[webgpu] support Pad operator #23141
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/azp run ONNX Runtime Web CI Pipeline,Windows GPU CI Pipeline,Linux Android Emulator QNN CI Pipeline |
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/azp run Linux CPU CI Pipeline,Linux CPU Minimal Build E2E CI Pipeline,Linux GPU CI Pipeline,Linux GPU TensorRT CI Pipeline,Linux OpenVINO CI Pipeline,Linux QNN CI Pipeline,MacOS CI Pipeline,Windows ARM64 QNN CI Pipeline,Windows CPU CI Pipeline |
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/azp run Windows GPU TensorRT CI Pipeline,onnxruntime-binary-size-checks-ci-pipeline,orttraining-linux-ci-pipeline,orttraining-linux-gpu-ci-pipeline,orttraining-ortmodule-distributed,Windows x64 QNN CI Pipeline,Big Models |
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![github-advanced-security[bot]](https://avatars.githubusercontent.com/in/57789?s=60&v=4){kind=small}
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@fs-eire @guschmue Please help to trigger the bots again. Last version failed on Mac OS, but could compile correctly on Windows. I had changed the code, but not ensured it worked correctly on Mac OS. The compiling error was shown as below. |
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/azp run ONNX Runtime Web CI Pipeline,Windows GPU CI Pipeline,Linux Android Emulator QNN CI Pipeline |
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/azp run Linux CPU CI Pipeline,Linux CPU Minimal Build E2E CI Pipeline,Linux GPU CI Pipeline,Linux GPU TensorRT CI Pipeline,Linux OpenVINO CI Pipeline,Linux QNN CI Pipeline,MacOS CI Pipeline,Windows ARM64 QNN CI Pipeline,Windows CPU CI Pipeline |
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/azp run Windows GPU TensorRT CI Pipeline,onnxruntime-binary-size-checks-ci-pipeline,orttraining-linux-ci-pipeline,orttraining-linux-gpu-ci-pipeline,orttraining-ortmodule-distributed,Windows x64 QNN CI Pipeline,Big Models |
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jchen10
reviewed
Dec 26, 2024
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/azp run Win_TRT_Minimal_CUDA_Test_CI |
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Azure Pipelines successfully started running 1 pipeline(s). |
fs-eire
reviewed
Jan 14, 2025
guschmue
previously approved these changes
Jan 14, 2025
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It seems that onnx_backend_test_series fails (SIGSEGV) on macOS and the error stably reproduces after retry. Maybe need to verify if this can be reproducible locally on a macOS device. |
guschmue
previously approved these changes
Feb 14, 2025
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macos pipeline is failing with: seems to be ok on main as far I can tell, so maybe something in this PR triggers it. |
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/azp run ONNX Runtime Web CI Pipeline,Windows GPU CI Pipeline,Linux Android Emulator QNN CI Pipeline |
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/azp run Linux CPU CI Pipeline,Linux CPU Minimal Build E2E CI Pipeline,Linux GPU CI Pipeline,Linux GPU TensorRT CI Pipeline, Linux OpenVINO CI Pipeline,Linux QNN CI Pipeline,MacOS CI Pipeline,Windows ARM64 QNN CI Pipeline,Windows CPU CI Pipeline |
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/azp run Windows GPU TensorRT CI Pipeline,onnxruntime-binary-size-checks-ci-pipeline,orttraining-linux-ci-pipeline,orttraining-linux-gpu-ci-pipeline,orttraining-ortmodule-distributed,Windows x64 QNN CI Pipeline,Big Models |
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1 similar comment
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logs should be back - still some segv. |
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/azp run ONNX Runtime Web CI Pipeline,Windows GPU CI Pipeline,Linux Android Emulator QNN CI Pipeline |
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/azp run Linux CPU CI Pipeline,Linux CPU Minimal Build E2E CI Pipeline,Linux GPU CI Pipeline,Linux GPU TensorRT CI Pipeline, Linux OpenVINO CI Pipeline,Linux QNN CI Pipeline,MacOS CI Pipeline,Windows ARM64 QNN CI Pipeline,Windows CPU CI Pipeline |
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/azp run Windows GPU TensorRT CI Pipeline,onnxruntime-binary-size-checks-ci-pipeline,orttraining-linux-ci-pipeline,orttraining-linux-gpu-ci-pipeline,orttraining-ortmodule-distributed,Windows x64 QNN CI Pipeline,Big Models |
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Azure Pipelines successfully started running 2 pipeline(s). |
