pixtral.py 44.9 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import math
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from collections.abc import Iterable, Mapping, Sequence
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from dataclasses import dataclass, fields
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from typing import Annotated, Literal
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import torch
import torch.nn as nn
import torch.nn.functional as F
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from mistral_common.protocol.instruct.chunk import ImageChunk, TextChunk
from mistral_common.protocol.instruct.messages import UserMessage
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from mistral_common.protocol.instruct.request import ChatCompletionRequest
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from transformers import PixtralVisionConfig
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from transformers.models.pixtral.image_processing_pixtral import (
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    _num_image_tokens as _get_pixtral_hf_num_image_tokens,
)
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from transformers.models.pixtral.modeling_pixtral import (
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    PixtralRotaryEmbedding,
    apply_rotary_pos_emb,
    position_ids_in_meshgrid,
)
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from vllm.config import VllmConfig
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from vllm.config.multimodal import BaseDummyOptions
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from vllm.distributed import divide, get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import get_act_and_mul_fn
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from vllm.model_executor.layers.conv import Conv2dLayer
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (
    MergedColumnParallelLinear,
    QKVParallelLinear,
    RowParallelLinear,
)
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from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargsItems
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from vllm.multimodal.inputs import (
    MultiModalDataDict,
    MultiModalFieldConfig,
    NestedTensors,
)
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from vllm.multimodal.parse import (
    ImageProcessorItems,
    ImageSize,
    MultiModalDataItems,
)
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from vllm.multimodal.processing import BaseDummyInputsBuilder
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from vllm.multimodal.processing.processor import (
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    BaseMultiModalProcessor,
    BaseProcessingInfo,
    MultiModalProcessingInfo,
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    ProcessorInputs,
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    PromptReplacement,
    PromptUpdate,
    PromptUpdateDetails,
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    TimingContext,
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)
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from vllm.platforms import current_platform
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from vllm.sequence import IntermediateTensors
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from vllm.tokenizers import cached_tokenizer_from_config
from vllm.tokenizers.mistral import MistralTokenizer
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from vllm.transformers_utils.processors.pixtral import MistralCommonPixtralProcessor
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from vllm.utils.tensor_schema import TensorSchema, TensorShape
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from .interfaces import (
    MultiModalEmbeddings,
    SupportsLoRA,
    SupportsMultiModal,
    SupportsPP,
)
from .module_mapping import MultiModelKeys
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from .utils import StageMissingLayer, init_vllm_registered_model, maybe_prefix
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from .vision import (
    VisionEncoderInfo,
    VisionFeatureSelectStrategy,
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    is_vit_use_data_parallel,
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    resolve_visual_encoder_outputs,
)
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try:
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    # Note: vLLM does not install xformers by default.
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    from xformers import ops as xops
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    if current_platform.is_cuda() and current_platform.has_device_capability(100):
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        # Xformers FA is not compatible with B200
        USE_XFORMERS_OPS = False
    else:
        USE_XFORMERS_OPS = True
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except ImportError:
    USE_XFORMERS_OPS = False

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PATCH_MERGE = "patch_merge"

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def _is_layer_none_or_staged(layer: nn.Module) -> bool:
    return layer is None or isinstance(layer, StageMissingLayer)


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class PixtralImagePixelInputs(TensorSchema):
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    """
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    Dimensions:
        - bn: Batch size * number of images
        - c: Number of channels (3)
        - h: Height of each image
        - w: Width of each image
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    The result of stacking `ImageEncoding.tokens` from each prompt.
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    """
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    type: Literal["pixel_values"] = "pixel_values"

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    images: Annotated[
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        torch.Tensor | list[torch.Tensor],
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        TensorShape("bn", 3, "h", "w", dynamic_dims={"h", "w"}),
    ]
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class PixtralProcessingInfo(BaseProcessingInfo):
    def get_tokenizer(self) -> MistralTokenizer:
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        tokenizer = cached_tokenizer_from_config(self.ctx.model_config)
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        if not isinstance(tokenizer, MistralTokenizer):
            raise ValueError("This model requires `--tokenizer-mode mistral`")

        return tokenizer

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    def get_hf_processor(self, **kwargs) -> MistralCommonPixtralProcessor:
        return self.ctx.init_processor(
            MistralCommonPixtralProcessor,
            tokenizer=self.get_tokenizer(),
            **kwargs,
        )
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    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
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        return {"image": None}

    def get_image_size_with_most_features(self) -> ImageSize:
        image_processor = self.get_hf_processor().image_processor
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        max_image_size = image_processor.mm_encoder.mm_config.max_image_size
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        return ImageSize(width=max_image_size, height=max_image_size)


class PixtralDummyInputsBuilder(BaseDummyInputsBuilder[PixtralProcessingInfo]):
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    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        return ""

    def get_dummy_mm_data(
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        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
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        mm_options: Mapping[str, BaseDummyOptions],
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    ) -> MultiModalDataDict:
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        num_images = mm_counts.get("image", 0)

