[Model] Support Mistral3 in the HF Transformers format (#15505)

Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>

docs/source/models/supported_models.md

@@ -865,6 +865,13 @@ See [this page](#generative-models) for more information on how to use generativ
   * ✅︎
   * ✅︎
   * ✅︎
+- * `Mistral3ForConditionalGeneration`
+  * Mistral3
+  * T + I<sup>+</sup>
+  * `mistralai/Mistral-Small-3.1-24B-Instruct-2503`, etc.
+  *
+  * ✅︎
+  *
 - * `MllamaForConditionalGeneration`
   * Llama 3.2
   * T + I<sup>+</sup>

examples/offline_inference/vision_language.py

@@ -498,6 +498,29 @@ def run_minicpmv(questions: list[str], modality: str) -> ModelRequestData:
     return run_minicpmv_base(questions, modality, "openbmb/MiniCPM-V-2_6")
 
 
+# Mistral-3 HF-format
+def run_mistral3(questions: list[str], modality: str) -> ModelRequestData:
+    assert modality == "image"
+
+    model_name = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
+
+    # NOTE: Need L40 (or equivalent) to avoid OOM
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=8192,
+        max_num_seqs=2,
+        tensor_parallel_size=2,
+        disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
+    )
+
+    prompts = [f"<s>[INST]{question}\n[IMG][/INST]" for question in questions]
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
+
+
 # LLama 3.2
 def run_mllama(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
@@ -859,6 +882,7 @@ model_example_map = {
     "mantis": run_mantis,
     "minicpmo": run_minicpmo,
     "minicpmv": run_minicpmv,
+    "mistral3": run_mistral3,
     "mllama": run_mllama,
     "molmo": run_molmo,
     "NVLM_D": run_nvlm_d,

examples/offline_inference/vision_language_multi_image.py

@@ -218,6 +218,28 @@ def load_internvl(question: str, image_urls: list[str]) -> ModelRequestData:
     )
 
 
+def load_mistral3(question: str, image_urls: list[str]) -> ModelRequestData:
+    model_name = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
+
+    # Adjust this as necessary to fit in GPU
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=8192,
+        max_num_seqs=2,
+        tensor_parallel_size=2,
+        limit_mm_per_prompt={"image": len(image_urls)},
+    )
+
+    placeholders = "[IMG]" * len(image_urls)
+    prompt = f"<s>[INST]{question}\n{placeholders}[/INST]"
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompt=prompt,
+        image_data=[fetch_image(url) for url in image_urls],
+    )
+
+
 def load_mllama(question: str, image_urls: list[str]) -> ModelRequestData:
     model_name = "meta-llama/Llama-3.2-11B-Vision-Instruct"
 
@@ -509,6 +531,7 @@ model_example_map = {
     "h2ovl_chat": load_h2ovl,
     "idefics3": load_idefics3,
     "internvl_chat": load_internvl,
+    "mistral3": load_mistral3,
     "mllama": load_mllama,
     "NVLM_D": load_nvlm_d,
     "phi3_v": load_phi3v,

tests/models/registry.py

@@ -297,6 +297,9 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     "MiniCPMV": _HfExamplesInfo("openbmb/MiniCPM-Llama3-V-2_5",
                                 extras={"2.6": "openbmb/MiniCPM-V-2_6"},  # noqa: E501
                                 trust_remote_code=True),
+    "Mistral3ForConditionalGeneration": _HfExamplesInfo("mistralai/Mistral-Small-3.1-24B-Instruct-2503",  # noqa: E501
+                                                        min_transformers_version="4.50",  # noqa: E501
+                                                        extras={"fp8": "nm-testing/Mistral-Small-3.1-24B-Instruct-2503-FP8-dynamic"}),  # noqa: E501
     "MolmoForCausalLM": _HfExamplesInfo("allenai/Molmo-7B-D-0924",
                                         max_transformers_version="4.48",
                                         transformers_version_reason="Use of private method which no longer exists.",  # noqa: E501

vllm/entrypoints/chat_utils.py

@@ -487,7 +487,8 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
                 return "<|endoftext10|>"  # 200010 (see vocab.json in hf model)
             if model_type in ("minicpmo", "minicpmv"):
                 return "(<image>./</image>)"
-            if model_type in ("blip-2", "fuyu", "paligemma", "pixtral"):
+            if model_type in ("blip-2", "fuyu", "paligemma", "pixtral",
+                              "mistral3"):
                 # These models do not use image tokens in the prompt
                 return None
             if model_type == "qwen":

