[V1][VLM][Pixtral-HF] Support Pixtral-HF on V1 (vllm-project#14275)

Signed-off-by: Linkun Chen <github@lkchen.net>

Author: lk-chen

docs/source/models/supported_models.md

@@ -866,7 +866,7 @@ See [this page](#generative-models) for more information on how to use generativ
 - * `PixtralForConditionalGeneration`
   * Pixtral
   * T + I<sup>+</sup>
-  * `mistralai/Pixtral-12B-2409`, `mistral-community/pixtral-12b` (see note), etc.
+  * `mistralai/Pixtral-12B-2409`, `mistral-community/pixtral-12b`, etc.
   *
   * ✅︎
   * ✅︎
@@ -930,10 +930,6 @@ For more details, please see: <gh-pr:4087#issuecomment-2250397630>
 Currently the PaliGemma model series is implemented without PrefixLM attention mask. This model series may be deprecated in a future release.
 :::
 
-:::{note}
-`mistral-community/pixtral-12b` does not support V1 yet.
-:::
-
 :::{note}
 To use Qwen2.5-VL series models, you have to install Hugging Face Transformers library from source via `pip install git+https://github.com/huggingface/transformers`.
 :::

vllm/model_executor/models/llava.py

@@ -4,7 +4,7 @@
 from collections.abc import Iterable, Mapping, Sequence
 from functools import cached_property
 from typing import (Final, List, Literal, Optional, Protocol, Set, Tuple,
-                    TypedDict, TypeVar, Union)
+                    TypedDict, TypeVar, Union, cast)
 
 import torch
 import torch.nn as nn
@@ -35,6 +35,7 @@
                                         PromptReplacement, PromptUpdate)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
 from vllm.sequence import IntermediateTensors
+from vllm.utils import JSONTree, flatten_2d_lists, json_map_leaves
 
 from .clip import CLIPVisionModel
 from .interfaces import SupportsMultiModal, SupportsPP
@@ -56,6 +57,25 @@ class LlavaImagePixelInputs(TypedDict):
     in which case the data is passed as a list instead of a batched tensor.
     """
 
+    feat_is_patch: Union[torch.Tensor, List[torch.Tensor]]
+    """
+    A boolean mask indicating which image features correspond
+    to patch tokens.
+
+    Shape: `(batch_size, num_crops, num_patch)`
+    """
+
+    embed_is_patch: Union[torch.Tensor, List[torch.Tensor]]
+    """
+    A boolean mask indicating which image embeddings correspond
+    to patch tokens.
+    
+    Shape: `(batch_size, num_embeds)`
+    """
+
+    num_crops: torch.Tensor
+    """Shape: `(batch_size, num_images)`"""
+
 
 class LlavaImageEmbeddingInputs(TypedDict):
     type: Literal["image_embeds"]
@@ -65,6 +85,25 @@ class LlavaImageEmbeddingInputs(TypedDict):
     `hidden_size` must match the hidden size of language model backbone.
     """
 
+    feat_is_patch: Union[torch.Tensor, List[torch.Tensor]]
+    """
+    A boolean mask indicating which image features correspond
+    to patch tokens.
+
+    Shape: `(batch_size, num_crops, num_patch)`
+    """
+
+    embed_is_patch: Union[torch.Tensor, List[torch.Tensor]]
+    """
+    A boolean mask indicating which image embeddings correspond
+    to patch tokens.
+    
+    Shape: `(batch_size, num_embeds)`
+    """
+
+    num_crops: torch.Tensor
+    """Shape: `(batch_size, num_images)`"""
+
 
 LlavaImageInputs = Union[LlavaImagePixelInputs, LlavaImageEmbeddingInputs]
 
@@ -317,14 +356,40 @@ def _call_hf_processor(
                     for p, (h, w) in zip(pixel_values, image_sizes)
                 ]
 
+            hf_config = self.info.get_hf_config()
+
+            tile_sizes = [
+                get_pixtral_hf_image_feature_grid_size(
+                    hf_config.vision_config,
+                    image_width=pixel_value.shape[-1],
+                    image_height=pixel_value.shape[-2])
+                for pixel_value in processed_outputs["pixel_values"]
+            ]
+            num_crops = torch.tensor([(ncols + 1) * nrows
+                                      for ncols, nrows in tile_sizes])
+            # Each image may result to masks of different sizes, so we need to
+            # flatten the list and later use `num_crops` to get per-image masks.
+            embed_is_patch = torch.tensor(
+                flatten_2d_lists([([True] * ncols + [False]) * nrows
+                                  for ncols, nrows in tile_sizes]))
+            processed_outputs["num_crops"] = num_crops
+            processed_outputs["embed_is_patch"] = embed_is_patch
+            processed_outputs["feat_is_patch"] = embed_is_patch
+
         return processed_outputs
 
