[Bugfix] Fix broken Minimax-01-VL model (#22116)

Signed-off-by: Isotr0py <2037008807@qq.com>
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>

examples/offline_inference/vision_language.py

@@ -889,6 +889,39 @@ def run_minicpmv(questions: list[str], modality: str) -> ModelRequestData:
     return run_minicpmv_base(questions, modality, "openbmb/MiniCPM-V-2_6")
 
 
+def run_minimax_vl_01(questions: list[str], modality: str) -> ModelRequestData:
+    assert modality == "image"
+
+    model_name = "MiniMaxAI/MiniMax-VL-01"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        max_num_seqs=2,
+        limit_mm_per_prompt={modality: 1},
+        trust_remote_code=True,
+        tensor_parallel_size=8,
+    )
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    messages = [
+        [
+            {
+                "role": "user",
+                "content": [{"type": "image"}, {"type": "text", "text": question}],
+            }
+        ]
+        for question in questions
+    ]
+    prompts = tokenizer.apply_chat_template(
+        messages, add_generation_prompt=True, tokenize=False
+    )
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
+
+
 # Mistral-3 HF-format
 def run_mistral3(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
@@ -1539,6 +1572,7 @@ model_example_map = {
     "mantis": run_mantis,
     "minicpmo": run_minicpmo,
     "minicpmv": run_minicpmv,
+    "minimax_vl_01": run_minimax_vl_01,
     "mistral3": run_mistral3,
     "mllama": run_mllama,
     "molmo": run_molmo,

tests/models/multimodal/test_tensor_schema.py

@@ -30,7 +30,6 @@ from ..utils import dummy_hf_overrides
 
 ARCH_TO_SKIP = {
     "MolmoForCausalLM": "incompatible requirements",
-    "MiniMaxVL01ForConditionalGeneration": "broken model",
 }
 ARCH_NEEDS_EXTRAS = [
     "InternVLChatModel",

vllm/model_executor/models/minimax_vl_01.py

@@ -1,11 +1,13 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from collections.abc import Iterable, Mapping
-from typing import Literal, Optional, TypedDict, Union, cast
+from typing import Annotated, Literal, Optional, Union, cast
 
 import torch
 import torch.nn as nn
 from transformers import BatchFeature, PretrainedConfig
+from transformers.models.llava_next.modeling_llava_next import (
+    get_anyres_image_grid_shape, unpad_image)
 
 from vllm.config import VllmConfig
 from vllm.model_executor.layers.activation import get_act_fn
@@ -17,6 +19,7 @@ from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import MultiModalFieldConfig
 from vllm.sequence import IntermediateTensors
 from vllm.utils.jsontree import json_map_leaves
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .clip import CLIPVisionModel
 from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
@@ -29,24 +32,36 @@ from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
                     maybe_prefix, merge_multimodal_embeddings)
 
 
-class MiniMaxVL01ImagePixelInputs(TypedDict):
-    type: Literal["pixel_values"]
-    pixel_values: torch.Tensor
+class MiniMaxVL01ImagePixelInputs(TensorSchema):
     """
-    Shape: `(batch_size * num_images, num_channels, height, width)`
+    Dimensions:
+        - bn: Batch size * number of images
+        - np: Number of patches + 1
+        - c: Number of channels (3)
+        - h: Height
+        - w: Width
 
-    Note that `height` or `width` may be different per batch and image,
+    Note that `num_patches` may be different per batch and image,
     in which case the data is passed as a list instead of a batched tensor.
     """
+    type: Literal["pixel_values"] = "pixel_values"
+    pixel_values: Annotated[
+        Union[torch.Tensor, list[torch.Tensor]],
+        TensorShape("bn", "np", 3, "h", "w", dynamic_dims={"np", "h", "w"})]
+
+    image_sizes: Annotated[Optional[torch.Tensor], TensorShape("bn", 2)]
+    # This should be in `(height, width)` format.
 
 
-class MiniMaxVL01ImageEmbeddingInputs(TypedDict):
-    type: Literal["image_embeds"]
-    data: torch.Tensor
-    """Shape: `(batch_size * num_images, image_feature_size, hidden_size)`
-
-    `hidden_size` must match the hidden size of language model backbone.
+class MiniMaxVL01ImageEmbeddingInputs(TensorSchema):
     """
+    Dimensions:
+        - bn: Batch size * number of images
+        - ifs: Image feature size
+        - hs: Hidden size (must match language model backbone)
+    """
+    type: Literal["image_embeds"] = "image_embeds"
+    data: Annotated[torch.Tensor, TensorShape("bn", "ifs", "hs")]
 
 
 MiniMaxVL01ImageInputs = Union[MiniMaxVL01ImagePixelInputs,
@@ -141,6 +156,7 @@ class MiniMaxVL01MultiModalProcessor(
     ) -> Mapping[str, MultiModalFieldConfig]:
         return {
             "pixel_values": MultiModalFieldConfig.batched("image"),
+            "image_sizes": MultiModalFieldConfig.batched("image"),
             "image_embeds": MultiModalFieldConfig.batched("image"),
         }
 
