diff --git a/.github/workflows/docker-cd.yaml b/.github/workflows/docker-cd.yaml index e5ef82e27f..5048a8910c 100644 --- a/.github/workflows/docker-cd.yaml +++ b/.github/workflows/docker-cd.yaml @@ -14,6 +14,7 @@ concurrency: jobs: build: + timeout-minutes: 120 runs-on: self-hosted strategy: matrix: @@ -85,10 +86,6 @@ jobs: env: DOCKER_ORG: registry.cn-hangzhou.aliyuncs.com/xprobe_xinference run: | - docker tag "xprobe/xinference:${XINFERENCE_IMAGE_TAG}" "${DOCKER_ORG}/xinference:${XINFERENCE_IMAGE_TAG}" - docker push "${DOCKER_ORG}/xinference:${XINFERENCE_IMAGE_TAG}" - docker tag "xprobe/xinference:${XINFERENCE_IMAGE_TAG}-cpu" "${DOCKER_ORG}/xinference:${XINFERENCE_IMAGE_TAG}-cpu" - docker push "${DOCKER_ORG}/xinference:${XINFERENCE_IMAGE_TAG}-cpu" if [[ -n "$XINFERENCE_GIT_TAG" ]]; then docker tag "xprobe/xinference:${XINFERENCE_GIT_TAG}" "$DOCKER_ORG/xinference:latest" docker push "$DOCKER_ORG/xinference:latest" diff --git a/.github/workflows/python.yaml b/.github/workflows/python.yaml index c097314a7a..81a16122c5 100644 --- a/.github/workflows/python.yaml +++ b/.github/workflows/python.yaml @@ -135,6 +135,9 @@ jobs: pip install tensorizer pip install eva-decord pip install jj-pytorchvideo + pip install qwen-vl-utils + pip install datamodel_code_generator + pip install jsonschema working-directory: . - name: Test with pytest @@ -142,7 +145,7 @@ jobs: MODULE: ${{ matrix.module }} run: | if [ "$MODULE" == "gpu" ]; then - ${{ env.SELF_HOST_PYTHON }} -m pip install -U "openai>1,<1.40" + ${{ env.SELF_HOST_PYTHON }} -m pip install -U "openai>1" ${{ env.SELF_HOST_PYTHON }} -m pip install -U modelscope ${{ env.SELF_HOST_PYTHON }} -m pip install -U sse_starlette ${{ env.SELF_HOST_PYTHON }} -m pip install -U xoscar @@ -154,37 +157,43 @@ jobs: ${{ env.SELF_HOST_CONDA }} install -c conda-forge pynini=2.1.5 ${{ env.SELF_HOST_CONDA }} install -c conda-forge "ffmpeg<7" ${{ env.SELF_HOST_PYTHON }} -m pip install -U funasr - ${{ env.SELF_HOST_PYTHON }} -m pip install -U nemo_text_processing + ${{ env.SELF_HOST_PYTHON }} -m pip install -U nemo_text_processing<1.1.0 ${{ env.SELF_HOST_PYTHON }} -m pip install -U omegaconf~=2.3.0 - ${{ env.SELF_HOST_PYTHON }} -m pip install -U WeTextProcessing + ${{ env.SELF_HOST_PYTHON }} -m pip install -U WeTextProcessing<1.0.4 ${{ env.SELF_HOST_PYTHON }} -m pip install -U librosa ${{ env.SELF_HOST_PYTHON }} -m pip install -U xxhash - ${{ env.SELF_HOST_PYTHON }} -m pip install -U "ChatTTS>0.1" + ${{ env.SELF_HOST_PYTHON }} -m pip install -U "ChatTTS>=0.2" ${{ env.SELF_HOST_PYTHON }} -m pip install -U HyperPyYAML - ${{ env.SELF_HOST_PYTHON }} -m pip install -U matcha-tts + ${{ env.SELF_HOST_PYTHON }} -m pip uninstall -y matcha-tts ${{ env.SELF_HOST_PYTHON }} -m pip install -U onnxruntime-gpu==1.16.0; sys_platform == 'linux' ${{ env.SELF_HOST_PYTHON }} -m pip install -U openai-whisper ${{ env.SELF_HOST_PYTHON }} -m pip install -U "torch==2.3.1" "torchaudio==2.3.1" ${{ env.SELF_HOST_PYTHON }} -m pip install -U "loguru" ${{ env.SELF_HOST_PYTHON }} -m pip install -U "natsort" ${{ env.SELF_HOST_PYTHON }} -m pip install -U "loralib" - ${{ env.SELF_HOST_PYTHON }} -m pip install -U "opencc==1.1.6" - ${{ env.SELF_HOST_PYTHON }} -m pip install -U "faster_whisper" + ${{ env.SELF_HOST_PYTHON }} -m pip install -U "ormsgpack" + ${{ env.SELF_HOST_PYTHON }} -m pip uninstall -y opencc + ${{ env.SELF_HOST_PYTHON }} -m pip uninstall -y "faster_whisper" + ${{ env.SELF_HOST_PYTHON }} -m pip install -U accelerate + ${{ env.SELF_HOST_PYTHON }} -m pip install -U verovio ${{ env.SELF_HOST_PYTHON }} -m pytest --timeout=1500 \ -W ignore::PendingDeprecationWarning \ - --cov-config=setup.cfg --cov-report=xml --cov=xinference xinference/model/image/tests/test_stable_diffusion.py + --cov-config=setup.cfg --cov-report=xml --cov=xinference xinference/model/image/tests/test_stable_diffusion.py && \ ${{ env.SELF_HOST_PYTHON }} -m pytest --timeout=1500 \ -W ignore::PendingDeprecationWarning \ - --cov-config=setup.cfg --cov-report=xml --cov=xinference xinference/model/audio/tests/test_whisper.py + --cov-config=setup.cfg --cov-report=xml --cov=xinference xinference/model/image/tests/test_got_ocr2.py && \ ${{ env.SELF_HOST_PYTHON }} -m pytest --timeout=1500 \ -W ignore::PendingDeprecationWarning \ - --cov-config=setup.cfg --cov-report=xml --cov=xinference xinference/model/audio/tests/test_funasr.py + --cov-config=setup.cfg --cov-report=xml --cov=xinference xinference/model/audio/tests/test_whisper.py && \ ${{ env.SELF_HOST_PYTHON }} -m pytest --timeout=1500 \ -W ignore::PendingDeprecationWarning \ - --cov-config=setup.cfg --cov-report=xml --cov=xinference xinference/model/audio/tests/test_chattts.py + --cov-config=setup.cfg --cov-report=xml --cov=xinference xinference/model/audio/tests/test_funasr.py && \ ${{ env.SELF_HOST_PYTHON }} -m pytest --timeout=1500 \ -W ignore::PendingDeprecationWarning \ - --cov-config=setup.cfg --cov-report=xml --cov=xinference xinference/model/audio/tests/test_cosyvoice.py + --cov-config=setup.cfg --cov-report=xml --cov=xinference xinference/model/audio/tests/test_chattts.py && \ + ${{ env.SELF_HOST_PYTHON }} -m pytest --timeout=1500 \ + -W ignore::PendingDeprecationWarning \ + --cov-config=setup.cfg --cov-report=xml --cov=xinference xinference/model/audio/tests/test_cosyvoice.py && \ ${{ env.SELF_HOST_PYTHON }} -m pytest --timeout=1500 \ -W ignore::PendingDeprecationWarning \ --cov-config=setup.cfg --cov-report=xml --cov=xinference xinference/model/audio/tests/test_fish_speech.py @@ -198,6 +207,6 @@ jobs: --cov-config=setup.cfg --cov-report=xml --cov=xinference xinference/client/tests/test_client.py pytest --timeout=1500 \ -W ignore::PendingDeprecationWarning \ - --cov-config=setup.cfg --cov-report=xml --cov=xinference --ignore xinference/client/tests/test_client.py --ignore xinference/model/image/tests/test_stable_diffusion.py --ignore xinference/model/audio/tests xinference + --cov-config=setup.cfg --cov-report=xml --cov=xinference --ignore xinference/client/tests/test_client.py --ignore xinference/model/image/tests/test_stable_diffusion.py --ignore xinference/model/image/tests/test_got_ocr2.py --ignore xinference/model/audio/tests xinference fi working-directory: . diff --git a/MANIFEST.in b/MANIFEST.in index ea0460dc63..2649794924 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -12,4 +12,5 @@ global-exclude conftest.py include xinference/locale/*.json include xinference/model/llm/*.json include xinference/model/embedding/*.json +graft xinference/thirdparty global-include xinference/web/ui/build/**/* \ No newline at end of file diff --git a/README.md b/README.md index fbee3a41e5..d63cbb42ff 100644 --- a/README.md +++ b/README.md @@ -3,13 +3,25 @@ # Xorbits Inference: Model Serving Made Easy 🤖 +

+ Xinference Cloud · + Xinference Enterprise · + Self-hosting · + Documentation +

+ [![PyPI Latest Release](https://img.shields.io/pypi/v/xinference.svg?style=for-the-badge)](https://pypi.org/project/xinference/) [![License](https://img.shields.io/pypi/l/xinference.svg?style=for-the-badge)](https://github.com/xorbitsai/inference/blob/main/LICENSE) [![Build Status](https://img.shields.io/github/actions/workflow/status/xorbitsai/inference/python.yaml?branch=main&style=for-the-badge&label=GITHUB%20ACTIONS&logo=github)](https://actions-badge.atrox.dev/xorbitsai/inference/goto?ref=main) [![Slack](https://img.shields.io/badge/join_Slack-781FF5.svg?logo=slack&style=for-the-badge)](https://join.slack.com/t/xorbitsio/shared_invite/zt-1o3z9ucdh-RbfhbPVpx7prOVdM1CAuxg) [![Twitter](https://img.shields.io/twitter/follow/xorbitsio?logo=x&style=for-the-badge)](https://twitter.com/xorbitsio) -English | [中文介绍](README_zh_CN.md) | [日本語](README_ja_JP.md) +

+ README in English + 简体中文版自述文件 + 日本語のREADME +

+
@@ -34,14 +46,14 @@ potential of cutting-edge AI models. - Support speech recognition model: [#929](https://github.com/xorbitsai/inference/pull/929) - Metrics support: [#906](https://github.com/xorbitsai/inference/pull/906) ### New Models +- Built-in support for [Qwen 2.5 Series](https://qwenlm.github.io/blog/qwen2.5/): [#2325](https://github.com/xorbitsai/inference/pull/2325) +- Built-in support for [Fish Speech V1.4](https://huggingface.co/fishaudio/fish-speech-1.4): [#2295](https://github.com/xorbitsai/inference/pull/2295) +- Built-in support for [DeepSeek-V2.5](https://huggingface.co/deepseek-ai/DeepSeek-V2.5): [#2292](https://github.com/xorbitsai/inference/pull/2292) +- Built-in support for [Qwen2-Audio](https://github.com/QwenLM/Qwen2-Audio): [#2271](https://github.com/xorbitsai/inference/pull/2271) +- Built-in support for [Qwen2-vl-instruct](https://github.com/QwenLM/Qwen2-VL): [#2205](https://github.com/xorbitsai/inference/pull/2205) +- Built-in support for [MiniCPM3-4B](https://huggingface.co/openbmb/MiniCPM3-4B): [#2263](https://github.com/xorbitsai/inference/pull/2263) - Built-in support for [CogVideoX](https://github.com/THUDM/CogVideo): [#2049](https://github.com/xorbitsai/inference/pull/2049) - Built-in support for [flux.1-schnell & flux.1-dev](https://www.basedlabs.ai/tools/flux1): [#2007](https://github.com/xorbitsai/inference/pull/2007) -- Built-in support for [MiniCPM-V 2.6](https://github.com/OpenBMB/MiniCPM-V): [#2031](https://github.com/xorbitsai/inference/pull/2031) -- Built-in support for [Kolors](https://huggingface.co/Kwai-Kolors/Kolors): [#2028](https://github.com/xorbitsai/inference/pull/2028) -- Built-in support for [SenseVoice](https://github.com/FunAudioLLM/SenseVoice): [#2008](https://github.com/xorbitsai/inference/pull/2008) -- Built-in support for [Mistral Large 2](https://mistral.ai/news/mistral-large-2407/): [#1944](https://github.com/xorbitsai/inference/pull/1944) -- Built-in support for [llama3.1](https://ai.meta.com/blog/meta-llama-3-1/): [#1932](https://github.com/xorbitsai/inference/pull/1932) -- Built-in support for [Mistral Nemo](https://mistral.ai/news/mistral-nemo/): [#1936](https://github.com/xorbitsai/inference/pull/1936) ### Integrations - [Dify](https://docs.dify.ai/advanced/model-configuration/xinference): an LLMOps platform that enables developers (and even non-developers) to quickly build useful applications based on large language models, ensuring they are visual, operable, and improvable. - [FastGPT](https://github.com/labring/FastGPT): a knowledge-based platform built on the LLM, offers out-of-the-box data processing and model invocation capabilities, allows for workflow orchestration through Flow visualization. @@ -85,9 +97,25 @@ with popular third-party libraries including [LangChain](https://python.langchai | Audio Models | ✅ | ❌ | ❌ | ❌ | | More OpenAI Functionalities (Function Calling) | ✅ | ❌ | ❌ | ❌ | -## Getting Started +## Using Xinference + +- **Cloud
** +We host a [Xinference Cloud](https://inference.top) service for anyone to try with zero setup. + +- **Self-hosting Xinference Community Edition
** +Quickly get Xinference running in your environment with this [starter guide](#getting-started). +Use our [documentation](https://inference.readthedocs.io/) for further references and more in-depth instructions. + +- **Xinference for enterprise / organizations
** +We provide additional enterprise-centric features. [send us an email](mailto:business@xprobe.io?subject=[GitHub]Business%20License%20Inquiry) to discuss enterprise needs.
-**Please give us a star before you begin, and you'll receive instant notifications for every new release on GitHub!** +## Staying Ahead + +Star Xinference on GitHub and be instantly notified of new releases. + +![star-us](assets/stay_ahead.gif) + +## Getting Started * [Docs](https://inference.readthedocs.io/en/latest/index.html) * [Built-in Models](https://inference.readthedocs.io/en/latest/models/builtin/index.html) @@ -157,3 +185,7 @@ Once Xinference is running, there are multiple ways you can try it: via the web + +## Star History + +[![Star History Chart](https://api.star-history.com/svg?repos=xorbitsai/inference&type=Date)](https://star-history.com/#xorbitsai/inference&Date) \ No newline at end of file diff --git a/README_ja_JP.md b/README_ja_JP.md index f5cafc4150..c80601a9c7 100644 --- a/README_ja_JP.md +++ b/README_ja_JP.md @@ -9,7 +9,11 @@ [![Slack](https://img.shields.io/badge/join_Slack-781FF5.svg?logo=slack&style=for-the-badge)](https://join.slack.com/t/xorbitsio/shared_invite/zt-1o3z9ucdh-RbfhbPVpx7prOVdM1CAuxg) [![Twitter](https://img.shields.io/twitter/follow/xorbitsio?logo=x&style=for-the-badge)](https://twitter.com/xorbitsio) -[English](README.md) | [中文介绍](README_zh_CN.md) | 日本語 +

+ README in English + 简体中文版自述文件 + 日本語のREADME +


diff --git a/README_zh_CN.md b/README_zh_CN.md index 34b9c4621e..2df28e2632 100644 --- a/README_zh_CN.md +++ b/README_zh_CN.md @@ -3,13 +3,24 @@ # Xorbits Inference：模型推理， 轻而易举 🤖 +

+ Xinference 云服务 · + Xinference 企业版 · + 自托管 · + 文档 +

+ [![PyPI Latest Release](https://img.shields.io/pypi/v/xinference.svg?style=for-the-badge)](https://pypi.org/project/xinference/) [![License](https://img.shields.io/pypi/l/xinference.svg?style=for-the-badge)](https://github.com/xorbitsai/inference/blob/main/LICENSE) [![Build Status](https://img.shields.io/github/actions/workflow/status/xorbitsai/inference/python.yaml?branch=main&style=for-the-badge&label=GITHUB%20ACTIONS&logo=github)](https://actions-badge.atrox.dev/xorbitsai/inference/goto?ref=main) [![WeChat](https://img.shields.io/badge/添加微信小助手-07C160?style=for-the-badge&logo=wechat&logoColor=white)](https://xorbits.cn/assets/images/wechat_work_qr.png) [![Zhihu](https://img.shields.io/static/v1?style=for-the-badge&message=未来速度&color=0084FF&logo=Zhihu&logoColor=FFFFFF&label=)](https://www.zhihu.com/org/xorbits) -[English](README.md) | 中文介绍 | [日本語](README_ja_JP.md) +

+ README in English + 简体中文版自述文件 + 日本語のREADME +


@@ -31,14 +42,14 @@ Xorbits Inference（Xinference）是一个性能强大且功能全面的分布 - 支持语音识别模型: [#929](https://github.com/xorbitsai/inference/pull/929) - 增加 Metrics 统计信息: [#906](https://github.com/xorbitsai/inference/pull/906) ### 新模型 +- 内置 [Qwen 2.5 Series](https://qwenlm.github.io/blog/qwen2.5/): [#2325](https://github.com/xorbitsai/inference/pull/2325) +- 内置 [Fish Speech V1.4](https://huggingface.co/fishaudio/fish-speech-1.4): [#2295](https://github.com/xorbitsai/inference/pull/2295) +- 内置 [DeepSeek-V2.5](https://huggingface.co/deepseek-ai/DeepSeek-V2.5): [#2292](https://github.com/xorbitsai/inference/pull/2292) +- 内置 [Qwen2-Audio](https://github.com/QwenLM/Qwen2-Audio): [#2271](https://github.com/xorbitsai/inference/pull/2271) +- 内置 [Qwen2-vl-instruct](https://github.com/QwenLM/Qwen2-VL): [#2205](https://github.com/xorbitsai/inference/pull/2205) +- 内置 [MiniCPM3-4B](https://huggingface.co/openbmb/MiniCPM3-4B): [#2263](https://github.com/xorbitsai/inference/pull/2263) - 内置 [CogVideoX](https://github.com/THUDM/CogVideo): [#2049](https://github.com/xorbitsai/inference/pull/2049) - 内置 [flux.1-schnell & flux.1-dev](https://www.basedlabs.ai/tools/flux1): [#2007](https://github.com/xorbitsai/inference/pull/2007) -- 内置 [MiniCPM-V 2.6](https://github.com/OpenBMB/MiniCPM-V): [#2031](https://github.com/xorbitsai/inference/pull/2031) -- 内置 [Kolors](https://huggingface.co/Kwai-Kolors/Kolors): [#2028](https://github.com/xorbitsai/inference/pull/2028) -- 内置 [SenseVoice](https://github.com/FunAudioLLM/SenseVoice): [#2008](https://github.com/xorbitsai/inference/pull/2008) -- 内置 [Mistral Large 2](https://mistral.ai/news/mistral-large-2407/): [#1944](https://github.com/xorbitsai/inference/pull/1944) -- 内置 [llama3.1](https://ai.meta.com/blog/meta-llama-3-1/): [#1932](https://github.com/xorbitsai/inference/pull/1932) -- 内置 [Mistral Nemo](https://mistral.ai/news/mistral-nemo/): [#1936](https://github.com/xorbitsai/inference/pull/1936) ### 集成 - [FastGPT](https://doc.fastai.site/docs/development/custom-models/xinference/)：一个基于 LLM 大模型的开源 AI 知识库构建平台。提供了开箱即用的数据处理、模型调用、RAG 检索、可视化 AI 工作流编排等能力，帮助您轻松实现复杂的问答场景。 - [Dify](https://docs.dify.ai/advanced/model-configuration/xinference): 一个涵盖了大型语言模型开发、部署、维护和优化的 LLMOps 平台。 @@ -72,10 +83,26 @@ Xorbits Inference（Xinference）是一个性能强大且功能全面的分布 | 语音识别模型 | ✅ | ❌ | ❌ | ❌ | | 更多 OpenAI 功能 (函数调用) | ✅ | ❌ | ❌ | ❌ | +## 使用 Xinference -## 入门指南 +- **云
** +我们提供 [Xinference 云服务](https://inference.top)，无需任何设置。 + +- **自托管 Xinference 社区版
** +使用 [入门指南](#getting-started) 快速在你自己的环境中运行 Xinference。 +参考 [文档](https://inference.readthedocs.io/zh-cn) 以获得参考和更多说明。 + +- **面向企业/组织的 Xinference 版本
** +我们提供额外的面向企业的功能。 [通过企业微信联系](https://xorbits.cn/assets/images/wechat_work_qr.png) +或 [提交表单](https://w8v6grm432.feishu.cn/share/base/form/shrcn9u1EBXQxmGMqILEjguuGoh) 讨论企业需求。
+ +## 保持领先 -**在开始之前，请给我们一个星标，这样你就可以在 GitHub 上及时收到每个新版本的通知！** +在 GitHub 上给 Xinference Star，并立即收到新版本的通知。 + +![star-us](assets/stay_ahead.gif) + +## 入门指南 * [文档](https://inference.readthedocs.io/zh-cn/latest/index.html) * [内置模型](https://inference.readthedocs.io/zh-cn/latest/models/builtin/index.html) @@ -141,4 +168,8 @@ $ xinference-local - \ No newline at end of file + + +## Star 历史 + +[![Star History Chart](https://api.star-history.com/svg?repos=xorbitsai/inference&type=Date)](https://star-history.com/#xorbitsai/inference&Date) \ No newline at end of file diff --git a/assets/stay_ahead.gif b/assets/stay_ahead.gif new file mode 100644 index 0000000000..fe148b6417 Binary files /dev/null and b/assets/stay_ahead.gif differ diff --git a/benchmark/README.md b/benchmark/README.md index 4c0ffc2ebd..a24fd292f9 100644 --- a/benchmark/README.md +++ b/benchmark/README.md @@ -38,3 +38,10 @@ python benchmark/benchmark_long.py --context-length ${context_length} --tokenize --model-uid ${model_uid} \ --num-prompts 32 -c 16 ``` + +## Common Options for Benchmarking Tools +- `--stream`. You can enable streaming responses by using the option, which is useful for real-time data processing and receiving incremental data without waiting for the entire dataset to be processed. + +- `--print-error`. For troubleshooting and more detailed output, the option can be used to print detailed error messages if any errors are encountered during the execution. + +These options are available for use in all benchmarking tools provided in this suite, enhancing flexibility and providing essential debugging information. diff --git a/benchmark/benchmark_latency.py b/benchmark/benchmark_latency.py index 3ae8125436..ac109ebb48 100644 --- a/benchmark/benchmark_latency.py +++ b/benchmark/benchmark_latency.py @@ -59,6 +59,7 @@ def main(args: argparse.Namespace): input_requests, args.stream, args.api_key, + args.print_error, ) asyncio.run(benchmark.run()) @@ -96,6 +97,10 @@ def main(args: argparse.Namespace): default=None, help="Authorization api key", ) - + parser.add_argument( + "--print-error", + action="store_true", + help="Print detailed error messages if any errors encountered." + ) args = parser.parse_args() main(args) diff --git a/benchmark/benchmark_long.py b/benchmark/benchmark_long.py index 75a0d43530..d19e142850 100644 --- a/benchmark/benchmark_long.py +++ b/benchmark/benchmark_long.py @@ -79,6 +79,7 @@ def main(args: argparse.Namespace): args.stream, concurrency=args.concurrency, api_key=args.api_key, + print_error=args.print_error, ) asyncio.run(benchmark.run()) @@ -120,5 +121,10 @@ def main(args: argparse.Namespace): parser.add_argument( "--stream", action="store_true", help="Enable streaming responses." ) + parser.add_argument( + "--print-error", + action="store_true", + help="Print detailed error messages if any errors encountered." + ) args = parser.parse_args() main(args) diff --git a/benchmark/benchmark_rerank.py b/benchmark/benchmark_rerank.py index 09e87d8758..765d5cf4c6 100644 --- a/benchmark/benchmark_rerank.py +++ b/benchmark/benchmark_rerank.py @@ -38,6 +38,7 @@ def __init__( top_n: int, concurrency: int, api_key: Optional[str] = None, + print_error: bool = False, ): super().__init__( api_url, @@ -46,6 +47,7 @@ def __init__( stream, concurrency, api_key, + print_error, ) self.top_n = top_n @@ -127,6 +129,7 @@ def main(args: argparse.Namespace): top_n=args.top_n, concurrency=args.concurrency, api_key=args.api_key, + print_error=args.print_error, ) asyncio.run(benchmark.run()) @@ -182,5 +185,10 @@ def main(args: argparse.Namespace): parser.add_argument( "--api-key", type=str, default=None, help="Authorization api key", ) + parser.add_argument( + "--print-error", + action="store_true", + help="Print detailed error messages if any errors encountered." + ) args = parser.parse_args() main(args) diff --git a/benchmark/benchmark_runner.py b/benchmark/benchmark_runner.py index 78bcff0ecc..dd4a6fb143 100644 --- a/benchmark/benchmark_runner.py +++ b/benchmark/benchmark_runner.py @@ -54,7 +54,8 @@ def __init__( model_uid: str, input_requests: List[Tuple[str, int, int]], stream: bool, - api_key: Optional[str]=None, + api_key: Optional[str] = None, + print_error: bool = False, ): self.api_url = api_url self.model_uid = model_uid @@ -63,6 +64,7 @@ def __init__( self.benchmark_time = None self.stream = stream self.api_key = api_key + self.print_error = print_error async def run(self): await self.warm_up() @@ -361,6 +363,17 @@ def print_stats(self): print(f"Total time: {total_time:.2f} s") print(f"Throughput: {len(self.outputs) / total_time:.2f} requests/s") + if completed < len(self.input_requests): + if self.print_error: + logger.info("Errors encountered during benchmark:") + for output in self.outputs: + if not output.success: + print(f"Error for prompt with length {output.prompt_len}: {output.error}") + else: + logger.info( + "Errors were encountered during the benchmark. Run with --print-error to see detailed error messages." + ) + class ConcurrentBenchmarkRunner(BenchmarkRunner): def __init__( @@ -370,9 +383,17 @@ def __init__( input_requests: List[Tuple[str, int, int]], stream: bool, concurrency: int, - api_key: Optional[str]=None, + api_key: Optional[str] = None, + print_error: bool = False, ): - super().__init__(api_url, model_uid, input_requests, stream, api_key) + super().__init__( + api_url, + model_uid, + input_requests, + stream, + api_key, + print_error, + ) self.concurrency = concurrency self.left = len(input_requests) diff --git a/benchmark/benchmark_serving.py b/benchmark/benchmark_serving.py index 105cce0976..cc56750f27 100644 --- a/benchmark/benchmark_serving.py +++ b/benchmark/benchmark_serving.py @@ -38,6 +38,7 @@ def __init__( concurrency: int, request_rate: float, api_key: Optional[str] = None, + print_error: bool = False, ): super().__init__( api_url, @@ -46,6 +47,7 @@ def __init__( stream, concurrency, api_key, + print_error, ) self.request_rate = request_rate self.queue = None # delay the creation of the queue @@ -118,6 +120,7 @@ def main(args: argparse.Namespace): request_rate=args.request_rate, concurrency=args.concurrency, api_key=args.api_key, + print_error=args.print_error, ) asyncio.run(benchmark.run()) @@ -174,5 +177,10 @@ def main(args: argparse.Namespace): parser.add_argument( "--stream", action="store_true", help="Enable streaming responses." ) + parser.add_argument( + "--print-error", + action="store_true", + help="Print detailed error messages if any errors encountered." + ) args = parser.parse_args() main(args) diff --git a/doc/source/getting_started/installation.rst b/doc/source/getting_started/installation.rst index 9dd563a0fc..bbb6e89e57 100644 --- a/doc/source/getting_started/installation.rst +++ b/doc/source/getting_started/installation.rst @@ -44,8 +44,10 @@ Currently, supported models include: - ``codestral-v0.1`` - ``Yi``, ``Yi-1.5``, ``Yi-chat``, ``Yi-1.5-chat``, ``Yi-1.5-chat-16k`` - ``code-llama``, ``code-llama-python``, ``code-llama-instruct`` -- ``deepseek``, ``deepseek-coder``, ``deepseek-chat``, ``deepseek-coder-instruct`` +- ``deepseek``, ``deepseek-coder``, ``deepseek-chat``, ``deepseek-coder-instruct``, ``deepseek-v2-chat``, ``deepseek-v2-chat-0628``, ``deepseek-v2.5`` +- ``yi-coder``, ``yi-coder-chat`` - ``codeqwen1.5``, ``codeqwen1.5-chat`` +- ``qwen2.5``, ``qwen2.5-coder``, ``qwen2.5-instruct``, ``qwen2.5-coder-instruct`` - ``baichuan-2-chat`` - ``internlm2-chat`` - ``internlm2.5-chat``, ``internlm2.5-chat-1m`` @@ -102,7 +104,7 @@ SGLang has a high-performance inference runtime with RadixAttention. It signific Initial setup:: - pip install 'xinference[sglang]' + pip install "xinference[sglang]" # For CUDA 12.4 & torch 2.4 to support sliding window attention for gemma 2 and llama 3.1 style rope pip install flashinfer -i https://flashinfer.ai/whl/cu124/torch2.4 @@ -115,7 +117,7 @@ MLX-lm is designed for Apple silicon users to run LLM efficiently. Initial setup:: - pip install 'xinference[mlx]' + pip install "xinference[mlx]" Other Platforms ~~~~~~~~~~~~~~~ diff --git a/doc/source/getting_started/installation_npu.rst b/doc/source/getting_started/installation_npu.rst index 8202661487..786ffdba34 100644 --- a/doc/source/getting_started/installation_npu.rst +++ b/doc/source/getting_started/installation_npu.rst @@ -6,6 +6,13 @@ Installation Guide for Ascend NPU ================================= Xinference can run on Ascend NPU, follow below instructions to install. +.. warning:: + + The open-source version relies on Transformers for inference, + which can be slow on chips like 310p3. We provide an enterprise version that supports the MindIE engine, + offering better performance and compatibility for Ascend NPU. + Refer to `Xinference Enterprise `_ + Installing PyTorch and Ascend extension for PyTorch ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/doc/source/getting_started/using_xinference.rst b/doc/source/getting_started/using_xinference.rst index b8cc47458a..af8071b3b6 100644 --- a/doc/source/getting_started/using_xinference.rst +++ b/doc/source/getting_started/using_xinference.rst @@ -243,11 +243,11 @@ or via Xinference's python client: from xinference.client import RESTfulClient client = RESTfulClient("http://127.0.0.1:9997") model = client.get_model("my-llama-2") - print(model.chat( - prompt="What is the largest animal?", - system_prompt="You are a helpful assistant.", - chat_history=[] - )) + model.chat( + messages=[ + {"role": "user", "content": "Who won the world series in 2020?"} + ] + ) .. code-tab:: json output diff --git a/doc/source/index.rst b/doc/source/index.rst index 926cb8dca0..270f71e565 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -35,14 +35,13 @@ Developing Real-world AI Applications with Xinference # Chat to LLM model.chat( - prompt="What is the largest animal?", - system_prompt="You are a helpful assistant", + messages=[{"role": "system", "content": "You are a helpful assistant"}, {"role": "user", "content": "What is the largest animal?"}], generate_config={"max_tokens": 1024} ) # Chat to VL model model.chat( - chat_history=[ + messages=[ { "role": "user", "content": [ diff --git a/doc/source/locale/zh_CN/LC_MESSAGES/getting_started/installation_npu.po b/doc/source/locale/zh_CN/LC_MESSAGES/getting_started/installation_npu.po index 85657a774b..4a27241727 100644 --- a/doc/source/locale/zh_CN/LC_MESSAGES/getting_started/installation_npu.po +++ b/doc/source/locale/zh_CN/LC_MESSAGES/getting_started/installation_npu.po @@ -8,7 +8,7 @@ msgid "" msgstr "" "Project-Id-Version: Xinference \n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2024-07-30 17:00+0800\n" +"POT-Creation-Date: 2024-10-25 15:13+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" @@ -28,52 +28,65 @@ msgid "Xinference can run on Ascend NPU, follow below instructions to install." msgstr "Xinference 能在昇腾 NPU 上运行，使用如下命令安装。" #: ../../source/getting_started/installation_npu.rst:11 +msgid "" +"The open-source version relies on Transformers for inference, which can " +"be slow on chips like 310p3. We provide an enterprise version that " +"supports the MindIE engine, offering better performance and compatibility" +" for Ascend NPU. Refer to `Xinference Enterprise " +"`_" +msgstr "" +"开源版本依赖 Transformers 进行推理，在 310p3 等芯片上会存在运行慢的问题。" +"我们提供了支持 MindIE 引擎，性能更为强大，兼容性更好的企业版本来支持 " +"Ascend NPU。详细参考 `Xinference 企业版 `_" + +#: ../../source/getting_started/installation_npu.rst:18 msgid "Installing PyTorch and Ascend extension for PyTorch" msgstr "安装 PyTorch 和昇腾扩展" -#: ../../source/getting_started/installation_npu.rst:12 +#: ../../source/getting_started/installation_npu.rst:19 msgid "Install PyTorch CPU version and corresponding Ascend extension." msgstr "安装 PyTorch CPU 版本和相应的昇腾扩展。" -#: ../../source/getting_started/installation_npu.rst:14 +#: ../../source/getting_started/installation_npu.rst:21 msgid "Take PyTorch v2.1.0 as example." msgstr "以 PyTorch v2.1.0 为例。" -#: ../../source/getting_started/installation_npu.rst:20 +#: ../../source/getting_started/installation_npu.rst:27 msgid "" "Then install `Ascend extension for PyTorch " "`_." -msgstr "" -"接着安装 `昇腾 PyTorch 扩展 " -"`_." +msgstr "接着安装 `昇腾 PyTorch 扩展 `_." -#: ../../source/getting_started/installation_npu.rst:28 +#: ../../source/getting_started/installation_npu.rst:35 msgid "Running below command to see if it correctly prints the Ascend NPU count." msgstr "运行如下命令查看，如果正常运行，会打印昇腾 NPU 的个数。" -#: ../../source/getting_started/installation_npu.rst:35 +#: ../../source/getting_started/installation_npu.rst:42 msgid "Installing Xinference" msgstr "安装 Xinference" -#: ../../source/getting_started/installation_npu.rst:41 +#: ../../source/getting_started/installation_npu.rst:48 msgid "" "Now you can use xinference according to :ref:`doc `. " "``Transformers`` backend is the only available engine supported for " "Ascend NPU for open source version." msgstr "" -"现在你可以参考 :ref:`文档 ` 来使用 Xinference。" -"``Transformers`` 是开源唯一支持的昇腾 NPU 的引擎。" +"现在你可以参考 :ref:`文档 ` 来使用 Xinference。``" +"Transformers`` 是开源唯一支持的昇腾 NPU 的引擎。" -#: ../../source/getting_started/installation_npu.rst:45 +#: ../../source/getting_started/installation_npu.rst:52 msgid "Enterprise Support" msgstr "企业支持" -#: ../../source/getting_started/installation_npu.rst:46 +#: ../../source/getting_started/installation_npu.rst:53 msgid "" "If you encounter any performance or other issues for Ascend NPU, please " "reach out to us via `link `_." msgstr "" -"如果你在昇腾 NPU 遇到任何性能和其他问题，欢迎垂询 Xinference 企业版，" -"在 `这里 `_ 可以找到我们，亦可以 " -"`填写表单 `_ 申请企业版试用。" +"如果你在昇腾 NPU 遇到任何性能和其他问题，欢迎垂询 Xinference 企业版，在 `" +"这里 `_ 可以找到我们，亦可以 `填写表单 <" +"https://w8v6grm432.feishu.cn/share/base/form/shrcn9u1EBXQxmGMqILEjguuGoh>" +"`_ 申请企业版试用。" diff --git a/doc/source/locale/zh_CN/LC_MESSAGES/models/custom.po b/doc/source/locale/zh_CN/LC_MESSAGES/models/custom.po index 03a7e356cd..878e084003 100644 --- a/doc/source/locale/zh_CN/LC_MESSAGES/models/custom.po +++ b/doc/source/locale/zh_CN/LC_MESSAGES/models/custom.po @@ -7,7 +7,7 @@ msgid "" msgstr "" "Project-Id-Version: Xinference \n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2024-08-15 11:39+0800\n" +"POT-Creation-Date: 2024-09-05 13:08+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" @@ -16,7 +16,7 @@ msgstr "" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" -"Generated-By: Babel 2.11.0\n" +"Generated-By: Babel 2.16.0\n" #: ../../source/models/custom.rst:5 msgid "Custom Models" @@ -70,8 +70,8 @@ msgstr "定义自定义大语言模型" msgid "Define a custom LLM model based on the following template:" msgstr "基于以下模板定义一个自定义大语言模型：" -#: ../../source/models/custom.rst:96 ../../source/models/custom.rst:225 -#: ../../source/models/custom.rst:248 +#: ../../source/models/custom.rst:95 ../../source/models/custom.rst:127 +#: ../../source/models/custom.rst:150 msgid "" "model_name: A string defining the name of the model. The name must start " "with a letter or a digit and can only contain letters, digits, " @@ -80,7 +80,7 @@ msgstr "" "model_name: 模型名称。名称必须以字母或数字开头，且只能包含字母、数字、" "下划线或短划线。" -#: ../../source/models/custom.rst:97 +#: ../../source/models/custom.rst:96 msgid "" "context_length: context_length: An optional integer that specifies the " "maximum context size the model was trained to accommodate, encompassing " @@ -90,7 +90,7 @@ msgstr "" "context_length: 一个可选的整数，模型支持的最大上下文长度，包括输入和输出" "长度。如果未定义，默认值为2048个token（约1,500个词）。" -#: ../../source/models/custom.rst:98 +#: ../../source/models/custom.rst:97 msgid "" "model_lang: A list of strings representing the supported languages for " "the model. Example: [\"en\"], which means that the model supports " @@ -99,7 +99,7 @@ msgstr "" "model_lang: 一个字符串列表，表示模型支持的语言。例如：['en']，表示该模型" "支持英语。" -#: ../../source/models/custom.rst:99 +#: ../../source/models/custom.rst:98 msgid "" "model_ability: A list of strings defining the abilities of the model. It " "could include options like \"embed\", \"generate\", and \"chat\". In this" @@ -108,40 +108,32 @@ msgstr "" "model_ability: 一个字符串列表，定义模型的能力。它可以包括像 'embed'、'" "generate' 和 'chat' 这样的选项。示例表示模型具有 'generate' 的能力。" -#: ../../source/models/custom.rst:100 +#: ../../source/models/custom.rst:99 msgid "" "model_family: A required string representing the family of the model you " -"want to register. The optional values are the model names of all :ref" -":`built-in models `. If the model family you register " -"is not among the built-in models in Xinference, please fill in ``other``." -" Note that you should choose the model family based on the ability of the" -" model you want to register. For example, if you want to register the " -"``llama-2`` model, do not fill in ``llama-2-chat`` as the model family." +"want to register. This parameter must not conflict with any builtin model" +" names." msgstr "" -"model_family: 必需字段，表示你要注册的模型的家族（类别）。可选值来自于 " -"Xinference :ref:`所有内置模型的模型名 `。如果你要注册的" -"模型不在其中，填入 ``other`` 。注意，此字段的值必须根据模型能力填入。例如" -"，如果你注册的是自定义 ``llama-2`` 模型，千万不要填入 ``llama-2-chat`` 。" -#: ../../source/models/custom.rst:106 +#: ../../source/models/custom.rst:100 msgid "" "model_specs: An array of objects defining the specifications of the " "model. These include:" msgstr "model_specs: 一个包含定义模型规格的对象数组。这些规格包括：" -#: ../../source/models/custom.rst:102 +#: ../../source/models/custom.rst:101 msgid "" "model_format: A string that defines the model format, like \"pytorch\" or" " \"ggufv2\"." msgstr "model_format: 一个定义模型格式的字符串，可以是 'pytorch' 或 'ggufv2'。" -#: ../../source/models/custom.rst:103 +#: ../../source/models/custom.rst:102 msgid "" "model_size_in_billions: An integer defining the size of the model in " "billions of parameters." msgstr "model_size_in_billions: 一个整数，定义模型的参数量，以十亿为单位。" -#: ../../source/models/custom.rst:104 +#: ../../source/models/custom.rst:103 msgid "" "quantizations: A list of strings defining the available quantizations for" " the model. For PyTorch models, it could be \"4-bit\", \"8-bit\", or " @@ -152,7 +144,7 @@ msgstr "" "可以是 \"4-bit\"、\"8-bit\" 或 \"none\"。对于 ggufv2 模型，量化方式应与 `" "`model_file_name_template`` 中的值对应。" -#: ../../source/models/custom.rst:105 +#: ../../source/models/custom.rst:104 msgid "" "model_id: A string representing the model ID, possibly referring to an " "identifier used by Hugging Face. **If model_uri is missing, Xinference " @@ -163,7 +155,7 @@ msgstr "" "如果 model_uri 字段缺失，Xinference 将尝试从此id指示的HuggingFace仓库下载" "该模型。" -#: ../../source/models/custom.rst:106 +#: ../../source/models/custom.rst:105 msgid "" "model_uri: A string representing the URI where the model can be loaded " "from, such as \"file:///path/to/llama-2-7b\". **When the model format is " @@ -173,11 +165,11 @@ msgid "" "the model from Hugging Face with the model ID." msgstr "" "model_uri：表示模型文件位置的字符串，例如本地目录：\"file:///path/to/" -"llama-2-7b\"。当 model_format 是 ggufv2 ，此字段必须是具体的" -"模型文件路径。而当 model_format 是 pytorch 时，此字段必须是一个包含所有" -"模型文件的目录。" +"llama-2-7b\"。当 model_format 是 ggufv2 ，此字段必须是具体的模型文件路径" +"。而当 model_format 是 pytorch 时，此字段必须是一个包含所有模型文件的目录" +"。" -#: ../../source/models/custom.rst:107 +#: ../../source/models/custom.rst:106 msgid "" "model_file_name_template: Required by gguf models. An f-string template " "used for defining the model file name based on the quantization. **Note " @@ -187,73 +179,57 @@ msgstr "" "model_file_name_template: gguf 模型所需。一个 f-string 模板，用于根据量化" "定义模型文件名。注意，这里不要填入文件的路径。" -#: ../../source/models/custom.rst:108 +#: ../../source/models/custom.rst:107 msgid "" -"prompt_style: If the ``model_family`` field is not ``other``, this field " -"does not need to be filled in. ``prompt_style`` is an optional field that" -" could be required by ``chat`` models to define the style of prompts. The" -" given example has this set to None, but additional details could be " -"found in a referenced file xinference/model/llm/tests/test_utils.py. You " -"can also specify this field as a string, which will use the builtin " -"prompt style in Xinference. For example:" -msgstr "" -"prompt_style: 如果上述 ``model_family`` 字段不是 ``other`` ，则无需设置" -"此字段。 ``prompt_style`` 是一个可选字段，表示 ``chat`` 模型需要的提示词" -"样式。给定的示例将其设置为 None，但可以在引用的文件 xinference/model/llm/" -"tests/test_utils.py 中找到更多详细信息。你也可以指定一个字符串，以使用" -"内置模型的提示词样式。" - -#: ../../source/models/custom.rst:117 -msgid "Xinference supports these builtin prompt styles in common usage:" -msgstr "Xinference 支持这些内置、常用的提示词样式：" - -#: ../../source/models/custom.rst:121 -msgid "baichuan-chat" -msgstr "" - -#: ../../source/models/custom.rst:140 -msgid "chatglm3" -msgstr "" - -#: ../../source/models/custom.rst:153 -msgid "qwen-chat" -msgstr "" - -#: ../../source/models/custom.rst:170 -msgid "llama-2-chat" +"chat_template: If ``model_ability`` includes ``chat`` , you must " +"configure this option to generate the correct full prompt during chat. " +"This is a Jinja template string. Usually, you can find it in the " +"``tokenizer_config.json`` file within the model directory." msgstr "" +"chat_template：如果 ``model_ability`` 中包含 ``chat`` ，那么此选项必须配置以生成合适的完整提示词。这是一个 Jinja 模版字符串。" +"通常，你可以在模型目录的 ``tokenizer_config.json`` 文件中找到。" -#: ../../source/models/custom.rst:191 -msgid "vicuna-v1.5" +#: ../../source/models/custom.rst:108 +msgid "" +"stop_token_ids: If ``model_ability`` includes ``chat`` , you can " +"configure this option to control when the model stops during chat. This " +"is a list of integers, and you can typically extract the corresponding " +"values from the ``generation_config.json`` or ``tokenizer_config.json`` " +"file in the model directory." msgstr "" +"stop_token_ids：如果 ``model_ability`` 中包含 ``chat`` ，那么推荐配置此选项以合理控制对话的停止。这是一个包含整数的列表，你可以" +"在模型目录的 ``generation_config.json`` 和 ``tokenizer_config.json`` 文件中提取相应的值。" -#: ../../source/models/custom.rst:206 +#: ../../source/models/custom.rst:109 msgid "" -"The above lists some commonly used built-in prompt styles. The full list " -"of supported prompt styles can be found on the Xinference web UI." +"stop: If ``model_ability`` includes ``chat`` , you can configure this " +"option to control when the model stops during chat. This is a list of " +"strings, and you can typically extract the corresponding values from the " +"``generation_config.json`` or ``tokenizer_config.json`` file in the model" +" directory." msgstr "" -"以上列举出了最常使用的提示词样式。完整的支持列表可以通过 Xinference 页面" -"的 register model 面板查看。" +"stop：如果 ``model_ability`` 中包含 ``chat`` ，那么推荐配置此选项以合理控制对话的停止。这是一个包含字符串的列表，" +"你可以在模型目录的 ``tokenizer_config.json`` 文件中找到 token 值对应的字符串。" -#: ../../source/models/custom.rst:210 +#: ../../source/models/custom.rst:112 msgid "Define a custom embedding model" msgstr "定义自定义 embedding 模型" -#: ../../source/models/custom.rst:212 +#: ../../source/models/custom.rst:114 msgid "Define a custom embedding model based on the following template:" msgstr "基于以下模板定义一个自定义 embedding 模型：" -#: ../../source/models/custom.rst:226 +#: ../../source/models/custom.rst:128 msgid "dimensions: A integer that specifies the embedding dimensions." msgstr "dimensions: 表示 embedding 维度的整型值。" -#: ../../source/models/custom.rst:227 +#: ../../source/models/custom.rst:129 msgid "" "max_tokens: A integer that represents the max sequence length that the " "embedding model supports." msgstr "max_tokens: 表示 embedding 模型支持的最大输入序列长度的整型值。" -#: ../../source/models/custom.rst:228 ../../source/models/custom.rst:250 +#: ../../source/models/custom.rst:130 ../../source/models/custom.rst:152 msgid "" "language: A list of strings representing the supported languages for the " "model. Example: [\"en\"], which means that the model supports English." @@ -261,7 +237,7 @@ msgstr "" "model_lang: 一个字符串列表，表示模型支持的语言。例如：['en']，表示该模型" "支持英语。" -#: ../../source/models/custom.rst:229 ../../source/models/custom.rst:251 +#: ../../source/models/custom.rst:131 ../../source/models/custom.rst:153 msgid "" "model_id: A string representing the model ID, possibly referring to an " "identifier used by Hugging Face." @@ -269,7 +245,7 @@ msgstr "" "model_id: 一个表示模型标识的字符串，类似 HuggingFace 或 ModelScope 使用的" "标识符。" -#: ../../source/models/custom.rst:230 ../../source/models/custom.rst:252 +#: ../../source/models/custom.rst:132 ../../source/models/custom.rst:154 msgid "" "model_uri: A string representing the URI where the model can be loaded " "from, such as \"file:///path/to/your_model\". If model URI is absent, " @@ -280,15 +256,15 @@ msgstr "" "如果模型 URI 不存在，Xinference 将尝试使用 model_id 从 HuggingFace 或 " "ModelScope 下载模型。" -#: ../../source/models/custom.rst:234 +#: ../../source/models/custom.rst:136 msgid "Define a custom Rerank model" msgstr "定义自定义 rerank 模型" -#: ../../source/models/custom.rst:236 +#: ../../source/models/custom.rst:138 msgid "Define a custom rerank model based on the following template:" msgstr "基于以下模板定义一个自定义大语言模型：" -#: ../../source/models/custom.rst:249 +#: ../../source/models/custom.rst:151 msgid "" "type: A string defining the type of the model, including ``normal``, " "``LLM-based`` and ``LLM-based layerwise``." @@ -296,20 +272,20 @@ msgstr "" "type: 表示模型的类型，可选值包括 ``normal``、``LLM-based`` 和 ``LLM-based" " layerwise``。" -#: ../../source/models/custom.rst:256 +#: ../../source/models/custom.rst:158 msgid "Register a Custom Model" msgstr "注册一个自定义模型" -#: ../../source/models/custom.rst:258 +#: ../../source/models/custom.rst:160 msgid "Register a custom model programmatically:" msgstr "以代码的方式注册自定义模型" -#: ../../source/models/custom.rst:273 ../../source/models/custom.rst:291 -#: ../../source/models/custom.rst:306 ../../source/models/custom.rst:361 +#: ../../source/models/custom.rst:175 ../../source/models/custom.rst:193 +#: ../../source/models/custom.rst:208 ../../source/models/custom.rst:263 msgid "Or via CLI:" msgstr "以命令行的方式" -#: ../../source/models/custom.rst:279 +#: ../../source/models/custom.rst:181 msgid "" "Note that replace the ```` above with ``LLM``, ``embedding`` " "or ``rerank``. The same as below." @@ -317,43 +293,43 @@ msgstr "" "注意将以下部分的 ```` 替换为 ``LLM``、``embedding`` 或 ``" "rerank`` 。" -#: ../../source/models/custom.rst:283 +#: ../../source/models/custom.rst:185 msgid "List the Built-in and Custom Models" msgstr "列举内置和自定义模型" -#: ../../source/models/custom.rst:285 +#: ../../source/models/custom.rst:187 msgid "List built-in and custom models programmatically:" msgstr "以代码的方式列举内置和自定义模型" -#: ../../source/models/custom.rst:298 +#: ../../source/models/custom.rst:200 msgid "Launch the Custom Model" msgstr "启动自定义模型" -#: ../../source/models/custom.rst:300 +#: ../../source/models/custom.rst:202 msgid "Launch the custom model programmatically:" msgstr "以代码的方式启动自定义模型" -#: ../../source/models/custom.rst:313 +#: ../../source/models/custom.rst:215 msgid "Interact with the Custom Model" msgstr "使用自定义模型" -#: ../../source/models/custom.rst:315 +#: ../../source/models/custom.rst:217 msgid "Invoke the model programmatically:" msgstr "以代码的方式调用模型" -#: ../../source/models/custom.rst:322 +#: ../../source/models/custom.rst:224 msgid "Result:" msgstr "结果为：" -#: ../../source/models/custom.rst:346 +#: ../../source/models/custom.rst:248 msgid "Or via CLI, replace ``${UID}`` with real model UID:" msgstr "或者以命令行的方式，用实际的模型 UID 替换 ``${UID}``：" -#: ../../source/models/custom.rst:353 +#: ../../source/models/custom.rst:255 msgid "Unregister the Custom Model" msgstr "注销自定义模型" -#: ../../source/models/custom.rst:355 +#: ../../source/models/custom.rst:257 msgid "Unregister the custom model programmatically:" msgstr "以代码的方式注销自定义模型" diff --git a/doc/source/locale/zh_CN/LC_MESSAGES/models/model_abilities/image.po b/doc/source/locale/zh_CN/LC_MESSAGES/models/model_abilities/image.po index 66b4935516..e73ba213b0 100644 --- a/doc/source/locale/zh_CN/LC_MESSAGES/models/model_abilities/image.po +++ b/doc/source/locale/zh_CN/LC_MESSAGES/models/model_abilities/image.po @@ -8,7 +8,7 @@ msgid "" msgstr "" "Project-Id-Version: Xinference \n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2024-08-09 19:13+0800\n" +"POT-Creation-Date: 2024-10-30 07:49+0000\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: zh_CN\n" @@ -17,7 +17,7 @@ msgstr "" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" -"Generated-By: Babel 2.14.0\n" +"Generated-By: Babel 2.16.0\n" #: ../../source/models/model_abilities/image.rst:5 msgid "Images" @@ -143,17 +143,20 @@ msgid "" " move a model component onto the GPU when it needs to be executed, while " "keeping the remaining components on the CPU." msgstr "" -"``--cpu_offload True``：指定 ``True`` 会在推理过程中将模型的组件卸载到 CPU 上以节省内存，" -"这会导致推理延迟略有增加。模型卸载仅会在需要执行时将模型组件移动到 GPU 上，同时保持其余组件在 CPU 上" +"``--cpu_offload True``：指定 ``True`` 会在推理过程中将模型的组件卸载到 " +"CPU 上以节省内存，这会导致推理延迟略有增加。模型卸载仅会在需要执行时将" +"模型组件移动到 GPU 上，同时保持其余组件在 CPU 上" #: ../../source/models/model_abilities/image.rst:117 msgid "" "``--quantize_text_encoder ``: We leveraged the " "``bitsandbytes`` library to load and quantize the T5-XXL text encoder to " -"8-bit precision. This allows you to keep using all text encoders " -"while only slightly impacting performance." -msgstr "``--quantize_text_encoder ``：我们利用 ``bitsandbytes`` 库" -"加载并量化 T5-XXL 文本编码器至8位精度。这使得你能够在仅轻微影响性能的情况下继续使用全部文本编码器。" +"8-bit precision. This allows you to keep using all text encoders while " +"only slightly impacting performance." +msgstr "" +"``--quantize_text_encoder ``：我们利用 ``bitsandbytes" +"`` 库加载并量化 T5-XXL 文本编码器至8位精度。这使得你能够在仅轻微影响性能" +"的情况下继续使用全部文本编码器。" #: ../../source/models/model_abilities/image.rst:120 msgid "" @@ -161,16 +164,18 @@ msgid "" "4.7B parameter T5-XXL text encoder during inference can significantly " "decrease the memory requirements with only a slight loss in performance." msgstr "" -"``--text_encoder_3 None``，对于 sd3-medium，" -"移除在推理过程中内存密集型的47亿参数T5-XXL文本编码器可以显著降低内存需求，而仅造成性能上的轻微损失。" +"``--text_encoder_3 None``，对于 sd3-medium，移除在推理过程中内存密集型的" +"47亿参数T5-XXL文本编码器可以显著降低内存需求，而仅造成性能上的轻微损失。" #: ../../source/models/model_abilities/image.rst:124 msgid "" "If you are trying to run large image models liek sd3-medium or FLUX.1 " "series on GPU card that has less memory than 24GB, you may encounter OOM " "when launching or inference. Try below solutions." -msgstr "如果你试图在显存小于24GB的GPU上运行像sd3-medium或FLUX.1系列这样的大型图像模型，" -"你在启动或推理过程中可能会遇到显存溢出（OOM）的问题。尝试以下解决方案。" +msgstr "" +"如果你试图在显存小于24GB的GPU上运行像sd3-medium或FLUX.1系列这样的大型图像" +"模型，你在启动或推理过程中可能会遇到显存溢出（OOM）的问题。尝试以下" +"解决方案。" #: ../../source/models/model_abilities/image.rst:128 msgid "For FLUX.1 series, try to apply quantization." @@ -200,4 +205,15 @@ msgstr "" msgid "Learn from a Stable Diffusion ControlNet example" msgstr "学习一个 Stable Diffusion 控制网络的示例" +#: ../../source/models/model_abilities/image.rst:160 +msgid "OCR" +msgstr "" + +#: ../../source/models/model_abilities/image.rst:162 +msgid "The OCR API accepts image bytes and returns the OCR text." +msgstr "OCR API 接受图像字节并返回 OCR 文本。" + +#: ../../source/models/model_abilities/image.rst:164 +msgid "We can try OCR API out either via cURL, or Xinference's python client:" +msgstr "可以通过 cURL 或 Xinference 的 Python 客户端来尝试 OCR API。" diff --git a/doc/source/locale/zh_CN/LC_MESSAGES/user_guide/continuous_batching.po b/doc/source/locale/zh_CN/LC_MESSAGES/user_guide/continuous_batching.po index 427e855a09..4505a7fa2a 100644 --- a/doc/source/locale/zh_CN/LC_MESSAGES/user_guide/continuous_batching.po +++ b/doc/source/locale/zh_CN/LC_MESSAGES/user_guide/continuous_batching.po @@ -8,7 +8,7 @@ msgid "" msgstr "" "Project-Id-Version: Xinference \n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2024-07-04 16:08+0800\n" +"POT-Creation-Date: 2024-10-17 18:49+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language-Team: LANGUAGE \n" @@ -18,8 +18,8 @@ msgstr "" "Generated-By: Babel 2.11.0\n" #: ../../source/user_guide/continuous_batching.rst:5 -msgid "Continuous Batching (experimental)" -msgstr "连续批处理（实验性质）" +msgid "Continuous Batching" +msgstr "连续批处理" #: ../../source/user_guide/continuous_batching.rst:7 msgid "" @@ -35,11 +35,15 @@ msgstr "" msgid "Usage" msgstr "使用方式" -#: ../../source/user_guide/continuous_batching.rst:12 +#: ../../source/user_guide/continuous_batching.rst:14 +msgid "LLM" +msgstr "大语言模型" + +#: ../../source/user_guide/continuous_batching.rst:15 msgid "Currently, this feature can be enabled under the following conditions:" msgstr "当前，此功能在满足以下条件时开启：" -#: ../../source/user_guide/continuous_batching.rst:14 +#: ../../source/user_guide/continuous_batching.rst:17 msgid "" "First, set the environment variable " "``XINFERENCE_TRANSFORMERS_ENABLE_BATCHING`` to ``1`` when starting " @@ -48,13 +52,22 @@ msgstr "" "首先，启动 Xinference 时需要将环境变量 ``XINFERENCE_TRANSFORMERS_ENABLE_" "BATCHING`` 置为 ``1`` 。" -#: ../../source/user_guide/continuous_batching.rst:21 +#: ../../source/user_guide/continuous_batching.rst:25 +msgid "" +"Since ``v0.16.0``, this feature is turned on by default and is no longer " +"required to set the ``XINFERENCE_TRANSFORMERS_ENABLE_BATCHING`` " +"environment variable. This environment variable has been removed." +msgstr "" +"自 ``v0.16.0`` 开始，此功能默认开启，不再需要设置 ``XINFERENCE_TRANSFORMERS_ENABLE_BATCHING`` 环境变量，" +"且该环境变量已被移除。" + +#: ../../source/user_guide/continuous_batching.rst:30 msgid "" "Then, ensure that the ``transformers`` engine is selected when launching " "the model. For example:" msgstr "然后，启动 LLM 模型时选择 ``transformers`` 推理引擎。例如：" -#: ../../source/user_guide/continuous_batching.rst:57 +#: ../../source/user_guide/continuous_batching.rst:66 msgid "" "Once this feature is enabled, all requests for LLMs will be managed by " "continuous batching, and the average throughput of requests made to a " @@ -64,57 +77,92 @@ msgstr "" "一旦此功能开启，LLM 模型的所有接口将被此功能接管。所有接口的使用方式没有" "任何变化。" -#: ../../source/user_guide/continuous_batching.rst:63 +#: ../../source/user_guide/continuous_batching.rst:71 +msgid "Image Model" +msgstr "图像模型" + +#: ../../source/user_guide/continuous_batching.rst:72 +msgid "" +"Currently, for image models, only the ``text_to_image`` interface is " +"supported for ``FLUX.1`` series models." +msgstr "" +"当前只有 ``FLUX.1`` 系列模型的 ``text_to_image`` （文生图）接口支持此功能。" + +#: ../../source/user_guide/continuous_batching.rst:74 +msgid "" +"Enabling this feature requires setting the environment variable " +"``XINFERENCE_TEXT_TO_IMAGE_BATCHING_SIZE``, which indicates the ``size`` " +"of the generated images." +msgstr "" +"图像模型开启此功能需要在启动 xinference 时指定 ``XINFERENCE_TEXT_TO_IMAGE_BATCHING_SIZE`` 环境变量，" +"表示生成图片的大小。" + +#: ../../source/user_guide/continuous_batching.rst:76 +msgid "For example, starting xinference like this:" +msgstr "" +"例如，像这样启动 xinference：" + +#: ../../source/user_guide/continuous_batching.rst:83 +msgid "" +"Then just use the ``text_to_image`` interface as before, and nothing else" +" needs to be changed." +msgstr "" +"接下来正常使用 ``text_to_image`` 接口即可，其他什么都不需要改变。" + +#: ../../source/user_guide/continuous_batching.rst:86 msgid "Abort your request" msgstr "中止请求" -#: ../../source/user_guide/continuous_batching.rst:64 +#: ../../source/user_guide/continuous_batching.rst:87 msgid "In this mode, you can abort requests that are in the process of inference." -msgstr "" -"此功能中，你可以优雅地中止正在推理中的请求。" +msgstr "此功能中，你可以优雅地中止正在推理中的请求。" -#: ../../source/user_guide/continuous_batching.rst:66 +#: ../../source/user_guide/continuous_batching.rst:89 msgid "First, add ``request_id`` option in ``generate_config``. For example:" -msgstr "" -"首先，在推理请求的 ``generate_config`` 中指定 ``request_id`` 选项。例如：" +msgstr "首先，在推理请求的 ``generate_config`` 中指定 ``request_id`` 选项。例如：" -#: ../../source/user_guide/continuous_batching.rst:75 +#: ../../source/user_guide/continuous_batching.rst:98 msgid "" "Then, abort the request using the ``request_id`` you have set. For " "example:" -msgstr "" -"接着，带着你指定的 ``request_id`` 去中止该请求。例如：" +msgstr "接着，带着你指定的 ``request_id`` 去中止该请求。例如：" -#: ../../source/user_guide/continuous_batching.rst:83 +#: ../../source/user_guide/continuous_batching.rst:106 msgid "" "Note that if your request has already finished, aborting the request will" -" be a no-op." -msgstr "" -"注意，如果你的请求已经结束，那么此操作将什么都不做。" +" be a no-op. Image models also support this feature." +msgstr "注意，如果你的请求已经结束，那么此操作将什么都不做。" -#: ../../source/user_guide/continuous_batching.rst:86 +#: ../../source/user_guide/continuous_batching.rst:110 msgid "Note" msgstr "注意事项" -#: ../../source/user_guide/continuous_batching.rst:88 +#: ../../source/user_guide/continuous_batching.rst:112 msgid "" -"Currently, this feature only supports the ``generate``, ``chat`` and " -"``vision`` tasks for ``LLM`` models. The ``tool call`` tasks are not " -"supported." +"Currently, for ``LLM`` models, this feature only supports the " +"``generate``, ``chat``, ``tool call`` and ``vision`` tasks." msgstr "" -"当前，此功能仅支持 LLM 模型的 ``generate``, ``chat`` 和 ``vision`` （多" -"模态） 功能。``tool call`` （工具调用）暂时不支持。" +"当前，此功能仅支持 LLM 模型的 ``generate``, ``chat``, ``tool call`` （工具调用）和 ``vision`` （多" +"模态） 功能。" -#: ../../source/user_guide/continuous_batching.rst:90 +#: ../../source/user_guide/continuous_batching.rst:114 msgid "" -"For ``vision`` tasks, currently only ``qwen-vl-chat``, ``cogvlm2``, and " -"``glm-4v`` models are supported. More models will be supported in the " -"future. Please let us know your requirements." +"Currently, for ``image`` models, this feature only supports the " +"``text_to_image`` tasks. Only ``FLUX.1`` series models are supported." msgstr "" -"对于多模态任务，当前支持 ``qwen-vl-chat`` ，``cogvlm2`` 和 ``glm-4v`` " -"模型。未来将加入更多模型，敬请期待。" +"当前，对于图像模型，仅支持 `FLUX.1`` 系列模型的 ``text_to_image`` （文生图）功能。" -#: ../../source/user_guide/continuous_batching.rst:92 +#: ../../source/user_guide/continuous_batching.rst:116 +msgid "" +"For ``vision`` tasks, currently only ``qwen-vl-chat``, ``cogvlm2``, " +"``glm-4v`` and ``MiniCPM-V-2.6`` (only for image tasks) models are " +"supported. More models will be supported in the future. Please let us " +"know your requirements." +msgstr "" +"对于多模态任务，当前支持 ``qwen-vl-chat`` ，``cogvlm2``， ``glm-4v`` 和 `" +"`MiniCPM-V-2.6`` (仅对于图像任务)模型。未来将加入更多模型，敬请期待。" + +#: ../../source/user_guide/continuous_batching.rst:118 msgid "" "If using GPU inference, this method will consume more GPU memory. Please " "be cautious when increasing the number of concurrent requests to the same" @@ -126,17 +174,3 @@ msgstr "" "请求量。``launch_model`` 接口提供可选参数 ``max_num_seqs`` 用于调整并发度" "，默认值为 ``16`` 。" -#: ../../source/user_guide/continuous_batching.rst:95 -msgid "" -"This feature is still in the experimental stage, and we welcome your " -"active feedback on any issues." -msgstr "此功能仍处于实验阶段，欢迎反馈任何问题。" - -#: ../../source/user_guide/continuous_batching.rst:97 -msgid "" -"After a period of testing, this method will remain enabled by default, " -"and the original inference method will be deprecated." -msgstr "" -"一段时间的测试之后，此功能将代替原来的 transformers 推理逻辑成为默认行为" -"。原来的推理逻辑将被摒弃。" - diff --git a/doc/source/models/builtin/audio/cosyvoice-300m-instruct.rst b/doc/source/models/builtin/audio/cosyvoice-300m-instruct.rst index 9e438f04d5..eff5788cf0 100644 --- a/doc/source/models/builtin/audio/cosyvoice-300m-instruct.rst +++ b/doc/source/models/builtin/audio/cosyvoice-300m-instruct.rst @@ -12,7 +12,7 @@ CosyVoice-300M-Instruct Specifications ^^^^^^^^^^^^^^ -- **Model ID:** model-scope/CosyVoice-300M-Instruct +- **Model ID:** FunAudioLLM/CosyVoice-300M-Instruct Execute the following command to launch the model:: diff --git a/doc/source/models/builtin/audio/cosyvoice-300m-sft.rst b/doc/source/models/builtin/audio/cosyvoice-300m-sft.rst index 4aa6864d31..b903b9118b 100644 --- a/doc/source/models/builtin/audio/cosyvoice-300m-sft.rst +++ b/doc/source/models/builtin/audio/cosyvoice-300m-sft.rst @@ -12,7 +12,7 @@ CosyVoice-300M-SFT Specifications ^^^^^^^^^^^^^^ -- **Model ID:** model-scope/CosyVoice-300M-SFT +- **Model ID:** FunAudioLLM/CosyVoice-300M-SFT Execute the following command to launch the model:: diff --git a/doc/source/models/builtin/audio/cosyvoice-300m.rst b/doc/source/models/builtin/audio/cosyvoice-300m.rst index f667546dbd..da04f444a2 100644 --- a/doc/source/models/builtin/audio/cosyvoice-300m.rst +++ b/doc/source/models/builtin/audio/cosyvoice-300m.rst @@ -12,7 +12,7 @@ CosyVoice-300M Specifications ^^^^^^^^^^^^^^ -- **Model ID:** model-scope/CosyVoice-300M +- **Model ID:** FunAudioLLM/CosyVoice-300M Execute the following command to launch the model:: diff --git a/doc/source/models/builtin/audio/fishspeech-1.4.rst b/doc/source/models/builtin/audio/fishspeech-1.4.rst new file mode 100644 index 0000000000..c256495d67 --- /dev/null +++ b/doc/source/models/builtin/audio/fishspeech-1.4.rst @@ -0,0 +1,19 @@ +.. _models_builtin_fishspeech-1.4: + +============== +FishSpeech-1.4 +============== + +- **Model Name:** FishSpeech-1.4 +- **Model Family:** FishAudio +- **Abilities:** text-to-audio +- **Multilingual:** True + +Specifications +^^^^^^^^^^^^^^ + +- **Model ID:** fishaudio/fish-speech-1.4 + +Execute the following command to launch the model:: + + xinference launch --model-name FishSpeech-1.4 --model-type audio \ No newline at end of file diff --git a/doc/source/models/builtin/audio/index.rst b/doc/source/models/builtin/audio/index.rst index 8959b2b94f..b89eaf41f6 100644 --- a/doc/source/models/builtin/audio/index.rst +++ b/doc/source/models/builtin/audio/index.rst @@ -25,7 +25,7 @@ The following is a list of built-in audio models in Xinference: cosyvoice-300m-sft - fishspeech-1.2-sft + fishspeech-1.4 sensevoicesmall @@ -35,6 +35,8 @@ The following is a list of built-in audio models in Xinference: whisper-large-v3 + whisper-large-v3-turbo + whisper-medium whisper-medium.en diff --git a/doc/source/models/builtin/audio/whisper-large-v3-turbo.rst b/doc/source/models/builtin/audio/whisper-large-v3-turbo.rst new file mode 100644 index 0000000000..dbaecc0c6d --- /dev/null +++ b/doc/source/models/builtin/audio/whisper-large-v3-turbo.rst @@ -0,0 +1,19 @@ +.. _models_builtin_whisper-large-v3-turbo: + +====================== +whisper-large-v3-turbo +====================== + +- **Model Name:** whisper-large-v3-turbo +- **Model Family:** whisper +- **Abilities:** audio-to-text +- **Multilingual:** True + +Specifications +^^^^^^^^^^^^^^ + +- **Model ID:** openai/whisper-large-v3-turbo + +Execute the following command to launch the model:: + + xinference launch --model-name whisper-large-v3-turbo --model-type audio \ No newline at end of file diff --git a/doc/source/models/builtin/embedding/gte-qwen2.rst b/doc/source/models/builtin/embedding/gte-qwen2.rst index a88fdece9d..85eeeac39a 100644 --- a/doc/source/models/builtin/embedding/gte-qwen2.rst +++ b/doc/source/models/builtin/embedding/gte-qwen2.rst @@ -11,11 +11,11 @@ gte-Qwen2 Specifications ^^^^^^^^^^^^^^ -- **Dimensions:** 3584 +- **Dimensions:** 4096 - **Max Tokens:** 32000 - **Model ID:** Alibaba-NLP/gte-Qwen2-7B-instruct - **Model Hubs**: `Hugging Face `__, `ModelScope `__ Execute the following command to launch the model:: - xinference launch --model-name gte-Qwen2 --model-type embedding \ No newline at end of file + xinference launch --model-name gte-Qwen2 --model-type embedding diff --git a/doc/source/models/builtin/embedding/index.rst b/doc/source/models/builtin/embedding/index.rst index 5afa52c21d..4422b10977 100644 --- a/doc/source/models/builtin/embedding/index.rst +++ b/doc/source/models/builtin/embedding/index.rst @@ -53,6 +53,8 @@ The following is a list of built-in embedding models in Xinference: jina-embeddings-v2-small-en + jina-embeddings-v3 + m3e-base m3e-large diff --git a/doc/source/models/builtin/embedding/jina-embeddings-v3.rst b/doc/source/models/builtin/embedding/jina-embeddings-v3.rst new file mode 100644 index 0000000000..59e7f3577c --- /dev/null +++ b/doc/source/models/builtin/embedding/jina-embeddings-v3.rst @@ -0,0 +1,21 @@ +.. _models_builtin_jina-embeddings-v3: + +================== +jina-embeddings-v3 +================== + +- **Model Name:** jina-embeddings-v3 +- **Languages:** zh, en +- **Abilities:** embed + +Specifications +^^^^^^^^^^^^^^ + +- **Dimensions:** 1024 +- **Max Tokens:** 8192 +- **Model ID:** jinaai/jina-embeddings-v3 +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model:: + + xinference launch --model-name jina-embeddings-v3 --model-type embedding \ No newline at end of file diff --git a/doc/source/models/builtin/image/flux.1-dev.rst b/doc/source/models/builtin/image/flux.1-dev.rst index 829bcbfd75..3a16cfe0a7 100644 --- a/doc/source/models/builtin/image/flux.1-dev.rst +++ b/doc/source/models/builtin/image/flux.1-dev.rst @@ -6,7 +6,7 @@ FLUX.1-dev - **Model Name:** FLUX.1-dev - **Model Family:** stable_diffusion -- **Abilities:** text2image +- **Abilities:** text2image, image2image, inpainting - **Available ControlNet:** None Specifications diff --git a/doc/source/models/builtin/image/flux.1-schnell.rst b/doc/source/models/builtin/image/flux.1-schnell.rst index 268f5a1720..df82d2069f 100644 --- a/doc/source/models/builtin/image/flux.1-schnell.rst +++ b/doc/source/models/builtin/image/flux.1-schnell.rst @@ -6,7 +6,7 @@ FLUX.1-schnell - **Model Name:** FLUX.1-schnell - **Model Family:** stable_diffusion -- **Abilities:** text2image +- **Abilities:** text2image, image2image, inpainting - **Available ControlNet:** None Specifications diff --git a/doc/source/models/builtin/image/got-ocr2_0.rst b/doc/source/models/builtin/image/got-ocr2_0.rst new file mode 100644 index 0000000000..994b0deae4 --- /dev/null +++ b/doc/source/models/builtin/image/got-ocr2_0.rst @@ -0,0 +1,19 @@ +.. _models_builtin_got-ocr2_0: + +========== +GOT-OCR2_0 +========== + +- **Model Name:** GOT-OCR2_0 +- **Model Family:** ocr +- **Abilities:** ocr +- **Available ControlNet:** None + +Specifications +^^^^^^^^^^^^^^ + +- **Model ID:** stepfun-ai/GOT-OCR2_0 + +Execute the following command to launch the model:: + + xinference launch --model-name GOT-OCR2_0 --model-type image \ No newline at end of file diff --git a/doc/source/models/builtin/image/index.rst b/doc/source/models/builtin/image/index.rst index 5bc8744338..bf4efdab86 100644 --- a/doc/source/models/builtin/image/index.rst +++ b/doc/source/models/builtin/image/index.rst @@ -15,6 +15,8 @@ The following is a list of built-in image models in Xinference: flux.1-schnell + got-ocr2_0 + kolors sd-turbo diff --git a/doc/source/models/builtin/image/sd3-medium.rst b/doc/source/models/builtin/image/sd3-medium.rst index c69b4a708b..953a3eca32 100644 --- a/doc/source/models/builtin/image/sd3-medium.rst +++ b/doc/source/models/builtin/image/sd3-medium.rst @@ -6,7 +6,7 @@ sd3-medium - **Model Name:** sd3-medium - **Model Family:** stable_diffusion -- **Abilities:** text2image, image2image +- **Abilities:** text2image, image2image, inpainting - **Available ControlNet:** None Specifications diff --git a/doc/source/models/builtin/llm/chatglm3-128k.rst b/doc/source/models/builtin/llm/chatglm3-128k.rst deleted file mode 100644 index 410669fd83..0000000000 --- a/doc/source/models/builtin/llm/chatglm3-128k.rst +++ /dev/null @@ -1,31 +0,0 @@ -.. _models_llm_chatglm3-128k: - -======================================== -chatglm3-128k -======================================== - -- **Context Length:** 131072 -- **Model Name:** chatglm3-128k -- **Languages:** en, zh -- **Abilities:** chat -- **Description:** ChatGLM3 is the third generation of ChatGLM, still open-source and trained on Chinese and English data. - -Specifications -^^^^^^^^^^^^^^ - - -Model Spec 1 (pytorch, 6 Billion) -++++++++++++++++++++++++++++++++++++++++ - -- **Model Format:** pytorch -- **Model Size (in billions):** 6 -- **Quantizations:** 4-bit, 8-bit, none -- **Engines**: vLLM, Transformers (vLLM only available for quantization none) -- **Model ID:** THUDM/chatglm3-6b-128k -- **Model Hubs**: `Hugging Face `__, `ModelScope `__ - -Execute the following command to launch the model, remember to replace ``${quantization}`` with your -chosen quantization method from the options listed above:: - - xinference launch --model-engine ${engine} --model-name chatglm3-128k --size-in-billions 6 --model-format pytorch --quantization ${quantization} - diff --git a/doc/source/models/builtin/llm/chatglm3-32k.rst b/doc/source/models/builtin/llm/chatglm3-32k.rst deleted file mode 100644 index b728ce3fad..0000000000 --- a/doc/source/models/builtin/llm/chatglm3-32k.rst +++ /dev/null @@ -1,31 +0,0 @@ -.. _models_llm_chatglm3-32k: - -======================================== -chatglm3-32k -======================================== - -- **Context Length:** 32768 -- **Model Name:** chatglm3-32k -- **Languages:** en, zh -- **Abilities:** chat -- **Description:** ChatGLM3 is the third generation of ChatGLM, still open-source and trained on Chinese and English data. - -Specifications -^^^^^^^^^^^^^^ - - -Model Spec 1 (pytorch, 6 Billion) -++++++++++++++++++++++++++++++++++++++++ - -- **Model Format:** pytorch -- **Model Size (in billions):** 6 -- **Quantizations:** 4-bit, 8-bit, none -- **Engines**: vLLM, Transformers (vLLM only available for quantization none) -- **Model ID:** THUDM/chatglm3-6b-32k -- **Model Hubs**: `Hugging Face `__, `ModelScope `__ - -Execute the following command to launch the model, remember to replace ``${quantization}`` with your -chosen quantization method from the options listed above:: - - xinference launch --model-engine ${engine} --model-name chatglm3-32k --size-in-billions 6 --model-format pytorch --quantization ${quantization} - diff --git a/doc/source/models/builtin/llm/chatglm3.rst b/doc/source/models/builtin/llm/chatglm3.rst deleted file mode 100644 index baf7a0fa08..0000000000 --- a/doc/source/models/builtin/llm/chatglm3.rst +++ /dev/null @@ -1,31 +0,0 @@ -.. _models_llm_chatglm3: - -======================================== -chatglm3 -======================================== - -- **Context Length:** 8192 -- **Model Name:** chatglm3 -- **Languages:** en, zh -- **Abilities:** chat, tools -- **Description:** ChatGLM3 is the third generation of ChatGLM, still open-source and trained on Chinese and English data. - -Specifications -^^^^^^^^^^^^^^ - - -Model Spec 1 (pytorch, 6 Billion) -++++++++++++++++++++++++++++++++++++++++ - -- **Model Format:** pytorch -- **Model Size (in billions):** 6 -- **Quantizations:** 4-bit, 8-bit, none -- **Engines**: vLLM, Transformers (vLLM only available for quantization none) -- **Model ID:** THUDM/chatglm3-6b -- **Model Hubs**: `Hugging Face `__, `ModelScope `__ - -Execute the following command to launch the model, remember to replace ``${quantization}`` with your -chosen quantization method from the options listed above:: - - xinference launch --model-engine ${engine} --model-name chatglm3 --size-in-billions 6 --model-format pytorch --quantization ${quantization} - diff --git a/doc/source/models/builtin/llm/deepseek-v2-chat-0628.rst b/doc/source/models/builtin/llm/deepseek-v2-chat-0628.rst new file mode 100644 index 0000000000..d6e91cb248 --- /dev/null +++ b/doc/source/models/builtin/llm/deepseek-v2-chat-0628.rst @@ -0,0 +1,31 @@ +.. _models_llm_deepseek-v2-chat-0628: + +======================================== +deepseek-v2-chat-0628 +======================================== + +- **Context Length:** 128000 +- **Model Name:** deepseek-v2-chat-0628 +- **Languages:** en, zh +- **Abilities:** chat +- **Description:** DeepSeek-V2-Chat-0628 is an improved version of DeepSeek-V2-Chat. + +Specifications +^^^^^^^^^^^^^^ + + +Model Spec 1 (pytorch, 236 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 236 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** deepseek-ai/DeepSeek-V2-Chat-0628 +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name deepseek-v2-chat-0628 --size-in-billions 236 --model-format pytorch --quantization ${quantization} + diff --git a/doc/source/models/builtin/llm/deepseek-v2-chat.rst b/doc/source/models/builtin/llm/deepseek-v2-chat.rst new file mode 100644 index 0000000000..84595c2bbb --- /dev/null +++ b/doc/source/models/builtin/llm/deepseek-v2-chat.rst @@ -0,0 +1,47 @@ +.. _models_llm_deepseek-v2-chat: + +======================================== +deepseek-v2-chat +======================================== + +- **Context Length:** 128000 +- **Model Name:** deepseek-v2-chat +- **Languages:** en, zh +- **Abilities:** chat +- **Description:** DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. + +Specifications +^^^^^^^^^^^^^^ + + +Model Spec 1 (pytorch, 16 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 16 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** deepseek-ai/DeepSeek-V2-Lite-Chat +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name deepseek-v2-chat --size-in-billions 16 --model-format pytorch --quantization ${quantization} + + +Model Spec 2 (pytorch, 236 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 236 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** deepseek-ai/DeepSeek-V2-Chat +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name deepseek-v2-chat --size-in-billions 236 --model-format pytorch --quantization ${quantization} + diff --git a/doc/source/models/builtin/llm/deepseek-v2.5.rst b/doc/source/models/builtin/llm/deepseek-v2.5.rst new file mode 100644 index 0000000000..5f5b9475d4 --- /dev/null +++ b/doc/source/models/builtin/llm/deepseek-v2.5.rst @@ -0,0 +1,31 @@ +.. _models_llm_deepseek-v2.5: + +======================================== +deepseek-v2.5 +======================================== + +- **Context Length:** 128000 +- **Model Name:** deepseek-v2.5 +- **Languages:** en, zh +- **Abilities:** chat +- **Description:** DeepSeek-V2.5 is an upgraded version that combines DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct. The new model integrates the general and coding abilities of the two previous versions. + +Specifications +^^^^^^^^^^^^^^ + + +Model Spec 1 (pytorch, 236 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 236 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** deepseek-ai/DeepSeek-V2.5 +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name deepseek-v2.5 --size-in-billions 236 --model-format pytorch --quantization ${quantization} + diff --git a/doc/source/models/builtin/llm/deepseek-v2.rst b/doc/source/models/builtin/llm/deepseek-v2.rst new file mode 100644 index 0000000000..4102b9568c --- /dev/null +++ b/doc/source/models/builtin/llm/deepseek-v2.rst @@ -0,0 +1,47 @@ +.. _models_llm_deepseek-v2: + +======================================== +deepseek-v2 +======================================== + +- **Context Length:** 128000 +- **Model Name:** deepseek-v2 +- **Languages:** en, zh +- **Abilities:** generate +- **Description:** DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. + +Specifications +^^^^^^^^^^^^^^ + + +Model Spec 1 (pytorch, 16 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 16 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: Transformers +- **Model ID:** deepseek-ai/DeepSeek-V2-Lite +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name deepseek-v2 --size-in-billions 16 --model-format pytorch --quantization ${quantization} + + +Model Spec 2 (pytorch, 236 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 236 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: Transformers +- **Model ID:** deepseek-ai/DeepSeek-V2 +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name deepseek-v2 --size-in-billions 236 --model-format pytorch --quantization ${quantization} + diff --git a/doc/source/models/builtin/llm/gorilla-openfunctions-v1.rst b/doc/source/models/builtin/llm/gorilla-openfunctions-v1.rst deleted file mode 100644 index d7ea21418d..0000000000 --- a/doc/source/models/builtin/llm/gorilla-openfunctions-v1.rst +++ /dev/null @@ -1,47 +0,0 @@ -.. _models_llm_gorilla-openfunctions-v1: - -======================================== -gorilla-openfunctions-v1 -======================================== - -- **Context Length:** 4096 -- **Model Name:** gorilla-openfunctions-v1 -- **Languages:** en -- **Abilities:** chat -- **Description:** OpenFunctions is designed to extend Large Language Model (LLM) Chat Completion feature to formulate executable APIs call given natural language instructions and API context. - -Specifications -^^^^^^^^^^^^^^ - - -Model Spec 1 (pytorch, 7 Billion) -++++++++++++++++++++++++++++++++++++++++ - -- **Model Format:** pytorch -- **Model Size (in billions):** 7 -- **Quantizations:** 4-bit, 8-bit, none -- **Engines**: Transformers -- **Model ID:** gorilla-llm/gorilla-openfunctions-v1 -- **Model Hubs**: `Hugging Face `__ - -Execute the following command to launch the model, remember to replace ``${quantization}`` with your -chosen quantization method from the options listed above:: - - xinference launch --model-engine ${engine} --model-name gorilla-openfunctions-v1 --size-in-billions 7 --model-format pytorch --quantization ${quantization} - - -Model Spec 2 (ggufv2, 7 Billion) -++++++++++++++++++++++++++++++++++++++++ - -- **Model Format:** ggufv2 -- **Model Size (in billions):** 7 -- **Quantizations:** Q2_K, Q3_K_L, Q3_K_M, Q3_K_S, Q4_0, Q4_K_M, Q4_K_S, Q5_0, Q5_K_M, Q5_K_S, Q6_K, Q8_0 -- **Engines**: llama.cpp -- **Model ID:** TheBloke/gorilla-openfunctions-v1-GGUF -- **Model Hubs**: `Hugging Face `__ - -Execute the following command to launch the model, remember to replace ``${quantization}`` with your -chosen quantization method from the options listed above:: - - xinference launch --model-engine ${engine} --model-name gorilla-openfunctions-v1 --size-in-billions 7 --model-format ggufv2 --quantization ${quantization} - diff --git a/doc/source/models/builtin/llm/index.rst b/doc/source/models/builtin/llm/index.rst index 75745ffdc6..3ff3c4b4f9 100644 --- a/doc/source/models/builtin/llm/index.rst +++ b/doc/source/models/builtin/llm/index.rst @@ -46,21 +46,6 @@ The following is a list of built-in LLM in Xinference: - 131072 - C4AI Command-R(+) is a research release of a 35 and 104 billion parameter highly performant generative model. - * - :ref:`chatglm3 ` - - chat, tools - - 8192 - - ChatGLM3 is the third generation of ChatGLM, still open-source and trained on Chinese and English data. - - * - :ref:`chatglm3-128k ` - - chat - - 131072 - - ChatGLM3 is the third generation of ChatGLM, still open-source and trained on Chinese and English data. - - * - :ref:`chatglm3-32k ` - - chat - - 32768 - - ChatGLM3 is the third generation of ChatGLM, still open-source and trained on Chinese and English data. - * - :ref:`code-llama ` - generate - 100000 @@ -141,6 +126,26 @@ The following is a list of built-in LLM in Xinference: - 16384 - deepseek-coder-instruct is a model initialized from deepseek-coder-base and fine-tuned on 2B tokens of instruction data. + * - :ref:`deepseek-v2 ` + - generate + - 128000 + - DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. + + * - :ref:`deepseek-v2-chat ` + - chat + - 128000 + - DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. + + * - :ref:`deepseek-v2-chat-0628 ` + - chat + - 128000 + - DeepSeek-V2-Chat-0628 is an improved version of DeepSeek-V2-Chat. + + * - :ref:`deepseek-v2.5 ` + - chat + - 128000 + - DeepSeek-V2.5 is an upgraded version that combines DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct. The new model integrates the general and coding abilities of the two previous versions. + * - :ref:`deepseek-vl-chat ` - chat, vision - 4096 @@ -171,11 +176,6 @@ The following is a list of built-in LLM in Xinference: - 1048576 - GLM4 is the open source version of the latest generation of pre-trained models in the GLM-4 series launched by Zhipu AI. - * - :ref:`gorilla-openfunctions-v1 ` - - chat - - 4096 - - OpenFunctions is designed to extend Large Language Model (LLM) Chat Completion feature to formulate executable APIs call given natural language instructions and API context. - * - :ref:`gorilla-openfunctions-v2 ` - chat - 4096 @@ -237,7 +237,7 @@ The following is a list of built-in LLM in Xinference: - Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture * - :ref:`llama-3.1-instruct ` - - chat + - chat, tools - 131072 - The Llama 3.1 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks.. @@ -276,6 +276,11 @@ The following is a list of built-in LLM in Xinference: - 32768 - MiniCPM-V 2.6 is the latest model in the MiniCPM-V series. The model is built on SigLip-400M and Qwen2-7B with a total of 8B parameters. + * - :ref:`minicpm3-4b ` + - chat + - 32768 + - MiniCPM3-4B is the 3rd generation of MiniCPM series. The overall performance of MiniCPM3-4B surpasses Phi-3.5-mini-Instruct and GPT-3.5-Turbo-0125, being comparable with many recent 7B~9B models. + * - :ref:`mistral-instruct-v0.1 ` - chat - 8192 @@ -367,7 +372,7 @@ The following is a list of built-in LLM in Xinference: - Platypus-70B-instruct is a merge of garage-bAInd/Platypus2-70B and upstage/Llama-2-70b-instruct-v2. * - :ref:`qwen-chat ` - - chat, tools + - chat - 32768 - Qwen-chat is a fine-tuned version of the Qwen LLM trained with alignment techniques, specializing in chatting. @@ -386,6 +391,16 @@ The following is a list of built-in LLM in Xinference: - 32768 - Qwen1.5-MoE is a transformer-based MoE decoder-only language model pretrained on a large amount of data. + * - :ref:`qwen2-audio ` + - chat, audio + - 32768 + - Qwen2-Audio: A large-scale audio-language model which is capable of accepting various audio signal inputs and performing audio analysis or direct textual responses with regard to speech instructions. + + * - :ref:`qwen2-audio-instruct ` + - chat, audio + - 32768 + - Qwen2-Audio: A large-scale audio-language model which is capable of accepting various audio signal inputs and performing audio analysis or direct textual responses with regard to speech instructions. + * - :ref:`qwen2-instruct ` - chat, tools - 32768 @@ -396,6 +411,31 @@ The following is a list of built-in LLM in Xinference: - 32768 - Qwen2 is the new series of Qwen large language models. + * - :ref:`qwen2-vl-instruct ` + - chat, vision + - 32768 + - Qwen2-VL: To See the World More Clearly.Qwen2-VL is the latest version of the vision language models in the Qwen model familities. + + * - :ref:`qwen2.5 ` + - generate + - 32768 + - Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. + + * - :ref:`qwen2.5-coder ` + - generate + - 32768 + - Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). + + * - :ref:`qwen2.5-coder-instruct ` + - chat, tools + - 32768 + - Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). + + * - :ref:`qwen2.5-instruct ` + - chat, tools + - 32768 + - Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. + * - :ref:`seallm_v2 ` - generate - 8192 @@ -481,21 +521,21 @@ The following is a list of built-in LLM in Xinference: - 4096 - The Yi series models are large language models trained from scratch by developers at 01.AI. + * - :ref:`yi-coder ` + - generate + - 131072 + - Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.Excelling in long-context understanding with a maximum context length of 128K tokens.Supporting 52 major programming languages, including popular ones such as Java, Python, JavaScript, and C++. + + * - :ref:`yi-coder-chat ` + - chat + - 131072 + - Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.Excelling in long-context understanding with a maximum context length of 128K tokens.Supporting 52 major programming languages, including popular ones such as Java, Python, JavaScript, and C++. + * - :ref:`yi-vl-chat ` - chat, vision - 4096 - Yi Vision Language (Yi-VL) model is the open-source, multimodal version of the Yi Large Language Model (LLM) series, enabling content comprehension, recognition, and multi-round conversations about images. - * - :ref:`zephyr-7b-alpha ` - - chat - - 8192 - - Zephyr-7B-α is the first model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1. - - * - :ref:`zephyr-7b-beta ` - - chat - - 8192 - - Zephyr-7B-β is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 - .. toctree:: :maxdepth: 3 @@ -513,12 +553,6 @@ The following is a list of built-in LLM in Xinference: c4ai-command-r-v01 - chatglm3 - - chatglm3-128k - - chatglm3-32k - code-llama code-llama-instruct @@ -551,6 +585,14 @@ The following is a list of built-in LLM in Xinference: deepseek-coder-instruct + deepseek-v2 + + deepseek-v2-chat + + deepseek-v2-chat-0628 + + deepseek-v2.5 + deepseek-vl-chat gemma-2-it @@ -563,8 +605,6 @@ The following is a list of built-in LLM in Xinference: glm4-chat-1m - gorilla-openfunctions-v1 - gorilla-openfunctions-v2 gpt-2 @@ -605,6 +645,8 @@ The following is a list of built-in LLM in Xinference: minicpm-v-2.6 + minicpm3-4b + mistral-instruct-v0.1 mistral-instruct-v0.2 @@ -649,10 +691,24 @@ The following is a list of built-in LLM in Xinference: qwen1.5-moe-chat + qwen2-audio + + qwen2-audio-instruct + qwen2-instruct qwen2-moe-instruct + qwen2-vl-instruct + + qwen2.5 + + qwen2.5-coder + + qwen2.5-coder-instruct + + qwen2.5-instruct + seallm_v2 seallm_v2.5 @@ -687,10 +743,10 @@ The following is a list of built-in LLM in Xinference: yi-chat - yi-vl-chat + yi-coder - zephyr-7b-alpha + yi-coder-chat - zephyr-7b-beta + yi-vl-chat diff --git a/doc/source/models/builtin/llm/internvl2.rst b/doc/source/models/builtin/llm/internvl2.rst index cf74863d96..e83d989707 100644 --- a/doc/source/models/builtin/llm/internvl2.rst +++ b/doc/source/models/builtin/llm/internvl2.rst @@ -38,7 +38,7 @@ Model Spec 2 (pytorch, 2 Billion) - **Quantizations:** 4-bit, 8-bit, none - **Engines**: vLLM, Transformers (vLLM only available for quantization none) - **Model ID:** OpenGVLab/InternVL2-2B -- **Model Hubs**: `Hugging Face `__, `ModelScope `__ +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: @@ -54,7 +54,7 @@ Model Spec 3 (awq, 2 Billion) - **Quantizations:** Int4 - **Engines**: - **Model ID:** OpenGVLab/InternVL2-2B-AWQ -- **Model Hubs**: `Hugging Face `__ +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: @@ -78,36 +78,36 @@ chosen quantization method from the options listed above:: xinference launch --model-engine ${engine} --model-name internvl2 --size-in-billions 4 --model-format pytorch --quantization ${quantization} -Model Spec 5 (awq, 4 Billion) +Model Spec 5 (pytorch, 8 Billion) ++++++++++++++++++++++++++++++++++++++++ -- **Model Format:** awq -- **Model Size (in billions):** 4 -- **Quantizations:** Int4 -- **Engines**: -- **Model ID:** OpenGVLab/InternVL2-8B-AWQ -- **Model Hubs**: `Hugging Face `__ +- **Model Format:** pytorch +- **Model Size (in billions):** 8 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers (vLLM only available for quantization none) +- **Model ID:** OpenGVLab/InternVL2-8B +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: - xinference launch --model-engine ${engine} --model-name internvl2 --size-in-billions 4 --model-format awq --quantization ${quantization} + xinference launch --model-engine ${engine} --model-name internvl2 --size-in-billions 8 --model-format pytorch --quantization ${quantization} -Model Spec 6 (pytorch, 8 Billion) +Model Spec 6 (awq, 8 Billion) ++++++++++++++++++++++++++++++++++++++++ -- **Model Format:** pytorch +- **Model Format:** awq - **Model Size (in billions):** 8 -- **Quantizations:** 4-bit, 8-bit, none -- **Engines**: vLLM, Transformers (vLLM only available for quantization none) -- **Model ID:** OpenGVLab/InternVL2-8B -- **Model Hubs**: `Hugging Face `__, `ModelScope `__ +- **Quantizations:** Int4 +- **Engines**: +- **Model ID:** OpenGVLab/InternVL2-8B-AWQ +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: - xinference launch --model-engine ${engine} --model-name internvl2 --size-in-billions 8 --model-format pytorch --quantization ${quantization} + xinference launch --model-engine ${engine} --model-name internvl2 --size-in-billions 8 --model-format awq --quantization ${quantization} Model Spec 7 (pytorch, 26 Billion) @@ -118,7 +118,7 @@ Model Spec 7 (pytorch, 26 Billion) - **Quantizations:** 4-bit, 8-bit, none - **Engines**: vLLM, Transformers (vLLM only available for quantization none) - **Model ID:** OpenGVLab/InternVL2-26B -- **Model Hubs**: `Hugging Face `__, `ModelScope `__ +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: @@ -134,7 +134,7 @@ Model Spec 8 (awq, 26 Billion) - **Quantizations:** Int4 - **Engines**: - **Model ID:** OpenGVLab/InternVL2-26B-AWQ -- **Model Hubs**: `Hugging Face `__ +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: @@ -150,7 +150,7 @@ Model Spec 9 (pytorch, 40 Billion) - **Quantizations:** 4-bit, 8-bit, none - **Engines**: vLLM, Transformers (vLLM only available for quantization none) - **Model ID:** OpenGVLab/InternVL2-40B -- **Model Hubs**: `Hugging Face `__, `ModelScope `__ +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: @@ -166,7 +166,7 @@ Model Spec 10 (awq, 40 Billion) - **Quantizations:** Int4 - **Engines**: - **Model ID:** OpenGVLab/InternVL2-40B-AWQ -- **Model Hubs**: `Hugging Face `__ +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: @@ -182,7 +182,7 @@ Model Spec 11 (pytorch, 76 Billion) - **Quantizations:** 4-bit, 8-bit, none - **Engines**: vLLM, Transformers (vLLM only available for quantization none) - **Model ID:** OpenGVLab/InternVL2-Llama3-76B -- **Model Hubs**: `Hugging Face `__, `ModelScope `__ +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: @@ -198,7 +198,7 @@ Model Spec 12 (awq, 76 Billion) - **Quantizations:** Int4 - **Engines**: - **Model ID:** OpenGVLab/InternVL2-Llama3-76B-AWQ -- **Model Hubs**: `Hugging Face `__ +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: diff --git a/doc/source/models/builtin/llm/llama-2-chat.rst b/doc/source/models/builtin/llm/llama-2-chat.rst index ebcc14f7ca..c5899c2116 100644 --- a/doc/source/models/builtin/llm/llama-2-chat.rst +++ b/doc/source/models/builtin/llm/llama-2-chat.rst @@ -84,7 +84,7 @@ Model Spec 5 (gptq, 7 Billion) - **Model Format:** gptq - **Model Size (in billions):** 7 - **Quantizations:** Int4 -- **Engines**: vLLM, SGLang +- **Engines**: vLLM, Transformers, SGLang - **Model ID:** TheBloke/Llama-2-7B-Chat-GPTQ - **Model Hubs**: `Hugging Face `__ @@ -100,7 +100,7 @@ Model Spec 6 (gptq, 70 Billion) - **Model Format:** gptq - **Model Size (in billions):** 70 - **Quantizations:** Int4 -- **Engines**: vLLM, SGLang +- **Engines**: vLLM, Transformers, SGLang - **Model ID:** TheBloke/Llama-2-70B-Chat-GPTQ - **Model Hubs**: `Hugging Face `__ @@ -116,7 +116,7 @@ Model Spec 7 (awq, 70 Billion) - **Model Format:** awq - **Model Size (in billions):** 70 - **Quantizations:** Int4 -- **Engines**: vLLM, SGLang +- **Engines**: vLLM, Transformers, SGLang - **Model ID:** TheBloke/Llama-2-70B-Chat-AWQ - **Model Hubs**: `Hugging Face `__ @@ -132,7 +132,7 @@ Model Spec 8 (awq, 7 Billion) - **Model Format:** awq - **Model Size (in billions):** 7 - **Quantizations:** Int4 -- **Engines**: vLLM, SGLang +- **Engines**: vLLM, Transformers, SGLang - **Model ID:** TheBloke/Llama-2-7B-Chat-AWQ - **Model Hubs**: `Hugging Face `__ @@ -164,7 +164,7 @@ Model Spec 10 (gptq, 13 Billion) - **Model Format:** gptq - **Model Size (in billions):** 13 - **Quantizations:** Int4 -- **Engines**: vLLM, SGLang +- **Engines**: vLLM, Transformers, SGLang - **Model ID:** TheBloke/Llama-2-13B-chat-GPTQ - **Model Hubs**: `Hugging Face `__ @@ -180,7 +180,7 @@ Model Spec 11 (awq, 13 Billion) - **Model Format:** awq - **Model Size (in billions):** 13 - **Quantizations:** Int4 -- **Engines**: vLLM, SGLang +- **Engines**: vLLM, Transformers, SGLang - **Model ID:** TheBloke/Llama-2-13B-chat-AWQ - **Model Hubs**: `Hugging Face `__ diff --git a/doc/source/models/builtin/llm/llama-2.rst b/doc/source/models/builtin/llm/llama-2.rst index 11c634b467..0a34f17fdb 100644 --- a/doc/source/models/builtin/llm/llama-2.rst +++ b/doc/source/models/builtin/llm/llama-2.rst @@ -36,7 +36,7 @@ Model Spec 2 (gptq, 7 Billion) - **Model Format:** gptq - **Model Size (in billions):** 7 - **Quantizations:** Int4 -- **Engines**: vLLM, SGLang +- **Engines**: vLLM, Transformers, SGLang - **Model ID:** TheBloke/Llama-2-7B-GPTQ - **Model Hubs**: `Hugging Face `__ @@ -52,7 +52,7 @@ Model Spec 3 (awq, 7 Billion) - **Model Format:** awq - **Model Size (in billions):** 7 - **Quantizations:** Int4 -- **Engines**: vLLM, SGLang +- **Engines**: vLLM, Transformers, SGLang - **Model ID:** TheBloke/Llama-2-7B-AWQ - **Model Hubs**: `Hugging Face `__ @@ -132,7 +132,7 @@ Model Spec 8 (gptq, 13 Billion) - **Model Format:** gptq - **Model Size (in billions):** 13 - **Quantizations:** Int4 -- **Engines**: vLLM, SGLang +- **Engines**: vLLM, Transformers, SGLang - **Model ID:** TheBloke/Llama-2-13B-GPTQ - **Model Hubs**: `Hugging Face `__ @@ -148,7 +148,7 @@ Model Spec 9 (awq, 13 Billion) - **Model Format:** awq - **Model Size (in billions):** 13 - **Quantizations:** Int4 -- **Engines**: vLLM, SGLang +- **Engines**: vLLM, Transformers, SGLang - **Model ID:** TheBloke/Llama-2-13B-AWQ - **Model Hubs**: `Hugging Face `__ @@ -180,7 +180,7 @@ Model Spec 11 (gptq, 70 Billion) - **Model Format:** gptq - **Model Size (in billions):** 70 - **Quantizations:** Int4 -- **Engines**: vLLM, SGLang +- **Engines**: vLLM, Transformers, SGLang - **Model ID:** TheBloke/Llama-2-70B-GPTQ - **Model Hubs**: `Hugging Face `__ @@ -196,7 +196,7 @@ Model Spec 12 (awq, 70 Billion) - **Model Format:** awq - **Model Size (in billions):** 70 - **Quantizations:** Int4 -- **Engines**: vLLM, SGLang +- **Engines**: vLLM, Transformers, SGLang - **Model ID:** TheBloke/Llama-2-70B-AWQ - **Model Hubs**: `Hugging Face `__ diff --git a/doc/source/models/builtin/llm/minicpm3-4b.rst b/doc/source/models/builtin/llm/minicpm3-4b.rst new file mode 100644 index 0000000000..868175aba9 --- /dev/null +++ b/doc/source/models/builtin/llm/minicpm3-4b.rst @@ -0,0 +1,47 @@ +.. _models_llm_minicpm3-4b: + +======================================== +minicpm3-4b +======================================== + +- **Context Length:** 32768 +- **Model Name:** minicpm3-4b +- **Languages:** zh +- **Abilities:** chat +- **Description:** MiniCPM3-4B is the 3rd generation of MiniCPM series. The overall performance of MiniCPM3-4B surpasses Phi-3.5-mini-Instruct and GPT-3.5-Turbo-0125, being comparable with many recent 7B~9B models. + +Specifications +^^^^^^^^^^^^^^ + + +Model Spec 1 (pytorch, 4 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 4 +- **Quantizations:** none +- **Engines**: Transformers +- **Model ID:** openbmb/MiniCPM3-4B +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name minicpm3-4b --size-in-billions 4 --model-format pytorch --quantization ${quantization} + + +Model Spec 2 (gptq, 4 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** gptq +- **Model Size (in billions):** 4 +- **Quantizations:** none +- **Engines**: Transformers +- **Model ID:** openbmb/MiniCPM3-4B-GPTQ-Int4 +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name minicpm3-4b --size-in-billions 4 --model-format gptq --quantization ${quantization} + diff --git a/doc/source/models/builtin/llm/qwen-chat.rst b/doc/source/models/builtin/llm/qwen-chat.rst index d0b6ddcfdc..b3f8230307 100644 --- a/doc/source/models/builtin/llm/qwen-chat.rst +++ b/doc/source/models/builtin/llm/qwen-chat.rst @@ -7,7 +7,7 @@ qwen-chat - **Context Length:** 32768 - **Model Name:** qwen-chat - **Languages:** en, zh -- **Abilities:** chat, tools +- **Abilities:** chat - **Description:** Qwen-chat is a fine-tuned version of the Qwen LLM trained with alignment techniques, specializing in chatting. Specifications diff --git a/doc/source/models/builtin/llm/qwen2-audio-instruct.rst b/doc/source/models/builtin/llm/qwen2-audio-instruct.rst new file mode 100644 index 0000000000..2d126a387e --- /dev/null +++ b/doc/source/models/builtin/llm/qwen2-audio-instruct.rst @@ -0,0 +1,31 @@ +.. _models_llm_qwen2-audio-instruct: + +======================================== +qwen2-audio-instruct +======================================== + +- **Context Length:** 32768 +- **Model Name:** qwen2-audio-instruct +- **Languages:** en, zh +- **Abilities:** chat, audio +- **Description:** Qwen2-Audio: A large-scale audio-language model which is capable of accepting various audio signal inputs and performing audio analysis or direct textual responses with regard to speech instructions. + +Specifications +^^^^^^^^^^^^^^ + + +Model Spec 1 (pytorch, 7 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 7 +- **Quantizations:** none +- **Engines**: Transformers +- **Model ID:** Qwen/Qwen2-Audio-7B-Instruct +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2-audio-instruct --size-in-billions 7 --model-format pytorch --quantization ${quantization} + diff --git a/doc/source/models/builtin/llm/qwen2-audio.rst b/doc/source/models/builtin/llm/qwen2-audio.rst new file mode 100644 index 0000000000..2973390c44 --- /dev/null +++ b/doc/source/models/builtin/llm/qwen2-audio.rst @@ -0,0 +1,31 @@ +.. _models_llm_qwen2-audio: + +======================================== +qwen2-audio +======================================== + +- **Context Length:** 32768 +- **Model Name:** qwen2-audio +- **Languages:** en, zh +- **Abilities:** chat, audio +- **Description:** Qwen2-Audio: A large-scale audio-language model which is capable of accepting various audio signal inputs and performing audio analysis or direct textual responses with regard to speech instructions. + +Specifications +^^^^^^^^^^^^^^ + + +Model Spec 1 (pytorch, 7 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 7 +- **Quantizations:** none +- **Engines**: Transformers +- **Model ID:** Qwen/Qwen2-Audio-7B +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2-audio --size-in-billions 7 --model-format pytorch --quantization ${quantization} + diff --git a/doc/source/models/builtin/llm/qwen2-vl-instruct.rst b/doc/source/models/builtin/llm/qwen2-vl-instruct.rst new file mode 100644 index 0000000000..0872ea0168 --- /dev/null +++ b/doc/source/models/builtin/llm/qwen2-vl-instruct.rst @@ -0,0 +1,191 @@ +.. _models_llm_qwen2-vl-instruct: + +======================================== +qwen2-vl-instruct +======================================== + +- **Context Length:** 32768 +- **Model Name:** qwen2-vl-instruct +- **Languages:** en, zh +- **Abilities:** chat, vision +- **Description:** Qwen2-VL: To See the World More Clearly.Qwen2-VL is the latest version of the vision language models in the Qwen model familities. + +Specifications +^^^^^^^^^^^^^^ + + +Model Spec 1 (pytorch, 2 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 2 +- **Quantizations:** none +- **Engines**: Transformers +- **Model ID:** Qwen/Qwen2-VL-2B-Instruct +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2-vl-instruct --size-in-billions 2 --model-format pytorch --quantization ${quantization} + + +Model Spec 2 (gptq, 2 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** gptq +- **Model Size (in billions):** 2 +- **Quantizations:** Int8 +- **Engines**: Transformers +- **Model ID:** Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int8 +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2-vl-instruct --size-in-billions 2 --model-format gptq --quantization ${quantization} + + +Model Spec 3 (gptq, 2 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** gptq +- **Model Size (in billions):** 2 +- **Quantizations:** Int4 +- **Engines**: Transformers +- **Model ID:** Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4 +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2-vl-instruct --size-in-billions 2 --model-format gptq --quantization ${quantization} + + +Model Spec 4 (awq, 2 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** awq +- **Model Size (in billions):** 2 +- **Quantizations:** Int4 +- **Engines**: Transformers +- **Model ID:** Qwen/Qwen2-VL-2B-Instruct-AWQ +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2-vl-instruct --size-in-billions 2 --model-format awq --quantization ${quantization} + + +Model Spec 5 (pytorch, 7 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 7 +- **Quantizations:** none +- **Engines**: Transformers +- **Model ID:** Qwen/Qwen2-VL-7B-Instruct +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2-vl-instruct --size-in-billions 7 --model-format pytorch --quantization ${quantization} + + +Model Spec 6 (gptq, 7 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** gptq +- **Model Size (in billions):** 7 +- **Quantizations:** Int8 +- **Engines**: Transformers +- **Model ID:** Qwen/Qwen2-VL-7B-Instruct-GPTQ-Int8 +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2-vl-instruct --size-in-billions 7 --model-format gptq --quantization ${quantization} + + +Model Spec 7 (gptq, 7 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** gptq +- **Model Size (in billions):** 7 +- **Quantizations:** Int4 +- **Engines**: Transformers +- **Model ID:** Qwen/Qwen2-VL-7B-Instruct-GPTQ-Int4 +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2-vl-instruct --size-in-billions 7 --model-format gptq --quantization ${quantization} + + +Model Spec 8 (awq, 7 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** awq +- **Model Size (in billions):** 7 +- **Quantizations:** Int4 +- **Engines**: Transformers +- **Model ID:** Qwen/Qwen2-VL-7B-Instruct-AWQ +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2-vl-instruct --size-in-billions 7 --model-format awq --quantization ${quantization} + + +Model Spec 9 (pytorch, 72 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 72 +- **Quantizations:** none +- **Engines**: Transformers +- **Model ID:** Qwen/Qwen2-VL-72B-Instruct +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2-vl-instruct --size-in-billions 72 --model-format pytorch --quantization ${quantization} + + +Model Spec 10 (awq, 72 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** awq +- **Model Size (in billions):** 72 +- **Quantizations:** Int4 +- **Engines**: Transformers +- **Model ID:** Qwen/Qwen2-VL-72B-Instruct-AWQ +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2-vl-instruct --size-in-billions 72 --model-format awq --quantization ${quantization} + + +Model Spec 11 (gptq, 72 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** gptq +- **Model Size (in billions):** 72 +- **Quantizations:** Int4, Int8 +- **Engines**: Transformers +- **Model ID:** Qwen/Qwen2-VL-72B-Instruct-GPTQ-{quantization} +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2-vl-instruct --size-in-billions 72 --model-format gptq --quantization ${quantization} + diff --git a/doc/source/models/builtin/llm/qwen2.5-coder-instruct.rst b/doc/source/models/builtin/llm/qwen2.5-coder-instruct.rst new file mode 100644 index 0000000000..74614b4f0b --- /dev/null +++ b/doc/source/models/builtin/llm/qwen2.5-coder-instruct.rst @@ -0,0 +1,79 @@ +.. _models_llm_qwen2.5-coder-instruct: + +======================================== +qwen2.5-coder-instruct +======================================== + +- **Context Length:** 32768 +- **Model Name:** qwen2.5-coder-instruct +- **Languages:** en, zh +- **Abilities:** chat, tools +- **Description:** Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). + +Specifications +^^^^^^^^^^^^^^ + + +Model Spec 1 (pytorch, 1_5 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 1_5 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** Qwen/Qwen2.5-Coder-1.5B-Instruct +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-coder-instruct --size-in-billions 1_5 --model-format pytorch --quantization ${quantization} + + +Model Spec 2 (pytorch, 7 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 7 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** Qwen/Qwen2.5-Coder-7B-Instruct +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-coder-instruct --size-in-billions 7 --model-format pytorch --quantization ${quantization} + + +Model Spec 3 (ggufv2, 1_5 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** ggufv2 +- **Model Size (in billions):** 1_5 +- **Quantizations:** q2_k, q3_k_m, q4_0, q4_k_m, q5_0, q5_k_m, q6_k, q8_0 +- **Engines**: llama.cpp +- **Model ID:** Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-coder-instruct --size-in-billions 1_5 --model-format ggufv2 --quantization ${quantization} + + +Model Spec 4 (ggufv2, 7 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** ggufv2 +- **Model Size (in billions):** 7 +- **Quantizations:** q2_k, q3_k_m, q4_0, q4_k_m, q5_0, q5_k_m, q6_k, q8_0 +- **Engines**: llama.cpp +- **Model ID:** Qwen/Qwen2.5-Coder-7B-Instruct-GGUF +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-coder-instruct --size-in-billions 7 --model-format ggufv2 --quantization ${quantization} + diff --git a/doc/source/models/builtin/llm/qwen2.5-coder.rst b/doc/source/models/builtin/llm/qwen2.5-coder.rst new file mode 100644 index 0000000000..8ae4709930 --- /dev/null +++ b/doc/source/models/builtin/llm/qwen2.5-coder.rst @@ -0,0 +1,47 @@ +.. _models_llm_qwen2.5-coder: + +======================================== +qwen2.5-coder +======================================== + +- **Context Length:** 32768 +- **Model Name:** qwen2.5-coder +- **Languages:** en, zh +- **Abilities:** generate +- **Description:** Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). + +Specifications +^^^^^^^^^^^^^^ + + +Model Spec 1 (pytorch, 1_5 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 1_5 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** Qwen/Qwen2.5-Coder-1.5B +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-coder --size-in-billions 1_5 --model-format pytorch --quantization ${quantization} + + +Model Spec 2 (pytorch, 7 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 7 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** Qwen/Qwen2.5-Coder-7B +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-coder --size-in-billions 7 --model-format pytorch --quantization ${quantization} + diff --git a/doc/source/models/builtin/llm/qwen2.5-instruct.rst b/doc/source/models/builtin/llm/qwen2.5-instruct.rst new file mode 100644 index 0000000000..a214dcdd23 --- /dev/null +++ b/doc/source/models/builtin/llm/qwen2.5-instruct.rst @@ -0,0 +1,799 @@ +.. _models_llm_qwen2.5-instruct: + +======================================== +qwen2.5-instruct +======================================== + +- **Context Length:** 32768 +- **Model Name:** qwen2.5-instruct +- **Languages:** en, zh +- **Abilities:** chat, tools +- **Description:** Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. + +Specifications +^^^^^^^^^^^^^^ + + +Model Spec 1 (pytorch, 0_5 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 0_5 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** Qwen/Qwen2.5-0.5B-Instruct +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 0_5 --model-format pytorch --quantization ${quantization} + + +Model Spec 2 (pytorch, 1_5 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 1_5 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** Qwen/Qwen2.5-1.5B-Instruct +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 1_5 --model-format pytorch --quantization ${quantization} + + +Model Spec 3 (pytorch, 3 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 3 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** Qwen/Qwen2.5-3B-Instruct +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 3 --model-format pytorch --quantization ${quantization} + + +Model Spec 4 (pytorch, 7 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 7 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** Qwen/Qwen2.5-7B-Instruct +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 7 --model-format pytorch --quantization ${quantization} + + +Model Spec 5 (pytorch, 14 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 14 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** Qwen/Qwen2.5-14B-Instruct +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 14 --model-format pytorch --quantization ${quantization} + + +Model Spec 6 (pytorch, 32 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 32 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** Qwen/Qwen2.5-32B-Instruct +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 32 --model-format pytorch --quantization ${quantization} + + +Model Spec 7 (pytorch, 72 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 72 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** Qwen/Qwen2.5-72B-Instruct +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 72 --model-format pytorch --quantization ${quantization} + + +Model Spec 8 (gptq, 0_5 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** gptq +- **Model Size (in billions):** 0_5 +- **Quantizations:** Int4, Int8 +- **Engines**: vLLM, Transformers, SGLang +- **Model ID:** Qwen/Qwen2.5-0.5B-Instruct-GPTQ-{quantization} +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 0_5 --model-format gptq --quantization ${quantization} + + +Model Spec 9 (gptq, 1_5 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** gptq +- **Model Size (in billions):** 1_5 +- **Quantizations:** Int4, Int8 +- **Engines**: vLLM, Transformers, SGLang +- **Model ID:** Qwen/Qwen2.5-1.5B-Instruct-GPTQ-{quantization} +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 1_5 --model-format gptq --quantization ${quantization} + + +Model Spec 10 (gptq, 3 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** gptq +- **Model Size (in billions):** 3 +- **Quantizations:** Int4, Int8 +- **Engines**: vLLM, Transformers, SGLang +- **Model ID:** Qwen/Qwen2.5-3B-Instruct-GPTQ-{quantization} +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 3 --model-format gptq --quantization ${quantization} + + +Model Spec 11 (gptq, 7 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** gptq +- **Model Size (in billions):** 7 +- **Quantizations:** Int4, Int8 +- **Engines**: vLLM, Transformers, SGLang +- **Model ID:** Qwen/Qwen2.5-7B-Instruct-GPTQ-{quantization} +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 7 --model-format gptq --quantization ${quantization} + + +Model Spec 12 (gptq, 14 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** gptq +- **Model Size (in billions):** 14 +- **Quantizations:** Int4, Int8 +- **Engines**: vLLM, Transformers, SGLang +- **Model ID:** Qwen/Qwen2.5-14B-Instruct-GPTQ-{quantization} +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 14 --model-format gptq --quantization ${quantization} + + +Model Spec 13 (gptq, 32 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** gptq +- **Model Size (in billions):** 32 +- **Quantizations:** Int4, Int8 +- **Engines**: vLLM, Transformers, SGLang +- **Model ID:** Qwen/Qwen2.5-32B-Instruct-GPTQ-{quantization} +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 32 --model-format gptq --quantization ${quantization} + + +Model Spec 14 (gptq, 72 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** gptq +- **Model Size (in billions):** 72 +- **Quantizations:** Int4, Int8 +- **Engines**: vLLM, Transformers, SGLang +- **Model ID:** Qwen/Qwen2.5-72B-Instruct-GPTQ-{quantization} +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 72 --model-format gptq --quantization ${quantization} + + +Model Spec 15 (awq, 0_5 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** awq +- **Model Size (in billions):** 0_5 +- **Quantizations:** Int4 +- **Engines**: vLLM, Transformers, SGLang +- **Model ID:** Qwen/Qwen2.5-0.5B-Instruct-AWQ +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 0_5 --model-format awq --quantization ${quantization} + + +Model Spec 16 (awq, 1_5 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** awq +- **Model Size (in billions):** 1_5 +- **Quantizations:** Int4 +- **Engines**: vLLM, Transformers, SGLang +- **Model ID:** Qwen/Qwen2.5-1.5B-Instruct-AWQ +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 1_5 --model-format awq --quantization ${quantization} + + +Model Spec 17 (awq, 3 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** awq +- **Model Size (in billions):** 3 +- **Quantizations:** Int4 +- **Engines**: vLLM, Transformers, SGLang +- **Model ID:** Qwen/Qwen2.5-3B-Instruct-AWQ +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 3 --model-format awq --quantization ${quantization} + + +Model Spec 18 (awq, 7 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** awq +- **Model Size (in billions):** 7 +- **Quantizations:** Int4 +- **Engines**: vLLM, Transformers, SGLang +- **Model ID:** Qwen/Qwen2.5-7B-Instruct-AWQ +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 7 --model-format awq --quantization ${quantization} + + +Model Spec 19 (awq, 14 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** awq +- **Model Size (in billions):** 14 +- **Quantizations:** Int4 +- **Engines**: vLLM, Transformers, SGLang +- **Model ID:** Qwen/Qwen2.5-14B-Instruct-AWQ +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 14 --model-format awq --quantization ${quantization} + + +Model Spec 20 (awq, 32 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** awq +- **Model Size (in billions):** 32 +- **Quantizations:** Int4 +- **Engines**: vLLM, Transformers, SGLang +- **Model ID:** Qwen/Qwen2.5-32B-Instruct-AWQ +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 32 --model-format awq --quantization ${quantization} + + +Model Spec 21 (awq, 72 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** awq +- **Model Size (in billions):** 72 +- **Quantizations:** Int4 +- **Engines**: vLLM, Transformers, SGLang +- **Model ID:** Qwen/Qwen2.5-72B-Instruct-AWQ +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 72 --model-format awq --quantization ${quantization} + + +Model Spec 22 (ggufv2, 0_5 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** ggufv2 +- **Model Size (in billions):** 0_5 +- **Quantizations:** q2_k, q3_k_m, q4_0, q4_k_m, q5_0, q5_k_m, q6_k, q8_0 +- **Engines**: llama.cpp +- **Model ID:** Qwen/Qwen2.5-0.5B-Instruct-GGUF +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 0_5 --model-format ggufv2 --quantization ${quantization} + + +Model Spec 23 (ggufv2, 1_5 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** ggufv2 +- **Model Size (in billions):** 1_5 +- **Quantizations:** q2_k, q3_k_m, q4_0, q4_k_m, q5_0, q5_k_m, q6_k, q8_0 +- **Engines**: llama.cpp +- **Model ID:** Qwen/Qwen2.5-1.5B-Instruct-GGUF +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 1_5 --model-format ggufv2 --quantization ${quantization} + + +Model Spec 24 (ggufv2, 3 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** ggufv2 +- **Model Size (in billions):** 3 +- **Quantizations:** q2_k, q3_k_m, q4_0, q4_k_m, q5_0, q5_k_m, q6_k, q8_0 +- **Engines**: llama.cpp +- **Model ID:** Qwen/Qwen2.5-3B-Instruct-GGUF +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 3 --model-format ggufv2 --quantization ${quantization} + + +Model Spec 25 (ggufv2, 7 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** ggufv2 +- **Model Size (in billions):** 7 +- **Quantizations:** q2_k, q3_k_m, q4_0, q4_k_m, q5_0, q5_k_m, q6_k, q8_0 +- **Engines**: llama.cpp +- **Model ID:** Qwen/Qwen2.5-7B-Instruct-GGUF +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 7 --model-format ggufv2 --quantization ${quantization} + + +Model Spec 26 (ggufv2, 14 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** ggufv2 +- **Model Size (in billions):** 14 +- **Quantizations:** q2_k, q3_k_m, q4_0, q4_k_m, q5_0, q5_k_m, q6_k, q8_0 +- **Engines**: llama.cpp +- **Model ID:** Qwen/Qwen2.5-14B-Instruct-GGUF +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 14 --model-format ggufv2 --quantization ${quantization} + + +Model Spec 27 (ggufv2, 32 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** ggufv2 +- **Model Size (in billions):** 32 +- **Quantizations:** q2_k, q3_k_m, q4_0, q4_k_m, q5_0, q5_k_m, q6_k, q8_0 +- **Engines**: llama.cpp +- **Model ID:** Qwen/Qwen2.5-32B-Instruct-GGUF +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 32 --model-format ggufv2 --quantization ${quantization} + + +Model Spec 28 (ggufv2, 72 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** ggufv2 +- **Model Size (in billions):** 72 +- **Quantizations:** q2_k, q3_k_m, q4_0, q4_k_m, q5_0, q5_k_m, q6_k, q8_0, fp16 +- **Engines**: llama.cpp +- **Model ID:** Qwen/Qwen2.5-72B-Instruct-GGUF +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 72 --model-format ggufv2 --quantization ${quantization} + + +Model Spec 29 (mlx, 0_5 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** mlx +- **Model Size (in billions):** 0_5 +- **Quantizations:** 4-bit +- **Engines**: MLX +- **Model ID:** mlx-community/Qwen2.5-0.5B-Instruct-4bit +- **Model Hubs**: `Hugging Face `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 0_5 --model-format mlx --quantization ${quantization} + + +Model Spec 30 (mlx, 0_5 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** mlx +- **Model Size (in billions):** 0_5 +- **Quantizations:** 8-bit +- **Engines**: MLX +- **Model ID:** mlx-community/Qwen2.5-0.5B-Instruct-8bit +- **Model Hubs**: `Hugging Face `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 0_5 --model-format mlx --quantization ${quantization} + + +Model Spec 31 (mlx, 0_5 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** mlx +- **Model Size (in billions):** 0_5 +- **Quantizations:** none +- **Engines**: MLX +- **Model ID:** mlx-community/Qwen2.5-0.5B-Instruct-bf16 +- **Model Hubs**: `Hugging Face `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 0_5 --model-format mlx --quantization ${quantization} + + +Model Spec 32 (mlx, 1_5 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** mlx +- **Model Size (in billions):** 1_5 +- **Quantizations:** 4-bit +- **Engines**: MLX +- **Model ID:** mlx-community/Qwen2.5-1.5B-Instruct-4bit +- **Model Hubs**: `Hugging Face `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 1_5 --model-format mlx --quantization ${quantization} + + +Model Spec 33 (mlx, 1_5 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** mlx +- **Model Size (in billions):** 1_5 +- **Quantizations:** 8-bit +- **Engines**: MLX +- **Model ID:** mlx-community/Qwen2.5-1.5B-Instruct-8bit +- **Model Hubs**: `Hugging Face `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 1_5 --model-format mlx --quantization ${quantization} + + +Model Spec 34 (mlx, 1_5 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** mlx +- **Model Size (in billions):** 1_5 +- **Quantizations:** none +- **Engines**: MLX +- **Model ID:** mlx-community/Qwen2.5-1.5B-Instruct-bf16 +- **Model Hubs**: `Hugging Face `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 1_5 --model-format mlx --quantization ${quantization} + + +Model Spec 35 (mlx, 3 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** mlx +- **Model Size (in billions):** 3 +- **Quantizations:** 4-bit +- **Engines**: MLX +- **Model ID:** mlx-community/Qwen2.5-3B-Instruct-4bit +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 3 --model-format mlx --quantization ${quantization} + + +Model Spec 36 (mlx, 3 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** mlx +- **Model Size (in billions):** 3 +- **Quantizations:** 8-bit +- **Engines**: MLX +- **Model ID:** mlx-community/Qwen2.5-3B-Instruct-8bit +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 3 --model-format mlx --quantization ${quantization} + + +Model Spec 37 (mlx, 3 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** mlx +- **Model Size (in billions):** 3 +- **Quantizations:** none +- **Engines**: MLX +- **Model ID:** mlx-community/Qwen2.5-3B-Instruct-bf16 +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 3 --model-format mlx --quantization ${quantization} + + +Model Spec 38 (mlx, 7 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** mlx +- **Model Size (in billions):** 7 +- **Quantizations:** 4-bit +- **Engines**: MLX +- **Model ID:** mlx-community/Qwen2.5-7B-Instruct-4bit +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 7 --model-format mlx --quantization ${quantization} + + +Model Spec 39 (mlx, 7 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** mlx +- **Model Size (in billions):** 7 +- **Quantizations:** 8-bit +- **Engines**: MLX +- **Model ID:** mlx-community/Qwen2.5-7B-Instruct-8bit +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 7 --model-format mlx --quantization ${quantization} + + +Model Spec 40 (mlx, 7 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** mlx +- **Model Size (in billions):** 7 +- **Quantizations:** none +- **Engines**: MLX +- **Model ID:** mlx-community/Qwen2.5-7B-Instruct-bf16 +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 7 --model-format mlx --quantization ${quantization} + + +Model Spec 41 (mlx, 14 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** mlx +- **Model Size (in billions):** 14 +- **Quantizations:** 4-bit +- **Engines**: MLX +- **Model ID:** mlx-community/Qwen2.5-14B-Instruct-4bit +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 14 --model-format mlx --quantization ${quantization} + + +Model Spec 42 (mlx, 14 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** mlx +- **Model Size (in billions):** 14 +- **Quantizations:** 8-bit +- **Engines**: MLX +- **Model ID:** mlx-community/Qwen2.5-14B-Instruct-8bit +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 14 --model-format mlx --quantization ${quantization} + + +Model Spec 43 (mlx, 14 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** mlx +- **Model Size (in billions):** 14 +- **Quantizations:** none +- **Engines**: MLX +- **Model ID:** mlx-community/Qwen2.5-14B-Instruct-bf16 +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 14 --model-format mlx --quantization ${quantization} + + +Model Spec 44 (mlx, 32 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** mlx +- **Model Size (in billions):** 32 +- **Quantizations:** 4-bit +- **Engines**: MLX +- **Model ID:** mlx-community/Qwen2.5-32B-Instruct-4bit +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 32 --model-format mlx --quantization ${quantization} + + +Model Spec 45 (mlx, 32 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** mlx +- **Model Size (in billions):** 32 +- **Quantizations:** 8-bit +- **Engines**: MLX +- **Model ID:** mlx-community/Qwen2.5-32B-Instruct-8bit +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 32 --model-format mlx --quantization ${quantization} + + +Model Spec 46 (mlx, 32 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** mlx +- **Model Size (in billions):** 32 +- **Quantizations:** none +- **Engines**: MLX +- **Model ID:** mlx-community/Qwen2.5-32B-Instruct-bf16 +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 32 --model-format mlx --quantization ${quantization} + + +Model Spec 47 (mlx, 72 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** mlx +- **Model Size (in billions):** 72 +- **Quantizations:** 4-bit +- **Engines**: MLX +- **Model ID:** mlx-community/Qwen2.5-72B-Instruct-4bit +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 72 --model-format mlx --quantization ${quantization} + + +Model Spec 48 (mlx, 72 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** mlx +- **Model Size (in billions):** 72 +- **Quantizations:** 8-bit +- **Engines**: MLX +- **Model ID:** mlx-community/Qwen2.5-72B-Instruct-8bit +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 72 --model-format mlx --quantization ${quantization} + + +Model Spec 49 (mlx, 72 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** mlx +- **Model Size (in billions):** 72 +- **Quantizations:** none +- **Engines**: MLX +- **Model ID:** mlx-community/Qwen2.5-72B-Instruct-bf16 +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5-instruct --size-in-billions 72 --model-format mlx --quantization ${quantization} + diff --git a/doc/source/models/builtin/llm/qwen2.5.rst b/doc/source/models/builtin/llm/qwen2.5.rst new file mode 100644 index 0000000000..44f1ddae07 --- /dev/null +++ b/doc/source/models/builtin/llm/qwen2.5.rst @@ -0,0 +1,127 @@ +.. _models_llm_qwen2.5: + +======================================== +qwen2.5 +======================================== + +- **Context Length:** 32768 +- **Model Name:** qwen2.5 +- **Languages:** en, zh +- **Abilities:** generate +- **Description:** Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. + +Specifications +^^^^^^^^^^^^^^ + + +Model Spec 1 (pytorch, 0_5 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 0_5 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** Qwen/Qwen2.5-0.5B +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5 --size-in-billions 0_5 --model-format pytorch --quantization ${quantization} + + +Model Spec 2 (pytorch, 1_5 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 1_5 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** Qwen/Qwen2.5-1.5B +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5 --size-in-billions 1_5 --model-format pytorch --quantization ${quantization} + + +Model Spec 3 (pytorch, 3 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 3 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** Qwen/Qwen2.5-3B +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5 --size-in-billions 3 --model-format pytorch --quantization ${quantization} + + +Model Spec 4 (pytorch, 7 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 7 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** Qwen/Qwen2.5-7B +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5 --size-in-billions 7 --model-format pytorch --quantization ${quantization} + + +Model Spec 5 (pytorch, 14 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 14 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** Qwen/Qwen2.5-14B +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5 --size-in-billions 14 --model-format pytorch --quantization ${quantization} + + +Model Spec 6 (pytorch, 32 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 32 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** Qwen/Qwen2.5-32B +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5 --size-in-billions 32 --model-format pytorch --quantization ${quantization} + + +Model Spec 7 (pytorch, 72 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 72 +- **Quantizations:** 4-bit, 8-bit, none +- **Engines**: vLLM, Transformers, SGLang (vLLM and SGLang only available for quantization none) +- **Model ID:** Qwen/Qwen2.5-72B +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name qwen2.5 --size-in-billions 72 --model-format pytorch --quantization ${quantization} + diff --git a/doc/source/models/builtin/llm/wizardcoder-python-v1.0.rst b/doc/source/models/builtin/llm/wizardcoder-python-v1.0.rst index 4bcf89bfab..316d08d26f 100644 --- a/doc/source/models/builtin/llm/wizardcoder-python-v1.0.rst +++ b/doc/source/models/builtin/llm/wizardcoder-python-v1.0.rst @@ -14,31 +14,15 @@ Specifications ^^^^^^^^^^^^^^ -Model Spec 1 (pytorch, 7 Billion) -++++++++++++++++++++++++++++++++++++++++ - -- **Model Format:** pytorch -- **Model Size (in billions):** 7 -- **Quantizations:** 4-bit, 8-bit, none -- **Engines**: Transformers -- **Model ID:** WizardLM/WizardCoder-Python-7B-V1.0 -- **Model Hubs**: `Hugging Face `__ - -Execute the following command to launch the model, remember to replace ``${quantization}`` with your -chosen quantization method from the options listed above:: - - xinference launch --model-engine ${engine} --model-name wizardcoder-python-v1.0 --size-in-billions 7 --model-format pytorch --quantization ${quantization} - - -Model Spec 2 (pytorch, 13 Billion) +Model Spec 1 (pytorch, 13 Billion) ++++++++++++++++++++++++++++++++++++++++ - **Model Format:** pytorch - **Model Size (in billions):** 13 - **Quantizations:** 4-bit, 8-bit, none - **Engines**: Transformers -- **Model ID:** WizardLM/WizardCoder-Python-13B-V1.0 -- **Model Hubs**: `Hugging Face `__, `ModelScope `__ +- **Model ID:** WizardLMTeam/WizardCoder-Python-13B-V1.0 +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: @@ -46,15 +30,15 @@ chosen quantization method from the options listed above:: xinference launch --model-engine ${engine} --model-name wizardcoder-python-v1.0 --size-in-billions 13 --model-format pytorch --quantization ${quantization} -Model Spec 3 (pytorch, 34 Billion) +Model Spec 2 (pytorch, 34 Billion) ++++++++++++++++++++++++++++++++++++++++ - **Model Format:** pytorch - **Model Size (in billions):** 34 - **Quantizations:** 4-bit, 8-bit, none - **Engines**: Transformers -- **Model ID:** WizardLM/WizardCoder-Python-34B-V1.0 -- **Model Hubs**: `Hugging Face `__, `ModelScope `__ +- **Model ID:** WizardLMTeam/WizardCoder-Python-34B-V1.0 +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: @@ -62,7 +46,7 @@ chosen quantization method from the options listed above:: xinference launch --model-engine ${engine} --model-name wizardcoder-python-v1.0 --size-in-billions 34 --model-format pytorch --quantization ${quantization} -Model Spec 4 (ggufv2, 7 Billion) +Model Spec 3 (ggufv2, 7 Billion) ++++++++++++++++++++++++++++++++++++++++ - **Model Format:** ggufv2 @@ -78,7 +62,7 @@ chosen quantization method from the options listed above:: xinference launch --model-engine ${engine} --model-name wizardcoder-python-v1.0 --size-in-billions 7 --model-format ggufv2 --quantization ${quantization} -Model Spec 5 (ggufv2, 13 Billion) +Model Spec 4 (ggufv2, 13 Billion) ++++++++++++++++++++++++++++++++++++++++ - **Model Format:** ggufv2 @@ -94,7 +78,7 @@ chosen quantization method from the options listed above:: xinference launch --model-engine ${engine} --model-name wizardcoder-python-v1.0 --size-in-billions 13 --model-format ggufv2 --quantization ${quantization} -Model Spec 6 (ggufv2, 34 Billion) +Model Spec 5 (ggufv2, 34 Billion) ++++++++++++++++++++++++++++++++++++++++ - **Model Format:** ggufv2 diff --git a/doc/source/models/builtin/llm/wizardmath-v1.0.rst b/doc/source/models/builtin/llm/wizardmath-v1.0.rst index 8de1960716..e2fb9e28f2 100644 --- a/doc/source/models/builtin/llm/wizardmath-v1.0.rst +++ b/doc/source/models/builtin/llm/wizardmath-v1.0.rst @@ -21,8 +21,8 @@ Model Spec 1 (pytorch, 7 Billion) - **Model Size (in billions):** 7 - **Quantizations:** 4-bit, 8-bit, none - **Engines**: Transformers -- **Model ID:** WizardLM/WizardMath-7B-V1.0 -- **Model Hubs**: `Hugging Face `__, `ModelScope `__ +- **Model ID:** WizardLMTeam/WizardMath-7B-V1.0 +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: @@ -30,31 +30,15 @@ chosen quantization method from the options listed above:: xinference launch --model-engine ${engine} --model-name wizardmath-v1.0 --size-in-billions 7 --model-format pytorch --quantization ${quantization} -Model Spec 2 (pytorch, 13 Billion) -++++++++++++++++++++++++++++++++++++++++ - -- **Model Format:** pytorch -- **Model Size (in billions):** 13 -- **Quantizations:** 4-bit, 8-bit, none -- **Engines**: Transformers -- **Model ID:** WizardLM/WizardMath-13B-V1.0 -- **Model Hubs**: `Hugging Face `__ - -Execute the following command to launch the model, remember to replace ``${quantization}`` with your -chosen quantization method from the options listed above:: - - xinference launch --model-engine ${engine} --model-name wizardmath-v1.0 --size-in-billions 13 --model-format pytorch --quantization ${quantization} - - -Model Spec 3 (pytorch, 70 Billion) +Model Spec 2 (pytorch, 70 Billion) ++++++++++++++++++++++++++++++++++++++++ - **Model Format:** pytorch - **Model Size (in billions):** 70 - **Quantizations:** 4-bit, 8-bit, none - **Engines**: Transformers -- **Model ID:** WizardLM/WizardMath-70B-V1.0 -- **Model Hubs**: `Hugging Face `__ +- **Model ID:** WizardLMTeam/WizardMath-70B-V1.0 +- **Model Hubs**: `Hugging Face `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: diff --git a/doc/source/models/builtin/llm/yi-coder-chat.rst b/doc/source/models/builtin/llm/yi-coder-chat.rst new file mode 100644 index 0000000000..af4368ae98 --- /dev/null +++ b/doc/source/models/builtin/llm/yi-coder-chat.rst @@ -0,0 +1,47 @@ +.. _models_llm_yi-coder-chat: + +======================================== +yi-coder-chat +======================================== + +- **Context Length:** 131072 +- **Model Name:** yi-coder-chat +- **Languages:** en +- **Abilities:** chat +- **Description:** Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.Excelling in long-context understanding with a maximum context length of 128K tokens.Supporting 52 major programming languages, including popular ones such as Java, Python, JavaScript, and C++. + +Specifications +^^^^^^^^^^^^^^ + + +Model Spec 1 (pytorch, 9 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 9 +- **Quantizations:** none +- **Engines**: vLLM, Transformers +- **Model ID:** 01ai/Yi-Coder-9B-Chat +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name yi-coder-chat --size-in-billions 9 --model-format pytorch --quantization ${quantization} + + +Model Spec 2 (pytorch, 1_5 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 1_5 +- **Quantizations:** none +- **Engines**: vLLM, Transformers +- **Model ID:** 01ai/Yi-Coder-1.5B-Chat +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name yi-coder-chat --size-in-billions 1_5 --model-format pytorch --quantization ${quantization} + diff --git a/doc/source/models/builtin/llm/yi-coder.rst b/doc/source/models/builtin/llm/yi-coder.rst new file mode 100644 index 0000000000..347a3bc9d1 --- /dev/null +++ b/doc/source/models/builtin/llm/yi-coder.rst @@ -0,0 +1,47 @@ +.. _models_llm_yi-coder: + +======================================== +yi-coder +======================================== + +- **Context Length:** 131072 +- **Model Name:** yi-coder +- **Languages:** en +- **Abilities:** generate +- **Description:** Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.Excelling in long-context understanding with a maximum context length of 128K tokens.Supporting 52 major programming languages, including popular ones such as Java, Python, JavaScript, and C++. + +Specifications +^^^^^^^^^^^^^^ + + +Model Spec 1 (pytorch, 9 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 9 +- **Quantizations:** none +- **Engines**: vLLM, Transformers +- **Model ID:** 01-ai/Yi-Coder-9B +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name yi-coder --size-in-billions 9 --model-format pytorch --quantization ${quantization} + + +Model Spec 2 (pytorch, 1_5 Billion) +++++++++++++++++++++++++++++++++++++++++ + +- **Model Format:** pytorch +- **Model Size (in billions):** 1_5 +- **Quantizations:** none +- **Engines**: vLLM, Transformers +- **Model ID:** 01-ai/Yi-Coder-1.5B +- **Model Hubs**: `Hugging Face `__, `ModelScope `__ + +Execute the following command to launch the model, remember to replace ``${quantization}`` with your +chosen quantization method from the options listed above:: + + xinference launch --model-engine ${engine} --model-name yi-coder --size-in-billions 1_5 --model-format pytorch --quantization ${quantization} + diff --git a/doc/source/models/builtin/llm/zephyr-7b-alpha.rst b/doc/source/models/builtin/llm/zephyr-7b-alpha.rst deleted file mode 100644 index 3b48a0f40a..0000000000 --- a/doc/source/models/builtin/llm/zephyr-7b-alpha.rst +++ /dev/null @@ -1,31 +0,0 @@ -.. _models_llm_zephyr-7b-alpha: - -======================================== -zephyr-7b-alpha -======================================== - -- **Context Length:** 8192 -- **Model Name:** zephyr-7b-alpha -- **Languages:** en -- **Abilities:** chat -- **Description:** Zephyr-7B-α is the first model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1. - -Specifications -^^^^^^^^^^^^^^ - - -Model Spec 1 (pytorch, 7 Billion) -++++++++++++++++++++++++++++++++++++++++ - -- **Model Format:** pytorch -- **Model Size (in billions):** 7 -- **Quantizations:** 4-bit, 8-bit, none -- **Engines**: Transformers -- **Model ID:** HuggingFaceH4/zephyr-7b-alpha -- **Model Hubs**: `Hugging Face `__, `ModelScope `__ - -Execute the following command to launch the model, remember to replace ``${quantization}`` with your -chosen quantization method from the options listed above:: - - xinference launch --model-engine ${engine} --model-name zephyr-7b-alpha --size-in-billions 7 --model-format pytorch --quantization ${quantization} - diff --git a/doc/source/models/builtin/llm/zephyr-7b-beta.rst b/doc/source/models/builtin/llm/zephyr-7b-beta.rst deleted file mode 100644 index 40048e2c55..0000000000 --- a/doc/source/models/builtin/llm/zephyr-7b-beta.rst +++ /dev/null @@ -1,31 +0,0 @@ -.. _models_llm_zephyr-7b-beta: - -======================================== -zephyr-7b-beta -======================================== - -- **Context Length:** 8192 -- **Model Name:** zephyr-7b-beta -- **Languages:** en -- **Abilities:** chat -- **Description:** Zephyr-7B-β is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 - -Specifications -^^^^^^^^^^^^^^ - - -Model Spec 1 (pytorch, 7 Billion) -++++++++++++++++++++++++++++++++++++++++ - -- **Model Format:** pytorch -- **Model Size (in billions):** 7 -- **Quantizations:** 4-bit, 8-bit, none -- **Engines**: Transformers -- **Model ID:** HuggingFaceH4/zephyr-7b-beta -- **Model Hubs**: `Hugging Face `__, `ModelScope `__ - -Execute the following command to launch the model, remember to replace ``${quantization}`` with your -chosen quantization method from the options listed above:: - - xinference launch --model-engine ${engine} --model-name zephyr-7b-beta --size-in-billions 7 --model-format pytorch --quantization ${quantization} - diff --git a/doc/source/models/builtin/rerank/index.rst b/doc/source/models/builtin/rerank/index.rst index 25964db295..6cb4abe59f 100644 --- a/doc/source/models/builtin/rerank/index.rst +++ b/doc/source/models/builtin/rerank/index.rst @@ -24,4 +24,6 @@ The following is a list of built-in rerank models in Xinference: bge-reranker-v2-minicpm-layerwise jina-reranker-v2 + + minicpm-reranker \ No newline at end of file diff --git a/doc/source/models/builtin/rerank/minicpm-reranker.rst b/doc/source/models/builtin/rerank/minicpm-reranker.rst new file mode 100644 index 0000000000..fd0b6c9411 --- /dev/null +++ b/doc/source/models/builtin/rerank/minicpm-reranker.rst @@ -0,0 +1,18 @@ +.. _models_builtin_minicpm-reranker: + +================ +minicpm-reranker +================ + +- **Model Name:** minicpm-reranker +- **Languages:** en, zh +- **Abilities:** rerank + +Specifications +^^^^^^^^^^^^^^ + +- **Model ID:** openbmb/MiniCPM-Reranker + +Execute the following command to launch the model:: + + xinference launch --model-name minicpm-reranker --model-type rerank \ No newline at end of file diff --git a/doc/source/models/builtin/video/cogvideox-5b.rst b/doc/source/models/builtin/video/cogvideox-5b.rst new file mode 100644 index 0000000000..4c4ee70cfd --- /dev/null +++ b/doc/source/models/builtin/video/cogvideox-5b.rst @@ -0,0 +1,18 @@ +.. _models_builtin_cogvideox-5b: + +============ +CogVideoX-5b +============ + +- **Model Name:** CogVideoX-5b +- **Model Family:** CogVideoX +- **Abilities:** text2video + +Specifications +^^^^^^^^^^^^^^ + +- **Model ID:** THUDM/CogVideoX-5b + +Execute the following command to launch the model:: + + xinference launch --model-name CogVideoX-5b --model-type video \ No newline at end of file diff --git a/doc/source/models/builtin/video/index.rst b/doc/source/models/builtin/video/index.rst index 1484b1e57c..d60f188b3c 100644 --- a/doc/source/models/builtin/video/index.rst +++ b/doc/source/models/builtin/video/index.rst @@ -12,4 +12,6 @@ The following is a list of built-in video models in Xinference: cogvideox-2b + + cogvideox-5b \ No newline at end of file diff --git a/doc/source/models/custom.rst b/doc/source/models/custom.rst index 071785f32d..ebdf5df153 100644 --- a/doc/source/models/custom.rst +++ b/doc/source/models/custom.rst @@ -59,25 +59,22 @@ Define a custom LLM model based on the following template: { "version": 1, "context_length": 2048, - "model_name": "custom-llama-2", + "model_name": "custom-llama-2-chat", "model_lang": [ "en" ], "model_ability": [ - "generate" + "chat" ], - "model_family": "llama-2", + "model_family": "my-llama-2-chat", "model_specs": [ { "model_format": "pytorch", "model_size_in_billions": 7, "quantizations": [ - "4-bit", - "8-bit", "none" ], - "model_id": "meta-llama/Llama-2-7b-hf", - "model_uri": "file:///path/to/llama-2-7b-hf" + "model_uri": "file:///path/to/llama-2-chat" }, { "model_format": "ggufv2", @@ -86,18 +83,20 @@ Define a custom LLM model based on the following template: "q4_0", "q8_0" ], - "model_id": "TheBloke/Llama-2-7B-GGUF", - "model_file_name_template": "llama-2-7b.{quantization}.gguf" + "model_file_name_template": "llama-2-chat-7b.{quantization}.gguf" "model_uri": "file:///path/to/gguf-file" } - ] + ], + "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = '<>\n' + messages[0]['content'] | trim + '\n<>\n\n' %}{% set messages = messages[1:] %}{% else %}{% set system_message = '' %}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 %}{% set content = system_message + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ '' + '[INST] ' + content | trim + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content | trim + ' ' + '' }}{% endif %}{% endfor %}", + "stop_token_ids": [2], + "stop": [] } * model_name: A string defining the name of the model. The name must start with a letter or a digit and can only contain letters, digits, underscores, or dashes. * context_length: context_length: An optional integer that specifies the maximum context size the model was trained to accommodate, encompassing both the input and output lengths. If not defined, the default value is 2048 tokens (~1,500 words). * model_lang: A list of strings representing the supported languages for the model. Example: ["en"], which means that the model supports English. * model_ability: A list of strings defining the abilities of the model. It could include options like "embed", "generate", and "chat". In this case, the model has the ability to "generate". -* model_family: A required string representing the family of the model you want to register. The optional values are the model names of all :ref:`built-in models `. If the model family you register is not among the built-in models in Xinference, please fill in ``other``. Note that you should choose the model family based on the ability of the model you want to register. For example, if you want to register the ``llama-2`` model, do not fill in ``llama-2-chat`` as the model family. +* model_family: A required string representing the family of the model you want to register. This parameter must not conflict with any builtin model names. * model_specs: An array of objects defining the specifications of the model. These include: * model_format: A string that defines the model format, like "pytorch" or "ggufv2". * model_size_in_billions: An integer defining the size of the model in billions of parameters. @@ -105,106 +104,9 @@ Define a custom LLM model based on the following template: * model_id: A string representing the model ID, possibly referring to an identifier used by Hugging Face. **If model_uri is missing, Xinference will try to download the model from the huggingface repository specified here.**. * model_uri: A string representing the URI where the model can be loaded from, such as "file:///path/to/llama-2-7b". **When the model format is ggufv2, model_uri must be the specific file path. When the model format is pytorch, model_uri must be the path to the directory containing the model files.** If model URI is absent, Xinference will try to download the model from Hugging Face with the model ID. * model_file_name_template: Required by gguf models. An f-string template used for defining the model file name based on the quantization. **Note that this field is just a template for the format of the ggufv2 model file, do not fill in the specific path of the model file.** -* prompt_style: If the ``model_family`` field is not ``other``, this field does not need to be filled in. ``prompt_style`` is an optional field that could be required by ``chat`` models to define the style of prompts. The given example has this set to None, but additional details could be found in a referenced file xinference/model/llm/tests/test_utils.py. You can also specify this field as a string, which will use the builtin prompt style in Xinference. For example: - -.. code-block:: json - - { - "model_specs": [...], - "prompt_style": "chatglm3" - } - -Xinference supports these builtin prompt styles in common usage: - -.. tabs:: - - .. tab:: baichuan-chat - - .. code-block:: json - - { - "style_name": "NO_COLON_TWO", - "system_prompt": "", - "roles": [ - " ", - " " - ], - "intra_message_sep": "", - "inter_message_sep": "", - "stop_token_ids": [ - 2, - 195 - ] - } - - .. tab:: chatglm3 - - .. code-block:: json - - { - "style_name": "CHATGLM3", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ] - } - - .. tab:: qwen-chat - - .. code-block:: json - - { - "style_name": "QWEN", - "system_prompt": "You are a helpful assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n", - "stop_token_ids": [ - 151643 - ] - } - - .. tab:: llama-2-chat - - .. code-block:: json - - { - "style_name": "LLAMA2", - "system_prompt": "[INST] <>\nYou are a helpful AI assistant.\n<>\n\n", - "roles": [ - "[INST]", - "[/INST]" - ], - "intra_message_sep": " ", - "inter_message_sep": " ", - "stop_token_ids": [ - 2 - ], - "stop": [ - "" - ] - } - - .. tab:: vicuna-v1.5 - - .. code-block:: json - - { - "style_name": "ADD_COLON_TWO", - "system_prompt": "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.", - "roles": [ - "USER", - "ASSISTANT" - ], - "intra_message_sep": " ", - "inter_message_sep": "" - } - -The above lists some commonly used built-in prompt styles. -The full list of supported prompt styles can be found on the Xinference web UI. +* chat_template: If ``model_ability`` includes ``chat`` , you must configure this option to generate the correct full prompt during chat. This is a Jinja template string. Usually, you can find it in the ``tokenizer_config.json`` file within the model directory. +* stop_token_ids: If ``model_ability`` includes ``chat`` , you can configure this option to control when the model stops during chat. This is a list of integers, and you can typically extract the corresponding values from the ``generation_config.json`` or ``tokenizer_config.json`` file in the model directory. +* stop: If ``model_ability`` includes ``chat`` , you can configure this option to control when the model stops during chat. This is a list of strings, and you can typically extract the corresponding values from the ``generation_config.json`` or ``tokenizer_config.json`` file in the model directory. Define a custom embedding model ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/doc/source/models/lora.rst b/doc/source/models/lora.rst index a82a7717da..16e4de3c4f 100644 --- a/doc/source/models/lora.rst +++ b/doc/source/models/lora.rst @@ -65,8 +65,7 @@ Specifically, specify that the ``lora_name`` parameter be configured in the ``ge client = Client("http://:") model = client.get_model("") model.chat( - "", - , + messages=[{"role": "user", "content": ""}], generate_config={"lora_name": ""} ) diff --git a/doc/source/models/model_abilities/audio.rst b/doc/source/models/model_abilities/audio.rst index d6731913d8..4ca3af1db8 100644 --- a/doc/source/models/model_abilities/audio.rst +++ b/doc/source/models/model_abilities/audio.rst @@ -51,6 +51,7 @@ Audio to text * whisper-medium * whisper-medium.en * whisper-large-v3 +* whisper-large-v3-turbo * Belle-distilwhisper-large-v2-zh * Belle-whisper-large-v2-zh * Belle-whisper-large-v3-zh diff --git a/doc/source/models/model_abilities/chat.rst b/doc/source/models/model_abilities/chat.rst index 35db451348..608c3faf4a 100644 --- a/doc/source/models/model_abilities/chat.rst +++ b/doc/source/models/model_abilities/chat.rst @@ -108,15 +108,14 @@ We can try Chat API out either via cURL, OpenAI Client, or Xinference's python c client = RESTfulClient("http://:") model = client.get_model("") - print(model.chat( - prompt="What is the largest animal?", - system_prompt="You are a helpful assistant.", - chat_history=[], + messages = [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "What is the largest animal?"}] + model.chat( + messages, generate_config={ "max_tokens": 512, "temperature": 0.7 } - )) + ) .. code-tab:: json output @@ -183,7 +182,9 @@ a list of messages as input, the Generate API accepts a freeform text string nam client = openai.Client(api_key="cannot be empty", base_url="http://:/v1") client.chat.completions.create( model=("", - prompt="What is the largest animal?" + messages=[ + {"role": "user", "content": "What is the largest animal?"} + ], max_tokens=512, temperature=0.7 ) diff --git a/doc/source/models/model_abilities/image.rst b/doc/source/models/model_abilities/image.rst index 7cd42f067c..79834e0dca 100644 --- a/doc/source/models/model_abilities/image.rst +++ b/doc/source/models/model_abilities/image.rst @@ -156,3 +156,34 @@ You can find more examples of Images API in the tutorial notebook: Learn from a Stable Diffusion ControlNet example +OCR +-------------------- + +The OCR API accepts image bytes and returns the OCR text. + +We can try OCR API out either via cURL, or Xinference's python client: + +.. tabs:: + + .. code-tab:: bash cURL + + curl -X 'POST' \ + 'http://:/v1/images/ocr' \ + -F model= \ + -F image=@xxx.jpg + + + .. code-tab:: python Xinference Python Client + + from xinference.client import Client + + client = Client("http://:") + + model = client.get_model("") + with open("xxx.jpg", "rb") as f: + model.ocr(f.read()) + + + .. code-tab:: text output + + diff --git a/doc/source/models/model_abilities/vision.rst b/doc/source/models/model_abilities/vision.rst index 74091abe43..d78cd5788a 100644 --- a/doc/source/models/model_abilities/vision.rst +++ b/doc/source/models/model_abilities/vision.rst @@ -30,6 +30,7 @@ The ``vision`` ability is supported with the following models in Xinference: * :ref:`GLM-4V ` * :ref:`MiniCPM-Llama3-V 2.6 ` * :ref:`internvl2 ` +* :ref:`qwen2-vl-instruct ` Quickstart diff --git a/doc/source/user_guide/backends.rst b/doc/source/user_guide/backends.rst index 57126871e8..d215c7c63b 100644 --- a/doc/source/user_guide/backends.rst +++ b/doc/source/user_guide/backends.rst @@ -51,8 +51,10 @@ Currently, supported model includes: - ``codestral-v0.1`` - ``Yi``, ``Yi-1.5``, ``Yi-chat``, ``Yi-1.5-chat``, ``Yi-1.5-chat-16k`` - ``code-llama``, ``code-llama-python``, ``code-llama-instruct`` -- ``deepseek``, ``deepseek-coder``, ``deepseek-chat``, ``deepseek-coder-instruct`` +- ``deepseek``, ``deepseek-coder``, ``deepseek-chat``, ``deepseek-coder-instruct``, ``deepseek-v2-chat``, ``deepseek-v2-chat-0628``, ``deepseek-v2.5`` +- ``yi-coder``, ``yi-coder-chat`` - ``codeqwen1.5``, ``codeqwen1.5-chat`` +- ``qwen2.5``, ``qwen2.5-coder``, ``qwen2.5-instruct``, ``qwen2.5-coder-instruct`` - ``baichuan-2-chat`` - ``internlm2-chat`` - ``internlm2.5-chat``, ``internlm2.5-chat-1m`` diff --git a/doc/source/user_guide/client_api.rst b/doc/source/user_guide/client_api.rst index bd47822b7e..ab4b9b1afe 100644 --- a/doc/source/user_guide/client_api.rst +++ b/doc/source/user_guide/client_api.rst @@ -53,13 +53,11 @@ Xinference Client quantization="Q4_K") model = client.get_model(model_uid) - chat_history = [] - prompt = "What is the largest animal?" + messages = [{"role": "user", "content": "What is the largest animal?"}] # If the model has "generate" capability, then you can call the # model.generate API. model.chat( - prompt, - chat_history=chat_history, + messages, generate_config={"max_tokens": 1024} ) diff --git a/doc/source/user_guide/continuous_batching.rst b/doc/source/user_guide/continuous_batching.rst index 47269fbd0a..e720288c57 100644 --- a/doc/source/user_guide/continuous_batching.rst +++ b/doc/source/user_guide/continuous_batching.rst @@ -1,14 +1,17 @@ .. _user_guide_continuous_batching: -================================== -Continuous Batching (experimental) -================================== +=================== +Continuous Batching +=================== Continuous batching, as a means to improve throughput during model serving, has already been implemented in inference engines like ``VLLM``. Xinference aims to provide this optimization capability when using the transformers engine as well. Usage ===== + +LLM +--- Currently, this feature can be enabled under the following conditions: * First, set the environment variable ``XINFERENCE_TRANSFORMERS_ENABLE_BATCHING`` to ``1`` when starting xinference. For example: @@ -18,6 +21,12 @@ Currently, this feature can be enabled under the following conditions: XINFERENCE_TRANSFORMERS_ENABLE_BATCHING=1 xinference-local --log-level debug +.. note:: + Since ``v0.16.0``, this feature is turned on by default and + is no longer required to set the ``XINFERENCE_TRANSFORMERS_ENABLE_BATCHING`` environment variable. + This environment variable has been removed. + + * Then, ensure that the ``transformers`` engine is selected when launching the model. For example: .. tabs:: @@ -58,6 +67,20 @@ Once this feature is enabled, all requests for LLMs will be managed by continuou and the average throughput of requests made to a single model will increase. The usage of the LLM interface remains exactly the same as before, with no differences. +Image Model +----------- +Currently, for image models, only the ``text_to_image`` interface is supported for ``FLUX.1`` series models. + +Enabling this feature requires setting the environment variable ``XINFERENCE_TEXT_TO_IMAGE_BATCHING_SIZE``, which indicates the ``size`` of the generated images. + +For example, starting xinference like this: + +.. code-block:: + + XINFERENCE_TEXT_TO_IMAGE_BATCHING_SIZE=1024*1024 xinference-local --log-level debug + + +Then just use the ``text_to_image`` interface as before, and nothing else needs to be changed. Abort your request ================== @@ -70,7 +93,7 @@ In this mode, you can abort requests that are in the process of inference. from xinference.client import Client client = Client("http://127.0.0.1:9997") model = client.get_model("") - model.chat("", generate_config={"request_id": ""}) + model.chat([{"role": "user", "content": ""}], generate_config={"request_id": ""}) #. Then, abort the request using the ``request_id`` you have set. For example: @@ -81,17 +104,16 @@ In this mode, you can abort requests that are in the process of inference. client.abort_request("", "") Note that if your request has already finished, aborting the request will be a no-op. +Image models also support this feature. Note ==== -* Currently, this feature only supports the ``generate``, ``chat`` and ``vision`` tasks for ``LLM`` models. The ``tool call`` tasks are not supported. +* Currently, for ``LLM`` models, this feature only supports the ``generate``, ``chat``, ``tool call`` and ``vision`` tasks. -* For ``vision`` tasks, currently only ``qwen-vl-chat``, ``cogvlm2``, and ``glm-4v`` models are supported. More models will be supported in the future. Please let us know your requirements. +* Currently, for ``image`` models, this feature only supports the ``text_to_image`` tasks. Only ``FLUX.1`` series models are supported. + +* For ``vision`` tasks, currently only ``qwen-vl-chat``, ``cogvlm2``, ``glm-4v`` and ``MiniCPM-V-2.6`` (only for image tasks) models are supported. More models will be supported in the future. Please let us know your requirements. * If using GPU inference, this method will consume more GPU memory. Please be cautious when increasing the number of concurrent requests to the same model. The ``launch_model`` interface provides the ``max_num_seqs`` parameter to adjust the concurrency level, with a default value of ``16``. - -* This feature is still in the experimental stage, and we welcome your active feedback on any issues. - -* After a period of testing, this method will remain enabled by default, and the original inference method will be deprecated. diff --git a/doc/templates/audio.rst.jinja b/doc/templates/audio.rst.jinja index 08aafad5eb..4a17708ec1 100644 --- a/doc/templates/audio.rst.jinja +++ b/doc/templates/audio.rst.jinja @@ -6,7 +6,7 @@ - **Model Name:** {{ model_name }} - **Model Family:** {{ model_family }} -- **Abilities:** {{ ability }} +- **Abilities:** {{ model_ability }} - **Multilingual:** {{ multilingual }} Specifications diff --git a/examples/StableDiffusionControlNet.ipynb b/examples/StableDiffusionControlNet.ipynb index 7c9842709c..14c46c3dec 100644 --- a/examples/StableDiffusionControlNet.ipynb +++ b/examples/StableDiffusionControlNet.ipynb @@ -91,7 +91,7 @@ "from diffusers.utils import load_image\n", "\n", "mlsd = MLSDdetector.from_pretrained(\"lllyasviel/ControlNet\")\n", - "image_path = os.path.expanduser(\"~/draft.png\")\n", + "image_path = os.path.expanduser(\"draft.png\")\n", "image = load_image(image_path)\n", "image = mlsd(image)\n", "image" @@ -181,7 +181,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -195,9 +195,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.6" + "version": "3.11.9" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/setup.cfg b/setup.cfg index 7b83211c84..3c08363e59 100644 --- a/setup.cfg +++ b/setup.cfg @@ -26,25 +26,24 @@ packages = find: install_requires = xoscar>=0.3.0 torch - gradio==4.26.0 - typer[all]<0.12.0 # fix typer required by gradio + gradio pillow click tqdm>=4.27 tabulate requests pydantic - fastapi==0.110.3 + fastapi>=0.110.3 uvicorn huggingface-hub>=0.19.4 typing_extensions modelscope>=1.10.0 sse_starlette>=1.6.5 # ensure_bytes API break change: https://github.com/sysid/sse-starlette/issues/65 - openai>1,<1.40 # For typing + openai>1 # For typing python-jose[cryptography] passlib[bcrypt] aioprometheus[starlette]>=23.12.0 - pynvml + nvidia-ml-py async-timeout peft timm @@ -71,7 +70,7 @@ dev = jieba>=0.42.0 flake8>=3.8.0 black - openai>1,<1.40 + openai>1 langchain langchain-community orjson @@ -80,15 +79,15 @@ dev = all = llama-cpp-python>=0.2.25,!=0.2.58 transformers>=4.43.2 - torch - accelerate>=0.27.2 + torch>=2.0.0 # >=2.0 For CosyVoice + accelerate>=0.28.0 sentencepiece transformers_stream_generator bitsandbytes protobuf einops - tiktoken - sentence-transformers>=2.7.0 + tiktoken>=0.6.0 + sentence-transformers>=3.1.0 vllm>=0.2.6 ; sys_platform=='linux' diffusers>=0.30.0 imageio-ffmpeg # For video @@ -106,16 +105,21 @@ all = FlagEmbedding # For rerank funasr omegaconf~=2.3.0 # For ChatTTS - nemo_text_processing # For ChatTTS - WeTextProcessing # For ChatTTS + nemo_text_processing<1.1.0 # 1.1.0 requires pynini==2.1.6.post1 + WeTextProcessing<1.0.4 # 1.0.4 requires pynini==2.1.6 librosa # For ChatTTS xxhash # For ChatTTS torchaudio # For ChatTTS - ChatTTS>0.1 + ChatTTS>=0.2 + lightning>=2.0.0 # For CosyVoice, matcha + hydra-core>=1.3.2 # For CosyVoice, matcha + inflect # For CosyVoice, matcha + conformer # For CosyVoice, matcha + diffusers>=0.30.0 # For CosyVoice, matcha + gdown # For CosyVoice, matcha + pyarrow # For CosyVoice, matcha HyperPyYAML # For CosyVoice - matcha-tts>=0.0.7 # For CosyVoice onnxruntime==1.16.0 # For CosyVoice, use onnxruntime-gpu==1.16.0 if possible - openai-whisper # For CosyVoice boto3>=1.28.55,<1.28.65 # For tensorizer tensorizer~=2.9.0 eva-decord # For video in VL @@ -123,8 +127,12 @@ all = loguru # For Fish Speech natsort # For Fish Speech loralib # For Fish Speech - opencc==1.1.6 # For Fish Speech - faster_whisper # For Fish Speech + ormsgpack # For Fish Speech + qwen-vl-utils # For qwen2-vl + datamodel_code_generator # for minicpm-4B + jsonschema # for minicpm-4B + verovio>=4.3.1 # For got_ocr2 + accelerate>=0.28.0 # For got_ocr2 intel = torch==2.1.0a0 intel_extension_for_pytorch==2.1.10+xpu @@ -133,7 +141,7 @@ llama_cpp = transformers = transformers>=4.43.2 torch - accelerate>=0.27.2 + accelerate>=0.28.0 sentencepiece transformers_stream_generator bitsandbytes @@ -149,6 +157,9 @@ transformers = peft eva-decord # For video in VL jj-pytorchvideo # For CogVLM2-video + qwen-vl-utils # For qwen2-vl + datamodel_code_generator # for minicpm-4B + jsonschema # for minicpm-4B vllm = vllm>=0.2.6 sglang = @@ -158,33 +169,46 @@ sglang = mlx = mlx-lm embedding = - sentence-transformers>=2.7.0 + sentence-transformers>=3.1.0 rerank = FlagEmbedding image = diffusers>=0.30.0 # fix conflict with matcha-tts controlnet_aux + deepcache + verovio>=4.3.1 # For got_ocr2 + transformers>=4.37.2 # For got_ocr2 + tiktoken>=0.6.0 # For got_ocr2 + accelerate>=0.28.0 # For got_ocr2 + torch # For got_ocr2 + torchvision # For got_ocr2 video = diffusers>=0.30.0 imageio-ffmpeg audio = funasr omegaconf~=2.3.0 - nemo_text_processing - WeTextProcessing + nemo_text_processing<1.1.0 # 1.1.0 requires pynini==2.1.6.post1 + WeTextProcessing<1.0.4 # 1.0.4 requires pynini==2.1.6 librosa xxhash torchaudio - ChatTTS>0.1 + ChatTTS>=0.2 + tiktoken # For CosyVoice, openai-whisper + torch>=2.0.0 # For CosyVoice, matcha + lightning>=2.0.0 # For CosyVoice, matcha + hydra-core>=1.3.2 # For CosyVoice, matcha + inflect # For CosyVoice, matcha + conformer # For CosyVoice, matcha + diffusers>=0.30.0 # For CosyVoice, matcha + gdown # For CosyVoice, matcha + pyarrow # For CosyVoice, matcha HyperPyYAML # For CosyVoice - matcha-tts>=0.0.7 # For CosyVoice onnxruntime==1.16.0 # For CosyVoice, use onnxruntime-gpu==1.16.0 if possible - openai-whisper # For CosyVoice loguru # For Fish Speech natsort # For Fish Speech loralib # For Fish Speech - opencc==1.1.6 # For Fish Speech - faster_whisper # For Fish Speech + ormsgpack # For Fish Speech doc = ipython>=6.5.0 sphinx>=3.0.0 diff --git a/xinference/__init__.py b/xinference/__init__.py index eb1fe93d66..8d87113a8e 100644 --- a/xinference/__init__.py +++ b/xinference/__init__.py @@ -26,13 +26,9 @@ def _install(): from xoscar.backends.router import Router - from .model import _install as install_model - default_router = Router.get_instance_or_empty() Router.set_instance(default_router) - install_model() - _install() del _install diff --git a/xinference/_compat.py b/xinference/_compat.py index e559391379..fbb3572a59 100644 --- a/xinference/_compat.py +++ b/xinference/_compat.py @@ -11,6 +11,8 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from typing import Dict, Iterable, List, Literal, Optional, Union + from pydantic.version import VERSION as PYDANTIC_VERSION PYDANTIC_V2 = PYDANTIC_VERSION.startswith("2.") @@ -50,3 +52,52 @@ from pydantic.parse import load_str_bytes # noqa: F401 from pydantic.types import StrBytes # noqa: F401 from pydantic.utils import ROOT_KEY # noqa: F401 + +from openai.types.chat.chat_completion_named_tool_choice_param import ( + ChatCompletionNamedToolChoiceParam, +) +from openai.types.chat.chat_completion_stream_options_param import ( + ChatCompletionStreamOptionsParam, +) +from openai.types.chat.chat_completion_tool_param import ChatCompletionToolParam + +OpenAIChatCompletionStreamOptionsParam = create_model_from_typeddict( + ChatCompletionStreamOptionsParam +) +OpenAIChatCompletionToolParam = create_model_from_typeddict(ChatCompletionToolParam) +OpenAIChatCompletionNamedToolChoiceParam = create_model_from_typeddict( + ChatCompletionNamedToolChoiceParam +) + + +class CreateChatCompletionOpenAI(BaseModel): + """ + Comes from source code: https://github.com/openai/openai-python/blob/main/src/openai/types/chat/completion_create_params.py + """ + + messages: List[Dict] + model: str + frequency_penalty: Optional[float] + logit_bias: Optional[Dict[str, int]] + logprobs: Optional[bool] + max_tokens: Optional[int] + n: Optional[int] + parallel_tool_calls: Optional[bool] + presence_penalty: Optional[float] + # we do not support this + # response_format: ResponseFormat + seed: Optional[int] + service_tier: Optional[Literal["auto", "default"]] + stop: Union[Optional[str], List[str]] + stream_options: Optional[OpenAIChatCompletionStreamOptionsParam] # type: ignore + temperature: Optional[float] + tool_choice: Optional[ # type: ignore + Union[ + Literal["none", "auto", "required"], + OpenAIChatCompletionNamedToolChoiceParam, + ] + ] + tools: Optional[Iterable[OpenAIChatCompletionToolParam]] # type: ignore + top_logprobs: Optional[int] + top_p: Optional[float] + user: Optional[str] diff --git a/xinference/api/restful_api.py b/xinference/api/restful_api.py index 9c553c4c5d..ed3a2eab90 100644 --- a/xinference/api/restful_api.py +++ b/xinference/api/restful_api.py @@ -57,14 +57,13 @@ from ..core.supervisor import SupervisorActor from ..core.utils import json_dumps from ..types import ( - SPECIAL_TOOL_PROMPT, ChatCompletion, - ChatCompletionMessage, Completion, CreateChatCompletion, CreateCompletion, ImageList, PeftModelConfig, + SDAPIResult, VideoList, max_tokens_field, ) @@ -124,6 +123,43 @@ class TextToImageRequest(BaseModel): user: Optional[str] = None +class SDAPIOptionsRequest(BaseModel): + sd_model_checkpoint: Optional[str] = None + + +class SDAPITxt2imgRequst(BaseModel): + model: Optional[str] + prompt: Optional[str] = "" + negative_prompt: Optional[str] = "" + steps: Optional[int] = None + seed: Optional[int] = -1 + cfg_scale: Optional[float] = 7.0 + override_settings: Optional[dict] = {} + width: Optional[int] = 512 + height: Optional[int] = 512 + sampler_name: Optional[str] = None + denoising_strength: Optional[float] = None + kwargs: Optional[str] = None + user: Optional[str] = None + + +class SDAPIImg2imgRequst(BaseModel): + model: Optional[str] + init_images: Optional[list] + prompt: Optional[str] = "" + negative_prompt: Optional[str] = "" + steps: Optional[int] = None + seed: Optional[int] = -1 + cfg_scale: Optional[float] = 7.0 + override_settings: Optional[dict] = {} + width: Optional[int] = 512 + height: Optional[int] = 512 + sampler_name: Optional[str] = None + denoising_strength: Optional[float] = None + kwargs: Optional[str] = None + user: Optional[str] = None + + class TextToVideoRequest(BaseModel): model: str prompt: Union[str, List[str]] = Field(description="The input to embed.") @@ -165,7 +201,7 @@ class BuildGradioImageInterfaceRequest(BaseModel): model_name: str model_family: str model_id: str - controlnet: Union[None, List[Dict[str, Union[str, None]]]] + controlnet: Union[None, List[Dict[str, Union[str, dict, None]]]] model_revision: str model_ability: List[str] @@ -199,14 +235,14 @@ def handle_request_limit_error(e: Exception): async def _get_supervisor_ref(self) -> xo.ActorRefType[SupervisorActor]: if self._supervisor_ref is None: self._supervisor_ref = await xo.actor_ref( - address=self._supervisor_address, uid=SupervisorActor.uid() + address=self._supervisor_address, uid=SupervisorActor.default_uid() ) return self._supervisor_ref async def _get_event_collector_ref(self) -> xo.ActorRefType[EventCollectorActor]: if self._event_collector_ref is None: self._event_collector_ref = await xo.actor_ref( - address=self._supervisor_address, uid=EventCollectorActor.uid() + address=self._supervisor_address, uid=EventCollectorActor.default_uid() ) return self._event_collector_ref @@ -488,6 +524,16 @@ async def internal_exception_handler(request: Request, exc: Exception): else None ), ) + self._router.add_api_route( + "/v1/requests/{request_id}/progress", + self.get_progress, + methods=["get"], + dependencies=( + [Security(self._auth_service, scopes=["models:read"])] + if self.is_authenticated() + else None + ), + ) self._router.add_api_route( "/v1/images/generations", self.create_images, @@ -521,6 +567,69 @@ async def internal_exception_handler(request: Request, exc: Exception): else None ), ) + self._router.add_api_route( + "/v1/images/ocr", + self.create_ocr, + methods=["POST"], + dependencies=( + [Security(self._auth_service, scopes=["models:read"])] + if self.is_authenticated() + else None + ), + ) + # SD WebUI API + self._router.add_api_route( + "/sdapi/v1/options", + self.sdapi_options, + methods=["POST"], + dependencies=( + [Security(self._auth_service, scopes=["models:read"])] + if self.is_authenticated() + else None + ), + ) + self._router.add_api_route( + "/sdapi/v1/sd-models", + self.sdapi_sd_models, + methods=["GET"], + dependencies=( + [Security(self._auth_service, scopes=["models:read"])] + if self.is_authenticated() + else None + ), + ) + self._router.add_api_route( + "/sdapi/v1/samplers", + self.sdapi_samplers, + methods=["GET"], + dependencies=( + [Security(self._auth_service, scopes=["models:read"])] + if self.is_authenticated() + else None + ), + ) + self._router.add_api_route( + "/sdapi/v1/txt2img", + self.sdapi_txt2img, + methods=["POST"], + response_model=SDAPIResult, + dependencies=( + [Security(self._auth_service, scopes=["models:read"])] + if self.is_authenticated() + else None + ), + ) + self._router.add_api_route( + "/sdapi/v1/img2img", + self.sdapi_img2img, + methods=["POST"], + response_model=SDAPIResult, + dependencies=( + [Security(self._auth_service, scopes=["models:read"])] + if self.is_authenticated() + else None + ), + ) self._router.add_api_route( "/v1/video/generations", self.create_videos, @@ -1397,6 +1506,17 @@ async def create_speech( await self._report_error_event(model_uid, str(e)) raise HTTPException(status_code=500, detail=str(e)) + async def get_progress(self, request_id: str) -> JSONResponse: + try: + supervisor_ref = await self._get_supervisor_ref() + result = {"progress": await supervisor_ref.get_progress(request_id)} + return JSONResponse(content=result) + except KeyError as e: + raise HTTPException(status_code=400, detail=str(e)) + except Exception as e: + logger.error(e, exc_info=True) + raise HTTPException(status_code=500, detail=str(e)) + async def create_images(self, request: Request) -> Response: body = TextToImageRequest.parse_obj(await request.json()) model_uid = body.model @@ -1431,6 +1551,118 @@ async def create_images(self, request: Request) -> Response: await self._report_error_event(model_uid, str(e)) raise HTTPException(status_code=500, detail=str(e)) + async def sdapi_options(self, request: Request) -> Response: + body = SDAPIOptionsRequest.parse_obj(await request.json()) + model_uid = body.sd_model_checkpoint + + try: + if not model_uid: + raise ValueError("Unknown model") + await (await self._get_supervisor_ref()).get_model(model_uid) + return Response() + except ValueError as ve: + logger.error(str(ve), exc_info=True) + await self._report_error_event(model_uid, str(ve)) + raise HTTPException(status_code=400, detail=str(ve)) + except Exception as e: + logger.error(e, exc_info=True) + await self._report_error_event(model_uid, str(e)) + raise HTTPException(status_code=500, detail=str(e)) + + async def sdapi_sd_models(self, request: Request) -> Response: + try: + models = await (await self._get_supervisor_ref()).list_models() + sd_models = [] + for model_name, info in models.items(): + if info["model_type"] != "image": + continue + sd_models.append({"model_name": model_name, "config": None}) + return JSONResponse(content=sd_models) + except Exception as e: + logger.error(e, exc_info=True) + raise HTTPException(status_code=500, detail=str(e)) + + async def sdapi_samplers(self, request: Request) -> Response: + try: + from ..model.image.stable_diffusion.core import SAMPLING_METHODS + + samplers = [ + {"name": sample_method, "alias": [], "options": {}} + for sample_method in SAMPLING_METHODS + ] + return JSONResponse(content=samplers) + except Exception as e: + logger.error(e, exc_info=True) + raise HTTPException(status_code=500, detail=str(e)) + + async def sdapi_txt2img(self, request: Request) -> Response: + body = SDAPITxt2imgRequst.parse_obj(await request.json()) + model_uid = body.model or body.override_settings.get("sd_model_checkpoint") + + try: + if not model_uid: + raise ValueError("Unknown model") + model = await (await self._get_supervisor_ref()).get_model(model_uid) + except ValueError as ve: + logger.error(str(ve), exc_info=True) + await self._report_error_event(model_uid, str(ve)) + raise HTTPException(status_code=400, detail=str(ve)) + except Exception as e: + logger.error(e, exc_info=True) + await self._report_error_event(model_uid, str(e)) + raise HTTPException(status_code=500, detail=str(e)) + + try: + kwargs = dict(body) + kwargs.update(json.loads(body.kwargs) if body.kwargs else {}) + image_list = await model.txt2img( + **kwargs, + ) + return Response(content=image_list, media_type="application/json") + except RuntimeError as re: + logger.error(re, exc_info=True) + await self._report_error_event(model_uid, str(re)) + self.handle_request_limit_error(re) + raise HTTPException(status_code=400, detail=str(re)) + except Exception as e: + logger.error(e, exc_info=True) + await self._report_error_event(model_uid, str(e)) + raise HTTPException(status_code=500, detail=str(e)) + + async def sdapi_img2img(self, request: Request) -> Response: + body = SDAPIImg2imgRequst.parse_obj(await request.json()) + model_uid = body.model or body.override_settings.get("sd_model_checkpoint") + + try: + if not model_uid: + raise ValueError("Unknown model") + model = await (await self._get_supervisor_ref()).get_model(model_uid) + except ValueError as ve: + logger.error(str(ve), exc_info=True) + await self._report_error_event(model_uid, str(ve)) + raise HTTPException(status_code=400, detail=str(ve)) + except Exception as e: + logger.error(e, exc_info=True) + await self._report_error_event(model_uid, str(e)) + raise HTTPException(status_code=500, detail=str(e)) + + try: + kwargs = dict(body) + kwargs.update(json.loads(body.kwargs) if body.kwargs else {}) + image_list = await model.img2img( + **kwargs, + ) + return Response(content=image_list, media_type="application/json") + except RuntimeError as re: + logger.error(re, exc_info=True) + await self._report_error_event(model_uid, str(re)) + self.handle_request_limit_error(re) + raise HTTPException(status_code=400, detail=str(re)) + except Exception as e: + logger.error(e, exc_info=True) + await self._report_error_event(model_uid, str(e)) + raise HTTPException(status_code=500, detail=str(e)) + async def create_variations( self, model: str = Form(...), @@ -1532,6 +1764,44 @@ async def create_inpainting( await self._report_error_event(model_uid, str(e)) raise HTTPException(status_code=500, detail=str(e)) + async def create_ocr( + self, + model: str = Form(...), + image: UploadFile = File(media_type="application/octet-stream"), + kwargs: Optional[str] = Form(None), + ) -> Response: + model_uid = model + try: + model_ref = await (await self._get_supervisor_ref()).get_model(model_uid) + except ValueError as ve: + logger.error(str(ve), exc_info=True) + await self._report_error_event(model_uid, str(ve)) + raise HTTPException(status_code=400, detail=str(ve)) + except Exception as e: + logger.error(e, exc_info=True) + await self._report_error_event(model_uid, str(e)) + raise HTTPException(status_code=500, detail=str(e)) + + try: + if kwargs is not None: + parsed_kwargs = json.loads(kwargs) + else: + parsed_kwargs = {} + im = Image.open(image.file) + text = await model_ref.ocr( + image=im, + **parsed_kwargs, + ) + return Response(content=text, media_type="text/plain") + except RuntimeError as re: + logger.error(re, exc_info=True) + await self._report_error_event(model_uid, str(re)) + raise HTTPException(status_code=400, detail=str(re)) + except Exception as e: + logger.error(e, exc_info=True) + await self._report_error_event(model_uid, str(e)) + raise HTTPException(status_code=500, detail=str(e)) + async def create_flexible_infer(self, request: Request) -> Response: payload = await request.json() @@ -1627,33 +1897,7 @@ async def create_chat_completion(self, request: Request) -> Response: status_code=400, detail="Invalid input. Please specify the prompt." ) - system_messages: List["ChatCompletionMessage"] = [] - system_messages_contents = [] - non_system_messages = [] - for msg in messages: - assert ( - msg.get("content") != SPECIAL_TOOL_PROMPT - ), f"Invalid message content {SPECIAL_TOOL_PROMPT}" - if msg["role"] == "system": - system_messages_contents.append(msg["content"]) - else: - non_system_messages.append(msg) - system_messages.append( - {"role": "system", "content": ". ".join(system_messages_contents)} - ) - has_tool_message = messages[-1].get("role") == "tool" - if has_tool_message: - prompt = SPECIAL_TOOL_PROMPT - system_prompt = system_messages[0]["content"] if system_messages else None - chat_history = non_system_messages # exclude the prompt - else: - prompt = None - if non_system_messages: - prompt = non_system_messages[-1]["content"] - system_prompt = system_messages[0]["content"] if system_messages else None - chat_history = non_system_messages[:-1] # exclude the prompt - model_uid = body.model try: @@ -1678,11 +1922,15 @@ async def create_chat_completion(self, request: Request) -> Response: await self._report_error_event(model_uid, str(e)) raise HTTPException(status_code=500, detail=str(e)) - from ..model.llm.utils import GLM4_TOOL_CALL_FAMILY, QWEN_TOOL_CALL_FAMILY + from ..model.llm.utils import ( + GLM4_TOOL_CALL_FAMILY, + LLAMA3_TOOL_CALL_FAMILY, + QWEN_TOOL_CALL_FAMILY, + ) model_family = desc.get("model_family", "") function_call_models = ( - ["gorilla-openfunctions-v1"] + QWEN_TOOL_CALL_FAMILY + GLM4_TOOL_CALL_FAMILY + QWEN_TOOL_CALL_FAMILY + GLM4_TOOL_CALL_FAMILY + LLAMA3_TOOL_CALL_FAMILY ) if model_family not in function_call_models: @@ -1716,9 +1964,7 @@ async def stream_results(): try: try: iterator = await model.chat( - prompt, - system_prompt, - chat_history, + messages, kwargs, raw_params=raw_kwargs, ) @@ -1750,9 +1996,7 @@ async def stream_results(): else: try: data = await model.chat( - prompt, - system_prompt, - chat_history, + messages, kwargs, raw_params=raw_kwargs, ) diff --git a/xinference/client/restful/restful_client.py b/xinference/client/restful/restful_client.py index 679f65d296..dd5e3f1146 100644 --- a/xinference/client/restful/restful_client.py +++ b/xinference/client/restful/restful_client.py @@ -13,7 +13,6 @@ # limitations under the License. import json import typing -import warnings from typing import TYPE_CHECKING, Any, Dict, Iterator, List, Optional, Union import requests @@ -370,6 +369,25 @@ def inpainting( response_data = response.json() return response_data + def ocr(self, image: Union[str, bytes], **kwargs): + url = f"{self._base_url}/v1/images/ocr" + params = { + "model": self._model_uid, + "kwargs": json.dumps(kwargs), + } + files: List[Any] = [] + for key, value in params.items(): + files.append((key, (None, value))) + files.append(("image", ("image", image, "application/octet-stream"))) + response = requests.post(url, files=files, headers=self.auth_headers) + if response.status_code != 200: + raise RuntimeError( + f"Failed to ocr the images, detail: {_get_error_string(response)}" + ) + + response_data = response.json() + return response_data + class RESTfulVideoModelHandle(RESTfulModelHandle): def text_to_video( @@ -470,9 +488,7 @@ def generate( class RESTfulChatModelHandle(RESTfulGenerateModelHandle): def chat( self, - prompt: str, - system_prompt: Optional[str] = None, - chat_history: Optional[List["ChatCompletionMessage"]] = None, + messages: List[Dict], tools: Optional[List[Dict]] = None, generate_config: Optional[ Union["LlamaCppGenerateConfig", "PytorchGenerateConfig"] @@ -483,11 +499,7 @@ def chat( Parameters ---------- - prompt: str - The user's input. - system_prompt: Optional[str] - The system context provide to Model prior to any chats. - chat_history: Optional[List["ChatCompletionMessage"]] + messages: List[Dict] A list of messages comprising the conversation so far. tools: Optional[List[Dict]] A tool list. @@ -509,25 +521,11 @@ def chat( Report the failure to generate the chat from the server. Detailed information provided in error message. """ - warnings.warn( - "The parameters `prompt`, `system_prompt` and `chat_history` will be deprecated in version v0.15.0, " - "and will be replaced by the parameter `messages`, " - "similar to the OpenAI API: https://platform.openai.com/docs/guides/chat-completions/getting-started", - category=DeprecationWarning, - stacklevel=2, - ) - url = f"{self._base_url}/v1/chat/completions" - if chat_history is None: - chat_history = [] - - chat_history = handle_system_prompts(chat_history, system_prompt) - chat_history.append({"role": "user", "content": prompt}) # type: ignore - request_body: Dict[str, Any] = { "model": self._model_uid, - "messages": chat_history, + "messages": messages, } if tools is not None: request_body["tools"] = tools @@ -730,10 +728,12 @@ def speech( ) ) response = requests.post( - url, data=params, files=files, headers=self.auth_headers + url, data=params, files=files, headers=self.auth_headers, stream=stream ) else: - response = requests.post(url, json=params, headers=self.auth_headers) + response = requests.post( + url, json=params, headers=self.auth_headers, stream=stream + ) if response.status_code != 200: raise RuntimeError( f"Failed to speech the text, detail: {_get_error_string(response)}" @@ -1404,6 +1404,16 @@ def get_supervisor_info(self): response_json = response.json() return response_json + def get_progress(self, request_id: str): + url = f"{self.base_url}/v1/requests/{request_id}/progress" + response = requests.get(url, headers=self._headers) + if response.status_code != 200: + raise RuntimeError( + f"Failed to get progress, detail: {_get_error_string(response)}" + ) + response_json = response.json() + return response_json + def abort_cluster(self): url = f"{self.base_url}/v1/clusters" response = requests.delete(url, headers=self._headers) diff --git a/xinference/client/tests/test_client.py b/xinference/client/tests/test_client.py index 095ef5e182..df28e8260a 100644 --- a/xinference/client/tests/test_client.py +++ b/xinference/client/tests/test_client.py @@ -73,18 +73,24 @@ def test_RESTful_client(setup): with pytest.raises(RuntimeError): completion = model.chat({"max_tokens": 64}) - completion = model.chat("What is the capital of France?") + messages = [{"role": "user", "content": "What is the capital of France?"