     def _get_mm_fields_config(
         self,
         hf_inputs: BatchFeature,
         hf_processor_mm_kwargs: Mapping[str, object],
     ) -> Mapping[str, MultiModalFieldConfig]:
+        num_crops = hf_inputs.get("num_crops", torch.empty(0)).view(-1)
         return dict(
+            feat_is_patch=MultiModalFieldConfig.flat_from_sizes(
+                "image", num_crops),
+            embed_is_patch=MultiModalFieldConfig.flat_from_sizes(
+                "image", num_crops),
+            num_crops=MultiModalFieldConfig.batched("image"),
             pixel_values=MultiModalFieldConfig.batched("image"),
             image_embeds=MultiModalFieldConfig.batched("image"),
         )
@@ -562,6 +627,23 @@ def _parse_and_validate_image_input(
         if pixel_values is None and image_embeds is None:
             return None
 
+        feat_is_patch = kwargs.pop("feat_is_patch", None)
+        if feat_is_patch is not None and not isinstance(
+                feat_is_patch, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of feat_is_patch. "
+                             f"Got type: {type(feat_is_patch)}")
+
+        embed_is_patch = kwargs.pop("embed_is_patch", None)
+        if embed_is_patch is not None and not isinstance(
+                embed_is_patch, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of embed_is_patch. "
+                             f"Got type: {type(embed_is_patch)}")
+
+        num_crops = kwargs.pop("num_crops", None)
+        if num_crops is not None and not isinstance(num_crops, torch.Tensor):
+            raise ValueError("Incorrect type of num_crops. "
+                             f"Got type: {type(num_crops)}")
+
         if pixel_values is not None:
             if not isinstance(pixel_values, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of pixel values. "
@@ -571,12 +653,18 @@ def _parse_and_validate_image_input(
                 return LlavaImagePixelInputs(
                     type="pixel_values",
                     data=flatten_bn(pixel_values),
+                    feat_is_patch=feat_is_patch,
+                    embed_is_patch=embed_is_patch,
+                    num_crops=num_crops,
                 )
 
             return LlavaImagePixelInputs(
                 type="pixel_values",
                 data=self._validate_pixel_values(
                     flatten_bn(pixel_values, concat=True)),
+                feat_is_patch=feat_is_patch,
+                embed_is_patch=embed_is_patch,
+                num_crops=num_crops,
             )
 
         if image_embeds is not None:
@@ -587,6 +675,9 @@ def _parse_and_validate_image_input(
             return LlavaImageEmbeddingInputs(
                 type="image_embeds",
                 data=flatten_bn(image_embeds, concat=True),
+                feat_is_patch=feat_is_patch,
+                embed_is_patch=embed_is_patch,
+                num_crops=num_crops,
             )
 
         raise AssertionError("This line should be unreachable.")
@@ -633,16 +724,74 @@ def _process_image_input(self,
 
         assert self.vision_tower is not None
         image_features = self._process_image_pixels(image_input)
-        return self.multi_modal_projector(image_features)
 