@@ -239,7 +255,7 @@ class MiniMaxVL01ForConditionalGeneration(nn.Module, SupportsMultiModal,
     ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
         # NOTE: we skip the step to select the vision feature layer since
         # this is already done inside the vision tower
-        image_features = vision_tower(pixel_values)
+        image_features = tuple(vision_tower(p) for p in pixel_values)
 
         def select_features(leaf: torch.Tensor):
             return self._select_image_features(
@@ -252,6 +268,56 @@ class MiniMaxVL01ForConditionalGeneration(nn.Module, SupportsMultiModal,
             json_map_leaves(select_features, image_features),
         )
 
+    # adapted from https://huggingface.co/MiniMaxAI/MiniMax-VL-01/blob/main/modeling_minimax_vl_01.py#L616-L631
+    def pack_image_features(self, image_features: list[torch.Tensor],
+                            image_sizes: torch.Tensor):
+        new_image_features = []
+        for image_idx, image_feature in enumerate(image_features):
+            if image_feature.shape[0] > 1:
+                base_image_feature = image_feature[0]
+                image_feature = image_feature[1:]
+                height = width = (self.config.vision_config.image_size //
+                                  self.config.vision_config.patch_size)
+                if height * width != base_image_feature.shape[0]:
+                    raise ValueError(
+                        "The number of patches is not consistent with "
+                        "the image size.")
+                num_patch_height, num_patch_width = get_anyres_image_grid_shape(
+                    image_sizes[image_idx],
+                    self.config.image_grid_pinpoints,
+                    self.config.vision_config.image_size,
+                )
+
+                image_feature = image_feature.view(num_patch_height,
+                                                   num_patch_width, height,
+                                                   width, -1)
+                image_feature = image_feature.permute(4, 0, 2, 1,
+                                                      3).contiguous()
+                image_feature = image_feature.flatten(1, 2).flatten(2, 3)
+                image_feature = unpad_image(image_feature,
+                                            image_sizes[image_idx])
+
+                image_feature = torch.cat(
+                    (
+                        image_feature,
+                        self.image_newline[:, None, None].expand(
+                            *image_feature.shape[:-1], 1).to(
+                                image_feature.dtype),
+                    ),
+                    dim=-1,
+                )
+                image_feature = image_feature.flatten(1, 2).transpose(0, 1)
+                image_feature = torch.cat((base_image_feature, image_feature),
+                                          dim=0)
+            else:
+                image_feature = image_feature[0]
+                image_feature = torch.cat(
+                    (image_feature,
+                     self.image_newline[None].to(image_feature)),
+                    dim=0)
+            new_image_features.append(image_feature)
+        return new_image_features
+
     def _process_image_pixels(
         self,
         inputs: MiniMaxVL01ImagePixelInputs,
@@ -259,7 +325,6 @@ class MiniMaxVL01ForConditionalGeneration(nn.Module, SupportsMultiModal,
         assert self.vision_tower is not None
 
         pixel_values = inputs["pixel_values"]
-
         return self._image_pixels_to_features(self.vision_tower, pixel_values)
 
     def _process_image_input(
@@ -281,38 +346,31 @@ class MiniMaxVL01ForConditionalGeneration(nn.Module, SupportsMultiModal,
 
         image_embeds = self.multi_modal_projector(torch.cat(image_features))
         image_embeds = torch.split(image_embeds, feature_sizes)
-        return image_embeds
-
-    def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
-        h = w = self.config.vision_config.image_size
-        expected_dims = (3, h, w)
-        actual_dims = tuple(data.shape[1:])
-
-        if actual_dims != expected_dims:
-            expected_expr = ("batch_size", *map(str, expected_dims))
-            raise ValueError(
-                f"The expected shape of pixel values is {expected_expr}. "
-                f"You supplied {tuple(data.shape)}.")
-
-        return data
+        image_sizes = image_input.get("image_sizes")
+        return self.pack_image_features(image_embeds, image_sizes)
 
     def _parse_and_validate_image_input(
             self, **kwargs: object) -> Optional[MiniMaxVL01ImageInputs]:
         pixel_values = kwargs.pop("pixel_values", None)
+        image_sizes = kwargs.pop("image_sizes", None)
         image_embeds = kwargs.pop("image_embeds", None)
 
         if pixel_values is None and image_embeds is None:
             return None
 
-        if pixel_values is not None:
+        if pixel_values is not None and image_sizes is not None:
             if not isinstance(pixel_values, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of pixel values. "
                                  f"Got type: {type(pixel_values)}")
 
+            if not isinstance(image_sizes, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image sizes. "
+                                 f"Got type: {type(image_sizes)}")
+
             return MiniMaxVL01ImagePixelInputs(
                 type="pixel_values",
-                pixel_values=self._validate_pixel_values(
-                    flatten_bn(pixel_values, concat=True)),
+                pixel_values=flatten_bn(pixel_values),
+                image_sizes=flatten_bn(image_sizes, concat=True),
             )
 
         if image_embeds is not None: