Add patch merger (#14957)

requirements/common.txt

@@ -28,7 +28,7 @@ pyzmq
 msgspec
 gguf == 0.10.0
 importlib_metadata
-mistral_common[opencv] >= 1.5.0
+mistral_common[opencv] >= 1.5.4
 pyyaml
 six>=1.16.0; python_version > '3.11' # transitive dependency of pandas that needs to be the latest version for python 3.12
 setuptools>=74.1.1; python_version > '3.11' # Setuptools is used by triton, we need to ensure a modern version is installed for 3.12+ so that it does not try to import distutils, which was removed in 3.12

requirements/docs.txt

@@ -15,7 +15,7 @@ pydantic >= 2.8
 torch
 py-cpuinfo
 transformers
-mistral_common >= 1.5.0
+mistral_common >= 1.5.4
 aiohttp
 starlette
 openai # Required by docs/source/serving/openai_compatible_server.md's vllm.entrypoints.openai.cli_args

requirements/test.in

@@ -27,7 +27,7 @@ torchaudio==2.6.0
 torchvision==0.21.0
 transformers_stream_generator # required for qwen-vl test
 matplotlib # required for qwen-vl test
-mistral_common[opencv] >= 1.5.0 # required for pixtral test
+mistral_common[opencv] >= 1.5.4 # required for pixtral test
 datamodel_code_generator # required for minicpm3 test
 lm-eval[api]==0.4.4 # required for model evaluation test
 transformers==4.48.2 
@@ -40,4 +40,4 @@ tritonclient==2.51.0
 
 numpy < 2.0.0
 runai-model-streamer==0.11.0
-runai-model-streamer-s3==0.11.0
+runai-model-streamer-s3==0.11.0
\ No newline at end of file

vllm/model_executor/models/pixtral.py

@@ -56,6 +56,8 @@ try:
 except ImportError:
     USE_XFORMERS_OPS = False
 
+PATCH_MERGE = "patch_merge"
+
 
 class PixtralImagePixelInputs(TypedDict):
     type: Literal["pixel_values"]
@@ -155,7 +157,6 @@ class PixtralProcessorAdapter:
 
         for image in images:
             image_inputs = self.image_processor(ImageChunk(image=image))
-
             image_processed = torch.tensor(image_inputs.image)
             image_tokens = torch.tensor(image_inputs.tokens)
 
@@ -353,6 +354,27 @@ class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal,
         )
 
         self.vision_encoder = VisionTransformer(self.vision_args)
+
+        if self.vision_args.add_pre_mm_projector_layer_norm:
+            self.pre_mm_projector_norm = RMSNorm(self.vision_args.hidden_size,
+                                                 eps=1e-5)
+
+        if self.vision_args.mm_projector_id == PATCH_MERGE:
+            self.patch_merger = PatchMerger(
+                vision_encoder_dim=self.vision_args.hidden_size,
+                spatial_merge_size=self.vision_args.spatial_merge_size,
+                use_mlp_bias=False,
+            )
+        if self.vision_args.add_pre_mm_projector_layer_norm:
+            self.pre_mm_projector_norm = RMSNorm(self.vision_args.hidden_size,
+                                                 eps=1e-5)
+
+        if self.vision_args.mm_projector_id == PATCH_MERGE:
+            self.patch_merger = PatchMerger(
+                vision_encoder_dim=self.vision_args.hidden_size,
+                spatial_merge_size=self.vision_args.spatial_merge_size,
+                use_mlp_bias=False,
+            )
         self.vision_language_adapter = VisionLanguageAdapter(
             self.vision_args, dim=config.text_config.hidden_size)
 
@@ -398,13 +420,25 @@ class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal,
         image_input: PixtralImagePixelInputs,
     ) -> tuple[torch.Tensor, ...]:
         images = image_input["images"]
-
         image_features = self.vision_encoder(images)
         feature_sizes = [
             image_feature.shape[0] for image_feature in image_features
         ]
-
-        image_embeds = self.vision_language_adapter(torch.cat(image_features))
+        image_features = torch.cat(image_features)
+        if self.vision_args.add_pre_mm_projector_layer_norm:
+            image_features = self.pre_mm_projector_norm(image_features)
+        if self.vision_args.mm_projector_id == PATCH_MERGE:
+            patch_size = self.vision_args.patch_size
+            spatial_merge_size_square = self.vision_args.spatial_merge_size**2
+            img_patch_dims = [(img.shape[1] // patch_size,
+                               img.shape[2] // patch_size) for img in images]
+            feature_sizes = [
+                feature_size // spatial_merge_size_square
+                for feature_size in feature_sizes
+            ]
+            image_features = self.patch_merger(image_features,
+                                               image_sizes=img_patch_dims)
+        image_embeds = self.vision_language_adapter(image_features)
         image_embeds = torch.split(image_embeds, feature_sizes)
         return image_embeds
 
@@ -524,8 +558,19 @@ class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal,
         def is_vision_lang_adapter_weights(weight: Tuple[str, torch.Tensor]):
             return weight[0].startswith("vision_language_adapter")
 
+        def is_patch_merger(weight: Tuple[str, torch.Tensor]):
+            return weight[0].startswith("patch_merger")
+
+        def is_pre_mm_projector_norm(weight: Tuple[str, torch.Tensor]):
+            return weight[0].startswith("pre_mm_projector_norm")
+
         # Get references to parameters for direct loading
         vision_encoder_dict = dict(self.vision_encoder.named_parameters())
+        patch_merger_dict = dict(self.patch_merger.named_parameters(
+        )) if self.vision_args.mm_projector_id == PATCH_MERGE else dict()
+        pre_mm_projector_norm_dict = dict(
+            self.pre_mm_projector_norm.named_parameters(
+            )) if self.vision_args.add_pre_mm_projector_layer_norm else dict()
         vision_lang_adapter_dict = dict(
             self.vision_language_adapter.named_parameters())
 