-    def get_multimodal_embeddings(
-        self, **kwargs
-    ) -> Union[list[torch.Tensor], torch.Tensor, tuple[torch.Tensor, ...]]:
+        if isinstance(image_features, torch.Tensor):
+            return self.multi_modal_projector(image_features)
+
+        feature_sizes = [
+            image_feature.shape[0] for image_feature in image_features
+        ]
+
+        image_embeds = self.multi_modal_projector(torch.cat(image_features))
+        image_embeds = torch.split(image_embeds, feature_sizes)
+        return image_embeds
+
+    def _get_mm_embeds(
+            self,
+            features: torch.Tensor,  # Shape: (num_crop, num_patch, d)
+            feat_is_patch: torch.Tensor,  # Shape: (num_crop, num_patch)
+            num_crops: torch.Tensor,  # Shape: (num_images,)
+            embed_is_patch: torch.Tensor,  # Shape: (num_embeds,)
+    ) -> list[torch.Tensor]:
+        """Scatter the patch features into a contiguous tensor that corresponds
+        to the embedding tokens defined by the multimodal processor.
+
+        Mostly copied from `Molmo._get_mm_embeds`. See following fixme comment.
+        """
+
+        # Insert columns of nan values according to `feat_is_patch`. This work
+        # ideally should be done in `_process_image_input`, but
+        # `_process_image_input` is used in both V0 and V1 path. It's safer to
+        # put the logic here.
+        # FIXME: Move this logic to `_process_image_input` when v0 is
+        # deprecated. Merge this function with `Molmo._get_mm_embeds`.
+        feat_is_patch = feat_is_patch.view(-1)
+        embed_is_patch = embed_is_patch.view(-1)
+        expanded_embedding = torch.full(
+            (sum(num_crops), *features.shape[1:]),
+            torch.nan,
+            dtype=features.dtype).to(features.device)
+        expanded_embedding[feat_is_patch] = features
+
+        num_crops_per_image = num_crops.tolist()
+        feats_per_image = expanded_embedding.split(num_crops_per_image)
+        f_is_patch_per_image = feat_is_patch.split(num_crops_per_image)
+
+        embed_dim = expanded_embedding.shape[-1]
+        num_embeds = embed_is_patch.shape[0]
+
+        embeds_in_batch = list[torch.Tensor]()
+        for feats, f_is_patch in zip(feats_per_image, f_is_patch_per_image):
+            embeds = feats.new_full((num_embeds, embed_dim), torch.nan)
+            embeds[embed_is_patch] = feats[f_is_patch]
+            embeds_in_batch.append(embeds)
+
+        return embeds_in_batch
+
+    def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
             return None
         vision_embeddings = self._process_image_input(image_input)
-        return vision_embeddings
+        if kwargs.get("v0_path", False):
+            return vision_embeddings
+        else:
+            nested_emb = [
+                self._get_mm_embeds(*args) for args in zip(
+                    vision_embeddings, image_input["feat_is_patch"],
+                    image_input["num_crops"], image_input["embed_is_patch"])
+            ]
+            return flatten_2d_lists(nested_emb)
 
     def get_input_embeddings(
         self,
@@ -651,8 +800,15 @@ def get_input_embeddings(
     ) -> torch.Tensor:
         inputs_embeds = self.language_model.get_input_embeddings(input_ids)
         if multimodal_embeddings is not None:
+            # Extract the patch tokens
+            patch_embeddings = json_map_leaves(
+                lambda x: x[~x.isnan()].view(-1, *x.shape[1:]),
+                cast(JSONTree[torch.Tensor], multimodal_embeddings),
+            )
+
             inputs_embeds = merge_multimodal_embeddings(
-                input_ids, inputs_embeds, multimodal_embeddings,
+                input_ids, inputs_embeds, cast(NestedTensors,
+                                               patch_embeddings),
                 self.config.image_token_index)
         return inputs_embeds
 
@@ -705,6 +861,7 @@ def forward(
         # NOTE: In v1, inputs_embeds is always generated at model runner, this
         # condition is for v0 compatibility.
         elif inputs_embeds is None:
+            kwargs.update({"v0_path": True})
             vision_embeddings = self.get_multimodal_embeddings(**kwargs)
             inputs_embeds = self.get_input_embeddings(input_ids,
                                                       vision_embeddings)

vllm/model_executor/models/molmo.py

@@ -1484,8 +1484,8 @@ def _parse_and_validate_image_input(
 
         img_patch_id = kwargs.pop("img_patch_id", None)
         if not isinstance(img_patch_id, torch.Tensor):
-            raise ValueError("Incorrect type of num_crops. "
-                             f"Got type: {type(num_crops)}")
+            raise ValueError("Incorrect type of img_patch_id. "
+                             f"Got type: {type(img_patch_id)}")
         self.img_patch_id = img_patch_id.flatten().unique().item()
 
         return MolmoImageInputs(

vllm/model_executor/models/pixtral.py

@@ -1042,9 +1042,13 @@ def forward(
             for img in pixel_values
         ]
 
+        patch_embeds = [
+            p.flatten(2).permute(0, 2, 1) for p in patch_embeds_list
+        ]
+        embed_sizes = [p.shape[1] for p in patch_embeds]
+
         # flatten to a single sequence
-        patch_embeds = torch.cat(
-            [p.flatten(2).permute(0, 2, 1) for p in patch_embeds_list], dim=1)
+        patch_embeds = torch.cat(patch_embeds, dim=1)
         patch_embeds = self.ln_pre(patch_embeds)
 
         # positional embeddings
@@ -1075,6 +1079,8 @@ def forward(
         out = resolve_visual_encoder_outputs(out, feature_sample_layers, None,
                                              self.config.num_hidden_layers)
 
+        # squeeze dim 0 and split into separate tensors for each image
+        out = torch.split(torch.squeeze(out), embed_sizes)
         return out
 
     # (TODO) Add prefix argument for filtering out weights to be loaded