vllm/model_executor/models/mistral3.py

@@ -0,0 +1,656 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from abc import abstractmethod
+from collections.abc import Iterable, Mapping, Sequence
+from functools import cached_property
+from typing import (Final, Literal, Optional, Protocol, Set, Tuple, TypedDict,
+                    TypeVar, Union)
+
+import torch
+import torch.nn as nn
+from transformers import (BatchFeature, Mistral3Config, PixtralVisionConfig,
+                          PretrainedConfig)
+from transformers.models.pixtral import PixtralProcessor
+
+from vllm.config import VllmConfig
+from vllm.inputs import InputProcessingContext
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import MultiModalFieldConfig, MultiModalKwargs
+from vllm.multimodal.parse import (ImageProcessorItems, ImageSize,
+                                   MultiModalDataItems)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, ProcessingCache,
+                                        PromptReplacement, PromptUpdate)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import (MultiModalEmbeddings, SupportsMultiModal, SupportsPP,
+                         SupportsV0Only)
+from .pixtral import PixtralHFEncoderInfo, PixtralHFVisionModel
+from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
+                    maybe_prefix, merge_multimodal_embeddings)
+from .vision import get_vision_encoder_info, select_patch_features
+
+
+class Mistral3ImagePixelInputs(TypedDict):
+    type: Literal["pixel_values_pixtral"]
+    pixel_values: Union[torch.Tensor, list[torch.Tensor]]
+    """
+    Shape: `(batch_size * num_images, num_channels, height, width)`
+
+    Note that `height` or `width` may be different per batch and image,
+    in which case the data is passed as a list instead of a batched tensor.
+    """
+
+    embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
+    """
+    A boolean mask indicating which image embeddings correspond
+    to patch tokens.
+    
+    Shape: `(batch_size, num_images, num_embeds)`
+    """
+
+
+class Mistral3PatchMerger(nn.Module):
+    """
+    Learned merging of spatial_merge_size ** 2 patches
+    """
+
+    def __init__(self, vision_hidden_size: int, spatial_merge_size: int,
+                 patch_size: int):
+        super().__init__()
+
+        self.vision_hidden_size = vision_hidden_size
+        self.spatial_merge_size = spatial_merge_size
+        self.patch_size = patch_size
+        self.merging_layer = nn.Linear(vision_hidden_size *
+                                       self.spatial_merge_size**2,
+                                       vision_hidden_size,
+                                       bias=False)
+
+    def forward(self, image_features: torch.Tensor,
+                image_sizes: torch.Tensor) -> torch.Tensor:
+        image_sizes = [(image_size[0] // self.patch_size,
+                        image_size[1] // self.patch_size)
+                       for image_size in image_sizes]
+
+        tokens_per_image = [h * w for h, w in image_sizes]
+        d = image_features.shape[-1]
+
+        permuted_tensor = []
+        for image_index, image_tokens in enumerate(
+                image_features.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).unsqueeze(0)
+            grid = torch.nn.functional.unfold(
+                image_grid,
+                kernel_size=self.spatial_merge_size,
+                stride=self.spatial_merge_size)
+            grid = grid.view(d * self.spatial_merge_size**2, -1).t()
+            permuted_tensor.append(grid)
+
+        image_features = torch.cat(permuted_tensor, dim=0)
+        image_features = self.merging_layer(image_features)
+        return image_features
+
+
+class Mistral3MultiModalProjector(nn.Module):
+
+    def __init__(self,
+                 vision_hidden_size: int,
+                 text_hidden_size: int,
+                 spatial_merge_size: int,
+                 patch_size: int,
+                 projector_hidden_act: str,
+                 multimodal_projector_bias: bool,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+
+        self.norm = RMSNorm(vision_hidden_size, eps=1e-5)
+        self.patch_merger = Mistral3PatchMerger(
+            vision_hidden_size=vision_hidden_size,
+            spatial_merge_size=spatial_merge_size,
+            patch_size=patch_size)
+
+        self.linear_1 = ColumnParallelLinear(vision_hidden_size,
+                                             text_hidden_size,
+                                             bias=multimodal_projector_bias,
+                                             quant_config=quant_config,
+                                             prefix=f"{prefix}.linear_1")
+        self.act = get_act_fn(projector_hidden_act)
+        self.linear_2 = RowParallelLinear(text_hidden_size,
+                                          text_hidden_size,
+                                          bias=multimodal_projector_bias,
+                                          quant_config=quant_config,
+                                          prefix=f"{prefix}.linear_2")
+
+    def forward(self, image_features: torch.Tensor,
