[Bugfix] Fix feature size calculation for LLaVA-NeXT (vllm-project#6982)

Author: DarkLight1337

tests/models/test_llava_next.py

@@ -1,7 +1,7 @@
-from typing import List, Optional, Tuple, Type
+from typing import List, Optional, Tuple, Type, overload
 
 import pytest
-from transformers import AutoConfig, AutoTokenizer
+from transformers import AutoTokenizer
 
 from vllm.multimodal.utils import rescale_image_size
 from vllm.sequence import SampleLogprobs
@@ -50,6 +50,7 @@ def vllm_to_hf_output(vllm_output: Tuple[List[int], str,
     return hf_output_ids, hf_output_str, out_logprobs
 
 
+@overload
 def run_test(
     hf_runner: Type[HfRunner],
     vllm_runner: Type[VllmRunner],
@@ -62,13 +63,55 @@ def run_test(
     num_logprobs: int,
     tensor_parallel_size: int,
     distributed_executor_backend: Optional[str] = None,
+):
+    ...
+
+
+@overload
+def run_test(
+    hf_runner: Type[HfRunner],
+    vllm_runner: Type[VllmRunner],
+    image_assets: _ImageAssets,
+    model: str,
+    *,
+    sizes: List[Tuple[int, int]],
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+    tensor_parallel_size: int,
+    distributed_executor_backend: Optional[str] = None,
+):
+    ...
+
+
+def run_test(
+    hf_runner: Type[HfRunner],
+    vllm_runner: Type[VllmRunner],
+    image_assets: _ImageAssets,
+    model: str,
+    *,
+    size_factors: Optional[List[float]] = None,
+    sizes: Optional[List[Tuple[int, int]]] = None,
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+    tensor_parallel_size: int,
+    distributed_executor_backend: Optional[str] = None,
 ):
     images = [asset.pil_image for asset in image_assets]
 
-    inputs_per_image = [(
-        [prompt for _ in size_factors],
-        [rescale_image_size(image, factor) for factor in size_factors],
-    ) for image, prompt in zip(images, HF_IMAGE_PROMPTS)]
+    if size_factors is not None:
+        inputs_per_image = [(
+            [prompt for _ in size_factors],
+            [rescale_image_size(image, factor) for factor in size_factors],
+        ) for image, prompt in zip(images, HF_IMAGE_PROMPTS)]
+    elif sizes is not None:
+        inputs_per_image = [(
+            [prompt for _ in sizes],
+            [image.resize(size) for size in sizes],
+        ) for image, prompt in zip(images, HF_IMAGE_PROMPTS)]
+    else:
+        raise ValueError("You must provide either `size_factors` or `sizes`")
 
     # max_model_len should be greater than image_feature_size
     with vllm_runner(model,
@@ -150,15 +193,24 @@ def test_models(hf_runner, vllm_runner, image_assets, model, size_factors,
     )
 
 
-@pytest.mark.parametrize("height_and_width_and_result", [(1669, 2560, 2144),
-                                                         (183, 488, 776)])
-def test_image_feature_size(height_and_width_and_result):
-    # Avoid initializing CUDA too early in distributed tests
-    from vllm.model_executor.models.llava_next import (
-        get_llava_next_image_feature_size)
-
-    height, width, result = height_and_width_and_result
-    config = AutoConfig.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
-    assert get_llava_next_image_feature_size(config,
-                                             input_height=height,
-                                             input_width=width) == result
+@pytest.mark.parametrize("model", models)
+@pytest.mark.parametrize(
+    "sizes",
+    [[(1669, 2560), (2560, 1669), (183, 488), (488, 183)]],
+)
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("max_tokens", [128])
+@pytest.mark.parametrize("num_logprobs", [5])
+def test_models_fixed_sizes(hf_runner, vllm_runner, image_assets, model, sizes,
+                            dtype, max_tokens, num_logprobs) -> None:
+    run_test(
+        hf_runner,
+        vllm_runner,
+        image_assets,
+        model,
+        sizes=sizes,
+        dtype=dtype,
+        max_tokens=max_tokens,
+        num_logprobs=num_logprobs,
+        tensor_parallel_size=1,
+    )

vllm/model_executor/models/fuyu.py

@@ -169,7 +169,7 @@ def input_processor_for_fuyu(ctx: InputContext, llm_inputs: LLMInputs):
         raise TypeError(f"Invalid image type: {type(image_data)}")
 