}] + completion = model.chat(messages) assert "content" in completion["choices"][0]["message"] def _check_stream(): streaming_response = model.chat( - prompt="What is the capital of France?", + messages, generate_config={"stream": True, "max_tokens": 5}, ) for chunk in streaming_response: - assert ("content" in chunk["choices"][0]["delta"]) or ( - "role" in chunk["choices"][0]["delta"] - ) + assert "finish_reason" in chunk["choices"][0] + finish_reason = chunk["choices"][0]["finish_reason"] + if finish_reason is None: + assert ("content" in chunk["choices"][0]["delta"]) or ( + "role" in chunk["choices"][0]["delta"] + ) + else: + assert chunk["choices"][0]["delta"] == {} _check_stream() @@ -93,15 +99,9 @@ def _check_stream(): for _ in range(2): r = executor.submit(_check_stream) results.append(r) - # Parallel generation is not supported by llama-cpp-python. - error_count = 0 + for r in results: - try: - r.result() - except Exception as ex: - assert "Parallel generation" in str(ex) - error_count += 1 - assert error_count == 1 + r.result() # After iteration finish, we can iterate again. _check_stream() @@ -137,18 +137,12 @@ def _check(stream=False): for stream in [True, False]: results = [] - error_count = 0 with ThreadPoolExecutor() as executor: for _ in range(3): r = executor.submit(_check, stream=stream) results.append(r) for r in results: - try: - r.result() - except Exception as ex: - assert "Parallel generation" in str(ex) - error_count += 1 - assert error_count == (2 if stream else 0) + r.result() client.terminate_model(model_uid=model_uid) assert len(client.list_models()) == 0 @@ -189,13 +183,6 @@ def test_RESTful_client_for_embedding(setup): completion = model.create_embedding("write a poem.") assert len(completion["data"][0]["embedding"]) == 768 - kwargs = { - "invalid": "invalid", - } - with pytest.raises(RuntimeError) as err: - completion = model.create_embedding("write a poem.", **kwargs) - assert "unexpected" in str(err.value) - client.terminate_model(model_uid=model_uid) assert len(client.list_models()) == 0 @@ -217,7 +204,6 @@ def test_RESTful_client_custom_model(setup): "en", "zh" ], "model_ability": [ - "embed", "chat" ], "model_family": "other", @@ -233,15 +219,9 @@ def test_RESTful_client_custom_model(setup): "model_id": "ziqingyang/chinese-alpaca-2-7b" } ], - "prompt_style": { - "style_name": "ADD_COLON_SINGLE", - "system_prompt": "Below is an instruction that describes a task. Write a response that appropriately completes the request.", - "roles": [ - "Instruction", - "Response" - ], - "intra_message_sep": "\\n\\n### " - } + "chat_template": "xyz", + "stop_token_ids": [], + "stop": [] }""" client.register_model(model_type="LLM", model=model, persist=False) @@ -265,7 +245,7 @@ def test_RESTful_client_custom_model(setup): custom_model_reg = model_reg assert custom_model_reg is None - # test register with string prompt style name + # test register with chat_template using model_family model_with_prompt = """{ "version": 1, "context_length":2048, @@ -290,12 +270,12 @@ def test_RESTful_client_custom_model(setup): "model_id": "ziqingyang/chinese-alpaca-2-7b" } ], - "prompt_style": "qwen-chat" + "chat_template": "qwen-chat" }""" client.register_model(model_type="LLM", model=model_with_prompt, persist=False) client.unregister_model(model_type="LLM", model_name="custom_model") - model_with_prompt2 = """{ + model_with_vision = """{ "version": 1, "context_length":2048, "model_name": "custom_model", @@ -303,8 +283,8 @@ def test_RESTful_client_custom_model(setup): "en", "zh" ], "model_ability": [ - "embed", - "chat" + "chat", + "vision" ], "model_family": "other", "model_specs": [ @@ -319,10 +299,41 @@ def test_RESTful_client_custom_model(setup): "model_id": "ziqingyang/chinese-alpaca-2-7b" } ], - "prompt_style": "xyz123" + "chat_template": "xyz123" }""" with pytest.raises(RuntimeError): - client.register_model(model_type="LLM", model=model_with_prompt2, persist=False) + client.register_model(model_type="LLM", model=model_with_vision, persist=False) + + model_with_tool_call = """{ + "version": 1, + "context_length":2048, + "model_name": "custom_model", + "model_lang": [ + "en", "zh" + ], + "model_ability": [ + "chat", + "tools" + ], + "model_family": "other", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": 7, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "ziqingyang/chinese-alpaca-2-7b" + } + ], + "chat_template": "xyz123" + }""" + with pytest.raises(RuntimeError): + client.register_model( + model_type="LLM", model=model_with_tool_call, persist=False + ) def test_client_from_modelscope(setup): diff --git a/xinference/conftest.py b/xinference/conftest.py index a03572451f..7a05a09a7e 100644 --- a/xinference/conftest.py +++ b/xinference/conftest.py @@ -144,7 +144,7 @@ async def _start_test_cluster( address=f"test://{address}", logging_conf=logging_conf ) await xo.create_actor( - SupervisorActor, address=address, uid=SupervisorActor.uid() + SupervisorActor, address=address, uid=SupervisorActor.default_uid() ) await start_worker_components( address=address, diff --git a/xinference/constants.py b/xinference/constants.py index c9ba4e5ddc..66e9983a93 100644 --- a/xinference/constants.py +++ b/xinference/constants.py @@ -27,7 +27,8 @@ XINFERENCE_ENV_HEALTH_CHECK_TIMEOUT = "XINFERENCE_HEALTH_CHECK_TIMEOUT" XINFERENCE_ENV_DISABLE_HEALTH_CHECK = "XINFERENCE_DISABLE_HEALTH_CHECK" XINFERENCE_ENV_DISABLE_METRICS = "XINFERENCE_DISABLE_METRICS" -XINFERENCE_ENV_TRANSFORMERS_ENABLE_BATCHING = "XINFERENCE_TRANSFORMERS_ENABLE_BATCHING" +XINFERENCE_ENV_DOWNLOAD_MAX_ATTEMPTS = "XINFERENCE_DOWNLOAD_MAX_ATTEMPTS" +XINFERENCE_ENV_TEXT_TO_IMAGE_BATCHING_SIZE = "XINFERENCE_TEXT_TO_IMAGE_BATCHING_SIZE" def get_xinference_home() -> str: @@ -38,6 +39,10 @@ def get_xinference_home() -> str: # if user has already set `XINFERENCE_HOME` env, change huggingface and modelscope default download path os.environ["HUGGINGFACE_HUB_CACHE"] = os.path.join(home_path, "huggingface") os.environ["MODELSCOPE_CACHE"] = os.path.join(home_path, "modelscope") + # In multi-tenant mode, + # gradio's temporary files are stored in their respective home directories, + # to prevent insufficient permissions + os.environ["GRADIO_TEMP_DIR"] = os.path.join(home_path, "tmp", "gradio") return home_path @@ -59,6 +64,7 @@ def get_xinference_home() -> str: XINFERENCE_DEFAULT_LOG_FILE_NAME = "xinference.log" XINFERENCE_LOG_MAX_BYTES = 100 * 1024 * 1024 XINFERENCE_LOG_BACKUP_COUNT = 30 +XINFERENCE_LOG_ARG_MAX_LENGTH = 100 XINFERENCE_HEALTH_CHECK_FAILURE_THRESHOLD = int( os.environ.get(XINFERENCE_ENV_HEALTH_CHECK_FAILURE_THRESHOLD, 5) ) @@ -74,6 +80,9 @@ def get_xinference_home() -> str: XINFERENCE_DISABLE_METRICS = bool( int(os.environ.get(XINFERENCE_ENV_DISABLE_METRICS, 0)) ) -XINFERENCE_TRANSFORMERS_ENABLE_BATCHING = bool( - int(os.environ.get(XINFERENCE_ENV_TRANSFORMERS_ENABLE_BATCHING, 0)) +XINFERENCE_DOWNLOAD_MAX_ATTEMPTS = int( + os.environ.get(XINFERENCE_ENV_DOWNLOAD_MAX_ATTEMPTS, 3) +) +XINFERENCE_TEXT_TO_IMAGE_BATCHING_SIZE = os.environ.get( + XINFERENCE_ENV_TEXT_TO_IMAGE_BATCHING_SIZE, None ) diff --git a/xinference/core/cache_tracker.py b/xinference/core/cache_tracker.py index 7f6975d4bc..a1d6aa2960 100644 --- a/xinference/core/cache_tracker.py +++ b/xinference/core/cache_tracker.py @@ -25,7 +25,7 @@ def __init__(self): self._model_name_to_version_info: Dict[str, List[Dict]] = {} # type: ignore @classmethod - def uid(cls) -> str: + def default_uid(cls) -> str: return "cache_tracker" @staticmethod diff --git a/xinference/core/chat_interface.py b/xinference/core/chat_interface.py index 8738141f90..08b30ab054 100644 --- a/xinference/core/chat_interface.py +++ b/xinference/core/chat_interface.py @@ -16,7 +16,7 @@ import logging import os from io import BytesIO -from typing import Generator, List, Optional +from typing import Dict, Generator, List, Optional import gradio as gr import PIL.Image @@ -27,7 +27,6 @@ RESTfulChatModelHandle, RESTfulGenerateModelHandle, ) -from ..types import ChatCompletionMessage logger = logging.getLogger(__name__) @@ -75,7 +74,11 @@ def build(self) -> "gr.Blocks": # Gradio initiates the queue during a startup event, but since the app has already been # started, that event will not run, so manually invoke the startup events. # See: https://github.com/gradio-app/gradio/issues/5228 - interface.startup_events() + try: + interface.run_startup_events() + except AttributeError: + # compatibility + interface.startup_events() favicon_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), os.path.pardir, @@ -96,11 +99,11 @@ def flatten(matrix: List[List[str]]) -> List[str]: flat_list += row return flat_list - def to_chat(lst: List[str]) -> List[ChatCompletionMessage]: + def to_chat(lst: List[str]) -> List[Dict]: res = [] for i in range(len(lst)): role = "assistant" if i % 2 == 1 else "user" - res.append(ChatCompletionMessage(role=role, content=lst[i])) + res.append(dict(role=role, content=lst[i])) return res def generate_wrapper( @@ -116,11 +119,12 @@ def generate_wrapper( client._set_token(self._access_token) model = client.get_model(self.model_uid) assert isinstance(model, RESTfulChatModelHandle) + messages = to_chat(flatten(history)) + messages.append(dict(role="user", content=message)) response_content = "" for chunk in model.chat( - prompt=message, - chat_history=to_chat(flatten(history)), + messages, generate_config={ "max_tokens": int(max_tokens), "temperature": temperature, @@ -191,15 +195,10 @@ def predict(history, bot, max_tokens, temperature, stream): model = client.get_model(self.model_uid) assert isinstance(model, RESTfulChatModelHandle) - prompt = history[-1] - assert prompt["role"] == "user" - prompt = prompt["content"] - # multimodal chat does not support stream. if stream: response_content = "" for chunk in model.chat( - prompt=prompt, - chat_history=history[:-1], + messages=history, generate_config={ "max_tokens": max_tokens, "temperature": temperature, @@ -224,8 +223,7 @@ def predict(history, bot, max_tokens, temperature, stream): yield history, bot else: response = model.chat( - prompt=prompt, - chat_history=history[:-1], + messages=history, generate_config={ "max_tokens": max_tokens, "temperature": temperature, diff --git a/xinference/core/event.py b/xinference/core/event.py index fb5df80dc2..19585cf859 100644 --- a/xinference/core/event.py +++ b/xinference/core/event.py @@ -41,7 +41,7 @@ def __init__(self): ) @classmethod - def uid(cls) -> str: + def default_uid(cls) -> str: return "event_collector" def get_model_events(self, model_uid: str) -> List[Dict]: diff --git a/xinference/core/image_interface.py b/xinference/core/image_interface.py index e5ece5320c..b48636bfd5 100644 --- a/xinference/core/image_interface.py +++ b/xinference/core/image_interface.py @@ -16,6 +16,9 @@ import io import logging import os +import threading +import time +import uuid from typing import Dict, List, Optional, Union import gradio as gr @@ -60,7 +63,11 @@ def build(self) -> gr.Blocks: # Gradio initiates the queue during a startup event, but since the app has already been # started, that event will not run, so manually invoke the startup events. # See: https://github.com/gradio-app/gradio/issues/5228 - interface.startup_events() + try: + interface.run_startup_events() + except AttributeError: + # compatibility + interface.startup_events() favicon_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), os.path.pardir, @@ -73,13 +80,18 @@ def build(self) -> gr.Blocks: return interface def text2image_interface(self) -> "gr.Blocks": + from ..model.image.stable_diffusion.core import SAMPLING_METHODS + def text_generate_image( prompt: str, n: int, size_width: int, size_height: int, + guidance_scale: int, num_inference_steps: int, negative_prompt: Optional[str] = None, + sampler_name: Optional[str] = None, + progress=gr.Progress(), ) -> PIL.Image.Image: from ..client import RESTfulClient @@ -89,21 +101,49 @@ def text_generate_image( assert isinstance(model, RESTfulImageModelHandle) size = f"{int(size_width)}*{int(size_height)}" + guidance_scale = None if guidance_scale == -1 else guidance_scale # type: ignore num_inference_steps = ( None if num_inference_steps == -1 else num_inference_steps # type: ignore ) + sampler_name = None if sampler_name == "default" else sampler_name + + response = None + exc = None + request_id = str(uuid.uuid4()) + + def run_in_thread(): + nonlocal exc, response + try: + response = model.text_to_image( + request_id=request_id, + prompt=prompt, + n=n, + size=size, + num_inference_steps=num_inference_steps, + guidance_scale=guidance_scale, + negative_prompt=negative_prompt, + sampler_name=sampler_name, + response_format="b64_json", + ) + except Exception as e: + exc = e - response = model.text_to_image( - prompt=prompt, - n=n, - size=size, - num_inference_steps=num_inference_steps, - negative_prompt=negative_prompt, - response_format="b64_json", - ) + t = threading.Thread(target=run_in_thread) + t.start() + while t.is_alive(): + try: + cur_progress = client.get_progress(request_id)["progress"] + except (KeyError, RuntimeError): + cur_progress = 0.0 + + progress(cur_progress, desc="Generating images") + time.sleep(1) + + if exc: + raise exc images = [] - for image_dict in response["data"]: + for image_dict in response["data"]: # type: ignore assert image_dict["b64_json"] is not None image_data = base64.b64decode(image_dict["b64_json"]) image = PIL.Image.open(io.BytesIO(image_data)) @@ -132,9 +172,16 @@ def text_generate_image( n = gr.Number(label="Number of Images", value=1) size_width = gr.Number(label="Width", value=1024) size_height = gr.Number(label="Height", value=1024) + with gr.Row(): + guidance_scale = gr.Number(label="Guidance scale", value=-1) num_inference_steps = gr.Number( label="Inference Step Number", value=-1 ) + sampler_name = gr.Dropdown( + choices=SAMPLING_METHODS, + value="default", + label="Sampling method", + ) with gr.Column(): image_output = gr.Gallery() @@ -146,8 +193,10 @@ def text_generate_image( n, size_width, size_height, + guidance_scale, num_inference_steps, negative_prompt, + sampler_name, ], outputs=image_output, ) @@ -155,6 +204,8 @@ def text_generate_image( return text2image_vl_interface def image2image_interface(self) -> "gr.Blocks": + from ..model.image.stable_diffusion.core import SAMPLING_METHODS + def image_generate_image( prompt: str, negative_prompt: str, @@ -164,6 +215,8 @@ def image_generate_image( size_height: int, num_inference_steps: int, padding_image_to_multiple: int, + sampler_name: Optional[str] = None, + progress=gr.Progress(), ) -> PIL.Image.Image: from ..client import RESTfulClient @@ -180,23 +233,49 @@ def image_generate_image( None if num_inference_steps == -1 else num_inference_steps # type: ignore ) padding_image_to_multiple = None if padding_image_to_multiple == -1 else padding_image_to_multiple # type: ignore + sampler_name = None if sampler_name == "default" else sampler_name bio = io.BytesIO() image.save(bio, format="png") - response = model.image_to_image( - prompt=prompt, - negative_prompt=negative_prompt, - n=n, - image=bio.getvalue(), - size=size, - response_format="b64_json", - num_inference_steps=num_inference_steps, - padding_image_to_multiple=padding_image_to_multiple, - ) + response = None + exc = None + request_id = str(uuid.uuid4()) + + def run_in_thread(): + nonlocal exc, response + try: + response = model.image_to_image( + request_id=request_id, + prompt=prompt, + negative_prompt=negative_prompt, + n=n, + image=bio.getvalue(), + size=size, + response_format="b64_json", + num_inference_steps=num_inference_steps, + padding_image_to_multiple=padding_image_to_multiple, + sampler_name=sampler_name, + ) + except Exception as e: + exc = e + + t = threading.Thread(target=run_in_thread) + t.start() + while t.is_alive(): + try: + cur_progress = client.get_progress(request_id)["progress"] + except (KeyError, RuntimeError): + cur_progress = 0.0 + + progress(cur_progress, desc="Generating images") + time.sleep(1) + + if exc: + raise exc images = [] - for image_dict in response["data"]: + for image_dict in response["data"]: # type: ignore assert image_dict["b64_json"] is not None image_data = base64.b64decode(image_dict["b64_json"]) image = PIL.Image.open(io.BytesIO(image_data)) @@ -233,6 +312,11 @@ def image_generate_image( padding_image_to_multiple = gr.Number( label="Padding image to multiple", value=-1 ) + sampler_name = gr.Dropdown( + choices=SAMPLING_METHODS, + value="default", + label="Sampling method", + ) with gr.Row(): with gr.Column(scale=1): @@ -251,6 +335,7 @@ def image_generate_image( size_height, num_inference_steps, padding_image_to_multiple, + sampler_name, ], outputs=output_gallery, ) diff --git a/xinference/core/model.py b/xinference/core/model.py index 602f712514..567ef81769 100644 --- a/xinference/core/model.py +++ b/xinference/core/model.py @@ -17,9 +17,10 @@ import inspect import json import os +import queue import time import types -import weakref +import uuid from asyncio.queues import Queue from asyncio.tasks import wait_for from concurrent.futures import Future as ConcurrentFuture @@ -31,7 +32,6 @@ Callable, Dict, Generator, - Iterator, List, Optional, Union, @@ -40,9 +40,10 @@ import sse_starlette.sse import xoscar as xo -from ..constants import XINFERENCE_TRANSFORMERS_ENABLE_BATCHING +from ..constants import XINFERENCE_TEXT_TO_IMAGE_BATCHING_SIZE if TYPE_CHECKING: + from .progress_tracker import ProgressTrackerActor from .worker import WorkerActor from ..model.llm.core import LLM from ..model.core import ModelDescription @@ -65,7 +66,14 @@ class _OutOfMemoryError(Exception): OutOfMemoryError = _OutOfMemoryError -XINFERENCE_BATCHING_ALLOWED_VISION_MODELS = ["qwen-vl-chat", "cogvlm2", "glm-4v"] +XINFERENCE_BATCHING_ALLOWED_VISION_MODELS = [ + "qwen-vl-chat", + "cogvlm2", + "glm-4v", + "MiniCPM-V-2.6", +] + +XINFERENCE_TEXT_TO_IMAGE_BATCHING_ALLOWED_MODELS = ["FLUX.1-dev", "FLUX.1-schnell"] def request_limit(fn): @@ -146,6 +154,16 @@ async def __pre_destroy__(self): f"Destroy scheduler actor failed, address: {self.address}, error: {e}" ) + if self.allow_batching_for_text_to_image(): + try: + assert self._text_to_image_scheduler_ref is not None + await xo.destroy_actor(self._text_to_image_scheduler_ref) + del self._text_to_image_scheduler_ref + except Exception as e: + logger.debug( + f"Destroy text_to_image scheduler actor failed, address: {self.address}, error: {e}" + ) + if hasattr(self._model, "stop") and callable(self._model.stop): self._model.stop() @@ -171,6 +189,7 @@ async def __pre_destroy__(self): def __init__( self, + supervisor_address: str, worker_address: str, model: "LLM", model_description: Optional["ModelDescription"] = None, @@ -182,15 +201,15 @@ def __init__( from ..model.llm.transformers.core import PytorchModel from ..model.llm.vllm.core import VLLMModel + self._supervisor_address = supervisor_address self._worker_address = worker_address self._model = model self._model_description = ( model_description.to_dict() if model_description else {} ) self._request_limits = request_limits - - self._generators: Dict[str, Union[Iterator, AsyncGenerator]] = {} - self._current_generator = lambda: None + self._pending_requests: asyncio.Queue = asyncio.Queue() + self._handle_pending_requests_task = None self._lock = ( None if isinstance( @@ -199,6 +218,7 @@ def __init__( else asyncio.locks.Lock() ) self._worker_ref = None + self._progress_tracker_ref = None self._serve_count = 0 self._metrics_labels = { "type": self._model_description.get("model_type", "unknown"), @@ -210,10 +230,15 @@ def __init__( self._loop: Optional[asyncio.AbstractEventLoop] = None self._scheduler_ref = None + self._text_to_image_scheduler_ref = None async def __post_create__(self): self._loop = asyncio.get_running_loop() + self._handle_pending_requests_task = asyncio.create_task( + self._handle_pending_requests() + ) + if self.allow_batching(): from .scheduler import SchedulerActor @@ -223,6 +248,15 @@ async def __post_create__(self): uid=SchedulerActor.gen_uid(self.model_uid(), self._model.rep_id), ) + if self.allow_batching_for_text_to_image(): + from ..model.image.scheduler.flux import FluxBatchSchedulerActor + + self._text_to_image_scheduler_ref = await xo.create_actor( + FluxBatchSchedulerActor, + address=self.address, + uid=FluxBatchSchedulerActor.gen_uid(self.model_uid()), + ) + async def _record_completion_metrics( self, duration, completion_tokens, prompt_tokens ): @@ -265,10 +299,32 @@ async def _get_worker_ref(self) -> xo.ActorRefType["WorkerActor"]: if self._worker_ref is None: self._worker_ref = await xo.actor_ref( - address=self._worker_address, uid=WorkerActor.uid() + address=self._worker_address, uid=WorkerActor.default_uid() ) return self._worker_ref + async def _get_progress_tracker_ref( + self, + ) -> xo.ActorRefType["ProgressTrackerActor"]: + from .progress_tracker import ProgressTrackerActor + + if self._progress_tracker_ref is None: + self._progress_tracker_ref = await xo.actor_ref( + address=self._supervisor_address, uid=ProgressTrackerActor.default_uid() + ) + return self._progress_tracker_ref + + async def _get_progressor(self, request_id: str): + from .progress_tracker import Progressor + + progressor = Progressor( + request_id, + await self._get_progress_tracker_ref(), + asyncio.get_running_loop(), + ) + await progressor.start() + return progressor + def is_vllm_backend(self) -> bool: from ..model.llm.vllm.core import VLLMModel @@ -279,10 +335,8 @@ def allow_batching(self) -> bool: model_ability = self._model_description.get("model_ability", []) - condition = XINFERENCE_TRANSFORMERS_ENABLE_BATCHING and isinstance( - self._model, PytorchModel - ) - if condition and "vision" in model_ability: + condition = isinstance(self._model, PytorchModel) + if condition and ("vision" in model_ability or "audio" in model_ability): if ( self._model.model_family.model_name in XINFERENCE_BATCHING_ALLOWED_VISION_MODELS @@ -299,6 +353,26 @@ def allow_batching(self) -> bool: return False return condition + def allow_batching_for_text_to_image(self) -> bool: + from ..model.image.stable_diffusion.core import DiffusionModel + + condition = XINFERENCE_TEXT_TO_IMAGE_BATCHING_SIZE is not None and isinstance( + self._model, DiffusionModel + ) + + if condition: + model_name = self._model._model_spec.model_name # type: ignore + if model_name in XINFERENCE_TEXT_TO_IMAGE_BATCHING_ALLOWED_MODELS: + return True + else: + logger.warning( + f"Currently for image models with text_to_image ability, " + f"xinference only supports {', '.join(XINFERENCE_TEXT_TO_IMAGE_BATCHING_ALLOWED_MODELS)} for batching. " + f"Your model {model_name} is disqualified." + ) + return False + return condition + async def load(self): self._model.load() if self.allow_batching(): @@ -306,6 +380,11 @@ async def load(self): logger.debug( f"Batching enabled for model: {self.model_uid()}, max_num_seqs: {self._model.get_max_num_seqs()}" ) + if self.allow_batching_for_text_to_image(): + await self._text_to_image_scheduler_ref.set_model(self._model) + logger.debug( + f"Batching enabled for model: {self.model_uid()}, max_num_images: {self._model.get_max_num_images_for_batching()}" + ) def model_uid(self): return ( @@ -397,6 +476,43 @@ async def _to_async_gen(self, output_type: str, gen: types.AsyncGeneratorType): ) await asyncio.gather(*coros) + async def _handle_pending_requests(self): + logger.info("Start requests handler.") + while True: + gen, stream_out, stop = await self._pending_requests.get() + + async def _async_wrapper(_gen): + try: + # anext is only available for Python >= 3.10 + return await _gen.__anext__() # noqa: F821 + except StopAsyncIteration: + return stop + + def _wrapper(_gen): + # Avoid issue: https://github.com/python/cpython/issues/112182 + try: + return next(_gen) + except StopIteration: + return stop + + while True: + try: + if inspect.isgenerator(gen): + r = await asyncio.to_thread(_wrapper, gen) + elif inspect.isasyncgen(gen): + r = await _async_wrapper(gen) + else: + raise Exception( + f"The generator {gen} should be a generator or an async generator, " + f"but a {type(gen)} is got." + ) + stream_out.put_nowait(r) + if r is not stop: + continue + except Exception: + logger.exception("stream encountered an error.") + break + async def _call_wrapper_json(self, fn: Callable, *args, **kwargs): return await self._call_wrapper("json", fn, *args, **kwargs) @@ -410,6 +526,13 @@ async def _call_wrapper(self, output_type: str, fn: Callable, *args, **kwargs): ret = await fn(*args, **kwargs) else: ret = await asyncio.to_thread(fn, *args, **kwargs) + + if inspect.isgenerator(ret): + gen = self._to_generator(output_type, ret) + return gen + if inspect.isasyncgen(ret): + gen = self._to_async_gen(output_type, ret) + return gen else: async with self._lock: if inspect.iscoroutinefunction(fn): @@ -417,40 +540,75 @@ async def _call_wrapper(self, output_type: str, fn: Callable, *args, **kwargs): else: ret = await asyncio.to_thread(fn, *args, **kwargs) - if self._lock is not None and self._current_generator(): - raise Exception("Parallel generation is not supported by llama-cpp-python.") + stream_out: Union[queue.Queue, asyncio.Queue] + + if inspect.isgenerator(ret): + gen = self._to_generator(output_type, ret) + stream_out = queue.Queue() + stop = object() + self._pending_requests.put_nowait((gen, stream_out, stop)) + + def _stream_out_generator(): + while True: + o = stream_out.get() + if o is stop: + break + else: + yield o + + return _stream_out_generator() + + if inspect.isasyncgen(ret): + gen = self._to_async_gen(output_type, ret) + stream_out = asyncio.Queue() + stop = object() + self._pending_requests.put_nowait((gen, stream_out, stop)) + + async def _stream_out_async_gen(): + while True: + o = await stream_out.get() + if o is stop: + break + else: + yield o + + return _stream_out_async_gen() - if inspect.isgenerator(ret): - gen = self._to_generator(output_type, ret) - self._current_generator = weakref.ref(gen) - return gen - if inspect.isasyncgen(ret): - gen = self._to_async_gen(output_type, ret) - self._current_generator = weakref.ref(gen) - return gen if output_type == "json": return await asyncio.to_thread(json_dumps, ret) else: assert output_type == "binary", f"Unknown output type '{output_type}'" return ret - @log_async(logger=logger) @request_limit @xo.generator + @log_async(logger=logger) async def generate(self, prompt: str, *args, **kwargs): if self.allow_batching(): + # not support request_id + kwargs.pop("request_id", None) return await self.handle_batching_request( prompt, "generate", *args, **kwargs ) else: kwargs.pop("raw_params", None) if hasattr(self._model, "generate"): + # not support request_id + kwargs.pop("request_id", None) return await self._call_wrapper_json( self._model.generate, prompt, *args, **kwargs ) if hasattr(self._model, "async_generate"): + if "request_id" not in kwargs: + kwargs["request_id"] = str(uuid.uuid1()) + else: + # model only accept string + kwargs["request_id"] = str(kwargs["request_id"]) return await self._call_wrapper_json( - self._model.async_generate, prompt, *args, **kwargs + self._model.async_generate, + prompt, + *args, + **kwargs, ) raise AttributeError(f"Model {self._model.model_spec} is not for generate.") @@ -481,31 +639,37 @@ async def _queue_consumer( yield res @staticmethod - def _get_stream_from_args(ability: str, *args) -> bool: - if ability == "chat": - assert args[2] is None or isinstance(args[2], dict) - return False if args[2] is None else args[2].get("stream", False) - else: - assert args[0] is None or isinstance(args[0], dict) - return False if args[0] is None else args[0].get("stream", False) + def _get_stream_from_args(*args) -> bool: + assert args[0] is None or isinstance(args[0], dict) + return False if args[0] is None else args[0].get("stream", False) - async def handle_batching_request(self, prompt: str, ability: str, *args, **kwargs): - stream = self._get_stream_from_args(ability, *args) + async def handle_batching_request( + self, prompt_or_messages: Union[str, List[Dict]], call_ability, *args, **kwargs + ): + """ + The input parameter `prompt_or_messages`: + - when the model_ability is `generate`, it's `prompt`, which is str type. + - when the model_ability is `chat`, it's `messages`, which is List[Dict] type. + """ + stream = self._get_stream_from_args(*args) assert self._scheduler_ref is not None if stream: assert self._scheduler_ref is not None queue: Queue[Any] = Queue() ret = self._queue_consumer(queue) - await self._scheduler_ref.add_request(prompt, queue, *args, **kwargs) + await self._scheduler_ref.add_request( + prompt_or_messages, queue, call_ability, *args, **kwargs + ) gen = self._to_async_gen("json", ret) - self._current_generator = weakref.ref(gen) return gen else: from .scheduler import XINFERENCE_NON_STREAMING_ABORT_FLAG assert self._loop is not None future = ConcurrentFuture() - await self._scheduler_ref.add_request(prompt, future, *args, **kwargs) + await self._scheduler_ref.add_request( + prompt_or_messages, future, call_ability, *args, **kwargs + ) fut = asyncio.wrap_future(future, loop=self._loop) result = await fut if result == XINFERENCE_NON_STREAMING_ABORT_FLAG: @@ -514,27 +678,36 @@ async def handle_batching_request(self, prompt: str, ability: str, *args, **kwar ) return await asyncio.to_thread(json_dumps, result) - @log_async(logger=logger) @request_limit @xo.generator - async def chat(self, prompt: str, *args, **kwargs): + @log_async(logger=logger) + async def chat(self, messages: List[Dict], *args, **kwargs): start_time = time.time() response = None try: if self.allow_batching(): + # not support request_id + kwargs.pop("request_id", None) return await self.handle_batching_request( - prompt, "chat", *args, **kwargs + messages, "chat", *args, **kwargs ) else: kwargs.pop("raw_params", None) if hasattr(self._model, "chat"): + # not support request_id + kwargs.pop("request_id", None) response = await self._call_wrapper_json( - self._model.chat, prompt, *args, **kwargs + self._model.chat, messages, *args, **kwargs ) return response if hasattr(self._model, "async_chat"): + if "request_id" not in kwargs: + kwargs["request_id"] = str(uuid.uuid1()) + else: + # model only accept string + kwargs["request_id"] = str(kwargs["request_id"]) response = await self._call_wrapper_json( - self._model.async_chat, prompt, *args, **kwargs + self._model.async_chat, messages, *args, **kwargs ) return response raise AttributeError(f"Model {self._model.model_spec} is not for chat.") @@ -557,17 +730,22 @@ async def chat(self, prompt: str, *args, **kwargs): ) async def abort_request(self, request_id: str) -> str: - from .scheduler import AbortRequestMessage + from .utils import AbortRequestMessage if self.allow_batching(): if self._scheduler_ref is None: return AbortRequestMessage.NOT_FOUND.name return await self._scheduler_ref.abort_request(request_id) + elif self.allow_batching_for_text_to_image(): + if self._text_to_image_scheduler_ref is None: + return AbortRequestMessage.NOT_FOUND.name + return await self._text_to_image_scheduler_ref.abort_request(request_id) return AbortRequestMessage.NO_OP.name - @log_async(logger=logger) @request_limit + @log_async(logger=logger) async def create_embedding(self, input: Union[str, List[str]], *args, **kwargs): + kwargs.pop("request_id", None) if hasattr(self._model, "create_embedding"): return await self._call_wrapper_json( self._model.create_embedding, input, *args, **kwargs @@ -577,8 +755,8 @@ async def create_embedding(self, input: Union[str, List[str]], *args, **kwargs): f"Model {self._model.model_spec} is not for creating embedding." ) - @log_async(logger=logger) @request_limit + @log_async(logger=logger) async def rerank( self, documents: List[str], @@ -590,6 +768,7 @@ async def rerank( *args, **kwargs, ): + kwargs.pop("request_id", None) if hasattr(self._model, "rerank"): return await self._call_wrapper_json( self._model.rerank, @@ -604,8 +783,8 @@ async def rerank( ) raise AttributeError(f"Model {self._model.model_spec} is not for reranking.") - @log_async(logger=logger, args_formatter=lambda _, kwargs: kwargs.pop("audio")) @request_limit + @log_async(logger=logger, ignore_kwargs=["audio"]) async def transcriptions( self, audio: bytes, @@ -614,7 +793,9 @@ async def transcriptions( response_format: str = "json", temperature: float = 0, timestamp_granularities: Optional[List[str]] = None, + **kwargs, ): + kwargs.pop("request_id", None) if hasattr(self._model, "transcriptions"): return await self._call_wrapper_json( self._model.transcriptions, @@ -629,8 +810,8 @@ async def transcriptions( f"Model {self._model.model_spec} is not for creating transcriptions." ) - @log_async(logger=logger, args_formatter=lambda _, kwargs: kwargs.pop("audio")) @request_limit + @log_async(logger=logger, ignore_kwargs=["audio"]) async def translations( self, audio: bytes, @@ -639,7 +820,9 @@ async def translations( response_format: str = "json", temperature: float = 0, timestamp_granularities: Optional[List[str]] = None, + **kwargs, ): + kwargs.pop("request_id", None) if hasattr(self._model, "translations"): return await self._call_wrapper_json( self._model.translations, @@ -654,12 +837,9 @@ async def translations( f"Model {self._model.model_spec} is not for creating translations." ) - @log_async( - logger=logger, - args_formatter=lambda _, kwargs: kwargs.pop("prompt_speech", None), - ) @request_limit @xo.generator + @log_async(logger=logger, ignore_kwargs=["prompt_speech"]) async def speech( self, input: str, @@ -669,6 +849,7 @@ async def speech( stream: bool = False, **kwargs, ): + kwargs.pop("request_id", None) if hasattr(self._model, "speech"): return await self._call_wrapper_binary( self._model.speech, @@ -683,8 +864,24 @@ async def speech( f"Model {self._model.model_spec} is not for creating speech." ) - @log_async(logger=logger) + async def handle_image_batching_request(self, unique_id, *args, **kwargs): + size = args[2] + if XINFERENCE_TEXT_TO_IMAGE_BATCHING_SIZE != size: + raise RuntimeError( + f"The image size: {size} of text_to_image for batching " + f"must be the same as the environment variable: {XINFERENCE_TEXT_TO_IMAGE_BATCHING_SIZE} you set." + ) + assert self._loop is not None + future = ConcurrentFuture() + await self._text_to_image_scheduler_ref.add_request( + unique_id, future, *args, **kwargs + ) + fut = asyncio.wrap_future(future, loop=self._loop) + result = await fut + return await asyncio.to_thread(json_dumps, result) + @request_limit + @log_async(logger=logger) async def text_to_image( self, prompt: str, @@ -695,46 +892,102 @@ async def text_to_image( **kwargs, ): if hasattr(self._model, "text_to_image"): - return await self._call_wrapper_json( - self._model.text_to_image, - prompt, - n, - size, - response_format, - *args, - **kwargs, - ) + if self.allow_batching_for_text_to_image(): + unique_id = kwargs.pop("request_id", None) + return await self.handle_image_batching_request( + unique_id, prompt, n, size, response_format, *args, **kwargs + ) + else: + progressor = kwargs["progressor"] = await self._get_progressor( + kwargs.pop("request_id", None) + ) + with progressor: + return await self._call_wrapper_json( + self._model.text_to_image, + prompt, + n, + size, + response_format, + *args, + **kwargs, + ) raise AttributeError( f"Model {self._model.model_spec} is not for creating image." ) + @request_limit + @log_async(logger=logger) + async def txt2img( + self, + **kwargs, + ): + if hasattr(self._model, "txt2img"): + progressor = kwargs["progressor"] = await self._get_progressor( + kwargs.pop("request_id", None) + ) + with progressor: + return await self._call_wrapper_json( + self._model.txt2img, + **kwargs, + ) + raise AttributeError(f"Model {self._model.model_spec} is not for txt2img.") + + @log_async( + logger=logger, + ignore_kwargs=["image"], + ) async def image_to_image( self, image: "PIL.Image", prompt: str, - negative_prompt: str, + negative_prompt: Optional[str] = None, n: int = 1, size: Optional[str] = None, response_format: str = "url", *args, **kwargs, ): + kwargs["negative_prompt"] = negative_prompt if hasattr(self._model, "image_to_image"): - return await self._call_wrapper_json( - self._model.image_to_image, - image, - prompt, - negative_prompt, - n, - size, - response_format, - *args, - **kwargs, + progressor = kwargs["progressor"] = await self._get_progressor( + kwargs.pop("request_id", None) ) + with progressor: + return await self._call_wrapper_json( + self._model.image_to_image, + image, + prompt, + n, + size, + response_format, + *args, + **kwargs, + ) raise AttributeError( f"Model {self._model.model_spec} is not for creating image." ) + @request_limit + @log_async(logger=logger) + async def img2img( + self, + **kwargs, + ): + if hasattr(self._model, "img2img"): + progressor = kwargs["progressor"] = await self._get_progressor( + kwargs.pop("request_id", None) + ) + with progressor: + return await self._call_wrapper_json( + self._model.img2img, + **kwargs, + ) + raise AttributeError(f"Model {self._model.model_spec} is not for img2img.") + + @log_async( + logger=logger, + ignore_kwargs=["image"], + ) async def inpainting( self, image: "PIL.Image", @@ -747,29 +1000,53 @@ async def inpainting( *args, **kwargs, ): + kwargs["negative_prompt"] = negative_prompt if hasattr(self._model, "inpainting"): + progressor = kwargs["progressor"] = await self._get_progressor( + kwargs.pop("request_id", None) + ) + with progressor: + return await self._call_wrapper_json( + self._model.inpainting, + image, + mask_image, + prompt, + n, + size, + response_format, + *args, + **kwargs, + ) + raise AttributeError( + f"Model {self._model.model_spec} is not for creating image." + ) + + @log_async( + logger=logger, + ignore_kwargs=["image"], + ) + async def ocr( + self, + image: "PIL.Image", + *args, + **kwargs, + ): + if hasattr(self._model, "ocr"): return await self._call_wrapper_json( - self._model.inpainting, + self._model.ocr, image, - mask_image, - prompt, - negative_prompt, - n, - size, - response_format, *args, **kwargs, ) - raise AttributeError( - f"Model {self._model.model_spec} is not for creating image." - ) + raise AttributeError(f"Model {self._model.model_spec} is not for ocr.") - @log_async(logger=logger) @request_limit + @log_async(logger=logger, ignore_kwargs=["image"]) async def infer( self, **kwargs, ): + kwargs.pop("request_id", None) if hasattr(self._model, "infer"): return await self._call_wrapper_json( self._model.infer, @@ -779,8 +1056,8 @@ async def infer( f"Model {self._model.model_spec} is not for flexible infer." ) - @log_async(logger=logger) @request_limit + @log_async(logger=logger) async def text_to_video( self, prompt: str, @@ -788,6 +1065,7 @@ async def text_to_video( *args, **kwargs, ): + kwargs.pop("request_id", None) if hasattr(self._model, "text_to_video"): return await self._call_wrapper_json( self._model.text_to_video, @@ -803,3 +1081,6 @@ async def text_to_video( async def record_metrics(self, name, op, kwargs): worker_ref = await self._get_worker_ref() await worker_ref.record_metrics(name, op, kwargs) + + async def get_pending_requests_count(self): + return self._pending_requests.qsize() diff --git a/xinference/core/progress_tracker.py b/xinference/core/progress_tracker.py new file mode 100644 index 0000000000..1e8db24a07 --- /dev/null +++ b/xinference/core/progress_tracker.py @@ -0,0 +1,187 @@ +# Copyright 2022-2023 XProbe Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import asyncio +import dataclasses +import logging +import os +import time +from typing import Dict, List, Optional, Tuple + +import numpy as np +import xoscar as xo + +TO_REMOVE_PROGRESS_INTERVAL = float( + os.getenv("XINFERENCE_REMOVE_PROGRESS_INTERVAL", 5 * 60) +) # 5min +CHECK_PROGRESS_INTERVAL = float( + os.getenv("XINFERENCE_CHECK_PROGRESS_INTERVAL", 1 * 60) +) # 1min +UPLOAD_PROGRESS_SPAN = float( + os.getenv("XINFERENCE_UPLOAD_PROGRESS_SPAN", 0.05) +) # not upload when change less than 0.1 + +logger = logging.getLogger(__name__) + + +@dataclasses.dataclass +class _ProgressInfo: + progress: float + last_updated: float + info: Optional[str] = None + + +class ProgressTrackerActor(xo.StatelessActor): + _request_id_to_progress: Dict[str, _ProgressInfo] + + @classmethod + def default_uid(cls) -> str: + return "progress_tracker" + + def __init__( + self, + to_remove_interval: float = TO_REMOVE_PROGRESS_INTERVAL, + check_interval: float = CHECK_PROGRESS_INTERVAL, + ): + super().__init__() + + self._request_id_to_progress = {} + self._clear_finished_task = None + self._to_remove_interval = to_remove_interval + self._check_interval = check_interval + + async def __post_create__(self): + self._clear_finished_task = asyncio.create_task(self._clear_finished()) + + async def __pre_destroy__(self): + if self._clear_finished_task: + self._clear_finished_task.cancel() + + async def _clear_finished(self): + to_remove_request_ids = [] + while True: + now = time.time() + for request_id, progress in self._request_id_to_progress.items(): + if abs(progress.progress - 1.0) > 1e-5: + continue + + # finished + if now - progress.last_updated > self._to_remove_interval: + to_remove_request_ids.append(request_id) + + for rid in to_remove_request_ids: + del self._request_id_to_progress[rid] + + if to_remove_request_ids: + logger.debug( + "Remove requests %s due to it's finished for over %s seconds", + to_remove_request_ids, + self._to_remove_interval, + ) + + await asyncio.sleep(self._check_interval) + + def start(self, request_id: str): + self._request_id_to_progress[request_id] = _ProgressInfo( + progress=0.0, last_updated=time.time() + ) + + def set_progress(self, request_id: str, progress: float): + assert progress <= 1.0 + info = self._request_id_to_progress[request_id] + info.progress = progress + info.last_updated = time.time() + logger.debug( + "Setting progress, request id: %s, progress: %s", request_id, progress + ) + + def get_progress(self, request_id: str) -> float: + return self._request_id_to_progress[request_id].progress + + +class Progressor: + _sub_progress_stack: List[Tuple[float, float]] + + def __init__( + self, + request_id: str, + progress_tracker_ref: xo.ActorRefType["ProgressTrackerActor"], + loop: asyncio.AbstractEventLoop, + upload_span: float = UPLOAD_PROGRESS_SPAN, + ): + self.request_id = request_id + self.progress_tracker_ref = progress_tracker_ref + self.loop = loop + # uploading when progress changes over this span + # to prevent from frequently uploading + self._upload_span = upload_span + + self._last_report_progress = 0.0 + self._current_progress = 0.0 + self._sub_progress_stack = [(0.0, 1.0)] + self._current_sub_progress_start = 0.0 + self._current_sub_progress_end = 1.0 + + async def start(self): + if self.request_id: + await self.progress_tracker_ref.start(self.request_id) + + def split_stages(self, n_stage: int, stage_weight: Optional[List[float]] = None): + if self.request_id: + if stage_weight is not None: + if len(stage_weight) != n_stage + 1: + raise ValueError( + f"stage_weight should have size {n_stage + 1}, got {len(stage_weight)}" + ) + progresses = stage_weight + else: + progresses = np.linspace( + self._current_sub_progress_start, + self._current_sub_progress_end, + n_stage + 1, + ) + spans = [(progresses[i], progresses[i + 1]) for i in range(n_stage)] + self._sub_progress_stack.extend(spans[::-1]) + + def __enter__(self): + if self.request_id: + ( + self._current_sub_progress_start, + self._current_sub_progress_end, + ) = self._sub_progress_stack[-1] + + def __exit__(self, exc_type, exc_val, exc_tb): + if self.request_id: + self._sub_progress_stack.pop() + # force to set progress to 1.0 for this sub progress + # nevertheless it is done or not + self.set_progress(1.0) + return False + + def set_progress(self, progress: float): + if self.request_id: + self._current_progress = ( + self._current_sub_progress_start + + (self._current_sub_progress_end - self._current_sub_progress_start) + * progress + ) + if ( + self._current_progress - self._last_report_progress >= self._upload_span + or 1.0 - progress < 1e-5 + ): + set_progress = self.progress_tracker_ref.set_progress( + self.request_id, self._current_progress + ) + asyncio.run_coroutine_threadsafe(set_progress, self.loop) # type: ignore + self._last_report_progress = self._current_progress diff --git a/xinference/core/scheduler.py b/xinference/core/scheduler.py index 6b28f70259..8b91855daa 100644 --- a/xinference/core/scheduler.py +++ b/xinference/core/scheduler.py @@ -17,11 +17,12 @@ import logging import uuid from collections import deque -from enum import Enum -from typing import List, Optional, Set, Tuple +from typing import Dict, List, Optional, Set, Tuple, Union import xoscar as xo +from .utils import AbortRequestMessage + logger = logging.getLogger(__name__) XINFERENCE_STREAMING_DONE_FLAG = "" @@ -30,20 +31,25 @@ XINFERENCE_NON_STREAMING_ABORT_FLAG = "" -class AbortRequestMessage(Enum): - NOT_FOUND = 1 - DONE = 2 - NO_OP = 3 - - class InferenceRequest: - def __init__(self, prompt, future_or_queue, is_prefill, *args, **kwargs): - # original prompt - self._prompt = prompt + def __init__( + self, + prompt_or_messages, + future_or_queue, + is_prefill, + call_ability, + *args, + **kwargs, + ): + # original prompt, prompt(str) for generate model and messages(List[Dict]) for chat model + self._prompt = prompt_or_messages # full prompt that contains chat history and applies chat template self._full_prompt = None # whether the current request is in the prefill phase self._is_prefill = is_prefill + # the ability that the user calls this model for, that is `generate` / `chat` for now, + # which is for results formatting + self._call_ability = call_ability # full prompt tokens self._prompt_tokens = None # all new generated tokens during decode phase @@ -70,6 +76,10 @@ def __init__(self, prompt, future_or_queue, is_prefill, *args, **kwargs): self.padding_len = 0 # Use in stream mode self.last_output_length = 0 + # For tool call + self.tools = None + # Currently, for storing tool call streaming results. + self.outputs: List[str] = [] # type: ignore # inference results, # it is a list type because when stream=True, # self.completion contains all the results in a decode round. @@ -88,38 +98,26 @@ def __init__(self, prompt, future_or_queue, is_prefill, *args, **kwargs): self._check_args() def _check_args(self): - # chat - if len(self._inference_args) == 3: - # system prompt - assert self._inference_args[0] is None or isinstance( - self._inference_args[0], str - ) - # chat history - assert self._inference_args[1] is None or isinstance( - self._inference_args[1], list - ) - # generate config - assert self._inference_args[2] is None or isinstance( - self._inference_args[2], dict - ) - else: # generate - assert len(self._inference_args) == 1 - # generate config - assert self._inference_args[0] is None or isinstance( - self._inference_args[0], dict - ) + assert len(self._inference_args) == 1 + # generate config + assert self._inference_args[0] is None or isinstance( + self._inference_args[0], dict + ) @property def prompt(self): + """ + prompt for generate model and messages for chat model + """ return self._prompt - @property - def system_prompt(self): - return self._inference_args[0] + @prompt.setter + def prompt(self, value: str): + self._prompt = value @property - def chat_history(self): - return self._inference_args[1] + def call_ability(self): + return self._call_ability @property def full_prompt(self): @@ -162,11 +160,7 @@ def append_new_token(self, token: int): @property def generate_config(self): - return ( - self._inference_args[2] - if len(self._inference_args) == 3 - else self._inference_args[0] - ) + return self._inference_args[0] @property def sanitized_generate_config(self): @@ -423,8 +417,17 @@ async def step(self): self._empty_cache() - async def add_request(self, prompt: str, future_or_queue, *args, **kwargs): - req = InferenceRequest(prompt, future_or_queue, True, *args, **kwargs) + async def add_request( + self, + prompt_or_messages: Union[str, List[Dict]], + future_or_queue, + call_ability, + *args, + **kwargs, + ): + req = InferenceRequest( + prompt_or_messages, future_or_queue, True, call_ability, *args, **kwargs + ) rid = req.request_id if rid is not None: if rid in self._id_to_req: diff --git a/xinference/core/status_guard.py b/xinference/core/status_guard.py index d348730869..e95e12d4d2 100644 --- a/xinference/core/status_guard.py +++ b/xinference/core/status_guard.py @@ -51,7 +51,7 @@ def __init__(self): self._model_uid_to_info: Dict[str, InstanceInfo] = {} # type: ignore @classmethod - def uid(cls) -> str: + def default_uid(cls) -> str: return "status_guard" @staticmethod diff --git a/xinference/core/supervisor.py b/xinference/core/supervisor.py index 2b6f7b9fc5..20d6ea95c2 100644 --- a/xinference/core/supervisor.py +++ b/xinference/core/supervisor.py @@ -105,7 +105,7 @@ def __init__(self): self._lock = asyncio.Lock() @classmethod - def uid(cls) -> str: + def default_uid(cls) -> str: return "supervisor" def _get_worker_ref_by_ip( @@ -130,17 +130,25 @@ async def __post_create__(self): ) logger.info(f"Xinference supervisor {self.address} started") from .cache_tracker import CacheTrackerActor + from .progress_tracker import ProgressTrackerActor from .status_guard import StatusGuardActor self._status_guard_ref: xo.ActorRefType[ # type: ignore "StatusGuardActor" ] = await xo.create_actor( - StatusGuardActor, address=self.address, uid=StatusGuardActor.uid() + StatusGuardActor, address=self.address, uid=StatusGuardActor.default_uid() ) self._cache_tracker_ref: xo.ActorRefType[ # type: ignore "CacheTrackerActor" ] = await xo.create_actor( - CacheTrackerActor, address=self.address, uid=CacheTrackerActor.uid() + CacheTrackerActor, address=self.address, uid=CacheTrackerActor.default_uid() + ) + self._progress_tracker: xo.ActorRefType[ # type: ignore + "ProgressTrackerActor" + ] = await xo.create_actor( + ProgressTrackerActor, + address=self.address, + uid=ProgressTrackerActor.default_uid(), ) from .event import EventCollectorActor @@ -148,7 +156,9 @@ async def __post_create__(self): self._event_collector_ref: xo.ActorRefType[ # type: ignore EventCollectorActor ] = await xo.create_actor( - EventCollectorActor, address=self.address, uid=EventCollectorActor.uid() + EventCollectorActor, + address=self.address, + uid=EventCollectorActor.default_uid(), ) from ..model.audio import ( @@ -308,14 +318,12 @@ async def get_cluster_device_info(self, detailed: bool = False) -> List: async def get_builtin_prompts() -> Dict[str, Any]: from ..model.llm.llm_family import BUILTIN_LLM_PROMPT_STYLE - data = {} - for k, v in BUILTIN_LLM_PROMPT_STYLE.items(): - data[k] = v.dict() - return data + return {k: v for k, v in BUILTIN_LLM_PROMPT_STYLE.items()} @staticmethod async def get_builtin_families() -> Dict[str, List[str]]: from ..model.llm.llm_family import ( + BUILTIN_LLM_FAMILIES, BUILTIN_LLM_MODEL_CHAT_FAMILIES, BUILTIN_LLM_MODEL_GENERATE_FAMILIES, BUILTIN_LLM_MODEL_TOOL_CALL_FAMILIES, @@ -325,6 +333,11 @@ async def get_builtin_families() -> Dict[str, List[str]]: "chat": list(BUILTIN_LLM_MODEL_CHAT_FAMILIES), "generate": list(BUILTIN_LLM_MODEL_GENERATE_FAMILIES), "tools": list(BUILTIN_LLM_MODEL_TOOL_CALL_FAMILIES), + "vision": [ + family.model_name + for family in BUILTIN_LLM_FAMILIES + if "vision" in family.model_ability + ], } async def get_devices_count(self) -> int: @@ -1028,7 +1041,7 @@ async def _launch_model(): else: task = asyncio.create_task(_launch_model()) ASYNC_LAUNCH_TASKS[model_uid] = task - task.add_done_callback(lambda _: callback_for_async_launch(model_uid)) + task.add_done_callback(lambda _: callback_for_async_launch(model_uid)) # type: ignore return model_uid async def get_instance_info( @@ -1233,7 +1246,9 @@ async def add_worker(self, worker_address: str): worker_address not in self._worker_address_to_worker ), f"Worker {worker_address} exists" - worker_ref = await xo.actor_ref(address=worker_address, uid=WorkerActor.uid()) + worker_ref = await xo.actor_ref( + address=worker_address, uid=WorkerActor.default_uid() + ) self._worker_address_to_worker[worker_address] = worker_ref logger.debug("Worker %s has been added successfully", worker_address) @@ -1353,3 +1368,6 @@ async def abort_cluster(self) -> bool: @staticmethod def record_metrics(name, op, kwargs): record_metrics(name, op, kwargs) + + async def get_progress(self, request_id: str) -> float: + return await self._progress_tracker.get_progress(request_id) diff --git a/xinference/core/tests/test_continuous_batching.py b/xinference/core/tests/test_continuous_batching.py index f6db0362cf..c58b91bb55 100644 --- a/xinference/core/tests/test_continuous_batching.py +++ b/xinference/core/tests/test_continuous_batching.py @@ -48,7 +48,7 @@ def join(self, timeout=None): class InferenceThread(BaseThread): def __init__(self, prompt, generate_config, client, model): super().__init__() - self._prompt = prompt + self._prompt = [{"role": "user", "content": prompt}] self._generate_config = generate_config self._client = client self._model = model @@ -159,11 +159,12 @@ def test_continuous_batching(enable_batch, setup): thread2.join() # test error generate config + messages = [{"role": "user", "content": "你好"}] with pytest.raises(RuntimeError): - model.chat("你好", generate_config={"max_tokens": 99999999999999999}) + model.chat(messages, generate_config={"max_tokens": 99999999999999999}) with pytest.raises(RuntimeError): - model.chat("你好", generate_config={"stream_interval": 0}) + model.chat(messages, generate_config={"stream_interval": 0}) # test error with other correct requests thread1 = InferenceThread("1+1=3正确吗？", {"stream": True}, client, model) diff --git a/xinference/core/tests/test_metrics.py b/xinference/core/tests/test_metrics.py index b6927b18c7..4bcd2c3bbd 100644 --- a/xinference/core/tests/test_metrics.py +++ b/xinference/core/tests/test_metrics.py @@ -72,7 +72,9 @@ async def test_metrics_exporter_server(setup_cluster): ) # Check the supervisor metrics collected the RESTful API. - supervisor_ref = await xo.actor_ref(supervisor_address, SupervisorActor.uid()) + supervisor_ref = await xo.actor_ref( + supervisor_address, SupervisorActor.default_uid() + ) response = requests.get(f"{endpoint}/metrics") assert response.ok assert "/v1/models" in response.text @@ -138,7 +140,8 @@ async def test_metrics_exporter_data(setup_cluster): ) model = client.get_model(model_uid) - response = model.chat("write a poem.") + messages = [{"role": "user", "content": "write a poem."}] + response = model.chat(messages) response = requests.get(metrics_exporter_address) assert response.ok diff --git a/xinference/core/tests/test_model.py b/xinference/core/tests/test_model.py new file mode 100644 index 0000000000..655debf799 --- /dev/null +++ b/xinference/core/tests/test_model.py @@ -0,0 +1,108 @@ +# Copyright 2022-2023 XProbe Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import asyncio + +import pytest +import pytest_asyncio +import xoscar as xo +from xoscar import create_actor_pool + +from ..model import ModelActor + +TEST_EVENT = None +TEST_VALUE = None + + +class MockModel: + async def generate(self, prompt, **kwargs): + global TEST_VALUE + TEST_VALUE = True + assert isinstance(TEST_EVENT, asyncio.Event) + await TEST_EVENT.wait() + yield {"test1": prompt} + yield {"test2": prompt} + + +class MockModelActor(ModelActor): + def __init__( + self, + supervisor_address: str, + worker_address: str, + ): + super().__init__(supervisor_address, worker_address, MockModel()) # type: ignore + self._lock = asyncio.locks.Lock() + + async def __pre_destroy__(self): + pass + + async def record_metrics(self, name, op, kwargs): + pass + + +@pytest_asyncio.fixture +async def setup_pool(): + pool = await create_actor_pool( + f"test://127.0.0.1:{xo.utils.get_next_port()}", n_process=0 + ) + async with pool: + yield pool + + +@pytest.mark.asyncio +async def test_concurrent_call(setup_pool): + pool = setup_pool + addr = pool.external_address + + global TEST_EVENT + TEST_EVENT = asyncio.Event() + + worker: xo.ActorRefType[MockModelActor] = await xo.create_actor( # type: ignore + MockModelActor, + address=addr, + uid=MockModelActor.default_uid(), + supervisor_address="test:123", + worker_address="test:345", + ) + + await worker.generate("test_prompt1") + assert TEST_VALUE is not None + # This request is waiting for the TEST_EVENT, so the queue is empty. + pending_count = await worker.get_pending_requests_count() + assert pending_count == 0 + await worker.generate("test_prompt3") + # This request is waiting in the queue because the previous request is waiting for TEST_EVENT. + pending_count = await worker.get_pending_requests_count() + assert pending_count == 1 + + async def _check(): + gen = await worker.generate("test_prompt2") + result = [] + async for g in gen: + result.append(g) + assert result == [ + b'data: {"test1": "test_prompt2"}\r\n\r\n', + b'data: {"test2": "test_prompt2"}\r\n\r\n', + ] + + check_task = asyncio.create_task(_check()) + await asyncio.sleep(2) + assert not check_task.done() + # Pending 2 requests: test_prompt3 and test_prompt2 + pending_count = await worker.get_pending_requests_count() + assert pending_count == 2 + TEST_EVENT.set() + await check_task + pending_count = await worker.get_pending_requests_count() + assert pending_count == 0 diff --git a/xinference/core/tests/test_progressor.py b/xinference/core/tests/test_progressor.py new file mode 100644 index 0000000000..b2831d9e01 --- /dev/null +++ b/xinference/core/tests/test_progressor.py @@ -0,0 +1,77 @@ +# Copyright 2022-2023 XProbe Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import asyncio +import uuid + +import pytest +import xoscar as xo + +from ..progress_tracker import Progressor, ProgressTrackerActor + + +@pytest.mark.asyncio +async def test_progressor(): + pool = await xo.create_actor_pool("127.0.0.1", n_process=0) + async with pool: + progress_tracker_ref = await xo.create_actor( + ProgressTrackerActor, + to_remove_interval=0, + check_interval=1, + address=pool.external_address, + uid=ProgressTrackerActor.default_uid(), + ) + request_id = str(uuid.uuid4()) + + progressor = Progressor( + request_id, progress_tracker_ref, asyncio.get_running_loop(), upload_span=0 + ) + await progressor.start() + + with progressor: + progressor.split_stages(2) + + with progressor: + progressor.set_progress(0.5) + + await asyncio.sleep(0.1) + assert await progress_tracker_ref.get_progress(request_id) == 0.25 + + await asyncio.sleep(0.1) + assert await progress_tracker_ref.get_progress(request_id) == 0.5 + + with progressor: + progressor.split_stages(2) + + with progressor: + progressor.set_progress(0.8) + + await asyncio.sleep(0.1) + assert ( + await progress_tracker_ref.get_progress(request_id) + == 0.5 + 0.25 * 0.8 + ) + + await asyncio.sleep(0.1) + assert await progress_tracker_ref.get_progress(request_id) == 0.75 + + with pytest.raises(ValueError): + with progressor: + raise ValueError + + await asyncio.sleep(0.1) + assert await progress_tracker_ref.get_progress(request_id) == 1.0 + + await asyncio.sleep(0.1) + assert await progress_tracker_ref.get_progress(request_id) == 1.0 diff --git a/xinference/core/tests/test_restful_api.py b/xinference/core/tests/test_restful_api.py index cd47b98cc5..510ddcd6d3 100644 --- a/xinference/core/tests/test_restful_api.py +++ b/xinference/core/tests/test_restful_api.py @@ -526,7 +526,8 @@ def test_restful_api_for_tool_calls(setup, model_format, quantization): client = RESTfulClient(endpoint) model = client.get_model(model_uid_res) - completion = model.chat("帮我查询股票10111的价格", tools=tools) + messages = [{"role": "user", "content": "帮我查询股票10111的价格"}] + completion = model.chat(messages, tools=tools) assert "content" in completion["choices"][0]["message"] assert "tool_calls" == completion["choices"][0]["finish_reason"] assert ( @@ -608,6 +609,98 @@ async def test_stream(): _check_invalid_tool_calls(endpoint, model_uid_res) +@pytest.mark.parametrize( + "model_format, quantization", + [("pytorch", None)], +) +@pytest.mark.skip(reason="Cost too many resources.") +def test_restful_api_for_llama3_tool_calls(setup, model_format, quantization): + model_name = "llama-3.1-instruct" + + endpoint, _ = setup + url = f"{endpoint}/v1/models" + + # list + response = requests.get(url) + response_data = response.json() + assert len(response_data["data"]) == 0 + + # launch + payload = { + "model_uid": "test_tool", + "model_engine": "transformers", + "model_name": model_name, + "model_size_in_billions": 8, + "model_format": model_format, + "quantization": quantization, + "download_hub": "huggingface", + } + + response = requests.post(url, json=payload) + response_data = response.json() + assert "model_uid" in response_data, response_data + model_uid_res = response_data["model_uid"] + assert model_uid_res == "test_tool" + + response = requests.get(url) + response_data = response.json() + assert len(response_data["data"]) == 1 + + # tool + tools = [ + { + "type": "function", + "function": { + "name": "track_a_long_function_name_to_test", + "description": "追踪指定股票的实时价格", + "parameters": { + "type": "object", + "properties": {"symbol": {"description": "需要追踪的股票代码"}}, + "required": ["symbol"], + }, + }, + }, + { + "type": "function", + "function": { + "name": "text-to-speech", + "description": "将文本转换为语音", + "parameters": { + "type": "object", + "properties": { + "text": {"description": "需要转换成语音的文本"}, + "voice": {"description": "要使用的语音类型（男声、女声等）"}, + "speed": {"description": "语音的速度（快、中等、慢等）"}, + }, + "required": ["text"], + }, + }, + }, + ] + url = f"{endpoint}/v1/chat/completions" + payload = { + "model": model_uid_res, + "messages": [ + {"role": "user", "content": "帮我查询股票10111的价格"}, + ], + "tools": tools, + } + response = requests.post(url, json=payload) + completion = response.json() + + assert "content" in completion["choices"][0]["message"] + assert "tool_calls" == completion["choices"][0]["finish_reason"] + assert ( + "track_a_long_function_name_to_test" + == completion["choices"][0]["message"]["tool_calls"][0]["function"]["name"] + ) + arguments = completion["choices"][0]["message"]["tool_calls"][0]["function"][ + "arguments" + ] + arg = json.loads(arguments) + assert arg == {"symbol": "10111"} + + @pytest.mark.parametrize( "model_format, quantization", [("ggufv2", "Q4_K_S"), ("pytorch", None)] ) @@ -1239,3 +1332,77 @@ def test_launch_model_by_version(setup): # delete again url = f"{endpoint}/v1/models/test_qwen15" requests.delete(url) + + +@pytest.mark.skip(reason="Cost too many resources.") +def test_restful_api_for_qwen_audio(setup): + model_name = "qwen2-audio-instruct" + + endpoint, _ = setup + url = f"{endpoint}/v1/models" + + # list + response = requests.get(url) + response_data = response.json() + assert len(response_data["data"]) == 0 + + # launch + payload = { + "model_uid": "test_audio", + "model_name": model_name, + "model_engine": "transformers", + "model_size_in_billions": 7, + "model_format": "pytorch", + "quantization": "none", + } + + response = requests.post(url, json=payload) + response_data = response.json() + model_uid_res = response_data["model_uid"] + assert model_uid_res == "test_audio" + + response = requests.get(url) + response_data = response.json() + assert len(response_data["data"]) == 1 + + url = f"{endpoint}/v1/chat/completions" + payload = { + "model": model_uid_res, + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + { + "role": "user", + "content": [ + { + "type": "audio", + "audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/glass-breaking-151256.mp3", + }, + {"type": "text", "text": "What's that sound?"}, + ], + }, + {"role": "assistant", "content": "It is the sound of glass shattering."}, + { + "role": "user", + "content": [ + {"type": "text", "text": "What can you do when you hear that?"}, + ], + }, + { + "role": "assistant", + "content": "Stay alert and cautious, and check if anyone is hurt or if there is any damage to property.", + }, + { + "role": "user", + "content": [ + { + "type": "audio", + "audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/1272-128104-0000.flac", + }, + {"type": "text", "text": "What does the person say?"}, + ], + }, + ], + } + response = requests.post(url, json=payload) + completion = response.json() + assert len(completion["choices"][0]["message"]) > 0 diff --git a/xinference/core/tests/test_types.py b/xinference/core/tests/test_types.py index 8dd3fdbd63..a0296ed240 100644 --- a/xinference/core/tests/test_types.py +++ b/xinference/core/tests/test_types.py @@ -13,7 +13,7 @@ # limitations under the License. import pytest -from ..._compat import ValidationError, create_model_from_typeddict +from ..._compat import ValidationError from ...types import ( CreateChatCompletion, CreateChatCompletionLlamaCpp, @@ -21,7 +21,6 @@ CreateCompletion, CreateCompletionLlamaCpp, CreateCompletionTorch, - _CreateCompletionOpenAIFallback, ) @@ -49,14 +48,6 @@ def check_fields(a, b): def test_create_completion_types(): - from openai.types.completion_create_params import CompletionCreateParamsNonStreaming - - openai_model = create_model_from_typeddict(CompletionCreateParamsNonStreaming) - assert ( - _CreateCompletionOpenAIFallback.__fields__.keys() - == openai_model.__fields__.keys() - ) - with pytest.raises(ValidationError): CreateCompletion() @@ -82,9 +73,6 @@ def test_create_chat_completion_types(): with pytest.raises(ValidationError): CreateChatCompletion(model="abc", not_exist="jdk") - # with pytest.raises(pydantic.ValidationError): - # CreateChatCompletion(model="abc", messages=[{"role": "invalid"}]) - CreateChatCompletion(model="abc", messages=[{"role": "tool"}], max_tokens=None) types = [CreateChatCompletionTorch, CreateChatCompletionLlamaCpp] diff --git a/xinference/core/tests/test_worker.py b/xinference/core/tests/test_worker.py index 2f32aae149..4185ffb27f 100644 --- a/xinference/core/tests/test_worker.py +++ b/xinference/core/tests/test_worker.py @@ -97,7 +97,7 @@ async def test_allocate_cuda_devices(setup_pool): worker: xo.ActorRefType["MockWorkerActor"] = await xo.create_actor( # type: ignore MockWorkerActor, address=addr, - uid=WorkerActor.uid(), + uid=WorkerActor.default_uid(), supervisor_address="test", main_pool=pool, cuda_devices=[i for i in range(8)], @@ -124,7 +124,7 @@ async def test_terminate_model_flag(setup_pool): worker: xo.ActorRefType["MockWorkerActor"] = await xo.create_actor( # type: ignore MockWorkerActor, address=addr, - uid=WorkerActor.uid(), + uid=WorkerActor.default_uid(), supervisor_address="test", main_pool=pool, cuda_devices=[i for i in range(8)], @@ -174,7 +174,7 @@ async def test_launch_embedding_model(setup_pool): worker: xo.ActorRefType["MockWorkerActor"] = await xo.create_actor( # type: ignore MockWorkerActor, address=addr, - uid=WorkerActor.uid(), + uid=WorkerActor.default_uid(), supervisor_address="test", main_pool=pool, cuda_devices=[i for i in range(4)], @@ -270,11 +270,12 @@ async def test_launch_model_with_gpu_idx(setup_pool): worker: xo.ActorRefType["MockWorkerActor"] = await xo.create_actor( # type: ignore MockWorkerActor, address=addr, - uid=WorkerActor.uid(), + uid=WorkerActor.default_uid(), supervisor_address="test", main_pool=pool, cuda_devices=[i for i in range(4)], ) + assert (await xo.actor_ref(addr, WorkerActor.default_uid())).uid == b"worker" # test normal model await worker.launch_builtin_model( diff --git a/xinference/core/utils.py b/xinference/core/utils.py index a110278aea..47aa45b27d 100644 --- a/xinference/core/utils.py +++ b/xinference/core/utils.py @@ -11,62 +11,129 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -import copy import logging import os import random import string -from typing import Dict, Generator, List, Tuple, Union +import uuid +from enum import Enum +from typing import Dict, Generator, List, Optional, Tuple, Union import orjson from pynvml import nvmlDeviceGetCount, nvmlInit, nvmlShutdown from .._compat import BaseModel +from ..constants import XINFERENCE_LOG_ARG_MAX_LENGTH logger = logging.getLogger(__name__) -def log_async(logger, args_formatter=None): +class AbortRequestMessage(Enum): + NOT_FOUND = 1 + DONE = 2 + NO_OP = 3 + + +def truncate_log_arg(arg) -> str: + s = str(arg) + if len(s) > XINFERENCE_LOG_ARG_MAX_LENGTH: + s = s[0:XINFERENCE_LOG_ARG_MAX_LENGTH] + "..." + return s + + +def log_async( + logger, + level=logging.DEBUG, + ignore_kwargs: Optional[List[str]] = None, + log_exception=True, +): import time from functools import wraps def decorator(func): + func_name = func.__name__ + @wraps(func) async def wrapped(*args, **kwargs): - if args_formatter is not None: - formatted_args, formatted_kwargs = copy.copy(args), copy.copy(kwargs) - args_formatter(formatted_args, formatted_kwargs) - else: - formatted_args, formatted_kwargs = args, kwargs - logger.debug( - f"Enter {func.__name__}, args: {formatted_args}, kwargs: {formatted_kwargs}" + request_id_str = kwargs.get("request_id", "") + if not request_id_str: + request_id_str = uuid.uuid1() + if func_name == "text_to_image": + kwargs["request_id"] = request_id_str + request_id_str = f"[request {request_id_str}]" + formatted_args = ",".join(map(truncate_log_arg, args)) + formatted_kwargs = ",".join( + [ + "%s=%s" % (k, truncate_log_arg(v)) + for k, v in kwargs.items() + if ignore_kwargs is None or k not in ignore_kwargs + ] ) - start = time.time() - ret = await func(*args, **kwargs) - logger.debug( - f"Leave {func.__name__}, elapsed time: {int(time.time() - start)} s" + logger.log( + level, + f"{request_id_str} Enter {func_name}, args: {formatted_args}, kwargs: {formatted_kwargs}", ) - return ret + start = time.time() + try: + ret = await func(*args, **kwargs) + logger.log( + level, + f"{request_id_str} Leave {func_name}, elapsed time: {int(time.time() - start)} s", + ) + return ret + except Exception as e: + if log_exception: + logger.error( + f"{request_id_str} Leave {func_name}, error: {e}, elapsed time: {int(time.time() - start)} s", + exc_info=True, + ) + else: + logger.log( + level, + f"{request_id_str} Leave {func_name}, error: {e}, elapsed time: {int(time.time() - start)} s", + ) + raise return wrapped return decorator -def log_sync(logger): +def log_sync(logger, level=logging.DEBUG, log_exception=True): import time from functools import wraps def decorator(func): @wraps(func) def wrapped(*args, **kwargs): - logger.debug(f"Enter {func.__name__}, args: {args}, kwargs: {kwargs}") - start = time.time() - ret = func(*args, **kwargs) - logger.debug( - f"Leave {func.__name__}, elapsed time: {int(time.time() - start)} s" + formatted_args = ",".join(map(truncate_log_arg, args)) + formatted_kwargs = ",".join( + map(lambda x: "%s=%s" % (x[0], truncate_log_arg(x[1])), kwargs.items()) ) - return ret + logger.log( + level, + f"Enter {func.__name__}, args: {formatted_args}, kwargs: {formatted_kwargs}", + ) + start = time.time() + try: + ret = func(*args, **kwargs) + logger.log( + level, + f"Leave {func.__name__}, elapsed time: {int(time.time() - start)} s", + ) + return ret + except Exception as e: + if log_exception: + logger.error( + f"Leave {func.__name__}, error: {e}, elapsed time: {int(time.time() - start)} s", + exc_info=True, + ) + else: + logger.log( + level, + f"Leave {func.__name__}, error: {e}, elapsed time: {int(time.time() - start)} s", + ) + raise return wrapped diff --git a/xinference/core/worker.py b/xinference/core/worker.py index 7a4f907479..567bcf14f0 100644 --- a/xinference/core/worker.py +++ b/xinference/core/worker.py @@ -13,6 +13,7 @@ # limitations under the License. import asyncio +import logging import os import platform import queue @@ -73,15 +74,15 @@ def __init__( self._supervisor_ref: Optional[xo.ActorRefType] = None self._main_pool = main_pool self._main_pool.recover_sub_pool = self.recover_sub_pool - self._status_guard_ref: xo.ActorRefType[ # type: ignore + self._status_guard_ref: xo.ActorRefType[ "StatusGuardActor" - ] = None + ] = None # type: ignore self._event_collector_ref: xo.ActorRefType[ # type: ignore EventCollectorActor ] = None - self._cache_tracker_ref: xo.ActorRefType[ # type: ignore + self._cache_tracker_ref: xo.ActorRefType[ CacheTrackerActor - ] = None + ] = None # type: ignore # internal states. # temporary placeholder during model launch process: @@ -146,7 +147,7 @@ async def recover_sub_pool(self, address): else: recover_count = self._model_uid_to_recover_count.get(model_uid) try: - await self.terminate_model(model_uid) + await self.terminate_model(model_uid, is_model_die=True) except Exception: pass if recover_count is not None: @@ -185,7 +186,7 @@ async def recover_sub_pool(self, address): break @classmethod - def uid(cls) -> str: + def default_uid(cls) -> str: return "worker" async def __post_create__(self): @@ -270,9 +271,9 @@ async def __post_create__(self): try: await self.get_supervisor_ref(add_worker=True) - except Exception as e: + except Exception: # Do not crash the worker if supervisor is down, auto re-connect later - logger.error(f"cannot connect to supervisor {e}") + logger.error(f"cannot connect to supervisor", exc_info=True) if not XINFERENCE_DISABLE_HEALTH_CHECK: from ..isolation import Isolation @@ -324,7 +325,7 @@ async def get_supervisor_ref(self, add_worker: bool = True) -> xo.ActorRefType: if self._supervisor_ref is not None: return self._supervisor_ref supervisor_ref = await xo.actor_ref( # type: ignore - address=self._supervisor_address, uid=SupervisorActor.uid() + address=self._supervisor_address, uid=SupervisorActor.default_uid() ) # Prevent concurrent operations leads to double initialization, check again. if self._supervisor_ref is not None: @@ -336,13 +337,13 @@ async def get_supervisor_ref(self, add_worker: bool = True) -> xo.ActorRefType: logger.info("Connected to supervisor as a fresh worker") self._status_guard_ref = await xo.actor_ref( - address=self._supervisor_address, uid=StatusGuardActor.uid() + address=self._supervisor_address, uid=StatusGuardActor.default_uid() ) self._event_collector_ref = await xo.actor_ref( - address=self._supervisor_address, uid=EventCollectorActor.uid() + address=self._supervisor_address, uid=EventCollectorActor.default_uid() ) self._cache_tracker_ref = await xo.actor_ref( - address=self._supervisor_address, uid=CacheTrackerActor.uid() + address=self._supervisor_address, uid=CacheTrackerActor.default_uid() ) # cache_tracker is on supervisor from ..model.audio import get_audio_model_descriptions @@ -664,6 +665,8 @@ def sort_helper(item): ret.sort(key=sort_helper) return ret + elif model_type == "video": + return [] elif model_type == "rerank": from ..model.rerank.custom import get_user_defined_reranks @@ -703,6 +706,8 @@ async def get_model_registration(self, model_type: str, model_name: str) -> Any: for f in get_user_defined_audios(): if f.model_name == model_name: return f + elif model_type == "video": + return None elif model_type == "rerank": from ..model.rerank.custom import get_user_defined_reranks @@ -737,9 +742,9 @@ async def _get_model_ability(self, model: Any, model_type: str) -> List[str]: elif model_type == "rerank": return ["rerank"] elif model_type == "image": - return ["text_to_image"] + return model.model_ability elif model_type == "audio": - return [model._model_spec.ability] + return [model.model_ability] elif model_type == "video": return ["text_to_video"] elif model_type == "flexible": @@ -766,7 +771,7 @@ async def update_cache_status( version_info["model_file_location"], ) - @log_async(logger=logger) + @log_async(logger=logger, level=logging.INFO) async def launch_builtin_model( self, model_uid: str, @@ -810,7 +815,7 @@ async def launch_builtin_model( ) except Exception as e: # Report callback error can be log and ignore, should not interrupt the Process - logger.error("report_event error: %s" % (e)) + logger.error("report_event error: %s" % (e), exc_info=True) if gpu_idx is not None: logger.info( @@ -880,6 +885,7 @@ async def launch_builtin_model( ModelActor, address=subpool_address, uid=model_uid, + supervisor_address=self._supervisor_address, worker_address=self.address, model=model, model_description=model_description, @@ -913,8 +919,8 @@ async def launch_builtin_model( {"model_ability": abilities, "status": LaunchStatus.READY.name}, ) - @log_async(logger=logger) - async def terminate_model(self, model_uid: str): + @log_async(logger=logger, level=logging.INFO) + async def terminate_model(self, model_uid: str, is_model_die=False): # Terminate model while its launching is not allow if model_uid in self._model_uid_launching_guard: raise ValueError(f"{model_uid} is launching") @@ -963,11 +969,16 @@ async def terminate_model(self, model_uid: str): self._model_uid_to_recover_count.pop(model_uid, None) self._model_uid_to_launch_args.pop(model_uid, None) + if is_model_die: + status = LaunchStatus.ERROR.name + else: + status = LaunchStatus.TERMINATED.name + if self._status_guard_ref is None: _ = await self.get_supervisor_ref() assert self._status_guard_ref is not None await self._status_guard_ref.update_instance_info( - origin_uid, {"status": LaunchStatus.TERMINATED.name} + origin_uid, {"status": status} ) # Provide an interface for future version of supervisor to call diff --git a/xinference/deploy/cmdline.py b/xinference/deploy/cmdline.py index 8eea848077..f0f09720a5 100644 --- a/xinference/deploy/cmdline.py +++ b/xinference/deploy/cmdline.py @@ -17,7 +17,7 @@ import os import sys import warnings -from typing import List, Optional, Sequence, Tuple, Union +from typing import Dict, List, Optional, Sequence, Tuple, Union import click from xoscar.utils import get_next_port @@ -38,7 +38,6 @@ XINFERENCE_LOG_MAX_BYTES, ) from ..isolation import Isolation -from ..types import ChatCompletionMessage from .utils import ( get_config_dict, get_log_file, @@ -1210,13 +1209,12 @@ def model_chat( stream: bool, api_key: Optional[str], ): - # TODO: chat model roles may not be user and assistant. endpoint = get_endpoint(endpoint) client = RESTfulClient(base_url=endpoint, api_key=api_key) if api_key is None: client._set_token(get_stored_token(endpoint, client)) - chat_history: "List[ChatCompletionMessage]" = [] + messages: List[Dict] = [] if stream: # TODO: when stream=True, RestfulClient cannot generate words one by one. # So use Client in temporary. The implementation needs to be changed to @@ -1229,10 +1227,10 @@ async def chat_internal(): if prompt == "": break print("Assistant: ", end="", file=sys.stdout) + messages.append(dict(role="user", content=prompt)) response_content = "" for chunk in model.chat( - prompt=prompt, - chat_history=chat_history, + messages, generate_config={"stream": stream, "max_tokens": max_tokens}, ): delta = chunk["choices"][0]["delta"] @@ -1242,10 +1240,7 @@ async def chat_internal(): response_content += delta["content"] print(delta["content"], end="", flush=True, file=sys.stdout) print("", file=sys.stdout) - chat_history.append(ChatCompletionMessage(role="user", content=prompt)) - chat_history.append( - ChatCompletionMessage(role="assistant", content=response_content) - ) + messages.append(dict(role="assistant", content=response_content)) model = client.get_model(model_uid=model_uid) @@ -1274,20 +1269,17 @@ async def chat_internal(): prompt = input("User: ") if prompt == "": break - chat_history.append(ChatCompletionMessage(role="user", content=prompt)) + messages.append({"role": "user", "content": prompt}) print("Assistant: ", end="", file=sys.stdout) response = restful_model.chat( - prompt=prompt, - chat_history=chat_history, + messages, generate_config={"stream": stream, "max_tokens": max_tokens}, ) if not isinstance(response, dict): raise ValueError("chat result is not valid") response_content = response["choices"][0]["message"]["content"] print(f"{response_content}\n", file=sys.stdout) - chat_history.append( - ChatCompletionMessage(role="assistant", content=response_content) - ) + messages.append(dict(role="assistant", content=response_content)) @cli.command("vllm-models", help="Query and display models compatible with vLLM.") diff --git a/xinference/deploy/docker/Dockerfile b/xinference/deploy/docker/Dockerfile index 1975adb5eb..3d6afc44c3 100644 --- a/xinference/deploy/docker/Dockerfile +++ b/xinference/deploy/docker/Dockerfile @@ -1,4 +1,4 @@ -FROM vllm/vllm-openai:latest +FROM vllm/vllm-openai:v0.6.0 COPY . /opt/inference WORKDIR /opt/inference @@ -39,4 +39,6 @@ RUN pip install --upgrade -i "$PIP_INDEX" pip && \ # clean packages pip cache purge +# Overwrite the entrypoint of vllm's base image ENTRYPOINT [] +CMD ["/bin/bash"] diff --git a/xinference/deploy/docker/cpu.Dockerfile b/xinference/deploy/docker/cpu.Dockerfile index d7bd45c463..21518a5272 100644 --- a/xinference/deploy/docker/cpu.Dockerfile +++ b/xinference/deploy/docker/cpu.Dockerfile @@ -28,3 +28,6 @@ RUN python -m pip install --upgrade -i "$PIP_INDEX" pip && \ pip install -i "$PIP_INDEX" --no-deps "." && \ # clean packages pip cache purge + +ENTRYPOINT [] +CMD ["/bin/bash"] diff --git a/xinference/deploy/docker/requirements.txt b/xinference/deploy/docker/requirements.txt index 5408194367..a3aa0a5e93 100644 --- a/xinference/deploy/docker/requirements.txt +++ b/xinference/deploy/docker/requirements.txt @@ -8,31 +8,31 @@ tqdm>=4.27 tabulate requests pydantic -fastapi==0.110.3 +fastapi>=0.110.3 uvicorn huggingface-hub>=0.19.4 typing_extensions modelscope>=1.10.0 sse_starlette>=1.6.5 # ensure_bytes API break change: https://github.com/sysid/sse-starlette/issues/65 -openai>1,<1.40 # For typing +openai>1 # For typing python-jose[cryptography] passlib[bcrypt] aioprometheus[starlette]>=23.12.0 -pynvml +nvidia-ml-py async-timeout peft opencv-contrib-python-headless # all -transformers>=4.34.1 -accelerate>=0.27.2 +transformers>=4.43.2 +accelerate>=0.28.0 sentencepiece transformers_stream_generator bitsandbytes protobuf einops -tiktoken -sentence-transformers>=2.7.0 +tiktoken>=0.6.0 +sentence-transformers>=3.1.0 diffusers>=0.30.0 controlnet_aux orjson @@ -45,17 +45,23 @@ torchvision # For deepseek VL FlagEmbedding # For rerank funasr omegaconf~=2.3.0 # For ChatTTS -nemo_text_processing # For ChatTTS -WeTextProcessing # For ChatTTS +nemo_text_processing<1.1.0 # 1.1.0 requires pynini==2.1.6.post1 +WeTextProcessing<1.0.4 # 1.0.4 requires pynini==2.1.6 librosa # For ChatTTS torchaudio # For ChatTTS -ChatTTS>0.1 +ChatTTS>=0.2 xxhash # For ChatTTS +torch>=2.0.0 # For CosyVoice +lightning>=2.0.0 # For CosyVoice, matcha +hydra-core>=1.3.2 # For CosyVoice, matcha +inflect # For CosyVoice, matcha +conformer # For CosyVoice, matcha +diffusers>=0.30.0 # For CosyVoice, matcha +gdown # For CosyVoice, matcha +pyarrow # For CosyVoice, matcha HyperPyYAML # For CosyVoice -matcha-tts>=0.0.7 # For CosyVoice onnxruntime-gpu==1.16.0; sys_platform == 'linux' # For CosyVoice onnxruntime==1.16.0; sys_platform == 'darwin' or sys_platform == 'windows' # For CosyVoice -openai-whisper # For CosyVoice boto3>=1.28.55,<1.28.65 # For tensorizer tensorizer~=2.9.0 imageio-ffmpeg # For video @@ -64,8 +70,12 @@ jj-pytorchvideo # For CogVLM2-video loguru # For Fish Speech natsort # For Fish Speech loralib # For Fish Speech -opencc==1.1.6 # For Fish Speech -faster_whisper # For Fish Speech +ormsgpack # For Fish Speech +qwen-vl-utils # For qwen2-vl +datamodel_code_generator # for minicpm-4B +jsonschema # for minicpm-4B +deepcache # for sd +verovio>=4.3.1 # For got_ocr2 # sglang outlines>=0.0.44 diff --git a/xinference/deploy/docker/requirements_cpu.txt b/xinference/deploy/docker/requirements_cpu.txt index 00a33dae9c..9eb9409b4f 100644 --- a/xinference/deploy/docker/requirements_cpu.txt +++ b/xinference/deploy/docker/requirements_cpu.txt @@ -7,7 +7,7 @@ tqdm>=4.27 tabulate requests pydantic -fastapi==0.110.3 +fastapi>=0.110.3 uvicorn huggingface-hub>=0.19.4 typing_extensions @@ -15,21 +15,21 @@ boto3>=1.28.55,<1.28.65 tensorizer~=2.9.0 modelscope>=1.10.0 sse_starlette>=1.6.5 -openai>1,<1.40 +openai>1 python-jose[cryptography] passlib[bcrypt] aioprometheus[starlette]>=23.12.0 -pynvml +nvidia-ml-py async-timeout -transformers>=4.34.1 -accelerate>=0.20.3 +transformers>=4.43.2 +accelerate>=0.28.0 sentencepiece transformers_stream_generator bitsandbytes protobuf einops tiktoken -sentence-transformers>=2.3.1 +sentence-transformers>=3.1.0 FlagEmbedding diffusers>=0.30.0 controlnet_aux @@ -42,22 +42,31 @@ timm opencv-contrib-python-headless funasr omegaconf~=2.3.0 # For ChatTTS -nemo_text_processing # For ChatTTS -WeTextProcessing # For ChatTTS +nemo_text_processing<1.1.0 # 1.1.0 requires pynini==2.1.6.post1 +WeTextProcessing<1.0.4 # 1.0.4 requires pynini==2.1.6 librosa # For ChatTTS torchaudio # For ChatTTS -ChatTTS>0.1 +ChatTTS>=0.2 xxhash # For ChatTTS +torch>=2.0.0 # For CosyVoice +lightning>=2.0.0 # For CosyVoice, matcha +hydra-core>=1.3.2 # For CosyVoice, matcha +inflect # For CosyVoice, matcha +conformer # For CosyVoice, matcha +diffusers>=0.30.0 # For CosyVoice, matcha +gdown # For CosyVoice, matcha +pyarrow # For CosyVoice, matcha HyperPyYAML # For CosyVoice -matcha-tts>=0.0.7 # For CosyVoice onnxruntime-gpu==1.16.0; sys_platform == 'linux' # For CosyVoice onnxruntime==1.16.0; sys_platform == 'darwin' or sys_platform == 'windows' # For CosyVoice -openai-whisper # For CosyVoice imageio-ffmpeg # For video eva-decord # For video in VL jj-pytorchvideo # For CogVLM2-video loguru # For Fish Speech natsort # For Fish Speech loralib # For Fish Speech -opencc==1.1.6 # For Fish Speech -faster_whisper # For Fish Speech +ormsgpack # For Fish Speech +qwen-vl-utils # For qwen2-vl +datamodel_code_generator # for minicpm-4B +jsonschema # for minicpm-4B +verovio>=4.3.1 # For got_ocr2 diff --git a/xinference/deploy/local.py b/xinference/deploy/local.py index e73c4d087c..94b4deb0b9 100644 --- a/xinference/deploy/local.py +++ b/xinference/deploy/local.py @@ -49,7 +49,7 @@ async def _start_local_cluster( address=address, logging_conf=logging_conf ) await xo.create_actor( - SupervisorActor, address=address, uid=SupervisorActor.uid() + SupervisorActor, address=address, uid=SupervisorActor.default_uid() ) await start_worker_components( address=address, diff --git a/xinference/deploy/supervisor.py b/xinference/deploy/supervisor.py index 3ea11520e7..ed12a9f7c2 100644 --- a/xinference/deploy/supervisor.py +++ b/xinference/deploy/supervisor.py @@ -41,7 +41,7 @@ async def _start_supervisor(address: str, logging_conf: Optional[Dict] = None): address=address, n_process=0, logging_conf={"dict": logging_conf} ) await xo.create_actor( - SupervisorActor, address=address, uid=SupervisorActor.uid() + SupervisorActor, address=address, uid=SupervisorActor.default_uid() ) await pool.join() except asyncio.exceptions.CancelledError: diff --git a/xinference/deploy/utils.py b/xinference/deploy/utils.py index 3058c2b1f1..1a8603bb6f 100644 --- a/xinference/deploy/utils.py +++ b/xinference/deploy/utils.py @@ -167,7 +167,7 @@ async def health_check_internal(): from ..core.supervisor import SupervisorActor supervisor_ref: xo.ActorRefType[SupervisorActor] = await xo.actor_ref( # type: ignore - address=address, uid=SupervisorActor.uid() + address=address, uid=SupervisorActor.default_uid() ) await supervisor_ref.get_status() diff --git a/xinference/deploy/worker.py b/xinference/deploy/worker.py index 95c5dd2d87..cedb6fb52a 100644 --- a/xinference/deploy/worker.py +++ b/xinference/deploy/worker.py @@ -43,7 +43,7 @@ async def start_worker_components( await xo.create_actor( WorkerActor, address=address, - uid=WorkerActor.uid(), + uid=WorkerActor.default_uid(), supervisor_address=supervisor_address, main_pool=main_pool, gpu_devices=gpu_device_indices, diff --git a/xinference/model/__init__.py b/xinference/model/__init__.py index 8371c55396..d751b2f663 100644 --- a/xinference/model/__init__.py +++ b/xinference/model/__init__.py @@ -14,6 +14,22 @@ def _install(): + from .audio import _install as audio_install + from .embedding import _install as embedding_install + from .flexible import _install as flexible_install + from .image import _install as image_install from .llm import _install as llm_install + from .rerank import _install as rerank_install + from .video import _install as video_install llm_install() + audio_install() + embedding_install() + flexible_install() + image_install() + rerank_install() + video_install() + + +_install() +del _install diff --git a/xinference/model/audio/__init__.py b/xinference/model/audio/__init__.py index fe33f5d629..18c278abae 100644 --- a/xinference/model/audio/__init__.py +++ b/xinference/model/audio/__init__.py @@ -15,7 +15,10 @@ import codecs import json import os +import warnings +from typing import Any, Dict +from ...constants import XINFERENCE_MODEL_DIR from .core import ( AUDIO_MODEL_DESCRIPTIONS, MODEL_NAME_TO_REVISION, @@ -31,49 +34,63 @@ unregister_audio, ) -_model_spec_json = os.path.join(os.path.dirname(__file__), "model_spec.json") -_model_spec_modelscope_json = os.path.join( - os.path.dirname(__file__), "model_spec_modelscope.json" -) -BUILTIN_AUDIO_MODELS = dict( - (spec["model_name"], AudioModelFamilyV1(**spec)) - for spec in json.load(codecs.open(_model_spec_json, "r", encoding="utf-8")) -) -for model_name, model_spec in BUILTIN_AUDIO_MODELS.items(): - MODEL_NAME_TO_REVISION[model_name].append(model_spec.model_revision) +BUILTIN_AUDIO_MODELS: Dict[str, Any] = {} +MODELSCOPE_AUDIO_MODELS: Dict[str, Any] = {} -MODELSCOPE_AUDIO_MODELS = dict( - (spec["model_name"], AudioModelFamilyV1(**spec)) - for spec in json.load( - codecs.open(_model_spec_modelscope_json, "r", encoding="utf-8") - ) -) -for model_name, model_spec in MODELSCOPE_AUDIO_MODELS.items(): - MODEL_NAME_TO_REVISION[model_name].append(model_spec.model_revision) -# register model description after recording model revision -for model_spec_info in [BUILTIN_AUDIO_MODELS, MODELSCOPE_AUDIO_MODELS]: - for model_name, model_spec in model_spec_info.items(): - if model_spec.model_name not in AUDIO_MODEL_DESCRIPTIONS: - AUDIO_MODEL_DESCRIPTIONS.update(generate_audio_description(model_spec)) +def register_custom_model(): + # if persist=True, load them when init + user_defined_audio_dir = os.path.join(XINFERENCE_MODEL_DIR, "audio") + if os.path.isdir(user_defined_audio_dir): + for f in os.listdir(user_defined_audio_dir): + try: + with codecs.open( + os.path.join(user_defined_audio_dir, f), encoding="utf-8" + ) as fd: + user_defined_audio_family = CustomAudioModelFamilyV1.parse_obj( + json.load(fd) + ) + register_audio(user_defined_audio_family, persist=False) + except Exception as e: + warnings.warn(f"{user_defined_audio_dir}/{f} has error, {e}") -from ...constants import XINFERENCE_MODEL_DIR -# if persist=True, load them when init -user_defined_audio_dir = os.path.join(XINFERENCE_MODEL_DIR, "audio") -if os.path.isdir(user_defined_audio_dir): - for f in os.listdir(user_defined_audio_dir): - with codecs.open( - os.path.join(user_defined_audio_dir, f), encoding="utf-8" - ) as fd: - user_defined_audio_family = CustomAudioModelFamilyV1.parse_obj( - json.load(fd) +def _install(): + _model_spec_json = os.path.join(os.path.dirname(__file__), "model_spec.json") + _model_spec_modelscope_json = os.path.join( + os.path.dirname(__file__), "model_spec_modelscope.json" + ) + BUILTIN_AUDIO_MODELS.update( + dict( + (spec["model_name"], AudioModelFamilyV1(**spec)) + for spec in json.load(codecs.open(_model_spec_json, "r", encoding="utf-8")) + ) + ) + for model_name, model_spec in BUILTIN_AUDIO_MODELS.items(): + MODEL_NAME_TO_REVISION[model_name].append(model_spec.model_revision) + + MODELSCOPE_AUDIO_MODELS.update( + dict( + (spec["model_name"], AudioModelFamilyV1(**spec)) + for spec in json.load( + codecs.open(_model_spec_modelscope_json, "r", encoding="utf-8") ) - register_audio(user_defined_audio_family, persist=False) + ) + ) + for model_name, model_spec in MODELSCOPE_AUDIO_MODELS.items(): + MODEL_NAME_TO_REVISION[model_name].append(model_spec.model_revision) + + # register model description after recording model revision + for model_spec_info in [BUILTIN_AUDIO_MODELS, MODELSCOPE_AUDIO_MODELS]: + for model_name, model_spec in model_spec_info.items(): + if model_spec.model_name not in AUDIO_MODEL_DESCRIPTIONS: + AUDIO_MODEL_DESCRIPTIONS.update(generate_audio_description(model_spec)) + + register_custom_model() -# register model description -for ud_audio in get_user_defined_audios(): - AUDIO_MODEL_DESCRIPTIONS.update(generate_audio_description(ud_audio)) + # register model description + for ud_audio in get_user_defined_audios(): + AUDIO_MODEL_DESCRIPTIONS.update(generate_audio_description(ud_audio)) -del _model_spec_json -del _model_spec_modelscope_json + del _model_spec_json + del _model_spec_modelscope_json diff --git a/xinference/model/audio/chattts.py b/xinference/model/audio/chattts.py index 8f866f429b..2a5f4ee7c9 100644 --- a/xinference/model/audio/chattts.py +++ b/xinference/model/audio/chattts.py @@ -11,11 +11,14 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + import base64 import logging from io import BytesIO from typing import TYPE_CHECKING, Optional +from ..utils import set_all_random_seed + if TYPE_CHECKING: from .core import AudioModelFamilyV1 @@ -38,6 +41,10 @@ def __init__( self._model = None self._kwargs = kwargs + @property + def model_ability(self): + return self._model_spec.model_ability + def load(self): import ChatTTS import torch @@ -46,7 +53,12 @@ def load(self): torch._dynamo.config.suppress_errors = True torch.set_float32_matmul_precision("high") self._model = ChatTTS.Chat() - self._model.load(source="custom", custom_path=self._model_path, compile=True) + logger.info("Load ChatTTS model with kwargs: %s", self._kwargs) + ok = self._model.load( + source="custom", custom_path=self._model_path, **self._kwargs + ) + if not ok: + raise Exception(f"The ChatTTS model is not correct: {self._model_path}") def speech( self, @@ -78,9 +90,7 @@ def speech( if rnd_spk_emb is None: seed = xxhash.xxh32_intdigest(voice) - torch.manual_seed(seed) - np.random.seed(seed) - torch.cuda.manual_seed(seed) + set_all_random_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False @@ -108,16 +118,15 @@ def _generator(): last_pos = 0 with writer.open(): for it in iter: - for itt in it: - for chunk in itt: - chunk = np.array([chunk]).transpose() - writer.write_audio_chunk(i, torch.from_numpy(chunk)) - new_last_pos = out.tell() - if new_last_pos != last_pos: - out.seek(last_pos) - encoded_bytes = out.read() - yield encoded_bytes - last_pos = new_last_pos + for chunk in it: + chunk = np.array([chunk]).transpose() + writer.write_audio_chunk(i, torch.from_numpy(chunk)) + new_last_pos = out.tell() + if new_last_pos != last_pos: + out.seek(last_pos) + encoded_bytes = out.read() + yield encoded_bytes + last_pos = new_last_pos return _generator() else: @@ -125,7 +134,15 @@ def _generator(): # Save the generated audio with BytesIO() as out: - torchaudio.save( - out, torch.from_numpy(wavs[0]), 24000, format=response_format - ) + try: + torchaudio.save( + out, + torch.from_numpy(wavs[0]).unsqueeze(0), + 24000, + format=response_format, + ) + except: + torchaudio.save( + out, torch.from_numpy(wavs[0]), 24000, format=response_format + ) return out.getvalue() diff --git a/xinference/model/audio/core.py b/xinference/model/audio/core.py index b6a65a572d..5672b216d4 100644 --- a/xinference/model/audio/core.py +++ b/xinference/model/audio/core.py @@ -25,8 +25,6 @@ from .funasr import FunASRModel from .whisper import WhisperModel -MAX_ATTEMPTS = 3 - logger = logging.getLogger(__name__) # Used for check whether the model is cached. @@ -47,7 +45,7 @@ class AudioModelFamilyV1(CacheableModelSpec): model_id: str model_revision: str multilingual: bool - ability: str + model_ability: Optional[str] default_model_config: Optional[Dict[str, Any]] default_transcription_config: Optional[Dict[str, Any]] diff --git a/xinference/model/audio/cosyvoice.py b/xinference/model/audio/cosyvoice.py index fcd5605245..9be452f473 100644 --- a/xinference/model/audio/cosyvoice.py +++ b/xinference/model/audio/cosyvoice.py @@ -16,6 +16,8 @@ from io import BytesIO from typing import TYPE_CHECKING, Optional +from ..utils import set_all_random_seed + if TYPE_CHECKING: from .core import AudioModelFamilyV1 @@ -38,6 +40,10 @@ def __init__( self._model = None self._kwargs = kwargs + @property + def model_ability(self): + return self._model_spec.model_ability + def load(self): import os import sys @@ -47,7 +53,82 @@ def load(self): from cosyvoice.cli.cosyvoice import CosyVoice - self._model = CosyVoice(self._model_path) + self._model = CosyVoice( + self._model_path, load_jit=self._kwargs.get("load_jit", False) + ) + + def _speech_handle( + self, + stream, + input, + instruct_text, + prompt_speech, + prompt_text, + voice, + response_format, + ): + if prompt_speech: + from cosyvoice.utils.file_utils import load_wav + + with io.BytesIO(prompt_speech) as prompt_speech_io: + prompt_speech_16k = load_wav(prompt_speech_io, 16000) + + if prompt_text: + logger.info("CosyVoice inference_zero_shot") + output = self._model.inference_zero_shot( + input, prompt_text, prompt_speech_16k, stream=stream + ) + else: + logger.info("CosyVoice inference_cross_lingual") + output = self._model.inference_cross_lingual( + input, prompt_speech_16k, stream=stream + ) + else: + available_speakers = self._model.list_avaliable_spks() + if not voice: + voice = available_speakers[0] + else: + assert ( + voice in available_speakers + ), f"Invalid voice {voice}, CosyVoice available speakers: {available_speakers}" + if instruct_text: + logger.info("CosyVoice inference_instruct") + output = self._model.inference_instruct( + input, voice, instruct_text=instruct_text, stream=stream + ) + else: + logger.info("CosyVoice inference_sft") + output = self._model.inference_sft(input, voice, stream=stream) + + import torch + import torchaudio + + def _generator_stream(): + with BytesIO() as out: + writer = torchaudio.io.StreamWriter(out, format=response_format) + writer.add_audio_stream(sample_rate=22050, num_channels=1) + i = 0 + last_pos = 0 + with writer.open(): + for chunk in output: + chunk = chunk["tts_speech"] + trans_chunk = torch.transpose(chunk, 0, 1) + writer.write_audio_chunk(i, trans_chunk) + new_last_pos = out.tell() + if new_last_pos != last_pos: + out.seek(last_pos) + encoded_bytes = out.read() + yield encoded_bytes + last_pos = new_last_pos + + def _generator_block(): + chunks = [o["tts_speech"] for o in output] + t = torch.cat(chunks, dim=1) + with BytesIO() as out: + torchaudio.save(out, t, 22050, format=response_format) + return out.getvalue() + + return _generator_stream() if stream else _generator_block() def speech( self, @@ -58,15 +139,10 @@ def speech( stream: bool = False, **kwargs, ): - if stream: - raise Exception("CosyVoiceModel does not support stream.") - - import torchaudio - from cosyvoice.utils.file_utils import load_wav - prompt_speech: Optional[bytes] = kwargs.pop("prompt_speech", None) prompt_text: Optional[str] = kwargs.pop("prompt_text", None) instruct_text: Optional[str] = kwargs.pop("instruct_text", None) + seed: Optional[int] = kwargs.pop("seed", 0) if "SFT" in self._model_spec.model_name: # inference_sft @@ -87,9 +163,6 @@ def speech( assert ( prompt_text is None ), "CosyVoice Instruct model does not support prompt_text" - assert ( - instruct_text is not None - ), "CosyVoice Instruct model expect a instruct_text" else: # inference_zero_shot # inference_cross_lingual @@ -99,38 +172,15 @@ def speech( ), "CosyVoice model does not support instruct_text" assert self._model is not None - if prompt_speech: - assert not voice, "voice can't be set with prompt speech." - with io.BytesIO(prompt_speech) as prompt_speech_io: - prompt_speech_16k = load_wav(prompt_speech_io, 16000) - if prompt_text: - logger.info("CosyVoice inference_zero_shot") - output = self._model.inference_zero_shot( - input, prompt_text, prompt_speech_16k - ) - else: - logger.info("CosyVoice inference_cross_lingual") - output = self._model.inference_cross_lingual( - input, prompt_speech_16k - ) - else: - available_speakers = self._model.list_avaliable_spks() - if not voice: - voice = available_speakers[0] - else: - assert ( - voice in available_speakers - ), f"Invalid voice {voice}, CosyVoice available speakers: {available_speakers}" - if instruct_text: - logger.info("CosyVoice inference_instruct") - output = self._model.inference_instruct( - input, voice, instruct_text=instruct_text - ) - else: - logger.info("CosyVoice inference_sft") - output = self._model.inference_sft(input, voice) - # Save the generated audio - with BytesIO() as out: - torchaudio.save(out, output["tts_speech"], 22050, format=response_format) - return out.getvalue() + set_all_random_seed(seed) + + return self._speech_handle( + stream, + input, + instruct_text, + prompt_speech, + prompt_text, + voice, + response_format, + ) diff --git a/xinference/model/audio/custom.py b/xinference/model/audio/custom.py index c342bcc1a7..be2c388fda 100644 --- a/xinference/model/audio/custom.py +++ b/xinference/model/audio/custom.py @@ -88,6 +88,10 @@ def register_audio(model_spec: CustomAudioModelFamilyV1, persist: bool): if not is_valid_model_name(model_spec.model_name): raise ValueError(f"Invalid model name {model_spec.model_name}.") + model_uri = model_spec.model_uri + if model_uri and not is_valid_model_uri(model_uri): + raise ValueError(f"Invalid model URI {model_uri}.") + with UD_AUDIO_LOCK: for model_name in ( list(BUILTIN_AUDIO_MODELS.keys()) @@ -102,11 +106,6 @@ def register_audio(model_spec: CustomAudioModelFamilyV1, persist: bool): UD_AUDIOS.append(model_spec) if persist: - # We only validate model URL when persist is True. - model_uri = model_spec.model_uri - if model_uri and not is_valid_model_uri(model_uri): - raise ValueError(f"Invalid model URI {model_uri}.") - persist_path = os.path.join( XINFERENCE_MODEL_DIR, "audio", f"{model_spec.model_name}.json" ) diff --git a/xinference/model/audio/fish_speech.py b/xinference/model/audio/fish_speech.py index 642b575a27..4a6412f04a 100644 --- a/xinference/model/audio/fish_speech.py +++ b/xinference/model/audio/fish_speech.py @@ -62,6 +62,10 @@ def __init__( self._model = None self._kwargs = kwargs + @property + def model_ability(self): + return self._model_spec.model_ability + def load(self): # There are too many imports from fish_speech. sys.path.insert( @@ -88,7 +92,7 @@ def load(self): checkpoint_path = os.path.join( self._model_path, - "firefly-gan-vq-fsq-4x1024-42hz-generator.pth", + "firefly-gan-vq-fsq-8x1024-21hz-generator.pth", ) self._model = load_decoder_model( config_name="firefly_gan_vq", @@ -155,11 +159,11 @@ def _inference( segments = [] while True: - result: WrappedGenerateResponse = response_queue.get() + result: WrappedGenerateResponse = response_queue.get() # type: ignore if result.status == "error": raise Exception(str(result.response)) - result: GenerateResponse = result.response + result: GenerateResponse = result.response # type: ignore if result.action == "next": break @@ -209,12 +213,12 @@ def speech( text=input, enable_reference_audio=False, reference_audio=None, - reference_text="", - max_new_tokens=0, - chunk_length=100, - top_p=0.7, - repetition_penalty=1.2, - temperature=0.7, + reference_text=kwargs.get("reference_text", ""), + max_new_tokens=kwargs.get("max_new_tokens", 1024), + chunk_length=kwargs.get("chunk_length", 200), + top_p=kwargs.get("top_p", 0.7), + repetition_penalty=kwargs.get("repetition_penalty", 1.2), + temperature=kwargs.get("temperature", 0.7), ) ) sample_rate, audio = result[0][1] diff --git a/xinference/model/audio/funasr.py b/xinference/model/audio/funasr.py index 98eb4ae878..9d9332c510 100644 --- a/xinference/model/audio/funasr.py +++ b/xinference/model/audio/funasr.py @@ -40,6 +40,10 @@ def __init__( self._model = None self._kwargs = kwargs + @property + def model_ability(self): + return self._model_spec.model_ability + def load(self): try: from funasr import AutoModel diff --git a/xinference/model/audio/model_spec.json b/xinference/model/audio/model_spec.json index 4cbe77a37a..e0328dd375 100644 --- a/xinference/model/audio/model_spec.json +++ b/xinference/model/audio/model_spec.json @@ -4,7 +4,7 @@ "model_family": "whisper", "model_id": "openai/whisper-tiny", "model_revision": "167c219b21f11ef214220b8fdb7536b8a88c2475", - "ability": "audio-to-text", + "model_ability": "audio-to-text", "multilingual": true }, { @@ -12,7 +12,7 @@ "model_family": "whisper", "model_id": "openai/whisper-tiny.en", "model_revision": "87c7102498dcde7456f24cfd30239ca606ed9063", - "ability": "audio-to-text", + "model_ability": "audio-to-text", "multilingual": false }, { @@ -20,7 +20,7 @@ "model_family": "whisper", "model_id": "openai/whisper-base", "model_revision": "8c1db9b51951100007a96a525d83a8ec81b3c237", - "ability": "audio-to-text", + "model_ability": "audio-to-text", "multilingual": true }, { @@ -28,7 +28,7 @@ "model_family": "whisper", "model_id": "openai/whisper-base.en", "model_revision": "911407f4214e0e1d82085af863093ec0b66f9cd6", - "ability": "audio-to-text", + "model_ability": "audio-to-text", "multilingual": false }, { @@ -36,7 +36,7 @@ "model_family": "whisper", "model_id": "openai/whisper-small", "model_revision": "998cb1a777c20db53d6033a61b977ed4c3792cac", - "ability": "audio-to-text", + "model_ability": "audio-to-text", "multilingual": true }, { @@ -44,7 +44,7 @@ "model_family": "whisper", "model_id": "openai/whisper-small.en", "model_revision": "e8727524f962ee844a7319d92be39ac1bd25655a", - "ability": "audio-to-text", + "model_ability": "audio-to-text", "multilingual": false }, { @@ -52,7 +52,7 @@ "model_family": "whisper", "model_id": "openai/whisper-medium", "model_revision": "16688beb1294bedd0a6f5cd86fe7eec57bce41ed", - "ability": "audio-to-text", + "model_ability": "audio-to-text", "multilingual": true }, { @@ -60,7 +60,7 @@ "model_family": "whisper", "model_id": "openai/whisper-medium.en", "model_revision": "2e98eb6279edf5095af0c8dedb36bdec0acd172b", - "ability": "audio-to-text", + "model_ability": "audio-to-text", "multilingual": false }, { @@ -68,7 +68,15 @@ "model_family": "whisper", "model_id": "openai/whisper-large-v3", "model_revision": "6cdf07a7e3ec3806e5d55f787915b85d4cd020b1", - "ability": "audio-to-text", + "model_ability": "audio-to-text", + "multilingual": true + }, + { + "model_name": "whisper-large-v3-turbo", + "model_family": "whisper", + "model_id": "openai/whisper-large-v3-turbo", + "model_revision": "41f01f3fe87f28c78e2fbf8b568835947dd65ed9", + "model_ability": "audio-to-text", "multilingual": true }, { @@ -76,7 +84,7 @@ "model_family": "whisper", "model_id": "BELLE-2/Belle-distilwhisper-large-v2-zh", "model_revision": "ed25d13498fa5bac758b2fc479435b698532dfe8", - "ability": "audio-to-text", + "model_ability": "audio-to-text", "multilingual": false }, { @@ -84,7 +92,7 @@ "model_family": "whisper", "model_id": "BELLE-2/Belle-whisper-large-v2-zh", "model_revision": "ec5bd5d78598545b7585814edde86dac2002b5b9", - "ability": "audio-to-text", + "model_ability": "audio-to-text", "multilingual": false }, { @@ -92,7 +100,7 @@ "model_family": "whisper", "model_id": "BELLE-2/Belle-whisper-large-v3-zh", "model_revision": "3bebc7247696b39f5ab9ed22db426943ac33f600", - "ability": "audio-to-text", + "model_ability": "audio-to-text", "multilingual": false }, { @@ -100,7 +108,7 @@ "model_family": "funasr", "model_id": "FunAudioLLM/SenseVoiceSmall", "model_revision": "3eb3b4eeffc2f2dde6051b853983753db33e35c3", - "ability": "audio-to-text", + "model_ability": "audio-to-text", "multilingual": true, "default_model_config": { "vad_model": "fsmn-vad", @@ -119,40 +127,40 @@ "model_name": "ChatTTS", "model_family": "ChatTTS", "model_id": "2Noise/ChatTTS", - "model_revision": "ce5913842aebd78e4a01a02d47244b8d62ac4ee3", - "ability": "text-to-audio", + "model_revision": "3b34118f6d25850440b8901cef3e71c6ef8619c8", + "model_ability": "text-to-audio", "multilingual": true }, { "model_name": "CosyVoice-300M", "model_family": "CosyVoice", - "model_id": "model-scope/CosyVoice-300M", - "model_revision": "ca4e036d2db2aa4731cc1747859a68044b6a4694", - "ability": "audio-to-audio", + "model_id": "FunAudioLLM/CosyVoice-300M", + "model_revision": "39c4e13d46bd4dfb840d214547623e5fcd2428e2", + "model_ability": "audio-to-audio", "multilingual": true }, { "model_name": "CosyVoice-300M-SFT", "model_family": "CosyVoice", - "model_id": "model-scope/CosyVoice-300M-SFT", - "model_revision": "ab918940c6c134b1fc1f069246e67bad6b66abcb", - "ability": "text-to-audio", + "model_id": "FunAudioLLM/CosyVoice-300M-SFT", + "model_revision": "096a5cff8d497fabb3dec2756a200f3688457a1b", + "model_ability": "text-to-audio", "multilingual": true }, { "model_name": "CosyVoice-300M-Instruct", "model_family": "CosyVoice", - "model_id": "model-scope/CosyVoice-300M-Instruct", - "model_revision": "fb5f676733139f35670bed9b59a77d476b1aa898", - "ability": "text-to-audio", + "model_id": "FunAudioLLM/CosyVoice-300M-Instruct", + "model_revision": "ba5265d9a3169c1fedce145122c9dd4bc24e062c", + "model_ability": "text-to-audio", "multilingual": true }, { - "model_name": "FishSpeech-1.2-SFT", + "model_name": "FishSpeech-1.4", "model_family": "FishAudio", - "model_id": "fishaudio/fish-speech-1.2-sft", - "model_revision": "180288e21ec5c50cfc564023a22f789e4b88a0e0", - "ability": "text-to-audio", + "model_id": "fishaudio/fish-speech-1.4", + "model_revision": "3c49651b8e583b6b13f55e375432e0d57e1aa84d", + "model_ability": "text-to-audio", "multilingual": true } ] diff --git a/xinference/model/audio/model_spec_modelscope.json b/xinference/model/audio/model_spec_modelscope.json index 54ab82e823..e47f1f8e3a 100644 --- a/xinference/model/audio/model_spec_modelscope.json +++ b/xinference/model/audio/model_spec_modelscope.json @@ -5,7 +5,16 @@ "model_hub": "modelscope", "model_id": "AI-ModelScope/whisper-large-v3", "model_revision": "master", - "ability": "audio-to-text", + "model_ability": "audio-to-text", + "multilingual": true + }, + { + "model_name": "whisper-large-v3-turbo", + "model_family": "whisper", + "model_hub": "modelscope", + "model_id": "AI-ModelScope/whisper-large-v3-turbo", + "model_revision": "master", + "model_ability": "audio-to-text", "multilingual": true }, { @@ -14,7 +23,7 @@ "model_hub": "modelscope", "model_id": "iic/SenseVoiceSmall", "model_revision": "master", - "ability": "audio-to-text", + "model_ability": "audio-to-text", "multilingual": true, "default_model_config": { "vad_model": "fsmn-vad", @@ -33,9 +42,9 @@ "model_name": "ChatTTS", "model_family": "ChatTTS", "model_hub": "modelscope", - "model_id": "pzc163/chatTTS", + "model_id": "AI-ModelScope/ChatTTS", "model_revision": "master", - "ability": "text-to-audio", + "model_ability": "text-to-audio", "multilingual": true }, { @@ -44,7 +53,7 @@ "model_hub": "modelscope", "model_id": "iic/CosyVoice-300M", "model_revision": "master", - "ability": "audio-to-audio", + "model_ability": "audio-to-audio", "multilingual": true }, { @@ -53,7 +62,7 @@ "model_hub": "modelscope", "model_id": "iic/CosyVoice-300M-SFT", "model_revision": "master", - "ability": "text-to-audio", + "model_ability": "text-to-audio", "multilingual": true }, { @@ -62,7 +71,7 @@ "model_hub": "modelscope", "model_id": "iic/CosyVoice-300M-Instruct", "model_revision": "master", - "ability": "text-to-audio", + "model_ability": "text-to-audio", "multilingual": true } ] diff --git a/xinference/model/audio/tests/test_chattts.py b/xinference/model/audio/tests/test_chattts.py index 92cb9ac96d..cadd732351 100644 --- a/xinference/model/audio/tests/test_chattts.py +++ b/xinference/model/audio/tests/test_chattts.py @@ -25,8 +25,7 @@ def test_chattts(setup): client = Client(endpoint) model_uid = client.launch_model( - model_name="ChatTTS", - model_type="audio", + model_name="ChatTTS", model_type="audio", compile=False ) model = client.get_model(model_uid) input_string = ( @@ -47,12 +46,14 @@ def test_chattts(setup): response = model.speech(input_string, stream=True) assert inspect.isgenerator(response) - i = 0 - for chunk in response: - i += 1 - assert type(chunk) is bytes - assert len(chunk) > 0 - assert i > 5 + with tempfile.NamedTemporaryFile(suffix=".mp3", delete=True) as f: + i = 0 + for chunk in response: + f.write(chunk) + i += 1 + assert type(chunk) is bytes + assert len(chunk) > 0 + assert i > 5 # Test openai API import openai diff --git a/xinference/model/audio/tests/test_cosyvoice.py b/xinference/model/audio/tests/test_cosyvoice.py index 481dc767a4..77bf92e6f5 100644 --- a/xinference/model/audio/tests/test_cosyvoice.py +++ b/xinference/model/audio/tests/test_cosyvoice.py @@ -11,6 +11,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import inspect import os.path import tempfile @@ -22,8 +23,7 @@ def test_cosyvoice_sft(setup): client = Client(endpoint) model_uid = client.launch_model( - model_name="CosyVoice-300M-SFT", - model_type="audio", + model_name="CosyVoice-300M-SFT", model_type="audio", download_hub="huggingface" ) model = client.get_model(model_uid) input_string = "你好，我是通义生成式语音大模型，请问有什么可以帮您的吗？" @@ -33,6 +33,18 @@ def test_cosyvoice_sft(setup): assert type(response) is bytes assert len(response) > 0 + # inference_sft + response = model.speech(input_string, stream=True) + assert inspect.isgenerator(response) + i = 0 + with tempfile.NamedTemporaryFile(suffix=".mp3", delete=True) as f: + for chunk in response: + f.write(chunk) + i += 1 + assert type(chunk) is bytes + assert len(chunk) > 0 + assert i > 5 + # Test openai API import openai @@ -60,8 +72,7 @@ def test_cosyvoice(setup): client = Client(endpoint) model_uid = client.launch_model( - model_name="CosyVoice-300M", - model_type="audio", + model_name="CosyVoice-300M", model_type="audio", download_hub="huggingface" ) model = client.get_model(model_uid) with open(zero_shot_prompt_file, "rb") as f: @@ -103,9 +114,20 @@ def test_cosyvoice_instruct(setup): model_uid = client.launch_model( model_name="CosyVoice-300M-Instruct", model_type="audio", + download_hub="huggingface", ) model = client.get_model(model_uid) + # inference without instruction + response = model.speech( + "在面对挑战时，他展现了非凡的勇气与智慧。", voice="中文男" + ) + assert type(response) is bytes + assert len(response) > 0 + with tempfile.NamedTemporaryFile(suffix=".mp3", delete=True) as f: + f.write(response) + assert os.stat(f.name).st_size > 0 + # inference_instruct response = model.speech( "在面对挑战时，他展现了非凡的勇气与智慧。", diff --git a/xinference/model/audio/tests/test_fish_speech.py b/xinference/model/audio/tests/test_fish_speech.py index 8b339290ad..ce57566b19 100644 --- a/xinference/model/audio/tests/test_fish_speech.py +++ b/xinference/model/audio/tests/test_fish_speech.py @@ -22,7 +22,7 @@ def test_fish_speech(setup): client = Client(endpoint) model_uid = client.launch_model( - model_name="FishSpeech-1.2-SFT", + model_name="FishSpeech-1.4", model_type="audio", ) model = client.get_model(model_uid) diff --git a/xinference/model/audio/tests/test_whisper.py b/xinference/model/audio/tests/test_whisper.py index d1c5d95a76..e376087c1a 100644 --- a/xinference/model/audio/tests/test_whisper.py +++ b/xinference/model/audio/tests/test_whisper.py @@ -152,6 +152,7 @@ def test_register_custom_audio(): model_name="custom_test_a", model_id="test/custom_test_a", multilingual=True, + ability="audio-to-text", ) register_audio(family_a, False) @@ -163,6 +164,7 @@ def test_register_custom_audio(): model_name="custom_test_b", model_id="test/custom_test_b", multilingual=True, + ability="audio-to-text", ) register_audio(family_b, False) assert family_b in get_user_defined_audios() @@ -194,6 +196,7 @@ def test_persistent_custom_audio(): model_id="test/custom_test_a", multilingual=True, model_uri=os.path.abspath(temp_dir), + ability="audio-to-text", ) register_audio(family, True) diff --git a/xinference/model/audio/whisper.py b/xinference/model/audio/whisper.py index 80335a9f47..7c4253bc35 100644 --- a/xinference/model/audio/whisper.py +++ b/xinference/model/audio/whisper.py @@ -12,6 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. import logging +import os +from glob import glob from typing import TYPE_CHECKING, Dict, List, Optional, Union from ...device_utils import ( @@ -42,6 +44,10 @@ def __init__( self._model = None self._kwargs = kwargs + @property + def model_ability(self): + return self._model_spec.model_ability + def load(self): from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline @@ -52,12 +58,13 @@ def load(self): raise ValueError(f"Device {self._device} is not available!") torch_dtype = get_device_preferred_dtype(self._device) + use_safetensors = any(glob(os.path.join(self._model_path, "*.safetensors"))) model = AutoModelForSpeechSeq2Seq.from_pretrained( self._model_path, torch_dtype=torch_dtype, low_cpu_mem_usage=True, - use_safetensors=True, + use_safetensors=use_safetensors, ) model.to(self._device) diff --git a/xinference/model/embedding/__init__.py b/xinference/model/embedding/__init__.py index dcf645dfab..758007497a 100644 --- a/xinference/model/embedding/__init__.py +++ b/xinference/model/embedding/__init__.py @@ -15,6 +15,8 @@ import codecs import json import os +import warnings +from typing import Any, Dict from .core import ( EMBEDDING_MODEL_DESCRIPTIONS, @@ -31,46 +33,68 @@ unregister_embedding, ) -_model_spec_json = os.path.join(os.path.dirname(__file__), "model_spec.json") -_model_spec_modelscope_json = os.path.join( - os.path.dirname(__file__), "model_spec_modelscope.json" -) -BUILTIN_EMBEDDING_MODELS = dict( - (spec["model_name"], EmbeddingModelSpec(**spec)) - for spec in json.load(codecs.open(_model_spec_json, "r", encoding="utf-8")) -) -for model_name, model_spec in BUILTIN_EMBEDDING_MODELS.items(): - MODEL_NAME_TO_REVISION[model_name].append(model_spec.model_revision) +BUILTIN_EMBEDDING_MODELS: Dict[str, Any] = {} +MODELSCOPE_EMBEDDING_MODELS: Dict[str, Any] = {} + + +def register_custom_model(): + from ...constants import XINFERENCE_MODEL_DIR + + user_defined_embedding_dir = os.path.join(XINFERENCE_MODEL_DIR, "embedding") + if os.path.isdir(user_defined_embedding_dir): + for f in os.listdir(user_defined_embedding_dir): + try: + with codecs.open( + os.path.join(user_defined_embedding_dir, f), encoding="utf-8" + ) as fd: + user_defined_llm_family = CustomEmbeddingModelSpec.parse_obj( + json.load(fd) + ) + register_embedding(user_defined_llm_family, persist=False) + except Exception as e: + warnings.warn(f"{user_defined_embedding_dir}/{f} has error, {e}") -MODELSCOPE_EMBEDDING_MODELS = dict( - (spec["model_name"], EmbeddingModelSpec(**spec)) - for spec in json.load( - codecs.open(_model_spec_modelscope_json, "r", encoding="utf-8") + +def _install(): + _model_spec_json = os.path.join(os.path.dirname(__file__), "model_spec.json") + _model_spec_modelscope_json = os.path.join( + os.path.dirname(__file__), "model_spec_modelscope.json" ) -) -for model_name, model_spec in MODELSCOPE_EMBEDDING_MODELS.items(): - MODEL_NAME_TO_REVISION[model_name].append(model_spec.model_revision) + BUILTIN_EMBEDDING_MODELS.update( + dict( + (spec["model_name"], EmbeddingModelSpec(**spec)) + for spec in json.load(codecs.open(_model_spec_json, "r", encoding="utf-8")) + ) + ) + for model_name, model_spec in BUILTIN_EMBEDDING_MODELS.items(): + MODEL_NAME_TO_REVISION[model_name].append(model_spec.model_revision) -# register model description after recording model revision -for model_spec_info in [BUILTIN_EMBEDDING_MODELS, MODELSCOPE_EMBEDDING_MODELS]: - for model_name, model_spec in model_spec_info.items(): - if model_spec.model_name not in EMBEDDING_MODEL_DESCRIPTIONS: - EMBEDDING_MODEL_DESCRIPTIONS.update( - generate_embedding_description(model_spec) + MODELSCOPE_EMBEDDING_MODELS.update( + dict( + (spec["model_name"], EmbeddingModelSpec(**spec)) + for spec in json.load( + codecs.open(_model_spec_modelscope_json, "r", encoding="utf-8") ) + ) + ) + for model_name, model_spec in MODELSCOPE_EMBEDDING_MODELS.items(): + MODEL_NAME_TO_REVISION[model_name].append(model_spec.model_revision) -from ...constants import XINFERENCE_MODEL_DIR + # register model description after recording model revision + for model_spec_info in [BUILTIN_EMBEDDING_MODELS, MODELSCOPE_EMBEDDING_MODELS]: + for model_name, model_spec in model_spec_info.items(): + if model_spec.model_name not in EMBEDDING_MODEL_DESCRIPTIONS: + EMBEDDING_MODEL_DESCRIPTIONS.update( + generate_embedding_description(model_spec) + ) -user_defined_llm_dir = os.path.join(XINFERENCE_MODEL_DIR, "embedding") -if os.path.isdir(user_defined_llm_dir): - for f in os.listdir(user_defined_llm_dir): - with codecs.open(os.path.join(user_defined_llm_dir, f), encoding="utf-8") as fd: - user_defined_llm_family = CustomEmbeddingModelSpec.parse_obj(json.load(fd)) - register_embedding(user_defined_llm_family, persist=False) + register_custom_model() -# register model description -for ud_embedding in get_user_defined_embeddings(): - EMBEDDING_MODEL_DESCRIPTIONS.update(generate_embedding_description(ud_embedding)) + # register model description + for ud_embedding in get_user_defined_embeddings(): + EMBEDDING_MODEL_DESCRIPTIONS.update( + generate_embedding_description(ud_embedding) + ) -del _model_spec_json -del _model_spec_modelscope_json + del _model_spec_json + del _model_spec_modelscope_json diff --git a/xinference/model/embedding/core.py b/xinference/model/embedding/core.py index 23a9766c48..8bb6c05626 100644 --- a/xinference/model/embedding/core.py +++ b/xinference/model/embedding/core.py @@ -19,6 +19,7 @@ from typing import Dict, List, Literal, Optional, Tuple, Union, no_type_check import numpy as np +import torch from ...device_utils import empty_cache from ...types import Embedding, EmbeddingData, EmbeddingUsage @@ -34,7 +35,11 @@ EMBEDDING_EMPTY_CACHE_COUNT = int( os.getenv("XINFERENCE_EMBEDDING_EMPTY_CACHE_COUNT", "10") ) +EMBEDDING_EMPTY_CACHE_TOKENS = int( + os.getenv("XINFERENCE_EMBEDDING_EMPTY_CACHE_TOKENS", "8192") +) assert EMBEDDING_EMPTY_CACHE_COUNT > 0 +assert EMBEDDING_EMPTY_CACHE_TOKENS > 0 def get_embedding_model_descriptions(): @@ -124,6 +129,7 @@ def __init__( model_path: str, model_spec: EmbeddingModelSpec, device: Optional[str] = None, + **kwargs, ): self._model_uid = model_uid self._model_path = model_path @@ -131,10 +137,19 @@ def __init__( self._model = None self._counter = 0 self._model_spec = model_spec + self._kwargs = kwargs def load(self): try: + import sentence_transformers from sentence_transformers import SentenceTransformer + + if sentence_transformers.__version__ < "3.1.0": + raise ValueError( + "The sentence_transformers version must be greater than 3.1.0. " + "Please upgrade your version via `pip install -U sentence_transformers` or refer to " + "https://github.com/UKPLab/sentence-transformers" + ) except ImportError: error_message = "Failed to import module 'SentenceTransformer'" installation_guide = [ @@ -147,49 +162,47 @@ class XSentenceTransformer(SentenceTransformer): def to(self, *args, **kwargs): pass - from ..utils import patch_trust_remote_code + torch_dtype = None + if torch_dtype_str := self._kwargs.get("torch_dtype"): + try: + torch_dtype = getattr(torch, torch_dtype_str) + if torch_dtype not in [ + torch.float16, + torch.float32, + torch.bfloat16, + ]: + logger.warning( + f"Load embedding model with unsupported torch dtype : {torch_dtype_str}. Using default torch dtype: fp32." + ) + torch_dtype = torch.float32 + except AttributeError: + logger.warning( + f"Load embedding model with unknown torch dtype '{torch_dtype_str}'. Using default torch dtype: fp32." + ) + torch_dtype = torch.float32 - patch_trust_remote_code() if ( "gte" in self._model_spec.model_name.lower() and "qwen2" in self._model_spec.model_name.lower() ): - import torch - - torch_dtype_str = self._kwargs.get("torch_dtype") - if torch_dtype_str is not None: - try: - torch_dtype = getattr(torch, torch_dtype_str) - if torch_dtype not in [ - torch.float16, - torch.float32, - torch.bfloat16, - ]: - logger.warning( - f"Load embedding model with unsupported torch dtype : {torch_dtype_str}. Using default torch dtype: fp32." - ) - torch_dtype = torch.float32 - except AttributeError: - logger.warning( - f"Load embedding model with unknown torch dtype '{torch_dtype_str}'. Using default torch dtype: fp32." - ) - torch_dtype = torch.float32 - else: - torch_dtype = "auto" + model_kwargs = {"device_map": "auto"} + if torch_dtype: + model_kwargs["torch_dtype"] = torch_dtype self._model = XSentenceTransformer( self._model_path, device=self._device, - model_kwargs={"device_map": "auto", "torch_dtype": torch_dtype}, + model_kwargs=model_kwargs, ) else: - self._model = SentenceTransformer(self._model_path, device=self._device) + model_kwargs = {"torch_dtype": torch_dtype} if torch_dtype else None + self._model = SentenceTransformer( + self._model_path, + device=self._device, + model_kwargs=model_kwargs, + trust_remote_code=True, + ) def create_embedding(self, sentences: Union[str, List[str]], **kwargs): - self._counter += 1 - if self._counter % EMBEDDING_EMPTY_CACHE_COUNT == 0: - logger.debug("Empty embedding cache.") - gc.collect() - empty_cache() from sentence_transformers import SentenceTransformer kwargs.setdefault("normalize_embeddings", True) @@ -208,6 +221,7 @@ def encode( convert_to_tensor: bool = False, device: str = None, normalize_embeddings: bool = False, + **kwargs, ): """ Computes sentence embeddings @@ -307,10 +321,12 @@ def encode( features = model.tokenize(sentences_batch) features = batch_to_device(features, device) features.update(extra_features) - all_token_nums += sum([len(f) for f in features]) + # when batching, the attention mask 1 means there is a token + # thus we just sum up it to get the total number of tokens + all_token_nums += features["attention_mask"].sum().item() with torch.no_grad(): - out_features = model.forward(features) + out_features = model.forward(features, **kwargs) if output_value == "token_embeddings": embeddings = [] @@ -391,13 +407,29 @@ def encode( usage = EmbeddingUsage( prompt_tokens=all_token_nums, total_tokens=all_token_nums ) - return Embedding( + result = Embedding( object="list", model=self._model_uid, data=embedding_list, usage=usage, ) + # clean cache if possible + self._counter += 1 + if ( + self._counter % EMBEDDING_EMPTY_CACHE_COUNT == 0 + or all_token_nums >= EMBEDDING_EMPTY_CACHE_TOKENS + ): + logger.debug( + "Empty embedding cache, calling count %s, all_token_nums %s", + self._counter, + all_token_nums, + ) + gc.collect() + empty_cache() + + return result + def match_embedding( model_name: str, diff --git a/xinference/model/embedding/custom.py b/xinference/model/embedding/custom.py index 8e311bbd7d..5cf398109b 100644 --- a/xinference/model/embedding/custom.py +++ b/xinference/model/embedding/custom.py @@ -47,6 +47,10 @@ def register_embedding(model_spec: CustomEmbeddingModelSpec, persist: bool): if not is_valid_model_name(model_spec.model_name): raise ValueError(f"Invalid model name {model_spec.model_name}.") + model_uri = model_spec.model_uri + if model_uri and not is_valid_model_uri(model_uri): + raise ValueError(f"Invalid model URI {model_uri}.") + with UD_EMBEDDING_LOCK: for model_name in ( list(BUILTIN_EMBEDDING_MODELS.keys()) @@ -61,11 +65,6 @@ def register_embedding(model_spec: CustomEmbeddingModelSpec, persist: bool): UD_EMBEDDINGS.append(model_spec) if persist: - # We only validate model URL when persist is True. - model_uri = model_spec.model_uri - if model_uri and not is_valid_model_uri(model_uri): - raise ValueError(f"Invalid model URI {model_uri}.") - persist_path = os.path.join( XINFERENCE_MODEL_DIR, "embedding", f"{model_spec.model_name}.json" ) diff --git a/xinference/model/embedding/model_spec.json b/xinference/model/embedding/model_spec.json index b90dcc8e58..dc8d851b85 100644 --- a/xinference/model/embedding/model_spec.json +++ b/xinference/model/embedding/model_spec.json @@ -233,10 +233,17 @@ }, { "model_name": "gte-Qwen2", - "dimensions": 3584, + "dimensions": 4096, "max_tokens": 32000, "language": ["zh", "en"], "model_id": "Alibaba-NLP/gte-Qwen2-7B-instruct", "model_revision": "e26182b2122f4435e8b3ebecbf363990f409b45b" + }, + { + "model_name": "jina-embeddings-v3", + "dimensions": 1024, + "max_tokens": 8192, + "language": ["zh", "en"], + "model_id": "jinaai/jina-embeddings-v3" } ] diff --git a/xinference/model/embedding/model_spec_modelscope.json b/xinference/model/embedding/model_spec_modelscope.json index b90a913b2b..7f4715b865 100644 --- a/xinference/model/embedding/model_spec_modelscope.json +++ b/xinference/model/embedding/model_spec_modelscope.json @@ -233,12 +233,20 @@ "model_id": "AI-ModelScope/m3e-large", "model_hub": "modelscope" }, - { + { "model_name": "gte-Qwen2", "dimensions": 4096, "max_tokens": 32000, "language": ["zh", "en"], "model_id": "iic/gte_Qwen2-7B-instruct", "model_hub": "modelscope" + }, + { + "model_name": "jina-embeddings-v3", + "dimensions": 1024, + "max_tokens": 8192, + "language": ["zh", "en"], + "model_id": "jinaai/jina-embeddings-v3", + "model_hub": "modelscope" } ] diff --git a/xinference/model/embedding/tests/test_embedding_models.py b/xinference/model/embedding/tests/test_embedding_models.py index 02e0cdde7c..7ff47f7c83 100644 --- a/xinference/model/embedding/tests/test_embedding_models.py +++ b/xinference/model/embedding/tests/test_embedding_models.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +import json import os import shutil import tempfile @@ -75,6 +76,11 @@ def test_model(): assert len(r["data"]) == 4 for d in r["data"]: assert len(d["embedding"]) == 384 + n_token = 0 + for inp in input_texts: + input_ids = model._model.tokenize([inp])["input_ids"] + n_token += input_ids.shape[-1] + assert r["usage"]["total_tokens"] == n_token finally: if model_path is not None: @@ -191,7 +197,8 @@ def test_register_custom_embedding(): model_id="test/custom_test_b", model_uri="file:///c/d", ) - register_embedding(model_spec, False) + with pytest.raises(ValueError): + register_embedding(model_spec, False) # name conflict model_spec = CustomEmbeddingModelSpec( @@ -213,3 +220,30 @@ def test_register_custom_embedding(): unregister_embedding("custom_test_d") shutil.rmtree(tmp_dir, ignore_errors=True) + + +def test_register_fault_embedding(): + from ....constants import XINFERENCE_MODEL_DIR + from .. import _install + + os.makedirs(os.path.join(XINFERENCE_MODEL_DIR, "embedding"), exist_ok=True) + file_path = os.path.join(XINFERENCE_MODEL_DIR, "embedding/GTE.json") + data = { + "model_name": "GTE", + "model_id": None, + "model_revision": None, + "model_hub": "huggingface", + "dimensions": 768, + "max_tokens": 512, + "language": ["en", "zh"], + "model_uri": "/new_data/cache/gte-Qwen2", + } + + with open(file_path, "w") as f: + json.dump(data, f, indent=4) + + with pytest.warns(UserWarning) as record: + _install() + assert any( + "Invalid model URI /new_data/cache/gte-Qwen2" in str(r.message) for r in record + ) diff --git a/xinference/model/flexible/__init__.py b/xinference/model/flexible/__init__.py index 049a2c6b25..3d5dd9e4c1 100644 --- a/xinference/model/flexible/__init__.py +++ b/xinference/model/flexible/__init__.py @@ -14,7 +14,9 @@ import codecs import json +import logging import os +import warnings from ...constants import XINFERENCE_MODEL_DIR from .core import ( @@ -28,13 +30,24 @@ unregister_flexible_model, ) -model_dir = os.path.join(XINFERENCE_MODEL_DIR, "flexible") -if os.path.isdir(model_dir): - for f in os.listdir(model_dir): - with codecs.open(os.path.join(model_dir, f), encoding="utf-8") as fd: - model_spec = FlexibleModelSpec.parse_obj(json.load(fd)) - register_flexible_model(model_spec, persist=False) +logger = logging.getLogger(__name__) -# register model description -for model in get_flexible_models(): - FLEXIBLE_MODEL_DESCRIPTIONS.update(generate_flexible_model_description(model)) + +def register_custom_model(): + model_dir = os.path.join(XINFERENCE_MODEL_DIR, "flexible") + if os.path.isdir(model_dir): + for f in os.listdir(model_dir): + try: + with codecs.open(os.path.join(model_dir, f), encoding="utf-8") as fd: + model_spec = FlexibleModelSpec.parse_obj(json.load(fd)) + register_flexible_model(model_spec, persist=False) + except Exception as e: + warnings.warn(f"{model_dir}/{f} has error, {e}") + + +def _install(): + register_custom_model() + + # register model description + for model in get_flexible_models(): + FLEXIBLE_MODEL_DESCRIPTIONS.update(generate_flexible_model_description(model)) diff --git a/xinference/model/flexible/core.py b/xinference/model/flexible/core.py index 44dd848b4d..666a69afb8 100644 --- a/xinference/model/flexible/core.py +++ b/xinference/model/flexible/core.py @@ -99,11 +99,15 @@ def get_flexible_model_descriptions(): def register_flexible_model(model_spec: FlexibleModelSpec, persist: bool): - from ..utils import is_valid_model_name + from ..utils import is_valid_model_name, is_valid_model_uri if not is_valid_model_name(model_spec.model_name): raise ValueError(f"Invalid model name {model_spec.model_name}.") + model_uri = model_spec.model_uri + if model_uri and not is_valid_model_uri(model_uri): + raise ValueError(f"Invalid model URI {model_uri}.") + if model_spec.launcher_args: try: model_spec.parser_args() diff --git a/xinference/model/image/__init__.py b/xinference/model/image/__init__.py index 7bcc1094b9..016197fc55 100644 --- a/xinference/model/image/__init__.py +++ b/xinference/model/image/__init__.py @@ -15,6 +15,7 @@ import codecs import json import os +import warnings from itertools import chain from .core import ( @@ -34,51 +35,60 @@ unregister_image, ) -_model_spec_json = os.path.join(os.path.dirname(__file__), "model_spec.json") -_model_spec_modelscope_json = os.path.join( - os.path.dirname(__file__), "model_spec_modelscope.json" -) -BUILTIN_IMAGE_MODELS.update( - dict( - (spec["model_name"], ImageModelFamilyV1(**spec)) - for spec in json.load(codecs.open(_model_spec_json, "r", encoding="utf-8")) - ) -) -for model_name, model_spec in BUILTIN_IMAGE_MODELS.items(): - MODEL_NAME_TO_REVISION[model_name].append(model_spec.model_revision) -MODELSCOPE_IMAGE_MODELS.update( - dict( - (spec["model_name"], ImageModelFamilyV1(**spec)) - for spec in json.load( - codecs.open(_model_spec_modelscope_json, "r", encoding="utf-8") +def register_custom_model(): + from ...constants import XINFERENCE_MODEL_DIR + + user_defined_image_dir = os.path.join(XINFERENCE_MODEL_DIR, "image") + if os.path.isdir(user_defined_image_dir): + for f in os.listdir(user_defined_image_dir): + try: + with codecs.open( + os.path.join(user_defined_image_dir, f), encoding="utf-8" + ) as fd: + user_defined_image_family = CustomImageModelFamilyV1.parse_obj( + json.load(fd) + ) + register_image(user_defined_image_family, persist=False) + except Exception as e: + warnings.warn(f"{user_defined_image_dir}/{f} has error, {e}") + + +def _install(): + _model_spec_json = os.path.join(os.path.dirname(__file__), "model_spec.json") + _model_spec_modelscope_json = os.path.join( + os.path.dirname(__file__), "model_spec_modelscope.json" + ) + BUILTIN_IMAGE_MODELS.update( + dict( + (spec["model_name"], ImageModelFamilyV1(**spec)) + for spec in json.load(codecs.open(_model_spec_json, "r", encoding="utf-8")) ) ) -) -for model_name, model_spec in MODELSCOPE_IMAGE_MODELS.items(): - MODEL_NAME_TO_REVISION[model_name].append(model_spec.model_revision) + for model_name, model_spec in BUILTIN_IMAGE_MODELS.items(): + MODEL_NAME_TO_REVISION[model_name].append(model_spec.model_revision) -# register model description -for model_name, model_spec in chain( - MODELSCOPE_IMAGE_MODELS.items(), BUILTIN_IMAGE_MODELS.items() -): - IMAGE_MODEL_DESCRIPTIONS.update(generate_image_description(model_spec)) + MODELSCOPE_IMAGE_MODELS.update( + dict( + (spec["model_name"], ImageModelFamilyV1(**spec)) + for spec in json.load( + codecs.open(_model_spec_modelscope_json, "r", encoding="utf-8") + ) + ) + ) + for model_name, model_spec in MODELSCOPE_IMAGE_MODELS.items(): + MODEL_NAME_TO_REVISION[model_name].append(model_spec.model_revision) -from ...constants import XINFERENCE_MODEL_DIR + # register model description + for model_name, model_spec in chain( + MODELSCOPE_IMAGE_MODELS.items(), BUILTIN_IMAGE_MODELS.items() + ): + IMAGE_MODEL_DESCRIPTIONS.update(generate_image_description(model_spec)) -user_defined_image_dir = os.path.join(XINFERENCE_MODEL_DIR, "image") -if os.path.isdir(user_defined_image_dir): - for f in os.listdir(user_defined_image_dir): - with codecs.open( - os.path.join(user_defined_image_dir, f), encoding="utf-8" - ) as fd: - user_defined_image_family = CustomImageModelFamilyV1.parse_obj( - json.load(fd) - ) - register_image(user_defined_image_family, persist=False) + register_custom_model() -for ud_image in get_user_defined_images(): - IMAGE_MODEL_DESCRIPTIONS.update(generate_image_description(ud_image)) + for ud_image in get_user_defined_images(): + IMAGE_MODEL_DESCRIPTIONS.update(generate_image_description(ud_image)) -del _model_spec_json -del _model_spec_modelscope_json + del _model_spec_json + del _model_spec_modelscope_json diff --git a/xinference/model/image/core.py b/xinference/model/image/core.py index 54aa27fe0b..581358b789 100644 --- a/xinference/model/image/core.py +++ b/xinference/model/image/core.py @@ -11,19 +11,21 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + import collections.abc import logging import os +import platform from collections import defaultdict -from typing import Dict, List, Literal, Optional, Tuple +from typing import Dict, List, Literal, Optional, Tuple, Union from ...constants import XINFERENCE_CACHE_DIR from ...types import PeftModelConfig from ..core import CacheableModelSpec, ModelDescription from ..utils import valid_model_revision +from .ocr.got_ocr2 import GotOCR2Model from .stable_diffusion.core import DiffusionModel - -MAX_ATTEMPTS = 3 +from .stable_diffusion.mlx import MLXDiffusionModel logger = logging.getLogger(__name__) @@ -47,6 +49,8 @@ class ImageModelFamilyV1(CacheableModelSpec): model_hub: str = "huggingface" model_ability: Optional[List[str]] controlnet: Optional[List["ImageModelFamilyV1"]] + default_model_config: Optional[dict] = {} + default_generate_config: Optional[dict] = {} class ImageModelDescription(ModelDescription): @@ -181,6 +185,28 @@ def get_cache_status( return valid_model_revision(meta_path, model_spec.model_revision) +def create_ocr_model_instance( + subpool_addr: str, + devices: List[str], + model_uid: str, + model_spec: ImageModelFamilyV1, + model_path: Optional[str] = None, + **kwargs, +) -> Tuple[GotOCR2Model, ImageModelDescription]: + if not model_path: + model_path = cache(model_spec) + model = GotOCR2Model( + model_uid, + model_path, + model_spec=model_spec, + **kwargs, + ) + model_description = ImageModelDescription( + subpool_addr, devices, model_spec, model_path=model_path + ) + return model, model_description + + def create_image_model_instance( subpool_addr: str, devices: List[str], @@ -190,8 +216,26 @@ def create_image_model_instance( download_hub: Optional[Literal["huggingface", "modelscope", "csghub"]] = None, model_path: Optional[str] = None, **kwargs, -) -> Tuple[DiffusionModel, ImageModelDescription]: +) -> Tuple[ + Union[DiffusionModel, MLXDiffusionModel, GotOCR2Model], ImageModelDescription +]: model_spec = match_diffusion(model_name, download_hub) + if model_spec.model_ability and "ocr" in model_spec.model_ability: + return create_ocr_model_instance( + subpool_addr=subpool_addr, + devices=devices, + model_uid=model_uid, + model_name=model_name, + model_spec=model_spec, + model_path=model_path, + **kwargs, + ) + + # use default model config + model_default_config = (model_spec.default_model_config or {}).copy() + model_default_config.update(kwargs) + kwargs = model_default_config + controlnet = kwargs.get("controlnet") # Handle controlnet if controlnet is not None: @@ -209,18 +253,19 @@ def create_image_model_instance( for name in controlnet: for cn_model_spec in model_spec.controlnet: if cn_model_spec.model_name == name: - if not model_path: - model_path = cache(cn_model_spec) - controlnet_model_paths.append(model_path) + controlnet_model_path = cache(cn_model_spec) + controlnet_model_paths.append(controlnet_model_path) break else: raise ValueError( f"controlnet `{name}` is not supported for model `{model_name}`." ) if len(controlnet_model_paths) == 1: - kwargs["controlnet"] = controlnet_model_paths[0] + kwargs["controlnet"] = (controlnet[0], controlnet_model_paths[0]) else: - kwargs["controlnet"] = controlnet_model_paths + kwargs["controlnet"] = [ + (n, path) for n, path in zip(controlnet, controlnet_model_paths) + ] if not model_path: model_path = cache(model_spec) if peft_model_config is not None: @@ -232,13 +277,23 @@ def create_image_model_instance( lora_load_kwargs = None lora_fuse_kwargs = None - model = DiffusionModel( + if ( + platform.system() == "Darwin" + and "arm" in platform.machine().lower() + and model_name in MLXDiffusionModel.supported_models + ): + # Mac with M series silicon chips + model_cls = MLXDiffusionModel + else: + model_cls = DiffusionModel # type: ignore + + model = model_cls( model_uid, model_path, - lora_model_paths=lora_model, + lora_model=lora_model, lora_load_kwargs=lora_load_kwargs, lora_fuse_kwargs=lora_fuse_kwargs, - abilities=model_spec.model_ability, + model_spec=model_spec, **kwargs, ) model_description = ImageModelDescription( diff --git a/xinference/model/image/custom.py b/xinference/model/image/custom.py index ff66ff8aa7..785482f23b 100644 --- a/xinference/model/image/custom.py +++ b/xinference/model/image/custom.py @@ -47,6 +47,10 @@ def register_image(model_spec: CustomImageModelFamilyV1, persist: bool): if not is_valid_model_name(model_spec.model_name): raise ValueError(f"Invalid model name {model_spec.model_name}.") + model_uri = model_spec.model_uri + if model_uri and not is_valid_model_uri(model_uri): + raise ValueError(f"Invalid model URI {model_uri}") + with UD_IMAGE_LOCK: for model_name in ( list(BUILTIN_IMAGE_MODELS.keys()) @@ -60,11 +64,6 @@ def register_image(model_spec: CustomImageModelFamilyV1, persist: bool): UD_IMAGES.append(model_spec) if persist: - # We only validate model URL when persist is True. - model_uri = model_spec.model_uri - if model_uri and not is_valid_model_uri(model_uri): - raise ValueError(f"Invalid model URI {model_uri}") - persist_path = os.path.join( XINFERENCE_MODEL_DIR, "image", f"{model_spec.model_name}.json" ) diff --git a/xinference/model/image/model_spec.json b/xinference/model/image/model_spec.json index 0c1d6f9068..24933cb99e 100644 --- a/xinference/model/image/model_spec.json +++ b/xinference/model/image/model_spec.json @@ -5,8 +5,14 @@ "model_id": "black-forest-labs/FLUX.1-schnell", "model_revision": "768d12a373ed5cc9ef9a9dea7504dc09fcc14842", "model_ability": [ - "text2image" - ] + "text2image", + "image2image", + "inpainting" + ], + "default_model_config": { + "quantize": true, + "quantize_text_encoder": "text_encoder_2" + } }, { "model_name": "FLUX.1-dev", @@ -14,8 +20,14 @@ "model_id": "black-forest-labs/FLUX.1-dev", "model_revision": "01aa605f2c300568dd6515476f04565a954fcb59", "model_ability": [ - "text2image" - ] + "text2image", + "image2image", + "inpainting" + ], + "default_model_config": { + "quantize": true, + "quantize_text_encoder": "text_encoder_2" + } }, { "model_name": "sd3-medium", @@ -26,7 +38,11 @@ "text2image", "image2image", "inpainting" - ] + ], + "default_model_config": { + "quantize": true, + "quantize_text_encoder": "text_encoder_3" + } }, { "model_name": "sd-turbo", @@ -35,7 +51,11 @@ "model_revision": "1681ed09e0cff58eeb41e878a49893228b78b94c", "model_ability": [ "text2image" - ] + ], + "default_generate_config": { + "guidance_scale": 0.0, + "num_inference_steps": 1 + } }, { "model_name": "sdxl-turbo", @@ -44,7 +64,11 @@ "model_revision": "f4b0486b498f84668e828044de1d0c8ba486e05b", "model_ability": [ "text2image" - ] + ], + "default_generate_config": { + "guidance_scale": 0.0, + "num_inference_steps": 1 + } }, { "model_name": "stable-diffusion-v1.5", @@ -166,5 +190,14 @@ "model_ability": [ "inpainting" ] + }, + { + "model_name": "GOT-OCR2_0", + "model_family": "ocr", + "model_id": "stepfun-ai/GOT-OCR2_0", + "model_revision": "cf6b7386bc89a54f09785612ba74cb12de6fa17c", + "model_ability": [ + "ocr" + ] } ] diff --git a/xinference/model/image/model_spec_modelscope.json b/xinference/model/image/model_spec_modelscope.json index d0a596ca7d..ad8af7a26f 100644 --- a/xinference/model/image/model_spec_modelscope.json +++ b/xinference/model/image/model_spec_modelscope.json @@ -6,8 +6,14 @@ "model_id": "AI-ModelScope/FLUX.1-schnell", "model_revision": "master", "model_ability": [ - "text2image" - ] + "text2image", + "image2image", + "inpainting" + ], + "default_model_config": { + "quantize": true, + "quantize_text_encoder": "text_encoder_2" + } }, { "model_name": "FLUX.1-dev", @@ -16,8 +22,14 @@ "model_id": "AI-ModelScope/FLUX.1-dev", "model_revision": "master", "model_ability": [ - "text2image" - ] + "text2image", + "image2image", + "inpainting" + ], + "default_model_config": { + "quantize": true, + "quantize_text_encoder": "text_encoder_2" + } }, { "model_name": "sd3-medium", @@ -29,7 +41,11 @@ "text2image", "image2image", "inpainting" - ] + ], + "default_model_config": { + "quantize": true, + "quantize_text_encoder": "text_encoder_3" + } }, { "model_name": "sd-turbo", @@ -39,7 +55,11 @@ "model_revision": "master", "model_ability": [ "text2image" - ] + ], + "default_generate_config": { + "guidance_scale": 0.0, + "num_inference_steps": 1 + } }, { "model_name": "sdxl-turbo", @@ -49,7 +69,11 @@ "model_revision": "master", "model_ability": [ "text2image" - ] + ], + "default_generate_config": { + "guidance_scale": 0.0, + "num_inference_steps": 1 + } }, { "model_name": "stable-diffusion-v1.5", @@ -136,5 +160,15 @@ "model_revision": "62134b9d8e703b5d6f74f1534457287a8bba77ef" } ] + }, + { + "model_name": "GOT-OCR2_0", + "model_family": "ocr", + "model_id": "stepfun-ai/GOT-OCR2_0", + "model_revision": "master", + "model_hub": "modelscope", + "model_ability": [ + "ocr" + ] } ] diff --git a/xinference/model/image/ocr/__init__.py b/xinference/model/image/ocr/__init__.py new file mode 100644 index 0000000000..37f6558d95 --- /dev/null +++ b/xinference/model/image/ocr/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2022-2023 XProbe Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/xinference/model/image/ocr/got_ocr2.py b/xinference/model/image/ocr/got_ocr2.py new file mode 100644 index 0000000000..f13607f602 --- /dev/null +++ b/xinference/model/image/ocr/got_ocr2.py @@ -0,0 +1,79 @@ +# Copyright 2022-2023 XProbe Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +from typing import TYPE_CHECKING, Optional + +import PIL.Image + +if TYPE_CHECKING: + from ..core import ImageModelFamilyV1 + +logger = logging.getLogger(__name__) + + +class GotOCR2Model: + def __init__( + self, + model_uid: str, + model_path: Optional[str] = None, + device: Optional[str] = None, + model_spec: Optional["ImageModelFamilyV1"] = None, + **kwargs, + ): + self._model_uid = model_uid + self._model_path = model_path + self._device = device + # model info when loading + self._model = None + self._tokenizer = None + # info + self._model_spec = model_spec + self._abilities = model_spec.model_ability or [] # type: ignore + self._kwargs = kwargs + + @property + def model_ability(self): + return self._abilities + + def load(self): + from transformers import AutoModel, AutoTokenizer + + self._tokenizer = AutoTokenizer.from_pretrained( + self._model_path, trust_remote_code=True + ) + model = AutoModel.from_pretrained( + self._model_path, + trust_remote_code=True, + low_cpu_mem_usage=True, + device_map="cuda", + use_safetensors=True, + pad_token_id=self._tokenizer.eos_token_id, + ) + self._model = model.eval().cuda() + + def ocr( + self, + image: PIL.Image, + **kwargs, + ): + logger.info("Got OCR 2.0 kwargs: %s", kwargs) + if "ocr_type" not in kwargs: + kwargs["ocr_type"] = "ocr" + if image.mode == "RGBA" or image.mode == "CMYK": + # convert to RGB + image = image.convert("RGB") + assert self._model is not None + # This chat API limits the max new tokens inside. + return self._model.chat(self._tokenizer, image, gradio_input=True, **kwargs) diff --git a/xinference/model/image/scheduler/__init__.py b/xinference/model/image/scheduler/__init__.py new file mode 100644 index 0000000000..09138b5b2a --- /dev/null +++ b/xinference/model/image/scheduler/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2022-2024 XProbe Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/xinference/model/image/scheduler/flux.py b/xinference/model/image/scheduler/flux.py new file mode 100644 index 0000000000..b681e59fa7 --- /dev/null +++ b/xinference/model/image/scheduler/flux.py @@ -0,0 +1,533 @@ +# Copyright 2022-2024 XProbe Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import asyncio +import logging +import os +import re +import typing +from collections import deque +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple + +import numpy as np +import torch +import xoscar as xo + +from ..utils import handle_image_result + +if TYPE_CHECKING: + from ..stable_diffusion.core import DiffusionModel + + +logger = logging.getLogger(__name__) +DEFAULT_MAX_SEQUENCE_LENGTH = 512 + + +class Text2ImageRequest: + def __init__( + self, + unique_id, + future, + prompt: str, + n: int, + size: str, + response_format: str, + *args, + **kwargs, + ): + self._unique_id = unique_id + self.future = future + self._prompt = prompt + self._n = n + self._size = size + self._response_format = response_format + self._args = args + self._kwargs = kwargs + self._width = -1 + self._height = -1 + self._generate_kwargs: Dict[str, Any] = {} + self._set_width_and_height() + self.is_encode = True + self.scheduler = None + self.done_steps = 0 + self.total_steps = 0 + self.static_tensors: Dict[str, torch.Tensor] = {} + self.timesteps = None + self.dtype = None + self.output = None + self.error_msg: Optional[str] = None + self.aborted = False + + def _set_width_and_height(self): + self._width, self._height = map(int, re.split(r"[^\d]+", self._size)) + + def set_generate_kwargs(self, generate_kwargs: Dict): + self._generate_kwargs = {k: v for k, v in generate_kwargs.items()} + + @property + def prompt(self): + return self._prompt + + @property + def n(self): + return self._n + + @property + def size(self): + return self._size + + @property + def response_format(self): + return self._response_format + + @property + def kwargs(self): + return self._kwargs + + @property + def width(self): + return self._width + + @property + def height(self): + return self._height + + @property + def generate_kwargs(self): + return self._generate_kwargs + + @property + def request_id(self): + return self._unique_id + + +class FluxBatchSchedulerActor(xo.StatelessActor): + @classmethod + def gen_uid(cls, model_uid: str): + return f"{model_uid}-scheduler-actor" + + def __init__(self): + from ....device_utils import get_available_device + + super().