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        target_width, target_height = self.info.get_image_size_with_most_features()
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        image_overrides = mm_options.get("image")
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        return {
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            "image": self._get_dummy_images(
                width=target_width,
                height=target_height,
                num_images=num_images,
                overrides=image_overrides,
            )
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        }

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    def get_dummy_processor_inputs(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
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        mm_options: Mapping[str, BaseDummyOptions],
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    ) -> ProcessorInputs:
        tokenizer = self.info.get_tokenizer()

        dummy_text = self.get_dummy_text(mm_counts)
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        dummy_mm_data = self.get_dummy_mm_data(seq_len, mm_counts, mm_options)
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        dummy_images = dummy_mm_data.get("image", [])

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        request = ChatCompletionRequest(
            messages=[
                UserMessage(
                    content=[
                        TextChunk(text=dummy_text),
                        *(ImageChunk(image=image) for image in dummy_images),
                    ]
                ),
            ]
        )
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        res = tokenizer.mistral.encode_chat_completion(request)
        dummy_tokens = res.tokens

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        dummy_mm_items = self.info.parse_mm_data(dummy_mm_data)

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        return ProcessorInputs(prompt=dummy_tokens, mm_data_items=dummy_mm_items)
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class PixtralMultiModalProcessor(BaseMultiModalProcessor[PixtralProcessingInfo]):
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    def _get_mm_fields_config(
        self,
        hf_inputs: Mapping[str, NestedTensors],
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
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        return dict(images=MultiModalFieldConfig.batched("image"))
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    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
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        out_mm_kwargs: MultiModalKwargsItems,
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    ) -> Sequence[PromptUpdate]:
        processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)

        image_break_id = processor.image_break_id
        image_token_id = processor.image_token_id
        image_end_id = processor.image_end_id

        def get_replacement(item_idx: int):
            images = mm_items.get_items("image", ImageProcessorItems)
            image_size = images.get_image_size(item_idx)

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            _, nrows, ncols = processor.image_processor.get_number_of_image_patches(
                image_size.height,
                image_size.width,
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            )
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            tokens = ([image_token_id] * ncols + [image_break_id]) * nrows
            tokens[-1] = image_end_id

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            return PromptUpdateDetails.select_token_id(tokens, image_token_id)
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        return [
            PromptReplacement(
                modality="image",
                target="",  # Never match the prompt (see below note)
                replacement=get_replacement,
            ),
        ]

    def _cached_apply_hf_processor(
        self,
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        inputs: ProcessorInputs,
        timing_ctx: TimingContext,
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    ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
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        prompt_ids, mm_info, _ = super()._cached_apply_hf_processor(inputs, timing_ctx)
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        # NOTE: The tokens are already inserted by the chat template
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        return prompt_ids, mm_info, True
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@MULTIMODAL_REGISTRY.register_processor(
    PixtralMultiModalProcessor,
    info=PixtralProcessingInfo,
    dummy_inputs=PixtralDummyInputsBuilder,
)
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class PixtralForConditionalGeneration(
    nn.Module, SupportsLoRA, SupportsMultiModal, SupportsPP
):
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    @classmethod
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    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
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        if modality.startswith("image"):
            return None

        raise ValueError("Only image modality is supported")

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    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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        super().__init__()
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        config = vllm_config.model_config.hf_config
        multimodal_config = vllm_config.model_config.multimodal_config
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        self.config = config
        self.multimodal_config = multimodal_config

        dataclass_fields = {field.name for field in fields(VisionEncoderArgs)}
        vision_args = {
            key: value
            for key, value in self.config.vision_config.to_dict().items()
            if key in dataclass_fields
        }

        self.vision_args = VisionEncoderArgs(**vision_args)

        # init MistralForCausalLM
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        with self._mark_language_model(vllm_config):
            self.language_model = init_vllm_registered_model(
                vllm_config=vllm_config,
                hf_config=config.text_config,
                prefix=maybe_prefix(prefix, "language_model"),
            )
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        with self._mark_tower_model(vllm_config, "image"):
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            self.vision_encoder = VisionTransformer(self.vision_args)
            self.pre_mm_projector_norm = (
                RMSNorm(self.vision_args.hidden_size, eps=1e-5)
                if self.vision_args.add_pre_mm_projector_layer_norm
                else None
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            )
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            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.mm_projector_id == PATCH_MERGE
                else None
            )
            self.vision_language_adapter = VisionLanguageAdapter(
                self.vision_args, dim=config.text_config.hidden_size
            )
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        self.make_empty_intermediate_tensors = (
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            self.language_model.make_empty_intermediate_tensors
        )
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    def _parse_and_validate_image_input(
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        self, **kwargs: object
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    ) -> PixtralImagePixelInputs | None:
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        images = kwargs.pop("images", None)
        if images is None:
            return None