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/azp run Windows GPU CUDA CI Pipeline,Windows GPU DML CI Pipeline,Windows GPU Doc Gen CI Pipeline, Win_TRT_Minimal_CUDA_Test_CI |
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Azure Pipelines successfully started running 4 pipeline(s). |
1 similar comment
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Azure Pipelines successfully started running 4 pipeline(s). |
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Azure Pipelines successfully started running 9 pipeline(s). |
guschmue
approved these changes
Mar 6, 2025
ankitm3k
pushed a commit
to intel/onnxruntime
that referenced
this pull request
Mar 10, 2025
* Fix flash attention for GQA (Phi4) (microsoft#23850) ### Description This change fixes GQA for Flash Attention on Nvidia GPUs. The root cause appears to be `k_start + capped_sg_id < seq_causal_length` check. This is either because, a. seq_causal_length varies per lane, so the check becomes non uniform control flow, which is having interactions with subgroupShuffle. or b. The check itself is incorrect and is wiping out values of v based on the source lane's seq_causal_length. While in actualness values of v need to be causal as per the lane that is going to multiply it with qkt. qkt is already causal because earlier values of qk for out of bounds k are set to min_value, and exp(<-4) are 0. This fix works by removing that causal check and relying on the qk being wiped out earlier. The documentation for causality behavior for GQA is missing to determine which of this reason is the true reason. Prior to this prompts with sequence length > 16 < 32 or 1k would break with Phi 4 but smaller prompts would work. Tested on Intel Alderlake, Nvidia 4070. * Model Builder API (microsoft#23223) ### Description <!-- Describe your changes. --> Supports creating a model programmatically using the ORT C or C++ API. Supports augmenting an existing model to add nodes. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> * Fix typo: change `Upample` to `Upsample`. (microsoft#23838) ### Description <!-- Describe your changes. --> Fixed a typo in function names related to the Upsample CUDA kernel. Changed incorrect spelling Upample to Upsample across relevant functions. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> This change is necessary to maintain consistency and prevent potential confusion caused by incorrect function names. * [doc] Fix typos in csharp/src/Microsoft.ML.OnnxRuntime/ (microsoft#23848) ### Description <!-- Describe your changes. --> Fix typos in csharp/src/Microsoft.ML.OnnxRuntime/ ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> * Quant tool: Consistent `get_qdq_config` and `get_qnn_qdq_config` behavior (microsoft#23856) * Change the logic to generate the default ep context file name (microsoft#23788) Change the logic to generate the default ep context file name ### Description Applies to all EPs: replace the .onnx to _ctx.onnx, instead of directly append extra string _ctx.onnx to existing model path. In QNN EP, also make the context binary .bin file shorter by removing QNNExecutionProvider_ from the file name. * Make Nuget QNN package pipeline 1ES compliant (microsoft#23805) ### Description Make [QNN_Nuget_Windows](https://aiinfra.visualstudio.com/Lotus/_build?definitionId=1234)1ES compliant ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> * [js/common] allows using Uint16Array as data for float16 tensor (microsoft#23827) ### Description Resolve microsoft#23817 ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> * [js/webgpu] Reland the optimization of ConvTranspose (microsoft#23858) This PR fixes the errors in the ConvTranspose optimization and adds tests to ensure the correctness of the implementation. * [OpenVINO] Fix a build warning (microsoft#23877) ### Description Fix a warning with std::move usage ### Motivation and Context Possibly allow building without --compile_no_warning_as_error flag * Change gsl::byte to std::byte (microsoft#23872) To be compatible with the latest GSL library. Without this fix we will get: ``` onnxruntime\core\providers\cpu\controlflow\loop.cc(247): error C4996: 'gsl::byte': Use std::byte instead. ``` * Allow using extended minimal build for several EPs (microsoft#23834) ### Description #### Background From code search, the following EPs use `onnxruntime::GetCpuPreferredNodes()` in their `GetCapabilities()` methods: - CANN - CUDA - DML - JS - ROCM - WebGPU However, the source file that implements `onnxruntime::GetCpuPreferredNodes()` is excluded when minimal build is ON: https://github.com/microsoft/onnxruntime/blob/6df0973e58ba5399fcaa98686f70ed9a9e59aaef/cmake/onnxruntime_framework.cmake#L38-L42 This means that all EPs mentioned above is not able to compile with minimal build. #### Solution The excluded file `core/framework/fallback_cpu_capability.cc` cannot build in minimal build because some of its dependencies are not included in the minimal build. However, in extended minimal build mode, all dependencies are available. This PR looses the restrict and allows to compile this file when it is extended minimal build. After this change, those EPs are able to compile in extended minimal build. * Add dawn to ThirdPartyNotices (microsoft#23876) ### Description Add `dawn` to ThirdPartyNotices. * Enable QNN EP weight sharing generation using public API (microsoft#23702) ### Description Enable QNN EP weight sharing generation using public API instead of internal interfaces, so that user can integrate into their own toolchain. The change is to share the QnnBackendManager across ORT sessions if ep.share_ep_contexts is enabled. And there is extra option to end the share so that we know when to remove the shared QnnBackendManager from the singleton. Change the tool name from onnxruntime_qnn_ctx_gen to ep_weight_sharing_ctx_gen, so that it can be shared for other EPs. * [QNN-EP]: Fix inference failures while running with htp_shared_memory (microsoft#23892) ### Description When using the enable_htp_shared_memory feature, we see that the address of the buffer passed to rpcmem_free is incorrect. So the rpc buffers are not freed leading to memory exhaustion. ### Motivation and Context When using the enable_htp_shared_memory_allocator feature for QNN in GenAI extensions, it leads to inference failures during the second prompt. As GenAI memory asks are higher, it surfaces sooner in gen AI use cases. Co-authored-by: Ashish Garg <[email protected]> * Fix enable_pix_capture build for WebGPU (microsoft#23857) The build option --enable_pix_capture is broken. This fixes the problem. --------- Co-authored-by: wp <[email protected]> * [WebGPU-EP Native] Add ReduceMean (microsoft#23860) ### Description <!-- Describe your changes. --> ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> * [WebGPU EP] introduce BiasAdd contrib op (microsoft#23861) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * Dynamo export and improve benchmark script for SAM2 encoder (microsoft#23887) ### Description * Add dynamo export for Sam2 image encoder * Verify fp32 onnx model with CPU EP (to avoid error message from TRT EP). * Update benchmark script: - output ORT profiling - output torch compiled code and unique kernel name for compiled kernel - add an option for nightly package installation - uninstall existing ort packages before installing The node metadata of dynamo exported model can help mapping node in onnx model back to pytorch modeling script. Currently, the graph optimization is not done on dynamo exported model, so it is experimental right now. ### Motivation and Context To support profiling of torch compiled CUDA kernel. * [js/web] improve workaround for bundlers (microsoft#23902) ### Description This PR improves the workaround for bundlers in onnxruntime-web. Specifically, the following changes have been made: - Use [this workaround](xenova@9c50aa2) as suggested by @xenova in huggingface/transformers.js#1161 (comment) - Use `url > "file:" && url < "file;"` instead of `url.startsWith("file:")` to allow minifiers to remove dead code correctly. This change allows to remove unnecessary dependencies of file parsed from `new URL("ort.bundle.min.js", import.meta.url)` in Vite, and optimize code like `if("file://filepath.js".startsWith("file:")) {do_sth1(); } else {do_sth2();}` into `do_sth1()` for webpack/terser usages. Resolves huggingface/transformers.js#1161 * [webgpu] Restore MatMulNBits workgroup size for Phi-3.5 (microsoft#23349) ### Description This change restores the MatMulNBits workgroup size from (8, 8, 1) back to (16, 8, 1) to resolve a performance regression observed on Intel iGPUs during token generation (M=1). ### Motivation and Context As above. Signed-off-by: Jianhui Dai <[email protected]> * [webgpu] support Pad operator (microsoft#23141) ### Description <!