@@ -538,6 +583,18 @@ class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal,
                     param = vision_encoder_dict[trimmed_name]
                     with torch.no_grad():
                         default_weight_loader(param, w)
+                elif is_patch_merger((name, w)):
+                    # Load vision patch merger weights directly
+                    trimmed_name = '.'.join(name.split(".")[1:])
+                    param = patch_merger_dict[trimmed_name]
+                    with torch.no_grad():
+                        default_weight_loader(param, w)
+                elif is_pre_mm_projector_norm((name, w)):
+                    # Load vision pre_mm_projector_norm weights directly
+                    trimmed_name = '.'.join(name.split(".")[1:])
+                    param = pre_mm_projector_norm_dict[trimmed_name]
+                    with torch.no_grad():
+                        default_weight_loader(param, w)
                 elif is_vision_lang_adapter_weights((name, w)):
                     # Load vision-language adapter weights directly
                     trimmed_name = '.'.join(name.split(".")[1:])
@@ -566,6 +623,9 @@ class VisionEncoderArgs:
     rope_theta: float  # for rope-2D
     image_token_id: int
     adapter_bias: bool = True
+    spatial_merge_size: int = 1
+    add_pre_mm_projector_layer_norm: bool = False
+    mm_projector_id: str = ""
 
 
 def _reshape_for_broadcast(freqs_cis: torch.Tensor,
@@ -843,6 +903,104 @@ class VisionLanguageAdapter(nn.Module):
         return self.w_out(self.gelu(self.w_in(x)))
 
 
+class PatchMerger(nn.Module):
+    """
+    Learned merging of spatial_merge_size ** 2 patches
+    """
+
+    def __init__(
+        self,
+        vision_encoder_dim: int,
+        spatial_merge_size: int,
+        use_mlp_bias: bool = False,
+    ) -> None:
+        super().__init__()
+
+        mlp_input_dim = vision_encoder_dim * (spatial_merge_size**2)
+
+        self.spatial_merge_size = spatial_merge_size
+        self.mlp_input_dim = mlp_input_dim
+
+        self.merging_layer = nn.Linear(
+            mlp_input_dim,
+            vision_encoder_dim,
+            bias=use_mlp_bias,
+        )
+
+    def forward(self, x: torch.Tensor,
+                image_sizes: list[tuple[int, int]]) -> torch.Tensor:
+        # image_sizes specified in tokens
+        assert sum([h * w for h, w in image_sizes]) == len(x)
+
+        # x is (N, vision_encoder_dim)
+        x = self.permute(x, image_sizes)
+
+        # x is (N / spatial_merge_size ** 2, vision_encoder_dim * spatial_merge_size ** 2)
+        x = self.merging_layer(x)
+
+        # x is (N / spatial_merge_size ** 2, vision_encoder_dim)
+        return x
+
+    def permute(
+        self,
+        x: torch.Tensor,
+        image_sizes: list[tuple[int, int]],
+    ) -> torch.Tensor:
+        """
+        Args:
+            x: (N, D) where N is flattened and concatenated patch tokens
+                for all images
+            image_sizes: list of tuple of (height, width) in tokens for
+                each image
+        Returns:
+            image_features: reorders patch tokens so each grid of
+                (spatial_merge_size, spatial_merge_size) is contiguous.
+                now (N / spatial_merge_size ** 2, D * spatial_merge_size ** 2)
+        """
+
+        sub_grids = get_sub_grids(
+            x=x,
+            image_sizes=image_sizes,
+            spatial_merge_size=self.spatial_merge_size
+        )  # list of [d x sub_grid_size x sub_grid_size x n_patches]
+        permuted_tensor: list[torch.Tensor] = []
+        for grid in sub_grids:
+            n_patches = grid.shape[-1]
+            permuted_tensor.append(grid.view(-1, n_patches).t(
+            ))  # n_patches x d * sub_grid_size * sub_grid_size
+        return torch.cat(
+            permuted_tensor, dim=0
+        )  # (N / spatial_merge_size ** 2, d * spatial_merge_size ** 2)
+
+
+def get_sub_grids(
+    x: torch.Tensor,
+    image_sizes: list[tuple[int, int]],
+    spatial_merge_size: int,
+) -> list[torch.Tensor]:
+    # image_sizes specified in tokens
+    tokens_per_image = [h * w for h, w in image_sizes]
+    d = x.shape[-1]
+    all_img_sub_grids: list[torch.Tensor] = []
+    sub_grid_size = spatial_merge_size
+
+    for image_index, image_tokens in enumerate(x.split(tokens_per_image)):
+        # Reshape image_tokens into a 2D grid
+        h, w = image_sizes[image_index]
+        image_grid = image_tokens.view(h, w, d).permute(
+            2, 0, 1)[None, :, :, :]  # 1 x d x h x w
+        sub_grids = torch.nn.functional.unfold(image_grid,
+                                               kernel_size=sub_grid_size,
+                                               stride=sub_grid_size)
+        sub_grids = sub_grids.view(
+            1, d, sub_grid_size, sub_grid_size,
+            -1)  # 1 x d x sub_grid_size x sub_grid_size x n_patches
+
+        all_img_sub_grids.append(sub_grids[0])
+
+    return all_img_sub_grids
+
+
 #### HF Transformers version of Pixtral ####
 # Based off https://github.com/huggingface/transformers/blob/d7950bff82b18c823193d17d72188c5e46d06c83/src/transformers/models/pixtral/modeling_pixtral.py
 # This model follows the Llava family, meaning image embeddings are placed