        return PixtralImagePixelInputs(
            type="pixel_values",
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            images=images,
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        )

    def _process_image_input(
        self,
        image_input: PixtralImagePixelInputs,
    ) -> tuple[torch.Tensor, ...]:
        images = image_input["images"]
        image_features = self.vision_encoder(images)
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        feature_sizes = [image_feature.shape[0] for image_feature in image_features]
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        image_features = torch.cat(image_features)
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        if self.pre_mm_projector_norm is not None:
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            image_features = self.pre_mm_projector_norm(image_features)
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        if self.patch_merger is not None:
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            patch_size = self.vision_args.patch_size
            spatial_merge_size_square = self.vision_args.spatial_merge_size**2
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            img_patch_dims = [
                (img.shape[1] // patch_size, img.shape[2] // patch_size)
                for img in images
            ]
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            feature_sizes = [
                feature_size // spatial_merge_size_square
                for feature_size in feature_sizes
            ]
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            image_features = self.patch_merger(
                image_features, image_sizes=img_patch_dims
            )
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        image_embeds = self.vision_language_adapter(image_features)
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        image_embeds = torch.split(image_embeds, feature_sizes)
        return image_embeds

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    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
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        image_input = self._parse_and_validate_image_input(**kwargs)
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        if image_input is None:
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            return []
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        return self._process_image_input(image_input)
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    def forward(
        self,
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        input_ids: torch.Tensor | None,
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        positions: torch.Tensor,
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        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
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        **kwargs: object,
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    ) -> torch.Tensor | IntermediateTensors:
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        """Run forward pass for pixtral."""
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        if intermediate_tensors is not None:
            inputs_embeds = None
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        hidden_states = self.language_model.model(
            input_ids, positions, intermediate_tensors, inputs_embeds=inputs_embeds
        )
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        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
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    ) -> torch.Tensor | None:
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        return self.language_model.compute_logits(hidden_states)
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    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
        def is_vision_encoder_weights(weight: tuple[str, torch.Tensor]):
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            return weight[0].startswith(("vision_encoder", "vision_tower"))
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        def is_vision_lang_adapter_weights(weight: tuple[str, torch.Tensor]):
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            return weight[0].startswith(
                ("vision_language_adapter", "multi_modal_projector")
            )
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        def is_patch_merger(weight: tuple[str, torch.Tensor]):
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            return weight[0].startswith("patch_merger")

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        def is_pre_mm_projector_norm(weight: tuple[str, torch.Tensor]):
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            return weight[0].startswith("pre_mm_projector_norm")

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        # Get references to parameters for direct loading
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        vision_encoder_dict = (
            dict(self.vision_encoder.named_parameters())
            if self.vision_encoder is not None
            else {}
        )
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        patch_merger_dict = (
            dict(self.patch_merger.named_parameters())
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            if self.patch_merger is not None
            else {}
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        )
        pre_mm_projector_norm_dict = (
            dict(self.pre_mm_projector_norm.named_parameters())
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            if self.pre_mm_projector_norm is not None
            else {}
        )
        vision_lang_adapter_dict = (
            dict(self.vision_language_adapter.named_parameters())
            if self.vision_language_adapter is not None
            else {}
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        )
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        def llm_weights_generator():
            # Single pass over weights
            for name, w in weights:
                if is_vision_encoder_weights((name, w)):
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                    if _is_layer_none_or_staged(self.vision_encoder):
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                        continue
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                    # Load vision encoder weights directly
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                    trimmed_name = ".".join(name.split(".")[1:])
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                    param = vision_encoder_dict.get(trimmed_name)
                    if param is not None:
                        with torch.no_grad():
                            default_weight_loader(param, w)
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                elif is_patch_merger((name, w)):
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                    if _is_layer_none_or_staged(self.patch_merger):
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                        continue
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                    # Load vision patch merger weights directly
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                    trimmed_name = ".".join(name.split(".")[1:])
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                    param = patch_merger_dict[trimmed_name]
                    with torch.no_grad():
                        default_weight_loader(param, w)
                elif is_pre_mm_projector_norm((name, w)):
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                    if _is_layer_none_or_staged(self.pre_mm_projector_norm):
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                        continue
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                    # Load vision pre_mm_projector_norm weights directly
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                    trimmed_name = ".".join(name.split(".")[1:])
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                    param = pre_mm_projector_norm_dict[trimmed_name]
                    with torch.no_grad():
                        default_weight_loader(param, w)
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                elif is_vision_lang_adapter_weights((name, w)):
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                    if _is_layer_none_or_staged(self.vision_language_adapter):
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                        continue
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                    # Load vision-language adapter weights directly
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                    trimmed_name = ".".join(name.split(".")[1:])
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                    param = vision_lang_adapter_dict.get(trimmed_name)
                    if param is not None:
                        with torch.no_grad():
                            default_weight_loader(param, w)
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                else:
                    # LLM weights: yield them to be loaded
                    # by language_model.load_weights
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                    # Strip "language_model." prefix if present (HF sharded format)
                    name = name.removeprefix("language_model.")
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                    yield (name, w)