     # process prompts
-    prompt = llm_inputs["prompt"]
+    prompt = llm_inputs.get("prompt")
     prompt_token_ids = llm_inputs["prompt_token_ids"]
     tokenizer = cached_get_tokenizer(model_config.model)
     # dim0 is batch_size, dim1 is subseq_size which will always be 1

vllm/model_executor/models/internvl.py

@@ -20,7 +20,7 @@
 from vllm.model_executor.models import ModelRegistry
 from vllm.model_executor.models.intern_vit import InternVisionModel
 from vllm.model_executor.sampling_metadata import SamplingMetadata
-from vllm.multimodal import MULTIMODAL_REGISTRY, BatchedTensors
+from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.base import MultiModalInputs
 from vllm.multimodal.image import cached_get_tokenizer
 from vllm.sequence import IntermediateTensors, SamplerOutput
@@ -43,7 +43,7 @@
 
 class InternVLImagePixelInputs(TypedDict):
     type: Literal["pixel_values"]
-    data: BatchedTensors
+    data: Union[torch.Tensor, List[torch.Tensor]]
     """
     Shape: `(batch_size, 1 + num_patches, num_channels, height, width)`
 
@@ -193,7 +193,7 @@ def input_processor_for_internvl(ctx: InputContext, llm_inputs: LLMInputs):
     tokenizer = cached_get_tokenizer(model_config.tokenizer,
                                      trust_remote_code=True)
 
-    prompt = llm_inputs["prompt"]
+    prompt = llm_inputs.get("prompt")
     prompt_token_ids = llm_inputs["prompt_token_ids"]
     if prompt is None:
         prompt = tokenizer.decode(prompt_token_ids)

vllm/model_executor/models/llava_next.py

@@ -21,7 +21,7 @@
 from vllm.model_executor.models.clip import CLIPVisionModel
 from vllm.model_executor.models.llama import LlamaModel
 from vllm.model_executor.sampling_metadata import SamplingMetadata
-from vllm.multimodal import MULTIMODAL_REGISTRY, BatchedTensors
+from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.sequence import IntermediateTensors, SamplerOutput
 
 from .clip import (dummy_image_for_clip, dummy_seq_data_for_clip,
@@ -43,7 +43,7 @@
 
 class LlavaNextImagePixelInputs(TypedDict):
     type: Literal["pixel_values"]
-    data: BatchedTensors
+    data: Union[torch.Tensor, List[torch.Tensor]]
     """
     Shape: `(batch_size, 1 + num_patches, num_channels, height, width)`
 
@@ -62,31 +62,26 @@ class LlavaNextImagePixelInputs(TypedDict):
 LlavaNextImageInputs = LlavaNextImagePixelInputs
 
 
-# Taken from: https://github.com/huggingface/text-generation-inference/blob/v2.0.4/server/text_generation_server/models/vlm_causal_lm.py#L91
-# NOTE: new_height and new_width are further incremented to properly invert the
-# floordiv operation: https://github.com/huggingface/transformers/blob/v4.42.2/src/transformers/models/llava_next/modeling_llava_next.py#L133
+# Based on: https://github.com/huggingface/text-generation-inference/blob/v2.2.0/server/text_generation_server/models/vlm_causal_lm.py#L79
 def _get_llava_next_num_unpadded_features(
-    height: int,
-    width: int,
+    original_height: int,
+    original_width: int,
     npatches: int,
     num_patch_height: int,
     num_patch_width: int,
 ) -> Tuple[int, int]:
     current_height = npatches * num_patch_height
     current_width = npatches * num_patch_width
-    current_height = torch.tensor(current_height).to("cuda")
-    current_width = torch.tensor(current_width).to("cuda")
 
-    aspect_ratio: float = width / height
-    current_aspect_ratio: float = current_width / current_height
+    aspect_ratio = original_width / original_height
+    current_aspect_ratio = current_width / current_height
+
     if aspect_ratio > current_aspect_ratio:
-        scale_factor = current_width / width
-        new_height = int(height * scale_factor)
+        new_height = (original_height * current_width) // original_width
         padding = (current_height - new_height) // 2
         current_height -= padding * 2
     else:
-        scale_factor = current_height / height
-        new_width = int(width * scale_factor)
+        new_width = (original_width * current_height) // original_height
         padding = (current_width - new_width) // 2
         current_width -= padding * 2
 