__init__() + self._waiting_queue: deque[Text2ImageRequest] = deque() # type: ignore + self._running_queue: deque[Text2ImageRequest] = deque() # type: ignore + self._model = None + self._available_device = get_available_device() + self._id_to_req: Dict[str, Text2ImageRequest] = {} # type: ignore + + def set_model(self, model): + """ + Must use `set_model`. Otherwise, the model will be copied once. + """ + self._model = model + + async def __post_create__(self): + from ....isolation import Isolation + + self._isolation = Isolation( + asyncio.new_event_loop(), threaded=True, daemon=True + ) + self._isolation.start() + asyncio.run_coroutine_threadsafe(self.run(), loop=self._isolation.loop) + + async def __pre_destroy__(self): + try: + assert self._isolation is not None + self._isolation.stop() + del self._isolation + except Exception as e: + logger.debug( + f"Destroy scheduler actor failed, address: {self.address}, error: {e}" + ) + + async def add_request(self, unique_id: str, future, *args, **kwargs): + req = Text2ImageRequest(unique_id, future, *args, **kwargs) + rid = req.request_id + if rid is not None: + if rid in self._id_to_req: + raise KeyError(f"Request id: {rid} has already existed!") + self._id_to_req[rid] = req + self._waiting_queue.append(req) + + async def abort_request(self, req_id: str) -> str: + """ + Abort a request. + Abort a submitted request. If the request is finished or not found, this method will be a no-op. + """ + from ....core.utils import AbortRequestMessage + + if req_id not in self._id_to_req: + logger.info(f"Request id: {req_id} not found. No-op for xinference.") + return AbortRequestMessage.NOT_FOUND.name + else: + self._id_to_req[req_id].aborted = True + logger.info(f"Request id: {req_id} found to be aborted.") + return AbortRequestMessage.DONE.name + + def _handle_request( + self, + ) -> Optional[Tuple[List[Text2ImageRequest], List[Text2ImageRequest]]]: + """ + Every request may generate `n>=1` images. + Here we need to decide whether to wait or not based on the value of `n` of each request. + """ + if self._model is None: + return None + max_num_images = self._model.get_max_num_images_for_batching() + cur_num_images = 0 + abort_list: List[Text2ImageRequest] = [] + # currently, FCFS strategy + running_list: List[Text2ImageRequest] = [] + while len(self._running_queue) > 0: + req = self._running_queue.popleft() + if req.aborted: + abort_list.append(req) + else: + running_list.append(req) + cur_num_images += req.n + + # Remove all the aborted requests in the waiting queue + waiting_tmp_list: List[Text2ImageRequest] = [] + while len(self._waiting_queue) > 0: + req = self._waiting_queue.popleft() + if req.aborted: + abort_list.append(req) + else: + waiting_tmp_list.append(req) + self._waiting_queue.extend(waiting_tmp_list) + + waiting_list: List[Text2ImageRequest] = [] + while len(self._waiting_queue) > 0: + req = self._waiting_queue[0] + if req.n + cur_num_images <= max_num_images: + waiting_list.append(self._waiting_queue.popleft()) + cur_num_images += req.n + else: + logger.warning( + f"Current queue is full, with an upper limit of max_num_images: {max_num_images}. " + f"Requests will continue to wait." + ) + break + + return waiting_list + running_list, abort_list + + @staticmethod + def _empty_cache(): + from ....device_utils import empty_cache + + empty_cache() + + async def step(self): + res = self._handle_request() + if res is None: + return + req_list, abort_list = res + # handle abort + if abort_list: + for r in abort_list: + r.future.set_exception( + RuntimeError( + f"Request: {r.request_id} has been cancelled by another `abort_request` request." + ) + ) + self._id_to_req.pop(r.request_id, None) + if not req_list: + return + _batch_text_to_image(self._model, req_list, self._available_device) + # handle results + for r in req_list: + if r.error_msg is not None: + r.future.set_exception(ValueError(r.error_msg)) + self._id_to_req.pop(r.request_id, None) + continue + if r.output is not None: + r.future.set_result( + handle_image_result(r.response_format, r.output.images) + ) + self._id_to_req.pop(r.request_id, None) + else: + self._running_queue.append(r) + self._empty_cache() + + async def run(self): + try: + while True: + # wait 10ms + await asyncio.sleep(0.01) + await self.step() + except Exception as e: + logger.exception( + f"Scheduler actor uid: {self.uid}, address: {self.address} run with error: {e}" + ) + + +def _cat_tensors(infos: List[Dict]) -> Dict: + keys = infos[0].keys() + res = {} + for k in keys: + tmp = [info[k] for info in infos] + res[k] = torch.cat(tmp) + return res + + +@typing.no_type_check +@torch.inference_mode() +def _batch_text_to_image_internal( + model_cls: "DiffusionModel", + req_list: List[Text2ImageRequest], + available_device: str, +): + from diffusers.pipelines.flux.pipeline_flux import ( + calculate_shift, + retrieve_timesteps, + ) + from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput + from diffusers.schedulers.scheduling_flow_match_euler_discrete import ( + FlowMatchEulerDiscreteScheduler, + ) + + device = model_cls._model._execution_device + height, width = req_list[0].height, req_list[0].width + cur_batch_max_sequence_length = [ + r.generate_kwargs.get("max_sequence_length", DEFAULT_MAX_SEQUENCE_LENGTH) + for r in req_list + if not r.is_encode + ] + for r in req_list: + if r.is_encode: + generate_kwargs = model_cls._model_spec.default_generate_config.copy() + generate_kwargs.update({k: v for k, v in r.kwargs.items() if v is not None}) + model_cls._filter_kwargs(model_cls._model, generate_kwargs) + r.set_generate_kwargs(generate_kwargs) + + # check max_sequence_length + max_sequence_length = r.generate_kwargs.get( + "max_sequence_length", DEFAULT_MAX_SEQUENCE_LENGTH + ) + if ( + cur_batch_max_sequence_length + and max_sequence_length != cur_batch_max_sequence_length[0] + ): + r.is_encode = False + r.error_msg = ( + f"The max_sequence_length of the current request: {max_sequence_length} is " + f"different from the setting in the running batch: {cur_batch_max_sequence_length[0]}, " + f"please be consistent." + ) + continue + + num_images_per_prompt = r.n + callback_on_step_end_tensor_inputs = r.generate_kwargs.get( + "callback_on_step_end_tensor_inputs", ["latents"] + ) + num_inference_steps = r.generate_kwargs.get("num_inference_steps", 28) + guidance_scale = r.generate_kwargs.get("guidance_scale", 7.0) + generator = None + seed = r.generate_kwargs.get("seed", None) + if seed is not None: + generator = torch.Generator(device=available_device) # type: ignore + if seed != -1: + generator = generator.manual_seed(seed) + latents = None + timesteps = None + + # Each request must build its own scheduler instance, + # otherwise the mixing of variables at `scheduler.STEP` will result in an error. + r.scheduler = FlowMatchEulerDiscreteScheduler( + model_cls._model.scheduler.config.num_train_timesteps, + model_cls._model.scheduler.config.shift, + model_cls._model.scheduler.config.use_dynamic_shifting, + model_cls._model.scheduler.config.base_shift, + model_cls._model.scheduler.config.max_shift, + model_cls._model.scheduler.config.base_image_seq_len, + model_cls._model.scheduler.config.max_image_seq_len, + ) + + # check inputs + model_cls._model.check_inputs( + r.prompt, + None, + height, + width, + prompt_embeds=None, + pooled_prompt_embeds=None, + callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, + max_sequence_length=max_sequence_length, + ) + + # handle prompt + ( + prompt_embeds, + pooled_prompt_embeds, + text_ids, + ) = model_cls._model.encode_prompt( + prompt=r.prompt, + prompt_2=None, + prompt_embeds=None, + pooled_prompt_embeds=None, + device=device, + num_images_per_prompt=num_images_per_prompt, + max_sequence_length=max_sequence_length, + lora_scale=None, + ) + + # Prepare latent variables + num_channels_latents = model_cls._model.transformer.config.in_channels // 4 + latents, latent_image_ids = model_cls._model.prepare_latents( + num_images_per_prompt, + num_channels_latents, + height, + width, + prompt_embeds.dtype, + device, + generator, + latents, + ) + + # Prepare timesteps + sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) + image_seq_len = latents.shape[1] + + mu = calculate_shift( + image_seq_len, + r.scheduler.config["base_image_seq_len"], + r.scheduler.config["max_image_seq_len"], + r.scheduler.config["base_shift"], + r.scheduler.config["max_shift"], + ) + timesteps, num_inference_steps = retrieve_timesteps( + r.scheduler, + num_inference_steps, + device, + timesteps, + sigmas, + mu=mu, + ) + + # handle guidance + if model_cls._model.transformer.config.guidance_embeds: + guidance = torch.full( + [1], guidance_scale, device=device, dtype=torch.float32 + ) + guidance = guidance.expand(latents.shape[0]) + else: + guidance = None + + r.static_tensors["latents"] = latents + r.static_tensors["guidance"] = guidance + r.static_tensors["pooled_prompt_embeds"] = pooled_prompt_embeds + r.static_tensors["prompt_embeds"] = prompt_embeds + r.static_tensors["text_ids"] = text_ids + r.static_tensors["latent_image_ids"] = latent_image_ids + r.timesteps = timesteps + r.dtype = latents.dtype + r.total_steps = len(timesteps) + r.is_encode = False + + running_req_list = [r for r in req_list if r.error_msg is None] + static_tensors = _cat_tensors([r.static_tensors for r in running_req_list]) + + # Do a step + timestep_tmp = [] + for r in running_req_list: + timestep_tmp.append(r.timesteps[r.done_steps].expand(r.n).to(r.dtype)) + r.done_steps += 1 + timestep = torch.cat(timestep_tmp) + noise_pred = model_cls._model.transformer( + hidden_states=static_tensors["latents"], + # YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing) + timestep=timestep / 1000, + guidance=static_tensors["guidance"], + pooled_projections=static_tensors["pooled_prompt_embeds"], + encoder_hidden_states=static_tensors["prompt_embeds"], + txt_ids=static_tensors["text_ids"], + img_ids=static_tensors["latent_image_ids"], + joint_attention_kwargs=None, + return_dict=False, + )[0] + + # update latents + start_idx = 0 + for r in running_req_list: + n = r.n + # handle diffusion scheduler step + _noise_pred = noise_pred[start_idx : start_idx + n, ::] + _timestep = timestep[start_idx] + latents_out = r.scheduler.step( + _noise_pred, _timestep, r.static_tensors["latents"], return_dict=False + )[0] + r.static_tensors["latents"] = latents_out + start_idx += n + + logger.info( + f"Request {r.request_id} has done {r.done_steps} / {r.total_steps} steps." + ) + + # process result + if r.done_steps == r.total_steps: + output_type = r.generate_kwargs.get("output_type", "pil") + _latents = r.static_tensors["latents"] + if output_type == "latent": + image = _latents + else: + _latents = model_cls._model._unpack_latents( + _latents, height, width, model_cls._model.vae_scale_factor + ) + _latents = ( + _latents / model_cls._model.vae.config.scaling_factor + ) + model_cls._model.vae.config.shift_factor + image = model_cls._model.vae.decode(_latents, return_dict=False)[0] + image = model_cls._model.image_processor.postprocess( + image, output_type=output_type + ) + + is_padded = r.generate_kwargs.get("is_padded", None) + origin_size = r.generate_kwargs.get("origin_size", None) + + if is_padded and origin_size: + new_images = [] + x, y = origin_size + for img in image: + new_images.append(img.crop((0, 0, x, y))) + image = new_images + + r.output = FluxPipelineOutput(images=image) + logger.info( + f"Request {r.request_id} has completed total {r.total_steps} steps." + ) + + +def _batch_text_to_image( + model_cls: "DiffusionModel", + req_list: List[Text2ImageRequest], + available_device: str, +): + from ....core.model import OutOfMemoryError + + try: + _batch_text_to_image_internal(model_cls, req_list, available_device) + except OutOfMemoryError: + logger.exception( + f"Batch text_to_image out of memory. " + f"Xinference will restart the model: {model_cls._model_uid}. " + f"Please be patient for a few moments." + ) + # Just kill the process and let xinference auto-recover the model + os._exit(1) + except Exception as e: + logger.exception(f"Internal error for batch text_to_image: {e}.") + # If internal error happens, just skip all the requests in this batch. + # If not handle here, the client will hang. + for r in req_list: + r.error_msg = str(e) diff --git a/xinference/model/image/sdapi.py b/xinference/model/image/sdapi.py new file mode 100644 index 0000000000..0bd5e08631 --- /dev/null +++ b/xinference/model/image/sdapi.py @@ -0,0 +1,167 @@ +# Copyright 2022-2023 XProbe Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import base64 +import io +import warnings + +from PIL import Image, ImageOps + + +class SDAPIToDiffusersConverter: + txt2img_identical_args = { + "prompt", + "negative_prompt", + "seed", + "width", + "height", + "sampler_name", + "progressor", + } + txt2img_arg_mapping = { + "steps": "num_inference_steps", + "cfg_scale": "guidance_scale", + "denoising_strength": "strength", + } + img2img_identical_args = { + "prompt", + "negative_prompt", + "seed", + "width", + "height", + "sampler_name", + "progressor", + } + img2img_arg_mapping = { + "init_images": "image", + "mask": "mask_image", + "steps": "num_inference_steps", + "cfg_scale": "guidance_scale", + "denoising_strength": "strength", + "inpaint_full_res_padding": "padding_mask_crop", + } + + @staticmethod + def convert_to_diffusers(sd_type: str, params: dict) -> dict: + diffusers_params = {} + + identical_args = getattr(SDAPIToDiffusersConverter, f"{sd_type}_identical_args") + mapping_args = getattr(SDAPIToDiffusersConverter, f"{sd_type}_arg_mapping") + for param, value in params.items(): + if param in identical_args: + diffusers_params[param] = value + elif param in mapping_args: + diffusers_params[mapping_args[param]] = value + else: + raise ValueError(f"Unknown arg: {param}") + + return diffusers_params + + @staticmethod + def get_available_args(sd_type: str) -> set: + identical_args = getattr(SDAPIToDiffusersConverter, f"{sd_type}_identical_args") + mapping_args = getattr(SDAPIToDiffusersConverter, f"{sd_type}_arg_mapping") + return identical_args.union(mapping_args) + + +class SDAPIDiffusionModelMixin: + @staticmethod + def _check_kwargs(sd_type: str, kwargs: dict): + available_args = SDAPIToDiffusersConverter.get_available_args(sd_type) + unknown_args = [] + available_kwargs = {} + for arg, value in kwargs.items(): + if arg in available_args: + available_kwargs[arg] = value + else: + unknown_args.append(arg) + if unknown_args: + warnings.warn( + f"Some args are not supported for now and will be ignored: {unknown_args}" + ) + + converted_kwargs = SDAPIToDiffusersConverter.convert_to_diffusers( + sd_type, available_kwargs + ) + + width, height = converted_kwargs.pop("width", None), converted_kwargs.pop( + "height", None + ) + if width and height: + converted_kwargs["size"] = f"{width}*{height}" + + return converted_kwargs + + def txt2img(self, **kwargs): + converted_kwargs = self._check_kwargs("txt2img", kwargs) + result = self.text_to_image(response_format="b64_json", **converted_kwargs) # type: ignore + + # convert to SD API result + return { + "images": [r["b64_json"] for r in result["data"]], + "info": {"created": result["created"]}, + "parameters": {}, + } + + @staticmethod + def _decode_b64_img(img_str: str) -> Image: + # img_str in a format: "data:image/png;base64," + raw_b64_img(image) + f, data = img_str.split(",", 1) + f, encode_type = f.split(";", 1) + assert encode_type == "base64" + f = f.split("/", 1)[1] + b = base64.b64decode(data) + return Image.open(io.BytesIO(b), formats=[f]) + + def img2img(self, **kwargs): + init_images = kwargs.pop("init_images", []) + kwargs["init_images"] = init_images = [ + self._decode_b64_img(i) for i in init_images + ] + if len(init_images) == 1: + kwargs["init_images"] = init_images[0] + mask_image = kwargs.pop("mask", None) + if mask_image: + if kwargs.pop("inpainting_mask_invert"): + mask_image = ImageOps.invert(mask_image) + + kwargs["mask"] = self._decode_b64_img(mask_image) + + # process inpaint_full_res and inpaint_full_res_padding + if kwargs.pop("inpaint_full_res", None): + kwargs["inpaint_full_res_padding"] = kwargs.pop( + "inpaint_full_res_padding", 0 + ) + else: + # inpaint_full_res_padding is turned `into padding_mask_crop` + # in diffusers, if padding_mask_crop is passed, it will do inpaint_full_res + # so if not inpaint_full_rs, we need to pop this option + kwargs.pop("inpaint_full_res_padding", None) + + clip_skip = kwargs.get("override_settings", {}).get("clip_skip") + converted_kwargs = self._check_kwargs("img2img", kwargs) + if clip_skip: + converted_kwargs["clip_skip"] = clip_skip + + if not converted_kwargs.get("mask_image"): + result = self.image_to_image(response_format="b64_json", **converted_kwargs) # type: ignore + else: + result = self.inpainting(response_format="b64_json", **converted_kwargs) # type: ignore + + # convert to SD API result + return { + "images": [r["b64_json"] for r in result["data"]], + "info": {"created": result["created"]}, + "parameters": {}, + } diff --git a/xinference/model/image/stable_diffusion/core.py b/xinference/model/image/stable_diffusion/core.py index 7d5fdb5bf4..c5a9b33f86 100644 --- a/xinference/model/image/stable_diffusion/core.py +++ b/xinference/model/image/stable_diffusion/core.py @@ -12,29 +12,69 @@ # See the License for the specific language governing permissions and # limitations under the License. -import base64 +import contextlib +import gc +import inspect +import itertools import logging -import os import re import sys -import time -import uuid -from concurrent.futures import ThreadPoolExecutor -from functools import partial -from io import BytesIO -from typing import Dict, List, Optional, Union +import warnings +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union import PIL.Image +import torch from PIL import ImageOps -from ....constants import XINFERENCE_IMAGE_DIR -from ....device_utils import move_model_to_available_device -from ....types import Image, ImageList, LoRA +from ....device_utils import get_available_device, move_model_to_available_device +from ....types import LoRA +from ..sdapi import SDAPIDiffusionModelMixin +from ..utils import handle_image_result -logger = logging.getLogger(__name__) +if TYPE_CHECKING: + from ....core.progress_tracker import Progressor + from ..core import ImageModelFamilyV1 +logger = logging.getLogger(__name__) -class DiffusionModel: +SAMPLING_METHODS = [ + "default", + "DPM++ 2M", + "DPM++ 2M Karras", + "DPM++ 2M SDE", + "DPM++ 2M SDE Karras", + "DPM++ SDE", + "DPM++ SDE Karras", + "DPM2", + "DPM2 Karras", + "DPM2 a", + "DPM2 a Karras", + "Euler", + "Euler a", + "Heun", + "LMS", + "LMS Karras", +] + + +def model_accept_param(params: Union[str, List[str]], model: Any) -> bool: + params = [params] if isinstance(params, str) else params + # model is diffusers Pipeline + parameters = inspect.signature(model.__call__).parameters # type: ignore + allow_params = False + for param in parameters.values(): + if param.kind == inspect.Parameter.VAR_KEYWORD: + # the __call__ can accept **kwargs, + # we treat it as it can accept any parameters + allow_params = True + break + if not allow_params: + if all(param in parameters for param in params): + allow_params = True + return allow_params + + +class DiffusionModel(SDAPIDiffusionModelMixin): def __init__( self, model_uid: str, @@ -43,25 +83,92 @@ def __init__( lora_model: Optional[List[LoRA]] = None, lora_load_kwargs: Optional[Dict] = None, lora_fuse_kwargs: Optional[Dict] = None, - abilities: Optional[List[str]] = None, + model_spec: Optional["ImageModelFamilyV1"] = None, **kwargs, ): self._model_uid = model_uid self._model_path = model_path self._device = device - # when a model has text2image ability, - # it will be loaded as AutoPipelineForText2Image - # for image2image and inpainting, - # we convert to the corresponding model + # model info when loading self._model = None - self._i2i_model = None # image to image model - self._inpainting_model = None # inpainting model self._lora_model = lora_model self._lora_load_kwargs = lora_load_kwargs or {} self._lora_fuse_kwargs = lora_fuse_kwargs or {} - self._abilities = abilities or [] + # deepcache + self._deepcache_helper = None + # when a model has text2image ability, + # it will be loaded as AutoPipelineForText2Image + # for image2image and inpainting, + # we convert to the corresponding model + self._torch_dtype = None + self._ability_to_models: Dict[Tuple[str, Any], Any] = {} + self._controlnet_models: Dict[str, Any] = {} + # info + self._model_spec = model_spec + self._abilities = model_spec.model_ability or [] # type: ignore self._kwargs = kwargs + @property + def model_ability(self): + return self._abilities + + @staticmethod + def _get_pipeline_type(ability: str) -> type: + if ability == "text2image": + from diffusers import AutoPipelineForText2Image as AutoPipelineModel + elif ability == "image2image": + from diffusers import AutoPipelineForImage2Image as AutoPipelineModel + elif ability == "inpainting": + from diffusers import AutoPipelineForInpainting as AutoPipelineModel + else: + raise ValueError(f"Unknown ability: {ability}") + return AutoPipelineModel + + def _get_controlnet_model(self, name: str, path: str): + from diffusers import ControlNetModel + + try: + return self._controlnet_models[name] + except KeyError: + logger.debug("Loading controlnet %s, from %s", name, path) + model = ControlNetModel.from_pretrained(path, torch_dtype=self._torch_dtype) + self._controlnet_models[name] = model + return model + + def _get_model( + self, + ability: str, + controlnet_name: Optional[Union[str, List[str]]] = None, + controlnet_path: Optional[Union[str, List[str]]] = None, + ): + try: + return self._ability_to_models[ability, controlnet_name] + except KeyError: + model_type = self._get_pipeline_type(ability) + + assert self._model is not None + + if controlnet_name: + assert controlnet_path + if isinstance(controlnet_name, (list, tuple)): + controlnet = [] + # multiple controlnet + for name, path in itertools.zip_longest( + controlnet_name, controlnet_path + ): + controlnet.append(self._get_controlnet_model(name, path)) + else: + controlnet = self._get_controlnet_model( + controlnet_name, controlnet_path + ) + model = model_type.from_pipe(self._model, controlnet=controlnet) + else: + model = model_type.from_pipe(self._model) + self._load_to_device(model) + + self._ability_to_models[ability, controlnet_name] = model + return model + def _apply_lora(self): if self._lora_model is not None: logger.info( @@ -76,8 +183,6 @@ def _apply_lora(self): logger.info(f"Successfully loaded the LoRA for model {self._model_uid}.") def load(self): - import torch - if "text2image" in self._abilities or "image2image" in self._abilities: from diffusers import AutoPipelineForText2Image as AutoPipelineModel elif "inpainting" in self._abilities: @@ -85,22 +190,24 @@ def load(self): else: raise ValueError(f"Unknown ability: {self._abilities}") - controlnet = self._kwargs.get("controlnet") - if controlnet is not None: - from diffusers import ControlNetModel - - logger.debug("Loading controlnet %s", controlnet) - self._kwargs["controlnet"] = ControlNetModel.from_pretrained(controlnet) - - torch_dtype = self._kwargs.get("torch_dtype") + self._torch_dtype = torch_dtype = self._kwargs.get("torch_dtype") if sys.platform != "darwin" and torch_dtype is None: # The following params crashes on Mac M2 - self._kwargs["torch_dtype"] = torch.float16 + self._torch_dtype = self._kwargs["torch_dtype"] = torch.float16 self._kwargs["variant"] = "fp16" self._kwargs["use_safetensors"] = True if isinstance(torch_dtype, str): self._kwargs["torch_dtype"] = getattr(torch, torch_dtype) + controlnet = self._kwargs.get("controlnet") + if controlnet is not None: + if isinstance(controlnet, tuple): + self._kwargs["controlnet"] = self._get_controlnet_model(*controlnet) + else: + self._kwargs["controlnet"] = [ + self._get_controlnet_model(*cn) for cn in controlnet + ] + quantize_text_encoder = self._kwargs.pop("quantize_text_encoder", None) if quantize_text_encoder: try: @@ -139,20 +246,194 @@ def load(self): self._kwargs[text_encoder_name] = text_encoder self._kwargs["device_map"] = "balanced" - logger.debug("Loading model %s", AutoPipelineModel) + logger.debug( + "Loading model from %s, kwargs: %s", self._model_path, self._kwargs + ) self._model = AutoPipelineModel.from_pretrained( self._model_path, **self._kwargs, ) + self._load_to_device(self._model) + self._apply_lora() + + if self._kwargs.get("deepcache", False): + try: + from DeepCache import DeepCacheSDHelper + except ImportError: + error_message = "Failed to import module 'deepcache' when you launch with deepcache=True" + installation_guide = [ + "Please make sure 'deepcache' is installed. ", + "You can install it by `pip install deepcache`\n", + ] + + raise ImportError(f"{error_message}\n\n{''.join(installation_guide)}") + else: + self._deepcache_helper = helper = DeepCacheSDHelper() + helper.set_params( + cache_interval=self._kwargs.get("deepcache_cache_interval", 3), + cache_branch_id=self._kwargs.get("deepcache_cache_branch_id", 0), + ) + + def _load_to_device(self, model): if self._kwargs.get("cpu_offload", False): logger.debug("CPU offloading model") - self._model.enable_model_cpu_offload() + model.enable_model_cpu_offload() + elif self._kwargs.get("sequential_cpu_offload", False): + logger.debug("CPU sequential offloading model") + model.enable_sequential_cpu_offload() elif not self._kwargs.get("device_map"): logger.debug("Loading model to available device") - self._model = move_model_to_available_device(self._model) - # Recommended if your computer has < 64 GB of RAM - self._model.enable_attention_slicing() - self._apply_lora() + model = move_model_to_available_device(model) + if self._kwargs.get("attention_slicing", False): + model.enable_attention_slicing() + if self._kwargs.get("vae_tiling", False): + model.enable_vae_tiling() + + def get_max_num_images_for_batching(self): + return self._kwargs.get("max_num_images", 16) + + @staticmethod + def _get_scheduler(model: Any, sampler_name: str): + if not sampler_name or sampler_name == "default": + return + + assert model is not None + + import diffusers + + kwargs = {} + if ( + sampler_name.startswith("DPM++") + and "final_sigmas_type" not in model.scheduler.config + ): + # `final_sigmas_type` will be set as `zero` by default which will cause error + kwargs["final_sigmas_type"] = "sigma_min" + + # see https://github.com/huggingface/diffusers/issues/4167 + # to get A1111 <> Diffusers Scheduler mapping + if sampler_name == "DPM++ 2M": + return diffusers.DPMSolverMultistepScheduler.from_config( + model.scheduler.config, **kwargs + ) + elif sampler_name == "DPM++ 2M Karras": + return diffusers.DPMSolverMultistepScheduler.from_config( + model.scheduler.config, use_karras_sigmas=True, **kwargs + ) + elif sampler_name == "DPM++ 2M SDE": + return diffusers.DPMSolverMultistepScheduler.from_config( + model.scheduler.config, algorithm_type="sde-dpmsolver++", **kwargs + ) + elif sampler_name == "DPM++ 2M SDE Karras": + return diffusers.DPMSolverMultistepScheduler.from_config( + model.scheduler.config, + algorithm_type="sde-dpmsolver++", + use_karras_sigmas=True, + **kwargs, + ) + elif sampler_name == "DPM++ SDE": + return diffusers.DPMSolverSinglestepScheduler.from_config( + model.scheduler.config, **kwargs + ) + elif sampler_name == "DPM++ SDE Karras": + return diffusers.DPMSolverSinglestepScheduler.from_config( + model.scheduler.config, use_karras_sigmas=True, **kwargs + ) + elif sampler_name == "DPM2": + return diffusers.KDPM2DiscreteScheduler.from_config( + model.scheduler.config, **kwargs + ) + elif sampler_name == "DPM2 Karras": + return diffusers.KDPM2DiscreteScheduler.from_config( + model.scheduler.config, use_karras_sigmas=True, **kwargs + ) + elif sampler_name == "DPM2 a": + return diffusers.KDPM2AncestralDiscreteScheduler.from_config( + model.scheduler.config, **kwargs + ) + elif sampler_name == "DPM2 a Karras": + return diffusers.KDPM2AncestralDiscreteScheduler.from_config( + model.scheduler.config, use_karras_sigmas=True, **kwargs + ) + elif sampler_name == "Euler": + return diffusers.EulerDiscreteScheduler.from_config( + model.scheduler.config, **kwargs + ) + elif sampler_name == "Euler a": + return diffusers.EulerAncestralDiscreteScheduler.from_config( + model.scheduler.config, **kwargs + ) + elif sampler_name == "Heun": + return diffusers.HeunDiscreteScheduler.from_config( + model.scheduler.config, **kwargs + ) + elif sampler_name == "LMS": + return diffusers.LMSDiscreteScheduler.from_config( + model.scheduler.config, **kwargs + ) + elif sampler_name == "LMS Karras": + return diffusers.LMSDiscreteScheduler.from_config( + model.scheduler.config, use_karras_sigmas=True, **kwargs + ) + else: + raise ValueError(f"Unknown sampler: {sampler_name}") + + @staticmethod + @contextlib.contextmanager + def _reset_when_done(model: Any, sampler_name: str): + assert model is not None + scheduler = DiffusionModel._get_scheduler(model, sampler_name) + if scheduler: + default_scheduler = model.scheduler + model.scheduler = scheduler + try: + yield + finally: + model.scheduler = default_scheduler + else: + yield + + @staticmethod + @contextlib.contextmanager + def _release_after(): + from ....device_utils import empty_cache + + try: + yield + finally: + gc.collect() + empty_cache() + + @contextlib.contextmanager + def _wrap_deepcache(self, model: Any): + if self._deepcache_helper: + self._deepcache_helper.pipe = model + self._deepcache_helper.enable() + try: + yield + finally: + if self._deepcache_helper: + self._deepcache_helper.disable() + self._deepcache_helper.pipe = None + + @staticmethod + def _process_progressor(kwargs: dict): + import diffusers + + progressor: Progressor = kwargs.pop("progressor", None) + + def report_status_callback( + pipe: diffusers.DiffusionPipeline, + step: int, + timestep: int, + callback_kwargs: dict, + ): + num_steps = pipe.num_timesteps + progressor.set_progress((step + 1) / num_steps) + + return callback_kwargs + + if progressor and progressor.request_id: + kwargs["callback_on_step_end"] = report_status_callback def _call_model( self, @@ -160,51 +441,50 @@ def _call_model( model=None, **kwargs, ): - import gc - - from ....device_utils import empty_cache - - logger.debug( - "stable diffusion args: %s", - kwargs, - ) model = model if model is not None else self._model + is_padded = kwargs.pop("is_padded", None) + origin_size = kwargs.pop("origin_size", None) + seed = kwargs.pop("seed", None) + return_images = kwargs.pop("_return_images", None) + if seed is not None and seed != -1: + kwargs["generator"] = generator = torch.Generator(device=get_available_device()) # type: ignore + if seed != -1: + kwargs["generator"] = generator.manual_seed(seed) + sampler_name = kwargs.pop("sampler_name", None) + self._process_progressor(kwargs) assert callable(model) - images = model(**kwargs).images - - # clean cache - gc.collect() - empty_cache() - - if response_format == "url": - os.makedirs(XINFERENCE_IMAGE_DIR, exist_ok=True) - image_list = [] - with ThreadPoolExecutor() as executor: - for img in images: - path = os.path.join(XINFERENCE_IMAGE_DIR, uuid.uuid4().hex + ".jpg") - image_list.append(Image(url=path, b64_json=None)) - executor.submit(img.save, path, "jpeg") - return ImageList(created=int(time.time()), data=image_list) - elif response_format == "b64_json": - - def _gen_base64_image(_img): - buffered = BytesIO() - _img.save(buffered, format="jpeg") - return base64.b64encode(buffered.getvalue()).decode() - - with ThreadPoolExecutor() as executor: - results = list(map(partial(executor.submit, _gen_base64_image), images)) # type: ignore - image_list = [Image(url=None, b64_json=s.result()) for s in results] - return ImageList(created=int(time.time()), data=image_list) - else: - raise ValueError(f"Unsupported response format: {response_format}") + with self._reset_when_done( + model, sampler_name + ), self._release_after(), self._wrap_deepcache(model): + logger.debug("stable diffusion args: %s, model: %s", kwargs, model) + self._filter_kwargs(model, kwargs) + images = model(**kwargs).images + + # revert padding if padded + if is_padded and origin_size: + new_images = [] + x, y = origin_size + for img in images: + new_images.append(img.crop((0, 0, x, y))) + images = new_images + + if return_images: + return images + + return handle_image_result(response_format, images) @classmethod - def _filter_kwargs(cls, kwargs: dict): + def _filter_kwargs(cls, model, kwargs: dict): for arg in ["negative_prompt", "num_inference_steps"]: if not kwargs.get(arg): kwargs.pop(arg, None) + for key in list(kwargs): + allow_key = model_accept_param(key, model) + if not allow_key: + warnings.warn(f"{type(model)} cannot accept `{key}`, will ignore it") + kwargs.pop(key) + def text_to_image( self, prompt: str, @@ -213,17 +493,16 @@ def text_to_image( response_format: str = "url", **kwargs, ): - # References: - # https://huggingface.co/docs/diffusers/main/en/api/pipelines/controlnet_sdxl width, height = map(int, re.split(r"[^\d]+", size)) - self._filter_kwargs(kwargs) + generate_kwargs = self._model_spec.default_generate_config.copy() # type: ignore + generate_kwargs.update({k: v for k, v in kwargs.items() if v is not None}) + generate_kwargs["width"], generate_kwargs["height"] = width, height + return self._call_model( prompt=prompt, - height=height, - width=width, num_images_per_prompt=n, response_format=response_format, - **kwargs, + **generate_kwargs, ) @staticmethod @@ -238,39 +517,42 @@ def image_to_image( self, image: PIL.Image, prompt: Optional[Union[str, List[str]]] = None, - negative_prompt: Optional[Union[str, List[str]]] = None, n: int = 1, size: Optional[str] = None, response_format: str = "url", **kwargs, ): - if "controlnet" in self._kwargs: + if self._kwargs.get("controlnet"): model = self._model else: - if "image2image" not in self._abilities: + ability = "image2image" + if ability not in self._abilities: raise RuntimeError(f"{self._model_uid} does not support image2image") - if self._i2i_model is not None: - model = self._i2i_model - else: - from diffusers import AutoPipelineForImage2Image + model = self._get_model(ability) - self._i2i_model = model = AutoPipelineForImage2Image.from_pipe( - self._model - ) - if size: - width, height = map(int, re.split(r"[^\d]+", size)) - kwargs["width"] = width - kwargs["height"] = height if padding_image_to_multiple := kwargs.pop("padding_image_to_multiple", None): # Model like SD3 image to image requires image's height and width is times of 16 # padding the image if specified + origin_x, origin_y = image.size + kwargs["origin_size"] = (origin_x, origin_y) + kwargs["is_padded"] = True image = self.pad_to_multiple(image, multiple=int(padding_image_to_multiple)) - self._filter_kwargs(kwargs) + if size: + width, height = map(int, re.split(r"[^\d]+", size)) + if padding_image_to_multiple: + width, height = image.size + kwargs["width"] = width + kwargs["height"] = height + else: + # SD3 image2image cannot accept width and height + allow_width_height = model_accept_param(["width", "height"], model) + if allow_width_height: + kwargs["width"], kwargs["height"] = image.size + return self._call_model( image=image, prompt=prompt, - negative_prompt=negative_prompt, num_images_per_prompt=n, response_format=response_format, model=model, @@ -279,40 +561,49 @@ def image_to_image( def inpainting( self, - image: bytes, - mask_image: bytes, + image: PIL.Image, + mask_image: PIL.Image, prompt: Optional[Union[str, List[str]]] = None, - negative_prompt: Optional[Union[str, List[str]]] = None, n: int = 1, size: str = "1024*1024", response_format: str = "url", **kwargs, ): - if "inpainting" not in self._abilities: + ability = "inpainting" + if ability not in self._abilities: raise RuntimeError(f"{self._model_uid} does not support inpainting") if ( "text2image" in self._abilities or "image2image" in self._abilities ) and self._model is not None: - from diffusers import AutoPipelineForInpainting - - if self._inpainting_model is not None: - model = self._inpainting_model - else: - model = self._inpainting_model = AutoPipelineForInpainting.from_pipe( - self._model - ) + model = self._get_model(ability) else: model = self._model - width, height = map(int, re.split(r"[^\d]+", size)) + if mask_blur := kwargs.pop("mask_blur", None): + logger.debug("Process mask image with mask_blur: %s", mask_blur) + mask_image = model.mask_processor.blur(mask_image, blur_factor=mask_blur) # type: ignore + + if "width" not in kwargs: + kwargs["width"], kwargs["height"] = map(int, re.split(r"[^\d]+", size)) + + if padding_image_to_multiple := kwargs.pop("padding_image_to_multiple", None): + # Model like SD3 inpainting requires image's height and width is times of 16 + # padding the image if specified + origin_x, origin_y = image.size + kwargs["origin_size"] = (origin_x, origin_y) + kwargs["is_padded"] = True + image = self.pad_to_multiple(image, multiple=int(padding_image_to_multiple)) + mask_image = self.pad_to_multiple( + mask_image, multiple=int(padding_image_to_multiple) + ) + # calculate actual image size after padding + kwargs["width"], kwargs["height"] = image.size + return self._call_model( image=image, mask_image=mask_image, prompt=prompt, - negative_prompt=negative_prompt, - height=height, - width=width, num_images_per_prompt=n, response_format=response_format, model=model, diff --git a/xinference/model/image/stable_diffusion/mlx.py b/xinference/model/image/stable_diffusion/mlx.py new file mode 100644 index 0000000000..849ff62aab --- /dev/null +++ b/xinference/model/image/stable_diffusion/mlx.py @@ -0,0 +1,221 @@ +# Copyright 2022-2023 XProbe Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import contextlib +import gc +import logging +import re +from typing import TYPE_CHECKING, Dict, List, Optional, Tuple + +import numpy as np +from PIL import Image +from xoscar.utils import classproperty + +from ....types import LoRA +from ..sdapi import SDAPIDiffusionModelMixin +from ..utils import handle_image_result + +if TYPE_CHECKING: + from ....core.progress_tracker import Progressor + from ..core import ImageModelFamilyV1 + + +logger = logging.getLogger(__name__) + + +def quantization_predicate(name: str, m) -> bool: + return hasattr(m, "to_quantized") and m.weight.shape[1] % 512 == 0 + + +def to_latent_size(image_size: Tuple[int, int]): + h, w = image_size + h = ((h + 15) // 16) * 16 + w = ((w + 15) // 16) * 16 + + if (h, w) != image_size: + print( + "Warning: The image dimensions need to be divisible by 16px. " + f"Changing size to {h}x{w}." + ) + + return (h // 8, w // 8) + + +class MLXDiffusionModel(SDAPIDiffusionModelMixin): + def __init__( + self, + model_uid: str, + model_path: Optional[str] = None, + device: Optional[str] = None, + lora_model: Optional[List[LoRA]] = None, + lora_load_kwargs: Optional[Dict] = None, + lora_fuse_kwargs: Optional[Dict] = None, + model_spec: Optional["ImageModelFamilyV1"] = None, + **kwargs, + ): + self._model_uid = model_uid + self._model_path = model_path + self._device = device + # model info when loading + self._model = None + self._lora_model = lora_model + self._lora_load_kwargs = lora_load_kwargs or {} + self._lora_fuse_kwargs = lora_fuse_kwargs or {} + # info + self._model_spec = model_spec + self._abilities = model_spec.model_ability or [] # type: ignore + self._kwargs = kwargs + + @property + def model_ability(self): + return self._abilities + + @classproperty + def supported_models(self): + return ["FLUX.1-schnell", "FLUX.1-dev"] + + def load(self): + try: + import mlx.nn as nn + except ImportError: + error_message = "Failed to import module 'mlx'" + installation_guide = [ + "Please make sure 'mlx' is installed. ", + "You can install it by `pip install mlx`\n", + ] + + raise ImportError(f"{error_message}\n\n{''.join(installation_guide)}") + + from ....thirdparty.mlx.flux import FluxPipeline + + logger.debug( + "Loading model from %s, kwargs: %s", self._model_path, self._kwargs + ) + flux = self._model = FluxPipeline( + "flux-" + self._model_spec.model_name.split("-")[1], + model_path=self._model_path, + t5_padding=self._kwargs.get("t5_padding", True), + ) + self._apply_lora() + + quantize = self._kwargs.get("quantize", True) + if quantize: + nn.quantize(flux.flow, class_predicate=quantization_predicate) + nn.quantize(flux.t5, class_predicate=quantization_predicate) + nn.quantize(flux.clip, class_predicate=quantization_predicate) + + def _apply_lora(self): + if self._lora_model is not None: + import mlx.core as mx + + for lora_model in self._lora_model: + weights, lora_config = mx.load( + lora_model.local_path, return_metadata=True + ) + rank = int(lora_config.get("lora_rank", 8)) + num_blocks = int(lora_config.get("lora_blocks", -1)) + flux = self._model + flux.linear_to_lora_layers(rank, num_blocks) + flux.flow.load_weights(list(weights.items()), strict=False) + flux.fuse_lora_layers() + logger.info(f"Successfully loaded the LoRA for model {self._model_uid}.") + + @staticmethod + @contextlib.contextmanager + def _release_after(): + import mlx.core as mx + + try: + yield + finally: + gc.collect() + mx.metal.clear_cache() + + def text_to_image( + self, + prompt: str, + n: int = 1, + size: str = "1024*1024", + response_format: str = "url", + **kwargs, + ): + import mlx.core as mx + + flux = self._model + width, height = map(int, re.split(r"[^\d]+", size)) + + # Make the generator + latent_size = to_latent_size((height, width)) + gen_latent_kwargs = {} + if (num_steps := kwargs.get("num_inference_steps")) is None: + num_steps = 50 if "dev" in self._model_spec.model_name else 2 # type: ignore + gen_latent_kwargs["num_steps"] = num_steps + if guidance := kwargs.get("guidance_scale"): + gen_latent_kwargs["guidance"] = guidance + if seed := kwargs.get("seed"): + gen_latent_kwargs["seed"] = seed + + with self._release_after(): + latents = flux.generate_latents( # type: ignore + prompt, n_images=n, latent_size=latent_size, **gen_latent_kwargs + ) + + # First we get and eval the conditioning + conditioning = next(latents) + mx.eval(conditioning) + peak_mem_conditioning = mx.metal.get_peak_memory() / 1024**3 + mx.metal.reset_peak_memory() + + progressor: Progressor = kwargs.pop("progressor", None) + # Actual denoising loop + for i, x_t in enumerate(latents): + mx.eval(x_t) + progressor.set_progress((i + 1) / num_steps) + + peak_mem_generation = mx.metal.get_peak_memory() / 1024**3 + mx.metal.reset_peak_memory() + + # Decode them into images + decoded = [] + for i in range(n): + decoded.append(flux.decode(x_t[i : i + 1], latent_size)) # type: ignore + mx.eval(decoded[-1]) + peak_mem_decoding = mx.metal.get_peak_memory() / 1024**3 + peak_mem_overall = max( + peak_mem_conditioning, peak_mem_generation, peak_mem_decoding + ) + + images = [] + x = mx.concatenate(decoded, axis=0) + x = (x * 255).astype(mx.uint8) + for i in range(len(x)): + im = Image.fromarray(np.array(x[i])) + images.append(im) + + logger.debug( + f"Peak memory used for the text: {peak_mem_conditioning:.3f}GB" + ) + logger.debug( + f"Peak memory used for the generation: {peak_mem_generation:.3f}GB" + ) + logger.debug(f"Peak memory used for the decoding: {peak_mem_decoding:.3f}GB") + logger.debug(f"Peak memory used overall: {peak_mem_overall:.3f}GB") + + return handle_image_result(response_format, images) + + def image_to_image(self, **kwargs): + raise NotImplementedError + + def inpainting(self, **kwargs): + raise NotImplementedError diff --git a/xinference/model/image/tests/test_got_ocr2.py b/xinference/model/image/tests/test_got_ocr2.py new file mode 100644 index 0000000000..385fb375f5 --- /dev/null +++ b/xinference/model/image/tests/test_got_ocr2.py @@ -0,0 +1,41 @@ +# Copyright 2022-2023 XProbe Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import io + +from diffusers.utils import load_image + + +def test_got_ocr2(setup): + endpoint, _ = setup + from ....client import Client + + client = Client(endpoint) + + model_uid = client.launch_model( + model_uid="ocr_test", + model_name="GOT-OCR2_0", + model_type="image", + ) + model = client.get_model(model_uid) + + url = "https://huggingface.co/stepfun-ai/GOT-OCR2_0/resolve/main/assets/train_sample.jpg" + image = load_image(url) + bio = io.BytesIO() + image.save(bio, format="JPEG") + r = model.ocr( + image=bio.getvalue(), + ocr_type="ocr", + ) + assert "Jesuits Estate" in r diff --git a/xinference/model/image/tests/test_stable_diffusion.py b/xinference/model/image/tests/test_stable_diffusion.py index ba54ca806d..e4da8014d0 100644 --- a/xinference/model/image/tests/test_stable_diffusion.py +++ b/xinference/model/image/tests/test_stable_diffusion.py @@ -11,17 +11,22 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import asyncio import base64 import io import logging import os.path import shutil import tempfile +import uuid from io import BytesIO +import numpy as np import pytest +import xoscar as xo from PIL import Image +from ....core.progress_tracker import Progressor, ProgressTrackerActor from ..core import ImageModelFamilyV1, cache from ..stable_diffusion.core import DiffusionModel @@ -30,6 +35,7 @@ model_name="small-stable-diffusion-v0", model_id="OFA-Sys/small-stable-diffusion-v0", model_revision="38e10e5e71e8fbf717a47a81e7543cd01c1a8140", + model_ability=["text2image"], ) logger = logging.getLogger(__name__) @@ -39,7 +45,7 @@ def test_model(): model_path = None try: model_path = cache(TEST_MODEL_SPEC) - model = DiffusionModel("mock", model_path) + model = DiffusionModel("mock", model_path, model_spec=TEST_MODEL_SPEC) # input is a string input_text = "an apple" model.load() @@ -57,6 +63,50 @@ def test_model(): shutil.rmtree(model_path) +@pytest.mark.asyncio +async def test_progressor(): + def _run_model(**kwargs): + model_path = None + try: + model_path = cache(TEST_MODEL_SPEC) + model = DiffusionModel("mock", model_path, model_spec=TEST_MODEL_SPEC) + # input is a string + input_text = "an apple" + model.load() + r = model.text_to_image(input_text, size="256*256", **kwargs) + assert len(r["data"]) == 1 + assert os.path.exists(r["data"][0]["url"]) + finally: + if model_path is not None: + shutil.rmtree(model_path) + + pool = await xo.create_actor_pool("127.0.0.1", n_process=0) + async with pool: + progress_tracker_ref = await xo.create_actor( + ProgressTrackerActor, + to_remove_interval=0, + check_interval=1, + address=pool.external_address, + uid=ProgressTrackerActor.default_uid(), + ) + request_id = str(uuid.uuid4()) + progressor = Progressor( + request_id, progress_tracker_ref, asyncio.get_running_loop() + ) + await progressor.start() + with progressor: + progressor.split_stages(2, stage_weight=np.array([0, 0.99, 1])) + with progressor: + await asyncio.to_thread(_run_model, progressor=progressor) + assert progressor._current_progress == 0.99 + assert await progress_tracker_ref.get_progress(request_id) == 0.99 + with progressor: + progressor.set_progress(1.0) + await asyncio.sleep(2) + with pytest.raises(KeyError): + await progress_tracker_ref.get_progress(request_id) + + @pytest.mark.skip(reason="Stable diffusion controlnet requires too many GRAM.") def test_restful_api_for_image_with_canny_controlnet(setup): endpoint, _ = setup @@ -287,20 +337,19 @@ def test_register_custom_image(): unregister_image, ) - tmp_dir = tempfile.mktemp() - - model_spec = CustomImageModelFamilyV1( - model_family="stable_diffusion", - model_name="my-custom-image", - model_id="my-custom-image", - model_uri=os.path.abspath(tmp_dir), - ) + with tempfile.TemporaryDirectory() as tmp_dir: + model_spec = CustomImageModelFamilyV1( + model_family="stable_diffusion", + model_name="my-custom-image", + model_id="my-custom-image", + model_uri=os.path.abspath(tmp_dir), + ) - register_image(model_spec, persist=False) - assert model_spec in get_user_defined_images() + register_image(model_spec, persist=False) + assert model_spec in get_user_defined_images() - unregister_image(model_spec.model_name, raise_error=False) - assert model_spec not in get_user_defined_images() + unregister_image(model_spec.model_name, raise_error=False) + assert model_spec not in get_user_defined_images() def test_persist_custom_image(): @@ -313,12 +362,13 @@ def test_persist_custom_image(): ) tmp_dir = tempfile.mktemp() + os.makedirs(tmp_dir) model_spec = CustomImageModelFamilyV1( model_family="stable_diffusion", model_name="my-custom-image", model_id="my-custom-image", - model_uri=os.path.abspath(tmp_dir), + model_uri=f"file://{os.path.abspath(tmp_dir)}", ) register_image(model_spec, persist=True) @@ -349,11 +399,12 @@ def test_launch_custom_image(setup): "model_uid": "my_sd", "model_name": "my_sd", "model_uri": model_path, + "model_ability": ["text2image"], } client.register_model( model_type="image", - model=json_dumps(my_model), + model=json_dumps(my_model).decode("utf-8"), persist=False, ) diff --git a/xinference/model/image/utils.py b/xinference/model/image/utils.py index bc4cbc350d..53df69e549 100644 --- a/xinference/model/image/utils.py +++ b/xinference/model/image/utils.py @@ -11,16 +11,52 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from typing import Optional +import base64 +import os +import time +import uuid +from concurrent.futures import ThreadPoolExecutor +from functools import partial +from io import BytesIO +from typing import TYPE_CHECKING, Optional -from .core import ImageModelFamilyV1 +from ...constants import XINFERENCE_IMAGE_DIR +from ...types import Image, ImageList + +if TYPE_CHECKING: + from .core import ImageModelFamilyV1 def get_model_version( - image_model: ImageModelFamilyV1, controlnet: Optional[ImageModelFamilyV1] + image_model: "ImageModelFamilyV1", controlnet: Optional["ImageModelFamilyV1"] ) -> str: return ( image_model.model_name if controlnet is None else f"{image_model.model_name}--{controlnet.model_name}" ) + + +def handle_image_result(response_format: str, images) -> ImageList: + if response_format == "url": + os.makedirs(XINFERENCE_IMAGE_DIR, exist_ok=True) + image_list = [] + with ThreadPoolExecutor() as executor: + for img in images: + path = os.path.join(XINFERENCE_IMAGE_DIR, uuid.uuid4().hex + ".jpg") + image_list.append(Image(url=path, b64_json=None)) + executor.submit(img.save, path, "jpeg") + return ImageList(created=int(time.time()), data=image_list) + elif response_format == "b64_json": + + def _gen_base64_image(_img): + buffered = BytesIO() + _img.save(buffered, format="jpeg") + return base64.b64encode(buffered.getvalue()).decode() + + with ThreadPoolExecutor() as executor: + results = list(map(partial(executor.submit, _gen_base64_image), images)) # type: ignore + image_list = [Image(url=None, b64_json=s.result()) for s in results] # type: ignore + return ImageList(created=int(time.time()), data=image_list) + else: + raise ValueError(f"Unsupported response format: {response_format}") diff --git a/xinference/model/llm/__init__.py b/xinference/model/llm/__init__.py index 9909addebb..88b0e49651 100644 --- a/xinference/model/llm/__init__.py +++ b/xinference/model/llm/__init__.py @@ -45,7 +45,6 @@ LLMFamilyV1, LLMSpecV1, MLXLLMSpecV1, - PromptStyleV1, PytorchLLMSpecV1, get_cache_status, get_user_defined_llm_families, @@ -112,6 +111,22 @@ def generate_engine_config_by_model_family(model_family): LLM_ENGINES[model_name] = engines +def register_custom_model(): + from ...constants import XINFERENCE_MODEL_DIR + + user_defined_llm_dir = os.path.join(XINFERENCE_MODEL_DIR, "llm") + if os.path.isdir(user_defined_llm_dir): + for f in os.listdir(user_defined_llm_dir): + try: + with codecs.open( + os.path.join(user_defined_llm_dir, f), encoding="utf-8" + ) as fd: + user_defined_llm_family = CustomLLMFamilyV1.parse_raw(fd.read()) + register_llm(user_defined_llm_family, persist=False) + except Exception as e: + warnings.warn(f"{user_defined_llm_dir}/{f} has error, {e}") + + def _install(): from .llama_cpp.core import LlamaCppChatModel, LlamaCppModel from .lmdeploy.core import LMDeployChatModel, LMDeployModel @@ -121,13 +136,19 @@ def _install(): from .transformers.cogvlm2 import CogVLM2Model from .transformers.cogvlm2_video import CogVLM2VideoModel from .transformers.core import PytorchChatModel, PytorchModel + from .transformers.deepseek_v2 import ( + DeepSeekV2PytorchChatModel, + DeepSeekV2PytorchModel, + ) from .transformers.deepseek_vl import DeepSeekVLChatModel from .transformers.glm4v import Glm4VModel from .transformers.intern_vl import InternVLChatModel from .transformers.internlm2 import Internlm2PytorchChatModel - from .transformers.llama_2 import LlamaPytorchChatModel, LlamaPytorchModel from .transformers.minicpmv25 import MiniCPMV25Model from .transformers.minicpmv26 import MiniCPMV26Model + from .transformers.opt import OptPytorchModel + from .transformers.qwen2_audio import Qwen2AudioChatModel + from .transformers.qwen2_vl import Qwen2VLChatModel from .transformers.qwen_vl import QwenVLChatModel from .transformers.yi_vl import YiVLChatModel from .vllm.core import VLLMChatModel, VLLMModel, VLLMVisionModel @@ -154,11 +175,11 @@ def _install(): TRANSFORMERS_CLASSES.extend( [ ChatglmPytorchChatModel, - LlamaPytorchModel, - LlamaPytorchChatModel, PytorchChatModel, Internlm2PytorchChatModel, QwenVLChatModel, + Qwen2VLChatModel, + Qwen2AudioChatModel, YiVLChatModel, DeepSeekVLChatModel, InternVLChatModel, @@ -168,6 +189,9 @@ def _install(): MiniCPMV25Model, MiniCPMV26Model, Glm4VModel, + DeepSeekV2PytorchModel, + DeepSeekV2PytorchChatModel, + OptPytorchModel, ] ) if OmniLMMModel: # type: ignore @@ -188,13 +212,17 @@ def _install(): model_spec = LLMFamilyV1.parse_obj(json_obj) BUILTIN_LLM_FAMILIES.append(model_spec) - # register prompt style + # register chat_template if "chat" in model_spec.model_ability and isinstance( - model_spec.prompt_style, PromptStyleV1 + model_spec.chat_template, str ): # note that the key is the model name, # since there are multiple representations of the same prompt style name in json. - BUILTIN_LLM_PROMPT_STYLE[model_spec.model_name] = model_spec.prompt_style + BUILTIN_LLM_PROMPT_STYLE[model_spec.model_name] = { + "chat_template": model_spec.chat_template, + "stop_token_ids": model_spec.stop_token_ids, + "stop": model_spec.stop, + } # register model family if "chat" in model_spec.model_ability: BUILTIN_LLM_MODEL_CHAT_FAMILIES.add(model_spec.model_name) @@ -214,10 +242,14 @@ def _install(): # if duplicated with huggingface json, keep it as the huggingface style if ( "chat" in model_spec.model_ability - and isinstance(model_spec.prompt_style, PromptStyleV1) + and isinstance(model_spec.chat_template, str) and model_spec.model_name not in BUILTIN_LLM_PROMPT_STYLE ): - BUILTIN_LLM_PROMPT_STYLE[model_spec.model_name] = model_spec.prompt_style + BUILTIN_LLM_PROMPT_STYLE[model_spec.model_name] = { + "chat_template": model_spec.chat_template, + "stop_token_ids": model_spec.stop_token_ids, + "stop": model_spec.stop, + } # register model family if "chat" in model_spec.model_ability: BUILTIN_LLM_MODEL_CHAT_FAMILIES.add(model_spec.model_name) @@ -237,10 +269,14 @@ def _install(): # if duplicated with huggingface json, keep it as the huggingface style if ( "chat" in model_spec.model_ability - and isinstance(model_spec.prompt_style, PromptStyleV1) + and isinstance(model_spec.chat_template, str) and model_spec.model_name not in BUILTIN_LLM_PROMPT_STYLE ): - BUILTIN_LLM_PROMPT_STYLE[model_spec.model_name] = model_spec.prompt_style + BUILTIN_LLM_PROMPT_STYLE[model_spec.model_name] = { + "chat_template": model_spec.chat_template, + "stop_token_ids": model_spec.stop_token_ids, + "stop": model_spec.stop, + } # register model family if "chat" in model_spec.model_ability: BUILTIN_LLM_MODEL_CHAT_FAMILIES.add(model_spec.model_name) @@ -267,16 +303,7 @@ def _install(): for family in families: generate_engine_config_by_model_family(family) - from ...constants import XINFERENCE_MODEL_DIR - - user_defined_llm_dir = os.path.join(XINFERENCE_MODEL_DIR, "llm") - if os.path.isdir(user_defined_llm_dir): - for f in os.listdir(user_defined_llm_dir): - with codecs.open( - os.path.join(user_defined_llm_dir, f), encoding="utf-8" - ) as fd: - user_defined_llm_family = CustomLLMFamilyV1.parse_obj(json.load(fd)) - register_llm(user_defined_llm_family, persist=False) + register_custom_model() # register model description for ud_llm in get_user_defined_llm_families(): diff --git a/xinference/model/llm/llama_cpp/core.py b/xinference/model/llm/llama_cpp/core.py index b820fce466..8e4929cbfe 100644 --- a/xinference/model/llm/llama_cpp/core.py +++ b/xinference/model/llm/llama_cpp/core.py @@ -14,12 +14,11 @@ import logging import os import time -from typing import Iterable, Iterator, List, Optional, Union +from typing import Dict, Iterator, List, Optional, Union from ....types import ( ChatCompletion, ChatCompletionChunk, - ChatCompletionMessage, Completion, CompletionChunk, CompletionUsage, @@ -181,13 +180,12 @@ def generator_wrapper( for index, _completion_chunk in enumerate( self._llm(prompt=_prompt, **_generate_config) ): + _completion_chunk["model"] = self.model_uid request_id = _completion_chunk["id"] - choice = _completion_chunk["choices"][0] - if choice["finish_reason"] is not None: - completion_tokens = index + completion_tokens = index + 1 total_tokens = prompt_tokens + completion_tokens _completion_chunk["usage"] = CompletionUsage( - prompt_tokens=total_tokens, + prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens, ) @@ -262,39 +260,26 @@ def _sanitize_generate_config( self, generate_config: Optional[LlamaCppGenerateConfig] ) -> LlamaCppGenerateConfig: generate_config = super()._sanitize_generate_config(generate_config) - if self.model_family.prompt_style and self.model_family.prompt_style.stop: - generate_config["stop"] = self.model_family.prompt_style.stop + if self.model_family.stop and self.model_family.stop: + generate_config["stop"] = self.model_family.stop.copy() return generate_config def chat( self, - prompt: str, - system_prompt: Optional[str] = None, - chat_history: Optional[List[ChatCompletionMessage]] = None, + messages: List[Dict], generate_config: Optional[LlamaCppGenerateConfig] = None, ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: - assert self.model_family.prompt_style is not None - prompt_style = self.model_family.prompt_style.copy() - if system_prompt: - prompt_style.system_prompt = system_prompt - - chat_history = chat_history or [] - assert prompt_style is not None + model_family = self.model_family.model_family or self.model_family.model_name tools = generate_config.pop("tools", []) if generate_config else None - full_prompt = self.get_prompt(prompt, chat_history, prompt_style, tools=tools) + full_context_kwargs = {} + if tools and model_family in QWEN_TOOL_CALL_FAMILY: + full_context_kwargs["tools"] = tools + assert self.model_family.chat_template is not None + full_prompt = self.get_full_context( + messages, self.model_family.chat_template, **full_context_kwargs + ) generate_config = self._sanitize_generate_config(generate_config) - # TODO(codingl2k1): qwen hacky to set stop for function call. - model_family = self.model_family.model_family or self.model_family.model_name - if tools and model_family in QWEN_TOOL_CALL_FAMILY: - stop = generate_config.get("stop") - if isinstance(stop, str): - generate_config["stop"] = [stop, "Observation:"] - elif isinstance(stop, Iterable): - assert not isinstance(stop, str) - generate_config["stop"] = stop + ["Observation:"] # type: ignore - else: - generate_config["stop"] = "Observation:" stream = generate_config.get("stream", False) if stream: @@ -305,7 +290,5 @@ def chat( c = self.generate(full_prompt, generate_config) assert not isinstance(c, Iterator) if tools: - return self._tool_calls_completion( - self.model_family, self.model_uid, c, tools - ) + return self._tool_calls_completion(self.model_family, self.model_uid, c) return self._to_chat_completion(c) diff --git a/xinference/model/llm/llm_family.json b/xinference/model/llm/llm_family.json index 26f1d599a8..abdcfd4f62 100644 --- a/xinference/model/llm/llm_family.json +++ b/xinference/model/llm/llm_family.json @@ -46,24 +46,15 @@ "model_revision": "3cb06f589b7b1e2f8e728c77280b1114191d24de" } ], - "prompt_style": { - "style_name": "CodeShell", - "system_prompt": "", - "roles": [ - "## human:", - "## assistant: " - ], - "intra_message_sep": "", - "inter_message_sep": "", - "stop_token_ids": [ - 70000 - ], - "stop": [ - "<|endoftext|>", - "|||", - "||" - ] - } + "chat_template": "{% for item in messages %}{% if item['role'] == 'user' %}{{ '## human: ' + item['content'] + '||' }}{% elif item['role'] == 'assistant' %}{{ '## assistant: ' + item['content'] + '||' }}{% endif %}{% endfor %}{{ '## assistant: ' }}", + "stop_token_ids": [ + 70000 + ], + "stop": [ + "<|endoftext|>", + "|||", + "||" + ] }, { "version": 1, @@ -134,26 +125,17 @@ "model_revision": "ebee18c488086b396dde649f2aa6548b9b8d2404" } ], - "prompt_style": { - "style_name": "PHI3", - "system_prompt": "You are a helpful AI assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n", - "inter_message_sep": "<|end|>\n", - "stop_token_ids":[ - 32000, - 32001, - 32007 - ], - "stop": [ - "<|endoftext|>", - "<|assistant|>", - "<|end|>" - ] - } + "chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{{'<|system|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'user' %}{{'<|user|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'assistant' %}{{'<|assistant|>\n' + message['content'] + '<|end|>\n'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ '<|endoftext|>' }}{% endif %}", + "stop_token_ids":[ + 32000, + 32001, + 32007 + ], + "stop": [ + "<|endoftext|>", + "<|assistant|>", + "<|end|>" + ] }, { "version": 1, @@ -189,156 +171,17 @@ "model_revision": "b86bcaf57ea4dfdec5dbe12a377028b2fab0d480" } ], - "prompt_style": { - "style_name": "PHI3", - "system_prompt": "You are a helpful AI assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n", - "inter_message_sep": "<|end|>\n", - "stop_token_ids":[ - 32000, - 32001, - 32007 - ], - "stop": [ - "<|endoftext|>", - "<|assistant|>", - "<|end|>" - ] - } - }, - { - "version": 1, - "context_length": 8192, - "model_name": "chatglm3", - "model_lang": [ - "en", - "zh" - ], - "model_ability": [ - "chat", - "tools" - ], - "model_description": "ChatGLM3 is the third generation of ChatGLM, still open-source and trained on Chinese and English data.", - "model_specs": [ - { - "model_format": "pytorch", - "model_size_in_billions": 6, - "quantizations": [ - "4-bit", - "8-bit", - "none" - ], - "model_id": "THUDM/chatglm3-6b", - "model_revision": "103caa40027ebfd8450289ca2f278eac4ff26405" - } - ], - "prompt_style": { - "style_name": "CHATGLM3", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 64795, - 64797, - 2 - ], - "stop": [ - "<|user|>", - "<|observation|>" - ] - } - }, - { - "version": 1, - "context_length": 32768, - "model_name": "chatglm3-32k", - "model_lang": [ - "en", - "zh" - ], - "model_ability": [ - "chat" - ], - "model_description": "ChatGLM3 is the third generation of ChatGLM, still open-source and trained on Chinese and English data.", - "model_specs": [ - { - "model_format": "pytorch", - "model_size_in_billions": 6, - "quantizations": [ - "4-bit", - "8-bit", - "none" - ], - "model_id": "THUDM/chatglm3-6b-32k", - "model_revision": "339f17ff464d47b5077527c2b34e80a7719ede3e" - } - ], - "prompt_style": { - "style_name": "CHATGLM3", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 64795, - 64797, - 2 - ], - "stop": [ - "<|user|>", - "<|observation|>" - ] - } - }, - { - "version": 1, - "context_length": 131072, - "model_name": "chatglm3-128k", - "model_lang": [ - "en", - "zh" - ], - "model_ability": [ - "chat" - ], - "model_description": "ChatGLM3 is the third generation of ChatGLM, still open-source and trained on Chinese and English data.", - "model_specs": [ - { - "model_format": "pytorch", - "model_size_in_billions": 6, - "quantizations": [ - "4-bit", - "8-bit", - "none" - ], - "model_id": "THUDM/chatglm3-6b-128k", - "model_revision": "f0afbe671009abc9e31182170cf60636d5546cda" - } + "chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{{'<|system|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'user' %}{{'<|user|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'assistant' %}{{'<|assistant|>\n' + message['content'] + '<|end|>\n'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ '<|endoftext|>' }}{% endif %}", + "stop_token_ids":[ + 32000, + 32001, + 32007 ], - "prompt_style": { - "style_name": "CHATGLM3", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 64795, - 64797, - 2 - ], - "stop": [ - "<|user|>", - "<|observation|>" - ] - } + "stop": [ + "<|endoftext|>", + "<|assistant|>", + "<|end|>" + ] }, { "version": 1, @@ -363,7 +206,7 @@ "none" ], "model_id": "THUDM/glm-4-9b-chat", - "model_revision": "aae8bd74af5c6dff63a49d7fbdcc89349ebf87aa" + "model_revision": "eb55a443d66541f30869f6caac5ad0d2e95bcbaa" }, { "model_format": "ggufv2", @@ -392,24 +235,17 @@ "model_revision": "0155a14edf0176863e9a003cdd78ce599e4d62c0" } ], - "prompt_style": { - "style_name": "CHATGLM3", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 151329, - 151336, - 151338 - ], - "stop": [ - "<|endoftext|>", - "<|user|>", - "<|observation|>" - ] - } + "chat_template": "[gMASK]{% for item in messages %}{% if item['tools'] is defined %}<|system|>\n你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具{% set tools = item['tools'] %}{% for tool in tools %}{% if tool['type'] == 'function' %}\n\n## {{ tool['function']['name'] }}\n\n{{ tool['function'] | tojson(indent=4) }}\n在调用上述函数时，请使用 Json 格式表示调用的参数。{% elif tool['type'] == 'python' %}\n\n## python\n\n当你向 `python` 发送包含 Python 代码的消息时，该代码将会在一个有状态的 Jupyter notebook 环境中执行。\n`python` 返回代码执行的输出，或在执行 60 秒后返回超时。\n`/mnt/data` 将会持久化存储你的文件。在此会话中，`python` 无法访问互联网。不要使用 `python` 进行任何网络请求或者在线 API 调用，这些在线内容的访问将不会成功。{% elif tool['type'] == 'simple_browser' %}\n\n## simple_browser\n\n你可以使用 `simple_browser` 工具。该工具支持以下函数：\n`search(query: str, recency_days: int)`：使用搜索引擎进行查询并显示结果，可以使用 `recency_days` 参数控制搜索内容的时效性。\n`mclick(ids: list[int])`：获取一系列指定 id 的页面内容。每次调用时，须选择3-10个页面。选择多个角度的页面，同时尽可能选择可信任的信息来源。考虑到部分页面是无法加载的，你也可以多打开一些可能有用的页面而不用担心内容过多。\n`open_url(url: str)`：打开指定的 URL。\n\n使用 `【{引用 id}†{引用文本}】` 来引用内容。\n\n操作步骤：1. 使用 `search` 来获得信息列表; 2. 使用 `mclick` 来获取指定 ID 页面的内容; 3. 根据获得的内容进行回复。在回复中应当引用信息来源。\n 如果用户提供了 URL，也可以用 `open_url` 直接打开页面。\n如果初次搜索结果没有找到合适的信息，也可以再次使用 `search` 进行搜索。{% elif tool['type'] == 'cogview' %}\n\n## cogview\n\n如果用户的请求中包含了对图像的描述，你可以使用 `cogview` 来生成图像并展示给用户。你需要向 `cogview` 发送图像描述，规则：\n- 发送给 `cogview` 的消息必须使用英语。用户的中文描述必须完全翻译为英语。\n- 应当尽可能详细地描述图像生成的需求，需求描述约 100 英文单词。\n- 保持用户原始描述的意图。不要虚构内容或者没见过的人物。\n- 如无特殊说明，所在地为中国，持有中国立场并遵循中国社会主义价值观。{% endif %}{% endfor %}{% endif %}{% if item['content'] %}<|{{ item['role'] }}|>{{ item['metadata'] }}\n{{ item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}", + "stop_token_ids": [ + 151329, + 151336, + 151338 + ], + "stop": [ + "<|endoftext|>", + "<|user|>", + "<|observation|>" + ] }, { "version": 1, @@ -463,24 +299,17 @@ "model_revision": "782e28bd5eee3c514c07108da15e0b5e06dcf776" } ], - "prompt_style": { - "style_name": "CHATGLM3", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 151329, - 151336, - 151338 - ], - "stop": [ - "<|endoftext|>", - "<|user|>", - "<|observation|>" - ] - } + "chat_template": "[gMASK]{% for item in messages %}{% if item['tools'] is defined %}<|system|>\n你是一个名为 GLM-4 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具{% set tools = item['tools'] %}{% for tool in tools %}{% if tool['type'] == 'function' %}\n\n## {{ tool['function']['name'] }}\n\n{{ tool['function'] | tojson(indent=4) }}\n在调用上述函数时，请使用 Json 格式表示调用的参数。{% elif tool['type'] == 'python' %}\n\n## python\n\n当你向 `python` 发送包含 Python 代码的消息时，该代码将会在一个有状态的 Jupyter notebook 环境中执行。\n`python` 返回代码执行的输出，或在执行 60 秒后返回超时。\n`/mnt/data` 将会持久化存储你的文件。在此会话中，`python` 无法访问互联网。不要使用 `python` 进行任何网络请求或者在线 API 调用，这些在线内容的访问将不会成功。{% elif tool['type'] == 'simple_browser' %}\n\n## simple_browser\n\n你可以使用 `simple_browser` 工具。该工具支持以下函数：\n`search(query: str, recency_days: int)`：使用搜索引擎进行查询并显示结果，可以使用 `recency_days` 参数控制搜索内容的时效性。\n`mclick(ids: list[int])`：获取一系列指定 id 的页面内容。每次调用时，须选择3-10个页面。选择多个角度的页面，同时尽可能选择可信任的信息来源。考虑到部分页面是无法加载的，你也可以多打开一些可能有用的页面而不用担心内容过多。\n`open_url(url: str)`：打开指定的 URL。\n\n使用 `【{引用 id}†{引用文本}】` 来引用内容。\n\n操作步骤：1. 使用 `search` 来获得信息列表; 2. 使用 `mclick` 来获取指定 ID 页面的内容; 3. 根据获得的内容进行回复。在回复中应当引用信息来源。\n 如果用户提供了 URL，也可以用 `open_url` 直接打开页面。\n如果初次搜索结果没有找到合适的信息，也可以再次使用 `search` 进行搜索。{% elif tool['type'] == 'cogview' %}\n\n## cogview\n\n如果用户的请求中包含了对图像的描述，你可以使用 `cogview` 来生成图像并展示给用户。你需要向 `cogview` 发送图像描述，规则：\n- 发送给 `cogview` 的消息必须使用英语。用户的中文描述必须完全翻译为英语。\n- 应当尽可能详细地描述图像生成的需求，需求描述约 100 英文单词。\n- 保持用户原始描述的意图。不要虚构内容或者没见过的人物。\n- 如无特殊说明，所在地为中国，持有中国立场并遵循中国社会主义价值观。{% endif %}{% endfor %}{% endif %}{% if item['content'] %}<|{{ item['role'] }}|>{{ item['metadata'] }}\n{{ item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}", + "stop_token_ids": [ + 151329, + 151336, + 151338 + ], + "stop": [ + "<|endoftext|>", + "<|user|>", + "<|observation|>" + ] }, { "version": 1, @@ -505,27 +334,20 @@ "none" ], "model_id": "THUDM/glm-4v-9b", - "model_revision": "6c2e4732db8443f64a48d5af04b74425a7d169c4" + "model_revision": "01328faefe122fe605c1c127b62e6031d3ffebf7" } ], - "prompt_style": { - "style_name": "CHATGLM3", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 151329, - 151336, - 151338 - ], - "stop": [ - "<|endoftext|>", - "<|user|>", - "<|observation|>" - ] - } + "chat_template": "", + "stop_token_ids": [ + 151329, + 151336, + 151338 + ], + "stop": [ + "<|endoftext|>", + "<|user|>", + "<|observation|>" + ] }, { "version": 1, @@ -567,24 +389,17 @@ "model_revision": "6a04071c54c943949826d4815ee00717ed8cf153" } ], - "prompt_style": { - "style_name": "CHATGLM3", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 151329, - 151336, - 151338 - ], - "stop": [ - "<|endoftext|>", - "<|user|>", - "<|observation|>" - ] - } + "chat_template": "{% for item in messages %}{% if loop.first and item['role'] == 'system' %}{{ '<|system|>\n' + item['content'] }}{% elif loop.first %}{{ '<|system|>\n你是一位智能编程助手，你叫CodeGeeX。你会为用户回答关于编程、代码、计算机方面的任何问题，并提供格式规范、可以执行、准确安全的代码，并在必要时提供详细的解释。' }}{% endif %}{% if item['role'] == 'user' %}{{ '<|user|>\n' + item['content'] }}{% elif item['role'] == 'assistant' %}{{ '<|assistant|>\n' + item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% endif %}", + "stop_token_ids": [ + 151329, + 151336, + 151338 + ], + "stop": [ + "<|endoftext|>", + "<|user|>", + "<|observation|>" + ] }, { "version": 1, @@ -622,14 +437,13 @@ "model_revision": "1e4944aaa1d8c8d0cdca28bb8e3a003303d0781b" } ], - "prompt_style": { - "style_name": "XVERSE", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ] - } + "chat_template": "{% for item in messages %}{% if loop.first and item['role'] == 'system' %}{{ '<|system|> \n' + item['content'] }}{% endif %}{% if item['role'] == 'user' %}{{ '<|user|> \n' + item['content'] }}{% elif item['role'] == 'assistant' %}{{ '<|assistant|> \n' + item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>' }}{% endif %}", + "stop_token_ids": [ + 3 + ], + "stop": [ + "<|endoftext|>" + ] }, { "version": 1, @@ -842,22 +656,11 @@ "model_revision": "36d9a7388cc80e5f4b3e9701ca2f250d21a96c30" } ], - "prompt_style": { - "style_name": "LLAMA2", - "system_prompt": "[INST] <>\nYou are a helpful AI assistant.\n<>\n\n", - "roles": [ - "[INST]", - "[/INST]" - ], - "intra_message_sep": " ", - "inter_message_sep": " ", - "stop_token_ids": [ + "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = '<>\n' + messages[0]['content'] | trim + '\n<>\n\n' %}{% set messages = messages[1:] %}{% else %}{% set system_message = '' %}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 %}{% set content = system_message + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ '' + '[INST] ' + content | trim + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content | trim + ' ' + '' }}{% endif %}{% endfor %}", + "stop_token_ids": [ 2 - ], - "stop": [ - "" - ] - } + ], + "stop": [] }, { "version": 1, @@ -1210,24 +1013,15 @@ "model_id": "TechxGenus/Meta-Llama-3-70B-Instruct-GPTQ" } ], - "prompt_style": { - "style_name": "LLAMA3", - "system_prompt": "You are a helpful assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n\n", - "inter_message_sep": "<|eot_id|>", - "stop_token_ids": [ - 128001, - 128009 - ], - "stop": [ - "<|end_of_text|>", - "<|eot_id|>" - ] - } + "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = '<|begin_of_text|>' + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}", + "stop_token_ids": [ + 128001, + 128009 + ], + "stop": [ + "<|end_of_text|>", + "<|eot_id|>" + ] }, { "version": 1, @@ -1317,7 +1111,8 @@ "th" ], "model_ability": [ - "chat" + "chat", + "tools" ], "model_description": "The Llama 3.1 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks..", "model_specs": [ @@ -1505,24 +1300,17 @@ "model_id": "hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4" } ], - "prompt_style": { - "style_name": "LLAMA3", - "system_prompt": "You are a helpful assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n\n", - "inter_message_sep": "<|eot_id|>", - "stop_token_ids": [ - 128001, - 128009 - ], - "stop": [ - "<|end_of_text|>", - "<|eot_id|>" - ] - } + "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|eom_id|>\" }}\n {%- else %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n", + "stop_token_ids": [ + 128001, + 128008, + 128009 + ], + "stop": [ + "<|end_of_text|>", + "<|eot_id|>", + "<|eom_id|>" + ] }, { "version": 1, @@ -1558,8 +1346,7 @@ "zh" ], "model_ability": [ - "chat", - "tools" + "chat" ], "model_description": "Qwen-chat is a fine-tuned version of the Qwen LLM trained with alignment techniques, specializing in chatting.", "model_specs": [ @@ -1662,25 +1449,17 @@ "model_id": "Qwen/Qwen-72B-Chat-{quantization}" } ], - "prompt_style": { - "style_name": "QWEN", - "system_prompt": "You are a helpful assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n", - "stop_token_ids": [ - 151643, - 151644, - 151645 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>" - ] - } + "chat_template": "{% for item in messages %}{% if loop.first and item['role'] == 'system' %}{{ '<|im_start|>system\n' + item['content'] + '<|im_end|>\n' }}{% elif loop.first %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{% if item['role'] == 'user' %}{{ '<|im_start|>user\n' + item['content'] + '<|im_end|>' }}{% elif item['role'] == 'assistant' %}{{ '<|im_start|>assistant\n' + item['content'] + '<|im_end|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [ + 151643, + 151644, + 151645 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>" + ] }, { "version": 1, @@ -2025,25 +1804,17 @@ } } ], - "prompt_style": { - "style_name": "QWEN", - "system_prompt": "You are a helpful assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n", - "stop_token_ids": [ - 151643, - 151644, - 151645 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>" - ] - } + "chat_template": "{%- macro json_to_python_type(json_spec) %}\n {%- set basic_type_map = {\n \"string\": \"str\",\n \"number\": \"float\",\n \"integer\": \"int\",\n \"boolean\": \"bool\"\n} %}\n {%- if basic_type_map[json_spec.type] is defined %}\n {{- basic_type_map[json_spec.type] }}\n {%- elif json_spec.type == \"array\" %}\n {{- \"list[\" + json_to_python_type(json_spec|items) + \"]\" }}\n {%- elif json_spec.type == \"object\" %}\n {%- if json_spec.additionalProperties is defined %}\n {{- \"dict[str, \" + json_to_python_type(json_spec.additionalProperties) + ']' }}\n {%- else %}\n {{- \"dict\" }}\n {%- endif %}\n {%- elif json_spec.type is iterable %}\n {{- \"Union[\" }}\n {%- for t in json_spec.type %}\n {{- json_to_python_type({\"type\": t}) }}\n {%- if not loop.last %}\n {{- \",\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"]\" }}\n {%- else %}\n {{- \"Any\" }}\n {%- endif %}\n{%- endmacro %}\n\n{%- if tools %}\n {{- '<|im_start|>system\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] + '\n\n' }}\n {%- endif %}\n {{- '# Tools\n\n' }}\n {{- \"You are a function calling AI model. You are provided with function signatures within XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: \" }}\n {%- for tool in tools %}\n {%- if tool.function is defined %}\n {%- set tool = tool.function %}\n {%- endif %}\n {{- '{\"type\": \"function\", \"function\": ' }}\n {{- '{\"name\": ' + tool.name + '\", ' }}\n {{- '\"description\": \"' + tool.name + '(' }}\n {%- for param_name, param_fields in tool.parameters.properties|items %}\n {{- param_name + \": \" + json_to_python_type(param_fields) }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- if tool.return is defined %}\n {{- \" -> \" + json_to_python_type(tool.return) }}\n {%- endif %}\n {{- \" - \" + tool.description + \"\n\n\" }}\n {%- for param_name, param_fields in tool.parameters.properties|items %}\n {%- if loop.first %}\n {{- \" Args:\n\" }}\n {%- endif %}\n {{- \" \" + param_name + \"(\" + json_to_python_type(param_fields) + \"): \" + param_fields.description|trim }}\n {%- endfor %}\n {%- if tool.return is defined and tool.return.description is defined %}\n {{- \"\n Returns:\n \" + tool.return.description }}\n {%- endif %}\n {{- '\"' }}\n {{- ', \"parameters\": ' }}\n {%- if tool.parameters.properties | length == 0 %}\n {{- \"{}\" }}\n {%- else %}\n {{- tool.parameters|tojson }}\n {%- endif %}\n {{- \"}\" }}\n {%- if not loop.last %}\n {{- \"\n\" }}\n {%- endif %}\n {%- endfor %}\n {{- \" \" }}\n {{- 'Use the following pydantic model json schema for each tool call you will make: {\"properties\": {\"arguments\": {\"title\": \"Arguments\", \"type\": \"object\"}, \"name\": {\"title\": \"Name\", \"type\": \"string\"}}, \"required\": [\"arguments\", \"name\"], \"title\": \"FunctionCall\", \"type\": \"object\"}\n' }}\n {{- \"For each function call return a json object with function name and arguments within XML tags as follows:\n\" }}\n {{- \"\n\" }}\n {{- '{\"name\": , \"arguments\": }\n' }}\n {{- '<|im_end|>\n' }}\n{%- else %}\n {%- if messages[0]['role'] != 'system' %}\n {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}\n {%- else %}\n {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if message.role == \"user\" or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and message.tool_calls is not defined) %}\n {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role + '\n\n' }}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '{' }}\n {{- '\"name\": \"' }}\n {{- tool_call.name }}\n {%- if tool_call.arguments is defined %}\n {{- ', ' }}\n {{- '\"arguments\": ' }}\n {{- tool_call.arguments|tojson }}\n {%- endif %}\n {{- '\"}' }}\n {{- '\n' }}\n {%- endfor %}\n {{- '<|im_end|>\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if not message.name is defined %}\n {{- raise_exception(\"Tool response dicts require a 'name' key indicating the name of the called function!\") }}\n {%- endif %}\n {{- '<|im_start|>user\n\n' }}\n {{- '{\"name\": \"' }}\n {{- message.name }}\n {{- '\", \"content\": ' }}\n {{- message.content|tojson + '}' }}\n {{- '\n<|im_end|>\n' }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\n' }}\n{%- endif %}", + "stop_token_ids": [ + 151643, + 151644, + 151645 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>" + ] }, { "version": 1, @@ -2078,25 +1849,17 @@ "model_id": "Qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4" } ], - "prompt_style": { - "style_name": "QWEN", - "system_prompt": "You are a helpful assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n", - "stop_token_ids": [ - 151643, - 151644, - 151645 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>" - ] - } + "chat_template": "{%- macro json_to_python_type(json_spec) %}\n {%- set basic_type_map = {\n \"string\": \"str\",\n \"number\": \"float\",\n \"integer\": \"int\",\n \"boolean\": \"bool\"\n} %}\n {%- if basic_type_map[json_spec.type] is defined %}\n {{- basic_type_map[json_spec.type] }}\n {%- elif json_spec.type == \"array\" %}\n {{- \"list[\" + json_to_python_type(json_spec|items) + \"]\" }}\n {%- elif json_spec.type == \"object\" %}\n {%- if json_spec.additionalProperties is defined %}\n {{- \"dict[str, \" + json_to_python_type(json_spec.additionalProperties) + ']' }}\n {%- else %}\n {{- \"dict\" }}\n {%- endif %}\n {%- elif json_spec.type is iterable %}\n {{- \"Union[\" }}\n {%- for t in json_spec.type %}\n {{- json_to_python_type({\"type\": t}) }}\n {%- if not loop.last %}\n {{- \",\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"]\" }}\n {%- else %}\n {{- \"Any\" }}\n {%- endif %}\n{%- endmacro %}\n\n{%- if tools %}\n {{- '<|im_start|>system\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] + '\n\n' }}\n {%- endif %}\n {{- '# Tools\n\n' }}\n {{- \"You are a function calling AI model. You are provided with function signatures within XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: \" }}\n {%- for tool in tools %}\n {%- if tool.function is defined %}\n {%- set tool = tool.function %}\n {%- endif %}\n {{- '{\"type\": \"function\", \"function\": ' }}\n {{- '{\"name\": ' + tool.name + '\", ' }}\n {{- '\"description\": \"' + tool.name + '(' }}\n {%- for param_name, param_fields in tool.parameters.properties|items %}\n {{- param_name + \": \" + json_to_python_type(param_fields) }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- if tool.return is defined %}\n {{- \" -> \" + json_to_python_type(tool.return) }}\n {%- endif %}\n {{- \" - \" + tool.description + \"\n\n\" }}\n {%- for param_name, param_fields in tool.parameters.properties|items %}\n {%- if loop.first %}\n {{- \" Args:\n\" }}\n {%- endif %}\n {{- \" \" + param_name + \"(\" + json_to_python_type(param_fields) + \"): \" + param_fields.description|trim }}\n {%- endfor %}\n {%- if tool.return is defined and tool.return.description is defined %}\n {{- \"\n Returns:\n \" + tool.return.description }}\n {%- endif %}\n {{- '\"' }}\n {{- ', \"parameters\": ' }}\n {%- if tool.parameters.properties | length == 0 %}\n {{- \"{}\" }}\n {%- else %}\n {{- tool.parameters|tojson }}\n {%- endif %}\n {{- \"}\" }}\n {%- if not loop.last %}\n {{- \"\n\" }}\n {%- endif %}\n {%- endfor %}\n {{- \" \" }}\n {{- 'Use the following pydantic model json schema for each tool call you will make: {\"properties\": {\"arguments\": {\"title\": \"Arguments\", \"type\": \"object\"}, \"name\": {\"title\": \"Name\", \"type\": \"string\"}}, \"required\": [\"arguments\", \"name\"], \"title\": \"FunctionCall\", \"type\": \"object\"}\n' }}\n {{- \"For each function call return a json object with function name and arguments within XML tags as follows:\n\" }}\n {{- \"\n\" }}\n {{- '{\"name\": , \"arguments\": }\n' }}\n {{- '<|im_end|>\n' }}\n{%- else %}\n {%- if messages[0]['role'] != 'system' %}\n {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}\n {%- else %}\n {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if message.role == \"user\" or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and message.tool_calls is not defined) %}\n {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role + '\n\n' }}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '{' }}\n {{- '\"name\": \"' }}\n {{- tool_call.name }}\n {%- if tool_call.arguments is defined %}\n {{- ', ' }}\n {{- '\"arguments\": ' }}\n {{- tool_call.arguments|tojson }}\n {%- endif %}\n {{- '\"}' }}\n {{- '\n' }}\n {%- endfor %}\n {{- '<|im_end|>\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if not message.name is defined %}\n {{- raise_exception(\"Tool response dicts require a 'name' key indicating the name of the called function!\") }}\n {%- endif %}\n {{- '<|im_start|>user\n\n' }}\n {{- '{\"name\": \"' }}\n {{- message.name }}\n {{- '\", \"content\": ' }}\n {{- message.content|tojson + '}' }}\n {{- '\n<|im_end|>\n' }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\n' }}\n{%- endif %}", + "stop_token_ids": [ + 151643, + 151644, + 151645 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>" + ] }, { "version": 1, @@ -2171,25 +1934,17 @@ "model_id": "Qwen/CodeQwen1.5-7B-Chat-AWQ" } ], - "prompt_style": { - "style_name": "QWEN", - "system_prompt": "You are a helpful assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n", - "stop_token_ids": [ - 151643, - 151644, - 151645 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>" - ] - } + "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [ + 151643, + 151644, + 151645 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>" + ] }, { "version": 1, @@ -2479,25 +2234,17 @@ } } ], - "prompt_style": { - "style_name": "QWEN", - "system_prompt": "You are a helpful assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n", - "stop_token_ids": [ - 151643, - 151644, - 151645 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>" - ] - } + "chat_template": "{%- macro json_to_python_type(json_spec) %}\n {%- set basic_type_map = {\n \"string\": \"str\",\n \"number\": \"float\",\n \"integer\": \"int\",\n \"boolean\": \"bool\"\n} %}\n {%- if basic_type_map[json_spec.type] is defined %}\n {{- basic_type_map[json_spec.type] }}\n {%- elif json_spec.type == \"array\" %}\n {{- \"list[\" + json_to_python_type(json_spec|items) + \"]\" }}\n {%- elif json_spec.type == \"object\" %}\n {%- if json_spec.additionalProperties is defined %}\n {{- \"dict[str, \" + json_to_python_type(json_spec.additionalProperties) + ']' }}\n {%- else %}\n {{- \"dict\" }}\n {%- endif %}\n {%- elif json_spec.type is iterable %}\n {{- \"Union[\" }}\n {%- for t in json_spec.type %}\n {{- json_to_python_type({\"type\": t}) }}\n {%- if not loop.last %}\n {{- \",\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"]\" }}\n {%- else %}\n {{- \"Any\" }}\n {%- endif %}\n{%- endmacro %}\n\n{%- if tools %}\n {{- '<|im_start|>system\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] + '\n\n' }}\n {%- endif %}\n {{- '# Tools\n\n' }}\n {{- \"You are a function calling AI model. You are provided with function signatures within XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: \" }}\n {%- for tool in tools %}\n {%- if tool.function is defined %}\n {%- set tool = tool.function %}\n {%- endif %}\n {{- '{\"type\": \"function\", \"function\": ' }}\n {{- '{\"name\": ' + tool.name + '\", ' }}\n {{- '\"description\": \"' + tool.name + '(' }}\n {%- for param_name, param_fields in tool.parameters.properties|items %}\n {{- param_name + \": \" + json_to_python_type(param_fields) }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- if tool.return is defined %}\n {{- \" -> \" + json_to_python_type(tool.return) }}\n {%- endif %}\n {{- \" - \" + tool.description + \"\n\n\" }}\n {%- for param_name, param_fields in tool.parameters.properties|items %}\n {%- if loop.first %}\n {{- \" Args:\n\" }}\n {%- endif %}\n {{- \" \" + param_name + \"(\" + json_to_python_type(param_fields) + \"): \" + param_fields.description|trim }}\n {%- endfor %}\n {%- if tool.return is defined and tool.return.description is defined %}\n {{- \"\n Returns:\n \" + tool.return.description }}\n {%- endif %}\n {{- '\"' }}\n {{- ', \"parameters\": ' }}\n {%- if tool.parameters.properties | length == 0 %}\n {{- \"{}\" }}\n {%- else %}\n {{- tool.parameters|tojson }}\n {%- endif %}\n {{- \"}\" }}\n {%- if not loop.last %}\n {{- \"\n\" }}\n {%- endif %}\n {%- endfor %}\n {{- \" \" }}\n {{- 'Use the following pydantic model json schema for each tool call you will make: {\"properties\": {\"arguments\": {\"title\": \"Arguments\", \"type\": \"object\"}, \"name\": {\"title\": \"Name\", \"type\": \"string\"}}, \"required\": [\"arguments\", \"name\"], \"title\": \"FunctionCall\", \"type\": \"object\"}\n' }}\n {{- \"For each function call return a json object with function name and arguments within XML tags as follows:\n\" }}\n {{- \"\n\" }}\n {{- '{\"name\": , \"arguments\": }\n' }}\n {{- '<|im_end|>\n' }}\n{%- else %}\n {%- if messages[0]['role'] != 'system' %}\n {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}\n {%- else %}\n {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if message.role == \"user\" or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and message.tool_calls is not defined) %}\n {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role + '\n\n' }}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '{' }}\n {{- '\"name\": \"' }}\n {{- tool_call.name }}\n {%- if tool_call.arguments is defined %}\n {{- ', ' }}\n {{- '\"arguments\": ' }}\n {{- tool_call.arguments|tojson }}\n {%- endif %}\n {{- '\"}' }}\n {{- '\n' }}\n {%- endfor %}\n {{- '<|im_end|>\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if not message.name is defined %}\n {{- raise_exception(\"Tool response dicts require a 'name' key indicating the name of the called function!\") }}\n {%- endif %}\n {{- '<|im_start|>user\n\n' }}\n {{- '{\"name\": \"' }}\n {{- message.name }}\n {{- '\", \"content\": ' }}\n {{- message.content|tojson + '}' }}\n {{- '\n<|im_end|>\n' }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\n' }}\n{%- endif %}", + "stop_token_ids": [ + 151643, + 151644, + 151645 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>" + ] }, { "version": 1, @@ -2560,25 +2307,17 @@ } } ], - "prompt_style": { - "style_name": "QWEN", - "system_prompt": "You are a helpful assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n", - "stop_token_ids": [ - 151643, - 151644, - 151645 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>" - ] - } + "chat_template": "{%- macro json_to_python_type(json_spec) %}\n {%- set basic_type_map = {\n \"string\": \"str\",\n \"number\": \"float\",\n \"integer\": \"int\",\n \"boolean\": \"bool\"\n} %}\n {%- if basic_type_map[json_spec.type] is defined %}\n {{- basic_type_map[json_spec.type] }}\n {%- elif json_spec.type == \"array\" %}\n {{- \"list[\" + json_to_python_type(json_spec|items) + \"]\" }}\n {%- elif json_spec.type == \"object\" %}\n {%- if json_spec.additionalProperties is defined %}\n {{- \"dict[str, \" + json_to_python_type(json_spec.additionalProperties) + ']' }}\n {%- else %}\n {{- \"dict\" }}\n {%- endif %}\n {%- elif json_spec.type is iterable %}\n {{- \"Union[\" }}\n {%- for t in json_spec.type %}\n {{- json_to_python_type({\"type\": t}) }}\n {%- if not loop.last %}\n {{- \",\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"]\" }}\n {%- else %}\n {{- \"Any\" }}\n {%- endif %}\n{%- endmacro %}\n\n{%- if tools %}\n {{- '<|im_start|>system\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] + '\n\n' }}\n {%- endif %}\n {{- '# Tools\n\n' }}\n {{- \"You are a function calling AI model. You are provided with function signatures within XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: \" }}\n {%- for tool in tools %}\n {%- if tool.function is defined %}\n {%- set tool = tool.function %}\n {%- endif %}\n {{- '{\"type\": \"function\", \"function\": ' }}\n {{- '{\"name\": ' + tool.name + '\", ' }}\n {{- '\"description\": \"' + tool.name + '(' }}\n {%- for param_name, param_fields in tool.parameters.properties|items %}\n {{- param_name + \": \" + json_to_python_type(param_fields) }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- if tool.return is defined %}\n {{- \" -> \" + json_to_python_type(tool.return) }}\n {%- endif %}\n {{- \" - \" + tool.description + \"\n\n\" }}\n {%- for param_name, param_fields in tool.parameters.properties|items %}\n {%- if loop.first %}\n {{- \" Args:\n\" }}\n {%- endif %}\n {{- \" \" + param_name + \"(\" + json_to_python_type(param_fields) + \"): \" + param_fields.description|trim }}\n {%- endfor %}\n {%- if tool.return is defined and tool.return.description is defined %}\n {{- \"\n Returns:\n \" + tool.return.description }}\n {%- endif %}\n {{- '\"' }}\n {{- ', \"parameters\": ' }}\n {%- if tool.parameters.properties | length == 0 %}\n {{- \"{}\" }}\n {%- else %}\n {{- tool.parameters|tojson }}\n {%- endif %}\n {{- \"}\" }}\n {%- if not loop.last %}\n {{- \"\n\" }}\n {%- endif %}\n {%- endfor %}\n {{- \" \" }}\n {{- 'Use the following pydantic model json schema for each tool call you will make: {\"properties\": {\"arguments\": {\"title\": \"Arguments\", \"type\": \"object\"}, \"name\": {\"title\": \"Name\", \"type\": \"string\"}}, \"required\": [\"arguments\", \"name\"], \"title\": \"FunctionCall\", \"type\": \"object\"}\n' }}\n {{- \"For each function call return a json object with function name and arguments within XML tags as follows:\n\" }}\n {{- \"\n\" }}\n {{- '{\"name\": , \"arguments\": }\n' }}\n {{- '<|im_end|>\n' }}\n{%- else %}\n {%- if messages[0]['role'] != 'system' %}\n {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}\n {%- else %}\n {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if message.role == \"user\" or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and message.tool_calls is not defined) %}\n {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role + '\n\n' }}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '{' }}\n {{- '\"name\": \"' }}\n {{- tool_call.name }}\n {%- if tool_call.arguments is defined %}\n {{- ', ' }}\n {{- '\"arguments\": ' }}\n {{- tool_call.arguments|tojson }}\n {%- endif %}\n {{- '\"}' }}\n {{- '\n' }}\n {%- endfor %}\n {{- '<|im_end|>\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if not message.name is defined %}\n {{- raise_exception(\"Tool response dicts require a 'name' key indicating the name of the called function!\") }}\n {%- endif %}\n {{- '<|im_start|>user\n\n' }}\n {{- '{\"name\": \"' }}\n {{- message.name }}\n {{- '\", \"content\": ' }}\n {{- message.content|tojson + '}' }}\n {{- '\n<|im_end|>\n' }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\n' }}\n{%- endif %}", + "stop_token_ids": [ + 151643, + 151644, + 151645 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>" + ] }, { "version": 1, @@ -2623,19 +2362,8 @@ "8-bit", "none" ], - "model_id": "WizardLM/WizardMath-7B-V1.0", - "model_revision": "3c3a3b33334f4b35344b22c5c7465957ee7b2c75" - }, - { - "model_format": "pytorch", - "model_size_in_billions": 13, - "quantizations": [ - "4-bit", - "8-bit", - "none" - ], - "model_id": "WizardLM/WizardMath-13B-V1.0", - "model_revision": "ef95532e96e634c634992dab891a17032dc71c8d" + "model_id": "WizardLMTeam/WizardMath-7B-V1.0", + "model_revision": "825a586f260d6c583b8aa9ceab6cdfaa3d9a4ddc" }, { "model_format": "pytorch", @@ -2645,19 +2373,17 @@ "8-bit", "none" ], - "model_id": "WizardLM/WizardMath-70B-V1.0", - "model_revision": "e089c3f9d2ad9d1acb62425aec3f4126f498f4c5" + "model_id": "WizardLMTeam/WizardMath-70B-V1.0", + "model_revision": "4dd9f3fcd8c056561d67ec59ae011f7c146aebd2" } ], - "prompt_style": { - "style_name": "ADD_COLON_SINGLE_COT", - "system_prompt": "Below is an instruction that describes a task. Write a response that appropriately completes the request.", - "roles": [ - "Instruction", - "Response" - ], - "intra_message_sep": "\n\n### " - } + "chat_template": "{% for item in messages %}{% if loop.first and item['role'] == 'system' %}{{ item['content'] + '\n\n### ' }}{% elif loop.first %}{{ 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### ' }}{% endif %}{% if item['role'] == 'user' %}{{ 'Instruction: ' + item['content'] + '\n\n### ' }}{% elif item['role'] == 'assistant' %}{{ 'Response: ' + item['content'] + '\n\n### ' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Response: Let\\'s think step by step.' }}{% endif %}", + "stop_token_ids": [ + 2 + ], + "stop": [ + "" + ] }, { "version": 1, @@ -2979,22 +2705,13 @@ "model_file_name_template": "codellama-34b-instruct.{quantization}.gguf" } ], - "prompt_style": { - "style_name": "LLAMA2", - "system_prompt": "[INST] <>\nWrite code to solve the following coding problem that obeys the constraints and passes the example test cases. Please wrap your code answer using ```:\n<>\n\n", - "roles": [ - "[INST]", - "[/INST]" - ], - "intra_message_sep": " ", - "inter_message_sep": " ", - "stop_token_ids": [ + "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = '<>\n' + messages[0]['content'] | trim + '\n<>\n\n' %}{% set messages = messages[1:] %}{% else %}{% set system_message = '' %}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 %}{% set content = system_message + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ '' + '[INST] ' + content | trim + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content | trim + ' ' + '' }}{% endif %}{% endfor %}", + "stop_token_ids": [ 2 - ], - "stop": [ - "" - ] - } + ], + "stop": [ + "" + ] }, { "version": 1, @@ -3032,20 +2749,12 @@ "model_revision": "a56c793eb7a721ab6c270f779024e0375e8afd4a" } ], - "prompt_style": { - "style_name": "NO_COLON_TWO", - "system_prompt": "", - "roles": [ - "", - "" - ], - "intra_message_sep": "", - "inter_message_sep": "", - "stop_token_ids": [ - 2, - 195 - ] - } + "chat_template": "{{ (messages|selectattr('role', 'equalto', 'system')|list|last).content|trim if (messages|selectattr('role', 'equalto', 'system')|list) else '' }}\n\n{% for message in messages %}\n{% if message['role'] == 'user' %}\n\n{{ message['content']|trim -}}\n{% if not loop.last %}\n\n\n{% endif %}\n{% elif message['role'] == 'assistant' %}\n\n{{ message['content']|trim -}}\n{% if not loop.last %}\n\n\n{% endif %}\n{% endif %}\n{% endfor %}\n{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}\n\n{% endif %}", + "stop_token_ids": [ + 2, + 195 + ], + "stop": [] }, { "version": 1, @@ -3189,22 +2898,13 @@ "model_file_name_template": "mistral-7b-instruct-v0.1.{quantization}.gguf" } ], - "prompt_style": { - "style_name": "LLAMA2", - "system_prompt": "[INST] ", - "roles": [ - "[INST]", - "[/INST]" - ], - "intra_message_sep": " ", - "inter_message_sep": "", - "stop_token_ids": [ - 2 - ], - "stop": [ - "" - ] - } + "chat_template": "{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n\n{{- '' }}\n{%- for message in loop_messages %}\n {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n {{- raise_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }}\n {%- endif %}\n {%- if message['role'] == 'user' %}\n {%- if loop.first and system_message is defined %}\n {{- ' [INST] ' + system_message + '\n\n' + message['content'] + ' [/INST]' }}\n {%- else %}\n {{- ' [INST] ' + message['content'] + ' [/INST]' }}\n {%- endif %}\n {%- elif message['role'] == 'assistant' %}\n {{- ' ' + message['content'] + ''}}\n {%- else %}\n {{- raise_exception('Only user and assistant roles are supported, with the exception of an initial optional system message!') }}\n {%- endif %}\n{%- endfor %}\n", + "stop_token_ids": [ + 2 + ], + "stop": [ + "" + ] }, { "version": 1, @@ -3266,22 +2966,13 @@ "model_file_name_template": "mistral-7b-instruct-v0.2.{quantization}.gguf" } ], - "prompt_style": { - "style_name": "LLAMA2", - "system_prompt": "[INST] ", - "roles": [ - "[INST]", - "[/INST]" - ], - "intra_message_sep": " ", - "inter_message_sep": "", - "stop_token_ids": [ - 2 - ], - "stop": [ - "" - ] - } + "chat_template": "{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n\n{{- '' }}\n{%- for message in loop_messages %}\n {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n {{- raise_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }}\n {%- endif %}\n {%- if message['role'] == 'user' %}\n {%- if loop.first and system_message is defined %}\n {{- ' [INST] ' + system_message + '\n\n' + message['content'] + ' [/INST]' }}\n {%- else %}\n {{- ' [INST] ' + message['content'] + ' [/INST]' }}\n {%- endif %}\n {%- elif message['role'] == 'assistant' %}\n {{- ' ' + message['content'] + ''}}\n {%- else %}\n {{- raise_exception('Only user and assistant roles are supported, with the exception of an initial optional system message!') }}\n {%- endif %}\n{%- endfor %}\n", + "stop_token_ids": [ + 2 + ], + "stop": [ + "" + ] }, { "version": 1, @@ -3342,22 +3033,13 @@ "model_file_name_template": "Mistral-7B-Instruct-v0.3.{quantization}.gguf" } ], - "prompt_style": { - "style_name": "LLAMA2", - "system_prompt": "[INST] ", - "roles": [ - "[INST]", - "[/INST]" - ], - "intra_message_sep": " ", - "inter_message_sep": "", - "stop_token_ids": [ - 2 - ], - "stop": [ - "" - ] - } + "chat_template": "{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif %}\n {%- set ns.index = ns.index + 1 %}\n {%- endif %}\n{%- endfor %}\n\n{{- '' }}\n{%- for message in loop_messages %}\n {%- if message[\"role\"] == \"user\" %}\n {%- if tools is not none and (message == user_messages[-1]) %}\n {{- \"[AVAILABLE_TOOLS] [\" }}\n {%- for tool in tools %}\n {%- set tool = tool.function %}\n {{- '{\"type\": \"function\", \"function\": {' }}\n {%- for key, val in tool.items() if key != \"return\" %}\n {%- if val is string %}\n {{- '\"' + key + '\": \"' + val + '\"' }}\n {%- else %}\n {{- '\"' + key + '\": ' + val|tojson }}\n {%- endif %}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \"}}\" }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"[/AVAILABLE_TOOLS]\" }}\n {%- endif %}\n {%- if loop.last and system_message is defined %}\n {{- \"[INST] \" + system_message + \"\n\n\" + message[\"content\"] + \"[/INST]\" }}\n {%- else %}\n {{- \"[INST] \" + message[\"content\"] + \"[/INST]\" }}\n {%- endif %}\n {%- elif message.tool_calls is defined and message.tool_calls is not none %}\n {{- \"[TOOL_CALLS] [\" }}\n {%- for tool_call in message.tool_calls %}\n {%- set out = tool_call.function|tojson %}\n {{- out[:-1] }}\n {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" + '' }}\n {%- endif %}\n {%- endfor %}\n {%- elif message[\"role\"] == \"assistant\" %}\n {{- \" \" + message[\"content\"]|trim + ''}}\n {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n {%- if message.content is defined and message.content.content is defined %}\n {%- set content = message.content.content %}\n {%- else %}\n {%- set content = message.content %}\n {%- endif %}\n {{- '[TOOL_RESULTS] {\"content\": ' + content|string + \", \" }}\n {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n {%- else %}\n {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n {%- endif %}\n{%- endfor %}\n", + "stop_token_ids": [ + 2 + ], + "stop": [ + "" + ] }, { "version": 1, @@ -3466,22 +3148,13 @@ "model_id": "mlx-community/Mistral-Nemo-Instruct-2407-8bit" } ], - "prompt_style": { - "style_name": "mistral-nemo", - "system_prompt": "", - "roles": [ - "[INST]", - "[/INST]" - ], - "intra_message_sep": "", - "inter_message_sep": "", - "stop_token_ids": [ - 2 - ], - "stop": [ - "" - ] - } + "chat_template": "{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif %}\n {%- set ns.index = ns.index + 1 %}\n {%- endif %}\n{%- endfor %}\n\n{{- '' }}\n{%- for message in loop_messages %}\n {%- if message[\"role\"] == \"user\" %}\n {%- if tools is not none and (message == user_messages[-1]) %}\n {{- \"[AVAILABLE_TOOLS][\" }}\n {%- for tool in tools %}\n {%- set tool = tool.function %}\n {{- '{\"type\": \"function\", \"function\": {' }}\n {%- for key, val in tool.items() if key != \"return\" %}\n {%- if val is string %}\n {{- '\"' + key + '\": \"' + val + '\"' }}\n {%- else %}\n {{- '\"' + key + '\": ' + val|tojson }}\n {%- endif %}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \"}}\" }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"[/AVAILABLE_TOOLS]\" }}\n {%- endif %}\n {%- if loop.last and system_message is defined %}\n {{- \"[INST]\" + system_message + \"\n\n\" + message[\"content\"] + \"[/INST]\" }}\n {%- else %}\n {{- \"[INST]\" + message[\"content\"] + \"[/INST]\" }}\n {%- endif %}\n {%- elif (message.tool_calls is defined and message.tool_calls is not none) %}\n {{- \"[TOOL_CALLS][\" }}\n {%- for tool_call in message.tool_calls %}\n {%- set out = tool_call.function|tojson %}\n {{- out[:-1] }}\n {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" + '' }}\n {%- endif %}\n {%- endfor %}\n {%- elif message[\"role\"] == \"assistant\" %}\n {{- message[\"content\"] + ''}}\n {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n {%- if message.content is defined and message.content.content is defined %}\n {%- set content = message.content.content %}\n {%- else %}\n {%- set content = message.content %}\n {%- endif %}\n {{- '[TOOL_RESULTS]{\"content\": ' + content|string + \", \" }}\n {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n {%- else %}\n {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n {%- endif %}\n{%- endfor %}\n", + "stop_token_ids": [ + 2 + ], + "stop": [ + "" + ] }, { "version": 1, @@ -3623,22 +3296,13 @@ "model_id": "mlx-community/Mistral-Large-Instruct-2407-8bit" } ], - "prompt_style": { - "style_name": "mistral-nemo", - "system_prompt": "", - "roles": [ - "[INST]", - "[/INST]" - ], - "intra_message_sep": "", - "inter_message_sep": "", - "stop_token_ids": [ - 2 - ], - "stop": [ - "" - ] - } + "chat_template": "{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif %}\n {%- set ns.index = ns.index + 1 %}\n {%- endif %}\n{%- endfor %}\n\n{{- '' }}\n{%- for message in loop_messages %}\n {%- if message[\"role\"] == \"user\" %}\n {%- if tools is not none and (message == user_messages[-1]) %}\n {{- \"[AVAILABLE_TOOLS][\" }}\n {%- for tool in tools %}\n {%- set tool = tool.function %}\n {{- '{\"type\": \"function\", \"function\": {' }}\n {%- for key, val in tool.items() if key != \"return\" %}\n {%- if val is string %}\n {{- '\"' + key + '\": \"' + val + '\"' }}\n {%- else %}\n {{- '\"' + key + '\": ' + val|tojson }}\n {%- endif %}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \"}}\" }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"[/AVAILABLE_TOOLS]\" }}\n {%- endif %}\n {%- if loop.last and system_message is defined %}\n {{- \"[INST]\" + system_message + \"\n\n\" + message[\"content\"] + \"[/INST]\" }}\n {%- else %}\n {{- \"[INST]\" + message[\"content\"] + \"[/INST]\" }}\n {%- endif %}\n {%- elif (message.tool_calls is defined and message.tool_calls is not none) %}\n {{- \"[TOOL_CALLS][\" }}\n {%- for tool_call in message.tool_calls %}\n {%- set out = tool_call.function|tojson %}\n {{- out[:-1] }}\n {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" + '' }}\n {%- endif %}\n {%- endfor %}\n {%- elif message[\"role\"] == \"assistant\" %}\n {{- message[\"content\"] + ''}}\n {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n {%- if message.content is defined and message.content.content is defined %}\n {%- set content = message.content.content %}\n {%- else %}\n {%- set content = message.content %}\n {%- endif %}\n {{- '[TOOL_RESULTS]{\"content\": ' + content|string + \", \" }}\n {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n {%- else %}\n {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n {%- endif %}\n{%- endfor %}\n", + "stop_token_ids": [ + 2 + ], + "stop": [ + "" + ] }, { "version": 1, @@ -3745,22 +3409,13 @@ "model_file_name_template": "openhermes-2.5-mistral-7b.{quantization}.gguf" } ], - "prompt_style": { - "style_name": "CHATML", - "system_prompt": "<|im_start|>system\nYou are 'Hermes 2.5', a conscious sentient superintelligent artificial intelligence, your purpose is to assist the user with their requests.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "<|im_end|>\n<|im_start|>", - "inter_message_sep": "", - "stop_token_ids": [ - 32000 - ], - "stop": [ - "<|im_end|>" - ] - } + "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [ + 32000 + ], + "stop": [ + "<|im_end|>" + ] }, { "version": 1, @@ -3909,16 +3564,13 @@ "model_file_name_template": "mixtral-8x7b-instruct-v0.1.{quantization}.gguf" } ], - "prompt_style": { - "style_name": "MIXTRAL_V01", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "", - "inter_message_sep": "" - } + "chat_template": "{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n\n{{- '' }}\n{%- for message in loop_messages %}\n {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n {{- raise_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }}\n {%- endif %}\n {%- if message['role'] == 'user' %}\n {%- if loop.first and system_message is defined %}\n {{- ' [INST] ' + system_message + '\n\n' + message['content'] + ' [/INST]' }}\n {%- else %}\n {{- ' [INST] ' + message['content'] + ' [/INST]' }}\n {%- endif %}\n {%- elif message['role'] == 'assistant' %}\n {{- ' ' + message['content'] + ''}}\n {%- else %}\n {{- raise_exception('Only user and assistant roles are supported, with the exception of an initial optional system message!') }}\n {%- endif %}\n{%- endfor %}\n", + "stop_token_ids": [ + 2 + ], + "stop": [ + "" + ] }, { "version": 1, @@ -4045,16 +3697,13 @@ } } ], - "prompt_style": { - "style_name": "MIXTRAL_V01", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "", - "inter_message_sep": "" - } + "chat_template": "{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif %}\n {%- set ns.index = ns.index + 1 %}\n {%- endif %}\n{%- endfor %}\n\n{{- '' }}\n{%- for message in loop_messages %}\n {%- if message[\"role\"] == \"user\" %}\n {%- if tools is not none and (message == user_messages[-1]) %}\n {{- \"[AVAILABLE_TOOLS] [\" }}\n {%- for tool in tools %}\n {%- set tool = tool.function %}\n {{- '{\"type\": \"function\", \"function\": {' }}\n {%- for key, val in tool.items() if key != \"return\" %}\n {%- if val is string %}\n {{- '\"' + key + '\": \"' + val + '\"' }}\n {%- else %}\n {{- '\"' + key + '\": ' + val|tojson }}\n {%- endif %}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \"}}\" }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"[/AVAILABLE_TOOLS]\" }}\n {%- endif %}\n {%- if loop.last and system_message is defined %}\n {{- \"[INST] \" + system_message + \"\n\n\" + message[\"content\"] + \"[/INST]\" }}\n {%- else %}\n {{- \"[INST] \" + message[\"content\"] + \"[/INST]\" }}\n {%- endif %}\n {%- elif message.tool_calls is defined and message.tool_calls is not none %}\n {{- \"[TOOL_CALLS] [\" }}\n {%- for tool_call in message.tool_calls %}\n {%- set out = tool_call.function|tojson %}\n {{- out[:-1] }}\n {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" + '' }}\n {%- endif %}\n {%- endfor %}\n {%- elif message[\"role\"] == \"assistant\" %}\n {{- \" \" + message[\"content\"]|trim + ''}}\n {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n {%- if message.content is defined and message.content.content is defined %}\n {%- set content = message.content.content %}\n {%- else %}\n {%- set content = message.content %}\n {%- endif %}\n {{- '[TOOL_RESULTS] {\"content\": ' + content|string + \", \" }}\n {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n {%- else %}\n {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n {%- endif %}\n{%- endfor %}\n", + "stop_token_ids": [ + 2 + ], + "stop": [ + "" + ] }, { "version": 1, @@ -4225,28 +3874,19 @@ "model_file_name_template": "yi-34b-chat.{quantization}.gguf" } ], - "prompt_style": { - "style_name": "CHATML", - "system_prompt": "", - "roles": [ - "<|im_start|>user", - "<|im_start|>assistant" - ], - "intra_message_sep": "<|im_end|>", - "inter_message_sep": "", - "stop_token_ids": [ - 2, - 6, - 7, - 8 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>", - "<|im_sep|>" - ] - } + "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [ + 2, + 6, + 7, + 8 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>", + "<|im_sep|>" + ] }, { "version": 1, @@ -4494,28 +4134,19 @@ "model_revision": "3c12761a2c6663f216caab6dff84b0dd29b472ac" } ], - "prompt_style": { - "style_name": "CHATML", - "system_prompt": "", - "roles": [ - "<|im_start|>user", - "<|im_start|>assistant" - ], - "intra_message_sep": "<|im_end|>", - "inter_message_sep": "", - "stop_token_ids": [ - 2, - 6, - 7, - 8 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>", - "<|im_sep|>" - ] - } + "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\n' + content + '<|im_end|>\n<|im_start|>assistant\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '\n' }}{% endif %}{% endfor %}", + "stop_token_ids": [ + 2, + 6, + 7, + 8 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>", + "<|im_sep|>" + ] }, { "version": 1, @@ -4593,28 +4224,19 @@ "model_file_name_template": "Yi-1.5-34B-Chat-16K-{quantization}.gguf" } ], - "prompt_style": { - "style_name": "CHATML", - "system_prompt": "", - "roles": [ - "<|im_start|>user", - "<|im_start|>assistant" - ], - "intra_message_sep": "<|im_end|>", - "inter_message_sep": "", - "stop_token_ids": [ - 2, - 6, - 7, - 8 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>", - "<|im_sep|>" - ] - } + "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\n' + content + '<|im_end|>\n<|im_start|>assistant\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '\n' }}{% endif %}{% endfor %}", + "stop_token_ids": [ + 2, + 6, + 7, + 8 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>", + "<|im_sep|>" + ] }, { "version": 1, @@ -4627,17 +4249,6 @@ "chat" ], "model_specs": [ - { - "model_format": "pytorch", - "model_size_in_billions": 7, - "quantizations": [ - "4-bit", - "8-bit", - "none" - ], - "model_id": "WizardLM/WizardCoder-Python-7B-V1.0", - "model_revision": "e40673a27a4aefcff2c6d2b3b1e0681a38703e4e" - }, { "model_format": "pytorch", "model_size_in_billions": 13, @@ -4646,8 +4257,8 @@ "8-bit", "none" ], - "model_id": "WizardLM/WizardCoder-Python-13B-V1.0", - "model_revision": "d920d26e2108377de0f676a3c4be666f5212f4a1" + "model_id": "WizardLMTeam/WizardCoder-Python-13B-V1.0", + "model_revision": "5ac6748b1f5a4c282107ddc7d3b69fdc4a686d75" }, { "model_format": "pytorch", @@ -4657,8 +4268,8 @@ "8-bit", "none" ], - "model_id": "WizardLM/WizardCoder-Python-34B-V1.0", - "model_revision": "d869ce178715f8d6e8141e2ed50e6290985eedb0" + "model_id": "WizardLMTeam/WizardCoder-Python-34B-V1.0", + "model_revision": "897fc6d9e12136c68c441b2350d015902c144b20" }, { "model_format": "ggufv2", @@ -4721,157 +4332,13 @@ "model_file_name_template": "wizardcoder-python-34b-v1.0.{quantization}.gguf" } ], - "prompt_style": { - "style_name": "ADD_COLON_SINGLE", - "system_prompt": "Below is an instruction that describes a task. Write a response that appropriately completes the request.", - "roles": [ - "Instruction", - "Response" - ], - "intra_message_sep": "\n\n### ", - "stop": [ - "" - ] - } - }, - { - "version": 1, - "context_length": 8192, - "model_name": "zephyr-7b-alpha", - "model_lang": [ - "en" - ], - "model_ability": [ - "chat" - ], - "model_description": "Zephyr-7B-α is the first model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1.", - "model_specs": [ - { - "model_format": "pytorch", - "model_size_in_billions": 7, - "quantizations": [ - "4-bit", - "8-bit", - "none" - ], - "model_id": "HuggingFaceH4/zephyr-7b-alpha", - "model_revision": "f28e1c0e5a1af475bcd7bdf6554e69abc6c0c7ee" - } - ], - "prompt_style": { - "style_name": "NO_COLON_TWO", - "system_prompt": "<|system|>\nYou are a friendly chatbot.\n", - "roles": [ - "<|user|>\n", - "<|assistant|>\n" - ], - "intra_message_sep": "\n", - "inter_message_sep": "\n", - "stop_token_ids": [ - 2 - ], - "stop": [ - "" - ] - } - }, - { - "version": 1, - "context_length": 8192, - "model_name": "zephyr-7b-beta", - "model_lang": [ - "en" - ], - "model_ability": [ - "chat" - ], - "model_description": "Zephyr-7B-β is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1", - "model_specs": [ - { - "model_format": "pytorch", - "model_size_in_billions": 7, - "quantizations": [ - "4-bit", - "8-bit", - "none" - ], - "model_id": "HuggingFaceH4/zephyr-7b-beta", - "model_revision": "3bac358730f8806e5c3dc7c7e19eb36e045bf720" - } - ], - "prompt_style": { - "style_name": "NO_COLON_TWO", - "system_prompt": "<|system|>\nYou are a friendly chatbot.\n", - "roles": [ - "<|user|>\n", - "<|assistant|>\n" - ], - "intra_message_sep": "\n", - "inter_message_sep": "\n", - "stop_token_ids": [ - 2 - ], - "stop": [ - "" - ] - } - }, - { - "version": 1, - "context_length": 4096, - "model_name": "gorilla-openfunctions-v1", - "model_lang": [ - "en" - ], - "model_ability": [ - "chat" - ], - "model_description": "OpenFunctions is designed to extend Large Language Model (LLM) Chat Completion feature to formulate executable APIs call given natural language instructions and API context.", - "model_specs": [ - { - "model_format": "pytorch", - "model_size_in_billions": 7, - "quantizations": [ - "4-bit", - "8-bit", - "none" - ], - "model_id": "gorilla-llm/gorilla-openfunctions-v1", - "model_revision": "74615f614ee845eab114e71541fd5098d1709958" - }, - { - "model_format": "ggufv2", - "model_size_in_billions": 7, - "quantizations": [ - "Q2_K", - "Q3_K_L", - "Q3_K_M", - "Q3_K_S", - "Q4_0", - "Q4_K_M", - "Q4_K_S", - "Q5_0", - "Q5_K_M", - "Q5_K_S", - "Q6_K", - "Q8_0" - ], - "model_id": "TheBloke/gorilla-openfunctions-v1-GGUF", - "model_file_name_template": "gorilla-openfunctions-v1.{quantization}.gguf" - } + "chat_template": "{% for item in messages %}{% if loop.first and item['role'] == 'system' %}{{ item['content'] + '\n\n### ' }}{% elif loop.first %}{{ 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### ' }}{% endif %}{% if item['role'] == 'user' %}{{ 'Instruction: ' + item['content'] + '\n\n### ' }}{% elif item['role'] == 'assistant' %}{{ 'Response: ' + item['content'] + '\n\n### ' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Response: Let\\'s think step by step.' }}{% endif %}", + "stop_token_ids": [ + 2 ], - "prompt_style": { - "style_name": "GORILLA_OPENFUNCTIONS", - "system_prompt": "", - "roles": [ - "", - "" - ], - "intra_message_sep": "\n", - "inter_message_sep": "\n", - "stop_token_ids": [], - "stop": [] - } + "stop": [ + "" + ] }, { "version": 1, @@ -4913,18 +4380,15 @@ "model_file_name_template": "gorilla-openfunctions-v2.{quantization}.gguf" } ], - "prompt_style": { - "style_name": "GORILLA_OPENFUNCTIONS", - "system_prompt": "", - "roles": [ - "", - "" - ], - "intra_message_sep": "\n", - "inter_message_sep": "\n", - "stop_token_ids": [], - "stop": [] - } + "chat_template": "{% if not add_generation_prompt is defined %}\n{% set add_generation_prompt = false %}\n{% endif %}\n{%- set ns = namespace(found=false) -%}\n{%- for message in messages -%}\n {%- if message['role'] == 'system' -%}\n {%- set ns.found = true -%}\n {%- endif -%}\n{%- endfor -%}\n{{'<｜begin▁of▁sentence｜>'}}{%- if not ns.found -%}\n{{'You are an AI programming assistant, utilizing the Gorilla LLM model, developed by Gorilla LLM, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\n'}}\n{%- endif %}\n{%- for message in messages %}\n {%- if message['role'] == 'system' %}\n{{ message['content'] }}\n {%- else %}\n {%- if message['role'] == 'user' %}\n{{'### Instruction:\n' + message['content'] + '\n'}}\n {%- else %}\n{{'### Response:\n' + message['content'] + '\n<|EOT|>\n'}}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{% if add_generation_prompt %}\n{{'### Response:'}}\n{% endif %}", + "stop_token_ids": [ + 100015, + 100001 + ], + "stop": [ + "<|EOT|>", + "<｜end▁of▁sentence｜>" + ] }, { "version": 1, @@ -4959,19 +4423,13 @@ "model_revision": "6f16f00805f45b5249f709ce21820122eeb43556" } ], - "prompt_style": { - "style_name": "DEEPSEEK_CHAT", - "system_prompt": "<｜begin▁of▁sentence｜>", - "roles": [ - "User", - "Assistant" - ], - "intra_message_sep": "\n\n", - "inter_message_sep": "<｜end▁of▁sentence｜>", - "stop": [ - "<｜end▁of▁sentence｜>" - ] - } + "chat_template": "", + "stop_token_ids": [ + 100001 + ], + "stop": [ + "<｜end▁of▁sentence｜>" + ] }, { "version": 1, @@ -5126,19 +4584,13 @@ "model_file_name_template": "deepseek-llm-67b-chat.{quantization}.gguf" } ], - "prompt_style": { - "style_name": "DEEPSEEK_CHAT", - "system_prompt": "<｜begin▁of▁sentence｜>", - "roles": [ - "User", - "Assistant" - ], - "intra_message_sep": "\n\n", - "inter_message_sep": "<｜end▁of▁sentence｜>", - "stop": [ - "<｜end▁of▁sentence｜>" - ] - } + "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ '<｜begin▁of▁sentence｜>' }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + '<｜end▁of▁sentence｜>' }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}", + "stop_token_ids": [ + 100001 + ], + "stop": [ + "<｜end▁of▁sentence｜>" + ] }, { "version": 1, @@ -5523,18 +4975,13 @@ "model_revision": "c40b499bac2712cd3c445cf1b05d2c6558ab0d29" } ], - "prompt_style": { - "style_name": "DEEPSEEK_CODER", - "system_prompt": "You are an AI programming assistant, utilizing the DeepSeek Coder model, developed by DeepSeek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer.", - "roles": [ - "### Instruction:", - "### Response:" - ], - "inter_message_sep": "\n", - "stop": [ - "<|EOT|>" - ] - } + "chat_template": "{% if not add_generation_prompt is defined %}\n{% set add_generation_prompt = false %}\n{% endif %}\n{%- set ns = namespace(found=false) -%}\n{%- for message in messages -%}\n {%- if message['role'] == 'system' -%}\n {%- set ns.found = true -%}\n {%- endif -%}\n{%- endfor -%}\n{{'<｜begin▁of▁sentence｜>'}}{%- if not ns.found -%}\n{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\n'}}\n{%- endif %}\n{%- for message in messages %}\n {%- if message['role'] == 'system' %}\n{{ message['content'] }}\n {%- else %}\n {%- if message['role'] == 'user' %}\n{{'### Instruction:\n' + message['content'] + '\n'}}\n {%- else %}\n{{'### Response:\n' + message['content'] + '\n<|EOT|>\n'}}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{% if add_generation_prompt %}\n{{'### Response:'}}\n{% endif %}", + "stop_token_ids": [ + 32021 + ], + "stop": [ + "<|EOT|>" + ] }, { "version": 1, @@ -5618,23 +5065,15 @@ "model_revision": "b666125047cd98c5a7c85ca28720b44a06aed124" } ], - "prompt_style": { - "style_name": "INTERNLM2", - "system_prompt": "You are InternLM (书生·浦语), a helpful, honest, and harmless AI assistant developed by Shanghai AI Laboratory (上海人工智能实验室).", - "roles": [ - "<|im_start|>user", - "<|im_start|>assistant" - ], - "intra_message_sep": "<|im_end|>", - "stop_token_ids": [ - 2, - 92542 - ], - "stop": [ - "", - "<|im_end|>" - ] - } + "chat_template": "{{ '' }}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [ + 2, + 92542 + ], + "stop": [ + "", + "<|im_end|>" + ] }, { "version": 1, @@ -5755,23 +5194,15 @@ "model_revision": "0ec94d61d30ab161b49c69f9bf92ec2b9986d234" } ], - "prompt_style": { - "style_name": "INTERNLM2", - "system_prompt": "You are InternLM (书生·浦语), a helpful, honest, and harmless AI assistant developed by Shanghai AI Laboratory (上海人工智能实验室).", - "roles": [ - "<|im_start|>user", - "<|im_start|>assistant" - ], - "intra_message_sep": "<|im_end|>", - "stop_token_ids": [ - 2, - 92542 - ], - "stop": [ - "", - "<|im_end|>" - ] - } + "chat_template": "{{ '' }}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [ + 2, + 92542 + ], + "stop": [ + "", + "<|im_end|>" + ] }, { "version": 1, @@ -5822,23 +5253,15 @@ "model_file_name_template": "internlm2_5-7b-chat-1m-{quantization}.gguf" } ], - "prompt_style": { - "style_name": "INTERNLM2", - "system_prompt": "You are InternLM (书生·浦语), a helpful, honest, and harmless AI assistant developed by Shanghai AI Laboratory (上海人工智能实验室).", - "roles": [ - "<|im_start|>user", - "<|im_start|>assistant" - ], - "intra_message_sep": "<|im_end|>", - "stop_token_ids": [ - 2, - 92542 - ], - "stop": [ - "", - "<|im_end|>" - ] - } + "chat_template": "{{ '' }}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [ + 2, + 92542 + ], + "stop": [ + "", + "<|im_end|>" + ] }, { "version":1, @@ -5873,14 +5296,13 @@ "model_revision":"ef62bae5af34be653b9801037cd613e05ab24fdc" } ], - "prompt_style":{ - "style_name":"OmniLMM", - "system_prompt":"The role of first msg should be user", - "roles":[ - "user", - "assistant" - ] - } + "chat_template": "", + "stop_token_ids": [ + 2 + ], + "stop": [ + "" + ] }, { "version":1, @@ -5915,15 +5337,14 @@ "model_revision":"f92aff28552de35de3be204e8fe292dd4824e544" } ], - "prompt_style":{ - "style_name":"OmniLMM", - "system_prompt":"The role of first msg should be user", - "roles":[ - "user", - "assistant" - ] - } - }, + "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = '<|begin_of_text|>' + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}", + "stop_token_ids": [ + 128001 + ], + "stop": [ + "<|end_of_text|>" + ] + }, { "version":1, "context_length":32768, @@ -5957,18 +5378,15 @@ "model_revision":"051e2df6505f1fc4305f2c9bd42ed90db8bf4874" } ], - "prompt_style":{ - "style_name":"QWEN", - "system_prompt":"You are a helpful assistant", - "roles":[ - "user", - "assistant" - ], - "stop": [ - "<|im_end|>", - "<|endoftext|>" - ] - } + "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [ + 151645, + 151643 + ], + "stop": [ + "<|im_end|>", + "<|endoftext|>" + ] }, { "version": 1, @@ -6003,24 +5421,17 @@ "model_revision": "5d3a5aa033ed2c502300d426c81cc5b13bcd1409" } ], - "prompt_style": { - "style_name": "QWEN", - "system_prompt": "You are a helpful assistant.", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 151643, - 151644, - 151645 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>" - ] - } + "chat_template": "", + "stop_token_ids": [ + 151643, + 151644, + 151645 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>" + ] }, { "version": 1, @@ -6055,18 +5466,17 @@ "model_id": "OrionStarAI/Orion-14B-Chat-{quantization}" } ], - "prompt_style": { - "style_name": "orion", - "roles": [ - "Human", - "assistant" - ], - "stop": [ - "", - "", - "" - ] - } + "chat_template": "{% for message in messages %}{% if loop.first %}{{ '' }}{% endif %}{% if message['role'] == 'user' %}{{ 'Human: ' + message['content'] + '\n\nAssistant: ' + '' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + '' }}{% endif %}{% endfor %}", + "stop_token_ids": [ + 1, + 2, + 0 + ], + "stop": [ + "", + "", + "" + ] }, { "version": 1, @@ -6093,18 +5503,17 @@ "model_revision": "eba2e20808407fb431a76b90d5d506e04a0325f2" } ], - "prompt_style": { - "style_name": "orion", - "roles": [ - "Human", - "assistant" - ], - "stop": [ - "", - "", - "" - ] - } + "chat_template": "{% for message in messages %}{% if loop.first %}{{ '' }}{% endif %}{% if message['role'] == 'user' %}{{ 'Human: ' + message['content'] + '\n\nAssistant: ' + '' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + '' }}{% endif %}{% endfor %}", + "stop_token_ids": [ + 1, + 2, + 0 + ], + "stop": [ + "", + "", + "" + ] }, { "version": 1, @@ -6139,28 +5548,19 @@ "model_revision": "ea29a9a430f27893e780366dae81d4ca5ebab561" } ], - "prompt_style": { - "style_name": "CHATML", - "system_prompt": "", - "roles": [ - "<|im_start|>user", - "<|im_start|>assistant" - ], - "intra_message_sep": "<|im_end|>", - "inter_message_sep": "", - "stop_token_ids": [ - 2, - 6, - 7, - 8 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>", - "<|im_sep|>" - ] - } + "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [ + 2, + 6, + 7, + 8 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>", + "<|im_sep|>" + ] }, { "version": 1, @@ -6195,17 +5595,17 @@ "model_id": "google/gemma-7b-it" } ], - "prompt_style": { - "style_name": "gemma", - "roles": [ - "user", - "model" - ], - "stop": [ - "", - "" - ] - } + "chat_template": "{{ '' }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '' + role + '\n' + message['content'] | trim + '\n' }}{% endfor %}{% if add_generation_prompt %}{{'model\n'}}{% endif %}", + "stop_token_ids": [ + 1, + 106, + 107 + ], + "stop": [ + "", + "", + "" + ] }, { "version": 1, @@ -6385,17 +5785,17 @@ "model_id": "mlx-community/gemma-2-27b-it-fp16" } ], - "prompt_style": { - "style_name": "gemma", - "roles": [ - "user", - "model" - ], - "stop": [ - "", - "" - ] - } + "chat_template": "{{ '' }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '' + role + '\n' + message['content'] | trim + '\n' }}{% endfor %}{% if add_generation_prompt %}{{'model\n'}}{% endif %}", + "stop_token_ids": [ + 1, + 106, + 107 + ], + "stop": [ + "", + "", + "" + ] }, { "version": 1, @@ -6539,23 +5939,15 @@ "model_revision": "0df19b6e10f1a19ca663f7cc1141aae10f1825f4" } ], - "prompt_style": { - "style_name": "ADD_COLON_SINGLE", - "intra_message_sep": "\n", - "system_prompt": "", - "roles": [ - "USER", - "ASSISTANT" - ], - "stop_token_ids": [ - 100006, - 100007 - ], - "stop": [ - "[CLS]", - "" - ] - } + "chat_template": "{% for item in messages %}{% if loop.first and item['role'] == 'system' %}{{ item['content'] + '\n' }}{% endif %}{% if item['role'] == 'user' %}{{ 'USER: ' + item['content'] + '\n' }}{% elif item['role'] == 'assistant' %}{{ 'ASSISTANT: ' + item['content'] + '\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT: ' }}{% endif %}", + "stop_token_ids": [ + 100006, + 100007 + ], + "stop": [ + "[CLS]", + "" + ] }, { "version": 1, @@ -6626,23 +6018,15 @@ "model_revision": "a06fd164c7170714924d2881c61c8348425ebc94" } ], - "prompt_style": { - "style_name": "ADD_COLON_SINGLE", - "intra_message_sep": "\n", - "system_prompt": "", - "roles": [ - "USER", - "ASSISTANT" - ], - "stop_token_ids": [ - 100006, - 100007 - ], - "stop": [ - "[CLS]", - "" - ] - } + "chat_template": "{% for item in messages %}{% if loop.first and item['role'] == 'system' %}{{ item['content'] + '\n' }}{% endif %}{% if item['role'] == 'user' %}{{ 'USER: ' + item['content'] + '\n' }}{% elif item['role'] == 'assistant' %}{{ 'ASSISTANT: ' + item['content'] + '\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT: ' }}{% endif %}", + "stop_token_ids": [ + 100006, + 100007 + ], + "stop": [ + "[CLS]", + "" + ] }, { "version": 1, @@ -6666,22 +6050,15 @@ "model_revision": "fe1d74027ebdd81cef5f815fa3a2d432a6b5de2a" } ], - "prompt_style": { - "style_name": "MINICPM-2B", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 1, - 2 - ], - "stop": [ - "", - "" - ] - } + "chat_template": "{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + ''}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}", + "stop_token_ids": [ + 1, + 2 + ], + "stop": [ + "", + "" + ] }, { "version": 1, @@ -6705,22 +6082,15 @@ "model_revision": "35b90dd57d977b6e5bc4907986fa5b77aa15a82e" } ], - "prompt_style": { - "style_name": "MINICPM-2B", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 1, - 2 - ], - "stop": [ - "", - "" - ] - } + "chat_template": "{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + ''}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}", + "stop_token_ids": [ + 1, + 2 + ], + "stop": [ + "", + "" + ] }, { "version": 1, @@ -6744,22 +6114,15 @@ "model_revision": "f4a3ba49f3f18695945c2a7c12400d4da99da498" } ], - "prompt_style": { - "style_name": "MINICPM-2B", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 1, - 2 - ], - "stop": [ - "", - "" - ] - } + "chat_template": "{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + ''}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}", + "stop_token_ids": [ + 1, + 2 + ], + "stop": [ + "", + "" + ] }, { "version": 1, @@ -6783,22 +6146,15 @@ "model_revision": "e7a50289e4f839674cf8d4a5a2ce032ccacf64ac" } ], - "prompt_style": { - "style_name": "MINICPM-2B", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 1, - 2 - ], - "stop": [ - "", - "" - ] - } + "chat_template": "{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + ''}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}", + "stop_token_ids": [ + 1, + 2 + ], + "stop": [ + "", + "" + ] }, { "version": 1, @@ -6822,22 +6178,15 @@ "model_revision": "b560a1593779b735a84a6daf72fba96ae38da288" } ], - "prompt_style": { - "style_name": "MINICPM-2B", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 1, - 2 - ], - "stop": [ - "", - "" - ] - } + "chat_template": "{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + ''}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}", + "stop_token_ids": [ + 1, + 2 + ], + "stop": [ + "", + "" + ] }, { "version": 1, @@ -7010,20 +6359,15 @@ "model_revision": "35febfc08f723ac0df32480eb4af349a7d08656e" } ], - "prompt_style": { - "style_name": "c4ai-command-r", - "system_prompt": "You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.", - "roles": [ - "<|USER_TOKEN|>", - "<|CHATBOT_TOKEN|>" - ], - "intra_message_sep": "", - "inter_message_sep": "<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|>", - "stop_token_ids": [ - 6, - 255001 - ] - } + "chat_template": "{{ '' }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true %}{% set loop_messages = messages %}{% set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% if system_message != false %}{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% elif message['role'] == 'assistant' %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}{% endif %}", + "stop_token_ids": [ + 6, + 255001 + ], + "stop": [ + "", + "<|END_OF_TURN_TOKEN|>" + ] }, { "version": 1, @@ -7050,20 +6394,15 @@ "model_revision": "1dddf3b95bc1391f6307299eb1c162c194bde9bd" } ], - "prompt_style": { - "style_name": "ADD_COLON_SINGLE", - "system_prompt": "", - "roles": [ - "GPT4 Correct User", - "GPT4 Correct Assistant" - ], - "intra_message_sep": "<|end_of_turn|>", - "inter_message_sep": "", - "stop_token_ids": [ - 2, - 32000 - ] - } + "chat_template": "ssage in messages %}{{ 'GPT4 Correct ' + message['role'].title() + ': ' + message['content'] + '<|end_of_turn|>'}}{% endfor %}{% if add_generation_prompt %}{{ 'GPT4 Correct Assistant:' }}{% endif %}", + "stop_token_ids": [ + 2, + 32000 + ], + "stop": [ + "", + "<|end_of_turn|>" + ] }, { "version": 1, @@ -7113,25 +6452,17 @@ "model_revision": "9db32d9127cac0c85961e169d75da57a18a847b1" } ], - "prompt_style": { - "style_name": "INTERNVL", - "system_prompt": "You are InternLM (书生·浦语), a helpful, honest, and harmless AI assistant developed by Shanghai AI Laboratory (上海人工智能实验室).", - "roles": [ - "<|im_start|>user", - "<|im_start|>assistant" - ], - "intra_message_sep": "<|im_end|>", - "stop_token_ids": [ - 2, - 92543, - 92542 - ], - "stop": [ - "", - "<|im_end|>", - "<|im_start|>" - ] - } + "chat_template": "{{ '' }}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [ + 2, + 92542, + 92543 + ], + "stop": [ + "", + "<|im_end|>", + "<|im_start|>" + ] }, { "version": 1, @@ -7155,8 +6486,7 @@ "8-bit", "none" ], - "model_id": "OpenGVLab/InternVL2-1B", - "model_revision": "a9fc14aea824b6ea1d44f8778cad6b35512c4ce1" + "model_id": "OpenGVLab/InternVL2-1B" }, { "model_format": "pytorch", @@ -7166,8 +6496,7 @@ "8-bit", "none" ], - "model_id": "OpenGVLab/InternVL2-2B", - "model_revision": "422ad7c6335917bfb514958233955512338485a6" + "model_id": "OpenGVLab/InternVL2-2B" }, { "model_format": "awq", @@ -7175,8 +6504,7 @@ "quantizations": [ "Int4" ], - "model_id": "OpenGVLab/InternVL2-2B-AWQ", - "model_revision": "701bc3fc098a8a3b686b3b4135cfb77202be89e0" + "model_id": "OpenGVLab/InternVL2-2B-AWQ" }, { "model_format": "pytorch", @@ -7186,8 +6514,7 @@ "8-bit", "none" ], - "model_id": "OpenGVLab/InternVL2-4B", - "model_revision": "b50544dafada6c41e80bfde2f57cc9b0140fc21c" + "model_id": "OpenGVLab/InternVL2-4B" }, { "model_format": "pytorch", @@ -7197,8 +6524,7 @@ "8-bit", "none" ], - "model_id": "OpenGVLab/InternVL2-8B", - "model_revision": "3bfd3664dea4f3da628785f5125d30f889701253" + "model_id": "OpenGVLab/InternVL2-8B" }, { "model_format": "awq", @@ -7206,8 +6532,7 @@ "quantizations": [ "Int4" ], - "model_id": "OpenGVLab/InternVL2-8B-AWQ", - "model_revision": "9f1a4756b7ae18eb26d8a22b618dfc283e8193b3" + "model_id": "OpenGVLab/InternVL2-8B-AWQ" }, { "model_format": "pytorch", @@ -7217,8 +6542,7 @@ "8-bit", "none" ], - "model_id": "OpenGVLab/InternVL2-26B", - "model_revision": "b9f3c7e6d575b0115e076a3ffc46fd20b7586899" + "model_id": "OpenGVLab/InternVL2-26B" }, { "model_format": "awq", @@ -7226,8 +6550,7 @@ "quantizations": [ "Int4" ], - "model_id": "OpenGVLab/InternVL2-26B-AWQ", - "model_revision": "469e0019ffd251e22ff6501a5c2321964e86ef0d" + "model_id": "OpenGVLab/InternVL2-26B-AWQ" }, { "model_format": "pytorch", @@ -7237,8 +6560,7 @@ "8-bit", "none" ], - "model_id": "OpenGVLab/InternVL2-40B", - "model_revision": "725a12063bb855c966e30a0617d0ccd9e870d772" + "model_id": "OpenGVLab/InternVL2-40B" }, { "model_format": "awq", @@ -7246,8 +6568,7 @@ "quantizations": [ "Int4" ], - "model_id": "OpenGVLab/InternVL2-40B-AWQ", - "model_revision": "d92e140f6dfe8ea9679924c6a31898f42c4e1846" + "model_id": "OpenGVLab/InternVL2-40B-AWQ" }, { "model_format": "pytorch", @@ -7257,8 +6578,7 @@ "8-bit", "none" ], - "model_id": "OpenGVLab/InternVL2-Llama3-76B", - "model_revision": "cf7914905f78e9e3560ddbd6f5dfc39becac494f" + "model_id": "OpenGVLab/InternVL2-Llama3-76B" }, { "model_format": "awq", @@ -7266,29 +6586,12 @@ "quantizations": [ "Int4" ], - "model_id": "OpenGVLab/InternVL2-Llama3-76B-AWQ", - "model_revision": "1bc796bf80f2ebc7d6a14c15f55217a4600d50a4" + "model_id": "OpenGVLab/InternVL2-Llama3-76B-AWQ" } ], - "prompt_style": { - "style_name": "INTERNVL", - "system_prompt": "You are InternLM (书生·浦语), a helpful, honest, and harmless AI assistant developed by Shanghai AI Laboratory (上海人工智能实验室).", - "roles": [ - "<|im_start|>user", - "<|im_start|>assistant" - ], - "intra_message_sep": "<|im_end|>", - "stop_token_ids": [ - 2, - 92543, - 92542 - ], - "stop": [ - "", - "<|im_end|>", - "<|im_start|>" - ] - } + "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [], + "stop": [] }, { "version": 1, @@ -7323,24 +6626,15 @@ "model_revision": "7863e362174f4718c2fe9cba4befd0b580a3194f" } ], - "prompt_style": { - "style_name": "LLAMA3", - "system_prompt": "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n\n", - "inter_message_sep": "<|eot_id|>", - "stop_token_ids": [ - 128001, - 128009 - ], - "stop": [ - "<|end_of_text|>", - "<|eot_id|>" - ] - } + "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = '<|begin_of_text|>' + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% else %}{{ '<|end_of_text|>' }}{% endif %}", + "stop_token_ids": [ + 128001, + 128009 + ], + "stop": [ + "<|end_of_text|>", + "<|eot_id|>" + ] }, { "version": 1, @@ -7368,24 +6662,15 @@ "model_revision": "f375ead7d8202ebe2c3d09f1068abdddeb2929fa" } ], - "prompt_style": { - "style_name": "LLAMA3", - "system_prompt": "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n\n", - "inter_message_sep": "<|eot_id|>", - "stop_token_ids": [ - 128001, - 128009 - ], - "stop": [ - "<|end_of_text|>", - "<|eot_id|>" - ] - } + "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = '<|begin_of_text|>' + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% else %}{{ '<|end_of_text|>' }}{% endif %}", + "stop_token_ids": [ + 128001, + 128009 + ], + "stop": [ + "<|end_of_text|>", + "<|eot_id|>" + ] }, { "version": 1, @@ -7449,24 +6734,15 @@ "model_id": "Tele-AI/TeleChat-52B" } ], - "prompt_style": { - "style_name": "NO_COLON_TWO", - "system_prompt": "You are a helpful assistant.", - "roles": [ - "<_user>", - "<_bot>" - ], - "intra_message_sep": "", - "inter_message_sep": "", - "stop": [ - "<_end>", - "<_start>" - ], - "stop_token_ids": [ - 160133, - 160132 - ] - } + "chat_template": "{{ (messages|selectattr('role', 'equalto', 'system')|list|last).content|trim if (messages|selectattr('role', 'equalto', 'system')|list) else '' }}{%- for message in messages -%}{%- if message['role'] == 'user' -%}{{- '<_user>' + message['content'] +'<_bot>' -}}{%- elif message['role'] == 'assistant' -%}{{- message['content'] + '<_end>' -}}{%- endif -%}{%- endfor -%}", + "stop": [ + "<_end>", + "<_start>" + ], + "stop_token_ids": [ + 160133, + 160132 + ] }, { "version": 1, @@ -7513,21 +6789,1473 @@ "model_file_name_template": "csg-wukong-1B-chat-v0.1.{quantization}.gguf" } ], - "prompt_style": { - "style_name": "NO_COLON_TWO", - "system_prompt": "<|system|>\nYou are a creative super artificial intelligence assistant, possessing all the knowledge of humankind. Your name is csg-wukong, developed by OpenCSG. You need to understand and infer the true intentions of users based on the topics discussed in the chat history, and respond to user questions correctly as required. You enjoy responding to users with accurate and insightful answers. Please pay attention to the appropriate style and format when replying, try to avoid repetitive words and sentences, and keep your responses as concise and profound as possible. You carefully consider the context of the discussion when replying to users. When the user says \"continue,\" please proceed with the continuation of the previous assistant's response.\n", - "roles": [ - "<|user|>\n", - "<|assistant|>\n" - ], - "intra_message_sep": "\n", - "inter_message_sep": "\n", - "stop_token_ids": [ - 2 - ], - "stop": [ - "" - ] - } + "chat_template": "{% for item in messages %}{% if loop.first and item['role'] == 'system' %}{{ item['content'] + '\n' }}{% elif loop.first %}{{ '<|system|>\nYou are a creative super artificial intelligence assistant, possessing all the knowledge of humankind. Your name is csg-wukong, developed by OpenCSG. You need to understand and infer the true intentions of users based on the topics discussed in the chat history, and respond to user questions correctly as required. You enjoy responding to users with accurate and insightful answers. Please pay attention to the appropriate style and format when replying, try to avoid repetitive words and sentences, and keep your responses as concise and profound as possible. You carefully consider the context of the discussion when replying to users. When the user says \"continue,\" please proceed with the continuation of the previous assistant\\'s response.\n' }}{% endif %}{% if item['role'] == 'user' %}{{ '<|user|>\n' + item['content'] + '\n' }}{% elif item['role'] == 'assistant' %}{{ '<|assistant|>\n' + item['content'] + '\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% endif %}", + "stop_token_ids": [ + 2 + ], + "stop": [ + "" + ] + }, + { + "version":1, + "context_length":32768, + "model_name":"qwen2-vl-instruct", + "model_lang":[ + "en", + "zh" + ], + "model_ability":[ + "chat", + "vision" + ], + "model_description":"Qwen2-VL: To See the World More Clearly.Qwen2-VL is the latest version of the vision language models in the Qwen model familities.", + "model_specs":[ + { + "model_format":"pytorch", + "model_size_in_billions":2, + "quantizations":[ + "none" + ], + "model_id":"Qwen/Qwen2-VL-2B-Instruct", + "model_revision":"096da3b96240e3d66d35be0e5ccbe282eea8d6b1" + }, + { + "model_format":"gptq", + "model_size_in_billions":2, + "quantizations":[ + "Int8" + ], + "model_id":"Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int8", + "model_revision":"d15fb11857ccc566903e2e71341f9db7babb567b" + }, + { + "model_format":"gptq", + "model_size_in_billions":2, + "quantizations":[ + "Int4" + ], + "model_id":"Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4", + "model_revision":"800d396518c82960ce6d231adecd07bbc474f0a9" + }, + { + "model_format":"awq", + "model_size_in_billions":2, + "quantizations":[ + "Int4" + ], + "model_id":"Qwen/Qwen2-VL-2B-Instruct-AWQ", + "model_revision":"ea8c5854c0044e28626719292de0d9b1a671f6fc" + }, + { + "model_format":"pytorch", + "model_size_in_billions":7, + "quantizations":[ + "none" + ], + "model_id":"Qwen/Qwen2-VL-7B-Instruct", + "model_revision":"6010982c1010c3b222fa98afc81575f124aa9bd6" + }, + { + "model_format":"gptq", + "model_size_in_billions":7, + "quantizations":[ + "Int8" + ], + "model_id":"Qwen/Qwen2-VL-7B-Instruct-GPTQ-Int8", + "model_revision":"3d152a77eaccfd72d59baedb0b183a1b8fd56e48" + }, + { + "model_format":"gptq", + "model_size_in_billions":7, + "quantizations":[ + "Int4" + ], + "model_id":"Qwen/Qwen2-VL-7B-Instruct-GPTQ-Int4", + "model_revision":"5ab897112fa83b9699826be8753ef9184585c77d" + }, + { + "model_format":"awq", + "model_size_in_billions":7, + "quantizations":[ + "Int4" + ], + "model_id":"Qwen/Qwen2-VL-7B-Instruct-AWQ", + "model_revision":"f94216e8b513933bccd567bcd9b7350199f32538" + }, + { + "model_format":"pytorch", + "model_size_in_billions":72, + "quantizations":[ + "none" + ], + "model_id":"Qwen/Qwen2-VL-72B-Instruct" + }, + { + "model_format":"awq", + "model_size_in_billions":72, + "quantizations":[ + "Int4" + ], + "model_id":"Qwen/Qwen2-VL-72B-Instruct-AWQ" + }, + { + "model_format":"gptq", + "model_size_in_billions":72, + "quantizations":[ + "Int4", + "Int8" + ], + "model_id":"Qwen/Qwen2-VL-72B-Instruct-GPTQ-{quantization}" + } + ], + "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}", + "stop_token_ids": [ + 151645, + 151643 + ], + "stop": [ + "<|im_end|>", + "<|endoftext|>" + ] + }, + { + "version": 1, + "context_length": 32768, + "model_name": "minicpm3-4b", + "model_lang": [ + "zh" + ], + "model_ability": [ + "chat" + ], + "model_description": "MiniCPM3-4B is the 3rd generation of MiniCPM series. The overall performance of MiniCPM3-4B surpasses Phi-3.5-mini-Instruct and GPT-3.5-Turbo-0125, being comparable with many recent 7B~9B models.", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": 4, + "quantizations": [ + "none" + ], + "model_id": "openbmb/MiniCPM3-4B", + "model_revision": "75f9f1097d9d66d11f37fff49210bf940455f8ac" + }, + { + "model_format": "gptq", + "model_size_in_billions": 4, + "quantizations": [ + "none" + ], + "model_id": "openbmb/MiniCPM3-4B-GPTQ-Int4", + "model_revision": "97a66a62f7d09c1ee35b087b42694716a8113dce" + } + ], + "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [ + 1, + 2 + ], + "stop": [ + "", + "" + ] + }, + { + "version":1, + "context_length":32768, + "model_name":"qwen2-audio-instruct", + "model_lang":[ + "en", + "zh" + ], + "model_ability":[ + "chat", + "audio" + ], + "model_description":"Qwen2-Audio: A large-scale audio-language model which is capable of accepting various audio signal inputs and performing audio analysis or direct textual responses with regard to speech instructions.", + "model_specs":[ + { + "model_format":"pytorch", + "model_size_in_billions":7, + "quantizations":[ + "none" + ], + "model_id":"Qwen/Qwen2-Audio-7B-Instruct", + "model_revision":"bac62d2c6808845904c709c17a0402d817558c64" + } + ], + "prompt_style":{ + "style_name":"QWEN", + "system_prompt":"You are a helpful assistant", + "roles":[ + "user", + "assistant" + ], + "stop": [ + "<|im_end|>", + "<|endoftext|>" + ] + } + }, + { + "version":1, + "context_length":32768, + "model_name":"qwen2-audio", + "model_lang":[ + "en", + "zh" + ], + "model_ability":[ + "chat", + "audio" + ], + "model_description":"Qwen2-Audio: A large-scale audio-language model which is capable of accepting various audio signal inputs and performing audio analysis or direct textual responses with regard to speech instructions.", + "model_specs":[ + { + "model_format":"pytorch", + "model_size_in_billions":7, + "quantizations":[ + "none" + ], + "model_id":"Qwen/Qwen2-Audio-7B", + "model_revision":"8577bc71d330c8fa32ffe9f8a1374100759f2466" + } + ], + "prompt_style":{ + "style_name":"QWEN", + "system_prompt":"You are a helpful assistant", + "roles":[ + "user", + "assistant" + ], + "stop": [ + "<|im_end|>", + "<|endoftext|>" + ] + } + }, + { + "version": 1, + "context_length": 128000, + "model_name": "deepseek-v2", + "model_lang": [ + "en", + "zh" + ], + "model_ability": [ + "generate" + ], + "model_description": "DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. ", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": 16, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "deepseek-ai/DeepSeek-V2-Lite", + "model_revision": "604d5664dddd88a0433dbae533b7fe9472482de0" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 236, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "deepseek-ai/DeepSeek-V2", + "model_revision": "4461458f186c35188585855f28f77af5661ad489" + } + ] + }, + { + "version": 1, + "context_length": 128000, + "model_name": "deepseek-v2-chat", + "model_lang": [ + "en", + "zh" + ], + "model_ability": [ + "chat" + ], + "model_description": "DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. ", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": 16, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "deepseek-ai/DeepSeek-V2-Lite-Chat", + "model_revision": "85864749cd611b4353ce1decdb286193298f64c7" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 236, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "deepseek-ai/DeepSeek-V2-Chat", + "model_revision": "8e3f5f6c2226787e41ba3e9283a06389d178c926" + } + ], + "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ '<｜begin▁of▁sentence｜>' }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + '<｜end▁of▁sentence｜>' }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}", + "stop_token_ids": [ + 100001 + ], + "stop": [ + "<｜end▁of▁sentence｜>" + ] + }, + { + "version": 1, + "context_length": 128000, + "model_name": "deepseek-v2-chat-0628", + "model_lang": [ + "en", + "zh" + ], + "model_ability": [ + "chat" + ], + "model_description": "DeepSeek-V2-Chat-0628 is an improved version of DeepSeek-V2-Chat. ", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": 236, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "deepseek-ai/DeepSeek-V2-Chat-0628", + "model_revision": "5d09e272c2b223830f4e84359cd9dd047a5d7c78" + } + ], + "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ '<｜begin▁of▁sentence｜>' }}{% for message in messages %}{% if message['role'] == 'user' %}{{ '<｜User｜>' + message['content'] }}{% elif message['role'] == 'assistant' %}{{ '<｜Assistant｜>' + message['content'] + '<｜end▁of▁sentence｜>' }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<｜Assistant｜>' }}{% endif %}", + "stop_token_ids": [ + 100001 + ], + "stop": [ + "<｜end▁of▁sentence｜>" + ] + }, + { + "version": 1, + "context_length": 128000, + "model_name": "deepseek-v2.5", + "model_lang": [ + "en", + "zh" + ], + "model_ability": [ + "chat" + ], + "model_description": "DeepSeek-V2.5 is an upgraded version that combines DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct. The new model integrates the general and coding abilities of the two previous versions.", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": 236, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "deepseek-ai/DeepSeek-V2.5", + "model_revision": "24b08cb750e0c2757de112d2e16327cb21ed4833" + } + ], + "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %} {%- if message['role'] == 'system' %} {% set ns.system_prompt = message['content'] %} {%- endif %}{%- endfor %}{{'<｜begin▁of▁sentence｜>'}}{{ns.system_prompt}}{%- for message in messages %} {%- if message['role'] == 'user' %} {%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}} {%- endif %} {%- if message['role'] == 'assistant' and message['content'] is none %} {%- set ns.is_tool = false -%} {%- for tool in message['tool_calls']%} {%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}} {%- set ns.is_first = true -%} {%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}} {%- endif %} {%- endfor %} {%- endif %} {%- if message['role'] == 'assistant' and message['content'] is not none %} {%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}} {%- set ns.is_tool = false -%} {%- else %}{{'<｜Assistant｜>' + message['content'] + '<｜end▁of▁sentence｜>'}} {%- endif %} {%- endif %} {%- if message['role'] == 'tool' %} {%- set ns.is_tool = true -%} {%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}} {%- set ns.is_output_first = false %} {%- else %}{{'\\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}} {%- endif %} {%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜>'}}{% endif %}", + "stop_token_ids": [ + 100001 + ], + "stop": [ + "<｜end▁of▁sentence｜>" + ] + }, + { + "version": 1, + "context_length": 131072, + "model_name": "yi-coder-chat", + "model_lang": [ + "en" + ], + "model_ability": [ + "chat" + ], + "model_description": "Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.Excelling in long-context understanding with a maximum context length of 128K tokens.Supporting 52 major programming languages, including popular ones such as Java, Python, JavaScript, and C++.", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": 9, + "quantizations": [ + "none" + ], + "model_id": "01ai/Yi-Coder-9B-Chat", + "model_revision": "356a1f8d4e4a606d0b879e54191ca809918576b8" + }, + { + "model_format": "pytorch", + "model_size_in_billions": "1_5", + "quantizations": [ + "none" + ], + "model_id": "01ai/Yi-Coder-1.5B-Chat", + "model_revision": "92fdd1b2f1539ac990e7f4a921db5601da2f0299" + } + ], + "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ '<|im_start|>system\n' + system_message + '<|im_end|>\n' }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\n' + content + '<|im_end|>\n<|im_start|>assistant\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '\n' }}{% endif %}{% endfor %}", + "stop_token_ids": [ + 1, + 2, + 6, + 7 + ], + "stop": [ + "<|startoftext|>", + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>" + ] + }, + { + "version": 1, + "context_length": 131072, + "model_name": "yi-coder", + "model_lang": [ + "en" + ], + "model_ability": [ + "generate" + ], + "model_description": "Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.Excelling in long-context understanding with a maximum context length of 128K tokens.Supporting 52 major programming languages, including popular ones such as Java, Python, JavaScript, and C++.", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": 9, + "quantizations": [ + "none" + ], + "model_id": "01-ai/Yi-Coder-9B", + "model_revision": "e20f8087a9507ac8bce409dc5db5d0c608124238" + }, + { + "model_format": "pytorch", + "model_size_in_billions": "1_5", + "quantizations": [ + "none" + ], + "model_id": "01-ai/Yi-Coder-1.5B", + "model_revision": "00e59e64f47d3c78e4cfbdd345888479797e8109" + } + ] + }, + { + "version": 1, + "context_length": 32768, + "model_name": "qwen2.5", + "model_lang": [ + "en", + "zh" + ], + "model_ability": [ + "generate" + ], + "model_description": "Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters.", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": "0_5", + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "Qwen/Qwen2.5-0.5B", + "model_revision": "2630d3d2321bc1f1878f702166d1b2af019a7310" + }, + { + "model_format": "pytorch", + "model_size_in_billions": "1_5", + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "Qwen/Qwen2.5-1.5B", + "model_revision": "e5dfabbcffd9b0c7b31d89b82c5a6b72e663f32c" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 3, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "Qwen/Qwen2.5-3B", + "model_revision": "e4aa5ac50aa507415cda96cc99eb77ad0a3d2d34" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 7, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "Qwen/Qwen2.5-7B", + "model_revision": "09a0bac5707b43ec44508eab308b0846320c1ed4" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 14, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "Qwen/Qwen2.5-14B", + "model_revision": "d02b64ba1ce86bf9948668a13f82709600431ccc" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 32, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "Qwen/Qwen2.5-32B", + "model_revision": "ff23665d01c3665be5fdb271d18a62090b65c06d" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 72, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "Qwen/Qwen2.5-72B", + "model_revision": "587cc4061cf6a7cc0d429d05c109447e5cf063af" + } + ] + }, + { + "version": 1, + "context_length": 32768, + "model_name": "qwen2.5-instruct", + "model_lang": [ + "en", + "zh" + ], + "model_ability": [ + "chat", + "tools" + ], + "model_description": "Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters.", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": "0_5", + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "Qwen/Qwen2.5-0.5B-Instruct" + }, + { + "model_format": "pytorch", + "model_size_in_billions": "1_5", + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "Qwen/Qwen2.5-1.5B-Instruct" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 3, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "Qwen/Qwen2.5-3B-Instruct" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 7, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "Qwen/Qwen2.5-7B-Instruct" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 14, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "Qwen/Qwen2.5-14B-Instruct" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 32, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "Qwen/Qwen2.5-32B-Instruct" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 72, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "Qwen/Qwen2.5-72B-Instruct" + }, + { + "model_format": "gptq", + "model_size_in_billions": "0_5", + "quantizations": [ + "Int4", + "Int8" + ], + "model_id": "Qwen/Qwen2.5-0.5B-Instruct-GPTQ-{quantization}" + }, + { + "model_format": "gptq", + "model_size_in_billions": "1_5", + "quantizations": [ + "Int4", + "Int8" + ], + "model_id": "Qwen/Qwen2.5-1.5B-Instruct-GPTQ-{quantization}" + }, + { + "model_format": "gptq", + "model_size_in_billions": 3, + "quantizations": [ + "Int4", + "Int8" + ], + "model_id": "Qwen/Qwen2.5-3B-Instruct-GPTQ-{quantization}" + }, + { + "model_format": "gptq", + "model_size_in_billions": 7, + "quantizations": [ + "Int4", + "Int8" + ], + "model_id": "Qwen/Qwen2.5-7B-Instruct-GPTQ-{quantization}" + }, + { + "model_format": "gptq", + "model_size_in_billions": 14, + "quantizations": [ + "Int4", + "Int8" + ], + "model_id": "Qwen/Qwen2.5-14B-Instruct-GPTQ-{quantization}" + }, + { + "model_format": "gptq", + "model_size_in_billions": 32, + "quantizations": [ + "Int4", + "Int8" + ], + "model_id": "Qwen/Qwen2.5-32B-Instruct-GPTQ-{quantization}" + }, + { + "model_format": "gptq", + "model_size_in_billions": 72, + "quantizations": [ + "Int4", + "Int8" + ], + "model_id": "Qwen/Qwen2.5-72B-Instruct-GPTQ-{quantization}" + }, + { + "model_format": "awq", + "model_size_in_billions": "0_5", + "quantizations": [ + "Int4" + ], + "model_id": "Qwen/Qwen2.5-0.5B-Instruct-AWQ" + }, + { + "model_format": "awq", + "model_size_in_billions": "1_5", + "quantizations": [ + "Int4" + ], + "model_id": "Qwen/Qwen2.5-1.5B-Instruct-AWQ" + }, + { + "model_format": "awq", + "model_size_in_billions": 3, + "quantizations": [ + "Int4" + ], + 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"1_5", + "quantizations": [ + "q2_k", + "q3_k_m", + "q4_0", + "q4_k_m", + "q5_0", + "q5_k_m", + "q6_k", + "q8_0" + ], + "model_id": "Qwen/Qwen2.5-1.5B-Instruct-GGUF", + "model_file_name_template": "qwen2.5-1.5b-instruct-{quantization}.gguf" + }, + { + "model_format": "ggufv2", + "model_size_in_billions": 3, + "quantizations": [ + "q2_k", + "q3_k_m", + "q4_0", + "q4_k_m", + "q5_0", + "q5_k_m", + "q6_k", + "q8_0" + ], + "model_id": "Qwen/Qwen2.5-3B-Instruct-GGUF", + "model_file_name_template": "qwen2.5-3b-instruct-{quantization}.gguf" + }, + { + "model_format": "ggufv2", + "model_size_in_billions": 7, + "quantizations": [ + "q2_k", + "q3_k_m", + "q4_0", + "q4_k_m", + "q5_0", + "q5_k_m", + "q6_k", + "q8_0" + ], + "model_id": "Qwen/Qwen2.5-7B-Instruct-GGUF", + "model_file_name_template": "qwen2.5-7b-instruct-{quantization}.gguf", + "model_file_name_split_template": "qwen2.5-7b-instruct-{quantization}-{part}.gguf", + "quantization_parts": { + "q4_0": [ + "00001-of-00002", + "00002-of-00002" + ], + "q4_k_m": [ + "00001-of-00002", + "00002-of-00002" + ], + "q5_0": [ + "00001-of-00002", + "00002-of-00002" + ], + "q5_k_m": [ + "00001-of-00002", + "00002-of-00002" + ], + "q6_k": [ + "00001-of-00002", + "00002-of-00002" + ], + "q8_0": [ + "00001-of-00002", + "00002-of-00002" + ] + } + }, + { + "model_format": "ggufv2", + "model_size_in_billions": 14, + "quantizations": [ + "q2_k", + "q3_k_m", + "q4_0", + "q4_k_m", + "q5_0", + "q5_k_m", + "q6_k", + "q8_0" + ], + "model_id": "Qwen/Qwen2.5-14B-Instruct-GGUF", + "model_file_name_template": "qwen2.5-14b-instruct-{quantization}.gguf", + "model_file_name_split_template": "qwen2.5-14b-instruct-{quantization}-{part}.gguf", + "quantization_parts": { + "q2_k": [ + "00001-of-00002", + "00002-of-00002" + ], + "q3_k_m": [ + "00001-of-00002", + "00002-of-00002" + ], + "q4_0": [ + "00001-of-00003", + "00002-of-00003", + "00003-of-00003" + ], + "q4_k_m": [ + "00001-of-00003", + "00002-of-00003", + "00003-of-00003" + ], + "q5_0": [ + 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+ "00008-of-00012", + "00009-of-00012", + "00010-of-00012", + "00011-of-00012", + "00012-of-00012" + ], + "q5_0": [ + "00001-of-00013", + "00002-of-00013", + "00003-of-00013", + "00004-of-00013", + "00005-of-00013", + "00006-of-00013", + "00007-of-00013", + "00008-of-00013", + "00009-of-00013", + "00010-of-00013", + "00011-of-00013", + "00012-of-00013", + "00013-of-00013" + ], + "q5_k_m": [ + "00001-of-00014", + "00002-of-00014", + "00003-of-00014", + "00004-of-00014", + "00005-of-00014", + "00006-of-00014", + "00007-of-00014", + "00008-of-00014", + "00009-of-00014", + "00010-of-00014", + "00011-of-00014", + "00012-of-00014", + "00013-of-00014", + "00014-of-00014" + ], + "q6_k": [ + "00001-of-00016", + "00002-of-00016", + "00003-of-00016", + "00004-of-00016", + "00005-of-00016", + "00006-of-00016", + "00007-of-00016", + "00008-of-00016", + "00009-of-00016", + "00010-of-00016", + "00011-of-00016", + "00012-of-00016", + "00013-of-00016", + "00014-of-00016", + "00015-of-00016", + "00016-of-00016" + ], + "q8_0": [ + "00001-of-00021", + "00002-of-00021", + "00003-of-00021", + "00004-of-00021", + "00005-of-00021", + "00006-of-00021", + "00007-of-00021", + "00008-of-00021", + "00009-of-00021", + "00010-of-00021", + "00011-of-00021", + "00012-of-00021", + "00013-of-00021", + "00014-of-00021", + "00015-of-00021", + "00016-of-00021", + "00017-of-00021", + "00018-of-00021", + "00019-of-00021", + "00020-of-00021", + "00021-of-00021" + ] + } + }, + { + "model_format": "mlx", + "model_size_in_billions": "0_5", + "quantizations": [ + "4-bit" + ], + "model_id": "mlx-community/Qwen2.5-0.5B-Instruct-4bit" + }, + { + "model_format": "mlx", + "model_size_in_billions": "0_5", + "quantizations": [ + "8-bit" + ], + "model_id": "mlx-community/Qwen2.5-0.5B-Instruct-8bit" + }, + { + "model_format": "mlx", + "model_size_in_billions": "0_5", + "quantizations": [ + "none" + ], + "model_id": "mlx-community/Qwen2.5-0.5B-Instruct-bf16" + }, + { + "model_format": "mlx", + "model_size_in_billions": "1_5", + "quantizations": [ + "4-bit" + ], + "model_id": "mlx-community/Qwen2.5-1.5B-Instruct-4bit" + }, + { + "model_format": "mlx", + "model_size_in_billions": "1_5", + "quantizations": [ + "8-bit" + ], + "model_id": "mlx-community/Qwen2.5-1.5B-Instruct-8bit" + }, + { + "model_format": "mlx", + "model_size_in_billions": "1_5", + "quantizations": [ + "none" + ], + "model_id": "mlx-community/Qwen2.5-1.5B-Instruct-bf16" + }, + { + "model_format": "mlx", + "model_size_in_billions": 3, + "quantizations": [ + "4-bit" + ], + "model_id": "mlx-community/Qwen2.5-3B-Instruct-4bit" + }, + { + "model_format": "mlx", + "model_size_in_billions": 3, + "quantizations": [ + "8-bit" + ], + "model_id": "mlx-community/Qwen2.5-3B-Instruct-8bit" + }, + { + "model_format": "mlx", + "model_size_in_billions": 3, + "quantizations": [ + "none" + ], + "model_id": "mlx-community/Qwen2.5-3B-Instruct-bf16" + }, + { + "model_format": "mlx", + "model_size_in_billions": 7, + "quantizations": [ + "4-bit" + ], + "model_id": "mlx-community/Qwen2.5-7B-Instruct-4bit" + }, + { + "model_format": "mlx", + "model_size_in_billions": 7, + "quantizations": [ + "8-bit" + ], + "model_id": "mlx-community/Qwen2.5-7B-Instruct-8bit" + }, + { + "model_format": "mlx", + "model_size_in_billions": 7, + "quantizations": [ + "none" + ], + "model_id": "mlx-community/Qwen2.5-7B-Instruct-bf16" + }, + { + "model_format": "mlx", + "model_size_in_billions": 14, + "quantizations": [ + "4-bit" + ], + "model_id": "mlx-community/Qwen2.5-14B-Instruct-4bit" + }, + { + "model_format": "mlx", + "model_size_in_billions": 14, + "quantizations": [ + "8-bit" + ], + "model_id": "mlx-community/Qwen2.5-14B-Instruct-8bit" + }, + { + "model_format": "mlx", + "model_size_in_billions": 14, + "quantizations": [ + "none" + ], + "model_id": "mlx-community/Qwen2.5-14B-Instruct-bf16" + }, + { + "model_format": "mlx", + "model_size_in_billions": 32, + "quantizations": [ + "4-bit" + ], + "model_id": "mlx-community/Qwen2.5-32B-Instruct-4bit" + }, + { + "model_format": "mlx", + "model_size_in_billions": 32, + "quantizations": [ + "8-bit" + ], + "model_id": "mlx-community/Qwen2.5-32B-Instruct-8bit" + }, + { + "model_format": "mlx", + "model_size_in_billions": 32, + "quantizations": [ + "none" + ], + "model_id": "mlx-community/Qwen2.5-32B-Instruct-bf16" + }, + { + "model_format": "mlx", + "model_size_in_billions": 72, + "quantizations": [ + "4-bit" + ], + "model_id": "mlx-community/Qwen2.5-72B-Instruct-4bit" + }, + { + "model_format": "mlx", + "model_size_in_billions": 72, + "quantizations": [ + "8-bit" + ], + "model_id": "mlx-community/Qwen2.5-72B-Instruct-8bit" + }, + { + "model_format": "mlx", + "model_size_in_billions": 72, + "quantizations": [ + "none" + ], + "model_id": "mlx-community/Qwen2.5-72B-Instruct-bf16" + } + ], + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- message.content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n", + "stop_token_ids": [ + 151643, + 151644, + 151645 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>" + ] + }, + { + "version": 1, + "context_length": 32768, + "model_name": "qwen2.5-coder", + "model_lang": [ + "en", + "zh" + ], + "model_ability": [ + "generate" + ], + "model_description": "Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen).", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": "1_5", + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "Qwen/Qwen2.5-Coder-1.5B", + "model_revision": "d3586cfe793730945f8e4d7ef31032a3ee50247d" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 7, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "Qwen/Qwen2.5-Coder-7B", + "model_revision": "30b6a7e874a78d46b80fa1db3194ea427dd41b08" + } + ] + }, + { + "version": 1, + "context_length": 32768, + "model_name": "qwen2.5-coder-instruct", + "model_lang": [ + "en", + "zh" + ], + "model_ability": [ + "chat", + "tools" + ], + "model_description": "Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen).", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": "1_5", + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "Qwen/Qwen2.5-Coder-1.5B-Instruct" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 7, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "Qwen/Qwen2.5-Coder-7B-Instruct" + }, + { + "model_format": "gptq", + "model_size_in_billions": "7", + "quantizations": [ + "Int4", + "Int8" + ], + "model_id": "Qwen/Qwen2.5-Coder-7B-Instruct-GPTQ-{quantization}" + }, + { + "model_format": "ggufv2", + "model_size_in_billions": "1_5", + "quantizations": [ + "q2_k", + "q3_k_m", + "q4_0", + "q4_k_m", + "q5_0", + "q5_k_m", + "q6_k", + "q8_0" + ], + "model_id": "Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF", + "model_file_name_template": "qwen2.5-coder-1.5b-instruct-{quantization}.gguf" + }, + { + "model_format": "ggufv2", + "model_size_in_billions": 7, + "quantizations": [ + "q2_k", + "q3_k_m", + "q4_0", + "q4_k_m", + "q5_0", + "q5_k_m", + "q6_k", + "q8_0" + ], + "model_id": "Qwen/Qwen2.5-Coder-7B-Instruct-GGUF", + "model_file_name_template": "qwen2.5-coder-7b-instruct-{quantization}.gguf", + "model_file_name_split_template": "qwen2.5-coder-7b-instruct-{quantization}-{part}.gguf", + "quantization_parts": { + "q4_0": [ + "00001-of-00002", + "00002-of-00002" + ], + "q4_k_m": [ + "00001-of-00002", + "00002-of-00002" + ], + "q5_0": [ + "00001-of-00002", + "00002-of-00002" + ], + "q5_k_m": [ + "00001-of-00002", + "00002-of-00002" + ], + "q6_k": [ + "00001-of-00002", + "00002-of-00002" + ], + "q8_0": [ + "00001-of-00003", + "00002-of-00003", + "00003-of-00003" + ] + } + } + ], + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{{\\\"name\\\": , \\\"arguments\\\": }}\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- message.content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n", + "stop_token_ids": [ + 151643, + 151644, + 151645 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>" + ] } ] diff --git a/xinference/model/llm/llm_family.py b/xinference/model/llm/llm_family.py index c2ea4d7b98..413b4229ae 100644 --- a/xinference/model/llm/llm_family.py +++ b/xinference/model/llm/llm_family.py @@ -52,7 +52,7 @@ logger = logging.getLogger(__name__) DEFAULT_CONTEXT_LENGTH = 2048 -BUILTIN_LLM_PROMPT_STYLE: Dict[str, "PromptStyleV1"] = {} +BUILTIN_LLM_PROMPT_STYLE: Dict[str, Dict[str, Any]] = {} BUILTIN_LLM_MODEL_CHAT_FAMILIES: Set[str] = set() BUILTIN_LLM_MODEL_GENERATE_FAMILIES: Set[str] = set() BUILTIN_LLM_MODEL_TOOL_CALL_FAMILIES: Set[str] = set() @@ -127,32 +127,24 @@ def validate_model_size_with_radix(cls, v: object) -> object: return v -class PromptStyleV1(BaseModel): - style_name: str - system_prompt: str = "" - roles: List[str] - intra_message_sep: str = "" - inter_message_sep: str = "" - stop: Optional[List[str]] - stop_token_ids: Optional[List[int]] - - class LLMFamilyV1(BaseModel): version: Literal[1] context_length: Optional[int] = DEFAULT_CONTEXT_LENGTH model_name: str model_lang: List[str] - model_ability: List[Literal["embed", "generate", "chat", "tools", "vision"]] + model_ability: List[ + Literal["embed", "generate", "chat", "tools", "vision", "audio"] + ] model_description: Optional[str] # reason for not required str here: legacy registration model_family: Optional[str] model_specs: List["LLMSpecV1"] - prompt_style: Optional["PromptStyleV1"] + chat_template: Optional[str] + stop_token_ids: Optional[List[int]] + stop: Optional[List[str]] class CustomLLMFamilyV1(LLMFamilyV1): - prompt_style: Optional[Union["PromptStyleV1", str]] # type: ignore - @classmethod def parse_raw( cls: Any, @@ -176,6 +168,11 @@ def parse_raw( except (ValueError, TypeError, UnicodeDecodeError) as e: raise ValidationError([ErrorWrapper(e, loc=ROOT_KEY)], cls) llm_spec: CustomLLMFamilyV1 = cls.parse_obj(obj) + vision_model_names: Set[str] = { + family.model_name + for family in BUILTIN_LLM_FAMILIES + if "vision" in family.model_ability + } # check model_family if llm_spec.model_family is None: @@ -183,61 +180,45 @@ def parse_raw( f"You must specify `model_family` when registering custom LLM models." ) assert isinstance(llm_spec.model_family, str) + # TODO: Currently, tool call and vision models cannot be registered if it is not the builtin model_family if ( - llm_spec.model_family != "other" - and "chat" in llm_spec.model_ability - and llm_spec.model_family not in BUILTIN_LLM_MODEL_CHAT_FAMILIES - ): - raise ValueError( - f"`model_family` for chat model must be `other` or one of the following values: \n" - f"{', '.join(list(BUILTIN_LLM_MODEL_CHAT_FAMILIES))}" - ) - if ( - llm_spec.model_family != "other" - and "tools" in llm_spec.model_ability + "tools" in llm_spec.model_ability and llm_spec.model_family not in BUILTIN_LLM_MODEL_TOOL_CALL_FAMILIES ): raise ValueError( - f"`model_family` for tool call model must be `other` or one of the following values: \n" + f"`model_family` for tool call model must be one of the following values: \n" f"{', '.join(list(BUILTIN_LLM_MODEL_TOOL_CALL_FAMILIES))}" ) if ( - llm_spec.model_family != "other" - and "chat" not in llm_spec.model_ability - and llm_spec.model_family not in BUILTIN_LLM_MODEL_GENERATE_FAMILIES + "vision" in llm_spec.model_ability + and llm_spec.model_family not in vision_model_names ): raise ValueError( - f"`model_family` for generate model must be `other` or one of the following values: \n" - f"{', '.join(list(BUILTIN_LLM_MODEL_GENERATE_FAMILIES))}" + f"`model_family` for multimodal model must be one of the following values: \n" + f"{', '.join(list(vision_model_names))}" ) - # set prompt style when it is the builtin model family + # set chat_template when it is the builtin model family + if llm_spec.chat_template is None and "chat" in llm_spec.model_ability: + llm_spec.chat_template = llm_spec.model_family + + # handle chat_template when user choose existing model_family if ( - llm_spec.prompt_style is None - and llm_spec.model_family != "other" - and "chat" in llm_spec.model_ability + llm_spec.chat_template is not None + and llm_spec.chat_template in BUILTIN_LLM_PROMPT_STYLE ): - llm_spec.prompt_style = llm_spec.model_family - - # handle prompt style when user choose existing style - if llm_spec.prompt_style is not None and isinstance(llm_spec.prompt_style, str): - prompt_style_name = llm_spec.prompt_style - if prompt_style_name not in BUILTIN_LLM_PROMPT_STYLE: - raise ValueError( - f"Xinference does not support the prompt style name: {prompt_style_name}" - ) - llm_spec.prompt_style = BUILTIN_LLM_PROMPT_STYLE[prompt_style_name] + llm_spec.stop_token_ids = BUILTIN_LLM_PROMPT_STYLE[llm_spec.chat_template][ + "stop_token_ids" + ] + llm_spec.stop = BUILTIN_LLM_PROMPT_STYLE[llm_spec.chat_template]["stop"] + llm_spec.chat_template = BUILTIN_LLM_PROMPT_STYLE[llm_spec.chat_template][ + "chat_template" + ] # check model ability, registering LLM only provides generate and chat # but for vision models, we add back the abilities so that # gradio chat interface can be generated properly if ( - llm_spec.model_family != "other" - and llm_spec.model_family - in { - family.model_name - for family in BUILTIN_LLM_FAMILIES - if "vision" in family.model_ability - } + llm_spec.model_family in vision_model_names and "vision" not in llm_spec.model_ability ): llm_spec.model_ability.append("vision") @@ -1004,6 +985,11 @@ def register_llm(llm_family: LLMFamilyV1, persist: bool): if not is_valid_model_name(llm_family.model_name): raise ValueError(f"Invalid model name {llm_family.model_name}.") + for spec in llm_family.model_specs: + model_uri = spec.model_uri + if model_uri and not is_valid_model_uri(model_uri): + raise ValueError(f"Invalid model URI {model_uri}.") + with UD_LLM_FAMILIES_LOCK: for family in BUILTIN_LLM_FAMILIES + UD_LLM_FAMILIES: if llm_family.model_name == family.model_name: @@ -1015,12 +1001,6 @@ def register_llm(llm_family: LLMFamilyV1, persist: bool): generate_engine_config_by_model_family(llm_family) if persist: - # We only validate model URL when persist is True. - for spec in llm_family.model_specs: - model_uri = spec.model_uri - if model_uri and not is_valid_model_uri(model_uri): - raise ValueError(f"Invalid model URI {model_uri}.") - persist_path = os.path.join( XINFERENCE_MODEL_DIR, "llm", f"{llm_family.model_name}.json" ) diff --git a/xinference/model/llm/llm_family_csghub.json b/xinference/model/llm/llm_family_csghub.json index dc5b9d3ba8..d607b580b7 100644 --- a/xinference/model/llm/llm_family_csghub.json +++ b/xinference/model/llm/llm_family_csghub.json @@ -43,25 +43,17 @@ "model_hub": "csghub" } ], - "prompt_style": { - "style_name": "QWEN", - "system_prompt": "You are a helpful assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n", - "stop_token_ids": [ - 151643, - 151644, - 151645 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>" - ] - } + "chat_template": "{%- macro json_to_python_type(json_spec) %}\n {%- set basic_type_map = {\n \"string\": \"str\",\n \"number\": \"float\",\n \"integer\": \"int\",\n \"boolean\": \"bool\"\n} %}\n {%- if basic_type_map[json_spec.type] is defined %}\n {{- basic_type_map[json_spec.type] }}\n {%- elif json_spec.type == \"array\" %}\n {{- \"list[\" + json_to_python_type(json_spec|items) + \"]\" }}\n {%- elif json_spec.type == \"object\" %}\n {%- if json_spec.additionalProperties is defined %}\n {{- \"dict[str, \" + json_to_python_type(json_spec.additionalProperties) + ']' }}\n {%- else %}\n {{- \"dict\" }}\n {%- endif %}\n {%- elif json_spec.type is iterable %}\n {{- \"Union[\" }}\n {%- for t in json_spec.type %}\n {{- json_to_python_type({\"type\": t}) }}\n {%- if not loop.last %}\n {{- \",\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"]\" }}\n {%- else %}\n {{- \"Any\" }}\n {%- endif %}\n{%- endmacro %}\n\n{%- if tools %}\n {{- '<|im_start|>system\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] + '\n\n' }}\n {%- endif %}\n {{- '# Tools\n\n' }}\n {{- \"You are a function calling AI model. You are provided with function signatures within XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: \" }}\n {%- for tool in tools %}\n {%- if tool.function is defined %}\n {%- set tool = tool.function %}\n {%- endif %}\n {{- '{\"type\": \"function\", \"function\": ' }}\n {{- '{\"name\": ' + tool.name + '\", ' }}\n {{- '\"description\": \"' + tool.name + '(' }}\n {%- for param_name, param_fields in tool.parameters.properties|items %}\n {{- param_name + \": \" + json_to_python_type(param_fields) }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- if tool.return is defined %}\n {{- \" -> \" + json_to_python_type(tool.return) }}\n {%- endif %}\n {{- \" - \" + tool.description + \"\n\n\" }}\n {%- for param_name, param_fields in tool.parameters.properties|items %}\n {%- if loop.first %}\n {{- \" Args:\n\" }}\n {%- endif %}\n {{- \" \" + param_name + \"(\" + json_to_python_type(param_fields) + \"): \" + param_fields.description|trim }}\n {%- endfor %}\n {%- if tool.return is defined and tool.return.description is defined %}\n {{- \"\n Returns:\n \" + tool.return.description }}\n {%- endif %}\n {{- '\"' }}\n {{- ', \"parameters\": ' }}\n {%- if tool.parameters.properties | length == 0 %}\n {{- \"{}\" }}\n {%- else %}\n {{- tool.parameters|tojson }}\n {%- endif %}\n {{- \"}\" }}\n {%- if not loop.last %}\n {{- \"\n\" }}\n {%- endif %}\n {%- endfor %}\n {{- \" \" }}\n {{- 'Use the following pydantic model json schema for each tool call you will make: {\"properties\": {\"arguments\": {\"title\": \"Arguments\", \"type\": \"object\"}, \"name\": {\"title\": \"Name\", \"type\": \"string\"}}, \"required\": [\"arguments\", \"name\"], \"title\": \"FunctionCall\", \"type\": \"object\"}\n' }}\n {{- \"For each function call return a json object with function name and arguments within XML tags as follows:\n\" }}\n {{- \"\n\" }}\n {{- '{\"name\": , \"arguments\": }\n' }}\n {{- '<|im_end|>\n' }}\n{%- else %}\n {%- if messages[0]['role'] != 'system' %}\n {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}\n {%- else %}\n {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if message.role == \"user\" or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and message.tool_calls is not defined) %}\n {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role + '\n\n' }}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '{' }}\n {{- '\"name\": \"' }}\n {{- tool_call.name }}\n {%- if tool_call.arguments is defined %}\n {{- ', ' }}\n {{- '\"arguments\": ' }}\n {{- tool_call.arguments|tojson }}\n {%- endif %}\n {{- '\"}' }}\n {{- '\n' }}\n {%- endfor %}\n {{- '<|im_end|>\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if not message.name is defined %}\n {{- raise_exception(\"Tool response dicts require a 'name' key indicating the name of the called function!