        # Now we call the language model load with the generator
        self.language_model.load_weights(llm_weights_generator())
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    def get_mm_mapping(self) -> MultiModelKeys:
        return MultiModelKeys.from_string_field(
            language_model="language_model",
            connector="vision_language_adapter",
            tower_model="vision_encoder",
        )

    def get_num_mm_encoder_tokens(self, num_image_tokens: int) -> int:
        if getattr(self, "patch_merger", None) is None:
            return num_image_tokens
        merge_size = self.vision_args.spatial_merge_size
        return num_image_tokens * (merge_size**2)

    def get_num_mm_connector_tokens(self, num_vision_tokens: int) -> int:
        if getattr(self, "patch_merger", None) is None:
            return num_vision_tokens
        merge_size = self.vision_args.spatial_merge_size
        return num_vision_tokens // (merge_size**2)

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# Vision encoder
@dataclass
class VisionEncoderArgs:
    hidden_size: int
    num_channels: int
    image_size: int
    patch_size: int
    intermediate_size: int
    num_hidden_layers: int
    num_attention_heads: int
    rope_theta: float  # for rope-2D
    image_token_id: int
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    adapter_bias: bool = True
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    spatial_merge_size: int = 1
    add_pre_mm_projector_layer_norm: bool = False
    mm_projector_id: str = ""
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def _reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
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    """
    freqs_cis: complex - (seq_len, head_dim / 2)
    x: complex - (bsz, seq_len, head_dim / 2)
    """
    ndim = x.ndim
    assert ndim > 1
    assert freqs_cis.shape == (x.shape[1], x.shape[-1]), (
        freqs_cis.shape,
        (x.shape[1], x.shape[-1]),
    )
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    shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
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    return freqs_cis.view(*shape)


def precompute_freqs_cis_2d(
    dim: int,
    height: int,
    width: int,
    theta: float,
) -> torch.Tensor:
    """
    freqs_cis: 2D complex tensor of shape (height, width, dim // 2)
        to be indexed by (height, width) position tuples
    """
    # (dim / 2) frequency bases
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    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2).float() / dim))
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    h = torch.arange(height, device=freqs.device)
    w = torch.arange(width, device=freqs.device)

    freqs_h = torch.outer(h, freqs[::2]).float()
    freqs_w = torch.outer(w, freqs[1::2]).float()
    freqs_2d = torch.cat(
        [
            freqs_h[:, None, :].repeat(1, width, 1),
            freqs_w[None, :, :].repeat(height, 1, 1),
        ],
        dim=-1,
    )
    return torch.polar(torch.ones_like(freqs_2d), freqs_2d)


def apply_rotary_emb_vit(
    xq: torch.Tensor,
    xk: torch.Tensor,
    freqs_cis: torch.Tensor,
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) -> tuple[torch.Tensor, torch.Tensor]:
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    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
    assert freqs_cis.dtype == torch.complex64
    freqs_cis = _reshape_for_broadcast(freqs_cis, xq_)
    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
    return xq_out.type_as(xq), xk_out.type_as(xk)


class FeedForward(nn.Module):
    def __init__(self, args: VisionEncoderArgs):
        super().__init__()
        assert args.intermediate_size is not None
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        self.w1 = nn.Linear(args.hidden_size, args.intermediate_size, bias=False)
        self.w2 = nn.Linear(args.intermediate_size, args.hidden_size, bias=False)
        self.w3 = nn.Linear(args.hidden_size, args.intermediate_size, bias=False)
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    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.w2(F.silu(self.w1(x)) * self.w3(x))


class Attention(nn.Module):
    def __init__(self, args: VisionEncoderArgs):
        super().__init__()
        self.args = args
        assert not args.hidden_size % args.num_attention_heads
        self.n_heads = args.num_attention_heads
        self.head_dim = args.hidden_size // args.num_attention_heads

        self.wq = nn.Linear(args.hidden_size, args.hidden_size, bias=False)
        self.wk = nn.Linear(args.hidden_size, args.hidden_size, bias=False)
        self.wv = nn.Linear(args.hidden_size, args.hidden_size, bias=False)
        self.wo = nn.Linear(args.hidden_size, args.hidden_size, bias=False)

    def forward(
        self,
        x: torch.Tensor,
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        mask: torch.Tensor,
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        freqs_cis: torch.Tensor,
    ) -> torch.Tensor:
        batch, patches, _ = x.shape