@@ -95,7 +90,7 @@ def _get_llava_next_num_unpadded_features(
     return (unpadded_features, newline_features)
 
 
-# Based on: https://github.com/huggingface/text-generation-inference/blob/v2.0.4/server/text_generation_server/models/vlm_causal_lm.py#L111
+# Based on: https://github.com/huggingface/text-generation-inference/blob/v2.2.0/server/text_generation_server/models/vlm_causal_lm.py#L106
 def get_llava_next_image_feature_size(
     hf_config: LlavaNextConfig,
     *,
@@ -111,9 +106,7 @@ def get_llava_next_image_feature_size(
         )
         base_feature_size = num_patches * num_patches
 
-        # Note: We follow the "wrong" width/height order
-        # [ref: PR huggingface/transformers#31588]
-        num_patch_width, num_patch_height = get_anyres_image_grid_shape(
+        num_patch_height, num_patch_width = get_anyres_image_grid_shape(
             image_size=(input_height, input_width),
             grid_pinpoints=hf_config.image_grid_pinpoints,
             patch_size=vision_config.image_size,
@@ -349,11 +342,12 @@ def _merge_image_patch_embeddings(self, image_size: torch.Tensor,
             if patch_embeddings.shape[0] > 1:
                 other_patch_embeds = patch_embeddings[1:]
 
+                # Move to CPU to avoid floating-point errors
+                orig_height, orig_width = image_size.tolist()
+
                 # image_aspect_ratio == "anyres"
-                # Note: We follow the "wrong" width/height order
-                # [ref: PR huggingface/transformers#31588]
-                num_patch_width, num_patch_height = get_anyres_image_grid_shape(
-                    image_size,
+                num_patch_height, num_patch_width = get_anyres_image_grid_shape(
+                    (orig_height, orig_width),
                     self.config.image_grid_pinpoints,
                     self.config.vision_config.image_size,
                 )
@@ -365,7 +359,7 @@ def _merge_image_patch_embeddings(self, image_size: torch.Tensor,
                         .permute(4, 0, 2, 1, 3).contiguous() \
                         .flatten(1, 2).flatten(2, 3)
                     other_patch_embeds = unpad_image(other_patch_embeds,
-                                                     image_size)
+                                                     (orig_height, orig_width))
                     other_patch_embeds = torch.cat((
                         other_patch_embeds,
                         self.image_newline[:, None, None] \
@@ -398,7 +392,7 @@ def _merge_image_patch_embeddings(self, image_size: torch.Tensor,
     def _process_image_pixels(
         self,
         inputs: LlavaNextImagePixelInputs,
-    ) -> BatchedTensors:
+    ) -> Union[torch.Tensor, List[torch.Tensor]]:
         assert self.vision_tower is not None
 
         pixel_values = inputs["data"]
@@ -425,7 +419,9 @@ def _process_image_pixels(
         ]
 
     def _process_image_input(
-            self, image_input: LlavaNextImageInputs) -> BatchedTensors:
+        self,
+        image_input: LlavaNextImageInputs,
+    ) -> Union[torch.Tensor, List[torch.Tensor]]:
         patch_embeddings = self._process_image_pixels(image_input)
 
         image_sizes = image_input.get("image_sizes")

vllm/model_executor/models/phi3v.py

@@ -36,7 +36,7 @@
 from vllm.model_executor.models.clip import CLIPVisionModel
 from vllm.model_executor.models.llama import LlamaModel
 from vllm.model_executor.sampling_metadata import SamplingMetadata
-from vllm.multimodal import MULTIMODAL_REGISTRY, BatchedTensors
+from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.image import cached_get_tokenizer
 from vllm.sequence import IntermediateTensors, SamplerOutput
 
@@ -261,7 +261,7 @@ def add_image_newline(self, image_features_hd):
 
 class Phi3VImagePixelInputs(TypedDict):
     type: Literal["pixel_values"]
-    data: BatchedTensors
+    data: Union[torch.Tensor, List[torch.Tensor]]
     """
     Shape: `(batch_size, 1 + num_patches, num_channels, height, width)`