+                image_sizes: torch.Tensor) -> torch.Tensor:
+        image_features = self.norm(image_features)
+        image_features = self.patch_merger(image_features, image_sizes)
+        hidden_states, _ = self.linear_1(image_features)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.linear_2(hidden_states)
+        return hidden_states
+
+
+class LlavaLikeConfig(Protocol):
+    vision_config: Final[PretrainedConfig]
+    image_token_index: Final[int]
+    vision_feature_select_strategy: Final[str]
+    vision_feature_layer: Final[Union[int, list[int]]]
+
+
+class LlavaLikeProcessor(Protocol):
+    image_token: Final[str]
+
+
+class BaseLlavaProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_config(self) -> LlavaLikeConfig:
+        return self.ctx.get_hf_config(Mistral3Config)
+
+    def get_vision_encoder_info(self):
+        return get_vision_encoder_info(self.get_hf_config())
+
+    @abstractmethod
+    def get_hf_processor(self, **kwargs: object) -> LlavaLikeProcessor:
+        raise NotImplementedError
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+    def get_mm_max_tokens_per_item(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> Mapping[str, int]:
+        return {"image": self.get_max_image_tokens()}
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+    ) -> int:
+        vision_encoder_info = self.get_vision_encoder_info()
+        return vision_encoder_info.get_num_image_tokens(
+            image_width=image_width,
+            image_height=image_height,
+        )
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        vision_encoder_info = self.get_vision_encoder_info()
+        width = height = vision_encoder_info.get_image_size()
+        return ImageSize(width=width, height=height)
+
+    def get_max_image_tokens(self) -> int:
+        target_width, target_height = self.get_image_size_with_most_features()
+
+        return self.get_num_image_tokens(
+            image_width=target_width,
+            image_height=target_height,
+        )
+
+
+_I = TypeVar("_I", bound=BaseLlavaProcessingInfo)
+
+
+class Mistral3DummyInputsBuilder(BaseDummyInputsBuilder[_I]):
+
+    def get_dummy_processor_inputs(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> ProcessorInputs:
+        num_images = mm_counts.get("image", 0)
+
+        processor = self.info.get_hf_processor()
+        image_token = processor.image_token
+        target_width, target_height = \
+            self.info.get_image_size_with_most_features()
+
+        mm_data = {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images)
+        }
+
+        return ProcessorInputs(
+            prompt_text=image_token * num_images,
+            mm_data=mm_data,
+        )
+
+
+class Mistral3ProcessingInfo(BaseLlavaProcessingInfo):
+
+    def get_hf_processor(self, **kwargs: object):
+        return self.ctx.get_hf_processor(PixtralProcessor, **kwargs)
+
+
+class Mistral3MultiModalProcessor(
+        BaseMultiModalProcessor[Mistral3ProcessingInfo]):
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        processed_outputs = super()._call_hf_processor(
+            prompt=prompt,
+            mm_data=mm_data,
+            mm_kwargs=mm_kwargs,
+        )
+
+        pixel_values = processed_outputs.get("pixel_values")
+        if pixel_values is not None:
+
+            # Avoid padding since we need the output for each image to be
+            # independent of other images for the cache to work correctly
+            image_sizes = processed_outputs["image_sizes"]
+            assert len(pixel_values) == len(image_sizes)
+
+            processed_outputs["pixel_values"] = [
+                p[:, :h, :w] for p, (h, w) in zip(pixel_values, image_sizes)
+            ]
+
+            hf_config = self.info.get_hf_config()
+            vision_config = hf_config.vision_config
+            assert isinstance(vision_config, PixtralVisionConfig)
+            encoder_info = PixtralHFEncoderInfo(vision_config)
+
+            tile_sizes = [
+                encoder_info.get_patch_grid_size(
+                    image_width=pixel_value.shape[-1],
+                    image_height=pixel_value.shape[-2],
+                ) for pixel_value in processed_outputs["pixel_values"]
+            ]
+            embed_is_patch = [
+                torch.tensor(([True] * ncols + [False]) * nrows)
+                for ncols, nrows in tile_sizes
+            ]
+            processed_outputs["embed_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]:
+        return dict(
+            pixel_values=MultiModalFieldConfig.batched("image"),
+            embed_is_patch=MultiModalFieldConfig.batched("image"),
+            image_embeds=MultiModalFieldConfig.batched("image"),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        hf_config = self.info.get_hf_config()
+        tokenizer = self.info.get_tokenizer()
+        vocab = tokenizer.get_vocab()
+
+        image_break_id = vocab[processor.image_break_token]
+        image_token_id = hf_config.image_token_index
+        image_end_id = vocab[processor.image_end_token]
+
+        vision_config = hf_config.vision_config