        q, k, v = self.wq(x), self.wk(x), self.wv(x)
        q = q.reshape(batch, patches, self.n_heads, self.head_dim)
        k = k.reshape(batch, patches, self.n_heads, self.head_dim)
        v = v.reshape(batch, patches, self.n_heads, self.head_dim)

        q, k = apply_rotary_emb_vit(q, k, freqs_cis=freqs_cis)
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        if USE_XFORMERS_OPS:
            out = xops.memory_efficient_attention(q, k, v, attn_bias=mask)
        else:
            q = q.transpose(1, 2)
            k = k.transpose(1, 2)
            v = v.transpose(1, 2)
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            out = nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask)
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            out = out.transpose(1, 2)

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        out = out.reshape(batch, patches, self.n_heads * self.head_dim)
        return self.wo(out)


class TransformerBlock(nn.Module):
    def __init__(self, args: VisionEncoderArgs):
        super().__init__()
        self.attention = Attention(args)
        self.feed_forward = FeedForward(args)
        self.attention_norm = RMSNorm(args.hidden_size, eps=1e-5)
        self.ffn_norm = RMSNorm(args.hidden_size, eps=1e-5)

    def forward(
        self,
        x: torch.Tensor,
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        mask: torch.Tensor,
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        freqs_cis: torch.Tensor,
    ) -> torch.Tensor:
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        r = self.attention.forward(
            self.attention_norm(x), mask=mask, freqs_cis=freqs_cis
        )
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        h = x + r
        r = self.feed_forward.forward(self.ffn_norm(h))
        out = h + r
        return out


class Transformer(nn.Module):
    def __init__(self, args: VisionEncoderArgs):
        super().__init__()
        self.layers = torch.nn.ModuleList()
        for _ in range(args.num_hidden_layers):
            self.layers.append(TransformerBlock(args))

    def forward(
        self,
        x: torch.Tensor,
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        mask: torch.Tensor,
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        freqs_cis: torch.Tensor | None,
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    ) -> torch.Tensor:
        for layer in self.layers:
            x = layer(x, mask=mask, freqs_cis=freqs_cis)
        return x


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def position_meshgrid(
    patch_embeds_list: list[torch.Tensor],
) -> torch.Tensor:
    positions = torch.cat(
        [
            torch.stack(
                torch.meshgrid(
                    torch.arange(p.shape[-2]),
                    torch.arange(p.shape[-1]),
                    indexing="ij",
                ),
                dim=-1,
            ).reshape(-1, 2)
            for p in patch_embeds_list
        ]
    )
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    return positions


class VisionTransformer(nn.Module):
    def __init__(self, args: VisionEncoderArgs):
        super().__init__()
        self.args = args
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        self.patch_conv = Conv2dLayer(
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            in_channels=args.num_channels,
            out_channels=args.hidden_size,
            kernel_size=args.patch_size,
            stride=args.patch_size,
            bias=False,
        )
        self.ln_pre = RMSNorm(args.hidden_size, eps=1e-5)
        self.transformer = Transformer(args)

        head_dim = self.args.hidden_size // self.args.num_attention_heads
        assert head_dim % 2 == 0, "ROPE requires even head_dim"
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        self._freqs_cis: torch.Tensor | None = None
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    @property
    def max_patches_per_side(self) -> int:
        return self.args.image_size // self.args.patch_size

    @property
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    def device(self) -> torch.types.Device:
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        return next(self.parameters()).device

    @property
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    def dtype(self) -> torch.dtype:
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        return next(self.parameters()).dtype

    @property
    def freqs_cis(self) -> torch.Tensor:
        if self._freqs_cis is None:
            self._freqs_cis = precompute_freqs_cis_2d(
                dim=self.args.hidden_size // self.args.num_attention_heads,
                height=self.max_patches_per_side,
                width=self.max_patches_per_side,
                theta=self.args.rope_theta,
            )

        if self._freqs_cis.device != self.device:
            self._freqs_cis = self._freqs_cis.to(device=self.device)

        return self._freqs_cis

    def forward(
        self,
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        images: list[torch.Tensor],
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    ) -> torch.Tensor:
        """
        Args:
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            images: list of N_img images of variable sizes,
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                each of shape (C, H, W)
        Returns:
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            image_features: tensor of token features for
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                all tokens of all images of shape (N_toks, D)
        """
        # pass images through initial convolution independently
        patch_embeds_list = [
            self.patch_conv(img.unsqueeze(0).to(self.dtype)) for img in images
        ]

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        patch_embeds = [p.flatten(2).permute(0, 2, 1) for p in patch_embeds_list]
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        embed_sizes = [p.shape[1] for p in patch_embeds]

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        # flatten to a single sequence
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        patch_embeds = torch.cat(patch_embeds, dim=1)
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        patch_embeds = self.ln_pre(patch_embeds)