-- Describe your changes. --> ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> * [WebNN] Accept Float16Array for float16 data type if it is available (microsoft#23894) Float16Array is now shipping and WebNN Chromium implementation has accepted it. We should allow it in WebNN EP as well. * Ensure that the 'cmake_minimum_required' is version 3.5 or greater (microsoft#23888) ### Description CMake 4.0 release candidate 2.0 is available, and it cannot compile all of OnnxRuntime out-of-the-box. There's portions of the OnnxRuntime codebase that specify a `cmake_minimum_required` version of 3.0, and CMake 4.0 has removed support for compatibility with CMake < 3.5 - the following error is reported: ``` CMake Error at winml_sdk_helpers.cmake:4 (cmake_minimum_required): Compatibility with CMake < 3.5 has been removed from CMake. Update the VERSION argument <min> value. Or, use the <min>...<max> syntax to tell CMake that the project requires at least <min> but has been updated to work with policies introduced by <max> or earlier. Or, add -DCMAKE_POLICY_VERSION_MINIMUM=3.5 to try configuring anyway. ``` Since CMake 3.5 appears to have shipped in 2016, it seems reasonable to set that as a minimum version to fix the error. The root CMakeLists.txt does ask for a minimum version of 3.28, so we could snap to that, but I'm still ramping up on the build, so wanted to propose a minimally sufficient fix. ### Motivation and Context Being able to build with the latest CMake - when it ships - reduces the barrier to entry to building OnnxRuntime, and allows the OnnxRuntime to leverage the latest and greatest tooling. * WebGPU: Remove deprecated subgroups-f16 from WebGPU native and JS EP (microsoft#23898) This PR removes the deprecated subgroups-f16 from WebGPU native and JS EP, and also remove the unused deviceInfo in WebGPU JS EP. * [JSEP/WebGPU] Fixed error in softmax dispatch. (microsoft#23906) ### Description Fixed an error softmax dispatch ### Motivation and Context Produce expected results for LlaMA model * enable WebGPU EP in WebAssembly build (microsoft#23913) ### Description This PR is the first step for migrating the webgpu backend of onnxruntime-web from JSEP based to WebGPU EP based. In this change, we enable building WebGPU EP in a wasm build (ie. `--build_wasm` `--use_webgpu` `--use_jsep`). However, the old build flags should still keep previous behavior. * Adding OpenVINO Windows CI Pipeline (microsoft#23919) ### Description <!-- Describe your changes. --> Enable an OpenVINO Windows CI pipeline. This includes: - Downloading the OpenVINO toolkit for Windows from an external source. - Setting up OpenVINO environment variables. - Building the ONNX Runtime OpenVINO Execution Provider. - Running unit tests. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> This change is required to run checks on precommit and commit in the ONNX Runtime project. It ensures that the code is tested with the OpenVINO toolkit on Windows, improving the reliability and compatibility of the project. * [WebGPU EP] SoftMax Implementation (microsoft#23538) Increase coverage for WebGPU Op * Exclude MAUI projects from GPU C# packaging builds (microsoft#23923) ### Description <!-- Describe your changes. --> Use 'desktop only' solution in GPU C# packaging builds. We don't need to include any MAUI support for those builds. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> * Support all block sizes that are multiples of 32 for DP4A (microsoft#23907) ### Description Simple change 1. The DP4A shader actually supports all block sizes that are multiples of 32, relaxing the restriction and making a small tweak to support sizes other than 32. 2. Moved the shader to a separate file for maintainability. --------- Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * Example custom op with output type inferencing (microsoft#23916) ### Description <!-- Describe your changes. --> Add example of a custom op that is required to do type inference for the output type for the model load to work. Also acts as an example of how to override an ONNX op with a custom implementation. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> microsoft#23891 * Enabling L2+ Optimizations for EPs (microsoft#23517) There are some requirements to modify the graph which are specific to the EP/hardware. ORT has the hardcoded EP list for optimizations but that can't scale and it's hard be extended to enable EP custom optimizations. Here is the prototype to enable L2+ optimizations for EPs (The original overview is provided by @skottmckay) as well as the TRT EP implementation for the ConstantFoldingDQ optimization. Signatures for selection and optimization functions: ```` - Selection: std::function<std::vector<std::unique_ptr<ComputeCapability>>(const GraphViewer&, const KeyValueConfig&)> - Optimization: std::function<Status(const Graph&, const ComputeCapability& this_optimization, ComputeCapability& cc_to_update)> ```` GetCapability - call (new) provider bridge API to lookup pre-defined optimizer by name and get selection function - ComputeCapability.optimize_func, i.e. optimization function, would be set by the optimizer to the function that does the optimization - EP has to update the returning ComputeCapability to include the optimization ComputeCapability in nodes_to_optimize. So that later ORT can perform optimization/transformation accordingly. GraphPartitioner - After assigning the ComputeCapability to the EP and prior to Compile, if the ComputeCapability has nodes_to_optimize, iterate that list - optimization function needs to be called with - a mutable Graph instance - the ComputeCapability for the individual optimization - the overall ComputeCapability so it can be updated * fix binplace file in web pipeline (microsoft#23930) * Updated run_CIs_for_external_pr.py to support the Windows OpenVINO CI pipeline (microsoft#23931) * Fix ConvInteger handling of optional inputs. (microsoft#23935) ### Description <!-- Describe your changes. --> Fix ConvInteger handling of optional inputs. Need to check Exists() and not just the number of inputs. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> microsoft#23927 * Updated ov version in pipeline (#595) (microsoft#23882) ### Description This PR updates the OpenVINO version used in the pipeline from 2024.5.0 to 2025.0.0 Co-authored-by: jatinwadhwa921 <[email protected]> * [AIX] External data handling (microsoft#23859) ### Description In BE system, model tensor data coming from external file is not handled properly. This was found during the debugging of (microsoft/onnxruntime-genai#1104) This PR changes do the endianness conversion of data loaded from external file in BE system. * Create a packaging pipeline for a custom nuget package (microsoft#23918) * Fix license in example test code. (microsoft#23936) * replace usage of gsl::narrow and gsl::narrow_cast in WebGPU EP (microsoft#23926) ### Description `gsl::narrow` does not work in no exception build. - use `onnxruntime::narrow` if necessary; - or change to `static_cast` if it's obviously safe. also apply the changes to usage of `gsl::narrow_cast`, which does not apply checks. * VCPKG improvement: set VCPKG_OSX_DEPLOYMENT_TARGET (microsoft#23933) ### Description 1. Set VCPKG_OSX_DEPLOYMENT_TARGET for macOS targets 2. Enable VCPKG in more pipelines. * Allow using a different version of flatbuffers when building with vcpkg (microsoft#23946) ### Description Allow using a different version of flatbuffers when building with vcpkg, so that users do not need to pin flatbuffer's version, which provides more flexibility in the build process. Delete utf8_range from the dependencies, because it is an indirect dependency of protobuf, which is already included in the build process. ### Motivation and Context * Make python package pipeline 1ES compliant (microsoft#23800) ### Description Make [Python packaging pipeline](https://aiinfra.visualstudio.com/530acbc4-21bc-487d-8cd8-348ff451d2ff/_build?definitionId=841) 1ES compliant ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> ### Checklist - [x] Make Onnxruntime-QNNEP-Windows-2022-CPU stateless * Delete ROCM Nuget Publishing Pipeline (microsoft#23948) * Bump SixLabors.ImageSharp from 2.1.9 to 2.1.10 in /csharp/sample/Microsoft.ML.OnnxRuntime.FasterRcnnSample (microsoft#23924) Bumps [SixLabors.ImageSharp](https://github.com/SixLabors/ImageSharp) from 2.1.9 to 2.1.10. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/SixLabors/ImageSharp/releases">SixLabors.ImageSharp's releases</a>.</em></p> <blockquote> <h2>v2.1.10</h2> <h2>What's Changed</h2> <ul> <li>Backport <a href="https://redirect.github.com/SixLabors/ImageSharp/issues/2859">#2859</a> to release/2.1.x by <a href="https://github.com/antonfirsov"><code>@antonfirsov</code></a> in <a href="https://redirect.github.com/SixLabors/ImageSharp/pull/2890">SixLabors/ImageSharp#2890</a></li> <li>Backport <a href="https://redirect.github.com/SixLabors/ImageSharp/issues/2701">#2701</a> to 2.1.x [copy] by <a href="https://github.com/antonfirsov"><code>@antonfirsov</code></a> in <a href="https://redirect.github.com/SixLabors/ImageSharp/pull/2891">SixLabors/ImageSharp#2891</a></li> </ul> <p><strong>Full Changelog</strong>: <a href="https://github.com/SixLabors/ImageSharp/compare/v2.1.9...v2.1.10">https://github.com/SixLabors/ImageSharp/compare/v2.1.9...v2.1.10</a></p> </blockquote> </details> <details> <summary>Commits</summary> <ul> <li><a href="https://github.com/SixLabors/ImageSharp/commit/d133ef99e8becfc3b924b0bb4315e63b8681d307"><code>d133ef9</code></a> Set lang version</li> <li><a href="https://github.com/SixLabors/ImageSharp/commit/5dfe5a800367581239de442cc18de659da6e9b1d"><code>5dfe5a8</code></a> Missed cache action update</li> <li><a href="https://github.com/SixLabors/ImageSharp/commit/4d3a85112b03c89d2cb8616a5b747684b6e73730"><code>4d3a851</code></a> Use latest cache action</li> <li><a href="https://github.com/SixLabors/ImageSharp/commit/4cb9f40a722ab2b837157862f0320c6a652da4d0"><code>4cb9f40</code></a> Merge pull request <a href="https://redirect.github.com/SixLabors/ImageSharp/issues/2891">#2891</a> from SixLabors/af/backport-2701</li> <li><a href="https://github.com/SixLabors/ImageSharp/commit/bb82f79db0197166271d4355b5fb5ceda370a906"><code>bb82f79</code></a> <a href="https://redirect.github.com/SixLabors/ImageSharp/issues/2701">#2701</a> to 2.1.x [copy]</li> <li><a href="https://github.com/SixLabors/ImageSharp/commit/627b5f721f30f6d529acb50bd81f92bd3db754eb"><code>627b5f7</code></a> Merge pull request <a href="https://redirect.github.com/SixLabors/ImageSharp/issues/2890">#2890</a> from SixLabors/af/backport-2859</li> <li><a href="https://github.com/SixLabors/ImageSharp/commit/67f7848d6e975e7956c8056823555de49a5fdf6d"><code>67f7848</code></a> try to fix LFS for *.BMP</li> <li><a href="https://github.com/SixLabors/ImageSharp/commit/44d294e06606111195152ead3006452357ef1bb9"><code>44d294e</code></a> 8.0.x is not needed</li> <li><a href="https://github.com/SixLabors/ImageSharp/commit/adb85d9e66aa3a588a86f4a4ef9a0539a8502117"><code>adb85d9</code></a> Another attempt for a Linux-specific skip</li> <li><a href="https://github.com/SixLabors/ImageSharp/commit/efc3fc4ee15eec4e523c26f7130e786541b00df2"><code>efc3fc4</code></a> Disable BmpDecoder_CanDecode_Os2BitmapArray on Linux</li> <li>Additional commits viewable in <a href="https://github.com/SixLabors/ImageSharp/compare/v2.1.9...v2.1.10">compare view</a></li> </ul> </details> <br /> [](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) --- <details> <summary>Dependabot commands and options</summary> <br /> You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot show <dependency name> ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/microsoft/onnxruntime/network/alerts). </details> Signed-off-by: dependabot[bot] <[email protected]> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --------- Signed-off-by: Jianhui Dai <[email protected]> Signed-off-by: dependabot[bot] <[email protected]> Co-authored-by: Sushanth Rajasankar <[email protected]> Co-authored-by: Scott McKay <[email protected]> Co-authored-by: Seungtaek Kim <[email protected]> Co-authored-by: co63oc <[email protected]> Co-authored-by: Jambay Kinley <[email protected]> Co-authored-by: Hector Li <[email protected]> Co-authored-by: Jian Chen <[email protected]> Co-authored-by: Yulong Wang <[email protected]> Co-authored-by: Jiajia Qin <[email protected]> Co-authored-by: Alessio Soldano <[email protected]> Co-authored-by: Changming Sun <[email protected]> Co-authored-by: Ashish Garg <[email protected]> Co-authored-by: Ashish Garg <[email protected]> Co-authored-by: Jie Chen <[email protected]> Co-authored-by: wp <[email protected]> Co-authored-by: Satya Kumar Jandhyala <[email protected]> Co-authored-by: Prathik Rao <[email protected]> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: Tianlei Wu <[email protected]> Co-authored-by: Jianhui Dai <[email protected]> Co-authored-by: xhcao <[email protected]> Co-authored-by: Wanming Lin <[email protected]> Co-authored-by: Mark Schofield <[email protected]> Co-authored-by: jiangzhaoming <[email protected]> Co-authored-by: Yi-Hong Lyu <[email protected]> Co-authored-by: vraspar <[email protected]> Co-authored-by: Chi Lo <[email protected]> Co-authored-by: saurabh <[email protected]> Co-authored-by: Ranjit Ranjan <[email protected]> Co-authored-by: Baiju Meswani <[email protected]> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
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Description
Motivation and Context