+        assert isinstance(vision_config, PixtralVisionConfig)
+        encoder_info = PixtralHFEncoderInfo(vision_config)
+
+        def get_replacement(item_idx: int):
+            images = mm_items.get_items("image", ImageProcessorItems)
+            image_size = images.get_image_size(item_idx)
+
+            ncols, nrows = encoder_info.get_patch_grid_size(
+                image_width=image_size.width,
+                image_height=image_size.height,
+            )
+
+            tokens = ([image_token_id] * ncols + [image_break_id]) * nrows
+            tokens[-1] = image_end_id
+
+            return tokens
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target=[image_token_id],
+                replacement=get_replacement,
+            ),
+        ]
+
+
+def _build_mistral3_info(
+    ctx: InputProcessingContext, ) -> BaseLlavaProcessingInfo:
+    hf_config = ctx.get_hf_config(Mistral3Config)
+    assert isinstance(hf_config.vision_config, PixtralVisionConfig)
+    return Mistral3ProcessingInfo(ctx)
+
+
+def _build_mistral3_processor(
+    info: _I,
+    dummy_inputs: BaseDummyInputsBuilder[_I],
+    *,
+    cache: Optional[ProcessingCache] = None,
+    enable_sanity_checks: bool = True,
+) -> BaseMultiModalProcessor:
+    assert isinstance(info, Mistral3ProcessingInfo)
+    return Mistral3MultiModalProcessor(
+        info,
+        dummy_inputs,  # type: ignore
+        cache=cache,
+        enable_sanity_checks=enable_sanity_checks,
+    )
+
+
+def _get_num_hidden_layers(hf_config: LlavaLikeConfig) -> int:
+    """Determine the number of hidden layers to initialize up to in the
+    visual encoder.
+    
+    Args:
+        hf_config: Model config with vision feature layer(s).
+    """
+    feature_layers = hf_config.vision_feature_layer
+    num_hidden_layers = hf_config.vision_config.num_hidden_layers
+    # If we have one feature layer, initialize up to that layer
+    if isinstance(feature_layers, int):
+        return _get_layer_index(feature_layers, num_hidden_layers)
+    # If we have multiple feature layers, initialize up to the deepest one
+    elif isinstance(feature_layers, (list, tuple)):
+        return max(
+            _get_layer_index(idx, num_hidden_layers) for idx in feature_layers)
+    raise TypeError(f"vision_layer_feature type: {type(feature_layers)}"
+                    " is not supported")
+
+
+def _get_layer_index(feature_layer_index: int, num_hidden_layers: int) -> int:
+    """Given a signed vision feature layer, get the number of hidden layers
+    needed to leverage it.
+
+    Args:
+        feature_layer_index: Index of a required layer in the visual encoder.
+        num_hidden_layers: The total number of hidden layers in the visual
+            encoder.
+    """
+    if feature_layer_index < 0:
+        return num_hidden_layers + feature_layer_index + 1
+    return feature_layer_index
+
+
+def init_vision_tower_for_llava(
+    hf_config: LlavaLikeConfig,
+    quant_config: Optional[QuantizationConfig],
+    *,
+    require_post_norm: Optional[bool] = None,
+    prefix: str = "",
+) -> PixtralHFVisionModel:
+    vision_config = hf_config.vision_config
+
+    # Initialize the vision tower only up to the deepest required feature layer
+    num_hidden_layers = _get_num_hidden_layers(hf_config)
+
+    assert isinstance(vision_config, PixtralVisionConfig)
+
+    return PixtralHFVisionModel(
+        vision_config,
+        quant_config=quant_config,
+        num_hidden_layers_override=num_hidden_layers,
+        require_post_norm=require_post_norm,
+        prefix=prefix,
+    )
+
+
+# TODO(mgoin): Support V1, there are issues with image batching/chunking
+# that need to be resolved first.
+@MULTIMODAL_REGISTRY.register_processor(
+    _build_mistral3_processor,
+    info=_build_mistral3_info,
+    dummy_inputs=Mistral3DummyInputsBuilder)
+class Mistral3ForConditionalGeneration(nn.Module, SupportsMultiModal,
+                                       SupportsPP, SupportsV0Only):
+
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+        "gate_up_proj": ["gate_proj", "up_proj"]
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+
+        self.config = config
+        self.multimodal_config = multimodal_config
+
+        # NOTE: These are special cases for Pixtral-12B in the HF-format
+        # https://huggingface.co/mistral-community/pixtral-12b/blob/main/config.json  # noqa
+        if (config.text_config.architectures is None
+                and config.text_config.model_type == "mistral"):
+            config.text_config.architectures = ["MistralForCausalLM"]
+        if (config.projector_hidden_act is None
+                and config.vision_config.hidden_act == "gelu"):
+            config.projector_hidden_act = "gelu"
+
+        # TODO: Optionally initializes this for supporting embeddings.
+        self.vision_tower = init_vision_tower_for_llava(
+            config,
+            quant_config,
+            require_post_norm=False,
+            prefix=maybe_prefix(prefix, "vision_tower"))
+        self.multi_modal_projector = Mistral3MultiModalProjector(
+            vision_hidden_size=config.vision_config.hidden_size,
+            text_hidden_size=config.text_config.hidden_size,
+            projector_hidden_act=config.projector_hidden_act,
+            spatial_merge_size=config.spatial_merge_size,
+            patch_size=config.vision_config.patch_size,
+            multimodal_projector_bias=config.multimodal_projector_bias,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "multi_modal_projector"))
+
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    @cached_property
+    def sampler(self):
+        if hasattr(self.language_model, "sampler"):
+            return self.language_model.sampler
+
+        return get_sampler()
+
+    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
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[Mistral3ImagePixelInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+
+        if pixel_values is None and image_embeds is None:
+            return None
+
+        assert pixel_values is not None
+        if not isinstance(pixel_values, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of pixel values. "
+                             f"Got type: {type(pixel_values)}")
+
+        assert self.config.vision_config.model_type == "pixtral"
+        embed_is_patch = kwargs.pop("embed_is_patch")
+        if not isinstance(embed_is_patch, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of embed_is_patch. "
+                             f"Got type: {type(embed_is_patch)}")
+
+        return Mistral3ImagePixelInputs(
+            type="pixel_values_pixtral",
+            pixel_values=flatten_bn(pixel_values),
+            embed_is_patch=embed_is_patch,
+        )
+
+    def _process_image_input(
+        self,
+        image_input: Mistral3ImagePixelInputs,
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
+        if image_input["type"] == "image_embeds":
+            return image_input["data"]
+
+        image_sizes = [(img.shape[-2], img.shape[-1])
+                       for img in image_input["pixel_values"]]
+
+        image_features = self.vision_tower(image_input["pixel_values"])
+
+        if isinstance(image_features, torch.Tensor):
+            return self.multi_modal_projector(image_features, image_sizes)
+
+        feature_sizes = [
+            image_feature.shape[0] // self.config.spatial_merge_size**2
+            for image_feature in image_features
+        ]
+
+        image_embeds = self.multi_modal_projector(torch.cat(image_features),
+                                                  image_sizes)
+        if len(feature_sizes) > 1:
+            image_embeds = torch.split(image_embeds, feature_sizes)
+        else:
+            image_embeds = (image_embeds, )
+        return image_embeds
+
+    def get_multimodal_embeddings(
+            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+        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
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                select_patch_features(multimodal_embeddings),
+                self.config.image_token_index,
+            )
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        """Run forward pass for Mistral3.
+
+        One key thing to understand is the `input_ids` already accounts for the
+        positions of the to-be-inserted image embeddings.
+
+        Concretely, consider a text prompt:
+        `"USER: <image>\\nWhat's the content of the image?\\nASSISTANT:"`.
+
+        Tokenizer outputs:
+        `[1, 3148, 1001, 29901, 29871, 32000, 29871, 13, 5618, 29915, 29879,
+        278, 2793, 310, 278, 1967, 29973, 13, 22933, 9047, 13566, 29901]`.
+
+        To reserve space in KV cache, we have to insert placeholder tokens
+        before they are inputted to the model, so the input processor prepends
+        additional image tokens (denoted as `32000`), resulting in:
+        `[1, 3148, 1001, 29901, 29871, 32000, ..., 32000, 29871, 13, 5618,
+        29915, 29879, 278, 2793, 310, 278, 1967, 29973, 13, 22933, 9047, 13566,
+        29901]`.
+
+        We insert 575 tokens so that including the original image token in the
+        input, there are a total of 576 (24 * 24) image tokens, which
+        corresponds to the number of image tokens inputted to the language
+        model, i.e. the number of image tokens outputted by the visual encoder.
+
+        This way, the `positions` and `attn_metadata` are consistent
+        with the `input_ids`.
+
+        Args:
+            input_ids: Flattened (concatenated) input_ids corresponding to a
+                batch.
+            pixel_values: The pixels in each input image.
+
+        See also:
+            :class:`Mistral3ImagePixelInputs`
+        """
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
+
+        hidden_states = self.language_model.model(input_ids,
+                                                  positions,
+                                                  intermediate_tensors,
+                                                  inputs_embeds=inputs_embeds)
+
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        return self.language_model.sample(logits, sampling_metadata)
+
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)

vllm/model_executor/models/pixtral.py

@@ -979,7 +979,8 @@ class PixtralHFEncoderInfo(VisionEncoderInfo[PixtralVisionConfig]):
         return self.vision_config.image_size
 
     def get_patch_size(self) -> int:
-        return self.vision_config.patch_size
+        return (self.vision_config.patch_size *
+                self.vision_config.spatial_merge_size)
 
     def get_patch_grid_length(self) -> int:
         image_size, patch_size = self.get_image_size(), self.get_patch_size()
@@ -1001,8 +1002,8 @@ class PixtralHFEncoderInfo(VisionEncoderInfo[PixtralVisionConfig]):
         ratio = max(image_width / max_width, image_height / max_height)
 
         if ratio > 1:
-            image_width = int(math.ceil(image_width / ratio))
-            image_height = int(math.ceil(image_height / ratio))
+            image_width = int(math.floor(image_width / ratio))
+            image_height = int(math.floor(image_height / ratio))
 
         nrows, ncols = _get_pixtral_hf_num_image_tokens(
             (image_height, image_width),

vllm/model_executor/models/registry.py

@@ -177,6 +177,7 @@ _MULTIMODAL_MODELS = {
     "MantisForConditionalGeneration": ("llava", "MantisForConditionalGeneration"),  # noqa: E501
     "MiniCPMO": ("minicpmo", "MiniCPMO"),
     "MiniCPMV": ("minicpmv", "MiniCPMV"),
+    "Mistral3ForConditionalGeneration": ("mistral3", "Mistral3ForConditionalGeneration"),  # noqa: E501
     "MolmoForCausalLM": ("molmo", "MolmoForCausalLM"),
     "NVLM_D": ("nvlm_d", "NVLM_D_Model"),
     "PaliGemmaForConditionalGeneration": ("paligemma", "PaliGemmaForConditionalGeneration"),  # noqa: E501

vllm/model_executor/models/vision.py

@@ -69,6 +69,9 @@ def get_vision_encoder_info(
     if isinstance(vision_config, CLIPVisionConfig):
         return CLIPEncoderInfo(vision_config)
     if isinstance(vision_config, PixtralVisionConfig):
+        # Need to sneak in spatial_merge_size for Mistral3
+        vision_config.spatial_merge_size = getattr(hf_config,
+                                                   "spatial_merge_size", 1)
         return PixtralHFEncoderInfo(vision_config)
     if isinstance(vision_config, SiglipVisionConfig):
         return SiglipEncoderInfo(vision_config)