        # positional embeddings
        positions = position_meshgrid(patch_embeds_list).to(self.device)
        freqs_cis = self.freqs_cis[positions[:, 0], positions[:, 1]]

        # pass through Transformer with a block diagonal mask delimiting images
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        if USE_XFORMERS_OPS:
            mask = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens(
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                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list],
            )
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        else:
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            from transformers.models.pixtral.modeling_pixtral import (
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                generate_block_attention_mask,
            )

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            mask = generate_block_attention_mask(
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                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], patch_embeds
            )
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        out = self.transformer(patch_embeds, mask=mask, freqs_cis=freqs_cis)

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        # squeeze dim 0 and split into separate tensors for each image
        return torch.split(out.squeeze(0), embed_sizes)
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class VisionLanguageAdapter(nn.Module):
    def __init__(self, args: VisionEncoderArgs, dim: int):
        super().__init__()
        assert isinstance(args, VisionEncoderArgs)
        self.w_in = nn.Linear(
            args.hidden_size,
            dim,
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            bias=args.adapter_bias,
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        )
        self.gelu = nn.GELU()
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        self.w_out = nn.Linear(dim, dim, bias=args.adapter_bias)
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    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.w_out(self.gelu(self.w_in(x)))
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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,
        )

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    def forward(
        self, x: torch.Tensor, image_sizes: list[tuple[int, int]]
    ) -> torch.Tensor:
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        # 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)

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        # x is (N / spatial_merge_size ** 2,
        #       vision_encoder_dim * spatial_merge_size ** 2)
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        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(
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            x=x, image_sizes=image_sizes, spatial_merge_size=self.spatial_merge_size
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        )  # 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]
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            permuted_tensor.append(
                grid.view(-1, n_patches).t()
            )  # n_patches x d * sub_grid_size * sub_grid_size
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        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]
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        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
        )
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        sub_grids = sub_grids.view(
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            1, d, sub_grid_size, sub_grid_size, -1
        )  # 1 x d x sub_grid_size x sub_grid_size x n_patches
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        all_img_sub_grids.append(sub_grids[0])

    return all_img_sub_grids


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#### 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
# instead of the `[IMG]` token placeholders.
# The model uses [`PixtralVisionModel`] for its vision encoder,
# and [`MistralForCausalLM`] for its language decoder.


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class PixtralHFEncoderInfo(VisionEncoderInfo[PixtralVisionConfig]):
    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> int:
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        ncols, nrows = self.get_patch_grid_size(
            image_width=image_width,
            image_height=image_height,
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        )
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        return ncols * nrows
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    def get_image_size(self) -> int:
        return self.vision_config.image_size

    def get_patch_size(self) -> int:
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        # spatial_merge_size is needed for Mistral3
        spatial_merge_size = getattr(self.hf_config, "spatial_merge_size", 1)
        return self.vision_config.patch_size * spatial_merge_size
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    def get_patch_grid_length(self) -> int:
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        image_size, patch_size = self.get_image_size(), self.get_patch_size()

        # Since interpolation is applied, the image size need not be divisible
        # assert image_size % patch_size == 0
        return image_size // patch_size

    # Adapted from: https://github.com/huggingface/transformers/blob/v4.49.0/src/transformers/models/pixtral/image_processing_pixtral.py#L99
    def get_patch_grid_size(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> tuple[int, int]:
        max_width = max_height = self.get_image_size()
        patch_width = patch_height = self.get_patch_size()

        ratio = max(image_width / max_width, image_height / max_height)

        if ratio > 1:
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            image_width = int(math.floor(image_width / ratio))
            image_height = int(math.floor(image_height / ratio))
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        nrows, ncols = _get_pixtral_hf_num_image_tokens(
            (image_height, image_width),
            (patch_height, patch_width),
        )  # type: ignore

        return ncols, nrows
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class PixtralHFMLP(nn.Module):
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    def __init__(
        self,
        config: PixtralVisionConfig,
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        quant_config: QuantizationConfig | None = None,
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        *,
        prefix: str = "",
    ) -> None:
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        super().__init__()
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        use_data_parallel = is_vit_use_data_parallel()
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        assert config.intermediate_size is not None
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        self.gate_up_proj = MergedColumnParallelLinear(
            input_size=config.hidden_size,
            output_sizes=[config.intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
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            prefix=f"{prefix}.gate_up_proj",
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            disable_tp=use_data_parallel,
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        )
        self.down_proj = RowParallelLinear(
            input_size=config.intermediate_size,
            output_size=config.hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.down_proj",
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            disable_tp=use_data_parallel,
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        )
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        self.act_and_mul = get_act_and_mul_fn(config.hidden_act)
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    def forward(self, x: torch.Tensor) -> torch.Tensor:
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        gate_up, _ = self.gate_up_proj(x)
        x = self.act_and_mul(gate_up)
        x, _ = self.down_proj(x)
        return x
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class PixtralHFAttention(nn.Module):
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    def __init__(
        self,
        config: PixtralVisionConfig,
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        quant_config: QuantizationConfig | None = None,
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        *,
        prefix: str = "",
    ) -> None:
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        super().__init__()
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        self.config = config
        assert not config.hidden_size % config.num_attention_heads
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        self.total_num_heads = config.num_attention_heads
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        self.head_dim = config.hidden_size // config.num_attention_heads
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        assert self.total_num_heads * self.head_dim == config.hidden_size
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        use_data_parallel = is_vit_use_data_parallel()
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        self.qkv_proj = QKVParallelLinear(
            hidden_size=config.hidden_size,
            head_size=self.head_dim,
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            total_num_heads=self.total_num_heads,
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            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
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            disable_tp=use_data_parallel,
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        )
        self.o_proj = RowParallelLinear(
            input_size=config.hidden_size,
            output_size=config.hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
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            disable_tp=use_data_parallel,
        )

        self.tp_size = (
            1 if use_data_parallel else get_tensor_model_parallel_world_size()
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        )
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        self.n_heads = divide(config.num_attention_heads, self.tp_size)
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    def forward(
        self,
        hidden_states: torch.Tensor,
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        attention_mask: torch.Tensor,
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        position_embeddings: torch.Tensor,
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    ) -> tuple[torch.Tensor, torch.Tensor | None]:
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        batch, patches, _ = hidden_states.size()
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        qkv_states, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv_states.chunk(3, dim=-1)
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        # Transpose q and k to apply HF's Rotary Position Embedding
        q = q.view(batch, patches, self.n_heads, self.head_dim).transpose(1, 2)
        k = k.view(batch, patches, self.n_heads, self.head_dim).transpose(1, 2)
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        v = v.view(batch, patches, self.n_heads, self.head_dim)
1040
        cos, sin = position_embeddings
1041
        q, k = apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=0)
1042

1043
1044
1045
1046
        if USE_XFORMERS_OPS:
            # Transpose q and k back for attention
            q = q.transpose(1, 2).contiguous()
            k = k.transpose(1, 2).contiguous()
1047
            out = xops.memory_efficient_attention(q, k, v, attn_bias=attention_mask)
1048
        else:
1049
            v = v.transpose(1, 2)
1050
            out = nn.functional.scaled_dot_product_attention(
1051
1052
                q, k, v, attn_mask=attention_mask
            )
1053
            out = out.transpose(1, 2)
1054

1055
        out = out.reshape(batch, patches, self.n_heads * self.head_dim)
1056
        attn_output, _ = self.o_proj(out)
1057

1058
        return attn_output, None
1059
1060
1061


class PixtralHFTransformerBlock(nn.Module):
1062
1063
1064
    def __init__(
        self,
        config: PixtralVisionConfig,
1065
        quant_config: QuantizationConfig | None = None,
1066
1067
1068
        *,
        prefix: str = "",
    ) -> None:
1069
        super().__init__()
1070

1071
        self.attention_norm = RMSNorm(config.hidden_size, eps=1e-5)
1072
        self.attention = PixtralHFAttention(
1073
1074
1075
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.attention",
1076
1077
        )
        self.feed_forward = PixtralHFMLP(
1078
1079
1080
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.feed_forward",
1081
        )
1082
1083
1084
1085
1086
        self.ffn_norm = RMSNorm(config.hidden_size, eps=1e-5)

    def forward(
        self,
        hidden_states: torch.Tensor,
1087
        attention_mask: torch.Tensor,
1088
1089
        position_embeddings: torch.Tensor,
    ) -> torch.Tensor:
1090
1091
1092
1093
1094
        r, _ = self.attention.forward(
            self.attention_norm(hidden_states),
            attention_mask=attention_mask,
            position_embeddings=position_embeddings,
        )
1095
1096
1097
1098
1099
1100
1101
        h = hidden_states + r
        r = self.feed_forward.forward(self.ffn_norm(h))
        out = h + r
        return out


class PixtralHFTransformer(nn.Module):
1102
1103
1104
    def __init__(
        self,
        config: PixtralVisionConfig,
1105
        quant_config: QuantizationConfig | None = None,
1106
        *,
1107
        num_hidden_layers_override: int | None = None,
1108
1109
        prefix: str = "",
    ) -> None:
1110
        super().__init__()
1111
1112
1113
1114
1115
1116

        if num_hidden_layers_override is None:
            num_hidden_layers = config.num_hidden_layers
        else:
            num_hidden_layers = num_hidden_layers_override