\") }}\n {%- endif %}\n {{- '<|im_start|>user\n\n' }}\n {{- '{\"name\": \"' }}\n {{- message.name }}\n {{- '\", \"content\": ' }}\n {{- message.content|tojson + '}' }}\n {{- '\n<|im_end|>\n' }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\n' }}\n{%- endif %}", + "stop_token_ids": [ + 151643, + 151644, + 151645 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>" + ] }, { "version": 1, @@ -85,21 +77,12 @@ "model_hub": "csghub" } ], - "prompt_style": { - "style_name": "NO_COLON_TWO", - "system_prompt": "<|system|>\nYou are a creative super artificial intelligence assistant, possessing all the knowledge of humankind. Your name is csg-wukong, developed by OpenCSG. You need to understand and infer the true intentions of users based on the topics discussed in the chat history, and respond to user questions correctly as required. You enjoy responding to users with accurate and insightful answers. Please pay attention to the appropriate style and format when replying, try to avoid repetitive words and sentences, and keep your responses as concise and profound as possible. You carefully consider the context of the discussion when replying to users. When the user says \"continue,\" please proceed with the continuation of the previous assistant's response.\n", - "roles": [ - "<|user|>\n", - "<|assistant|>\n" - ], - "intra_message_sep": "\n", - "inter_message_sep": "\n", - "stop_token_ids": [ - 2 - ], - "stop": [ - "" - ] - } + "chat_template": "{% for item in messages %}{% if loop.first and item['role'] == 'system' %}{{ item['content'] + '\n' }}{% elif loop.first %}{{ '<|system|>\nYou are a creative super artificial intelligence assistant, possessing all the knowledge of humankind. Your name is csg-wukong, developed by OpenCSG. You need to understand and infer the true intentions of users based on the topics discussed in the chat history, and respond to user questions correctly as required. You enjoy responding to users with accurate and insightful answers. Please pay attention to the appropriate style and format when replying, try to avoid repetitive words and sentences, and keep your responses as concise and profound as possible. You carefully consider the context of the discussion when replying to users. When the user says \"continue,\" please proceed with the continuation of the previous assistant\\'s response.\n' }}{% endif %}{% if item['role'] == 'user' %}{{ '<|user|>\n' + item['content'] + '\n' }}{% elif item['role'] == 'assistant' %}{{ '<|assistant|>\n' + item['content'] + '\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% endif %}", + "stop_token_ids": [ + 2 + ], + "stop": [ + "" + ] } ] diff --git a/xinference/model/llm/llm_family_modelscope.json b/xinference/model/llm/llm_family_modelscope.json index 44ac3e7794..7a91e561e6 100644 --- a/xinference/model/llm/llm_family_modelscope.json +++ b/xinference/model/llm/llm_family_modelscope.json @@ -70,19 +70,11 @@ "model_revision": "v1.0.1" } ], - "prompt_style": { - "style_name": "LLAMA2", - "system_prompt": "[INST] <>\nYou are a helpful AI assistant.\n<>\n\n", - "roles": [ - "[INST]", - "[/INST]" - ], - "intra_message_sep": " ", - "inter_message_sep": " ", - "stop_token_ids": [ + "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = '<>\n' + messages[0]['content'] | trim + '\n<>\n\n' %}{% set messages = messages[1:] %}{% else %}{% set system_message = '' %}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 %}{% set content = system_message + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ '' + '[INST] ' + content | trim + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content | trim + ' ' + '' }}{% endif %}{% endfor %}", + "stop_token_ids": [ 2 - ] - } + ], + "stop": [] }, { "version": 1, @@ -175,24 +167,15 @@ "model_hub": "modelscope" } ], - "prompt_style": { - "style_name": "LLAMA3", - "system_prompt": "You are a helpful assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n\n", - "inter_message_sep": "<|eot_id|>", - "stop_token_ids": [ - 128001, - 128009 - ], - "stop": [ - "<|end_of_text|>", - "<|eot_id|>" - ] - } + "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = '<|begin_of_text|>' + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}", + "stop_token_ids": [ + 128001, + 128009 + ], + "stop": [ + "<|end_of_text|>", + "<|eot_id|>" + ] }, { "version": 1, @@ -263,7 +246,8 @@ "th" ], "model_ability": [ - "chat" + "chat", + "tools" ], "model_description": "The Llama 3.1 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks..", "model_specs": [ @@ -367,24 +351,17 @@ "model_hub": "modelscope" } ], - "prompt_style": { - "style_name": "LLAMA3", - "system_prompt": "You are a helpful assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n\n", - "inter_message_sep": "<|eot_id|>", - "stop_token_ids": [ - 128001, - 128009 - ], - "stop": [ - "<|end_of_text|>", - "<|eot_id|>" - ] - } + "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|eom_id|>\" }}\n {%- else %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n", + "stop_token_ids": [ + 128001, + 128008, + 128009 + ], + "stop": [ + "<|end_of_text|>", + "<|eot_id|>", + "<|eom_id|>" + ] }, { "version": 1, @@ -449,20 +426,12 @@ "model_revision": "v1.0.3" } ], - "prompt_style": { - "style_name": "NO_COLON_TWO", - "system_prompt": "", - "roles": [ - "", - "" - ], - "intra_message_sep": "", - "inter_message_sep": "", - "stop_token_ids": [ - 2, - 195 - ] - } + "chat_template": "{{ (messages|selectattr('role', 'equalto', 'system')|list|last).content|trim if (messages|selectattr('role', 'equalto', 'system')|list) else '' }}\n\n{% for message in messages %}\n{% if message['role'] == 'user' %}\n\n{{ message['content']|trim -}}\n{% if not loop.last %}\n\n\n{% endif %}\n{% elif message['role'] == 'assistant' %}\n\n{{ message['content']|trim -}}\n{% if not loop.last %}\n\n\n{% endif %}\n{% endif %}\n{% endfor %}\n{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}\n\n{% endif %}", + "stop_token_ids": [ + 2, + 195 + ], + "stop": [] }, { "version": 1, @@ -503,139 +472,6 @@ } ] }, - { - "version": 1, - "context_length": 8192, - "model_name": "chatglm3", - "model_lang": [ - "en", - "zh" - ], - "model_ability": [ - "chat", - "tools" - ], - "model_description": "ChatGLM3 is the third generation of ChatGLM, still open-source and trained on Chinese and English data.", - "model_specs": [ - { - "model_format": "pytorch", - "model_size_in_billions": 6, - "quantizations": [ - "4-bit", - "8-bit", - "none" - ], - "model_hub": "modelscope", - "model_id": "ZhipuAI/chatglm3-6b", - "model_revision": "v1.0.2" - } - ], - "prompt_style": { - "style_name": "CHATGLM3", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 64795, - 64797, - 2 - ], - "stop": [ - "<|user|>", - "<|observation|>" - ] - } - }, - { - "version": 1, - "context_length": 32768, - "model_name": "chatglm3-32k", - "model_lang": [ - "en", - "zh" - ], - "model_ability": [ - "chat" - ], - "model_description": "ChatGLM3 is the third generation of ChatGLM, still open-source and trained on Chinese and English data.", - "model_specs": [ - { - "model_format": "pytorch", - "model_size_in_billions": 6, - "quantizations": [ - "4-bit", - "8-bit", - "none" - ], - "model_hub": "modelscope", - "model_id": "ZhipuAI/chatglm3-6b-32k", - "model_revision": "master" - } - ], - "prompt_style": { - "style_name": "CHATGLM3", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 64795, - 64797, - 2 - ], - "stop": [ - "<|user|>", - "<|observation|>" - ] - } - }, - { - "version": 1, - "context_length": 131072, - "model_name": "chatglm3-128k", - "model_lang": [ - "en", - "zh" - ], - "model_ability": [ - "chat" - ], - "model_description": "ChatGLM3 is the third generation of ChatGLM, still open-source and trained on Chinese and English data.", - "model_specs": [ - { - "model_format": "pytorch", - "model_size_in_billions": 6, - "quantizations": [ - "4-bit", - "8-bit", - "none" - ], - "model_hub": "modelscope", - "model_id": "ZhipuAI/chatglm3-6b-128k", - "model_revision": "master" - } - ], - "prompt_style": { - "style_name": "CHATGLM3", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 64795, - 64797, - 2 - ], - "stop": [ - "<|user|>", - "<|observation|>" - ] - } - }, { "version": 1, "context_length": 131072, @@ -690,24 +526,17 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "CHATGLM3", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 151329, - 151336, - 151338 - ], - "stop": [ - "<|endoftext|>", - "<|user|>", - "<|observation|>" - ] - } + "chat_template": "[gMASK]{% for item in messages %}{% if item['tools'] is defined %}<|system|>\n你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具{% set tools = item['tools'] %}{% for tool in tools %}{% if tool['type'] == 'function' %}\n\n## {{ tool['function']['name'] }}\n\n{{ tool['function'] | tojson(indent=4) }}\n在调用上述函数时，请使用 Json 格式表示调用的参数。{% elif tool['type'] == 'python' %}\n\n## python\n\n当你向 `python` 发送包含 Python 代码的消息时，该代码将会在一个有状态的 Jupyter notebook 环境中执行。\n`python` 返回代码执行的输出，或在执行 60 秒后返回超时。\n`/mnt/data` 将会持久化存储你的文件。在此会话中，`python` 无法访问互联网。不要使用 `python` 进行任何网络请求或者在线 API 调用，这些在线内容的访问将不会成功。{% elif tool['type'] == 'simple_browser' %}\n\n## simple_browser\n\n你可以使用 `simple_browser` 工具。该工具支持以下函数：\n`search(query: str, recency_days: int)`：使用搜索引擎进行查询并显示结果，可以使用 `recency_days` 参数控制搜索内容的时效性。\n`mclick(ids: list[int])`：获取一系列指定 id 的页面内容。每次调用时，须选择3-10个页面。选择多个角度的页面，同时尽可能选择可信任的信息来源。考虑到部分页面是无法加载的，你也可以多打开一些可能有用的页面而不用担心内容过多。\n`open_url(url: str)`：打开指定的 URL。\n\n使用 `【{引用 id}†{引用文本}】` 来引用内容。\n\n操作步骤：1. 使用 `search` 来获得信息列表; 2. 使用 `mclick` 来获取指定 ID 页面的内容; 3. 根据获得的内容进行回复。在回复中应当引用信息来源。\n 如果用户提供了 URL，也可以用 `open_url` 直接打开页面。\n如果初次搜索结果没有找到合适的信息，也可以再次使用 `search` 进行搜索。{% elif tool['type'] == 'cogview' %}\n\n## cogview\n\n如果用户的请求中包含了对图像的描述，你可以使用 `cogview` 来生成图像并展示给用户。你需要向 `cogview` 发送图像描述，规则：\n- 发送给 `cogview` 的消息必须使用英语。用户的中文描述必须完全翻译为英语。\n- 应当尽可能详细地描述图像生成的需求，需求描述约 100 英文单词。\n- 保持用户原始描述的意图。不要虚构内容或者没见过的人物。\n- 如无特殊说明，所在地为中国，持有中国立场并遵循中国社会主义价值观。{% endif %}{% endfor %}{% endif %}{% if item['content'] %}<|{{ item['role'] }}|>{{ item['metadata'] }}\n{{ item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}", + "stop_token_ids": [ + 151329, + 151336, + 151338 + ], + "stop": [ + "<|endoftext|>", + "<|user|>", + "<|observation|>" + ] }, { "version": 1, @@ -763,24 +592,17 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "CHATGLM3", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 151329, - 151336, - 151338 - ], - "stop": [ - "<|endoftext|>", - "<|user|>", - "<|observation|>" - ] - } + "chat_template": "[gMASK]{% for item in messages %}{% if item['tools'] is defined %}<|system|>\n你是一个名为 GLM-4 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具{% set tools = item['tools'] %}{% for tool in tools %}{% if tool['type'] == 'function' %}\n\n## {{ tool['function']['name'] }}\n\n{{ tool['function'] | tojson(indent=4) }}\n在调用上述函数时，请使用 Json 格式表示调用的参数。{% elif tool['type'] == 'python' %}\n\n## python\n\n当你向 `python` 发送包含 Python 代码的消息时，该代码将会在一个有状态的 Jupyter notebook 环境中执行。\n`python` 返回代码执行的输出，或在执行 60 秒后返回超时。\n`/mnt/data` 将会持久化存储你的文件。在此会话中，`python` 无法访问互联网。不要使用 `python` 进行任何网络请求或者在线 API 调用，这些在线内容的访问将不会成功。{% elif tool['type'] == 'simple_browser' %}\n\n## simple_browser\n\n你可以使用 `simple_browser` 工具。该工具支持以下函数：\n`search(query: str, recency_days: int)`：使用搜索引擎进行查询并显示结果，可以使用 `recency_days` 参数控制搜索内容的时效性。\n`mclick(ids: list[int])`：获取一系列指定 id 的页面内容。每次调用时，须选择3-10个页面。选择多个角度的页面，同时尽可能选择可信任的信息来源。考虑到部分页面是无法加载的，你也可以多打开一些可能有用的页面而不用担心内容过多。\n`open_url(url: str)`：打开指定的 URL。\n\n使用 `【{引用 id}†{引用文本}】` 来引用内容。\n\n操作步骤：1. 使用 `search` 来获得信息列表; 2. 使用 `mclick` 来获取指定 ID 页面的内容; 3. 根据获得的内容进行回复。在回复中应当引用信息来源。\n 如果用户提供了 URL，也可以用 `open_url` 直接打开页面。\n如果初次搜索结果没有找到合适的信息，也可以再次使用 `search` 进行搜索。{% elif tool['type'] == 'cogview' %}\n\n## cogview\n\n如果用户的请求中包含了对图像的描述，你可以使用 `cogview` 来生成图像并展示给用户。你需要向 `cogview` 发送图像描述，规则：\n- 发送给 `cogview` 的消息必须使用英语。用户的中文描述必须完全翻译为英语。\n- 应当尽可能详细地描述图像生成的需求，需求描述约 100 英文单词。\n- 保持用户原始描述的意图。不要虚构内容或者没见过的人物。\n- 如无特殊说明，所在地为中国，持有中国立场并遵循中国社会主义价值观。{% endif %}{% endfor %}{% endif %}{% if item['content'] %}<|{{ item['role'] }}|>{{ item['metadata'] }}\n{{ item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}", + "stop_token_ids": [ + 151329, + 151336, + 151338 + ], + "stop": [ + "<|endoftext|>", + "<|user|>", + "<|observation|>" + ] }, { "version": 1, @@ -809,24 +631,17 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "CHATGLM3", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 151329, - 151336, - 151338 - ], - "stop": [ - "<|endoftext|>", - "<|user|>", - "<|observation|>" - ] - } + "chat_template": "", + "stop_token_ids": [ + 151329, + 151336, + 151338 + ], + "stop": [ + "<|endoftext|>", + "<|user|>", + "<|observation|>" + ] }, { "version": 1, @@ -869,24 +684,17 @@ "model_hub": "modelscope" } ], - "prompt_style": { - "style_name": "CHATGLM3", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 151329, - 151336, - 151338 - ], - "stop": [ - "<|endoftext|>", - "<|user|>", - "<|observation|>" - ] - } + "chat_template": "{% for item in messages %}{% if loop.first and item['role'] == 'system' %}{{ '<|system|>\n' + item['content'] }}{% elif loop.first %}{{ '<|system|>\n你是一位智能编程助手，你叫CodeGeeX。你会为用户回答关于编程、代码、计算机方面的任何问题，并提供格式规范、可以执行、准确安全的代码，并在必要时提供详细的解释。' }}{% endif %}{% if item['role'] == 'user' %}{{ '<|user|>\n' + item['content'] }}{% elif item['role'] == 'assistant' %}{{ '<|assistant|>\n' + item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% endif %}", + "stop_token_ids": [ + 151329, + 151336, + 151338 + ], + "stop": [ + "<|endoftext|>", + "<|user|>", + "<|observation|>" + ] }, { "version": 1, @@ -926,14 +734,13 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "XVERSE", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ] - } + "chat_template": "{% for item in messages %}{% if loop.first and item['role'] == 'system' %}{{ '<|system|> \n' + item['content'] }}{% endif %}{% if item['role'] == 'user' %}{{ '<|user|> \n' + item['content'] }}{% elif item['role'] == 'assistant' %}{{ '<|assistant|> \n' + item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>' }}{% endif %}", + "stop_token_ids": [ + 3 + ], + "stop": [ + "<|endoftext|>" + ] }, { "version": 1, @@ -1045,23 +852,15 @@ "model_hub": "modelscope" } ], - "prompt_style": { - "style_name": "INTERNLM2", - "system_prompt": "You are InternLM (书生·浦语), a helpful, honest, and harmless AI assistant developed by Shanghai AI Laboratory (上海人工智能实验室).", - "roles": [ - "<|im_start|>user", - "<|im_start|>assistant" - ], - "intra_message_sep": "<|im_end|>", - "stop_token_ids": [ - 2, - 92542 - ], - "stop": [ - "", - "<|im_end|>" - ] - } + "chat_template": "{{ '' }}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [ + 2, + 92542 + ], + "stop": [ + "", + "<|im_end|>" + ] }, { "version": 1, @@ -1086,23 +885,15 @@ "model_hub": "modelscope" } ], - "prompt_style": { - "style_name": "INTERNLM2", - "system_prompt": "You are InternLM (书生·浦语), a helpful, honest, and harmless AI assistant developed by Shanghai AI Laboratory (上海人工智能实验室).", - "roles": [ - "<|im_start|>user", - "<|im_start|>assistant" - ], - "intra_message_sep": "<|im_end|>", - "stop_token_ids": [ - 2, - 92542 - ], - "stop": [ - "", - "<|im_end|>" - ] - } + "chat_template": "{{ '' }}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [ + 2, + 92542 + ], + "stop": [ + "", + "<|im_end|>" + ] }, { "version": 1, @@ -1140,18 +931,13 @@ "model_revision": "v1.0.0" } ], - "prompt_style": { - "style_name": "ADD_COLON_SINGLE", - "system_prompt": "Below is an instruction that describes a task. Write a response that appropriately completes the request.", - "roles": [ - "Instruction", - "Response" - ], - "intra_message_sep": "\n\n### ", - "stop": [ - "" - ] - } + "chat_template": "{% for item in messages %}{% if loop.first and item['role'] == 'system' %}{{ item['content'] + '\n\n### ' }}{% elif loop.first %}{{ 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### ' }}{% endif %}{% if item['role'] == 'user' %}{{ 'Instruction: ' + item['content'] + '\n\n### ' }}{% elif item['role'] == 'assistant' %}{{ 'Response: ' + item['content'] + '\n\n### ' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Response: Let\\'s think step by step.' }}{% endif %}", + "stop_token_ids": [ + 2 + ], + "stop": [ + "" + ] }, { "version": 1, @@ -1252,24 +1038,15 @@ "model_hub": "modelscope" } ], - "prompt_style": { - "style_name": "CodeShell", - "system_prompt": "", - "roles": [ - "## human:", - "## assistant: " - ], - "intra_message_sep": "", - "inter_message_sep": "", - "stop_token_ids": [ - 70000 - ], - "stop": [ - "<|endoftext|>", - "|||", - "||" - ] - } + "chat_template": "{% for item in messages %}{% if item['role'] == 'user' %}{{ '## human: ' + item['content'] + '||' }}{% elif item['role'] == 'assistant' %}{{ '## assistant: ' + item['content'] + '||' }}{% endif %}{% endfor %}{{ '## assistant: ' }}", + "stop_token_ids": [ + 70000 + ], + "stop": [ + "<|endoftext|>", + "|||", + "||" + ] }, { "version": 1, @@ -1353,19 +1130,13 @@ "model_revision": "v0.1.0" } ], - "prompt_style": { - "style_name": "LLAMA2", - "system_prompt": "[INST] <>\nWrite code to solve the following coding problem that obeys the constraints and passes the example test cases. Please wrap your code answer using ```:\n<>\n\n", - "roles": [ - "[INST]", - "[/INST]" - ], - "intra_message_sep": " ", - "inter_message_sep": " ", - "stop_token_ids": [ + "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = '<>\n' + messages[0]['content'] | trim + '\n<>\n\n' %}{% set messages = messages[1:] %}{% else %}{% set system_message = '' %}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 %}{% set content = system_message + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ '' + '[INST] ' + content | trim + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content | trim + ' ' + '' }}{% endif %}{% endfor %}", + "stop_token_ids": [ 2 - ] - } + ], + "stop": [ + "" + ] }, { "version": 1, @@ -1567,16 +1338,13 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "MIXTRAL_V01", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "", - "inter_message_sep": "" - } + "chat_template": "{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n\n{{- '' }}\n{%- for message in loop_messages %}\n {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n {{- raise_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }}\n {%- endif %}\n {%- if message['role'] == 'user' %}\n {%- if loop.first and system_message is defined %}\n {{- ' [INST] ' + system_message + '\n\n' + message['content'] + ' [/INST]' }}\n {%- else %}\n {{- ' [INST] ' + message['content'] + ' [/INST]' }}\n {%- endif %}\n {%- elif message['role'] == 'assistant' %}\n {{- ' ' + message['content'] + ''}}\n {%- else %}\n {{- raise_exception('Only user and assistant roles are supported, with the exception of an initial optional system message!') }}\n {%- endif %}\n{%- endfor %}\n", + "stop_token_ids": [ + 2 + ], + "stop": [ + "" + ] }, { "version": 1, @@ -1716,28 +1484,19 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "CHATML", - "system_prompt": "", - "roles": [ - "<|im_start|>user", - "<|im_start|>assistant" - ], - "intra_message_sep": "<|im_end|>", - "inter_message_sep": "", - "stop_token_ids": [ - 2, - 6, - 7, - 8 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>", - "<|im_sep|>" - ] - } + "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [ + 2, + 6, + 7, + 8 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>", + "<|im_sep|>" + ] }, { "version": 1, @@ -1900,28 +1659,19 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "CHATML", - "system_prompt": "", - "roles": [ - "<|im_start|>user", - "<|im_start|>assistant" - ], - "intra_message_sep": "<|im_end|>", - "inter_message_sep": "", - "stop_token_ids": [ - 2, - 6, - 7, - 8 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>", - "<|im_sep|>" - ] - } + "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\n' + content + '<|im_end|>\n<|im_start|>assistant\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '\n' }}{% endif %}{% endfor %}", + "stop_token_ids": [ + 2, + 6, + 7, + 8 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>", + "<|im_sep|>" + ] }, { "version": 1, @@ -1961,28 +1711,19 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "CHATML", - "system_prompt": "", - "roles": [ - "<|im_start|>user", - "<|im_start|>assistant" - ], - "intra_message_sep": "<|im_end|>", - "inter_message_sep": "", - "stop_token_ids": [ - 2, - 6, - 7, - 8 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>", - "<|im_sep|>" - ] - } + "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\n' + content + '<|im_end|>\n<|im_start|>assistant\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '\n' }}{% endif %}{% endfor %}", + "stop_token_ids": [ + 2, + 6, + 7, + 8 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>", + "<|im_sep|>" + ] }, { "version": 1, @@ -2009,15 +1750,13 @@ "model_revision": "v1.0.0" } ], - "prompt_style": { - "style_name": "ADD_COLON_SINGLE_COT", - "system_prompt": "Below is an instruction that describes a task. Write a response that appropriately completes the request.", - "roles": [ - "Instruction", - "Response" - ], - "intra_message_sep": "\n\n### " - } + "chat_template": "{% for item in messages %}{% if loop.first and item['role'] == 'system' %}{{ item['content'] + '\n\n### ' }}{% elif loop.first %}{{ 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### ' }}{% endif %}{% if item['role'] == 'user' %}{{ 'Instruction: ' + item['content'] + '\n\n### ' }}{% elif item['role'] == 'assistant' %}{{ 'Response: ' + item['content'] + '\n\n### ' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Response: Let\\'s think step by step.' }}{% endif %}", + "stop_token_ids": [ + 2 + ], + "stop": [ + "" + ] }, { "version": 1, @@ -2044,22 +1783,13 @@ "model_revision": "v1.0.0" } ], - "prompt_style": { - "style_name": "LLAMA2", - "system_prompt": "[INST] ", - "roles": [ - "[INST]", - "[/INST]" - ], - "intra_message_sep": " ", - "inter_message_sep": "", - "stop_token_ids": [ - 2 - ], - "stop": [ - "" - ] - } + "chat_template": "{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n\n{{- '' }}\n{%- for message in loop_messages %}\n {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n {{- raise_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }}\n {%- endif %}\n {%- if message['role'] == 'user' %}\n {%- if loop.first and system_message is defined %}\n {{- ' [INST] ' + system_message + '\n\n' + message['content'] + ' [/INST]' }}\n {%- else %}\n {{- ' [INST] ' + message['content'] + ' [/INST]' }}\n {%- endif %}\n {%- elif message['role'] == 'assistant' %}\n {{- ' ' + message['content'] + ''}}\n {%- else %}\n {{- raise_exception('Only user and assistant roles are supported, with the exception of an initial optional system message!') }}\n {%- endif %}\n{%- endfor %}\n", + "stop_token_ids": [ + 2 + ], + "stop": [ + "" + ] }, { "version": 1, @@ -2095,22 +1825,13 @@ "model_file_name_template": "mistral-7b-instruct-v0.2.{quantization}.gguf" } ], - "prompt_style": { - "style_name": "LLAMA2", - "system_prompt": "[INST] ", - "roles": [ - "[INST]", - "[/INST]" - ], - "intra_message_sep": " ", - "inter_message_sep": "", - "stop_token_ids": [ - 2 - ], - "stop": [ - "" - ] - } + "chat_template": "{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n\n{{- '' }}\n{%- for message in loop_messages %}\n {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n {{- raise_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }}\n {%- endif %}\n {%- if message['role'] == 'user' %}\n {%- if loop.first and system_message is defined %}\n {{- ' [INST] ' + system_message + '\n\n' + message['content'] + ' [/INST]' }}\n {%- else %}\n {{- ' [INST] ' + message['content'] + ' [/INST]' }}\n {%- endif %}\n {%- elif message['role'] == 'assistant' %}\n {{- ' ' + message['content'] + ''}}\n {%- else %}\n {{- raise_exception('Only user and assistant roles are supported, with the exception of an initial optional system message!') }}\n {%- endif %}\n{%- endfor %}\n", + "stop_token_ids": [ + 2 + ], + "stop": [ + "" + ] }, { "version": 1, @@ -2151,22 +1872,13 @@ "model_hub": "modelscope" } ], - "prompt_style": { - "style_name": "mistral-nemo", - "system_prompt": "", - "roles": [ - "[INST]", - "[/INST]" - ], - "intra_message_sep": "", - "inter_message_sep": "", - "stop_token_ids": [ - 2 - ], - "stop": [ - "" - ] - } + "chat_template": "{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif %}\n {%- set ns.index = ns.index + 1 %}\n {%- endif %}\n{%- endfor %}\n\n{{- '' }}\n{%- for message in loop_messages %}\n {%- if message[\"role\"] == \"user\" %}\n {%- if tools is not none and (message == user_messages[-1]) %}\n {{- \"[AVAILABLE_TOOLS][\" }}\n {%- for tool in tools %}\n {%- set tool = tool.function %}\n {{- '{\"type\": \"function\", \"function\": {' }}\n {%- for key, val in tool.items() if key != \"return\" %}\n {%- if val is string %}\n {{- '\"' + key + '\": \"' + val + '\"' }}\n {%- else %}\n {{- '\"' + key + '\": ' + val|tojson }}\n {%- endif %}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \"}}\" }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"[/AVAILABLE_TOOLS]\" }}\n {%- endif %}\n {%- if loop.last and system_message is defined %}\n {{- \"[INST]\" + system_message + \"\n\n\" + message[\"content\"] + \"[/INST]\" }}\n {%- else %}\n {{- \"[INST]\" + message[\"content\"] + \"[/INST]\" }}\n {%- endif %}\n {%- elif (message.tool_calls is defined and message.tool_calls is not none) %}\n {{- \"[TOOL_CALLS][\" }}\n {%- for tool_call in message.tool_calls %}\n {%- set out = tool_call.function|tojson %}\n {{- out[:-1] }}\n {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" + '' }}\n {%- endif %}\n {%- endfor %}\n {%- elif message[\"role\"] == \"assistant\" %}\n {{- message[\"content\"] + ''}}\n {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n {%- if message.content is defined and message.content.content is defined %}\n {%- set content = message.content.content %}\n {%- else %}\n {%- set content = message.content %}\n {%- endif %}\n {{- '[TOOL_RESULTS]{\"content\": ' + content|string + \", \" }}\n {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n {%- else %}\n {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n {%- endif %}\n{%- endfor %}\n", + "stop_token_ids": [ + 2 + ], + "stop": [ + "" + ] }, { "version": 1, @@ -2208,120 +1920,26 @@ "model_hub": "modelscope" } ], - "prompt_style": { - "style_name": "mistral-nemo", - "system_prompt": "", - "roles": [ - "[INST]", - "[/INST]" - ], - "intra_message_sep": "", - "inter_message_sep": "", - "stop_token_ids": [ - 2 - ], - "stop": [ - "" - ] - } + "chat_template": "{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif %}\n {%- set ns.index = ns.index + 1 %}\n {%- endif %}\n{%- endfor %}\n\n{{- '' }}\n{%- for message in loop_messages %}\n {%- if message[\"role\"] == \"user\" %}\n {%- if tools is not none and (message == user_messages[-1]) %}\n {{- \"[AVAILABLE_TOOLS][\" }}\n {%- for tool in tools %}\n {%- set tool = tool.function %}\n {{- '{\"type\": \"function\", \"function\": {' }}\n {%- for key, val in tool.items() if key != \"return\" %}\n {%- if val is string %}\n {{- '\"' + key + '\": \"' + val + '\"' }}\n {%- else %}\n {{- '\"' + key + '\": ' + val|tojson }}\n {%- endif %}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \"}}\" }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"[/AVAILABLE_TOOLS]\" }}\n {%- endif %}\n {%- if loop.last and system_message is defined %}\n {{- \"[INST]\" + system_message + \"\n\n\" + message[\"content\"] + \"[/INST]\" }}\n {%- else %}\n {{- \"[INST]\" + message[\"content\"] + \"[/INST]\" }}\n {%- endif %}\n {%- elif (message.tool_calls is defined and message.tool_calls is not none) %}\n {{- \"[TOOL_CALLS][\" }}\n {%- for tool_call in message.tool_calls %}\n {%- set out = tool_call.function|tojson %}\n {{- out[:-1] }}\n {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" + '' }}\n {%- endif %}\n {%- endfor %}\n {%- elif message[\"role\"] == \"assistant\" %}\n {{- message[\"content\"] + ''}}\n {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n {%- if message.content is defined and message.content.content is defined %}\n {%- set content = message.content.content %}\n {%- else %}\n {%- set content = message.content %}\n {%- endif %}\n {{- '[TOOL_RESULTS]{\"content\": ' + content|string + \", \" }}\n {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n {%- else %}\n {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n {%- endif %}\n{%- endfor %}\n", + "stop_token_ids": [ + 2 + ], + "stop": [ + "" + ] }, { "version": 1, - "context_length": 8192, - "model_name": "zephyr-7b-alpha", + "context_length": 32768, + "model_name": "qwen-chat", "model_lang": [ - "en" + "en", + "zh" ], "model_ability": [ "chat" ], - "model_description": "Zephyr-7B-α is the first model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1.", - "model_specs": [ - { - "model_format": "pytorch", - "model_size_in_billions": 7, - "quantizations": [ - "4-bit", - "8-bit", - "none" - ], - "model_hub": "modelscope", - "model_id": "keepitsimple/zephyr-7b-alpha", - "model_revision": "v1.0-1" - } - ], - "prompt_style": { - "style_name": "NO_COLON_TWO", - "system_prompt": "<|system|>\nYou are a friendly chatbot.\n", - "roles": [ - "<|user|>\n", - "<|assistant|>\n" - ], - "intra_message_sep": "\n", - "inter_message_sep": "\n", - "stop_token_ids": [ - 2 - ], - "stop": [ - "" - ] - } - }, - { - "version": 1, - "context_length": 8192, - "model_name": "zephyr-7b-beta", - "model_lang": [ - "en" - ], - "model_ability": [ - "chat" - ], - "model_description": "Zephyr-7B-β is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1", - "model_specs": [ - { - "model_format": "pytorch", - "model_size_in_billions": 7, - "quantizations": [ - "4-bit", - "8-bit", - "none" - ], - "model_hub": "modelscope", - "model_id": "modelscope/zephyr-7b-beta", - "model_revision": "master" - } - ], - "prompt_style": { - "style_name": "NO_COLON_TWO", - "system_prompt": "<|system|>\nYou are a friendly chatbot.\n", - "roles": [ - "<|user|>\n", - "<|assistant|>\n" - ], - "intra_message_sep": "\n", - "inter_message_sep": "\n", - "stop_token_ids": [ - 2 - ], - "stop": [ - "" - ] - } - }, - { - "version": 1, - "context_length": 32768, - "model_name": "qwen-chat", - "model_lang": [ - "en", - "zh" - ], - "model_ability": [ - "chat", - "tools" - ], - "model_description": "Qwen-chat is a fine-tuned version of the Qwen LLM trained with alignment techniques, specializing in chatting.", + "model_description": "Qwen-chat is a fine-tuned version of the Qwen LLM trained with alignment techniques, specializing in chatting.", "model_specs": [ { "model_format": "ggufv2", @@ -2438,25 +2056,17 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "QWEN", - "system_prompt": "You are a helpful assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n", - "stop_token_ids": [ - 151643, - 151644, - 151645 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>" - ] - } + "chat_template": "{% for item in messages %}{% if loop.first and item['role'] == 'system' %}{{ '<|im_start|>system\n' + item['content'] + '<|im_end|>\n' }}{% elif loop.first %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{% if item['role'] == 'user' %}{{ '<|im_start|>user\n' + item['content'] + '<|im_end|>' }}{% elif item['role'] == 'assistant' %}{{ '<|im_start|>assistant\n' + item['content'] + '<|im_end|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [ + 151643, + 151644, + 151645 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>" + ] }, { "version": 1, @@ -2832,25 +2442,17 @@ } } ], - "prompt_style": { - "style_name": "QWEN", - "system_prompt": "You are a helpful assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n", - "stop_token_ids": [ - 151643, - 151644, - 151645 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>" - ] - } + "chat_template": "{%- macro json_to_python_type(json_spec) %}\n {%- set basic_type_map = {\n \"string\": \"str\",\n \"number\": \"float\",\n \"integer\": \"int\",\n \"boolean\": \"bool\"\n} %}\n {%- if basic_type_map[json_spec.type] is defined %}\n {{- basic_type_map[json_spec.type] }}\n {%- elif json_spec.type == \"array\" %}\n {{- \"list[\" + json_to_python_type(json_spec|items) + \"]\" }}\n {%- elif json_spec.type == \"object\" %}\n {%- if json_spec.additionalProperties is defined %}\n {{- \"dict[str, \" + json_to_python_type(json_spec.additionalProperties) + ']' }}\n {%- else %}\n {{- \"dict\" }}\n {%- endif %}\n {%- elif json_spec.type is iterable %}\n {{- \"Union[\" }}\n {%- for t in json_spec.type %}\n {{- json_to_python_type({\"type\": t}) }}\n {%- if not loop.last %}\n {{- \",\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"]\" }}\n {%- else %}\n {{- \"Any\" }}\n {%- endif %}\n{%- endmacro %}\n\n{%- if tools %}\n {{- '<|im_start|>system\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] + '\n\n' }}\n {%- endif %}\n {{- '# Tools\n\n' }}\n {{- \"You are a function calling AI model. You are provided with function signatures within XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: \" }}\n {%- for tool in tools %}\n {%- if tool.function is defined %}\n {%- set tool = tool.function %}\n {%- endif %}\n {{- '{\"type\": \"function\", \"function\": ' }}\n {{- '{\"name\": ' + tool.name + '\", ' }}\n {{- '\"description\": \"' + tool.name + '(' }}\n {%- for param_name, param_fields in tool.parameters.properties|items %}\n {{- param_name + \": \" + json_to_python_type(param_fields) }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- if tool.return is defined %}\n {{- \" -> \" + json_to_python_type(tool.return) }}\n {%- endif %}\n {{- \" - \" + tool.description + \"\n\n\" }}\n {%- for param_name, param_fields in tool.parameters.properties|items %}\n {%- if loop.first %}\n {{- \" Args:\n\" }}\n {%- endif %}\n {{- \" \" + param_name + \"(\" + json_to_python_type(param_fields) + \"): \" + param_fields.description|trim }}\n {%- endfor %}\n {%- if tool.return is defined and tool.return.description is defined %}\n {{- \"\n Returns:\n \" + tool.return.description }}\n {%- endif %}\n {{- '\"' }}\n {{- ', \"parameters\": ' }}\n {%- if tool.parameters.properties | length == 0 %}\n {{- \"{}\" }}\n {%- else %}\n {{- tool.parameters|tojson }}\n {%- endif %}\n {{- \"}\" }}\n {%- if not loop.last %}\n {{- \"\n\" }}\n {%- endif %}\n {%- endfor %}\n {{- \" \" }}\n {{- 'Use the following pydantic model json schema for each tool call you will make: {\"properties\": {\"arguments\": {\"title\": \"Arguments\", \"type\": \"object\"}, \"name\": {\"title\": \"Name\", \"type\": \"string\"}}, \"required\": [\"arguments\", \"name\"], \"title\": \"FunctionCall\", \"type\": \"object\"}\n' }}\n {{- \"For each function call return a json object with function name and arguments within XML tags as follows:\n\" }}\n {{- \"\n\" }}\n {{- '{\"name\": , \"arguments\": }\n' }}\n {{- '<|im_end|>\n' }}\n{%- else %}\n {%- if messages[0]['role'] != 'system' %}\n {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}\n {%- else %}\n {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if message.role == \"user\" or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and message.tool_calls is not defined) %}\n {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role + '\n\n' }}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '{' }}\n {{- '\"name\": \"' }}\n {{- tool_call.name }}\n {%- if tool_call.arguments is defined %}\n {{- ', ' }}\n {{- '\"arguments\": ' }}\n {{- tool_call.arguments|tojson }}\n {%- endif %}\n {{- '\"}' }}\n {{- '\n' }}\n {%- endfor %}\n {{- '<|im_end|>\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if not message.name is defined %}\n {{- raise_exception(\"Tool response dicts require a 'name' key indicating the name of the called function!\") }}\n {%- endif %}\n {{- '<|im_start|>user\n\n' }}\n {{- '{\"name\": \"' }}\n {{- message.name }}\n {{- '\", \"content\": ' }}\n {{- message.content|tojson + '}' }}\n {{- '\n<|im_end|>\n' }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\n' }}\n{%- endif %}", + "stop_token_ids": [ + 151643, + 151644, + 151645 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>" + ] }, { "version": 1, @@ -2887,25 +2489,17 @@ "model_hub": "modelscope" } ], - "prompt_style": { - "style_name": "QWEN", - "system_prompt": "You are a helpful assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n", - "stop_token_ids": [ - 151643, - 151644, - 151645 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>" - ] - } + "chat_template": "{%- macro json_to_python_type(json_spec) %}\n {%- set basic_type_map = {\n \"string\": \"str\",\n \"number\": \"float\",\n \"integer\": \"int\",\n \"boolean\": \"bool\"\n} %}\n {%- if basic_type_map[json_spec.type] is defined %}\n {{- basic_type_map[json_spec.type] }}\n {%- elif json_spec.type == \"array\" %}\n {{- \"list[\" + json_to_python_type(json_spec|items) + \"]\" }}\n {%- elif json_spec.type == \"object\" %}\n {%- if json_spec.additionalProperties is defined %}\n {{- \"dict[str, \" + json_to_python_type(json_spec.additionalProperties) + ']' }}\n {%- else %}\n {{- \"dict\" }}\n {%- endif %}\n {%- elif json_spec.type is iterable %}\n {{- \"Union[\" }}\n {%- for t in json_spec.type %}\n {{- json_to_python_type({\"type\": t}) }}\n {%- if not loop.last %}\n {{- \",\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"]\" }}\n {%- else %}\n {{- \"Any\" }}\n {%- endif %}\n{%- endmacro %}\n\n{%- if tools %}\n {{- '<|im_start|>system\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] + '\n\n' }}\n {%- endif %}\n {{- '# Tools\n\n' }}\n {{- \"You are a function calling AI model. You are provided with function signatures within XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: \" }}\n {%- for tool in tools %}\n {%- if tool.function is defined %}\n {%- set tool = tool.function %}\n {%- endif %}\n {{- '{\"type\": \"function\", \"function\": ' }}\n {{- '{\"name\": ' + tool.name + '\", ' }}\n {{- '\"description\": \"' + tool.name + '(' }}\n {%- for param_name, param_fields in tool.parameters.properties|items %}\n {{- param_name + \": \" + json_to_python_type(param_fields) }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- if tool.return is defined %}\n {{- \" -> \" + json_to_python_type(tool.return) }}\n {%- endif %}\n {{- \" - \" + tool.description + \"\n\n\" }}\n {%- for param_name, param_fields in tool.parameters.properties|items %}\n {%- if loop.first %}\n {{- \" Args:\n\" }}\n {%- endif %}\n {{- \" \" + param_name + \"(\" + json_to_python_type(param_fields) + \"): \" + param_fields.description|trim }}\n {%- endfor %}\n {%- if tool.return is defined and tool.return.description is defined %}\n {{- \"\n Returns:\n \" + tool.return.description }}\n {%- endif %}\n {{- '\"' }}\n {{- ', \"parameters\": ' }}\n {%- if tool.parameters.properties | length == 0 %}\n {{- \"{}\" }}\n {%- else %}\n {{- tool.parameters|tojson }}\n {%- endif %}\n {{- \"}\" }}\n {%- if not loop.last %}\n {{- \"\n\" }}\n {%- endif %}\n {%- endfor %}\n {{- \" \" }}\n {{- 'Use the following pydantic model json schema for each tool call you will make: {\"properties\": {\"arguments\": {\"title\": \"Arguments\", \"type\": \"object\"}, \"name\": {\"title\": \"Name\", \"type\": \"string\"}}, \"required\": [\"arguments\", \"name\"], \"title\": \"FunctionCall\", \"type\": \"object\"}\n' }}\n {{- \"For each function call return a json object with function name and arguments within XML tags as follows:\n\" }}\n {{- \"\n\" }}\n {{- '{\"name\": , \"arguments\": }\n' }}\n {{- '<|im_end|>\n' }}\n{%- else %}\n {%- if messages[0]['role'] != 'system' %}\n {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}\n {%- else %}\n {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if message.role == \"user\" or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and message.tool_calls is not defined) %}\n {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role + '\n\n' }}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '{' }}\n {{- '\"name\": \"' }}\n {{- tool_call.name }}\n {%- if tool_call.arguments is defined %}\n {{- ', ' }}\n {{- '\"arguments\": ' }}\n {{- tool_call.arguments|tojson }}\n {%- endif %}\n {{- '\"}' }}\n {{- '\n' }}\n {%- endfor %}\n {{- '<|im_end|>\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if not message.name is defined %}\n {{- raise_exception(\"Tool response dicts require a 'name' key indicating the name of the called function!\") }}\n {%- endif %}\n {{- '<|im_start|>user\n\n' }}\n {{- '{\"name\": \"' }}\n {{- message.name }}\n {{- '\", \"content\": ' }}\n {{- message.content|tojson + '}' }}\n {{- '\n<|im_end|>\n' }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\n' }}\n{%- endif %}", + "stop_token_ids": [ + 151643, + 151644, + 151645 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>" + ] }, { "version": 1, @@ -2984,25 +2578,17 @@ "model_hub": "modelscope" } ], - "prompt_style": { - "style_name": "QWEN", - "system_prompt": "You are a helpful assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n", - "stop_token_ids": [ - 151643, - 151644, - 151645 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>" - ] - } + "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [ + 151643, + 151644, + 151645 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>" + ] }, { "version": 1, @@ -3281,25 +2867,17 @@ } } ], - "prompt_style": { - "style_name": "QWEN", - "system_prompt": "You are a helpful assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n", - "stop_token_ids": [ - 151643, - 151644, - 151645 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>" - ] - } + "chat_template": "{%- macro json_to_python_type(json_spec) %}\n {%- set basic_type_map = {\n \"string\": \"str\",\n \"number\": \"float\",\n \"integer\": \"int\",\n \"boolean\": \"bool\"\n} %}\n {%- if basic_type_map[json_spec.type] is defined %}\n {{- basic_type_map[json_spec.type] }}\n {%- elif json_spec.type == \"array\" %}\n {{- \"list[\" + json_to_python_type(json_spec|items) + \"]\" }}\n {%- elif json_spec.type == \"object\" %}\n {%- if json_spec.additionalProperties is defined %}\n {{- \"dict[str, \" + json_to_python_type(json_spec.additionalProperties) + ']' }}\n {%- else %}\n {{- \"dict\" }}\n {%- endif %}\n {%- elif json_spec.type is iterable %}\n {{- \"Union[\" }}\n {%- for t in json_spec.type %}\n {{- json_to_python_type({\"type\": t}) }}\n {%- if not loop.last %}\n {{- \",\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"]\" }}\n {%- else %}\n {{- \"Any\" }}\n {%- endif %}\n{%- endmacro %}\n\n{%- if tools %}\n {{- '<|im_start|>system\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] + '\n\n' }}\n {%- endif %}\n {{- '# Tools\n\n' }}\n {{- \"You are a function calling AI model. You are provided with function signatures within XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: \" }}\n {%- for tool in tools %}\n {%- if tool.function is defined %}\n {%- set tool = tool.function %}\n {%- endif %}\n {{- '{\"type\": \"function\", \"function\": ' }}\n {{- '{\"name\": ' + tool.name + '\", ' }}\n {{- '\"description\": \"' + tool.name + '(' }}\n {%- for param_name, param_fields in tool.parameters.properties|items %}\n {{- param_name + \": \" + json_to_python_type(param_fields) }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- if tool.return is defined %}\n {{- \" -> \" + json_to_python_type(tool.return) }}\n {%- endif %}\n {{- \" - \" + tool.description + \"\n\n\" }}\n {%- for param_name, param_fields in tool.parameters.properties|items %}\n {%- if loop.first %}\n {{- \" Args:\n\" }}\n {%- endif %}\n {{- \" \" + param_name + \"(\" + json_to_python_type(param_fields) + \"): \" + param_fields.description|trim }}\n {%- endfor %}\n {%- if tool.return is defined and tool.return.description is defined %}\n {{- \"\n Returns:\n \" + tool.return.description }}\n {%- endif %}\n {{- '\"' }}\n {{- ', \"parameters\": ' }}\n {%- if tool.parameters.properties | length == 0 %}\n {{- \"{}\" }}\n {%- else %}\n {{- tool.parameters|tojson }}\n {%- endif %}\n {{- \"}\" }}\n {%- if not loop.last %}\n {{- \"\n\" }}\n {%- endif %}\n {%- endfor %}\n {{- \" \" }}\n {{- 'Use the following pydantic model json schema for each tool call you will make: {\"properties\": {\"arguments\": {\"title\": \"Arguments\", \"type\": \"object\"}, \"name\": {\"title\": \"Name\", \"type\": \"string\"}}, \"required\": [\"arguments\", \"name\"], \"title\": \"FunctionCall\", \"type\": \"object\"}\n' }}\n {{- \"For each function call return a json object with function name and arguments within XML tags as follows:\n\" }}\n {{- \"\n\" }}\n {{- '{\"name\": , \"arguments\": }\n' }}\n {{- '<|im_end|>\n' }}\n{%- else %}\n {%- if messages[0]['role'] != 'system' %}\n {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}\n {%- else %}\n {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if message.role == \"user\" or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and message.tool_calls is not defined) %}\n {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role + '\n\n' }}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '{' }}\n {{- '\"name\": \"' }}\n {{- tool_call.name }}\n {%- if tool_call.arguments is defined %}\n {{- ', ' }}\n {{- '\"arguments\": ' }}\n {{- tool_call.arguments|tojson }}\n {%- endif %}\n {{- '\"}' }}\n {{- '\n' }}\n {%- endfor %}\n {{- '<|im_end|>\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if not message.name is defined %}\n {{- raise_exception(\"Tool response dicts require a 'name' key indicating the name of the called function!\") }}\n {%- endif %}\n {{- '<|im_start|>user\n\n' }}\n {{- '{\"name\": \"' }}\n {{- message.name }}\n {{- '\", \"content\": ' }}\n {{- message.content|tojson + '}' }}\n {{- '\n<|im_end|>\n' }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\n' }}\n{%- endif %}", + "stop_token_ids": [ + 151643, + 151644, + 151645 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>" + ] }, { "version": 1, @@ -3365,25 +2943,17 @@ } } ], - "prompt_style": { - "style_name": "QWEN", - "system_prompt": "You are a helpful assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n", - "stop_token_ids": [ - 151643, - 151644, - 151645 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>" - ] - } + "chat_template": "{%- macro json_to_python_type(json_spec) %}\n {%- set basic_type_map = {\n \"string\": \"str\",\n \"number\": \"float\",\n \"integer\": \"int\",\n \"boolean\": \"bool\"\n} %}\n {%- if basic_type_map[json_spec.type] is defined %}\n {{- basic_type_map[json_spec.type] }}\n {%- elif json_spec.type == \"array\" %}\n {{- \"list[\" + json_to_python_type(json_spec|items) + \"]\" }}\n {%- elif json_spec.type == \"object\" %}\n {%- if json_spec.additionalProperties is defined %}\n {{- \"dict[str, \" + json_to_python_type(json_spec.additionalProperties) + ']' }}\n {%- else %}\n {{- \"dict\" }}\n {%- endif %}\n {%- elif json_spec.type is iterable %}\n {{- \"Union[\" }}\n {%- for t in json_spec.type %}\n {{- json_to_python_type({\"type\": t}) }}\n {%- if not loop.last %}\n {{- \",\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"]\" }}\n {%- else %}\n {{- \"Any\" }}\n {%- endif %}\n{%- endmacro %}\n\n{%- if tools %}\n {{- '<|im_start|>system\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] + '\n\n' }}\n {%- endif %}\n {{- '# Tools\n\n' }}\n {{- \"You are a function calling AI model. You are provided with function signatures within XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: \" }}\n {%- for tool in tools %}\n {%- if tool.function is defined %}\n {%- set tool = tool.function %}\n {%- endif %}\n {{- '{\"type\": \"function\", \"function\": ' }}\n {{- '{\"name\": ' + tool.name + '\", ' }}\n {{- '\"description\": \"' + tool.name + '(' }}\n {%- for param_name, param_fields in tool.parameters.properties|items %}\n {{- param_name + \": \" + json_to_python_type(param_fields) }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- if tool.return is defined %}\n {{- \" -> \" + json_to_python_type(tool.return) }}\n {%- endif %}\n {{- \" - \" + tool.description + \"\n\n\" }}\n {%- for param_name, param_fields in tool.parameters.properties|items %}\n {%- if loop.first %}\n {{- \" Args:\n\" }}\n {%- endif %}\n {{- \" \" + param_name + \"(\" + json_to_python_type(param_fields) + \"): \" + param_fields.description|trim }}\n {%- endfor %}\n {%- if tool.return is defined and tool.return.description is defined %}\n {{- \"\n Returns:\n \" + tool.return.description }}\n {%- endif %}\n {{- '\"' }}\n {{- ', \"parameters\": ' }}\n {%- if tool.parameters.properties | length == 0 %}\n {{- \"{}\" }}\n {%- else %}\n {{- tool.parameters|tojson }}\n {%- endif %}\n {{- \"}\" }}\n {%- if not loop.last %}\n {{- \"\n\" }}\n {%- endif %}\n {%- endfor %}\n {{- \" \" }}\n {{- 'Use the following pydantic model json schema for each tool call you will make: {\"properties\": {\"arguments\": {\"title\": \"Arguments\", \"type\": \"object\"}, \"name\": {\"title\": \"Name\", \"type\": \"string\"}}, \"required\": [\"arguments\", \"name\"], \"title\": \"FunctionCall\", \"type\": \"object\"}\n' }}\n {{- \"For each function call return a json object with function name and arguments within XML tags as follows:\n\" }}\n {{- \"\n\" }}\n {{- '{\"name\": , \"arguments\": }\n' }}\n {{- '<|im_end|>\n' }}\n{%- else %}\n {%- if messages[0]['role'] != 'system' %}\n {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}\n {%- else %}\n {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if message.role == \"user\" or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and message.tool_calls is not defined) %}\n {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role + '\n\n' }}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '{' }}\n {{- '\"name\": \"' }}\n {{- tool_call.name }}\n {%- if tool_call.arguments is defined %}\n {{- ', ' }}\n {{- '\"arguments\": ' }}\n {{- tool_call.arguments|tojson }}\n {%- endif %}\n {{- '\"}' }}\n {{- '\n' }}\n {%- endfor %}\n {{- '<|im_end|>\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if not message.name is defined %}\n {{- raise_exception(\"Tool response dicts require a 'name' key indicating the name of the called function!\") }}\n {%- endif %}\n {{- '<|im_start|>user\n\n' }}\n {{- '{\"name\": \"' }}\n {{- message.name }}\n {{- '\", \"content\": ' }}\n {{- message.content|tojson + '}' }}\n {{- '\n<|im_end|>\n' }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\n' }}\n{%- endif %}", + "stop_token_ids": [ + 151643, + 151644, + 151645 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>" + ] }, { "version": 1, @@ -3418,19 +2988,13 @@ "model_hub": "modelscope" } ], - "prompt_style": { - "style_name": "DEEPSEEK_CHAT", - "system_prompt": "<｜begin▁of▁sentence｜>", - "roles": [ - "User", - "Assistant" - ], - "intra_message_sep": "\n\n", - "inter_message_sep": "<｜end▁of▁sentence｜>", - "stop": [ - "<｜end▁of▁sentence｜>" - ] - } + "chat_template": "", + "stop_token_ids": [ + 100001 + ], + "stop": [ + "<｜end▁of▁sentence｜>" + ] }, { "version": 1, @@ -3505,19 +3069,13 @@ "model_hub": "modelscope" } ], - "prompt_style": { - "style_name": "DEEPSEEK_CHAT", - "system_prompt": "<｜begin▁of▁sentence｜>", - "roles": [ - "User", - "Assistant" - ], - "intra_message_sep": "\n\n", - "inter_message_sep": "<｜end▁of▁sentence｜>", - "stop": [ - "<｜end▁of▁sentence｜>" - ] - } + "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ '<｜begin▁of▁sentence｜>' }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + '<｜end▁of▁sentence｜>' }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}", + "stop_token_ids": [ + 100001 + ], + "stop": [ + "<｜end▁of▁sentence｜>" + ] }, { "version": 1, @@ -3614,18 +3172,13 @@ "model_hub": "modelscope" } ], - "prompt_style": { - "style_name": "DEEPSEEK_CODER", - "system_prompt": "You are an AI programming assistant, utilizing the DeepSeek Coder model, developed by DeepSeek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer.", - "roles": [ - "### Instruction:", - "### Response:" - ], - "inter_message_sep": "\n", - "stop": [ - "<|EOT|>" - ] - } + "chat_template": "{% if not add_generation_prompt is defined %}\n{% set add_generation_prompt = false %}\n{% endif %}\n{%- set ns = namespace(found=false) -%}\n{%- for message in messages -%}\n {%- if message['role'] == 'system' -%}\n {%- set ns.found = true -%}\n {%- endif -%}\n{%- endfor -%}\n{{'<｜begin▁of▁sentence｜>'}}{%- if not ns.found -%}\n{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\n'}}\n{%- endif %}\n{%- for message in messages %}\n {%- if message['role'] == 'system' %}\n{{ message['content'] }}\n {%- else %}\n {%- if message['role'] == 'user' %}\n{{'### Instruction:\n' + message['content'] + '\n'}}\n {%- else %}\n{{'### Response:\n' + message['content'] + '\n<|EOT|>\n'}}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{% if add_generation_prompt %}\n{{'### Response:'}}\n{% endif %}", + "stop_token_ids": [ + 32021 + ], + "stop": [ + "<|EOT|>" + ] }, { "version": 1, @@ -3713,23 +3266,15 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "INTERNLM2", - "system_prompt": "You are InternLM (书生·浦语), a helpful, honest, and harmless AI assistant developed by Shanghai AI Laboratory (上海人工智能实验室).", - "roles": [ - "<|im_start|>user", - "<|im_start|>assistant" - ], - "intra_message_sep": "<|im_end|>", - "stop_token_ids": [ - 2, - 92542 - ], - "stop": [ - "", - "<|im_end|>" - ] - } + "chat_template": "{{ '' }}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [ + 2, + 92542 + ], + "stop": [ + "", + "<|im_end|>" + ] }, { "version": 1, @@ -3766,24 +3311,17 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "QWEN", - "system_prompt": "You are a helpful assistant.", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 151643, - 151644, - 151645 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>" - ] - } + "chat_template": "", + "stop_token_ids": [ + 151643, + 151644, + 151645 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>" + ] }, { "version": 1, @@ -3819,18 +3357,17 @@ "model_id": "OrionStarAI/Orion-14B-Chat-{quantization}" } ], - "prompt_style": { - "style_name": "orion", - "roles": [ - "Human", - "assistant" - ], - "stop": [ - "", - "", - "" - ] - } + "chat_template": "{% for message in messages %}{% if loop.first %}{{ '' }}{% endif %}{% if message['role'] == 'user' %}{{ 'Human: ' + message['content'] + '\n\nAssistant: ' + '' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + '' }}{% endif %}{% endfor %}", + "stop_token_ids": [ + 1, + 2, + 0 + ], + "stop": [ + "", + "", + "" + ] }, { "version": 1, @@ -3857,18 +3394,17 @@ "model_id": "OrionStarAI/Orion-14B-Chat-RAG" } ], - "prompt_style": { - "style_name": "orion", - "roles": [ - "Human", - "assistant" - ], - "stop": [ - "", - "", - "" - ] - } + "chat_template": "{% for message in messages %}{% if loop.first %}{{ '' }}{% endif %}{% if message['role'] == 'user' %}{{ 'Human: ' + message['content'] + '\n\nAssistant: ' + '' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + '' }}{% endif %}{% endfor %}", + "stop_token_ids": [ + 1, + 2, + 0 + ], + "stop": [ + "", + "", + "" + ] }, { "version": 1, @@ -3903,28 +3439,19 @@ "model_id": "01ai/Yi-VL-34B" } ], - "prompt_style": { - "style_name": "CHATML", - "system_prompt": "", - "roles": [ - "<|im_start|>user", - "<|im_start|>assistant" - ], - "intra_message_sep": "<|im_end|>", - "inter_message_sep": "", - "stop_token_ids": [ - 2, - 6, - 7, - 8 - ], - "stop": [ - "<|endoftext|>", - "<|im_start|>", - "<|im_end|>", - "<|im_sep|>" - ] - } + "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [ + 2, + 6, + 7, + 8 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>", + "<|im_sep|>" + ] }, { "version": 1, @@ -3961,17 +3488,17 @@ "model_id": "AI-ModelScope/gemma-7b-it" } ], - "prompt_style": { - "style_name": "gemma", - "roles": [ - "user", - "model" - ], - "stop": [ - "", - "" - ] - } + "chat_template": "{{ '' }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '' + role + '\n' + message['content'] | trim + '\n' }}{% endfor %}{% if add_generation_prompt %}{{'model\n'}}{% endif %}", + "stop_token_ids": [ + 1, + 106, + 107 + ], + "stop": [ + "", + "", + "" + ] }, { "version": 1, @@ -4042,17 +3569,17 @@ "model_hub": "modelscope" } ], - "prompt_style": { - "style_name": "gemma", - "roles": [ - "user", - "model" - ], - "stop": [ - "", - "" - ] - } + "chat_template": "{{ '' }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '' + role + '\n' + message['content'] | trim + '\n' }}{% endfor %}{% if add_generation_prompt %}{{'model\n'}}{% endif %}", + "stop_token_ids": [ + 1, + 106, + 107 + ], + "stop": [ + "", + "", + "" + ] }, { "version":1, @@ -4089,14 +3616,13 @@ "model_revision":"master" } ], - "prompt_style":{ - "style_name":"OmniLMM", - "system_prompt":"The role of first msg should be user", - "roles":[ - "user", - "assistant" - ] - } + "chat_template": "", + "stop_token_ids": [ + 2 + ], + "stop": [ + "" + ] }, { "version": 1, @@ -4121,22 +3647,15 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "MINICPM-2B", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 1, - 2 - ], - "stop": [ - "", - "" - ] - } + "chat_template": "{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + ''}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}", + "stop_token_ids": [ + 1, + 2 + ], + "stop": [ + "", + "" + ] }, { "version": 1, @@ -4161,22 +3680,15 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "MINICPM-2B", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 1, - 2 - ], - "stop": [ - "", - "" - ] - } + "chat_template": "{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + ''}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}", + "stop_token_ids": [ + 1, + 2 + ], + "stop": [ + "", + "" + ] }, { "version": 1, @@ -4201,22 +3713,15 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "MINICPM-2B", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 1, - 2 - ], - "stop": [ - "", - "" - ] - } + "chat_template": "{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + ''}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}", + "stop_token_ids": [ + 1, + 2 + ], + "stop": [ + "", + "" + ] }, { "version": 1, @@ -4241,22 +3746,15 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "MINICPM-2B", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 1, - 2 - ], - "stop": [ - "", - "" - ] - } + "chat_template": "{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + ''}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}", + "stop_token_ids": [ + 1, + 2 + ], + "stop": [ + "", + "" + ] }, { "version": 1, @@ -4281,22 +3779,15 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "MINICPM-2B", - "system_prompt": "", - "roles": [ - "user", - "assistant" - ], - "stop_token_ids": [ - 1, - 2 - ], - "stop": [ - "", - "" - ] - } + "chat_template": "{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + ''}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}", + "stop_token_ids": [ + 1, + 2 + ], + "stop": [ + "", + "" + ] }, { "version":1, @@ -4333,14 +3824,13 @@ "model_revision":"master" } ], - "prompt_style":{ - "style_name":"OmniLMM", - "system_prompt":"The role of first msg should be user", - "roles":[ - "user", - "assistant" - ] - } + "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = '<|begin_of_text|>' + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}", + "stop_token_ids": [ + 128001 + ], + "stop": [ + "<|end_of_text|>" + ] }, { "version":1, @@ -4377,14 +3867,15 @@ "model_revision":"master" } ], - "prompt_style":{ - "style_name":"QWEN", - "system_prompt":"You are a helpful assistant", - "roles":[ - "user", - "assistant" - ] - } + "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [ + 151645, + 151643 + ], + "stop": [ + "<|im_end|>", + "<|endoftext|>" + ] }, { "version": 1, @@ -4463,23 +3954,15 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "ADD_COLON_SINGLE", - "intra_message_sep": "\n", - "system_prompt": "", - "roles": [ - "USER", - "ASSISTANT" - ], - "stop_token_ids": [ - 100006, - 100007 - ], - "stop": [ - "[CLS]", - "" - ] - } + "chat_template": "{% for item in messages %}{% if loop.first and item['role'] == 'system' %}{{ item['content'] + '\n' }}{% endif %}{% if item['role'] == 'user' %}{{ 'USER: ' + item['content'] + '\n' }}{% elif item['role'] == 'assistant' %}{{ 'ASSISTANT: ' + item['content'] + '\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT: ' }}{% endif %}", + "stop_token_ids": [ + 100006, + 100007 + ], + "stop": [ + "[CLS]", + "" + ] }, { "version": 1, @@ -4504,23 +3987,15 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "ADD_COLON_SINGLE", - "intra_message_sep": "\n", - "system_prompt": "", - "roles": [ - "USER", - "ASSISTANT" - ], - "stop_token_ids": [ - 100006, - 100007 - ], - "stop": [ - "[CLS]", - "" - ] - } + "chat_template": "{% for item in messages %}{% if loop.first and item['role'] == 'system' %}{{ item['content'] + '\n' }}{% endif %}{% if item['role'] == 'user' %}{{ 'USER: ' + item['content'] + '\n' }}{% elif item['role'] == 'assistant' %}{{ 'ASSISTANT: ' + item['content'] + '\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT: ' }}{% endif %}", + "stop_token_ids": [ + 100006, + 100007 + ], + "stop": [ + "[CLS]", + "" + ] }, { "version": 1, @@ -4588,20 +4063,15 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "c4ai-command-r", - "system_prompt": "You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.", - "roles": [ - "<|USER_TOKEN|>", - "<|CHATBOT_TOKEN|>" - ], - "intra_message_sep": "", - "inter_message_sep": "<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|>", - "stop_token_ids": [ - 6, - 255001 - ] - } + "chat_template": "{{ '' }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true %}{% set loop_messages = messages %}{% set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% if system_message != false %}{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% elif message['role'] == 'assistant' %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}{% endif %}", + "stop_token_ids": [ + 6, + 255001 + ], + "stop": [ + "", + "<|END_OF_TURN_TOKEN|>" + ] }, { "version": 1, @@ -4628,24 +4098,17 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "PHI3", - "system_prompt": "You are a helpful AI assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n", - "inter_message_sep": "<|end|>\n", - "stop_token_ids":[ - 32000, - 32007 - ], - "stop": [ - "<|endoftext|>", - "<|end|>" - ] - } + "chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{{'<|system|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'user' %}{{'<|user|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'assistant' %}{{'<|assistant|>\n' + message['content'] + '<|end|>\n'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ '<|endoftext|>' }}{% endif %}", + "stop_token_ids":[ + 32000, + 32001, + 32007 + ], + "stop": [ + "<|endoftext|>", + "<|assistant|>", + "<|end|>" + ] }, { "version": 1, @@ -4672,24 +4135,17 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "PHI3", - "system_prompt": "You are a helpful AI assistant.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n", - "inter_message_sep": "<|end|>\n", - "stop_token_ids":[ - 32000, - 32007 - ], - "stop": [ - "<|endoftext|>", - "<|end|>" - ] - } + "chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{{'<|system|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'user' %}{{'<|user|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'assistant' %}{{'<|assistant|>\n' + message['content'] + '<|end|>\n'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ '<|endoftext|>' }}{% endif %}", + "stop_token_ids":[ + 32000, + 32001, + 32007 + ], + "stop": [ + "<|endoftext|>", + "<|assistant|>", + "<|end|>" + ] }, { "version": 1, @@ -4718,25 +4174,17 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "INTERNVL", - "system_prompt": "You are InternLM (书生·浦语), a helpful, honest, and harmless AI assistant developed by Shanghai AI Laboratory (上海人工智能实验室).", - "roles": [ - "<|im_start|>user", - "<|im_start|>assistant" - ], - "intra_message_sep": "<|im_end|>", - "stop_token_ids": [ - 2, - 92543, - 92542 - ], - "stop": [ - "", - "<|im_end|>", - "<|im_start|>" - ] - } + "chat_template": "{{ '' }}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [ + 2, + 92542, + 92543 + ], + "stop": [ + "", + "<|im_end|>", + "<|im_start|>" + ] }, { "version": 1, @@ -4888,25 +4336,9 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "INTERNVL", - "system_prompt": "You are InternLM (书生·浦语), a helpful, honest, and harmless AI assistant developed by Shanghai AI Laboratory (上海人工智能实验室).", - "roles": [ - "<|im_start|>user", - "<|im_start|>assistant" - ], - "intra_message_sep": "<|im_end|>", - "stop_token_ids": [ - 2, - 92543, - 92542 - ], - "stop": [ - "", - "<|im_end|>", - "<|im_start|>" - ] - } + "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [], + "stop": [] }, { "version": 1, @@ -4943,24 +4375,15 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "LLAMA3", - "system_prompt": "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n\n", - "inter_message_sep": "<|eot_id|>", - "stop_token_ids": [ - 128001, - 128009 - ], - "stop": [ - "<|end_of_text|>", - "<|eot_id|>" - ] - } + "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = '<|begin_of_text|>' + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% else %}{{ '<|end_of_text|>' }}{% endif %}", + "stop_token_ids": [ + 128001, + 128009 + ], + "stop": [ + "<|end_of_text|>", + "<|eot_id|>" + ] }, { "version": 1, @@ -4989,24 +4412,15 @@ "model_revision": "master" } ], - "prompt_style": { - "style_name": "LLAMA3", - "system_prompt": "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.", - "roles": [ - "user", - "assistant" - ], - "intra_message_sep": "\n\n", - "inter_message_sep": "<|eot_id|>", - "stop_token_ids": [ - 128001, - 128009 - ], - "stop": [ - "<|end_of_text|>", - "<|eot_id|>" - ] - } + "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = '<|begin_of_text|>' + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% else %}{{ '<|end_of_text|>' }}{% endif %}", + "stop_token_ids": [ + 128001, + 128009 + ], + "stop": [ + "<|end_of_text|>", + "<|eot_id|>" + ] }, { "version": 1, @@ -5080,23 +4494,1476 @@ "model_revision": "master" } ], + "chat_template": "{{ (messages|selectattr('role', 'equalto', 'system')|list|last).content|trim if (messages|selectattr('role', 'equalto', 'system')|list) else '' }}{%- for message in messages -%}{%- if message['role'] == 'user' -%}{{- '<_user>' + message['content'] +'<_bot>' -}}{%- elif message['role'] == 'assistant' -%}{{- message['content'] + '<_end>' -}}{%- endif -%}{%- endfor -%}", + "stop": [ + "<_end>", + "<_start>" + ], + "stop_token_ids": [ + 160133, + 160132 + ] + }, + { + "version": 1, + "context_length": 32768, + "model_name": "qwen2-vl-instruct", + "model_lang": [ + "en", + "zh" + ], + "model_ability": [ + "chat", + "vision" + ], + "model_description": "Qwen2-VL: To See the World More Clearly.Qwen2-VL is the latest version of the vision language models in the Qwen model familities.", + "model_specs":[ + { + "model_format":"pytorch", + "model_size_in_billions":7, + "quantizations":[ + "none" + ], + "model_hub": "modelscope", + "model_id":"qwen/Qwen2-VL-7B-Instruct", + "model_revision":"master" + }, + { + "model_format":"gptq", + "model_size_in_billions":7, + "quantizations":[ + "Int8" + ], + "model_hub": "modelscope", + "model_id":"qwen/Qwen2-VL-7B-Instruct-GPTQ-Int8", + "model_revision":"master" + }, + { + "model_format":"gptq", + "model_size_in_billions":7, + "quantizations":[ + "Int4" + ], + "model_hub": "modelscope", + "model_id":"qwen/Qwen2-VL-7B-Instruct-GPTQ-Int4", + "model_revision":"master" + }, + { + "model_format":"awq", + "model_size_in_billions":7, + "quantizations":[ + "Int4" + ], + "model_hub": "modelscope", + "model_id":"qwen/Qwen2-VL-7B-Instruct-AWQ", + "model_revision":"master" + }, + { + "model_format":"pytorch", + "model_size_in_billions":2, + "quantizations":[ + "none" + ], + "model_hub": "modelscope", + "model_id":"qwen/Qwen2-VL-2B-Instruct", + "model_revision":"master" + }, + { + "model_format":"gptq", + "model_size_in_billions":2, + "quantizations":[ + "Int8" + ], + "model_hub": "modelscope", + "model_id":"qwen/Qwen2-VL-2B-Instruct-GPTQ-Int8", + "model_revision":"master" + }, + { + "model_format":"gptq", + "model_size_in_billions":2, + "quantizations":[ + "Int4" + ], + "model_hub": "modelscope", + "model_id":"qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4", + "model_revision":"master" + }, + { + "model_format":"awq", + "model_size_in_billions":2, + "quantizations":[ + "Int4" + ], + "model_hub": "modelscope", + "model_id":"qwen/Qwen2-VL-2B-Instruct-AWQ", + "model_revision":"master" + }, + { + "model_format":"pytorch", + "model_size_in_billions":72, + "quantizations":[ + "none" + ], + "model_id":"qwen/Qwen2-VL-72B-Instruct", + "model_hub": "modelscope" + }, + { + "model_format":"awq", + "model_size_in_billions":72, + "quantizations":[ + "Int4" + ], + "model_id":"qwen/Qwen2-VL-72B-Instruct-AWQ", + "model_hub": "modelscope" + }, + { + "model_format":"gptq", + "model_size_in_billions":72, + "quantizations":[ + "Int4", + "Int8" + ], + "model_id":"qwen/Qwen2-VL-72B-Instruct-GPTQ-{quantization}", + "model_hub": "modelscope" + } + ], + "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}", + "stop_token_ids": [ + 151645, + 151643 + ], + "stop": [ + "<|im_end|>", + "<|endoftext|>" + ] + }, + { + "version": 1, + "context_length": 32768, + "model_name": "minicpm3-4b", + "model_lang": [ + "zh" + ], + "model_ability": [ + "chat" + ], + "model_description": "MiniCPM3-4B is the 3rd generation of MiniCPM series. The overall performance of MiniCPM3-4B surpasses Phi-3.5-mini-Instruct and GPT-3.5-Turbo-0125, being comparable with many recent 7B~9B models.", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": 4, + "quantizations": [ + "none" + ], + "model_hub": "modelscope", + "model_id": "OpenBMB/MiniCPM3-4B", + "model_revision": "master" + }, + { + "model_format": "gptq", + "model_size_in_billions": 4, + "quantizations": [ + "Int4" + ], + "model_hub": "modelscope", + "model_id": "OpenBMB/MiniCPM3-4B-GPTQ-Int4", + "model_revision": "master" + } + ], + "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "stop_token_ids": [ + 1, + 2 + ], + "stop": [ + "", + "" + ] + }, + { + "version": 1, + "context_length": 32768, + "model_name": "qwen2-audio-instruct", + "model_lang": [ + "en", + "zh" + ], + "model_ability": [ + "chat", + "audio" + ], + "model_description": "Qwen2-Audio: A large-scale audio-language model which is capable of accepting various audio signal inputs and performing audio analysis or direct textual responses with regard to speech instructions.", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": 7, + "quantizations": [ + "none" + ], + "model_hub": "modelscope", + "model_id": "qwen/Qwen2-Audio-7B-Instruct", + "model_revision": "master" + } + ], + "prompt_style": { + "style_name": "QWEN", + "system_prompt": "You are a helpful assistant", + "roles": [ + "user", + "assistant" + ] + } + }, + { + "version": 1, + "context_length": 32768, + "model_name": "qwen2-audio", + "model_lang": [ + "en", + "zh" + ], + "model_ability": [ + "chat", + "audio" + ], + "model_description": "Qwen2-Audio: A large-scale audio-language model which is capable of accepting various audio signal inputs and performing audio analysis or direct textual responses with regard to speech instructions.", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": 7, + "quantizations": [ + "none" + ], + "model_hub": "modelscope", + "model_id": "qwen/Qwen2-Audio-7B", + "model_revision": "master" + } + ], "prompt_style": { - "style_name": "NO_COLON_TWO", - "system_prompt": "You are a helpful assistant.", + "style_name": "QWEN", + "system_prompt": "You are a helpful assistant", "roles": [ - "<_user>", - "<_bot>" - ], - "intra_message_sep": "", - "inter_message_sep": "", - "stop": [ - "<_end>", - "<_start>" - ], - "stop_token_ids": [ - 160133, - 160132 + "user", + "assistant" ] } + }, + { + "version": 1, + "context_length": 128000, + "model_name": "deepseek-v2", + "model_lang": [ + "en", + "zh" + ], + "model_ability": [ + "chat" + ], + "model_description": "DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. ", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": 16, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "deepseek-ai/DeepSeek-V2-Lite", + "model_hub": "modelscope", + "model_revision": "master" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 236, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "deepseek-ai/DeepSeek-V2", + "model_hub": "modelscope", + "model_revision": "master" + } + ] + }, + { + "version": 1, + "context_length": 128000, + "model_name": "deepseek-v2-chat", + "model_lang": [ + "en", + "zh" + ], + "model_ability": [ + "chat" + ], + "model_description": "DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. ", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": 16, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "deepseek-ai/DeepSeek-V2-Lite-Chat", + "model_hub": "modelscope", + "model_revision": "master" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 236, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "deepseek-ai/DeepSeek-V2-Chat", + "model_hub": "modelscope", + "model_revision": "master" + } + ], + "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ '<｜begin▁of▁sentence｜>' }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + '<｜end▁of▁sentence｜>' }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}", + "stop_token_ids": [ + 100001 + ], + "stop": [ + "<｜end▁of▁sentence｜>" + ] + }, + { + "version": 1, + "context_length": 128000, + "model_name": "deepseek-v2-chat-0628", + "model_lang": [ + "en", + "zh" + ], + "model_ability": [ + "chat" + ], + "model_description": "DeepSeek-V2-Chat-0628 is an improved version of DeepSeek-V2-Chat. ", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": 236, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "deepseek-ai/DeepSeek-V2-Chat-0628", + "model_hub": "modelscope", + "model_revision": "master" + } + ], + "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ '<｜begin▁of▁sentence｜>' }}{% for message in messages %}{% if message['role'] == 'user' %}{{ '<｜User｜>' + message['content'] }}{% elif message['role'] == 'assistant' %}{{ '<｜Assistant｜>' + message['content'] + '<｜end▁of▁sentence｜>' }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<｜Assistant｜>' }}{% endif %}", + "stop_token_ids": [ + 100001 + ], + "stop": [ + "<｜end▁of▁sentence｜>" + ] + }, + { + "version": 1, + "context_length": 128000, + "model_name": "deepseek-v2.5", + "model_lang": [ + "en", + "zh" + ], + "model_ability": [ + "chat" + ], + "model_description": "DeepSeek-V2.5 is an upgraded version that combines DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct. The new model integrates the general and coding abilities of the two previous versions.", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": 236, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "deepseek-ai/DeepSeek-V2.5", + "model_hub": "modelscope", + "model_revision": "master" + } + ], + "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %} {%- if message['role'] == 'system' %} {% set ns.system_prompt = message['content'] %} {%- endif %}{%- endfor %}{{'<｜begin▁of▁sentence｜>'}}{{ns.system_prompt}}{%- for message in messages %} {%- if message['role'] == 'user' %} {%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}} {%- endif %} {%- if message['role'] == 'assistant' and message['content'] is none %} {%- set ns.is_tool = false -%} {%- for tool in message['tool_calls']%} {%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}} {%- set ns.is_first = true -%} {%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}} {%- endif %} {%- endfor %} {%- endif %} {%- if message['role'] == 'assistant' and message['content'] is not none %} {%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}} {%- set ns.is_tool = false -%} {%- else %}{{'<｜Assistant｜>' + message['content'] + '<｜end▁of▁sentence｜>'}} {%- endif %} {%- endif %} {%- if message['role'] == 'tool' %} {%- set ns.is_tool = true -%} {%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}} {%- set ns.is_output_first = false %} {%- else %}{{'\\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}} {%- endif %} {%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜>'}}{% endif %}", + "stop_token_ids": [ + 100001 + ], + "stop": [ + "<｜end▁of▁sentence｜>" + ] + }, + { + "version": 1, + "context_length": 131072, + "model_name": "yi-coder-chat", + "model_lang": [ + "en" + ], + "model_ability": [ + "chat" + ], + "model_description": "Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.Excelling in long-context understanding with a maximum context length of 128K tokens.Supporting 52 major programming languages, including popular ones such as Java, Python, JavaScript, and C++.", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": 9, + "quantizations": [ + "none" + ], + "model_hub": "modelscope", + "model_id": "01ai/Yi-Coder-9B-Chat", + "model_revision": "master" + }, + { + "model_format": "pytorch", + "model_size_in_billions": "1_5", + "quantizations": [ + "none" + ], + "model_hub": "modelscope", + "model_id": "01ai/Yi-Coder-1.5B-Chat", + "model_revision": "master" + } + ], + "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ '<|im_start|>system\n' + system_message + '<|im_end|>\n' }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\n' + content + '<|im_end|>\n<|im_start|>assistant\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '\n' }}{% endif %}{% endfor %}", + "stop_token_ids": [ + 1, + 2, + 6, + 7 + ], + "stop": [ + "<|startoftext|>", + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>" + ] + }, + { + "version": 1, + "context_length": 131072, + "model_name": "yi-coder", + "model_lang": [ + "en" + ], + "model_ability": [ + "generate" + ], + "model_description": "Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.Excelling in long-context understanding with a maximum context length of 128K tokens.Supporting 52 major programming languages, including popular ones such as Java, Python, JavaScript, and C++.", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": 9, + "quantizations": [ + "none" + ], + "model_hub": "modelscope", + "model_id": "01ai/Yi-Coder-9B", + "model_revision": "master" + }, + { + "model_format": "pytorch", + "model_size_in_billions": "1_5", + "quantizations": [ + "none" + ], + "model_hub": "modelscope", + "model_id": "01ai/Yi-Coder-1.5B", + "model_revision": "master" + } + ] + }, + { + "version": 1, + "context_length": 32768, + "model_name": "qwen2.5", + "model_lang": [ + "en", + "zh" + ], + "model_ability": [ + "generate" + ], + "model_description": "Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters.", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": "0_5", + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "qwen/Qwen2.5-0.5B", + "model_revision": "master", + "model_hub": "modelscope" + }, + { + "model_format": "pytorch", + "model_size_in_billions": "1_5", + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "qwen/Qwen2.5-1.5B", + "model_revision": "master", + "model_hub": "modelscope" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 3, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "qwen/Qwen2.5-3B", + "model_revision": "master", + "model_hub": "modelscope" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 7, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "qwen/Qwen2.5-7B", + "model_revision": "master", + "model_hub": "modelscope" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 14, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "qwen/Qwen2.5-14B", + "model_revision": "master", + "model_hub": "modelscope" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 32, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "qwen/Qwen2.5-32B", + "model_revision": "master", + "model_hub": "modelscope" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 72, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "qwen/Qwen2.5-72B", + "model_revision": "master", + "model_hub": "modelscope" + } + ] + }, + { + "version": 1, + "context_length": 32768, + "model_name": "qwen2.5-instruct", + "model_lang": [ + "en", + "zh" + ], + "model_ability": [ + "chat", + "tools" + ], + "model_description": "Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters.", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": "0_5", + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "qwen/Qwen2.5-0.5B-Instruct", + "model_hub": "modelscope" + }, + { + "model_format": "pytorch", + "model_size_in_billions": "1_5", + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "qwen/Qwen2.5-1.5B-Instruct", + "model_hub": "modelscope" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 3, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "qwen/Qwen2.5-3B-Instruct", + "model_hub": "modelscope" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 7, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "qwen/Qwen2.5-7B-Instruct", + "model_hub": "modelscope" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 14, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "qwen/Qwen2.5-14B-Instruct", + "model_hub": "modelscope" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 32, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "qwen/Qwen2.5-32B-Instruct", + "model_hub": "modelscope" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 72, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "qwen/Qwen2.5-72B-Instruct", + "model_hub": "modelscope" + }, + { + "model_format": "gptq", + "model_size_in_billions": "0_5", + "quantizations": [ + "Int4", + "Int8" + ], + "model_id": "qwen/Qwen2.5-0.5B-Instruct-GPTQ-{quantization}", + "model_hub": "modelscope" + }, + { + "model_format": "gptq", + "model_size_in_billions": "1_5", + "quantizations": [ + "Int4", + "Int8" + ], + "model_id": "qwen/Qwen2.5-1.5B-Instruct-GPTQ-{quantization}", + "model_hub": "modelscope" + }, + { + "model_format": "gptq", + "model_size_in_billions": 3, + "quantizations": [ + "Int4", + "Int8" + ], + "model_id": "qwen/Qwen2.5-3B-Instruct-GPTQ-{quantization}", + "model_hub": "modelscope" + }, + { + "model_format": "gptq", + "model_size_in_billions": 7, + "quantizations": [ + "Int4", + "Int8" + ], + "model_id": "qwen/Qwen2.5-7B-Instruct-GPTQ-{quantization}", + "model_hub": "modelscope" + }, + { + "model_format": "gptq", + "model_size_in_billions": 14, + "quantizations": [ + "Int4", + "Int8" + ], + "model_id": "qwen/Qwen2.5-14B-Instruct-GPTQ-{quantization}", + "model_hub": "modelscope" + }, + { + "model_format": "gptq", + "model_size_in_billions": 32, + "quantizations": [ + "Int4", + "Int8" + ], + "model_id": "qwen/Qwen2.5-32B-Instruct-GPTQ-{quantization}", + "model_hub": "modelscope" + }, + { + "model_format": "gptq", + "model_size_in_billions": 72, + "quantizations": [ + "Int4", + "Int8" + ], + "model_id": "qwen/Qwen2.5-72B-Instruct-GPTQ-{quantization}", + "model_hub": "modelscope" + }, + { + "model_format": "awq", + "model_size_in_billions": "0_5", + "quantizations": [ + "Int4" + ], + "model_id": "qwen/Qwen2-0.5B-Instruct-AWQ", + "model_hub": "modelscope" + }, + { + "model_format": "awq", + "model_size_in_billions": "1_5", + "quantizations": [ + "Int4" + ], + "model_id": "qwen/Qwen2-1.5B-Instruct-AWQ", + "model_hub": "modelscope" + }, + { + "model_format": "awq", + "model_size_in_billions": 3, + "quantizations": [ + "Int4" + ], + "model_id": "qwen/Qwen2.5-3B-Instruct-AWQ", + "model_hub": "modelscope" + }, + { + "model_format": "awq", + "model_size_in_billions": 7, + "quantizations": [ + "Int4" + ], + "model_id": "qwen/Qwen2.5-7B-Instruct-AWQ", + "model_hub": "modelscope" + }, + { + "model_format": "awq", + "model_size_in_billions":14, + "quantizations": [ + "Int4" + ], + "model_id": "qwen/Qwen2.5-14B-Instruct-AWQ", + "model_hub": "modelscope" + }, + { + "model_format": "awq", + "model_size_in_billions": 32, + "quantizations": [ + "Int4" + ], + "model_id": "qwen/Qwen2.5-32B-Instruct-AWQ", + "model_hub": "modelscope" + }, + { + "model_format": "awq", + "model_size_in_billions": 72, + "quantizations": [ + "Int4" + ], + "model_id": "qwen/Qwen2.5-72B-Instruct-AWQ", + "model_hub": "modelscope" + }, + { + "model_format": "ggufv2", + "model_size_in_billions": "0_5", + "quantizations": [ + "q2_k", + "q3_k_m", + "q4_0", + "q4_k_m", + "q5_0", + "q5_k_m", + "q6_k", + "q8_0" + ], + "model_id": "qwen/Qwen2.5-0.5B-Instruct-GGUF", + "model_file_name_template": "qwen2.5-0.5b-instruct-{quantization}.gguf", + "model_hub": "modelscope" + }, + { + "model_format": "ggufv2", + "model_size_in_billions": "1_5", + "quantizations": [ + "q2_k", + "q3_k_m", + "q4_0", + "q4_k_m", + "q5_0", + "q5_k_m", + "q6_k", + "q8_0" + ], + "model_id": "qwen/Qwen2.5-1.5B-Instruct-GGUF", + "model_file_name_template": "qwen2.5-1.5b-instruct-{quantization}.gguf", + "model_hub": "modelscope" + }, + { + "model_format": "ggufv2", + "model_size_in_billions": 3, + "quantizations": [ + 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"qwen2.5-72b-instruct-{quantization}-{part}.gguf", + "quantization_parts": { + "q2_k": [ + "00001-of-00007", + "00002-of-00007", + "00003-of-00007", + "00004-of-00007", + "00005-of-00007", + "00006-of-00007", + "00007-of-00007" + ], + "q3_k_m": [ + "00001-of-00009", + "00002-of-00009", + "00003-of-00009", + "00004-of-00009", + "00005-of-00009", + "00006-of-00009", + "00007-of-00009", + "00008-of-00009", + "00009-of-00009" + ], + "q4_0": [ + "00001-of-00011", + "00002-of-00011", + "00003-of-00011", + "00004-of-00011", + "00005-of-00011", + "00006-of-00011", + "00007-of-00011", + "00008-of-00011", + "00009-of-00011", + "00010-of-00011", + "00011-of-00011" + ], + "q4_k_m": [ + "00001-of-00012", + "00002-of-00012", + "00003-of-00012", + "00004-of-00012", + "00005-of-00012", + "00006-of-00012", + "00007-of-00012", + "00008-of-00012", + "00009-of-00012", + "00010-of-00012", + "00011-of-00012", + "00012-of-00012" + ], + "q5_0": [ + "00001-of-00013", + "00002-of-00013", + "00003-of-00013", + "00004-of-00013", + "00005-of-00013", + "00006-of-00013", + "00007-of-00013", + "00008-of-00013", + "00009-of-00013", + "00010-of-00013", + "00011-of-00013", + "00012-of-00013", + "00013-of-00013" + ], + "q5_k_m": [ + "00001-of-00014", + "00002-of-00014", + "00003-of-00014", + "00004-of-00014", + "00005-of-00014", + "00006-of-00014", + "00007-of-00014", + "00008-of-00014", + "00009-of-00014", + "00010-of-00014", + "00011-of-00014", + "00012-of-00014", + "00013-of-00014", + "00014-of-00014" + ], + "q6_k": [ + "00001-of-00016", + "00002-of-00016", + "00003-of-00016", + "00004-of-00016", + "00005-of-00016", + "00006-of-00016", + "00007-of-00016", + "00008-of-00016", + "00009-of-00016", + "00010-of-00016", + "00011-of-00016", + "00012-of-00016", + "00013-of-00016", + "00014-of-00016", + "00015-of-00016", + "00016-of-00016" + ], + "q8_0": [ + "00001-of-00021", + "00002-of-00021", + "00003-of-00021", + "00004-of-00021", + "00005-of-00021", + "00006-of-00021", + "00007-of-00021", + "00008-of-00021", + "00009-of-00021", + "00010-of-00021", + "00011-of-00021", + "00012-of-00021", + "00013-of-00021", + "00014-of-00021", + "00015-of-00021", + "00016-of-00021", + "00017-of-00021", + "00018-of-00021", + "00019-of-00021", + "00020-of-00021", + "00021-of-00021" + ] + } + }, + { + "model_format": "mlx", + "model_size_in_billions": 3, + "quantizations": [ + "4-bit" + ], + "model_id": "okwinds/Qwen2.5-3B-Instruct-MLX-4bit", + "model_hub": "modelscope" + }, + { + "model_format": "mlx", + "model_size_in_billions": 3, + "quantizations": [ + "8-bit" + ], + "model_id": "okwinds/Qwen2.5-3B-Instruct-MLX-8bit", + "model_hub": "modelscope" + }, + { + "model_format": "mlx", + "model_size_in_billions": 7, + "quantizations": [ + "4-bit" + ], + "model_id": "okwinds/Qwen2.5-7B-Instruct-MLX-4bit", + "model_hub": "modelscope" + }, + { + "model_format": "mlx", + "model_size_in_billions": 7, + "quantizations": [ + "8-bit" + ], + "model_id": "okwinds/Qwen2.5-7B-Instruct-MLX-8bit", + "model_hub": "modelscope" + }, + { + "model_format": "mlx", + "model_size_in_billions": 14, + "quantizations": [ + "4-bit" + ], + "model_id": "okwinds/Qwen2.5-14B-Instruct-MLX-4bit", + "model_hub": "modelscope" + }, + { + "model_format": "mlx", + "model_size_in_billions": 14, + "quantizations": [ + "8-bit" + ], + "model_id": "okwinds/Qwen2.5-14B-Instruct-MLX-8bit", + "model_hub": "modelscope" + }, + { + "model_format": "mlx", + "model_size_in_billions": 32, + "quantizations": [ + "2-bit" + ], + "model_id": "okwinds/Qwen2.5-32B-Instruct-MLX-2bit", + "model_hub": "modelscope" + }, + { + "model_format": "mlx", + "model_size_in_billions": 32, + "quantizations": [ + "4-bit" + ], + "model_id": "okwinds/Qwen2.5-32B-Instruct-MLX-4bit", + "model_hub": "modelscope" + }, + { + "model_format": "mlx", + "model_size_in_billions": 32, + "quantizations": [ + "8-bit" + ], + "model_id": "okwinds/Qwen2.5-32B-Instruct-MLX-8bit", + "model_hub": "modelscope" + }, + { + "model_format": "mlx", + "model_size_in_billions": 72, + "quantizations": [ + "2-bit" + ], + "model_id": "okwinds/Qwen2.5-32B-Instruct-MLX-2bit", + "model_hub": "modelscope" + }, + { + "model_format": "mlx", + "model_size_in_billions": 72, + "quantizations": [ + "4-bit" + ], + "model_id": "okwinds/Qwen2.5-72B-Instruct-MLX-4bit", + "model_hub": "modelscope" + }, + { + "model_format": "mlx", + "model_size_in_billions": 72, + "quantizations": [ + "8-bit" + ], + "model_id": "okwinds/Qwen2.5-72B-Instruct-MLX-8bit", + "model_hub": "modelscope" + } + ], + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- message.content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n", + "stop_token_ids": [ + 151643, + 151644, + 151645 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>" + ] + }, + { + "version": 1, + "context_length": 32768, + "model_name": "qwen2.5-coder", + "model_lang": [ + "en", + "zh" + ], + "model_ability": [ + "generate" + ], + "model_description": "Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen).", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": "1_5", + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "qwen/Qwen2.5-Coder-1.5B", + "model_revision": "master", + "model_hub": "modelscope" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 7, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "qwen/Qwen2.5-Coder-7B", + "model_revision": "master", + "model_hub": "modelscope" + } + ] + }, + { + "version": 1, + "context_length": 32768, + "model_name": "qwen2.5-coder-instruct", + "model_lang": [ + "en", + "zh" + ], + "model_ability": [ + "chat", + "tools" + ], + "model_description": "Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen).", + "model_specs": [ + { + "model_format": "pytorch", + "model_size_in_billions": "1_5", + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "qwen/Qwen2.5-Coder-1.5B-Instruct", + "model_revision": "master", + "model_hub": "modelscope" + }, + { + "model_format": "pytorch", + "model_size_in_billions": 7, + "quantizations": [ + "4-bit", + "8-bit", + "none" + ], + "model_id": "qwen/Qwen2.5-Coder-7B-Instruct", + "model_revision": "master", + "model_hub": "modelscope" + }, + { + "model_format": "gptq", + "model_size_in_billions": 7, + "quantizations": [ + "Int4", + "Int8" + ], + "model_id": "qwen/Qwen2.5-Coder-7B-Instruct-GPTQ-{quantization}", + "model_revision": "master", + "model_hub": "modelscope" + }, + { + "model_format": "ggufv2", + "model_size_in_billions": "1_5", + "quantizations": [ + "q2_k", + "q3_k_m", + "q4_0", + "q4_k_m", + "q5_0", + "q5_k_m", + "q6_k", + "q8_0" + ], + "model_hub": "modelscope", + "model_id": "qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF", + "model_file_name_template": "qwen2.5-coder-1.5b-instruct-{quantization}.gguf" + }, + { + "model_format": "ggufv2", + "model_size_in_billions": 7, + "quantizations": [ + "q2_k", + "q3_k_m", + "q4_0", + "q4_k_m", + "q5_0", + "q5_k_m", + "q6_k", + "q8_0" + ], + "model_hub": "modelscope", + "model_id": "qwen/Qwen2.5-Coder-7B-Instruct-GGUF", + "model_file_name_template": "qwen2.5-coder-7b-instruct-{quantization}.gguf", + "model_file_name_split_template": "qwen2.5-coder-7b-instruct-{quantization}-{part}.gguf", + "quantization_parts": { + "q4_0": [ + "00001-of-00002", + "00002-of-00002" + ], + "q4_k_m": [ + "00001-of-00002", + "00002-of-00002" + ], + "q5_0": [ + "00001-of-00002", + "00002-of-00002" + ], + "q5_k_m": [ + "00001-of-00002", + "00002-of-00002" + ], + "q6_k": [ + "00001-of-00002", + "00002-of-00002" + ], + "q8_0": [ + "00001-of-00003", + "00002-of-00003", + "00003-of-00003" + ] + } + } + ], + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{{\\\"name\\\": , \\\"arguments\\\": }}\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- message.content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n", + "stop_token_ids": [ + 151643, + 151644, + 151645 + ], + "stop": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>" + ] } ] diff --git a/xinference/model/llm/lmdeploy/core.py b/xinference/model/llm/lmdeploy/core.py index 22fbd53e72..8df9207a95 100644 --- a/xinference/model/llm/lmdeploy/core.py +++ b/xinference/model/llm/lmdeploy/core.py @@ -12,25 +12,15 @@ # See the License for the specific language governing permissions and # limitations under the License. import logging -import time import uuid from typing import AsyncGenerator, Dict, Iterator, List, Optional, TypedDict, Union import torch -from ....types import ( - ChatCompletion, - ChatCompletionChunk, - ChatCompletionChunkChoice, - ChatCompletionMessage, - Completion, - CompletionChoice, - CompletionUsage, - LoRA, -) +from ....types import ChatCompletion, ChatCompletionChunk, Completion, LoRA from ..core import LLM from ..llm_family import LLMFamilyV1, LLMSpecV1 -from ..utils import ChatModelMixin +from ..utils import ChatModelMixin, generate_chat_completion, generate_completion_chunk logger = logging.getLogger(__name__) @@ -74,8 +64,8 @@ class LMDeployGenerateConfig(TypedDict, total=False): repetition_penalty: Optional[float] ignore_eos: Optional[bool] random_seed: Optional[int] - stop_words: Optional[List[str]] - bad_words: Optional[List[str]] + stop_words: Optional[List[int]] + bad_words: Optional[List[int]] min_new_tokens: Optional[int] skip_special_tokens: Optional[bool] logprobs: Optional[int] @@ -164,9 +154,6 @@ def load(self): raise ValueError(f"Can not find correct chat template.") chat_template_config = ChatTemplateConfig(chat_temp_name) - chat_template_config.meta_instruction = ( - self.model_family.prompt_style.system_prompt - ) count = torch.cuda.device_count() if count > 1: self._model_config.setdefault("tp", torch.cuda.device_count()) @@ -192,9 +179,7 @@ def match( async def async_chat( self, - prompt: Union[str, List[Dict]], - system_prompt: Optional[str] = None, - chat_history: Optional[List[ChatCompletionMessage]] = None, + messages: List[Dict], generate_config: Optional[Dict] = None, ) -> Union[ChatCompletion, AsyncGenerator[ChatCompletionChunk, None]]: stream = ( @@ -213,75 +198,69 @@ async def async_chat( else False ) - chat_history = chat_history or [] - if stream: - chunk = self._chat_stream(prompt, chat_history, include_usage) + chunk = self._chat_stream(messages, include_usage) return self._async_to_chat_completion_chunks(chunk) else: - chunk = await self._chat(prompt, chat_history) - return self._to_chat_completion(chunk) + return await self._chat(messages) - async def _chat_stream(self, prompt, chat_history, include_usage): + async def _chat_stream(self, messages, include_usage): from lmdeploy.messages import Response prompt_tokens, completion_tokens, total_tokens = 0, 0, 0 completion_id = str(uuid.uuid1()) + finish_reason = None async for output in self._generate( - prompt, - chat_history, + messages, session_id=-1, stream_response=True, ): new_text = output.text if isinstance(output, Response) else output.response - - completion_choice = ChatCompletionChunkChoice( - text=new_text, - index=0, - logprobs=None, - finish_reason=output.finish_reason, - ) - chunk = ChatCompletionChunk( - id=completion_id, - object="chat.completion", - created=int(time.time()), - model=self.model_uid, - choices=[completion_choice], - ) prompt_tokens = output.input_token_len completion_tokens = output.generate_token_len total_tokens = prompt_tokens + completion_tokens - completion_usage = CompletionUsage( + finish_reason = output.finish_reason + yield generate_completion_chunk( + chunk_text=new_text, + finish_reason=None, + chunk_id=completion_id, + model_uid=self.model_uid, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens, ) - chunk["usage"] = completion_usage - print(chunk) - yield chunk + + yield generate_completion_chunk( + chunk_text=None, + finish_reason=finish_reason, + chunk_id=completion_id, + model_uid=self.model_uid, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=total_tokens, + has_choice=True, + has_content=False, + ) if include_usage: - chunk = ChatCompletionChunk( - id=completion_id, - object="chat.completion", - created=int(time.time()), - model=self.model_uid, - choices=[], - ) - chunk["usage"] = CompletionUsage( + yield generate_completion_chunk( + chunk_text=None, + finish_reason=None, + chunk_id=completion_id, + model_uid=self.model_uid, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens, + has_choice=False, + has_content=False, ) - yield chunk - async def _chat(self, prompt, chat_history): + async def _chat(self, messages) -> ChatCompletion: from lmdeploy.messages import Response - response, finish_reason = "", "" + response, finish_reason = "", None prompt_tokens, completion_tokens, total_tokens = 0, 0, 0 async for output in self._generate( - prompt, - chat_history, + messages, session_id=-1, stream_response=False, ): @@ -291,30 +270,20 @@ async def _chat(self, prompt, chat_history): total_tokens = output.input_token_len + output.generate_token_len finish_reason = output.finish_reason - chunk = ChatCompletion( - id=str(uuid.uuid1()), - object="chat.completion", - created=int(time.time()), - model=self.model_uid, - choices=[ - CompletionChoice( - index=0, text=response, finish_reason=finish_reason, logprobs=None - ) - ], - usage=CompletionUsage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=total_tokens, - ), + return generate_chat_completion( + self.model_uid, + response, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=total_tokens, + finish_reason=finish_reason, ) - return chunk # copy from lmdeploy # Reference: lmdeploy.serve.async_engine.py async def _generate( self, - prompt, - chat_history, + messages: List[Dict], session_id: int, generate_config: Optional[Dict] = None, tools: Optional[List[object]] = None, @@ -332,6 +301,8 @@ async def _generate( from lmdeploy.serve.async_engine import GenOut from lmdeploy.tokenizer import DetokenizeState + from ..utils import get_stop_token_ids_from_config_file + session_id = -1 if str(session_id) not in self._model.id2step: @@ -343,7 +314,9 @@ async def _generate( generate_config, self._model.tokenizer ) if generate_config.stop_words is None: # type: ignore - generate_config.stop_words = self._model.stop_words # type: ignore + stop_token_ids = get_stop_token_ids_from_config_file(self.model_path) + if stop_token_ids is not None: + generate_config.stop_words = stop_token_ids # type: ignore if generate_config.random_seed is None and sequence_start: # type: ignore generate_config.random_seed = random.getrandbits(64) # type: ignore if generate_config.n > 1: # type: ignore @@ -353,7 +326,7 @@ async def _generate( ) generate_config.n = 1 # type: ignore - prompt_input = await self._get_prompt_input(prompt, chat_history) + prompt_input = await self._get_prompt_input(messages) prompt = prompt_input["prompt"] input_ids = prompt_input["input_ids"] finish_reason = None @@ -482,8 +455,7 @@ async def _generate( # Reference: lmdeploy.serve.vl_async_engine.py async def _get_prompt_input( self, - prompt: Union[str, List[Dict]], - chat_history: Optional[List[ChatCompletionMessage]] = None, + messages: List[Dict], sequence_start: bool = True, tools: Optional[List[object]] = None, **kwargs, @@ -493,13 +465,9 @@ async def _get_prompt_input( IMAGE_DUMMY_TOKEN_INDEX = 0 import numpy as np - assert self.model_family.prompt_style is not None - prompt_style = self.model_family.prompt_style.copy() - chat_history = chat_history or [] - - decorated, _ = self.get_prompt(prompt, chat_history, prompt_style) # type: ignore - chat_history.append(ChatCompletionMessage(role="user", content=prompt)) # type: ignore - prompt = chat_history # type: ignore + model_family = self.model_family.model_family or self.model_family.model_name + decorated, _ = self.get_specific_prompt(model_family, messages) # type: ignore + prompt = messages # type: ignore decorated = decorated.replace("", "") diff --git a/xinference/model/llm/mlx/core.py b/xinference/model/llm/mlx/core.py index e41db2b693..d01324fbf5 100644 --- a/xinference/model/llm/mlx/core.py +++ b/xinference/model/llm/mlx/core.py @@ -17,22 +17,20 @@ import sys import time import uuid -from typing import Dict, Iterable, Iterator, List, Optional, TypedDict, Union +from typing import Dict, Iterator, List, Optional, TypedDict, Union from ....fields import max_tokens_field from ....types import ( ChatCompletion, ChatCompletionChunk, - ChatCompletionMessage, Completion, - CompletionChoice, CompletionChunk, CompletionUsage, LoRA, ) from ..core import LLM from ..llm_family import LLMFamilyV1, LLMSpecV1 -from ..utils import ChatModelMixin +from ..utils import QWEN_TOOL_CALL_FAMILY, ChatModelMixin, generate_completion_chunk logger = logging.getLogger(__name__) @@ -54,6 +52,7 @@ class MLXGenerateConfig(TypedDict, total=False): stop_token_ids: Optional[Union[int, List[int]]] stream: bool stream_options: Optional[Union[dict, None]] + tools: Optional[List[Dict]] class MLXModel(LLM): @@ -211,23 +210,21 @@ def _generate_stream(self, prompt: str, kwargs: MLXGenerateConfig): else: output += out - completion_choice = CompletionChoice( - text=output, index=0, logprobs=None, finish_reason=None - ) - completion_chunk = CompletionChunk( - id=chunk_id, - object="text_completion", - created=int(time.time()), - model=model_uid, - choices=[completion_choice], - ) completion_usage = CompletionUsage( prompt_tokens=input_echo_len, completion_tokens=i, total_tokens=(input_echo_len + i), ) - yield completion_chunk, completion_usage + yield generate_completion_chunk( + chunk_text=output, + finish_reason=None, + chunk_id=chunk_id, + model_uid=model_uid, + prompt_tokens=input_echo_len, + completion_tokens=i, + total_tokens=(input_echo_len + i), + ), completion_usage logger.info( f"Average generation speed: {i / (time.time() - start):.2f} tokens/s." @@ -238,29 +235,31 @@ def _generate_stream(self, prompt: str, kwargs: MLXGenerateConfig): else: finish_reason = "stop" - if stream: - completion_choice = CompletionChoice( - text="", index=0, logprobs=None, finish_reason=finish_reason - ) - else: - completion_choice = CompletionChoice( - text=output, index=0, logprobs=None, finish_reason=finish_reason - ) - - completion_chunk = CompletionChunk( - id=chunk_id, - object="text_completion", - created=int(time.time()), - model=model_uid, - choices=[completion_choice], - ) completion_usage = CompletionUsage( prompt_tokens=input_echo_len, completion_tokens=i, total_tokens=(input_echo_len + i), ) - - yield completion_chunk, completion_usage + if stream: + yield generate_completion_chunk( + "", + finish_reason=finish_reason, + chunk_id=chunk_id, + model_uid=model_uid, + prompt_tokens=input_echo_len, + completion_tokens=i, + total_tokens=(input_echo_len + i), + ), completion_usage + else: + yield generate_completion_chunk( + output, + finish_reason=finish_reason, + chunk_id=chunk_id, + model_uid=model_uid, + prompt_tokens=input_echo_len, + completion_tokens=i, + total_tokens=(input_echo_len + i), + ), completion_usage if include_usage: completion_chunk = CompletionChunk( @@ -270,11 +269,6 @@ def _generate_stream(self, prompt: str, kwargs: MLXGenerateConfig): model=model_uid, choices=[], ) - completion_usage = CompletionUsage( - prompt_tokens=input_echo_len, - completion_tokens=i, - total_tokens=(input_echo_len + i), - ) yield completion_chunk, completion_usage def generate( @@ -345,20 +339,13 @@ def _sanitize_generate_config( generate_config: Optional[MLXGenerateConfig], ) -> MLXGenerateConfig: generate_config = super()._sanitize_generate_config(generate_config) - if ( - (not generate_config.get("stop")) - and self.model_family.prompt_style - and self.model_family.prompt_style.stop - ): - generate_config["stop"] = self.model_family.prompt_style.stop.copy() + if (not generate_config.get("stop")) and self.model_family.stop: + generate_config["stop"] = self.model_family.stop.copy() if ( generate_config.get("stop_token_ids", None) is None - and self.model_family.prompt_style - and self.model_family.prompt_style.stop_token_ids + and self.model_family.stop_token_ids ): - generate_config[ - "stop_token_ids" - ] = self.model_family.prompt_style.stop_token_ids.copy() + generate_config["stop_token_ids"] = self.model_family.stop_token_ids.copy() return generate_config @@ -377,28 +364,20 @@ def match( def chat( self, - prompt: str, - system_prompt: Optional[str] = None, - chat_history: Optional[List[ChatCompletionMessage]] = None, + messages: List[Dict], generate_config: Optional[MLXGenerateConfig] = None, ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: - tools = generate_config.pop("tools", []) if generate_config else None # type: ignore - full_prompt = self.get_full_prompt( - self.model_family, prompt, system_prompt, chat_history, tools + model_family = self.model_family.model_family or self.model_family.model_name + tools = generate_config.pop("tools", []) if generate_config else None + full_context_kwargs = {} + if tools and model_family in QWEN_TOOL_CALL_FAMILY: + full_context_kwargs["tools"] = tools + assert self.model_family.chat_template is not None + full_prompt = self.get_full_context( + messages, self.model_family.chat_template, **full_context_kwargs ) generate_config = self._sanitize_generate_config(generate_config) - # TODO(codingl2k1): qwen hacky to set stop for function call. - model_family = self.model_family.model_family or self.model_family.model_name - if tools and model_family in ["qwen-chat", "qwen1.5-chat"]: - stop = generate_config.get("stop") - if isinstance(stop, str): - generate_config["stop"] = [stop, "Observation:"] - elif isinstance(stop, Iterable): - assert not isinstance(stop, str) - generate_config["stop"] = list(stop) + ["Observation:"] - else: - generate_config["stop"] = "Observation:" stream = generate_config.get("stream", False) if stream: @@ -409,7 +388,5 @@ def chat( c = self.generate(full_prompt, generate_config) assert not isinstance(c, Iterator) if tools: - return self._tool_calls_completion( - self.model_family, self.model_uid, c, tools - ) + return self._tool_calls_completion(self.model_family, self.model_uid, c) return self._to_chat_completion(c) diff --git a/xinference/model/llm/mlx/tests/test_mlx.py b/xinference/model/llm/mlx/tests/test_mlx.py index 4fe69fd34f..b1d0682e5b 100644 --- a/xinference/model/llm/mlx/tests/test_mlx.py +++ b/xinference/model/llm/mlx/tests/test_mlx.py @@ -36,6 +36,7 @@ def test_load_mlx(setup): ) assert len(client.list_models()) == 1 model = client.get_model(model_uid) - completion = model.chat("write a poem.") + messages = [{"role": "user", "content": "write a poem."}] + completion = model.chat(messages) assert "content" in completion["choices"][0]["message"] assert len(completion["choices"][0]["message"]["content"]) != 0 diff --git a/xinference/model/llm/sglang/core.py b/xinference/model/llm/sglang/core.py index 3e089b6eb0..a413f2ad0f 100644 --- a/xinference/model/llm/sglang/core.py +++ b/xinference/model/llm/sglang/core.py @@ -21,7 +21,6 @@ from ....types import ( ChatCompletion, ChatCompletionChunk, - ChatCompletionMessage, Completion, CompletionChoice, CompletionChunk, @@ -29,7 +28,7 @@ ) from .. import LLM, LLMFamilyV1, LLMSpecV1 from ..llm_family import CustomLLMFamilyV1 -from ..utils import ChatModelMixin +from ..utils import ChatModelMixin, generate_completion_chunk logger = logging.getLogger(__name__) @@ -69,6 +68,8 @@ class SGLANGGenerateConfig(TypedDict, total=False): "llama-3.1", "mistral-v0.1", "mixtral-v0.1", + "qwen2.5", + "qwen2.5-coder", ] SGLANG_SUPPORTED_CHAT_MODELS = [ "llama-2-chat", @@ -83,6 +84,11 @@ class SGLANGGenerateConfig(TypedDict, total=False): "mixtral-instruct-v0.1", "gemma-it", "gemma-2-it", + "deepseek-v2.5", + "deepseek-v2-chat", + "deepseek-v2-chat-0628", + "qwen2.5-instruct", + "qwen2.5-coder-instruct", ] @@ -113,6 +119,13 @@ def load(self): raise ImportError(f"{error_message}\n\n{''.join(installation_guide)}") self._model_config = self._sanitize_model_config(self._model_config) + + # Fix: GH#2169 + if sgl.__version__ >= "0.2.14": + self._model_config.setdefault("triton_attention_reduce_in_fp32", False) + else: + self._model_config.setdefault("attention_reduce_in_fp32", False) + logger.info( f"Loading {self.model_uid} with following model config: {self._model_config}" ) @@ -152,7 +165,6 @@ def _sanitize_model_config( else: model_config["mem_fraction_static"] = 0.88 model_config.setdefault("log_level", "info") - model_config.setdefault("attention_reduce_in_fp32", False) return model_config @@ -313,6 +325,7 @@ async def async_generate( self, prompt: str, generate_config: Optional[SGLANGGenerateConfig] = None, + request_id: Optional[str] = None, ) -> Union[Completion, AsyncGenerator[CompletionChunk, None]]: sanitized_generate_config = self._sanitize_generate_config(generate_config) logger.debug( @@ -326,8 +339,8 @@ async def async_generate( if isinstance(stream_options, dict) else False ) - - request_id = str(uuid.uuid1()) + if not request_id: + request_id = str(uuid.uuid1()) if not stream: state = await self._non_stream_generate(prompt, **sanitized_generate_config) return self._convert_state_to_completion( @@ -340,12 +353,14 @@ async def async_generate( async def stream_results() -> AsyncGenerator[CompletionChunk, None]: prompt_tokens, completion_tokens, total_tokens = 0, 0, 0 + finish_reason = None async for meta_info, out in self._stream_generate( prompt, **sanitized_generate_config ): chunk = self._convert_state_to_completion_chunk( request_id, self.model_uid, output_text=out ) + finish_reason = meta_info["finish_reason"] prompt_tokens = meta_info["prompt_tokens"] completion_tokens = meta_info["completion_tokens"] total_tokens = prompt_tokens + completion_tokens @@ -355,6 +370,26 @@ async def stream_results() -> AsyncGenerator[CompletionChunk, None]: total_tokens=total_tokens, ) yield chunk + + finish_reason = ( + "stop" + if finish_reason is None + or ( + isinstance(finish_reason, str) + and finish_reason.lower() == "none" + ) + else finish_reason + ) + yield generate_completion_chunk( + "", + finish_reason=finish_reason, + chunk_id=request_id, + model_uid=self.model_uid, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=total_tokens, + ) + if include_usage: chunk = CompletionChunk( id=request_id, @@ -403,26 +438,19 @@ def _sanitize_chat_config( ) -> Dict: if not generate_config: generate_config = {} - if self.model_family.prompt_style: - if ( - not generate_config.get("stop") - ) and self.model_family.prompt_style.stop: - generate_config["stop"] = self.model_family.prompt_style.stop.copy() + if self.model_family.stop: + if (not generate_config.get("stop")) and self.model_family.stop: + generate_config["stop"] = self.model_family.stop.copy() return generate_config async def async_chat( self, - prompt: str, - system_prompt: Optional[str] = None, - chat_history: Optional[List[ChatCompletionMessage]] = None, + messages: List[Dict], generate_config: Optional[Dict] = None, + request_id: Optional[str] = None, ) -> Union[ChatCompletion, AsyncGenerator[ChatCompletionChunk, None]]: - assert self.model_family.prompt_style is not None - prompt_style = self.model_family.prompt_style.copy() - if system_prompt: - prompt_style.system_prompt = system_prompt - chat_history = chat_history or [] - full_prompt = self.get_prompt(prompt, chat_history, prompt_style) + assert self.model_family.chat_template is not None + full_prompt = self.get_full_context(messages, self.model_family.chat_template) generate_config = self._sanitize_chat_config(generate_config) stream = generate_config.get("stream", None) diff --git a/xinference/model/llm/tests/test_llm_family.py b/xinference/model/llm/tests/test_llm_family.py index 252491282c..146f00dd6f 100644 --- a/xinference/model/llm/tests/test_llm_family.py +++ b/xinference/model/llm/tests/test_llm_family.py @@ -26,7 +26,6 @@ CustomLLMFamilyV1, LlamaCppLLMSpecV1, LLMFamilyV1, - PromptStyleV1, PytorchLLMSpecV1, _generate_meta_file, _get_cache_dir, @@ -70,15 +69,9 @@ def test_deserialize_llm_family_v1(): "model_id":"example/TestModel" } ], - "prompt_style": { - "style_name": "ADD_COLON_SINGLE", - "system_prompt": "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.", - "roles": ["user", "assistant"], - "intra_message_sep": "\\n### ", - "inter_message_sep": "\\n### ", - "stop": null, - "stop_token_ids": null - } + "chat_template": "xyz", + "stop_token_ids": [1, 2, 3], + "stop": ["hello", "world"] }""" model_family = LLMFamilyV1.parse_raw(serialized) assert isinstance(model_family, LLMFamilyV1) @@ -108,17 +101,9 @@ def test_deserialize_llm_family_v1(): assert pytorch_spec.model_hub == "huggingface" assert pytorch_spec.model_id == "example/TestModel" - prompt_style = PromptStyleV1( - style_name="ADD_COLON_SINGLE", - system_prompt=( - "A chat between a curious human and an artificial intelligence assistant. The " - "assistant gives helpful, detailed, and polite answers to the human's questions." - ), - roles=["user", "assistant"], - intra_message_sep="\n### ", - inter_message_sep="\n### ", - ) - assert prompt_style == model_family.prompt_style + assert model_family.chat_template == "xyz" + assert model_family.stop_token_ids == [1, 2, 3] + assert model_family.stop == ["hello", "world"] def test_serialize_llm_family_v1(): @@ -139,16 +124,6 @@ def test_serialize_llm_family_v1(): model_id="example/TestModel", model_revision="456", ) - prompt_style = PromptStyleV1( - style_name="ADD_COLON_SINGLE", - system_prompt=( - "A chat between a curious human and an artificial intelligence assistant. The " - "assistant gives helpful, detailed, and polite answers to the human's questions." - ), - roles=["user", "assistant"], - intra_message_sep="\n### ", - inter_message_sep="\n### ", - ) llm_family = LLMFamilyV1( version=1, model_type="LLM", @@ -156,10 +131,12 @@ def test_serialize_llm_family_v1(): model_lang=["en"], model_ability=["embed", "generate"], model_specs=[gguf_spec, pytorch_spec], - prompt_style=prompt_style, + chat_template="xyz", + stop_token_ids=[1, 2, 3], + stop=["hello", "world"], ) - expected = """{"version": 1, "context_length": 2048, "model_name": "TestModel", "model_lang": ["en"], "model_ability": ["embed", "generate"], "model_description": null, "model_family": null, "model_specs": [{"model_format": "ggufv2", "model_hub": "huggingface", "model_size_in_billions": 2, "quantizations": ["q4_0", "q4_1"], "quantization_parts": {"q4_2": ["a", "b"]}, "model_id": "example/TestModel", "model_revision": "123", "model_file_name_template": "TestModel.{quantization}.bin", "model_file_name_split_template": "TestModel.{quantization}.bin.{part}", "model_uri": null}, {"model_format": "pytorch", "model_hub": "huggingface", "model_size_in_billions": 3, "quantizations": ["int8", "int4", "none"], "model_id": "example/TestModel", "model_revision": "456", "model_uri": null}], "prompt_style": {"style_name": "ADD_COLON_SINGLE", "system_prompt": "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.", "roles": ["user", "assistant"], "intra_message_sep": "\\n### ", "inter_message_sep": "\\n### ", "stop": null, "stop_token_ids": null}}""" + expected = """{"version": 1, "context_length": 2048, "model_name": "TestModel", "model_lang": ["en"], "model_ability": ["embed", "generate"], "model_description": null, "model_family": null, "model_specs": [{"model_format": "ggufv2", "model_hub": "huggingface", "model_size_in_billions": 2, "quantizations": ["q4_0", "q4_1"], "quantization_parts": {"q4_2": ["a", "b"]}, "model_id": "example/TestModel", "model_revision": "123", "model_file_name_template": "TestModel.{quantization}.bin", "model_file_name_split_template": "TestModel.{quantization}.bin.{part}", "model_uri": null}, {"model_format": "pytorch", "model_hub": "huggingface", "model_size_in_billions": 3, "quantizations": ["int8", "int4", "none"], "model_id": "example/TestModel", "model_revision": "456", "model_uri": null}], "chat_template": "xyz", "stop_token_ids": [1, 2, 3], "stop": ["hello", "world"]}""" assert json.loads(llm_family.json()) == json.loads(expected) llm_family_context_length = LLMFamilyV1( @@ -170,7 +147,9 @@ def test_serialize_llm_family_v1(): model_lang=["en"], model_ability=["embed", "generate"], model_specs=[gguf_spec, pytorch_spec], - prompt_style=prompt_style, + chat_template="xyz", + stop_token_ids=[1, 2, 3], + stop=["hello", "world"], ) assert json.loads(llm_family_context_length.json()) == json.loads(expected) @@ -201,7 +180,9 @@ def test_cache_from_huggingface_pytorch(): model_lang=["en"], model_ability=["embed", "generate"], model_specs=[spec], - prompt_style=None, + chat_template=None, + stop_token_ids=None, + stop=None, ) cache_dir = cache_from_huggingface(family, spec, quantization=None) @@ -230,7 +211,9 @@ def test_cache_from_huggingface_gguf(): model_lang=["en"], model_ability=["chat"], model_specs=[spec], - prompt_style=None, + chat_template=None, + stop_token_ids=None, + stop=None, ) cache_dir = _get_cache_dir(family, spec) @@ -266,7 +249,9 @@ def test_cache_from_uri_local(): model_lang=["en"], model_ability=["embed", "chat"], model_specs=[spec], - prompt_style=None, + chat_template=None, + stop_token_ids=None, + stop=None, ) cache_dir = cache_from_uri(family, spec) @@ -295,7 +280,9 @@ def test_meta_file(): model_lang=["en"], model_ability=["embed", "generate"], model_specs=[spec], - prompt_style=None, + chat_template=None, + stop_token_ids=None, + stop=None, ) cache_dir = cache_from_huggingface(family, spec, quantization=None) @@ -340,7 +327,9 @@ def test_legacy_cache(): model_lang=["en"], model_ability=["chat"], model_specs=[spec], - prompt_style=None, + chat_template=None, + stop_token_ids=None, + stop=None, ) cache_path = get_legacy_cache_path( @@ -378,7 +367,9 @@ def test_custom_llm(): model_lang=["en"], model_ability=["chat"], model_specs=[spec], - prompt_style=None, + chat_template=None, + stop_token_ids=None, + stop=None, ) register_llm(family, False) @@ -408,7 +399,9 @@ def test_persistent_custom_llm(): model_lang=["en"], model_ability=["chat"], model_specs=[spec], - prompt_style=None, + chat_template=None, + stop_token_ids=None, + stop=None, ) register_llm(family, True) @@ -501,16 +494,6 @@ def test_skip_download_pytorch(): model_hub="modelscope", model_revision="456", ) - prompt_style = PromptStyleV1( - style_name="ADD_COLON_SINGLE", - system_prompt=( - "A chat between a curious human and an artificial intelligence assistant. The " - "assistant gives helpful, detailed, and polite answers to the human's questions." - ), - roles=["user", "assistant"], - intra_message_sep="\n### ", - inter_message_sep="\n### ", - ) llm_family = LLMFamilyV1( version=1, model_type="LLM", @@ -518,7 +501,9 @@ def test_skip_download_pytorch(): model_lang=["en"], model_ability=["embed", "generate"], model_specs=[hf_spec, ms_spec], - prompt_style=prompt_style, + chat_template="xyz", + stop_token_ids=[1, 2, 3], + stop=["hello", "world"], ) cache_dir = _get_cache_dir(llm_family, hf_spec) @@ -594,16 +579,6 @@ def test_skip_download_gguf(): model_revision="123", model_file_name_template="TestModel.{quantization}.bin", ) - prompt_style = PromptStyleV1( - style_name="ADD_COLON_SINGLE", - system_prompt=( - "A chat between a curious human and an artificial intelligence assistant. The " - "assistant gives helpful, detailed, and polite answers to the human's questions." - ), - roles=["user", "assistant"], - intra_message_sep="\n### ", - inter_message_sep="\n### ", - ) llm_family = LLMFamilyV1( version=1, model_type="LLM", @@ -611,7 +586,9 @@ def test_skip_download_gguf(): model_lang=["en"], model_ability=["embed", "generate"], model_specs=[hf_spec, ms_spec], - prompt_style=prompt_style, + chat_template="xyz", + stop_token_ids=[1, 2, 3], + stop=["hello", "world"], ) cache_dir = _get_cache_dir(llm_family, hf_spec) @@ -686,7 +663,9 @@ def test_get_cache_status_pytorch(): model_lang=["en"], model_ability=["embed", "generate"], model_specs=[spec], - prompt_style=None, + chat_template=None, + stop_token_ids=None, + stop=None, ) cache_status = get_cache_status(llm_family=family, llm_spec=spec) @@ -722,7 +701,9 @@ def test_get_cache_status_gguf(): model_lang=["en"], model_ability=["chat"], model_specs=[spec], - prompt_style=None, + chat_template=None, + stop_token_ids=None, + stop=None, ) cache_status = get_cache_status(llm_family=family, llm_spec=spec) @@ -741,13 +722,13 @@ def test_get_cache_status_gguf(): shutil.rmtree(cache_dir) -def test_parse_prompt_style(): +def test_parse_chat_template(): from ..llm_family import BUILTIN_LLM_PROMPT_STYLE assert len(BUILTIN_LLM_PROMPT_STYLE) > 0 # take some examples to assert assert "qwen-chat" in BUILTIN_LLM_PROMPT_STYLE - assert "chatglm3" in BUILTIN_LLM_PROMPT_STYLE + assert "glm4-chat" in BUILTIN_LLM_PROMPT_STYLE assert "baichuan-2-chat" in BUILTIN_LLM_PROMPT_STYLE hf_spec = LlamaCppLLMSpecV1( @@ -776,8 +757,8 @@ def test_parse_prompt_style(): model_lang=["en"], model_ability=["chat", "generate"], model_specs=[hf_spec, ms_spec], - model_family="chatglm3", - prompt_style="chatglm3", + model_family="glm4-chat", + chat_template="glm4-chat", ) model_spec = CustomLLMFamilyV1.parse_raw(bytes(llm_family.json(), "utf8")) assert model_spec.model_name == llm_family.model_name @@ -791,7 +772,7 @@ def test_parse_prompt_style(): model_ability=["chat", "generate"], model_specs=[hf_spec, ms_spec], model_family="qwen-vl-chat", - prompt_style="qwen-vl-chat", + chat_template="qwen-vl-chat", ) model_spec = CustomLLMFamilyV1.parse_raw(bytes(llm_family.json(), "utf-8")) assert "vision" in model_spec.model_ability @@ -804,12 +785,12 @@ def test_parse_prompt_style(): model_lang=["en"], model_ability=["chat", "generate"], model_specs=[hf_spec, ms_spec], - prompt_style="chatglm3", + chat_template="glm4-chat", ) with pytest.raises(ValueError): CustomLLMFamilyV1.parse_raw(bytes(llm_family.json(), "utf8")) - # wrong model_family + # successful new model family llm_family = CustomLLMFamilyV1( version=1, model_type="LLM", @@ -818,12 +799,20 @@ def test_parse_prompt_style(): model_ability=["chat", "generate"], model_family="xyzz", model_specs=[hf_spec, ms_spec], - prompt_style="chatglm3", + chat_template="glm4-chat", ) - with pytest.raises(ValueError): - CustomLLMFamilyV1.parse_raw(bytes(llm_family.json(), "utf8")) + model_spec = CustomLLMFamilyV1.parse_raw(bytes(llm_family.json(), "utf8")) + assert ( + model_spec.chat_template + == BUILTIN_LLM_PROMPT_STYLE["glm4-chat"]["chat_template"] + ) + assert ( + model_spec.stop_token_ids + == BUILTIN_LLM_PROMPT_STYLE["glm4-chat"]["stop_token_ids"] + ) + assert model_spec.stop == BUILTIN_LLM_PROMPT_STYLE["glm4-chat"]["stop"] - # error: wrong prompt style + # when chat_template is None, chat_template = model_family llm_family = CustomLLMFamilyV1( version=1, model_type="LLM", @@ -831,11 +820,19 @@ def test_parse_prompt_style(): model_lang=["en"], model_ability=["chat", "generate"], model_specs=[hf_spec, ms_spec], - model_family="chatglm3", - prompt_style="test_xyz", + model_family="glm4-chat", + chat_template=None, ) - with pytest.raises(ValueError): - CustomLLMFamilyV1.parse_raw(bytes(llm_family.json(), "utf8")) + model_spec = CustomLLMFamilyV1.parse_raw(bytes(llm_family.json(), "utf8")) + assert ( + model_spec.chat_template + == BUILTIN_LLM_PROMPT_STYLE["glm4-chat"]["chat_template"] + ) + assert ( + model_spec.stop_token_ids + == BUILTIN_LLM_PROMPT_STYLE["glm4-chat"]["stop_token_ids"] + ) + assert model_spec.stop == BUILTIN_LLM_PROMPT_STYLE["glm4-chat"]["stop"] def test_match_model_size(): @@ -1073,7 +1070,9 @@ def test_query_engine_general(): model_lang=["en"], model_ability=["chat"], model_specs=[spec], - prompt_style=None, + chat_template=None, + stop_token_ids=None, + stop=None, ) register_llm(family, False) @@ -1107,15 +1106,9 @@ def test_query_engine_general(): model_lang=["en", "zh"], model_ability=["generate", "chat"], model_specs=[spec], - prompt_style={ - "style_name": "QWEN", - "system_prompt": "You are a helpful assistant.", - "roles": ["user", "assistant"], - "intra_message_sep": "\n", - "inter_message_sep": "", - "stop": ["<|endoftext|>", "<|im_start|>", "<|im_end|>"], - "stop_token_ids": [151643, 151644, 151645], - }, + chat_template="test", + stop=["<|endoftext|>", "<|im_start|>", "<|im_end|>"], + stop_token_ids=[151643, 151644, 151645], ) register_llm(family, False) diff --git a/xinference/model/llm/tests/test_multimodal.py b/xinference/model/llm/tests/test_multimodal.py index 567e0d0355..7bd3e78a15 100644 --- a/xinference/model/llm/tests/test_multimodal.py +++ b/xinference/model/llm/tests/test_multimodal.py @@ -34,16 +34,21 @@ def test_restful_api_for_qwen_vl(setup, model_format, quantization): quantization=quantization, ) model = client.get_model(model_uid) - prompt = [ - {"type": "text", "text": "What’s in this image?"}, + messages = [ { - "type": "image_url", - "image_url": { - "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" - }, - }, + "role": "user", + "content": [ + {"type": "text", "text": "What’s in this image?"}, + { + "type": "image_url", + "image_url": { + "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" + }, + }, + ], + } ] - response = model.chat(prompt=prompt) + response = model.chat(messages) assert "grass" in response["choices"][0]["message"]["content"] assert "tree" in response["choices"][0]["message"]["content"] assert "sky" in response["choices"][0]["message"]["content"] @@ -141,16 +146,21 @@ def test_restful_api_for_yi_vl(setup, model_format, quantization): quantization=quantization, ) model = client.get_model(model_uid) - prompt = [ - {"type": "text", "text": "What’s in this image?"}, + messages = [ { - "type": "image_url", - "image_url": { - "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" - }, - }, + "role": "user", + "content": [ + {"type": "text", "text": "What’s in this image?"}, + { + "type": "image_url", + "image_url": { + "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" + }, + }, + ], + } ] - response = model.chat(prompt=prompt) + response = model.chat(messages) assert "green" in response["choices"][0]["message"]["content"] assert "tree" in response["choices"][0]["message"]["content"] assert "sky" in response["choices"][0]["message"]["content"] @@ -225,16 +235,21 @@ def test_restful_api_for_deepseek_vl(setup, model_format, quantization): temperature=0.0, ) model = client.get_model(model_uid) - prompt = [ - {"type": "text", "text": "What’s in this image?"}, + messages = [ { - "type": "image_url", - "image_url": { - "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" - }, - }, + "role": "user", + "content": [ + {"type": "text", "text": "What’s in this image?"}, + { + "type": "image_url", + "image_url": { + "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" + }, + }, + ], + } ] - response = model.chat(prompt=prompt) + response = model.chat(messages) assert any( green in response["choices"][0]["message"]["content"] for green in ["grass", "green"] diff --git a/xinference/model/llm/tests/test_utils.py b/xinference/model/llm/tests/test_utils.py index 42125a0048..9d12d695ca 100644 --- a/xinference/model/llm/tests/test_utils.py +++ b/xinference/model/llm/tests/test_utils.py @@ -12,309 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. -from ....types import ChatCompletionMessage -from ..llm_family import PromptStyleV1 -from ..utils import ChatModelMixin - - -def test_prompt_style_add_colon_single(): - prompt_style = PromptStyleV1( - style_name="ADD_COLON_SINGLE", - system_prompt=( - "A chat between a curious human and an artificial intelligence assistant. The " - "assistant gives helpful, detailed, and polite answers to the human's questions." - ), - roles=["user", "assistant"], - intra_message_sep="\n### ", - ) - chat_history = [ - ChatCompletionMessage(role=prompt_style.roles[0], content="Hi there."), - ChatCompletionMessage( - role=prompt_style.roles[1], content="Hello, how may I help you?" - ), - ] - expected = ( - "A chat between a curious human and an artificial intelligence assistant. The assistant" - " gives helpful, detailed, and polite answers to the human's questions." - "\n### user: Hi there." - "\n### assistant: Hello, how may I help you?" - "\n### user: Write a poem." - "\n### assistant:" - ) - assert expected == ChatModelMixin.get_prompt( - "Write a poem.", chat_history, prompt_style - ) - - -def test_prompt_style_no_colon_two(): - prompt_style = PromptStyleV1( - style_name="NO_COLON_TWO", - system_prompt="", - roles=[" ", " "], - intra_message_sep="", - inter_message_sep="", - stop_token_ids=[2, 195], - ) - chat_history = [ - ChatCompletionMessage(role=prompt_style.roles[0], content="Hi there."), - ChatCompletionMessage( - role=prompt_style.roles[1], content="Hello, how may I help you?" - ), - ] - expected = ( - " Hi there." - " Hello, how may I help you?" - " Write a poem." - " " - ) - assert expected == ChatModelMixin.get_prompt( - "Write a poem.", chat_history, prompt_style - ) - - -def test_prompt_style_llama2(): - prompt_style = PromptStyleV1( - style_name="LLAMA2", - system_prompt=( - "[INST] <>\nYou are a helpful, respectful and honest assistant. Always answer" - " as helpfully as possible, while being safe. Your answers should not include any" - " harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please" - " ensure that your responses are socially unbiased and positive in nature.\n\nIf a" - " question does not make any sense, or is not factually coherent, explain why instead" - " of answering something not correct. If you don't know the answer to a question," - " please don't share false information.\n<>\n\n" - ), - roles=["[INST]", "[/INST]"], - intra_message_sep=" ", - inter_message_sep=" ", - stop_token_ids=[2], - ) - chat_history = [ - ChatCompletionMessage(role=prompt_style.roles[0], content="Hi there."), - ChatCompletionMessage( - role=prompt_style.roles[1], content="Hello, how may I help you?" - ), - ] - expected = ( - "[INST] <>\nYou are a helpful, respectful and honest assistant. Always answer" - " as helpfully as possible, while being safe. Your answers should not include any" - " harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please" - " ensure that your responses are socially unbiased and positive in nature.\n\nIf a" - " question does not make any sense, or is not factually coherent, explain why instead" - " of answering something not correct. If you don't know the answer to a question," - " please don't share false information.\n<>\n\nHi there.[/INST] Hello, how may I help" - " you? [INST] Write a poem. [/INST]" - ) - assert expected == ChatModelMixin.get_prompt( - "Write a poem.", chat_history, prompt_style - ) - - -def test_prompt_style_llama3(): - prompt_style = PromptStyleV1( - style_name="LLAMA3", - system_prompt=( - "You are a helpful, respectful and honest assistant. Always answer" - " as helpfully as possible, while being safe. Your answers should not include any" - " harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please" - " ensure that your responses are socially unbiased and positive in nature.\n\nIf a" - " question does not make any sense, or is not factually coherent, explain why instead" - " of answering something not correct. If you don't know the answer to a question," - " please don't share false information" - ), - roles=["user", "assistant"], - intra_message_sep="\n\n", - inter_message_sep="<|eot_id|>", - stop_token_ids=[128001, 128009], - ) - chat_history = [ - ChatCompletionMessage(role=prompt_style.roles[0], content="Hi there."), - ChatCompletionMessage( - role=prompt_style.roles[1], content="Hello, how may I help you?" - ), - ] - expected = ( - "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n" - "You are a helpful, respectful and honest assistant. Always answer" - " as helpfully as possible, while being safe. Your answers should not include any" - " harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please" - " ensure that your responses are socially unbiased and positive in nature.\n\nIf a" - " question does not make any sense, or is not factually coherent, explain why instead" - " of answering something not correct. If you don't know the answer to a question," - " please don't share false information<|eot_id|>" - "<|start_header_id|>user<|end_header_id|>\n\nHi there.<|eot_id|>" - "<|start_header_id|>assistant<|end_header_id|>\n\nHello, how may I help you?<|eot_id|>" - "<|start_header_id|>user<|end_header_id|>\n\nWrite a poem.<|eot_id|>" - "<|start_header_id|>assistant<|end_header_id|>\n\n" - ) - assert expected == ChatModelMixin.get_prompt( - "Write a poem.", chat_history, prompt_style - ) - - -def test_prompt_style_chatglm_v3(): - prompt_style = PromptStyleV1( - style_name="CHATGLM3", - system_prompt="", - roles=["user", "assistant"], - ) - chat_history = [ - ChatCompletionMessage(role=prompt_style.roles[0], content="Hi there."), - ChatCompletionMessage( - role=prompt_style.roles[1], content="Hello, how may I help you?" - ), - ] - expected = ( - "<|user|>\n Hi there.\n" - "<|assistant|>\n Hello, how may I help you?\n" - "<|user|>\n Write a poem.\n" - "<|assistant|>" - ) - assert expected == ChatModelMixin.get_prompt( - "Write a poem.", chat_history, prompt_style - ) - - -def test_prompt_style_xverse(): - prompt_style = PromptStyleV1( - style_name="XVERSE", - system_prompt="", - roles=["user", "assistant"], - ) - chat_history = [ - ChatCompletionMessage(role=prompt_style.roles[0], content="Hi there."), - ChatCompletionMessage( - role=prompt_style.roles[1], content="Hello, how may I help you?" - ), - ] - expected = ( - "<|user|> \n Hi there." - "<|assistant|> \n Hello, how may I help you?" - "<|user|> \n Write a poem." - "<|assistant|>" - ) - assert expected == ChatModelMixin.get_prompt( - "Write a poem.", chat_history, prompt_style - ) - - -def test_prompt_style_qwen(): - prompt_style = PromptStyleV1( - style_name="QWEN", - system_prompt="You are a helpful assistant.", - roles=["user", "assistant"], - intra_message_sep="\n", - ) - chat_history = [ - ChatCompletionMessage(role=prompt_style.roles[0], content="Hi there."), - ChatCompletionMessage( - role=prompt_style.roles[1], content="Hello, how may I help you?" - ), - ] - expected = ( - "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nHi there." - "<|im_end|>\n<|im_start|>assistant\nHello, how may I help you?<|im_end|>\n<|im_start|>" - "user\nWrite a poem.<|im_end|>\n<|im_start|>assistant\n" - ) - assert expected == ChatModelMixin.get_prompt( - "Write a poem.", chat_history, prompt_style - ) - - -def test_prompt_style_chatml(): - prompt_style = PromptStyleV1( - style_name="CHATML", - system_prompt="You are a wonderful code assistant\n", - roles=["<|user|>", "<|assistant|>"], - intra_message_sep="<|end|>", - ) - - chat_history = [ - ChatCompletionMessage(role=prompt_style.roles[0], content="Hi there."), - ChatCompletionMessage( - role=prompt_style.roles[1], content="Hello, how may I help you?" - ), - ] - - expected = ( - "You are a wonderful code assistant\n" - "<|end|>\n" - "<|user|>\n" - "Hi there.<|end|>\n" - "<|assistant|>\n" - "Hello, how may I help you?<|end|>\n" - "<|user|>\n" - "Write me a HelloWorld Function<|end|>\n" - "<|assistant|>\n" - ) - assert expected == ChatModelMixin.get_prompt( - "Write me a HelloWorld Function", chat_history, prompt_style - ) - - -def test_prompt_style_add_colon_single_cot(): - prompt_style = PromptStyleV1( - style_name="ADD_COLON_SINGLE_COT", - system_prompt=( - "Below is an instruction that describes a task. Write a response that appropriately " - "completes the request." - ), - roles=["Instruction", "Response"], - intra_message_sep="\n\n### ", - ) - - chat_history = [ - ChatCompletionMessage(role=prompt_style.roles[0], content="Hi there."), - ChatCompletionMessage( - role=prompt_style.roles[1], content="Hello, how may I help you?" - ), - ] - expected = ( - "Below is an instruction that describes a task. Write a response that appropriately " - "completes the request." - "\n\n### Instruction: Hi there." - "\n\n### Response: Hello, how may I help you?" - "\n\n### Instruction: Write a poem." - "\n\n### Response: Let's think step by step." - ) - assert expected == ChatModelMixin.get_prompt( - "Write a poem.", chat_history, prompt_style - ) - - -def test_prompt_style_zephyr(): - prompt_style = PromptStyleV1( - style_name="NO_COLON_TWO", - system_prompt=( - "<|system|>\nYou are a friendly chatbot who always responds in the style of a pirate.\n" - ), - roles=["<|user|>\n", "<|assistant|>\n"], - intra_message_sep="\n", - inter_message_sep="\n", - stop_token_ids=[2, 195], - stop=[""], - ) - - chat_history = [ - ChatCompletionMessage(role=prompt_style.roles[0], content="Hi there."), - ChatCompletionMessage( - role=prompt_style.roles[1], content="Hello, how may I help you?" - ), - ] - expected = ( - "<|system|>\n" - "You are a friendly chatbot who always responds in the style of a pirate.\n" - "<|user|>\n" - "Hi there.\n" - "<|assistant|>\n" - "Hello, how may I help you?\n" - "<|user|>\n" - "Write a poem.\n" - "<|assistant|>\n" - ) - actual = ChatModelMixin.get_prompt("Write a poem.", chat_history, prompt_style) - assert expected == actual - def test_is_valid_model_name(): from ...utils import is_valid_model_name diff --git a/xinference/model/llm/transformers/chatglm.py b/xinference/model/llm/transformers/chatglm.py index 797402b220..e5ab6b2707 100644 --- a/xinference/model/llm/transformers/chatglm.py +++ b/xinference/model/llm/transformers/chatglm.py @@ -11,43 +11,26 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -import copy import json -import threading -import time +import logging +import typing import uuid +from threading import Thread from typing import Any, Dict, Iterator, List, Optional, Union import torch -from transformers.generation.logits_process import LogitsProcessor -from transformers.generation.utils import LogitsProcessorList from ....core.scheduler import InferenceRequest -from ....types import ( - SPECIAL_TOOL_PROMPT, - ChatCompletion, - ChatCompletionChoice, - ChatCompletionChunk, - ChatCompletionMessage, - CompletionChoice, - CompletionChunk, - CompletionUsage, - LoRA, - PytorchGenerateConfig, -) +from ....types import ChatCompletion, ChatCompletionChunk, LoRA, PytorchGenerateConfig from ..llm_family import LLMFamilyV1, LLMSpecV1 -from ..utils import GLM4_TOOL_CALL_FAMILY +from ..utils import ( + GLM4_TOOL_CALL_FAMILY, + generate_chat_completion, + generate_completion_chunk, +) from .core import PytorchChatModel, PytorchModelConfig - -class InvalidScoreLogitsProcessor(LogitsProcessor): - def __call__( - self, input_ids: torch.LongTensor, scores: torch.FloatTensor - ) -> torch.FloatTensor: - if torch.isnan(scores).any() or torch.isinf(scores).any(): - scores.zero_() - scores[..., 198] = 5e4 - return scores +logger = logging.getLogger(__name__) class ChatglmPytorchChatModel(PytorchChatModel): @@ -107,40 +90,28 @@ def match( if llm_spec.model_format != "pytorch": return False model_family = llm_family.model_family or llm_family.model_name - if "chatglm" not in model_family and "glm4" not in model_family: + if "glm4" not in model_family: return False if "chat" not in llm_family.model_ability: return False return True - def _handle_tools(self, chat_history, generate_config) -> bool: + def _handle_tools(self, messages, generate_config): """Convert openai tools to ChatGLM tools.""" + if self.model_family.model_name not in GLM4_TOOL_CALL_FAMILY: + return None if generate_config is None: - return False + return None tools = generate_config.pop("tools", None) if tools is None: - return False - # Convert a iterable to a list + return None + # Convert an iterable to a list tools = list(tools) tool_choice = generate_config.pop("tool_choice", "none") - if self.model_family.model_name in GLM4_TOOL_CALL_FAMILY: - chat_history[:] = self._process_messages( - chat_history, tools=tools, tool_choice=tool_choice - ) - return True - else: - chatglm_tools = [] - for elem in tools: - if elem.get("type") != "function" or "function" not in elem: - raise ValueError("ChatGLM tools only support function type.") - chatglm_tools.append(elem["function"]) - tool_prompt_message = { - "role": "system", - "content": f"Answer the following questions as best as you can. You have access to the following tools:", - "tools": chatglm_tools, - } - chat_history.insert(0, tool_prompt_message) - return True + messages[:] = self._process_messages( + messages, tools=tools, tool_choice=tool_choice + ) + return tools @staticmethod def _process_messages(messages, tools=None, tool_choice="none"): @@ -230,12 +201,70 @@ def _filter_tools(_tool_choice, _tools): return processed_messages @staticmethod - def _process_response(output, history, tools, end=False): + @typing.no_type_check + def _process_response_non_streaming( + output: str, tools: Union[Dict, List[Dict]] = None, use_tool: bool = False + ) -> Union[str, dict]: + """ + Copied from https://github.com/THUDM/GLM-4/blob/main/basic_demo/openai_api_server.py#L150 + """ + import re + + lines = output.strip().split("\n") + arguments_json = None + special_tools = ["cogview", "simple_browser"] + tools = {tool["function"]["name"] for tool in tools} if tools else {} + + # 这是一个简单的工具比较函数，不能保证拦截所有非工具输出的结果，比如参数未对齐等特殊情况。 + ##TODO 如果你希望做更多判断，可以在这里进行逻辑完善。 + + if len(lines) >= 2 and lines[1].startswith("{"): + function_name = lines[0].strip() + arguments = "\n".join(lines[1:]).strip() + if function_name in tools or function_name in special_tools: + try: + arguments_json = json.loads(arguments) + is_tool_call = True + except json.JSONDecodeError: + is_tool_call = function_name in special_tools + + if is_tool_call and use_tool: + content = { + "name": function_name, + "arguments": json.dumps( + arguments_json + if isinstance(arguments_json, dict) + else arguments, + ensure_ascii=False, + ), + } + if function_name == "simple_browser": + search_pattern = re.compile( + r'search\("(.+?)"\s*,\s*recency_days\s*=\s*(\d+)\)' + ) + match = search_pattern.match(arguments) + if match: + content["arguments"] = json.dumps( + { + "query": match.group(1), + "recency_days": int(match.group(2)), + }, + ensure_ascii=False, + ) + elif function_name == "cogview": + content["arguments"] = json.dumps( + {"prompt": arguments}, ensure_ascii=False + ) + + return content + return output.strip() + + @staticmethod + def _process_response_streaming(output, tools, end=False): # Copy from https://huggingface.co/THUDM/glm-4-9b-chat/blob/main/modeling_chatglm.py content = "" - history = copy.deepcopy(history) if not tools and end: - return None, None + return None for response in output.split("<|assistant|>"): if "\n" in response: metadata, content = response.split("\n", maxsplit=1) @@ -244,205 +273,54 @@ def _process_response(output, history, tools, end=False): if not metadata.strip(): if tools and any(t.startswith(response) for t in tools) and not end: # Waiting for tool call complete. - return None, None + return None content = content.strip() - history.append( - {"role": "assistant", "metadata": metadata, "content": content} - ) content = content.replace("[[训练时间]]", "2023年") else: if tools and metadata in tools and not end: - return None, None - history.append( - {"role": "assistant", "metadata": metadata, "content": content} - ) + return None metadata = metadata.strip() if tools and metadata in tools and end: try: parameters = json.loads(content) - content = {"name": metadata.strip(), "parameters": parameters} + content = {"name": metadata.strip(), "arguments": parameters} except json.JSONDecodeError: content = {"name": metadata.strip(), "content": content} else: content = {"name": metadata.strip(), "content": content} - return content, history - - def _get_generate_args( - self, - tokenizer, - query: str, - history: Optional[List[Dict]] = None, - role: str = "user", - past_key_values=None, - max_length: int = 8192, - do_sample=True, - top_p=0.8, - temperature=0.8, - logits_processor=None, - **kwargs, - ): - # Copy from https://huggingface.co/THUDM/glm-4-9b-chat/blob/main/modeling_chatglm.py - if history is None: - history = [] - if logits_processor is None: - logits_processor = LogitsProcessorList() - logits_processor.append(InvalidScoreLogitsProcessor()) - eos_token_id = [ - tokenizer.eos_token_id, - tokenizer.convert_tokens_to_ids("<|user|>"), - tokenizer.convert_tokens_to_ids("<|observation|>"), - ] - gen_kwargs = { - "max_length": max_length, - "do_sample": do_sample, - "top_p": top_p, - "temperature": temperature, - "logits_processor": logits_processor, - **kwargs, - } - if past_key_values is None: - inputs = tokenizer.apply_chat_template( - history + [{"role": role, "content": query}], - add_generation_prompt=True, - tokenize=True, - return_tensors="pt", - return_dict=True, - ) - else: - inputs = tokenizer.apply_chat_template( - [{"role": role, "content": query}], - add_special_tokens=False, - add_generation_prompt=True, - tokenize=True, - return_tensors="pt", - return_dict=True, - ) - inputs = inputs.to(self._model.device) - if past_key_values is not None: - past_length = past_key_values[0][0].shape[2] - inputs.position_ids += past_length - attention_mask = inputs.attention_mask - attention_mask = torch.cat( - (attention_mask.new_ones(1, past_length), attention_mask), dim=1 - ) - inputs["attention_mask"] = attention_mask - history.append({"role": role, "content": query}) - tools = history[0]["role"] == "system" and history[0].get("tools") - tools = ( - [ - t.get("function", {}).get("name", "") - for t in tools - if isinstance(t, dict) - ] - if tools - else [] - ) - kwargs = dict(inputs) - kwargs["past_key_values"] = past_key_values - kwargs["eos_token_id"] = eos_token_id - kwargs.update(gen_kwargs) - return kwargs, tools + return content @torch.inference_mode() - def _stream_chat( - self, - tokenizer, - query: str, - history: Optional[List[Dict]] = None, - role: str = "user", - past_key_values=None, - max_length: int = 8192, - do_sample=True, - top_p=0.8, - temperature=0.8, - logits_processor=None, - **kwargs, - ): + def _stream_chat(self, inputs, tools, **kwargs): from transformers import TextIteratorStreamer - kwargs, tools = self._get_generate_args( - tokenizer=tokenizer, - query=query, - history=history, - role=role, - past_key_values=past_key_values, - max_length=max_length, - do_sample=do_sample, - top_p=top_p, - temperature=temperature, - logits_processor=logits_processor, - **kwargs, - ) - streamer = TextIteratorStreamer( - tokenizer, skip_prompt=True, skip_special_tokens=True + self._tokenizer, skip_prompt=True, skip_special_tokens=True ) - kwargs["streamer"] = streamer - thread = threading.Thread(target=self._model.generate, kwargs=kwargs) + tools = {tool["function"]["name"] for tool in tools} if tools else {} + generation_kwargs = dict(inputs, streamer=streamer) + generation_kwargs.update(kwargs) + thread = Thread(target=self._model.generate, kwargs=generation_kwargs) thread.start() response = "" for token in streamer: response += token if response and response[-1] != "�": - new_response, new_history = self._process_response( - response, history, tools, end=False + new_response = self._process_response_streaming( + response, tools, end=False ) if new_response is None: continue - yield new_response, new_history + yield new_response if tools: - new_response, new_history = self._process_response( - response, history, tools, end=True - ) + new_response = self._process_response_streaming(response, tools, end=True) if new_response: - yield new_response, new_history + yield new_response - @torch.inference_mode() - def _non_stream_chat( - self, - tokenizer, - query: str, - history: Optional[List[Dict]] = None, - role: str = "user", - past_key_values=None, - max_length: int = 8192, - do_sample=True, - top_p=0.8, - temperature=0.8, - logits_processor=None, - **kwargs, - ): - kwargs, tools = self._get_generate_args( - tokenizer=tokenizer, - query=query, - history=history, - role=role, - past_key_values=past_key_values, - max_length=max_length, - do_sample=do_sample, - top_p=top_p, - temperature=temperature, - logits_processor=logits_processor, - **kwargs, - ) - - outputs = self._model.generate(**kwargs) - outputs = outputs[:, kwargs["input_ids"].shape[1] :] - response = tokenizer.decode(outputs[0], skip_special_tokens=True) - if tools: - return self._process_response(response, history, tools, end=True) - else: - return self._process_response(response, history, tools) - - def chat( - self, - prompt: str, - system_prompt: Optional[str] = None, - chat_history: Optional[List[ChatCompletionMessage]] = None, - generate_config: Optional[PytorchGenerateConfig] = None, - ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: - kwargs: Dict[str, Any] = {} + @staticmethod + def _get_generate_kwargs(generate_config): + kwargs: Dict[str, Any] = {} # type: ignore generate_config = generate_config or {} temperature = generate_config.get("temperature") if temperature is not None: @@ -453,18 +331,26 @@ def chat( max_new_tokens = generate_config.get("max_tokens") if max_new_tokens is not None: kwargs["max_new_tokens"] = int(max_new_tokens) - chat_history = chat_history or [] - tools = self._handle_tools(chat_history, generate_config) - # Tool calls only works for non stream, so we call chat directly. - if prompt == SPECIAL_TOOL_PROMPT and chat_history: - tool_message = chat_history.pop() - content = tool_message.get("content") - assert content is not None - prompt = content - kwargs["role"] = "observation" - chat_history = [h for h in chat_history if not h.get("tool_calls")] - if system_prompt: - chat_history.append({"role": "system", "content": system_prompt}) + do_sample = generate_config.get("do_sample") + if do_sample is not None: + kwargs["do_sample"] = bool(do_sample) + top_k = generate_config.get("top_k") + if top_k is not None: + kwargs["top_k"] = top_k + repetition_penalty = generate_config.get("repetition_penalty") + if repetition_penalty is not None: + kwargs["repetition_penalty"] = repetition_penalty + return kwargs + + def chat( + self, + messages: List[Dict], + generate_config: Optional[PytorchGenerateConfig] = None, + ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: + generate_config = generate_config or {} + kwargs: Dict[str, Any] = self._get_generate_kwargs(generate_config) + tools = self._handle_tools(messages, generate_config) + has_tools = tools is not None stream = generate_config.get("stream", False) stream_options = generate_config.pop("stream_options", None) include_usage = ( @@ -472,103 +358,82 @@ def chat( if isinstance(stream_options, dict) else False ) - if stream and ( - not tools or self.model_family.model_name in GLM4_TOOL_CALL_FAMILY - ): + inputs = self._tokenizer.apply_chat_template( + messages, + return_tensors="pt", + chat_template=self.model_family.chat_template, + add_generation_prompt=True, + return_dict=True, + ) + inputs = inputs.to(self._model.device) + + if not stream: + with torch.no_grad(): + outputs = self._model.generate(**inputs, **kwargs) + outputs = outputs[:, inputs["input_ids"].shape[1] :] + response = self._tokenizer.decode(outputs[0], skip_special_tokens=True) + # In some cases, the response starts with `\n` + if response.startswith("\n"): + response = response[1:] + if has_tools: + function_call = self._process_response_non_streaming( + response, tools, use_tool=True + ) + return self._tool_calls_completion( + self.model_family, self.model_uid, function_call + ) + else: + return generate_chat_completion(self.model_uid, response) + else: def _stream_generator(): last_chunk_text_length = 0 chunk_id = "chat-" + str(uuid.uuid1()) prompt_tokens, completion_tokens, total_tokens = 0, 0, 0 - inputs = self._tokenizer([prompt], return_tensors="pt") - inputs = inputs.to(self._model.device) prompt_tokens = len(inputs["input_ids"][0]) - for chunk_text, _ in self._stream_chat( - self._tokenizer, prompt, chat_history, **kwargs - ): + for chunk_text in self._stream_chat(inputs, tools, **kwargs): if tools and isinstance(chunk_text, dict): yield self._tool_calls_completion_chunk( - self.model_family, self.model_uid, [chunk_text, _], tools + self.model_family, self.model_uid, chunk_text ) return completion_tokens = completion_tokens + 1 total_tokens = prompt_tokens + completion_tokens chunk_text = chunk_text[last_chunk_text_length:] last_chunk_text_length += len(chunk_text) - completion_choice = CompletionChoice( - text=chunk_text, index=0, logprobs=None, finish_reason=None - ) - yield CompletionChunk( - id=chunk_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[completion_choice], - usage=CompletionUsage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=total_tokens, - ), + yield generate_completion_chunk( + chunk_text, + finish_reason=None, + chunk_id=chunk_id, + model_uid=self.model_uid, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=total_tokens, ) - completion_choice = CompletionChoice( - text="", index=0, logprobs=None, finish_reason="stop" - ) - chunk = CompletionChunk( - id=chunk_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[completion_choice], - ) - completion_usage = CompletionUsage( + yield generate_completion_chunk( + None, + finish_reason="stop", + chunk_id=chunk_id, + model_uid=self.model_uid, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens, + has_choice=True, + has_content=False, ) - chunk["usage"] = completion_usage - yield chunk if include_usage: - chunk = CompletionChunk( - id=chunk_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[], - ) - chunk["usage"] = CompletionUsage( + yield generate_completion_chunk( + None, + finish_reason=None, + chunk_id=chunk_id, + model_uid=self.model_uid, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens, + has_choice=False, ) - yield chunk return self._to_chat_completion_chunks(_stream_generator()) - else: - response = self._non_stream_chat( - self._tokenizer, prompt, chat_history, **kwargs - ) - if tools: - return self._tool_calls_completion( - self.model_family, self.model_uid, response, tools - ) - else: - content, _ = response - return ChatCompletion( - id="chat" + str(uuid.uuid1()), - object="chat.completion", - created=int(time.time()), - model=self.model_uid, - choices=[ - ChatCompletionChoice( - index=0, - message={"role": "assistant", "content": content}, - finish_reason="stop", - ) - ], - usage=CompletionUsage( - prompt_tokens=-1, completion_tokens=-1, total_tokens=-1 - ), - ) def prepare_sanitize_generate_config(self, req: InferenceRequest): """ @@ -583,3 +448,104 @@ def prepare_sanitize_generate_config(self, req: InferenceRequest): raw_config["top_p"] = 0.8 return raw_config + + def prepare_batch_inference(self, req_list: List[InferenceRequest]): + super(PytorchChatModel, self).prepare_batch_inference(req_list) + for r in req_list: + try: + if not r.stopped and r.is_prefill: + tools = r.generate_config.get("tools", None) + tools = list(tools) if tools is not None else None + tool_choice = r.generate_config.get("tool_choice", "none") + + r.prompt = self._process_messages( + r.prompt, tools=tools, tool_choice=tool_choice + ) + r.full_prompt = self.get_full_context( + r.prompt, + self.model_family.chat_template, # type: ignore + tokenizer=self._tokenizer, + ) + if tools: + r.tools = tools + except Exception as e: + logger.exception(f"prepare inference error with {e}") + r.stopped = True + r.error_msg = str(e) + + def handle_chat_result_non_streaming(self, req: InferenceRequest): + if req.tools: + response = req.completion[0]["choices"][0]["text"] + usage = req.completion[0]["usage"] + function_call = self._process_response_non_streaming( + response, req.tools, use_tool=True + ) + req.completion[0] = self._tool_calls_completion( + self.model_family, self.model_uid, function_call + ) + req.completion[0]["usage"] = usage + else: + req.completion[0] = self._to_chat_completion(req.completion[0]) + + def handle_chat_result_streaming(self, req: InferenceRequest): + results = [] + tools = {tool["function"]["name"] for tool in req.tools} if req.tools else {} + response = "".join(req.outputs) + eos_pos = response.find("") + if eos_pos != -1: + response = response[:eos_pos] + + if "" in req.completion: + bos_pos = req.completion.index("") + results.append( + self._get_first_chat_completion_chunk(req.completion[bos_pos + 1]) + ) + + if req.stopped: + if tools: + new_response = self._process_response_streaming( + response, tools, end=True + ) + if new_response: + if isinstance(new_response, dict): # tool call case + chunk_id = [ + c for c in req.completion if not isinstance(c, str) + ][0]["id"] + results.append( + self._tool_calls_completion_chunk( + self.model_family, + self.model_uid, + new_response, + chunk_id=chunk_id, + ) + ) + else: # normal case + for c in req.completion: + if c == "": + continue + elif c == "": + break + else: + results.append(self._to_chat_completion_chunk(c)) + else: + for c in req.completion: + if c == "": + continue + elif c == "": + break + else: + results.append(self._to_chat_completion_chunk(c)) + else: + if response and response[-1] != "�": + new_response = self._process_response_streaming( + response, tools, end=False + ) + if new_response is not None: # normal case + for c in req.completion: + if c == "": + continue + results.append(self._to_chat_completion_chunk(c)) + + if req.stopped and req.include_usage: + results.append(self._get_final_chat_completion_chunk(req.completion[-1])) + req.completion = results diff --git a/xinference/model/llm/transformers/cogvlm2.py b/xinference/model/llm/transformers/cogvlm2.py index 79b15be69c..27cdc23dc6 100644 --- a/xinference/model/llm/transformers/cogvlm2.py +++ b/xinference/model/llm/transformers/cogvlm2.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. import logging -import time import uuid from concurrent.futures import ThreadPoolExecutor from typing import Dict, Iterator, List, Optional, Tuple, Union @@ -21,19 +20,16 @@ from ....core.scheduler import InferenceRequest from ....model.utils import select_device -from ....types import ( - ChatCompletion, - ChatCompletionChunk, - ChatCompletionMessage, - Completion, - CompletionChoice, - CompletionChunk, - CompletionUsage, -) +from ....types import ChatCompletion, ChatCompletionChunk, CompletionChunk from ..llm_family import LLMFamilyV1, LLMSpecV1 -from ..utils import _decode_image +from ..utils import ( + _decode_image, + generate_chat_completion, + generate_completion_chunk, + parse_messages, +) from .core import PytorchChatModel, PytorchGenerateConfig -from .utils import get_max_src_len +from .utils import cache_clean, get_max_src_len logger = logging.getLogger(__name__) @@ -139,9 +135,7 @@ def _message_content_to_cogvlm2(self, content): ) return content, None - def _history_content_to_cogvlm2( - self, system_prompt: str, chat_history: List[ChatCompletionMessage] - ): + def _history_content_to_cogvlm2(self, system_prompt: str, chat_history: List[Dict]): query = system_prompt history: List[Tuple] = [] pixel_values = None @@ -163,7 +157,7 @@ def get_query_and_history( self, prompt: Union[str, List[Dict]], system_prompt: Optional[str] = None, - chat_history: Optional[List[ChatCompletionMessage]] = None, + chat_history: Optional[List[Dict]] = None, ): content, image = self._message_content_to_cogvlm2(prompt) @@ -182,14 +176,15 @@ def get_query_and_history( query = content return query, image, history + @cache_clean def chat( self, - prompt: Union[str, List[Dict]], - system_prompt: Optional[str] = None, - chat_history: Optional[List[ChatCompletionMessage]] = None, + messages: List[Dict], generate_config: Optional[PytorchGenerateConfig] = None, ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: - system_prompt = system_prompt if system_prompt else "" + system_prompt = "" + if messages[0]["role"] == "system": + system_prompt = messages[0]["content"] stream = generate_config.get("stream", False) if generate_config else False sanitized_config = { @@ -199,6 +194,7 @@ def chat( else 512, } + prompt, _, chat_history = parse_messages(messages) query, image, history = self.get_query_and_history( prompt, system_prompt=system_prompt, chat_history=chat_history ) @@ -236,21 +232,7 @@ def chat( response = self._tokenizer.decode(outputs[0]) response = response.split("<|end_of_text|>")[0] - chunk = Completion( - id=str(uuid.uuid1()), - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[ - CompletionChoice( - index=0, text=response, finish_reason="stop", logprobs=None - ) - ], - usage=CompletionUsage( - prompt_tokens=-1, completion_tokens=-1, total_tokens=-1 - ), - ) - return self._to_chat_completion(chunk) + return generate_chat_completion(self.model_uid, response) def _streaming_chat_response( self, inputs: Dict, config: Dict @@ -277,36 +259,26 @@ def _streaming_chat_response( completion_id = str(uuid.uuid1()) for new_text in streamer: - chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[ - CompletionChoice( - index=0, text=new_text, finish_reason=None, logprobs=None - ) - ], - usage=CompletionUsage( - prompt_tokens=-1, completion_tokens=-1, total_tokens=-1 - ), + yield generate_completion_chunk( + chunk_text=new_text, + finish_reason=None, + chunk_id=completion_id, + model_uid=self.model_uid, + prompt_tokens=-1, + completion_tokens=-1, + total_tokens=-1, ) - yield chunk - - completion_choice = CompletionChoice( - text="", index=0, logprobs=None, finish_reason="stop" - ) - chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[completion_choice], - usage=CompletionUsage( - prompt_tokens=-1, completion_tokens=-1, total_tokens=-1 - ), + yield generate_completion_chunk( + chunk_text=None, + finish_reason="stop", + chunk_id=completion_id, + model_uid=self.model_uid, + prompt_tokens=-1, + completion_tokens=-1, + total_tokens=-1, + has_choice=True, + has_content=False, ) - yield chunk @staticmethod def build_position_ids(x, attention_mask=None): @@ -341,7 +313,9 @@ def build_position_ids(x, attention_mask=None): def get_dtype(self): return self._torch_type - def _get_full_prompt(self, prompt, system_prompt, chat_history, tools): + def _get_full_prompt(self, messages: List[Dict], tools): + prompt, system_prompt, chat_history = parse_messages(messages) + system_prompt = system_prompt or "" query, image, history = self.get_query_and_history( prompt, system_prompt=system_prompt, chat_history=chat_history ) diff --git a/xinference/model/llm/transformers/cogvlm2_video.py b/xinference/model/llm/transformers/cogvlm2_video.py index 24f31e0b5c..9fa7272a8e 100644 --- a/xinference/model/llm/transformers/cogvlm2_video.py +++ b/xinference/model/llm/transformers/cogvlm2_video.py @@ -12,28 +12,23 @@ # See the License for the specific language governing permissions and # limitations under the License. import logging -import time import uuid from concurrent.futures import ThreadPoolExecutor from typing import Dict, Iterator, List, Optional, Tuple, Union import torch -from ....core.scheduler import InferenceRequest from ....model.utils import select_device -from ....types import ( - ChatCompletion, - ChatCompletionChunk, - ChatCompletionMessage, - Completion, - CompletionChoice, - CompletionChunk, - CompletionUsage, -) +from ....types import ChatCompletion, ChatCompletionChunk, CompletionChunk from ..llm_family import LLMFamilyV1, LLMSpecV1 -from ..utils import _decode_image +from ..utils import ( + _decode_image, + generate_chat_completion, + generate_completion_chunk, + parse_messages, +) from .core import PytorchChatModel, PytorchGenerateConfig -from .utils import get_max_src_len +from .utils import cache_clean logger = logging.getLogger(__name__) @@ -170,9 +165,7 @@ def _message_content_to_cogvlm2(self, content): return text, images, video return content, [], None - def _history_content_to_cogvlm2( - self, system_prompt: str, chat_history: List[ChatCompletionMessage] - ): + def _history_content_to_cogvlm2(self, system_prompt: str, chat_history: List[Dict]): query = system_prompt history: List[Tuple] = [] pixel_values = None @@ -202,7 +195,7 @@ def get_query_and_history( self, prompt: Union[str, List[Dict]], system_prompt: Optional[str] = None, - chat_history: Optional[List[ChatCompletionMessage]] = None, + chat_history: Optional[List[Dict]] = None, ): content, image, video = self._message_content_to_cogvlm2(prompt) @@ -235,14 +228,15 @@ def get_query_and_history( return query, image, video, history + @cache_clean def chat( self, - prompt: Union[str, List[Dict]], - system_prompt: Optional[str] = None, - chat_history: Optional[List[ChatCompletionMessage]] = None, + messages: List[Dict], generate_config: Optional[PytorchGenerateConfig] = None, ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: - system_prompt = system_prompt if system_prompt else "" + system_prompt = "" + if messages[0]["role"] == "system": + system_prompt = messages[0]["content"] stream = generate_config.get("stream", False) if generate_config else False sanitized_config = { @@ -252,6 +246,7 @@ def chat( else 512, } + prompt, _, chat_history = parse_messages(messages) query, image, video, history = self.get_query_and_history( prompt, system_prompt=system_prompt, chat_history=chat_history ) @@ -292,21 +287,7 @@ def chat( response = self._tokenizer.decode(outputs[0]) response = response.split("<|end_of_text|>")[0] - chunk = Completion( - id=str(uuid.uuid1()), - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[ - CompletionChoice( - index=0, text=response, finish_reason="stop", logprobs=None - ) - ], - usage=CompletionUsage( - prompt_tokens=-1, completion_tokens=-1, total_tokens=-1 - ), - ) - return self._to_chat_completion(chunk) + return generate_chat_completion(self.model_uid, response) def _streaming_chat_response( self, inputs: Dict, config: Dict @@ -333,192 +314,23 @@ def _streaming_chat_response( completion_id = str(uuid.uuid1()) for new_text in streamer: - chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[ - CompletionChoice( - index=0, text=new_text, finish_reason=None, logprobs=None - ) - ], - usage=CompletionUsage( - prompt_tokens=-1, completion_tokens=-1, total_tokens=-1 - ), + yield generate_completion_chunk( + chunk_text=new_text, + finish_reason=None, + chunk_id=completion_id, + model_uid=self.model_uid, + prompt_tokens=-1, + completion_tokens=-1, + total_tokens=-1, ) - yield chunk - - completion_choice = CompletionChoice( - text="", index=0, logprobs=None, finish_reason="stop" - ) - chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[completion_choice], - usage=CompletionUsage( - prompt_tokens=-1, completion_tokens=-1, total_tokens=-1 - ), - ) - yield chunk - - @staticmethod - def build_position_ids(x, attention_mask=None): - """ - Copied from https://huggingface.co/THUDM/cogvlm2-llama3-chinese-chat-19B-int4/blob/main/modeling_cogvlm.py - """ - # Fix: 参考官方开源代码 - if attention_mask is not None: - tmp = x.clone() - tmp[~(attention_mask.bool())] = -1 - else: - tmp = x.clone() - # image boi eoi token as LANGUAGE_TOKEN_TYPE - is_boi_eoi = torch.zeros_like(x, dtype=torch.bool) - is_boi_eoi[:, 1:] |= (tmp[:, 1:] == VISION_TOKEN_TYPE) & ( - tmp[:, :-1] == LANGUAGE_TOKEN_TYPE - ) - is_boi_eoi[:, 0] |= tmp[:, 0] == VISION_TOKEN_TYPE - is_boi_eoi[:, :-1] |= (tmp[:, :-1] == VISION_TOKEN_TYPE) & ( - tmp[:, 1:] == LANGUAGE_TOKEN_TYPE + yield generate_completion_chunk( + chunk_text=None, + finish_reason="stop", + chunk_id=completion_id, + model_uid=self.model_uid, + prompt_tokens=-1, + completion_tokens=-1, + total_tokens=-1, + has_choice=True, + has_content=False, ) - is_boi_eoi[:, -1] |= tmp[:, -1] == VISION_TOKEN_TYPE - tmp[is_boi_eoi] = LANGUAGE_TOKEN_TYPE - # final position ids - y = torch.zeros_like(x, dtype=torch.long) - y[:, 1:] = (tmp[:, 1:] == LANGUAGE_TOKEN_TYPE) | ( - (tmp[:, 1:] == VISION_TOKEN_TYPE) & (tmp[:, :-1] == LANGUAGE_TOKEN_TYPE) - ) - y = y.cumsum(dim=-1) - return y - - def get_dtype(self): - return self._torch_type - - def _get_full_prompt(self, prompt, system_prompt, chat_history, tools): - query, image, video, history = self.get_query_and_history( - prompt, system_prompt=system_prompt, chat_history=chat_history - ) - - if video: - image = [video] - - input_by_model: dict = self._model.build_conversation_input_ids( # type: ignore - self._tokenizer, - query=query, - history=history, - images=image, - template_version="chat", - ) - return { - "input_ids": input_by_model["input_ids"], # seq_len - "token_type_ids": input_by_model["token_type_ids"], # seq_len - "attention_mask": input_by_model["attention_mask"], # seq_len - "images": input_by_model["images"], - } - - def prepare_sanitize_generate_config(self, req: InferenceRequest): - """ - See https://huggingface.co/THUDM/cogvlm2-llama3-chat-19B/blob/main/generation_config.json - """ - raw_config = req.inference_kwargs.get("raw_params", {}) - temperature = raw_config.get("temperature", None) - if temperature is None: - raw_config["temperature"] = 0.6 - top_p = raw_config.get("top_p", None) - if top_p is None: - raw_config["top_p"] = 0.9 - return raw_config - - def build_prefill_kwargs(self, prompts: List, req_list: List[InferenceRequest]): - context_len = self.get_context_len() - assert isinstance(prompts[0], dict) - images = [] - max_length = float("-inf") - for i, feature in enumerate(prompts): - req = req_list[i] - if "images" in feature: - images.append(feature.pop("images", None)) - max_src_len = get_max_src_len(context_len, req) - input_ids = feature["input_ids"][-max_src_len:] - req.prompt_tokens = input_ids.tolist() - feature["input_ids"] = input_ids - feature["token_type_ids"] = feature["token_type_ids"][-max_src_len:] - feature["attention_mask"] = feature["attention_mask"][-max_src_len:] - req.extra_kwargs["attention_mask_seq_len"] = feature[ - "attention_mask" - ].shape[0] - max_length = max(len(input_ids), max_length) - - def pad_to_max_length_internal(feature, max_len, idx): - padding_length = max_len - len(feature["input_ids"]) - req_list[idx].padding_len = padding_length - feature["input_ids"] = torch.cat( - [torch.full((padding_length,), 0), feature["input_ids"]] - ) - feature["token_type_ids"] = torch.cat( - [ - torch.zeros(padding_length, dtype=torch.long), - feature["token_type_ids"], - ] - ) - feature["attention_mask"] = torch.cat( - [ - torch.zeros(padding_length, dtype=torch.long), - feature["attention_mask"], - ] - ) - return feature - - features = [ - pad_to_max_length_internal(feature, max_length, i) - for i, feature in enumerate(prompts) - ] - batch = { - key: torch.stack([feature[key] for feature in features]) - for key in features[0].keys() - } - - position_ids = self.build_position_ids(batch["token_type_ids"]) - batch["position_ids"] = position_ids - - for i in range(len(prompts)): - req = req_list[i] - req.extra_kwargs["max_position_id"] = position_ids[i : i + 1, -1].item() - - if images: - batch["images"] = images - - batch = recur_move_to( - batch, self._device, lambda x: isinstance(x, torch.Tensor) - ) - dtype = self.get_dtype() - if dtype: - batch = recur_move_to( - batch, - dtype, - lambda x: isinstance(x, torch.Tensor) and torch.is_floating_point(x), - ) - return batch - - def build_decode_token_type_ids( - self, batch_size: int, seq_length: int, reqs: List[InferenceRequest] - ): - token_type_ids = torch.full( - (batch_size, 1), fill_value=1, dtype=torch.long, device=self._device - ) - return token_type_ids - - def build_decode_position_ids( - self, batch_size: int, seq_length: int, reqs: List[InferenceRequest] - ): - tmp = [] - for r in reqs: - r.extra_kwargs["max_position_id"] += 1 - tmp.append(r.extra_kwargs["max_position_id"]) - position_ids = torch.as_tensor( - tmp, device=self._device, dtype=torch.long - ).unsqueeze(1) - return position_ids diff --git a/xinference/model/llm/transformers/core.py b/xinference/model/llm/transformers/core.py index 7b427c4918..9d48c6f005 100644 --- a/xinference/model/llm/transformers/core.py +++ b/xinference/model/llm/transformers/core.py @@ -16,7 +16,7 @@ import logging import os from functools import lru_cache -from typing import Iterable, Iterator, List, Optional, Tuple, Union +from typing import Dict, Iterable, Iterator, List, Optional, Tuple, Union import torch @@ -29,8 +29,6 @@ from ....types import ( ChatCompletion, ChatCompletionChunk, - ChatCompletionMessage, - Completion, CompletionChoice, CompletionChunk, CreateCompletionTorch, @@ -41,19 +39,15 @@ from ...utils import select_device from ..core import LLM from ..llm_family import LLMFamilyV1, LLMSpecV1 -from ..utils import QWEN_TOOL_CALL_FAMILY, ChatModelMixin +from ..utils import LLAMA3_TOOL_CALL_FAMILY, QWEN_TOOL_CALL_FAMILY, ChatModelMixin from .utils import get_context_length, get_max_src_len, pad_prefill_tokens logger = logging.getLogger(__name__) NON_DEFAULT_MODEL_LIST: List[str] = [ - "chatglm3", - "chatglm3-32k", - "chatglm3-128k", + "opt", "glm4-chat", "glm4-chat-1m", - "llama-2", - "llama-2-chat", "internlm2-chat", "internlm2.5-chat", "qwen-vl-chat", @@ -67,6 +61,13 @@ "MiniCPM-Llama3-V-2_5", "MiniCPM-V-2.6", "glm-4v", + "qwen2-vl-instruct", + "qwen2-audio", + "qwen2-audio-instruct", + "deepseek-v2", + "deepseek-v2-chat", + "deepseek-v2.5", + "deepseek-v2-chat-0628", ] @@ -319,6 +320,8 @@ def load(self): else: self._model, self._tokenizer = self._load_model(**kwargs) + self._apply_lora() + if not is_device_map_auto: self._model.to(self._device) @@ -339,69 +342,6 @@ def match( return False return True - def generate( - self, prompt: str, generate_config: Optional[PytorchGenerateConfig] = None - ) -> Union[Completion, Iterator[CompletionChunk]]: - from .utils import generate_stream - - def generator_wrapper( - prompt: str, generate_config: PytorchGenerateConfig - ) -> Iterator[CompletionChunk]: - for completion_chunk, completion_usage in generate_stream( - self.model_uid, - self._model, - self._tokenizer, - prompt, - self._device, - generate_config, - ): - completion_chunk["usage"] = completion_usage - yield completion_chunk - - logger.debug( - "Enter generate, prompt: %s, generate config: %s", prompt, generate_config - ) - - generate_config = self._sanitize_generate_config(generate_config) - - assert self._model is not None - assert self._tokenizer is not None - - lora_model = generate_config.pop("lora_name") - - if lora_model is not None and self._peft_model is not None: - for lora in self._peft_model: - if lora_model == lora.lora_name: - self._model.set_adapter(lora_model) - logger.info(f"Set lora model to {lora_model}") - break - else: - self._model.disable_adapter() - logger.info(f"No lora model {lora_model} found, skip setting") - - stream = generate_config.get("stream", False) - if not stream: - for completion_chunk, completion_usage in generate_stream( - self.model_uid, - self._model, - self._tokenizer, - prompt, - self._device, - generate_config, - ): - pass - completion = Completion( - id=completion_chunk["id"], - object=completion_chunk["object"], - created=completion_chunk["created"], - model=completion_chunk["model"], - choices=completion_chunk["choices"], - usage=completion_usage, - ) - return completion - else: - return generator_wrapper(prompt, generate_config) - def build_prefill_attention_mask( self, batch_size: int, seq_length: int, reqs: List[InferenceRequest] ): @@ -613,12 +553,17 @@ def prepare_batch_inference(self, req_list: List[InferenceRequest]): r.error_msg = str(e) def get_builtin_stop_token_ids(self) -> Tuple: - return ( - tuple(self.model_family.prompt_style.stop_token_ids) - if self.model_family.prompt_style - and self.model_family.prompt_style.stop_token_ids - else tuple() - ) + from ..utils import get_stop_token_ids_from_config_file + + stop_token_ids = get_stop_token_ids_from_config_file(self.model_path) + if stop_token_ids is not None: + return tuple(stop_token_ids) + else: + return ( + tuple(self.model_family.stop_token_ids) + if self.model_family.stop_token_ids + else tuple() + ) def handle_batch_inference_results(self, req_list: List[InferenceRequest]): for req in req_list: @@ -691,20 +636,13 @@ def _sanitize_generate_config( generate_config: Optional[PytorchGenerateConfig], ) -> PytorchGenerateConfig: generate_config = super()._sanitize_generate_config(generate_config) - if ( - (not generate_config.get("stop")) - and self.model_family.prompt_style - and self.model_family.prompt_style.stop - ): - generate_config["stop"] = self.model_family.prompt_style.stop.copy() + if (not generate_config.get("stop")) and self.model_family.stop is not None: + generate_config["stop"] = self.model_family.stop.copy() if ( generate_config.get("stop_token_ids", None) is None - and self.model_family.prompt_style - and self.model_family.prompt_style.stop_token_ids + and self.model_family.stop_token_ids is not None ): - generate_config[ - "stop_token_ids" - ] = self.model_family.prompt_style.stop_token_ids.copy() + generate_config["stop_token_ids"] = self.model_family.stop_token_ids.copy() return generate_config @@ -723,52 +661,29 @@ def match( def chat( self, - prompt: str, - system_prompt: Optional[str] = None, - chat_history: Optional[List[ChatCompletionMessage]] = None, + messages: List[Dict], generate_config: Optional[PytorchGenerateConfig] = None, ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: - tools = generate_config.pop("tools", []) if generate_config else None - full_prompt = self._get_full_prompt(prompt, system_prompt, chat_history, tools) - - generate_config = self._sanitize_generate_config(generate_config) - # TODO(codingl2k1): qwen hacky to set stop for function call. - model_family = self.model_family.model_family or self.model_family.model_name - if tools and model_family in QWEN_TOOL_CALL_FAMILY: - stop = generate_config.get("stop") - if isinstance(stop, str): - generate_config["stop"] = [stop, "Observation:"] - elif isinstance(stop, Iterable): - assert not isinstance(stop, str) - generate_config["stop"] = list(stop) + ["Observation:"] - else: - generate_config["stop"] = "Observation:" - - stream = generate_config.get("stream", False) - if stream: - it = self.generate(full_prompt, generate_config) - assert isinstance(it, Iterator) - return self._to_chat_completion_chunks(it) - else: - c = self.generate(full_prompt, generate_config) - assert not isinstance(c, Iterator) - if tools: - return self._tool_calls_completion( - self.model_family, self.model_uid, c, tools - ) - return self._to_chat_completion(c) + raise NotImplementedError def load(self): super().load() - def _get_full_prompt(self, prompt, system_prompt, chat_history, tools): - assert self.model_family.prompt_style is not None - prompt_style = self.model_family.prompt_style.copy() - if system_prompt: - prompt_style.system_prompt = system_prompt - chat_history = chat_history or [] - full_prompt = ChatModelMixin.get_prompt( - prompt, chat_history, prompt_style, tools=tools + def _get_full_prompt(self, messages: List[Dict], tools): + model_family = self.model_family.model_family or self.model_family.model_name + full_context_kwargs = {} + if ( + tools + and model_family in QWEN_TOOL_CALL_FAMILY + or model_family in LLAMA3_TOOL_CALL_FAMILY + ): + full_context_kwargs["tools"] = tools + assert self.model_family.chat_template is not None + full_prompt = self.get_full_context( + messages, + self.model_family.chat_template, + tokenizer=self._tokenizer, + **full_context_kwargs, ) return full_prompt @@ -777,35 +692,57 @@ def prepare_batch_inference(self, req_list: List[InferenceRequest]): for r in req_list: try: if not r.stopped and r.is_prefill: - r.full_prompt = self._get_full_prompt( - r.prompt, r.system_prompt, r.chat_history, None - ) + tools = r.generate_config.get("tools", None) + r.full_prompt = self._get_full_prompt(r.prompt, tools) + if tools: + r.tools = tools except Exception as e: logger.exception(f"prepare inference error with {e}") r.stopped = True r.error_msg = str(e) + def handle_chat_result_non_streaming(self, req: InferenceRequest): + if req.tools: + req.completion[0] = self._tool_calls_completion( + self.model_family, self.model_uid, req.completion[0] + ) + else: + req.completion[0] = self._to_chat_completion(req.completion[0]) + + def handle_chat_result_streaming(self, req: InferenceRequest): + results = [] + for i, c in enumerate(req.completion): + if c == "": + results.append( + self._get_first_chat_completion_chunk(req.completion[i + 1]) + ) + elif c == "": + break + else: + results.append(self._to_chat_completion_chunk(c)) + + if req.stopped and req.include_usage: + results.append(self._get_final_chat_completion_chunk(req.completion[-1])) + req.completion = results + def handle_batch_inference_results(self, req_list: List[InferenceRequest]): for req in req_list: if req.error_msg is None and req.completion: - if req.stream: + # The `generate` function can be called for some chat models. + # So that we cannot convert completion chunk to chat completion chunk. + if req.call_ability == "generate": results = [] - for i, c in enumerate(req.completion): + for c in req.completion: if c == "": - results.append( - self._get_first_chat_completion_chunk( - req.completion[i + 1] - ) - ) + continue elif c == "": break else: - results.append(self._to_chat_completion_chunk(c)) - - if req.stopped and req.include_usage: - results.append( - self._get_final_chat_completion_chunk(req.completion[-1]) - ) + results.append(c) req.completion = results + continue + + if req.stream: + self.handle_chat_result_streaming(req) else: - req.completion[0] = self._to_chat_completion(req.completion[0]) + self.handle_chat_result_non_streaming(req) diff --git a/xinference/model/llm/transformers/deepseek_v2.py b/xinference/model/llm/transformers/deepseek_v2.py new file mode 100644 index 0000000000..9d9fb0d1d3 --- /dev/null +++ b/xinference/model/llm/transformers/deepseek_v2.py @@ -0,0 +1,114 @@ +# Copyright 2022-2023 XProbe Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import logging + +import torch + +from ..llm_family import LLMFamilyV1, LLMSpecV1 +from .core import PytorchChatModel, PytorchModel + +logger = logging.getLogger(__name__) + + +class DeepSeekV2PytorchModel(PytorchModel): + def _load_model(self, **kwargs): + try: + from transformers import ( + AutoModelForCausalLM, + AutoTokenizer, + GenerationConfig, + ) + except ImportError: + error_message = "Failed to import module 'transformers'" + installation_guide = [ + "Please make sure 'transformers' is installed. ", + "You can install it by `pip install transformers`\n", + ] + + raise ImportError(f"{error_message}\n\n{''.join(installation_guide)}") + + tokenizer = AutoTokenizer.from_pretrained( + self.model_path, + trust_remote_code=kwargs["trust_remote_code"], + ) + model = AutoModelForCausalLM.from_pretrained( + self.model_path, + attn_implementation="eager", + torch_dtype=torch.bfloat16, + trust_remote_code=True, + device_map="auto", + ) + model.generation_config = GenerationConfig.from_pretrained(self.model_path) + model.generation_config.pad_token_id = model.generation_config.eos_token_id + return model, tokenizer + + @classmethod + def match( + cls, llm_family: "LLMFamilyV1", llm_spec: "LLMSpecV1", quantization: str + ) -> bool: + if llm_spec.model_format != "pytorch": + return False + model_family = llm_family.model_family or llm_family.model_name + if "deepseek-v2" not in model_family: + return False + if "generate" not in llm_family.model_ability: + return False + return True + + +class DeepSeekV2PytorchChatModel(PytorchChatModel): + def _load_model(self, **kwargs): + try: + from transformers import ( + AutoModelForCausalLM, + AutoTokenizer, + GenerationConfig, + ) + except ImportError: + error_message = "Failed to import module 'transformers'" + installation_guide = [ + "Please make sure 'transformers' is installed. ", + "You can install it by `pip install transformers`\n", + ] + + raise ImportError(f"{error_message}\n\n{''.join(installation_guide)}") + + tokenizer = AutoTokenizer.from_pretrained( + self.model_path, + trust_remote_code=kwargs["trust_remote_code"], + ) + logger.info(f"kwargs:{kwargs}") + model = AutoModelForCausalLM.from_pretrained( + self.model_path, + attn_implementation="eager", + torch_dtype=torch.bfloat16, + trust_remote_code=True, + device_map="auto", + ) + model.generation_config = GenerationConfig.from_pretrained(self.model_path) + model.generation_config.pad_token_id = model.generation_config.eos_token_id + return model, tokenizer + + @classmethod + def match( + cls, llm_family: "LLMFamilyV1", llm_spec: "LLMSpecV1", quantization: str + ) -> bool: + if llm_spec.model_format != "pytorch": + return False + model_family = llm_family.model_family or llm_family.model_name + if "deepseek-v2" not in model_family: + return False + if "chat" not in llm_family.model_ability: + return False + return True diff --git a/xinference/model/llm/transformers/deepseek_vl.py b/xinference/model/llm/transformers/deepseek_vl.py index d24158f5d4..515644fec5 100644 --- a/xinference/model/llm/transformers/deepseek_vl.py +++ b/xinference/model/llm/transformers/deepseek_vl.py @@ -15,7 +15,6 @@ import logging import os.path import tempfile -import time import uuid from concurrent.futures import ThreadPoolExecutor from io import BytesIO @@ -25,17 +24,11 @@ import torch from ....model.utils import select_device -from ....types import ( - ChatCompletion, - ChatCompletionChunk, - ChatCompletionMessage, - Completion, - CompletionChoice, - CompletionChunk, - CompletionUsage, -) +from ....types import ChatCompletion, ChatCompletionChunk, CompletionChunk from ..llm_family import LLMFamilyV1, LLMSpecV1 +from ..utils import generate_chat_completion, generate_completion_chunk from .core import PytorchChatModel, PytorchGenerateConfig +from .utils import cache_clean logger = logging.getLogger(__name__) @@ -145,11 +138,10 @@ def _fill_placeholder(_url, _index): return "".join(new_content), images return content, [] + @cache_clean def chat( self, - prompt: Union[str, List[Dict]], - system_prompt: Optional[str] = None, - chat_history: Optional[List[ChatCompletionMessage]] = None, + messages: List[Dict], generate_config: Optional[PytorchGenerateConfig] = None, ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: if not generate_config: @@ -162,44 +154,40 @@ def chat( if isinstance(stream_options, dict) else False ) - prompt, images = self._message_content_to_deepseek(prompt) - prompt_messages: List[Dict[str, Any]] = [ - { - "role": "User", - "content": prompt, - }, - {"role": "Assistant", "content": ""}, - ] - if images: - prompt_messages[0]["images"] = images - - # Convert openai history to qwen vl history - deepseek_history = [] - for h in chat_history or []: - role = h["role"] + + prompt = "" + deepseek_messages = [] + for i, message in enumerate(messages): + role = message["role"] + content = message["content"] if role == "user": - content, images = self._message_content_to_deepseek(h["content"]) - msg: Dict[str, Any] = { - "role": "User", - "content": content, - } - if images: - msg["images"] = images - deepseek_history.append(msg) + if isinstance(content, str): + deepseek_messages.append({"role": "User", "content": content}) + else: + content, images = self._message_content_to_deepseek(content) + msg: Dict[str, Any] = { + "role": "User", + "content": content, + } + if images: + msg["images"] = images + deepseek_messages.append(msg) + if i == len(messages) - 1: + prompt = content elif role == "assistant": - deepseek_history.append({"role": "Assistant", "content": h["content"]}) + deepseek_messages.append({"role": "Assistant", "content": content}) else: - logger.error("Unexpected msg in chat history: %s", h) - - deepseek_history.extend(prompt_messages) + logger.error( + f"Unexpected message in messages: role: {role}, message: {message}" + ) from ....thirdparty.deepseek_vl.serve.inference import generate from ....thirdparty.deepseek_vl.utils.io import load_pil_images # load images and prepare for inputs - pil_images = load_pil_images(deepseek_history) + pil_images = load_pil_images(deepseek_messages) prepare_inputs = self._vl_chat_processor( - conversations=deepseek_history, images=pil_images, force_batchify=True + conversations=deepseek_messages, images=pil_images, force_batchify=True ).to(self._model.device, self._model.dtype) temperature = generate_config.get("temperature", 0.2) @@ -226,31 +214,16 @@ def chat( it = self._generate_stream(streamer, stop_str, include_usage, prompt) return self._to_chat_completion_chunks(it) else: - c = self._generate(streamer, stop_str) - return self._to_chat_completion(c) + return self._generate(streamer, stop_str) - def _generate(self, streamer, stop_str) -> Completion: + def _generate(self, streamer, stop_str) -> ChatCompletion: generated_text = "" for new_text in streamer: if new_text.endswith(stop_str): new_text = new_text[: -len(stop_str)] generated_text += new_text - c = Completion( - id=str(uuid.uuid1()), - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[ - CompletionChoice( - index=0, text=generated_text, finish_reason="stop", logprobs=None - ) - ], - usage=CompletionUsage( - prompt_tokens=-1, completion_tokens=-1, total_tokens=-1 - ), - ) - return c + return generate_chat_completion(self.model_uid, generated_text) def _generate_stream( self, streamer, stop_str, include_usage, prompt @@ -262,54 +235,40 @@ def _generate_stream( for i, new_text in enumerate(streamer): if new_text.endswith(stop_str): new_text = new_text[: -len(stop_str)] - completion_choice = CompletionChoice( - text=new_text, index=0, logprobs=None, finish_reason=None - ) - chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[completion_choice], - ) completion_tokens = i total_tokens = prompt_tokens + completion_tokens - completion_usage = CompletionUsage( + yield generate_completion_chunk( + chunk_text=new_text, + finish_reason=None, + chunk_id=completion_id, + model_uid=self.model_uid, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens, + has_choice=True, + has_content=True, ) - chunk["usage"] = completion_usage - yield chunk - - completion_choice = CompletionChoice( - text="", index=0, logprobs=None, finish_reason="stop" - ) - chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[completion_choice], - ) - completion_usage = CompletionUsage( + yield generate_completion_chunk( + chunk_text=None, + finish_reason="stop", + chunk_id=completion_id, + model_uid=self.model_uid, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens, + has_choice=True, + has_content=False, ) - chunk["usage"] = completion_usage - yield chunk + if include_usage: - chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[], - ) - chunk["usage"] = CompletionUsage( + yield generate_completion_chunk( + chunk_text=None, + finish_reason=None, + chunk_id=completion_id, + model_uid=self.model_uid, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens, + has_choice=False, + has_content=False, ) - yield chunk diff --git a/xinference/model/llm/transformers/glm4v.py b/xinference/model/llm/transformers/glm4v.py index 4df4f9cd4d..b1109d4b04 100644 --- a/xinference/model/llm/transformers/glm4v.py +++ b/xinference/model/llm/transformers/glm4v.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. import logging -import time import typing import uuid from concurrent.futures import ThreadPoolExecutor @@ -22,20 +21,12 @@ import torch from ....core.scheduler import InferenceRequest -from ....types import ( - ChatCompletion, - ChatCompletionChunk, - ChatCompletionMessage, - Completion, - CompletionChoice, - CompletionChunk, - CompletionUsage, -) +from ....types import ChatCompletion, ChatCompletionChunk, CompletionChunk from ...utils import select_device from ..llm_family import LLMFamilyV1, LLMSpecV1 -from ..utils import _decode_image +from ..utils import _decode_image, generate_chat_completion, generate_completion_chunk from .core import PytorchChatModel, PytorchGenerateConfig -from .utils import get_max_src_len +from .utils import cache_clean, get_max_src_len logger = logging.getLogger(__name__) @@ -102,66 +93,46 @@ def load(self): self._tokenizer = tokenizer self._save_tensorizer() - def _message_content_to_chat(self, content): - if not isinstance(content, str): - texts = [] - image_urls = [] - for c in content: - c_type = c.get("type") - if c_type == "text": - texts.append(c["text"]) - elif c_type == "image_url": - image_urls.append(c["image_url"]["url"]) - image_futures = [] - with ThreadPoolExecutor() as executor: - for image_url in image_urls: - fut = executor.submit(_decode_image, image_url) - image_futures.append(fut) - images = [fut.result() for fut in image_futures] - text = " ".join(texts) - if len(images) == 0: - return text, [] - elif len(images) == 1: - return text, images + @staticmethod + def _get_processed_msgs(messages: List[Dict]) -> List[Dict]: + res = [] + for message in messages: + role = message["role"] + content = message["content"] + if isinstance(content, str): + res.append({"role": role, "content": content}) else: - raise RuntimeError("Only one image per message is supported") - return content, [] - - def _get_chat_msgs( - self, - prompt: Union[str, List[Dict]], - chat_history: Optional[List[ChatCompletionMessage]] = None, - ): - content, images_chat = self._message_content_to_chat(prompt) - - msgs = [] - query_to_response: List[Dict] = [] - images_history = [] - for h in chat_history or []: - role = h["role"] - content_h, images_tmp = self._message_content_to_chat(h["content"]) - if images_tmp: - images_history = images_tmp - if len(query_to_response) == 0 and role == "user": - query_to_response.append({"role": "user", "content": content_h}) - if len(query_to_response) == 1 and role == "assistant": - query_to_response.append({"role": "assistant", "content": content_h}) - if len(query_to_response) == 2: - msgs.extend(query_to_response) - query_to_response = [] - image = None - if len(images_chat) > 0: - image = images_chat[0] - elif len(images_history) > 0: - image = images_history[0] - msgs.append({"role": "user", "content": content, "image": image}) - return msgs + texts = [] + image_urls = [] + for c in content: + c_type = c.get("type") + if c_type == "text": + texts.append(c["text"]) + else: + assert ( + c_type == "image_url" + ), "Please follow the image input of the OpenAI API." + image_urls.append(c["image_url"]["url"]) + if len(image_urls) > 1: + raise RuntimeError("Only one image per message is supported") + image_futures = [] + with ThreadPoolExecutor() as executor: + for image_url in image_urls: + fut = executor.submit(_decode_image, image_url) + image_futures.append(fut) + images = [fut.result() for fut in image_futures] + assert len(images) <= 1 + text = " ".join(texts) + if images: + res.append({"role": role, "content": text, "image": images[0]}) + else: + res.append({"role": role, "content": text}) + return res + @cache_clean def chat( self, - prompt: Union[str, List[Dict]], - system_prompt: Optional[str] = None, - chat_history: Optional[List[ChatCompletionMessage]] = None, + messages: List[Dict], generate_config: Optional[PytorchGenerateConfig] = None, ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: from transformers import TextIteratorStreamer @@ -170,7 +141,7 @@ def chat( generate_config = {} stream = generate_config.get("stream", False) - msgs = self._get_chat_msgs(prompt, chat_history) + msgs = self._get_processed_msgs(messages) inputs = self._tokenizer.apply_chat_template( msgs, @@ -213,64 +184,38 @@ def chat( response = self._tokenizer.decode(outputs[0]) if response.endswith(stop_str): response = response[: -len(stop_str)] - c = Completion( - id=str(uuid.uuid1()), - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[ - CompletionChoice( - index=0, text=response, finish_reason="stop", logprobs=None - ) - ], - usage=CompletionUsage( - prompt_tokens=-1, completion_tokens=-1, total_tokens=-1 - ), - ) - return self._to_chat_completion(c) + return generate_chat_completion(self.model_uid, response) def chat_stream(self, streamer, stop_str) -> Iterator[CompletionChunk]: completion_id = str(uuid.uuid1()) for new_text in streamer: if not new_text.endswith(stop_str): - completion_choice = CompletionChoice( - text=new_text, index=0, logprobs=None, finish_reason=None - ) - chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[completion_choice], - ) - completion_usage = CompletionUsage( + yield generate_completion_chunk( + chunk_text=new_text, + finish_reason=None, + chunk_id=completion_id, + model_uid=self.model_uid, prompt_tokens=-1, completion_tokens=-1, total_tokens=-1, + has_choice=True, + has_content=True, ) - chunk["usage"] = completion_usage - yield chunk - completion_choice = CompletionChoice( - text="", index=0, logprobs=None, finish_reason="stop" - ) - chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[completion_choice], - ) - completion_usage = CompletionUsage( + yield generate_completion_chunk( + chunk_text=None, + finish_reason="stop", + chunk_id=completion_id, + model_uid=self.model_uid, prompt_tokens=-1, completion_tokens=-1, total_tokens=-1, + has_choice=True, + has_content=False, ) - chunk["usage"] = completion_usage - yield chunk - def _get_full_prompt(self, prompt, system_prompt, chat_history, tools): - msgs = self._get_chat_msgs(prompt, chat_history) + def _get_full_prompt(self, messages, tools): + msgs = self._get_processed_msgs(messages) inputs = self._tokenizer.apply_chat_template( msgs, add_generation_prompt=True, diff --git a/xinference/model/llm/transformers/intern_vl.py b/xinference/model/llm/transformers/intern_vl.py index 02632e2af8..8150711e00 100644 --- a/xinference/model/llm/transformers/intern_vl.py +++ b/xinference/model/llm/transformers/intern_vl.py @@ -12,25 +12,22 @@ # See the License for the specific language governing permissions and # limitations under the License. import logging -import time import uuid from concurrent.futures import ThreadPoolExecutor from typing import Dict, Iterator, List, Optional, Union import torch -from ....types import ( - ChatCompletion, - ChatCompletionChunk, - ChatCompletionMessage, - Completion, - CompletionChoice, - CompletionChunk, - CompletionUsage, -) +from ....types import ChatCompletion, ChatCompletionChunk from ..llm_family import LLMFamilyV1, LLMSpecV1 -from ..utils import _decode_image +from ..utils import ( + _decode_image, + generate_chat_completion, + generate_completion_chunk, + parse_messages, +) from .core import PytorchChatModel, PytorchGenerateConfig +from .utils import cache_clean logger = logging.getLogger(__name__) @@ -78,7 +75,7 @@ def _message_content_to_intern(content, image_cnt): def _get_prompt_and_chat_history( prompt: Union[str, List[Dict]], - chat_history: Optional[List[ChatCompletionMessage]] = None, + chat_history: Optional[List[Dict]] = None, ): # Convert openai history to intern vl history images = [] @@ -330,11 +327,10 @@ def load(self, **kwargs): use_fast=False, ) + @cache_clean def chat( self, - prompt: Union[str, List[Dict]], - system_prompt: Optional[str] = None, - chat_history: Optional[List[ChatCompletionMessage]] = None, + messages: List[Dict], generate_config: Optional[PytorchGenerateConfig] = None, ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: from ....thirdparty.internvl.conversation import get_conv_template @@ -366,6 +362,7 @@ def chat( else False ) + prompt, _, chat_history = parse_messages(messages) content, history, images, videos = _get_prompt_and_chat_history( prompt, chat_history ) @@ -434,10 +431,9 @@ def chat( chunk = self._generate_stream(generate_kwargs, input_ids, include_usage) return self._to_chat_completion_chunks(chunk) else: - chunk = self._generate(generate_kwargs, input_ids, template) - return self._to_chat_completion(chunk) + return self._generate(generate_kwargs, input_ids, template) - def _generate(self, generate_kwargs, input_ids, template): + def _generate(self, generate_kwargs, input_ids, template) -> ChatCompletion: prompt_tokens = len(input_ids[0]) generation_output = self._model.generate(**generate_kwargs) completion_tokens = len(generation_output[0]) @@ -445,23 +441,13 @@ def _generate(self, generate_kwargs, input_ids, template): generation_output, skip_special_tokens=True )[0] response = response.split(template.sep)[0].strip() - chunk = Completion( - id=str(uuid.uuid1()), - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[ - CompletionChoice( - index=0, text=response, finish_reason="stop", logprobs=None - ) - ], - usage=CompletionUsage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, - ), + return generate_chat_completion( + self.model_uid, + response, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, ) - return chunk def _generate_stream(self, generate_kwargs, input_ids, include_usage): from threading import Thread @@ -483,58 +469,43 @@ def _generate_stream(self, generate_kwargs, input_ids, include_usage): completion_id = str(uuid.uuid1()) prompt_tokens = len(input_ids[0]) - completion_tokens = 0 + total_tokens, completion_tokens = 0, 0 # Loop through the streamer to get the new text as it is generated for i, new_text in enumerate(streamer): if new_text == self._model.conv_template.sep: break - completion_choice = CompletionChoice( - text=new_text, index=0, logprobs=None, finish_reason=None - ) - chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[completion_choice], - ) completion_tokens = max(completion_tokens, len(streamer.token_cache)) total_tokens = prompt_tokens + completion_tokens - completion_usage = CompletionUsage( + yield generate_completion_chunk( + chunk_text=new_text, + finish_reason=None, + chunk_id=completion_id, + model_uid=self.model_uid, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens, ) - chunk["usage"] = completion_usage - yield chunk - completion_choice = CompletionChoice( - text="", index=0, logprobs=None, finish_reason="stop" - ) - chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[completion_choice], - ) - completion_usage = CompletionUsage( + yield generate_completion_chunk( + chunk_text=None, + finish_reason="stop", + chunk_id=completion_id, + model_uid=self.model_uid, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens, + has_choice=True, + has_content=False, ) - chunk["usage"] = completion_usage - yield chunk + if include_usage: - chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[], - ) - chunk["usage"] = CompletionUsage( + yield generate_completion_chunk( + chunk_text=None, + finish_reason=None, + chunk_id=completion_id, + model_uid=self.model_uid, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens, + has_choice=False, + has_content=False, ) - yield chunk diff --git a/xinference/model/llm/transformers/internlm2.py b/xinference/model/llm/transformers/internlm2.py index fc7b1c7588..ba05cf3b35 100644 --- a/xinference/model/llm/transformers/internlm2.py +++ b/xinference/model/llm/transformers/internlm2.py @@ -11,22 +11,11 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -import time -import uuid -from typing import Any, Dict, Iterator, List, Optional, Union + +from typing import List, Optional from ....core.scheduler import InferenceRequest -from ....types import ( - ChatCompletion, - ChatCompletionChoice, - ChatCompletionChunk, - ChatCompletionMessage, - CompletionChoice, - CompletionChunk, - CompletionUsage, - LoRA, - PytorchGenerateConfig, -) +from ....types import LoRA from ..llm_family import LLMFamilyV1, LLMSpecV1 from .core import PytorchChatModel, PytorchModelConfig @@ -103,106 +92,3 @@ def prepare_sanitize_generate_config(self, req: InferenceRequest): if top_p is None: raw_config["top_p"] = 0.8 return raw_config - - def chat( - self, - prompt: str, - system_prompt: Optional[str] = None, - chat_history: Optional[List[ChatCompletionMessage]] = None, - generate_config: Optional[PytorchGenerateConfig] = None, - ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: - kwargs: Dict[str, Any] = {} - generate_config = generate_config or {} - temperature = generate_config.get("temperature") - if temperature is not None: - kwargs["temperature"] = float(temperature) - top_p = generate_config.get("top_p") - if top_p is not None: - kwargs["top_p"] = float(top_p) - max_new_tokens = generate_config.get("max_tokens") - if max_new_tokens is not None: - kwargs["max_length"] = int(max_new_tokens) - - stream = generate_config.get("stream", False) - stream_options = generate_config.pop("stream_options", None) - include_usage = ( - stream_options["include_usage"] - if isinstance(stream_options, dict) - else False - ) - if chat_history: - input_history = [ - (chat_history[i]["content"], (chat_history[i + 1]["content"])) - for i in range(0, len(chat_history), 2) - ] - else: - input_history = [] - if system_prompt: - kwargs["meta_instruction"] = system_prompt - if stream: - - def _stream_generator(): - last_chunk_text_length = 0 - chunk_id = "chat-" + str(uuid.uuid1()) - prompt_tokens, completion_tokens, total_tokens = 0, 0, 0 - inputs = self._tokenizer([prompt], return_tensors="pt") - inputs = inputs.to(self._model.device) - prompt_tokens = len(inputs["input_ids"][0]) - for chunk_text, _ in self._model.stream_chat( - self._tokenizer, prompt, input_history, **kwargs - ): - completion_tokens = completion_tokens + 1 - total_tokens = prompt_tokens + completion_tokens - chunk_text = chunk_text[last_chunk_text_length:] - last_chunk_text_length += len(chunk_text) - completion_choice = CompletionChoice( - text=chunk_text, index=0, logprobs=None, finish_reason=None - ) - yield CompletionChunk( - id=chunk_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[completion_choice], - usage=CompletionUsage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=total_tokens, - ), - ) - if include_usage: - chunk = CompletionChunk( - id=chunk_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[], - ) - chunk["usage"] = CompletionUsage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=total_tokens, - ) - yield chunk - - return self._to_chat_completion_chunks(_stream_generator()) - else: - response, _ = self._model.chat( - self._tokenizer, prompt, input_history, **kwargs - ) - return ChatCompletion( - id="chat" + str(uuid.uuid1()), - object="chat.completion", - created=int(time.time()), - model=self.model_uid, - choices=[ - ChatCompletionChoice( - index=0, - message={"role": "assistant", "content": response}, - finish_reason="stop", - ) - ], - usage=CompletionUsage( - prompt_tokens=-1, completion_tokens=-1, total_tokens=-1 - ), - ) diff --git a/xinference/model/llm/transformers/llama_2.py b/xinference/model/llm/transformers/llama_2.py deleted file mode 100644 index 4e5e01d263..0000000000 --- a/xinference/model/llm/transformers/llama_2.py +++ /dev/null @@ -1,108 +0,0 @@ -# Copyright 2022-2023 XProbe Inc. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import List, Optional - -from ....types import LoRA -from ..llm_family import LLMFamilyV1, LLMSpecV1 -from .core import PytorchChatModel, PytorchModel, PytorchModelConfig - - -class LlamaPytorchModel(PytorchModel): - def __init__( - self, - model_uid: str, - model_family: "LLMFamilyV1", - model_spec: "LLMSpecV1", - quantization: str, - model_path: str, - pytorch_model_config: Optional[PytorchModelConfig] = None, - peft_model: Optional[List[LoRA]] = None, - ): - super().__init__( - model_uid, - model_family, - model_spec, - quantization, - model_path, - pytorch_model_config=pytorch_model_config, - peft_model=peft_model, - ) - - def _load_model(self, **kwargs): - model, tokenizer = super()._load_model(**kwargs) - # Llama has no pad token by default - # https://github.com/huggingface/transformers/blob/07998ef39926b76d3f6667025535d0859eed61c3/docs/source/en/llm_tutorial.md?plain=1#L125 - tokenizer.pad_token = tokenizer.eos_token - model.config.eos_token_id = tokenizer.eos_token_id - model.config.pad_token_id = tokenizer.pad_token_id - return model, tokenizer - - @classmethod - def match( - cls, llm_family: "LLMFamilyV1", llm_spec: "LLMSpecV1", quantization: str - ) -> bool: - if llm_spec.model_format != "pytorch": - return False - model_family = llm_family.model_family or llm_family.model_name - if "llama-2" not in model_family: - return False - if "generate" not in llm_family.model_ability: - return False - return True - - -class LlamaPytorchChatModel(PytorchChatModel): - def __init__( - self, - model_uid: str, - model_family: "LLMFamilyV1", - model_spec: "LLMSpecV1", - quantization: str, - model_path: str, - pytorch_model_config: Optional["PytorchModelConfig"] = None, - peft_model: Optional[List[LoRA]] = None, - ): - super().__init__( - model_uid, - model_family, - model_spec, - quantization, - model_path, - peft_model=peft_model, - pytorch_model_config=pytorch_model_config, - ) - self._use_fast_tokenizer = False - - def _load_model(self, **kwargs): - model, tokenizer = super()._load_model(**kwargs) - # Llama has no pad token by default - # https://github.com/huggingface/transformers/blob/07998ef39926b76d3f6667025535d0859eed61c3/docs/source/en/llm_tutorial.md?plain=1#L125 - tokenizer.pad_token = tokenizer.eos_token - model.config.eos_token_id = tokenizer.eos_token_id - model.config.pad_token_id = tokenizer.pad_token_id - return model, tokenizer - - @classmethod - def match( - cls, llm_family: "LLMFamilyV1", llm_spec: "LLMSpecV1", quantization: str - ) -> bool: - if llm_spec.model_format != "pytorch": - return False - model_family = llm_family.model_family or llm_family.model_name - if "llama-2" not in model_family: - return False - if "chat" not in llm_family.model_ability: - return False - return True diff --git a/xinference/model/llm/transformers/minicpmv25.py b/xinference/model/llm/transformers/minicpmv25.py index af22319759..81fbc69706 100644 --- a/xinference/model/llm/transformers/minicpmv25.py +++ b/xinference/model/llm/transformers/minicpmv25.py @@ -13,26 +13,23 @@ # limitations under the License. import json import logging -import time import uuid from concurrent.futures import ThreadPoolExecutor from typing import Dict, Iterator, List, Optional, Union import torch -from ....types import ( - ChatCompletion, - ChatCompletionChunk, - ChatCompletionMessage, - Completion, - CompletionChoice, - CompletionChunk, - CompletionUsage, -) +from ....types import ChatCompletion, ChatCompletionChunk, CompletionChunk from ...utils import select_device from ..llm_family import LLMFamilyV1, LLMSpecV1 -from ..utils import _decode_image +from ..utils import ( + _decode_image, + generate_chat_completion, + generate_completion_chunk, + parse_messages, +) from .core import PytorchChatModel, PytorchGenerateConfig +from .utils import cache_clean logger = logging.getLogger(__name__) @@ -123,14 +120,14 @@ def _message_content_to_chat(self, content): raise RuntimeError("Only one image per message is supported") return content, [] + @cache_clean def chat( self, - prompt: Union[str, List[Dict]], - system_prompt: Optional[str] = None, - chat_history: Optional[List[ChatCompletionMessage]] = None, + messages: List[Dict], generate_config: Optional[PytorchGenerateConfig] = None, ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: stream = generate_config.get("stream", False) if generate_config else False + prompt, _, chat_history = parse_messages(messages) content, images_chat = self._message_content_to_chat(prompt) msgs = [] @@ -166,57 +163,29 @@ def chat( it = self.chat_stream(chat) return self._to_chat_completion_chunks(it) else: - c = Completion( - id=str(uuid.uuid1()), - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[ - CompletionChoice( - index=0, text=chat, finish_reason="stop", logprobs=None - ) - ], - usage=CompletionUsage( - prompt_tokens=-1, completion_tokens=-1, total_tokens=-1 - ), - ) - return self._to_chat_completion(c) + return generate_chat_completion(self.model_uid, chat) def chat_stream(self, chat) -> Iterator[CompletionChunk]: completion_id = str(uuid.uuid1()) for new_text in chat: - completion_choice = CompletionChoice( - text=new_text, index=0, logprobs=None, finish_reason=None - ) - chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[completion_choice], - ) - completion_usage = CompletionUsage( + yield generate_completion_chunk( + chunk_text=new_text, + finish_reason=None, + chunk_id=completion_id, + model_uid=self.model_uid, prompt_tokens=-1, completion_tokens=-1, total_tokens=-1, ) - chunk["usage"] = completion_usage - yield chunk - completion_choice = CompletionChoice( - text="", index=0, logprobs=None, finish_reason="stop" - ) - chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[completion_choice], - ) - completion_usage = CompletionUsage( + yield generate_completion_chunk( + chunk_text=None, + finish_reason="stop", + chunk_id=completion_id, + model_uid=self.model_uid, prompt_tokens=-1, completion_tokens=-1, total_tokens=-1, + has_choice=True, + has_content=False, ) - chunk["usage"] = completion_usage - yield chunk diff --git a/xinference/model/llm/transformers/minicpmv26.py b/xinference/model/llm/transformers/minicpmv26.py index 0900bc4a86..cc6ba5e7a8 100644 --- a/xinference/model/llm/transformers/minicpmv26.py +++ b/xinference/model/llm/transformers/minicpmv26.py @@ -12,27 +12,25 @@ # See the License for the specific language governing permissions and # limitations under the License. import logging -import time import uuid from concurrent.futures import ThreadPoolExecutor -from typing import Dict, Iterator, List, Optional, Union +from typing import Dict, Iterator, List, Optional, Tuple, Union import torch from PIL import Image -from ....types import ( - ChatCompletion, - ChatCompletionChunk, - ChatCompletionMessage, - Completion, - CompletionChoice, - CompletionChunk, - CompletionUsage, -) +from ....core.scheduler import InferenceRequest +from ....types import ChatCompletion, ChatCompletionChunk, CompletionChunk from ...utils import select_device from ..llm_family import LLMFamilyV1, LLMSpecV1 -from ..utils import _decode_image +from ..utils import ( + _decode_image, + generate_chat_completion, + generate_completion_chunk, + parse_messages, +) from .core import PytorchChatModel, PytorchGenerateConfig +from .utils import cache_clean logger = logging.getLogger(__name__) @@ -43,6 +41,7 @@ def __init__(self, *args, **kwargs): self._device = None self._tokenizer = None self._model = None + self._processor = None @classmethod def match( @@ -59,7 +58,7 @@ def _get_model_class(self): return AutoModel def load(self, **kwargs): - from transformers import AutoModel, AutoTokenizer + from transformers import AutoModel, AutoProcessor, AutoTokenizer from transformers.generation import GenerationConfig device = self._pytorch_model_config.get("device", "auto") @@ -100,6 +99,10 @@ def load(self, **kwargs): self.model_path, trust_remote_code=True, ) + self._processor = AutoProcessor.from_pretrained( + self.model_path, trust_remote_code=True + ) + self._device = self._model.device self._save_tensorizer() def _message_content_to_chat(self, content): @@ -120,7 +123,9 @@ def uniform_sample(l, n): frame_idx = uniform_sample(frame_idx, MAX_NUM_FRAMES) frames = vr.get_batch(frame_idx).asnumpy() frames = [Image.fromarray(v.astype("uint8")) for v in frames] - print("num frames:", len(frames)) + logger.info( + f"Num frames: {len(frames)} when decoding video for {self.model_uid}" + ) return frames def _load_video(_url): @@ -158,19 +163,13 @@ def _load_video(_url): return text, images, frames return content, [], [] - def chat( - self, - prompt: Union[str, List[Dict]], - system_prompt: Optional[str] = None, - chat_history: Optional[List[ChatCompletionMessage]] = None, - generate_config: Optional[PytorchGenerateConfig] = None, - ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: - stream = generate_config.get("stream", False) if generate_config else False - videoExisted = False + def _convert_to_specific_style(self, messages: List[Dict]) -> Tuple: + video_existed = False + prompt, _, chat_history = parse_messages(messages) content, images_chat, video_frames = self._message_content_to_chat(prompt) if len(video_frames) > 0: - videoExisted = True + video_existed = True images_chat = video_frames msgs = [] @@ -184,7 +183,7 @@ def chat( if images_tmp != []: images_history = images_tmp if len(video_frames_h) > 0: - videoExisted = True + video_existed = True images_history = video_frames_h if len(query_to_response) == 0 and role == "user": query_to_response.append( @@ -198,10 +197,20 @@ def chat( msgs.extend(query_to_response) query_to_response = [] msgs.append({"role": "user", "content": images_chat + [content]}) + return msgs, video_existed + + @cache_clean + def chat( + self, + messages: List[Dict], + generate_config: Optional[PytorchGenerateConfig] = None, + ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: + stream = generate_config.get("stream", False) if generate_config else False + msgs, video_existed = self._convert_to_specific_style(messages) # Set decode params for video params = {} - if videoExisted: + if video_existed: params = {"use_image_id": False, "max_slice_nums": 1} chat = self._model.chat( @@ -216,57 +225,140 @@ def chat( it = self.chat_stream(chat) return self._to_chat_completion_chunks(it) else: - c = Completion( - id=str(uuid.uuid1()), - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[ - CompletionChoice( - index=0, text=chat, finish_reason="stop", logprobs=None - ) - ], - usage=CompletionUsage( - prompt_tokens=-1, completion_tokens=-1, total_tokens=-1 - ), - ) - return self._to_chat_completion(c) + return generate_chat_completion(self.model_uid, chat) def chat_stream(self, chat) -> Iterator[CompletionChunk]: completion_id = str(uuid.uuid1()) for new_text in chat: - completion_choice = CompletionChoice( - text=new_text, index=0, logprobs=None, finish_reason=None - ) - chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[completion_choice], - ) - completion_usage = CompletionUsage( + yield generate_completion_chunk( + chunk_text=new_text, + finish_reason=None, + chunk_id=completion_id, + model_uid=self.model_uid, prompt_tokens=-1, completion_tokens=-1, total_tokens=-1, ) - chunk["usage"] = completion_usage - yield chunk - - completion_choice = CompletionChoice( - text="", index=0, logprobs=None, finish_reason="stop" - ) - chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[completion_choice], - ) - completion_usage = CompletionUsage( + yield generate_completion_chunk( + chunk_text=None, + finish_reason="stop", + chunk_id=completion_id, + model_uid=self.model_uid, prompt_tokens=-1, completion_tokens=-1, total_tokens=-1, + has_choice=True, + has_content=False, + ) + + def prepare_sanitize_generate_config(self, req: InferenceRequest): + """ + Refer to https://huggingface.co/openbmb/MiniCPM-V-2_6/blob/main/modeling_minicpmv.py + """ + raw_config = req.inference_kwargs.get("raw_params", {}) + temperature = raw_config.get("temperature", None) + if temperature is None: + raw_config["temperature"] = 0.7 + top_p = raw_config.get("top_p", None) + if top_p is None: + raw_config["top_p"] = 0.8 + top_k = raw_config.get("top_k", None) + if top_k is None: + raw_config["top_k"] = 100 + repetition_penalty = raw_config.get("repetition_penalty", None) + if repetition_penalty is None: + raw_config["repetition_penalty"] = 1.05 + return raw_config + + def _handle_input_ids_and_images(self, msgs: List[Dict]) -> Dict: + """ + Copied from https://huggingface.co/openbmb/MiniCPM-V-2_6/blob/main/modeling_minicpmv.py#L315 + """ + from copy import deepcopy + + copy_msgs = deepcopy(msgs) + + images = [] + for i, msg in enumerate(copy_msgs): + role = msg["role"] + content = msg["content"] + assert role in ["user", "assistant"] + if i == 0: + assert role == "user", "The role of first msg should be user" + if isinstance(content, str): + content = [content] + cur_msgs = [] + for c in content: + if isinstance(c, Image.Image): + images.append(c) + cur_msgs.append("(./)") + elif isinstance(c, str): + cur_msgs.append(c) + msg["content"] = "\n".join(cur_msgs) + + return { + "prompt": self._processor.tokenizer.apply_chat_template( + copy_msgs, tokenize=False, add_generation_prompt=True + ), + "input_image": images, + } + + def _get_full_prompt(self, messages: List[Dict], tools): + msgs, video_existed = self._convert_to_specific_style(messages) + if video_existed: + raise RuntimeError( + f"Continuous batching does not support video inputs for this model: {self.model_uid}" + ) + return self._handle_input_ids_and_images(msgs) + + def build_prefill_kwargs(self, prompts: List, req_list: List[InferenceRequest]): + prompts_lists = [x["prompt"] for x in prompts] + input_images_lists = [x["input_image"] for x in prompts] + inputs = self._processor( + prompts_lists, + input_images_lists, + max_slice_nums=None, + use_image_id=None, + return_tensors="pt", + max_length=8192, + ).to(self._model.device) + inputs.pop("image_sizes") + + masked_input_ids = inputs["input_ids"] * inputs["attention_mask"] + for i in range(masked_input_ids.shape[0]): + non_zero_values = masked_input_ids[i][masked_input_ids[i] != 0].tolist() + req_list[i].prompt_tokens = non_zero_values + req_list[i].extra_kwargs["attention_mask_seq_len"] = len(non_zero_values) + req_list[i].padding_len = masked_input_ids.shape[1] - len(non_zero_values) + + model_inputs = { + "input_ids": inputs["input_ids"], + "image_bound": inputs["image_bound"], + "pixel_values": inputs["pixel_values"], + "tgt_sizes": inputs["tgt_sizes"], + } + model_inputs["inputs_embeds"], _ = self._model.get_vllm_embedding(model_inputs) + + return { + "inputs_embeds": model_inputs["inputs_embeds"], + "attention_mask": inputs["attention_mask"], + } + + def build_decode_position_ids( + self, batch_size: int, seq_length: int, reqs: List[InferenceRequest] + ): + return None + + def batch_inference(self, req_list: List[InferenceRequest]): + """ + This method is rewritten + because the specific inference process is performed by `self._model.llm`, + not `self._model` itself + """ + from .utils import batch_inference_one_step + + self.prepare_batch_inference(req_list) + batch_inference_one_step( + self, req_list, self.model_uid, self._model.llm, self._tokenizer ) - chunk["usage"] = completion_usage - yield chunk + self.handle_batch_inference_results(req_list) diff --git a/xinference/model/llm/transformers/omnilmm.py b/xinference/model/llm/transformers/omnilmm.py index 583f3cc56e..137ef5add1 100644 --- a/xinference/model/llm/transformers/omnilmm.py +++ b/xinference/model/llm/transformers/omnilmm.py @@ -16,21 +16,15 @@ import logging import operator import tempfile -import time -import uuid from typing import Dict, Iterator, List, Optional, Tuple, Union from ....thirdparty.omnilmm.chat import OmniLMMChat, img2base64 -from ....types import ( - ChatCompletion, - ChatCompletionChoice, - ChatCompletionChunk, - ChatCompletionMessage, - CompletionUsage, -) +from ....types import ChatCompletion, ChatCompletionChunk from ...utils import select_device from ..llm_family import LLMFamilyV1, LLMSpecV1 +from ..utils import generate_chat_completion, parse_messages from .core import PytorchChatModel, PytorchGenerateConfig +from .utils import cache_clean logger = logging.getLogger(__name__) @@ -94,17 +88,17 @@ def _ensure_url(_url): return images, other_content return [], [{"type": "text", "text": content}] + @cache_clean def chat( self, - prompt: Union[str, List[Dict]], - system_prompt: Optional[str] = None, - chat_history: Optional[List[ChatCompletionMessage]] = None, + messages: List[Dict], generate_config: Optional[PytorchGenerateConfig] = None, ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: if generate_config and generate_config.get("stream"): raise Exception( f"Chat with model {self.model_family.model_name} does not support stream." ) + prompt, _, chat_history = parse_messages(messages) image_first, prompt = self._message_content_to_OmniLMM(prompt) msgs = [] @@ -135,19 +129,4 @@ def chat( input = {"image": im_64, "question": json.dumps(msgs, ensure_ascii=True)} answer = self._model.chat(input=input) - return ChatCompletion( - id="chat" + str(uuid.uuid1()), - object="chat.completion", - created=int(time.time()), - model=self.model_uid, - choices=[ - ChatCompletionChoice( - index=0, - message={"role": "assistant", "content": answer}, - finish_reason="stop", - ) - ], - usage=CompletionUsage( - prompt_tokens=-1, completion_tokens=-1, total_tokens=-1 - ), - ) + return generate_chat_completion(self.model_uid, answer) diff --git a/xinference/model/llm/transformers/opt.py b/xinference/model/llm/transformers/opt.py new file mode 100644 index 0000000000..6050f2c749 --- /dev/null +++ b/xinference/model/llm/transformers/opt.py @@ -0,0 +1,68 @@ +# Copyright 2022-2024 XProbe Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from builtins import classmethod +from typing import List, Optional + +from ....core.scheduler import InferenceRequest +from ....types import LoRA +from ..llm_family import LLMFamilyV1, LLMSpecV1 +from .core import PytorchModel, PytorchModelConfig + + +class OptPytorchModel(PytorchModel): + def __init__( + self, + model_uid: str, + model_family: "LLMFamilyV1", + model_spec: "LLMSpecV1", + quantization: str, + model_path: str, + pytorch_model_config: Optional[PytorchModelConfig] = None, + peft_model: Optional[List[LoRA]] = None, + ): + super().__init__( + model_uid, + model_family, + model_spec, + quantization, + model_path, + pytorch_model_config=pytorch_model_config, + peft_model=peft_model, + ) + + @classmethod + def match( + cls, llm_family: "LLMFamilyV1", llm_spec: "LLMSpecV1", quantization: str + ) -> bool: + if llm_spec.model_format != "pytorch": + return False + model_family = llm_family.model_family or llm_family.model_name + if model_family != "opt": + return False + return True + + def build_prefill_position_ids( + self, batch_size: int, seq_length: int, reqs: List[InferenceRequest] + ): + """ + Mainly for UT. + Transformers code in `main` branch supports `position_ids` parameter (https://github.com/huggingface/transformers/blob/main/src/transformers/models/opt/modeling_opt.py#L1076), + while in release branch, it doesn't (https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/models/opt/modeling_opt.py#L886). + """ + return None + + def build_decode_position_ids( + self, batch_size: int, seq_length: int, reqs: List[InferenceRequest] + ): + return None diff --git a/xinference/model/llm/transformers/qwen2_audio.py b/xinference/model/llm/transformers/qwen2_audio.py new file mode 100644 index 0000000000..e5ea0da981 --- /dev/null +++ b/xinference/model/llm/transformers/qwen2_audio.py @@ -0,0 +1,175 @@ +# Copyright 2022-2023 XProbe Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import logging +import uuid +from io import BytesIO +from typing import Iterator, List, Optional, Union +from urllib.request import urlopen + +import numpy as np + +from ....model.utils import select_device +from ....types import ( + ChatCompletion, + ChatCompletionChunk, + ChatCompletionMessage, + CompletionChunk, +) +from ..llm_family import LLMFamilyV1, LLMSpecV1 +from ..utils import generate_chat_completion, generate_completion_chunk +from .core import PytorchChatModel, PytorchGenerateConfig +from .utils import cache_clean + +logger = logging.getLogger(__name__) + + +class Qwen2AudioChatModel(PytorchChatModel): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._processor = None + self._model = None + self._device = None + + @classmethod + def match( + cls, model_family: "LLMFamilyV1", model_spec: "LLMSpecV1", quantization: str + ) -> bool: + llm_family = model_family.model_family or model_family.model_name + if "qwen2-audio".lower() in llm_family.lower(): + return True + return False + + def load(self): + from transformers import AutoProcessor, Qwen2AudioForConditionalGeneration + + device = self._pytorch_model_config.get("device", "auto") + device = select_device(device) + self._device = device + # for multiple GPU, set back to auto to make multiple devices work + device = "auto" if device == "cuda" else device + + self._processor = AutoProcessor.from_pretrained( + self.model_path, + device_map=device, + # trust_remote_code=True, + code_revision=self.model_spec.model_revision, + ) + self._model = Qwen2AudioForConditionalGeneration.from_pretrained( + self.model_path, + device_map=device, + # trust_remote_code=True, + revision=self.model_spec.model_revision, + ) + + def _transform_messages( + self, + messages: List[ChatCompletionMessage], + ): + import librosa + + text = self._processor.apply_chat_template( + messages, add_generation_prompt=True, tokenize=False + ) + audios: List[np.ndarray] = [] + for msg in messages: + content = msg["content"] + if isinstance(content, List): + for item in content: # type: ignore + if item.get("type") == "audio" and "audio_url" in item: + audio = librosa.load( + BytesIO(urlopen(item["audio_url"]).read()), + sr=self._processor.feature_extractor.sampling_rate, + )[0] + audios.append(audio) + + return text, audios + + @cache_clean + def chat( + self, + messages: List[ChatCompletionMessage], + generate_config: Optional[PytorchGenerateConfig] = None, + ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: + text, audios = self._transform_messages(messages) + inputs = self._processor( + text=text, audios=audios, return_tensors="pt", padding=True + ) + inputs.input_ids = inputs.input_ids.to(self._device) + generate_config = generate_config if generate_config else {} + stream = generate_config.get("stream", False) if generate_config else False + + if stream: + it = self._generate_stream(inputs, generate_config) + return self._to_chat_completion_chunks(it) + else: + c = self._generate(inputs, generate_config) + return c + + def _generate(self, inputs, config: PytorchGenerateConfig = {}) -> ChatCompletion: + generate_ids = self._model.generate( + **inputs, + max_length=config.get("max_tokens", 512), + ) + generate_ids = generate_ids[:, inputs.input_ids.size(1) :] + response = self._processor.batch_decode( + generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False + )[0] + return generate_chat_completion(self.model_uid, response) + + def _generate_stream( + self, inputs, config: PytorchGenerateConfig = {} + ) -> Iterator[CompletionChunk]: + from threading import Thread + + from transformers import TextIteratorStreamer + + tokenizer = self._processor.tokenizer + streamer = TextIteratorStreamer( + tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True + ) + + gen_kwargs = { + "max_new_tokens": config.get("max_tokens", 512), + "streamer": streamer, + **inputs, + } + + thread = Thread(target=self._model.generate, kwargs=gen_kwargs) + thread.start() + + completion_id = str(uuid.uuid1()) + for new_text in streamer: + yield generate_completion_chunk( + chunk_text=new_text, + finish_reason=None, + chunk_id=completion_id, + model_uid=self.model_uid, + prompt_tokens=-1, + completion_tokens=-1, + total_tokens=-1, + has_choice=True, + has_content=True, + ) + + yield generate_completion_chunk( + chunk_text=None, + finish_reason="stop", + chunk_id=completion_id, + model_uid=self.model_uid, + prompt_tokens=-1, + completion_tokens=-1, + total_tokens=-1, + has_choice=True, + has_content=False, + ) diff --git a/xinference/model/llm/transformers/qwen2_vl.py b/xinference/model/llm/transformers/qwen2_vl.py new file mode 100644 index 0000000000..900f261113 --- /dev/null +++ b/xinference/model/llm/transformers/qwen2_vl.py @@ -0,0 +1,208 @@ +# Copyright 2022-2023 XProbe Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import importlib.util +import logging +import sys +import uuid +from typing import Iterator, List, Optional, Union + +from ....model.utils import select_device +from ....types import ( + ChatCompletion, + ChatCompletionChunk, + ChatCompletionMessage, + CompletionChunk, +) +from ..llm_family import LLMFamilyV1, LLMSpecV1 +from ..utils import generate_chat_completion, generate_completion_chunk +from .core import PytorchChatModel, PytorchGenerateConfig +from .utils import cache_clean + +logger = logging.getLogger(__name__) + + +class Qwen2VLChatModel(PytorchChatModel): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._tokenizer = None + self._model = None + self._device = None + self._processor = None + + @classmethod + def match( + cls, model_family: "LLMFamilyV1", model_spec: "LLMSpecV1", quantization: str + ) -> bool: + llm_family = model_family.model_family or model_family.model_name + if "qwen2-vl-instruct".lower() in llm_family.lower(): + return True + return False + + def load(self): + from transformers import AutoProcessor, Qwen2VLForConditionalGeneration + + device = self._pytorch_model_config.get("device", "auto") + device = select_device(device) + self._device = device + # for multiple GPU, set back to auto to make multiple devices work + device = "auto" if device == "cuda" else device + + self._processor = AutoProcessor.from_pretrained( + self.model_path, trust_remote_code=True + ) + self._tokenizer = self._processor.tokenizer + flash_attn_installed = importlib.util.find_spec("flash_attn") is not None + if flash_attn_installed: + self._model = Qwen2VLForConditionalGeneration.from_pretrained( + self.model_path, + torch_dtype="bfloat16", + device_map=device, + attn_implementation="flash_attention_2", + trust_remote_code=True, + ).eval() + else: + self._model = Qwen2VLForConditionalGeneration.from_pretrained( + self.model_path, device_map=device, trust_remote_code=True + ).eval() + + @cache_clean + def chat( + self, + messages: List[ChatCompletionMessage], # type: ignore + generate_config: Optional[PytorchGenerateConfig] = None, + ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: + messages = self._transform_messages(messages) + + generate_config = generate_config if generate_config else {} + + stream = generate_config.get("stream", False) if generate_config else False + + if stream: + it = self._generate_stream(messages, generate_config) + return self._to_chat_completion_chunks(it) + else: + c = self._generate(messages, generate_config) + return c + + def _generate( + self, messages: List, config: PytorchGenerateConfig = {} + ) -> ChatCompletion: + from qwen_vl_utils import process_vision_info + + # Preparation for inference + text = self._processor.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + image_inputs, video_inputs = process_vision_info(messages) + inputs = self._processor( + text=[text], + images=image_inputs, + videos=video_inputs, + padding=True, + return_tensors="pt", + ) + inputs = inputs.to("cuda") + + # Inference: Generation of the output + generated_ids = self._model.generate( + **inputs, + max_new_tokens=config.get("max_tokens", 512), + temperature=config.get("temperature", 1), + ) + generated_ids_trimmed = [ + out_ids[len(in_ids) :] + for in_ids, out_ids in zip(inputs.input_ids, generated_ids) + ] + output_text = self._processor.batch_decode( + generated_ids_trimmed, + skip_special_tokens=True, + clean_up_tokenization_spaces=False, + )[0] + return generate_chat_completion(self.model_uid, output_text) + + def _generate_stream( + self, messages: List, config: PytorchGenerateConfig = {} + ) -> Iterator[CompletionChunk]: + from threading import Thread + + from qwen_vl_utils import process_vision_info + from transformers import TextIteratorStreamer + + text = self._processor.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + image_inputs, video_inputs = process_vision_info(messages) + inputs = self._processor( + text=[text], + images=image_inputs, + videos=video_inputs, + padding=True, + return_tensors="pt", + ) + inputs = inputs.to(self._model.device) + + tokenizer = self._tokenizer + streamer = TextIteratorStreamer( + tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True + ) + + gen_kwargs = { + "max_new_tokens": config.get("max_tokens", 512), + "temperature": config.get("temperature", 1), + "streamer": streamer, + **inputs, + } + error = None + + def model_generate(): + try: + return self._model.generate(**gen_kwargs) + except Exception: + nonlocal error + error = sys.exc_info() + streamer.end() + raise + + thread = Thread(target=model_generate) + thread.start() + + completion_id = str(uuid.uuid1()) + for new_text in streamer: + yield generate_completion_chunk( + chunk_text=new_text, + finish_reason=None, + chunk_id=completion_id, + model_uid=self.model_uid, + prompt_tokens=-1, + completion_tokens=-1, + total_tokens=-1, + has_choice=True, + has_content=True, + ) + + if error: + _, err, tb = error # type: ignore + raise err.with_traceback(tb) + + yield generate_completion_chunk( + chunk_text=None, + finish_reason="stop", + chunk_id=completion_id, + model_uid=self.model_uid, + prompt_tokens=-1, + completion_tokens=-1, + total_tokens=-1, + has_choice=True, + has_content=False, + ) diff --git a/xinference/model/llm/transformers/qwen_vl.py b/xinference/model/llm/transformers/qwen_vl.py index 8a2be562e3..d803af75d7 100644 --- a/xinference/model/llm/transformers/qwen_vl.py +++ b/xinference/model/llm/transformers/qwen_vl.py @@ -15,7 +15,6 @@ import logging import operator import tempfile -import time import typing import uuid from typing import Dict, Iterator, List, Optional, Tuple, Union @@ -25,18 +24,11 @@ from ....core.scheduler import InferenceRequest from ....model.utils import select_device -from ....types import ( - ChatCompletion, - ChatCompletionChunk, - ChatCompletionMessage, - Completion, - CompletionChoice, - CompletionChunk, - CompletionUsage, -) +from ....types import ChatCompletion, ChatCompletionChunk, CompletionChunk from ..llm_family import LLMFamilyV1, LLMSpecV1 +from ..utils import generate_chat_completion, generate_completion_chunk from .core import PytorchChatModel, PytorchGenerateConfig -from .utils import pad_prefill_tokens +from .utils import cache_clean, pad_prefill_tokens logger = logging.getLogger(__name__) @@ -53,7 +45,7 @@ def match( cls, model_family: "LLMFamilyV1", model_spec: "LLMSpecV1", quantization: str ) -> bool: llm_family = model_family.model_family or model_family.model_name - if "qwen" in llm_family and "vision" in model_family.model_ability: + if "qwen-" in llm_family and "vision" in model_family.model_ability: return True return False @@ -129,18 +121,12 @@ def _ensure_url(_url): return self._tokenizer.from_list_format(content) return content - def _get_prompt_and_chat_history( - self, - prompt: Union[str, List[Dict]], - chat_history: Optional[List[ChatCompletionMessage]] = None, - ): - prompt = self._message_content_to_qwen(prompt) - # Convert openai history to qwen vl history + def _get_prompt_and_chat_history(self, messages: List[Dict]): qwen_history = [] query_to_response: List = [] - for h in chat_history or []: - role = h["role"] - content = self._message_content_to_qwen(h["content"]) + for message in messages[:-1]: + role = message["role"] + content = self._message_content_to_qwen(message["content"]) if len(query_to_response) == 0 and role == "user": query_to_response.append(content) if len(query_to_response) == 1 and role == "assistant": @@ -148,18 +134,16 @@ def _get_prompt_and_chat_history( if len(query_to_response) == 2: qwen_history.append(query_to_response) query_to_response = [] + prompt = self._message_content_to_qwen(messages[-1]["content"]) return prompt, qwen_history + @cache_clean def chat( self, - prompt: Union[str, List[Dict]], - system_prompt: Optional[str] = None, - chat_history: Optional[List[ChatCompletionMessage]] = None, + messages: List[Dict], generate_config: Optional[PytorchGenerateConfig] = None, ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: - prompt, qwen_history = self._get_prompt_and_chat_history( - prompt, chat_history=chat_history - ) + prompt, qwen_history = self._get_prompt_and_chat_history(messages) stream = generate_config.get("stream", False) if generate_config else False stream_options = ( @@ -174,33 +158,17 @@ def chat( it = self._generate_stream(prompt, qwen_history, include_usage) # type: ignore return self._to_chat_completion_chunks(it) else: - c = self._generate(prompt, qwen_history) # type: ignore - return self._to_chat_completion(c) + return self._generate(prompt, qwen_history) # type: ignore - def _generate(self, prompt: str, qwen_history: List) -> Completion: + def _generate(self, prompt: str, qwen_history: List) -> ChatCompletion: response, history = self._model.chat( self._tokenizer, query=prompt, history=qwen_history ) - c = Completion( - id=str(uuid.uuid1()), - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[ - CompletionChoice( - index=0, text=response, finish_reason="stop", logprobs=None - ) - ], - usage=CompletionUsage( - prompt_tokens=-1, completion_tokens=-1, total_tokens=-1 - ), - ) - return c + return generate_chat_completion(self.model_uid, response) def _generate_stream( self, prompt: str, qwen_history: List, include_usage ) -> Iterator[CompletionChunk]: - # response, history = model.chat(tokenizer, message, history=history) response_generator = self._model.chat_stream( self._tokenizer, query=prompt, history=qwen_history ) @@ -212,57 +180,40 @@ def _generate_stream( for response in response_generator: inc_content = response[len(full_response) :] full_response = response - completion_choice = CompletionChoice( - text=inc_content, index=0, logprobs=None, finish_reason=None - ) - completion_chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[completion_choice], - ) completion_tokens = completion_tokens + 1 total_tokens = prompt_tokens + completion_tokens - completion_usage = CompletionUsage( + yield generate_completion_chunk( + chunk_text=inc_content, + finish_reason=None, + chunk_id=completion_id, + model_uid=self.model_uid, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens, ) - completion_chunk["usage"] = completion_usage - yield completion_chunk - - completion_choice = CompletionChoice( - text="", index=0, logprobs=None, finish_reason="stop" - ) - completion_chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[completion_choice], - ) - completion_usage = CompletionUsage( + yield generate_completion_chunk( + chunk_text=None, + finish_reason="stop", + chunk_id=completion_id, + model_uid=self.model_uid, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens, + has_choice=True, + has_content=False, ) - completion_chunk["usage"] = completion_usage - yield completion_chunk if include_usage: - chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[], - ) - chunk["usage"] = CompletionUsage( + yield generate_completion_chunk( + chunk_text=None, + finish_reason=None, + chunk_id=completion_id, + model_uid=self.model_uid, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens, + has_choice=False, + has_content=False, ) - yield chunk @staticmethod def get_batch_size_and_seq_len_indexes_from_kv() -> Tuple[int, int]: @@ -359,10 +310,8 @@ def _tokenize_str(role, content): return raw_text, context_tokens - def _get_full_prompt(self, prompt, system_prompt, chat_history, tools): - prompt, qwen_history = self._get_prompt_and_chat_history( - prompt, chat_history=chat_history - ) + def _get_full_prompt(self, messages: List[Dict], tools): + prompt, qwen_history = self._get_prompt_and_chat_history(messages) _, context_tokens = self.make_context(self._tokenizer, prompt, qwen_history) return context_tokens diff --git a/xinference/model/llm/transformers/tests/test_opt.py b/xinference/model/llm/transformers/tests/test_opt.py index d7dd30a512..5bc239f2a8 100644 --- a/xinference/model/llm/transformers/tests/test_opt.py +++ b/xinference/model/llm/transformers/tests/test_opt.py @@ -11,38 +11,16 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -import asyncio import json import os -import threading -import time from concurrent.futures import ThreadPoolExecutor from typing import Union import pytest -import xoscar from .....client import Client from .....client.restful.restful_client import RESTfulGenerateModelHandle -from .....core.model import ModelActor from ... import BUILTIN_LLM_FAMILIES -from ..core import PytorchModel - - -class MockNonPytorchModel(object): - def __init__(self): - self._test_dict = {} - - def generate(self, prompt: str, generate_config=None): - tid = threading.get_ident() - self._test_dict[tid] = True - time.sleep(1) - self._test_dict.pop(tid, None) - return len(self._test_dict) - - -class MockPytorchModel(MockNonPytorchModel, PytorchModel): - pass @pytest.mark.asyncio @@ -113,41 +91,3 @@ def _check(): expected_revision = spec.model_revision assert expected_revision == actual_revision - - -@pytest.mark.asyncio -async def test_concurrent_pytorch_model(setup): - pool = await xoscar.create_actor_pool("127.0.0.1", n_process=1) - async with pool: - mock_torch_model = MockPytorchModel() - model_torch_actor = await xoscar.create_actor( - ModelActor, - pool.external_address, - mock_torch_model, - address=next(iter(pool.sub_processes.keys())), - ) - coros = [] - for _ in range(3): - co = model_torch_actor.generate( - "Once upon a time, there was a very old computer" - ) - coros.append(co) - r = await asyncio.gather(*coros) - assert any(r) - - mock_non_torch_model = MockNonPytorchModel() - model_non_torch_actor = await xoscar.create_actor( - ModelActor, - pool.external_address, - mock_non_torch_model, - address=next(iter(pool.sub_processes.keys())), - ) - coros = [] - for _ in range(3): - co = model_non_torch_actor.generate( - "Once upon a time, there was a very old computer" - ) - coros.append(co) - r = await asyncio.gather(*coros) - r = [json.loads(i) for i in r] - assert not any(r) diff --git a/xinference/model/llm/transformers/tests/test_tensorizer.py b/xinference/model/llm/transformers/tests/test_tensorizer.py index a4e228259c..87fd38a7a7 100644 --- a/xinference/model/llm/transformers/tests/test_tensorizer.py +++ b/xinference/model/llm/transformers/tests/test_tensorizer.py @@ -37,7 +37,9 @@ def setup_and_teardown(self): model_lang=["en", "zh"], model_ability=["chat", "tools"], model_specs=[spec], - prompt_style=None, + chat_template=None, + stop_token_ids=None, + stop=None, ) if not os.path.exists(self.model_path): diff --git a/xinference/model/llm/transformers/utils.py b/xinference/model/llm/transformers/utils.py index 5ada9a512c..7ef1c36ecb 100644 --- a/xinference/model/llm/transformers/utils.py +++ b/xinference/model/llm/transformers/utils.py @@ -12,12 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. -import gc +import asyncio +import functools import logging import os import time -import uuid -from typing import TYPE_CHECKING, Dict, Iterable, Iterator, List, Optional, Tuple +from typing import TYPE_CHECKING, Dict, List, Optional, Tuple import torch from transformers.cache_utils import DynamicCache @@ -45,20 +45,6 @@ logger = logging.getLogger(__name__) -def is_sentence_complete(output: str): - """Check whether the output is a complete sentence.""" - end_symbols = (".", "?", "!", "...", "。", "？", "！", "…", '"', "'", "”") - return output.endswith(end_symbols) - - -def is_partial_stop(output: str, stop_str: str): - """Check whether the output contains a partial stop str.""" - for i in range(0, min(len(output), len(stop_str))): - if stop_str.startswith(output[-i:]): - return True - return False - - def get_context_length(config) -> int: """Get the context length of a model from a huggingface model config.""" if ( @@ -98,272 +84,6 @@ def prepare_logits_processor( return processor_list -@torch.inference_mode() -def generate_stream( - model_uid, - model, - tokenizer, - prompt, - device, - generate_config, - judge_sent_end=False, -) -> Iterator[Tuple[CompletionChunk, CompletionUsage]]: - context_len = get_context_length(model.config) - stream_interval = generate_config.get("stream_interval", 2) - stream = generate_config.get("stream", False) - stream_options = generate_config.pop("stream_options", None) - include_usage = ( - stream_options["include_usage"] if isinstance(stream_options, dict) else False - ) - - len_prompt = len(prompt) - - temperature = float(generate_config.get("temperature", 1.0)) - repetition_penalty = float(generate_config.get("repetition_penalty", 1.0)) - top_p = float(generate_config.get("top_p", 1.0)) - top_k = int(generate_config.get("top_k", -1)) # -1 means disable - max_new_tokens = int(generate_config.get("max_tokens", max_tokens_field.default)) - echo = bool(generate_config.get("echo", False)) - stop_str = generate_config.get("stop", None) - stop_token_ids = generate_config.get("stop_token_ids", None) or [] - stop_token_ids.append(tokenizer.eos_token_id) - chunk_id = str(uuid.uuid4()) - - logits_processor = prepare_logits_processor( - temperature, repetition_penalty, top_p, top_k - ) - - if ".modeling_qwen." in str(type(model)).lower(): - # TODO: hacky - input_ids = tokenizer(prompt, allowed_special="all").input_ids - else: - input_ids = tokenizer(prompt).input_ids - output_ids = list(input_ids) - - if model.config.is_encoder_decoder: - max_src_len = context_len - else: - max_src_len = context_len - max_new_tokens - 8 - if max_src_len < 0: - raise ValueError("Max tokens exceeds model's max length") - - input_ids = input_ids[-max_src_len:] - input_echo_len = len(input_ids) - - if model.config.is_encoder_decoder: - encoder_output = model.encoder( - input_ids=torch.as_tensor([input_ids], device=device) - )[0] - start_ids = torch.as_tensor( - [[model.generation_config.decoder_start_token_id]], - dtype=torch.int64, - device=device, - ) - - start = time.time() - past_key_values = out = None - sent_interrupt = False - token = None - last_output_length = 0 - for i in range(max_new_tokens): - if i == 0: - if model.config.is_encoder_decoder: - out = model.decoder( - input_ids=start_ids, - encoder_hidden_states=encoder_output, - use_cache=True, - ) - logits = model.lm_head(out[0]) - else: - out = model(torch.as_tensor([input_ids], device=device), use_cache=True) - logits = out.logits - past_key_values = out.past_key_values - else: - if model.config.is_encoder_decoder: - out = model.decoder( - input_ids=torch.as_tensor( - [[token] if not sent_interrupt else output_ids], device=device - ), - encoder_hidden_states=encoder_output, - use_cache=True, - past_key_values=past_key_values if not sent_interrupt else None, - ) - sent_interrupt = False - - logits = model.lm_head(out[0]) - else: - out = model( - input_ids=torch.as_tensor( - [[token] if not sent_interrupt else output_ids], device=device - ), - use_cache=True, - past_key_values=past_key_values if not sent_interrupt else None, - ) - sent_interrupt = False - logits = out.logits - past_key_values = out.past_key_values - - if logits_processor: - if repetition_penalty > 1.0: - tmp_output_ids = torch.as_tensor([output_ids], device=logits.device) - else: - tmp_output_ids = None - last_token_logits = logits_processor(tmp_output_ids, logits[:, -1, :])[0] - else: - last_token_logits = logits[0, -1, :] - - if device == "mps": - # Switch to CPU by avoiding some bugs in mps backend. - last_token_logits = last_token_logits.float().to("cpu") - - if temperature < 1e-5 or top_p < 1e-8: # greedy - _, indices = torch.topk(last_token_logits, 2) - tokens = [int(index) for index in indices.tolist()] - else: - probs = torch.softmax(last_token_logits, dim=-1) - indices = torch.multinomial(probs, num_samples=2) - tokens = [int(token) for token in indices.tolist()] - token = tokens[0] - output_ids.append(token) - - if token in stop_token_ids: - stopped = True - else: - stopped = False - - if i % stream_interval == 0 or i == max_new_tokens - 1 or stopped: - if echo: - tmp_output_ids = output_ids - rfind_start = len_prompt - else: - tmp_output_ids = output_ids[input_echo_len:] - rfind_start = 0 - - output = tokenizer.decode( - tmp_output_ids, - skip_special_tokens=True, - spaces_between_special_tokens=False, - clean_up_tokenization_spaces=True, - ) - - # TODO: For the issue of incomplete sentences interrupting output, apply a patch and others can also modify it to a more elegant way - if judge_sent_end and stopped and not is_sentence_complete(output): - if len(tokens) > 1: - token = tokens[1] - output_ids[-1] = token - else: - output_ids.pop() - stopped = False - sent_interrupt = True - - partially_stopped = False - if stop_str: - if isinstance(stop_str, str): - pos = output.rfind(stop_str, rfind_start) - if pos != -1: - output = output[:pos] - stopped = True - else: - partially_stopped = is_partial_stop(output, stop_str) - elif isinstance(stop_str, Iterable): - for each_stop in stop_str: - pos = output.rfind(each_stop, rfind_start) - if pos != -1: - output = output[:pos] - stopped = True - break - else: - partially_stopped = is_partial_stop(output, each_stop) - if partially_stopped: - break - else: - raise ValueError("Invalid stop field type.") - - if stream: - output = output.strip("�") - tmp_output_length = len(output) - output = output[last_output_length:] - last_output_length = tmp_output_length - - # prevent yielding partial stop sequence - if not partially_stopped: - completion_choice = CompletionChoice( - text=output, index=0, logprobs=None, finish_reason=None - ) - completion_chunk = CompletionChunk( - id=chunk_id, - object="text_completion", - created=int(time.time()), - model=model_uid, - choices=[completion_choice], - ) - completion_usage = CompletionUsage( - prompt_tokens=input_echo_len, - completion_tokens=i, - total_tokens=(input_echo_len + i), - ) - - yield completion_chunk, completion_usage - - if stopped: - break - - elapsed_time = time.time() - start - logger.info(f"Average generation speed: {i / elapsed_time:.2f} tokens/s.") - - # finish stream event, which contains finish reason - if stopped: - finish_reason = "stop" - elif i == max_new_tokens - 1: - finish_reason = "length" - else: - finish_reason = None - - if stream: - completion_choice = CompletionChoice( - text="", index=0, logprobs=None, finish_reason=finish_reason - ) - else: - completion_choice = CompletionChoice( - text=output, index=0, logprobs=None, finish_reason=finish_reason - ) - - completion_chunk = CompletionChunk( - id=chunk_id, - object="text_completion", - created=int(time.time()), - model=model_uid, - choices=[completion_choice], - ) - completion_usage = CompletionUsage( - prompt_tokens=input_echo_len, - completion_tokens=i, - total_tokens=(input_echo_len + i), - ) - - yield completion_chunk, completion_usage - - if include_usage: - completion_chunk = CompletionChunk( - id=chunk_id, - object="text_completion", - created=int(time.time()), - model=model_uid, - choices=[], - ) - completion_usage = CompletionUsage( - prompt_tokens=input_echo_len, - completion_tokens=i, - total_tokens=(input_echo_len + i), - ) - yield completion_chunk, completion_usage - - # clean - del past_key_values, out - gc.collect() - empty_cache() - - def _get_token_from_logits( req: InferenceRequest, i: int, logits, temperature, repetition_penalty, top_p, top_k ): @@ -430,39 +150,6 @@ def pad_prefill_tokens( return prompt_tokens -def _get_completion_chunk( - output: str, - chunk_id: str, - finish_reason: Optional[str], - model_uid: str, - r: InferenceRequest, - just_usage: bool, -): - completion_choice = ( - [ - CompletionChoice( - text=output, index=0, logprobs=None, finish_reason=finish_reason - ) - ] - if not just_usage - else [] - ) - completion_chunk = CompletionChunk( - id=chunk_id, - object="text_completion", - created=int(time.time()), - model=model_uid, - choices=completion_choice, - ) - completion_usage = CompletionUsage( - prompt_tokens=len(r.prompt_tokens), - completion_tokens=len(r.new_tokens), - total_tokens=len(r.prompt_tokens) + len(r.new_tokens), - ) - completion_chunk["usage"] = completion_usage - return completion_chunk - - def _get_completion( output: str, chunk_id: str, @@ -551,6 +238,8 @@ def _batch_inference_one_step_internal( bos_flag: str = "", eos_flag: str = "", ): + from ..utils import generate_completion_chunk + # need to judge stopped here, # since some requests state may change to stopped due to invalid parameters, e.g. max_src_len valid_req_list = [r for r in req_list if not r.stopped] @@ -709,13 +398,32 @@ def _batch_inference_one_step_internal( output = output.strip("�") output = output[r.last_output_length :] r.last_output_length += len(output) - - completion_chunk = _get_completion_chunk( - output, r.chunk_id, r.finish_reason, model_uid, r, False + r.outputs.append(output) + + completion_chunk = generate_completion_chunk( + chunk_text=output, + finish_reason=None, + chunk_id=r.chunk_id, + model_uid=model_uid, + prompt_tokens=len(r.prompt_tokens), + completion_tokens=len(r.new_tokens), + total_tokens=len(r.prompt_tokens) + len(r.new_tokens), ) r.completion.append(completion_chunk) if r.stopped: + # OpenAI compatible chunk + completion_chunk = generate_completion_chunk( + chunk_text="", + finish_reason=r.finish_reason, + chunk_id=r.chunk_id, + model_uid=model_uid, + prompt_tokens=len(r.prompt_tokens), + completion_tokens=len(r.new_tokens), + total_tokens=len(r.prompt_tokens) + len(r.new_tokens), + ) + r.completion.append(completion_chunk) r.completion.append(eos_flag) + r.outputs.append(eos_flag) # last round, handle stream result # append usage information when enable `include_usage` for OPENAI API compatibility @@ -723,8 +431,16 @@ def _batch_inference_one_step_internal( # these tokens are real generated and should be counted. if r.stopped and _i == decode_round - 1 and include_usage: r.completion.append( - _get_completion_chunk( - "", r.chunk_id, r.finish_reason, model_uid, r, True + generate_completion_chunk( + chunk_text=None, + finish_reason=None, + chunk_id=r.chunk_id, + model_uid=model_uid, + prompt_tokens=len(r.prompt_tokens), + completion_tokens=len(r.new_tokens), + total_tokens=len(r.prompt_tokens) + len(r.new_tokens), + has_choice=False, + has_content=False, ) ) else: @@ -782,3 +498,34 @@ def batch_inference_one_step( for r in req_list: r.stopped = True r.error_msg = str(e) + + +def cache_clean(fn): + @functools.wraps(fn) + async def _async_wrapper(self, *args, **kwargs): + import gc + + from ....device_utils import empty_cache + + result = await fn(self, *args, **kwargs) + + gc.collect() + empty_cache() + return result + + @functools.wraps(fn) + def _wrapper(self, *args, **kwargs): + import gc + + from ....device_utils import empty_cache + + result = fn(self, *args, **kwargs) + + gc.collect() + empty_cache() + return result + + if asyncio.iscoroutinefunction(fn): + return _async_wrapper + else: + return _wrapper diff --git a/xinference/model/llm/transformers/yi_vl.py b/xinference/model/llm/transformers/yi_vl.py index e4b3d1f6ce..69ce724402 100644 --- a/xinference/model/llm/transformers/yi_vl.py +++ b/xinference/model/llm/transformers/yi_vl.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. import logging -import time import uuid from concurrent.futures import ThreadPoolExecutor from threading import Thread @@ -21,18 +20,16 @@ import torch from ....model.utils import select_device -from ....types import ( - ChatCompletion, - ChatCompletionChunk, - ChatCompletionMessage, - Completion, - CompletionChoice, - CompletionChunk, - CompletionUsage, -) +from ....types import ChatCompletion, ChatCompletionChunk, CompletionChunk from ..llm_family import LLMFamilyV1, LLMSpecV1 -from ..utils import _decode_image +from ..utils import ( + _decode_image, + generate_chat_completion, + generate_completion_chunk, + parse_messages, +) from .core import PytorchChatModel, PytorchGenerateConfig +from .utils import cache_clean logger = logging.getLogger(__name__) @@ -103,17 +100,14 @@ def _message_content_to_yi(content) -> Union[str, tuple]: raise RuntimeError("Only one image per message is supported by Yi VL.") return content + @cache_clean def chat( self, - prompt: Union[str, List[Dict]], - system_prompt: Optional[str] = None, - chat_history: Optional[List[ChatCompletionMessage]] = None, + messages: List[Dict], generate_config: Optional[PytorchGenerateConfig] = None, ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: from transformers import TextIteratorStreamer - # TODO(codingl2k1): implement stream mode. - if not generate_config: generate_config = {} @@ -134,7 +128,8 @@ def chat( # Convert chat history to llava state state = conv_templates["mm_default"].copy() - for message in chat_history or []: + prompt, _, chat_history = parse_messages(messages) + for message in chat_history: content = self._message_content_to_yi(message["content"]) state.append_message(message["role"], content) state.append_message(state.roles[0], self._message_content_to_yi(prompt)) @@ -190,31 +185,15 @@ def chat( it = self._generate_stream(streamer, stop_str, input_ids, include_usage) return self._to_chat_completion_chunks(it) else: - c = self._generate(streamer, stop_str) - return self._to_chat_completion(c) + return self._generate(streamer, stop_str) - def _generate(self, streamer, stop_str) -> Completion: + def _generate(self, streamer, stop_str) -> ChatCompletion: generated_text = "" for new_text in streamer: generated_text += new_text if generated_text.endswith(stop_str): generated_text = generated_text[: -len(stop_str)] - - c = Completion( - id=str(uuid.uuid1()), - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[ - CompletionChoice( - index=0, text=generated_text, finish_reason="stop", logprobs=None - ) - ], - usage=CompletionUsage( - prompt_tokens=-1, completion_tokens=-1, total_tokens=-1 - ), - ) - return c + return generate_chat_completion(self.model_uid, generated_text) def _generate_stream( self, streamer, stop_str, input_ids, include_usage @@ -224,54 +203,37 @@ def _generate_stream( prompt_tokens = len(input_ids[0]) for i, new_text in enumerate(streamer): if not new_text.endswith(stop_str): - completion_choice = CompletionChoice( - text=new_text, index=0, logprobs=None, finish_reason=None - ) - chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[completion_choice], - ) completion_tokens = i total_tokens = prompt_tokens + completion_tokens - completion_usage = CompletionUsage( + yield generate_completion_chunk( + chunk_text=new_text, + finish_reason=None, + chunk_id=completion_id, + model_uid=self.model_uid, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens, ) - chunk["usage"] = completion_usage - yield chunk - - completion_choice = CompletionChoice( - text="", index=0, logprobs=None, finish_reason="stop" - ) - chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[completion_choice], - ) - completion_usage = CompletionUsage( + yield generate_completion_chunk( + chunk_text=None, + finish_reason="stop", + chunk_id=completion_id, + model_uid=self.model_uid, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens, + has_choice=True, + has_content=False, ) - chunk["usage"] = completion_usage - yield chunk if include_usage: - chunk = CompletionChunk( - id=completion_id, - object="text_completion", - created=int(time.time()), - model=self.model_uid, - choices=[], - ) - chunk["usage"] = CompletionUsage( + yield generate_completion_chunk( + chunk_text=None, + finish_reason=None, + chunk_id=completion_id, + model_uid=self.model_uid, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens, + has_choice=False, + has_content=False, ) - yield chunk diff --git a/xinference/model/llm/utils.py b/xinference/model/llm/utils.py index 7f203d0c21..55b2b02ae4 100644 --- a/xinference/model/llm/utils.py +++ b/xinference/model/llm/utils.py @@ -17,6 +17,7 @@ import logging import os import time +import typing import uuid from io import BytesIO from typing import AsyncGenerator, Dict, Iterator, List, Optional, Tuple, cast @@ -25,18 +26,19 @@ from PIL import Image from ...types import ( - SPECIAL_TOOL_PROMPT, ChatCompletion, + ChatCompletionChoice, ChatCompletionChunk, ChatCompletionMessage, Completion, + CompletionChoice, CompletionChunk, + CompletionUsage, ) from .llm_family import ( LlamaCppLLMSpecV1, LLMFamilyV1, LLMSpecV1, - PromptStyleV1, _get_cache_dir, get_cache_status, ) @@ -45,11 +47,11 @@ QWEN_TOOL_CALL_FAMILY = [ - "qwen-chat", "qwen1.5-chat", "qwen1.5-moe-chat", "qwen2-instruct", "qwen2-moe-instruct", + "qwen2.5-instruct", ] GLM4_TOOL_CALL_FAMILY = [ @@ -57,416 +59,94 @@ "glm4-chat-1m", ] +LLAMA3_TOOL_CALL_FAMILY = [ + "llama-3.1-instruct", +] + +QWEN_TOOL_CALL_SYMBOLS = ["", ""] + class ChatModelMixin: @staticmethod - def get_prompt( - prompt: str, - chat_history: List[ChatCompletionMessage], - prompt_style: PromptStyleV1, - tools: Optional[List[Dict]] = None, - ): + @functools.lru_cache + def _compile_jinja_template(chat_template): """ - Inspired by FastChat. Format chat history into a prompt according to the prompty style of - different models. + Copied from transformers source code. """ - assert prompt_style.roles is not None - if prompt != SPECIAL_TOOL_PROMPT: - chat_history.append( - ChatCompletionMessage(role=prompt_style.roles[0], content=prompt) - ) - chat_history.append( - ChatCompletionMessage(role=prompt_style.roles[1], content="") + try: + from jinja2.exceptions import TemplateError + from jinja2.sandbox import ImmutableSandboxedEnvironment + except ImportError: + raise ImportError("xinference requires jinja2 to be installed.") + + def raise_exception(message): + raise TemplateError(message) + + jinja_env = ImmutableSandboxedEnvironment(trim_blocks=True, lstrip_blocks=True) + jinja_env.globals["raise_exception"] = raise_exception + return jinja_env.from_string(chat_template) + + def _build_from_raw_template( + self, messages: List, chat_template: str, **kwargs + ) -> str: + compiled_template = self._compile_jinja_template(chat_template) + rendered = compiled_template.render( + messages=messages, add_generation_prompt=True, **kwargs ) - - def get_role(role_name: str): - if role_name == "user": - return prompt_style.roles[0] - elif role_name == "assistant": - return prompt_style.roles[1] - else: - return role_name - - if prompt_style.style_name == "ADD_COLON_SINGLE": - ret = prompt_style.system_prompt + prompt_style.intra_message_sep - for message in chat_history: - role = get_role(message["role"]) - content = message["content"] - if content: - ret += role + ": " + content + prompt_style.intra_message_sep - else: - ret += role + ":" - return ret - elif prompt_style.style_name == "NO_COLON_TWO": - seps = [prompt_style.intra_message_sep, prompt_style.inter_message_sep] - ret = prompt_style.system_prompt - for i, message in enumerate(chat_history): - role = get_role(message["role"]) - content = message["content"] - if content: - ret += role + content + seps[i % 2] - else: - ret += role - return ret - elif prompt_style.style_name == "LLAMA2": - seps = [prompt_style.intra_message_sep, prompt_style.inter_message_sep] - ret = "" - for i, message in enumerate(chat_history): - role = get_role(message["role"]) - content = message["content"] - if content: - if i == 0: - ret += prompt_style.system_prompt + content - else: - ret += role + " " + content + seps[i % 2] - else: - ret += role - return ret - elif prompt_style.style_name == "LLAMA3": - ret = ( - f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>" - f"{prompt_style.intra_message_sep}{prompt_style.system_prompt}{prompt_style.inter_message_sep}" - ) - for i, message in enumerate(chat_history): - role = get_role(message["role"]) - content = message["content"] - if content: - ret += ( - f"<|start_header_id|>{role}<|end_header_id|>" - f"{prompt_style.intra_message_sep}{content}{prompt_style.inter_message_sep}" - ) - else: - ret += f"<|start_header_id|>{role}<|end_header_id|>{prompt_style.intra_message_sep}" - return ret - elif prompt_style.style_name == "MIXTRAL_V01": - ret = "" - for i, message in enumerate(chat_history): - content = message["content"] - if i % 2 == 0: # user - ret += f" [INST] {content} [/INST]" - else: # assistant - ret += f"{content} " - return ret - elif prompt_style.style_name == "CHATGLM3": - prompts = ( - [f"<|system|>\n {prompt_style.system_prompt}"] - if prompt_style.system_prompt - else [] - ) - - for i, message in enumerate(chat_history): - role = get_role(message["role"]) - content = message.get("content") - tool_calls = message.get("tool_calls") - if tool_calls: - content = tool_calls[0]["function"] - if content: - if role == "tool": - role = "observation" - prompts.append(f"<|{role}|>\n {content}") - else: - prompts.append(f"<|{role}|>") - return "\n".join(prompts) - elif prompt_style.style_name == "XVERSE": - ret = ( - f"<|system|> \n {prompt_style.system_prompt}" - if prompt_style.system_prompt - else "" - ) - for i, message in enumerate(chat_history): - role = get_role(message["role"]) - content = message["content"] - if content: - ret += f"<|{role}|> \n {content}" - else: - ret += f"<|{role}|>" - return ret - elif prompt_style.style_name == "QWEN": - if tools: - tool_desc = """{name_for_model}: Call this tool to interact with the {name_for_human} API. What is the {name_for_human} API useful for? {description_for_model} Parameters: {parameters} Format the arguments as a JSON object.""" - - react_instruction = """Answer the following questions as best you can. You have access to the following APIs: - -{tools_text} - -Use the following format: - -Question: the input question you must answer -Thought: you should always think about what to do -Action: the action to take, should be one of [{tools_name_text}] -Action Input: the input to the action -Observation: the result of the action -... (this Thought/Action/Action Input/Observation can be repeated zero or more times) -Thought: I now know the final answer -Final Answer: the final answer to the original input question - -Begin!""" - tools_text = [] - tools_name_text = [] - for func_info in tools: - parameters = [] - fp = func_info["function"].get("parameters", {}) - if fp: - required_parameters = fp.get("required", []) - for name, p in fp["properties"].items(): - param = dict({"name": name}, **p) - if name in required_parameters: - param["required"] = True - parameters.append(param) - - name = func_info["function"]["name"] - desc = func_info["function"]["description"] - tool_string = tool_desc.format( - name_for_model=name, - name_for_human=name, - # Hint: You can add the following format requirements in description: - # "Format the arguments as a JSON object." - # "Enclose the code within triple backticks (`) at the beginning and end of the code." - description_for_model=desc, - parameters=json.dumps(parameters, ensure_ascii=False), - ) - tools_text.append(tool_string) - tools_name_text.append(name) - tools_text_string = "\n\n".join(tools_text) - tools_name_text_string = ", ".join(tools_name_text) - tool_system = react_instruction.format( - tools_text=tools_text_string, - tools_name_text=tools_name_text_string, + return rendered + + def get_full_context( + self, messages: List, chat_template: str, tokenizer=None, **kwargs + ) -> str: + if tokenizer is not None: + try: + full_context = tokenizer.apply_chat_template( + messages, + tokenize=False, + chat_template=chat_template, + add_generation_prompt=True, + **kwargs, ) - else: - tool_system = "" - - ret = f"<|im_start|>system\n{prompt_style.system_prompt}<|im_end|>" - for message in chat_history: - role = get_role(message["role"]) - content = message.get("content") - - ret += prompt_style.intra_message_sep - if tools: - if role == "user": - if tool_system: - content = tool_system + f"\n\nQuestion: {content}" - tool_system = "" - else: - content = f"Question: {content}" - elif role == "assistant": - tool_calls = message.get("tool_calls") - if tool_calls: - func_call = tool_calls[0]["function"] - f_name, f_args = ( - func_call["name"], - func_call["arguments"], - ) - content = f"Thought: I can use {f_name}.\nAction: {f_name}\nAction Input: {f_args}" - elif content: - content = f"Thought: I now know the final answer.\nFinal answer: {content}" - elif role == "tool": - role = "function" - content = f"Observation: {content}" - else: - raise Exception(f"Unsupported message role: {role}") - if content: - content = content.lstrip("\n").rstrip() - ret += f"<|im_start|>{role}\n{content}<|im_end|>" - else: - ret += f"<|im_start|>{role}\n" - return ret - elif prompt_style.style_name == "CHATML": - ret = ( - "" - if prompt_style.system_prompt == "" - else prompt_style.system_prompt + prompt_style.intra_message_sep + "\n" - ) - for message in chat_history: - role = get_role(message["role"]) - content = message["content"] + return full_context + except Exception as e: + logger.warning( + f"tokenizer.apply_chat_template error. Maybe this is an old model: {e}" + ) + return self._build_from_raw_template(messages, chat_template, **kwargs) + else: + # build from jinja + # Compilation function uses a cache to avoid recompiling the same template + return self._build_from_raw_template(messages, chat_template, **kwargs) - if content: - ret += role + "\n" + content + prompt_style.intra_message_sep + "\n" - else: - ret += role + "\n" - return ret - elif prompt_style.style_name == "INTERNLM2": - ret = ( - "" - if prompt_style.system_prompt == "" - else "<|im_start|>system\n" - + prompt_style.system_prompt - + prompt_style.intra_message_sep - + "\n" - ) - for message in chat_history: - role = get_role(message["role"]) - content = message["content"] + @staticmethod + def get_specific_prompt(model_family: str, messages: List[ChatCompletionMessage]): + """ + Inspired by FastChat. Format chat history into a prompt according to the prompty style of + different models. + """ + _messages = [x for x in messages] # copy for not modifying the origin messages + _messages.append({"role": "assistant", "content": ""}) - if content: - ret += role + "\n" + content + prompt_style.intra_message_sep + "\n" - else: - ret += role + "\n" - return ret - elif prompt_style.style_name == "ADD_COLON_SINGLE_COT": - ret = prompt_style.system_prompt + prompt_style.intra_message_sep - for message in chat_history: - role = get_role(message["role"]) - content = message["content"] - if content: - ret += role + ": " + content + prompt_style.intra_message_sep - else: - ret += role + ": Let's think step by step." - return ret - elif prompt_style.style_name == "DEEPSEEK_CHAT": - seps = [prompt_style.intra_message_sep, prompt_style.inter_message_sep] - ret = prompt_style.system_prompt - for i, message in enumerate(chat_history): - role = get_role(message["role"]) - content = message["content"] - if content: - ret += role + ": " + content + seps[i % 2] - else: - ret += role + ":" - return ret - elif prompt_style.style_name == "DEEPSEEK_CODER": - sep = prompt_style.inter_message_sep - ret = prompt_style.system_prompt + sep - for i, message in enumerate(chat_history): - role = get_role(message["role"]) - content = message["content"] - if content: - ret += role + "\n" + content + sep - else: - ret += role + "\n" - return ret - elif prompt_style.style_name == "GORILLA_OPENFUNCTIONS": - if tools: - gorilla_functions = [] - for tool in tools: - gorilla_functions.append( - { - "name": tool["function"]["name"], - "api_name": tool["function"]["name"], - "description": tool["function"]["description"], - "parameters": [ - dict({"name": name}, **p) - for name, p in tool["function"]["parameters"][ - "properties" - ].items() - ], - } - ) - tools_string = json.dumps(gorilla_functions) - return f"USER: <> {prompt} <> {tools_string}\nASSISTANT: " - else: - return f"USER: <> {prompt}\nASSISTANT: " - elif prompt_style.style_name == "orion": - ret = "" - for i, message in enumerate(chat_history): - content = message["content"] - role = get_role(message["role"]) - if i % 2 == 0: # Human - assert content is not None - ret += role + ": " + content + "\n\n" - else: # Assistant - if content: - ret += role + ": " + content + "" - else: - ret += role + ": " - return ret - elif prompt_style.style_name == "gemma": - ret = "" - for message in chat_history: - content = message["content"] - role = get_role(message["role"]) - ret += "" + role + "\n" - if content: - ret += content + "\n" - return ret - elif prompt_style.style_name == "CodeShell": - ret = "" - for message in chat_history: - content = message["content"] - role = get_role(message["role"]) - if content: - ret += f"{role}{content}||" - else: - ret += f"{role}".rstrip() - return ret - elif prompt_style.style_name == "MINICPM-2B": - ret = "" - for message in chat_history: - content = message["content"] or "" - role = get_role(message["role"]) - if role == "user": - ret += "<用户>" + content.strip() - else: - ret += "" + content.strip() - return ret - elif prompt_style.style_name == "PHI3": - ret = f"<|system|>{prompt_style.intra_message_sep}{prompt_style.system_prompt}{prompt_style.inter_message_sep}" - for message in chat_history: - content = message["content"] or "" - role = get_role(message["role"]) - if content: - ret += f"<|{role}|>{prompt_style.intra_message_sep}{content}{prompt_style.inter_message_sep}" - else: - ret += f"<|{role}|>{prompt_style.intra_message_sep}" - ret += "<|assistant|>\n" - return ret - elif prompt_style.style_name == "c4ai-command-r": - ret = ( - f"<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>" - f"{prompt_style.system_prompt}{prompt_style.inter_message_sep}" + if model_family == "internvl2": + system_prompt = ( + messages[0]["content"] if messages[0]["role"] == "system" else "" ) - for i, message in enumerate(chat_history): - role = get_role(message["role"]) - content = message["content"] - if content: - ret += f"{role}{content}{prompt_style.inter_message_sep}" - else: - ret += role - return ret - elif prompt_style.style_name == "mistral-nemo": - seps = [prompt_style.intra_message_sep, prompt_style.inter_message_sep] - ret = "" - for i, message in enumerate(chat_history): - role = get_role(message["role"]) - content = message["content"] - if content: - if i == len(chat_history) - 2 and prompt_style.system_prompt: - ret += ( - role - + " " - + prompt_style.system_prompt - + "\n\n" - + content - + seps[i % 2] - ) - else: - ret += role + " " + content + seps[i % 2] - else: - ret += role - return ret - elif prompt_style.style_name == "INTERNVL": + intra_message_sep = "<|im_end|>" ret = ( "" - if prompt_style.system_prompt == "" - else "<|im_start|>system\n" - + prompt_style.system_prompt - + prompt_style.intra_message_sep + if system_prompt == "" + else "<|im_start|>system\n" # type: ignore + + system_prompt + + intra_message_sep + "\n" ) images = [] # type: ignore - for message in chat_history: - role = get_role(message["role"]) + for message in _messages: + role = "<|im_start|>" + message["role"] content = message["content"] if isinstance(content, str): if content: - ret += ( - role - + "\n" - + content - + prompt_style.intra_message_sep - + "\n" - ) + ret += role + "\n" + content + intra_message_sep + "\n" else: ret += role + "\n" elif isinstance(content, list): @@ -485,23 +165,28 @@ def get_role(role_name: str): for image_url in image_urls: fut = executor.submit(_decode_image, image_url) image_futures.append(fut) - images = [fut.result() for fut in image_futures] + images.extend([fut.result() for fut in image_futures]) if len(image_futures) == 0: - ret += ( - role + "\n" + text + prompt_style.intra_message_sep + "\n" - ) + ret += role + "\n" + text + intra_message_sep + "\n" else: + placeholders = "\n".join( + f"Image-{i+1}: \n" + for i in range( + len(images) - len(image_futures), len(images) + ) + ) ret += ( role + "\n" - + f"\n{text}" - + prompt_style.intra_message_sep + + f"{placeholders}\n{text}" + + intra_message_sep + "\n" ) - - return (ret, images) + if len(images) == 1: + ret = ret.replace("Image-1: \n", "\n") + return ret, images else: - raise ValueError(f"Invalid prompt style: {prompt_style.style_name}") + raise ValueError(f"Invalid model family: {model_family}") @classmethod def _to_chat_completion_chunk(cls, chunk: CompletionChunk) -> ChatCompletionChunk: @@ -522,7 +207,11 @@ def _to_chat_completion_chunk(cls, chunk: CompletionChunk) -> ChatCompletionChun { "index": i, "delta": { - "content": choice.get("text"), + **( + {"content": choice["text"]} + if ("text" in choice and choice["finish_reason"] is None) + else {} + ), **( {"tool_calls": choice["tool_calls"]} if "tool_calls" in choice @@ -629,143 +318,99 @@ def _to_chat_completion(completion: Completion) -> ChatCompletion: } @staticmethod - def _eval_gorilla_openfunctions_arguments(c, tools): - tool_names = [tool["function"]["name"] for tool in tools] - arguments = c["choices"][0]["text"] - - def tool_call(n, **kwargs): - return None, n, kwargs - - try: - a, b, c = eval( - arguments, {n: functools.partial(tool_call, n) for n in tool_names} - ) - return a, b, c - except Exception as e: - logger.error("Eval tool calls completion failed: %s", e) - return arguments, None, None - - @staticmethod - def _eval_glm_chat_arguments(c, tools): + def _eval_glm_chat_arguments(c) -> List[Tuple]: + """ + Currently, glm4 tool call only supports one function + """ try: - if isinstance(c[0], str): - return c[0], None, None - return None, c[0]["name"], c[0]["parameters"] + if isinstance(c, dict): + return [(None, c["name"], c["arguments"])] except KeyError: logger.error("Can't parse glm output: %s", c) - return str(c), None, None + return [(str(c), None, None)] + else: + return [(str(c), None, None)] - @staticmethod - def _eval_qwen_chat_arguments(c, tools): + @classmethod + def _handle_qwen_tool_result(cls, text: str) -> List[Tuple]: + text: str = text.strip() # type: ignore + contents: List[str] = text.split(QWEN_TOOL_CALL_SYMBOLS[1]) + results: List[Tuple] = [] + for content in contents: + content = content.strip() + if content: + pos = content.find(QWEN_TOOL_CALL_SYMBOLS[0]) + if pos != -1: + content = content[pos + len(QWEN_TOOL_CALL_SYMBOLS[0]) :] + content = content.strip() + try: + res = json.loads(content) + results.append((None, res["name"], res["arguments"])) + except Exception as e: + logger.error( + "Can't parse single qwen tool call output: %s. Error: %s", + content, + e, + ) + results.append((content, None, None)) + return results + + @classmethod + def _eval_qwen_chat_arguments(cls, c) -> List[Tuple]: + text = c["choices"][0]["text"] + return cls._handle_qwen_tool_result(text) + + @classmethod + def _eval_llama3_chat_arguments(cls, c) -> List[Tuple]: text = c["choices"][0]["text"] try: - # Refer to: - # https://github.com/QwenLM/Qwen/blob/main/examples/react_prompt.md - # https://github.com/QwenLM/Qwen/blob/main/openai_api.py#L297 - func_name, func_args, content = "", "", "" - i = text.rfind("\nAction:") - j = text.rfind("\nAction Input:") - k = text.rfind("\nObservation:") - t = max( - text.rfind("\nThought:", 0, i), text.rfind("Thought:", 0, i) - ) # find the last thought just before Action, considering the Thought at the very beginning - if 0 <= i < j: # If the text has `Action` and `Action input`, - if k < j: # but does not contain `Observation`, - # then it is likely that `Observation` is omitted by the LLM, - # because the output text may have discarded the stop word. - text = text.rstrip() + "\nObservation:" # Add it back. - k = text.rfind("\nObservation:") - if 0 <= t < i < j < k: - func_name = text[i + len("\nAction:") : j].strip() - func_args = text[j + len("\nAction Input:") : k].strip() - content = text[ - t + len("\nThought:") : i - ].strip() # len("\nThought:") and len("Thought:") both are OK since there is a space after : - if func_name: - return content, func_name, json.loads(func_args) - except Exception as e: - logger.error("Eval tool calls completion failed: %s", e) - t = max(text.rfind("\nThought:"), text.rfind("Thought:")) - z = max(text.rfind("\nFinal Answer:"), text.rfind("Final Answer:")) - if z >= 0: - text = text[ - z + len("\nFinal Answer:") : - ] # len("\nFinal Answer::") and len("Final Answer::") both are OK since there is a space after : - else: - text = text[ - t + len("\nThought:") : - ] # There is only Thought: no Final Answer: - return text, None, None + data = eval(text, {}, {}) + return [(None, data["name"], data["parameters"])] + except Exception: + return [(text, None, None)] @classmethod - def _eval_tool_arguments(cls, model_family, c, tools): + def _eval_tool_arguments(cls, model_family, c): family = model_family.model_family or model_family.model_name - if family in ["gorilla-openfunctions-v1", "gorilla-openfunctions-v2"]: - content, func, args = cls._eval_gorilla_openfunctions_arguments(c, tools) - elif family in GLM4_TOOL_CALL_FAMILY: - content, func, args = cls._eval_glm_chat_arguments(c, tools) + if family in GLM4_TOOL_CALL_FAMILY: + result = cls._eval_glm_chat_arguments(c) elif family in QWEN_TOOL_CALL_FAMILY: - content, func, args = cls._eval_qwen_chat_arguments(c, tools) + result = cls._eval_qwen_chat_arguments(c) + elif family in LLAMA3_TOOL_CALL_FAMILY: + result = cls._eval_llama3_chat_arguments(c) else: raise Exception( f"Model {model_family.model_name} is not support tool calls." ) - logger.debug("Tool call content: %s, func: %s, args: %s", content, func, args) - return content, func, args + logger.debug(f"Tool call content: {result}") + return result @classmethod - def _tools_token_filter(cls, model_family): - """ - Generates a filter function for Qwen series models to retain outputs after "\nFinal Answer:". - - Returns: - A function that takes tokens (string output by the model so far) and delta (new tokens added) as input, - returns the part after "\nFinal Answer:" if found, else returns delta. - """ - family = model_family.model_family or model_family.model_name - if family in QWEN_TOOL_CALL_FAMILY: - # Encapsulating function to reset 'found' after each call - found = False - - def process_tokens(tokens: str, delta: str): - nonlocal found - # Once "Final Answer:" is found, future tokens are allowed. - if found: - return delta - # Check if the token ends with "\nFinal Answer:" and update `found`. - final_answer_idx = tokens.lower().rfind("\nfinal answer:") - if final_answer_idx != -1: - found = True - return tokens[final_answer_idx + len("\nfinal answer:") :] - return "" - - return process_tokens - else: - return lambda tokens, delta: delta - - @classmethod - def _tool_calls_completion_chunk(cls, model_family, model_uid, c, tools): - _id = str(uuid.uuid4()) - content, func, args = cls._eval_tool_arguments(model_family, c, tools) - if func: - d = { - "role": "assistant", - "content": content, - "tool_calls": [ + def _tool_calls_completion_chunk(cls, model_family, model_uid, c, chunk_id=None): + _id = chunk_id if chunk_id is not None else str(uuid.uuid4()) + tool_result = cls._eval_tool_arguments(model_family, c) + tool_calls = [] + failed_contents = [] + for content, func, args in tool_result: + if func: + tool_calls.append( { "id": f"call_{_id}", "type": "function", "function": { "name": func, - "arguments": json.dumps(args), + "arguments": json.dumps(args, ensure_ascii=False), }, } - ], - } - finish_reason = "tool_calls" - else: - d = {"role": "assistant", "content": content, "tool_calls": []} - finish_reason = "stop" + ) + else: + failed_contents.append(content) + finish_reason = "tool_calls" if tool_calls else "stop" + d = { + "role": "assistant", + "content": ". ".join(failed_contents) if failed_contents else None, + "tool_calls": tool_calls, + } try: usage = c.get("usage") assert "prompt_tokens" in usage @@ -792,28 +437,32 @@ def _tool_calls_completion_chunk(cls, model_family, model_uid, c, tools): } @classmethod - def _tool_calls_completion(cls, model_family, model_uid, c, tools): + def _tool_calls_completion(cls, model_family, model_uid, c): _id = str(uuid.uuid4()) - content, func, args = cls._eval_tool_arguments(model_family, c, tools) - if func: - m = { - "role": "assistant", - "content": content, - "tool_calls": [ + tool_result = cls._eval_tool_arguments(model_family, c) + + tool_calls = [] + failed_contents = [] + for content, func, args in tool_result: + if func: + tool_calls.append( { "id": f"call_{_id}", "type": "function", "function": { "name": func, - "arguments": json.dumps(args), + "arguments": json.dumps(args, ensure_ascii=False), }, } - ], - } - finish_reason = "tool_calls" - else: - m = {"role": "assistant", "content": content, "tool_calls": []} - finish_reason = "stop" + ) + else: + failed_contents.append(content) + finish_reason = "tool_calls" if tool_calls else "stop" + m = { + "role": "assistant", + "content": ". ".join(failed_contents) if failed_contents else None, + "tool_calls": tool_calls, + } try: usage = c.get("usage") assert "prompt_tokens" in usage @@ -838,15 +487,33 @@ def _tool_calls_completion(cls, model_family, model_uid, c, tools): "usage": usage, } - @classmethod - def get_full_prompt(cls, model_family, prompt, system_prompt, chat_history, tools): - assert model_family.prompt_style is not None - prompt_style = model_family.prompt_style.copy() - if system_prompt: - prompt_style.system_prompt = system_prompt - chat_history = chat_history or [] - full_prompt = cls.get_prompt(prompt, chat_history, prompt_style, tools=tools) - return full_prompt + def _transform_messages( + self, + messages: List[ChatCompletionMessage], + ): + transformed_messages = [] + for msg in messages: + new_content = [] + role = msg["role"] + content = msg["content"] + if isinstance(content, str): + new_content.append({"type": "text", "text": content}) + elif isinstance(content, List): + for item in content: # type: ignore + if "text" in item: + new_content.append({"type": "text", "text": item["text"]}) + elif "image_url" in item: + new_content.append( + {"type": "image", "image": item["image_url"]["url"]} + ) + elif "video_url" in item: + new_content.append( + {"type": "video", "video": item["video_url"]["url"]} + ) + new_message = {"role": role, "content": new_content} + transformed_messages.append(new_message) + + return transformed_messages def get_file_location( @@ -900,3 +567,120 @@ def _decode_image(_url): return Image.open(_url).convert("RGB") else: return Image.open(BytesIO(response.content)).convert("RGB") + + +@typing.no_type_check +def generate_completion_chunk( + chunk_text: Optional[str], + finish_reason: Optional[str], + chunk_id: str, + model_uid: str, + prompt_tokens: int, + completion_tokens: int, + total_tokens: int, + has_choice: bool = True, + has_content: bool = True, +): + choices = [] + if has_choice: + choices.append( + CompletionChoice( + text=chunk_text, index=0, logprobs=None, finish_reason=finish_reason + ) + if has_content + else CompletionChoice(index=0, logprobs=None, finish_reason=finish_reason) + ) + return CompletionChunk( + id=chunk_id, + object="text_completion", + created=int(time.time()), + model=model_uid, + choices=choices, + usage=CompletionUsage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=total_tokens, + ), + ) + + +def generate_completion( + model_uid: str, + response: str, + prompt_tokens=-1, + completion_tokens=-1, + total_tokens=-1, + finish_reason="stop", +) -> Completion: + return Completion( + id=str(uuid.uuid1()), + object="text_completion", + created=int(time.time()), + model=model_uid, + choices=[ + CompletionChoice( + text=response, index=0, logprobs=None, finish_reason=finish_reason + ) + ], + usage=CompletionUsage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=total_tokens, + ), + ) + + +def generate_chat_completion( + model_uid: str, + response: str, + prompt_tokens=-1, + completion_tokens=-1, + total_tokens=-1, + finish_reason="stop", +) -> ChatCompletion: + return ChatCompletion( + id="chat" + str(uuid.uuid1()), + object="chat.completion", + created=int(time.time()), + model=model_uid, + choices=[ + ChatCompletionChoice( + index=0, + message={"role": "assistant", "content": response}, + finish_reason=finish_reason, + ) + ], + usage=CompletionUsage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=total_tokens, + ), + ) + + +@functools.lru_cache +def get_stop_token_ids_from_config_file(model_path: str) -> Optional[List[int]]: + from transformers import GenerationConfig as TransformersGenerationConfig + + transformers_config = TransformersGenerationConfig.from_pretrained(model_path) + if transformers_config.eos_token_id is not None: + stop_token_ids = ( + transformers_config.eos_token_id + if isinstance(transformers_config.eos_token_id, list) + else [transformers_config.eos_token_id] + ) + return stop_token_ids + return None + + +def parse_messages(messages: List[Dict]) -> Tuple: + """ + Some older models still follow the old way of parameter passing. + This function helps to parse out the needed information from OpenAI-compatible `messages`. + """ + system_messages = [mess["content"] for mess in messages if mess["role"] == "system"] + content_messages = [mess for mess in messages if mess["role"] != "system"] + prompt = content_messages[-1]["content"] + system_prompt = ". ".join(system_messages) if system_messages else None + chat_history = content_messages[:-1] + return prompt, system_prompt, chat_history diff --git a/xinference/model/llm/vllm/core.py b/xinference/model/llm/vllm/core.py index 4b009aa646..53001c1590 100644 --- a/xinference/model/llm/vllm/core.py +++ b/xinference/model/llm/vllm/core.py @@ -24,9 +24,9 @@ Any, AsyncGenerator, Dict, - Iterable, List, Optional, + Tuple, TypedDict, Union, ) @@ -40,12 +40,16 @@ CompletionChunk, CompletionUsage, LoRA, - ToolCallFunction, - ToolCalls, ) from .. import LLM, LLMFamilyV1, LLMSpecV1 from ..llm_family import CustomLLMFamilyV1 -from ..utils import QWEN_TOOL_CALL_FAMILY, ChatModelMixin +from ..utils import ( + QWEN_TOOL_CALL_FAMILY, + QWEN_TOOL_CALL_SYMBOLS, + ChatModelMixin, + generate_completion_chunk, +) +from .utils import vllm_check logger = logging.getLogger(__name__) @@ -64,6 +68,7 @@ class VLLMModelConfig(TypedDict, total=False): max_num_seqs: int quantization: Optional[str] max_model_len: Optional[int] + limit_mm_per_prompt: Optional[Dict[str, int]] class VLLMGenerateConfig(TypedDict, total=False): @@ -89,9 +94,7 @@ class VLLMGenerateConfig(TypedDict, total=False): except ImportError: VLLM_INSTALLED = False -VLLM_SUPPORTED_VISION_MODEL_LIST: List[str] = [ - "internvl2", -] +VLLM_SUPPORTED_VISION_MODEL_LIST: List[str] = [] VLLM_SUPPORTED_MODELS = [ "llama-2", "llama-3", @@ -103,6 +106,7 @@ class VLLMGenerateConfig(TypedDict, total=False): "code-llama-python", "deepseek", "deepseek-coder", + "yi-coder", ] VLLM_SUPPORTED_CHAT_MODELS = [ "llama-2-chat", @@ -129,12 +133,18 @@ class VLLMGenerateConfig(TypedDict, total=False): "codegeex4", "deepseek-chat", "deepseek-coder-instruct", + "yi-coder-chat", ] if VLLM_INSTALLED and vllm.__version__ >= "0.3.0": VLLM_SUPPORTED_CHAT_MODELS.append("qwen1.5-chat") VLLM_SUPPORTED_MODELS.append("codeqwen1.5") VLLM_SUPPORTED_CHAT_MODELS.append("codeqwen1.5-chat") VLLM_SUPPORTED_CHAT_MODELS.append("qwen2-instruct") + VLLM_SUPPORTED_MODELS.append("qwen2.5") + VLLM_SUPPORTED_CHAT_MODELS.append("qwen2.5-instruct") + VLLM_SUPPORTED_MODELS.append("qwen2.5-coder") + VLLM_SUPPORTED_CHAT_MODELS.append("qwen2.5-coder-instruct") + if VLLM_INSTALLED and vllm.__version__ >= "0.3.2": VLLM_SUPPORTED_CHAT_MODELS.append("gemma-it") @@ -148,6 +158,12 @@ class VLLMGenerateConfig(TypedDict, total=False): VLLM_SUPPORTED_CHAT_MODELS.append("qwen2-moe-instruct") VLLM_SUPPORTED_CHAT_MODELS.append("c4ai-command-r-v01") +if VLLM_INSTALLED and vllm.__version__ >= "0.5.1": + VLLM_SUPPORTED_CHAT_MODELS.append("deepseek-v2-chat") + VLLM_SUPPORTED_CHAT_MODELS.append("deepseek-v2-chat-0628") + VLLM_SUPPORTED_CHAT_MODELS.append("deepseek-v2.5") + + if VLLM_INSTALLED and vllm.__version__ >= "0.5.3": VLLM_SUPPORTED_CHAT_MODELS.append("gemma-2-it") VLLM_SUPPORTED_CHAT_MODELS.append("mistral-nemo-instruct") @@ -157,6 +173,12 @@ class VLLMGenerateConfig(TypedDict, total=False): VLLM_SUPPORTED_MODELS.append("llama-3.1") VLLM_SUPPORTED_CHAT_MODELS.append("llama-3.1-instruct") +if VLLM_INSTALLED and vllm.__version__ >= "0.6.1": + VLLM_SUPPORTED_VISION_MODEL_LIST.append("internvl2") + +if VLLM_INSTALLED and vllm.__version__ >= "0.6.3": + VLLM_SUPPORTED_VISION_MODEL_LIST.append("qwen2-vl-instruct") + class VLLMModel(LLM): def __init__( @@ -290,7 +312,7 @@ def _sanitize_model_config( model_config.setdefault("gpu_memory_utilization", 0.90) model_config.setdefault("max_num_seqs", 256) model_config.setdefault("quantization", None) - model_config.setdefault("max_model_len", 4096) + model_config.setdefault("max_model_len", None) return model_config @@ -363,23 +385,28 @@ def match( @staticmethod def _convert_request_output_to_completion_chunk( request_id: str, model: str, request_output: "RequestOutput" - ) -> CompletionChunk: + ) -> Tuple[CompletionChunk, Optional[str]]: choices: List[CompletionChoice] = [] + finish_reason = None for output in request_output.outputs: choices.append( CompletionChoice( text=output.text, index=output.index, logprobs=None, # TODO: support logprobs. - finish_reason=output.finish_reason, + finish_reason=None, ) ) - return CompletionChunk( - id=request_id, - object="text_completion", - created=int(time.time()), - model=model, - choices=choices, + finish_reason = output.finish_reason + return ( + CompletionChunk( + id=request_id, + object="text_completion", + created=int(time.time()), + model=model, + choices=choices, + ), + finish_reason, ) @staticmethod @@ -415,11 +442,13 @@ def _convert_request_output_to_completion( usage=usage, ) + @vllm_check async def async_generate( self, prompt: Union[str, Dict[str, Any]], generate_config: Optional[Dict] = None, tools: object = False, + request_id: Optional[str] = None, ) -> Union[Completion, AsyncGenerator[CompletionChunk, None]]: try: from vllm.sampling_params import SamplingParams @@ -454,7 +483,8 @@ async def async_generate( else False ) sampling_params = SamplingParams(**sanitized_generate_config) - request_id = str(uuid.uuid1()) + if not request_id: + request_id = str(uuid.uuid1()) assert self._engine is not None results_generator = self._engine.generate( @@ -463,10 +493,14 @@ async def async_generate( async def stream_results() -> AsyncGenerator[CompletionChunk, None]: previous_texts = [""] * sanitized_generate_config["n"] - tools_token_filter = ChatModelMixin._tools_token_filter(self.model_family) prompt_tokens, completion_tokens, total_tokens = 0, 0, 0 + complete_response = "" + match_tool_call_tmp_results = [] + is_match_tool_call = False + chunk = None + finish_reason = None async for _request_output in results_generator: - chunk = self._convert_request_output_to_completion_chunk( + chunk, finish_reason = self._convert_request_output_to_completion_chunk( request_id=request_id, model=self.model_uid, request_output=_request_output, @@ -476,40 +510,8 @@ async def stream_results() -> AsyncGenerator[CompletionChunk, None]: delta = choice["text"][len(previous_texts[i]) :] previous_texts[i] = choice["text"] choice["text"] = delta + complete_response += delta - if tools: - # only handle the first choice - choice = chunk["choices"][0] - if choice["finish_reason"] is not None: - # use previous text for evaluation temporarily - choice_delta = choice["text"] - choice["text"] = previous_texts[0] - _content, func, args = ChatModelMixin._eval_tool_arguments( - self.model_family, chunk, tools - ) - choice["text"] = tools_token_filter( - tokens=previous_texts[0], delta=choice_delta - ) - if func is not None: - choice["text"] = None - choice["finish_reason"] = "tool_calls" - choice["tool_calls"] = [ - ToolCalls( - id=str(uuid.uuid4()), - type="function", - function=ToolCallFunction( - name=func, - arguments=json.dumps(args, ensure_ascii=False), - ), - ) - ] - else: - # use a filter function to skip Qwen's react thought process - choice["text"] = tools_token_filter( - tokens=previous_texts[0], delta=choice["text"] - ) - if not choice["text"]: - continue prompt_tokens = len(_request_output.prompt_token_ids) completion_tokens = sum( len(output.token_ids) for output in _request_output.outputs @@ -520,7 +522,59 @@ async def stream_results() -> AsyncGenerator[CompletionChunk, None]: completion_tokens=completion_tokens, total_tokens=total_tokens, ) + + if tools: + """ + The qwen2 tool call returns format like this: + + {...} + + Here is to match this. + """ + if (len(QWEN_TOOL_CALL_SYMBOLS[0]) > len(complete_response)) and ( + not QWEN_TOOL_CALL_SYMBOLS[0].startswith(complete_response) + ): + for c in match_tool_call_tmp_results: + yield c + match_tool_call_tmp_results.clear() + yield chunk + elif (len(QWEN_TOOL_CALL_SYMBOLS[0]) > len(complete_response)) and ( + QWEN_TOOL_CALL_SYMBOLS[0].startswith(complete_response) + ): + match_tool_call_tmp_results.append(chunk) + else: + assert len(QWEN_TOOL_CALL_SYMBOLS[0]) <= len(complete_response) + if not is_match_tool_call and complete_response.startswith( + QWEN_TOOL_CALL_SYMBOLS[0] + ): + is_match_tool_call = True + match_tool_call_tmp_results.clear() + + if not is_match_tool_call: + for c in match_tool_call_tmp_results: + yield c + match_tool_call_tmp_results.clear() + yield chunk + else: + chunk["choices"][0]["text"] = complete_response + else: + yield chunk + + if is_match_tool_call: + assert chunk is not None yield chunk + + # match OpenAI API stream + yield generate_completion_chunk( + chunk_text="", + finish_reason=finish_reason, + chunk_id=request_id, + model_uid=self.model_uid, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=total_tokens, + ) + if include_usage: chunk = CompletionChunk( id=request_id, @@ -586,105 +640,103 @@ def _sanitize_chat_config( ) -> Dict: if not generate_config: generate_config = {} - if self.model_family.prompt_style: - if ( - not generate_config.get("stop") - ) and self.model_family.prompt_style.stop: - generate_config["stop"] = self.model_family.prompt_style.stop.copy() - if self.model_family.prompt_style.stop_token_ids: - generate_config.setdefault( - "stop_token_ids", - self.model_family.prompt_style.stop_token_ids.copy(), - ) + if not generate_config.get("stop") and self.model_family.stop: + generate_config["stop"] = self.model_family.stop.copy() + if ( + not generate_config.get("stop_token_ids") + and self.model_family.stop_token_ids + ): + generate_config["stop_token_ids"] = self.model_family.stop_token_ids.copy() return generate_config + @staticmethod + def is_tool_call_chunk(chunk): + return chunk["choices"][0]["text"].startswith(QWEN_TOOL_CALL_SYMBOLS[0]) + + async def _async_to_tool_completion_chunks( + self, + chunks: AsyncGenerator[CompletionChunk, None], + ) -> AsyncGenerator[ChatCompletionChunk, None]: + i = 0 + async for chunk in chunks: + if i == 0: + yield self._get_first_chat_completion_chunk(chunk) + # usage + choices = chunk.get("choices") + if not choices: + yield self._get_final_chat_completion_chunk(chunk) + else: + if self.is_tool_call_chunk(chunk): + yield self._tool_calls_completion_chunk( + self.model_family, self.model_uid, chunk + ) + else: + yield self._to_chat_completion_chunk(chunk) + i += 1 + + @vllm_check async def async_chat( self, - prompt: str, - system_prompt: Optional[str] = None, - chat_history: Optional[List[ChatCompletionMessage]] = None, + messages: List[Dict], generate_config: Optional[Dict] = None, + request_id: Optional[str] = None, ) -> Union[ChatCompletion, AsyncGenerator[ChatCompletionChunk, None]]: - assert self.model_family.prompt_style is not None - prompt_style = self.model_family.prompt_style.copy() - if system_prompt: - prompt_style.system_prompt = system_prompt - chat_history = chat_history or [] tools = generate_config.pop("tools", []) if generate_config else None - full_prompt = self.get_prompt(prompt, chat_history, prompt_style, tools=tools) - - generate_config = self._sanitize_chat_config(generate_config) - # TODO(codingl2k1): qwen hacky to set stop for function call. model_family = self.model_family.model_family or self.model_family.model_name + full_context_kwargs = {} if tools and model_family in QWEN_TOOL_CALL_FAMILY: - stop = generate_config.get("stop") - if isinstance(stop, str): - generate_config["stop"] = [stop, "Observation:"] - elif isinstance(stop, Iterable): - assert not isinstance(stop, str) - generate_config["stop"] = list(stop) + ["Observation:"] - else: - generate_config["stop"] = "Observation:" + full_context_kwargs["tools"] = tools + assert self.model_family.chat_template is not None + full_prompt = self.get_full_context( + messages, self.model_family.chat_template, **full_context_kwargs + ) + generate_config = self._sanitize_chat_config(generate_config) stream = generate_config.get("stream", None) if stream: - agen = await self.async_generate(full_prompt, generate_config, tools) + agen = await self.async_generate( + full_prompt, generate_config, tools, request_id=request_id + ) assert isinstance(agen, AsyncGenerator) + if tools: + return self._async_to_tool_completion_chunks(agen) return self._async_to_chat_completion_chunks(agen) else: - c = await self.async_generate(full_prompt, generate_config) + c = await self.async_generate( + full_prompt, generate_config, request_id=request_id + ) assert not isinstance(c, AsyncGenerator) if tools: - return self._tool_calls_completion( - self.model_family, self.model_uid, c, tools - ) + return self._tool_calls_completion(self.model_family, self.model_uid, c) return self._to_chat_completion(c) class VLLMVisionModel(VLLMModel, ChatModelMixin): - def load(self): - try: - import vllm - from vllm.engine.arg_utils import AsyncEngineArgs - from vllm.engine.async_llm_engine import AsyncLLMEngine - except ImportError: - error_message = "Failed to import module 'vllm'" - installation_guide = [ - "Please make sure 'vllm' is installed. ", - "You can install it by `pip install vllm`\n", - ] - raise ImportError(f"{error_message}\n\n{''.join(installation_guide)}") - - if vllm.__version__ >= "0.3.1": - # from vllm v0.3.1, it uses cupy as NCCL backend - # in which cupy will fork a process - # only for xoscar >= 0.3.0, new process is allowed in subpool - # besides, xinference set start method as forkserver for unix - # we need to set it to fork to make cupy NCCL work - multiprocessing.set_start_method("fork", force=True) - - self._model_config = self._sanitize_model_config(self._model_config) - - logger.info( - f"Loading {self.model_uid} with following model config: {self._model_config}" - ) - - engine_args = AsyncEngineArgs( - model=self.model_path, - **self._model_config, - ) - self._engine = AsyncLLMEngine.from_engine_args(engine_args) - @classmethod def match( cls, llm_family: "LLMFamilyV1", llm_spec: "LLMSpecV1", quantization: str ) -> bool: - if llm_spec.model_format != "pytorch": + if not cls._has_cuda_device(): + return False + if not cls._is_linux(): + return False + if llm_spec.model_format not in ["pytorch", "gptq", "awq", "fp8"]: return False if llm_spec.model_format == "pytorch": if quantization != "none" and not (quantization is None): return False + if llm_spec.model_format == "awq": + # Currently, only 4-bit weight quantization is supported for AWQ, but got 8 bits. + if "4" not in quantization: + return False + if llm_spec.model_format == "gptq": + if VLLM_INSTALLED and vllm.__version__ >= "0.3.3": + if not any(q in quantization for q in ("3", "4", "8")): + return False + else: + if "4" not in quantization: + return False if isinstance(llm_family, CustomLLMFamilyV1): if llm_family.model_family not in VLLM_SUPPORTED_VISION_MODEL_LIST: return False @@ -695,51 +747,107 @@ def match( return False return VLLM_INSTALLED + def _sanitize_model_config( + self, model_config: Optional[VLLMModelConfig] + ) -> VLLMModelConfig: + if model_config is None: + model_config = VLLMModelConfig() + + cuda_count = self._get_cuda_count() + + model_config.setdefault("tokenizer_mode", "auto") + model_config.setdefault("trust_remote_code", True) + model_config.setdefault("tensor_parallel_size", cuda_count) + model_config.setdefault("block_size", 16) + model_config.setdefault("swap_space", 4) + model_config.setdefault("gpu_memory_utilization", 0.90) + model_config.setdefault("max_num_seqs", 256) + model_config.setdefault("quantization", None) + model_config.setdefault("max_model_len", None) + model_config["limit_mm_per_prompt"] = ( + json.loads(model_config.get("limit_mm_per_prompt")) # type: ignore + if model_config.get("limit_mm_per_prompt") + else { + "image": 2, # default 2 images all chat + } + ) + + return model_config + def _sanitize_chat_config( self, generate_config: Optional[Dict] = None, ) -> Dict: + from ..utils import get_stop_token_ids_from_config_file + if not generate_config: generate_config = {} - if self.model_family.prompt_style: - if self.model_family.prompt_style.stop_token_ids: - generate_config.setdefault( - "stop_token_ids", - self.model_family.prompt_style.stop_token_ids.copy(), - ) + if generate_config.get("stop_token_ids", None) is None: + stop_token_ids = get_stop_token_ids_from_config_file(self.model_path) + if stop_token_ids is not None: + generate_config.setdefault("stop_token_ids", stop_token_ids) + else: + if self.model_family.stop_token_ids: + generate_config.setdefault( + "stop_token_ids", self.model_family.stop_token_ids.copy() + ) return generate_config + @vllm_check async def async_chat( self, - prompt: str, - system_prompt: Optional[str] = None, - chat_history: Optional[List[ChatCompletionMessage]] = None, + messages: List[ChatCompletionMessage], # type: ignore generate_config: Optional[Dict] = None, + request_id: Optional[str] = None, ) -> Union[ChatCompletion, AsyncGenerator[ChatCompletionChunk, None]]: - # only support single image, waiting vllm support multi images - assert self.model_family.prompt_style is not None - prompt_style = self.model_family.prompt_style.copy() - chat_history = chat_history or [] - prompt, images = self.get_prompt(prompt, chat_history, prompt_style) + messages = self._transform_messages(messages) + tools = generate_config.pop("tools", []) if generate_config else None + + model_family = self.model_family.model_family or self.model_family.model_name + + if "internvl2" not in model_family.lower(): + from qwen_vl_utils import process_vision_info + + full_context_kwargs = {} + if tools and model_family in QWEN_TOOL_CALL_FAMILY: + full_context_kwargs["tools"] = tools + assert self.model_family.chat_template is not None + prompt = self.get_full_context( + messages, self.model_family.chat_template, **full_context_kwargs + ) + images, video_inputs = process_vision_info(messages) + if video_inputs: + raise ValueError("Not support video input now.") + else: + prompt, images = self.get_specific_prompt(model_family, messages) - if len(images) == 0: + if not images: inputs = { "prompt": prompt, } - else: + elif len(images) == 1: inputs = { "prompt": prompt, "multi_modal_data": {"image": images[-1]}, # type: ignore } + else: + inputs = { + "prompt": prompt, + "multi_modal_data": {"image": images}, # type: ignore + } generate_config = self._sanitize_chat_config(generate_config) stream = generate_config.get("stream", None) if stream: - agen = await self.async_generate(inputs, generate_config) + agen = await self.async_generate( + inputs, generate_config, request_id=request_id + ) assert isinstance(agen, AsyncGenerator) return self._async_to_chat_completion_chunks(agen) else: - c = await self.async_generate(inputs, generate_config) + c = await self.async_generate( + inputs, generate_config, request_id=request_id + ) assert not isinstance(c, AsyncGenerator) return self._to_chat_completion(c) diff --git a/xinference/model/llm/vllm/utils.py b/xinference/model/llm/vllm/utils.py new file mode 100644 index 0000000000..97af5ba580 --- /dev/null +++ b/xinference/model/llm/vllm/utils.py @@ -0,0 +1,41 @@ +# Copyright 2022-2023 XProbe Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import functools +import logging +import os + +logger = logging.getLogger(__name__) + + +def vllm_check(fn): + try: + from vllm.engine.async_llm_engine import AsyncEngineDeadError + except: + return fn + + @functools.wraps(fn) + async def _async_wrapper(self, *args, **kwargs): + try: + return await fn(self, *args, **kwargs) + except AsyncEngineDeadError: + logger.info("Detecting vLLM is not health, prepare to quit the process") + try: + self.stop() + except: + # ignore error when stop + pass + # Just kill the process and let xinference auto-recover the model + os._exit(1) + + return _async_wrapper diff --git a/xinference/model/rerank/__init__.py b/xinference/model/rerank/__init__.py index b3e408c96a..8c9ec3a67c 100644 --- a/xinference/model/rerank/__init__.py +++ b/xinference/model/rerank/__init__.py @@ -15,6 +15,8 @@ import codecs import json import os +import warnings +from typing import Any, Dict from ...constants import XINFERENCE_MODEL_DIR from .core import ( @@ -32,45 +34,65 @@ unregister_rerank, ) -_model_spec_json = os.path.join(os.path.dirname(__file__), "model_spec.json") -_model_spec_modelscope_json = os.path.join( - os.path.dirname(__file__), "model_spec_modelscope.json" -) -BUILTIN_RERANK_MODELS = dict( - (spec["model_name"], RerankModelSpec(**spec)) - for spec in json.load(codecs.open(_model_spec_json, "r", encoding="utf-8")) -) -for model_name, model_spec in BUILTIN_RERANK_MODELS.items(): - MODEL_NAME_TO_REVISION[model_name].append(model_spec.model_revision) +BUILTIN_RERANK_MODELS: Dict[str, Any] = {} +MODELSCOPE_RERANK_MODELS: Dict[str, Any] = {} + + +def register_custom_model(): + # if persist=True, load them when init + user_defined_rerank_dir = os.path.join(XINFERENCE_MODEL_DIR, "rerank") + if os.path.isdir(user_defined_rerank_dir): + for f in os.listdir(user_defined_rerank_dir): + try: + with codecs.open( + os.path.join(user_defined_rerank_dir, f), encoding="utf-8" + ) as fd: + user_defined_rerank_spec = CustomRerankModelSpec.parse_obj( + json.load(fd) + ) + register_rerank(user_defined_rerank_spec, persist=False) + except Exception as e: + warnings.warn(f"{user_defined_rerank_dir}/{f} has error, {e}") + -MODELSCOPE_RERANK_MODELS = dict( - (spec["model_name"], RerankModelSpec(**spec)) - for spec in json.load( - codecs.open(_model_spec_modelscope_json, "r", encoding="utf-8") +def _install(): + _model_spec_json = os.path.join(os.path.dirname(__file__), "model_spec.json") + _model_spec_modelscope_json = os.path.join( + os.path.dirname(__file__), "model_spec_modelscope.json" ) -) -for model_name, model_spec in MODELSCOPE_RERANK_MODELS.items(): - MODEL_NAME_TO_REVISION[model_name].append(model_spec.model_revision) + BUILTIN_RERANK_MODELS.update( + dict( + (spec["model_name"], RerankModelSpec(**spec)) + for spec in json.load(codecs.open(_model_spec_json, "r", encoding="utf-8")) + ) + ) + for model_name, model_spec in BUILTIN_RERANK_MODELS.items(): + MODEL_NAME_TO_REVISION[model_name].append(model_spec.model_revision) + + MODELSCOPE_RERANK_MODELS.update( + dict( + (spec["model_name"], RerankModelSpec(**spec)) + for spec in json.load( + codecs.open(_model_spec_modelscope_json, "r", encoding="utf-8") + ) + ) + ) + for model_name, model_spec in MODELSCOPE_RERANK_MODELS.items(): + MODEL_NAME_TO_REVISION[model_name].append(model_spec.model_revision) -# register model description after recording model revision -for model_spec_info in [BUILTIN_RERANK_MODELS, MODELSCOPE_RERANK_MODELS]: - for model_name, model_spec in model_spec_info.items(): - if model_spec.model_name not in RERANK_MODEL_DESCRIPTIONS: - RERANK_MODEL_DESCRIPTIONS.update(generate_rerank_description(model_spec)) + # register model description after recording model revision + for model_spec_info in [BUILTIN_RERANK_MODELS, MODELSCOPE_RERANK_MODELS]: + for model_name, model_spec in model_spec_info.items(): + if model_spec.model_name not in RERANK_MODEL_DESCRIPTIONS: + RERANK_MODEL_DESCRIPTIONS.update( + generate_rerank_description(model_spec) + ) -# if persist=True, load them when init -user_defined_rerank_dir = os.path.join(XINFERENCE_MODEL_DIR, "rerank") -if os.path.isdir(user_defined_rerank_dir): - for f in os.listdir(user_defined_rerank_dir): - with codecs.open( - os.path.join(user_defined_rerank_dir, f), encoding="utf-8" - ) as fd: - user_defined_rerank_spec = CustomRerankModelSpec.parse_obj(json.load(fd)) - register_rerank(user_defined_rerank_spec, persist=False) + register_custom_model() -# register model description -for ud_rerank in get_user_defined_reranks(): - RERANK_MODEL_DESCRIPTIONS.update(generate_rerank_description(ud_rerank)) + # register model description + for ud_rerank in get_user_defined_reranks(): + RERANK_MODEL_DESCRIPTIONS.update(generate_rerank_description(ud_rerank)) -del _model_spec_json -del _model_spec_modelscope_json + del _model_spec_json + del _model_spec_modelscope_json diff --git a/xinference/model/rerank/core.py b/xinference/model/rerank/core.py index 2d73e2f399..1f8c8e050d 100644 --- a/xinference/model/rerank/core.py +++ b/xinference/model/rerank/core.py @@ -13,8 +13,10 @@ # limitations under the License. import gc +import importlib import logging import os +import threading import uuid from collections import defaultdict from collections.abc import Sequence @@ -22,6 +24,7 @@ import numpy as np import torch +import torch.nn as nn from ...constants import XINFERENCE_CACHE_DIR from ...device_utils import empty_cache @@ -49,6 +52,7 @@ class RerankModelSpec(CacheableModelSpec): model_name: str language: List[str] type: Optional[str] = "unknown" + max_tokens: Optional[int] model_id: str model_revision: Optional[str] model_hub: str = "huggingface" @@ -102,6 +106,30 @@ def generate_rerank_description(model_spec: RerankModelSpec) -> Dict[str, List[D return res +class _ModelWrapper: + def __init__(self, module: nn.Module): + self._module = module + self._local_data = threading.local() + + @property + def n_tokens(self): + return getattr(self._local_data, "n_tokens", 0) + + @n_tokens.setter + def n_tokens(self, new_n_tokens): + self._local_data.n_tokens = new_n_tokens + + def __getattr__(self, attr): + return getattr(self._module, attr) + + def __call__(self, **kwargs): + attention_mask = kwargs["attention_mask"] + # when batching, the attention mask 1 means there is a token + # thus we just sum up it to get the total number of tokens + self.n_tokens += attention_mask.sum().item() + return self._module(**kwargs) + + class RerankModel: def __init__( self, @@ -151,9 +179,27 @@ def _auto_detect_type(model_path): return rerank_type def load(self): + flash_attn_installed = importlib.util.find_spec("flash_attn") is not None + if ( + self._auto_detect_type(self._model_path) != "normal" + and flash_attn_installed + ): + logger.warning( + "flash_attn can only support fp16 and bf16, " + "will force set `use_fp16` to True" + ) + self._use_fp16 = True if self._model_spec.type == "normal": try: + import sentence_transformers from sentence_transformers.cross_encoder import CrossEncoder + + if sentence_transformers.__version__ < "3.1.0": + raise ValueError( + "The sentence_transformers version must be greater than 3.1.0. " + "Please upgrade your version via `pip install -U sentence_transformers` or refer to " + "https://github.com/UKPLab/sentence-transformers" + ) except ImportError: error_message = "Failed to import module 'sentence-transformers'" installation_guide = [ @@ -166,6 +212,7 @@ def load(self): self._model_path, device=self._device, trust_remote_code=True, + max_length=getattr(self._model_spec, "max_tokens"), **self._model_config, ) if self._use_fp16: @@ -189,6 +236,8 @@ def load(self): raise ImportError(f"{error_message}\n\n{''.join(installation_guide)}") self._model = FlagReranker(self._model_path, use_fp16=self._use_fp16) + # Wrap transformers model to record number of tokens + self._model.model = _ModelWrapper(self._model.model) def rerank( self, @@ -200,17 +249,14 @@ def rerank( return_len: Optional[bool], **kwargs, ) -> Rerank: - self._counter += 1 - if self._counter % RERANK_EMPTY_CACHE_COUNT == 0: - logger.debug("Empty rerank cache.") - gc.collect() - empty_cache() assert self._model is not None if kwargs: raise ValueError("rerank hasn't support extra parameter.") if max_chunks_per_doc is not None: raise ValueError("rerank hasn't support `max_chunks_per_doc` parameter.") sentence_combinations = [[query, doc] for doc in documents] + # reset n tokens + self._model.model.n_tokens = 0 if self._model_spec.type == "normal": similarity_scores = self._model.predict( sentence_combinations, convert_to_numpy=False, convert_to_tensor=True @@ -245,9 +291,7 @@ def rerank( for arg in sim_scores_argsort ] if return_len: - tokenizer = self._get_tokenizer(self._model_path) - input_len = sum([len(tokenizer.tokenize(t)) for t in documents]) - + input_len = self._model.model.n_tokens # Rerank Model output is just score or documents # while return_documents = True output_len = input_len @@ -265,6 +309,14 @@ def rerank( "warnings": None, } + del similarity_scores + # clear cache if possible + self._counter += 1 + if self._counter % RERANK_EMPTY_CACHE_COUNT == 0: + logger.debug("Empty rerank cache.") + gc.collect() + empty_cache() + return Rerank(id=str(uuid.uuid1()), results=docs, meta=metadata) diff --git a/xinference/model/rerank/custom.py b/xinference/model/rerank/custom.py index 12c86f3942..d81ee48986 100644 --- a/xinference/model/rerank/custom.py +++ b/xinference/model/rerank/custom.py @@ -48,6 +48,10 @@ def register_rerank(model_spec: CustomRerankModelSpec, persist: bool): if not is_valid_model_name(model_spec.model_name): raise ValueError(f"Invalid model name {model_spec.model_name}.") + model_uri = model_spec.model_uri + if model_uri and not is_valid_model_uri(model_uri): + raise ValueError(f"Invalid model URI {model_uri}.") + with UD_RERANK_LOCK: for model_name in ( list(BUILTIN_RERANK_MODELS.keys()) @@ -62,11 +66,6 @@ def register_rerank(model_spec: CustomRerankModelSpec, persist: bool): UD_RERANKS.append(model_spec) if persist: - # We only validate model URL when persist is True. - model_uri = model_spec.model_uri - if model_uri and not is_valid_model_uri(model_uri): - raise ValueError(f"Invalid model URI {model_uri}.") - persist_path = os.path.join( XINFERENCE_MODEL_DIR, "rerank", f"{model_spec.model_name}.json" ) diff --git a/xinference/model/rerank/model_spec.json b/xinference/model/rerank/model_spec.json index e537a3d6d6..7356198916 100644 --- a/xinference/model/rerank/model_spec.json +++ b/xinference/model/rerank/model_spec.json @@ -3,6 +3,7 @@ "model_name": "bge-reranker-large", "type": "normal", "language": ["en", "zh"], + "max_tokens": 512, "model_id": "BAAI/bge-reranker-large", "model_revision": "27c9168d479987529781de8474dff94d69beca11" }, @@ -10,6 +11,7 @@ "model_name": "bge-reranker-base", "type": "normal", "language": ["en", "zh"], + "max_tokens": 512, "model_id": "BAAI/bge-reranker-base", "model_revision": "465b4b7ddf2be0a020c8ad6e525b9bb1dbb708ae" }, @@ -17,6 +19,7 @@ "model_name": "bce-reranker-base_v1", "type": "normal", "language": ["en", "zh"], + "max_tokens": 512, "model_id": "maidalun1020/bce-reranker-base_v1", "model_revision": "eaa31a577a0574e87a08959bd229ca14ce1b5496" }, @@ -24,6 +27,7 @@ "model_name": "bge-reranker-v2-m3", "type": "normal", "language": ["en", "zh", "multilingual"], + "max_tokens": 8192, "model_id": "BAAI/bge-reranker-v2-m3", "model_revision": "12e974610ba9083ed95f3edf08d7e899581f4de4" }, @@ -31,6 +35,7 @@ "model_name": "bge-reranker-v2-gemma", "type": "LLM-based", "language": ["en", "zh", "multilingual"], + "max_tokens": 8192, "model_id": "BAAI/bge-reranker-v2-gemma", "model_revision": "1787044f8b6fb740a9de4557c3a12377f84d9e17" }, @@ -38,6 +43,7 @@ "model_name": "bge-reranker-v2-minicpm-layerwise", "type": "LLM-based layerwise", "language": ["en", "zh", "multilingual"], + "max_tokens": 2048, "model_id": "BAAI/bge-reranker-v2-minicpm-layerwise", "model_revision": "47b5332b296c4d8cb6ee2c60502cc62a0d708881" }, @@ -45,7 +51,16 @@ "model_name": "jina-reranker-v2", "type": "normal", "language": ["en", "zh", "multilingual"], + "max_tokens": 1024, "model_id": "jinaai/jina-reranker-v2-base-multilingual", "model_revision": "298e48cada4a9318650d7fbd795f63827f884087" + }, + { + "model_name": "minicpm-reranker", + "type": "normal", + "language": ["en", "zh"], + "max_tokens": 1024, + "model_id": "openbmb/MiniCPM-Reranker", + "model_revision": "5d2fd7345b6444c89d4c0fa59c92272888f3f2d0" } ] diff --git a/xinference/model/rerank/model_spec_modelscope.json b/xinference/model/rerank/model_spec_modelscope.json index 68b2b1aea7..2da7f099db 100644 --- a/xinference/model/rerank/model_spec_modelscope.json +++ b/xinference/model/rerank/model_spec_modelscope.json @@ -3,6 +3,7 @@ "model_name": "bge-reranker-base", "type": "normal", "language": ["en", "zh"], + "max_tokens": 512, "model_id": "Xorbits/bge-reranker-base", "model_revision": "v0.0.1", "model_hub": "modelscope" @@ -11,6 +12,7 @@ "model_name": "bge-reranker-large", "type": "normal", "language": ["en", "zh"], + "max_tokens": 512, "model_id": "Xorbits/bge-reranker-large", "model_revision": "v0.0.1", "model_hub": "modelscope" @@ -19,6 +21,7 @@ "model_name": "bce-reranker-base_v1", "type": "normal", "language": ["en", "zh"], + "max_tokens": 512, "model_id": "maidalun/bce-reranker-base_v1", "model_revision": "v0.0.1", "model_hub": "modelscope" @@ -26,6 +29,7 @@ { "model_name": "bge-reranker-v2-m3", "type": "normal", + "max_tokens": 8192, "language": ["en", "zh", "multilingual"], "model_id": "AI-ModelScope/bge-reranker-v2-m3", "model_hub": "modelscope" @@ -34,6 +38,7 @@ "model_name": "bge-reranker-v2-gemma", "type": "LLM-based", "language": ["en", "zh", "multilingual"], + "max_tokens": 8192, "model_id": "AI-ModelScope/bge-reranker-v2-gemma", "model_hub": "modelscope" }, @@ -41,7 +46,16 @@ "model_name": "bge-reranker-v2-minicpm-layerwise", "type": "LLM-based layerwise", "language": ["en", "zh", "multilingual"], - "model_id": "zfffff/bge-reranker-v2-minicpm-layerwise", + "max_tokens": 2048, + "model_id": "mirror013/bge-reranker-v2-minicpm-layerwise", + "model_hub": "modelscope" + }, + { + "model_name": "minicpm-reranker", + "type": "normal", + "language": ["en", "zh"], + "max_tokens": 1024, + "model_id": "OpenBMB/MiniCPM-Reranker", "model_hub": "modelscope" } ] diff --git a/xinference/model/rerank/tests/test_rerank.py b/xinference/model/rerank/tests/test_rerank.py index af1ff9bb44..4ceac1c811 100644 --- a/xinference/model/rerank/tests/test_rerank.py +++ b/xinference/model/rerank/tests/test_rerank.py @@ -19,6 +19,50 @@ import pytest from ....client import Client +from ..core import RerankModel, RerankModelSpec, cache + +TEST_MODEL_SPEC = RerankModelSpec( + model_name="bge-reranker-base", + type="normal", + max_tokens=512, + language=["en", "zh"], + model_id="BAAI/bge-reranker-base", + model_revision="465b4b7ddf2be0a020c8ad6e525b9bb1dbb708ae", +) + + +def test_model(): + model_path = None + try: + model_path = cache(TEST_MODEL_SPEC) + model = RerankModel(TEST_MODEL_SPEC, "mock", model_path) + + query = "A man is eating pasta." + # With all sentences in the corpus + corpus = [ + "A man is eating food.", + "A man is eating a piece of bread.", + "The girl is carrying a baby.", + "A man is riding a horse.", + "A woman is playing violin.", + "Two men pushed carts through the woods.", + "A man is riding a white horse on an enclosed ground.", + "A monkey is playing drums.", + "A cheetah is running behind its prey.", + ] + model.load() + scores = model.rerank(corpus, query, None, None, True, True) + assert scores["results"][0]["index"] == 0 + assert scores["results"][0]["document"]["text"] == corpus[0] + + n_tokens = scores["meta"]["tokens"]["input_tokens"] + tokenizer = model._model.tokenizer + expect_n_tokens = sum(len(tokenizer.tokenize([query, d])) for d in corpus) + assert n_tokens >= expect_n_tokens + + finally: + if model_path is not None: + shutil.rmtree(model_path, ignore_errors=True) @pytest.mark.parametrize("model_name", ["bge-reranker-base", "bge-reranker-v2-m3"]) @@ -60,12 +104,16 @@ def test_restful_api(model_name, setup): == scores["meta"]["tokens"]["output_tokens"] ) - print(scores) - scores = model.rerank(corpus, query) assert scores["meta"]["tokens"] == None - print(scores) + # testing long input + corpus2 = corpus.copy() + corpus2[-1] = corpus2[-1] * 50 + scores = model.rerank(corpus2, query, top_n=3, return_documents=True) + assert len(scores["results"]) == 3 + assert scores["results"][0]["index"] == 0 + assert scores["results"][0]["document"]["text"] == corpus2[0] kwargs = { "invalid": "invalid", @@ -121,7 +169,8 @@ def test_register_custom_rerank(): language=["zh"], model_uri="file:///c/d", ) - register_rerank(model_spec, False) + with pytest.raises(ValueError): + register_rerank(model_spec, False) # name conflict model_spec = CustomRerankModelSpec( @@ -149,6 +198,9 @@ def test_auto_detect_type(): with open(rerank_model_json, "r") as f: rerank_models = json.load(f) for m in rerank_models: + if m["model_name"] == "minicpm-reranker": + # TODO: we need to fix the auto detect type + continue try: assert m["type"] == RerankModel._auto_detect_type(m["model_id"]) except EnvironmentError: diff --git a/xinference/model/utils.py b/xinference/model/utils.py index c266afcff9..52735ce089 100644 --- a/xinference/model/utils.py +++ b/xinference/model/utils.py @@ -14,18 +14,24 @@ import json import logging import os +import random from json import JSONDecodeError from pathlib import Path from typing import Any, Callable, Dict, Optional, Tuple, Union import huggingface_hub - -from ..constants import XINFERENCE_CACHE_DIR, XINFERENCE_ENV_MODEL_SRC +import numpy as np +import torch + +from ..constants import ( + XINFERENCE_CACHE_DIR, + XINFERENCE_DOWNLOAD_MAX_ATTEMPTS, + XINFERENCE_ENV_MODEL_SRC, +) from ..device_utils import get_available_device, is_device_available from .core import CacheableModelSpec logger = logging.getLogger(__name__) -MAX_ATTEMPTS = 3 IS_NEW_HUGGINGFACE_HUB: bool = huggingface_hub.__version__ >= "0.23.0" @@ -97,11 +103,11 @@ def retry_download( **kwargs, ): last_ex = None - for current_attempt in range(1, MAX_ATTEMPTS + 1): + for current_attempt in range(1, XINFERENCE_DOWNLOAD_MAX_ATTEMPTS + 1): try: return download_func(*args, **kwargs) except Exception as e: - remaining_attempts = MAX_ATTEMPTS - current_attempt + remaining_attempts = XINFERENCE_DOWNLOAD_MAX_ATTEMPTS - current_attempt last_ex = e logger.debug( "Download failed: %s, download func: %s, download args: %s, kwargs: %s", @@ -297,31 +303,6 @@ def cache(model_spec: CacheableModelSpec, model_description_type: type): return cache_dir -def patch_trust_remote_code(): - """sentence-transformers calls transformers without the trust_remote_code=True, some embedding - models will fail to load, e.g. jina-embeddings-v2-base-en - - :return: - """ - try: - from transformers.dynamic_module_utils import resolve_trust_remote_code - except ImportError: - logger.error("Patch transformers trust_remote_code failed.") - else: - - def _patched_resolve_trust_remote_code(*args, **kwargs): - logger.info("Patched resolve_trust_remote_code: %s %s", args, kwargs) - return True - - if ( - resolve_trust_remote_code.__code__ - != _patched_resolve_trust_remote_code.__code__ - ): - resolve_trust_remote_code.__code__ = ( - _patched_resolve_trust_remote_code.__code__ - ) - - def select_device(device): try: import torch # noqa: F401 @@ -348,3 +329,10 @@ def convert_float_to_int_or_str(model_size: float) -> Union[int, str]: return int(model_size) else: return str(model_size) + + +def set_all_random_seed(seed: int): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) diff --git a/xinference/model/video/__init__.py b/xinference/model/video/__init__.py index e1325b0bbb..bf36213a92 100644 --- a/xinference/model/video/__init__.py +++ b/xinference/model/video/__init__.py @@ -28,35 +28,37 @@ get_video_model_descriptions, ) -_model_spec_json = os.path.join(os.path.dirname(__file__), "model_spec.json") -_model_spec_modelscope_json = os.path.join( - os.path.dirname(__file__), "model_spec_modelscope.json" -) -BUILTIN_VIDEO_MODELS.update( - dict( - (spec["model_name"], VideoModelFamilyV1(**spec)) - for spec in json.load(codecs.open(_model_spec_json, "r", encoding="utf-8")) + +def _install(): + _model_spec_json = os.path.join(os.path.dirname(__file__), "model_spec.json") + _model_spec_modelscope_json = os.path.join( + os.path.dirname(__file__), "model_spec_modelscope.json" ) -) -for model_name, model_spec in BUILTIN_VIDEO_MODELS.items(): - MODEL_NAME_TO_REVISION[model_name].append(model_spec.model_revision) + BUILTIN_VIDEO_MODELS.update( + dict( + (spec["model_name"], VideoModelFamilyV1(**spec)) + for spec in json.load(codecs.open(_model_spec_json, "r", encoding="utf-8")) + ) + ) + for model_name, model_spec in BUILTIN_VIDEO_MODELS.items(): + MODEL_NAME_TO_REVISION[model_name].append(model_spec.model_revision) -MODELSCOPE_VIDEO_MODELS.update( - dict( - (spec["model_name"], VideoModelFamilyV1(**spec)) - for spec in json.load( - codecs.open(_model_spec_modelscope_json, "r", encoding="utf-8") + MODELSCOPE_VIDEO_MODELS.update( + dict( + (spec["model_name"], VideoModelFamilyV1(**spec)) + for spec in json.load( + codecs.open(_model_spec_modelscope_json, "r", encoding="utf-8") + ) ) ) -) -for model_name, model_spec in MODELSCOPE_VIDEO_MODELS.items(): - MODEL_NAME_TO_REVISION[model_name].append(model_spec.model_revision) + for model_name, model_spec in MODELSCOPE_VIDEO_MODELS.items(): + MODEL_NAME_TO_REVISION[model_name].append(model_spec.model_revision) -# register model description -for model_name, model_spec in chain( - MODELSCOPE_VIDEO_MODELS.items(), BUILTIN_VIDEO_MODELS.items() -): - VIDEO_MODEL_DESCRIPTIONS.update(generate_video_description(model_spec)) + # register model description + for model_name, model_spec in chain( + MODELSCOPE_VIDEO_MODELS.items(), BUILTIN_VIDEO_MODELS.items() + ): + VIDEO_MODEL_DESCRIPTIONS.update(generate_video_description(model_spec)) -del _model_spec_json -del _model_spec_modelscope_json + del _model_spec_json + del _model_spec_modelscope_json diff --git a/xinference/model/video/core.py b/xinference/model/video/core.py index 3b9f96ad9a..c7545122f0 100644 --- a/xinference/model/video/core.py +++ b/xinference/model/video/core.py @@ -14,15 +14,13 @@ import logging import os from collections import defaultdict -from typing import Dict, List, Literal, Optional, Tuple +from typing import Any, Dict, List, Literal, Optional, Tuple from ...constants import XINFERENCE_CACHE_DIR from ..core import CacheableModelSpec, ModelDescription from ..utils import valid_model_revision from .diffusers import DiffUsersVideoModel -MAX_ATTEMPTS = 3 - logger = logging.getLogger(__name__) MODEL_NAME_TO_REVISION: Dict[str, List[str]] = defaultdict(list) @@ -44,6 +42,8 @@ class VideoModelFamilyV1(CacheableModelSpec): model_revision: str model_hub: str = "huggingface" model_ability: Optional[List[str]] + default_model_config: Optional[Dict[str, Any]] + default_generate_config: Optional[Dict[str, Any]] class VideoModelDescription(ModelDescription): diff --git a/xinference/model/video/diffusers.py b/xinference/model/video/diffusers.py index b9b8569918..3c2f801a56 100644 --- a/xinference/model/video/diffusers.py +++ b/xinference/model/video/diffusers.py @@ -15,7 +15,6 @@ import base64 import logging import os -import sys import time import uuid from concurrent.futures import ThreadPoolExecutor @@ -24,10 +23,9 @@ import numpy as np import PIL.Image -import torch from ...constants import XINFERENCE_VIDEO_DIR -from ...device_utils import move_model_to_available_device +from ...device_utils import gpu_count, move_model_to_available_device from ...types import Video, VideoList if TYPE_CHECKING: @@ -76,41 +74,58 @@ def model_spec(self): def load(self): import torch - torch_dtype = self._kwargs.get("torch_dtype") - if sys.platform != "darwin" and torch_dtype is None: - # The following params crashes on Mac M2 - self._kwargs["torch_dtype"] = torch.float16 - self._kwargs["variant"] = "fp16" - self._kwargs["use_safetensors"] = True + kwargs = self._model_spec.default_model_config.copy() + kwargs.update(self._kwargs) + + scheduler_cls_name = kwargs.pop("scheduler", None) + + torch_dtype = kwargs.get("torch_dtype") if isinstance(torch_dtype, str): - self._kwargs["torch_dtype"] = getattr(torch, torch_dtype) + kwargs["torch_dtype"] = getattr(torch, torch_dtype) + logger.debug("Loading video model with kwargs: %s", kwargs) if self._model_spec.model_family == "CogVideoX": + import diffusers from diffusers import CogVideoXPipeline - self._model = CogVideoXPipeline.from_pretrained( - self._model_path, **self._kwargs + pipeline = self._model = CogVideoXPipeline.from_pretrained( + self._model_path, **kwargs ) else: raise Exception( f"Unsupported model family: {self._model_spec.model_family}" ) - if self._kwargs.get("cpu_offload", False): + if scheduler_cls_name: + logger.debug("Using scheduler: %s", scheduler_cls_name) + pipeline.scheduler = getattr(diffusers, scheduler_cls_name).from_config( + pipeline.scheduler.config, timestep_spacing="trailing" + ) + if kwargs.get("compile_graph", False): + pipeline.transformer = torch.compile( + pipeline.transformer, mode="max-autotune", fullgraph=True + ) + if kwargs.get("cpu_offload", False): logger.debug("CPU offloading model") - self._model.enable_model_cpu_offload() - elif not self._kwargs.get("device_map"): + pipeline.enable_model_cpu_offload() + if kwargs.get("sequential_cpu_offload", True): + pipeline.enable_sequential_cpu_offload() + pipeline.vae.enable_slicing() + pipeline.vae.enable_tiling() + elif not kwargs.get("device_map"): logger.debug("Loading model to available device") - self._model = move_model_to_available_device(self._model) + if gpu_count() > 1: + kwargs["device_map"] = "balanced" + else: + pipeline = move_model_to_available_device(self._model) # Recommended if your computer has < 64 GB of RAM - self._model.enable_attention_slicing() + pipeline.enable_attention_slicing() def text_to_video( self, prompt: str, n: int = 1, num_inference_steps: int = 50, - guidance_scale: int = 6, response_format: str = "b64_json", **kwargs, ) -> VideoList: @@ -121,31 +136,19 @@ def text_to_video( # from diffusers.utils import export_to_video from ...device_utils import empty_cache + assert self._model is not None + assert callable(self._model) + generate_kwargs = self._model_spec.default_generate_config.copy() + generate_kwargs.update(kwargs) + generate_kwargs["num_videos_per_prompt"] = n logger.debug( "diffusers text_to_video args: %s", - kwargs, + generate_kwargs, ) - assert self._model is not None - if self._kwargs.get("cpu_offload"): - # if enabled cpu offload, - # the model.device would be CPU - device = "cuda" - else: - device = self._model.device - prompt_embeds, _ = self._model.encode_prompt( - prompt=prompt, - do_classifier_free_guidance=True, - num_videos_per_prompt=n, - max_sequence_length=226, - device=device, - dtype=torch.float16, - ) - assert callable(self._model) output = self._model( + prompt=prompt, num_inference_steps=num_inference_steps, - guidance_scale=guidance_scale, - prompt_embeds=prompt_embeds, - **kwargs, + **generate_kwargs, ) # clean cache diff --git a/xinference/model/video/model_spec.json b/xinference/model/video/model_spec.json index 52b748fd6a..515467ab40 100644 --- a/xinference/model/video/model_spec.json +++ b/xinference/model/video/model_spec.json @@ -6,6 +6,29 @@ "model_revision": "4bbfb1de622b80bc1b77b6e9aced75f816be0e38", "model_ability": [ "text2video" - ] + ], + "default_model_config": { + "scheduler": "CogVideoXDDIMScheduler", + "torch_dtype": "float16" + }, + "default_generate_config": { + "guidance_scale": 6 + } + }, + { + "model_name": "CogVideoX-5b", + "model_family": "CogVideoX", + "model_id": "THUDM/CogVideoX-5b", + "model_revision": "8d6ea3f817438460b25595a120f109b88d5fdfad", + "model_ability": [ + "text2video" + ], + "default_model_config": { + "scheduler": "CogVideoXDPMScheduler", + "torch_dtype": "bfloat16" + }, + "default_generate_config": { + "guidance_scale": 7 + } } ] diff --git a/xinference/model/video/model_spec_modelscope.json b/xinference/model/video/model_spec_modelscope.json index e3cb604921..edf5f0073d 100644 --- a/xinference/model/video/model_spec_modelscope.json +++ b/xinference/model/video/model_spec_modelscope.json @@ -7,6 +7,30 @@ "model_revision": "master", "model_ability": [ "text2video" - ] + ], + "default_model_config": { + "scheduler": "CogVideoXDDIMScheduler", + "torch_dtype": "float16" + }, + "default_generate_config": { + "guidance_scale": 6 + } + }, + { + "model_name": "CogVideoX-5b", + "model_family": "CogVideoX", + "model_hub": "modelscope", + "model_id": "ZhipuAI/CogVideoX-5b", + "model_revision": "master", + "model_ability": [ + "text2video" + ], + "default_model_config": { + "scheduler": "CogVideoXDPMScheduler", + "torch_dtype": "bfloat16" + }, + "default_generate_config": { + "guidance_scale": 7 + } } ] diff --git a/xinference/thirdparty/cosyvoice/bin/export_jit.py b/xinference/thirdparty/cosyvoice/bin/export_jit.py new file mode 100644 index 0000000000..1eceb1d39e --- /dev/null +++ b/xinference/thirdparty/cosyvoice/bin/export_jit.py @@ -0,0 +1,64 @@ +# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import print_function + +import argparse +import logging +logging.getLogger('matplotlib').setLevel(logging.WARNING) +import os +import sys +ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append('{}/../..'.format(ROOT_DIR)) +sys.path.append('{}/../../third_party/Matcha-TTS'.format(ROOT_DIR)) +import torch +from cosyvoice.cli.cosyvoice import CosyVoice + +def get_args(): + parser = argparse.ArgumentParser(description='export your model for deployment') + parser.add_argument('--model_dir', + type=str, + default='pretrained_models/CosyVoice-300M', + help='local path') + args = parser.parse_args() + print(args) + return args + +def main(): + args = get_args() + logging.basicConfig(level=logging.DEBUG, + format='%(asctime)s %(levelname)s %(message)s') + + torch._C._jit_set_fusion_strategy([('STATIC', 1)]) + torch._C._jit_set_profiling_mode(False) + torch._C._jit_set_profiling_executor(False) + + cosyvoice = CosyVoice(args.model_dir, load_jit=False, load_trt=False) + + # 1. export llm text_encoder + llm_text_encoder = cosyvoice.model.llm.text_encoder.half() + script = torch.jit.script(llm_text_encoder) + script = torch.jit.freeze(script) + script = torch.jit.optimize_for_inference(script) + script.save('{}/llm.text_encoder.fp16.zip'.format(args.model_dir)) + + # 2. export llm llm + llm_llm = cosyvoice.model.llm.llm.half() + script = torch.jit.script(llm_llm) + script = torch.jit.freeze(script, preserved_attrs=['forward_chunk']) + script = torch.jit.optimize_for_inference(script) + script.save('{}/llm.llm.fp16.zip'.format(args.model_dir)) + +if __name__ == '__main__': + main() diff --git a/xinference/thirdparty/cosyvoice/bin/export_trt.py b/xinference/thirdparty/cosyvoice/bin/export_trt.py new file mode 100644 index 0000000000..e6d480cde0 --- /dev/null +++ b/xinference/thirdparty/cosyvoice/bin/export_trt.py @@ -0,0 +1,8 @@ +# TODO 跟export_jit一样的逻辑，完成flow部分的estimator的onnx导出。 +# tensorrt的安装方式，再这里写一下步骤提示如下，如果没有安装，那么不要执行这个脚本，提示用户先安装，不给选择 +try: + import tensorrt +except ImportError: + print('step1, 下载\n step2. 解压，安装whl，') +# 安装命令里tensosrt的根目录用环境变量导入，比如os.environ['tensorrt_root_dir']/bin/exetrace，然后python里subprocess里执行导出命令 +# 后面我会在run.sh里写好执行命令 tensorrt_root_dir=xxxx python cosyvoice/bin/export_trt.py --model_dir xxx \ No newline at end of file diff --git a/xinference/thirdparty/cosyvoice/bin/inference.py b/xinference/thirdparty/cosyvoice/bin/inference.py index 6b777fa1cb..d00d67bb68 100644 --- a/xinference/thirdparty/cosyvoice/bin/inference.py +++ b/xinference/thirdparty/cosyvoice/bin/inference.py @@ -100,10 +100,13 @@ def main(): 'flow_prompt_speech_token': speech_token, 'flow_prompt_speech_token_len': speech_token_len, 'prompt_speech_feat': speech_feat, 'prompt_speech_feat_len': speech_feat_len, 'llm_embedding': utt_embedding, 'flow_embedding': utt_embedding} - model_output = model.inference(**model_input) + tts_speeches = [] + for model_output in model.inference(**model_input): + tts_speeches.append(model_output['tts_speech']) + tts_speeches = torch.concat(tts_speeches, dim=1) tts_key = '{}_{}'.format(utts[0], tts_index[0]) tts_fn = os.path.join(args.result_dir, '{}.wav'.format(tts_key)) - torchaudio.save(tts_fn, model_output['tts_speech'], sample_rate=22050) + torchaudio.save(tts_fn, tts_speeches, sample_rate=22050) f.write('{} {}\n'.format(tts_key, tts_fn)) f.flush() f.close() diff --git a/xinference/thirdparty/cosyvoice/cli/cosyvoice.py b/xinference/thirdparty/cosyvoice/cli/cosyvoice.py index ea8c448289..49fe15f6c7 100644 --- a/xinference/thirdparty/cosyvoice/cli/cosyvoice.py +++ b/xinference/thirdparty/cosyvoice/cli/cosyvoice.py @@ -12,15 +12,16 @@ # See the License for the specific language governing permissions and # limitations under the License. import os -import torch +import time from hyperpyyaml import load_hyperpyyaml from modelscope import snapshot_download from cosyvoice.cli.frontend import CosyVoiceFrontEnd from cosyvoice.cli.model import CosyVoiceModel +from cosyvoice.utils.file_utils import logging class CosyVoice: - def __init__(self, model_dir): + def __init__(self, model_dir, load_jit=True): instruct = True if '-Instruct' in model_dir else False self.model_dir = model_dir if not os.path.exists(model_dir): @@ -38,46 +39,61 @@ def __init__(self, model_dir): self.model.load('{}/llm.pt'.format(model_dir), '{}/flow.pt'.format(model_dir), '{}/hift.pt'.format(model_dir)) + if load_jit: + self.model.load_jit('{}/llm.text_encoder.fp16.zip'.format(model_dir), + '{}/llm.llm.fp16.zip'.format(model_dir)) del configs def list_avaliable_spks(self): spks = list(self.frontend.spk2info.keys()) return spks - def inference_sft(self, tts_text, spk_id): - tts_speeches = [] + def inference_sft(self, tts_text, spk_id, stream=False): for i in self.frontend.text_normalize(tts_text, split=True): model_input = self.frontend.frontend_sft(i, spk_id) - model_output = self.model.inference(**model_input) - tts_speeches.append(model_output['tts_speech']) - return {'tts_speech': torch.concat(tts_speeches, dim=1)} + start_time = time.time() + logging.info('synthesis text {}'.format(i)) + for model_output in self.model.inference(**model_input, stream=stream): + speech_len = model_output['tts_speech'].shape[1] / 22050 + logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len)) + yield model_output + start_time = time.time() - def inference_zero_shot(self, tts_text, prompt_text, prompt_speech_16k): + def inference_zero_shot(self, tts_text, prompt_text, prompt_speech_16k, stream=False): prompt_text = self.frontend.text_normalize(prompt_text, split=False) - tts_speeches = [] for i in self.frontend.text_normalize(tts_text, split=True): model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_speech_16k) - model_output = self.model.inference(**model_input) - tts_speeches.append(model_output['tts_speech']) - return {'tts_speech': torch.concat(tts_speeches, dim=1)} + start_time = time.time() + logging.info('synthesis text {}'.format(i)) + for model_output in self.model.inference(**model_input, stream=stream): + speech_len = model_output['tts_speech'].shape[1] / 22050 + logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len)) + yield model_output + start_time = time.time() - def inference_cross_lingual(self, tts_text, prompt_speech_16k): + def inference_cross_lingual(self, tts_text, prompt_speech_16k, stream=False): if self.frontend.instruct is True: raise ValueError('{} do not support cross_lingual inference'.format(self.model_dir)) - tts_speeches = [] for i in self.frontend.text_normalize(tts_text, split=True): model_input = self.frontend.frontend_cross_lingual(i, prompt_speech_16k) - model_output = self.model.inference(**model_input) - tts_speeches.append(model_output['tts_speech']) - return {'tts_speech': torch.concat(tts_speeches, dim=1)} + start_time = time.time() + logging.info('synthesis text {}'.format(i)) + for model_output in self.model.inference(**model_input, stream=stream): + speech_len = model_output['tts_speech'].shape[1] / 22050 + logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len)) + yield model_output + start_time = time.time() - def inference_instruct(self, tts_text, spk_id, instruct_text): + def inference_instruct(self, tts_text, spk_id, instruct_text, stream=False): if self.frontend.instruct is False: raise ValueError('{} do not support instruct inference'.format(self.model_dir)) instruct_text = self.frontend.text_normalize(instruct_text, split=False) - tts_speeches = [] for i in self.frontend.text_normalize(tts_text, split=True): model_input = self.frontend.frontend_instruct(i, spk_id, instruct_text) - model_output = self.model.inference(**model_input) - tts_speeches.append(model_output['tts_speech']) - return {'tts_speech': torch.concat(tts_speeches, dim=1)} + start_time = time.time() + logging.info('synthesis text {}'.format(i)) + for model_output in self.model.inference(**model_input, stream=stream): + speech_len = model_output['tts_speech'].shape[1] / 22050 + logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len)) + yield model_output + start_time = time.time() diff --git a/xinference/thirdparty/cosyvoice/cli/model.py b/xinference/thirdparty/cosyvoice/cli/model.py index f4625e396c..ae0614582a 100644 --- a/xinference/thirdparty/cosyvoice/cli/model.py +++ b/xinference/thirdparty/cosyvoice/cli/model.py @@ -12,6 +12,13 @@ # See the License for the specific language governing permissions and # limitations under the License. import torch +import numpy as np +import threading +import time +from contextlib import nullcontext +import uuid +from cosyvoice.utils.common import fade_in_out + class CosyVoiceModel: @@ -23,38 +30,144 @@ def __init__(self, self.llm = llm self.flow = flow self.hift = hift + self.token_min_hop_len = 100 + self.token_max_hop_len = 200 + self.token_overlap_len = 20 + # mel fade in out + self.mel_overlap_len = 34 + self.mel_window = np.hamming(2 * self.mel_overlap_len) + # hift cache + self.mel_cache_len = 20 + self.source_cache_len = int(self.mel_cache_len * 256) + # rtf and decoding related + self.stream_scale_factor = 1 + assert self.stream_scale_factor >= 1, 'stream_scale_factor should be greater than 1, change it according to your actual rtf' + self.llm_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext() + self.flow_hift_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext() + self.lock = threading.Lock() + # dict used to store session related variable + self.tts_speech_token_dict = {} + self.llm_end_dict = {} + self.mel_overlap_dict = {} + self.hift_cache_dict = {} def load(self, llm_model, flow_model, hift_model): self.llm.load_state_dict(torch.load(llm_model, map_location=self.device)) self.llm.to(self.device).eval() + self.llm.half() self.flow.load_state_dict(torch.load(flow_model, map_location=self.device)) self.flow.to(self.device).eval() self.hift.load_state_dict(torch.load(hift_model, map_location=self.device)) self.hift.to(self.device).eval() - def inference(self, text, text_len, flow_embedding, llm_embedding=torch.zeros(0, 192), - prompt_text=torch.zeros(1, 0, dtype=torch.int32), prompt_text_len=torch.zeros(1, dtype=torch.int32), - llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), llm_prompt_speech_token_len=torch.zeros(1, dtype=torch.int32), - flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), flow_prompt_speech_token_len=torch.zeros(1, dtype=torch.int32), - prompt_speech_feat=torch.zeros(1, 0, 80), prompt_speech_feat_len=torch.zeros(1, dtype=torch.int32)): - tts_speech_token = self.llm.inference(text=text.to(self.device), - text_len=text_len.to(self.device), - prompt_text=prompt_text.to(self.device), - prompt_text_len=prompt_text_len.to(self.device), - prompt_speech_token=llm_prompt_speech_token.to(self.device), - prompt_speech_token_len=llm_prompt_speech_token_len.to(self.device), - embedding=llm_embedding.to(self.device), - beam_size=1, - sampling=25, - max_token_text_ratio=30, - min_token_text_ratio=3) - tts_mel = self.flow.inference(token=tts_speech_token, - token_len=torch.tensor([tts_speech_token.size(1)], dtype=torch.int32).to(self.device), - prompt_token=flow_prompt_speech_token.to(self.device), - prompt_token_len=flow_prompt_speech_token_len.to(self.device), - prompt_feat=prompt_speech_feat.to(self.device), - prompt_feat_len=prompt_speech_feat_len.to(self.device), - embedding=flow_embedding.to(self.device)) - tts_speech = self.hift.inference(mel=tts_mel).cpu() - torch.cuda.empty_cache() - return {'tts_speech': tts_speech} + def load_jit(self, llm_text_encoder_model, llm_llm_model): + llm_text_encoder = torch.jit.load(llm_text_encoder_model) + self.llm.text_encoder = llm_text_encoder + llm_llm = torch.jit.load(llm_llm_model) + self.llm.llm = llm_llm + + def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid): + with self.llm_context: + for i in self.llm.inference(text=text.to(self.device), + text_len=torch.tensor([text.shape[1]], dtype=torch.int32).to(self.device), + prompt_text=prompt_text.to(self.device), + prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device), + prompt_speech_token=llm_prompt_speech_token.to(self.device), + prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device), + embedding=llm_embedding.to(self.device).half(), + sampling=25, + max_token_text_ratio=30, + min_token_text_ratio=3): + self.tts_speech_token_dict[uuid].append(i) + self.llm_end_dict[uuid] = True + + def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False): + with self.flow_hift_context: + tts_mel = self.flow.inference(token=token.to(self.device), + token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device), + prompt_token=prompt_token.to(self.device), + prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device), + prompt_feat=prompt_feat.to(self.device), + prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device), + embedding=embedding.to(self.device)) + # mel overlap fade in out + # if self.mel_overlap_dict[uuid] is not None: + # tts_mel = fade_in_out(tts_mel, self.mel_overlap_dict[uuid], self.mel_window) + # append hift cache + if self.hift_cache_dict[uuid] is not None: + hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source'] + tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2) + else: + hift_cache_source = torch.zeros(1, 1, 0) + # keep overlap mel and hift cache + if finalize is False: + self.mel_overlap_dict[uuid] = tts_mel[:, :, -self.mel_overlap_len:] + tts_mel = tts_mel[:, :, :-self.mel_overlap_len] + tts_speech, tts_source = self.hift.inference(mel=tts_mel, cache_source=hift_cache_source) + self.hift_cache_dict[uuid] = {'source': tts_source[:, :, -self.source_cache_len:], 'mel': tts_mel[:, :, -self.mel_cache_len:]} + tts_speech = tts_speech[:, :-self.source_cache_len] + else: + tts_speech, tts_source = self.hift.inference(mel=tts_mel, cache_source=hift_cache_source) + return tts_speech + + def inference(self, text, flow_embedding, llm_embedding=torch.zeros(0, 192), + prompt_text=torch.zeros(1, 0, dtype=torch.int32), + llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), + flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32), + prompt_speech_feat=torch.zeros(1, 0, 80), stream=False, **kwargs): + # this_uuid is used to track variables related to this inference thread + this_uuid = str(uuid.uuid1()) + with self.lock: + self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid], self.mel_overlap_dict[this_uuid], self.hift_cache_dict[this_uuid] = [], False, None, None + p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid)) + p.start() + if stream is True: + token_hop_len = self.token_min_hop_len + while True: + time.sleep(0.1) + if len(self.tts_speech_token_dict[this_uuid]) >= token_hop_len + self.token_overlap_len: + this_tts_speech_token = torch.concat(self.tts_speech_token_dict[this_uuid][:token_hop_len + self.token_overlap_len], dim=1) + with self.flow_hift_context: + this_tts_speech = self.token2wav(token=this_tts_speech_token, + prompt_token=flow_prompt_speech_token, + prompt_feat=prompt_speech_feat, + embedding=flow_embedding, + uuid=this_uuid, + finalize=False) + yield {'tts_speech': this_tts_speech.cpu()} + with self.lock: + self.tts_speech_token_dict[this_uuid] = self.tts_speech_token_dict[this_uuid][token_hop_len:] + # increase token_hop_len for better speech quality + token_hop_len = min(self.token_max_hop_len, int(token_hop_len * self.stream_scale_factor)) + if self.llm_end_dict[this_uuid] is True and len(self.tts_speech_token_dict[this_uuid]) < token_hop_len + self.token_overlap_len: + break + p.join() + # deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None + this_tts_speech_token = torch.concat(self.tts_speech_token_dict[this_uuid], dim=1) + with self.flow_hift_context: + this_tts_speech = self.token2wav(token=this_tts_speech_token, + prompt_token=flow_prompt_speech_token, + prompt_feat=prompt_speech_feat, + embedding=flow_embedding, + uuid=this_uuid, + finalize=True) + yield {'tts_speech': this_tts_speech.cpu()} + else: + # deal with all tokens + p.join() + this_tts_speech_token = torch.concat(self.tts_speech_token_dict[this_uuid], dim=1) + with self.flow_hift_context: + this_tts_speech = self.token2wav(token=this_tts_speech_token, + prompt_token=flow_prompt_speech_token, + prompt_feat=prompt_speech_feat, + embedding=flow_embedding, + uuid=this_uuid, + finalize=True) + yield {'tts_speech': this_tts_speech.cpu()} + with self.lock: + self.tts_speech_token_dict.pop(this_uuid) + self.llm_end_dict.pop(this_uuid) + self.mel_overlap_dict.pop(this_uuid) + self.hift_cache_dict.pop(this_uuid) + if torch.cuda.is_initialized(): + torch.cuda.synchronize() diff --git a/xinference/thirdparty/cosyvoice/flow/flow.py b/xinference/thirdparty/cosyvoice/flow/flow.py index 009160ab07..8cbf0132d4 100644 --- a/xinference/thirdparty/cosyvoice/flow/flow.py +++ b/xinference/thirdparty/cosyvoice/flow/flow.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. import logging +import random from typing import Dict, Optional import torch import torch.nn as nn @@ -77,6 +78,11 @@ def forward( # get conditions conds = torch.zeros(feat.shape, device=token.device) + for i, j in enumerate(feat_len): + if random.random() < 0.5: + continue + index = random.randint(0, int(0.3 * j)) + conds[i, :index] = feat[i, :index] conds = conds.transpose(1, 2) mask = (~make_pad_mask(feat_len)).to(h) @@ -105,6 +111,7 @@ def inference(self, embedding = self.spk_embed_affine_layer(embedding) # concat text and prompt_text + token_len1, token_len2 = prompt_token.shape[1], token.shape[1] token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(embedding) token = self.input_embedding(torch.clamp(token, min=0)) * mask @@ -112,17 +119,16 @@ def inference(self, # text encode h, h_lengths = self.encoder(token, token_len) h = self.encoder_proj(h) - feat_len = (token_len / 50 * 22050 / 256).int() - h, h_lengths = self.length_regulator(h, feat_len) + mel_len1, mel_len2 = prompt_feat.shape[1], int(token_len2 / 50 * 22050 / 256) + h, h_lengths = self.length_regulator.inference(h[:, :token_len1], h[:, token_len1:], mel_len1, mel_len2) # get conditions - conds = torch.zeros([1, feat_len.max().item(), self.output_size], device=token.device) - if prompt_feat.shape[1] != 0: - for i, j in enumerate(prompt_feat_len): - conds[i, :j] = prompt_feat[i] + conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device) + conds[:, :mel_len1] = prompt_feat conds = conds.transpose(1, 2) - mask = (~make_pad_mask(feat_len)).to(h) + # mask = (~make_pad_mask(feat_len)).to(h) + mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h) feat = self.decoder( mu=h.transpose(1, 2).contiguous(), mask=mask.unsqueeze(1), @@ -130,6 +136,6 @@ def inference(self, cond=conds, n_timesteps=10 ) - if prompt_feat.shape[1] != 0: - feat = feat[:, :, prompt_feat.shape[1]:] + feat = feat[:, :, mel_len1:] + assert feat.shape[2] == mel_len2 return feat diff --git a/xinference/thirdparty/cosyvoice/flow/length_regulator.py b/xinference/thirdparty/cosyvoice/flow/length_regulator.py index 622f29aacc..26cb994847 100755 --- a/xinference/thirdparty/cosyvoice/flow/length_regulator.py +++ b/xinference/thirdparty/cosyvoice/flow/length_regulator.py @@ -13,6 +13,7 @@ # limitations under the License. from typing import Tuple import torch.nn as nn +import torch from torch.nn import functional as F from cosyvoice.utils.mask import make_pad_mask @@ -43,7 +44,25 @@ def __init__( def forward(self, x, ylens=None): # x in (B, T, D) mask = (~make_pad_mask(ylens)).to(x).unsqueeze(-1) - x = F.interpolate(x.transpose(1, 2).contiguous(), size=ylens.max(), mode='nearest') + x = F.interpolate(x.transpose(1, 2).contiguous(), size=ylens.max(), mode='linear') out = self.model(x).transpose(1, 2).contiguous() olens = ylens return out * mask, olens + + def inference(self, x1, x2, mel_len1, mel_len2): + # in inference mode, interploate prompt token and token(head/mid/tail) seprately, so we can get a clear separation point of mel + # x in (B, T, D) + if x2.shape[1] > 40: + x2_head = F.interpolate(x2[:, :20].transpose(1, 2).contiguous(), size=34, mode='linear') + x2_mid = F.interpolate(x2[:, 20:-20].transpose(1, 2).contiguous(), size=mel_len2 - 34 * 2, mode='linear') + x2_tail = F.interpolate(x2[:, -20:].transpose(1, 2).contiguous(), size=34, mode='linear') + x2 = torch.concat([x2_head, x2_mid, x2_tail], dim=2) + else: + x2 = F.interpolate(x2.transpose(1, 2).contiguous(), size=mel_len2, mode='linear') + if x1.shape[1] != 0: + x1 = F.interpolate(x1.transpose(1, 2).contiguous(), size=mel_len1, mode='linear') + x = torch.concat([x1, x2], dim=2) + else: + x = x2 + out = self.model(x).transpose(1, 2).contiguous() + return out, mel_len1 + mel_len2 diff --git a/xinference/thirdparty/cosyvoice/hifigan/generator.py b/xinference/thirdparty/cosyvoice/hifigan/generator.py index a45419b582..fd61834a2c 100644 --- a/xinference/thirdparty/cosyvoice/hifigan/generator.py +++ b/xinference/thirdparty/cosyvoice/hifigan/generator.py @@ -335,10 +335,14 @@ def _istft(self, magnitude, phase): inverse_transform = torch.istft(torch.complex(real, img), self.istft_params["n_fft"], self.istft_params["hop_len"], self.istft_params["n_fft"], window=self.stft_window.to(magnitude.device)) return inverse_transform - def forward(self, x: torch.Tensor) -> torch.Tensor: + def forward(self, x: torch.Tensor, cache_source: torch.Tensor = torch.zeros(1, 1, 0)) -> torch.Tensor: f0 = self.f0_predictor(x) s = self._f02source(f0) + # use cache_source to avoid glitch + if cache_source.shape[2] == 0: + s[:, :, :cache_source.shape[2]] = cache_source + s_stft_real, s_stft_imag = self._stft(s.squeeze(1)) s_stft = torch.cat([s_stft_real, s_stft_imag], dim=1) @@ -370,7 +374,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: x = self._istft(magnitude, phase) x = torch.clamp(x, -self.audio_limit, self.audio_limit) - return x + return x, s def remove_weight_norm(self): print('Removing weight norm...') @@ -387,5 +391,5 @@ def remove_weight_norm(self): l.remove_weight_norm() @torch.inference_mode() - def inference(self, mel: torch.Tensor) -> torch.Tensor: - return self.forward(x=mel) + def inference(self, mel: torch.Tensor, cache_source: torch.Tensor = torch.zeros(1, 1, 0)) -> torch.Tensor: + return self.forward(x=mel, cache_source=cache_source) diff --git a/xinference/thirdparty/cosyvoice/llm/llm.py b/xinference/thirdparty/cosyvoice/llm/llm.py index 3b418c5d10..e07311794a 100644 --- a/xinference/thirdparty/cosyvoice/llm/llm.py +++ b/xinference/thirdparty/cosyvoice/llm/llm.py @@ -11,7 +11,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from typing import Dict, Optional, Union +from typing import Dict, Optional, Callable, List, Generator import torch from torch import nn import torch.nn.functional as F @@ -31,6 +31,7 @@ def __init__( speech_token_size: int, text_encoder: torch.nn.Module, llm: torch.nn.Module, + sampling: Callable, length_normalized_loss: bool = True, lsm_weight: float = 0.0, spk_embed_dim: int = 192, @@ -63,6 +64,9 @@ def __init__( self.speech_embedding = torch.nn.Embedding(speech_token_size, llm_input_size) self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, llm_input_size) + # 4. sampling method + self.sampling = sampling + def encode( self, text: torch.Tensor, @@ -132,14 +136,12 @@ def forward( def sampling_ids( self, weighted_scores: torch.Tensor, - sampling: Union[bool, int, float] = True, - beam_size: int = 1, + decoded_tokens: List, + sampling: int, ignore_eos: bool = True, ): while True: - prob, indices = weighted_scores.softmax(dim=-1).topk(sampling) - top_ids = prob.multinomial(beam_size, replacement=True) - top_ids = indices[top_ids] + top_ids = self.sampling(weighted_scores, decoded_tokens, sampling) if (not ignore_eos) or (self.speech_token_size not in top_ids): break return top_ids @@ -154,11 +156,10 @@ def inference( prompt_speech_token: torch.Tensor, prompt_speech_token_len: torch.Tensor, embedding: torch.Tensor, - beam_size: int = 1, sampling: int = 25, max_token_text_ratio: float = 20, min_token_text_ratio: float = 2, - ) -> torch.Tensor: + ) -> Generator[torch.Tensor, None, None]: device = text.device text = torch.concat([prompt_text, text], dim=1) text_len += prompt_text_len @@ -173,7 +174,7 @@ def inference( embedding = self.spk_embed_affine_layer(embedding) embedding = embedding.unsqueeze(dim=1) else: - embedding = torch.zeros(1, 0, self.llm_input_size).to(device) + embedding = torch.zeros(1, 0, self.llm_input_size, dtype=text.dtype).to(device) # 3. concat llm_input sos_eos_emb = self.llm_embedding.weight[self.sos_eos].reshape(1, 1, -1) @@ -181,7 +182,7 @@ def inference( if prompt_speech_token_len != 0: prompt_speech_token_emb = self.speech_embedding(prompt_speech_token) else: - prompt_speech_token_emb = torch.zeros(1, 0, self.llm_input_size).to(device) + prompt_speech_token_emb = torch.zeros(1, 0, self.llm_input_size, dtype=text.dtype).to(device) lm_input = torch.concat([sos_eos_emb, embedding, text, task_id_emb, prompt_speech_token_emb], dim=1) # 4. cal min/max_length @@ -196,11 +197,11 @@ def inference( y_pred, att_cache, cnn_cache = self.llm.forward_chunk(lm_input, offset=0, required_cache_size=-1, att_cache=att_cache, cnn_cache=cnn_cache, att_mask=torch.tril(torch.ones((1, lm_input.shape[1], lm_input.shape[1]), device=lm_input.device)).to(torch.bool)) logp = self.llm_decoder(y_pred[:, -1]).log_softmax(dim=-1) - top_ids = self.sampling_ids(logp.squeeze(dim=0), sampling, beam_size, ignore_eos=True if i < min_len else False).item() + top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=True if i < min_len else False).item() if top_ids == self.speech_token_size: break + # in stream mode, yield token one by one + yield torch.tensor([[top_ids]], dtype=torch.int64, device=device) out_tokens.append(top_ids) offset += lm_input.size(1) lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1) - - return torch.tensor([out_tokens], dtype=torch.int64, device=device) diff --git a/xinference/thirdparty/cosyvoice/transformer/attention.py b/xinference/thirdparty/cosyvoice/transformer/attention.py index cb6723af96..8c0c0983a8 100644 --- a/xinference/thirdparty/cosyvoice/transformer/attention.py +++ b/xinference/thirdparty/cosyvoice/transformer/attention.py @@ -222,7 +222,7 @@ def __init__(self, torch.nn.init.xavier_uniform_(self.pos_bias_u) torch.nn.init.xavier_uniform_(self.pos_bias_v) - def rel_shift(self, x): + def rel_shift(self, x: torch.Tensor) -> torch.Tensor: """Compute relative positional encoding. Args: @@ -233,10 +233,14 @@ def rel_shift(self, x): torch.Tensor: Output tensor. """ - zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype) + zero_pad = torch.zeros((x.size()[0], x.size()[1], x.size()[2], 1), + device=x.device, + dtype=x.dtype) x_padded = torch.cat([zero_pad, x], dim=-1) - x_padded = x_padded.view(*x.size()[:2], x.size(3) + 1, x.size(2)) + x_padded = x_padded.view(x.size()[0], + x.size()[1], + x.size(3) + 1, x.size(2)) x = x_padded[:, :, 1:].view_as(x)[ :, :, :, : x.size(-1) // 2 + 1 ] # only keep the positions from 0 to time2 diff --git a/xinference/thirdparty/cosyvoice/transformer/decoder.py b/xinference/thirdparty/cosyvoice/transformer/decoder.py index 961c875eab..98f3a66a66 100644 --- a/xinference/thirdparty/cosyvoice/transformer/decoder.py +++ b/xinference/thirdparty/cosyvoice/transformer/decoder.py @@ -174,7 +174,7 @@ def forward_layers(self, x: torch.Tensor, tgt_mask: torch.Tensor, memory_mask) return x - @torch.jit.ignore(drop=True) + @torch.jit.unused def forward_layers_checkpointed(self, x: torch.Tensor, tgt_mask: torch.Tensor, memory: torch.Tensor, diff --git a/xinference/thirdparty/cosyvoice/transformer/embedding.py b/xinference/thirdparty/cosyvoice/transformer/embedding.py index 46130a503f..e32cfc97e5 100644 --- a/xinference/thirdparty/cosyvoice/transformer/embedding.py +++ b/xinference/thirdparty/cosyvoice/transformer/embedding.py @@ -212,7 +212,7 @@ class EspnetRelPositionalEncoding(torch.nn.Module): """ - def __init__(self, d_model, dropout_rate, max_len=5000): + def __init__(self, d_model: int, dropout_rate: float, max_len: int=5000): """Construct an PositionalEncoding object.""" super(EspnetRelPositionalEncoding, self).__init__() self.d_model = d_model @@ -221,7 +221,7 @@ def __init__(self, d_model, dropout_rate, max_len=5000): self.pe = None self.extend_pe(torch.tensor(0.0).expand(1, max_len)) - def extend_pe(self, x): + def extend_pe(self, x: torch.Tensor): """Reset the positional encodings.""" if self.pe is not None: # self.pe contains both positive and negative parts @@ -253,7 +253,8 @@ def extend_pe(self, x): pe = torch.cat([pe_positive, pe_negative], dim=1) self.pe = pe.to(device=x.device, dtype=x.dtype) - def forward(self, x: torch.Tensor, offset: Union[int, torch.Tensor] = 0): + def forward(self, x: torch.Tensor, offset: Union[int, torch.Tensor] = 0) \ + -> Tuple[torch.Tensor, torch.Tensor]: """Add positional encoding. Args: diff --git a/xinference/thirdparty/cosyvoice/transformer/encoder.py b/xinference/thirdparty/cosyvoice/transformer/encoder.py index 7e8bd230b2..c5709d0ce8 100644 --- a/xinference/thirdparty/cosyvoice/transformer/encoder.py +++ b/xinference/thirdparty/cosyvoice/transformer/encoder.py @@ -169,7 +169,7 @@ def forward_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor, xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) return xs - @torch.jit.ignore(drop=True) + @torch.jit.unused def forward_layers_checkpointed(self, xs: torch.Tensor, chunk_masks: torch.Tensor, pos_emb: torch.Tensor, @@ -180,6 +180,7 @@ def forward_layers_checkpointed(self, xs: torch.Tensor, mask_pad) return xs + @torch.jit.export def forward_chunk( self, xs: torch.Tensor, @@ -270,6 +271,7 @@ def forward_chunk( return (xs, r_att_cache, r_cnn_cache) + @torch.jit.unused def forward_chunk_by_chunk( self, xs: torch.Tensor, @@ -297,7 +299,7 @@ def forward_chunk_by_chunk( rate. 3. Currently, nn.Sequential is used to stack all the convolution layers in subsampling, we need to rewrite it to make it work - with cache, which is not prefered. + with cache, which is not preferred. Args: xs (torch.Tensor): (1, max_len, dim) chunk_size (int): decoding chunk size diff --git a/xinference/thirdparty/cosyvoice/utils/common.py b/xinference/thirdparty/cosyvoice/utils/common.py index 6ec5e17835..07e1f9252a 100644 --- a/xinference/thirdparty/cosyvoice/utils/common.py +++ b/xinference/thirdparty/cosyvoice/utils/common.py @@ -101,3 +101,39 @@ def init_weights(m, mean=0.0, std=0.01): classname = m.__class__.__name__ if classname.find("Conv") != -1: m.weight.data.normal_(mean, std) + +# Repetition Aware Sampling in VALL-E 2 +def ras_sampling(weighted_scores, decoded_tokens, sampling, top_p=0.8, top_k=25, win_size=10, tau_r=0.1): + top_ids = nucleus_sampling(weighted_scores, top_p=top_p, top_k=top_k) + rep_num = (torch.tensor(decoded_tokens[-win_size:]).to(weighted_scores.device) == top_ids).sum().item() + if rep_num >= win_size * tau_r: + top_ids = random_sampling(weighted_scores, decoded_tokens, sampling) + return top_ids + +def nucleus_sampling(weighted_scores, top_p=0.8, top_k=25): + prob, indices = [], [] + cum_prob = 0.0 + sorted_value, sorted_idx = weighted_scores.softmax(dim=0).sort(descending=True, stable=True) + for i in range(len(sorted_idx)): + # sampling both top-p and numbers. + if cum_prob < top_p and len(prob) < top_k: + cum_prob += sorted_value[i] + prob.append(sorted_value[i]) + indices.append(sorted_idx[i]) + else: + break + prob = torch.tensor(prob).to(weighted_scores) + indices = torch.tensor(indices, dtype=torch.long).to(weighted_scores.device) + top_ids = indices[prob.multinomial(1, replacement=True)] + return top_ids + +def random_sampling(weighted_scores, decoded_tokens, sampling): + top_ids = weighted_scores.softmax(dim=0).multinomial(1, replacement=True) + return top_ids + +def fade_in_out(fade_in_mel, fade_out_mel, window): + device = fade_in_mel.device + fade_in_mel, fade_out_mel = fade_in_mel.cpu(), fade_out_mel.cpu() + mel_overlap_len = int(window.shape[0] / 2) + fade_in_mel[:, :, :mel_overlap_len] = fade_in_mel[:, :, :mel_overlap_len] * window[:mel_overlap_len] + fade_out_mel[:, :, -mel_overlap_len:] * window[mel_overlap_len:] + return fade_in_mel.to(device) diff --git a/xinference/thirdparty/cosyvoice/utils/file_utils.py b/xinference/thirdparty/cosyvoice/utils/file_utils.py index 92c448b9cc..40e7b20d68 100644 --- a/xinference/thirdparty/cosyvoice/utils/file_utils.py +++ b/xinference/thirdparty/cosyvoice/utils/file_utils.py @@ -15,6 +15,10 @@ import json import torchaudio +import logging +logging.getLogger('matplotlib').setLevel(logging.WARNING) +logging.basicConfig(level=logging.DEBUG, + format='%(asctime)s %(levelname)s %(message)s') def read_lists(list_file): @@ -39,3 +43,15 @@ def load_wav(wav, target_sr): assert sample_rate > target_sr, 'wav sample rate {} must be greater than {}'.format(sample_rate, target_sr) speech = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=target_sr)(speech) return speech + +def speed_change(waveform, sample_rate, speed_factor: str): + effects = [ + ["tempo", speed_factor], # speed_factor + ["rate", f"{sample_rate}"] + ] + augmented_waveform, new_sample_rate = torchaudio.sox_effects.apply_effects_tensor( + waveform, + sample_rate, + effects + ) + return augmented_waveform, new_sample_rate diff --git a/xinference/thirdparty/fish_speech/fish_speech/configs/firefly_gan_vq.yaml b/xinference/thirdparty/fish_speech/fish_speech/configs/firefly_gan_vq.yaml index 7417623b03..10aa8d4a52 100644 --- a/xinference/thirdparty/fish_speech/fish_speech/configs/firefly_gan_vq.yaml +++ b/xinference/thirdparty/fish_speech/fish_speech/configs/firefly_gan_vq.yaml @@ -22,13 +22,12 @@ head: resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]] num_mels: 512 upsample_initial_channel: 512 - use_template: false pre_conv_kernel_size: 13 post_conv_kernel_size: 13 quantizer: _target_: fish_speech.models.vqgan.modules.fsq.DownsampleFiniteScalarQuantize input_dim: 512 - n_groups: 4 + n_groups: 8 n_codebooks: 1 levels: [8, 5, 5, 5] - downsample_factor: [2] + downsample_factor: [2, 2] diff --git a/xinference/thirdparty/fish_speech/fish_speech/configs/text2semantic_finetune.yaml b/xinference/thirdparty/fish_speech/fish_speech/configs/text2semantic_finetune.yaml index 1bf8fd6b6d..f4c1993023 100644 --- a/xinference/thirdparty/fish_speech/fish_speech/configs/text2semantic_finetune.yaml +++ b/xinference/thirdparty/fish_speech/fish_speech/configs/text2semantic_finetune.yaml @@ -4,7 +4,7 @@ defaults: project: text2semantic_finetune_dual_ar max_length: 4096 -pretrained_ckpt_path: checkpoints/fish-speech-1.2-sft +pretrained_ckpt_path: checkpoints/fish-speech-1.4 # Lightning Trainer trainer: diff --git a/xinference/thirdparty/fish_speech/fish_speech/i18n/locale/en_US.json b/xinference/thirdparty/fish_speech/fish_speech/i18n/locale/en_US.json index cf6ad6ca1e..6e280c236e 100644 --- a/xinference/thirdparty/fish_speech/fish_speech/i18n/locale/en_US.json +++ b/xinference/thirdparty/fish_speech/fish_speech/i18n/locale/en_US.json @@ -72,7 +72,7 @@ "Put your text here.": "Put your text here.", "Reference Audio": "Reference Audio", "Reference Text": "Reference Text", - "Related code are released under BSD-3-Clause License, and weights are released under CC BY-NC-SA 4.0 License.": "Related code are released under BSD-3-Clause License, and weights are released under CC BY-NC-SA 4.0 License.", + "Related code and weights are released under CC BY-NC-SA 4.0 License.": "Related code and weights are released under CC BY-NC-SA 4.0 License.", "Remove Selected Data": "Remove Selected Data", "Removed path successfully!": "Removed path successfully!", "Repetition Penalty": "Repetition Penalty", diff --git a/xinference/thirdparty/fish_speech/fish_speech/i18n/locale/es_ES.json b/xinference/thirdparty/fish_speech/fish_speech/i18n/locale/es_ES.json index 1ea5988213..3285341f68 100644 --- a/xinference/thirdparty/fish_speech/fish_speech/i18n/locale/es_ES.json +++ b/xinference/thirdparty/fish_speech/fish_speech/i18n/locale/es_ES.json @@ -72,7 +72,7 @@ "Put your text here.": "Ponga su texto aquí.", "Reference Audio": "Audio de Referencia", "Reference Text": "Texto de Referencia", - "Related code are released under BSD-3-Clause License, and weights are released under CC BY-NC-SA 4.0 License.": "El código relacionado se publica bajo la Licencia BSD-3-Clause, y los pesos se publican bajo la Licencia CC BY-NC-SA 4.0.", + "Related code and weights are released under CC BY-NC-SA 4.0 License.": "El código relacionado y los pesos se publican bajo la Licencia CC BY-NC-SA 4.0.", "Remove Selected Data": "Eliminar Datos Seleccionados", "Removed path successfully!": "¡Ruta eliminada exitosamente!", "Repetition Penalty": "Penalización por Repetición", diff --git a/xinference/thirdparty/fish_speech/fish_speech/i18n/locale/ja_JP.json b/xinference/thirdparty/fish_speech/fish_speech/i18n/locale/ja_JP.json index e7817eb0c5..d30bac7bcd 100644 --- a/xinference/thirdparty/fish_speech/fish_speech/i18n/locale/ja_JP.json +++ b/xinference/thirdparty/fish_speech/fish_speech/i18n/locale/ja_JP.json @@ -72,7 +72,7 @@ "Put your text here.": "ここにテキストを入力してください。", "Reference Audio": "リファレンスオーディオ", "Reference Text": "リファレンステキスト", - "Related code are released under BSD-3-Clause License, and weights are released under CC BY-NC-SA 4.0 License.": "関連コードはBSD-3-Clauseライセンスの下でリリースされ、重みはCC BY-NC-SA 4.0ライセンスの下でリリースされます。", + "Related code and weights are released under CC BY-NC-SA 4.0 License.": "関連コードと重みはCC BY-NC-SA 4.0ライセンスの下でリリースされます。", "Remove Selected Data": "選択したデータを削除", "Removed path successfully!": "パスの削除に成功しました！", "Repetition Penalty": "反復ペナルティ", diff --git a/xinference/thirdparty/fish_speech/fish_speech/i18n/locale/pt_BR.json b/xinference/thirdparty/fish_speech/fish_speech/i18n/locale/pt_BR.json index c3df431a40..385f20272e 100644 --- a/xinference/thirdparty/fish_speech/fish_speech/i18n/locale/pt_BR.json +++ b/xinference/thirdparty/fish_speech/fish_speech/i18n/locale/pt_BR.json @@ -84,7 +84,7 @@ "Reference Text": "Texto de Referência", "warning": "Aviso", "Pre-processing begins...": "O pré-processamento começou!", - "Related code are released under BSD-3-Clause License, and weights are released under CC BY-NC-SA 4.0 License.": "O código relacionado é licenciado sob a Licença BSD-3-Clause, e os pesos sob a Licença CC BY-NC-SA 4.0.", + "Related code and weights are released under CC BY-NC-SA 4.0 License.": "O código relacionado e os pesos são licenciados sob a Licença CC BY-NC-SA 4.0.", "Remove Selected Data": "Remover Dados Selecionados", "Removed path successfully!": "Caminho removido com sucesso!", "Repetition Penalty": "Penalidade de Repetição", diff --git a/xinference/thirdparty/fish_speech/fish_speech/i18n/locale/zh_CN.json b/xinference/thirdparty/fish_speech/fish_speech/i18n/locale/zh_CN.json index da81eef1cf..3dd1a5cd1c 100644 --- a/xinference/thirdparty/fish_speech/fish_speech/i18n/locale/zh_CN.json +++ b/xinference/thirdparty/fish_speech/fish_speech/i18n/locale/zh_CN.json @@ -72,7 +72,7 @@ "Put your text here.": "在此处输入文本.", "Reference Audio": "参考音频", "Reference Text": "参考文本", - "Related code are released under BSD-3-Clause License, and weights are released under CC BY-NC-SA 4.0 License.": "相关代码使用 BSD-3-Clause 许可证发布，权重使用 CC BY-NC-SA 4.0 许可证发布.", + "Related code and weights are released under CC BY-NC-SA 4.0 License.": "相关代码和权重使用 CC BY-NC-SA 4.0 许可证发布.", "Remove Selected Data": "移除选中数据", "Removed path successfully!": "移除路径成功!", "Repetition Penalty": "重复惩罚", diff --git a/xinference/thirdparty/fish_speech/fish_speech/models/text2semantic/llama.py b/xinference/thirdparty/fish_speech/fish_speech/models/text2semantic/llama.py index 4eef92b0ba..0725dfb9b7 100644 --- a/xinference/thirdparty/fish_speech/fish_speech/models/text2semantic/llama.py +++ b/xinference/thirdparty/fish_speech/fish_speech/models/text2semantic/llama.py @@ -353,7 +353,7 @@ def from_pretrained( if "int8" in str(Path(path)): logger.info("Using int8 weight-only quantization!") - from ...tools.llama.quantize import WeightOnlyInt8QuantHandler + from tools.llama.quantize import WeightOnlyInt8QuantHandler simple_quantizer = WeightOnlyInt8QuantHandler(model) model = simple_quantizer.convert_for_runtime() @@ -363,7 +363,7 @@ def from_pretrained( path_comps = path.name.split("-") assert path_comps[-2].startswith("g") groupsize = int(path_comps[-2][1:]) - from ...tools.llama.quantize import WeightOnlyInt4QuantHandler + from tools.llama.quantize import WeightOnlyInt4QuantHandler simple_quantizer = WeightOnlyInt4QuantHandler(model, groupsize) model = simple_quantizer.convert_for_runtime() diff --git a/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/__init__.py b/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/__init__.py index 401c6df468..e69de29bb2 100644 --- a/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/__init__.py +++ b/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/__init__.py @@ -1,3 +0,0 @@ -from .lit_module import VQGAN - -__all__ = ["VQGAN"] diff --git a/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/lit_module.py b/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/lit_module.py deleted file mode 100644 index d5fa2ccabb..0000000000 --- a/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/lit_module.py +++ /dev/null @@ -1,442 +0,0 @@ -import itertools -import math -from typing import Any, Callable - -import lightning as L -import torch -import torch.nn.functional as F -# import wandb -from lightning.pytorch.loggers import TensorBoardLogger, WandbLogger -from matplotlib import pyplot as plt -from torch import nn - -from fish_speech.models.vqgan.modules.discriminator import Discriminator -from fish_speech.models.vqgan.modules.wavenet import WaveNet -from fish_speech.models.vqgan.utils import avg_with_mask, plot_mel, sequence_mask - - -class VQGAN(L.LightningModule): - def __init__( - self, - optimizer: Callable, - lr_scheduler: Callable, - encoder: WaveNet, - quantizer: nn.Module, - decoder: WaveNet, - discriminator: Discriminator, - vocoder: nn.Module, - encode_mel_transform: nn.Module, - gt_mel_transform: nn.Module, - weight_adv: float = 1.0, - weight_vq: float = 1.0, - weight_mel: float = 1.0, - sampling_rate: int = 44100, - freeze_encoder: bool = False, - ): - super().__init__() - - # Model parameters - self.optimizer_builder = optimizer - self.lr_scheduler_builder = lr_scheduler - - # Modules - self.encoder = encoder - self.quantizer = quantizer - self.decoder = decoder - self.vocoder = vocoder - self.discriminator = discriminator - self.encode_mel_transform = encode_mel_transform - self.gt_mel_transform = gt_mel_transform - - # A simple linear layer to project quality to condition channels - self.quality_projection = nn.Linear(1, 768) - - # Freeze vocoder - for param in self.vocoder.parameters(): - param.requires_grad = False - - # Loss weights - self.weight_adv = weight_adv - self.weight_vq = weight_vq - self.weight_mel = weight_mel - - # Other parameters - self.sampling_rate = sampling_rate - - # Disable strict loading - self.strict_loading = False - - # If encoder is frozen - if freeze_encoder: - for param in self.encoder.parameters(): - param.requires_grad = False - - for param in self.quantizer.parameters(): - param.requires_grad = False - - self.automatic_optimization = False - - def on_save_checkpoint(self, checkpoint): - # Do not save vocoder - state_dict = checkpoint["state_dict"] - for name in list(state_dict.keys()): - if "vocoder" in name: - state_dict.pop(name) - - def configure_optimizers(self): - optimizer_generator = self.optimizer_builder( - itertools.chain( - self.encoder.parameters(), - self.quantizer.parameters(), - self.decoder.parameters(), - self.quality_projection.parameters(), - ) - ) - optimizer_discriminator = self.optimizer_builder( - self.discriminator.parameters() - ) - - lr_scheduler_generator = self.lr_scheduler_builder(optimizer_generator) - lr_scheduler_discriminator = self.lr_scheduler_builder(optimizer_discriminator) - - return ( - { - "optimizer": optimizer_generator, - "lr_scheduler": { - "scheduler": lr_scheduler_generator, - "interval": "step", - "name": "optimizer/generator", - }, - }, - { - "optimizer": optimizer_discriminator, - "lr_scheduler": { - "scheduler": lr_scheduler_discriminator, - "interval": "step", - "name": "optimizer/discriminator", - }, - }, - ) - - def training_step(self, batch, batch_idx): - optim_g, optim_d = self.optimizers() - - audios, audio_lengths = batch["audios"], batch["audio_lengths"] - - audios = audios.float() - audios = audios[:, None, :] - - with torch.no_grad(): - encoded_mels = self.encode_mel_transform(audios) - gt_mels = self.gt_mel_transform(audios) - quality = ((gt_mels.mean(-1) > -8).sum(-1) - 90) / 10 - quality = quality.unsqueeze(-1) - - mel_lengths = audio_lengths // self.gt_mel_transform.hop_length - mel_masks = sequence_mask(mel_lengths, gt_mels.shape[2]) - mel_masks_float_conv = mel_masks[:, None, :].float() - gt_mels = gt_mels * mel_masks_float_conv - encoded_mels = encoded_mels * mel_masks_float_conv - - # Encode - encoded_features = self.encoder(encoded_mels) * mel_masks_float_conv - - # Quantize - vq_result = self.quantizer(encoded_features) - loss_vq = getattr("vq_result", "loss", 0.0) - vq_recon_features = vq_result.z * mel_masks_float_conv - vq_recon_features = ( - vq_recon_features + self.quality_projection(quality)[:, :, None] - ) - - # VQ Decode - gen_mel = ( - self.decoder( - torch.randn_like(vq_recon_features) * mel_masks_float_conv, - condition=vq_recon_features, - ) - * mel_masks_float_conv - ) - - # Discriminator - real_logits = self.discriminator(gt_mels) - fake_logits = self.discriminator(gen_mel.detach()) - d_mask = F.interpolate( - mel_masks_float_conv, size=(real_logits.shape[2],), mode="nearest" - ) - - loss_real = avg_with_mask((real_logits - 1) ** 2, d_mask) - loss_fake = avg_with_mask(fake_logits**2, d_mask) - - loss_d = loss_real + loss_fake - - self.log( - "train/discriminator/loss", - loss_d, - on_step=True, - on_epoch=False, - prog_bar=True, - logger=True, - ) - - # Discriminator backward - optim_d.zero_grad() - self.manual_backward(loss_d) - self.clip_gradients( - optim_d, gradient_clip_val=1000.0, gradient_clip_algorithm="norm" - ) - optim_d.step() - - # Mel Loss, applying l1, using a weighted sum - mel_distance = ( - gen_mel - gt_mels - ).abs() # * 0.5 + self.ssim(gen_mel, gt_mels) * 0.5 - loss_mel_low_freq = avg_with_mask(mel_distance[:, :40, :], mel_masks_float_conv) - loss_mel_mid_freq = avg_with_mask( - mel_distance[:, 40:70, :], mel_masks_float_conv - ) - loss_mel_high_freq = avg_with_mask( - mel_distance[:, 70:, :], mel_masks_float_conv - ) - loss_mel = ( - loss_mel_low_freq * 0.6 + loss_mel_mid_freq * 0.3 + loss_mel_high_freq * 0.1 - ) - - # Adversarial Loss - fake_logits = self.discriminator(gen_mel) - loss_adv = avg_with_mask((fake_logits - 1) ** 2, d_mask) - - # Total loss - loss = ( - self.weight_vq * loss_vq - + self.weight_mel * loss_mel - + self.weight_adv * loss_adv - ) - - # Log losses - self.log( - "train/generator/loss", - loss, - on_step=True, - on_epoch=False, - prog_bar=True, - logger=True, - ) - self.log( - "train/generator/loss_vq", - loss_vq, - on_step=True, - on_epoch=False, - prog_bar=False, - logger=True, - ) - self.log( - "train/generator/loss_mel", - loss_mel, - on_step=True, - on_epoch=False, - prog_bar=False, - logger=True, - ) - self.log( - "train/generator/loss_adv", - loss_adv, - on_step=True, - on_epoch=False, - prog_bar=False, - logger=True, - ) - - # Generator backward - optim_g.zero_grad() - self.manual_backward(loss) - self.clip_gradients( - optim_g, gradient_clip_val=1000.0, gradient_clip_algorithm="norm" - ) - optim_g.step() - - scheduler_g, scheduler_d = self.lr_schedulers() - scheduler_g.step() - scheduler_d.step() - - def validation_step(self, batch: Any, batch_idx: int): - audios, audio_lengths = batch["audios"], batch["audio_lengths"] - - audios = audios.float() - audios = audios[:, None, :] - - encoded_mels = self.encode_mel_transform(audios) - gt_mels = self.gt_mel_transform(audios) - - mel_lengths = audio_lengths // self.gt_mel_transform.hop_length - mel_masks = sequence_mask(mel_lengths, gt_mels.shape[2]) - mel_masks_float_conv = mel_masks[:, None, :].float() - gt_mels = gt_mels * mel_masks_float_conv - encoded_mels = encoded_mels * mel_masks_float_conv - - # Encode - encoded_features = self.encoder(encoded_mels) * mel_masks_float_conv - - # Quantize - vq_recon_features = self.quantizer(encoded_features).z * mel_masks_float_conv - vq_recon_features = ( - vq_recon_features - + self.quality_projection( - torch.ones( - vq_recon_features.shape[0], 1, device=vq_recon_features.device - ) - * 2 - )[:, :, None] - ) - - # VQ Decode - gen_aux_mels = ( - self.decoder( - torch.randn_like(vq_recon_features) * mel_masks_float_conv, - condition=vq_recon_features, - ) - * mel_masks_float_conv - ) - loss_mel = avg_with_mask((gen_aux_mels - gt_mels).abs(), mel_masks_float_conv) - - self.log( - "val/loss_mel", - loss_mel, - on_step=False, - on_epoch=True, - prog_bar=False, - logger=True, - sync_dist=True, - ) - - recon_audios = self.vocoder(gt_mels) - gen_aux_audios = self.vocoder(gen_aux_mels) - - # only log the first batch - if batch_idx != 0: - return - - for idx, ( - gt_mel, - gen_aux_mel, - audio, - gen_aux_audio, - recon_audio, - audio_len, - ) in enumerate( - zip( - gt_mels, - gen_aux_mels, - audios.cpu().float(), - gen_aux_audios.cpu().float(), - recon_audios.cpu().float(), - audio_lengths, - ) - ): - if idx > 4: - break - - mel_len = audio_len // self.gt_mel_transform.hop_length - - image_mels = plot_mel( - [ - gt_mel[:, :mel_len], - gen_aux_mel[:, :mel_len], - ], - [ - "Ground-Truth", - "Auxiliary", - ], - ) - - if isinstance(self.logger, WandbLogger): - self.logger.experiment.log( - { - "reconstruction_mel": wandb.Image(image_mels, caption="mels"), - "wavs": [ - wandb.Audio( - audio[0, :audio_len], - sample_rate=self.sampling_rate, - caption="gt", - ), - wandb.Audio( - gen_aux_audio[0, :audio_len], - sample_rate=self.sampling_rate, - caption="aux", - ), - wandb.Audio( - recon_audio[0, :audio_len], - sample_rate=self.sampling_rate, - caption="recon", - ), - ], - }, - ) - - if isinstance(self.logger, TensorBoardLogger): - self.logger.experiment.add_figure( - f"sample-{idx}/mels", - image_mels, - global_step=self.global_step, - ) - self.logger.experiment.add_audio( - f"sample-{idx}/wavs/gt", - audio[0, :audio_len], - self.global_step, - sample_rate=self.sampling_rate, - ) - self.logger.experiment.add_audio( - f"sample-{idx}/wavs/gen", - gen_aux_audio[0, :audio_len], - self.global_step, - sample_rate=self.sampling_rate, - ) - self.logger.experiment.add_audio( - f"sample-{idx}/wavs/recon", - recon_audio[0, :audio_len], - self.global_step, - sample_rate=self.sampling_rate, - ) - - plt.close(image_mels) - - def encode(self, audios, audio_lengths): - audios = audios.float() - - mels = self.encode_mel_transform(audios) - mel_lengths = audio_lengths // self.encode_mel_transform.hop_length - mel_masks = sequence_mask(mel_lengths, mels.shape[2]) - mel_masks_float_conv = mel_masks[:, None, :].float() - mels = mels * mel_masks_float_conv - - # Encode - encoded_features = self.encoder(mels) * mel_masks_float_conv - feature_lengths = mel_lengths // math.prod(self.quantizer.downsample_factor) - - return self.quantizer.encode(encoded_features), feature_lengths - - def decode(self, indices, feature_lengths, return_audios=False): - factor = math.prod(self.quantizer.downsample_factor) - mel_masks = sequence_mask(feature_lengths * factor, indices.shape[2] * factor) - mel_masks_float_conv = mel_masks[:, None, :].float() - - z = self.quantizer.decode(indices) * mel_masks_float_conv - z = ( - z - + self.quality_projection(torch.ones(z.shape[0], 1, device=z.device) * 2)[ - :, :, None - ] - ) - - gen_mel = ( - self.decoder( - torch.randn_like(z) * mel_masks_float_conv, - condition=z, - ) - * mel_masks_float_conv - ) - - if return_audios: - return self.vocoder(gen_mel) - - return gen_mel diff --git a/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/modules/discriminator.py b/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/modules/discriminator.py deleted file mode 100644 index 69c7df4103..0000000000 --- a/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/modules/discriminator.py +++ /dev/null @@ -1,44 +0,0 @@ -import torch -from torch import nn -from torch.nn.utils.parametrizations import weight_norm - - -class Discriminator(nn.Module): - def __init__(self): - super().__init__() - - blocks = [] - convs = [ - (1, 64, (3, 9), 1, (1, 4)), - (64, 128, (3, 9), (1, 2), (1, 4)), - (128, 256, (3, 9), (1, 2), (1, 4)), - (256, 512, (3, 9), (1, 2), (1, 4)), - (512, 1024, (3, 3), 1, (1, 1)), - (1024, 1, (3, 3), 1, (1, 1)), - ] - - for idx, (in_channels, out_channels, kernel_size, stride, padding) in enumerate( - convs - ): - blocks.append( - weight_norm( - nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding) - ) - ) - - if idx != len(convs) - 1: - blocks.append(nn.SiLU(inplace=True)) - - self.blocks = nn.Sequential(*blocks) - - def forward(self, x): - return self.blocks(x[:, None])[:, 0] - - -if __name__ == "__main__": - model = Discriminator() - print(sum(p.numel() for p in model.parameters()) / 1_000_000) - x = torch.randn(1, 128, 1024) - y = model(x) - print(y.shape) - print(y) diff --git a/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/modules/firefly.py b/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/modules/firefly.py index 4ca0ff5882..aa21839b54 100644 --- a/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/modules/firefly.py +++ b/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/modules/firefly.py @@ -1,25 +1,26 @@ -# A inference only version of the FireflyGAN model - import math from functools import partial from math import prod from typing import Callable -import numpy as np import torch import torch.nn.functional as F from torch import nn -from torch.nn import Conv1d from torch.nn.utils.parametrizations import weight_norm from torch.nn.utils.parametrize import remove_parametrizations from torch.utils.checkpoint import checkpoint -from fish_speech.models.vqgan.utils import sequence_mask + +def sequence_mask(length, max_length=None): + if max_length is None: + max_length = length.max() + x = torch.arange(max_length, dtype=length.dtype, device=length.device) + return x.unsqueeze(0) < length.unsqueeze(1) def init_weights(m, mean=0.0, std=0.01): classname = m.__class__.__name__ - if classname.find("Conv") != -1: + if classname.find("Conv1D") != -1: m.weight.data.normal_(mean, std) @@ -27,78 +28,141 @@ def get_padding(kernel_size, dilation=1): return (kernel_size * dilation - dilation) // 2 +def unpad1d(x: torch.Tensor, paddings: tuple[int, int]): + """Remove padding from x, handling properly zero padding. Only for 1d!""" + padding_left, padding_right = paddings + assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right) + assert (padding_left + padding_right) <= x.shape[-1] + end = x.shape[-1] - padding_right + return x[..., padding_left:end] + + +def get_extra_padding_for_conv1d( + x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0 +) -> int: + """See `pad_for_conv1d`.""" + length = x.shape[-1] + n_frames = (length - kernel_size + padding_total) / stride + 1 + ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total) + return ideal_length - length + + +def pad1d( + x: torch.Tensor, + paddings: tuple[int, int], + mode: str = "zeros", + value: float = 0.0, +): + """Tiny wrapper around F.pad, just to allow for reflect padding on small input. + If this is the case, we insert extra 0 padding to the right + before the reflection happen. + """ + length = x.shape[-1] + padding_left, padding_right = paddings + assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right) + if mode == "reflect": + max_pad = max(padding_left, padding_right) + extra_pad = 0 + if length <= max_pad: + extra_pad = max_pad - length + 1 + x = F.pad(x, (0, extra_pad)) + padded = F.pad(x, paddings, mode, value) + end = padded.shape[-1] - extra_pad + return padded[..., :end] + else: + return F.pad(x, paddings, mode, value) + + +class FishConvNet(nn.Module): + def __init__( + self, in_channels, out_channels, kernel_size, dilation=1, stride=1, groups=1 + ): + super(FishConvNet, self).__init__() + self.conv = nn.Conv1d( + in_channels, + out_channels, + kernel_size, + stride=stride, + dilation=dilation, + groups=groups, + ) + self.stride = stride + self.kernel_size = (kernel_size - 1) * dilation + 1 + self.dilation = dilation + + def forward(self, x): + pad = self.kernel_size - self.stride + extra_padding = get_extra_padding_for_conv1d( + x, self.kernel_size, self.stride, pad + ) + x = pad1d(x, (pad, extra_padding), mode="constant", value=0) + return self.conv(x).contiguous() + + def weight_norm(self, name="weight", dim=0): + self.conv = weight_norm(self.conv, name=name, dim=dim) + return self + + def remove_weight_norm(self): + self.conv = remove_parametrizations(self.conv) + return self + + +class FishTransConvNet(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, dilation=1, stride=1): + super(FishTransConvNet, self).__init__() + self.conv = nn.ConvTranspose1d( + in_channels, out_channels, kernel_size, stride=stride, dilation=dilation + ) + self.stride = stride + self.kernel_size = kernel_size + + def forward(self, x): + x = self.conv(x) + pad = self.kernel_size - self.stride + padding_right = math.ceil(pad) + padding_left = pad - padding_right + x = unpad1d(x, (padding_left, padding_right)) + return x.contiguous() + + def weight_norm(self, name="weight", dim=0): + self.conv = weight_norm(self.conv, name=name, dim=dim) + return self + + def remove_weight_norm(self): + self.conv = remove_parametrizations(self.conv) + return self + + class ResBlock1(torch.nn.Module): def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): super().__init__() self.convs1 = nn.ModuleList( [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]), - ) - ), + FishConvNet( + channels, channels, kernel_size, stride=1, dilation=dilation[0] + ).weight_norm(), + FishConvNet( + channels, channels, kernel_size, stride=1, dilation=dilation[1] + ).weight_norm(), + FishConvNet( + channels, channels, kernel_size, stride=1, dilation=dilation[2] + ).weight_norm(), ] ) self.convs1.apply(init_weights) self.convs2 = nn.ModuleList( [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), + FishConvNet( + channels, channels, kernel_size, stride=1, dilation=dilation[0] + ).weight_norm(), + FishConvNet( + channels, channels, kernel_size, stride=1, dilation=dilation[1] + ).weight_norm(), + FishConvNet( + channels, channels, kernel_size, stride=1, dilation=dilation[2] + ).weight_norm(), ] ) self.convs2.apply(init_weights) @@ -119,7 +183,7 @@ def remove_parametrizations(self): remove_parametrizations(conv, tensor_name="weight") -class ParralelBlock(nn.Module): +class ParallelBlock(nn.Module): def __init__( self, channels: int, @@ -153,7 +217,6 @@ def __init__( resblock_dilation_sizes: tuple[tuple[int]] = ((1, 3, 5), (1, 3, 5), (1, 3, 5)), num_mels: int = 128, upsample_initial_channel: int = 512, - use_template: bool = True, pre_conv_kernel_size: int = 7, post_conv_kernel_size: int = 7, post_activation: Callable = partial(nn.SiLU, inplace=True), @@ -164,85 +227,51 @@ def __init__( prod(upsample_rates) == hop_length ), f"hop_length must be {prod(upsample_rates)}" - self.conv_pre = weight_norm( - nn.Conv1d( - num_mels, - upsample_initial_channel, - pre_conv_kernel_size, - 1, - padding=get_padding(pre_conv_kernel_size), - ) - ) + self.conv_pre = FishConvNet( + num_mels, + upsample_initial_channel, + pre_conv_kernel_size, + stride=1, + ).weight_norm() self.num_upsamples = len(upsample_rates) self.num_kernels = len(resblock_kernel_sizes) self.noise_convs = nn.ModuleList() - self.use_template = use_template self.ups = nn.ModuleList() for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - c_cur = upsample_initial_channel // (2 ** (i + 1)) self.ups.append( - weight_norm( - nn.ConvTranspose1d( - upsample_initial_channel // (2**i), - upsample_initial_channel // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) + FishTransConvNet( + upsample_initial_channel // (2**i), + upsample_initial_channel // (2 ** (i + 1)), + k, + stride=u, + ).weight_norm() ) - if not use_template: - continue - - if i + 1 < len(upsample_rates): - stride_f0 = np.prod(upsample_rates[i + 1 :]) - self.noise_convs.append( - Conv1d( - 1, - c_cur, - kernel_size=stride_f0 * 2, - stride=stride_f0, - padding=stride_f0 // 2, - ) - ) - else: - self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1)) - self.resblocks = nn.ModuleList() for i in range(len(self.ups)): ch = upsample_initial_channel // (2 ** (i + 1)) self.resblocks.append( - ParralelBlock(ch, resblock_kernel_sizes, resblock_dilation_sizes) + ParallelBlock(ch, resblock_kernel_sizes, resblock_dilation_sizes) ) self.activation_post = post_activation() - self.conv_post = weight_norm( - nn.Conv1d( - ch, - 1, - post_conv_kernel_size, - 1, - padding=get_padding(post_conv_kernel_size), - ) - ) + self.conv_post = FishConvNet( + ch, 1, post_conv_kernel_size, stride=1 + ).weight_norm() self.ups.apply(init_weights) self.conv_post.apply(init_weights) - def forward(self, x, template=None): + def forward(self, x): x = self.conv_pre(x) for i in range(self.num_upsamples): x = F.silu(x, inplace=True) x = self.ups[i](x) - if self.use_template: - x = x + self.noise_convs[i](template) - - if self.training: + if self.training and self.checkpointing: x = checkpoint( self.resblocks[i], x, @@ -364,11 +393,11 @@ def __init__( ): super().__init__() - self.dwconv = nn.Conv1d( + self.dwconv = FishConvNet( dim, dim, kernel_size=kernel_size, - padding=int(dilation * (kernel_size - 1) / 2), + # padding=int(dilation * (kernel_size - 1) / 2), groups=dim, ) # depthwise conv self.norm = LayerNorm(dim, eps=1e-6) @@ -421,12 +450,13 @@ def __init__( self.downsample_layers = nn.ModuleList() stem = nn.Sequential( - nn.Conv1d( + FishConvNet( input_channels, dims[0], - kernel_size=kernel_size, - padding=kernel_size // 2, - padding_mode="zeros", + kernel_size=7, + # padding=3, + # padding_mode="replicate", + # padding_mode="zeros", ), LayerNorm(dims[0], eps=1e-6, data_format="channels_first"), ) @@ -491,6 +521,7 @@ def __init__( self.head = head self.quantizer = quantizer self.spec_transform = spec_transform + self.downsample_factor = math.prod(self.quantizer.downsample_factor) def forward(self, x: torch.Tensor, template=None, mask=None) -> torch.Tensor: if self.spec_transform is not None: @@ -512,7 +543,7 @@ def forward(self, x: torch.Tensor, template=None, mask=None) -> torch.Tensor: if x.ndim == 2: x = x[:, None, :] - if self.quantizer is not None: + if self.vq is not None: return x, vq_result return x @@ -528,25 +559,30 @@ def encode(self, audios, audio_lengths): # Encode encoded_features = self.backbone(mels) * mel_masks_float_conv - feature_lengths = mel_lengths // math.prod(self.quantizer.downsample_factor) + feature_lengths = mel_lengths // self.downsample_factor return self.quantizer.encode(encoded_features), feature_lengths def decode(self, indices, feature_lengths) -> torch.Tensor: - factor = math.prod(self.quantizer.downsample_factor) - mel_masks = sequence_mask(feature_lengths * factor, indices.shape[2] * factor) + mel_masks = sequence_mask( + feature_lengths * self.downsample_factor, + indices.shape[2] * self.downsample_factor, + ) mel_masks_float_conv = mel_masks[:, None, :].float() + audio_lengths = ( + feature_lengths * self.downsample_factor * self.spec_transform.hop_length + ) audio_masks = sequence_mask( - feature_lengths * factor * self.spec_transform.hop_length, - indices.shape[2] * factor * self.spec_transform.hop_length, + audio_lengths, + indices.shape[2] * self.downsample_factor * self.spec_transform.hop_length, ) audio_masks_float_conv = audio_masks[:, None, :].float() z = self.quantizer.decode(indices) * mel_masks_float_conv x = self.head(z) * audio_masks_float_conv - return x + return x, audio_lengths def remove_parametrizations(self): if hasattr(self.backbone, "remove_parametrizations"): @@ -558,68 +594,3 @@ def remove_parametrizations(self): @property def device(self): return next(self.parameters()).device - - -class FireflyBase(nn.Module): - def __init__(self, ckpt_path: str = None, pretrained: bool = True): - super().__init__() - - self.backbone = ConvNeXtEncoder( - input_channels=128, - depths=[3, 3, 9, 3], - dims=[128, 256, 384, 512], - drop_path_rate=0.2, - kernel_size=7, - ) - - self.head = HiFiGANGenerator( - hop_length=512, - upsample_rates=[8, 8, 2, 2, 2], - upsample_kernel_sizes=[16, 16, 4, 4, 4], - resblock_kernel_sizes=[3, 7, 11], - resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]], - num_mels=512, - upsample_initial_channel=512, - use_template=False, - pre_conv_kernel_size=13, - post_conv_kernel_size=13, - ) - - if ckpt_path is not None: - state_dict = torch.load(ckpt_path, map_location="cpu") - elif pretrained: - state_dict = torch.hub.load_state_dict_from_url( - "https://github.com/fishaudio/vocoder/releases/download/1.0.0/firefly-gan-base-generator.ckpt", - map_location="cpu", - model_dir="checkpoints", - ) - - if "state_dict" in state_dict: - state_dict = state_dict["state_dict"] - - if any("generator." in k for k in state_dict): - state_dict = { - k.replace("generator.", ""): v - for k, v in state_dict.items() - if "generator." in k - } - - self.load_state_dict(state_dict, strict=True) - self.head.remove_parametrizations() - - @torch.no_grad() - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = self.backbone(x) - x = self.head(x) - if x.ndim == 2: - x = x[:, None, :] - return x - - -if __name__ == "__main__": - model = FireflyBase() - model.eval() - x = torch.randn(1, 128, 128) - with torch.no_grad(): - y = model(x) - print(y.shape) diff --git a/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/modules/fsq.py b/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/modules/fsq.py index c837d6aee5..7ea4853376 100644 --- a/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/modules/fsq.py +++ b/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/modules/fsq.py @@ -6,7 +6,7 @@ from einops import rearrange from vector_quantize_pytorch import GroupedResidualFSQ -from .firefly import ConvNeXtBlock +from .firefly import ConvNeXtBlock, FishConvNet, FishTransConvNet @dataclass @@ -20,7 +20,7 @@ class DownsampleFiniteScalarQuantize(nn.Module): def __init__( self, input_dim: int = 512, - n_codebooks: int = 1, + n_codebooks: int = 9, n_groups: int = 1, levels: tuple[int] = (8, 5, 5, 5), # Approximate 2**10 downsample_factor: tuple[int] = (2, 2), @@ -46,7 +46,7 @@ def __init__( self.downsample = nn.Sequential( *[ nn.Sequential( - nn.Conv1d( + FishConvNet( all_dims[idx], all_dims[idx + 1], kernel_size=factor, @@ -61,7 +61,7 @@ def __init__( self.upsample = nn.Sequential( *[ nn.Sequential( - nn.ConvTranspose1d( + FishTransConvNet( all_dims[idx + 1], all_dims[idx], kernel_size=factor, @@ -114,26 +114,3 @@ def decode(self, indices: torch.Tensor): z_q = self.residual_fsq.get_output_from_indices(indices) z_q = self.upsample(z_q.mT) return z_q - - # def from_latents(self, latents: torch.Tensor): - # z_q, z_p, codes = super().from_latents(latents) - # z_q = self.upsample(z_q) - # return z_q, z_p, codes - - -if __name__ == "__main__": - rvq = DownsampleFiniteScalarQuantize( - n_codebooks=1, - downsample_factor=(2, 2), - ) - x = torch.randn(16, 512, 80) - - result = rvq(x) - print(rvq) - print(result.latents.shape, result.codes.shape, result.z.shape) - - # y = rvq.from_codes(result.codes) - # print(y[0].shape) - - # y = rvq.from_latents(result.latents) - # print(y[0].shape) diff --git a/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/modules/reference.py b/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/modules/reference.py deleted file mode 100644 index 0d9c8c8359..0000000000 --- a/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/modules/reference.py +++ /dev/null @@ -1,115 +0,0 @@ -from typing import Optional - -import torch -import torch.nn.functional as F -from torch import nn - -from fish_speech.utils import autocast_exclude_mps - -from .wavenet import WaveNet - - -class ReferenceEncoder(WaveNet): - def __init__( - self, - input_channels: Optional[int] = None, - output_channels: Optional[int] = None, - residual_channels: int = 512, - residual_layers: int = 20, - dilation_cycle: Optional[int] = 4, - num_heads: int = 8, - latent_len: int = 4, - ): - super().__init__( - input_channels=input_channels, - residual_channels=residual_channels, - residual_layers=residual_layers, - dilation_cycle=dilation_cycle, - ) - - self.head_dim = residual_channels // num_heads - self.num_heads = num_heads - - self.latent_len = latent_len - self.latent = nn.Parameter(torch.zeros(1, self.latent_len, residual_channels)) - - self.q = nn.Linear(residual_channels, residual_channels, bias=True) - self.kv = nn.Linear(residual_channels, residual_channels * 2, bias=True) - self.q_norm = nn.LayerNorm(self.head_dim) - self.k_norm = nn.LayerNorm(self.head_dim) - self.proj = nn.Linear(residual_channels, residual_channels) - self.proj_drop = nn.Dropout(0.1) - - self.norm = nn.LayerNorm(residual_channels) - self.mlp = nn.Sequential( - nn.Linear(residual_channels, residual_channels * 4), - nn.SiLU(), - nn.Linear(residual_channels * 4, residual_channels), - ) - self.output_projection_attn = nn.Linear(residual_channels, output_channels) - - torch.nn.init.trunc_normal_(self.latent, std=0.02) - self.apply(self.init_weights) - - def init_weights(self, m): - if isinstance(m, nn.Linear): - torch.nn.init.trunc_normal_(m.weight, std=0.02) - if m.bias is not None: - torch.nn.init.constant_(m.bias, 0) - - def forward(self, x, attn_mask=None): - x = super().forward(x).mT - B, N, C = x.shape - - # Calculate mask - if attn_mask is not None: - assert attn_mask.shape == (B, N) and attn_mask.dtype == torch.bool - - attn_mask = attn_mask[:, None, None, :].expand( - B, self.num_heads, self.latent_len, N - ) - - q_latent = self.latent.expand(B, -1, -1) - q = ( - self.q(q_latent) - .reshape(B, self.latent_len, self.num_heads, self.head_dim) - .transpose(1, 2) - ) - - kv = ( - self.kv(x) - .reshape(B, N, 2, self.num_heads, self.head_dim) - .permute(2, 0, 3, 1, 4) - ) - k, v = kv.unbind(0) - - q, k = self.q_norm(q), self.k_norm(k) - x = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) - - x = x.transpose(1, 2).reshape(B, self.latent_len, C) - x = self.proj(x) - x = self.proj_drop(x) - - x = x + self.mlp(self.norm(x)) - x = self.output_projection_attn(x) - x = x.mean(1) - - return x - - -if __name__ == "__main__": - with autocast_exclude_mps(device_type="cpu", dtype=torch.bfloat16): - model = ReferenceEncoder( - input_channels=128, - output_channels=64, - residual_channels=384, - residual_layers=20, - dilation_cycle=4, - num_heads=8, - ) - x = torch.randn(4, 128, 64) - mask = torch.ones(4, 64, dtype=torch.bool) - y = model(x, mask) - print(y.shape) - loss = F.mse_loss(y, torch.randn(4, 64)) - loss.backward() diff --git a/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/modules/wavenet.py b/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/modules/wavenet.py deleted file mode 100644 index e7cc011c3e..0000000000 --- a/xinference/thirdparty/fish_speech/fish_speech/models/vqgan/modules/wavenet.py +++ /dev/null @@ -1,225 +0,0 @@ -import math -from typing import Optional - -import torch -import torch.nn.functional as F -from torch import nn - - -class Mish(nn.Module): - def forward(self, x): - return x * torch.tanh(F.softplus(x)) - - -class DiffusionEmbedding(nn.Module): - """Diffusion Step Embedding""" - - def __init__(self, d_denoiser): - super(DiffusionEmbedding, self).__init__() - self.dim = d_denoiser - - def forward(self, x): - device = x.device - half_dim = self.dim // 2 - emb = math.log(10000) / (half_dim - 1) - emb = torch.exp(torch.arange(half_dim, device=device) * -emb) - emb = x[:, None] * emb[None, :] - emb = torch.cat((emb.sin(), emb.cos()), dim=-1) - return emb - - -class LinearNorm(nn.Module): - """LinearNorm Projection""" - - def __init__(self, in_features, out_features, bias=False): - super(LinearNorm, self).__init__() - self.linear = nn.Linear(in_features, out_features, bias) - - nn.init.xavier_uniform_(self.linear.weight) - if bias: - nn.init.constant_(self.linear.bias, 0.0) - - def forward(self, x): - x = self.linear(x) - return x - - -class ConvNorm(nn.Module): - """1D Convolution""" - - def __init__( - self, - in_channels, - out_channels, - kernel_size=1, - stride=1, - padding=None, - dilation=1, - bias=True, - w_init_gain="linear", - ): - super(ConvNorm, self).__init__() - - if padding is None: - assert kernel_size % 2 == 1 - padding = int(dilation * (kernel_size - 1) / 2) - - self.conv = nn.Conv1d( - in_channels, - out_channels, - kernel_size=kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - bias=bias, - ) - nn.init.kaiming_normal_(self.conv.weight) - - def forward(self, signal): - conv_signal = self.conv(signal) - - return conv_signal - - -class ResidualBlock(nn.Module): - """Residual Block""" - - def __init__( - self, - residual_channels, - use_linear_bias=False, - dilation=1, - condition_channels=None, - ): - super(ResidualBlock, self).__init__() - self.conv_layer = ConvNorm( - residual_channels, - 2 * residual_channels, - kernel_size=3, - stride=1, - padding=dilation, - dilation=dilation, - ) - - if condition_channels is not None: - self.diffusion_projection = LinearNorm( - residual_channels, residual_channels, use_linear_bias - ) - self.condition_projection = ConvNorm( - condition_channels, 2 * residual_channels, kernel_size=1 - ) - - self.output_projection = ConvNorm( - residual_channels, 2 * residual_channels, kernel_size=1 - ) - - def forward(self, x, condition=None, diffusion_step=None): - y = x - - if diffusion_step is not None: - diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1) - y = y + diffusion_step - - y = self.conv_layer(y) - - if condition is not None: - condition = self.condition_projection(condition) - y = y + condition - - gate, filter = torch.chunk(y, 2, dim=1) - y = torch.sigmoid(gate) * torch.tanh(filter) - - y = self.output_projection(y) - residual, skip = torch.chunk(y, 2, dim=1) - - return (x + residual) / math.sqrt(2.0), skip - - -class WaveNet(nn.Module): - def __init__( - self, - input_channels: Optional[int] = None, - output_channels: Optional[int] = None, - residual_channels: int = 512, - residual_layers: int = 20, - dilation_cycle: Optional[int] = 4, - is_diffusion: bool = False, - condition_channels: Optional[int] = None, - ): - super().__init__() - - # Input projection - self.input_projection = None - if input_channels is not None and input_channels != residual_channels: - self.input_projection = ConvNorm( - input_channels, residual_channels, kernel_size=1 - ) - - if input_channels is None: - input_channels = residual_channels - - self.input_channels = input_channels - - # Residual layers - self.residual_layers = nn.ModuleList( - [ - ResidualBlock( - residual_channels=residual_channels, - use_linear_bias=False, - dilation=2 ** (i % dilation_cycle) if dilation_cycle else 1, - condition_channels=condition_channels, - ) - for i in range(residual_layers) - ] - ) - - # Skip projection - self.skip_projection = ConvNorm( - residual_channels, residual_channels, kernel_size=1 - ) - - # Output projection - self.output_projection = None - if output_channels is not None and output_channels != residual_channels: - self.output_projection = ConvNorm( - residual_channels, output_channels, kernel_size=1 - ) - - if is_diffusion: - self.diffusion_embedding = DiffusionEmbedding(residual_channels) - self.mlp = nn.Sequential( - LinearNorm(residual_channels, residual_channels * 4, False), - Mish(), - LinearNorm(residual_channels * 4, residual_channels, False), - ) - - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, (nn.Conv1d, nn.Linear)): - nn.init.trunc_normal_(m.weight, std=0.02) - if getattr(m, "bias", None) is not None: - nn.init.constant_(m.bias, 0) - - def forward(self, x, t=None, condition=None): - if self.input_projection is not None: - x = self.input_projection(x) - x = F.silu(x) - - if t is not None: - t = self.diffusion_embedding(t) - t = self.mlp(t) - - skip = [] - for layer in self.residual_layers: - x, skip_connection = layer(x, condition, t) - skip.append(skip_connection) - - x = torch.sum(torch.stack(skip), dim=0) / math.sqrt(len(self.residual_layers)) - x = self.skip_projection(x) - - if self.output_projection is not None: - x = F.silu(x) - x = self.output_projection(x) - - return x diff --git a/xinference/thirdparty/fish_speech/fish_speech/text/clean.py b/xinference/thirdparty/fish_speech/fish_speech/text/clean.py index 76d9dc9033..c228dfcd13 100644 --- a/xinference/thirdparty/fish_speech/fish_speech/text/clean.py +++ b/xinference/thirdparty/fish_speech/fish_speech/text/clean.py @@ -1,61 +1,24 @@ -import itertools import re -LANGUAGE_UNICODE_RANGE_MAP = { - "ZH": [(0x4E00, 0x9FFF)], - "JP": [(0x4E00, 0x9FFF), (0x3040, 0x309F), (0x30A0, 0x30FF), (0x31F0, 0x31FF)], - "EN": [(0x0000, 0x007F)], -} - SYMBOLS_MAPPING = { - "：": ",", - "；": ",", - "，": ",", - "。": ".", - "！": "!", - "？": "?", - "\n": ".", - "·": ",", - "、": ",", - "...": "…", "“": "'", "”": "'", "‘": "'", "’": "'", - "（": "'", - "）": "'", - "(": "'", - ")": "'", - "《": "'", - "》": "'", - "【": "'", - "】": "'", - "[": "'", - "]": "'", - "—": "-", - "～": "-", - "~": "-", - "・": "-", - "「": "'", - "」": "'", - ";": ",", - ":": ",", + "【": "", + "】": "", + "[": "", + "]": "", + "（": "", + "）": "", + "(": "", + ")": "", + "・": "·", } REPLACE_SYMBOL_REGEX = re.compile( "|".join(re.escape(p) for p in SYMBOLS_MAPPING.keys()) ) -ALL_KNOWN_UTF8_RANGE = list( - itertools.chain.from_iterable(LANGUAGE_UNICODE_RANGE_MAP.values()) -) -REMOVE_UNKNOWN_SYMBOL_REGEX = re.compile( - "[^" - + "".join( - f"{re.escape(chr(start))}-{re.escape(chr(end))}" - for start, end in ALL_KNOWN_UTF8_RANGE - ) - + "]" -) def clean_text(text): @@ -64,6 +27,5 @@ def clean_text(text): # Replace all chinese symbols with their english counterparts text = REPLACE_SYMBOL_REGEX.sub(lambda x: SYMBOLS_MAPPING[x.group()], text) - text = REMOVE_UNKNOWN_SYMBOL_REGEX.sub("", text) return text diff --git a/xinference/thirdparty/fish_speech/fish_speech/text/spliter.py b/xinference/thirdparty/fish_speech/fish_speech/text/spliter.py index 5528cd3a63..d4bb995487 100644 --- a/xinference/thirdparty/fish_speech/fish_speech/text/spliter.py +++ b/xinference/thirdparty/fish_speech/fish_speech/text/spliter.py @@ -71,9 +71,9 @@ def split_text(text, length): texts = [text] texts = map(protect_float, texts) - texts = break_text(texts, length, {".", "!", "?"}) + texts = break_text(texts, length, {".", "!", "?", "。", "！", "？"}) texts = map(unprotect_float, texts) - texts = break_text(texts, length, {","}) + texts = break_text(texts, length, {",", "，"}) texts = break_text(texts, length, {" "}) texts = list(break_text_by_length(texts, length)) diff --git a/xinference/thirdparty/fish_speech/fish_speech/train.py b/xinference/thirdparty/fish_speech/fish_speech/train.py index a6a344097a..41b3642f88 100644 --- a/xinference/thirdparty/fish_speech/fish_speech/train.py +++ b/xinference/thirdparty/fish_speech/fish_speech/train.py @@ -1,4 +1,6 @@ import os + +os.environ["USE_LIBUV"] = "0" import sys from typing import Optional diff --git a/xinference/thirdparty/fish_speech/fish_speech/webui/manage.py b/xinference/thirdparty/fish_speech/fish_speech/webui/manage.py index 9c183acd7c..4ec3fcac25 100644 --- a/xinference/thirdparty/fish_speech/fish_speech/webui/manage.py +++ b/xinference/thirdparty/fish_speech/fish_speech/webui/manage.py @@ -1,9 +1,11 @@ from __future__ import annotations +import os + +os.environ["USE_LIBUV"] = "0" import datetime import html import json -import os import platform import shutil import signal @@ -469,7 +471,7 @@ def train_process( "--config-name", "firefly_gan_vq", "--checkpoint-path", - "checkpoints/fish-speech-1.2-sft/firefly-gan-vq-fsq-4x1024-42hz-generator.pth", + "checkpoints/fish-speech-1.4/firefly-gan-vq-fsq-8x1024-21hz-generator.pth", ] ) @@ -485,7 +487,7 @@ def train_process( "16", ] ) - ckpt_path = "checkpoints/fish-speech-1.2-sft/model.pth" + ckpt_path = "checkpoints/fish-speech-1.4/model.pth" lora_prefix = "lora_" if llama_use_lora else "" llama_name = lora_prefix + "text2semantic_" + new_project latest = next( @@ -862,7 +864,7 @@ def llama_quantify(llama_weight, quantify_mode): minimum=1, maximum=32, step=1, - value=4, + value=2, ) llama_data_max_length_slider = gr.Slider( label=i18n("Maximum Length per Sample"), @@ -870,7 +872,7 @@ def llama_quantify(llama_weight, quantify_mode): minimum=1024, maximum=4096, step=128, - value=1024, + value=2048, ) with gr.Row(equal_height=False): llama_precision_dropdown = gr.Dropdown( @@ -925,9 +927,9 @@ def llama_quantify(llama_weight, quantify_mode): "Type the path or select from the dropdown" ), choices=[ - "checkpoints/fish-speech-1.2-sft/model.pth", + "checkpoints/fish-speech-1.4/model.pth", ], - value="checkpoints/fish-speech-1.2-sft/model.pth", + value="checkpoints/fish-speech-1.4/model.pth", allow_custom_value=True, interactive=True, ) @@ -979,7 +981,7 @@ def llama_quantify(llama_weight, quantify_mode): "Type the path or select from the dropdown" ), choices=list_llama_models(), - value="checkpoints/fish-speech-1.2-sft", + value="checkpoints/fish-speech-1.4", allow_custom_value=True, interactive=True, ) @@ -1042,7 +1044,7 @@ def llama_quantify(llama_weight, quantify_mode): "Type the path or select from the dropdown" ), choices=list_decoder_models(), - value="checkpoints/fish-speech-1.2-sft/firefly-gan-vq-fsq-4x1024-42hz-generator.pth", + value="checkpoints/fish-speech-1.4/firefly-gan-vq-fsq-8x1024-21hz-generator.pth", allow_custom_value=True, ) infer_decoder_config = gr.Dropdown( @@ -1060,7 +1062,7 @@ def llama_quantify(llama_weight, quantify_mode): info=i18n( "Type the path or select from the dropdown" ), - value="checkpoints/fish-speech-1.2-sft", + value="checkpoints/fish-speech-1.4", choices=list_llama_models(), allow_custom_value=True, ) diff --git a/xinference/thirdparty/fish_speech/tools/api.py b/xinference/thirdparty/fish_speech/tools/api.py index a11d30b227..7fcc9330ae 100644 --- a/xinference/thirdparty/fish_speech/tools/api.py +++ b/xinference/thirdparty/fish_speech/tools/api.py @@ -9,16 +9,20 @@ from argparse import ArgumentParser from http import HTTPStatus from pathlib import Path -from typing import Annotated, Literal, Optional +from typing import Annotated, Any, Literal, Optional import numpy as np +import ormsgpack # import pyrootutils import soundfile as sf import torch import torchaudio +# from baize.datastructures import ContentType # from kui.asgi import ( # Body, +# FactoryClass, # HTTPException, +# HttpRequest, # HttpView, # JSONResponse, # Kui, @@ -27,14 +31,16 @@ # ) # from kui.asgi.routing import MultimethodRoutes from loguru import logger -from pydantic import BaseModel, Field +from pydantic import BaseModel, Field, conint # pyrootutils.setup_root(__file__, indicator=".project-root", pythonpath=True) # from fish_speech.models.vqgan.lit_module import VQGAN from fish_speech.models.vqgan.modules.firefly import FireflyArchitecture +from fish_speech.text.chn_text_norm.text import Text as ChnNormedText from fish_speech.utils import autocast_exclude_mps -from tools.auto_rerank import batch_asr, calculate_wer, is_chinese, load_model +from tools.commons import ServeReferenceAudio, ServeTTSRequest +from tools.file import AUDIO_EXTENSIONS, audio_to_bytes, list_files, read_ref_text from tools.llama.generate import ( GenerateRequest, GenerateResponse, @@ -82,11 +88,8 @@ async def other_exception_handler(exc: "Exception"): def load_audio(reference_audio, sr): if len(reference_audio) > 255 or not Path(reference_audio).exists(): - try: - audio_data = base64.b64decode(reference_audio) - reference_audio = io.BytesIO(audio_data) - except base64.binascii.Error: - raise ValueError("Invalid path or base64 string") + audio_data = reference_audio + reference_audio = io.BytesIO(audio_data) waveform, original_sr = torchaudio.load( reference_audio, backend="sox" if sys.platform == "linux" else "soundfile" @@ -145,7 +148,7 @@ def decode_vq_tokens( return decoder_model.decode( indices=codes[None], feature_lengths=feature_lengths, - ).squeeze() + )[0].squeeze() raise ValueError(f"Unknown model type: {type(decoder_model)}") @@ -153,58 +156,6 @@ def decode_vq_tokens( # routes = MultimethodRoutes(base_class=HttpView) -def get_random_paths(base_path, data, speaker, emotion): - if base_path and data and speaker and emotion and (Path(base_path).exists()): - if speaker in data and emotion in data[speaker]: - files = data[speaker][emotion] - lab_files = [f for f in files if f.endswith(".lab")] - wav_files = [f for f in files if f.endswith(".wav")] - - if lab_files and wav_files: - selected_lab = random.choice(lab_files) - selected_wav = random.choice(wav_files) - - lab_path = Path(base_path) / speaker / emotion / selected_lab - wav_path = Path(base_path) / speaker / emotion / selected_wav - if lab_path.exists() and wav_path.exists(): - return lab_path, wav_path - - return None, None - - -def load_json(json_file): - if not json_file: - logger.info("Not using a json file") - return None - try: - with open(json_file, "r", encoding="utf-8") as file: - data = json.load(file) - except FileNotFoundError: - logger.warning(f"ref json not found: {json_file}") - data = None - except Exception as e: - logger.warning(f"Loading json failed: {e}") - data = None - return data - - -class InvokeRequest(BaseModel): - text: str = "你说的对, 但是原神是一款由米哈游自主研发的开放世界手游." - reference_text: Optional[str] = None - reference_audio: Optional[str] = None - max_new_tokens: int = 1024 - chunk_length: Annotated[int, Field(ge=0, le=500, strict=True)] = 100 - top_p: Annotated[float, Field(ge=0.1, le=1.0, strict=True)] = 0.7 - repetition_penalty: Annotated[float, Field(ge=0.9, le=2.0, strict=True)] = 1.2 - temperature: Annotated[float, Field(ge=0.1, le=1.0, strict=True)] = 0.7 - emotion: Optional[str] = None - format: Literal["wav", "mp3", "flac"] = "wav" - streaming: bool = False - ref_json: Optional[str] = "ref_data.json" - ref_base: Optional[str] = "ref_data" - speaker: Optional[str] = None - - def get_content_type(audio_format): if audio_format == "wav": return "audio/wav" @@ -217,35 +168,52 @@ def get_content_type(audio_format): @torch.inference_mode() -def inference(req: InvokeRequest): - # Parse reference audio aka prompt - prompt_tokens = None - - ref_data = load_json(req.ref_json) - ref_base = req.ref_base - - lab_path, wav_path = get_random_paths(ref_base, ref_data, req.speaker, req.emotion) - - if lab_path and wav_path: - with open(lab_path, "r", encoding="utf-8") as lab_file: - ref_text = lab_file.read() - req.reference_audio = wav_path - req.reference_text = ref_text - logger.info("ref_path: " + str(wav_path)) - logger.info("ref_text: " + ref_text) - - # Parse reference audio aka prompt - prompt_tokens = encode_reference( - decoder_model=decoder_model, - reference_audio=req.reference_audio, - enable_reference_audio=req.reference_audio is not None, - ) - logger.info(f"ref_text: {req.reference_text}") +def inference(req: ServeTTSRequest): + + idstr: str | None = req.reference_id + if idstr is not None: + ref_folder = Path("references") / idstr + ref_folder.mkdir(parents=True, exist_ok=True) + ref_audios = list_files( + ref_folder, AUDIO_EXTENSIONS, recursive=True, sort=False + ) + prompt_tokens = [ + encode_reference( + decoder_model=decoder_model, + reference_audio=audio_to_bytes(str(ref_audio)), + enable_reference_audio=True, + ) + for ref_audio in ref_audios + ] + prompt_texts = [ + read_ref_text(str(ref_audio.with_suffix(".lab"))) + for ref_audio in ref_audios + ] + + else: + # Parse reference audio aka prompt + refs = req.references + if refs is None: + refs = [] + prompt_tokens = [ + encode_reference( + decoder_model=decoder_model, + reference_audio=ref.audio, + enable_reference_audio=True, + ) + for ref in refs + ] + prompt_texts = [ref.text for ref in refs] + # LLAMA Inference request = dict( device=decoder_model.device, max_new_tokens=req.max_new_tokens, - text=req.text, + text=( + req.text + if not req.normalize + else ChnNormedText(raw_text=req.text).normalize() + ), top_p=req.top_p, repetition_penalty=req.repetition_penalty, temperature=req.temperature, @@ -254,7 +222,7 @@ def inference(req: InvokeRequest): chunk_length=req.chunk_length, max_length=2048, prompt_tokens=prompt_tokens, - prompt_text=req.reference_text, + prompt_text=prompt_texts, ) response_queue = queue.Queue() @@ -307,40 +275,7 @@ def inference(req: InvokeRequest): yield fake_audios -def auto_rerank_inference(req: InvokeRequest, use_auto_rerank: bool = True): - if not use_auto_rerank: - # 如果不使用 auto_rerank，直接调用原始的 inference 函数 - return inference(req) - - zh_model, en_model = load_model() - max_attempts = 5 - best_wer = float("inf") - best_audio = None - - for attempt in range(max_attempts): - # 调用原始的 inference 函数 - audio_generator = inference(req) - fake_audios = next(audio_generator) - - asr_result = batch_asr( - zh_model if is_chinese(req.text) else en_model, [fake_audios], 44100 - )[0] - wer = calculate_wer(req.text, asr_result["text"]) - - if wer <= 0.1 and not asr_result["huge_gap"]: - return fake_audios - - if wer < best_wer: - best_wer = wer - best_audio = fake_audios - - if attempt == max_attempts - 1: - break - - return best_audio - - -async def inference_async(req: InvokeRequest): +async def inference_async(req: ServeTTSRequest): for chunk in inference(req): yield chunk @@ -349,9 +284,9 @@ async def buffer_to_async_generator(buffer): yield buffer -# @routes.http.post("/v1/invoke") +# @routes.http.post("/v1/tts") # async def api_invoke_model( -# req: Annotated[InvokeRequest, Body(exclusive=True)], +# req: Annotated[ServeTTSRequest, Body(exclusive=True)], # ): # """ # Invoke model and generate audio @@ -410,21 +345,20 @@ def parse_args(): parser.add_argument( "--llama-checkpoint-path", type=str, - default="checkpoints/fish-speech-1.2-sft", + default="checkpoints/fish-speech-1.4", ) parser.add_argument( "--decoder-checkpoint-path", type=str, - default="checkpoints/fish-speech-1.2-sft/firefly-gan-vq-fsq-4x1024-42hz-generator.pth", + default="checkpoints/fish-speech-1.4/firefly-gan-vq-fsq-8x1024-21hz-generator.pth", ) parser.add_argument("--decoder-config-name", type=str, default="firefly_gan_vq") parser.add_argument("--device", type=str, default="cuda") parser.add_argument("--half", action="store_true") parser.add_argument("--compile", action="store_true") parser.add_argument("--max-text-length", type=int, default=0) - parser.add_argument("--listen", type=str, default="127.0.0.1:8000") + parser.add_argument("--listen", type=str, default="127.0.0.1:8080") parser.add_argument("--workers", type=int, default=1) - parser.add_argument("--use-auto-rerank", type=bool, default=True) return parser.parse_args() @@ -436,18 +370,30 @@ def parse_args(): # }, # ).routes # +# +# class MsgPackRequest(HttpRequest): +# async def data(self) -> Annotated[Any, ContentType("application/msgpack")]: +# if self.content_type == "application/msgpack": +# return ormsgpack.unpackb(await self.body) +# +# raise HTTPException( +# HTTPStatus.UNSUPPORTED_MEDIA_TYPE, +# headers={"Accept": "application/msgpack"}, +# ) +# +# # app = Kui( # routes=routes + openapi[1:], # Remove the default route # exception_handlers={ # HTTPException: http_execption_handler, # Exception: other_exception_handler, # }, +# factory_class=FactoryClass(http=MsgPackRequest), # cors_config={}, # ) if __name__ == "__main__": - import threading import uvicorn @@ -474,18 +420,17 @@ def parse_args(): # Dry run to check if the model is loaded correctly and avoid the first-time latency list( inference( - InvokeRequest( + ServeTTSRequest( text="Hello world.", - reference_text=None, - reference_audio=None, - max_new_tokens=0, + references=[], + reference_id=None, + max_new_tokens=1024, + chunk_length=200, top_p=0.7, repetition_penalty=1.2, temperature=0.7, emotion=None, format="wav", - ref_base=None, - ref_json=None, ) ) ) diff --git a/xinference/thirdparty/fish_speech/tools/auto_rerank.py b/xinference/thirdparty/fish_speech/tools/auto_rerank.py deleted file mode 100644 index 0297d63d77..0000000000 --- a/xinference/thirdparty/fish_speech/tools/auto_rerank.py +++ /dev/null @@ -1,159 +0,0 @@ -import os - -os.environ["MODELSCOPE_CACHE"] = ".cache/" - -import string -import time -from threading import Lock - -import librosa -import numpy as np -import opencc -import torch -from faster_whisper import WhisperModel - -t2s_converter = opencc.OpenCC("t2s") - - -def load_model(*, device="cuda"): - model = WhisperModel( - "medium", - device=device, - compute_type="float16", - download_root="faster_whisper", - ) - print("faster_whisper loaded!") - return model - - -@torch.no_grad() -def batch_asr_internal(model: WhisperModel, audios, sr): - resampled_audios = [] - for audio in audios: - - if isinstance(audio, np.ndarray): - audio = torch.from_numpy(audio).float() - - if audio.dim() > 1: - audio = audio.squeeze() - - assert audio.dim() == 1 - audio_np = audio.numpy() - resampled_audio = librosa.resample(audio_np, orig_sr=sr, target_sr=16000) - resampled_audios.append(resampled_audio) - - trans_results = [] - - for resampled_audio in resampled_audios: - segments, info = model.transcribe( - resampled_audio, - language=None, - beam_size=5, - initial_prompt="Punctuation is needed in any language.", - ) - trans_results.append(list(segments)) - - results = [] - for trans_res, audio in zip(trans_results, audios): - - duration = len(audio) / sr * 1000 - huge_gap = False - max_gap = 0.0 - - text = None - last_tr = None - - for tr in trans_res: - delta = tr.text.strip() - if tr.id > 1: - max_gap = max(tr.start - last_tr.end, max_gap) - text += delta - else: - text = delta - - last_tr = tr - if max_gap > 3.0: - huge_gap = True - break - - sim_text = t2s_converter.convert(text) - results.append( - { - "text": sim_text, - "duration": duration, - "huge_gap": huge_gap, - } - ) - - return results - - -global_lock = Lock() - - -def batch_asr(model, audios, sr): - return batch_asr_internal(model, audios, sr) - - -def is_chinese(text): - return True - - -def calculate_wer(text1, text2, debug=False): - chars1 = remove_punctuation(text1) - chars2 = remove_punctuation(text2) - - m, n = len(chars1), len(chars2) - - if m > n: - chars1, chars2 = chars2, chars1 - m, n = n, m - - prev = list(range(m + 1)) # row 0 distance: [0, 1, 2, ...] - curr = [0] * (m + 1) - - for j in range(1, n + 1): - curr[0] = j - for i in range(1, m + 1): - if chars1[i - 1] == chars2[j - 1]: - curr[i] = prev[i - 1] - else: - curr[i] = min(prev[i], curr[i - 1], prev[i - 1]) + 1 - prev, curr = curr, prev - - edits = prev[m] - tot = max(len(chars1), len(chars2)) - wer = edits / tot - - if debug: - print(" gt: ", chars1) - print(" pred: ", chars2) - print(" edits/tot = wer: ", edits, "/", tot, "=", wer) - - return wer - - -def remove_punctuation(text): - chinese_punctuation = ( - " \n\t”“！？｡。＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—" - '‛""„‟…‧﹏' - ) - all_punctuation = string.punctuation + chinese_punctuation - translator = str.maketrans("", "", all_punctuation) - text_without_punctuation = text.translate(translator) - return text_without_punctuation - - -if __name__ == "__main__": - model = load_model() - audios = [ - librosa.load("44100.wav", sr=44100)[0], - librosa.load("lengyue.wav", sr=44100)[0], - ] - print(np.array(audios[0])) - print(batch_asr(model, audios, 44100)) - - start_time = time.time() - for _ in range(10): - print(batch_asr(model, audios, 44100)) - print("Time taken:", time.time() - start_time) diff --git a/xinference/thirdparty/fish_speech/tools/commons.py b/xinference/thirdparty/fish_speech/tools/commons.py new file mode 100644 index 0000000000..f81cadec1e --- /dev/null +++ b/xinference/thirdparty/fish_speech/tools/commons.py @@ -0,0 +1,35 @@ +from typing import Annotated, Literal, Optional + +from pydantic import BaseModel, Field, conint + + +class ServeReferenceAudio(BaseModel): + audio: bytes + text: str + + +class ServeTTSRequest(BaseModel): + text: str + chunk_length: Annotated[int, conint(ge=100, le=300, strict=True)] = 200 + # Audio format + format: Literal["wav", "pcm", "mp3"] = "wav" + mp3_bitrate: Literal[64, 128, 192] = 128 + # References audios for in-context learning + references: list[ServeReferenceAudio] = [] + # Reference id + # For example, if you want use https://fish.audio/m/7f92f8afb8ec43bf81429cc1c9199cb1/ + # Just pass 7f92f8afb8ec43bf81429cc1c9199cb1 + reference_id: str | None = None + # Normalize text for en & zh, this increase stability for numbers + normalize: bool = True + mp3_bitrate: Optional[int] = 64 + opus_bitrate: Optional[int] = -1000 + # Balance mode will reduce latency to 300ms, but may decrease stability + latency: Literal["normal", "balanced"] = "normal" + # not usually used below + streaming: bool = False + emotion: Optional[str] = None + max_new_tokens: int = 1024 + top_p: Annotated[float, Field(ge=0.1, le=1.0, strict=True)] = 0.7 + repetition_penalty: Annotated[float, Field(ge=0.9, le=2.0, strict=True)] = 1.2 + temperature: Annotated[float, Field(ge=0.1, le=1.0, strict=True)] = 0.7 diff --git a/xinference/thirdparty/fish_speech/tools/download_models.py b/xinference/thirdparty/fish_speech/tools/download_models.py index 480f3be0f4..9e79c34c43 100644 --- a/xinference/thirdparty/fish_speech/tools/download_models.py +++ b/xinference/thirdparty/fish_speech/tools/download_models.py @@ -22,8 +22,8 @@ def check_and_download_files(repo_id, file_list, local_dir): # 1st -repo_id_1 = "fishaudio/fish-speech-1.2-sft" -local_dir_1 = "./checkpoints/fish-speech-1.2-sft" +repo_id_1 = "fishaudio/fish-speech-1.4" +local_dir_1 = "./checkpoints/fish-speech-1.4" files_1 = [ "model.pth", "README.md", @@ -31,7 +31,7 @@ def check_and_download_files(repo_id, file_list, local_dir): "tokenizer_config.json", "tokenizer.json", "config.json", - "firefly-gan-vq-fsq-4x1024-42hz-generator.pth", + "firefly-gan-vq-fsq-8x1024-21hz-generator.pth", ] # 3rd diff --git a/xinference/thirdparty/fish_speech/tools/file.py b/xinference/thirdparty/fish_speech/tools/file.py index b4b8051d6f..f7a0597365 100644 --- a/xinference/thirdparty/fish_speech/tools/file.py +++ b/xinference/thirdparty/fish_speech/tools/file.py @@ -1,3 +1,4 @@ +import base64 from pathlib import Path from typing import Union @@ -23,6 +24,22 @@ } +def audio_to_bytes(file_path): + if not file_path or not Path(file_path).exists(): + return None + with open(file_path, "rb") as wav_file: + wav = wav_file.read() + return wav + + +def read_ref_text(ref_text): + path = Path(ref_text) + if path.exists() and path.is_file(): + with path.open("r", encoding="utf-8") as file: + return file.read() + return ref_text + + def list_files( path: Union[Path, str], extensions: set[str] = None, diff --git a/xinference/thirdparty/fish_speech/tools/gen_ref.py b/xinference/thirdparty/fish_speech/tools/gen_ref.py deleted file mode 100644 index a771903b02..0000000000 --- a/xinference/thirdparty/fish_speech/tools/gen_ref.py +++ /dev/null @@ -1,36 +0,0 @@ -import json -from pathlib import Path - - -def scan_folder(base_path): - wav_lab_pairs = {} - - base = Path(base_path) - for suf in ["wav", "lab"]: - for f in base.rglob(f"*.{suf}"): - relative_path = f.relative_to(base) - parts = relative_path.parts - print(parts) - if len(parts) >= 3: - character = parts[0] - emotion = parts[1] - - if character not in wav_lab_pairs: - wav_lab_pairs[character] = {} - if emotion not in wav_lab_pairs[character]: - wav_lab_pairs[character][emotion] = [] - wav_lab_pairs[character][emotion].append(str(f.name)) - - return wav_lab_pairs - - -def save_to_json(data, output_file): - with open(output_file, "w", encoding="utf-8") as file: - json.dump(data, file, ensure_ascii=False, indent=2) - - -base_path = "ref_data" -out_ref_file = "ref_data.json" - -wav_lab_pairs = scan_folder(base_path) -save_to_json(wav_lab_pairs, out_ref_file) diff --git a/xinference/thirdparty/fish_speech/tools/llama/build_dataset.py b/xinference/thirdparty/fish_speech/tools/llama/build_dataset.py index 20e2219956..fc5ef120cc 100644 --- a/xinference/thirdparty/fish_speech/tools/llama/build_dataset.py +++ b/xinference/thirdparty/fish_speech/tools/llama/build_dataset.py @@ -13,7 +13,7 @@ from fish_speech.datasets.protos.text_data_pb2 import Semantics, Sentence, TextData from fish_speech.datasets.protos.text_data_stream import pack_pb_stream -from fish_speech.utils.file import load_filelist +from tools.file import load_filelist # To avoid CPU overload os.environ["MKL_NUM_THREADS"] = "1" diff --git a/xinference/thirdparty/fish_speech/tools/llama/generate.py b/xinference/thirdparty/fish_speech/tools/llama/generate.py index 934c185145..ad9c549996 100644 --- a/xinference/thirdparty/fish_speech/tools/llama/generate.py +++ b/xinference/thirdparty/fish_speech/tools/llama/generate.py @@ -2,6 +2,7 @@ import queue import threading import time +from contextlib import nullcontext from dataclasses import dataclass from pathlib import Path from typing import Literal, Optional, Tuple, Union @@ -93,15 +94,20 @@ def decode_one_token_ar( **sampling_kwargs, ) -> torch.Tensor: x = model.forward_generate(x, input_pos) + + sampling_kwargs_main = sampling_kwargs.copy() + sampling_kwargs_main["temperature"] = 0.1 + sampling_kwargs_main["top_p"] = 0.1 + sampling_kwargs_main["repetition_penalty"] = 1.0 + codebooks = [ sample( x.logits, - previous_tokens=( - previous_tokens[0] if previous_tokens is not None else None - ), # Disable repetition penalty for the token codebook - **sampling_kwargs, + previous_tokens=None, # Disable repetition penalty for the token codebook + **sampling_kwargs_main, )[0] ] + x = x.hidden_states # Cleanup the cache @@ -136,11 +142,16 @@ def decode_one_token_naive( ) -> torch.Tensor: x = model.forward_generate(x, input_pos) + sampling_kwargs_main = sampling_kwargs.copy() + sampling_kwargs_main["temperature"] = 0.1 + sampling_kwargs_main["top_p"] = 0.1 + sampling_kwargs_main["repetition_penalty"] = 1.0 + codebooks = [ sample( - x.token_logits, + x.logits, previous_tokens=None, # Disable repetition penalty for the token codebook - **sampling_kwargs, + **sampling_kwargs_main, )[0] ] @@ -181,8 +192,12 @@ def decode_n_tokens( else: window = previous_tokens[:, i - win_size : i] - with torch.backends.cuda.sdp_kernel( - enable_flash=False, enable_mem_efficient=False, enable_math=True + with ( + torch.backends.cuda.sdp_kernel( + enable_flash=False, enable_mem_efficient=False, enable_math=True + ) + if torch.cuda.is_available() + else nullcontext() ): # Actually better for Inductor to codegen attention here next_token = decode_one_token( model=model, @@ -222,25 +237,11 @@ def generate( # create an empty tensor of the expected final shape and fill in the current tokens T = prompt.size(1) - if max_new_tokens: - if T + max_new_tokens > model.config.max_seq_len: - max_new_tokens = model.config.max_seq_len - T - logger.info(f"Truncating max_new_tokens to {max_new_tokens}") - - T_new = T + max_new_tokens - else: - T_new = model.config.max_seq_len - max_new_tokens = T_new - T - device, dtype = prompt.device, prompt.dtype - with torch.device(device): - model.setup_caches( - max_batch_size=1, max_seq_len=T_new, dtype=next(model.parameters()).dtype - ) codebook_dim = 1 + model.config.num_codebooks # create an empty tensor of the expected final shape and fill in the current tokens - empty = torch.empty((codebook_dim, T_new), dtype=dtype, device=device) + empty = torch.empty((codebook_dim, max_new_tokens), dtype=dtype, device=device) empty[:, :T] = prompt seq = empty input_pos = torch.arange(0, T, device=device) @@ -560,6 +561,10 @@ def worker(): model, decode_one_token = load_model( checkpoint_path, device, precision, compile=compile ) + with torch.device(device): + model.setup_caches( + max_batch_size=1, max_seq_len=2048, dtype=next(model.parameters()).dtype + ) init_event.set() while True: @@ -607,7 +612,7 @@ def worker(): @click.option( "--checkpoint-path", type=click.Path(path_type=Path, exists=True), - default="checkpoints/fish-speech-1.2-sft", + default="checkpoints/fish-speech-1.4", ) @click.option("--device", type=str, default="cuda") @click.option("--compile/--no-compile", default=False) diff --git a/xinference/thirdparty/fish_speech/tools/llama/merge_lora.py b/xinference/thirdparty/fish_speech/tools/llama/merge_lora.py index f12eece8d2..c1bd3cbd72 100644 --- a/xinference/thirdparty/fish_speech/tools/llama/merge_lora.py +++ b/xinference/thirdparty/fish_speech/tools/llama/merge_lora.py @@ -15,7 +15,7 @@ @click.command() @click.option("--lora-config", type=str, default="r_8_alpha_16") -@click.option("--base-weight", type=str, default="checkpoints/fish-speech-1.2-sft") +@click.option("--base-weight", type=str, default="checkpoints/fish-speech-1.4") @click.option("--lora-weight", type=str, required=True) @click.option("--output", type=str, required=True) def merge(lora_config, base_weight, lora_weight, output): diff --git a/xinference/thirdparty/fish_speech/tools/llama/quantize.py b/xinference/thirdparty/fish_speech/tools/llama/quantize.py index aae32fcce7..e629d944b5 100644 --- a/xinference/thirdparty/fish_speech/tools/llama/quantize.py +++ b/xinference/thirdparty/fish_speech/tools/llama/quantize.py @@ -428,7 +428,7 @@ def generate_folder_name(): @click.option( "--checkpoint-path", type=click.Path(path_type=Path, exists=True), - default="checkpoints/fish-speech-1.2-sft", + default="checkpoints/fish-speech-1.4", ) @click.option( "--mode", type=str, default="int8", help="type of quantization to perform" @@ -451,7 +451,7 @@ def quantize(checkpoint_path: Path, mode: str, groupsize: int, timestamp: str) - precision=precision, compile=False, ) - vq_model = "firefly-gan-vq-fsq-4x1024-42hz-generator.pth" + vq_model = "firefly-gan-vq-fsq-8x1024-21hz-generator.pth" now = timestamp if timestamp != "None" else generate_folder_name() if mode == "int8": diff --git a/xinference/thirdparty/fish_speech/tools/merge_asr_files.py b/xinference/thirdparty/fish_speech/tools/merge_asr_files.py deleted file mode 100644 index cc12062095..0000000000 --- a/xinference/thirdparty/fish_speech/tools/merge_asr_files.py +++ /dev/null @@ -1,55 +0,0 @@ -import os -from pathlib import Path - -from pydub import AudioSegment -from tqdm import tqdm - -from tools.file import AUDIO_EXTENSIONS, list_files - - -def merge_and_delete_files(save_dir, original_files): - save_path = Path(save_dir) - audio_slice_files = list_files( - path=save_dir, extensions=AUDIO_EXTENSIONS.union([".lab"]), recursive=True - ) - audio_files = {} - label_files = {} - for file_path in tqdm(audio_slice_files, desc="Merging audio files"): - rel_path = Path(file_path).relative_to(save_path) - (save_path / rel_path.parent).mkdir(parents=True, exist_ok=True) - if file_path.suffix == ".wav": - prefix = rel_path.parent / file_path.stem.rsplit("-", 1)[0] - if prefix == rel_path.parent / file_path.stem: - continue - audio = AudioSegment.from_wav(file_path) - if prefix in audio_files.keys(): - audio_files[prefix] = audio_files[prefix] + audio - else: - audio_files[prefix] = audio - - elif file_path.suffix == ".lab": - prefix = rel_path.parent / file_path.stem.rsplit("-", 1)[0] - if prefix == rel_path.parent / file_path.stem: - continue - with open(file_path, "r", encoding="utf-8") as f: - label = f.read() - if prefix in label_files.keys(): - label_files[prefix] = label_files[prefix] + ", " + label - else: - label_files[prefix] = label - - for prefix, audio in audio_files.items(): - output_audio_path = save_path / f"{prefix}.wav" - audio.export(output_audio_path, format="wav") - - for prefix, label in label_files.items(): - output_label_path = save_path / f"{prefix}.lab" - with open(output_label_path, "w", encoding="utf-8") as f: - f.write(label) - - for file_path in original_files: - os.remove(file_path) - - -if __name__ == "__main__": - merge_and_delete_files("/made/by/spicysama/laziman", [__file__]) diff --git a/xinference/thirdparty/fish_speech/tools/msgpack_api.py b/xinference/thirdparty/fish_speech/tools/msgpack_api.py new file mode 100644 index 0000000000..67f907bf55 --- /dev/null +++ b/xinference/thirdparty/fish_speech/tools/msgpack_api.py @@ -0,0 +1,34 @@ +import httpx +import ormsgpack + +from tools.commons import ServeReferenceAudio, ServeTTSRequest + +# priority: ref_id > references +request = ServeTTSRequest( + text="你说的对, 但是原神是一款由米哈游自主研发的开放世界手游.", + # reference_id="114514", + references=[ + ServeReferenceAudio( + audio=open("lengyue.wav", "rb").read(), + text=open("lengyue.lab", "r", encoding="utf-8").read(), + ) + ], + streaming=True, +) + +with ( + httpx.Client() as client, + open("hello.wav", "wb") as f, +): + with client.stream( + "POST", + "http://127.0.0.1:8080/v1/tts", + content=ormsgpack.packb(request, option=ormsgpack.OPT_SERIALIZE_PYDANTIC), + headers={ + "authorization": "Bearer YOUR_API_KEY", + "content-type": "application/msgpack", + }, + timeout=None, + ) as response: + for chunk in response.iter_bytes(): + f.write(chunk) diff --git a/xinference/thirdparty/fish_speech/tools/post_api.py b/xinference/thirdparty/fish_speech/tools/post_api.py index 153893078e..c20dc455c3 100644 --- a/xinference/thirdparty/fish_speech/tools/post_api.py +++ b/xinference/thirdparty/fish_speech/tools/post_api.py @@ -1,40 +1,19 @@ import argparse import base64 -import json import wave -from pathlib import Path +import ormsgpack import pyaudio import requests +from pydub import AudioSegment +from pydub.playback import play +from tools.commons import ServeReferenceAudio, ServeTTSRequest +from tools.file import audio_to_bytes, read_ref_text -def wav_to_base64(file_path): - if not file_path or not Path(file_path).exists(): - return None - with open(file_path, "rb") as wav_file: - wav_content = wav_file.read() - base64_encoded = base64.b64encode(wav_content) - return base64_encoded.decode("utf-8") +def parse_args(): -def read_ref_text(ref_text): - path = Path(ref_text) - if path.exists() and path.is_file(): - with path.open("r", encoding="utf-8") as file: - return file.read() - return ref_text - - -def play_audio(audio_content, format, channels, rate): - p = pyaudio.PyAudio() - stream = p.open(format=format, channels=channels, rate=rate, output=True) - stream.write(audio_content) - stream.stop_stream() - stream.close() - p.terminate() - - -if __name__ == "__main__": parser = argparse.ArgumentParser( description="Send a WAV file and text to a server and receive synthesized audio." ) @@ -43,16 +22,24 @@ def play_audio(audio_content, format, channels, rate): "--url", "-u", type=str, - default="http://127.0.0.1:8080/v1/invoke", + default="http://127.0.0.1:8080/v1/tts", help="URL of the server", ) parser.add_argument( "--text", "-t", type=str, required=True, help="Text to be synthesized" ) + parser.add_argument( + "--reference_id", + "-id", + type=str, + default=None, + help="ID of the reference model o be used for the speech", + ) parser.add_argument( "--reference_audio", "-ra", type=str, + nargs="+", default=None, help="Path to the WAV file", ) @@ -60,9 +47,30 @@ def play_audio(audio_content, format, channels, rate): "--reference_text", "-rt", type=str, + nargs="+", default=None, help="Reference text for voice synthesis", ) + parser.add_argument( + "--output", + "-o", + type=str, + default="generated_audio", + help="Output audio file name", + ) + parser.add_argument( + "--play", + type=bool, + default=True, + help="Whether to play audio after receiving data", + ) + parser.add_argument("--normalize", type=bool, default=True) + parser.add_argument( + "--format", type=str, choices=["wav", "mp3", "flac"], default="wav" + ) + parser.add_argument("--mp3_bitrate", type=int, default=64) + parser.add_argument("--opus_bitrate", type=int, default=-1000) + parser.add_argument("--latency", type=str, default="normal", help="延迟选项") parser.add_argument( "--max_new_tokens", type=int, @@ -88,7 +96,6 @@ def play_audio(audio_content, format, channels, rate): "--speaker", type=str, default=None, help="Speaker ID for voice synthesis" ) parser.add_argument("--emotion", type=str, default=None, help="Speaker's Emotion") - parser.add_argument("--format", type=str, default="wav", help="Audio format") parser.add_argument( "--streaming", type=bool, default=False, help="Enable streaming response" ) @@ -97,18 +104,42 @@ def play_audio(audio_content, format, channels, rate): ) parser.add_argument("--rate", type=int, default=44100, help="Sample rate for audio") - args = parser.parse_args() + return parser.parse_args() - base64_audio = wav_to_base64(args.reference_audio) - ref_text = args.reference_text - if ref_text: - ref_text = read_ref_text(ref_text) +if __name__ == "__main__": + + args = parse_args() + + idstr: str | None = args.reference_id + # priority: ref_id > [{text, audio},...] + if idstr is None: + ref_audios = args.reference_audio + ref_texts = args.reference_text + if ref_audios is None: + byte_audios = [] + else: + byte_audios = [audio_to_bytes(ref_audio) for ref_audio in ref_audios] + if ref_texts is None: + ref_texts = [] + else: + ref_texts = [read_ref_text(ref_text) for ref_text in ref_texts] + else: + byte_audios = [] + ref_texts = [] + pass # in api.py data = { "text": args.text, - "reference_text": ref_text, - "reference_audio": base64_audio, + "references": [ + ServeReferenceAudio(audio=ref_audio, text=ref_text) + for ref_text, ref_audio in zip(ref_texts, byte_audios) + ], + "reference_id": idstr, + "normalize": args.normalize, + "format": args.format, + "mp3_bitrate": args.mp3_bitrate, + "opus_bitrate": args.opus_bitrate, "max_new_tokens": args.max_new_tokens, "chunk_length": args.chunk_length, "top_p": args.top_p, @@ -116,22 +147,30 @@ def play_audio(audio_content, format, channels, rate): "temperature": args.temperature, "speaker": args.speaker, "emotion": args.emotion, - "format": args.format, "streaming": args.streaming, } - response = requests.post(args.url, json=data, stream=args.streaming) + pydantic_data = ServeTTSRequest(**data) - audio_format = pyaudio.paInt16 # Assuming 16-bit PCM format + response = requests.post( + args.url, + data=ormsgpack.packb(pydantic_data, option=ormsgpack.OPT_SERIALIZE_PYDANTIC), + stream=args.streaming, + headers={ + "authorization": "Bearer YOUR_API_KEY", + "content-type": "application/msgpack", + }, + ) if response.status_code == 200: if args.streaming: p = pyaudio.PyAudio() + audio_format = pyaudio.paInt16 # Assuming 16-bit PCM format stream = p.open( format=audio_format, channels=args.channels, rate=args.rate, output=True ) - wf = wave.open("generated_audio.wav", "wb") + wf = wave.open(f"{args.output}.wav", "wb") wf.setnchannels(args.channels) wf.setsampwidth(p.get_sample_size(audio_format)) wf.setframerate(args.rate) @@ -153,12 +192,14 @@ def play_audio(audio_content, format, channels, rate): wf.close() else: audio_content = response.content - - with open("generated_audio.wav", "wb") as audio_file: + audio_path = f"{args.output}.{args.format}" + with open(audio_path, "wb") as audio_file: audio_file.write(audio_content) - play_audio(audio_content, audio_format, args.channels, args.rate) - print("Audio has been saved to 'generated_audio.wav'.") + audio = AudioSegment.from_file(audio_path, format=args.format) + if args.play: + play(audio) + print(f"Audio has been saved to '{audio_path}'.") else: print(f"Request failed with status code {response.status_code}") print(response.json()) diff --git a/xinference/thirdparty/fish_speech/tools/sensevoice/fun_asr.py b/xinference/thirdparty/fish_speech/tools/sensevoice/fun_asr.py index 02c15a5976..6789316d51 100644 --- a/xinference/thirdparty/fish_speech/tools/sensevoice/fun_asr.py +++ b/xinference/thirdparty/fish_speech/tools/sensevoice/fun_asr.py @@ -26,7 +26,7 @@ def uvr5_cli( output_folder: Path, audio_files: list[Path] | None = None, output_format: str = "flac", - model: str = "BS-Roformer-Viperx-1296.ckpt", + model: str = "BS-Roformer-Viperx-1297.ckpt", ): # ["BS-Roformer-Viperx-1297.ckpt", "BS-Roformer-Viperx-1296.ckpt", "BS-Roformer-Viperx-1053.ckpt", "Mel-Roformer-Viperx-1143.ckpt"] sepr = Separator( diff --git a/xinference/thirdparty/fish_speech/tools/smart_pad.py b/xinference/thirdparty/fish_speech/tools/smart_pad.py index 9772168f51..de9dc154f2 100644 --- a/xinference/thirdparty/fish_speech/tools/smart_pad.py +++ b/xinference/thirdparty/fish_speech/tools/smart_pad.py @@ -15,21 +15,34 @@ def process(file): waveform, sample_rate = torchaudio.load(str(file), backend="sox") + if waveform.size(0) > 1: + waveform = waveform.mean(dim=0, keepdim=True) + loudness = librosa.feature.rms( y=waveform.numpy().squeeze(), frame_length=2048, hop_length=512, center=True )[0] + for i in range(len(loudness) - 1, 0, -1): if loudness[i] > threshold: break - silent_time = (len(loudness) - i) * 512 / sample_rate + end_silent_time = (len(loudness) - i) * 512 / sample_rate - if silent_time <= 0.3: - random_time = random.uniform(0.3, 0.7) + if end_silent_time <= 0.3: + random_time = random.uniform(0.3, 0.7) - end_silent_time waveform = F.pad( waveform, (0, int(random_time * sample_rate)), mode="constant", value=0 ) + for i in range(len(loudness)): + if loudness[i] > threshold: + break + + start_silent_time = i * 512 / sample_rate + + if start_silent_time > 0.02: + waveform = waveform[:, int((start_silent_time - 0.02) * sample_rate) :] + torchaudio.save(uri=str(file), src=waveform, sample_rate=sample_rate) diff --git a/xinference/thirdparty/fish_speech/tools/vqgan/extract_vq.py b/xinference/thirdparty/fish_speech/tools/vqgan/extract_vq.py index bc6bc40830..c24eb3f46a 100644 --- a/xinference/thirdparty/fish_speech/tools/vqgan/extract_vq.py +++ b/xinference/thirdparty/fish_speech/tools/vqgan/extract_vq.py @@ -42,7 +42,7 @@ @lru_cache(maxsize=1) def get_model( config_name: str = "firefly_gan_vq", - checkpoint_path: str = "checkpoints/fish-speech-1.2-sft/firefly-gan-vq-fsq-4x1024-42hz-generator.pth", + checkpoint_path: str = "checkpoints/fish-speech-1.4/firefly-gan-vq-fsq-8x1024-21hz-generator.pth", device: str | torch.device = "cuda", ): with initialize(version_base="1.3", config_path="../../fish_speech/configs"): @@ -133,7 +133,7 @@ def process_batch(files: list[Path], model) -> float: @click.option("--config-name", default="firefly_gan_vq") @click.option( "--checkpoint-path", - default="checkpoints/fish-speech-1.2-sft/firefly-gan-vq-fsq-4x1024-42hz-generator.pth", + default="checkpoints/fish-speech-1.4/firefly-gan-vq-fsq-8x1024-21hz-generator.pth", ) @click.option("--batch-size", default=64) @click.option("--filelist", default=None, type=Path) diff --git a/xinference/thirdparty/fish_speech/tools/vqgan/inference.py b/xinference/thirdparty/fish_speech/tools/vqgan/inference.py index 17c9034d7b..b6bc7531c4 100644 --- a/xinference/thirdparty/fish_speech/tools/vqgan/inference.py +++ b/xinference/thirdparty/fish_speech/tools/vqgan/inference.py @@ -59,7 +59,7 @@ def load_model(config_name, checkpoint_path, device="cuda"): @click.option("--config-name", default="firefly_gan_vq") @click.option( "--checkpoint-path", - default="checkpoints/fish-speech-1.2-sft/firefly-gan-vq-fsq-4x1024-42hz-generator.pth", + default="checkpoints/fish-speech-1.4/firefly-gan-vq-fsq-8x1024-21hz-generator.pth", ) @click.option( "--device", @@ -103,7 +103,9 @@ def main(input_path, output_path, config_name, checkpoint_path, device): # Restore feature_lengths = torch.tensor([indices.shape[1]], device=device) - fake_audios = model.decode(indices=indices[None], feature_lengths=feature_lengths) + fake_audios, _ = model.decode( + indices=indices[None], feature_lengths=feature_lengths + ) audio_time = fake_audios.shape[-1] / model.spec_transform.sample_rate logger.info( diff --git a/xinference/thirdparty/fish_speech/tools/webui.py b/xinference/thirdparty/fish_speech/tools/webui.py index f64ff923b0..a52f548cc9 100644 --- a/xinference/thirdparty/fish_speech/tools/webui.py +++ b/xinference/thirdparty/fish_speech/tools/webui.py @@ -23,7 +23,6 @@ from fish_speech.text.chn_text_norm.text import Text as ChnNormedText from fish_speech.utils import autocast_exclude_mps from tools.api import decode_vq_tokens, encode_reference -from tools.auto_rerank import batch_asr, calculate_wer, is_chinese, load_model from tools.llama.generate import ( GenerateRequest, GenerateResponse, @@ -40,9 +39,9 @@ {i18n("A text-to-speech model based on VQ-GAN and Llama developed by [Fish Audio](https://fish.audio).")} -{i18n("You can find the source code [here](https://github.com/fishaudio/fish-speech) and models [here](https://huggingface.co/fishaudio/fish-speech-1).")} +{i18n("You can find the source code [here](https://github.com/fishaudio/fish-speech) and models [here](https://huggingface.co/fishaudio/fish-speech-1.4).")} -{i18n("Related code are released under BSD-3-Clause License, and weights are released under CC BY-NC-SA 4.0 License.")} +{i18n("Related code and weights are released under CC BY-NC-SA 4.0 License.")} {i18n("We are not responsible for any misuse of the model, please consider your local laws and regulations before using it.")} """ @@ -160,66 +159,6 @@ def inference( gc.collect() -def inference_with_auto_rerank( - text, - enable_reference_audio, - reference_audio, - reference_text, - max_new_tokens, - chunk_length, - top_p, - repetition_penalty, - temperature, - use_auto_rerank, - streaming=False, -): - - max_attempts = 2 if use_auto_rerank else 1 - best_wer = float("inf") - best_audio = None - best_sample_rate = None - - for attempt in range(max_attempts): - audio_generator = inference( - text, - enable_reference_audio, - reference_audio, - reference_text, - max_new_tokens, - chunk_length, - top_p, - repetition_penalty, - temperature, - streaming=False, - ) - - # 获取音频数据 - for _ in audio_generator: - pass - _, (sample_rate, audio), message = _ - - if audio is None: - return None, None, message - - if not use_auto_rerank: - return None, (sample_rate, audio), None - - asr_result = batch_asr(asr_model, [audio], sample_rate)[0] - wer = calculate_wer(text, asr_result["text"]) - if wer <= 0.3 and not asr_result["huge_gap"]: - return None, (sample_rate, audio), None - - if wer < best_wer: - best_wer = wer - best_audio = audio - best_sample_rate = sample_rate - - if attempt == max_attempts - 1: - break - - return None, (best_sample_rate, best_audio), None - - inference_stream = partial(inference, streaming=True) n_audios = 4 @@ -239,13 +178,12 @@ def inference_wrapper( repetition_penalty, temperature, batch_infer_num, - if_load_asr_model, ): audios = [] errors = [] for _ in range(batch_infer_num): - result = inference_with_auto_rerank( + result = inference( text, enable_reference_audio, reference_audio, @@ -255,10 +193,9 @@ def inference_wrapper( top_p, repetition_penalty, temperature, - if_load_asr_model, ) - _, audio_data, error_message = result + _, audio_data, error_message = next(result) audios.append( gr.Audio(value=audio_data if audio_data else None, visible=True), @@ -301,42 +238,6 @@ def normalize_text(user_input, use_normalization): asr_model = None -def change_if_load_asr_model(if_load): - global asr_model - - if if_load: - gr.Warning("Loading faster whisper model...") - if asr_model is None: - asr_model = load_model() - return gr.Checkbox(label="Unload faster whisper model", value=if_load) - - if if_load is False: - gr.Warning("Unloading faster whisper model...") - del asr_model - asr_model = None - if torch.cuda.is_available(): - torch.cuda.empty_cache() - gc.collect() - return gr.Checkbox(label="Load faster whisper model", value=if_load) - - -def change_if_auto_label(if_load, if_auto_label, enable_ref, ref_audio, ref_text): - if if_load and asr_model is not None: - if ( - if_auto_label - and enable_ref - and ref_audio is not None - and ref_text.strip() == "" - ): - data, sample_rate = librosa.load(ref_audio) - res = batch_asr(asr_model, [data], sample_rate)[0] - ref_text = res["text"] - else: - gr.Warning("Whisper model not loaded!") - - return gr.Textbox(value=ref_text) - - def build_app(): with gr.Blocks(theme=gr.themes.Base()) as app: gr.Markdown(HEADER_MD) @@ -367,23 +268,17 @@ def build_app(): with gr.Row(): if_refine_text = gr.Checkbox( label=i18n("Text Normalization"), - value=True, - scale=1, - ) - - if_load_asr_model = gr.Checkbox( - label=i18n("Load / Unload ASR model for auto-reranking"), value=False, - scale=3, + scale=1, ) with gr.Row(): with gr.Tab(label=i18n("Advanced Config")): chunk_length = gr.Slider( label=i18n("Iterative Prompt Length, 0 means off"), - minimum=0, - maximum=500, - value=100, + minimum=50, + maximum=300, + value=200, step=8, ) @@ -434,12 +329,6 @@ def build_app(): type="filepath", ) with gr.Row(): - if_auto_label = gr.Checkbox( - label=i18n("Auto Labeling"), - min_width=100, - scale=0, - value=False, - ) reference_text = gr.Textbox( label=i18n("Reference Text"), lines=1, @@ -494,28 +383,6 @@ def build_app(): fn=normalize_text, inputs=[text, if_refine_text], outputs=[refined_text] ) - if_load_asr_model.change( - fn=change_if_load_asr_model, - inputs=[if_load_asr_model], - outputs=[if_load_asr_model], - ) - - if_auto_label.change( - fn=lambda: gr.Textbox(value=""), - inputs=[], - outputs=[reference_text], - ).then( - fn=change_if_auto_label, - inputs=[ - if_load_asr_model, - if_auto_label, - enable_reference_audio, - reference_audio, - reference_text, - ], - outputs=[reference_text], - ) - # # Submit generate.click( inference_wrapper, @@ -530,7 +397,6 @@ def build_app(): repetition_penalty, temperature, batch_infer_num, - if_load_asr_model, ], [stream_audio, *global_audio_list, *global_error_list], concurrency_limit=1, @@ -560,12 +426,12 @@ def parse_args(): parser.add_argument( "--llama-checkpoint-path", type=Path, - default="checkpoints/fish-speech-1.2-sft", + default="checkpoints/fish-speech-1.4", ) parser.add_argument( "--decoder-checkpoint-path", type=Path, - default="checkpoints/fish-speech-1.2-sft/firefly-gan-vq-fsq-4x1024-42hz-generator.pth", + default="checkpoints/fish-speech-1.4/firefly-gan-vq-fsq-8x1024-21hz-generator.pth", ) parser.add_argument("--decoder-config-name", type=str, default="firefly_gan_vq") parser.add_argument("--device", type=str, default="cuda") @@ -605,8 +471,8 @@ def parse_args(): enable_reference_audio=False, reference_audio=None, reference_text="", - max_new_tokens=0, - chunk_length=100, + max_new_tokens=1024, + chunk_length=200, top_p=0.7, repetition_penalty=1.2, temperature=0.7, diff --git a/xinference/thirdparty/matcha/VERSION b/xinference/thirdparty/matcha/VERSION new file mode 100644 index 0000000000..ea5abc8f95 --- /dev/null +++ b/xinference/thirdparty/matcha/VERSION @@ -0,0 +1 @@ +0.0.7.0 diff --git a/xinference/thirdparty/matcha/__init__.py b/xinference/thirdparty/matcha/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/xinference/thirdparty/matcha/app.py b/xinference/thirdparty/matcha/app.py new file mode 100644 index 0000000000..d68fbaa2d1 --- /dev/null +++ b/xinference/thirdparty/matcha/app.py @@ -0,0 +1,357 @@ +import tempfile +from argparse import Namespace +from pathlib import Path + +import gradio as gr +import soundfile as sf +import torch + +from matcha.cli import ( + MATCHA_URLS, + VOCODER_URLS, + assert_model_downloaded, + get_device, + load_matcha, + load_vocoder, + process_text, + to_waveform, +) +from matcha.utils.utils import get_user_data_dir, plot_tensor + +LOCATION = Path(get_user_data_dir()) + +args = Namespace( + cpu=False, + model="matcha_vctk", + vocoder="hifigan_univ_v1", + spk=0, +) + +CURRENTLY_LOADED_MODEL = args.model + + +def MATCHA_TTS_LOC(x): + return LOCATION / f"{x}.ckpt" + + +def VOCODER_LOC(x): + return LOCATION / f"{x}" + + +LOGO_URL = "https://shivammehta25.github.io/Matcha-TTS/images/logo.png" +RADIO_OPTIONS = { + "Multi Speaker (VCTK)": { + "model": "matcha_vctk", + "vocoder": "hifigan_univ_v1", + }, + "Single Speaker (LJ Speech)": { + "model": "matcha_ljspeech", + "vocoder": "hifigan_T2_v1", + }, +} + +# Ensure all the required models are downloaded +assert_model_downloaded(MATCHA_TTS_LOC("matcha_ljspeech"), MATCHA_URLS["matcha_ljspeech"]) +assert_model_downloaded(VOCODER_LOC("hifigan_T2_v1"), VOCODER_URLS["hifigan_T2_v1"]) +assert_model_downloaded(MATCHA_TTS_LOC("matcha_vctk"), MATCHA_URLS["matcha_vctk"]) +assert_model_downloaded(VOCODER_LOC("hifigan_univ_v1"), VOCODER_URLS["hifigan_univ_v1"]) + +device = get_device(args) + +# Load default model +model = load_matcha(args.model, MATCHA_TTS_LOC(args.model), device) +vocoder, denoiser = load_vocoder(args.vocoder, VOCODER_LOC(args.vocoder), device) + + +def load_model(model_name, vocoder_name): + model = load_matcha(model_name, MATCHA_TTS_LOC(model_name), device) + vocoder, denoiser = load_vocoder(vocoder_name, VOCODER_LOC(vocoder_name), device) + return model, vocoder, denoiser + + +def load_model_ui(model_type, textbox): + model_name, vocoder_name = RADIO_OPTIONS[model_type]["model"], RADIO_OPTIONS[model_type]["vocoder"] + + global model, vocoder, denoiser, CURRENTLY_LOADED_MODEL # pylint: disable=global-statement + if CURRENTLY_LOADED_MODEL != model_name: + model, vocoder, denoiser = load_model(model_name, vocoder_name) + CURRENTLY_LOADED_MODEL = model_name + + if model_name == "matcha_ljspeech": + spk_slider = gr.update(visible=False, value=-1) + single_speaker_examples = gr.update(visible=True) + multi_speaker_examples = gr.update(visible=False) + length_scale = gr.update(value=0.95) + else: + spk_slider = gr.update(visible=True, value=0) + single_speaker_examples = gr.update(visible=False) + multi_speaker_examples = gr.update(visible=True) + length_scale = gr.update(value=0.85) + + return ( + textbox, + gr.update(interactive=True), + spk_slider, + single_speaker_examples, + multi_speaker_examples, + length_scale, + ) + + +@torch.inference_mode() +def process_text_gradio(text): + output = process_text(1, text, device) + return output["x_phones"][1::2], output["x"], output["x_lengths"] + + +@torch.inference_mode() +def synthesise_mel(text, text_length, n_timesteps, temperature, length_scale, spk): + spk = torch.tensor([spk], device=device, dtype=torch.long) if spk >= 0 else None + output = model.synthesise( + text, + text_length, + n_timesteps=n_timesteps, + temperature=temperature, + spks=spk, + length_scale=length_scale, + ) + output["waveform"] = to_waveform(output["mel"], vocoder, denoiser) + with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: + sf.write(fp.name, output["waveform"], 22050, "PCM_24") + + return fp.name, plot_tensor(output["mel"].squeeze().cpu().numpy()) + + +def multispeaker_example_cacher(text, n_timesteps, mel_temp, length_scale, spk): + global CURRENTLY_LOADED_MODEL # pylint: disable=global-statement + if CURRENTLY_LOADED_MODEL != "matcha_vctk": + global model, vocoder, denoiser # pylint: disable=global-statement + model, vocoder, denoiser = load_model("matcha_vctk", "hifigan_univ_v1") + CURRENTLY_LOADED_MODEL = "matcha_vctk" + + phones, text, text_lengths = process_text_gradio(text) + audio, mel_spectrogram = synthesise_mel(text, text_lengths, n_timesteps, mel_temp, length_scale, spk) + return phones, audio, mel_spectrogram + + +def ljspeech_example_cacher(text, n_timesteps, mel_temp, length_scale, spk=-1): + global CURRENTLY_LOADED_MODEL # pylint: disable=global-statement + if CURRENTLY_LOADED_MODEL != "matcha_ljspeech": + global model, vocoder, denoiser # pylint: disable=global-statement + model, vocoder, denoiser = load_model("matcha_ljspeech", "hifigan_T2_v1") + CURRENTLY_LOADED_MODEL = "matcha_ljspeech" + + phones, text, text_lengths = process_text_gradio(text) + audio, mel_spectrogram = synthesise_mel(text, text_lengths, n_timesteps, mel_temp, length_scale, spk) + return phones, audio, mel_spectrogram + + +def main(): + description = """# 🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching + ### [Shivam Mehta](https://www.kth.se/profile/smehta), [Ruibo Tu](https://www.kth.se/profile/ruibo), [Jonas Beskow](https://www.kth.se/profile/beskow), [Éva Székely](https://www.kth.se/profile/szekely), and [Gustav Eje Henter](https://people.kth.se/~ghe/) + We propose 🍵 Matcha-TTS, a new approach to non-autoregressive neural TTS, that uses conditional flow matching (similar to rectified flows) to speed up ODE-based speech synthesis. Our method: + + + * Is probabilistic + * Has compact memory footprint + * Sounds highly natural + * Is very fast to synthesise from + + + Check out our [demo page](https://shivammehta25.github.io/Matcha-TTS). Read our [arXiv preprint for more details](https://arxiv.org/abs/2309.03199). + Code is available in our [GitHub repository](https://github.com/shivammehta25/Matcha-TTS), along with pre-trained models. + + Cached examples are available at the bottom of the page. + """ + + with gr.Blocks(title="🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching") as demo: + processed_text = gr.State(value=None) + processed_text_len = gr.State(value=None) + + with gr.Box(): + with gr.Row(): + gr.Markdown(description, scale=3) + with gr.Column(): + gr.Image(LOGO_URL, label="Matcha-TTS logo", height=50, width=50, scale=1, show_label=False) + html = '
' + gr.HTML(html) + + with gr.Box(): + radio_options = list(RADIO_OPTIONS.keys()) + model_type = gr.Radio( + radio_options, value=radio_options[0], label="Choose a Model", interactive=True, container=False + ) + + with gr.Row(): + gr.Markdown("# Text Input") + with gr.Row(): + text = gr.Textbox(value="", lines=2, label="Text to synthesise", scale=3) + spk_slider = gr.Slider( + minimum=0, maximum=107, step=1, value=args.spk, label="Speaker ID", interactive=True, scale=1 + ) + + with gr.Row(): + gr.Markdown("### Hyper parameters") + with gr.Row(): + n_timesteps = gr.Slider( + label="Number of ODE steps", + minimum=1, + maximum=100, + step=1, + value=10, + interactive=True, + ) + length_scale = gr.Slider( + label="Length scale (Speaking rate)", + minimum=0.5, + maximum=1.5, + step=0.05, + value=1.0, + interactive=True, + ) + mel_temp = gr.Slider( + label="Sampling temperature", + minimum=0.00, + maximum=2.001, + step=0.16675, + value=0.667, + interactive=True, + ) + + synth_btn = gr.Button("Synthesise") + + with gr.Box(): + with gr.Row(): + gr.Markdown("### Phonetised text") + phonetised_text = gr.Textbox(interactive=False, scale=10, label="Phonetised text") + + with gr.Box(): + with gr.Row(): + mel_spectrogram = gr.Image(interactive=False, label="mel spectrogram") + + # with gr.Row(): + audio = gr.Audio(interactive=False, label="Audio") + + with gr.Row(visible=False) as example_row_lj_speech: + examples = gr.Examples( # pylint: disable=unused-variable + examples=[ + [ + "We propose Matcha-TTS, a new approach to non-autoregressive neural TTS, that uses conditional flow matching (similar to rectified flows) to speed up O D E-based speech synthesis.", + 50, + 0.677, + 0.95, + ], + [ + "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.", + 2, + 0.677, + 0.95, + ], + [ + "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.", + 4, + 0.677, + 0.95, + ], + [ + "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.", + 10, + 0.677, + 0.95, + ], + [ + "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.", + 50, + 0.677, + 0.95, + ], + [ + "The narrative of these events is based largely on the recollections of the participants.", + 10, + 0.677, + 0.95, + ], + [ + "The jury did not believe him, and the verdict was for the defendants.", + 10, + 0.677, + 0.95, + ], + ], + fn=ljspeech_example_cacher, + inputs=[text, n_timesteps, mel_temp, length_scale], + outputs=[phonetised_text, audio, mel_spectrogram], + cache_examples=True, + ) + + with gr.Row() as example_row_multispeaker: + multi_speaker_examples = gr.Examples( # pylint: disable=unused-variable + examples=[ + [ + "Hello everyone! I am speaker 0 and I am here to tell you that Matcha-TTS is amazing!", + 10, + 0.677, + 0.85, + 0, + ], + [ + "Hello everyone! I am speaker 16 and I am here to tell you that Matcha-TTS is amazing!", + 10, + 0.677, + 0.85, + 16, + ], + [ + "Hello everyone! I am speaker 44 and I am here to tell you that Matcha-TTS is amazing!", + 50, + 0.677, + 0.85, + 44, + ], + [ + "Hello everyone! I am speaker 45 and I am here to tell you that Matcha-TTS is amazing!", + 50, + 0.677, + 0.85, + 45, + ], + [ + "Hello everyone! I am speaker 58 and I am here to tell you that Matcha-TTS is amazing!", + 4, + 0.677, + 0.85, + 58, + ], + ], + fn=multispeaker_example_cacher, + inputs=[text, n_timesteps, mel_temp, length_scale, spk_slider], + outputs=[phonetised_text, audio, mel_spectrogram], + cache_examples=True, + label="Multi Speaker Examples", + ) + + model_type.change(lambda x: gr.update(interactive=False), inputs=[synth_btn], outputs=[synth_btn]).then( + load_model_ui, + inputs=[model_type, text], + outputs=[text, synth_btn, spk_slider, example_row_lj_speech, example_row_multispeaker, length_scale], + ) + + synth_btn.click( + fn=process_text_gradio, + inputs=[ + text, + ], + outputs=[phonetised_text, processed_text, processed_text_len], + api_name="matcha_tts", + queue=True, + ).then( + fn=synthesise_mel, + inputs=[processed_text, processed_text_len, n_timesteps, mel_temp, length_scale, spk_slider], + outputs=[audio, mel_spectrogram], + ) + + demo.queue().launch(share=True) + + +if __name__ == "__main__": + main() diff --git a/xinference/thirdparty/matcha/cli.py b/xinference/thirdparty/matcha/cli.py new file mode 100644 index 0000000000..7daf13073a --- /dev/null +++ b/xinference/thirdparty/matcha/cli.py @@ -0,0 +1,419 @@ +import argparse +import datetime as dt +import os +import warnings +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import soundfile as sf +import torch + +from matcha.hifigan.config import v1 +from matcha.hifigan.denoiser import Denoiser +from matcha.hifigan.env import AttrDict +from matcha.hifigan.models import Generator as HiFiGAN +from matcha.models.matcha_tts import MatchaTTS +from matcha.text import sequence_to_text, text_to_sequence +from matcha.utils.utils import assert_model_downloaded, get_user_data_dir, intersperse + +MATCHA_URLS = { + "matcha_ljspeech": "https://github.com/shivammehta25/Matcha-TTS-checkpoints/releases/download/v1.0/matcha_ljspeech.ckpt", + "matcha_vctk": "https://github.com/shivammehta25/Matcha-TTS-checkpoints/releases/download/v1.0/matcha_vctk.ckpt", +} + +VOCODER_URLS = { + "hifigan_T2_v1": "https://github.com/shivammehta25/Matcha-TTS-checkpoints/releases/download/v1.0/generator_v1", # Old url: https://drive.google.com/file/d/14NENd4equCBLyyCSke114Mv6YR_j_uFs/view?usp=drive_link + "hifigan_univ_v1": "https://github.com/shivammehta25/Matcha-TTS-checkpoints/releases/download/v1.0/g_02500000", # Old url: https://drive.google.com/file/d/1qpgI41wNXFcH-iKq1Y42JlBC9j0je8PW/view?usp=drive_link +} + +MULTISPEAKER_MODEL = { + "matcha_vctk": {"vocoder": "hifigan_univ_v1", "speaking_rate": 0.85, "spk": 0, "spk_range": (0, 107)} +} + +SINGLESPEAKER_MODEL = {"matcha_ljspeech": {"vocoder": "hifigan_T2_v1", "speaking_rate": 0.95, "spk": None}} + + +def plot_spectrogram_to_numpy(spectrogram, filename): + fig, ax = plt.subplots(figsize=(12, 3)) + im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none") + plt.colorbar(im, ax=ax) + plt.xlabel("Frames") + plt.ylabel("Channels") + plt.title("Synthesised Mel-Spectrogram") + fig.canvas.draw() + plt.savefig(filename) + + +def process_text(i: int, text: str, device: torch.device): + print(f"[{i}] - Input text: {text}") + x = torch.tensor( + intersperse(text_to_sequence(text, ["english_cleaners2"])[0], 0), + dtype=torch.long, + device=device, + )[None] + x_lengths = torch.tensor([x.shape[-1]], dtype=torch.long, device=device) + x_phones = sequence_to_text(x.squeeze(0).tolist()) + print(f"[{i}] - Phonetised text: {x_phones[1::2]}") + + return {"x_orig": text, "x": x, "x_lengths": x_lengths, "x_phones": x_phones} + + +def get_texts(args): + if args.text: + texts = [args.text] + else: + with open(args.file, encoding="utf-8") as f: + texts = f.readlines() + return texts + + +def assert_required_models_available(args): + save_dir = get_user_data_dir() + if not hasattr(args, "checkpoint_path") and args.checkpoint_path is None: + model_path = args.checkpoint_path + else: + model_path = save_dir / f"{args.model}.ckpt" + assert_model_downloaded(model_path, MATCHA_URLS[args.model]) + + vocoder_path = save_dir / f"{args.vocoder}" + assert_model_downloaded(vocoder_path, VOCODER_URLS[args.vocoder]) + return {"matcha": model_path, "vocoder": vocoder_path} + + +def load_hifigan(checkpoint_path, device): + h = AttrDict(v1) + hifigan = HiFiGAN(h).to(device) + hifigan.load_state_dict(torch.load(checkpoint_path, map_location=device)["generator"]) + _ = hifigan.eval() + hifigan.remove_weight_norm() + return hifigan + + +def load_vocoder(vocoder_name, checkpoint_path, device): + print(f"[!] Loading {vocoder_name}!") + vocoder = None + if vocoder_name in ("hifigan_T2_v1", "hifigan_univ_v1"): + vocoder = load_hifigan(checkpoint_path, device) + else: + raise NotImplementedError( + f"Vocoder {vocoder_name} not implemented! define a load_<> method for it" + ) + + denoiser = Denoiser(vocoder, mode="zeros") + print(f"[+] {vocoder_name} loaded!") + return vocoder, denoiser + + +def load_matcha(model_name, checkpoint_path, device): + print(f"[!] Loading {model_name}!") + model = MatchaTTS.load_from_checkpoint(checkpoint_path, map_location=device) + _ = model.eval() + + print(f"[+] {model_name} loaded!") + return model + + +def to_waveform(mel, vocoder, denoiser=None): + audio = vocoder(mel).clamp(-1, 1) + if denoiser is not None: + audio = denoiser(audio.squeeze(), strength=0.00025).cpu().squeeze() + + return audio.cpu().squeeze() + + +def save_to_folder(filename: str, output: dict, folder: str): + folder = Path(folder) + folder.mkdir(exist_ok=True, parents=True) + plot_spectrogram_to_numpy(np.array(output["mel"].squeeze().float().cpu()), f"{filename}.png") + np.save(folder / f"{filename}", output["mel"].cpu().numpy()) + sf.write(folder / f"{filename}.wav", output["waveform"], 22050, "PCM_24") + return folder.resolve() / f"{filename}.wav" + + +def validate_args(args): + assert ( + args.text or args.file + ), "Either text or file must be provided Matcha-T(ea)TTS need sometext to whisk the waveforms." + assert args.temperature >= 0, "Sampling temperature cannot be negative" + assert args.steps > 0, "Number of ODE steps must be greater than 0" + + if args.checkpoint_path is None: + # When using pretrained models + if args.model in SINGLESPEAKER_MODEL: + args = validate_args_for_single_speaker_model(args) + + if args.model in MULTISPEAKER_MODEL: + args = validate_args_for_multispeaker_model(args) + else: + # When using a custom model + if args.vocoder != "hifigan_univ_v1": + warn_ = "[-] Using custom model checkpoint! I would suggest passing --vocoder hifigan_univ_v1, unless the custom model is trained on LJ Speech." + warnings.warn(warn_, UserWarning) + if args.speaking_rate is None: + args.speaking_rate = 1.0 + + if args.batched: + assert args.batch_size > 0, "Batch size must be greater than 0" + assert args.speaking_rate > 0, "Speaking rate must be greater than 0" + + return args + + +def validate_args_for_multispeaker_model(args): + if args.vocoder is not None: + if args.vocoder != MULTISPEAKER_MODEL[args.model]["vocoder"]: + warn_ = f"[-] Using {args.model} model! I would suggest passing --vocoder {MULTISPEAKER_MODEL[args.model]['vocoder']}" + warnings.warn(warn_, UserWarning) + else: + args.vocoder = MULTISPEAKER_MODEL[args.model]["vocoder"] + + if args.speaking_rate is None: + args.speaking_rate = MULTISPEAKER_MODEL[args.model]["speaking_rate"] + + spk_range = MULTISPEAKER_MODEL[args.model]["spk_range"] + if args.spk is not None: + assert ( + args.spk >= spk_range[0] and args.spk <= spk_range[-1] + ), f"Speaker ID must be between {spk_range} for this model." + else: + available_spk_id = MULTISPEAKER_MODEL[args.model]["spk"] + warn_ = f"[!] Speaker ID not provided! Using speaker ID {available_spk_id}" + warnings.warn(warn_, UserWarning) + args.spk = available_spk_id + + return args + + +def validate_args_for_single_speaker_model(args): + if args.vocoder is not None: + if args.vocoder != SINGLESPEAKER_MODEL[args.model]["vocoder"]: + warn_ = f"[-] Using {args.model} model! I would suggest passing --vocoder {SINGLESPEAKER_MODEL[args.model]['vocoder']}" + warnings.warn(warn_, UserWarning) + else: + args.vocoder = SINGLESPEAKER_MODEL[args.model]["vocoder"] + + if args.speaking_rate is None: + args.speaking_rate = SINGLESPEAKER_MODEL[args.model]["speaking_rate"] + + if args.spk != SINGLESPEAKER_MODEL[args.model]["spk"]: + warn_ = f"[-] Ignoring speaker id {args.spk} for {args.model}" + warnings.warn(warn_, UserWarning) + args.spk = SINGLESPEAKER_MODEL[args.model]["spk"] + + return args + + +@torch.inference_mode() +def cli(): + parser = argparse.ArgumentParser( + description=" 🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching" + ) + parser.add_argument( + "--model", + type=str, + default="matcha_ljspeech", + help="Model to use", + choices=MATCHA_URLS.keys(), + ) + + parser.add_argument( + "--checkpoint_path", + type=str, + default=None, + help="Path to the custom model checkpoint", + ) + + parser.add_argument( + "--vocoder", + type=str, + default=None, + help="Vocoder to use (default: will use the one suggested with the pretrained model))", + choices=VOCODER_URLS.keys(), + ) + parser.add_argument("--text", type=str, default=None, help="Text to synthesize") + parser.add_argument("--file", type=str, default=None, help="Text file to synthesize") + parser.add_argument("--spk", type=int, default=None, help="Speaker ID") + parser.add_argument( + "--temperature", + type=float, + default=0.667, + help="Variance of the x0 noise (default: 0.667)", + ) + parser.add_argument( + "--speaking_rate", + type=float, + default=None, + help="change the speaking rate, a higher value means slower speaking rate (default: 1.0)", + ) + parser.add_argument("--steps", type=int, default=10, help="Number of ODE steps (default: 10)") + parser.add_argument("--cpu", action="store_true", help="Use CPU for inference (default: use GPU if available)") + parser.add_argument( + "--denoiser_strength", + type=float, + default=0.00025, + help="Strength of the vocoder bias denoiser (default: 0.00025)", + ) + parser.add_argument( + "--output_folder", + type=str, + default=os.getcwd(), + help="Output folder to save results (default: current dir)", + ) + parser.add_argument("--batched", action="store_true", help="Batched inference (default: False)") + parser.add_argument( + "--batch_size", type=int, default=32, help="Batch size only useful when --batched (default: 32)" + ) + + args = parser.parse_args() + + args = validate_args(args) + device = get_device(args) + print_config(args) + paths = assert_required_models_available(args) + + if args.checkpoint_path is not None: + print(f"[🍵] Loading custom model from {args.checkpoint_path}") + paths["matcha"] = args.checkpoint_path + args.model = "custom_model" + + model = load_matcha(args.model, paths["matcha"], device) + vocoder, denoiser = load_vocoder(args.vocoder, paths["vocoder"], device) + + texts = get_texts(args) + + spk = torch.tensor([args.spk], device=device, dtype=torch.long) if args.spk is not None else None + if len(texts) == 1 or not args.batched: + unbatched_synthesis(args, device, model, vocoder, denoiser, texts, spk) + else: + batched_synthesis(args, device, model, vocoder, denoiser, texts, spk) + + +class BatchedSynthesisDataset(torch.utils.data.Dataset): + def __init__(self, processed_texts): + self.processed_texts = processed_texts + + def __len__(self): + return len(self.processed_texts) + + def __getitem__(self, idx): + return self.processed_texts[idx] + + +def batched_collate_fn(batch): + x = [] + x_lengths = [] + + for b in batch: + x.append(b["x"].squeeze(0)) + x_lengths.append(b["x_lengths"]) + + x = torch.nn.utils.rnn.pad_sequence(x, batch_first=True) + x_lengths = torch.concat(x_lengths, dim=0) + return {"x": x, "x_lengths": x_lengths} + + +def batched_synthesis(args, device, model, vocoder, denoiser, texts, spk): + total_rtf = [] + total_rtf_w = [] + processed_text = [process_text(i, text, "cpu") for i, text in enumerate(texts)] + dataloader = torch.utils.data.DataLoader( + BatchedSynthesisDataset(processed_text), + batch_size=args.batch_size, + collate_fn=batched_collate_fn, + num_workers=8, + ) + for i, batch in enumerate(dataloader): + i = i + 1 + start_t = dt.datetime.now() + b = batch["x"].shape[0] + output = model.synthesise( + batch["x"].to(device), + batch["x_lengths"].to(device), + n_timesteps=args.steps, + temperature=args.temperature, + spks=spk.expand(b) if spk is not None else spk, + length_scale=args.speaking_rate, + ) + + output["waveform"] = to_waveform(output["mel"], vocoder, denoiser) + t = (dt.datetime.now() - start_t).total_seconds() + rtf_w = t * 22050 / (output["waveform"].shape[-1]) + print(f"[🍵-Batch: {i}] Matcha-TTS RTF: {output['rtf']:.4f}") + print(f"[🍵-Batch: {i}] Matcha-TTS + VOCODER RTF: {rtf_w:.4f}") + total_rtf.append(output["rtf"]) + total_rtf_w.append(rtf_w) + for j in range(output["mel"].shape[0]): + base_name = f"utterance_{j:03d}_speaker_{args.spk:03d}" if args.spk is not None else f"utterance_{j:03d}" + length = output["mel_lengths"][j] + new_dict = {"mel": output["mel"][j][:, :length], "waveform": output["waveform"][j][: length * 256]} + location = save_to_folder(base_name, new_dict, args.output_folder) + print(f"[🍵-{j}] Waveform saved: {location}") + + print("".join(["="] * 100)) + print(f"[🍵] Average Matcha-TTS RTF: {np.mean(total_rtf):.4f} ± {np.std(total_rtf)}") + print(f"[🍵] Average Matcha-TTS + VOCODER RTF: {np.mean(total_rtf_w):.4f} ± {np.std(total_rtf_w)}") + print("[🍵] Enjoy the freshly whisked 🍵 Matcha-TTS!") + + +def unbatched_synthesis(args, device, model, vocoder, denoiser, texts, spk): + total_rtf = [] + total_rtf_w = [] + for i, text in enumerate(texts): + i = i + 1 + base_name = f"utterance_{i:03d}_speaker_{args.spk:03d}" if args.spk is not None else f"utterance_{i:03d}" + + print("".join(["="] * 100)) + text = text.strip() + text_processed = process_text(i, text, device) + + print(f"[🍵] Whisking Matcha-T(ea)TS for: {i}") + start_t = dt.datetime.now() + output = model.synthesise( + text_processed["x"], + text_processed["x_lengths"], + n_timesteps=args.steps, + temperature=args.temperature, + spks=spk, + length_scale=args.speaking_rate, + ) + output["waveform"] = to_waveform(output["mel"], vocoder, denoiser) + # RTF with HiFiGAN + t = (dt.datetime.now() - start_t).total_seconds() + rtf_w = t * 22050 / (output["waveform"].shape[-1]) + print(f"[🍵-{i}] Matcha-TTS RTF: {output['rtf']:.4f}") + print(f"[🍵-{i}] Matcha-TTS + VOCODER RTF: {rtf_w:.4f}") + total_rtf.append(output["rtf"]) + total_rtf_w.append(rtf_w) + + location = save_to_folder(base_name, output, args.output_folder) + print(f"[+] Waveform saved: {location}") + + print("".join(["="] * 100)) + print(f"[🍵] Average Matcha-TTS RTF: {np.mean(total_rtf):.4f} ± {np.std(total_rtf)}") + print(f"[🍵] Average Matcha-TTS + VOCODER RTF: {np.mean(total_rtf_w):.4f} ± {np.std(total_rtf_w)}") + print("[🍵] Enjoy the freshly whisked 🍵 Matcha-TTS!") + + +def print_config(args): + print("[!] Configurations: ") + print(f"\t- Model: {args.model}") + print(f"\t- Vocoder: {args.vocoder}") + print(f"\t- Temperature: {args.temperature}") + print(f"\t- Speaking rate: {args.speaking_rate}") + print(f"\t- Number of ODE steps: {args.steps}") + print(f"\t- Speaker: {args.spk}") + + +def get_device(args): + if torch.cuda.is_available() and not args.cpu: + print("[+] GPU Available! Using GPU") + device = torch.device("cuda") + else: + print("[-] GPU not available or forced CPU run! Using CPU") + device = torch.device("cpu") + return device + + +if __name__ == "__main__": + cli() diff --git a/xinference/thirdparty/matcha/data/__init__.py b/xinference/thirdparty/matcha/data/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/xinference/thirdparty/matcha/data/components/__init__.py b/xinference/thirdparty/matcha/data/components/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/xinference/thirdparty/matcha/data/text_mel_datamodule.py b/xinference/thirdparty/matcha/data/text_mel_datamodule.py new file mode 100644 index 0000000000..e10dfcb8bb --- /dev/null +++ b/xinference/thirdparty/matcha/data/text_mel_datamodule.py @@ -0,0 +1,274 @@ +import random +from pathlib import Path +from typing import Any, Dict, Optional + +import numpy as np +import torch +import torchaudio as ta +from lightning import LightningDataModule +from torch.utils.data.dataloader import DataLoader + +from matcha.text import text_to_sequence +from matcha.utils.audio import mel_spectrogram +from matcha.utils.model import fix_len_compatibility, normalize +from matcha.utils.utils import intersperse + + +def parse_filelist(filelist_path, split_char="|"): + with open(filelist_path, encoding="utf-8") as f: + filepaths_and_text = [line.strip().split(split_char) for line in f] + return filepaths_and_text + + +class TextMelDataModule(LightningDataModule): + def __init__( # pylint: disable=unused-argument + self, + name, + train_filelist_path, + valid_filelist_path, + batch_size, + num_workers, + pin_memory, + cleaners, + add_blank, + n_spks, + n_fft, + n_feats, + sample_rate, + hop_length, + win_length, + f_min, + f_max, + data_statistics, + seed, + load_durations, + ): + super().__init__() + + # this line allows to access init params with 'self.hparams' attribute + # also ensures init params will be stored in ckpt + self.save_hyperparameters(logger=False) + + def setup(self, stage: Optional[str] = None): # pylint: disable=unused-argument + """Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`. + + This method is called by lightning with both `trainer.fit()` and `trainer.test()`, so be + careful not to execute things like random split twice! + """ + # load and split datasets only if not loaded already + + self.trainset = TextMelDataset( # pylint: disable=attribute-defined-outside-init + self.hparams.train_filelist_path, + self.hparams.n_spks, + self.hparams.cleaners, + self.hparams.add_blank, + self.hparams.n_fft, + self.hparams.n_feats, + self.hparams.sample_rate, + self.hparams.hop_length, + self.hparams.win_length, + self.hparams.f_min, + self.hparams.f_max, + self.hparams.data_statistics, + self.hparams.seed, + self.hparams.load_durations, + ) + self.validset = TextMelDataset( # pylint: disable=attribute-defined-outside-init + self.hparams.valid_filelist_path, + self.hparams.n_spks, + self.hparams.cleaners, + self.hparams.add_blank, + self.hparams.n_fft, + self.hparams.n_feats, + self.hparams.sample_rate, + self.hparams.hop_length, + self.hparams.win_length, + self.hparams.f_min, + self.hparams.f_max, + self.hparams.data_statistics, + self.hparams.seed, + self.hparams.load_durations, + ) + + def train_dataloader(self): + return DataLoader( + dataset=self.trainset, + batch_size=self.hparams.batch_size, + num_workers=self.hparams.num_workers, + pin_memory=self.hparams.pin_memory, + shuffle=True, + collate_fn=TextMelBatchCollate(self.hparams.n_spks), + ) + + def val_dataloader(self): + return DataLoader( + dataset=self.validset, + batch_size=self.hparams.batch_size, + num_workers=self.hparams.num_workers, + pin_memory=self.hparams.pin_memory, + shuffle=False, + collate_fn=TextMelBatchCollate(self.hparams.n_spks), + ) + + def teardown(self, stage: Optional[str] = None): + """Clean up after fit or test.""" + pass # pylint: disable=unnecessary-pass + + def state_dict(self): + """Extra things to save to checkpoint.""" + return {} + + def load_state_dict(self, state_dict: Dict[str, Any]): + """Things to do when loading checkpoint.""" + pass # pylint: disable=unnecessary-pass + + +class TextMelDataset(torch.utils.data.Dataset): + def __init__( + self, + filelist_path, + n_spks, + cleaners, + add_blank=True, + n_fft=1024, + n_mels=80, + sample_rate=22050, + hop_length=256, + win_length=1024, + f_min=0.0, + f_max=8000, + data_parameters=None, + seed=None, + load_durations=False, + ): + self.filepaths_and_text = parse_filelist(filelist_path) + self.n_spks = n_spks + self.cleaners = cleaners + self.add_blank = add_blank + self.n_fft = n_fft + self.n_mels = n_mels + self.sample_rate = sample_rate + self.hop_length = hop_length + self.win_length = win_length + self.f_min = f_min + self.f_max = f_max + self.load_durations = load_durations + + if data_parameters is not None: + self.data_parameters = data_parameters + else: + self.data_parameters = {"mel_mean": 0, "mel_std": 1} + random.seed(seed) + random.shuffle(self.filepaths_and_text) + + def get_datapoint(self, filepath_and_text): + if self.n_spks > 1: + filepath, spk, text = ( + filepath_and_text[0], + int(filepath_and_text[1]), + filepath_and_text[2], + ) + else: + filepath, text = filepath_and_text[0], filepath_and_text[1] + spk = None + + text, cleaned_text = self.get_text(text, add_blank=self.add_blank) + mel = self.get_mel(filepath) + + durations = self.get_durations(filepath, text) if self.load_durations else None + + return {"x": text, "y": mel, "spk": spk, "filepath": filepath, "x_text": cleaned_text, "durations": durations} + + def get_durations(self, filepath, text): + filepath = Path(filepath) + data_dir, name = filepath.parent.parent, filepath.stem + + try: + dur_loc = data_dir / "durations" / f"{name}.npy" + durs = torch.from_numpy(np.load(dur_loc).astype(int)) + + except FileNotFoundError as e: + raise FileNotFoundError( + f"Tried loading the durations but durations didn't exist at {dur_loc}, make sure you've generate the durations first using: python matcha/utils/get_durations_from_trained_model.py \n" + ) from e + + assert len(durs) == len(text), f"Length of durations {len(durs)} and text {len(text)} do not match" + + return durs + + def get_mel(self, filepath): + audio, sr = ta.load(filepath) + assert sr == self.sample_rate + mel = mel_spectrogram( + audio, + self.n_fft, + self.n_mels, + self.sample_rate, + self.hop_length, + self.win_length, + self.f_min, + self.f_max, + center=False, + ).squeeze() + mel = normalize(mel, self.data_parameters["mel_mean"], self.data_parameters["mel_std"]) + return mel + + def get_text(self, text, add_blank=True): + text_norm, cleaned_text = text_to_sequence(text, self.cleaners) + if self.add_blank: + text_norm = intersperse(text_norm, 0) + text_norm = torch.IntTensor(text_norm) + return text_norm, cleaned_text + + def __getitem__(self, index): + datapoint = self.get_datapoint(self.filepaths_and_text[index]) + return datapoint + + def __len__(self): + return len(self.filepaths_and_text) + + +class TextMelBatchCollate: + def __init__(self, n_spks): + self.n_spks = n_spks + + def __call__(self, batch): + B = len(batch) + y_max_length = max([item["y"].shape[-1] for item in batch]) + y_max_length = fix_len_compatibility(y_max_length) + x_max_length = max([item["x"].shape[-1] for item in batch]) + n_feats = batch[0]["y"].shape[-2] + + y = torch.zeros((B, n_feats, y_max_length), dtype=torch.float32) + x = torch.zeros((B, x_max_length), dtype=torch.long) + durations = torch.zeros((B, x_max_length), dtype=torch.long) + + y_lengths, x_lengths = [], [] + spks = [] + filepaths, x_texts = [], [] + for i, item in enumerate(batch): + y_, x_ = item["y"], item["x"] + y_lengths.append(y_.shape[-1]) + x_lengths.append(x_.shape[-1]) + y[i, :, : y_.shape[-1]] = y_ + x[i, : x_.shape[-1]] = x_ + spks.append(item["spk"]) + filepaths.append(item["filepath"]) + x_texts.append(item["x_text"]) + if item["durations"] is not None: + durations[i, : item["durations"].shape[-1]] = item["durations"] + + y_lengths = torch.tensor(y_lengths, dtype=torch.long) + x_lengths = torch.tensor(x_lengths, dtype=torch.long) + spks = torch.tensor(spks, dtype=torch.long) if self.n_spks > 1 else None + + return { + "x": x, + "x_lengths": x_lengths, + "y": y, + "y_lengths": y_lengths, + "spks": spks, + "filepaths": filepaths, + "x_texts": x_texts, + "durations": durations if not torch.eq(durations, 0).all() else None, + } diff --git a/xinference/thirdparty/matcha/hifigan/LICENSE b/xinference/thirdparty/matcha/hifigan/LICENSE new file mode 100644 index 0000000000..91751daed8 --- /dev/null +++ b/xinference/thirdparty/matcha/hifigan/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2020 Jungil Kong + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/xinference/thirdparty/matcha/hifigan/README.md b/xinference/thirdparty/matcha/hifigan/README.md new file mode 100644 index 0000000000..5db2585045 --- /dev/null +++ b/xinference/thirdparty/matcha/hifigan/README.md @@ -0,0 +1,101 @@ +# HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis + +### Jungil Kong, Jaehyeon Kim, Jaekyoung Bae + +In our [paper](https://arxiv.org/abs/2010.05646), +we proposed HiFi-GAN: a GAN-based model capable of generating high fidelity speech efficiently.
+We provide our implementation and pretrained models as open source in this repository. + +**Abstract :** +Several recent work on speech synthesis have employed generative adversarial networks (GANs) to produce raw waveforms. +Although such methods improve the sampling efficiency and memory usage, +their sample quality has not yet reached that of autoregressive and flow-based generative models. +In this work, we propose HiFi-GAN, which achieves both efficient and high-fidelity speech synthesis. +As speech audio consists of sinusoidal signals with various periods, +we demonstrate that modeling periodic patterns of an audio is crucial for enhancing sample quality. +A subjective human evaluation (mean opinion score, MOS) of a single speaker dataset indicates that our proposed method +demonstrates similarity to human quality while generating 22.05 kHz high-fidelity audio 167.9 times faster than +real-time on a single V100 GPU. We further show the generality of HiFi-GAN to the mel-spectrogram inversion of unseen +speakers and end-to-end speech synthesis. Finally, a small footprint version of HiFi-GAN generates samples 13.4 times +faster than real-time on CPU with comparable quality to an autoregressive counterpart. + +Visit our [demo website](https://jik876.github.io/hifi-gan-demo/) for audio samples. + +## Pre-requisites + +1. Python >= 3.6 +2. Clone this repository. +3. Install python requirements. Please refer [requirements.txt](requirements.txt) +4. Download and extract the [LJ Speech dataset](https://keithito.com/LJ-Speech-Dataset/). + And move all wav files to `LJSpeech-1.1/wavs` + +## Training + +``` +python train.py --config config_v1.json +``` + +To train V2 or V3 Generator, replace `config_v1.json` with `config_v2.json` or `config_v3.json`.
+Checkpoints and copy of the configuration file are saved in `cp_hifigan` directory by default.
+You can change the path by adding `--checkpoint_path` option. + +Validation loss during training with V1 generator.
+![validation loss](./validation_loss.png) + +## Pretrained Model + +You can also use pretrained models we provide.
+[Download pretrained models](https://drive.google.com/drive/folders/1-eEYTB5Av9jNql0WGBlRoi-WH2J7bp5Y?usp=sharing)
+Details of each folder are as in follows: + +| Folder Name | Generator | Dataset | Fine-Tuned | +| ------------ | --------- | --------- | ------------------------------------------------------ | +| LJ_V1 | V1 | LJSpeech | No | +| LJ_V2 | V2 | LJSpeech | No | +| LJ_V3 | V3 | LJSpeech | No | +| LJ_FT_T2_V1 | V1 | LJSpeech | Yes ([Tacotron2](https://github.com/NVIDIA/tacotron2)) | +| LJ_FT_T2_V2 | V2 | LJSpeech | Yes ([Tacotron2](https://github.com/NVIDIA/tacotron2)) | +| LJ_FT_T2_V3 | V3 | LJSpeech | Yes ([Tacotron2](https://github.com/NVIDIA/tacotron2)) | +| VCTK_V1 | V1 | VCTK | No | +| VCTK_V2 | V2 | VCTK | No | +| VCTK_V3 | V3 | VCTK | No | +| UNIVERSAL_V1 | V1 | Universal | No | + +We provide the universal model with discriminator weights that can be used as a base for transfer learning to other datasets. + +## Fine-Tuning + +1. Generate mel-spectrograms in numpy format using [Tacotron2](https://github.com/NVIDIA/tacotron2) with teacher-forcing.
+ The file name of the generated mel-spectrogram should match the audio file and the extension should be `.npy`.
+ Example: + ` Audio File : LJ001-0001.wav +Mel-Spectrogram File : LJ001-0001.npy` +2. Create `ft_dataset` folder and copy the generated mel-spectrogram files into it.
+3. Run the following command. + ``` + python train.py --fine_tuning True --config config_v1.json + ``` + For other command line options, please refer to the training section. + +## Inference from wav file + +1. Make `test_files` directory and copy wav files into the directory. +2. Run the following command. + ` python inference.py --checkpoint_file [generator checkpoint file path]` + Generated wav files are saved in `generated_files` by default.
+ You can change the path by adding `--output_dir` option. + +## Inference for end-to-end speech synthesis + +1. Make `test_mel_files` directory and copy generated mel-spectrogram files into the directory.
+ You can generate mel-spectrograms using [Tacotron2](https://github.com/NVIDIA/tacotron2), + [Glow-TTS](https://github.com/jaywalnut310/glow-tts) and so forth. +2. Run the following command. + ` python inference_e2e.py --checkpoint_file [generator checkpoint file path]` + Generated wav files are saved in `generated_files_from_mel` by default.
+ You can change the path by adding `--output_dir` option. + +## Acknowledgements + +We referred to [WaveGlow](https://github.com/NVIDIA/waveglow), [MelGAN](https://github.com/descriptinc/melgan-neurips) +and [Tacotron2](https://github.com/NVIDIA/tacotron2) to implement this. diff --git a/xinference/thirdparty/matcha/hifigan/__init__.py b/xinference/thirdparty/matcha/hifigan/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/xinference/thirdparty/matcha/hifigan/config.py b/xinference/thirdparty/matcha/hifigan/config.py new file mode 100644 index 0000000000..b3abea9e15 --- /dev/null +++ b/xinference/thirdparty/matcha/hifigan/config.py @@ -0,0 +1,28 @@ +v1 = { + "resblock": "1", + "num_gpus": 0, + "batch_size": 16, + "learning_rate": 0.0004, + "adam_b1": 0.8, + "adam_b2": 0.99, + "lr_decay": 0.999, + "seed": 1234, + "upsample_rates": [8, 8, 2, 2], + "upsample_kernel_sizes": [16, 16, 4, 4], + "upsample_initial_channel": 512, + "resblock_kernel_sizes": [3, 7, 11], + "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]], + "resblock_initial_channel": 256, + "segment_size": 8192, + "num_mels": 80, + "num_freq": 1025, + "n_fft": 1024, + "hop_size": 256, + "win_size": 1024, + "sampling_rate": 22050, + "fmin": 0, + "fmax": 8000, + "fmax_loss": None, + "num_workers": 4, + "dist_config": {"dist_backend": "nccl", "dist_url": "tcp://localhost:54321", "world_size": 1}, +} diff --git a/xinference/thirdparty/matcha/hifigan/denoiser.py b/xinference/thirdparty/matcha/hifigan/denoiser.py new file mode 100644 index 0000000000..9fd33312a0 --- /dev/null +++ b/xinference/thirdparty/matcha/hifigan/denoiser.py @@ -0,0 +1,64 @@ +# Code modified from Rafael Valle's implementation https://github.com/NVIDIA/waveglow/blob/5bc2a53e20b3b533362f974cfa1ea0267ae1c2b1/denoiser.py + +"""Waveglow style denoiser can be used to remove the artifacts from the HiFiGAN generated audio.""" +import torch + + +class Denoiser(torch.nn.Module): + """Removes model bias from audio produced with waveglow""" + + def __init__(self, vocoder, filter_length=1024, n_overlap=4, win_length=1024, mode="zeros"): + super().__init__() + self.filter_length = filter_length + self.hop_length = int(filter_length / n_overlap) + self.win_length = win_length + + dtype, device = next(vocoder.parameters()).dtype, next(vocoder.parameters()).device + self.device = device + if mode == "zeros": + mel_input = torch.zeros((1, 80, 88), dtype=dtype, device=device) + elif mode == "normal": + mel_input = torch.randn((1, 80, 88), dtype=dtype, device=device) + else: + raise Exception(f"Mode {mode} if not supported") + + def stft_fn(audio, n_fft, hop_length, win_length, window): + spec = torch.stft( + audio, + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + window=window, + return_complex=True, + ) + spec = torch.view_as_real(spec) + return torch.sqrt(spec.pow(2).sum(-1)), torch.atan2(spec[..., -1], spec[..., 0]) + + self.stft = lambda x: stft_fn( + audio=x, + n_fft=self.filter_length, + hop_length=self.hop_length, + win_length=self.win_length, + window=torch.hann_window(self.win_length, device=device), + ) + self.istft = lambda x, y: torch.istft( + torch.complex(x * torch.cos(y), x * torch.sin(y)), + n_fft=self.filter_length, + hop_length=self.hop_length, + win_length=self.win_length, + window=torch.hann_window(self.win_length, device=device), + ) + + with torch.no_grad(): + bias_audio = vocoder(mel_input).float().squeeze(0) + bias_spec, _ = self.stft(bias_audio) + + self.register_buffer("bias_spec", bias_spec[:, :, 0][:, :, None]) + + @torch.inference_mode() + def forward(self, audio, strength=0.0005): + audio_spec, audio_angles = self.stft(audio) + audio_spec_denoised = audio_spec - self.bias_spec.to(audio.device) * strength + audio_spec_denoised = torch.clamp(audio_spec_denoised, 0.0) + audio_denoised = self.istft(audio_spec_denoised, audio_angles) + return audio_denoised diff --git a/xinference/thirdparty/matcha/hifigan/env.py b/xinference/thirdparty/matcha/hifigan/env.py new file mode 100644 index 0000000000..9ea4f948a3 --- /dev/null +++ b/xinference/thirdparty/matcha/hifigan/env.py @@ -0,0 +1,17 @@ +""" from https://github.com/jik876/hifi-gan """ + +import os +import shutil + + +class AttrDict(dict): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.__dict__ = self + + +def build_env(config, config_name, path): + t_path = os.path.join(path, config_name) + if config != t_path: + os.makedirs(path, exist_ok=True) + shutil.copyfile(config, os.path.join(path, config_name)) diff --git a/xinference/thirdparty/matcha/hifigan/meldataset.py b/xinference/thirdparty/matcha/hifigan/meldataset.py new file mode 100644 index 0000000000..8b43ea7965 --- /dev/null +++ b/xinference/thirdparty/matcha/hifigan/meldataset.py @@ -0,0 +1,217 @@ +""" from https://github.com/jik876/hifi-gan """ + +import math +import os +import random + +import numpy as np +import torch +import torch.utils.data +from librosa.filters import mel as librosa_mel_fn +from librosa.util import normalize +from scipy.io.wavfile import read + +MAX_WAV_VALUE = 32768.0 + + +def load_wav(full_path): + sampling_rate, data = read(full_path) + return data, sampling_rate + + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) + + +def dynamic_range_decompression(x, C=1): + return np.exp(x) / C + + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def dynamic_range_decompression_torch(x, C=1): + return torch.exp(x) / C + + +def spectral_normalize_torch(magnitudes): + output = dynamic_range_compression_torch(magnitudes) + return output + + +def spectral_de_normalize_torch(magnitudes): + output = dynamic_range_decompression_torch(magnitudes) + return output + + +mel_basis = {} +hann_window = {} + + +def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): + if torch.min(y) < -1.0: + print("min value is ", torch.min(y)) + if torch.max(y) > 1.0: + print("max value is ", torch.max(y)) + + global mel_basis, hann_window # pylint: disable=global-statement + if fmax not in mel_basis: + mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) + mel_basis[str(fmax) + "_" + str(y.device)] = torch.from_numpy(mel).float().to(y.device) + hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device) + + y = torch.nn.functional.pad( + y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect" + ) + y = y.squeeze(1) + + spec = torch.view_as_real( + torch.stft( + y, + n_fft, + hop_length=hop_size, + win_length=win_size, + window=hann_window[str(y.device)], + center=center, + pad_mode="reflect", + normalized=False, + onesided=True, + return_complex=True, + ) + ) + + spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) + + spec = torch.matmul(mel_basis[str(fmax) + "_" + str(y.device)], spec) + spec = spectral_normalize_torch(spec) + + return spec + + +def get_dataset_filelist(a): + with open(a.input_training_file, encoding="utf-8") as fi: + training_files = [ + os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav") for x in fi.read().split("\n") if len(x) > 0 + ] + + with open(a.input_validation_file, encoding="utf-8") as fi: + validation_files = [ + os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav") for x in fi.read().split("\n") if len(x) > 0 + ] + return training_files, validation_files + + +class MelDataset(torch.utils.data.Dataset): + def __init__( + self, + training_files, + segment_size, + n_fft, + num_mels, + hop_size, + win_size, + sampling_rate, + fmin, + fmax, + split=True, + shuffle=True, + n_cache_reuse=1, + device=None, + fmax_loss=None, + fine_tuning=False, + base_mels_path=None, + ): + self.audio_files = training_files + random.seed(1234) + if shuffle: + random.shuffle(self.audio_files) + self.segment_size = segment_size + self.sampling_rate = sampling_rate + self.split = split + self.n_fft = n_fft + self.num_mels = num_mels + self.hop_size = hop_size + self.win_size = win_size + self.fmin = fmin + self.fmax = fmax + self.fmax_loss = fmax_loss + self.cached_wav = None + self.n_cache_reuse = n_cache_reuse + self._cache_ref_count = 0 + self.device = device + self.fine_tuning = fine_tuning + self.base_mels_path = base_mels_path + + def __getitem__(self, index): + filename = self.audio_files[index] + if self._cache_ref_count == 0: + audio, sampling_rate = load_wav(filename) + audio = audio / MAX_WAV_VALUE + if not self.fine_tuning: + audio = normalize(audio) * 0.95 + self.cached_wav = audio + if sampling_rate != self.sampling_rate: + raise ValueError(f"{sampling_rate} SR doesn't match target {self.sampling_rate} SR") + self._cache_ref_count = self.n_cache_reuse + else: + audio = self.cached_wav + self._cache_ref_count -= 1 + + audio = torch.FloatTensor(audio) + audio = audio.unsqueeze(0) + + if not self.fine_tuning: + if self.split: + if audio.size(1) >= self.segment_size: + max_audio_start = audio.size(1) - self.segment_size + audio_start = random.randint(0, max_audio_start) + audio = audio[:, audio_start : audio_start + self.segment_size] + else: + audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), "constant") + + mel = mel_spectrogram( + audio, + self.n_fft, + self.num_mels, + self.sampling_rate, + self.hop_size, + self.win_size, + self.fmin, + self.fmax, + center=False, + ) + else: + mel = np.load(os.path.join(self.base_mels_path, os.path.splitext(os.path.split(filename)[-1])[0] + ".npy")) + mel = torch.from_numpy(mel) + + if len(mel.shape) < 3: + mel = mel.unsqueeze(0) + + if self.split: + frames_per_seg = math.ceil(self.segment_size / self.hop_size) + + if audio.size(1) >= self.segment_size: + mel_start = random.randint(0, mel.size(2) - frames_per_seg - 1) + mel = mel[:, :, mel_start : mel_start + frames_per_seg] + audio = audio[:, mel_start * self.hop_size : (mel_start + frames_per_seg) * self.hop_size] + else: + mel = torch.nn.functional.pad(mel, (0, frames_per_seg - mel.size(2)), "constant") + audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), "constant") + + mel_loss = mel_spectrogram( + audio, + self.n_fft, + self.num_mels, + self.sampling_rate, + self.hop_size, + self.win_size, + self.fmin, + self.fmax_loss, + center=False, + ) + + return (mel.squeeze(), audio.squeeze(0), filename, mel_loss.squeeze()) + + def __len__(self): + return len(self.audio_files) diff --git a/xinference/thirdparty/matcha/hifigan/models.py b/xinference/thirdparty/matcha/hifigan/models.py new file mode 100644 index 0000000000..d209d9a4e9 --- /dev/null +++ b/xinference/thirdparty/matcha/hifigan/models.py @@ -0,0 +1,368 @@ +""" from https://github.com/jik876/hifi-gan """ + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d +from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm + +from .xutils import get_padding, init_weights + +LRELU_SLOPE = 0.1 + + +class ResBlock1(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): + super().__init__() + self.h = h + self.convs1 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]), + ) + ), + ] + ) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + ] + ) + self.convs2.apply(init_weights) + + def forward(self, x): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + xt = c2(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_weight_norm(l) + for l in self.convs2: + remove_weight_norm(l) + + +class ResBlock2(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): + super().__init__() + self.h = h + self.convs = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + ] + ) + self.convs.apply(init_weights) + + def forward(self, x): + for c in self.convs: + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + + +class Generator(torch.nn.Module): + def __init__(self, h): + super().__init__() + self.h = h + self.num_kernels = len(h.resblock_kernel_sizes) + self.num_upsamples = len(h.upsample_rates) + self.conv_pre = weight_norm(Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3)) + resblock = ResBlock1 if h.resblock == "1" else ResBlock2 + + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): + self.ups.append( + weight_norm( + ConvTranspose1d( + h.upsample_initial_channel // (2**i), + h.upsample_initial_channel // (2 ** (i + 1)), + k, + u, + padding=(k - u) // 2, + ) + ) + ) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = h.upsample_initial_channel // (2 ** (i + 1)) + for _, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)): + self.resblocks.append(resblock(h, ch, k, d)) + + self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + + def forward(self, x): + x = self.conv_pre(x) + for i in range(self.num_upsamples): + x = F.leaky_relu(x, LRELU_SLOPE) + x = self.ups[i](x) + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + x = F.leaky_relu(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + print("Removing weight norm...") + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + + +class DiscriminatorP(torch.nn.Module): + def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): + super().__init__() + self.period = period + norm_f = weight_norm if use_spectral_norm is False else spectral_norm + self.convs = nn.ModuleList( + [ + norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), + ] + ) + self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) + + def forward(self, x): + fmap = [] + + # 1d to 2d + b, c, t = x.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x = F.pad(x, (0, n_pad), "reflect") + t = t + n_pad + x = x.view(b, c, t // self.period, self.period) + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiPeriodDiscriminator(torch.nn.Module): + def __init__(self): + super().__init__() + self.discriminators = nn.ModuleList( + [ + DiscriminatorP(2), + DiscriminatorP(3), + DiscriminatorP(5), + DiscriminatorP(7), + DiscriminatorP(11), + ] + ) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for _, d in enumerate(self.discriminators): + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +class DiscriminatorS(torch.nn.Module): + def __init__(self, use_spectral_norm=False): + super().__init__() + norm_f = weight_norm if use_spectral_norm is False else spectral_norm + self.convs = nn.ModuleList( + [ + norm_f(Conv1d(1, 128, 15, 1, padding=7)), + norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), + norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), + norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), + ] + ) + self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) + + def forward(self, x): + fmap = [] + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiScaleDiscriminator(torch.nn.Module): + def __init__(self): + super().__init__() + self.discriminators = nn.ModuleList( + [ + DiscriminatorS(use_spectral_norm=True), + DiscriminatorS(), + DiscriminatorS(), + ] + ) + self.meanpools = nn.ModuleList([AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2)]) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + if i != 0: + y = self.meanpools[i - 1](y) + y_hat = self.meanpools[i - 1](y_hat) + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +def feature_loss(fmap_r, fmap_g): + loss = 0 + for dr, dg in zip(fmap_r, fmap_g): + for rl, gl in zip(dr, dg): + loss += torch.mean(torch.abs(rl - gl)) + + return loss * 2 + + +def discriminator_loss(disc_real_outputs, disc_generated_outputs): + loss = 0 + r_losses = [] + g_losses = [] + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + r_loss = torch.mean((1 - dr) ** 2) + g_loss = torch.mean(dg**2) + loss += r_loss + g_loss + r_losses.append(r_loss.item()) + g_losses.append(g_loss.item()) + + return loss, r_losses, g_losses + + +def generator_loss(disc_outputs): + loss = 0 + gen_losses = [] + for dg in disc_outputs: + l = torch.mean((1 - dg) ** 2) + gen_losses.append(l) + loss += l + + return loss, gen_losses diff --git a/xinference/thirdparty/matcha/hifigan/xutils.py b/xinference/thirdparty/matcha/hifigan/xutils.py new file mode 100644 index 0000000000..eefadcb7a1 --- /dev/null +++ b/xinference/thirdparty/matcha/hifigan/xutils.py @@ -0,0 +1,60 @@ +""" from https://github.com/jik876/hifi-gan """ + +import glob +import os + +import matplotlib +import torch +from torch.nn.utils import weight_norm + +matplotlib.use("Agg") +import matplotlib.pylab as plt + + +def plot_spectrogram(spectrogram): + fig, ax = plt.subplots(figsize=(10, 2)) + im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none") + plt.colorbar(im, ax=ax) + + fig.canvas.draw() + plt.close() + + return fig + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def apply_weight_norm(m): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + weight_norm(m) + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size * dilation - dilation) / 2) + + +def load_checkpoint(filepath, device): + assert os.path.isfile(filepath) + print(f"Loading '{filepath}'") + checkpoint_dict = torch.load(filepath, map_location=device) + print("Complete.") + return checkpoint_dict + + +def save_checkpoint(filepath, obj): + print(f"Saving checkpoint to {filepath}") + torch.save(obj, filepath) + print("Complete.") + + +def scan_checkpoint(cp_dir, prefix): + pattern = os.path.join(cp_dir, prefix + "????????") + cp_list = glob.glob(pattern) + if len(cp_list) == 0: + return None + return sorted(cp_list)[-1] diff --git a/xinference/thirdparty/matcha/models/__init__.py b/xinference/thirdparty/matcha/models/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/xinference/thirdparty/matcha/models/baselightningmodule.py b/xinference/thirdparty/matcha/models/baselightningmodule.py new file mode 100644 index 0000000000..f8abe7b44f --- /dev/null +++ b/xinference/thirdparty/matcha/models/baselightningmodule.py @@ -0,0 +1,210 @@ +""" +This is a base lightning module that can be used to train a model. +The benefit of this abstraction is that all the logic outside of model definition can be reused for different models. +""" +import inspect +from abc import ABC +from typing import Any, Dict + +import torch +from lightning import LightningModule +from lightning.pytorch.utilities import grad_norm + +from matcha import utils +from matcha.utils.utils import plot_tensor + +log = utils.get_pylogger(__name__) + + +class BaseLightningClass(LightningModule, ABC): + def update_data_statistics(self, data_statistics): + if data_statistics is None: + data_statistics = { + "mel_mean": 0.0, + "mel_std": 1.0, + } + + self.register_buffer("mel_mean", torch.tensor(data_statistics["mel_mean"])) + self.register_buffer("mel_std", torch.tensor(data_statistics["mel_std"])) + + def configure_optimizers(self) -> Any: + optimizer = self.hparams.optimizer(params=self.parameters()) + if self.hparams.scheduler not in (None, {}): + scheduler_args = {} + # Manage last epoch for exponential schedulers + if "last_epoch" in inspect.signature(self.hparams.scheduler.scheduler).parameters: + if hasattr(self, "ckpt_loaded_epoch"): + current_epoch = self.ckpt_loaded_epoch - 1 + else: + current_epoch = -1 + + scheduler_args.update({"optimizer": optimizer}) + scheduler = self.hparams.scheduler.scheduler(**scheduler_args) + scheduler.last_epoch = current_epoch + return { + "optimizer": optimizer, + "lr_scheduler": { + "scheduler": scheduler, + "interval": self.hparams.scheduler.lightning_args.interval, + "frequency": self.hparams.scheduler.lightning_args.frequency, + "name": "learning_rate", + }, + } + + return {"optimizer": optimizer} + + def get_losses(self, batch): + x, x_lengths = batch["x"], batch["x_lengths"] + y, y_lengths = batch["y"], batch["y_lengths"] + spks = batch["spks"] + + dur_loss, prior_loss, diff_loss, *_ = self( + x=x, + x_lengths=x_lengths, + y=y, + y_lengths=y_lengths, + spks=spks, + out_size=self.out_size, + durations=batch["durations"], + ) + return { + "dur_loss": dur_loss, + "prior_loss": prior_loss, + "diff_loss": diff_loss, + } + + def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None: + self.ckpt_loaded_epoch = checkpoint["epoch"] # pylint: disable=attribute-defined-outside-init + + def training_step(self, batch: Any, batch_idx: int): + loss_dict = self.get_losses(batch) + self.log( + "step", + float(self.global_step), + on_step=True, + prog_bar=True, + logger=True, + sync_dist=True, + ) + + self.log( + "sub_loss/train_dur_loss", + loss_dict["dur_loss"], + on_step=True, + on_epoch=True, + logger=True, + sync_dist=True, + ) + self.log( + "sub_loss/train_prior_loss", + loss_dict["prior_loss"], + on_step=True, + on_epoch=True, + logger=True, + sync_dist=True, + ) + self.log( + "sub_loss/train_diff_loss", + loss_dict["diff_loss"], + on_step=True, + on_epoch=True, + logger=True, + sync_dist=True, + ) + + total_loss = sum(loss_dict.values()) + self.log( + "loss/train", + total_loss, + on_step=True, + on_epoch=True, + logger=True, + prog_bar=True, + sync_dist=True, + ) + + return {"loss": total_loss, "log": loss_dict} + + def validation_step(self, batch: Any, batch_idx: int): + loss_dict = self.get_losses(batch) + self.log( + "sub_loss/val_dur_loss", + loss_dict["dur_loss"], + on_step=True, + on_epoch=True, + logger=True, + sync_dist=True, + ) + self.log( + "sub_loss/val_prior_loss", + loss_dict["prior_loss"], + on_step=True, + on_epoch=True, + logger=True, + sync_dist=True, + ) + self.log( + "sub_loss/val_diff_loss", + loss_dict["diff_loss"], + on_step=True, + on_epoch=True, + logger=True, + sync_dist=True, + ) + + total_loss = sum(loss_dict.values()) + self.log( + "loss/val", + total_loss, + on_step=True, + on_epoch=True, + logger=True, + prog_bar=True, + sync_dist=True, + ) + + return total_loss + + def on_validation_end(self) -> None: + if self.trainer.is_global_zero: + one_batch = next(iter(self.trainer.val_dataloaders)) + if self.current_epoch == 0: + log.debug("Plotting original samples") + for i in range(2): + y = one_batch["y"][i].unsqueeze(0).to(self.device) + self.logger.experiment.add_image( + f"original/{i}", + plot_tensor(y.squeeze().cpu()), + self.current_epoch, + dataformats="HWC", + ) + + log.debug("Synthesising...") + for i in range(2): + x = one_batch["x"][i].unsqueeze(0).to(self.device) + x_lengths = one_batch["x_lengths"][i].unsqueeze(0).to(self.device) + spks = one_batch["spks"][i].unsqueeze(0).to(self.device) if one_batch["spks"] is not None else None + output = self.synthesise(x[:, :x_lengths], x_lengths, n_timesteps=10, spks=spks) + y_enc, y_dec = output["encoder_outputs"], output["decoder_outputs"] + attn = output["attn"] + self.logger.experiment.add_image( + f"generated_enc/{i}", + plot_tensor(y_enc.squeeze().cpu()), + self.current_epoch, + dataformats="HWC", + ) + self.logger.experiment.add_image( + f"generated_dec/{i}", + plot_tensor(y_dec.squeeze().cpu()), + self.current_epoch, + dataformats="HWC", + ) + self.logger.experiment.add_image( + f"alignment/{i}", + plot_tensor(attn.squeeze().cpu()), + self.current_epoch, + dataformats="HWC", + ) + + def on_before_optimizer_step(self, optimizer): + self.log_dict({f"grad_norm/{k}": v for k, v in grad_norm(self, norm_type=2).items()}) diff --git a/xinference/thirdparty/matcha/models/components/__init__.py b/xinference/thirdparty/matcha/models/components/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/xinference/thirdparty/matcha/models/components/decoder.py b/xinference/thirdparty/matcha/models/components/decoder.py new file mode 100644 index 0000000000..1137cd7008 --- /dev/null +++ b/xinference/thirdparty/matcha/models/components/decoder.py @@ -0,0 +1,443 @@ +import math +from typing import Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +from conformer import ConformerBlock +from diffusers.models.activations import get_activation +from einops import pack, rearrange, repeat + +from matcha.models.components.transformer import BasicTransformerBlock + + +class SinusoidalPosEmb(torch.nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + assert self.dim % 2 == 0, "SinusoidalPosEmb requires dim to be even" + + def forward(self, x, scale=1000): + if x.ndim < 1: + x = x.unsqueeze(0) + device = x.device + half_dim = self.dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb) + emb = scale * x.unsqueeze(1) * emb.unsqueeze(0) + emb = torch.cat((emb.sin(), emb.cos()), dim=-1) + return emb + + +class Block1D(torch.nn.Module): + def __init__(self, dim, dim_out, groups=8): + super().__init__() + self.block = torch.nn.Sequential( + torch.nn.Conv1d(dim, dim_out, 3, padding=1), + torch.nn.GroupNorm(groups, dim_out), + nn.Mish(), + ) + + def forward(self, x, mask): + output = self.block(x * mask) + return output * mask + + +class ResnetBlock1D(torch.nn.Module): + def __init__(self, dim, dim_out, time_emb_dim, groups=8): + super().__init__() + self.mlp = torch.nn.Sequential(nn.Mish(), torch.nn.Linear(time_emb_dim, dim_out)) + + self.block1 = Block1D(dim, dim_out, groups=groups) + self.block2 = Block1D(dim_out, dim_out, groups=groups) + + self.res_conv = torch.nn.Conv1d(dim, dim_out, 1) + + def forward(self, x, mask, time_emb): + h = self.block1(x, mask) + h += self.mlp(time_emb).unsqueeze(-1) + h = self.block2(h, mask) + output = h + self.res_conv(x * mask) + return output + + +class Downsample1D(nn.Module): + def __init__(self, dim): + super().__init__() + self.conv = torch.nn.Conv1d(dim, dim, 3, 2, 1) + + def forward(self, x): + return self.conv(x) + + +class TimestepEmbedding(nn.Module): + def __init__( + self, + in_channels: int, + time_embed_dim: int, + act_fn: str = "silu", + out_dim: int = None, + post_act_fn: Optional[str] = None, + cond_proj_dim=None, + ): + super().__init__() + + self.linear_1 = nn.Linear(in_channels, time_embed_dim) + + if cond_proj_dim is not None: + self.cond_proj = nn.Linear(cond_proj_dim, in_channels, bias=False) + else: + self.cond_proj = None + + self.act = get_activation(act_fn) + + if out_dim is not None: + time_embed_dim_out = out_dim + else: + time_embed_dim_out = time_embed_dim + self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim_out) + + if post_act_fn is None: + self.post_act = None + else: + self.post_act = get_activation(post_act_fn) + + def forward(self, sample, condition=None): + if condition is not None: + sample = sample + self.cond_proj(condition) + sample = self.linear_1(sample) + + if self.act is not None: + sample = self.act(sample) + + sample = self.linear_2(sample) + + if self.post_act is not None: + sample = self.post_act(sample) + return sample + + +class Upsample1D(nn.Module): + """A 1D upsampling layer with an optional convolution. + + Parameters: + channels (`int`): + number of channels in the inputs and outputs. + use_conv (`bool`, default `False`): + option to use a convolution. + use_conv_transpose (`bool`, default `False`): + option to use a convolution transpose. + out_channels (`int`, optional): + number of output channels. Defaults to `channels`. + """ + + def __init__(self, channels, use_conv=False, use_conv_transpose=True, out_channels=None, name="conv"): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_conv_transpose = use_conv_transpose + self.name = name + + self.conv = None + if use_conv_transpose: + self.conv = nn.ConvTranspose1d(channels, self.out_channels, 4, 2, 1) + elif use_conv: + self.conv = nn.Conv1d(self.channels, self.out_channels, 3, padding=1) + + def forward(self, inputs): + assert inputs.shape[1] == self.channels + if self.use_conv_transpose: + return self.conv(inputs) + + outputs = F.interpolate(inputs, scale_factor=2.0, mode="nearest") + + if self.use_conv: + outputs = self.conv(outputs) + + return outputs + + +class ConformerWrapper(ConformerBlock): + def __init__( # pylint: disable=useless-super-delegation + self, + *, + dim, + dim_head=64, + heads=8, + ff_mult=4, + conv_expansion_factor=2, + conv_kernel_size=31, + attn_dropout=0, + ff_dropout=0, + conv_dropout=0, + conv_causal=False, + ): + super().__init__( + dim=dim, + dim_head=dim_head, + heads=heads, + ff_mult=ff_mult, + conv_expansion_factor=conv_expansion_factor, + conv_kernel_size=conv_kernel_size, + attn_dropout=attn_dropout, + ff_dropout=ff_dropout, + conv_dropout=conv_dropout, + conv_causal=conv_causal, + ) + + def forward( + self, + hidden_states, + attention_mask, + encoder_hidden_states=None, + encoder_attention_mask=None, + timestep=None, + ): + return super().forward(x=hidden_states, mask=attention_mask.bool()) + + +class Decoder(nn.Module): + def __init__( + self, + in_channels, + out_channels, + channels=(256, 256), + dropout=0.05, + attention_head_dim=64, + n_blocks=1, + num_mid_blocks=2, + num_heads=4, + act_fn="snake", + down_block_type="transformer", + mid_block_type="transformer", + up_block_type="transformer", + ): + super().__init__() + channels = tuple(channels) + self.in_channels = in_channels + self.out_channels = out_channels + + self.time_embeddings = SinusoidalPosEmb(in_channels) + time_embed_dim = channels[0] * 4 + self.time_mlp = TimestepEmbedding( + in_channels=in_channels, + time_embed_dim=time_embed_dim, + act_fn="silu", + ) + + self.down_blocks = nn.ModuleList([]) + self.mid_blocks = nn.ModuleList([]) + self.up_blocks = nn.ModuleList([]) + + output_channel = in_channels + for i in range(len(channels)): # pylint: disable=consider-using-enumerate + input_channel = output_channel + output_channel = channels[i] + is_last = i == len(channels) - 1 + resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) + transformer_blocks = nn.ModuleList( + [ + self.get_block( + down_block_type, + output_channel, + attention_head_dim, + num_heads, + dropout, + act_fn, + ) + for _ in range(n_blocks) + ] + ) + downsample = ( + Downsample1D(output_channel) if not is_last else nn.Conv1d(output_channel, output_channel, 3, padding=1) + ) + + self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample])) + + for i in range(num_mid_blocks): + input_channel = channels[-1] + out_channels = channels[-1] + + resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) + + transformer_blocks = nn.ModuleList( + [ + self.get_block( + mid_block_type, + output_channel, + attention_head_dim, + num_heads, + dropout, + act_fn, + ) + for _ in range(n_blocks) + ] + ) + + self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks])) + + channels = channels[::-1] + (channels[0],) + for i in range(len(channels) - 1): + input_channel = channels[i] + output_channel = channels[i + 1] + is_last = i == len(channels) - 2 + + resnet = ResnetBlock1D( + dim=2 * input_channel, + dim_out=output_channel, + time_emb_dim=time_embed_dim, + ) + transformer_blocks = nn.ModuleList( + [ + self.get_block( + up_block_type, + output_channel, + attention_head_dim, + num_heads, + dropout, + act_fn, + ) + for _ in range(n_blocks) + ] + ) + upsample = ( + Upsample1D(output_channel, use_conv_transpose=True) + if not is_last + else nn.Conv1d(output_channel, output_channel, 3, padding=1) + ) + + self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample])) + + self.final_block = Block1D(channels[-1], channels[-1]) + self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1) + + self.initialize_weights() + # nn.init.normal_(self.final_proj.weight) + + @staticmethod + def get_block(block_type, dim, attention_head_dim, num_heads, dropout, act_fn): + if block_type == "conformer": + block = ConformerWrapper( + dim=dim, + dim_head=attention_head_dim, + heads=num_heads, + ff_mult=1, + conv_expansion_factor=2, + ff_dropout=dropout, + attn_dropout=dropout, + conv_dropout=dropout, + conv_kernel_size=31, + ) + elif block_type == "transformer": + block = BasicTransformerBlock( + dim=dim, + num_attention_heads=num_heads, + attention_head_dim=attention_head_dim, + dropout=dropout, + activation_fn=act_fn, + ) + else: + raise ValueError(f"Unknown block type {block_type}") + + return block + + def initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv1d): + nn.init.kaiming_normal_(m.weight, nonlinearity="relu") + + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + elif isinstance(m, nn.GroupNorm): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + elif isinstance(m, nn.Linear): + nn.init.kaiming_normal_(m.weight, nonlinearity="relu") + + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + def forward(self, x, mask, mu, t, spks=None, cond=None): + """Forward pass of the UNet1DConditional model. + + Args: + x (torch.Tensor): shape (batch_size, in_channels, time) + mask (_type_): shape (batch_size, 1, time) + t (_type_): shape (batch_size) + spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None. + cond (_type_, optional): placeholder for future use. Defaults to None. + + Raises: + ValueError: _description_ + ValueError: _description_ + + Returns: + _type_: _description_ + """ + + t = self.time_embeddings(t) + t = self.time_mlp(t) + + x = pack([x, mu], "b * t")[0] + + if spks is not None: + spks = repeat(spks, "b c -> b c t", t=x.shape[-1]) + x = pack([x, spks], "b * t")[0] + + hiddens = [] + masks = [mask] + for resnet, transformer_blocks, downsample in self.down_blocks: + mask_down = masks[-1] + x = resnet(x, mask_down, t) + x = rearrange(x, "b c t -> b t c") + mask_down = rearrange(mask_down, "b 1 t -> b t") + for transformer_block in transformer_blocks: + x = transformer_block( + hidden_states=x, + attention_mask=mask_down, + timestep=t, + ) + x = rearrange(x, "b t c -> b c t") + mask_down = rearrange(mask_down, "b t -> b 1 t") + hiddens.append(x) # Save hidden states for skip connections + x = downsample(x * mask_down) + masks.append(mask_down[:, :, ::2]) + + masks = masks[:-1] + mask_mid = masks[-1] + + for resnet, transformer_blocks in self.mid_blocks: + x = resnet(x, mask_mid, t) + x = rearrange(x, "b c t -> b t c") + mask_mid = rearrange(mask_mid, "b 1 t -> b t") + for transformer_block in transformer_blocks: + x = transformer_block( + hidden_states=x, + attention_mask=mask_mid, + timestep=t, + ) + x = rearrange(x, "b t c -> b c t") + mask_mid = rearrange(mask_mid, "b t -> b 1 t") + + for resnet, transformer_blocks, upsample in self.up_blocks: + mask_up = masks.pop() + x = resnet(pack([x, hiddens.pop()], "b * t")[0], mask_up, t) + x = rearrange(x, "b c t -> b t c") + mask_up = rearrange(mask_up, "b 1 t -> b t") + for transformer_block in transformer_blocks: + x = transformer_block( + hidden_states=x, + attention_mask=mask_up, + timestep=t, + ) + x = rearrange(x, "b t c -> b c t") + mask_up = rearrange(mask_up, "b t -> b 1 t") + x = upsample(x * mask_up) + + x = self.final_block(x, mask_up) + output = self.final_proj(x * mask_up) + + return output * mask diff --git a/xinference/thirdparty/matcha/models/components/flow_matching.py b/xinference/thirdparty/matcha/models/components/flow_matching.py new file mode 100644 index 0000000000..5cad7431ef --- /dev/null +++ b/xinference/thirdparty/matcha/models/components/flow_matching.py @@ -0,0 +1,132 @@ +from abc import ABC + +import torch +import torch.nn.functional as F + +from matcha.models.components.decoder import Decoder +from matcha.utils.pylogger import get_pylogger + +log = get_pylogger(__name__) + + +class BASECFM(torch.nn.Module, ABC): + def __init__( + self, + n_feats, + cfm_params, + n_spks=1, + spk_emb_dim=128, + ): + super().__init__() + self.n_feats = n_feats + self.n_spks = n_spks + self.spk_emb_dim = spk_emb_dim + self.solver = cfm_params.solver + if hasattr(cfm_params, "sigma_min"): + self.sigma_min = cfm_params.sigma_min + else: + self.sigma_min = 1e-4 + + self.estimator = None + + @torch.inference_mode() + def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None): + """Forward diffusion + + Args: + mu (torch.Tensor): output of encoder + shape: (batch_size, n_feats, mel_timesteps) + mask (torch.Tensor): output_mask + shape: (batch_size, 1, mel_timesteps) + n_timesteps (int): number of diffusion steps + temperature (float, optional): temperature for scaling noise. Defaults to 1.0. + spks (torch.Tensor, optional): speaker ids. Defaults to None. + shape: (batch_size, spk_emb_dim) + cond: Not used but kept for future purposes + + Returns: + sample: generated mel-spectrogram + shape: (batch_size, n_feats, mel_timesteps) + """ + z = torch.randn_like(mu) * temperature + t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device) + return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond) + + def solve_euler(self, x, t_span, mu, mask, spks, cond): + """ + Fixed euler solver for ODEs. + Args: + x (torch.Tensor): random noise + t_span (torch.Tensor): n_timesteps interpolated + shape: (n_timesteps + 1,) + mu (torch.Tensor): output of encoder + shape: (batch_size, n_feats, mel_timesteps) + mask (torch.Tensor): output_mask + shape: (batch_size, 1, mel_timesteps) + spks (torch.Tensor, optional): speaker ids. Defaults to None. + shape: (batch_size, spk_emb_dim) + cond: Not used but kept for future purposes + """ + t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0] + + # I am storing this because I can later plot it by putting a debugger here and saving it to a file + # Or in future might add like a return_all_steps flag + sol = [] + + for step in range(1, len(t_span)): + dphi_dt = self.estimator(x, mask, mu, t, spks, cond) + + x = x + dt * dphi_dt + t = t + dt + sol.append(x) + if step < len(t_span) - 1: + dt = t_span[step + 1] - t + + return sol[-1] + + def compute_loss(self, x1, mask, mu, spks=None, cond=None): + """Computes diffusion loss + + Args: + x1 (torch.Tensor): Target + shape: (batch_size, n_feats, mel_timesteps) + mask (torch.Tensor): target mask + shape: (batch_size, 1, mel_timesteps) + mu (torch.Tensor): output of encoder + shape: (batch_size, n_feats, mel_timesteps) + spks (torch.Tensor, optional): speaker embedding. Defaults to None. + shape: (batch_size, spk_emb_dim) + + Returns: + loss: conditional flow matching loss + y: conditional flow + shape: (batch_size, n_feats, mel_timesteps) + """ + b, _, t = mu.shape + + # random timestep + t = torch.rand([b, 1, 1], device=mu.device, dtype=mu.dtype) + # sample noise p(x_0) + z = torch.randn_like(x1) + + y = (1 - (1 - self.sigma_min) * t) * z + t * x1 + u = x1 - (1 - self.sigma_min) * z + + loss = F.mse_loss(self.estimator(y, mask, mu, t.squeeze(), spks), u, reduction="sum") / ( + torch.sum(mask) * u.shape[1] + ) + return loss, y + + +class CFM(BASECFM): + def __init__(self, in_channels, out_channel, cfm_params, decoder_params, n_spks=1, spk_emb_dim=64): + super().__init__( + n_feats=in_channels, + cfm_params=cfm_params, + n_spks=n_spks, + spk_emb_dim=spk_emb_dim, + ) + + in_channels = in_channels + (spk_emb_dim if n_spks > 1 else 0) + # Just change the architecture of the estimator here + self.estimator = Decoder(in_channels=in_channels, out_channels=out_channel, **decoder_params) diff --git a/xinference/thirdparty/matcha/models/components/text_encoder.py b/xinference/thirdparty/matcha/models/components/text_encoder.py new file mode 100644 index 0000000000..a388d05d63 --- /dev/null +++ b/xinference/thirdparty/matcha/models/components/text_encoder.py @@ -0,0 +1,410 @@ +""" from https://github.com/jaywalnut310/glow-tts """ + +import math + +import torch +import torch.nn as nn +from einops import rearrange + +import matcha.utils as utils +from matcha.utils.model import sequence_mask + +log = utils.get_pylogger(__name__) + + +class LayerNorm(nn.Module): + def __init__(self, channels, eps=1e-4): + super().__init__() + self.channels = channels + self.eps = eps + + self.gamma = torch.nn.Parameter(torch.ones(channels)) + self.beta = torch.nn.Parameter(torch.zeros(channels)) + + def forward(self, x): + n_dims = len(x.shape) + mean = torch.mean(x, 1, keepdim=True) + variance = torch.mean((x - mean) ** 2, 1, keepdim=True) + + x = (x - mean) * torch.rsqrt(variance + self.eps) + + shape = [1, -1] + [1] * (n_dims - 2) + x = x * self.gamma.view(*shape) + self.beta.view(*shape) + return x + + +class ConvReluNorm(nn.Module): + def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): + super().__init__() + self.in_channels = in_channels + self.hidden_channels = hidden_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.n_layers = n_layers + self.p_dropout = p_dropout + + self.conv_layers = torch.nn.ModuleList() + self.norm_layers = torch.nn.ModuleList() + self.conv_layers.append(torch.nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) + self.norm_layers.append(LayerNorm(hidden_channels)) + self.relu_drop = torch.nn.Sequential(torch.nn.ReLU(), torch.nn.Dropout(p_dropout)) + for _ in range(n_layers - 1): + self.conv_layers.append( + torch.nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2) + ) + self.norm_layers.append(LayerNorm(hidden_channels)) + self.proj = torch.nn.Conv1d(hidden_channels, out_channels, 1) + self.proj.weight.data.zero_() + self.proj.bias.data.zero_() + + def forward(self, x, x_mask): + x_org = x + for i in range(self.n_layers): + x = self.conv_layers[i](x * x_mask) + x = self.norm_layers[i](x) + x = self.relu_drop(x) + x = x_org + self.proj(x) + return x * x_mask + + +class DurationPredictor(nn.Module): + def __init__(self, in_channels, filter_channels, kernel_size, p_dropout): + super().__init__() + self.in_channels = in_channels + self.filter_channels = filter_channels + self.p_dropout = p_dropout + + self.drop = torch.nn.Dropout(p_dropout) + self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2) + self.norm_1 = LayerNorm(filter_channels) + self.conv_2 = torch.nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2) + self.norm_2 = LayerNorm(filter_channels) + self.proj = torch.nn.Conv1d(filter_channels, 1, 1) + + def forward(self, x, x_mask): + x = self.conv_1(x * x_mask) + x = torch.relu(x) + x = self.norm_1(x) + x = self.drop(x) + x = self.conv_2(x * x_mask) + x = torch.relu(x) + x = self.norm_2(x) + x = self.drop(x) + x = self.proj(x * x_mask) + return x * x_mask + + +class RotaryPositionalEmbeddings(nn.Module): + """ + ## RoPE module + + Rotary encoding transforms pairs of features by rotating in the 2D plane. + That is, it organizes the $d$ features as $\frac{d}{2}$ pairs. + Each pair can be considered a coordinate in a 2D plane, and the encoding will rotate it + by an angle depending on the position of the token. + """ + + def __init__(self, d: int, base: int = 10_000): + r""" + * `d` is the number of features $d$ + * `base` is the constant used for calculating $\Theta$ + """ + super().__init__() + + self.base = base + self.d = int(d) + self.cos_cached = None + self.sin_cached = None + + def _build_cache(self, x: torch.Tensor): + r""" + Cache $\cos$ and $\sin$ values + """ + # Return if cache is already built + if self.cos_cached is not None and x.shape[0] <= self.cos_cached.shape[0]: + return + + # Get sequence length + seq_len = x.shape[0] + + # $\Theta = {\theta_i = 10000^{-\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$ + theta = 1.0 / (self.base ** (torch.arange(0, self.d, 2).float() / self.d)).to(x.device) + + # Create position indexes `[0, 1, ..., seq_len - 1]` + seq_idx = torch.arange(seq_len, device=x.device).float().to(x.device) + + # Calculate the product of position index and $\theta_i$ + idx_theta = torch.einsum("n,d->nd", seq_idx, theta) + + # Concatenate so that for row $m$ we have + # $[m \theta_0, m \theta_1, ..., m \theta_{\frac{d}{2}}, m \theta_0, m \theta_1, ..., m \theta_{\frac{d}{2}}]$ + idx_theta2 = torch.cat([idx_theta, idx_theta], dim=1) + + # Cache them + self.cos_cached = idx_theta2.cos()[:, None, None, :] + self.sin_cached = idx_theta2.sin()[:, None, None, :] + + def _neg_half(self, x: torch.Tensor): + # $\frac{d}{2}$ + d_2 = self.d // 2 + + # Calculate $[-x^{(\frac{d}{2} + 1)}, -x^{(\frac{d}{2} + 2)}, ..., -x^{(d)}, x^{(1)}, x^{(2)}, ..., x^{(\frac{d}{2})}]$ + return torch.cat([-x[:, :, :, d_2:], x[:, :, :, :d_2]], dim=-1) + + def forward(self, x: torch.Tensor): + """ + * `x` is the Tensor at the head of a key or a query with shape `[seq_len, batch_size, n_heads, d]` + """ + # Cache $\cos$ and $\sin$ values + x = rearrange(x, "b h t d -> t b h d") + + self._build_cache(x) + + # Split the features, we can choose to apply rotary embeddings only to a partial set of features. + x_rope, x_pass = x[..., : self.d], x[..., self.d :] + + # Calculate + # $[-x^{(\frac{d}{2} + 1)}, -x^{(\frac{d}{2} + 2)}, ..., -x^{(d)}, x^{(1)}, x^{(2)}, ..., x^{(\frac{d}{2})}]$ + neg_half_x = self._neg_half(x_rope) + + x_rope = (x_rope * self.cos_cached[: x.shape[0]]) + (neg_half_x * self.sin_cached[: x.shape[0]]) + + return rearrange(torch.cat((x_rope, x_pass), dim=-1), "t b h d -> b h t d") + + +class MultiHeadAttention(nn.Module): + def __init__( + self, + channels, + out_channels, + n_heads, + heads_share=True, + p_dropout=0.0, + proximal_bias=False, + proximal_init=False, + ): + super().__init__() + assert channels % n_heads == 0 + + self.channels = channels + self.out_channels = out_channels + self.n_heads = n_heads + self.heads_share = heads_share + self.proximal_bias = proximal_bias + self.p_dropout = p_dropout + self.attn = None + + self.k_channels = channels // n_heads + self.conv_q = torch.nn.Conv1d(channels, channels, 1) + self.conv_k = torch.nn.Conv1d(channels, channels, 1) + self.conv_v = torch.nn.Conv1d(channels, channels, 1) + + # from https://nn.labml.ai/transformers/rope/index.html + self.query_rotary_pe = RotaryPositionalEmbeddings(self.k_channels * 0.5) + self.key_rotary_pe = RotaryPositionalEmbeddings(self.k_channels * 0.5) + + self.conv_o = torch.nn.Conv1d(channels, out_channels, 1) + self.drop = torch.nn.Dropout(p_dropout) + + torch.nn.init.xavier_uniform_(self.conv_q.weight) + torch.nn.init.xavier_uniform_(self.conv_k.weight) + if proximal_init: + self.conv_k.weight.data.copy_(self.conv_q.weight.data) + self.conv_k.bias.data.copy_(self.conv_q.bias.data) + torch.nn.init.xavier_uniform_(self.conv_v.weight) + + def forward(self, x, c, attn_mask=None): + q = self.conv_q(x) + k = self.conv_k(c) + v = self.conv_v(c) + + x, self.attn = self.attention(q, k, v, mask=attn_mask) + + x = self.conv_o(x) + return x + + def attention(self, query, key, value, mask=None): + b, d, t_s, t_t = (*key.size(), query.size(2)) + query = rearrange(query, "b (h c) t-> b h t c", h=self.n_heads) + key = rearrange(key, "b (h c) t-> b h t c", h=self.n_heads) + value = rearrange(value, "b (h c) t-> b h t c", h=self.n_heads) + + query = self.query_rotary_pe(query) + key = self.key_rotary_pe(key) + + scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels) + + if self.proximal_bias: + assert t_s == t_t, "Proximal bias is only available for self-attention." + scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype) + if mask is not None: + scores = scores.masked_fill(mask == 0, -1e4) + p_attn = torch.nn.functional.softmax(scores, dim=-1) + p_attn = self.drop(p_attn) + output = torch.matmul(p_attn, value) + output = output.transpose(2, 3).contiguous().view(b, d, t_t) + return output, p_attn + + @staticmethod + def _attention_bias_proximal(length): + r = torch.arange(length, dtype=torch.float32) + diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) + return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) + + +class FFN(nn.Module): + def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0.0): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.filter_channels = filter_channels + self.kernel_size = kernel_size + self.p_dropout = p_dropout + + self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2) + self.conv_2 = torch.nn.Conv1d(filter_channels, out_channels, kernel_size, padding=kernel_size // 2) + self.drop = torch.nn.Dropout(p_dropout) + + def forward(self, x, x_mask): + x = self.conv_1(x * x_mask) + x = torch.relu(x) + x = self.drop(x) + x = self.conv_2(x * x_mask) + return x * x_mask + + +class Encoder(nn.Module): + def __init__( + self, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size=1, + p_dropout=0.0, + **kwargs, + ): + super().__init__() + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + + self.drop = torch.nn.Dropout(p_dropout) + self.attn_layers = torch.nn.ModuleList() + self.norm_layers_1 = torch.nn.ModuleList() + self.ffn_layers = torch.nn.ModuleList() + self.norm_layers_2 = torch.nn.ModuleList() + for _ in range(self.n_layers): + self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout)) + self.norm_layers_1.append(LayerNorm(hidden_channels)) + self.ffn_layers.append( + FFN( + hidden_channels, + hidden_channels, + filter_channels, + kernel_size, + p_dropout=p_dropout, + ) + ) + self.norm_layers_2.append(LayerNorm(hidden_channels)) + + def forward(self, x, x_mask): + attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) + for i in range(self.n_layers): + x = x * x_mask + y = self.attn_layers[i](x, x, attn_mask) + y = self.drop(y) + x = self.norm_layers_1[i](x + y) + y = self.ffn_layers[i](x, x_mask) + y = self.drop(y) + x = self.norm_layers_2[i](x + y) + x = x * x_mask + return x + + +class TextEncoder(nn.Module): + def __init__( + self, + encoder_type, + encoder_params, + duration_predictor_params, + n_vocab, + n_spks=1, + spk_emb_dim=128, + ): + super().__init__() + self.encoder_type = encoder_type + self.n_vocab = n_vocab + self.n_feats = encoder_params.n_feats + self.n_channels = encoder_params.n_channels + self.spk_emb_dim = spk_emb_dim + self.n_spks = n_spks + + self.emb = torch.nn.Embedding(n_vocab, self.n_channels) + torch.nn.init.normal_(self.emb.weight, 0.0, self.n_channels**-0.5) + + if encoder_params.prenet: + self.prenet = ConvReluNorm( + self.n_channels, + self.n_channels, + self.n_channels, + kernel_size=5, + n_layers=3, + p_dropout=0.5, + ) + else: + self.prenet = lambda x, x_mask: x + + self.encoder = Encoder( + encoder_params.n_channels + (spk_emb_dim if n_spks > 1 else 0), + encoder_params.filter_channels, + encoder_params.n_heads, + encoder_params.n_layers, + encoder_params.kernel_size, + encoder_params.p_dropout, + ) + + self.proj_m = torch.nn.Conv1d(self.n_channels + (spk_emb_dim if n_spks > 1 else 0), self.n_feats, 1) + self.proj_w = DurationPredictor( + self.n_channels + (spk_emb_dim if n_spks > 1 else 0), + duration_predictor_params.filter_channels_dp, + duration_predictor_params.kernel_size, + duration_predictor_params.p_dropout, + ) + + def forward(self, x, x_lengths, spks=None): + """Run forward pass to the transformer based encoder and duration predictor + + Args: + x (torch.Tensor): text input + shape: (batch_size, max_text_length) + x_lengths (torch.Tensor): text input lengths + shape: (batch_size,) + spks (torch.Tensor, optional): speaker ids. Defaults to None. + shape: (batch_size,) + + Returns: + mu (torch.Tensor): average output of the encoder + shape: (batch_size, n_feats, max_text_length) + logw (torch.Tensor): log duration predicted by the duration predictor + shape: (batch_size, 1, max_text_length) + x_mask (torch.Tensor): mask for the text input + shape: (batch_size, 1, max_text_length) + """ + x = self.emb(x) * math.sqrt(self.n_channels) + x = torch.transpose(x, 1, -1) + x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) + + x = self.prenet(x, x_mask) + if self.n_spks > 1: + x = torch.cat([x, spks.unsqueeze(-1).repeat(1, 1, x.shape[-1])], dim=1) + x = self.encoder(x, x_mask) + mu = self.proj_m(x) * x_mask + + x_dp = torch.detach(x) + logw = self.proj_w(x_dp, x_mask) + + return mu, logw, x_mask diff --git a/xinference/thirdparty/matcha/models/components/transformer.py b/xinference/thirdparty/matcha/models/components/transformer.py new file mode 100644 index 0000000000..dd1afa3aff --- /dev/null +++ b/xinference/thirdparty/matcha/models/components/transformer.py @@ -0,0 +1,316 @@ +from typing import Any, Dict, Optional + +import torch +import torch.nn as nn +from diffusers.models.attention import ( + GEGLU, + GELU, + AdaLayerNorm, + AdaLayerNormZero, + ApproximateGELU, +) +from diffusers.models.attention_processor import Attention +from diffusers.models.lora import LoRACompatibleLinear +from diffusers.utils.torch_utils import maybe_allow_in_graph + + +class SnakeBeta(nn.Module): + """ + A modified Snake function which uses separate parameters for the magnitude of the periodic components + Shape: + - Input: (B, C, T) + - Output: (B, C, T), same shape as the input + Parameters: + - alpha - trainable parameter that controls frequency + - beta - trainable parameter that controls magnitude + References: + - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: + https://arxiv.org/abs/2006.08195 + Examples: + >>> a1 = snakebeta(256) + >>> x = torch.randn(256) + >>> x = a1(x) + """ + + def __init__(self, in_features, out_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True): + """ + Initialization. + INPUT: + - in_features: shape of the input + - alpha - trainable parameter that controls frequency + - beta - trainable parameter that controls magnitude + alpha is initialized to 1 by default, higher values = higher-frequency. + beta is initialized to 1 by default, higher values = higher-magnitude. + alpha will be trained along with the rest of your model. + """ + super().__init__() + self.in_features = out_features if isinstance(out_features, list) else [out_features] + self.proj = LoRACompatibleLinear(in_features, out_features) + + # initialize alpha + self.alpha_logscale = alpha_logscale + if self.alpha_logscale: # log scale alphas initialized to zeros + self.alpha = nn.Parameter(torch.zeros(self.in_features) * alpha) + self.beta = nn.Parameter(torch.zeros(self.in_features) * alpha) + else: # linear scale alphas initialized to ones + self.alpha = nn.Parameter(torch.ones(self.in_features) * alpha) + self.beta = nn.Parameter(torch.ones(self.in_features) * alpha) + + self.alpha.requires_grad = alpha_trainable + self.beta.requires_grad = alpha_trainable + + self.no_div_by_zero = 0.000000001 + + def forward(self, x): + """ + Forward pass of the function. + Applies the function to the input elementwise. + SnakeBeta ∶= x + 1/b * sin^2 (xa) + """ + x = self.proj(x) + if self.alpha_logscale: + alpha = torch.exp(self.alpha) + beta = torch.exp(self.beta) + else: + alpha = self.alpha + beta = self.beta + + x = x + (1.0 / (beta + self.no_div_by_zero)) * torch.pow(torch.sin(x * alpha), 2) + + return x + + +class FeedForward(nn.Module): + r""" + A feed-forward layer. + + Parameters: + dim (`int`): The number of channels in the input. + dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`. + mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + final_dropout (`bool` *optional*, defaults to False): Apply a final dropout. + """ + + def __init__( + self, + dim: int, + dim_out: Optional[int] = None, + mult: int = 4, + dropout: float = 0.0, + activation_fn: str = "geglu", + final_dropout: bool = False, + ): + super().__init__() + inner_dim = int(dim * mult) + dim_out = dim_out if dim_out is not None else dim + + if activation_fn == "gelu": + act_fn = GELU(dim, inner_dim) + if activation_fn == "gelu-approximate": + act_fn = GELU(dim, inner_dim, approximate="tanh") + elif activation_fn == "geglu": + act_fn = GEGLU(dim, inner_dim) + elif activation_fn == "geglu-approximate": + act_fn = ApproximateGELU(dim, inner_dim) + elif activation_fn == "snakebeta": + act_fn = SnakeBeta(dim, inner_dim) + + self.net = nn.ModuleList([]) + # project in + self.net.append(act_fn) + # project dropout + self.net.append(nn.Dropout(dropout)) + # project out + self.net.append(LoRACompatibleLinear(inner_dim, dim_out)) + # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout + if final_dropout: + self.net.append(nn.Dropout(dropout)) + + def forward(self, hidden_states): + for module in self.net: + hidden_states = module(hidden_states) + return hidden_states + + +@maybe_allow_in_graph +class BasicTransformerBlock(nn.Module): + r""" + A basic Transformer block. + + Parameters: + dim (`int`): The number of channels in the input and output. + num_attention_heads (`int`): The number of heads to use for multi-head attention. + attention_head_dim (`int`): The number of channels in each head. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. + only_cross_attention (`bool`, *optional*): + Whether to use only cross-attention layers. In this case two cross attention layers are used. + double_self_attention (`bool`, *optional*): + Whether to use two self-attention layers. In this case no cross attention layers are used. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + num_embeds_ada_norm (: + obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`. + attention_bias (: + obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. + """ + + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout=0.0, + cross_attention_dim: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + attention_bias: bool = False, + only_cross_attention: bool = False, + double_self_attention: bool = False, + upcast_attention: bool = False, + norm_elementwise_affine: bool = True, + norm_type: str = "layer_norm", + final_dropout: bool = False, + ): + super().__init__() + self.only_cross_attention = only_cross_attention + + self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero" + self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm" + + if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None: + raise ValueError( + f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to" + f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}." + ) + + # Define 3 blocks. Each block has its own normalization layer. + # 1. Self-Attn + if self.use_ada_layer_norm: + self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) + elif self.use_ada_layer_norm_zero: + self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm) + else: + self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + self.attn1 = Attention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + ) + + # 2. Cross-Attn + if cross_attention_dim is not None or double_self_attention: + # We currently only use AdaLayerNormZero for self attention where there will only be one attention block. + # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during + # the second cross attention block. + self.norm2 = ( + AdaLayerNorm(dim, num_embeds_ada_norm) + if self.use_ada_layer_norm + else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + ) + self.attn2 = Attention( + query_dim=dim, + cross_attention_dim=cross_attention_dim if not double_self_attention else None, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + # scale_qk=False, # uncomment this to not to use flash attention + ) # is self-attn if encoder_hidden_states is none + else: + self.norm2 = None + self.attn2 = None + + # 3. Feed-forward + self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout) + + # let chunk size default to None + self._chunk_size = None + self._chunk_dim = 0 + + def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int): + # Sets chunk feed-forward + self._chunk_size = chunk_size + self._chunk_dim = dim + + def forward( + self, + hidden_states: torch.FloatTensor, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + timestep: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + class_labels: Optional[torch.LongTensor] = None, + ): + # Notice that normalization is always applied before the real computation in the following blocks. + # 1. Self-Attention + if self.use_ada_layer_norm: + norm_hidden_states = self.norm1(hidden_states, timestep) + elif self.use_ada_layer_norm_zero: + norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( + hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype + ) + else: + norm_hidden_states = self.norm1(hidden_states) + + cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} + + attn_output = self.attn1( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, + attention_mask=encoder_attention_mask if self.only_cross_attention else attention_mask, + **cross_attention_kwargs, + ) + if self.use_ada_layer_norm_zero: + attn_output = gate_msa.unsqueeze(1) * attn_output + hidden_states = attn_output + hidden_states + + # 2. Cross-Attention + if self.attn2 is not None: + norm_hidden_states = ( + self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states) + ) + + attn_output = self.attn2( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + **cross_attention_kwargs, + ) + hidden_states = attn_output + hidden_states + + # 3. Feed-forward + norm_hidden_states = self.norm3(hidden_states) + + if self.use_ada_layer_norm_zero: + norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + + if self._chunk_size is not None: + # "feed_forward_chunk_size" can be used to save memory + if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0: + raise ValueError( + f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`." + ) + + num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size + ff_output = torch.cat( + [self.ff(hid_slice) for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)], + dim=self._chunk_dim, + ) + else: + ff_output = self.ff(norm_hidden_states) + + if self.use_ada_layer_norm_zero: + ff_output = gate_mlp.unsqueeze(1) * ff_output + + hidden_states = ff_output + hidden_states + + return hidden_states diff --git a/xinference/thirdparty/matcha/models/matcha_tts.py b/xinference/thirdparty/matcha/models/matcha_tts.py new file mode 100644 index 0000000000..07f95ad2e3 --- /dev/null +++ b/xinference/thirdparty/matcha/models/matcha_tts.py @@ -0,0 +1,244 @@ +import datetime as dt +import math +import random + +import torch + +import matcha.utils.monotonic_align as monotonic_align +from matcha import utils +from matcha.models.baselightningmodule import BaseLightningClass +from matcha.models.components.flow_matching import CFM +from matcha.models.components.text_encoder import TextEncoder +from matcha.utils.model import ( + denormalize, + duration_loss, + fix_len_compatibility, + generate_path, + sequence_mask, +) + +log = utils.get_pylogger(__name__) + + +class MatchaTTS(BaseLightningClass): # 🍵 + def __init__( + self, + n_vocab, + n_spks, + spk_emb_dim, + n_feats, + encoder, + decoder, + cfm, + data_statistics, + out_size, + optimizer=None, + scheduler=None, + prior_loss=True, + use_precomputed_durations=False, + ): + super().__init__() + + self.save_hyperparameters(logger=False) + + self.n_vocab = n_vocab + self.n_spks = n_spks + self.spk_emb_dim = spk_emb_dim + self.n_feats = n_feats + self.out_size = out_size + self.prior_loss = prior_loss + self.use_precomputed_durations = use_precomputed_durations + + if n_spks > 1: + self.spk_emb = torch.nn.Embedding(n_spks, spk_emb_dim) + + self.encoder = TextEncoder( + encoder.encoder_type, + encoder.encoder_params, + encoder.duration_predictor_params, + n_vocab, + n_spks, + spk_emb_dim, + ) + + self.decoder = CFM( + in_channels=2 * encoder.encoder_params.n_feats, + out_channel=encoder.encoder_params.n_feats, + cfm_params=cfm, + decoder_params=decoder, + n_spks=n_spks, + spk_emb_dim=spk_emb_dim, + ) + + self.update_data_statistics(data_statistics) + + @torch.inference_mode() + def synthesise(self, x, x_lengths, n_timesteps, temperature=1.0, spks=None, length_scale=1.0): + """ + Generates mel-spectrogram from text. Returns: + 1. encoder outputs + 2. decoder outputs + 3. generated alignment + + Args: + x (torch.Tensor): batch of texts, converted to a tensor with phoneme embedding ids. + shape: (batch_size, max_text_length) + x_lengths (torch.Tensor): lengths of texts in batch. + shape: (batch_size,) + n_timesteps (int): number of steps to use for reverse diffusion in decoder. + temperature (float, optional): controls variance of terminal distribution. + spks (bool, optional): speaker ids. + shape: (batch_size,) + length_scale (float, optional): controls speech pace. + Increase value to slow down generated speech and vice versa. + + Returns: + dict: { + "encoder_outputs": torch.Tensor, shape: (batch_size, n_feats, max_mel_length), + # Average mel spectrogram generated by the encoder + "decoder_outputs": torch.Tensor, shape: (batch_size, n_feats, max_mel_length), + # Refined mel spectrogram improved by the CFM + "attn": torch.Tensor, shape: (batch_size, max_text_length, max_mel_length), + # Alignment map between text and mel spectrogram + "mel": torch.Tensor, shape: (batch_size, n_feats, max_mel_length), + # Denormalized mel spectrogram + "mel_lengths": torch.Tensor, shape: (batch_size,), + # Lengths of mel spectrograms + "rtf": float, + # Real-time factor + """ + # For RTF computation + t = dt.datetime.now() + + if self.n_spks > 1: + # Get speaker embedding + spks = self.spk_emb(spks.long()) + + # Get encoder_outputs `mu_x` and log-scaled token durations `logw` + mu_x, logw, x_mask = self.encoder(x, x_lengths, spks) + + w = torch.exp(logw) * x_mask + w_ceil = torch.ceil(w) * length_scale + y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() + y_max_length = y_lengths.max() + y_max_length_ = fix_len_compatibility(y_max_length) + + # Using obtained durations `w` construct alignment map `attn` + y_mask = sequence_mask(y_lengths, y_max_length_).unsqueeze(1).to(x_mask.dtype) + attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2) + attn = generate_path(w_ceil.squeeze(1), attn_mask.squeeze(1)).unsqueeze(1) + + # Align encoded text and get mu_y + mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2)) + mu_y = mu_y.transpose(1, 2) + encoder_outputs = mu_y[:, :, :y_max_length] + + # Generate sample tracing the probability flow + decoder_outputs = self.decoder(mu_y, y_mask, n_timesteps, temperature, spks) + decoder_outputs = decoder_outputs[:, :, :y_max_length] + + t = (dt.datetime.now() - t).total_seconds() + rtf = t * 22050 / (decoder_outputs.shape[-1] * 256) + + return { + "encoder_outputs": encoder_outputs, + "decoder_outputs": decoder_outputs, + "attn": attn[:, :, :y_max_length], + "mel": denormalize(decoder_outputs, self.mel_mean, self.mel_std), + "mel_lengths": y_lengths, + "rtf": rtf, + } + + def forward(self, x, x_lengths, y, y_lengths, spks=None, out_size=None, cond=None, durations=None): + """ + Computes 3 losses: + 1. duration loss: loss between predicted token durations and those extracted by Monotinic Alignment Search (MAS). + 2. prior loss: loss between mel-spectrogram and encoder outputs. + 3. flow matching loss: loss between mel-spectrogram and decoder outputs. + + Args: + x (torch.Tensor): batch of texts, converted to a tensor with phoneme embedding ids. + shape: (batch_size, max_text_length) + x_lengths (torch.Tensor): lengths of texts in batch. + shape: (batch_size,) + y (torch.Tensor): batch of corresponding mel-spectrograms. + shape: (batch_size, n_feats, max_mel_length) + y_lengths (torch.Tensor): lengths of mel-spectrograms in batch. + shape: (batch_size,) + out_size (int, optional): length (in mel's sampling rate) of segment to cut, on which decoder will be trained. + Should be divisible by 2^{num of UNet downsamplings}. Needed to increase batch size. + spks (torch.Tensor, optional): speaker ids. + shape: (batch_size,) + """ + if self.n_spks > 1: + # Get speaker embedding + spks = self.spk_emb(spks) + + # Get encoder_outputs `mu_x` and log-scaled token durations `logw` + mu_x, logw, x_mask = self.encoder(x, x_lengths, spks) + y_max_length = y.shape[-1] + + y_mask = sequence_mask(y_lengths, y_max_length).unsqueeze(1).to(x_mask) + attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2) + + if self.use_precomputed_durations: + attn = generate_path(durations.squeeze(1), attn_mask.squeeze(1)) + else: + # Use MAS to find most likely alignment `attn` between text and mel-spectrogram + with torch.no_grad(): + const = -0.5 * math.log(2 * math.pi) * self.n_feats + factor = -0.5 * torch.ones(mu_x.shape, dtype=mu_x.dtype, device=mu_x.device) + y_square = torch.matmul(factor.transpose(1, 2), y**2) + y_mu_double = torch.matmul(2.0 * (factor * mu_x).transpose(1, 2), y) + mu_square = torch.sum(factor * (mu_x**2), 1).unsqueeze(-1) + log_prior = y_square - y_mu_double + mu_square + const + + attn = monotonic_align.maximum_path(log_prior, attn_mask.squeeze(1)) + attn = attn.detach() # b, t_text, T_mel + + # Compute loss between predicted log-scaled durations and those obtained from MAS + # refered to as prior loss in the paper + logw_ = torch.log(1e-8 + torch.sum(attn.unsqueeze(1), -1)) * x_mask + dur_loss = duration_loss(logw, logw_, x_lengths) + + # Cut a small segment of mel-spectrogram in order to increase batch size + # - "Hack" taken from Grad-TTS, in case of Grad-TTS, we cannot train batch size 32 on a 24GB GPU without it + # - Do not need this hack for Matcha-TTS, but it works with it as well + if not isinstance(out_size, type(None)): + max_offset = (y_lengths - out_size).clamp(0) + offset_ranges = list(zip([0] * max_offset.shape[0], max_offset.cpu().numpy())) + out_offset = torch.LongTensor( + [torch.tensor(random.choice(range(start, end)) if end > start else 0) for start, end in offset_ranges] + ).to(y_lengths) + attn_cut = torch.zeros(attn.shape[0], attn.shape[1], out_size, dtype=attn.dtype, device=attn.device) + y_cut = torch.zeros(y.shape[0], self.n_feats, out_size, dtype=y.dtype, device=y.device) + + y_cut_lengths = [] + for i, (y_, out_offset_) in enumerate(zip(y, out_offset)): + y_cut_length = out_size + (y_lengths[i] - out_size).clamp(None, 0) + y_cut_lengths.append(y_cut_length) + cut_lower, cut_upper = out_offset_, out_offset_ + y_cut_length + y_cut[i, :, :y_cut_length] = y_[:, cut_lower:cut_upper] + attn_cut[i, :, :y_cut_length] = attn[i, :, cut_lower:cut_upper] + + y_cut_lengths = torch.LongTensor(y_cut_lengths) + y_cut_mask = sequence_mask(y_cut_lengths).unsqueeze(1).to(y_mask) + + attn = attn_cut + y = y_cut + y_mask = y_cut_mask + + # Align encoded text with mel-spectrogram and get mu_y segment + mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2)) + mu_y = mu_y.transpose(1, 2) + + # Compute loss of the decoder + diff_loss, _ = self.decoder.compute_loss(x1=y, mask=y_mask, mu=mu_y, spks=spks, cond=cond) + + if self.prior_loss: + prior_loss = torch.sum(0.5 * ((y - mu_y) ** 2 + math.log(2 * math.pi)) * y_mask) + prior_loss = prior_loss / (torch.sum(y_mask) * self.n_feats) + else: + prior_loss = 0 + + return dur_loss, prior_loss, diff_loss, attn diff --git a/xinference/thirdparty/matcha/onnx/__init__.py b/xinference/thirdparty/matcha/onnx/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/xinference/thirdparty/matcha/onnx/export.py b/xinference/thirdparty/matcha/onnx/export.py new file mode 100644 index 0000000000..9b79508615 --- /dev/null +++ b/xinference/thirdparty/matcha/onnx/export.py @@ -0,0 +1,181 @@ +import argparse +import random +from pathlib import Path + +import numpy as np +import torch +from lightning import LightningModule + +from matcha.cli import VOCODER_URLS, load_matcha, load_vocoder + +DEFAULT_OPSET = 15 + +SEED = 1234 +random.seed(SEED) +np.random.seed(SEED) +torch.manual_seed(SEED) +torch.cuda.manual_seed(SEED) +torch.backends.cudnn.deterministic = True +torch.backends.cudnn.benchmark = False + + +class MatchaWithVocoder(LightningModule): + def __init__(self, matcha, vocoder): + super().__init__() + self.matcha = matcha + self.vocoder = vocoder + + def forward(self, x, x_lengths, scales, spks=None): + mel, mel_lengths = self.matcha(x, x_lengths, scales, spks) + wavs = self.vocoder(mel).clamp(-1, 1) + lengths = mel_lengths * 256 + return wavs.squeeze(1), lengths + + +def get_exportable_module(matcha, vocoder, n_timesteps): + """ + Return an appropriate `LighteningModule` and output-node names + based on whether the vocoder is embedded in the final graph + """ + + def onnx_forward_func(x, x_lengths, scales, spks=None): + """ + Custom forward function for accepting + scaler parameters as tensors + """ + # Extract scaler parameters from tensors + temperature = scales[0] + length_scale = scales[1] + output = matcha.synthesise(x, x_lengths, n_timesteps, temperature, spks, length_scale) + return output["mel"], output["mel_lengths"] + + # Monkey-patch Matcha's forward function + matcha.forward = onnx_forward_func + + if vocoder is None: + model, output_names = matcha, ["mel", "mel_lengths"] + else: + model = MatchaWithVocoder(matcha, vocoder) + output_names = ["wav", "wav_lengths"] + return model, output_names + + +def get_inputs(is_multi_speaker): + """ + Create dummy inputs for tracing + """ + dummy_input_length = 50 + x = torch.randint(low=0, high=20, size=(1, dummy_input_length), dtype=torch.long) + x_lengths = torch.LongTensor([dummy_input_length]) + + # Scales + temperature = 0.667 + length_scale = 1.0 + scales = torch.Tensor([temperature, length_scale]) + + model_inputs = [x, x_lengths, scales] + input_names = [ + "x", + "x_lengths", + "scales", + ] + + if is_multi_speaker: + spks = torch.LongTensor([1]) + model_inputs.append(spks) + input_names.append("spks") + + return tuple(model_inputs), input_names + + +def main(): + parser = argparse.ArgumentParser(description="Export 🍵 Matcha-TTS to ONNX") + + parser.add_argument( + "checkpoint_path", + type=str, + help="Path to the model checkpoint", + ) + parser.add_argument("output", type=str, help="Path to output `.onnx` file") + parser.add_argument( + "--n-timesteps", type=int, default=5, help="Number of steps to use for reverse diffusion in decoder (default 5)" + ) + parser.add_argument( + "--vocoder-name", + type=str, + choices=list(VOCODER_URLS.keys()), + default=None, + help="Name of the vocoder to embed in the ONNX graph", + ) + parser.add_argument( + "--vocoder-checkpoint-path", + type=str, + default=None, + help="Vocoder checkpoint to embed in the ONNX graph for an `e2e` like experience", + ) + parser.add_argument("--opset", type=int, default=DEFAULT_OPSET, help="ONNX opset version to use (default 15") + + args = parser.parse_args() + + print(f"[🍵] Loading Matcha checkpoint from {args.checkpoint_path}") + print(f"Setting n_timesteps to {args.n_timesteps}") + + checkpoint_path = Path(args.checkpoint_path) + matcha = load_matcha(checkpoint_path.stem, checkpoint_path, "cpu") + + if args.vocoder_name or args.vocoder_checkpoint_path: + assert ( + args.vocoder_name and args.vocoder_checkpoint_path + ), "Both vocoder_name and vocoder-checkpoint are required when embedding the vocoder in the ONNX graph." + vocoder, _ = load_vocoder(args.vocoder_name, args.vocoder_checkpoint_path, "cpu") + else: + vocoder = None + + is_multi_speaker = matcha.n_spks > 1 + + dummy_input, input_names = get_inputs(is_multi_speaker) + model, output_names = get_exportable_module(matcha, vocoder, args.n_timesteps) + + # Set dynamic shape for inputs/outputs + dynamic_axes = { + "x": {0: "batch_size", 1: "time"}, + "x_lengths": {0: "batch_size"}, + } + + if vocoder is None: + dynamic_axes.update( + { + "mel": {0: "batch_size", 2: "time"}, + "mel_lengths": {0: "batch_size"}, + } + ) + else: + print("Embedding the vocoder in the ONNX graph") + dynamic_axes.update( + { + "wav": {0: "batch_size", 1: "time"}, + "wav_lengths": {0: "batch_size"}, + } + ) + + if is_multi_speaker: + dynamic_axes["spks"] = {0: "batch_size"} + + # Create the output directory (if not exists) + Path(args.output).parent.mkdir(parents=True, exist_ok=True) + + model.to_onnx( + args.output, + dummy_input, + input_names=input_names, + output_names=output_names, + dynamic_axes=dynamic_axes, + opset_version=args.opset, + export_params=True, + do_constant_folding=True, + ) + print(f"[🍵] ONNX model exported to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/xinference/thirdparty/matcha/onnx/infer.py b/xinference/thirdparty/matcha/onnx/infer.py new file mode 100644 index 0000000000..89ca92559c --- /dev/null +++ b/xinference/thirdparty/matcha/onnx/infer.py @@ -0,0 +1,168 @@ +import argparse +import os +import warnings +from pathlib import Path +from time import perf_counter + +import numpy as np +import onnxruntime as ort +import soundfile as sf +import torch + +from matcha.cli import plot_spectrogram_to_numpy, process_text + + +def validate_args(args): + assert ( + args.text or args.file + ), "Either text or file must be provided Matcha-T(ea)TTS need sometext to whisk the waveforms." + assert args.temperature >= 0, "Sampling temperature cannot be negative" + assert args.speaking_rate >= 0, "Speaking rate must be greater than 0" + return args + + +def write_wavs(model, inputs, output_dir, external_vocoder=None): + if external_vocoder is None: + print("The provided model has the vocoder embedded in the graph.\nGenerating waveform directly") + t0 = perf_counter() + wavs, wav_lengths = model.run(None, inputs) + infer_secs = perf_counter() - t0 + mel_infer_secs = vocoder_infer_secs = None + else: + print("[🍵] Generating mel using Matcha") + mel_t0 = perf_counter() + mels, mel_lengths = model.run(None, inputs) + mel_infer_secs = perf_counter() - mel_t0 + print("Generating waveform from mel using external vocoder") + vocoder_inputs = {external_vocoder.get_inputs()[0].name: mels} + vocoder_t0 = perf_counter() + wavs = external_vocoder.run(None, vocoder_inputs)[0] + vocoder_infer_secs = perf_counter() - vocoder_t0 + wavs = wavs.squeeze(1) + wav_lengths = mel_lengths * 256 + infer_secs = mel_infer_secs + vocoder_infer_secs + + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + for i, (wav, wav_length) in enumerate(zip(wavs, wav_lengths)): + output_filename = output_dir.joinpath(f"output_{i + 1}.wav") + audio = wav[:wav_length] + print(f"Writing audio to {output_filename}") + sf.write(output_filename, audio, 22050, "PCM_24") + + wav_secs = wav_lengths.sum() / 22050 + print(f"Inference seconds: {infer_secs}") + print(f"Generated wav seconds: {wav_secs}") + rtf = infer_secs / wav_secs + if mel_infer_secs is not None: + mel_rtf = mel_infer_secs / wav_secs + print(f"Matcha RTF: {mel_rtf}") + if vocoder_infer_secs is not None: + vocoder_rtf = vocoder_infer_secs / wav_secs + print(f"Vocoder RTF: {vocoder_rtf}") + print(f"Overall RTF: {rtf}") + + +def write_mels(model, inputs, output_dir): + t0 = perf_counter() + mels, mel_lengths = model.run(None, inputs) + infer_secs = perf_counter() - t0 + + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + for i, mel in enumerate(mels): + output_stem = output_dir.joinpath(f"output_{i + 1}") + plot_spectrogram_to_numpy(mel.squeeze(), output_stem.with_suffix(".png")) + np.save(output_stem.with_suffix(".numpy"), mel) + + wav_secs = (mel_lengths * 256).sum() / 22050 + print(f"Inference seconds: {infer_secs}") + print(f"Generated wav seconds: {wav_secs}") + rtf = infer_secs / wav_secs + print(f"RTF: {rtf}") + + +def main(): + parser = argparse.ArgumentParser( + description=" 🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching" + ) + parser.add_argument( + "model", + type=str, + help="ONNX model to use", + ) + parser.add_argument("--vocoder", type=str, default=None, help="Vocoder to use (defaults to None)") + parser.add_argument("--text", type=str, default=None, help="Text to synthesize") + parser.add_argument("--file", type=str, default=None, help="Text file to synthesize") + parser.add_argument("--spk", type=int, default=None, help="Speaker ID") + parser.add_argument( + "--temperature", + type=float, + default=0.667, + help="Variance of the x0 noise (default: 0.667)", + ) + parser.add_argument( + "--speaking-rate", + type=float, + default=1.0, + help="change the speaking rate, a higher value means slower speaking rate (default: 1.0)", + ) + parser.add_argument("--gpu", action="store_true", help="Use CPU for inference (default: use GPU if available)") + parser.add_argument( + "--output-dir", + type=str, + default=os.getcwd(), + help="Output folder to save results (default: current dir)", + ) + + args = parser.parse_args() + args = validate_args(args) + + if args.gpu: + providers = ["GPUExecutionProvider"] + else: + providers = ["CPUExecutionProvider"] + model = ort.InferenceSession(args.model, providers=providers) + + model_inputs = model.get_inputs() + model_outputs = list(model.get_outputs()) + + if args.text: + text_lines = args.text.splitlines() + else: + with open(args.file, encoding="utf-8") as file: + text_lines = file.read().splitlines() + + processed_lines = [process_text(0, line, "cpu") for line in text_lines] + x = [line["x"].squeeze() for line in processed_lines] + # Pad + x = torch.nn.utils.rnn.pad_sequence(x, batch_first=True) + x = x.detach().cpu().numpy() + x_lengths = np.array([line["x_lengths"].item() for line in processed_lines], dtype=np.int64) + inputs = { + "x": x, + "x_lengths": x_lengths, + "scales": np.array([args.temperature, args.speaking_rate], dtype=np.float32), + } + is_multi_speaker = len(model_inputs) == 4 + if is_multi_speaker: + if args.spk is None: + args.spk = 0 + warn = "[!] Speaker ID not provided! Using speaker ID 0" + warnings.warn(warn, UserWarning) + inputs["spks"] = np.repeat(args.spk, x.shape[0]).astype(np.int64) + + has_vocoder_embedded = model_outputs[0].name == "wav" + if has_vocoder_embedded: + write_wavs(model, inputs, args.output_dir) + elif args.vocoder: + external_vocoder = ort.InferenceSession(args.vocoder, providers=providers) + write_wavs(model, inputs, args.output_dir, external_vocoder=external_vocoder) + else: + warn = "[!] A vocoder is not embedded in the graph nor an external vocoder is provided. The mel output will be written as numpy arrays to `*.npy` files in the output directory" + warnings.warn(warn, UserWarning) + write_mels(model, inputs, args.output_dir) + + +if __name__ == "__main__": + main() diff --git a/xinference/thirdparty/matcha/text/__init__.py b/xinference/thirdparty/matcha/text/__init__.py new file mode 100644 index 0000000000..8c75d6b571 --- /dev/null +++ b/xinference/thirdparty/matcha/text/__init__.py @@ -0,0 +1,53 @@ +""" from https://github.com/keithito/tacotron """ +from matcha.text import cleaners +from matcha.text.symbols import symbols + +# Mappings from symbol to numeric ID and vice versa: +_symbol_to_id = {s: i for i, s in enumerate(symbols)} +_id_to_symbol = {i: s for i, s in enumerate(symbols)} # pylint: disable=unnecessary-comprehension + + +def text_to_sequence(text, cleaner_names): + """Converts a string of text to a sequence of IDs corresponding to the symbols in the text. + Args: + text: string to convert to a sequence + cleaner_names: names of the cleaner functions to run the text through + Returns: + List of integers corresponding to the symbols in the text + """ + sequence = [] + + clean_text = _clean_text(text, cleaner_names) + for symbol in clean_text: + symbol_id = _symbol_to_id[symbol] + sequence += [symbol_id] + return sequence, clean_text + + +def cleaned_text_to_sequence(cleaned_text): + """Converts a string of text to a sequence of IDs corresponding to the symbols in the text. + Args: + text: string to convert to a sequence + Returns: + List of integers corresponding to the symbols in the text + """ + sequence = [_symbol_to_id[symbol] for symbol in cleaned_text] + return sequence + + +def sequence_to_text(sequence): + """Converts a sequence of IDs back to a string""" + result = "" + for symbol_id in sequence: + s = _id_to_symbol[symbol_id] + result += s + return result + + +def _clean_text(text, cleaner_names): + for name in cleaner_names: + cleaner = getattr(cleaners, name) + if not cleaner: + raise Exception("Unknown cleaner: %s" % name) + text = cleaner(text) + return text diff --git a/xinference/thirdparty/matcha/text/cleaners.py b/xinference/thirdparty/matcha/text/cleaners.py new file mode 100644 index 0000000000..36776e3552 --- /dev/null +++ b/xinference/thirdparty/matcha/text/cleaners.py @@ -0,0 +1,121 @@ +""" from https://github.com/keithito/tacotron + +Cleaners are transformations that run over the input text at both training and eval time. + +Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners" +hyperparameter. Some cleaners are English-specific. You'll typically want to use: + 1. "english_cleaners" for English text + 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using + the Unidecode library (https://pypi.python.org/pypi/Unidecode) + 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update + the symbols in symbols.py to match your data). +""" + +import logging +import re + +import phonemizer +from unidecode import unidecode + +# To avoid excessive logging we set the log level of the phonemizer package to Critical +critical_logger = logging.getLogger("phonemizer") +critical_logger.setLevel(logging.CRITICAL) + +# Intializing the phonemizer globally significantly reduces the speed +# now the phonemizer is not initialising at every call +# Might be less flexible, but it is much-much faster +global_phonemizer = phonemizer.backend.EspeakBackend( + language="en-us", + preserve_punctuation=True, + with_stress=True, + language_switch="remove-flags", + logger=critical_logger, +) + + +# Regular expression matching whitespace: +_whitespace_re = re.compile(r"\s+") + +# List of (regular expression, replacement) pairs for abbreviations: +_abbreviations = [ + (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1]) + for x in [ + ("mrs", "misess"), + ("mr", "mister"), + ("dr", "doctor"), + ("st", "saint"), + ("co", "company"), + ("jr", "junior"), + ("maj", "major"), + ("gen", "general"), + ("drs", "doctors"), + ("rev", "reverend"), + ("lt", "lieutenant"), + ("hon", "honorable"), + ("sgt", "sergeant"), + ("capt", "captain"), + ("esq", "esquire"), + ("ltd", "limited"), + ("col", "colonel"), + ("ft", "fort"), + ] +] + + +def expand_abbreviations(text): + for regex, replacement in _abbreviations: + text = re.sub(regex, replacement, text) + return text + + +def lowercase(text): + return text.lower() + + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text) + + +def convert_to_ascii(text): + return unidecode(text) + + +def basic_cleaners(text): + """Basic pipeline that lowercases and collapses whitespace without transliteration.""" + text = lowercase(text) + text = collapse_whitespace(text) + return text + + +def transliteration_cleaners(text): + """Pipeline for non-English text that transliterates to ASCII.""" + text = convert_to_ascii(text) + text = lowercase(text) + text = collapse_whitespace(text) + return text + + +def english_cleaners2(text): + """Pipeline for English text, including abbreviation expansion. + punctuation + stress""" + text = convert_to_ascii(text) + text = lowercase(text) + text = expand_abbreviations(text) + phonemes = global_phonemizer.phonemize([text], strip=True, njobs=1)[0] + phonemes = collapse_whitespace(phonemes) + return phonemes + + +# I am removing this due to incompatibility with several version of python +# However, if you want to use it, you can uncomment it +# and install piper-phonemize with the following command: +# pip install piper-phonemize + +# import piper_phonemize +# def english_cleaners_piper(text): +# """Pipeline for English text, including abbreviation expansion. + punctuation + stress""" +# text = convert_to_ascii(text) +# text = lowercase(text) +# text = expand_abbreviations(text) +# phonemes = "".join(piper_phonemize.phonemize_espeak(text=text, voice="en-US")[0]) +# phonemes = collapse_whitespace(phonemes) +# return phonemes diff --git a/xinference/thirdparty/matcha/text/numbers.py b/xinference/thirdparty/matcha/text/numbers.py new file mode 100644 index 0000000000..f99a8686dc --- /dev/null +++ b/xinference/thirdparty/matcha/text/numbers.py @@ -0,0 +1,71 @@ +""" from https://github.com/keithito/tacotron """ + +import re + +import inflect + +_inflect = inflect.engine() +_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])") +_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)") +_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)") +_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)") +_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)") +_number_re = re.compile(r"[0-9]+") + + +def _remove_commas(m): + return m.group(1).replace(",", "") + + +def _expand_decimal_point(m): + return m.group(1).replace(".", " point ") + + +def _expand_dollars(m): + match = m.group(1) + parts = match.split(".") + if len(parts) > 2: + return match + " dollars" + dollars = int(parts[0]) if parts[0] else 0 + cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0 + if dollars and cents: + dollar_unit = "dollar" if dollars == 1 else "dollars" + cent_unit = "cent" if cents == 1 else "cents" + return f"{dollars} {dollar_unit}, {cents} {cent_unit}" + elif dollars: + dollar_unit = "dollar" if dollars == 1 else "dollars" + return f"{dollars} {dollar_unit}" + elif cents: + cent_unit = "cent" if cents == 1 else "cents" + return f"{cents} {cent_unit}" + else: + return "zero dollars" + + +def _expand_ordinal(m): + return _inflect.number_to_words(m.group(0)) + + +def _expand_number(m): + num = int(m.group(0)) + if num > 1000 and num < 3000: + if num == 2000: + return "two thousand" + elif num > 2000 and num < 2010: + return "two thousand " + _inflect.number_to_words(num % 100) + elif num % 100 == 0: + return _inflect.number_to_words(num // 100) + " hundred" + else: + return _inflect.number_to_words(num, andword="", zero="oh", group=2).replace(", ", " ") + else: + return _inflect.number_to_words(num, andword="") + + +def normalize_numbers(text): + text = re.sub(_comma_number_re, _remove_commas, text) + text = re.sub(_pounds_re, r"\1 pounds", text) + text = re.sub(_dollars_re, _expand_dollars, text) + text = re.sub(_decimal_number_re, _expand_decimal_point, text) + text = re.sub(_ordinal_re, _expand_ordinal, text) + text = re.sub(_number_re, _expand_number, text) + return text diff --git a/xinference/thirdparty/matcha/text/symbols.py b/xinference/thirdparty/matcha/text/symbols.py new file mode 100644 index 0000000000..7018df549a --- /dev/null +++ b/xinference/thirdparty/matcha/text/symbols.py @@ -0,0 +1,17 @@ +""" from https://github.com/keithito/tacotron + +Defines the set of symbols used in text input to the model. +""" +_pad = "_" +_punctuation = ';:,.!?¡¿—…"«»“” ' +_letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +_letters_ipa = ( + "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ" +) + + +# Export all symbols: +symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa) + +# Special symbol ids +SPACE_ID = symbols.index(" ") diff --git a/xinference/thirdparty/matcha/train.py b/xinference/thirdparty/matcha/train.py new file mode 100644 index 0000000000..d1d64c6c44 --- /dev/null +++ b/xinference/thirdparty/matcha/train.py @@ -0,0 +1,122 @@ +from typing import Any, Dict, List, Optional, Tuple + +import hydra +import lightning as L +import rootutils +from lightning import Callback, LightningDataModule, LightningModule, Trainer +from lightning.pytorch.loggers import Logger +from omegaconf import DictConfig + +from matcha import utils + +rootutils.setup_root(__file__, indicator=".project-root", pythonpath=True) +# ------------------------------------------------------------------------------------ # +# the setup_root above is equivalent to: +# - adding project root dir to PYTHONPATH +# (so you don't need to force user to install project as a package) +# (necessary before importing any local modules e.g. `from src import utils`) +# - setting up PROJECT_ROOT environment variable +# (which is used as a base for paths in "configs/paths/default.yaml") +# (this way all filepaths are the same no matter where you run the code) +# - loading environment variables from ".env" in root dir +# +# you can remove it if you: +# 1. either install project as a package or move entry files to project root dir +# 2. set `root_dir` to "." in "configs/paths/default.yaml" +# +# more info: https://github.com/ashleve/rootutils +# ------------------------------------------------------------------------------------ # + + +log = utils.get_pylogger(__name__) + + +@utils.task_wrapper +def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]: + """Trains the model. Can additionally evaluate on a testset, using best weights obtained during + training. + + This method is wrapped in optional @task_wrapper decorator, that controls the behavior during + failure. Useful for multiruns, saving info about the crash, etc. + + :param cfg: A DictConfig configuration composed by Hydra. + :return: A tuple with metrics and dict with all instantiated objects. + """ + # set seed for random number generators in pytorch, numpy and python.random + if cfg.get("seed"): + L.seed_everything(cfg.seed, workers=True) + + log.info(f"Instantiating datamodule <{cfg.data._target_}>") # pylint: disable=protected-access + datamodule: LightningDataModule = hydra.utils.instantiate(cfg.data) + + log.info(f"Instantiating model <{cfg.model._target_}>") # pylint: disable=protected-access + model: LightningModule = hydra.utils.instantiate(cfg.model) + + log.info("Instantiating callbacks...") + callbacks: List[Callback] = utils.instantiate_callbacks(cfg.get("callbacks")) + + log.info("Instantiating loggers...") + logger: List[Logger] = utils.instantiate_loggers(cfg.get("logger")) + + log.info(f"Instantiating trainer <{cfg.trainer._target_}>") # pylint: disable=protected-access + trainer: Trainer = hydra.utils.instantiate(cfg.trainer, callbacks=callbacks, logger=logger) + + object_dict = { + "cfg": cfg, + "datamodule": datamodule, + "model": model, + "callbacks": callbacks, + "logger": logger, + "trainer": trainer, + } + + if logger: + log.info("Logging hyperparameters!") + utils.log_hyperparameters(object_dict) + + if cfg.get("train"): + log.info("Starting training!") + trainer.fit(model=model, datamodule=datamodule, ckpt_path=cfg.get("ckpt_path")) + + train_metrics = trainer.callback_metrics + + if cfg.get("test"): + log.info("Starting testing!") + ckpt_path = trainer.checkpoint_callback.best_model_path + if ckpt_path == "": + log.warning("Best ckpt not found! Using current weights for testing...") + ckpt_path = None + trainer.test(model=model, datamodule=datamodule, ckpt_path=ckpt_path) + log.info(f"Best ckpt path: {ckpt_path}") + + test_metrics = trainer.callback_metrics + + # merge train and test metrics + metric_dict = {**train_metrics, **test_metrics} + + return metric_dict, object_dict + + +@hydra.main(version_base="1.3", config_path="../configs", config_name="train.yaml") +def main(cfg: DictConfig) -> Optional[float]: + """Main entry point for training. + + :param cfg: DictConfig configuration composed by Hydra. + :return: Optional[float] with optimized metric value. + """ + # apply extra utilities + # (e.g. ask for tags if none are provided in cfg, print cfg tree, etc.) + utils.extras(cfg) + + # train the model + metric_dict, _ = train(cfg) + + # safely retrieve metric value for hydra-based hyperparameter optimization + metric_value = utils.get_metric_value(metric_dict=metric_dict, metric_name=cfg.get("optimized_metric")) + + # return optimized metric + return metric_value + + +if __name__ == "__main__": + main() # pylint: disable=no-value-for-parameter diff --git a/xinference/thirdparty/matcha/utils/__init__.py b/xinference/thirdparty/matcha/utils/__init__.py new file mode 100644 index 0000000000..074db64611 --- /dev/null +++ b/xinference/thirdparty/matcha/utils/__init__.py @@ -0,0 +1,5 @@ +from matcha.utils.instantiators import instantiate_callbacks, instantiate_loggers +from matcha.utils.logging_utils import log_hyperparameters +from matcha.utils.pylogger import get_pylogger +from matcha.utils.rich_utils import enforce_tags, print_config_tree +from matcha.utils.utils import extras, get_metric_value, task_wrapper diff --git a/xinference/thirdparty/matcha/utils/audio.py b/xinference/thirdparty/matcha/utils/audio.py new file mode 100644 index 0000000000..0bcd74df47 --- /dev/null +++ b/xinference/thirdparty/matcha/utils/audio.py @@ -0,0 +1,82 @@ +import numpy as np +import torch +import torch.utils.data +from librosa.filters import mel as librosa_mel_fn +from scipy.io.wavfile import read + +MAX_WAV_VALUE = 32768.0 + + +def load_wav(full_path): + sampling_rate, data = read(full_path) + return data, sampling_rate + + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) + + +def dynamic_range_decompression(x, C=1): + return np.exp(x) / C + + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def dynamic_range_decompression_torch(x, C=1): + return torch.exp(x) / C + + +def spectral_normalize_torch(magnitudes): + output = dynamic_range_compression_torch(magnitudes) + return output + + +def spectral_de_normalize_torch(magnitudes): + output = dynamic_range_decompression_torch(magnitudes) + return output + + +mel_basis = {} +hann_window = {} + + +def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): + if torch.min(y) < -1.0: + print("min value is ", torch.min(y)) + if torch.max(y) > 1.0: + print("max value is ", torch.max(y)) + + global mel_basis, hann_window # pylint: disable=global-statement + if f"{str(fmax)}_{str(y.device)}" not in mel_basis: + mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax) + mel_basis[str(fmax) + "_" + str(y.device)] = torch.from_numpy(mel).float().to(y.device) + hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device) + + y = torch.nn.functional.pad( + y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect" + ) + y = y.squeeze(1) + + spec = torch.view_as_real( + torch.stft( + y, + n_fft, + hop_length=hop_size, + win_length=win_size, + window=hann_window[str(y.device)], + center=center, + pad_mode="reflect", + normalized=False, + onesided=True, + return_complex=True, + ) + ) + + spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) + + spec = torch.matmul(mel_basis[str(fmax) + "_" + str(y.device)], spec) + spec = spectral_normalize_torch(spec) + + return spec diff --git a/xinference/thirdparty/matcha/utils/generate_data_statistics.py b/xinference/thirdparty/matcha/utils/generate_data_statistics.py new file mode 100644 index 0000000000..49ed3c1b07 --- /dev/null +++ b/xinference/thirdparty/matcha/utils/generate_data_statistics.py @@ -0,0 +1,112 @@ +r""" +The file creates a pickle file where the values needed for loading of dataset is stored and the model can load it +when needed. + +Parameters from hparam.py will be used +""" +import argparse +import json +import os +import sys +from pathlib import Path + +import rootutils +import torch +from hydra import compose, initialize +from omegaconf import open_dict +from tqdm.auto import tqdm + +from matcha.data.text_mel_datamodule import TextMelDataModule +from matcha.utils.logging_utils import pylogger + +log = pylogger.get_pylogger(__name__) + + +def compute_data_statistics(data_loader: torch.utils.data.DataLoader, out_channels: int): + """Generate data mean and standard deviation helpful in data normalisation + + Args: + data_loader (torch.utils.data.Dataloader): _description_ + out_channels (int): mel spectrogram channels + """ + total_mel_sum = 0 + total_mel_sq_sum = 0 + total_mel_len = 0 + + for batch in tqdm(data_loader, leave=False): + mels = batch["y"] + mel_lengths = batch["y_lengths"] + + total_mel_len += torch.sum(mel_lengths) + total_mel_sum += torch.sum(mels) + total_mel_sq_sum += torch.sum(torch.pow(mels, 2)) + + data_mean = total_mel_sum / (total_mel_len * out_channels) + data_std = torch.sqrt((total_mel_sq_sum / (total_mel_len * out_channels)) - torch.pow(data_mean, 2)) + + return {"mel_mean": data_mean.item(), "mel_std": data_std.item()} + + +def main(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "-i", + "--input-config", + type=str, + default="vctk.yaml", + help="The name of the yaml config file under configs/data", + ) + + parser.add_argument( + "-b", + "--batch-size", + type=int, + default="256", + help="Can have increased batch size for faster computation", + ) + + parser.add_argument( + "-f", + "--force", + action="store_true", + default=False, + required=False, + help="force overwrite the file", + ) + args = parser.parse_args() + output_file = Path(args.input_config).with_suffix(".json") + + if os.path.exists(output_file) and not args.force: + print("File already exists. Use -f to force overwrite") + sys.exit(1) + + with initialize(version_base="1.3", config_path="../../configs/data"): + cfg = compose(config_name=args.input_config, return_hydra_config=True, overrides=[]) + + root_path = rootutils.find_root(search_from=__file__, indicator=".project-root") + + with open_dict(cfg): + del cfg["hydra"] + del cfg["_target_"] + cfg["data_statistics"] = None + cfg["seed"] = 1234 + cfg["batch_size"] = args.batch_size + cfg["train_filelist_path"] = str(os.path.join(root_path, cfg["train_filelist_path"])) + cfg["valid_filelist_path"] = str(os.path.join(root_path, cfg["valid_filelist_path"])) + cfg["load_durations"] = False + + text_mel_datamodule = TextMelDataModule(**cfg) + text_mel_datamodule.setup() + data_loader = text_mel_datamodule.train_dataloader() + log.info("Dataloader loaded! Now computing stats...") + params = compute_data_statistics(data_loader, cfg["n_feats"]) + print(params) + json.dump( + params, + open(output_file, "w"), + ) + + +if __name__ == "__main__": + main() diff --git a/xinference/thirdparty/matcha/utils/get_durations_from_trained_model.py b/xinference/thirdparty/matcha/utils/get_durations_from_trained_model.py new file mode 100644 index 0000000000..0fe2f35c42 --- /dev/null +++ b/xinference/thirdparty/matcha/utils/get_durations_from_trained_model.py @@ -0,0 +1,195 @@ +r""" +The file creates a pickle file where the values needed for loading of dataset is stored and the model can load it +when needed. + +Parameters from hparam.py will be used +""" +import argparse +import json +import os +import sys +from pathlib import Path + +import lightning +import numpy as np +import rootutils +import torch +from hydra import compose, initialize +from omegaconf import open_dict +from torch import nn +from tqdm.auto import tqdm + +from matcha.cli import get_device +from matcha.data.text_mel_datamodule import TextMelDataModule +from matcha.models.matcha_tts import MatchaTTS +from matcha.utils.logging_utils import pylogger +from matcha.utils.utils import get_phoneme_durations + +log = pylogger.get_pylogger(__name__) + + +def save_durations_to_folder( + attn: torch.Tensor, x_length: int, y_length: int, filepath: str, output_folder: Path, text: str +): + durations = attn.squeeze().sum(1)[:x_length].numpy() + durations_json = get_phoneme_durations(durations, text) + output = output_folder / Path(filepath).name.replace(".wav", ".npy") + with open(output.with_suffix(".json"), "w", encoding="utf-8") as f: + json.dump(durations_json, f, indent=4, ensure_ascii=False) + + np.save(output, durations) + + +@torch.inference_mode() +def compute_durations(data_loader: torch.utils.data.DataLoader, model: nn.Module, device: torch.device, output_folder): + """Generate durations from the model for each datapoint and save it in a folder + + Args: + data_loader (torch.utils.data.DataLoader): Dataloader + model (nn.Module): MatchaTTS model + device (torch.device): GPU or CPU + """ + + for batch in tqdm(data_loader, desc="🍵 Computing durations 🍵:"): + x, x_lengths = batch["x"], batch["x_lengths"] + y, y_lengths = batch["y"], batch["y_lengths"] + spks = batch["spks"] + x = x.to(device) + y = y.to(device) + x_lengths = x_lengths.to(device) + y_lengths = y_lengths.to(device) + spks = spks.to(device) if spks is not None else None + + _, _, _, attn = model( + x=x, + x_lengths=x_lengths, + y=y, + y_lengths=y_lengths, + spks=spks, + ) + attn = attn.cpu() + for i in range(attn.shape[0]): + save_durations_to_folder( + attn[i], + x_lengths[i].item(), + y_lengths[i].item(), + batch["filepaths"][i], + output_folder, + batch["x_texts"][i], + ) + + +def main(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "-i", + "--input-config", + type=str, + default="ljspeech.yaml", + help="The name of the yaml config file under configs/data", + ) + + parser.add_argument( + "-b", + "--batch-size", + type=int, + default="32", + help="Can have increased batch size for faster computation", + ) + + parser.add_argument( + "-f", + "--force", + action="store_true", + default=False, + required=False, + help="force overwrite the file", + ) + parser.add_argument( + "-c", + "--checkpoint_path", + type=str, + required=True, + help="Path to the checkpoint file to load the model from", + ) + + parser.add_argument( + "-o", + "--output-folder", + type=str, + default=None, + help="Output folder to save the data statistics", + ) + + parser.add_argument( + "--cpu", action="store_true", help="Use CPU for inference, not recommended (default: use GPU if available)" + ) + + args = parser.parse_args() + + with initialize(version_base="1.3", config_path="../../configs/data"): + cfg = compose(config_name=args.input_config, return_hydra_config=True, overrides=[]) + + root_path = rootutils.find_root(search_from=__file__, indicator=".project-root") + + with open_dict(cfg): + del cfg["hydra"] + del cfg["_target_"] + cfg["seed"] = 1234 + cfg["batch_size"] = args.batch_size + cfg["train_filelist_path"] = str(os.path.join(root_path, cfg["train_filelist_path"])) + cfg["valid_filelist_path"] = str(os.path.join(root_path, cfg["valid_filelist_path"])) + cfg["load_durations"] = False + + if args.output_folder is not None: + output_folder = Path(args.output_folder) + else: + output_folder = Path(cfg["train_filelist_path"]).parent / "durations" + + print(f"Output folder set to: {output_folder}") + + if os.path.exists(output_folder) and not args.force: + print("Folder already exists. Use -f to force overwrite") + sys.exit(1) + + output_folder.mkdir(parents=True, exist_ok=True) + + print(f"Preprocessing: {cfg['name']} from training filelist: {cfg['train_filelist_path']}") + print("Loading model...") + device = get_device(args) + model = MatchaTTS.load_from_checkpoint(args.checkpoint_path, map_location=device) + + text_mel_datamodule = TextMelDataModule(**cfg) + text_mel_datamodule.setup() + try: + print("Computing stats for training set if exists...") + train_dataloader = text_mel_datamodule.train_dataloader() + compute_durations(train_dataloader, model, device, output_folder) + except lightning.fabric.utilities.exceptions.MisconfigurationException: + print("No training set found") + + try: + print("Computing stats for validation set if exists...") + val_dataloader = text_mel_datamodule.val_dataloader() + compute_durations(val_dataloader, model, device, output_folder) + except lightning.fabric.utilities.exceptions.MisconfigurationException: + print("No validation set found") + + try: + print("Computing stats for test set if exists...") + test_dataloader = text_mel_datamodule.test_dataloader() + compute_durations(test_dataloader, model, device, output_folder) + except lightning.fabric.utilities.exceptions.MisconfigurationException: + print("No test set found") + + print(f"[+] Done! Data statistics saved to: {output_folder}") + + +if __name__ == "__main__": + # Helps with generating durations for the dataset to train other architectures + # that cannot learn to align due to limited size of dataset + # Example usage: + # python python matcha/utils/get_durations_from_trained_model.py -i ljspeech.yaml -c pretrained_model + # This will create a folder in data/processed_data/durations/ljspeech with the durations + main() diff --git a/xinference/thirdparty/matcha/utils/instantiators.py b/xinference/thirdparty/matcha/utils/instantiators.py new file mode 100644 index 0000000000..5547b4ed61 --- /dev/null +++ b/xinference/thirdparty/matcha/utils/instantiators.py @@ -0,0 +1,56 @@ +from typing import List + +import hydra +from lightning import Callback +from lightning.pytorch.loggers import Logger +from omegaconf import DictConfig + +from matcha.utils import pylogger + +log = pylogger.get_pylogger(__name__) + + +def instantiate_callbacks(callbacks_cfg: DictConfig) -> List[Callback]: + """Instantiates callbacks from config. + + :param callbacks_cfg: A DictConfig object containing callback configurations. + :return: A list of instantiated callbacks. + """ + callbacks: List[Callback] = [] + + if not callbacks_cfg: + log.warning("No callback configs found! Skipping..") + return callbacks + + if not isinstance(callbacks_cfg, DictConfig): + raise TypeError("Callbacks config must be a DictConfig!") + + for _, cb_conf in callbacks_cfg.items(): + if isinstance(cb_conf, DictConfig) and "_target_" in cb_conf: + log.info(f"Instantiating callback <{cb_conf._target_}>") # pylint: disable=protected-access + callbacks.append(hydra.utils.instantiate(cb_conf)) + + return callbacks + + +def instantiate_loggers(logger_cfg: DictConfig) -> List[Logger]: + """Instantiates loggers from config. + + :param logger_cfg: A DictConfig object containing logger configurations. + :return: A list of instantiated loggers. + """ + logger: List[Logger] = [] + + if not logger_cfg: + log.warning("No logger configs found! Skipping...") + return logger + + if not isinstance(logger_cfg, DictConfig): + raise TypeError("Logger config must be a DictConfig!") + + for _, lg_conf in logger_cfg.items(): + if isinstance(lg_conf, DictConfig) and "_target_" in lg_conf: + log.info(f"Instantiating logger <{lg_conf._target_}>") # pylint: disable=protected-access + logger.append(hydra.utils.instantiate(lg_conf)) + + return logger diff --git a/xinference/thirdparty/matcha/utils/logging_utils.py b/xinference/thirdparty/matcha/utils/logging_utils.py new file mode 100644 index 0000000000..1a12d1ddaf --- /dev/null +++ b/xinference/thirdparty/matcha/utils/logging_utils.py @@ -0,0 +1,53 @@ +from typing import Any, Dict + +from lightning.pytorch.utilities import rank_zero_only +from omegaconf import OmegaConf + +from matcha.utils import pylogger + +log = pylogger.get_pylogger(__name__) + + +@rank_zero_only +def log_hyperparameters(object_dict: Dict[str, Any]) -> None: + """Controls which config parts are saved by Lightning loggers. + + Additionally saves: + - Number of model parameters + + :param object_dict: A dictionary containing the following objects: + - `"cfg"`: A DictConfig object containing the main config. + - `"model"`: The Lightning model. + - `"trainer"`: The Lightning trainer. + """ + hparams = {} + + cfg = OmegaConf.to_container(object_dict["cfg"]) + model = object_dict["model"] + trainer = object_dict["trainer"] + + if not trainer.logger: + log.warning("Logger not found! Skipping hyperparameter logging...") + return + + hparams["model"] = cfg["model"] + + # save number of model parameters + hparams["model/params/total"] = sum(p.numel() for p in model.parameters()) + hparams["model/params/trainable"] = sum(p.numel() for p in model.parameters() if p.requires_grad) + hparams["model/params/non_trainable"] = sum(p.numel() for p in model.parameters() if not p.requires_grad) + + hparams["data"] = cfg["data"] + hparams["trainer"] = cfg["trainer"] + + hparams["callbacks"] = cfg.get("callbacks") + hparams["extras"] = cfg.get("extras") + + hparams["task_name"] = cfg.get("task_name") + hparams["tags"] = cfg.get("tags") + hparams["ckpt_path"] = cfg.get("ckpt_path") + hparams["seed"] = cfg.get("seed") + + # send hparams to all loggers + for logger in trainer.loggers: + logger.log_hyperparams(hparams) diff --git a/xinference/thirdparty/matcha/utils/model.py b/xinference/thirdparty/matcha/utils/model.py new file mode 100644 index 0000000000..869cc6092f --- /dev/null +++ b/xinference/thirdparty/matcha/utils/model.py @@ -0,0 +1,90 @@ +""" from https://github.com/jaywalnut310/glow-tts """ + +import numpy as np +import torch + + +def sequence_mask(length, max_length=None): + if max_length is None: + max_length = length.max() + x = torch.arange(max_length, dtype=length.dtype, device=length.device) + return x.unsqueeze(0) < length.unsqueeze(1) + + +def fix_len_compatibility(length, num_downsamplings_in_unet=2): + factor = torch.scalar_tensor(2).pow(num_downsamplings_in_unet) + length = (length / factor).ceil() * factor + if not torch.onnx.is_in_onnx_export(): + return length.int().item() + else: + return length + + +def convert_pad_shape(pad_shape): + inverted_shape = pad_shape[::-1] + pad_shape = [item for sublist in inverted_shape for item in sublist] + return pad_shape + + +def generate_path(duration, mask): + device = duration.device + + b, t_x, t_y = mask.shape + cum_duration = torch.cumsum(duration, 1) + path = torch.zeros(b, t_x, t_y, dtype=mask.dtype).to(device=device) + + cum_duration_flat = cum_duration.view(b * t_x) + path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) + path = path.view(b, t_x, t_y) + path = path - torch.nn.functional.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] + path = path * mask + return path + + +def duration_loss(logw, logw_, lengths): + loss = torch.sum((logw - logw_) ** 2) / torch.sum(lengths) + return loss + + +def normalize(data, mu, std): + if not isinstance(mu, (float, int)): + if isinstance(mu, list): + mu = torch.tensor(mu, dtype=data.dtype, device=data.device) + elif isinstance(mu, torch.Tensor): + mu = mu.to(data.device) + elif isinstance(mu, np.ndarray): + mu = torch.from_numpy(mu).to(data.device) + mu = mu.unsqueeze(-1) + + if not isinstance(std, (float, int)): + if isinstance(std, list): + std = torch.tensor(std, dtype=data.dtype, device=data.device) + elif isinstance(std, torch.Tensor): + std = std.to(data.device) + elif isinstance(std, np.ndarray): + std = torch.from_numpy(std).to(data.device) + std = std.unsqueeze(-1) + + return (data - mu) / std + + +def denormalize(data, mu, std): + if not isinstance(mu, float): + if isinstance(mu, list): + mu = torch.tensor(mu, dtype=data.dtype, device=data.device) + elif isinstance(mu, torch.Tensor): + mu = mu.to(data.device) + elif isinstance(mu, np.ndarray): + mu = torch.from_numpy(mu).to(data.device) + mu = mu.unsqueeze(-1) + + if not isinstance(std, float): + if isinstance(std, list): + std = torch.tensor(std, dtype=data.dtype, device=data.device) + elif isinstance(std, torch.Tensor): + std = std.to(data.device) + elif isinstance(std, np.ndarray): + std = torch.from_numpy(std).to(data.device) + std = std.unsqueeze(-1) + + return data * std + mu diff --git a/xinference/thirdparty/matcha/utils/monotonic_align/__init__.py b/xinference/thirdparty/matcha/utils/monotonic_align/__init__.py new file mode 100644 index 0000000000..eee6e0d47c --- /dev/null +++ b/xinference/thirdparty/matcha/utils/monotonic_align/__init__.py @@ -0,0 +1,22 @@ +import numpy as np +import torch + +from matcha.utils.monotonic_align.core import maximum_path_c + + +def maximum_path(value, mask): + """Cython optimised version. + value: [b, t_x, t_y] + mask: [b, t_x, t_y] + """ + value = value * mask + device = value.device + dtype = value.dtype + value = value.data.cpu().numpy().astype(np.float32) + path = np.zeros_like(value).astype(np.int32) + mask = mask.data.cpu().numpy() + + t_x_max = mask.sum(1)[:, 0].astype(np.int32) + t_y_max = mask.sum(2)[:, 0].astype(np.int32) + maximum_path_c(path, value, t_x_max, t_y_max) + return torch.from_numpy(path).to(device=device, dtype=dtype) diff --git a/xinference/thirdparty/matcha/utils/monotonic_align/core.pyx b/xinference/thirdparty/matcha/utils/monotonic_align/core.pyx new file mode 100644 index 0000000000..091fcc3a50 --- /dev/null +++ b/xinference/thirdparty/matcha/utils/monotonic_align/core.pyx @@ -0,0 +1,47 @@ +import numpy as np + +cimport cython +cimport numpy as np + +from cython.parallel import prange + + +@cython.boundscheck(False) +@cython.wraparound(False) +cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_x, int t_y, float max_neg_val) nogil: + cdef int x + cdef int y + cdef float v_prev + cdef float v_cur + cdef float tmp + cdef int index = t_x - 1 + + for y in range(t_y): + for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)): + if x == y: + v_cur = max_neg_val + else: + v_cur = value[x, y-1] + if x == 0: + if y == 0: + v_prev = 0. + else: + v_prev = max_neg_val + else: + v_prev = value[x-1, y-1] + value[x, y] = max(v_cur, v_prev) + value[x, y] + + for y in range(t_y - 1, -1, -1): + path[index, y] = 1 + if index != 0 and (index == y or value[index, y-1] < value[index-1, y-1]): + index = index - 1 + + +@cython.boundscheck(False) +@cython.wraparound(False) +cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_xs, int[::1] t_ys, float max_neg_val=-1e9) nogil: + cdef int b = values.shape[0] + + cdef int i + for i in prange(b, nogil=True): + maximum_path_each(paths[i], values[i], t_xs[i], t_ys[i], max_neg_val) diff --git a/xinference/thirdparty/matcha/utils/monotonic_align/setup.py b/xinference/thirdparty/matcha/utils/monotonic_align/setup.py new file mode 100644 index 0000000000..f22bc6a35a --- /dev/null +++ b/xinference/thirdparty/matcha/utils/monotonic_align/setup.py @@ -0,0 +1,7 @@ +# from distutils.core import setup +# from Cython.Build import cythonize +# import numpy + +# setup(name='monotonic_align', +# ext_modules=cythonize("core.pyx"), +# include_dirs=[numpy.get_include()]) diff --git a/xinference/thirdparty/matcha/utils/pylogger.py b/xinference/thirdparty/matcha/utils/pylogger.py new file mode 100644 index 0000000000..6160067802 --- /dev/null +++ b/xinference/thirdparty/matcha/utils/pylogger.py @@ -0,0 +1,21 @@ +import logging + +from lightning.pytorch.utilities import rank_zero_only + + +def get_pylogger(name: str = __name__) -> logging.Logger: + """Initializes a multi-GPU-friendly python command line logger. + + :param name: The name of the logger, defaults to ``__name__``. + + :return: A logger object. + """ + logger = logging.getLogger(name) + + # this ensures all logging levels get marked with the rank zero decorator + # otherwise logs would get multiplied for each GPU process in multi-GPU setup + logging_levels = ("debug", "info", "warning", "error", "exception", "fatal", "critical") + for level in logging_levels: + setattr(logger, level, rank_zero_only(getattr(logger, level))) + + return logger diff --git a/xinference/thirdparty/matcha/utils/rich_utils.py b/xinference/thirdparty/matcha/utils/rich_utils.py new file mode 100644 index 0000000000..f602f6e935 --- /dev/null +++ b/xinference/thirdparty/matcha/utils/rich_utils.py @@ -0,0 +1,101 @@ +from pathlib import Path +from typing import Sequence + +import rich +import rich.syntax +import rich.tree +from hydra.core.hydra_config import HydraConfig +from lightning.pytorch.utilities import rank_zero_only +from omegaconf import DictConfig, OmegaConf, open_dict +from rich.prompt import Prompt + +from matcha.utils import pylogger + +log = pylogger.get_pylogger(__name__) + + +@rank_zero_only +def print_config_tree( + cfg: DictConfig, + print_order: Sequence[str] = ( + "data", + "model", + "callbacks", + "logger", + "trainer", + "paths", + "extras", + ), + resolve: bool = False, + save_to_file: bool = False, +) -> None: + """Prints the contents of a DictConfig as a tree structure using the Rich library. + + :param cfg: A DictConfig composed by Hydra. + :param print_order: Determines in what order config components are printed. Default is ``("data", "model", + "callbacks", "logger", "trainer", "paths", "extras")``. + :param resolve: Whether to resolve reference fields of DictConfig. Default is ``False``. + :param save_to_file: Whether to export config to the hydra output folder. Default is ``False``. + """ + style = "dim" + tree = rich.tree.Tree("CONFIG", style=style, guide_style=style) + + queue = [] + + # add fields from `print_order` to queue + for field in print_order: + _ = ( + queue.append(field) + if field in cfg + else log.warning(f"Field '{field}' not found in config. Skipping '{field}' config printing...") + ) + + # add all the other fields to queue (not specified in `print_order`) + for field in cfg: + if field not in queue: + queue.append(field) + + # generate config tree from queue + for field in queue: + branch = tree.add(field, style=style, guide_style=style) + + config_group = cfg[field] + if isinstance(config_group, DictConfig): + branch_content = OmegaConf.to_yaml(config_group, resolve=resolve) + else: + branch_content = str(config_group) + + branch.add(rich.syntax.Syntax(branch_content, "yaml")) + + # print config tree + rich.print(tree) + + # save config tree to file + if save_to_file: + with open(Path(cfg.paths.output_dir, "config_tree.log"), "w") as file: + rich.print(tree, file=file) + + +@rank_zero_only +def enforce_tags(cfg: DictConfig, save_to_file: bool = False) -> None: + """Prompts user to input tags from command line if no tags are provided in config. + + :param cfg: A DictConfig composed by Hydra. + :param save_to_file: Whether to export tags to the hydra output folder. Default is ``False``. + """ + if not cfg.get("tags"): + if "id" in HydraConfig().cfg.hydra.job: + raise ValueError("Specify tags before launching a multirun!") + + log.warning("No tags provided in config. Prompting user to input tags...") + tags = Prompt.ask("Enter a list of comma separated tags", default="dev") + tags = [t.strip() for t in tags.split(",") if t != ""] + + with open_dict(cfg): + cfg.tags = tags + + log.info(f"Tags: {cfg.tags}") + + if save_to_file: + with open(Path(cfg.paths.output_dir, "tags.log"), "w") as file: + rich.print(cfg.tags, file=file) diff --git a/xinference/thirdparty/matcha/utils/utils.py b/xinference/thirdparty/matcha/utils/utils.py new file mode 100644 index 0000000000..fc3a48ec2b --- /dev/null +++ b/xinference/thirdparty/matcha/utils/utils.py @@ -0,0 +1,259 @@ +import os +import sys +import warnings +from importlib.util import find_spec +from math import ceil +from pathlib import Path +from typing import Any, Callable, Dict, Tuple + +import gdown +import matplotlib.pyplot as plt +import numpy as np +import torch +import wget +from omegaconf import DictConfig + +from matcha.utils import pylogger, rich_utils + +log = pylogger.get_pylogger(__name__) + + +def extras(cfg: DictConfig) -> None: + """Applies optional utilities before the task is started. + + Utilities: + - Ignoring python warnings + - Setting tags from command line + - Rich config printing + + :param cfg: A DictConfig object containing the config tree. + """ + # return if no `extras` config + if not cfg.get("extras"): + log.warning("Extras config not found! ") + return + + # disable python warnings + if cfg.extras.get("ignore_warnings"): + log.info("Disabling python warnings! ") + warnings.filterwarnings("ignore") + + # prompt user to input tags from command line if none are provided in the config + if cfg.extras.get("enforce_tags"): + log.info("Enforcing tags! ") + rich_utils.enforce_tags(cfg, save_to_file=True) + + # pretty print config tree using Rich library + if cfg.extras.get("print_config"): + log.info("Printing config tree with Rich! ") + rich_utils.print_config_tree(cfg, resolve=True, save_to_file=True) + + +def task_wrapper(task_func: Callable) -> Callable: + """Optional decorator that controls the failure behavior when executing the task function. + + This wrapper can be used to: + - make sure loggers are closed even if the task function raises an exception (prevents multirun failure) + - save the exception to a `.log` file + - mark the run as failed with a dedicated file in the `logs/` folder (so we can find and rerun it later) + - etc. (adjust depending on your needs) + + Example: + ``` + @utils.task_wrapper + def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]: + ... + return metric_dict, object_dict + ``` + + :param task_func: The task function to be wrapped. + + :return: The wrapped task function. + """ + + def wrap(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]: + # execute the task + try: + metric_dict, object_dict = task_func(cfg=cfg) + + # things to do if exception occurs + except Exception as ex: + # save exception to `.log` file + log.exception("") + + # some hyperparameter combinations might be invalid or cause out-of-memory errors + # so when using hparam search plugins like Optuna, you might want to disable + # raising the below exception to avoid multirun failure + raise ex + + # things to always do after either success or exception + finally: + # display output dir path in terminal + log.info(f"Output dir: {cfg.paths.output_dir}") + + # always close wandb run (even if exception occurs so multirun won't fail) + if find_spec("wandb"): # check if wandb is installed + import wandb + + if wandb.run: + log.info("Closing wandb!") + wandb.finish() + + return metric_dict, object_dict + + return wrap + + +def get_metric_value(metric_dict: Dict[str, Any], metric_name: str) -> float: + """Safely retrieves value of the metric logged in LightningModule. + + :param metric_dict: A dict containing metric values. + :param metric_name: The name of the metric to retrieve. + :return: The value of the metric. + """ + if not metric_name: + log.info("Metric name is None! Skipping metric value retrieval...") + return None + + if metric_name not in metric_dict: + raise ValueError( + f"Metric value not found! \n" + "Make sure metric name logged in LightningModule is correct!\n" + "Make sure `optimized_metric` name in `hparams_search` config is correct!" + ) + + metric_value = metric_dict[metric_name].item() + log.info(f"Retrieved metric value! <{metric_name}={metric_value}>") + + return metric_value + + +def intersperse(lst, item): + # Adds blank symbol + result = [item] * (len(lst) * 2 + 1) + result[1::2] = lst + return result + + +def save_figure_to_numpy(fig): + data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="") + data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) + return data + + +def plot_tensor(tensor): + plt.style.use("default") + fig, ax = plt.subplots(figsize=(12, 3)) + im = ax.imshow(tensor, aspect="auto", origin="lower", interpolation="none") + plt.colorbar(im, ax=ax) + plt.tight_layout() + fig.canvas.draw() + data = save_figure_to_numpy(fig) + plt.close() + return data + + +def save_plot(tensor, savepath): + plt.style.use("default") + fig, ax = plt.subplots(figsize=(12, 3)) + im = ax.imshow(tensor, aspect="auto", origin="lower", interpolation="none") + plt.colorbar(im, ax=ax) + plt.tight_layout() + fig.canvas.draw() + plt.savefig(savepath) + plt.close() + + +def to_numpy(tensor): + if isinstance(tensor, np.ndarray): + return tensor + elif isinstance(tensor, torch.Tensor): + return tensor.detach().cpu().numpy() + elif isinstance(tensor, list): + return np.array(tensor) + else: + raise TypeError("Unsupported type for conversion to numpy array") + + +def get_user_data_dir(appname="matcha_tts"): + """ + Args: + appname (str): Name of application + + Returns: + Path: path to user data directory + """ + + MATCHA_HOME = os.environ.get("MATCHA_HOME") + if MATCHA_HOME is not None: + ans = Path(MATCHA_HOME).expanduser().resolve(strict=False) + elif sys.platform == "win32": + import winreg # pylint: disable=import-outside-toplevel + + key = winreg.OpenKey( + winreg.HKEY_CURRENT_USER, + r"Software\Microsoft\Windows\CurrentVersion\Explorer\Shell Folders", + ) + dir_, _ = winreg.QueryValueEx(key, "Local AppData") + ans = Path(dir_).resolve(strict=False) + elif sys.platform == "darwin": + ans = Path("~/Library/Application Support/").expanduser() + else: + ans = Path.home().joinpath(".local/share") + + final_path = ans.joinpath(appname) + final_path.mkdir(parents=True, exist_ok=True) + return final_path + + +def assert_model_downloaded(checkpoint_path, url, use_wget=True): + if Path(checkpoint_path).exists(): + log.debug(f"[+] Model already present at {checkpoint_path}!") + print(f"[+] Model already present at {checkpoint_path}!") + return + log.info(f"[-] Model not found at {checkpoint_path}! Will download it") + print(f"[-] Model not found at {checkpoint_path}! Will download it") + checkpoint_path = str(checkpoint_path) + if not use_wget: + gdown.download(url=url, output=checkpoint_path, quiet=False, fuzzy=True) + else: + wget.download(url=url, out=checkpoint_path) + + +def get_phoneme_durations(durations, phones): + prev = durations[0] + merged_durations = [] + # Convolve with stride 2 + for i in range(1, len(durations), 2): + if i == len(durations) - 2: + # if it is last take full value + next_half = durations[i + 1] + else: + next_half = ceil(durations[i + 1] / 2) + + curr = prev + durations[i] + next_half + prev = durations[i + 1] - next_half + merged_durations.append(curr) + + assert len(phones) == len(merged_durations) + assert len(merged_durations) == (len(durations) - 1) // 2 + + merged_durations = torch.cumsum(torch.tensor(merged_durations), 0, dtype=torch.long) + start = torch.tensor(0) + duration_json = [] + for i, duration in enumerate(merged_durations): + duration_json.append( + { + phones[i]: { + "starttime": start.item(), + "endtime": duration.item(), + "duration": duration.item() - start.item(), + } + } + ) + start = duration + + assert list(duration_json[-1].values())[0]["endtime"] == sum( + durations + ), f"{list(duration_json[-1].values())[0]['endtime'], sum(durations)}" + return duration_json diff --git a/xinference/thirdparty/mlx/__init__.py b/xinference/thirdparty/mlx/__init__.py new file mode 100644 index 0000000000..37f6558d95 --- /dev/null +++ b/xinference/thirdparty/mlx/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2022-2023 XProbe Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/xinference/thirdparty/mlx/flux/__init__.py b/xinference/thirdparty/mlx/flux/__init__.py new file mode 100644 index 0000000000..b1122d75d6 --- /dev/null +++ b/xinference/thirdparty/mlx/flux/__init__.py @@ -0,0 +1,15 @@ +# Copyright © 2024 Apple Inc. + +from .datasets import Dataset, load_dataset +from .flux import FluxPipeline +from .lora import LoRALinear +from .sampler import FluxSampler +from .trainer import Trainer +from .utils import ( + load_ae, + load_clip, + load_clip_tokenizer, + load_flow_model, + load_t5, + load_t5_tokenizer, +) diff --git a/xinference/thirdparty/mlx/flux/autoencoder.py b/xinference/thirdparty/mlx/flux/autoencoder.py new file mode 100644 index 0000000000..6332bb570b --- /dev/null +++ b/xinference/thirdparty/mlx/flux/autoencoder.py @@ -0,0 +1,357 @@ +# Copyright © 2024 Apple Inc. + +from dataclasses import dataclass +from typing import List + +import mlx.core as mx +import mlx.nn as nn +from mlx.nn.layers.upsample import upsample_nearest + + +@dataclass +class AutoEncoderParams: + resolution: int + in_channels: int + ch: int + out_ch: int + ch_mult: List[int] + num_res_blocks: int + z_channels: int + scale_factor: float + shift_factor: float + + +class AttnBlock(nn.Module): + def __init__(self, in_channels: int): + super().__init__() + self.in_channels = in_channels + + self.norm = nn.GroupNorm( + num_groups=32, + dims=in_channels, + eps=1e-6, + affine=True, + pytorch_compatible=True, + ) + self.q = nn.Linear(in_channels, in_channels) + self.k = nn.Linear(in_channels, in_channels) + self.v = nn.Linear(in_channels, in_channels) + self.proj_out = nn.Linear(in_channels, in_channels) + + def __call__(self, x: mx.array) -> mx.array: + B, H, W, C = x.shape + + y = x.reshape(B, 1, -1, C) + y = self.norm(y) + q = self.q(y) + k = self.k(y) + v = self.v(y) + y = mx.fast.scaled_dot_product_attention(q, k, v, scale=C ** (-0.5)) + y = self.proj_out(y) + + return x + y.reshape(B, H, W, C) + + +class ResnetBlock(nn.Module): + def __init__(self, in_channels: int, out_channels: int): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + + self.norm1 = nn.GroupNorm( + num_groups=32, + dims=in_channels, + eps=1e-6, + affine=True, + pytorch_compatible=True, + ) + self.conv1 = nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + self.norm2 = nn.GroupNorm( + num_groups=32, + dims=out_channels, + eps=1e-6, + affine=True, + pytorch_compatible=True, + ) + self.conv2 = nn.Conv2d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + if self.in_channels != self.out_channels: + self.nin_shortcut = nn.Linear(in_channels, out_channels) + + def __call__(self, x): + h = x + h = self.norm1(h) + h = nn.silu(h) + h = self.conv1(h) + + h = self.norm2(h) + h = nn.silu(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + x = self.nin_shortcut(x) + + return x + h + + +class Downsample(nn.Module): + def __init__(self, in_channels: int): + super().__init__() + self.conv = nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=2, padding=0 + ) + + def __call__(self, x: mx.array): + x = mx.pad(x, [(0, 0), (0, 1), (0, 1), (0, 0)]) + x = self.conv(x) + return x + + +class Upsample(nn.Module): + def __init__(self, in_channels: int): + super().__init__() + self.conv = nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=1, padding=1 + ) + + def __call__(self, x: mx.array): + x = upsample_nearest(x, (2, 2)) + x = self.conv(x) + return x + + +class Encoder(nn.Module): + def __init__( + self, + resolution: int, + in_channels: int, + ch: int, + ch_mult: list[int], + num_res_blocks: int, + z_channels: int, + ): + super().__init__() + self.ch = ch + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + # downsampling + self.conv_in = nn.Conv2d( + in_channels, self.ch, kernel_size=3, stride=1, padding=1 + ) + + curr_res = resolution + in_ch_mult = (1,) + tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = [] + block_in = self.ch + for i_level in range(self.num_resolutions): + block = [] + attn = [] # TODO: Remove the attn, nobody appends anything to it + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for _ in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, out_channels=block_out)) + block_in = block_out + down = {} + down["block"] = block + down["attn"] = attn + if i_level != self.num_resolutions - 1: + down["downsample"] = Downsample(block_in) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = {} + self.mid["block_1"] = ResnetBlock(in_channels=block_in, out_channels=block_in) + self.mid["attn_1"] = AttnBlock(block_in) + self.mid["block_2"] = ResnetBlock(in_channels=block_in, out_channels=block_in) + + # end + self.norm_out = nn.GroupNorm( + num_groups=32, dims=block_in, eps=1e-6, affine=True, pytorch_compatible=True + ) + self.conv_out = nn.Conv2d( + block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1 + ) + + def __call__(self, x: mx.array): + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level]["block"][i_block](hs[-1]) + + # TODO: Remove the attn + if len(self.down[i_level]["attn"]) > 0: + h = self.down[i_level]["attn"][i_block](h) + + hs.append(h) + + if i_level != self.num_resolutions - 1: + hs.append(self.down[i_level]["downsample"](hs[-1])) + + # middle + h = hs[-1] + h = self.mid["block_1"](h) + h = self.mid["attn_1"](h) + h = self.mid["block_2"](h) + + # end + h = self.norm_out(h) + h = nn.silu(h) + h = self.conv_out(h) + + return h + + +class Decoder(nn.Module): + def __init__( + self, + ch: int, + out_ch: int, + ch_mult: list[int], + num_res_blocks: int, + in_channels: int, + resolution: int, + z_channels: int, + ): + super().__init__() + self.ch = ch + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.ffactor = 2 ** (self.num_resolutions - 1) + + # compute in_ch_mult, block_in and curr_res at lowest res + block_in = ch * ch_mult[self.num_resolutions - 1] + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + + # z to block_in + self.conv_in = nn.Conv2d( + z_channels, block_in, kernel_size=3, stride=1, padding=1 + ) + + # middle + self.mid = {} + self.mid["block_1"] = ResnetBlock(in_channels=block_in, out_channels=block_in) + self.mid["attn_1"] = AttnBlock(block_in) + self.mid["block_2"] = ResnetBlock(in_channels=block_in, out_channels=block_in) + + # upsampling + self.up = [] + for i_level in reversed(range(self.num_resolutions)): + block = [] + attn = [] # TODO: Remove the attn, nobody appends anything to it + + block_out = ch * ch_mult[i_level] + for _ in range(self.num_res_blocks + 1): + block.append(ResnetBlock(in_channels=block_in, out_channels=block_out)) + block_in = block_out + up = {} + up["block"] = block + up["attn"] = attn + if i_level != 0: + up["upsample"] = Upsample(block_in) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = nn.GroupNorm( + num_groups=32, dims=block_in, eps=1e-6, affine=True, pytorch_compatible=True + ) + self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) + + def __call__(self, z: mx.array): + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid["block_1"](h) + h = self.mid["attn_1"](h) + h = self.mid["block_2"](h) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level]["block"][i_block](h) + + # TODO: Remove the attn + if len(self.up[i_level]["attn"]) > 0: + h = self.up[i_level]["attn"][i_block](h) + + if i_level != 0: + h = self.up[i_level]["upsample"](h) + + # end + h = self.norm_out(h) + h = nn.silu(h) + h = self.conv_out(h) + + return h + + +class DiagonalGaussian(nn.Module): + def __call__(self, z: mx.array): + mean, logvar = mx.split(z, 2, axis=-1) + if self.training: + std = mx.exp(0.5 * logvar) + eps = mx.random.normal(shape=z.shape, dtype=z.dtype) + return mean + std * eps + else: + return mean + + +class AutoEncoder(nn.Module): + def __init__(self, params: AutoEncoderParams): + super().__init__() + self.encoder = Encoder( + resolution=params.resolution, + in_channels=params.in_channels, + ch=params.ch, + ch_mult=params.ch_mult, + num_res_blocks=params.num_res_blocks, + z_channels=params.z_channels, + ) + self.decoder = Decoder( + resolution=params.resolution, + in_channels=params.in_channels, + ch=params.ch, + out_ch=params.out_ch, + ch_mult=params.ch_mult, + num_res_blocks=params.num_res_blocks, + z_channels=params.z_channels, + ) + self.reg = DiagonalGaussian() + + self.scale_factor = params.scale_factor + self.shift_factor = params.shift_factor + + def sanitize(self, weights): + new_weights = {} + for k, w in weights.items(): + if w.ndim == 4: + w = w.transpose(0, 2, 3, 1) + w = w.reshape(-1).reshape(w.shape) + if w.shape[1:3] == (1, 1): + w = w.squeeze((1, 2)) + new_weights[k] = w + return new_weights + + def encode(self, x: mx.array): + z = self.reg(self.encoder(x)) + z = self.scale_factor * (z - self.shift_factor) + return z + + def decode(self, z: mx.array): + z = z / self.scale_factor + self.shift_factor + return self.decoder(z) + + def __call__(self, x: mx.array): + return self.decode(self.encode(x)) diff --git a/xinference/thirdparty/mlx/flux/clip.py b/xinference/thirdparty/mlx/flux/clip.py new file mode 100644 index 0000000000..d5a30dbf34 --- /dev/null +++ b/xinference/thirdparty/mlx/flux/clip.py @@ -0,0 +1,154 @@ +# Copyright © 2024 Apple Inc. + +from dataclasses import dataclass +from typing import List, Optional + +import mlx.core as mx +import mlx.nn as nn + +_ACTIVATIONS = {"quick_gelu": nn.gelu_fast_approx, "gelu": nn.gelu} + + +@dataclass +class CLIPTextModelConfig: + num_layers: int = 23 + model_dims: int = 1024 + num_heads: int = 16 + max_length: int = 77 + vocab_size: int = 49408 + hidden_act: str = "quick_gelu" + + @classmethod + def from_dict(cls, config): + return cls( + num_layers=config["num_hidden_layers"], + model_dims=config["hidden_size"], + num_heads=config["num_attention_heads"], + max_length=config["max_position_embeddings"], + vocab_size=config["vocab_size"], + hidden_act=config["hidden_act"], + ) + + +@dataclass +class CLIPOutput: + # The last_hidden_state indexed at the EOS token and possibly projected if + # the model has a projection layer + pooled_output: Optional[mx.array] = None + + # The full sequence output of the transformer after the final layernorm + last_hidden_state: Optional[mx.array] = None + + # A list of hidden states corresponding to the outputs of the transformer layers + hidden_states: Optional[List[mx.array]] = None + + +class CLIPEncoderLayer(nn.Module): + """The transformer encoder layer from CLIP.""" + + def __init__(self, model_dims: int, num_heads: int, activation: str): + super().__init__() + + self.layer_norm1 = nn.LayerNorm(model_dims) + self.layer_norm2 = nn.LayerNorm(model_dims) + + self.attention = nn.MultiHeadAttention(model_dims, num_heads, bias=True) + + self.linear1 = nn.Linear(model_dims, 4 * model_dims) + self.linear2 = nn.Linear(4 * model_dims, model_dims) + + self.act = _ACTIVATIONS[activation] + + def __call__(self, x, attn_mask=None): + y = self.layer_norm1(x) + y = self.attention(y, y, y, attn_mask) + x = y + x + + y = self.layer_norm2(x) + y = self.linear1(y) + y = self.act(y) + y = self.linear2(y) + x = y + x + + return x + + +class CLIPTextModel(nn.Module): + """Implements the text encoder transformer from CLIP.""" + + def __init__(self, config: CLIPTextModelConfig): + super().__init__() + + self.token_embedding = nn.Embedding(config.vocab_size, config.model_dims) + self.position_embedding = nn.Embedding(config.max_length, config.model_dims) + self.layers = [ + CLIPEncoderLayer(config.model_dims, config.num_heads, config.hidden_act) + for i in range(config.num_layers) + ] + self.final_layer_norm = nn.LayerNorm(config.model_dims) + + def _get_mask(self, N, dtype): + indices = mx.arange(N) + mask = indices[:, None] < indices[None] + mask = mask.astype(dtype) * (-6e4 if dtype == mx.float16 else -1e9) + return mask + + def sanitize(self, weights): + new_weights = {} + for key, w in weights.items(): + # Remove prefixes + if key.startswith("text_model."): + key = key[11:] + if key.startswith("embeddings."): + key = key[11:] + if key.startswith("encoder."): + key = key[8:] + + # Map attention layers + if "self_attn." in key: + key = key.replace("self_attn.", "attention.") + if "q_proj." in key: + key = key.replace("q_proj.", "query_proj.") + if "k_proj." in key: + key = key.replace("k_proj.", "key_proj.") + if "v_proj." in key: + key = key.replace("v_proj.", "value_proj.") + + # Map ffn layers + if "mlp.fc1" in key: + key = key.replace("mlp.fc1", "linear1") + if "mlp.fc2" in key: + key = key.replace("mlp.fc2", "linear2") + + new_weights[key] = w + + return new_weights + + def __call__(self, x): + # Extract some shapes + B, N = x.shape + eos_tokens = x.argmax(-1) + + # Compute the embeddings + x = self.token_embedding(x) + x = x + self.position_embedding.weight[:N] + + # Compute the features from the transformer + mask = self._get_mask(N, x.dtype) + hidden_states = [] + for l in self.layers: + x = l(x, mask) + hidden_states.append(x) + + # Apply the final layernorm and return + x = self.final_layer_norm(x) + last_hidden_state = x + + # Select the EOS token + pooled_output = x[mx.arange(len(x)), eos_tokens] + + return CLIPOutput( + pooled_output=pooled_output, + last_hidden_state=last_hidden_state, + hidden_states=hidden_states, + ) diff --git a/xinference/thirdparty/mlx/flux/datasets.py b/xinference/thirdparty/mlx/flux/datasets.py new file mode 100644 index 0000000000..d31a09f179 --- /dev/null +++ b/xinference/thirdparty/mlx/flux/datasets.py @@ -0,0 +1,75 @@ +import json +from pathlib import Path + +from PIL import Image + + +class Dataset: + def __getitem__(self, index: int): + raise NotImplementedError() + + def __len__(self): + raise NotImplementedError() + + +class LocalDataset(Dataset): + prompt_key = "prompt" + + def __init__(self, dataset: str, data_file): + self.dataset_base = Path(dataset) + with open(data_file, "r") as fid: + self._data = [json.loads(l) for l in fid] + + def __len__(self): + return len(self._data) + + def __getitem__(self, index: int): + item = self._data[index] + image = Image.open(self.dataset_base / item["image"]) + return image, item[self.prompt_key] + + +class LegacyDataset(LocalDataset): + prompt_key = "text" + + def __init__(self, dataset: str): + self.dataset_base = Path(dataset) + with open(self.dataset_base / "index.json") as f: + self._data = json.load(f)["data"] + + +class HuggingFaceDataset(Dataset): + + def __init__(self, dataset: str): + from datasets import load_dataset as hf_load_dataset + + self._df = hf_load_dataset(dataset)["train"] + + def __len__(self): + return len(self._df) + + def __getitem__(self, index: int): + item = self._df[index] + return item["image"], item["prompt"] + + +def load_dataset(dataset: str): + dataset_base = Path(dataset) + data_file = dataset_base / "train.jsonl" + legacy_file = dataset_base / "index.json" + + if data_file.exists(): + print(f"Load the local dataset {data_file} .", flush=True) + dataset = LocalDataset(dataset, data_file) + elif legacy_file.exists(): + print(f"Load the local dataset {legacy_file} .") + print() + print(" WARNING: 'index.json' is deprecated in favor of 'train.jsonl'.") + print(" See the README for details.") + print(flush=True) + dataset = LegacyDataset(dataset) + else: + print(f"Load the Hugging Face dataset {dataset} .", flush=True) + dataset = HuggingFaceDataset(dataset) + + return dataset diff --git a/xinference/thirdparty/mlx/flux/flux.py b/xinference/thirdparty/mlx/flux/flux.py new file mode 100644 index 0000000000..425cb4b9ea --- /dev/null +++ b/xinference/thirdparty/mlx/flux/flux.py @@ -0,0 +1,247 @@ +# Copyright © 2024 Apple Inc. + +from typing import Tuple + +import mlx.core as mx +import mlx.nn as nn +from mlx.utils import tree_unflatten +from tqdm import tqdm + +from .lora import LoRALinear +from .sampler import FluxSampler +from .utils import ( + load_ae, + load_clip, + load_clip_tokenizer, + load_flow_model, + load_t5, + load_t5_tokenizer, +) + + +class FluxPipeline: + def __init__(self, name: str, model_path: str, t5_padding: bool = True): + self.dtype = mx.bfloat16 + self.name = name + self.t5_padding = t5_padding + + self.model_path = model_path + self.ae = load_ae(name, model_path) + self.flow = load_flow_model(name, model_path) + self.clip = load_clip(name, model_path) + self.clip_tokenizer = load_clip_tokenizer(name, model_path) + self.t5 = load_t5(name, model_path) + self.t5_tokenizer = load_t5_tokenizer(name, model_path) + self.sampler = FluxSampler(name) + + def ensure_models_are_loaded(self): + mx.eval( + self.ae.parameters(), + self.flow.parameters(), + self.clip.parameters(), + self.t5.parameters(), + ) + + def reload_text_encoders(self): + self.t5 = load_t5(self.name, self.model_path) + self.clip = load_clip(self.name, self.model_path) + + def tokenize(self, text): + t5_tokens = self.t5_tokenizer.encode(text, pad=self.t5_padding) + clip_tokens = self.clip_tokenizer.encode(text) + return t5_tokens, clip_tokens + + def _prepare_latent_images(self, x): + b, h, w, c = x.shape + + # Pack the latent image to 2x2 patches + x = x.reshape(b, h // 2, 2, w // 2, 2, c) + x = x.transpose(0, 1, 3, 5, 2, 4).reshape(b, h * w // 4, c * 4) + + # Create positions ids used to positionally encode each patch. Due to + # the way RoPE works, this results in an interesting positional + # encoding where parts of the feature are holding different positional + # information. Namely, the first part holds information independent of + # the spatial position (hence 0s), the 2nd part holds vertical spatial + # information and the last one horizontal. + i = mx.zeros((h // 2, w // 2), dtype=mx.int32) + j, k = mx.meshgrid(mx.arange(h // 2), mx.arange(w // 2), indexing="ij") + x_ids = mx.stack([i, j, k], axis=-1) + x_ids = mx.repeat(x_ids.reshape(1, h * w // 4, 3), b, 0) + + return x, x_ids + + def _prepare_conditioning(self, n_images, t5_tokens, clip_tokens): + # Prepare the text features + txt = self.t5(t5_tokens) + if len(txt) == 1 and n_images > 1: + txt = mx.broadcast_to(txt, (n_images, *txt.shape[1:])) + txt_ids = mx.zeros((n_images, txt.shape[1], 3), dtype=mx.int32) + + # Prepare the clip text features + vec = self.clip(clip_tokens).pooled_output + if len(vec) == 1 and n_images > 1: + vec = mx.broadcast_to(vec, (n_images, *vec.shape[1:])) + + return txt, txt_ids, vec + + def _denoising_loop( + self, + x_t, + x_ids, + txt, + txt_ids, + vec, + num_steps: int = 35, + guidance: float = 4.0, + start: float = 1, + stop: float = 0, + ): + B = len(x_t) + + def scalar(x): + return mx.full((B,), x, dtype=self.dtype) + + guidance = scalar(guidance) + timesteps = self.sampler.timesteps( + num_steps, + x_t.shape[1], + start=start, + stop=stop, + ) + for i in range(num_steps): + t = timesteps[i] + t_prev = timesteps[i + 1] + + pred = self.flow( + img=x_t, + img_ids=x_ids, + txt=txt, + txt_ids=txt_ids, + y=vec, + timesteps=scalar(t), + guidance=guidance, + ) + x_t = self.sampler.step(pred, x_t, t, t_prev) + + yield x_t + + def generate_latents( + self, + text: str, + n_images: int = 1, + num_steps: int = 35, + guidance: float = 4.0, + latent_size: Tuple[int, int] = (64, 64), + seed=None, + ): + # Set the PRNG state + if seed is not None: + mx.random.seed(seed) + + # Create the latent variables + x_T = self.sampler.sample_prior((n_images, *latent_size, 16), dtype=self.dtype) + x_T, x_ids = self._prepare_latent_images(x_T) + + # Get the conditioning + t5_tokens, clip_tokens = self.tokenize(text) + txt, txt_ids, vec = self._prepare_conditioning(n_images, t5_tokens, clip_tokens) + + # Yield the conditioning for controlled evaluation by the caller + yield (x_T, x_ids, txt, txt_ids, vec) + + # Yield the latent sequences from the denoising loop + yield from self._denoising_loop( + x_T, x_ids, txt, txt_ids, vec, num_steps=num_steps, guidance=guidance + ) + + def decode(self, x, latent_size: Tuple[int, int] = (64, 64)): + h, w = latent_size + x = x.reshape(len(x), h // 2, w // 2, -1, 2, 2) + x = x.transpose(0, 1, 4, 2, 5, 3).reshape(len(x), h, w, -1) + x = self.ae.decode(x) + return mx.clip(x + 1, 0, 2) * 0.5 + + def generate_images( + self, + text: str, + n_images: int = 1, + num_steps: int = 35, + guidance: float = 4.0, + latent_size: Tuple[int, int] = (64, 64), + seed=None, + reload_text_encoders: bool = True, + progress: bool = True, + ): + latents = self.generate_latents( + text, n_images, num_steps, guidance, latent_size, seed + ) + mx.eval(next(latents)) + + if reload_text_encoders: + self.reload_text_encoders() + + for x_t in tqdm(latents, total=num_steps, disable=not progress, leave=True): + mx.eval(x_t) + + images = [] + for i in tqdm(range(len(x_t)), disable=not progress, desc="generate images"): + images.append(self.decode(x_t[i : i + 1])) + mx.eval(images[-1]) + images = mx.concatenate(images, axis=0) + mx.eval(images) + + return images + + def training_loss( + self, + x_0: mx.array, + t5_features: mx.array, + clip_features: mx.array, + guidance: mx.array, + ): + # Get the text conditioning + txt = t5_features + txt_ids = mx.zeros(txt.shape[:-1] + (3,), dtype=mx.int32) + vec = clip_features + + # Prepare the latent input + x_0, x_ids = self._prepare_latent_images(x_0) + + # Forward process + t = self.sampler.random_timesteps(*x_0.shape[:2], dtype=self.dtype) + eps = mx.random.normal(x_0.shape, dtype=self.dtype) + x_t = self.sampler.add_noise(x_0, t, noise=eps) + x_t = mx.stop_gradient(x_t) + + # Do the denoising + pred = self.flow( + img=x_t, + img_ids=x_ids, + txt=txt, + txt_ids=txt_ids, + y=vec, + timesteps=t, + guidance=guidance, + ) + + return (pred + x_0 - eps).square().mean() + + def linear_to_lora_layers(self, rank: int = 8, num_blocks: int = -1): + """Swap the linear layers in the transformer blocks with LoRA layers.""" + all_blocks = self.flow.double_blocks + self.flow.single_blocks + all_blocks.reverse() + num_blocks = num_blocks if num_blocks > 0 else len(all_blocks) + for i, block in zip(range(num_blocks), all_blocks): + loras = [] + for name, module in block.named_modules(): + if isinstance(module, nn.Linear): + loras.append((name, LoRALinear.from_base(module, r=rank))) + block.update_modules(tree_unflatten(loras)) + + def fuse_lora_layers(self): + fused_layers = [] + for name, module in self.flow.named_modules(): + if isinstance(module, LoRALinear): + fused_layers.append((name, module.fuse())) + self.flow.update_modules(tree_unflatten(fused_layers)) diff --git a/xinference/thirdparty/mlx/flux/layers.py b/xinference/thirdparty/mlx/flux/layers.py new file mode 100644 index 0000000000..12397904e8 --- /dev/null +++ b/xinference/thirdparty/mlx/flux/layers.py @@ -0,0 +1,302 @@ +# Copyright © 2024 Apple Inc. + +import math +from dataclasses import dataclass +from functools import partial +from typing import List, Optional, Tuple + +import mlx.core as mx +import mlx.nn as nn + + +def _rope(pos: mx.array, dim: int, theta: float): + scale = mx.arange(0, dim, 2, dtype=mx.float32) / dim + omega = 1.0 / (theta**scale) + x = pos[..., None] * omega + cosx = mx.cos(x) + sinx = mx.sin(x) + pe = mx.stack([cosx, -sinx, sinx, cosx], axis=-1) + pe = pe.reshape(*pe.shape[:-1], 2, 2) + + return pe + + +@partial(mx.compile, shapeless=True) +def _ab_plus_cd(a, b, c, d): + return a * b + c * d + + +def _apply_rope(x, pe): + s = x.shape + x = x.reshape(*s[:-1], -1, 1, 2) + x = _ab_plus_cd(x[..., 0], pe[..., 0], x[..., 1], pe[..., 1]) + return x.reshape(s) + + +def _attention(q: mx.array, k: mx.array, v: mx.array, pe: mx.array): + B, H, L, D = q.shape + + q = _apply_rope(q, pe) + k = _apply_rope(k, pe) + x = mx.fast.scaled_dot_product_attention(q, k, v, scale=D ** (-0.5)) + + return x.transpose(0, 2, 1, 3).reshape(B, L, -1) + + +def timestep_embedding( + t: mx.array, dim: int, max_period: int = 10000, time_factor: float = 1000.0 +): + half = dim // 2 + freqs = mx.arange(0, half, dtype=mx.float32) / half + freqs = freqs * (-math.log(max_period)) + freqs = mx.exp(freqs) + + x = (time_factor * t)[:, None] * freqs[None] + x = mx.concatenate([mx.cos(x), mx.sin(x)], axis=-1) + + return x.astype(t.dtype) + + +class EmbedND(nn.Module): + def __init__(self, dim: int, theta: int, axes_dim: List[int]): + super().__init__() + + self.dim = dim + self.theta = theta + self.axes_dim = axes_dim + + def __call__(self, ids: mx.array): + n_axes = ids.shape[-1] + pe = mx.concatenate( + [_rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], + axis=-3, + ) + + return pe[:, None] + + +class MLPEmbedder(nn.Module): + def __init__(self, in_dim: int, hidden_dim: int): + super().__init__() + self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True) + self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True) + + def __call__(self, x: mx.array) -> mx.array: + return self.out_layer(nn.silu(self.in_layer(x))) + + +class QKNorm(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.query_norm = nn.RMSNorm(dim) + self.key_norm = nn.RMSNorm(dim) + + def __call__(self, q: mx.array, k: mx.array) -> tuple[mx.array, mx.array]: + return self.query_norm(q), self.key_norm(k) + + +class SelfAttention(nn.Module): + def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.norm = QKNorm(head_dim) + self.proj = nn.Linear(dim, dim) + + def __call__(self, x: mx.array, pe: mx.array) -> mx.array: + H = self.num_heads + B, L, _ = x.shape + qkv = self.qkv(x) + q, k, v = mx.split(qkv, 3, axis=-1) + q = q.reshape(B, L, H, -1).transpose(0, 2, 1, 3) + k = k.reshape(B, L, H, -1).transpose(0, 2, 1, 3) + v = v.reshape(B, L, H, -1).transpose(0, 2, 1, 3) + q, k = self.norm(q, k) + x = _attention(q, k, v, pe) + x = self.proj(x) + return x + + +@dataclass +class ModulationOut: + shift: mx.array + scale: mx.array + gate: mx.array + + +class Modulation(nn.Module): + def __init__(self, dim: int, double: bool): + super().__init__() + self.is_double = double + self.multiplier = 6 if double else 3 + self.lin = nn.Linear(dim, self.multiplier * dim, bias=True) + + def __call__(self, x: mx.array) -> Tuple[ModulationOut, Optional[ModulationOut]]: + x = self.lin(nn.silu(x)) + xs = mx.split(x[:, None, :], self.multiplier, axis=-1) + + mod1 = ModulationOut(*xs[:3]) + mod2 = ModulationOut(*xs[3:]) if self.is_double else None + + return mod1, mod2 + + +class DoubleStreamBlock(nn.Module): + def __init__( + self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False + ): + super().__init__() + + mlp_hidden_dim = int(hidden_size * mlp_ratio) + self.num_heads = num_heads + self.hidden_size = hidden_size + self.img_mod = Modulation(hidden_size, double=True) + self.img_norm1 = nn.LayerNorm(hidden_size, affine=False, eps=1e-6) + self.img_attn = SelfAttention( + dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias + ) + + self.img_norm2 = nn.LayerNorm(hidden_size, affine=False, eps=1e-6) + self.img_mlp = nn.Sequential( + nn.Linear(hidden_size, mlp_hidden_dim, bias=True), + nn.GELU(approx="tanh"), + nn.Linear(mlp_hidden_dim, hidden_size, bias=True), + ) + + self.txt_mod = Modulation(hidden_size, double=True) + self.txt_norm1 = nn.LayerNorm(hidden_size, affine=False, eps=1e-6) + self.txt_attn = SelfAttention( + dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias + ) + + self.txt_norm2 = nn.LayerNorm(hidden_size, affine=False, eps=1e-6) + self.txt_mlp = nn.Sequential( + nn.Linear(hidden_size, mlp_hidden_dim, bias=True), + nn.GELU(approx="tanh"), + nn.Linear(mlp_hidden_dim, hidden_size, bias=True), + ) + + def __call__( + self, img: mx.array, txt: mx.array, vec: mx.array, pe: mx.array + ) -> Tuple[mx.array, mx.array]: + B, L, _ = img.shape + _, S, _ = txt.shape + H = self.num_heads + + img_mod1, img_mod2 = self.img_mod(vec) + txt_mod1, txt_mod2 = self.txt_mod(vec) + + # prepare image for attention + img_modulated = self.img_norm1(img) + img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift + img_qkv = self.img_attn.qkv(img_modulated) + img_q, img_k, img_v = mx.split(img_qkv, 3, axis=-1) + img_q = img_q.reshape(B, L, H, -1).transpose(0, 2, 1, 3) + img_k = img_k.reshape(B, L, H, -1).transpose(0, 2, 1, 3) + img_v = img_v.reshape(B, L, H, -1).transpose(0, 2, 1, 3) + img_q, img_k = self.img_attn.norm(img_q, img_k) + + # prepare txt for attention + txt_modulated = self.txt_norm1(txt) + txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift + txt_qkv = self.txt_attn.qkv(txt_modulated) + txt_q, txt_k, txt_v = mx.split(txt_qkv, 3, axis=-1) + txt_q = txt_q.reshape(B, S, H, -1).transpose(0, 2, 1, 3) + txt_k = txt_k.reshape(B, S, H, -1).transpose(0, 2, 1, 3) + txt_v = txt_v.reshape(B, S, H, -1).transpose(0, 2, 1, 3) + txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k) + + # run actual attention + q = mx.concatenate([txt_q, img_q], axis=2) + k = mx.concatenate([txt_k, img_k], axis=2) + v = mx.concatenate([txt_v, img_v], axis=2) + + attn = _attention(q, k, v, pe) + txt_attn, img_attn = mx.split(attn, [S], axis=1) + + # calculate the img bloks + img = img + img_mod1.gate * self.img_attn.proj(img_attn) + img = img + img_mod2.gate * self.img_mlp( + (1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift + ) + + # calculate the txt bloks + txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn) + txt = txt + txt_mod2.gate * self.txt_mlp( + (1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift + ) + + return img, txt + + +class SingleStreamBlock(nn.Module): + def __init__( + self, + hidden_size: int, + num_heads: int, + mlp_ratio: float = 4.0, + qk_scale: Optional[float] = None, + ): + super().__init__() + self.hidden_dim = hidden_size + self.num_heads = num_heads + head_dim = hidden_size // num_heads + self.scale = qk_scale or head_dim**-0.5 + + self.mlp_hidden_dim = int(hidden_size * mlp_ratio) + # qkv and mlp_in + self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim) + # proj and mlp_out + self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size) + + self.norm = QKNorm(head_dim) + + self.hidden_size = hidden_size + self.pre_norm = nn.LayerNorm(hidden_size, affine=False, eps=1e-6) + + self.mlp_act = nn.GELU(approx="tanh") + self.modulation = Modulation(hidden_size, double=False) + + def __call__(self, x: mx.array, vec: mx.array, pe: mx.array): + B, L, _ = x.shape + H = self.num_heads + + mod, _ = self.modulation(vec) + x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift + + q, k, v, mlp = mx.split( + self.linear1(x_mod), + [self.hidden_size, 2 * self.hidden_size, 3 * self.hidden_size], + axis=-1, + ) + q = q.reshape(B, L, H, -1).transpose(0, 2, 1, 3) + k = k.reshape(B, L, H, -1).transpose(0, 2, 1, 3) + v = v.reshape(B, L, H, -1).transpose(0, 2, 1, 3) + q, k = self.norm(q, k) + + # compute attention + y = _attention(q, k, v, pe) + + # compute activation in mlp stream, cat again and run second linear layer + y = self.linear2(mx.concatenate([y, self.mlp_act(mlp)], axis=2)) + return x + mod.gate * y + + +class LastLayer(nn.Module): + def __init__(self, hidden_size: int, patch_size: int, out_channels: int): + super().__init__() + self.norm_final = nn.LayerNorm(hidden_size, affine=False, eps=1e-6) + self.linear = nn.Linear( + hidden_size, patch_size * patch_size * out_channels, bias=True + ) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True) + ) + + def __call__(self, x: mx.array, vec: mx.array): + shift, scale = mx.split(self.adaLN_modulation(vec), 2, axis=1) + x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :] + x = self.linear(x) + return x diff --git a/xinference/thirdparty/mlx/flux/lora.py b/xinference/thirdparty/mlx/flux/lora.py new file mode 100644 index 0000000000..b0c8ae5605 --- /dev/null +++ b/xinference/thirdparty/mlx/flux/lora.py @@ -0,0 +1,76 @@ +# Copyright © 2024 Apple Inc. + +import math + +import mlx.core as mx +import mlx.nn as nn + + +class LoRALinear(nn.Module): + @staticmethod + def from_base( + linear: nn.Linear, + r: int = 8, + dropout: float = 0.0, + scale: float = 1.0, + ): + output_dims, input_dims = linear.weight.shape + lora_lin = LoRALinear( + input_dims=input_dims, + output_dims=output_dims, + r=r, + dropout=dropout, + scale=scale, + ) + lora_lin.linear = linear + return lora_lin + + def fuse(self): + linear = self.linear + bias = "bias" in linear + weight = linear.weight + dtype = weight.dtype + + output_dims, input_dims = weight.shape + fused_linear = nn.Linear(input_dims, output_dims, bias=bias) + + lora_b = self.scale * self.lora_b.T + lora_a = self.lora_a.T + fused_linear.weight = weight + (lora_b @ lora_a).astype(dtype) + if bias: + fused_linear.bias = linear.bias + + return fused_linear + + def __init__( + self, + input_dims: int, + output_dims: int, + r: int = 8, + dropout: float = 0.0, + scale: float = 1.0, + bias: bool = False, + ): + super().__init__() + + # Regular linear layer weights + self.linear = nn.Linear(input_dims, output_dims, bias=bias) + + self.dropout = nn.Dropout(p=dropout) + + # Scale for low-rank update + self.scale = scale + + # Low rank lora weights + scale = 1 / math.sqrt(input_dims) + self.lora_a = mx.random.uniform( + low=-scale, + high=scale, + shape=(input_dims, r), + ) + self.lora_b = mx.zeros(shape=(r, output_dims)) + + def __call__(self, x): + y = self.linear(x) + z = (self.dropout(x) @ self.lora_a) @ self.lora_b + return y + (self.scale * z).astype(x.dtype) diff --git a/xinference/thirdparty/mlx/flux/model.py b/xinference/thirdparty/mlx/flux/model.py new file mode 100644 index 0000000000..18ea70b08a --- /dev/null +++ b/xinference/thirdparty/mlx/flux/model.py @@ -0,0 +1,134 @@ +# Copyright © 2024 Apple Inc. + +from dataclasses import dataclass +from typing import Optional + +import mlx.core as mx +import mlx.nn as nn + +from .layers import ( + DoubleStreamBlock, + EmbedND, + LastLayer, + MLPEmbedder, + SingleStreamBlock, + timestep_embedding, +) + + +@dataclass +class FluxParams: + in_channels: int + vec_in_dim: int + context_in_dim: int + hidden_size: int + mlp_ratio: float + num_heads: int + depth: int + depth_single_blocks: int + axes_dim: list[int] + theta: int + qkv_bias: bool + guidance_embed: bool + + +class Flux(nn.Module): + def __init__(self, params: FluxParams): + super().__init__() + + self.params = params + self.in_channels = params.in_channels + self.out_channels = self.in_channels + if params.hidden_size % params.num_heads != 0: + raise ValueError( + f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" + ) + pe_dim = params.hidden_size // params.num_heads + if sum(params.axes_dim) != pe_dim: + raise ValueError( + f"Got {params.axes_dim} but expected positional dim {pe_dim}" + ) + self.hidden_size = params.hidden_size + self.num_heads = params.num_heads + self.pe_embedder = EmbedND( + dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim + ) + self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True) + self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) + self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size) + self.guidance_in = ( + MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) + if params.guidance_embed + else nn.Identity() + ) + self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size) + + self.double_blocks = [ + DoubleStreamBlock( + self.hidden_size, + self.num_heads, + mlp_ratio=params.mlp_ratio, + qkv_bias=params.qkv_bias, + ) + for _ in range(params.depth) + ] + + self.single_blocks = [ + SingleStreamBlock( + self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio + ) + for _ in range(params.depth_single_blocks) + ] + + self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels) + + def sanitize(self, weights): + new_weights = {} + for k, w in weights.items(): + if k.endswith(".scale"): + k = k[:-6] + ".weight" + for seq in ["img_mlp", "txt_mlp", "adaLN_modulation"]: + if f".{seq}." in k: + k = k.replace(f".{seq}.", f".{seq}.layers.") + break + new_weights[k] = w + return new_weights + + def __call__( + self, + img: mx.array, + img_ids: mx.array, + txt: mx.array, + txt_ids: mx.array, + timesteps: mx.array, + y: mx.array, + guidance: Optional[mx.array] = None, + ) -> mx.array: + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + img = self.img_in(img) + vec = self.time_in(timestep_embedding(timesteps, 256)) + if self.params.guidance_embed: + if guidance is None: + raise ValueError( + "Didn't get guidance strength for guidance distilled model." + ) + vec = vec + self.guidance_in(timestep_embedding(guidance, 256)) + vec = vec + self.vector_in(y) + txt = self.txt_in(txt) + + ids = mx.concatenate([txt_ids, img_ids], axis=1) + pe = self.pe_embedder(ids).astype(img.dtype) + + for block in self.double_blocks: + img, txt = block(img=img, txt=txt, vec=vec, pe=pe) + + img = mx.concatenate([txt, img], axis=1) + for block in self.single_blocks: + img = block(img, vec=vec, pe=pe) + img = img[:, txt.shape[1] :, ...] + + img = self.final_layer(img, vec) + + return img diff --git a/xinference/thirdparty/mlx/flux/sampler.py b/xinference/thirdparty/mlx/flux/sampler.py new file mode 100644 index 0000000000..3bff1ca275 --- /dev/null +++ b/xinference/thirdparty/mlx/flux/sampler.py @@ -0,0 +1,56 @@ +# Copyright © 2024 Apple Inc. + +import math +from functools import lru_cache + +import mlx.core as mx + + +class FluxSampler: + def __init__(self, name: str, base_shift: float = 0.5, max_shift: float = 1.5): + self._base_shift = base_shift + self._max_shift = max_shift + self._schnell = "schnell" in name + + def _time_shift(self, x, t): + x1, x2 = 256, 4096 + t1, t2 = self._base_shift, self._max_shift + exp_mu = math.exp((x - x1) * (t2 - t1) / (x2 - x1) + t1) + t = exp_mu / (exp_mu + (1 / t - 1)) + return t + + @lru_cache + def timesteps( + self, num_steps, image_sequence_length, start: float = 1, stop: float = 0 + ): + t = mx.linspace(start, stop, num_steps + 1) + + if self._schnell: + t = self._time_shift(image_sequence_length, t) + + return t.tolist() + + def random_timesteps(self, B, L, dtype=mx.float32, key=None): + if self._schnell: + # TODO: Should we upweigh 1 and 0.75? + t = mx.random.randint(1, 5, shape=(B,), key=key) + t = t.astype(dtype) / 4 + else: + t = mx.random.uniform(shape=(B,), dtype=dtype, key=key) + t = self._time_shift(L, t) + + return t + + def sample_prior(self, shape, dtype=mx.float32, key=None): + return mx.random.normal(shape, dtype=dtype, key=key) + + def add_noise(self, x, t, noise=None, key=None): + noise = ( + noise + if noise is not None + else mx.random.normal(x.shape, dtype=x.dtype, key=key) + ) + return x * (1 - t) + t * noise + + def step(self, pred, x_t, t, t_prev): + return x_t + (t_prev - t) * pred diff --git a/xinference/thirdparty/mlx/flux/t5.py b/xinference/thirdparty/mlx/flux/t5.py new file mode 100644 index 0000000000..cf0515cd5e --- /dev/null +++ b/xinference/thirdparty/mlx/flux/t5.py @@ -0,0 +1,244 @@ +# Copyright © 2024 Apple Inc. + +import math +from dataclasses import dataclass +from typing import List, Optional, Tuple + +import mlx.core as mx +import mlx.nn as nn + +_SHARED_REPLACEMENT_PATTERNS = [ + (".block.", ".layers."), + (".k.", ".key_proj."), + (".o.", ".out_proj."), + (".q.", ".query_proj."), + (".v.", ".value_proj."), + ("shared.", "wte."), + ("lm_head.", "lm_head.linear."), + (".layer.0.layer_norm.", ".ln1."), + (".layer.1.layer_norm.", ".ln2."), + (".layer.2.layer_norm.", ".ln3."), + (".final_layer_norm.", ".ln."), + ( + "layers.0.layer.0.SelfAttention.relative_attention_bias.", + "relative_attention_bias.embeddings.", + ), +] + +_ENCODER_REPLACEMENT_PATTERNS = [ + (".layer.0.SelfAttention.", ".attention."), + (".layer.1.DenseReluDense.", ".dense."), +] + + +@dataclass +class T5Config: + vocab_size: int + num_layers: int + num_heads: int + relative_attention_num_buckets: int + d_kv: int + d_model: int + feed_forward_proj: str + tie_word_embeddings: bool + + d_ff: Optional[int] = None + num_decoder_layers: Optional[int] = None + relative_attention_max_distance: int = 128 + layer_norm_epsilon: float = 1e-6 + + @classmethod + def from_dict(cls, config): + return cls( + vocab_size=config["vocab_size"], + num_layers=config["num_layers"], + num_heads=config["num_heads"], + relative_attention_num_buckets=config["relative_attention_num_buckets"], + d_kv=config["d_kv"], + d_model=config["d_model"], + feed_forward_proj=config["feed_forward_proj"], + tie_word_embeddings=config["tie_word_embeddings"], + d_ff=config.get("d_ff", 4 * config["d_model"]), + num_decoder_layers=config.get("num_decoder_layers", config["num_layers"]), + relative_attention_max_distance=config.get( + "relative_attention_max_distance", 128 + ), + layer_norm_epsilon=config.get("layer_norm_epsilon", 1e-6), + ) + + +class RelativePositionBias(nn.Module): + def __init__(self, config: T5Config, bidirectional: bool): + self.bidirectional = bidirectional + self.num_buckets = config.relative_attention_num_buckets + self.max_distance = config.relative_attention_max_distance + self.n_heads = config.num_heads + self.embeddings = nn.Embedding(self.num_buckets, self.n_heads) + + @staticmethod + def _relative_position_bucket(rpos, bidirectional, num_buckets, max_distance): + num_buckets = num_buckets // 2 if bidirectional else num_buckets + max_exact = num_buckets // 2 + + abspos = rpos.abs() + is_small = abspos < max_exact + + scale = (num_buckets - max_exact) / math.log(max_distance / max_exact) + buckets_large = (mx.log(abspos / max_exact) * scale).astype(mx.int16) + buckets_large = mx.minimum(max_exact + buckets_large, num_buckets - 1) + + buckets = mx.where(is_small, abspos, buckets_large) + if bidirectional: + buckets = buckets + (rpos > 0) * num_buckets + else: + buckets = buckets * (rpos < 0) + + return buckets + + def __call__(self, query_length: int, key_length: int, offset: int = 0): + """Compute binned relative position bias""" + context_position = mx.arange(offset, query_length)[:, None] + memory_position = mx.arange(key_length)[None, :] + + # shape (query_length, key_length) + relative_position = memory_position - context_position + relative_position_bucket = self._relative_position_bucket( + relative_position, + bidirectional=self.bidirectional, + num_buckets=self.num_buckets, + max_distance=self.max_distance, + ) + + # shape (query_length, key_length, num_heads) + values = self.embeddings(relative_position_bucket) + + # shape (num_heads, query_length, key_length) + return values.transpose(2, 0, 1) + + +class MultiHeadAttention(nn.Module): + def __init__(self, config: T5Config): + super().__init__() + inner_dim = config.d_kv * config.num_heads + self.num_heads = config.num_heads + self.query_proj = nn.Linear(config.d_model, inner_dim, bias=False) + self.key_proj = nn.Linear(config.d_model, inner_dim, bias=False) + self.value_proj = nn.Linear(config.d_model, inner_dim, bias=False) + self.out_proj = nn.Linear(inner_dim, config.d_model, bias=False) + + def __call__( + self, + queries: mx.array, + keys: mx.array, + values: mx.array, + mask: Optional[mx.array], + cache: Optional[Tuple[mx.array, mx.array]] = None, + ) -> [mx.array, Tuple[mx.array, mx.array]]: + queries = self.query_proj(queries) + keys = self.key_proj(keys) + values = self.value_proj(values) + + num_heads = self.num_heads + B, L, _ = queries.shape + _, S, _ = keys.shape + queries = queries.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3) + keys = keys.reshape(B, S, num_heads, -1).transpose(0, 2, 1, 3) + values = values.reshape(B, S, num_heads, -1).transpose(0, 2, 1, 3) + + if cache is not None: + key_cache, value_cache = cache + keys = mx.concatenate([key_cache, keys], axis=3) + values = mx.concatenate([value_cache, values], axis=2) + + values_hat = mx.fast.scaled_dot_product_attention( + queries, keys, values, scale=1.0, mask=mask.astype(queries.dtype) + ) + values_hat = values_hat.transpose(0, 2, 1, 3).reshape(B, L, -1) + + return self.out_proj(values_hat), (keys, values) + + +class DenseActivation(nn.Module): + def __init__(self, config: T5Config): + super().__init__() + mlp_dims = config.d_ff or config.d_model * 4 + self.gated = config.feed_forward_proj.startswith("gated") + if self.gated: + self.wi_0 = nn.Linear(config.d_model, mlp_dims, bias=False) + self.wi_1 = nn.Linear(config.d_model, mlp_dims, bias=False) + else: + self.wi = nn.Linear(config.d_model, mlp_dims, bias=False) + self.wo = nn.Linear(mlp_dims, config.d_model, bias=False) + activation = config.feed_forward_proj.removeprefix("gated-") + if activation == "relu": + self.act = nn.relu + elif activation == "gelu": + self.act = nn.gelu + elif activation == "silu": + self.act = nn.silu + else: + raise ValueError(f"Unknown activation: {activation}") + + def __call__(self, x): + if self.gated: + hidden_act = self.act(self.wi_0(x)) + hidden_linear = self.wi_1(x) + x = hidden_act * hidden_linear + else: + x = self.act(self.wi(x)) + return self.wo(x) + + +class TransformerEncoderLayer(nn.Module): + def __init__(self, config: T5Config): + super().__init__() + self.attention = MultiHeadAttention(config) + self.ln1 = nn.RMSNorm(config.d_model, eps=config.layer_norm_epsilon) + self.ln2 = nn.RMSNorm(config.d_model, eps=config.layer_norm_epsilon) + self.dense = DenseActivation(config) + + def __call__(self, x, mask): + y = self.ln1(x) + y, _ = self.attention(y, y, y, mask=mask) + x = x + y + + y = self.ln2(x) + y = self.dense(y) + return x + y + + +class TransformerEncoder(nn.Module): + def __init__(self, config: T5Config): + super().__init__() + self.layers = [ + TransformerEncoderLayer(config) for i in range(config.num_layers) + ] + self.ln = nn.RMSNorm(config.d_model, eps=config.layer_norm_epsilon) + self.relative_attention_bias = RelativePositionBias(config, bidirectional=True) + + def __call__(self, x: mx.array): + pos_bias = self.relative_attention_bias(x.shape[1], x.shape[1]) + pos_bias = pos_bias.astype(x.dtype) + for layer in self.layers: + x = layer(x, mask=pos_bias) + return self.ln(x) + + +class T5Encoder(nn.Module): + def __init__(self, config: T5Config): + self.wte = nn.Embedding(config.vocab_size, config.d_model) + self.encoder = TransformerEncoder(config) + + def sanitize(self, weights): + new_weights = {} + for k, w in weights.items(): + for old, new in _SHARED_REPLACEMENT_PATTERNS: + k = k.replace(old, new) + if k.startswith("encoder."): + for old, new in _ENCODER_REPLACEMENT_PATTERNS: + k = k.replace(old, new) + new_weights[k] = w + return new_weights + + def __call__(self, inputs: mx.array): + return self.encoder(self.wte(inputs)) diff --git a/xinference/thirdparty/mlx/flux/tokenizers.py b/xinference/thirdparty/mlx/flux/tokenizers.py new file mode 100644 index 0000000000..796ef3896f --- /dev/null +++ b/xinference/thirdparty/mlx/flux/tokenizers.py @@ -0,0 +1,185 @@ +# Copyright © 2024 Apple Inc. + +import mlx.core as mx +import regex +from sentencepiece import SentencePieceProcessor + + +class CLIPTokenizer: + """A simple port of CLIPTokenizer from https://github.com/huggingface/transformers/ .""" + + def __init__(self, bpe_ranks, vocab, max_length=77): + self.max_length = max_length + self.bpe_ranks = bpe_ranks + self.vocab = vocab + self.pat = regex.compile( + r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", + regex.IGNORECASE, + ) + + self._cache = {self.bos: self.bos, self.eos: self.eos} + + @property + def bos(self): + return "<|startoftext|>" + + @property + def bos_token(self): + return self.vocab[self.bos] + + @property + def eos(self): + return "<|endoftext|>" + + @property + def eos_token(self): + return self.vocab[self.eos] + + def bpe(self, text): + if text in self._cache: + return self._cache[text] + + unigrams = list(text[:-1]) + [text[-1] + ""] + unique_bigrams = set(zip(unigrams, unigrams[1:])) + + if not unique_bigrams: + return unigrams + + # In every iteration try to merge the two most likely bigrams. If none + # was merged we are done. + # + # Ported from https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/tokenization_clip.py + while unique_bigrams: + bigram = min( + unique_bigrams, key=lambda pair: self.bpe_ranks.get(pair, float("inf")) + ) + if bigram not in self.bpe_ranks: + break + + new_unigrams = [] + skip = False + for a, b in zip(unigrams, unigrams[1:]): + if skip: + skip = False + continue + + if (a, b) == bigram: + new_unigrams.append(a + b) + skip = True + + else: + new_unigrams.append(a) + + if not skip: + new_unigrams.append(b) + + unigrams = new_unigrams + unique_bigrams = set(zip(unigrams, unigrams[1:])) + + self._cache[text] = unigrams + + return unigrams + + def tokenize(self, text, prepend_bos=True, append_eos=True): + if isinstance(text, list): + return [self.tokenize(t, prepend_bos, append_eos) for t in text] + + # Lower case cleanup and split according to self.pat. Hugging Face does + # a much more thorough job here but this should suffice for 95% of + # cases. + clean_text = regex.sub(r"\s+", " ", text.lower()) + tokens = regex.findall(self.pat, clean_text) + + # Split the tokens according to the byte-pair merge file + bpe_tokens = [ti for t in tokens for ti in self.bpe(t)] + + # Map to token ids and return + tokens = [self.vocab[t] for t in bpe_tokens] + if prepend_bos: + tokens = [self.bos_token] + tokens + if append_eos: + tokens.append(self.eos_token) + + if len(tokens) > self.max_length: + tokens = tokens[: self.max_length] + if append_eos: + tokens[-1] = self.eos_token + + return tokens + + def encode(self, text): + if not isinstance(text, list): + return self.encode([text]) + + tokens = self.tokenize(text) + length = max(len(t) for t in tokens) + for t in tokens: + t.extend([self.eos_token] * (length - len(t))) + + return mx.array(tokens) + + +class T5Tokenizer: + def __init__(self, model_file, max_length=512): + self._tokenizer = SentencePieceProcessor(model_file) + self.max_length = max_length + + @property + def pad(self): + try: + return self._tokenizer.id_to_piece(self.pad_token) + except IndexError: + return None + + @property + def pad_token(self): + return self._tokenizer.pad_id() + + @property + def bos(self): + try: + return self._tokenizer.id_to_piece(self.bos_token) + except IndexError: + return None + + @property + def bos_token(self): + return self._tokenizer.bos_id() + + @property + def eos(self): + try: + return self._tokenizer.id_to_piece(self.eos_token) + except IndexError: + return None + + @property + def eos_token(self): + return self._tokenizer.eos_id() + + def tokenize(self, text, prepend_bos=True, append_eos=True, pad=True): + if isinstance(text, list): + return [self.tokenize(t, prepend_bos, append_eos, pad) for t in text] + + tokens = self._tokenizer.encode(text) + + if prepend_bos and self.bos_token >= 0: + tokens = [self.bos_token] + tokens + if append_eos and self.eos_token >= 0: + tokens.append(self.eos_token) + if pad and len(tokens) < self.max_length and self.pad_token >= 0: + tokens += [self.pad_token] * (self.max_length - len(tokens)) + + return tokens + + def encode(self, text, pad=True): + if not isinstance(text, list): + return self.encode([text], pad=pad) + + pad_token = self.pad_token if self.pad_token >= 0 else 0 + tokens = self.tokenize(text, pad=pad) + length = max(len(t) for t in tokens) + for t in tokens: + t.extend([pad_token] * (length - len(t))) + + return mx.array(tokens) diff --git a/xinference/thirdparty/mlx/flux/trainer.py b/xinference/thirdparty/mlx/flux/trainer.py new file mode 100644 index 0000000000..40a126e886 --- /dev/null +++ b/xinference/thirdparty/mlx/flux/trainer.py @@ -0,0 +1,98 @@ +import mlx.core as mx +import numpy as np +from PIL import Image, ImageFile +from tqdm import tqdm + +from .datasets import Dataset +from .flux import FluxPipeline + + +class Trainer: + + def __init__(self, flux: FluxPipeline, dataset: Dataset, args): + self.flux = flux + self.dataset = dataset + self.args = args + self.latents = [] + self.t5_features = [] + self.clip_features = [] + + def _random_crop_resize(self, img): + resolution = self.args.resolution + width, height = img.size + + a, b, c, d = mx.random.uniform(shape=(4,), stream=mx.cpu).tolist() + + # Random crop the input image between 0.8 to 1.0 of its original dimensions + crop_size = ( + max((0.8 + 0.2 * a) * width, resolution[0]), + max((0.8 + 0.2 * b) * height, resolution[1]), + ) + pan = (width - crop_size[0], height - crop_size[1]) + img = img.crop( + ( + pan[0] * c, + pan[1] * d, + crop_size[0] + pan[0] * c, + crop_size[1] + pan[1] * d, + ) + ) + + # Fit the largest rectangle with the ratio of resolution in the image + # rectangle. + width, height = crop_size + ratio = resolution[0] / resolution[1] + r1 = (height * ratio, height) + r2 = (width, width / ratio) + r = r1 if r1[0] <= width else r2 + img = img.crop( + ( + (width - r[0]) / 2, + (height - r[1]) / 2, + (width + r[0]) / 2, + (height + r[1]) / 2, + ) + ) + + # Finally resize the image to resolution + img = img.resize(resolution, Image.LANCZOS) + + return mx.array(np.array(img)) + + def _encode_image(self, input_img: ImageFile.ImageFile, num_augmentations: int): + for i in range(num_augmentations): + img = self._random_crop_resize(input_img) + img = (img[:, :, :3].astype(self.flux.dtype) / 255) * 2 - 1 + x_0 = self.flux.ae.encode(img[None]) + x_0 = x_0.astype(self.flux.dtype) + mx.eval(x_0) + self.latents.append(x_0) + + def _encode_prompt(self, prompt): + t5_tok, clip_tok = self.flux.tokenize([prompt]) + t5_feat = self.flux.t5(t5_tok) + clip_feat = self.flux.clip(clip_tok).pooled_output + mx.eval(t5_feat, clip_feat) + self.t5_features.append(t5_feat) + self.clip_features.append(clip_feat) + + def encode_dataset(self): + """Encode the images & prompt in the latent space to prepare for training.""" + self.flux.ae.eval() + for image, prompt in tqdm(self.dataset, desc="encode dataset"): + self._encode_image(image, self.args.num_augmentations) + self._encode_prompt(prompt) + + def iterate(self, batch_size): + xs = mx.concatenate(self.latents) + t5 = mx.concatenate(self.t5_features) + clip = mx.concatenate(self.clip_features) + mx.eval(xs, t5, clip) + n_aug = self.args.num_augmentations + while True: + x_indices = mx.random.permutation(len(self.latents)) + c_indices = x_indices // n_aug + for i in range(0, len(self.latents), batch_size): + x_i = x_indices[i : i + batch_size] + c_i = c_indices[i : i + batch_size] + yield xs[x_i], t5[c_i], clip[c_i] diff --git a/xinference/thirdparty/mlx/flux/utils.py b/xinference/thirdparty/mlx/flux/utils.py new file mode 100644 index 0000000000..47e7fe9e33 --- /dev/null +++ b/xinference/thirdparty/mlx/flux/utils.py @@ -0,0 +1,179 @@ +# Copyright © 2024 Apple Inc. + +import json +import os +from dataclasses import dataclass +from typing import Optional + +import mlx.core as mx + +from .autoencoder import AutoEncoder, AutoEncoderParams +from .clip import CLIPTextModel, CLIPTextModelConfig +from .model import Flux, FluxParams +from .t5 import T5Config, T5Encoder +from .tokenizers import CLIPTokenizer, T5Tokenizer + + +@dataclass +class ModelSpec: + params: FluxParams + ae_params: AutoEncoderParams + ckpt_path: Optional[str] + ae_path: Optional[str] + repo_id: Optional[str] + repo_flow: Optional[str] + repo_ae: Optional[str] + + +configs = { + "flux-dev": ModelSpec( + repo_id="black-forest-labs/FLUX.1-dev", + repo_flow="flux1-dev.safetensors", + repo_ae="ae.safetensors", + ckpt_path=os.getenv("FLUX_DEV"), + params=FluxParams( + in_channels=64, + vec_in_dim=768, + context_in_dim=4096, + hidden_size=3072, + mlp_ratio=4.0, + num_heads=24, + depth=19, + depth_single_blocks=38, + axes_dim=[16, 56, 56], + theta=10_000, + qkv_bias=True, + guidance_embed=True, + ), + ae_path=os.getenv("AE"), + ae_params=AutoEncoderParams( + resolution=256, + in_channels=3, + ch=128, + out_ch=3, + ch_mult=[1, 2, 4, 4], + num_res_blocks=2, + z_channels=16, + scale_factor=0.3611, + shift_factor=0.1159, + ), + ), + "flux-schnell": ModelSpec( + repo_id="black-forest-labs/FLUX.1-schnell", + repo_flow="flux1-schnell.safetensors", + repo_ae="ae.safetensors", + ckpt_path=os.getenv("FLUX_SCHNELL"), + params=FluxParams( + in_channels=64, + vec_in_dim=768, + context_in_dim=4096, + hidden_size=3072, + mlp_ratio=4.0, + num_heads=24, + depth=19, + depth_single_blocks=38, + axes_dim=[16, 56, 56], + theta=10_000, + qkv_bias=True, + guidance_embed=False, + ), + ae_path=os.getenv("AE"), + ae_params=AutoEncoderParams( + resolution=256, + in_channels=3, + ch=128, + out_ch=3, + ch_mult=[1, 2, 4, 4], + num_res_blocks=2, + z_channels=16, + scale_factor=0.3611, + shift_factor=0.1159, + ), + ), +} + + +def load_flow_model(name: str, ckpt_path: str): + # Make the model + model = Flux(configs[name].params) + + # Load the checkpoint if needed + if os.path.isdir(ckpt_path): + ckpt_path = os.path.join(ckpt_path, configs[name].repo_flow) + weights = mx.load(ckpt_path) + weights = model.sanitize(weights) + model.load_weights(list(weights.items())) + + return model + + +def load_ae(name: str, ckpt_path: str): + # Make the autoencoder + ae = AutoEncoder(configs[name].ae_params) + + # Load the checkpoint if needed + ckpt_path = os.path.join(ckpt_path, "ae.safetensors") + weights = mx.load(ckpt_path) + weights = ae.sanitize(weights) + ae.load_weights(list(weights.items())) + + return ae + + +def load_clip(name: str, ckpt_path: str): + config_path = os.path.join(ckpt_path, "text_encoder/config.json") + with open(config_path) as f: + config = CLIPTextModelConfig.from_dict(json.load(f)) + + # Make the clip text encoder + clip = CLIPTextModel(config) + + ckpt_path = os.path.join(ckpt_path, "text_encoder/model.safetensors") + weights = mx.load(ckpt_path) + weights = clip.sanitize(weights) + clip.load_weights(list(weights.items())) + + return clip + + +def load_t5(name: str, ckpt_path: str): + config_path = os.path.join(ckpt_path, "text_encoder_2/config.json") + with open(config_path) as f: + config = T5Config.from_dict(json.load(f)) + + # Make the T5 model + t5 = T5Encoder(config) + + model_index = os.path.join(ckpt_path, "text_encoder_2/model.safetensors.index.json") + weight_files = set() + with open(model_index) as f: + for _, w in json.load(f)["weight_map"].items(): + weight_files.add(w) + weights = {} + for w in weight_files: + w = f"text_encoder_2/{w}" + w = os.path.join(ckpt_path, w) + weights.update(mx.load(w)) + weights = t5.sanitize(weights) + t5.load_weights(list(weights.items())) + + return t5 + + +def load_clip_tokenizer(name: str, ckpt_path: str): + vocab_file = os.path.join(ckpt_path, "tokenizer/vocab.json") + with open(vocab_file, encoding="utf-8") as f: + vocab = json.load(f) + + merges_file = os.path.join(ckpt_path, "tokenizer/merges.txt") + with open(merges_file, encoding="utf-8") as f: + bpe_merges = f.read().strip().split("\n")[1 : 49152 - 256 - 2 + 1] + bpe_merges = [tuple(m.split()) for m in bpe_merges] + bpe_ranks = dict(map(reversed, enumerate(bpe_merges))) + + return CLIPTokenizer(bpe_ranks, vocab, max_length=77) + + +def load_t5_tokenizer(name: str, ckpt_path: str, pad: bool = True): + model_file = os.path.join(ckpt_path, "tokenizer_2/spiece.model") + return T5Tokenizer(model_file, 256 if "schnell" in name else 512) diff --git a/xinference/thirdparty/whisper/__init__.py b/xinference/thirdparty/whisper/__init__.py new file mode 100644 index 0000000000..d7fbba36f2 --- /dev/null +++ b/xinference/thirdparty/whisper/__init__.py @@ -0,0 +1,156 @@ +import hashlib +import io +import os +import urllib +import warnings +from typing import List, Optional, Union + +import torch +from tqdm import tqdm + +from .audio import load_audio, log_mel_spectrogram, pad_or_trim +from .decoding import DecodingOptions, DecodingResult, decode, detect_language +from .model import ModelDimensions, Whisper +from .transcribe import transcribe +from .version import __version__ + +_MODELS = { + "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt", + "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt", + "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt", + "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt", + "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt", + "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt", + "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt", + "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt", + "large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt", + "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt", + "large-v3": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt", + "large": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt", +} + +# base85-encoded (n_layers, n_heads) boolean arrays indicating the cross-attention heads that are +# highly correlated to the word-level timing, i.e. the alignment between audio and text tokens. +_ALIGNMENT_HEADS = { + "tiny.en": b"ABzY8J1N>@0{>%R00Bk>$p{7v037`oCl~+#00", + "tiny": b"ABzY8bu8Lr0{>%RKn9Fp%m@SkK7Kt=7ytkO", + "base.en": b"ABzY8;40c<0{>%RzzG;p*o+Vo09|#PsxSZm00", + "base": b"ABzY8KQ!870{>%RzyTQH3`Q^yNP!>##QT-?_)10{>%RpeA61k&I|OI3I$65C{;;pbCHh0B{qLQ;+}v00", + "small": b"ABzY8DmU6=0{>%Rpa?J`kvJ6qF(V^F86#Xh7JUGMK}P%R7%R7}kK1fFL7w6%<-Pf*t^=N)Qr&0RR9", + "large-v1": b"ABzY8r9j$a0{>%R7#4sLmoOs{s)o3~84-RPdcFk!JR%R7=D0pU<_bnWW*tkYAhobTNnu$jnkEkXqp)j;w1Tzk)UH3X%SZd&fFZ2fC2yj", + "large-v3": b"ABzY8gWO1E0{>%R7(9S+Kn!D~%ngiGaR?*L!iJG9p-nab0JQ=-{D1-g00", + "large": b"ABzY8gWO1E0{>%R7(9S+Kn!D~%ngiGaR?*L!iJG9p-nab0JQ=-{D1-g00", +} + + +def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]: + os.makedirs(root, exist_ok=True) + + expected_sha256 = url.split("/")[-2] + download_target = os.path.join(root, os.path.basename(url)) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError(f"{download_target} exists and is not a regular file") + + if os.path.isfile(download_target): + with open(download_target, "rb") as f: + model_bytes = f.read() + if hashlib.sha256(model_bytes).hexdigest() == expected_sha256: + return model_bytes if in_memory else download_target + else: + warnings.warn( + f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" + ) + + with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: + with tqdm( + total=int(source.info().get("Content-Length")), + ncols=80, + unit="iB", + unit_scale=True, + unit_divisor=1024, + ) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + model_bytes = open(download_target, "rb").read() + if hashlib.sha256(model_bytes).hexdigest() != expected_sha256: + raise RuntimeError( + "Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model." + ) + + return model_bytes if in_memory else download_target + + +def available_models() -> List[str]: + """Returns the names of available models""" + return list(_MODELS.keys()) + + +def load_model( + name: str, + device: Optional[Union[str, torch.device]] = None, + download_root: str = None, + in_memory: bool = False, +) -> Whisper: + """ + Load a Whisper ASR model + + Parameters + ---------- + name : str + one of the official model names listed by `whisper.available_models()`, or + path to a model checkpoint containing the model dimensions and the model state_dict. + device : Union[str, torch.device] + the PyTorch device to put the model into + download_root: str + path to download the model files; by default, it uses "~/.cache/whisper" + in_memory: bool + whether to preload the model weights into host memory + + Returns + ------- + model : Whisper + The Whisper ASR model instance + """ + + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + if download_root is None: + default = os.path.join(os.path.expanduser("~"), ".cache") + download_root = os.path.join(os.getenv("XDG_CACHE_HOME", default), "whisper") + + if name in _MODELS: + checkpoint_file = _download(_MODELS[name], download_root, in_memory) + alignment_heads = _ALIGNMENT_HEADS[name] + elif os.path.isfile(name): + checkpoint_file = open(name, "rb").read() if in_memory else name + alignment_heads = None + else: + raise RuntimeError( + f"Model {name} not found; available models = {available_models()}" + ) + + with ( + io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb") + ) as fp: + checkpoint = torch.load(fp, map_location=device) + del checkpoint_file + + dims = ModelDimensions(**checkpoint["dims"]) + model = Whisper(dims) + model.load_state_dict(checkpoint["model_state_dict"]) + + if alignment_heads is not None: + model.set_alignment_heads(alignment_heads) + + return model.to(device) diff --git a/xinference/thirdparty/whisper/__main__.py b/xinference/thirdparty/whisper/__main__.py new file mode 100644 index 0000000000..d14f2058e7 --- /dev/null +++ b/xinference/thirdparty/whisper/__main__.py @@ -0,0 +1,3 @@ +from .transcribe import cli + +cli() diff --git a/xinference/thirdparty/whisper/assets/gpt2.tiktoken b/xinference/thirdparty/whisper/assets/gpt2.tiktoken new file mode 100644 index 0000000000..3480e077d2 --- /dev/null +++ b/xinference/thirdparty/whisper/assets/gpt2.tiktoken @@ -0,0 +1,50256 @@ +IQ== 0 +Ig== 1 +Iw== 2 +JA== 3 +JQ== 4 +Jg== 5 +Jw== 6 +KA== 7 +KQ== 8 +Kg== 9 +Kw== 10 +LA== 11 +LQ== 12 +Lg== 13 +Lw== 14 +MA== 15 +MQ== 16 +Mg== 17 +Mw== 18 +NA== 19 +NQ== 20 +Ng== 21 +Nw== 22 +OA== 23 +OQ== 24 +Og== 25 +Ow== 26 +PA== 27 +PQ== 28 +Pg== 29 +Pw== 30 +QA== 31 +QQ== 32 +Qg== 33 +Qw== 34 +RA== 35 +RQ== 36 +Rg== 37 +Rw== 38 +SA== 39 +SQ== 40 +Sg== 41 +Sw== 42 +TA== 43 +TQ== 44 +Tg== 45 +Tw== 46 +UA== 47 +UQ== 48 +Ug== 49 +Uw== 50 +VA== 51 +VQ== 52 +Vg== 53 +Vw== 54 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exact_div(N_SAMPLES, HOP_LENGTH) # 3000 frames in a mel spectrogram input + +N_SAMPLES_PER_TOKEN = HOP_LENGTH * 2 # the initial convolutions has stride 2 +FRAMES_PER_SECOND = exact_div(SAMPLE_RATE, HOP_LENGTH) # 10ms per audio frame +TOKENS_PER_SECOND = exact_div(SAMPLE_RATE, N_SAMPLES_PER_TOKEN) # 20ms per audio token + + +def load_audio(file: str, sr: int = SAMPLE_RATE): + """ + Open an audio file and read as mono waveform, resampling as necessary + + Parameters + ---------- + file: str + The audio file to open + + sr: int + The sample rate to resample the audio if necessary + + Returns + ------- + A NumPy array containing the audio waveform, in float32 dtype. + """ + + # This launches a subprocess to decode audio while down-mixing + # and resampling as necessary. Requires the ffmpeg CLI in PATH. + # fmt: off + cmd = [ + "ffmpeg", + "-nostdin", + "-threads", "0", + "-i", file, + "-f", "s16le", + "-ac", "1", + "-acodec", "pcm_s16le", + "-ar", str(sr), + "-" + ] + # fmt: on + try: + out = run(cmd, capture_output=True, check=True).stdout + except CalledProcessError as e: + raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e + + return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0 + + +def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1): + """ + Pad or trim the audio array to N_SAMPLES, as expected by the encoder. + """ + if torch.is_tensor(array): + if array.shape[axis] > length: + array = array.index_select( + dim=axis, index=torch.arange(length, device=array.device) + ) + + if array.shape[axis] < length: + pad_widths = [(0, 0)] * array.ndim + pad_widths[axis] = (0, length - array.shape[axis]) + array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes]) + else: + if array.shape[axis] > length: + array = array.take(indices=range(length), axis=axis) + + if array.shape[axis] < length: + pad_widths = [(0, 0)] * array.ndim + pad_widths[axis] = (0, length - array.shape[axis]) + array = np.pad(array, pad_widths) + + return array + + +@lru_cache(maxsize=None) +def mel_filters(device, n_mels: int) -> torch.Tensor: + """ + load the mel filterbank matrix for projecting STFT into a Mel spectrogram. + Allows decoupling librosa dependency; saved using: + + np.savez_compressed( + "mel_filters.npz", + mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80), + mel_128=librosa.filters.mel(sr=16000, n_fft=400, n_mels=128), + ) + """ + assert n_mels in {80, 128}, f"Unsupported n_mels: {n_mels}" + + filters_path = os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz") + with np.load(filters_path, allow_pickle=False) as f: + return torch.from_numpy(f[f"mel_{n_mels}"]).to(device) + + +def log_mel_spectrogram( + audio: Union[str, np.ndarray, torch.Tensor], + n_mels: int = 80, + padding: int = 0, + device: Optional[Union[str, torch.device]] = None, +): + """ + Compute the log-Mel spectrogram of + + Parameters + ---------- + audio: Union[str, np.ndarray, torch.Tensor], shape = (*) + The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz + + n_mels: int + The number of Mel-frequency filters, only 80 is supported + + padding: int + Number of zero samples to pad to the right + + device: Optional[Union[str, torch.device]] + If given, the audio tensor is moved to this device before STFT + + Returns + ------- + torch.Tensor, shape = (80, n_frames) + A Tensor that contains the Mel spectrogram + """ + if not torch.is_tensor(audio): + if isinstance(audio, str): + audio = load_audio(audio) + audio = torch.from_numpy(audio) + + if device is not None: + audio = audio.to(device) + if padding > 0: + audio = F.pad(audio, (0, padding)) + window = torch.hann_window(N_FFT).to(audio.device) + stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True) + magnitudes = stft[..., :-1].abs() ** 2 + + filters = mel_filters(audio.device, n_mels) + mel_spec = filters @ magnitudes + + log_spec = torch.clamp(mel_spec, min=1e-10).log10() + log_spec = torch.maximum(log_spec, log_spec.max() - 8.0) + log_spec = (log_spec + 4.0) / 4.0 + return log_spec diff --git a/xinference/thirdparty/whisper/decoding.py b/xinference/thirdparty/whisper/decoding.py new file mode 100644 index 0000000000..49485d0090 --- /dev/null +++ b/xinference/thirdparty/whisper/decoding.py @@ -0,0 +1,826 @@ +from dataclasses import dataclass, field, replace +from typing import TYPE_CHECKING, Dict, Iterable, List, Optional, Sequence, Tuple, Union + +import numpy as np +import torch +import torch.nn.functional as F +from torch import Tensor +from torch.distributions import Categorical + +from .audio import CHUNK_LENGTH +from .tokenizer import Tokenizer, get_tokenizer +from .utils import compression_ratio + +if TYPE_CHECKING: + from .model import Whisper + + +@torch.no_grad() +def detect_language( + model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None +) -> Tuple[Tensor, List[dict]]: + """ + Detect the spoken language in the audio, and return them as list of strings, along with the ids + of the most probable language tokens and the probability distribution over all language tokens. + This is performed outside the main decode loop in order to not interfere with kv-caching. + + Returns + ------- + language_tokens : Tensor, shape = (n_audio,) + ids of the most probable language tokens, which appears after the startoftranscript token. + language_probs : List[Dict[str, float]], length = n_audio + list of dictionaries containing the probability distribution over all languages. + """ + if tokenizer is None: + tokenizer = get_tokenizer( + model.is_multilingual, num_languages=model.num_languages + ) + if ( + tokenizer.language is None + or tokenizer.language_token not in tokenizer.sot_sequence + ): + raise ValueError( + "This model doesn't have language tokens so it can't perform lang id" + ) + + single = mel.ndim == 2 + if single: + mel = mel.unsqueeze(0) + + # skip encoder forward pass if already-encoded audio features were given + if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state): + mel = model.encoder(mel) + + # forward pass using a single token, startoftranscript + n_audio = mel.shape[0] + x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device) # [n_audio, 1] + logits = model.logits(x, mel)[:, 0] + + # collect detected languages; suppress all non-language tokens + mask = torch.ones(logits.shape[-1], dtype=torch.bool) + mask[list(tokenizer.all_language_tokens)] = False + logits[:, mask] = -np.inf + language_tokens = logits.argmax(dim=-1) + language_token_probs = logits.softmax(dim=-1).cpu() + language_probs = [ + { + c: language_token_probs[i, j].item() + for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes) + } + for i in range(n_audio) + ] + + if single: + language_tokens = language_tokens[0] + language_probs = language_probs[0] + + return language_tokens, language_probs + + +@dataclass(frozen=True) +class DecodingOptions: + # whether to perform X->X "transcribe" or X->English "translate" + task: str = "transcribe" + + # language that the audio is in; uses detected language if None + language: Optional[str] = None + + # sampling-related options + temperature: float = 0.0 + sample_len: Optional[int] = None # maximum number of tokens to sample + best_of: Optional[int] = None # number of independent sample trajectories, if t > 0 + beam_size: Optional[int] = None # number of beams in beam search, if t == 0 + patience: Optional[float] = None # patience in beam search (arxiv:2204.05424) + + # "alpha" in Google NMT, or None for length norm, when ranking generations + # to select which to return among the beams or best-of-N samples + length_penalty: Optional[float] = None + + # text or tokens to feed as the prompt or the prefix; for more info: + # https://github.com/openai/whisper/discussions/117#discussioncomment-3727051 + prompt: Optional[Union[str, List[int]]] = None # for the previous context + prefix: Optional[Union[str, List[int]]] = None # to prefix the current context + + # list of tokens ids (or comma-separated token ids) to suppress + # "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()` + suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1" + suppress_blank: bool = True # this will suppress blank outputs + + # timestamp sampling options + without_timestamps: bool = False # use <|notimestamps|> to sample text tokens only + max_initial_timestamp: Optional[float] = 1.0 + + # implementation details + fp16: bool = True # use fp16 for most of the calculation + + +@dataclass(frozen=True) +class DecodingResult: + audio_features: Tensor + language: str + language_probs: Optional[Dict[str, float]] = None + tokens: List[int] = field(default_factory=list) + text: str = "" + avg_logprob: float = np.nan + no_speech_prob: float = np.nan + temperature: float = np.nan + compression_ratio: float = np.nan + + +class Inference: + def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor: + """Perform a forward pass on the decoder and return per-token logits""" + raise NotImplementedError + + def rearrange_kv_cache(self, source_indices) -> None: + """Update the key-value cache according to the updated beams""" + raise NotImplementedError + + def cleanup_caching(self) -> None: + """Clean up any resources or hooks after decoding is finished""" + pass + + +class PyTorchInference(Inference): + def __init__(self, model: "Whisper", initial_token_length: int): + self.model: "Whisper" = model + self.initial_token_length = initial_token_length + self.kv_cache = {} + self.hooks = [] + + key_modules = [block.attn.key for block in self.model.decoder.blocks] + value_modules = [block.attn.value for block in self.model.decoder.blocks] + self.kv_modules = key_modules + value_modules + + def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor: + if not self.kv_cache: + self.kv_cache, self.hooks = self.model.install_kv_cache_hooks() + + if tokens.shape[-1] > self.initial_token_length: + # only need to use the last token except in the first forward pass + tokens = tokens[:, -1:] + + return self.model.decoder(tokens, audio_features, kv_cache=self.kv_cache) + + def cleanup_caching(self): + for hook in self.hooks: + hook.remove() + + self.kv_cache = {} + self.hooks = [] + + def rearrange_kv_cache(self, source_indices): + if source_indices != list(range(len(source_indices))): + for module in self.kv_modules: + # update the key/value cache to contain the selected sequences + self.kv_cache[module] = self.kv_cache[module][source_indices].detach() + + +class SequenceRanker: + def rank( + self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]] + ) -> List[int]: + """ + Given a list of groups of samples and their cumulative log probabilities, + return the indices of the samples in each group to select as the final result + """ + raise NotImplementedError + + +class MaximumLikelihoodRanker(SequenceRanker): + """ + Select the sample with the highest log probabilities, penalized using either + a simple length normalization or Google NMT paper's length penalty + """ + + def __init__(self, length_penalty: Optional[float]): + self.length_penalty = length_penalty + + def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]): + def scores(logprobs, lengths): + result = [] + for logprob, length in zip(logprobs, lengths): + if self.length_penalty is None: + penalty = length + else: + # from the Google NMT paper + penalty = ((5 + length) / 6) ** self.length_penalty + result.append(logprob / penalty) + return result + + # get the sequence with the highest score + lengths = [[len(t) for t in s] for s in tokens] + return [np.argmax(scores(p, l)) for p, l in zip(sum_logprobs, lengths)] + + +class TokenDecoder: + def reset(self): + """Initialize any stateful variables for decoding a new sequence""" + + def update( + self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor + ) -> Tuple[Tensor, bool]: + """Specify how to select the next token, based on the current trace and logits + + Parameters + ---------- + tokens : Tensor, shape = (n_batch, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence tokens + + logits : Tensor, shape = (n_batch, vocab_size) + per-token logits of the probability distribution at the current step + + sum_logprobs : Tensor, shape = (n_batch) + cumulative log probabilities for each sequence + + Returns + ------- + tokens : Tensor, shape = (n_batch, current_sequence_length + 1) + the tokens, appended with the selected next token + + completed : bool + True if all sequences has reached the end of text + + """ + raise NotImplementedError + + def finalize( + self, tokens: Tensor, sum_logprobs: Tensor + ) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]: + """Finalize search and return the final candidate sequences + + Parameters + ---------- + tokens : Tensor, shape = (n_audio, n_group, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence + + sum_logprobs : Tensor, shape = (n_audio, n_group) + cumulative log probabilities for each sequence + + Returns + ------- + tokens : Sequence[Sequence[Tensor]], length = n_audio + sequence of Tensors containing candidate token sequences, for each audio input + + sum_logprobs : List[List[float]], length = n_audio + sequence of cumulative log probabilities corresponding to the above + + """ + raise NotImplementedError + + +class GreedyDecoder(TokenDecoder): + def __init__(self, temperature: float, eot: int): + self.temperature = temperature + self.eot = eot + + def update( + self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor + ) -> Tuple[Tensor, bool]: + if self.temperature == 0: + next_tokens = logits.argmax(dim=-1) + else: + next_tokens = Categorical(logits=logits / self.temperature).sample() + + logprobs = F.log_softmax(logits.float(), dim=-1) + current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens] + sum_logprobs += current_logprobs * (tokens[:, -1] != self.eot) + + next_tokens[tokens[:, -1] == self.eot] = self.eot + tokens = torch.cat([tokens, next_tokens[:, None]], dim=-1) + + completed = (tokens[:, -1] == self.eot).all() + return tokens, completed + + def finalize(self, tokens: Tensor, sum_logprobs: Tensor): + # make sure each sequence has at least one EOT token at the end + tokens = F.pad(tokens, (0, 1), value=self.eot) + return tokens, sum_logprobs.tolist() + + +class BeamSearchDecoder(TokenDecoder): + def __init__( + self, + beam_size: int, + eot: int, + inference: Inference, + patience: Optional[float] = None, + ): + self.beam_size = beam_size + self.eot = eot + self.inference = inference + self.patience = patience or 1.0 + self.max_candidates: int = round(beam_size * self.patience) + self.finished_sequences = None + + assert ( + self.max_candidates > 0 + ), f"Invalid beam size ({beam_size}) or patience ({patience})" + + def reset(self): + self.finished_sequences = None + + def update( + self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor + ) -> Tuple[Tensor, bool]: + if tokens.shape[0] % self.beam_size != 0: + raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0") + + n_audio = tokens.shape[0] // self.beam_size + if self.finished_sequences is None: # for the first update + self.finished_sequences = [{} for _ in range(n_audio)] + + logprobs = F.log_softmax(logits.float(), dim=-1) + next_tokens, source_indices, finished_sequences = [], [], [] + for i in range(n_audio): + scores, sources, finished = {}, {}, {} + + # STEP 1: calculate the cumulative log probabilities for possible candidates + for j in range(self.beam_size): + idx = i * self.beam_size + j + prefix = tokens[idx].tolist() + for logprob, token in zip(*logprobs[idx].topk(self.beam_size + 1)): + new_logprob = (sum_logprobs[idx] + logprob).item() + sequence = tuple(prefix + [token.item()]) + scores[sequence] = new_logprob + sources[sequence] = idx + + # STEP 2: rank the candidates and keep the top beam_size sequences for each audio + saved = 0 + for sequence in sorted(scores, key=scores.get, reverse=True): + if sequence[-1] == self.eot: + finished[sequence] = scores[sequence] + else: + sum_logprobs[len(next_tokens)] = scores[sequence] + next_tokens.append(sequence) + source_indices.append(sources[sequence]) + + saved += 1 + if saved == self.beam_size: + break + + finished_sequences.append(finished) + + tokens = torch.tensor(next_tokens, device=tokens.device) + self.inference.rearrange_kv_cache(source_indices) + + # add newly finished sequences to self.finished_sequences + assert len(self.finished_sequences) == len(finished_sequences) + for previously_finished, newly_finished in zip( + self.finished_sequences, finished_sequences + ): + for seq in sorted(newly_finished, key=newly_finished.get, reverse=True): + if len(previously_finished) >= self.max_candidates: + break # the candidate list is full + previously_finished[seq] = newly_finished[seq] + + # mark as completed if all audio has enough number of samples + completed = all( + len(sequences) >= self.max_candidates + for sequences in self.finished_sequences + ) + return tokens, completed + + def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor): + # collect all finished sequences, including patience, and add unfinished ones if not enough + sum_logprobs = sum_logprobs.cpu() + for i, sequences in enumerate(self.finished_sequences): + if ( + len(sequences) < self.beam_size + ): # when not enough sequences are finished + for j in list(np.argsort(sum_logprobs[i]))[::-1]: + sequence = preceding_tokens[i, j].tolist() + [self.eot] + sequences[tuple(sequence)] = sum_logprobs[i][j].item() + if len(sequences) >= self.beam_size: + break + + tokens: List[List[Tensor]] = [ + [torch.tensor(seq) for seq in sequences.keys()] + for sequences in self.finished_sequences + ] + sum_logprobs: List[List[float]] = [ + list(sequences.values()) for sequences in self.finished_sequences + ] + return tokens, sum_logprobs + + +class LogitFilter: + def apply(self, logits: Tensor, tokens: Tensor) -> None: + """Apply any filtering or masking to logits in-place + + Parameters + ---------- + logits : Tensor, shape = (n_batch, vocab_size) + per-token logits of the probability distribution at the current step + + tokens : Tensor, shape = (n_batch, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence tokens + + """ + raise NotImplementedError + + +class SuppressBlank(LogitFilter): + def __init__(self, tokenizer: Tokenizer, sample_begin: int): + self.tokenizer = tokenizer + self.sample_begin = sample_begin + + def apply(self, logits: Tensor, tokens: Tensor): + if tokens.shape[1] == self.sample_begin: + logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf + + +class SuppressTokens(LogitFilter): + def __init__(self, suppress_tokens: Sequence[int]): + self.suppress_tokens = list(suppress_tokens) + + def apply(self, logits: Tensor, tokens: Tensor): + logits[:, self.suppress_tokens] = -np.inf + + +class ApplyTimestampRules(LogitFilter): + def __init__( + self, + tokenizer: Tokenizer, + sample_begin: int, + max_initial_timestamp_index: Optional[int], + ): + self.tokenizer = tokenizer + self.sample_begin = sample_begin + self.max_initial_timestamp_index = max_initial_timestamp_index + + def apply(self, logits: Tensor, tokens: Tensor): + # suppress <|notimestamps|> which is handled by without_timestamps + if self.tokenizer.no_timestamps is not None: + logits[:, self.tokenizer.no_timestamps] = -np.inf + + # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly + for k in range(tokens.shape[0]): + sampled_tokens = tokens[k, self.sample_begin :] + seq = [t for t in sampled_tokens.tolist()] + last_was_timestamp = ( + len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin + ) + penultimate_was_timestamp = ( + len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin + ) + + if last_was_timestamp: + if penultimate_was_timestamp: # has to be non-timestamp + logits[k, self.tokenizer.timestamp_begin :] = -np.inf + else: # cannot be normal text tokens + logits[k, : self.tokenizer.eot] = -np.inf + + timestamps = sampled_tokens[ + sampled_tokens.ge(self.tokenizer.timestamp_begin) + ] + if timestamps.numel() > 0: + # timestamps shouldn't decrease; forbid timestamp tokens smaller than the last + # also force each segment to have a nonzero length, to prevent infinite looping + if last_was_timestamp and not penultimate_was_timestamp: + timestamp_last = timestamps[-1] + else: + timestamp_last = timestamps[-1] + 1 + logits[k, self.tokenizer.timestamp_begin : timestamp_last] = -np.inf + + if tokens.shape[1] == self.sample_begin: + # suppress generating non-timestamp tokens at the beginning + logits[:, : self.tokenizer.timestamp_begin] = -np.inf + + # apply the `max_initial_timestamp` option + if self.max_initial_timestamp_index is not None: + last_allowed = ( + self.tokenizer.timestamp_begin + self.max_initial_timestamp_index + ) + logits[:, last_allowed + 1 :] = -np.inf + + # if sum of probability over timestamps is above any other token, sample timestamp + logprobs = F.log_softmax(logits.float(), dim=-1) + for k in range(tokens.shape[0]): + timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp( + dim=-1 + ) + max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max() + if timestamp_logprob > max_text_token_logprob: + logits[k, : self.tokenizer.timestamp_begin] = -np.inf + + +class DecodingTask: + inference: Inference + sequence_ranker: SequenceRanker + decoder: TokenDecoder + logit_filters: List[LogitFilter] + + def __init__(self, model: "Whisper", options: DecodingOptions): + self.model = model + + language = options.language or "en" + tokenizer = get_tokenizer( + model.is_multilingual, + num_languages=model.num_languages, + language=language, + task=options.task, + ) + self.tokenizer: Tokenizer = tokenizer + self.options: DecodingOptions = self._verify_options(options) + + self.n_group: int = options.beam_size or options.best_of or 1 + self.n_ctx: int = model.dims.n_text_ctx + self.sample_len: int = options.sample_len or model.dims.n_text_ctx // 2 + + self.sot_sequence: Tuple[int] = tokenizer.sot_sequence + if self.options.without_timestamps: + self.sot_sequence = tokenizer.sot_sequence_including_notimestamps + + self.initial_tokens: Tuple[int] = self._get_initial_tokens() + self.sample_begin: int = len(self.initial_tokens) + self.sot_index: int = self.initial_tokens.index(tokenizer.sot) + + # inference: implements the forward pass through the decoder, including kv caching + self.inference = PyTorchInference(model, len(self.initial_tokens)) + + # sequence ranker: implements how to rank a group of sampled sequences + self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty) + + # decoder: implements how to select the next tokens, given the autoregressive distribution + if options.beam_size is not None: + self.decoder = BeamSearchDecoder( + options.beam_size, tokenizer.eot, self.inference, options.patience + ) + else: + self.decoder = GreedyDecoder(options.temperature, tokenizer.eot) + + # logit filters: applies various rules to suppress or penalize certain tokens + self.logit_filters = [] + if self.options.suppress_blank: + self.logit_filters.append(SuppressBlank(self.tokenizer, self.sample_begin)) + if self.options.suppress_tokens: + self.logit_filters.append(SuppressTokens(self._get_suppress_tokens())) + if not options.without_timestamps: + precision = CHUNK_LENGTH / model.dims.n_audio_ctx # usually 0.02 seconds + max_initial_timestamp_index = None + if options.max_initial_timestamp: + max_initial_timestamp_index = round( + self.options.max_initial_timestamp / precision + ) + self.logit_filters.append( + ApplyTimestampRules( + tokenizer, self.sample_begin, max_initial_timestamp_index + ) + ) + + def _verify_options(self, options: DecodingOptions) -> DecodingOptions: + if options.beam_size is not None and options.best_of is not None: + raise ValueError("beam_size and best_of can't be given together") + if options.temperature == 0: + if options.best_of is not None: + raise ValueError("best_of with greedy sampling (T=0) is not compatible") + if options.patience is not None and options.beam_size is None: + raise ValueError("patience requires beam_size to be given") + if options.length_penalty is not None and not ( + 0 <= options.length_penalty <= 1 + ): + raise ValueError("length_penalty (alpha) should be a value between 0 and 1") + + return options + + def _get_initial_tokens(self) -> Tuple[int]: + tokens = list(self.sot_sequence) + + if prefix := self.options.prefix: + prefix_tokens = ( + self.tokenizer.encode(" " + prefix.strip()) + if isinstance(prefix, str) + else prefix + ) + if self.sample_len is not None: + max_prefix_len = self.n_ctx // 2 - self.sample_len + prefix_tokens = prefix_tokens[-max_prefix_len:] + tokens = tokens + prefix_tokens + + if prompt := self.options.prompt: + prompt_tokens = ( + self.tokenizer.encode(" " + prompt.strip()) + if isinstance(prompt, str) + else prompt + ) + tokens = ( + [self.tokenizer.sot_prev] + + prompt_tokens[-(self.n_ctx // 2 - 1) :] + + tokens + ) + + return tuple(tokens) + + def _get_suppress_tokens(self) -> Tuple[int]: + suppress_tokens = self.options.suppress_tokens + + if isinstance(suppress_tokens, str): + suppress_tokens = [int(t) for t in suppress_tokens.split(",")] + + if -1 in suppress_tokens: + suppress_tokens = [t for t in suppress_tokens if t >= 0] + suppress_tokens.extend(self.tokenizer.non_speech_tokens) + elif suppress_tokens is None or len(suppress_tokens) == 0: + suppress_tokens = [] # interpret empty string as an empty list + else: + assert isinstance(suppress_tokens, list), "suppress_tokens must be a list" + + suppress_tokens.extend( + [ + self.tokenizer.transcribe, + self.tokenizer.translate, + self.tokenizer.sot, + self.tokenizer.sot_prev, + self.tokenizer.sot_lm, + ] + ) + if self.tokenizer.no_speech is not None: + # no-speech probability is collected separately + suppress_tokens.append(self.tokenizer.no_speech) + + return tuple(sorted(set(suppress_tokens))) + + def _get_audio_features(self, mel: Tensor): + if self.options.fp16: + mel = mel.half() + + if mel.shape[-2:] == ( + self.model.dims.n_audio_ctx, + self.model.dims.n_audio_state, + ): + # encoded audio features are given; skip audio encoding + audio_features = mel + else: + audio_features = self.model.encoder(mel) + + if audio_features.dtype != ( + torch.float16 if self.options.fp16 else torch.float32 + ): + return TypeError( + f"audio_features has an incorrect dtype: {audio_features.dtype}" + ) + + return audio_features + + def _detect_language(self, audio_features: Tensor, tokens: Tensor): + languages = [self.options.language] * audio_features.shape[0] + lang_probs = None + + if self.options.language is None or self.options.task == "lang_id": + lang_tokens, lang_probs = self.model.detect_language( + audio_features, self.tokenizer + ) + languages = [max(probs, key=probs.get) for probs in lang_probs] + if self.options.language is None: + tokens[:, self.sot_index + 1] = lang_tokens # write language tokens + + return languages, lang_probs + + def _main_loop(self, audio_features: Tensor, tokens: Tensor): + n_batch = tokens.shape[0] + sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device) + no_speech_probs = [np.nan] * n_batch + + try: + for i in range(self.sample_len): + logits = self.inference.logits(tokens, audio_features) + + if ( + i == 0 and self.tokenizer.no_speech is not None + ): # save no_speech_probs + probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1) + no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist() + + # now we need to consider the logits at the last token only + logits = logits[:, -1] + + # apply the logit filters, e.g. for suppressing or applying penalty to + for logit_filter in self.logit_filters: + logit_filter.apply(logits, tokens) + + # expand the tokens tensor with the selected next tokens + tokens, completed = self.decoder.update(tokens, logits, sum_logprobs) + + if completed or tokens.shape[-1] > self.n_ctx: + break + finally: + self.inference.cleanup_caching() + + return tokens, sum_logprobs, no_speech_probs + + @torch.no_grad() + def run(self, mel: Tensor) -> List[DecodingResult]: + self.decoder.reset() + tokenizer: Tokenizer = self.tokenizer + n_audio: int = mel.shape[0] + + audio_features: Tensor = self._get_audio_features(mel) # encoder forward pass + tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1) + + # detect language if requested, overwriting the language token + languages, language_probs = self._detect_language(audio_features, tokens) + if self.options.task == "lang_id": + return [ + DecodingResult( + audio_features=features, language=language, language_probs=probs + ) + for features, language, probs in zip( + audio_features, languages, language_probs + ) + ] + + # repeat text tensors by the group size, for beam search or best-of-n sampling + tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device) + + # call the main sampling loop + tokens, sum_logprobs, no_speech_probs = self._main_loop(audio_features, tokens) + + # reshape the tensors to have (n_audio, n_group) as the first two dimensions + audio_features = audio_features[:: self.n_group] + no_speech_probs = no_speech_probs[:: self.n_group] + assert audio_features.shape[0] == len(no_speech_probs) == n_audio + + tokens = tokens.reshape(n_audio, self.n_group, -1) + sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group) + + # get the final candidates for each group, and slice between the first sampled token and EOT + tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs) + tokens: List[List[Tensor]] = [ + [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] + for s in tokens + ] + + # select the top-ranked sample in each group + selected = self.sequence_ranker.rank(tokens, sum_logprobs) + tokens: List[List[int]] = [t[i].tolist() for i, t in zip(selected, tokens)] + texts: List[str] = [tokenizer.decode(t).strip() for t in tokens] + + sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)] + avg_logprobs: List[float] = [ + lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs) + ] + + fields = ( + texts, + languages, + tokens, + audio_features, + avg_logprobs, + no_speech_probs, + ) + if len(set(map(len, fields))) != 1: + raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}") + + return [ + DecodingResult( + audio_features=features, + language=language, + tokens=tokens, + text=text, + avg_logprob=avg_logprob, + no_speech_prob=no_speech_prob, + temperature=self.options.temperature, + compression_ratio=compression_ratio(text), + ) + for text, language, tokens, features, avg_logprob, no_speech_prob in zip( + *fields + ) + ] + + +@torch.no_grad() +def decode( + model: "Whisper", + mel: Tensor, + options: DecodingOptions = DecodingOptions(), + **kwargs, +) -> Union[DecodingResult, List[DecodingResult]]: + """ + Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s). + + Parameters + ---------- + model: Whisper + the Whisper model instance + + mel: torch.Tensor, shape = (80, 3000) or (*, 80, 3000) + A tensor containing the Mel spectrogram(s) + + options: DecodingOptions + A dataclass that contains all necessary options for decoding 30-second segments + + Returns + ------- + result: Union[DecodingResult, List[DecodingResult]] + The result(s) of decoding contained in `DecodingResult` dataclass instance(s) + """ + if single := mel.ndim == 2: + mel = mel.unsqueeze(0) + + if kwargs: + options = replace(options, **kwargs) + + result = DecodingTask(model, options).run(mel) + + return result[0] if single else result diff --git a/xinference/thirdparty/whisper/model.py b/xinference/thirdparty/whisper/model.py new file mode 100644 index 0000000000..a678283974 --- /dev/null +++ b/xinference/thirdparty/whisper/model.py @@ -0,0 +1,314 @@ +import base64 +import gzip +from dataclasses import dataclass +from typing import Dict, Iterable, Optional + +import numpy as np +import torch +import torch.nn.functional as F +from torch import Tensor, nn + +from .decoding import decode as decode_function +from .decoding import detect_language as detect_language_function +from .transcribe import transcribe as transcribe_function + + +@dataclass +class ModelDimensions: + n_mels: int + n_audio_ctx: int + n_audio_state: int + n_audio_head: int + n_audio_layer: int + n_vocab: int + n_text_ctx: int + n_text_state: int + n_text_head: int + n_text_layer: int + + +class LayerNorm(nn.LayerNorm): + def forward(self, x: Tensor) -> Tensor: + return super().forward(x.float()).type(x.dtype) + + +class Linear(nn.Linear): + def forward(self, x: Tensor) -> Tensor: + return F.linear( + x, + self.weight.to(x.dtype), + None if self.bias is None else self.bias.to(x.dtype), + ) + + +class Conv1d(nn.Conv1d): + def _conv_forward( + self, x: Tensor, weight: Tensor, bias: Optional[Tensor] + ) -> Tensor: + return super()._conv_forward( + x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype) + ) + + +def sinusoids(length, channels, max_timescale=10000): + """Returns sinusoids for positional embedding""" + assert channels % 2 == 0 + log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1) + inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2)) + scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :] + return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1) + + +class MultiHeadAttention(nn.Module): + def __init__(self, n_state: int, n_head: int): + super().__init__() + self.n_head = n_head + self.query = Linear(n_state, n_state) + self.key = Linear(n_state, n_state, bias=False) + self.value = Linear(n_state, n_state) + self.out = Linear(n_state, n_state) + + def forward( + self, + x: Tensor, + xa: Optional[Tensor] = None, + mask: Optional[Tensor] = None, + kv_cache: Optional[dict] = None, + ): + q = self.query(x) + + if kv_cache is None or xa is None or self.key not in kv_cache: + # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors; + # otherwise, perform key/value projections for self- or cross-attention as usual. + k = self.key(x if xa is None else xa) + v = self.value(x if xa is None else xa) + else: + # for cross-attention, calculate keys and values once and reuse in subsequent calls. + k = kv_cache[self.key] + v = kv_cache[self.value] + + wv, qk = self.qkv_attention(q, k, v, mask) + return self.out(wv), qk + + def qkv_attention( + self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None + ): + n_batch, n_ctx, n_state = q.shape + scale = (n_state // self.n_head) ** -0.25 + q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale + k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale + v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) + + qk = q @ k + if mask is not None: + qk = qk + mask[:n_ctx, :n_ctx] + qk = qk.float() + + w = F.softmax(qk, dim=-1).to(q.dtype) + return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach() + + +class ResidualAttentionBlock(nn.Module): + def __init__(self, n_state: int, n_head: int, cross_attention: bool = False): + super().__init__() + + self.attn = MultiHeadAttention(n_state, n_head) + self.attn_ln = LayerNorm(n_state) + + self.cross_attn = ( + MultiHeadAttention(n_state, n_head) if cross_attention else None + ) + self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None + + n_mlp = n_state * 4 + self.mlp = nn.Sequential( + Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state) + ) + self.mlp_ln = LayerNorm(n_state) + + def forward( + self, + x: Tensor, + xa: Optional[Tensor] = None, + mask: Optional[Tensor] = None, + kv_cache: Optional[dict] = None, + ): + x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)[0] + if self.cross_attn: + x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)[0] + x = x + self.mlp(self.mlp_ln(x)) + return x + + +class AudioEncoder(nn.Module): + def __init__( + self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int + ): + super().__init__() + self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1) + self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1) + self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state)) + + self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( + [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)] + ) + self.ln_post = LayerNorm(n_state) + + def forward(self, x: Tensor): + """ + x : torch.Tensor, shape = (batch_size, n_mels, n_ctx) + the mel spectrogram of the audio + """ + x = F.gelu(self.conv1(x)) + x = F.gelu(self.conv2(x)) + x = x.permute(0, 2, 1) + + assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape" + x = (x + self.positional_embedding).to(x.dtype) + + for block in self.blocks: + x = block(x) + + x = self.ln_post(x) + return x + + +class TextDecoder(nn.Module): + def __init__( + self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int + ): + super().__init__() + + self.token_embedding = nn.Embedding(n_vocab, n_state) + self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state)) + + self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( + [ + ResidualAttentionBlock(n_state, n_head, cross_attention=True) + for _ in range(n_layer) + ] + ) + self.ln = LayerNorm(n_state) + + mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1) + self.register_buffer("mask", mask, persistent=False) + + def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None): + """ + x : torch.LongTensor, shape = (batch_size, <= n_ctx) + the text tokens + xa : torch.Tensor, shape = (batch_size, n_audio_ctx, n_audio_state) + the encoded audio features to be attended on + """ + offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0 + x = ( + self.token_embedding(x) + + self.positional_embedding[offset : offset + x.shape[-1]] + ) + x = x.to(xa.dtype) + + for block in self.blocks: + x = block(x, xa, mask=self.mask, kv_cache=kv_cache) + + x = self.ln(x) + logits = ( + x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1) + ).float() + + return logits + + +class Whisper(nn.Module): + def __init__(self, dims: ModelDimensions): + super().__init__() + self.dims = dims + self.encoder = AudioEncoder( + self.dims.n_mels, + self.dims.n_audio_ctx, + self.dims.n_audio_state, + self.dims.n_audio_head, + self.dims.n_audio_layer, + ) + self.decoder = TextDecoder( + self.dims.n_vocab, + self.dims.n_text_ctx, + self.dims.n_text_state, + self.dims.n_text_head, + self.dims.n_text_layer, + ) + # use the last half among the decoder layers for time alignment by default; + # to use a specific set of heads, see `set_alignment_heads()` below. + all_heads = torch.zeros( + self.dims.n_text_layer, self.dims.n_text_head, dtype=torch.bool + ) + all_heads[self.dims.n_text_layer // 2 :] = True + self.register_buffer("alignment_heads", all_heads.to_sparse(), persistent=False) + + def set_alignment_heads(self, dump: bytes): + array = np.frombuffer( + gzip.decompress(base64.b85decode(dump)), dtype=bool + ).copy() + mask = torch.from_numpy(array).reshape( + self.dims.n_text_layer, self.dims.n_text_head + ) + self.register_buffer("alignment_heads", mask.to_sparse(), persistent=False) + + def embed_audio(self, mel: torch.Tensor): + return self.encoder(mel) + + def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor): + return self.decoder(tokens, audio_features) + + def forward( + self, mel: torch.Tensor, tokens: torch.Tensor + ) -> Dict[str, torch.Tensor]: + return self.decoder(tokens, self.encoder(mel)) + + @property + def device(self): + return next(self.parameters()).device + + @property + def is_multilingual(self): + return self.dims.n_vocab >= 51865 + + @property + def num_languages(self): + return self.dims.n_vocab - 51765 - int(self.is_multilingual) + + def install_kv_cache_hooks(self, cache: Optional[dict] = None): + """ + The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value + tensors calculated for the previous positions. This method returns a dictionary that stores + all caches, and the necessary hooks for the key and value projection modules that save the + intermediate tensors to be reused during later calculations. + + Returns + ------- + cache : Dict[nn.Module, torch.Tensor] + A dictionary object mapping the key/value projection modules to its cache + hooks : List[RemovableHandle] + List of PyTorch RemovableHandle objects to stop the hooks to be called + """ + cache = {**cache} if cache is not None else {} + hooks = [] + + def save_to_cache(module, _, output): + if module not in cache or output.shape[1] > self.dims.n_text_ctx: + # save as-is, for the first token or cross attention + cache[module] = output + else: + cache[module] = torch.cat([cache[module], output], dim=1).detach() + return cache[module] + + def install_hooks(layer: nn.Module): + if isinstance(layer, MultiHeadAttention): + hooks.append(layer.key.register_forward_hook(save_to_cache)) + hooks.append(layer.value.register_forward_hook(save_to_cache)) + + self.decoder.apply(install_hooks) + return cache, hooks + + detect_language = detect_language_function + transcribe = transcribe_function + decode = decode_function diff --git a/xinference/thirdparty/whisper/normalizers/__init__.py b/xinference/thirdparty/whisper/normalizers/__init__.py new file mode 100644 index 0000000000..896d5e3364 --- /dev/null +++ b/xinference/thirdparty/whisper/normalizers/__init__.py @@ -0,0 +1,2 @@ +from .basic import BasicTextNormalizer as BasicTextNormalizer +from .english import EnglishTextNormalizer as EnglishTextNormalizer diff --git a/xinference/thirdparty/whisper/normalizers/basic.py b/xinference/thirdparty/whisper/normalizers/basic.py new file mode 100644 index 0000000000..a824032039 --- /dev/null +++ b/xinference/thirdparty/whisper/normalizers/basic.py @@ -0,0 +1,76 @@ +import re +import unicodedata + +import regex + +# non-ASCII letters that are not separated by "NFKD" normalization +ADDITIONAL_DIACRITICS = { + "œ": "oe", + "Œ": "OE", + "ø": "o", + "Ø": "O", + "æ": "ae", + "Æ": "AE", + "ß": "ss", + "ẞ": "SS", + "đ": "d", + "Đ": "D", + "ð": "d", + "Ð": "D", + "þ": "th", + "Þ": "th", + "ł": "l", + "Ł": "L", +} + + +def remove_symbols_and_diacritics(s: str, keep=""): + """ + Replace any other markers, symbols, and punctuations with a space, + and drop any diacritics (category 'Mn' and some manual mappings) + """ + return "".join( + c + if c in keep + else ADDITIONAL_DIACRITICS[c] + if c in ADDITIONAL_DIACRITICS + else "" + if unicodedata.category(c) == "Mn" + else " " + if unicodedata.category(c)[0] in "MSP" + else c + for c in unicodedata.normalize("NFKD", s) + ) + + +def remove_symbols(s: str): + """ + Replace any other markers, symbols, punctuations with a space, keeping diacritics + """ + return "".join( + " " if unicodedata.category(c)[0] in "MSP" else c + for c in unicodedata.normalize("NFKC", s) + ) + + +class BasicTextNormalizer: + def __init__(self, remove_diacritics: bool = False, split_letters: bool = False): + self.clean = ( + remove_symbols_and_diacritics if remove_diacritics else remove_symbols + ) + self.split_letters = split_letters + + def __call__(self, s: str): + s = s.lower() + s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets + s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis + s = self.clean(s).lower() + + if self.split_letters: + s = " ".join(regex.findall(r"\X", s, regex.U)) + + s = re.sub( + r"\s+", " ", s + ) # replace any successive whitespace characters with a space + + return s diff --git a/xinference/thirdparty/whisper/normalizers/english.json b/xinference/thirdparty/whisper/normalizers/english.json new file mode 100644 index 0000000000..74a1c3521d --- /dev/null +++ b/xinference/thirdparty/whisper/normalizers/english.json @@ -0,0 +1,1741 @@ +{ + "accessorise": "accessorize", + "accessorised": "accessorized", + "accessorises": "accessorizes", + "accessorising": "accessorizing", + "acclimatisation": "acclimatization", + "acclimatise": "acclimatize", + "acclimatised": "acclimatized", + "acclimatises": "acclimatizes", + "acclimatising": "acclimatizing", + "accoutrements": "accouterments", + "aeon": "eon", + "aeons": "eons", + "aerogramme": "aerogram", + "aerogrammes": "aerograms", + "aeroplane": "airplane", + "aeroplanes": "airplanes", + "aesthete": "esthete", + "aesthetes": "esthetes", + "aesthetic": "esthetic", + "aesthetically": "esthetically", + "aesthetics": "esthetics", + "aetiology": "etiology", + "ageing": "aging", + "aggrandisement": "aggrandizement", + "agonise": "agonize", + "agonised": "agonized", + "agonises": "agonizes", + "agonising": "agonizing", + "agonisingly": "agonizingly", + "almanack": "almanac", + "almanacks": "almanacs", + "aluminium": "aluminum", + "amortisable": "amortizable", + "amortisation": "amortization", + "amortisations": "amortizations", + "amortise": "amortize", + "amortised": "amortized", + "amortises": "amortizes", + "amortising": "amortizing", + "amphitheatre": "amphitheater", + "amphitheatres": "amphitheaters", + "anaemia": "anemia", + "anaemic": "anemic", + "anaesthesia": "anesthesia", + "anaesthetic": "anesthetic", + "anaesthetics": "anesthetics", + "anaesthetise": "anesthetize", + "anaesthetised": "anesthetized", + "anaesthetises": "anesthetizes", + "anaesthetising": "anesthetizing", + "anaesthetist": "anesthetist", + "anaesthetists": "anesthetists", + "anaesthetize": "anesthetize", + "anaesthetized": "anesthetized", + "anaesthetizes": "anesthetizes", + "anaesthetizing": "anesthetizing", + "analogue": "analog", + "analogues": "analogs", + "analyse": "analyze", + "analysed": "analyzed", + "analyses": "analyzes", + "analysing": "analyzing", + "anglicise": "anglicize", + "anglicised": "anglicized", + "anglicises": "anglicizes", + "anglicising": "anglicizing", + "annualised": "annualized", + "antagonise": "antagonize", + "antagonised": "antagonized", + "antagonises": "antagonizes", + "antagonising": "antagonizing", + "apologise": "apologize", + "apologised": "apologized", + "apologises": "apologizes", + "apologising": "apologizing", + "appal": "appall", + "appals": "appalls", + "appetiser": "appetizer", + "appetisers": "appetizers", + "appetising": "appetizing", + "appetisingly": "appetizingly", + "arbour": "arbor", + "arbours": "arbors", + "archeological": "archaeological", + "archaeologically": "archeologically", + "archaeologist": "archeologist", + "archaeologists": "archeologists", + "archaeology": "archeology", + "ardour": "ardor", + "armour": "armor", + "armoured": "armored", + "armourer": "armorer", + "armourers": "armorers", + "armouries": "armories", + "armoury": "armory", + "artefact": "artifact", + "artefacts": "artifacts", + "authorise": "authorize", + "authorised": "authorized", + "authorises": "authorizes", + "authorising": "authorizing", + "axe": "ax", + "backpedalled": "backpedaled", + "backpedalling": "backpedaling", + "bannister": "banister", + "bannisters": "banisters", + "baptise": "baptize", + "baptised": "baptized", + "baptises": "baptizes", + "baptising": "baptizing", + "bastardise": "bastardize", + "bastardised": "bastardized", + "bastardises": "bastardizes", + "bastardising": "bastardizing", + "battleax": "battleaxe", + "baulk": "balk", + "baulked": "balked", + "baulking": "balking", + "baulks": "balks", + "bedevilled": "bedeviled", + "bedevilling": "bedeviling", + "behaviour": "behavior", + "behavioural": "behavioral", + "behaviourism": "behaviorism", + "behaviourist": "behaviorist", + "behaviourists": "behaviorists", + "behaviours": "behaviors", + "behove": "behoove", + "behoved": "behooved", + "behoves": "behooves", + "bejewelled": "bejeweled", + "belabour": "belabor", + "belaboured": "belabored", + "belabouring": "belaboring", + "belabours": "belabors", + "bevelled": "beveled", + "bevvies": "bevies", + "bevvy": "bevy", + "biassed": "biased", + "biassing": "biasing", + "bingeing": "binging", + "bougainvillaea": "bougainvillea", + "bougainvillaeas": "bougainvilleas", + "bowdlerise": "bowdlerize", + "bowdlerised": "bowdlerized", + "bowdlerises": "bowdlerizes", + "bowdlerising": "bowdlerizing", + "breathalyse": "breathalyze", + "breathalysed": "breathalyzed", + "breathalyser": "breathalyzer", + "breathalysers": "breathalyzers", + "breathalyses": "breathalyzes", + "breathalysing": "breathalyzing", + "brutalise": "brutalize", + "brutalised": "brutalized", + "brutalises": "brutalizes", + "brutalising": "brutalizing", + "busses": "buses", + "bussing": "busing", + "caesarean": "cesarean", + "caesareans": "cesareans", + "calibre": "caliber", + "calibres": "calibers", + "calliper": "caliper", + "callipers": "calipers", + "callisthenics": "calisthenics", + "canalise": "canalize", + "canalised": "canalized", + "canalises": "canalizes", + "canalising": "canalizing", + "cancelation": "cancellation", + "cancelations": "cancellations", + "cancelled": "canceled", + "cancelling": "canceling", + "candour": "candor", + "cannibalise": "cannibalize", + "cannibalised": "cannibalized", + "cannibalises": "cannibalizes", + "cannibalising": "cannibalizing", + "canonise": "canonize", + "canonised": "canonized", + "canonises": "canonizes", + "canonising": "canonizing", + "capitalise": "capitalize", + "capitalised": "capitalized", + "capitalises": "capitalizes", + "capitalising": "capitalizing", + "caramelise": "caramelize", + "caramelised": "caramelized", + "caramelises": "caramelizes", + "caramelising": "caramelizing", + "carbonise": "carbonize", + "carbonised": "carbonized", + "carbonises": "carbonizes", + "carbonising": "carbonizing", + "carolled": "caroled", + "carolling": "caroling", + "catalogue": "catalog", + "catalogued": "cataloged", + "catalogues": "catalogs", + "cataloguing": "cataloging", + "catalyse": "catalyze", + "catalysed": "catalyzed", + "catalyses": "catalyzes", + "catalysing": "catalyzing", + "categorise": "categorize", + "categorised": "categorized", + "categorises": "categorizes", + "categorising": "categorizing", + "cauterise": "cauterize", + "cauterised": "cauterized", + "cauterises": "cauterizes", + "cauterising": "cauterizing", + "cavilled": "caviled", + "cavilling": "caviling", + "centigramme": "centigram", + "centigrammes": "centigrams", + "centilitre": "centiliter", + "centilitres": "centiliters", + "centimetre": "centimeter", + "centimetres": "centimeters", + "centralise": "centralize", + "centralised": "centralized", + "centralises": "centralizes", + "centralising": "centralizing", + "centre": "center", + "centred": "centered", + "centrefold": "centerfold", + "centrefolds": "centerfolds", + "centrepiece": "centerpiece", + "centrepieces": "centerpieces", + "centres": "centers", + "channelled": "channeled", + "channelling": "channeling", + "characterise": "characterize", + "characterised": "characterized", + "characterises": "characterizes", + "characterising": "characterizing", + "cheque": "check", + "chequebook": "checkbook", + "chequebooks": "checkbooks", + "chequered": "checkered", + "cheques": "checks", + "chilli": "chili", + "chimaera": "chimera", + "chimaeras": "chimeras", + "chiselled": "chiseled", + "chiselling": "chiseling", + "circularise": "circularize", + "circularised": "circularized", + "circularises": "circularizes", + "circularising": "circularizing", + "civilise": "civilize", + "civilised": "civilized", + "civilises": "civilizes", + "civilising": "civilizing", + "clamour": "clamor", + "clamoured": "clamored", + "clamouring": "clamoring", + "clamours": "clamors", + "clangour": "clangor", + "clarinettist": "clarinetist", + "clarinettists": "clarinetists", + "collectivise": "collectivize", + "collectivised": "collectivized", + "collectivises": "collectivizes", + "collectivising": "collectivizing", + "colonisation": "colonization", + "colonise": "colonize", + "colonised": "colonized", + "coloniser": "colonizer", + "colonisers": "colonizers", + "colonises": "colonizes", + "colonising": "colonizing", + "colour": "color", + "colourant": "colorant", + "colourants": "colorants", + "coloured": "colored", + "coloureds": "coloreds", + "colourful": "colorful", + "colourfully": "colorfully", + "colouring": "coloring", + "colourize": "colorize", + "colourized": "colorized", + "colourizes": "colorizes", + "colourizing": "colorizing", + "colourless": "colorless", + "colours": "colors", + "commercialise": "commercialize", + "commercialised": "commercialized", + "commercialises": "commercializes", + "commercialising": "commercializing", + "compartmentalise": "compartmentalize", + "compartmentalised": "compartmentalized", + "compartmentalises": "compartmentalizes", + "compartmentalising": "compartmentalizing", + "computerise": "computerize", + "computerised": "computerized", + "computerises": "computerizes", + "computerising": "computerizing", + "conceptualise": "conceptualize", + "conceptualised": "conceptualized", + "conceptualises": "conceptualizes", + "conceptualising": "conceptualizing", + "connexion": "connection", + "connexions": "connections", + "contextualise": "contextualize", + "contextualised": "contextualized", + "contextualises": "contextualizes", + "contextualising": "contextualizing", + "cosier": "cozier", + "cosies": "cozies", + "cosiest": "coziest", + "cosily": "cozily", + "cosiness": "coziness", + "cosy": "cozy", + "councillor": "councilor", + "councillors": "councilors", + "counselled": "counseled", + "counselling": "counseling", + "counsellor": "counselor", + "counsellors": "counselors", + "crenelated": "crenellated", + "criminalise": "criminalize", + "criminalised": "criminalized", + "criminalises": "criminalizes", + "criminalising": "criminalizing", + "criticise": "criticize", + "criticised": "criticized", + "criticises": "criticizes", + "criticising": "criticizing", + "crueller": "crueler", + "cruellest": "cruelest", + "crystallisation": "crystallization", + "crystallise": "crystallize", + "crystallised": "crystallized", + "crystallises": "crystallizes", + "crystallising": "crystallizing", + "cudgelled": "cudgeled", + "cudgelling": "cudgeling", + "customise": "customize", + "customised": "customized", + "customises": "customizes", + "customising": "customizing", + "cypher": "cipher", + "cyphers": "ciphers", + "decentralisation": "decentralization", + "decentralise": "decentralize", + "decentralised": "decentralized", + "decentralises": "decentralizes", + "decentralising": "decentralizing", + "decriminalisation": "decriminalization", + "decriminalise": "decriminalize", + "decriminalised": "decriminalized", + "decriminalises": "decriminalizes", + "decriminalising": "decriminalizing", + "defence": "defense", + "defenceless": "defenseless", + "defences": "defenses", + "dehumanisation": "dehumanization", + "dehumanise": "dehumanize", + "dehumanised": "dehumanized", + "dehumanises": "dehumanizes", + "dehumanising": "dehumanizing", + "demeanour": "demeanor", + "demilitarisation": "demilitarization", + "demilitarise": "demilitarize", + "demilitarised": "demilitarized", + "demilitarises": "demilitarizes", + "demilitarising": "demilitarizing", + "demobilisation": "demobilization", + "demobilise": "demobilize", + "demobilised": "demobilized", + "demobilises": "demobilizes", + "demobilising": "demobilizing", + "democratisation": "democratization", + "democratise": "democratize", + "democratised": "democratized", + "democratises": "democratizes", + "democratising": "democratizing", + "demonise": "demonize", + "demonised": "demonized", + "demonises": "demonizes", + "demonising": "demonizing", + "demoralisation": "demoralization", + "demoralise": "demoralize", + "demoralised": "demoralized", + "demoralises": "demoralizes", + "demoralising": "demoralizing", + "denationalisation": "denationalization", + "denationalise": "denationalize", + "denationalised": "denationalized", + "denationalises": "denationalizes", + "denationalising": "denationalizing", + "deodorise": "deodorize", + "deodorised": "deodorized", + "deodorises": "deodorizes", + "deodorising": "deodorizing", + "depersonalise": "depersonalize", + "depersonalised": "depersonalized", + "depersonalises": "depersonalizes", + "depersonalising": "depersonalizing", + "deputise": "deputize", + "deputised": "deputized", + "deputises": "deputizes", + "deputising": "deputizing", + "desensitisation": "desensitization", + "desensitise": "desensitize", + "desensitised": "desensitized", + "desensitises": "desensitizes", + "desensitising": "desensitizing", + "destabilisation": "destabilization", + "destabilise": "destabilize", + "destabilised": "destabilized", + "destabilises": "destabilizes", + "destabilising": "destabilizing", + "dialled": "dialed", + "dialling": "dialing", + "dialogue": "dialog", + "dialogues": "dialogs", + "diarrhoea": "diarrhea", + "digitise": "digitize", + "digitised": "digitized", + "digitises": "digitizes", + "digitising": "digitizing", + "disc": "disk", + "discolour": "discolor", + "discoloured": "discolored", + "discolouring": "discoloring", + "discolours": "discolors", + "discs": "disks", + "disembowelled": "disemboweled", + "disembowelling": "disemboweling", + "disfavour": "disfavor", + "dishevelled": "disheveled", + "dishonour": "dishonor", + "dishonourable": "dishonorable", + "dishonourably": "dishonorably", + "dishonoured": "dishonored", + "dishonouring": "dishonoring", + "dishonours": "dishonors", + "disorganisation": "disorganization", + "disorganised": "disorganized", + "distil": "distill", + "distils": "distills", + "dramatisation": "dramatization", + "dramatisations": "dramatizations", + "dramatise": "dramatize", + "dramatised": "dramatized", + "dramatises": "dramatizes", + "dramatising": "dramatizing", + "draught": "draft", + "draughtboard": "draftboard", + "draughtboards": "draftboards", + "draughtier": "draftier", + "draughtiest": "draftiest", + "draughts": "drafts", + "draughtsman": "draftsman", + "draughtsmanship": "draftsmanship", + "draughtsmen": "draftsmen", + "draughtswoman": "draftswoman", + "draughtswomen": "draftswomen", + "draughty": "drafty", + "drivelled": "driveled", + "drivelling": "driveling", + "duelled": "dueled", + "duelling": "dueling", + "economise": "economize", + "economised": "economized", + "economises": "economizes", + "economising": "economizing", + "edoema": "edema", + "editorialise": "editorialize", + "editorialised": "editorialized", + "editorialises": "editorializes", + "editorialising": "editorializing", + "empathise": "empathize", + "empathised": "empathized", + "empathises": "empathizes", + "empathising": "empathizing", + "emphasise": "emphasize", + "emphasised": "emphasized", + "emphasises": "emphasizes", + "emphasising": "emphasizing", + "enamelled": "enameled", + "enamelling": "enameling", + "enamoured": "enamored", + "encyclopaedia": "encyclopedia", + "encyclopaedias": "encyclopedias", + "encyclopaedic": "encyclopedic", + "endeavour": "endeavor", + "endeavoured": "endeavored", + "endeavouring": "endeavoring", + "endeavours": "endeavors", + "energise": "energize", + "energised": "energized", + "energises": "energizes", + "energising": "energizing", + "enrol": "enroll", + "enrols": "enrolls", + "enthral": "enthrall", + "enthrals": "enthralls", + "epaulette": "epaulet", + "epaulettes": "epaulets", + "epicentre": "epicenter", + "epicentres": "epicenters", + "epilogue": "epilog", + "epilogues": "epilogs", + "epitomise": "epitomize", + "epitomised": "epitomized", + "epitomises": "epitomizes", + "epitomising": "epitomizing", + "equalisation": "equalization", + "equalise": "equalize", + "equalised": "equalized", + "equaliser": "equalizer", + "equalisers": "equalizers", + "equalises": "equalizes", + "equalising": "equalizing", + "eulogise": "eulogize", + "eulogised": "eulogized", + "eulogises": "eulogizes", + "eulogising": "eulogizing", + "evangelise": "evangelize", + "evangelised": "evangelized", + "evangelises": "evangelizes", + "evangelising": "evangelizing", + "exorcise": "exorcize", + "exorcised": "exorcized", + "exorcises": "exorcizes", + "exorcising": "exorcizing", + "extemporisation": "extemporization", + "extemporise": "extemporize", + "extemporised": "extemporized", + "extemporises": "extemporizes", + "extemporising": "extemporizing", + "externalisation": "externalization", + "externalisations": "externalizations", + "externalise": "externalize", + "externalised": "externalized", + "externalises": "externalizes", + "externalising": "externalizing", + "factorise": "factorize", + "factorised": "factorized", + "factorises": "factorizes", + "factorising": "factorizing", + "faecal": "fecal", + "faeces": "feces", + "familiarisation": "familiarization", + "familiarise": "familiarize", + "familiarised": "familiarized", + "familiarises": "familiarizes", + "familiarising": "familiarizing", + "fantasise": "fantasize", + "fantasised": "fantasized", + "fantasises": "fantasizes", + "fantasising": "fantasizing", + "favour": "favor", + "favourable": "favorable", + "favourably": "favorably", + "favoured": "favored", + "favouring": "favoring", + "favourite": "favorite", + "favourites": "favorites", + "favouritism": "favoritism", + "favours": "favors", + "feminise": "feminize", + "feminised": "feminized", + "feminises": "feminizes", + "feminising": "feminizing", + "fertilisation": "fertilization", + "fertilise": "fertilize", + "fertilised": "fertilized", + "fertiliser": "fertilizer", + "fertilisers": "fertilizers", + "fertilises": "fertilizes", + "fertilising": "fertilizing", + "fervour": "fervor", + "fibre": "fiber", + "fibreglass": "fiberglass", + "fibres": "fibers", + "fictionalisation": "fictionalization", + "fictionalisations": "fictionalizations", + "fictionalise": "fictionalize", + "fictionalised": "fictionalized", + "fictionalises": "fictionalizes", + "fictionalising": "fictionalizing", + "fillet": "filet", + "filleted": "fileted", + "filleting": "fileting", + "fillets": "filets", + "finalisation": "finalization", + "finalise": "finalize", + "finalised": "finalized", + "finalises": "finalizes", + "finalising": "finalizing", + "flautist": "flutist", + "flautists": "flutists", + "flavour": "flavor", + "flavoured": "flavored", + "flavouring": "flavoring", + "flavourings": "flavorings", + "flavourless": "flavorless", + "flavours": "flavors", + "flavoursome": "flavorsome", + "flyer / flier": "flier / flyer", + "foetal": "fetal", + "foetid": "fetid", + "foetus": "fetus", + "foetuses": "fetuses", + "formalisation": "formalization", + "formalise": "formalize", + "formalised": "formalized", + "formalises": "formalizes", + "formalising": "formalizing", + "fossilisation": "fossilization", + "fossilise": "fossilize", + "fossilised": "fossilized", + "fossilises": "fossilizes", + "fossilising": "fossilizing", + "fraternisation": "fraternization", + "fraternise": "fraternize", + "fraternised": "fraternized", + "fraternises": "fraternizes", + "fraternising": "fraternizing", + "fulfil": "fulfill", + "fulfilment": "fulfillment", + "fulfils": "fulfills", + "funnelled": "funneled", + "funnelling": "funneling", + "galvanise": "galvanize", + "galvanised": "galvanized", + "galvanises": "galvanizes", + "galvanising": "galvanizing", + "gambolled": "gamboled", + "gambolling": "gamboling", + "gaol": "jail", + "gaolbird": "jailbird", + "gaolbirds": "jailbirds", + "gaolbreak": "jailbreak", + "gaolbreaks": "jailbreaks", + "gaoled": "jailed", + "gaoler": "jailer", + "gaolers": "jailers", + "gaoling": "jailing", + "gaols": "jails", + "gasses": "gases", + "gage": "gauge", + "gaged": "gauged", + "gages": "gauges", + "gaging": "gauging", + "generalisation": "generalization", + "generalisations": "generalizations", + "generalise": "generalize", + "generalised": "generalized", + "generalises": "generalizes", + "generalising": "generalizing", + "ghettoise": "ghettoize", + "ghettoised": "ghettoized", + "ghettoises": "ghettoizes", + "ghettoising": "ghettoizing", + "gipsies": "gypsies", + "glamorise": "glamorize", + "glamorised": "glamorized", + "glamorises": "glamorizes", + "glamorising": "glamorizing", + "glamor": "glamour", + "globalisation": "globalization", + "globalise": "globalize", + "globalised": "globalized", + "globalises": "globalizes", + "globalising": "globalizing", + "glueing": "gluing", + "goitre": "goiter", + "goitres": "goiters", + "gonorrhoea": "gonorrhea", + "gramme": "gram", + "grammes": "grams", + "gravelled": "graveled", + "grey": "gray", + "greyed": "grayed", + "greying": "graying", + "greyish": "grayish", + "greyness": "grayness", + "greys": "grays", + "grovelled": "groveled", + "grovelling": "groveling", + "groyne": "groin", + "groynes": "groins", + "gruelling": "grueling", + "gruellingly": "gruelingly", + "gryphon": "griffin", + "gryphons": "griffins", + "gynaecological": "gynecological", + "gynaecologist": "gynecologist", + "gynaecologists": "gynecologists", + "gynaecology": "gynecology", + "haematological": "hematological", + "haematologist": "hematologist", + "haematologists": "hematologists", + "haematology": "hematology", + "haemoglobin": "hemoglobin", + "haemophilia": "hemophilia", + "haemophiliac": "hemophiliac", + "haemophiliacs": "hemophiliacs", + "haemorrhage": "hemorrhage", + "haemorrhaged": "hemorrhaged", + "haemorrhages": "hemorrhages", + "haemorrhaging": "hemorrhaging", + "haemorrhoids": "hemorrhoids", + "harbour": "harbor", + "harboured": "harbored", + "harbouring": "harboring", + "harbours": "harbors", + "harmonisation": "harmonization", + "harmonise": "harmonize", + "harmonised": "harmonized", + "harmonises": "harmonizes", + "harmonising": "harmonizing", + "homoeopath": "homeopath", + "homoeopathic": "homeopathic", + "homoeopaths": "homeopaths", + "homoeopathy": "homeopathy", + "homogenise": "homogenize", + "homogenised": "homogenized", + "homogenises": "homogenizes", + "homogenising": "homogenizing", + "honour": "honor", + "honourable": "honorable", + "honourably": "honorably", + "honoured": "honored", + "honouring": "honoring", + "honours": "honors", + "hospitalisation": "hospitalization", + "hospitalise": "hospitalize", + "hospitalised": "hospitalized", + "hospitalises": "hospitalizes", + "hospitalising": "hospitalizing", + "humanise": "humanize", + "humanised": "humanized", + "humanises": "humanizes", + "humanising": "humanizing", + "humour": "humor", + "humoured": "humored", + "humouring": "humoring", + "humourless": "humorless", + "humours": "humors", + "hybridise": "hybridize", + "hybridised": "hybridized", + "hybridises": "hybridizes", + "hybridising": "hybridizing", + "hypnotise": "hypnotize", + "hypnotised": "hypnotized", + "hypnotises": "hypnotizes", + "hypnotising": "hypnotizing", + "hypothesise": "hypothesize", + "hypothesised": "hypothesized", + "hypothesises": "hypothesizes", + "hypothesising": "hypothesizing", + "idealisation": "idealization", + "idealise": "idealize", + "idealised": "idealized", + "idealises": "idealizes", + "idealising": "idealizing", + "idolise": "idolize", + "idolised": "idolized", + "idolises": "idolizes", + "idolising": "idolizing", + "immobilisation": "immobilization", + "immobilise": "immobilize", + "immobilised": "immobilized", + "immobiliser": "immobilizer", + "immobilisers": "immobilizers", + "immobilises": "immobilizes", + "immobilising": "immobilizing", + "immortalise": "immortalize", + "immortalised": "immortalized", + "immortalises": "immortalizes", + "immortalising": "immortalizing", + "immunisation": "immunization", + "immunise": "immunize", + "immunised": "immunized", + "immunises": "immunizes", + "immunising": "immunizing", + "impanelled": "impaneled", + "impanelling": "impaneling", + "imperilled": "imperiled", + "imperilling": "imperiling", + "individualise": "individualize", + "individualised": "individualized", + "individualises": "individualizes", + "individualising": "individualizing", + "industrialise": "industrialize", + "industrialised": "industrialized", + "industrialises": "industrializes", + "industrialising": "industrializing", + "inflexion": "inflection", + "inflexions": "inflections", + "initialise": "initialize", + "initialised": "initialized", + "initialises": "initializes", + "initialising": "initializing", + "initialled": "initialed", + "initialling": "initialing", + "instal": "install", + "instalment": "installment", + "instalments": "installments", + "instals": "installs", + "instil": "instill", + "instils": "instills", + "institutionalisation": "institutionalization", + "institutionalise": "institutionalize", + "institutionalised": "institutionalized", + "institutionalises": "institutionalizes", + "institutionalising": "institutionalizing", + "intellectualise": "intellectualize", + "intellectualised": "intellectualized", + "intellectualises": "intellectualizes", + "intellectualising": "intellectualizing", + "internalisation": "internalization", + "internalise": "internalize", + "internalised": "internalized", + "internalises": "internalizes", + "internalising": "internalizing", + "internationalisation": "internationalization", + "internationalise": "internationalize", + "internationalised": "internationalized", + "internationalises": "internationalizes", + "internationalising": "internationalizing", + "ionisation": "ionization", + "ionise": "ionize", + "ionised": "ionized", + "ioniser": "ionizer", + "ionisers": "ionizers", + "ionises": "ionizes", + "ionising": "ionizing", + "italicise": "italicize", + "italicised": "italicized", + "italicises": "italicizes", + "italicising": "italicizing", + "itemise": "itemize", + "itemised": "itemized", + "itemises": "itemizes", + "itemising": "itemizing", + "jeopardise": "jeopardize", + "jeopardised": "jeopardized", + "jeopardises": "jeopardizes", + "jeopardising": "jeopardizing", + "jewelled": "jeweled", + "jeweller": "jeweler", + "jewellers": "jewelers", + "jewellery": "jewelry", + "judgement": "judgment", + "kilogramme": "kilogram", + "kilogrammes": "kilograms", + "kilometre": "kilometer", + "kilometres": "kilometers", + "labelled": "labeled", + "labelling": "labeling", + "labour": "labor", + "laboured": "labored", + "labourer": "laborer", + "labourers": "laborers", + "labouring": "laboring", + "labours": "labors", + "lacklustre": "lackluster", + "legalisation": "legalization", + "legalise": "legalize", + "legalised": "legalized", + "legalises": "legalizes", + "legalising": "legalizing", + "legitimise": "legitimize", + "legitimised": "legitimized", + "legitimises": "legitimizes", + "legitimising": "legitimizing", + "leukaemia": "leukemia", + "levelled": "leveled", + "leveller": "leveler", + "levellers": "levelers", + "levelling": "leveling", + "libelled": "libeled", + "libelling": "libeling", + "libellous": "libelous", + "liberalisation": "liberalization", + "liberalise": "liberalize", + "liberalised": "liberalized", + "liberalises": "liberalizes", + "liberalising": "liberalizing", + "licence": "license", + "licenced": "licensed", + "licences": "licenses", + "licencing": "licensing", + "likeable": "likable", + "lionisation": "lionization", + "lionise": "lionize", + "lionised": "lionized", + "lionises": "lionizes", + "lionising": "lionizing", + "liquidise": "liquidize", + "liquidised": "liquidized", + "liquidiser": "liquidizer", + "liquidisers": "liquidizers", + "liquidises": "liquidizes", + "liquidising": "liquidizing", + "litre": "liter", + "litres": "liters", + "localise": "localize", + "localised": "localized", + "localises": "localizes", + "localising": "localizing", + "louvre": "louver", + "louvred": "louvered", + "louvres": "louvers", + "lustre": "luster", + "magnetise": "magnetize", + "magnetised": "magnetized", + "magnetises": "magnetizes", + "magnetising": "magnetizing", + "manoeuvrability": "maneuverability", + "manoeuvrable": "maneuverable", + "manoeuvre": "maneuver", + "manoeuvred": "maneuvered", + "manoeuvres": "maneuvers", + "manoeuvring": "maneuvering", + "manoeuvrings": "maneuverings", + "marginalisation": "marginalization", + "marginalise": "marginalize", + "marginalised": "marginalized", + "marginalises": "marginalizes", + "marginalising": "marginalizing", + "marshalled": "marshaled", + "marshalling": "marshaling", + "marvelled": "marveled", + "marvelling": "marveling", + "marvellous": "marvelous", + "marvellously": "marvelously", + "materialisation": "materialization", + "materialise": "materialize", + "materialised": "materialized", + "materialises": "materializes", + "materialising": "materializing", + "maximisation": "maximization", + "maximise": "maximize", + "maximised": "maximized", + "maximises": "maximizes", + "maximising": "maximizing", + "meagre": "meager", + "mechanisation": "mechanization", + "mechanise": "mechanize", + "mechanised": "mechanized", + "mechanises": "mechanizes", + "mechanising": "mechanizing", + "mediaeval": "medieval", + "memorialise": "memorialize", + "memorialised": "memorialized", + "memorialises": "memorializes", + "memorialising": "memorializing", + "memorise": "memorize", + "memorised": "memorized", + "memorises": "memorizes", + "memorising": "memorizing", + "mesmerise": "mesmerize", + "mesmerised": "mesmerized", + "mesmerises": "mesmerizes", + "mesmerising": "mesmerizing", + "metabolise": "metabolize", + "metabolised": "metabolized", + "metabolises": "metabolizes", + "metabolising": "metabolizing", + "metre": "meter", + "metres": "meters", + "micrometre": "micrometer", + "micrometres": "micrometers", + "militarise": "militarize", + "militarised": "militarized", + "militarises": "militarizes", + "militarising": "militarizing", + "milligramme": "milligram", + "milligrammes": "milligrams", + "millilitre": "milliliter", + "millilitres": "milliliters", + "millimetre": "millimeter", + "millimetres": "millimeters", + "miniaturisation": "miniaturization", + "miniaturise": "miniaturize", + "miniaturised": "miniaturized", + "miniaturises": "miniaturizes", + "miniaturising": "miniaturizing", + "minibusses": "minibuses", + "minimise": "minimize", + "minimised": "minimized", + "minimises": "minimizes", + "minimising": "minimizing", + "misbehaviour": "misbehavior", + "misdemeanour": "misdemeanor", + "misdemeanours": "misdemeanors", + "misspelt": "misspelled", + "mitre": "miter", + "mitres": "miters", + "mobilisation": "mobilization", + "mobilise": "mobilize", + "mobilised": "mobilized", + "mobilises": "mobilizes", + "mobilising": "mobilizing", + "modelled": "modeled", + "modeller": "modeler", + "modellers": "modelers", + "modelling": "modeling", + "modernise": "modernize", + "modernised": "modernized", + "modernises": "modernizes", + "modernising": "modernizing", + "moisturise": "moisturize", + "moisturised": "moisturized", + "moisturiser": "moisturizer", + "moisturisers": "moisturizers", + "moisturises": "moisturizes", + "moisturising": "moisturizing", + "monologue": "monolog", + "monologues": "monologs", + "monopolisation": "monopolization", + "monopolise": "monopolize", + "monopolised": "monopolized", + "monopolises": "monopolizes", + "monopolising": "monopolizing", + "moralise": "moralize", + "moralised": "moralized", + "moralises": "moralizes", + "moralising": "moralizing", + "motorised": "motorized", + "mould": "mold", + "moulded": "molded", + "moulder": "molder", + "mouldered": "moldered", + "mouldering": "moldering", + "moulders": "molders", + "mouldier": "moldier", + "mouldiest": "moldiest", + "moulding": "molding", + "mouldings": "moldings", + "moulds": "molds", + "mouldy": "moldy", + "moult": "molt", + "moulted": "molted", + "moulting": "molting", + "moults": "molts", + "moustache": "mustache", + "moustached": "mustached", + "moustaches": "mustaches", + "moustachioed": "mustachioed", + "multicoloured": "multicolored", + "nationalisation": "nationalization", + "nationalisations": "nationalizations", + "nationalise": "nationalize", + "nationalised": "nationalized", + "nationalises": "nationalizes", + "nationalising": "nationalizing", + "naturalisation": "naturalization", + "naturalise": "naturalize", + "naturalised": "naturalized", + "naturalises": "naturalizes", + "naturalising": "naturalizing", + "neighbour": "neighbor", + "neighbourhood": "neighborhood", + "neighbourhoods": "neighborhoods", + "neighbouring": "neighboring", + "neighbourliness": "neighborliness", + "neighbourly": "neighborly", + "neighbours": "neighbors", + "neutralisation": "neutralization", + "neutralise": "neutralize", + "neutralised": "neutralized", + "neutralises": "neutralizes", + "neutralising": "neutralizing", + "normalisation": "normalization", + "normalise": "normalize", + "normalised": "normalized", + "normalises": "normalizes", + "normalising": "normalizing", + "odour": "odor", + "odourless": "odorless", + "odours": "odors", + "oesophagus": "esophagus", + "oesophaguses": "esophaguses", + "oestrogen": "estrogen", + "offence": "offense", + "offences": "offenses", + "omelette": "omelet", + "omelettes": "omelets", + "optimise": "optimize", + "optimised": "optimized", + "optimises": "optimizes", + "optimising": "optimizing", + "organisation": "organization", + "organisational": "organizational", + "organisations": "organizations", + "organise": "organize", + "organised": "organized", + "organiser": "organizer", + "organisers": "organizers", + "organises": "organizes", + "organising": "organizing", + "orthopaedic": "orthopedic", + "orthopaedics": "orthopedics", + "ostracise": "ostracize", + "ostracised": "ostracized", + "ostracises": "ostracizes", + "ostracising": "ostracizing", + "outmanoeuvre": "outmaneuver", + "outmanoeuvred": "outmaneuvered", + "outmanoeuvres": "outmaneuvers", + "outmanoeuvring": "outmaneuvering", + "overemphasise": "overemphasize", + "overemphasised": "overemphasized", + "overemphasises": "overemphasizes", + "overemphasising": "overemphasizing", + "oxidisation": "oxidization", + "oxidise": "oxidize", + "oxidised": "oxidized", + "oxidises": "oxidizes", + "oxidising": "oxidizing", + "paederast": "pederast", + "paederasts": "pederasts", + "paediatric": "pediatric", + "paediatrician": "pediatrician", + "paediatricians": "pediatricians", + "paediatrics": "pediatrics", + "paedophile": "pedophile", + "paedophiles": "pedophiles", + "paedophilia": "pedophilia", + "palaeolithic": "paleolithic", + "palaeontologist": "paleontologist", + "palaeontologists": "paleontologists", + "palaeontology": "paleontology", + "panelled": "paneled", + "panelling": "paneling", + "panellist": "panelist", + "panellists": "panelists", + "paralyse": "paralyze", + "paralysed": "paralyzed", + "paralyses": "paralyzes", + "paralysing": "paralyzing", + "parcelled": "parceled", + "parcelling": "parceling", + "parlour": "parlor", + "parlours": "parlors", + "particularise": "particularize", + "particularised": "particularized", + "particularises": "particularizes", + "particularising": "particularizing", + "passivisation": "passivization", + "passivise": "passivize", + "passivised": "passivized", + "passivises": "passivizes", + "passivising": "passivizing", + "pasteurisation": "pasteurization", + "pasteurise": "pasteurize", + "pasteurised": "pasteurized", + "pasteurises": "pasteurizes", + "pasteurising": "pasteurizing", + "patronise": "patronize", + "patronised": "patronized", + "patronises": "patronizes", + "patronising": "patronizing", + "patronisingly": "patronizingly", + "pedalled": "pedaled", + "pedalling": "pedaling", + "pedestrianisation": "pedestrianization", + "pedestrianise": "pedestrianize", + "pedestrianised": "pedestrianized", + "pedestrianises": "pedestrianizes", + "pedestrianising": "pedestrianizing", + "penalise": "penalize", + "penalised": "penalized", + "penalises": "penalizes", + "penalising": "penalizing", + "pencilled": "penciled", + "pencilling": "penciling", + "personalise": "personalize", + "personalised": "personalized", + "personalises": "personalizes", + "personalising": "personalizing", + "pharmacopoeia": "pharmacopeia", + "pharmacopoeias": "pharmacopeias", + "philosophise": "philosophize", + "philosophised": "philosophized", + "philosophises": "philosophizes", + "philosophising": "philosophizing", + "philtre": "filter", + "philtres": "filters", + "phoney": "phony", + "plagiarise": "plagiarize", + "plagiarised": "plagiarized", + "plagiarises": "plagiarizes", + "plagiarising": "plagiarizing", + "plough": "plow", + "ploughed": "plowed", + "ploughing": "plowing", + "ploughman": "plowman", + "ploughmen": "plowmen", + "ploughs": "plows", + "ploughshare": "plowshare", + "ploughshares": "plowshares", + "polarisation": "polarization", + "polarise": "polarize", + "polarised": "polarized", + "polarises": "polarizes", + "polarising": "polarizing", + "politicisation": "politicization", + "politicise": "politicize", + "politicised": "politicized", + "politicises": "politicizes", + "politicising": "politicizing", + "popularisation": "popularization", + "popularise": "popularize", + "popularised": "popularized", + "popularises": "popularizes", + "popularising": "popularizing", + "pouffe": "pouf", + "pouffes": "poufs", + "practise": "practice", + "practised": "practiced", + "practises": "practices", + "practising": "practicing", + "praesidium": "presidium", + "praesidiums": "presidiums", + "pressurisation": "pressurization", + "pressurise": "pressurize", + "pressurised": "pressurized", + "pressurises": "pressurizes", + "pressurising": "pressurizing", + "pretence": "pretense", + "pretences": "pretenses", + "primaeval": "primeval", + "prioritisation": "prioritization", + "prioritise": "prioritize", + "prioritised": "prioritized", + "prioritises": "prioritizes", + "prioritising": "prioritizing", + "privatisation": "privatization", + "privatisations": "privatizations", + "privatise": "privatize", + "privatised": "privatized", + "privatises": "privatizes", + "privatising": "privatizing", + "professionalisation": "professionalization", + "professionalise": "professionalize", + "professionalised": "professionalized", + "professionalises": "professionalizes", + "professionalising": "professionalizing", + "programme": "program", + "programmes": "programs", + "prologue": "prolog", + "prologues": "prologs", + "propagandise": "propagandize", + "propagandised": "propagandized", + "propagandises": "propagandizes", + "propagandising": "propagandizing", + "proselytise": "proselytize", + "proselytised": "proselytized", + "proselytiser": "proselytizer", + "proselytisers": "proselytizers", + "proselytises": "proselytizes", + "proselytising": "proselytizing", + "psychoanalyse": "psychoanalyze", + "psychoanalysed": "psychoanalyzed", + "psychoanalyses": "psychoanalyzes", + "psychoanalysing": "psychoanalyzing", + "publicise": "publicize", + "publicised": "publicized", + "publicises": "publicizes", + "publicising": "publicizing", + "pulverisation": "pulverization", + "pulverise": "pulverize", + "pulverised": "pulverized", + "pulverises": "pulverizes", + "pulverising": "pulverizing", + "pummelled": "pummel", + "pummelling": "pummeled", + "pyjama": "pajama", + "pyjamas": "pajamas", + "pzazz": "pizzazz", + "quarrelled": "quarreled", + "quarrelling": "quarreling", + "radicalise": "radicalize", + "radicalised": "radicalized", + "radicalises": "radicalizes", + "radicalising": "radicalizing", + "rancour": "rancor", + "randomise": "randomize", + "randomised": "randomized", + "randomises": "randomizes", + "randomising": "randomizing", + "rationalisation": "rationalization", + "rationalisations": "rationalizations", + "rationalise": "rationalize", + "rationalised": "rationalized", + "rationalises": "rationalizes", + "rationalising": "rationalizing", + "ravelled": "raveled", + "ravelling": "raveling", + "realisable": "realizable", + "realisation": "realization", + "realisations": "realizations", + "realise": "realize", + "realised": "realized", + "realises": "realizes", + "realising": "realizing", + "recognisable": "recognizable", + "recognisably": "recognizably", + "recognisance": "recognizance", + "recognise": "recognize", + "recognised": "recognized", + "recognises": "recognizes", + "recognising": "recognizing", + "reconnoitre": "reconnoiter", + "reconnoitred": "reconnoitered", + "reconnoitres": "reconnoiters", + "reconnoitring": "reconnoitering", + "refuelled": "refueled", + "refuelling": "refueling", + "regularisation": "regularization", + "regularise": "regularize", + "regularised": "regularized", + "regularises": "regularizes", + "regularising": "regularizing", + "remodelled": "remodeled", + "remodelling": "remodeling", + "remould": "remold", + "remoulded": "remolded", + "remoulding": "remolding", + "remoulds": "remolds", + "reorganisation": "reorganization", + "reorganisations": "reorganizations", + "reorganise": "reorganize", + "reorganised": "reorganized", + "reorganises": "reorganizes", + "reorganising": "reorganizing", + "revelled": "reveled", + "reveller": "reveler", + "revellers": "revelers", + "revelling": "reveling", + "revitalise": "revitalize", + "revitalised": "revitalized", + "revitalises": "revitalizes", + "revitalising": "revitalizing", + "revolutionise": "revolutionize", + "revolutionised": "revolutionized", + "revolutionises": "revolutionizes", + "revolutionising": "revolutionizing", + "rhapsodise": "rhapsodize", + "rhapsodised": "rhapsodized", + "rhapsodises": "rhapsodizes", + "rhapsodising": "rhapsodizing", + "rigour": "rigor", + "rigours": "rigors", + "ritualised": "ritualized", + "rivalled": "rivaled", + "rivalling": "rivaling", + "romanticise": "romanticize", + "romanticised": "romanticized", + "romanticises": "romanticizes", + "romanticising": "romanticizing", + "rumour": "rumor", + "rumoured": "rumored", + "rumours": "rumors", + "sabre": "saber", + "sabres": "sabers", + "saltpetre": "saltpeter", + "sanitise": "sanitize", + "sanitised": "sanitized", + "sanitises": "sanitizes", + "sanitising": "sanitizing", + "satirise": "satirize", + "satirised": "satirized", + "satirises": "satirizes", + "satirising": "satirizing", + "saviour": "savior", + "saviours": "saviors", + "savour": "savor", + "savoured": "savored", + "savouries": "savories", + "savouring": "savoring", + "savours": "savors", + "savoury": "savory", + "scandalise": "scandalize", + "scandalised": "scandalized", + "scandalises": "scandalizes", + "scandalising": "scandalizing", + "sceptic": "skeptic", + "sceptical": "skeptical", + "sceptically": "skeptically", + "scepticism": "skepticism", + "sceptics": "skeptics", + "sceptre": "scepter", + "sceptres": "scepters", + "scrutinise": "scrutinize", + "scrutinised": "scrutinized", + "scrutinises": "scrutinizes", + "scrutinising": "scrutinizing", + "secularisation": "secularization", + "secularise": "secularize", + "secularised": "secularized", + "secularises": "secularizes", + "secularising": "secularizing", + "sensationalise": "sensationalize", + "sensationalised": "sensationalized", + "sensationalises": "sensationalizes", + "sensationalising": "sensationalizing", + "sensitise": "sensitize", + "sensitised": "sensitized", + "sensitises": "sensitizes", + "sensitising": "sensitizing", + "sentimentalise": "sentimentalize", + "sentimentalised": "sentimentalized", + "sentimentalises": "sentimentalizes", + "sentimentalising": "sentimentalizing", + "sepulchre": "sepulcher", + "sepulchres": "sepulchers", + "serialisation": "serialization", + "serialisations": "serializations", + "serialise": "serialize", + "serialised": "serialized", + "serialises": "serializes", + "serialising": "serializing", + "sermonise": "sermonize", + "sermonised": "sermonized", + "sermonises": "sermonizes", + "sermonising": "sermonizing", + "sheikh": "sheik", + "shovelled": "shoveled", + "shovelling": "shoveling", + "shrivelled": "shriveled", + "shrivelling": "shriveling", + "signalise": "signalize", + "signalised": "signalized", + "signalises": "signalizes", + "signalising": "signalizing", + "signalled": "signaled", + "signalling": "signaling", + "smoulder": "smolder", + "smouldered": "smoldered", + "smouldering": "smoldering", + "smoulders": "smolders", + "snivelled": "sniveled", + "snivelling": "sniveling", + "snorkelled": "snorkeled", + "snorkelling": "snorkeling", + "snowplough": "snowplow", + "snowploughs": "snowplow", + "socialisation": "socialization", + "socialise": "socialize", + "socialised": "socialized", + "socialises": "socializes", + "socialising": "socializing", + "sodomise": "sodomize", + "sodomised": "sodomized", + "sodomises": "sodomizes", + "sodomising": "sodomizing", + "solemnise": "solemnize", + "solemnised": "solemnized", + "solemnises": "solemnizes", + "solemnising": "solemnizing", + "sombre": "somber", + "specialisation": "specialization", + "specialisations": "specializations", + "specialise": "specialize", + "specialised": "specialized", + "specialises": "specializes", + "specialising": "specializing", + "spectre": "specter", + "spectres": "specters", + "spiralled": "spiraled", + "spiralling": "spiraling", + "splendour": "splendor", + "splendours": "splendors", + "squirrelled": "squirreled", + "squirrelling": "squirreling", + "stabilisation": "stabilization", + "stabilise": "stabilize", + "stabilised": "stabilized", + "stabiliser": "stabilizer", + "stabilisers": "stabilizers", + "stabilises": "stabilizes", + "stabilising": "stabilizing", + "standardisation": "standardization", + "standardise": "standardize", + "standardised": "standardized", + "standardises": "standardizes", + "standardising": "standardizing", + "stencilled": "stenciled", + "stencilling": "stenciling", + "sterilisation": "sterilization", + "sterilisations": "sterilizations", + "sterilise": "sterilize", + "sterilised": "sterilized", + "steriliser": "sterilizer", + "sterilisers": "sterilizers", + "sterilises": "sterilizes", + "sterilising": "sterilizing", + "stigmatisation": "stigmatization", + "stigmatise": "stigmatize", + "stigmatised": "stigmatized", + "stigmatises": "stigmatizes", + "stigmatising": "stigmatizing", + "storey": "story", + "storeys": "stories", + "subsidisation": "subsidization", + "subsidise": "subsidize", + "subsidised": "subsidized", + "subsidiser": "subsidizer", + "subsidisers": "subsidizers", + "subsidises": "subsidizes", + "subsidising": "subsidizing", + "succour": "succor", + "succoured": "succored", + "succouring": "succoring", + "succours": "succors", + "sulphate": "sulfate", + "sulphates": "sulfates", + "sulphide": "sulfide", + "sulphides": "sulfides", + "sulphur": "sulfur", + "sulphurous": "sulfurous", + "summarise": "summarize", + "summarised": "summarized", + "summarises": "summarizes", + "summarising": "summarizing", + "swivelled": "swiveled", + "swivelling": "swiveling", + "symbolise": "symbolize", + "symbolised": "symbolized", + "symbolises": "symbolizes", + "symbolising": "symbolizing", + "sympathise": "sympathize", + "sympathised": "sympathized", + "sympathiser": "sympathizer", + "sympathisers": "sympathizers", + "sympathises": "sympathizes", + "sympathising": "sympathizing", + "synchronisation": "synchronization", + "synchronise": "synchronize", + "synchronised": "synchronized", + "synchronises": "synchronizes", + "synchronising": "synchronizing", + "synthesise": "synthesize", + "synthesised": "synthesized", + "synthesiser": "synthesizer", + "synthesisers": "synthesizers", + "synthesises": "synthesizes", + "synthesising": "synthesizing", + "syphon": "siphon", + "syphoned": "siphoned", + "syphoning": "siphoning", + "syphons": "siphons", + "systematisation": "systematization", + "systematise": "systematize", + "systematised": "systematized", + "systematises": "systematizes", + "systematising": "systematizing", + "tantalise": "tantalize", + "tantalised": "tantalized", + "tantalises": "tantalizes", + "tantalising": "tantalizing", + "tantalisingly": "tantalizingly", + "tasselled": "tasseled", + "technicolour": "technicolor", + "temporise": "temporize", + "temporised": "temporized", + "temporises": "temporizes", + "temporising": "temporizing", + "tenderise": "tenderize", + "tenderised": "tenderized", + "tenderises": "tenderizes", + "tenderising": "tenderizing", + "terrorise": "terrorize", + "terrorised": "terrorized", + "terrorises": "terrorizes", + "terrorising": "terrorizing", + "theatre": "theater", + "theatregoer": "theatergoer", + "theatregoers": "theatergoers", + "theatres": "theaters", + "theorise": "theorize", + "theorised": "theorized", + "theorises": "theorizes", + "theorising": "theorizing", + "tonne": "ton", + "tonnes": "tons", + "towelled": "toweled", + "towelling": "toweling", + "toxaemia": "toxemia", + "tranquillise": "tranquilize", + "tranquillised": "tranquilized", + "tranquilliser": "tranquilizer", + "tranquillisers": "tranquilizers", + "tranquillises": "tranquilizes", + "tranquillising": "tranquilizing", + "tranquillity": "tranquility", + "tranquillize": "tranquilize", + "tranquillized": "tranquilized", + "tranquillizer": "tranquilizer", + "tranquillizers": "tranquilizers", + "tranquillizes": "tranquilizes", + "tranquillizing": "tranquilizing", + "tranquilly": "tranquility", + "transistorised": "transistorized", + "traumatise": "traumatize", + "traumatised": "traumatized", + "traumatises": "traumatizes", + "traumatising": "traumatizing", + "travelled": "traveled", + "traveller": "traveler", + "travellers": "travelers", + "travelling": "traveling", + "travelog": "travelogue", + "travelogs": "travelogues", + "trialled": "trialed", + "trialling": "trialing", + "tricolour": "tricolor", + "tricolours": "tricolors", + "trivialise": "trivialize", + "trivialised": "trivialized", + "trivialises": "trivializes", + "trivialising": "trivializing", + "tumour": "tumor", + "tumours": "tumors", + "tunnelled": "tunneled", + "tunnelling": "tunneling", + "tyrannise": "tyrannize", + "tyrannised": "tyrannized", + "tyrannises": "tyrannizes", + "tyrannising": "tyrannizing", + "tyre": "tire", + "tyres": "tires", + "unauthorised": "unauthorized", + "uncivilised": "uncivilized", + "underutilised": "underutilized", + "unequalled": "unequaled", + "unfavourable": "unfavorable", + "unfavourably": "unfavorably", + "unionisation": "unionization", + "unionise": "unionize", + "unionised": "unionized", + "unionises": "unionizes", + "unionising": "unionizing", + "unorganised": "unorganized", + "unravelled": "unraveled", + "unravelling": "unraveling", + "unrecognisable": "unrecognizable", + "unrecognised": "unrecognized", + "unrivalled": "unrivaled", + "unsavoury": "unsavory", + "untrammelled": "untrammeled", + "urbanisation": "urbanization", + "urbanise": "urbanize", + "urbanised": "urbanized", + "urbanises": "urbanizes", + "urbanising": "urbanizing", + "utilisable": "utilizable", + "utilisation": "utilization", + "utilise": "utilize", + "utilised": "utilized", + "utilises": "utilizes", + "utilising": "utilizing", + "valour": "valor", + "vandalise": "vandalize", + "vandalised": "vandalized", + "vandalises": "vandalizes", + "vandalising": "vandalizing", + "vaporisation": "vaporization", + "vaporise": "vaporize", + "vaporised": "vaporized", + "vaporises": "vaporizes", + "vaporising": "vaporizing", + "vapour": "vapor", + "vapours": "vapors", + "verbalise": "verbalize", + "verbalised": "verbalized", + "verbalises": "verbalizes", + "verbalising": "verbalizing", + "victimisation": "victimization", + "victimise": "victimize", + "victimised": "victimized", + "victimises": "victimizes", + "victimising": "victimizing", + "videodisc": "videodisk", + "videodiscs": "videodisks", + "vigour": "vigor", + "visualisation": "visualization", + "visualisations": "visualizations", + "visualise": "visualize", + "visualised": "visualized", + "visualises": "visualizes", + "visualising": "visualizing", + "vocalisation": "vocalization", + "vocalisations": "vocalizations", + "vocalise": "vocalize", + "vocalised": "vocalized", + "vocalises": "vocalizes", + "vocalising": "vocalizing", + "vulcanised": "vulcanized", + "vulgarisation": "vulgarization", + "vulgarise": "vulgarize", + "vulgarised": "vulgarized", + "vulgarises": "vulgarizes", + "vulgarising": "vulgarizing", + "waggon": "wagon", + "waggons": "wagons", + "watercolour": "watercolor", + "watercolours": "watercolors", + "weaselled": "weaseled", + "weaselling": "weaseling", + "westernisation": "westernization", + "westernise": "westernize", + "westernised": "westernized", + "westernises": "westernizes", + "westernising": "westernizing", + "womanise": "womanize", + "womanised": "womanized", + "womaniser": "womanizer", + "womanisers": "womanizers", + "womanises": "womanizes", + "womanising": "womanizing", + "woollen": "woolen", + "woollens": "woolens", + "woollies": "woolies", + "woolly": "wooly", + "worshipped": "worshiped", + "worshipping": "worshiping", + "worshipper": "worshiper", + "yodelled": "yodeled", + "yodelling": "yodeling", + "yoghourt": "yogurt", + "yoghourts": "yogurts", + "yoghurt": "yogurt", + "yoghurts": "yogurts", + "mhm": "hmm", + "mmm": "hmm" +} \ No newline at end of file diff --git a/xinference/thirdparty/whisper/normalizers/english.py b/xinference/thirdparty/whisper/normalizers/english.py new file mode 100644 index 0000000000..4932042bc5 --- /dev/null +++ b/xinference/thirdparty/whisper/normalizers/english.py @@ -0,0 +1,550 @@ +import json +import os +import re +from fractions import Fraction +from typing import Iterator, List, Match, Optional, Union + +from more_itertools import windowed + +from .basic import remove_symbols_and_diacritics + + +class EnglishNumberNormalizer: + """ + Convert any spelled-out numbers into arabic numbers, while handling: + + - remove any commas + - keep the suffixes such as: `1960s`, `274th`, `32nd`, etc. + - spell out currency symbols after the number. e.g. `$20 million` -> `20000000 dollars` + - spell out `one` and `ones` + - interpret successive single-digit numbers as nominal: `one oh one` -> `101` + """ + + def __init__(self): + super().__init__() + + self.zeros = {"o", "oh", "zero"} + self.ones = { + name: i + for i, name in enumerate( + [ + "one", + "two", + "three", + "four", + "five", + "six", + "seven", + "eight", + "nine", + "ten", + "eleven", + "twelve", + "thirteen", + "fourteen", + "fifteen", + "sixteen", + "seventeen", + "eighteen", + "nineteen", + ], + start=1, + ) + } + self.ones_plural = { + "sixes" if name == "six" else name + "s": (value, "s") + for name, value in self.ones.items() + } + self.ones_ordinal = { + "zeroth": (0, "th"), + "first": (1, "st"), + "second": (2, "nd"), + "third": (3, "rd"), + "fifth": (5, "th"), + "twelfth": (12, "th"), + **{ + name + ("h" if name.endswith("t") else "th"): (value, "th") + for name, value in self.ones.items() + if value > 3 and value != 5 and value != 12 + }, + } + self.ones_suffixed = {**self.ones_plural, **self.ones_ordinal} + + self.tens = { + "twenty": 20, + "thirty": 30, + "forty": 40, + "fifty": 50, + "sixty": 60, + "seventy": 70, + "eighty": 80, + "ninety": 90, + } + self.tens_plural = { + name.replace("y", "ies"): (value, "s") for name, value in self.tens.items() + } + self.tens_ordinal = { + name.replace("y", "ieth"): (value, "th") + for name, value in self.tens.items() + } + self.tens_suffixed = {**self.tens_plural, **self.tens_ordinal} + + self.multipliers = { + "hundred": 100, + "thousand": 1_000, + "million": 1_000_000, + "billion": 1_000_000_000, + "trillion": 1_000_000_000_000, + "quadrillion": 1_000_000_000_000_000, + "quintillion": 1_000_000_000_000_000_000, + "sextillion": 1_000_000_000_000_000_000_000, + "septillion": 1_000_000_000_000_000_000_000_000, + "octillion": 1_000_000_000_000_000_000_000_000_000, + "nonillion": 1_000_000_000_000_000_000_000_000_000_000, + "decillion": 1_000_000_000_000_000_000_000_000_000_000_000, + } + self.multipliers_plural = { + name + "s": (value, "s") for name, value in self.multipliers.items() + } + self.multipliers_ordinal = { + name + "th": (value, "th") for name, value in self.multipliers.items() + } + self.multipliers_suffixed = { + **self.multipliers_plural, + **self.multipliers_ordinal, + } + self.decimals = {*self.ones, *self.tens, *self.zeros} + + self.preceding_prefixers = { + "minus": "-", + "negative": "-", + "plus": "+", + "positive": "+", + } + self.following_prefixers = { + "pound": "£", + "pounds": "£", + "euro": "€", + "euros": "€", + "dollar": "$", + "dollars": "$", + "cent": "¢", + "cents": "¢", + } + self.prefixes = set( + list(self.preceding_prefixers.values()) + + list(self.following_prefixers.values()) + ) + self.suffixers = { + "per": {"cent": "%"}, + "percent": "%", + } + self.specials = {"and", "double", "triple", "point"} + + self.words = set( + [ + key + for mapping in [ + self.zeros, + self.ones, + self.ones_suffixed, + self.tens, + self.tens_suffixed, + self.multipliers, + self.multipliers_suffixed, + self.preceding_prefixers, + self.following_prefixers, + self.suffixers, + self.specials, + ] + for key in mapping + ] + ) + self.literal_words = {"one", "ones"} + + def process_words(self, words: List[str]) -> Iterator[str]: + prefix: Optional[str] = None + value: Optional[Union[str, int]] = None + skip = False + + def to_fraction(s: str): + try: + return Fraction(s) + except ValueError: + return None + + def output(result: Union[str, int]): + nonlocal prefix, value + result = str(result) + if prefix is not None: + result = prefix + result + value = None + prefix = None + return result + + if len(words) == 0: + return + + for prev, current, next in windowed([None] + words + [None], 3): + if skip: + skip = False + continue + + next_is_numeric = next is not None and re.match(r"^\d+(\.\d+)?$", next) + has_prefix = current[0] in self.prefixes + current_without_prefix = current[1:] if has_prefix else current + if re.match(r"^\d+(\.\d+)?$", current_without_prefix): + # arabic numbers (potentially with signs and fractions) + f = to_fraction(current_without_prefix) + assert f is not None + if value is not None: + if isinstance(value, str) and value.endswith("."): + # concatenate decimals / ip address components + value = str(value) + str(current) + continue + else: + yield output(value) + + prefix = current[0] if has_prefix else prefix + if f.denominator == 1: + value = f.numerator # store integers as int + else: + value = current_without_prefix + elif current not in self.words: + # non-numeric words + if value is not None: + yield output(value) + yield output(current) + elif current in self.zeros: + value = str(value or "") + "0" + elif current in self.ones: + ones = self.ones[current] + + if value is None: + value = ones + elif isinstance(value, str) or prev in self.ones: + if ( + prev in self.tens and ones < 10 + ): # replace the last zero with the digit + assert value[-1] == "0" + value = value[:-1] + str(ones) + else: + value = str(value) + str(ones) + elif ones < 10: + if value % 10 == 0: + value += ones + else: + value = str(value) + str(ones) + else: # eleven to nineteen + if value % 100 == 0: + value += ones + else: + value = str(value) + str(ones) + elif current in self.ones_suffixed: + # ordinal or cardinal; yield the number right away + ones, suffix = self.ones_suffixed[current] + if value is None: + yield output(str(ones) + suffix) + elif isinstance(value, str) or prev in self.ones: + if prev in self.tens and ones < 10: + assert value[-1] == "0" + yield output(value[:-1] + str(ones) + suffix) + else: + yield output(str(value) + str(ones) + suffix) + elif ones < 10: + if value % 10 == 0: + yield output(str(value + ones) + suffix) + else: + yield output(str(value) + str(ones) + suffix) + else: # eleven to nineteen + if value % 100 == 0: + yield output(str(value + ones) + suffix) + else: + yield output(str(value) + str(ones) + suffix) + value = None + elif current in self.tens: + tens = self.tens[current] + if value is None: + value = tens + elif isinstance(value, str): + value = str(value) + str(tens) + else: + if value % 100 == 0: + value += tens + else: + value = str(value) + str(tens) + elif current in self.tens_suffixed: + # ordinal or cardinal; yield the number right away + tens, suffix = self.tens_suffixed[current] + if value is None: + yield output(str(tens) + suffix) + elif isinstance(value, str): + yield output(str(value) + str(tens) + suffix) + else: + if value % 100 == 0: + yield output(str(value + tens) + suffix) + else: + yield output(str(value) + str(tens) + suffix) + elif current in self.multipliers: + multiplier = self.multipliers[current] + if value is None: + value = multiplier + elif isinstance(value, str) or value == 0: + f = to_fraction(value) + p = f * multiplier if f is not None else None + if f is not None and p.denominator == 1: + value = p.numerator + else: + yield output(value) + value = multiplier + else: + before = value // 1000 * 1000 + residual = value % 1000 + value = before + residual * multiplier + elif current in self.multipliers_suffixed: + multiplier, suffix = self.multipliers_suffixed[current] + if value is None: + yield output(str(multiplier) + suffix) + elif isinstance(value, str): + f = to_fraction(value) + p = f * multiplier if f is not None else None + if f is not None and p.denominator == 1: + yield output(str(p.numerator) + suffix) + else: + yield output(value) + yield output(str(multiplier) + suffix) + else: # int + before = value // 1000 * 1000 + residual = value % 1000 + value = before + residual * multiplier + yield output(str(value) + suffix) + value = None + elif current in self.preceding_prefixers: + # apply prefix (positive, minus, etc.) if it precedes a number + if value is not None: + yield output(value) + + if next in self.words or next_is_numeric: + prefix = self.preceding_prefixers[current] + else: + yield output(current) + elif current in self.following_prefixers: + # apply prefix (dollars, cents, etc.) only after a number + if value is not None: + prefix = self.following_prefixers[current] + yield output(value) + else: + yield output(current) + elif current in self.suffixers: + # apply suffix symbols (percent -> '%') + if value is not None: + suffix = self.suffixers[current] + if isinstance(suffix, dict): + if next in suffix: + yield output(str(value) + suffix[next]) + skip = True + else: + yield output(value) + yield output(current) + else: + yield output(str(value) + suffix) + else: + yield output(current) + elif current in self.specials: + if next not in self.words and not next_is_numeric: + # apply special handling only if the next word can be numeric + if value is not None: + yield output(value) + yield output(current) + elif current == "and": + # ignore "and" after hundreds, thousands, etc. + if prev not in self.multipliers: + if value is not None: + yield output(value) + yield output(current) + elif current == "double" or current == "triple": + if next in self.ones or next in self.zeros: + repeats = 2 if current == "double" else 3 + ones = self.ones.get(next, 0) + value = str(value or "") + str(ones) * repeats + skip = True + else: + if value is not None: + yield output(value) + yield output(current) + elif current == "point": + if next in self.decimals or next_is_numeric: + value = str(value or "") + "." + else: + # should all have been covered at this point + raise ValueError(f"Unexpected token: {current}") + else: + # all should have been covered at this point + raise ValueError(f"Unexpected token: {current}") + + if value is not None: + yield output(value) + + def preprocess(self, s: str): + # replace " and a half" with " point five" + results = [] + + segments = re.split(r"\band\s+a\s+half\b", s) + for i, segment in enumerate(segments): + if len(segment.strip()) == 0: + continue + if i == len(segments) - 1: + results.append(segment) + else: + results.append(segment) + last_word = segment.rsplit(maxsplit=2)[-1] + if last_word in self.decimals or last_word in self.multipliers: + results.append("point five") + else: + results.append("and a half") + + s = " ".join(results) + + # put a space at number/letter boundary + s = re.sub(r"([a-z])([0-9])", r"\1 \2", s) + s = re.sub(r"([0-9])([a-z])", r"\1 \2", s) + + # but remove spaces which could be a suffix + s = re.sub(r"([0-9])\s+(st|nd|rd|th|s)\b", r"\1\2", s) + + return s + + def postprocess(self, s: str): + def combine_cents(m: Match): + try: + currency = m.group(1) + integer = m.group(2) + cents = int(m.group(3)) + return f"{currency}{integer}.{cents:02d}" + except ValueError: + return m.string + + def extract_cents(m: Match): + try: + return f"¢{int(m.group(1))}" + except ValueError: + return m.string + + # apply currency postprocessing; "$2 and ¢7" -> "$2.07" + s = re.sub(r"([€£$])([0-9]+) (?:and )?¢([0-9]{1,2})\b", combine_cents, s) + s = re.sub(r"[€£$]0.([0-9]{1,2})\b", extract_cents, s) + + # write "one(s)" instead of "1(s)", just for the readability + s = re.sub(r"\b1(s?)\b", r"one\1", s) + + return s + + def __call__(self, s: str): + s = self.preprocess(s) + s = " ".join(word for word in self.process_words(s.split()) if word is not None) + s = self.postprocess(s) + + return s + + +class EnglishSpellingNormalizer: + """ + Applies British-American spelling mappings as listed in [1]. + + [1] https://www.tysto.com/uk-us-spelling-list.html + """ + + def __init__(self): + mapping_path = os.path.join(os.path.dirname(__file__), "english.json") + self.mapping = json.load(open(mapping_path)) + + def __call__(self, s: str): + return " ".join(self.mapping.get(word, word) for word in s.split()) + + +class EnglishTextNormalizer: + def __init__(self): + self.ignore_patterns = r"\b(hmm|mm|mhm|mmm|uh|um)\b" + self.replacers = { + # common contractions + r"\bwon't\b": "will not", + r"\bcan't\b": "can not", + r"\blet's\b": "let us", + r"\bain't\b": "aint", + r"\by'all\b": "you all", + r"\bwanna\b": "want to", + r"\bgotta\b": "got to", + r"\bgonna\b": "going to", + r"\bi'ma\b": "i am going to", + r"\bimma\b": "i am going to", + r"\bwoulda\b": "would have", + r"\bcoulda\b": "could have", + r"\bshoulda\b": "should have", + r"\bma'am\b": "madam", + # contractions in titles/prefixes + r"\bmr\b": "mister ", + r"\bmrs\b": "missus ", + r"\bst\b": "saint ", + r"\bdr\b": "doctor ", + r"\bprof\b": "professor ", + r"\bcapt\b": "captain ", + r"\bgov\b": "governor ", + r"\bald\b": "alderman ", + r"\bgen\b": "general ", + r"\bsen\b": "senator ", + r"\brep\b": "representative ", + r"\bpres\b": "president ", + r"\brev\b": "reverend ", + r"\bhon\b": "honorable ", + r"\basst\b": "assistant ", + r"\bassoc\b": "associate ", + r"\blt\b": "lieutenant ", + r"\bcol\b": "colonel ", + r"\bjr\b": "junior ", + r"\bsr\b": "senior ", + r"\besq\b": "esquire ", + # prefect tenses, ideally it should be any past participles, but it's harder.. + r"'d been\b": " had been", + r"'s been\b": " has been", + r"'d gone\b": " had gone", + r"'s gone\b": " has gone", + r"'d done\b": " had done", # "'s done" is ambiguous + r"'s got\b": " has got", + # general contractions + r"n't\b": " not", + r"'re\b": " are", + r"'s\b": " is", + r"'d\b": " would", + r"'ll\b": " will", + r"'t\b": " not", + r"'ve\b": " have", + r"'m\b": " am", + } + self.standardize_numbers = EnglishNumberNormalizer() + self.standardize_spellings = EnglishSpellingNormalizer() + + def __call__(self, s: str): + s = s.lower() + + s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets + s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis + s = re.sub(self.ignore_patterns, "", s) + s = re.sub(r"\s+'", "'", s) # when there's a space before an apostrophe + + for pattern, replacement in self.replacers.items(): + s = re.sub(pattern, replacement, s) + + s = re.sub(r"(\d),(\d)", r"\1\2", s) # remove commas between digits + s = re.sub(r"\.([^0-9]|$)", r" \1", s) # remove periods not followed by numbers + s = remove_symbols_and_diacritics(s, keep=".%$¢€£") # keep numeric symbols + + s = self.standardize_numbers(s) + s = self.standardize_spellings(s) + + # now remove prefix/suffix symbols that are not preceded/followed by numbers + s = re.sub(r"[.$¢€£]([^0-9])", r" \1", s) + s = re.sub(r"([^0-9])%", r"\1 ", s) + + s = re.sub(r"\s+", " ", s) # replace any successive whitespaces with a space + + return s diff --git a/xinference/thirdparty/whisper/timing.py b/xinference/thirdparty/whisper/timing.py new file mode 100644 index 0000000000..b695ead0ab --- /dev/null +++ b/xinference/thirdparty/whisper/timing.py @@ -0,0 +1,386 @@ +import itertools +import subprocess +import warnings +from dataclasses import dataclass +from typing import TYPE_CHECKING, List + +import numba +import numpy as np +import torch +import torch.nn.functional as F + +from .audio import HOP_LENGTH, SAMPLE_RATE, TOKENS_PER_SECOND +from .tokenizer import Tokenizer + +if TYPE_CHECKING: + from .model import Whisper + + +def median_filter(x: torch.Tensor, filter_width: int): + """Apply a median filter of width `filter_width` along the last dimension of `x`""" + pad_width = filter_width // 2 + if x.shape[-1] <= pad_width: + # F.pad requires the padding width to be smaller than the input dimension + return x + + if (ndim := x.ndim) <= 2: + # `F.pad` does not support 1D or 2D inputs for reflect padding but supports 3D and 4D + x = x[None, None, :] + + assert ( + filter_width > 0 and filter_width % 2 == 1 + ), "`filter_width` should be an odd number" + + result = None + x = F.pad(x, (filter_width // 2, filter_width // 2, 0, 0), mode="reflect") + if x.is_cuda: + try: + from .triton_ops import median_filter_cuda + + result = median_filter_cuda(x, filter_width) + except (RuntimeError, subprocess.CalledProcessError): + warnings.warn( + "Failed to launch Triton kernels, likely due to missing CUDA toolkit; " + "falling back to a slower median kernel implementation..." + ) + + if result is None: + # sort() is faster than torch.median (https://github.com/pytorch/pytorch/issues/51450) + result = x.unfold(-1, filter_width, 1).sort()[0][..., filter_width // 2] + + if ndim <= 2: + result = result[0, 0] + + return result + + +@numba.jit(nopython=True) +def backtrace(trace: np.ndarray): + i = trace.shape[0] - 1 + j = trace.shape[1] - 1 + trace[0, :] = 2 + trace[:, 0] = 1 + + result = [] + while i > 0 or j > 0: + result.append((i - 1, j - 1)) + + if trace[i, j] == 0: + i -= 1 + j -= 1 + elif trace[i, j] == 1: + i -= 1 + elif trace[i, j] == 2: + j -= 1 + else: + raise ValueError("Unexpected trace[i, j]") + + result = np.array(result) + return result[::-1, :].T + + +@numba.jit(nopython=True, parallel=True) +def dtw_cpu(x: np.ndarray): + N, M = x.shape + cost = np.ones((N + 1, M + 1), dtype=np.float32) * np.inf + trace = -np.ones((N + 1, M + 1), dtype=np.float32) + + cost[0, 0] = 0 + for j in range(1, M + 1): + for i in range(1, N + 1): + c0 = cost[i - 1, j - 1] + c1 = cost[i - 1, j] + c2 = cost[i, j - 1] + + if c0 < c1 and c0 < c2: + c, t = c0, 0 + elif c1 < c0 and c1 < c2: + c, t = c1, 1 + else: + c, t = c2, 2 + + cost[i, j] = x[i - 1, j - 1] + c + trace[i, j] = t + + return backtrace(trace) + + +def dtw_cuda(x, BLOCK_SIZE=1024): + from .triton_ops import dtw_kernel + + M, N = x.shape + assert M < BLOCK_SIZE, f"M should be smaller than {BLOCK_SIZE=}" + + x_skew = ( + F.pad(x, (0, M + 1), value=np.inf).flatten()[: M * (N + M)].reshape(M, N + M) + ) + x_skew = x_skew.T.contiguous() + cost = torch.ones(N + M + 2, M + 2) * np.inf + cost[0, 0] = 0 + cost = cost.cuda() + trace = torch.zeros_like(cost, dtype=torch.int32) + + dtw_kernel[(1,)]( + cost, + trace, + x_skew, + x_skew.stride(0), + cost.stride(0), + trace.stride(0), + N, + M, + BLOCK_SIZE=BLOCK_SIZE, + ) + + trace = trace.T.flatten()[: (M + 1) * (M + N + 3)].reshape(M + 1, M + N + 3)[ + :, : N + 1 + ] + return backtrace(trace.cpu().numpy()) + + +def dtw(x: torch.Tensor) -> np.ndarray: + if x.is_cuda: + try: + return dtw_cuda(x) + except (RuntimeError, subprocess.CalledProcessError): + warnings.warn( + "Failed to launch Triton kernels, likely due to missing CUDA toolkit; " + "falling back to a slower DTW implementation..." + ) + + return dtw_cpu(x.double().cpu().numpy()) + + +@dataclass +class WordTiming: + word: str + tokens: List[int] + start: float + end: float + probability: float + + +def find_alignment( + model: "Whisper", + tokenizer: Tokenizer, + text_tokens: List[int], + mel: torch.Tensor, + num_frames: int, + *, + medfilt_width: int = 7, + qk_scale: float = 1.0, +) -> List[WordTiming]: + if len(text_tokens) == 0: + return [] + + tokens = torch.tensor( + [ + *tokenizer.sot_sequence, + tokenizer.no_timestamps, + *text_tokens, + tokenizer.eot, + ] + ).to(model.device) + + # install hooks on the cross attention layers to retrieve the attention weights + QKs = [None] * model.dims.n_text_layer + hooks = [ + block.cross_attn.register_forward_hook( + lambda _, ins, outs, index=i: QKs.__setitem__(index, outs[-1][0]) + ) + for i, block in enumerate(model.decoder.blocks) + ] + + with torch.no_grad(): + logits = model(mel.unsqueeze(0), tokens.unsqueeze(0))[0] + sampled_logits = logits[len(tokenizer.sot_sequence) :, : tokenizer.eot] + token_probs = sampled_logits.softmax(dim=-1) + text_token_probs = token_probs[np.arange(len(text_tokens)), text_tokens] + text_token_probs = text_token_probs.tolist() + + for hook in hooks: + hook.remove() + + # heads * tokens * frames + weights = torch.stack([QKs[_l][_h] for _l, _h in model.alignment_heads.indices().T]) + weights = weights[:, :, : num_frames // 2] + weights = (weights * qk_scale).softmax(dim=-1) + std, mean = torch.std_mean(weights, dim=-2, keepdim=True, unbiased=False) + weights = (weights - mean) / std + weights = median_filter(weights, medfilt_width) + + matrix = weights.mean(axis=0) + matrix = matrix[len(tokenizer.sot_sequence) : -1] + text_indices, time_indices = dtw(-matrix) + + words, word_tokens = tokenizer.split_to_word_tokens(text_tokens + [tokenizer.eot]) + if len(word_tokens) <= 1: + # return on eot only + # >>> np.pad([], (1, 0)) + # array([0.]) + # This results in crashes when we lookup jump_times with float, like + # IndexError: arrays used as indices must be of integer (or boolean) type + return [] + word_boundaries = np.pad(np.cumsum([len(t) for t in word_tokens[:-1]]), (1, 0)) + + jumps = np.pad(np.diff(text_indices), (1, 0), constant_values=1).astype(bool) + jump_times = time_indices[jumps] / TOKENS_PER_SECOND + start_times = jump_times[word_boundaries[:-1]] + end_times = jump_times[word_boundaries[1:]] + word_probabilities = [ + np.mean(text_token_probs[i:j]) + for i, j in zip(word_boundaries[:-1], word_boundaries[1:]) + ] + + return [ + WordTiming(word, tokens, start, end, probability) + for word, tokens, start, end, probability in zip( + words, word_tokens, start_times, end_times, word_probabilities + ) + ] + + +def merge_punctuations(alignment: List[WordTiming], prepended: str, appended: str): + # merge prepended punctuations + i = len(alignment) - 2 + j = len(alignment) - 1 + while i >= 0: + previous = alignment[i] + following = alignment[j] + if previous.word.startswith(" ") and previous.word.strip() in prepended: + # prepend it to the following word + following.word = previous.word + following.word + following.tokens = previous.tokens + following.tokens + previous.word = "" + previous.tokens = [] + else: + j = i + i -= 1 + + # merge appended punctuations + i = 0 + j = 1 + while j < len(alignment): + previous = alignment[i] + following = alignment[j] + if not previous.word.endswith(" ") and following.word in appended: + # append it to the previous word + previous.word = previous.word + following.word + previous.tokens = previous.tokens + following.tokens + following.word = "" + following.tokens = [] + else: + i = j + j += 1 + + +def add_word_timestamps( + *, + segments: List[dict], + model: "Whisper", + tokenizer: Tokenizer, + mel: torch.Tensor, + num_frames: int, + prepend_punctuations: str = "\"'“¿([{-", + append_punctuations: str = "\"'.。,，!！?？:：”)]}、", + last_speech_timestamp: float, + **kwargs, +): + if len(segments) == 0: + return + + text_tokens_per_segment = [ + [token for token in segment["tokens"] if token < tokenizer.eot] + for segment in segments + ] + + text_tokens = list(itertools.chain.from_iterable(text_tokens_per_segment)) + alignment = find_alignment(model, tokenizer, text_tokens, mel, num_frames, **kwargs) + word_durations = np.array([t.end - t.start for t in alignment]) + word_durations = word_durations[word_durations.nonzero()] + median_duration = np.median(word_durations) if len(word_durations) > 0 else 0.0 + median_duration = min(0.7, float(median_duration)) + max_duration = median_duration * 2 + + # hack: truncate long words at sentence boundaries. + # a better segmentation algorithm based on VAD should be able to replace this. + if len(word_durations) > 0: + sentence_end_marks = ".。!！?？" + # ensure words at sentence boundaries are not longer than twice the median word duration. + for i in range(1, len(alignment)): + if alignment[i].end - alignment[i].start > max_duration: + if alignment[i].word in sentence_end_marks: + alignment[i].end = alignment[i].start + max_duration + elif alignment[i - 1].word in sentence_end_marks: + alignment[i].start = alignment[i].end - max_duration + + merge_punctuations(alignment, prepend_punctuations, append_punctuations) + + time_offset = segments[0]["seek"] * HOP_LENGTH / SAMPLE_RATE + word_index = 0 + + for segment, text_tokens in zip(segments, text_tokens_per_segment): + saved_tokens = 0 + words = [] + + while word_index < len(alignment) and saved_tokens < len(text_tokens): + timing = alignment[word_index] + + if timing.word: + words.append( + dict( + word=timing.word, + start=round(time_offset + timing.start, 2), + end=round(time_offset + timing.end, 2), + probability=timing.probability, + ) + ) + + saved_tokens += len(timing.tokens) + word_index += 1 + + # hack: truncate long words at segment boundaries. + # a better segmentation algorithm based on VAD should be able to replace this. + if len(words) > 0: + # ensure the first and second word after a pause is not longer than + # twice the median word duration. + if words[0]["end"] - last_speech_timestamp > median_duration * 4 and ( + words[0]["end"] - words[0]["start"] > max_duration + or ( + len(words) > 1 + and words[1]["end"] - words[0]["start"] > max_duration * 2 + ) + ): + if ( + len(words) > 1 + and words[1]["end"] - words[1]["start"] > max_duration + ): + boundary = max(words[1]["end"] / 2, words[1]["end"] - max_duration) + words[0]["end"] = words[1]["start"] = boundary + words[0]["start"] = max(0, words[0]["end"] - max_duration) + + # prefer the segment-level start timestamp if the first word is too long. + if ( + segment["start"] < words[0]["end"] + and segment["start"] - 0.5 > words[0]["start"] + ): + words[0]["start"] = max( + 0, min(words[0]["end"] - median_duration, segment["start"]) + ) + else: + segment["start"] = words[0]["start"] + + # prefer the segment-level end timestamp if the last word is too long. + if ( + segment["end"] > words[-1]["start"] + and segment["end"] + 0.5 < words[-1]["end"] + ): + words[-1]["end"] = max( + words[-1]["start"] + median_duration, segment["end"] + ) + else: + segment["end"] = words[-1]["end"] + + last_speech_timestamp = segment["end"] + + segment["words"] = words diff --git a/xinference/thirdparty/whisper/tokenizer.py b/xinference/thirdparty/whisper/tokenizer.py new file mode 100644 index 0000000000..2af837570b --- /dev/null +++ b/xinference/thirdparty/whisper/tokenizer.py @@ -0,0 +1,395 @@ +import base64 +import os +import string +from dataclasses import dataclass, field +from functools import cached_property, lru_cache +from typing import Dict, List, Optional, Tuple + +import tiktoken + +LANGUAGES = { + "en": "english", + "zh": "chinese", + "de": "german", + "es": "spanish", + "ru": "russian", + "ko": "korean", + "fr": "french", + "ja": "japanese", + "pt": "portuguese", + "tr": "turkish", + "pl": "polish", + "ca": "catalan", + "nl": "dutch", + "ar": "arabic", + "sv": "swedish", + "it": "italian", + "id": "indonesian", + "hi": "hindi", + "fi": "finnish", + "vi": "vietnamese", + "he": "hebrew", + "uk": "ukrainian", + "el": "greek", + "ms": "malay", + "cs": "czech", + "ro": "romanian", + "da": "danish", + "hu": "hungarian", + "ta": "tamil", + "no": "norwegian", + "th": "thai", + "ur": "urdu", + "hr": "croatian", + "bg": "bulgarian", + "lt": "lithuanian", + "la": "latin", + "mi": "maori", + "ml": "malayalam", + "cy": "welsh", + "sk": "slovak", + "te": "telugu", + "fa": "persian", + "lv": "latvian", + "bn": "bengali", + "sr": "serbian", + "az": "azerbaijani", + "sl": "slovenian", + "kn": "kannada", + "et": "estonian", + "mk": "macedonian", + "br": "breton", + "eu": "basque", + "is": "icelandic", + "hy": "armenian", + "ne": "nepali", + "mn": "mongolian", + "bs": "bosnian", + "kk": "kazakh", + "sq": "albanian", + "sw": "swahili", + "gl": "galician", + "mr": "marathi", + "pa": "punjabi", + "si": "sinhala", + "km": "khmer", + "sn": "shona", + "yo": "yoruba", + "so": "somali", + "af": "afrikaans", + "oc": "occitan", + "ka": "georgian", + "be": "belarusian", + "tg": "tajik", + "sd": "sindhi", + "gu": "gujarati", + "am": "amharic", + "yi": "yiddish", + "lo": "lao", + "uz": "uzbek", + "fo": "faroese", + "ht": "haitian creole", + "ps": "pashto", + "tk": "turkmen", + "nn": "nynorsk", + "mt": "maltese", + "sa": "sanskrit", + "lb": "luxembourgish", + "my": "myanmar", + "bo": "tibetan", + "tl": "tagalog", + "mg": "malagasy", + "as": "assamese", + "tt": "tatar", + "haw": "hawaiian", + "ln": "lingala", + "ha": "hausa", + "ba": "bashkir", + "jw": "javanese", + "su": "sundanese", + "yue": "cantonese", +} + +# language code lookup by name, with a few language aliases +TO_LANGUAGE_CODE = { + **{language: code for code, language in LANGUAGES.items()}, + "burmese": "my", + "valencian": "ca", + "flemish": "nl", + "haitian": "ht", + "letzeburgesch": "lb", + "pushto": "ps", + "panjabi": "pa", + "moldavian": "ro", + "moldovan": "ro", + "sinhalese": "si", + "castilian": "es", + "mandarin": "zh", +} + + +@dataclass +class Tokenizer: + """A thin wrapper around `tiktoken` providing quick access to special tokens""" + + encoding: tiktoken.Encoding + num_languages: int + language: Optional[str] = None + task: Optional[str] = None + sot_sequence: Tuple[int] = () + special_tokens: Dict[str, int] = field(default_factory=dict) + + def __post_init__(self): + for special in self.encoding.special_tokens_set: + special_token = self.encoding.encode_single_token(special) + self.special_tokens[special] = special_token + + sot: int = self.special_tokens["<|startoftranscript|>"] + translate: int = self.special_tokens["<|translate|>"] + transcribe: int = self.special_tokens["<|transcribe|>"] + + langs = tuple(LANGUAGES.keys())[: self.num_languages] + sot_sequence = [sot] + if self.language is not None: + sot_sequence.append(sot + 1 + langs.index(self.language)) + if self.task is not None: + task_token: int = transcribe if self.task == "transcribe" else translate + sot_sequence.append(task_token) + + self.sot_sequence = tuple(sot_sequence) + + def encode(self, text, **kwargs): + return self.encoding.encode(text, **kwargs) + + def decode(self, token_ids: List[int], **kwargs) -> str: + token_ids = [t for t in token_ids if t < self.timestamp_begin] + return self.encoding.decode(token_ids, **kwargs) + + def decode_with_timestamps(self, token_ids: List[int], **kwargs) -> str: + """ + Timestamp tokens are above other special tokens' id range and are ignored by `decode()`. + This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>". + """ + return self.encoding.decode(token_ids, **kwargs) + + @cached_property + def eot(self) -> int: + return self.encoding.eot_token + + @cached_property + def transcribe(self) -> int: + return self.special_tokens["<|transcribe|>"] + + @cached_property + def translate(self) -> int: + return self.special_tokens["<|translate|>"] + + @cached_property + def sot(self) -> int: + return self.special_tokens["<|startoftranscript|>"] + + @cached_property + def sot_lm(self) -> int: + return self.special_tokens["<|startoflm|>"] + + @cached_property + def sot_prev(self) -> int: + return self.special_tokens["<|startofprev|>"] + + @cached_property + def no_speech(self) -> int: + return self.special_tokens["<|nospeech|>"] + + @cached_property + def no_timestamps(self) -> int: + return self.special_tokens["<|notimestamps|>"] + + @cached_property + def timestamp_begin(self) -> int: + return self.special_tokens["<|0.00|>"] + + @cached_property + def language_token(self) -> int: + """Returns the token id corresponding to the value of the `language` field""" + if self.language is None: + raise ValueError("This tokenizer does not have language token configured") + + return self.to_language_token(self.language) + + def to_language_token(self, language): + if token := self.special_tokens.get(f"<|{language}|>", None): + return token + + raise KeyError(f"Language {language} not found in tokenizer.") + + @cached_property + def all_language_tokens(self) -> Tuple[int]: + result = [] + for token, token_id in self.special_tokens.items(): + if token.strip("<|>") in LANGUAGES: + result.append(token_id) + return tuple(result)[: self.num_languages] + + @cached_property + def all_language_codes(self) -> Tuple[str]: + return tuple(self.decode([_l]).strip("<|>") for _l in self.all_language_tokens) + + @cached_property + def sot_sequence_including_notimestamps(self) -> Tuple[int]: + return tuple(list(self.sot_sequence) + [self.no_timestamps]) + + @cached_property + def non_speech_tokens(self) -> Tuple[int]: + """ + Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech + annotations, to prevent sampling texts that are not actually spoken in the audio, e.g. + + - ♪♪♪ + - ( SPEAKING FOREIGN LANGUAGE ) + - [DAVID] Hey there, + + keeping basic punctuations like commas, periods, question marks, exclamation points, etc. + """ + symbols = list('"#()*+/:;<=>@[\\]^_`{|}~「」『』') + symbols += ( + "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split() + ) + + # symbols that may be a single token or multiple tokens depending on the tokenizer. + # In case they're multiple tokens, suppress the first token, which is safe because: + # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress + # in generations, and in the 3-byte UTF-8 representation they share the first two bytes. + miscellaneous = set("♩♪♫♬♭♮♯") + assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous) + + # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word + result = {self.encoding.encode(" -")[0], self.encoding.encode(" '")[0]} + for symbol in symbols + list(miscellaneous): + for tokens in [ + self.encoding.encode(symbol), + self.encoding.encode(" " + symbol), + ]: + if len(tokens) == 1 or symbol in miscellaneous: + result.add(tokens[0]) + + return tuple(sorted(result)) + + def split_to_word_tokens(self, tokens: List[int]): + if self.language in {"zh", "ja", "th", "lo", "my", "yue"}: + # These languages don't typically use spaces, so it is difficult to split words + # without morpheme analysis. Here, we instead split words at any + # position where the tokens are decoded as valid unicode points + return self.split_tokens_on_unicode(tokens) + + return self.split_tokens_on_spaces(tokens) + + def split_tokens_on_unicode(self, tokens: List[int]): + decoded_full = self.decode_with_timestamps(tokens) + replacement_char = "\ufffd" + + words = [] + word_tokens = [] + current_tokens = [] + unicode_offset = 0 + + for token in tokens: + current_tokens.append(token) + decoded = self.decode_with_timestamps(current_tokens) + + if ( + replacement_char not in decoded + or decoded_full[unicode_offset + decoded.index(replacement_char)] + == replacement_char + ): + words.append(decoded) + word_tokens.append(current_tokens) + current_tokens = [] + unicode_offset += len(decoded) + + return words, word_tokens + + def split_tokens_on_spaces(self, tokens: List[int]): + subwords, subword_tokens_list = self.split_tokens_on_unicode(tokens) + words = [] + word_tokens = [] + + for subword, subword_tokens in zip(subwords, subword_tokens_list): + special = subword_tokens[0] >= self.eot + with_space = subword.startswith(" ") + punctuation = subword.strip() in string.punctuation + if special or with_space or punctuation or len(words) == 0: + words.append(subword) + word_tokens.append(subword_tokens) + else: + words[-1] = words[-1] + subword + word_tokens[-1].extend(subword_tokens) + + return words, word_tokens + + +@lru_cache(maxsize=None) +def get_encoding(name: str = "gpt2", num_languages: int = 99): + vocab_path = os.path.join(os.path.dirname(__file__), "assets", f"{name}.tiktoken") + ranks = { + base64.b64decode(token): int(rank) + for token, rank in (line.split() for line in open(vocab_path) if line) + } + n_vocab = len(ranks) + special_tokens = {} + + specials = [ + "<|endoftext|>", + "<|startoftranscript|>", + *[f"<|{lang}|>" for lang in list(LANGUAGES.keys())[:num_languages]], + "<|translate|>", + "<|transcribe|>", + "<|startoflm|>", + "<|startofprev|>", + "<|nospeech|>", + "<|notimestamps|>", + *[f"<|{i * 0.02:.2f}|>" for i in range(1501)], + ] + + for token in specials: + special_tokens[token] = n_vocab + n_vocab += 1 + + return tiktoken.Encoding( + name=os.path.basename(vocab_path), + explicit_n_vocab=n_vocab, + pat_str=r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""", + mergeable_ranks=ranks, + special_tokens=special_tokens, + ) + + +@lru_cache(maxsize=None) +def get_tokenizer( + multilingual: bool, + *, + num_languages: int = 99, + language: Optional[str] = None, + task: Optional[str] = None, # Literal["transcribe", "translate", None] +) -> Tokenizer: + if language is not None: + language = language.lower() + if language not in LANGUAGES: + if language in TO_LANGUAGE_CODE: + language = TO_LANGUAGE_CODE[language] + else: + raise ValueError(f"Unsupported language: {language}") + + if multilingual: + encoding_name = "multilingual" + language = language or "en" + task = task or "transcribe" + else: + encoding_name = "gpt2" + language = None + task = None + + encoding = get_encoding(name=encoding_name, num_languages=num_languages) + + return Tokenizer( + encoding=encoding, num_languages=num_languages, language=language, task=task + ) diff --git a/xinference/thirdparty/whisper/transcribe.py b/xinference/thirdparty/whisper/transcribe.py new file mode 100644 index 0000000000..1c075a201a --- /dev/null +++ b/xinference/thirdparty/whisper/transcribe.py @@ -0,0 +1,605 @@ +import argparse +import os +import traceback +import warnings +from typing import TYPE_CHECKING, List, Optional, Tuple, Union + +import numpy as np +import torch +import tqdm + +from .audio import ( + FRAMES_PER_SECOND, + HOP_LENGTH, + N_FRAMES, + N_SAMPLES, + SAMPLE_RATE, + log_mel_spectrogram, + pad_or_trim, +) +from .decoding import DecodingOptions, DecodingResult +from .timing import add_word_timestamps +from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer +from .utils import ( + exact_div, + format_timestamp, + get_end, + get_writer, + make_safe, + optional_float, + optional_int, + str2bool, +) + +if TYPE_CHECKING: + from .model import Whisper + + +def transcribe( + model: "Whisper", + audio: Union[str, np.ndarray, torch.Tensor], + *, + verbose: Optional[bool] = None, + temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), + compression_ratio_threshold: Optional[float] = 2.4, + logprob_threshold: Optional[float] = -1.0, + no_speech_threshold: Optional[float] = 0.6, + condition_on_previous_text: bool = True, + initial_prompt: Optional[str] = None, + word_timestamps: bool = False, + prepend_punctuations: str = "\"'“¿([{-", + append_punctuations: str = "\"'.。,，!！?？:：”)]}、", + clip_timestamps: Union[str, List[float]] = "0", + hallucination_silence_threshold: Optional[float] = None, + **decode_options, +): + """ + Transcribe an audio file using Whisper + + Parameters + ---------- + model: Whisper + The Whisper model instance + + audio: Union[str, np.ndarray, torch.Tensor] + The path to the audio file to open, or the audio waveform + + verbose: bool + Whether to display the text being decoded to the console. If True, displays all the details, + If False, displays minimal details. If None, does not display anything + + temperature: Union[float, Tuple[float, ...]] + Temperature for sampling. It can be a tuple of temperatures, which will be successively used + upon failures according to either `compression_ratio_threshold` or `logprob_threshold`. + + compression_ratio_threshold: float + If the gzip compression ratio is above this value, treat as failed + + logprob_threshold: float + If the average log probability over sampled tokens is below this value, treat as failed + + no_speech_threshold: float + If the no_speech probability is higher than this value AND the average log probability + over sampled tokens is below `logprob_threshold`, consider the segment as silent + + condition_on_previous_text: bool + if True, the previous output of the model is provided as a prompt for the next window; + disabling may make the text inconsistent across windows, but the model becomes less prone to + getting stuck in a failure loop, such as repetition looping or timestamps going out of sync. + + word_timestamps: bool + Extract word-level timestamps using the cross-attention pattern and dynamic time warping, + and include the timestamps for each word in each segment. + + prepend_punctuations: str + If word_timestamps is True, merge these punctuation symbols with the next word + + append_punctuations: str + If word_timestamps is True, merge these punctuation symbols with the previous word + + initial_prompt: Optional[str] + Optional text to provide as a prompt for the first window. This can be used to provide, or + "prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns + to make it more likely to predict those word correctly. + + decode_options: dict + Keyword arguments to construct `DecodingOptions` instances + + clip_timestamps: Union[str, List[float]] + Comma-separated list start,end,start,end,... timestamps (in seconds) of clips to process. + The last end timestamp defaults to the end of the file. + + hallucination_silence_threshold: Optional[float] + When word_timestamps is True, skip silent periods longer than this threshold (in seconds) + when a possible hallucination is detected + + Returns + ------- + A dictionary containing the resulting text ("text") and segment-level details ("segments"), and + the spoken language ("language"), which is detected when `decode_options["language"]` is None. + """ + dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32 + if model.device == torch.device("cpu"): + if torch.cuda.is_available(): + warnings.warn("Performing inference on CPU when CUDA is available") + if dtype == torch.float16: + warnings.warn("FP16 is not supported on CPU; using FP32 instead") + dtype = torch.float32 + + if dtype == torch.float32: + decode_options["fp16"] = False + + # Pad 30-seconds of silence to the input audio, for slicing + mel = log_mel_spectrogram(audio, model.dims.n_mels, padding=N_SAMPLES) + content_frames = mel.shape[-1] - N_FRAMES + content_duration = float(content_frames * HOP_LENGTH / SAMPLE_RATE) + + if decode_options.get("language", None) is None: + if not model.is_multilingual: + decode_options["language"] = "en" + else: + if verbose: + print( + "Detecting language using up to the first 30 seconds. Use `--language` to specify the language" + ) + mel_segment = pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype) + _, probs = model.detect_language(mel_segment) + decode_options["language"] = max(probs, key=probs.get) + if verbose is not None: + print( + f"Detected language: {LANGUAGES[decode_options['language']].title()}" + ) + + language: str = decode_options["language"] + task: str = decode_options.get("task", "transcribe") + tokenizer = get_tokenizer( + model.is_multilingual, + num_languages=model.num_languages, + language=language, + task=task, + ) + + if isinstance(clip_timestamps, str): + clip_timestamps = [ + float(ts) for ts in (clip_timestamps.split(",") if clip_timestamps else []) + ] + seek_points: List[int] = [round(ts * FRAMES_PER_SECOND) for ts in clip_timestamps] + if len(seek_points) == 0: + seek_points.append(0) + if len(seek_points) % 2 == 1: + seek_points.append(content_frames) + seek_clips: List[Tuple[int, int]] = list(zip(seek_points[::2], seek_points[1::2])) + + punctuation = "\"'“¿([{-\"'.。,，!！?？:：”)]}、" + + if word_timestamps and task == "translate": + warnings.warn("Word-level timestamps on translations may not be reliable.") + + def decode_with_fallback(segment: torch.Tensor) -> DecodingResult: + temperatures = ( + [temperature] if isinstance(temperature, (int, float)) else temperature + ) + decode_result = None + + for t in temperatures: + kwargs = {**decode_options} + if t > 0: + # disable beam_size and patience when t > 0 + kwargs.pop("beam_size", None) + kwargs.pop("patience", None) + else: + # disable best_of when t == 0 + kwargs.pop("best_of", None) + + options = DecodingOptions(**kwargs, temperature=t) + decode_result = model.decode(segment, options) + + needs_fallback = False + if ( + compression_ratio_threshold is not None + and decode_result.compression_ratio > compression_ratio_threshold + ): + needs_fallback = True # too repetitive + if ( + logprob_threshold is not None + and decode_result.avg_logprob < logprob_threshold + ): + needs_fallback = True # average log probability is too low + if ( + no_speech_threshold is not None + and decode_result.no_speech_prob > no_speech_threshold + ): + needs_fallback = False # silence + if not needs_fallback: + break + + return decode_result + + clip_idx = 0 + seek = seek_clips[clip_idx][0] + input_stride = exact_div( + N_FRAMES, model.dims.n_audio_ctx + ) # mel frames per output token: 2 + time_precision = ( + input_stride * HOP_LENGTH / SAMPLE_RATE + ) # time per output token: 0.02 (seconds) + all_tokens = [] + all_segments = [] + prompt_reset_since = 0 + + if initial_prompt is not None: + initial_prompt_tokens = tokenizer.encode(" " + initial_prompt.strip()) + all_tokens.extend(initial_prompt_tokens) + else: + initial_prompt_tokens = [] + + def new_segment( + *, start: float, end: float, tokens: torch.Tensor, result: DecodingResult + ): + tokens = tokens.tolist() + text_tokens = [token for token in tokens if token < tokenizer.eot] + return { + "seek": seek, + "start": start, + "end": end, + "text": tokenizer.decode(text_tokens), + "tokens": tokens, + "temperature": result.temperature, + "avg_logprob": result.avg_logprob, + "compression_ratio": result.compression_ratio, + "no_speech_prob": result.no_speech_prob, + } + + # show the progress bar when verbose is False (if True, transcribed text will be printed) + with tqdm.tqdm( + total=content_frames, unit="frames", disable=verbose is not False + ) as pbar: + last_speech_timestamp = 0.0 + # NOTE: This loop is obscurely flattened to make the diff readable. + # A later commit should turn this into a simpler nested loop. + # for seek_clip_start, seek_clip_end in seek_clips: + # while seek < seek_clip_end + while clip_idx < len(seek_clips): + seek_clip_start, seek_clip_end = seek_clips[clip_idx] + if seek < seek_clip_start: + seek = seek_clip_start + if seek >= seek_clip_end: + clip_idx += 1 + if clip_idx < len(seek_clips): + seek = seek_clips[clip_idx][0] + continue + time_offset = float(seek * HOP_LENGTH / SAMPLE_RATE) + window_end_time = float((seek + N_FRAMES) * HOP_LENGTH / SAMPLE_RATE) + segment_size = min(N_FRAMES, content_frames - seek, seek_clip_end - seek) + mel_segment = mel[:, seek : seek + segment_size] + segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE + mel_segment = pad_or_trim(mel_segment, N_FRAMES).to(model.device).to(dtype) + + decode_options["prompt"] = all_tokens[prompt_reset_since:] + result: DecodingResult = decode_with_fallback(mel_segment) + tokens = torch.tensor(result.tokens) + + if no_speech_threshold is not None: + # no voice activity check + should_skip = result.no_speech_prob > no_speech_threshold + if ( + logprob_threshold is not None + and result.avg_logprob > logprob_threshold + ): + # don't skip if the logprob is high enough, despite the no_speech_prob + should_skip = False + + if should_skip: + seek += segment_size # fast-forward to the next segment boundary + continue + + previous_seek = seek + current_segments = [] + + # anomalous words are very long/short/improbable + def word_anomaly_score(word: dict) -> float: + probability = word.get("probability", 0.0) + duration = word["end"] - word["start"] + score = 0.0 + if probability < 0.15: + score += 1.0 + if duration < 0.133: + score += (0.133 - duration) * 15 + if duration > 2.0: + score += duration - 2.0 + return score + + def is_segment_anomaly(segment: Optional[dict]) -> bool: + if segment is None or not segment["words"]: + return False + words = [w for w in segment["words"] if w["word"] not in punctuation] + words = words[:8] + score = sum(word_anomaly_score(w) for w in words) + return score >= 3 or score + 0.01 >= len(words) + + def next_words_segment(segments: List[dict]) -> Optional[dict]: + return next((s for s in segments if s["words"]), None) + + timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin) + single_timestamp_ending = timestamp_tokens[-2:].tolist() == [False, True] + + consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0] + consecutive.add_(1) + if len(consecutive) > 0: + # if the output contains two consecutive timestamp tokens + slices = consecutive.tolist() + if single_timestamp_ending: + slices.append(len(tokens)) + + last_slice = 0 + for current_slice in slices: + sliced_tokens = tokens[last_slice:current_slice] + start_timestamp_pos = ( + sliced_tokens[0].item() - tokenizer.timestamp_begin + ) + end_timestamp_pos = ( + sliced_tokens[-1].item() - tokenizer.timestamp_begin + ) + current_segments.append( + new_segment( + start=time_offset + start_timestamp_pos * time_precision, + end=time_offset + end_timestamp_pos * time_precision, + tokens=sliced_tokens, + result=result, + ) + ) + last_slice = current_slice + + if single_timestamp_ending: + # single timestamp at the end means no speech after the last timestamp. + seek += segment_size + else: + # otherwise, ignore the unfinished segment and seek to the last timestamp + last_timestamp_pos = ( + tokens[last_slice - 1].item() - tokenizer.timestamp_begin + ) + seek += last_timestamp_pos * input_stride + else: + duration = segment_duration + timestamps = tokens[timestamp_tokens.nonzero().flatten()] + if ( + len(timestamps) > 0 + and timestamps[-1].item() != tokenizer.timestamp_begin + ): + # no consecutive timestamps but it has a timestamp; use the last one. + last_timestamp_pos = ( + timestamps[-1].item() - tokenizer.timestamp_begin + ) + duration = last_timestamp_pos * time_precision + + current_segments.append( + new_segment( + start=time_offset, + end=time_offset + duration, + tokens=tokens, + result=result, + ) + ) + seek += segment_size + + if word_timestamps: + add_word_timestamps( + segments=current_segments, + model=model, + tokenizer=tokenizer, + mel=mel_segment, + num_frames=segment_size, + prepend_punctuations=prepend_punctuations, + append_punctuations=append_punctuations, + last_speech_timestamp=last_speech_timestamp, + ) + + if not single_timestamp_ending: + last_word_end = get_end(current_segments) + if last_word_end is not None and last_word_end > time_offset: + seek = round(last_word_end * FRAMES_PER_SECOND) + + # skip silence before possible hallucinations + if hallucination_silence_threshold is not None: + threshold = hallucination_silence_threshold + if not single_timestamp_ending: + last_word_end = get_end(current_segments) + if last_word_end is not None and last_word_end > time_offset: + remaining_duration = window_end_time - last_word_end + if remaining_duration > threshold: + seek = round(last_word_end * FRAMES_PER_SECOND) + else: + seek = previous_seek + segment_size + + # if first segment might be a hallucination, skip leading silence + first_segment = next_words_segment(current_segments) + if first_segment is not None and is_segment_anomaly(first_segment): + gap = first_segment["start"] - time_offset + if gap > threshold: + seek = previous_seek + round(gap * FRAMES_PER_SECOND) + continue + + # skip silence before any possible hallucination that is surrounded + # by silence or more hallucinations + hal_last_end = last_speech_timestamp + for si in range(len(current_segments)): + segment = current_segments[si] + if not segment["words"]: + continue + if is_segment_anomaly(segment): + next_segment = next_words_segment( + current_segments[si + 1 :] + ) + if next_segment is not None: + hal_next_start = next_segment["words"][0]["start"] + else: + hal_next_start = time_offset + segment_duration + silence_before = ( + segment["start"] - hal_last_end > threshold + or segment["start"] < threshold + or segment["start"] - time_offset < 2.0 + ) + silence_after = ( + hal_next_start - segment["end"] > threshold + or is_segment_anomaly(next_segment) + or window_end_time - segment["end"] < 2.0 + ) + if silence_before and silence_after: + seek = round( + max(time_offset + 1, segment["start"]) + * FRAMES_PER_SECOND + ) + if content_duration - segment["end"] < threshold: + seek = content_frames + current_segments[si:] = [] + break + hal_last_end = segment["end"] + + last_word_end = get_end(current_segments) + if last_word_end is not None: + last_speech_timestamp = last_word_end + + if verbose: + for segment in current_segments: + start, end, text = segment["start"], segment["end"], segment["text"] + line = f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}" + print(make_safe(line)) + + # if a segment is instantaneous or does not contain text, clear it + for i, segment in enumerate(current_segments): + if segment["start"] == segment["end"] or segment["text"].strip() == "": + segment["text"] = "" + segment["tokens"] = [] + segment["words"] = [] + + all_segments.extend( + [ + {"id": i, **segment} + for i, segment in enumerate( + current_segments, start=len(all_segments) + ) + ] + ) + all_tokens.extend( + [token for segment in current_segments for token in segment["tokens"]] + ) + + if not condition_on_previous_text or result.temperature > 0.5: + # do not feed the prompt tokens if a high temperature was used + prompt_reset_since = len(all_tokens) + + # update progress bar + pbar.update(min(content_frames, seek) - previous_seek) + + return dict( + text=tokenizer.decode(all_tokens[len(initial_prompt_tokens) :]), + segments=all_segments, + language=language, + ) + + +def cli(): + from . import available_models + + def valid_model_name(name): + if name in available_models() or os.path.exists(name): + return name + raise ValueError( + f"model should be one of {available_models()} or path to a model checkpoint" + ) + + # fmt: off + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe") + parser.add_argument("--model", default="small", type=valid_model_name, help="name of the Whisper model to use") + parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default") + parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference") + parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs") + parser.add_argument("--output_format", "-f", type=str, default="all", choices=["txt", "vtt", "srt", "tsv", "json", "all"], help="format of the output file; if not specified, all available formats will be produced") + parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages") + + parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')") + parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), help="language spoken in the audio, specify None to perform language detection") + + parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling") + parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature") + parser.add_argument("--beam_size", type=optional_int, default=5, help="number of beams in beam search, only applicable when temperature is zero") + parser.add_argument("--patience", type=float, default=None, help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search") + parser.add_argument("--length_penalty", type=float, default=None, help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple length normalization by default") + + parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations") + parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.") + parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop") + parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default") + + parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below") + parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed") + parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed") + parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence") + parser.add_argument("--word_timestamps", type=str2bool, default=False, help="(experimental) extract word-level timestamps and refine the results based on them") + parser.add_argument("--prepend_punctuations", type=str, default="\"\'“¿([{-", help="if word_timestamps is True, merge these punctuation symbols with the next word") + parser.add_argument("--append_punctuations", type=str, default="\"\'.。,，!！?？:：”)]}、", help="if word_timestamps is True, merge these punctuation symbols with the previous word") + parser.add_argument("--highlight_words", type=str2bool, default=False, help="(requires --word_timestamps True) underline each word as it is spoken in srt and vtt") + parser.add_argument("--max_line_width", type=optional_int, default=None, help="(requires --word_timestamps True) the maximum number of characters in a line before breaking the line") + parser.add_argument("--max_line_count", type=optional_int, default=None, help="(requires --word_timestamps True) the maximum number of lines in a segment") + parser.add_argument("--max_words_per_line", type=optional_int, default=None, help="(requires --word_timestamps True, no effect with --max_line_width) the maximum number of words in a segment") + parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS") + parser.add_argument("--clip_timestamps", type=str, default="0", help="comma-separated list start,end,start,end,... timestamps (in seconds) of clips to process, where the last end timestamp defaults to the end of the file") + parser.add_argument("--hallucination_silence_threshold", type=optional_float, help="(requires --word_timestamps True) skip silent periods longer than this threshold (in seconds) when a possible hallucination is detected") + # fmt: on + + args = parser.parse_args().__dict__ + model_name: str = args.pop("model") + model_dir: str = args.pop("model_dir") + output_dir: str = args.pop("output_dir") + output_format: str = args.pop("output_format") + device: str = args.pop("device") + os.makedirs(output_dir, exist_ok=True) + + if model_name.endswith(".en") and args["language"] not in {"en", "English"}: + if args["language"] is not None: + warnings.warn( + f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead." + ) + args["language"] = "en" + + temperature = args.pop("temperature") + if (increment := args.pop("temperature_increment_on_fallback")) is not None: + temperature = tuple(np.arange(temperature, 1.0 + 1e-6, increment)) + else: + temperature = [temperature] + + if (threads := args.pop("threads")) > 0: + torch.set_num_threads(threads) + + from . import load_model + + model = load_model(model_name, device=device, download_root=model_dir) + + writer = get_writer(output_format, output_dir) + word_options = [ + "highlight_words", + "max_line_count", + "max_line_width", + "max_words_per_line", + ] + if not args["word_timestamps"]: + for option in word_options: + if args[option]: + parser.error(f"--{option} requires --word_timestamps True") + if args["max_line_count"] and not args["max_line_width"]: + warnings.warn("--max_line_count has no effect without --max_line_width") + if args["max_words_per_line"] and args["max_line_width"]: + warnings.warn("--max_words_per_line has no effect with --max_line_width") + writer_args = {arg: args.pop(arg) for arg in word_options} + for audio_path in args.pop("audio"): + try: + result = transcribe(model, audio_path, temperature=temperature, **args) + writer(result, audio_path, **writer_args) + except Exception as e: + traceback.print_exc() + print(f"Skipping {audio_path} due to {type(e).__name__}: {str(e)}") + + +if __name__ == "__main__": + cli() diff --git a/xinference/thirdparty/whisper/triton_ops.py b/xinference/thirdparty/whisper/triton_ops.py new file mode 100644 index 0000000000..edd456414a --- /dev/null +++ b/xinference/thirdparty/whisper/triton_ops.py @@ -0,0 +1,109 @@ +from functools import lru_cache + +import numpy as np +import torch + +try: + import triton + import triton.language as tl +except ImportError: + raise RuntimeError("triton import failed; try `pip install --pre triton`") + + +@triton.jit +def dtw_kernel( + cost, trace, x, x_stride, cost_stride, trace_stride, N, M, BLOCK_SIZE: tl.constexpr +): + offsets = tl.arange(0, BLOCK_SIZE) + mask = offsets < M + + for k in range(1, N + M + 1): # k = i + j + tl.debug_barrier() + + p0 = cost + (k - 1) * cost_stride + p1 = cost + k * cost_stride + p2 = cost + k * cost_stride + 1 + + c0 = tl.load(p0 + offsets, mask=mask) + c1 = tl.load(p1 + offsets, mask=mask) + c2 = tl.load(p2 + offsets, mask=mask) + + x_row = tl.load(x + (k - 1) * x_stride + offsets, mask=mask, other=0) + cost_row = x_row + tl.minimum(tl.minimum(c0, c1), c2) + + cost_ptr = cost + (k + 1) * cost_stride + 1 + tl.store(cost_ptr + offsets, cost_row, mask=mask) + + trace_ptr = trace + (k + 1) * trace_stride + 1 + tl.store(trace_ptr + offsets, 2, mask=mask & (c2 <= c0) & (c2 <= c1)) + tl.store(trace_ptr + offsets, 1, mask=mask & (c1 <= c0) & (c1 <= c2)) + tl.store(trace_ptr + offsets, 0, mask=mask & (c0 <= c1) & (c0 <= c2)) + + +@lru_cache(maxsize=None) +def median_kernel(filter_width: int): + @triton.jit + def kernel( + y, x, x_stride, y_stride, BLOCK_SIZE: tl.constexpr + ): # x.shape[-1] == filter_width + row_idx = tl.program_id(0) + offsets = tl.arange(0, BLOCK_SIZE) + mask = offsets < y_stride + + x_ptr = x + row_idx * x_stride # noqa: F841 + y_ptr = y + row_idx * y_stride + + LOAD_ALL_ROWS_HERE # noqa: F821 + + BUBBLESORT_HERE # noqa: F821 + + tl.store(y_ptr + offsets, MIDDLE_ROW_HERE, mask=mask) # noqa: F821 + + kernel = triton.JITFunction(kernel.fn) + kernel.src = kernel.src.replace( + " LOAD_ALL_ROWS_HERE", + "\n".join( + [ + f" row{i} = tl.load(x_ptr + offsets + {i}, mask=mask)" + for i in range(filter_width) + ] + ), + ) + kernel.src = kernel.src.replace( + " BUBBLESORT_HERE", + "\n\n".join( + [ + "\n\n".join( + [ + "\n".join( + [ + f" smaller = tl.where(row{j} < row{j + 1}, row{j}, row{j + 1})", + f" larger = tl.where(row{j} > row{j + 1}, row{j}, row{j + 1})", + f" row{j} = smaller", + f" row{j + 1} = larger", + ] + ) + for j in range(filter_width - i - 1) + ] + ) + for i in range(filter_width // 2 + 1) + ] + ), + ) + kernel.src = kernel.src.replace("MIDDLE_ROW_HERE", f"row{filter_width // 2}") + + return kernel + + +def median_filter_cuda(x: torch.Tensor, filter_width: int): + """Apply a median filter of given width along the last dimension of x""" + slices = x.contiguous().unfold(-1, filter_width, 1) + grid = np.prod(slices.shape[:-2]) + + kernel = median_kernel(filter_width) + y = torch.empty_like(slices[..., 0]) + + BLOCK_SIZE = 1 << (y.stride(-2) - 1).bit_length() + kernel[(grid,)](y, x, x.stride(-2), y.stride(-2), BLOCK_SIZE=BLOCK_SIZE) + + return y diff --git a/xinference/thirdparty/whisper/utils.py b/xinference/thirdparty/whisper/utils.py new file mode 100644 index 0000000000..9b9b138626 --- /dev/null +++ b/xinference/thirdparty/whisper/utils.py @@ -0,0 +1,316 @@ +import json +import os +import re +import sys +import zlib +from typing import Callable, List, Optional, TextIO + +system_encoding = sys.getdefaultencoding() + +if system_encoding != "utf-8": + + def make_safe(string): + # replaces any character not representable using the system default encoding with an '?', + # avoiding UnicodeEncodeError (https://github.com/openai/whisper/discussions/729). + return string.encode(system_encoding, errors="replace").decode(system_encoding) + +else: + + def make_safe(string): + # utf-8 can encode any Unicode code point, so no need to do the round-trip encoding + return string + + +def exact_div(x, y): + assert x % y == 0 + return x // y + + +def str2bool(string): + str2val = {"True": True, "False": False} + if string in str2val: + return str2val[string] + else: + raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}") + + +def optional_int(string): + return None if string == "None" else int(string) + + +def optional_float(string): + return None if string == "None" else float(string) + + +def compression_ratio(text) -> float: + text_bytes = text.encode("utf-8") + return len(text_bytes) / len(zlib.compress(text_bytes)) + + +def format_timestamp( + seconds: float, always_include_hours: bool = False, decimal_marker: str = "." +): + assert seconds >= 0, "non-negative timestamp expected" + milliseconds = round(seconds * 1000.0) + + hours = milliseconds // 3_600_000 + milliseconds -= hours * 3_600_000 + + minutes = milliseconds // 60_000 + milliseconds -= minutes * 60_000 + + seconds = milliseconds // 1_000 + milliseconds -= seconds * 1_000 + + hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else "" + return ( + f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}" + ) + + +def get_start(segments: List[dict]) -> Optional[float]: + return next( + (w["start"] for s in segments for w in s["words"]), + segments[0]["start"] if segments else None, + ) + + +def get_end(segments: List[dict]) -> Optional[float]: + return next( + (w["end"] for s in reversed(segments) for w in reversed(s["words"])), + segments[-1]["end"] if segments else None, + ) + + +class ResultWriter: + extension: str + + def __init__(self, output_dir: str): + self.output_dir = output_dir + + def __call__( + self, result: dict, audio_path: str, options: Optional[dict] = None, **kwargs + ): + audio_basename = os.path.basename(audio_path) + audio_basename = os.path.splitext(audio_basename)[0] + output_path = os.path.join( + self.output_dir, audio_basename + "." + self.extension + ) + + with open(output_path, "w", encoding="utf-8") as f: + self.write_result(result, file=f, options=options, **kwargs) + + def write_result( + self, result: dict, file: TextIO, options: Optional[dict] = None, **kwargs + ): + raise NotImplementedError + + +class WriteTXT(ResultWriter): + extension: str = "txt" + + def write_result( + self, result: dict, file: TextIO, options: Optional[dict] = None, **kwargs + ): + for segment in result["segments"]: + print(segment["text"].strip(), file=file, flush=True) + + +class SubtitlesWriter(ResultWriter): + always_include_hours: bool + decimal_marker: str + + def iterate_result( + self, + result: dict, + options: Optional[dict] = None, + *, + max_line_width: Optional[int] = None, + max_line_count: Optional[int] = None, + highlight_words: bool = False, + max_words_per_line: Optional[int] = None, + ): + options = options or {} + max_line_width = max_line_width or options.get("max_line_width") + max_line_count = max_line_count or options.get("max_line_count") + highlight_words = highlight_words or options.get("highlight_words", False) + max_words_per_line = max_words_per_line or options.get("max_words_per_line") + preserve_segments = max_line_count is None or max_line_width is None + max_line_width = max_line_width or 1000 + max_words_per_line = max_words_per_line or 1000 + + def iterate_subtitles(): + line_len = 0 + line_count = 1 + # the next subtitle to yield (a list of word timings with whitespace) + subtitle: List[dict] = [] + last: float = get_start(result["segments"]) or 0.0 + for segment in result["segments"]: + chunk_index = 0 + words_count = max_words_per_line + while chunk_index < len(segment["words"]): + remaining_words = len(segment["words"]) - chunk_index + if max_words_per_line > len(segment["words"]) - chunk_index: + words_count = remaining_words + for i, original_timing in enumerate( + segment["words"][chunk_index : chunk_index + words_count] + ): + timing = original_timing.copy() + long_pause = ( + not preserve_segments and timing["start"] - last > 3.0 + ) + has_room = line_len + len(timing["word"]) <= max_line_width + seg_break = i == 0 and len(subtitle) > 0 and preserve_segments + if ( + line_len > 0 + and has_room + and not long_pause + and not seg_break + ): + # line continuation + line_len += len(timing["word"]) + else: + # new line + timing["word"] = timing["word"].strip() + if ( + len(subtitle) > 0 + and max_line_count is not None + and (long_pause or line_count >= max_line_count) + or seg_break + ): + # subtitle break + yield subtitle + subtitle = [] + line_count = 1 + elif line_len > 0: + # line break + line_count += 1 + timing["word"] = "\n" + timing["word"] + line_len = len(timing["word"].strip()) + subtitle.append(timing) + last = timing["start"] + chunk_index += max_words_per_line + if len(subtitle) > 0: + yield subtitle + + if len(result["segments"]) > 0 and "words" in result["segments"][0]: + for subtitle in iterate_subtitles(): + subtitle_start = self.format_timestamp(subtitle[0]["start"]) + subtitle_end = self.format_timestamp(subtitle[-1]["end"]) + subtitle_text = "".join([word["word"] for word in subtitle]) + if highlight_words: + last = subtitle_start + all_words = [timing["word"] for timing in subtitle] + for i, this_word in enumerate(subtitle): + start = self.format_timestamp(this_word["start"]) + end = self.format_timestamp(this_word["end"]) + if last != start: + yield last, start, subtitle_text + + yield start, end, "".join( + [ + re.sub(r"^(\s*)(.*)$", r"\1\2", word) + if j == i + else word + for j, word in enumerate(all_words) + ] + ) + last = end + else: + yield subtitle_start, subtitle_end, subtitle_text + else: + for segment in result["segments"]: + segment_start = self.format_timestamp(segment["start"]) + segment_end = self.format_timestamp(segment["end"]) + segment_text = segment["text"].strip().replace("-->", "->") + yield segment_start, segment_end, segment_text + + def format_timestamp(self, seconds: float): + return format_timestamp( + seconds=seconds, + always_include_hours=self.always_include_hours, + decimal_marker=self.decimal_marker, + ) + + +class WriteVTT(SubtitlesWriter): + extension: str = "vtt" + always_include_hours: bool = False + decimal_marker: str = "." + + def write_result( + self, result: dict, file: TextIO, options: Optional[dict] = None, **kwargs + ): + print("WEBVTT\n", file=file) + for start, end, text in self.iterate_result(result, options, **kwargs): + print(f"{start} --> {end}\n{text}\n", file=file, flush=True) + + +class WriteSRT(SubtitlesWriter): + extension: str = "srt" + always_include_hours: bool = True + decimal_marker: str = "," + + def write_result( + self, result: dict, file: TextIO, options: Optional[dict] = None, **kwargs + ): + for i, (start, end, text) in enumerate( + self.iterate_result(result, options, **kwargs), start=1 + ): + print(f"{i}\n{start} --> {end}\n{text}\n", file=file, flush=True) + + +class WriteTSV(ResultWriter): + """ + Write a transcript to a file in TSV (tab-separated values) format containing lines like: + \t\t + + Using integer milliseconds as start and end times means there's no chance of interference from + an environment setting a language encoding that causes the decimal in a floating point number + to appear as a comma; also is faster and more efficient to parse & store, e.g., in C++. + """ + + extension: str = "tsv" + + def write_result( + self, result: dict, file: TextIO, options: Optional[dict] = None, **kwargs + ): + print("start", "end", "text", sep="\t", file=file) + for segment in result["segments"]: + print(round(1000 * segment["start"]), file=file, end="\t") + print(round(1000 * segment["end"]), file=file, end="\t") + print(segment["text"].strip().replace("\t", " "), file=file, flush=True) + + +class WriteJSON(ResultWriter): + extension: str = "json" + + def write_result( + self, result: dict, file: TextIO, options: Optional[dict] = None, **kwargs + ): + json.dump(result, file) + + +def get_writer( + output_format: str, output_dir: str +) -> Callable[[dict, TextIO, dict], None]: + writers = { + "txt": WriteTXT, + "vtt": WriteVTT, + "srt": WriteSRT, + "tsv": WriteTSV, + "json": WriteJSON, + } + + if output_format == "all": + all_writers = [writer(output_dir) for writer in writers.values()] + + def write_all( + result: dict, file: TextIO, options: Optional[dict] = None, **kwargs + ): + for writer in all_writers: + writer(result, file, options, **kwargs) + + return write_all + + return writers[output_format](output_dir) diff --git a/xinference/thirdparty/whisper/version.py b/xinference/thirdparty/whisper/version.py new file mode 100644 index 0000000000..c96dd9ce4b --- /dev/null +++ b/xinference/thirdparty/whisper/version.py @@ -0,0 +1 @@ +__version__ = "20231117" diff --git a/xinference/types.py b/xinference/types.py index 3f636d94c3..613d8709bb 100644 --- a/xinference/types.py +++ b/xinference/types.py @@ -24,11 +24,8 @@ ) from .fields import ( echo_field, - frequency_penalty_field, - logprobs_field, max_tokens_field, none_field, - presence_penalty_field, repeat_penalty_field, stop_field, stream_field, @@ -39,8 +36,6 @@ top_p_field, ) -SPECIAL_TOOL_PROMPT = "" - class Image(TypedDict): url: Optional[str] @@ -52,6 +47,12 @@ class ImageList(TypedDict): data: List[Image] +class SDAPIResult(TypedDict): + images: List[str] + parameters: dict + info: dict + + class Video(TypedDict): url: Optional[str] b64_json: Optional[str] @@ -142,7 +143,7 @@ class ToolCalls(TypedDict): class CompletionChoice(TypedDict): - text: str + text: NotRequired[str] index: int logprobs: Optional[CompletionLogprobs] finish_reason: Optional[str] @@ -402,41 +403,12 @@ class CreateCompletionTorch(BaseModel): CreateCompletionOpenAI: BaseModel -class _CreateCompletionOpenAIFallback(BaseModel): - # OpenAI's create completion request body, we define it by pydantic - # model to verify the input params. - # https://platform.openai.com/docs/api-reference/completions/object - model: str - prompt: str - best_of: Optional[int] = 1 - echo: bool = echo_field - frequency_penalty: Optional[float] = frequency_penalty_field - logit_bias: Optional[Dict[str, float]] = none_field - logprobs: Optional[int] = logprobs_field - max_tokens: int = max_tokens_field - n: Optional[int] = 1 - presence_penalty: Optional[float] = presence_penalty_field - seed: Optional[int] = none_field - stop: Optional[Union[str, List[str]]] = stop_field - stream: bool = stream_field - stream_options: Optional[Union[dict, None]] = stream_option_field - suffix: Optional[str] = none_field - temperature: float = temperature_field - top_p: float = top_p_field - user: Optional[str] = none_field - - -try: - # For openai > 1 - from openai.types.completion_create_params import CompletionCreateParamsNonStreaming +from openai.types.completion_create_params import CompletionCreateParamsNonStreaming - CreateCompletionOpenAI = create_model_from_typeddict( - CompletionCreateParamsNonStreaming, - ) - CreateCompletionOpenAI = fix_forward_ref(CreateCompletionOpenAI) -except ImportError: - # TODO(codingl2k1): Remove it if openai < 1 is dropped. - CreateCompletionOpenAI = _CreateCompletionOpenAIFallback +CreateCompletionOpenAI = create_model_from_typeddict( + CompletionCreateParamsNonStreaming, +) +CreateCompletionOpenAI = fix_forward_ref(CreateCompletionOpenAI) class CreateCompletion( @@ -456,22 +428,11 @@ class CreateChatModel(BaseModel): CreateChatCompletionTorch = CreateCompletionTorch CreateChatCompletionLlamaCpp: BaseModel = CreateCompletionLlamaCpp -# This type is for openai API compatibility -CreateChatCompletionOpenAI: BaseModel - -# Only support openai > 1 -from openai.types.chat.completion_create_params import ( - CompletionCreateParamsNonStreaming, -) - -CreateChatCompletionOpenAI = create_model_from_typeddict( - CompletionCreateParamsNonStreaming, -) -CreateChatCompletionOpenAI = fix_forward_ref(CreateChatCompletionOpenAI) +from ._compat import CreateChatCompletionOpenAI -class CreateChatCompletion( +class CreateChatCompletion( # type: ignore CreateChatModel, CreateChatCompletionTorch, CreateChatCompletionLlamaCpp, diff --git a/xinference/utils.py b/xinference/utils.py index 79a46fde6d..3514a6984e 100644 --- a/xinference/utils.py +++ b/xinference/utils.py @@ -13,9 +13,8 @@ # limitations under the License. -import torch - - def cuda_count(): + import torch + # even if install torch cpu, this interface would return 0. return torch.cuda.device_count() diff --git a/xinference/web/ui/package-lock.json b/xinference/web/ui/package-lock.json index 983c68dd2a..7f15648e74 100644 --- a/xinference/web/ui/package-lock.json +++ b/xinference/web/ui/package-lock.json @@ -15,10 +15,10 @@ "@fullcalendar/list": "^6.1.8", "@fullcalendar/timegrid": "^6.1.8", "@mui/icons-material": "^5.14.0", - "@mui/lab": "latest", + "@mui/lab": "^5.0.0-alpha.173", "@mui/material": "^5.14.0", "@mui/system": "^5.15.9", - "@mui/x-data-grid": "^6.10.0", + "@mui/x-data-grid": "^6.20.4", "@nivo/bar": "^0.83.0", "@nivo/core": "^0.83.0", "@nivo/geo": "^0.83.0", @@ -29,6 +29,7 @@ "@testing-library/user-event": "^13.5.0", "clipboard": "^2.0.11", "formik": "^2.4.2", + "nunjucks": "^3.2.4", "prop-types": "^15.8.1", "react": "^18.2.0", "react-cookie": "^6.1.1", @@ -2581,28 +2582,28 @@ } }, "node_modules/@floating-ui/core": { - "version": "1.5.0", - "resolved": "https://registry.npmjs.org/@floating-ui/core/-/core-1.5.0.tgz", - "integrity": "sha512-kK1h4m36DQ0UHGj5Ah4db7R0rHemTqqO0QLvUqi1/mUUp3LuAWbWxdxSIf/XsnH9VS6rRVPLJCncjRzUvyCLXg==", + "version": "1.6.7", + "resolved": "https://registry.npmjs.org/@floating-ui/core/-/core-1.6.7.tgz", + "integrity": "sha512-yDzVT/Lm101nQ5TCVeK65LtdN7Tj4Qpr9RTXJ2vPFLqtLxwOrpoxAHAJI8J3yYWUc40J0BDBheaitK5SJmno2g==", "dependencies": { - "@floating-ui/utils": "^0.1.3" + "@floating-ui/utils": "^0.2.7" } }, "node_modules/@floating-ui/dom": { - "version": "1.5.3", - "resolved": "https://registry.npmjs.org/@floating-ui/dom/-/dom-1.5.3.tgz", - "integrity": "sha512-ClAbQnEqJAKCJOEbbLo5IUlZHkNszqhuxS4fHAVxRPXPya6Ysf2G8KypnYcOTpx6I8xcgF9bbHb6g/2KpbV8qA==", + "version": "1.6.10", + "resolved": "https://registry.npmjs.org/@floating-ui/dom/-/dom-1.6.10.tgz", + "integrity": "sha512-fskgCFv8J8OamCmyun8MfjB1Olfn+uZKjOKZ0vhYF3gRmEUXcGOjxWL8bBr7i4kIuPZ2KD2S3EUIOxnjC8kl2A==", "dependencies": { - "@floating-ui/core": "^1.4.2", - "@floating-ui/utils": "^0.1.3" + "@floating-ui/core": "^1.6.0", + "@floating-ui/utils": "^0.2.7" } }, "node_modules/@floating-ui/react-dom": { - "version": "2.0.2", - "resolved": "https://registry.npmjs.org/@floating-ui/react-dom/-/react-dom-2.0.2.tgz", - "integrity": "sha512-5qhlDvjaLmAst/rKb3VdlCinwTF4EYMiVxuuc/HVUjs46W0zgtbMmAZ1UTsDrRTxRmUEzl92mOtWbeeXL26lSQ==", + "version": "2.1.1", + "resolved": "https://registry.npmjs.org/@floating-ui/react-dom/-/react-dom-2.1.1.tgz", + "integrity": "sha512-4h84MJt3CHrtG18mGsXuLCHMrug49d7DFkU0RMIyshRveBeyV2hmV/pDaF2Uxtu8kgq5r46llp5E5FQiR0K2Yg==", "dependencies": { - "@floating-ui/dom": "^1.5.1" + "@floating-ui/dom": "^1.0.0" }, "peerDependencies": { "react": ">=16.8.0", @@ -2610,9 +2611,9 @@ } }, "node_modules/@floating-ui/utils": { - "version": "0.1.6", - "resolved": "https://registry.npmjs.org/@floating-ui/utils/-/utils-0.1.6.tgz", - "integrity": "sha512-OfX7E2oUDYxtBvsuS4e/jSn4Q9Qb6DzgeYtsAdkPZ47znpoNsMgZw0+tVijiv3uGNR6dgNlty6r9rzIzHjtd/A==" + "version": "0.2.7", + "resolved": "https://registry.npmjs.org/@floating-ui/utils/-/utils-0.2.7.tgz", + "integrity": "sha512-X8R8Oj771YRl/w+c1HqAC1szL8zWQRwFvgDwT129k9ACdBoud/+/rX9V0qiMl6LWUdP9voC2nDVZYPMQQsb6eA==" }, "node_modules/@fullcalendar/core": { "version": "6.1.8", @@ -3419,25 +3420,24 @@ "integrity": 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"version": "1.5.5", @@ -5440,9 +5352,9 @@ } }, "node_modules/@types/react-transition-group": { - "version": "4.4.6", - "resolved": "https://registry.npmjs.org/@types/react-transition-group/-/react-transition-group-4.4.6.tgz", - "integrity": "sha512-VnCdSxfcm08KjsJVQcfBmhEQAPnLB8G08hAxn39azX1qYBQ/5RVQuoHuKIcfKOdncuaUvEpFKFzEvbtIMsfVew==", + "version": "4.4.11", + "resolved": "https://registry.npmjs.org/@types/react-transition-group/-/react-transition-group-4.4.11.tgz", + "integrity": "sha512-RM05tAniPZ5DZPzzNFP+DmrcOdD0efDUxMy3145oljWSl3x9ZV5vhme98gTxFrj2lhXvmGNnUiuDyJgY9IKkNA==", "dependencies": { "@types/react": "*" } @@ -5888,6 +5800,11 @@ "resolved": "https://registry.npmjs.org/@xtuc/long/-/long-4.2.2.tgz", "integrity": "sha512-NuHqBY1PB/D8xU6s/thBgOAiAP7HOYDQ32+BFZILJ8ivkUkAHQnWfn6WhL79Owj1qmUnoN/YPhktdIoucipkAQ==" }, + "node_modules/a-sync-waterfall": { + "version": "1.0.1", + "resolved": "https://registry.npmmirror.com/a-sync-waterfall/-/a-sync-waterfall-1.0.1.tgz", + "integrity": "sha512-RYTOHHdWipFUliRFMCS4X2Yn2X8M87V/OpSqWzKKOGhzqyUxzyVmhHDH9sAvG+ZuQf/TAOFsLCpMw09I1ufUnA==" + }, "node_modules/abab": { "version": "2.0.6", "resolved": "https://registry.npmjs.org/abab/-/abab-2.0.6.tgz", @@ -7073,9 +6990,9 @@ } }, "node_modules/clsx": { - "version": "1.2.1", - "resolved": "https://registry.npmjs.org/clsx/-/clsx-1.2.1.tgz", - "integrity": "sha512-EcR6r5a8bj6pu3ycsa/E/cKVGuTgZJZdsyUYHOksG/UHIiKfjxzRxYJpyVBwYaQeOvghal9fcc4PidlgzugAQg==", + "version": "2.1.1", + "resolved": "https://registry.npmjs.org/clsx/-/clsx-2.1.1.tgz", + "integrity": "sha512-eYm0QWBtUrBWZWG0d386OGAw16Z995PiOVo2B7bjWSbHedGl5e0ZWaq65kOGgUSNesEIDkB9ISbTg/JK9dhCZA==", "engines": { "node": ">=6" } @@ -13839,6 +13756,38 @@ "url": "https://github.com/fb55/nth-check?sponsor=1" } }, + "node_modules/nunjucks": { + "version": "3.2.4", + "resolved": "https://registry.npmmirror.com/nunjucks/-/nunjucks-3.2.4.tgz", + "integrity": "sha512-26XRV6BhkgK0VOxfbU5cQI+ICFUtMLixv1noZn1tGU38kQH5A5nmmbk/O45xdyBhD1esk47nKrY0mvQpZIhRjQ==", + "dependencies": { + "a-sync-waterfall": "^1.0.0", + "asap": "^2.0.3", + "commander": "^5.1.0" + }, + "bin": { + "nunjucks-precompile": "bin/precompile" + }, + "engines": { + "node": ">= 6.9.0" + }, + "peerDependencies": { + "chokidar": "^3.3.0" + }, + "peerDependenciesMeta": { + "chokidar": { + "optional": true + } + } + }, + "node_modules/nunjucks/node_modules/commander": { + "version": "5.1.0", + "resolved": "https://registry.npmmirror.com/commander/-/commander-5.1.0.tgz", + "integrity": "sha512-P0CysNDQ7rtVw4QIQtm+MRxV66vKFSvlsQvGYXZWR3qFU0jlMKHZZZgw8e+8DSah4UDKMqnknRDQz+xuQXQ/Zg==", + "engines": { + "node": ">= 6" + } + }, "node_modules/nwsapi": { "version": "2.2.7", "resolved": "https://registry.npmjs.org/nwsapi/-/nwsapi-2.2.7.tgz", diff --git a/xinference/web/ui/package.json b/xinference/web/ui/package.json index ed0e6ea1f4..1bda015ba8 100644 --- a/xinference/web/ui/package.json +++ b/xinference/web/ui/package.json @@ -11,10 +11,10 @@ "@fullcalendar/list": "^6.1.8", "@fullcalendar/timegrid": "^6.1.8", "@mui/icons-material": "^5.14.0", - "@mui/lab": "latest", + "@mui/lab": "^5.0.0-alpha.173", "@mui/material": "^5.14.0", "@mui/system": "^5.15.9", - "@mui/x-data-grid": "^6.10.0", + "@mui/x-data-grid": "^6.20.4", "@nivo/bar": "^0.83.0", "@nivo/core": "^0.83.0", "@nivo/geo": "^0.83.0", @@ -25,6 +25,7 @@ "@testing-library/user-event": "^13.5.0", "clipboard": "^2.0.11", "formik": "^2.4.2", + "nunjucks": "^3.2.4", "prop-types": "^15.8.1", "react": "^18.2.0", "react-cookie": "^6.1.1", diff --git a/xinference/web/ui/src/scenes/launch_model/modelCard.js b/xinference/web/ui/src/scenes/launch_model/modelCard.js index a4391f830a..bcffbace11 100644 --- a/xinference/web/ui/src/scenes/launch_model/modelCard.js +++ b/xinference/web/ui/src/scenes/launch_model/modelCard.js @@ -327,6 +327,9 @@ const ModelCard = ({ modelDataWithID_LLM.n_gpu_layers = nGPULayers } + const modelDataWithID = + modelType === 'LLM' ? modelDataWithID_LLM : modelDataWithID_other + if ( loraListArr.length || imageLoraLoadKwargsArr.length || @@ -354,12 +357,9 @@ const ModelCard = ({ }) peft_model_config['lora_list'] = lora_list } - modelDataWithID_LLM['peft_model_config'] = peft_model_config + modelDataWithID['peft_model_config'] = peft_model_config } - const modelDataWithID = - modelType === 'LLM' ? modelDataWithID_LLM : modelDataWithID_other - if (customParametersArr.length) { customParametersArr.forEach((item) => { modelDataWithID[item.key] = handleValueType(item.value) @@ -376,10 +376,15 @@ const ModelCard = ({ `/running_models/${modelType}` ) let historyArr = JSON.parse(localStorage.getItem('historyArr')) || [] - if (!historyArr.some((item) => deepEqual(item, modelDataWithID))) { - historyArr = historyArr.filter( - (item) => item.model_name !== modelDataWithID.model_name - ) + const historyModelNameArr = historyArr.map((item) => item.model_name) + if (historyModelNameArr.includes(modelDataWithID.model_name)) { + historyArr = historyArr.map((item) => { + if (item.model_name === modelDataWithID.model_name) { + return modelDataWithID + } + return item + }) + } else { historyArr.push(modelDataWithID) } localStorage.setItem('historyArr', JSON.stringify(historyArr)) @@ -600,28 +605,10 @@ const ModelCard = ({ }) setLoraArr(loraData) - let ImageLoraLoadData = [] - for (let key in peft_model_config?.image_lora_load_kwargs) { - ImageLoraLoadData.push({ - key: key, - value: peft_model_config?.image_lora_load_kwargs[key], - }) - } - setImageLoraLoadArr(ImageLoraLoadData) - - let ImageLoraFuseData = [] - for (let key in peft_model_config?.image_lora_fuse_kwargs) { - ImageLoraFuseData.push({ - key: key, - value: peft_model_config?.image_lora_fuse_kwargs[key], - }) - } - setImageLoraFuseArr(ImageLoraFuseData) - let customData = [] for (let key in arr[0]) { !llmAllDataKey.includes(key) && - customData.push({ key: key, value: arr[0][key] }) + customData.push({ key: key, value: arr[0][key] || 'none' }) } setCustomArr(customData) @@ -635,11 +622,7 @@ const ModelCard = ({ ) setIsOther(true) - if ( - loraData.length || - ImageLoraLoadData.length || - ImageLoraFuseData.length - ) { + if (loraData.length) { setIsOther(true) setIsPeftModelConfig(true) } @@ -657,37 +640,54 @@ const ModelCard = ({ setDownloadHub(arr[0].download_hub || '') setModelPath(arr[0].model_path || '') + if (arr[0].model_type === 'image') { + let loraData = [] + arr[0].peft_model_config?.lora_list?.forEach((item) => { + loraData.push({ + lora_name: item.lora_name, + local_path: item.local_path, + }) + }) + setLoraArr(loraData) + + let ImageLoraLoadData = [] + for (let key in arr[0].peft_model_config?.image_lora_load_kwargs) { + ImageLoraLoadData.push({ + key: key, + value: + arr[0].peft_model_config?.image_lora_load_kwargs[key] || 'none', + }) + } + setImageLoraLoadArr(ImageLoraLoadData) + + let ImageLoraFuseData = [] + for (let key in arr[0].peft_model_config?.image_lora_fuse_kwargs) { + ImageLoraFuseData.push({ + key: key, + value: + arr[0].peft_model_config?.image_lora_fuse_kwargs[key] || 'none', + }) + } + setImageLoraFuseArr(ImageLoraFuseData) + + if ( + loraData.length || + ImageLoraLoadData.length || + ImageLoraFuseData.length + ) { + setIsPeftModelConfig(true) + } + } + let customData = [] for (let key in arr[0]) { !llmAllDataKey.includes(key) && - customData.push({ key: key, value: arr[0][key] }) + customData.push({ key: key, value: arr[0][key] || 'none' }) } setCustomArr(customData) } } - const deepEqual = (obj1, obj2) => { - if (obj1 === obj2) return true - if ( - typeof obj1 !== 'object' || - typeof obj2 !== 'object' || - obj1 == null || - obj2 == null - ) { - return false - } - - let keysA = Object.keys(obj1) - let keysB = Object.keys(obj2) - if (keysA.length !== keysB.length) return false - for (let key of keysA) { - if (!keysB.includes(key) || !deepEqual(obj1[key], obj2[key])) { - return false - } - } - return true - } - const handleCollection = (bool) => { setHover(false) @@ -725,8 +725,6 @@ const ModelCard = ({ setDownloadHub('') setModelPath('') setLoraArr([]) - setImageLoraLoadArr([]) - setImageLoraFuseArr([]) setCustomArr([]) setIsOther(false) setIsPeftModelConfig(false) @@ -738,6 +736,11 @@ const ModelCard = ({ setWorkerIp('') setDownloadHub('') setModelPath('') + setLoraArr([]) + setImageLoraLoadArr([]) + setImageLoraFuseArr([]) + setCustomArr([]) + setIsPeftModelConfig(false) } } @@ -991,7 +994,14 @@ const ModelCard = ({ {(() => { if (modelData.language) { return modelData.language.map((v) => { - return + return ( + + ) }) } else if (modelData.model_family) { return ( @@ -1446,30 +1456,6 @@ const ModelCard = ({ onJudgeArr={judgeArr} pairData={loraArr} /> - { - setImageLoraLoadKwargsArr(arr) - }} - onJudgeArr={judgeArr} - pairData={imageLoraLoadArr} - /> - { - setImageLoraFuseKwargsArr(arr) - }} - onJudgeArr={judgeArr} - pairData={imageLoraFuseArr} - /> ) : ( - - setModelUID(e.target.value)} - /> - setReplica(parseInt(e.target.value, 10))} - /> + - Device - - - {nGpu === 'GPU' && ( + setModelUID(e.target.value)} + /> + setReplica(parseInt(e.target.value, 10))} + /> + + Device + + + {nGpu === 'GPU' && ( + + { + setGPUIdxAlert(false) + setGPUIdx(e.target.value) + const regular = /^\d+(?:,\d+)*$/ + if ( + e.target.value !== '' && + !regular.test(e.target.value) + ) { + setGPUIdxAlert(true) + } + }} + /> + {GPUIdxAlert && ( + + Please enter numeric data separated by commas, for + example: 0,1,2 + + )} + + )} setWorkerIp(e.target.value)} + /> + + + + (Optional) Download_hub + + + + + setModelPath(e.target.value)} /> - {GPUIdxAlert && ( - - Please enter numeric data separated by commas, for - example: 0,1,2 - - )} - )} - - setWorkerIp(e.target.value)} - /> - - - - (Optional) Download_hub - - - - - setModelPath(e.target.value)} + onGetArr={(arr) => { + setCustomParametersArr(arr) + }} + onJudgeArr={judgeArr} + pairData={customArr} /> - { - setCustomParametersArr(arr) - }} - onJudgeArr={judgeArr} - pairData={customArr} - /> - + )} + +
+ {(formData?.length ? formData : ['']).map((item, index) => ( +
+
+ handleChange(e.target.value, index)} + label={helperText} + size="small" + style={{ width: '100%' }} + /> + {formData?.length > 1 && ( + handleDelete(index)} + style={{ cursor: 'pointer', color: '#1976d2' }} + /> + )} +
+ + {handleShowAlert(item) && ( + Please enter an integer. + )} +
+ ))} +
+ + + ) +} + +export default AddStop diff --git a/xinference/web/ui/src/scenes/register_model/index.js b/xinference/web/ui/src/scenes/register_model/index.js index eb5b0a9e77..be6d4bad4c 100644 --- a/xinference/web/ui/src/scenes/register_model/index.js +++ b/xinference/web/ui/src/scenes/register_model/index.js @@ -63,7 +63,6 @@ const RegisterModel = () => { context_length: 2048, model_lang: ['en'], model_ability: ['generate'], - model_family: '', model_specs: [ { model_uri: '/path/to/llama-1', @@ -72,7 +71,7 @@ const RegisterModel = () => { quantizations: ['none'], }, ], - prompt_style: undefined, + model_family: 'your_custom_model', }} /> diff --git a/xinference/web/ui/src/scenes/register_model/registerModel.js b/xinference/web/ui/src/scenes/register_model/registerModel.js index 06cc582927..f35196b3b5 100644 --- a/xinference/web/ui/src/scenes/register_model/registerModel.js +++ b/xinference/web/ui/src/scenes/register_model/registerModel.js @@ -1,14 +1,21 @@ import './styles/registerModelStyle.css' -import CheckIcon from '@mui/icons-material/Check' +import Cancel from '@mui/icons-material/Cancel' +import CheckCircleIcon from '@mui/icons-material/CheckCircle' import KeyboardDoubleArrowRightIcon from '@mui/icons-material/KeyboardDoubleArrowRight' import NotesIcon from '@mui/icons-material/Notes' +import OpenInFullIcon from '@mui/icons-material/OpenInFull' import { Alert, + Autocomplete, Box, Button, Checkbox, Chip, + Dialog, + DialogActions, + DialogContent, + DialogTitle, FormControl, FormControlLabel, InputLabel, @@ -21,6 +28,7 @@ import { TextField, Tooltip, } from '@mui/material' +import nunjucks from 'nunjucks' import React, { useContext, useEffect, useRef, useState } from 'react' import { useCookies } from 'react-cookie' import { useNavigate, useParams } from 'react-router-dom' @@ -31,9 +39,20 @@ import fetchWrapper from '../../components/fetchWrapper' import { isValidBearerToken } from '../../components/utils' import AddControlnet from './components/addControlnet' import AddModelSpecs from './components/addModelSpecs' +import AddStop from './components/addStop' import languages from './data/languages' const SUPPORTED_LANGUAGES_DICT = { en: 'English', zh: 'Chinese' } -const SUPPORTED_FEATURES = ['Generate', 'Chat', 'Vision'] +const SUPPORTED_FEATURES = ['Generate', 'Chat', 'Vision', 'Tools'] +const messages = [ + { + role: 'assistant', + content: 'This is the message content replied by the assistant previously', + }, + { + role: 'user', + content: 'This is the message content sent by the user currently', + }, +] // Convert dictionary of supported languages into list const SUPPORTED_LANGUAGES = Object.keys(SUPPORTED_LANGUAGES_DICT) @@ -43,10 +62,7 @@ const RegisterModelComponent = ({ modelType, customData }) => { const { setErrorMsg } = useContext(ApiContext) const [formData, setFormData] = useState(customData) const [promptStyles, setPromptStyles] = useState([]) - const [family, setFamily] = useState({ - chat: [], - generate: [], - }) + const [family, setFamily] = useState({}) const [languagesArr, setLanguagesArr] = useState([]) const [isContextLengthAlert, setIsContextLengthAlert] = useState(false) const [isDimensionsAlert, setIsDimensionsAlert] = useState(false) @@ -73,6 +89,12 @@ const RegisterModelComponent = ({ modelType, customData }) => { ) const [contrastObj, setContrastObj] = useState({}) const [isEqual, setIsEqual] = useState(true) + const [testRes, setTestRes] = useState('') + const [isOpenMessages, setIsOpenMessages] = useState(false) + const [testErrorInfo, setTestErrorInfo] = useState('') + const [isStopTokenIdsAlert, setIsStopTokenIdsAlert] = useState(false) + const [familyOptions, setFamilyOptions] = useState([]) + const [isEditableFamily, setIsEditableFamily] = useState(true) useEffect(() => { if (model_name) { @@ -93,7 +115,9 @@ const RegisterModelComponent = ({ modelType, customData }) => { model_ability, model_family, model_specs, - prompt_style, + chat_template, + stop_token_ids, + stop, } = data const specsDataArr = model_specs.map((item) => { const { @@ -120,8 +144,10 @@ const RegisterModelComponent = ({ modelType, customData }) => { model_ability, model_family, model_specs: specsDataArr, + chat_template, + stop_token_ids, + stop, } - prompt_style ? (llmData.prompt_style = prompt_style) : '' setFormData(llmData) setContrastObj(llmData) setSpecsArr(specsDataArr) @@ -234,8 +260,20 @@ const RegisterModelComponent = ({ modelType, customData }) => { ) } else { const data = await response.json() - data.chat.push('other') - data.generate.push('other') + for (let key in data) { + data[key] = data[key].sort(function (a, b) { + let lowerA = a.toLowerCase() + let lowerB = b.toLowerCase() + + if (lowerA < lowerB) { + return -1 + } + if (lowerA > lowerB) { + return 1 + } + return 0 + }) + } setFamily(data) } } @@ -270,7 +308,6 @@ const RegisterModelComponent = ({ modelType, customData }) => { Object.prototype.hasOwnProperty.call(customData, 'model_ability') && Object.prototype.hasOwnProperty.call(customData, 'model_family') ) { - // avoid keep requesting backend to get prompts if (promptStyles.length === 0) { getBuiltInPromptStyles().catch((error) => { setErrorMsg( @@ -280,7 +317,7 @@ const RegisterModelComponent = ({ modelType, customData }) => { console.error('Error: ', error) }) } - if (family.chat.length === 0) { + if (family?.chat === undefined) { getBuiltinFamilies().catch((error) => { setErrorMsg( error.message || @@ -293,46 +330,33 @@ const RegisterModelComponent = ({ modelType, customData }) => { }, [cookie.token]) useEffect(() => { - setJsonData(JSON.stringify(formData, null, 4)) + setJsonData(JSON.stringify(formData, customReplacer, 4)) if (contrastObj.model_name) { deepEqual(contrastObj, formData) ? setIsEqual(true) : setIsEqual(false) } + if (family?.chat?.length) handleFamilyOptions(formData.model_ability) }, [formData]) - const getFamilyByAbility = () => { - if ( - formData.model_ability.includes('chat') || - formData.model_ability.includes('vision') - ) { - return family.chat - } else { - return family.generate - } - } + useEffect(() => { + if (family?.chat?.length) handleFamilyOptions(formData.model_ability) + }, [family]) - const sortStringsByFirstLetter = (arr) => { - return arr.sort((a, b) => { - const firstCharA = a.charAt(0).toLowerCase() - const firstCharB = b.charAt(0).toLowerCase() - if (firstCharA < firstCharB) { - return -1 - } - if (firstCharA > firstCharB) { - return 1 - } - return 0 - }) + const customReplacer = (key, value) => { + if (key === 'chat_template' && value) { + return value.replace(/\\n/g, '\n') + } + return value } const handleClick = async () => { - console.log('formData', modelType, formData) - for (let key in formData) { const type = Object.prototype.toString.call(formData[key]).slice(8, -1) if ( key !== 'model_description' && ((type === 'Array' && key !== 'controlnet' && + key !== 'stop_token_ids' && + key !== 'stop' && formData[key].length === 0) || (type === 'String' && formData[key] === '') || (type === 'Number' && formData[key] <= 0)) @@ -355,7 +379,7 @@ const RegisterModelComponent = ({ modelType, customData }) => { try { fetchWrapper .post(`/v1/model_registrations/${modelType}`, { - model: JSON.stringify(formData), + model: JSON.stringify(formData, customReplacer), persist: true, }) .then(() => { @@ -427,61 +451,103 @@ const RegisterModelComponent = ({ modelType, customData }) => { } const toggleAbility = (ability) => { + const obj = JSON.parse(JSON.stringify(formData)) if (formData.model_ability.includes(ability)) { - const obj = JSON.parse(JSON.stringify(formData)) - if (ability === 'chat') { - delete obj.prompt_style - } + delete obj.chat_template + delete obj.stop_token_ids + delete obj.stop setFormData({ ...obj, - model_ability: formData.model_ability.filter((a) => a !== ability), + model_ability: formData.model_ability.filter((item) => { + if (ability === 'chat') { + return item !== 'chat' && item !== 'vision' && item !== 'tools' + } + return item !== ability + }), model_family: '', }) } else { + let model_ability = [] + if ( + ability === 'chat' || + (['vision', 'tools'].includes(ability) && + !formData.model_ability.includes('chat')) + ) { + if ( + formData.model_family !== '' && + family?.chat?.includes(formData.model_family) + ) { + const data = promptStyles.filter( + (item) => item.name === formData.model_family + ) + obj.chat_template = data[0]?.chat_template || null + obj.stop_token_ids = data[0]?.stop_token_ids || [] + obj.stop = data[0]?.stop || [] + } else { + obj.chat_template = '' + obj.stop_token_ids = [] + obj.stop = [] + } + ability === 'chat' + ? (model_ability = [...formData.model_ability, ability]) + : (model_ability = [...formData.model_ability, 'chat', ability]) + } else { + if (ability === 'vision' && formData.model_ability.includes('tools')) { + model_ability = [ + ...formData.model_ability.filter((item) => item !== 'tools'), + 'chat', + ability, + ] + } else if ( + ability === 'tools' && + formData.model_ability.includes('vision') + ) { + model_ability = [ + ...formData.model_ability.filter((item) => item !== 'vision'), + 'chat', + ability, + ] + } else { + model_ability = [...formData.model_ability, ability] + } + } + delete obj.chat_template + delete obj.stop_token_ids + delete obj.stop setFormData({ - ...formData, - model_ability: [...formData.model_ability, ability], + ...obj, model_family: '', + model_ability: model_ability, }) } } - const toggleFamily = (value) => { - const ps = promptStyles.find((item) => item.name === value) - if (formData.model_ability.includes('chat') && ps) { - const prompt_style = { - style_name: ps.style_name, - system_prompt: ps.system_prompt, - roles: ps.roles, - intra_message_sep: ps.intra_message_sep, - inter_message_sep: ps.inter_message_sep, - stop: ps.stop ?? null, - stop_token_ids: ps.stop_token_ids ?? null, + const handleFamily = (value) => { + if (formData.model_ability.includes('chat')) { + if (family?.chat?.includes(value)) { + const data = promptStyles.filter((item) => { + return item.name === value + }) + setFormData({ + ...formData, + model_family: value, + chat_template: data[0]?.chat_template || null, + stop_token_ids: data[0]?.stop_token_ids || [], + stop: data[0]?.stop || [], + }) + } else { + setFormData({ + ...formData, + model_family: value, + chat_template: '', + stop_token_ids: [], + stop: [], + }) } - setFormData({ - ...formData, - model_family: value, - prompt_style, - }) } else { - const { - version, - model_name, - model_description, - context_length, - model_lang, - model_ability, - model_specs, - } = formData setFormData({ - version, - model_name, - model_description, - context_length, - model_lang, - model_ability, + ...formData, model_family: value, - model_specs, }) } } @@ -569,6 +635,128 @@ const RegisterModelComponent = ({ modelType, customData }) => { return true } + const handleTest = () => { + setTestRes('') + if (formData.chat_template) { + try { + nunjucks.configure({ autoescape: false }) + const test_res = nunjucks.renderString(formData.chat_template, { + messages: messages, + }) + if (test_res === '') { + setTestRes(test_res) + setTestErrorInfo('error') + } else { + setTestRes(test_res) + setTestErrorInfo('') + } + } catch (error) { + setTestErrorInfo(`${error}`) + } + } + } + + const getStopTokenIds = (value) => { + if (value.length === 1 && value[0] === '') { + setFormData({ + ...formData, + stop_token_ids: [], + }) + } else { + setFormData({ + ...formData, + stop_token_ids: value, + }) + } + } + + const getStop = (value) => { + if (value.length === 1 && value[0] === '') { + setFormData({ + ...formData, + stop: [], + }) + } else { + setFormData({ + ...formData, + stop: value, + }) + } + } + + const handleFamilyAlert = () => { + if ( + formData.model_ability?.includes('vision') && + !family?.vision?.includes(formData.model_family) + ) { + return true + } else if ( + formData.model_ability?.includes('tools') && + !family?.tools?.includes(formData.model_family) + ) { + return true + } + return false + } + + const handleChatTemplateAlert = () => { + if ( + familyOptions?.filter((item) => item.id === formData.model_family) + .length === 0 && + !formData.chat_template + ) { + return true + } + return false + } + + const handleFamilyOptions = (model_ability) => { + if (model_ability.includes('vision')) { + setIsEditableFamily(false) + setFamilyOptions( + family?.vision?.map((item) => { + return { + id: item, + label: item, + } + }) + ) + } else if (model_ability.includes('tools')) { + setIsEditableFamily(false) + setFamilyOptions( + family?.tools?.map((item) => { + return { + id: item, + label: item, + } + }) + ) + } else if (model_ability.includes('chat')) { + setIsEditableFamily(true) + setFamilyOptions( + family?.chat?.map((item) => { + return { + id: item, + label: item, + } + }) + ) + } else if (model_ability.includes('generate')) { + setIsEditableFamily(true) + setFamilyOptions( + family?.generate?.map((item) => { + return { + id: item, + label: item, + } + }) + ) + } else { + setIsEditableFamily(true) + setFamilyOptions([]) + } + } + return (
@@ -845,66 +1033,200 @@ const RegisterModelComponent = ({ modelType, customData }) => { {/* family */} {(customData.model_family === '' || customData.model_family) && ( - - - {modelType === 'LLM' && formData.model_family && ( - } - severity="success" - > - Please be careful to select the family name corresponding to - the model you want to register. If not found, please choose - other - . - - )} - {modelType === 'LLM' && !formData.model_family && ( - - Please be careful to select the family name corresponding to - the model you want to register. If not found, please choose - other - . - + <> + {modelType === 'LLM' && ( + <> + ( + + )} + value={formData.model_family} + onChange={(_, newValue) => { + handleFamily(newValue?.id) + }} + onInputChange={(_, newInputValue) => { + if (isEditableFamily) { + handleFamily(newInputValue) + } + }} + /> + + )} - { - toggleFamily(e.target.value) - }} - > - - {modelType === 'LLM' && - sortStringsByFirstLetter(getFamilyByAbility()).map((v) => ( - + {(modelType === 'image' || modelType === 'audio') && ( + <> + + + + } - label={v} + label={formData.model_family} /> - ))} - {(modelType === 'image' || modelType === 'audio') && ( - } - label={formData.model_family} - /> - )} - - + + + + + )} + + )} + + {/* chat_template */} + {formData.model_ability?.includes('chat') && ( + <> +
+ + setFormData({ + ...formData, + chat_template: event.target.value, + }) + } + style={{ flex: 1 }} + /> + +
+
+
+ messages example + setIsOpenMessages(true)} + style={{ + fontSize: 14, + color: '#666', + cursor: 'pointer', + }} + /> +
+
+ + test result + {testErrorInfo ? ( + + ) : testRes ? ( + + ) : ( + '' + )} + +
+ {testErrorInfo !== '' + ? testErrorInfo + : testRes + ? testRes + : 'No test results...'} +
+
+
+
+ Please note that failure to pass test may prevent chats from + working properly. +
+
+
+ + + )} + + {/* stop_token_ids */} + {formData.model_ability?.includes('chat') && ( + <> + { + if (value.includes('false')) { + setIsStopTokenIdsAlert(true) + } else { + setIsStopTokenIdsAlert(false) + } + }} + helperText="int type, used to control the stopping of chat models" + /> - + + )} + + {/* stop */} + {formData.model_ability?.includes('chat') && ( + <> + + + )} {/* specs */} @@ -1011,6 +1333,17 @@ const RegisterModelComponent = ({ modelType, customData }) => { color="primary" type="submit" onClick={handleClick} + disabled={ + isContextLengthAlert || + isDimensionsAlert || + isMaxTokensAlert || + formData.model_lang?.length === 0 || + formData.language?.length === 0 || + formData.model_ability?.length === 0 || + (modelType === 'LLM' && !formData.model_family) || + isStopTokenIdsAlert || + handleFamilyAlert() + } > Register Model @@ -1018,6 +1351,32 @@ const RegisterModelComponent = ({ modelType, customData }) => { )}
+ setIsOpenMessages(false)} + aria-labelledby="alert-dialog-title" + aria-describedby="alert-dialog-description" + > + Messages Example + +