1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
        self.layers = nn.ModuleList(
            [
                PixtralHFTransformerBlock(
                    config=config,
                    quant_config=quant_config,
                    prefix=f"{prefix}.layers.{layer_idx}",
                )
                for layer_idx in range(num_hidden_layers)
            ]
        )
1127
1128
1129
1130

    def forward(
        self,
        x: torch.Tensor,
1131
        attention_mask: torch.Tensor,
1132
        position_embeddings: torch.Tensor,
1133
        return_all_hidden_states: bool,
1134
    ) -> torch.Tensor:
1135
        hidden_states_pool = [x]
1136

1137
1138
        for layer in self.layers:
            x = layer(x, attention_mask, position_embeddings)
1139
1140
1141
1142
1143
1144
            if return_all_hidden_states:
                hidden_states_pool.append(x)
        # If we have multiple feature sample layers, we return all hidden
        # states in order and grab the ones we need by index.
        if return_all_hidden_states:
            return hidden_states_pool
1145
1146
1147
1148
        return x


class PixtralHFVisionModel(nn.Module):
1149
1150
1151
    def __init__(
        self,
        config: PixtralVisionConfig,
1152
        quant_config: QuantizationConfig | None = None,
1153
        *,
1154
1155
        num_hidden_layers_override: int | None = None,
        require_post_norm: bool | None = None,
1156
1157
        prefix: str = "",
    ) -> None:
1158
1159
1160
        super().__init__()

        self.config = config
1161

1162
        self.patch_conv = Conv2dLayer(
1163
1164
1165
1166
1167
1168
1169
            in_channels=config.num_channels,
            out_channels=config.hidden_size,
            kernel_size=config.patch_size,
            stride=config.patch_size,
            bias=False,
        )
        self.ln_pre = RMSNorm(config.hidden_size, eps=1e-5)
1170
1171
        self.transformer = PixtralHFTransformer(
            config,
1172
            quant_config=quant_config,
1173
1174
1175
1176
1177
1178
1179
1180
1181
            num_hidden_layers_override=num_hidden_layers_override,
            prefix=f"{prefix}.transformer",
        )

        num_hidden_layers = config.num_hidden_layers
        if len(self.transformer.layers) > config.num_hidden_layers:
            raise ValueError(
                f"The original encoder only has {num_hidden_layers} "
                f"layers, but you requested {len(self.transformer.layers)} "
1182
1183
                "layers."
            )
1184
1185
1186
1187
1188

        if require_post_norm is True:
            msg = "PixtralHFVisionModel does not have post-layernorm"
            raise ValueError(msg)

1189
1190
        self.dtype = next(self.parameters()).dtype
        self.device = next(self.parameters()).device
1191
        self.patch_positional_embedding = PixtralRotaryEmbedding(config, self.device)
1192
1193
1194

    def forward(
        self,
1195
        pixel_values: list[torch.Tensor],
1196
        *,
1197
1198
        select_layers: list[int] | None = None,
        feature_select_strategy: VisionFeatureSelectStrategy | None = None,
1199
    ) -> tuple[torch.Tensor, ...]:
1200
1201
        """
        Args:
1202
1203
1204
1205
            pixel_values: Each image to be processed will be a separate tensor
                in pixel_values. This means it will be a list of tensors
                because multiple requests batched can have multiple images,
                each with their own shape potentially
1206
            select_layers: Layer indices whose features should be
1207
1208
                concatenated and used as the visual encoder output. If none
                are provided, the last layer is used.
1209

1210
1211
1212
1213
1214
1215
        Returns:
            image_features: tensor of token features for
                all tokens of all images of shape (N_toks, D)
        """
        # pass images through initial convolution independently
        patch_embeds_list = [
1216
            self.patch_conv(img.unsqueeze(0).to(self.dtype)) for img in pixel_values
1217
1218
        ]

1219
        patch_embeds = [p.flatten(2).permute(0, 2, 1) for p in patch_embeds_list]
1220
1221
        embed_sizes = [p.shape[1] for p in patch_embeds]

1222
        # flatten to a single sequence
1223
        patch_embeds = torch.cat(patch_embeds, dim=1)
1224
1225
1226
1227
1228
        patch_embeds = self.ln_pre(patch_embeds)

        # positional embeddings
        position_ids = position_ids_in_meshgrid(
            patch_embeds_list,
1229
1230
1231
            max_width=self.config.image_size // self.config.patch_size,
        ).to(self.device)
        position_embedding = self.patch_positional_embedding(patch_embeds, position_ids)
1232
1233
1234

        if USE_XFORMERS_OPS:
            attention_mask = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens(
1235
1236
                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list],
            )
1237
1238
        else:
            from transformers.models.pixtral.modeling_pixtral import (
1239
1240
1241
                generate_block_attention_mask,
            )

1242
            attention_mask = generate_block_attention_mask(
1243
1244
                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], patch_embeds
            )
1245

1246
1247
1248
1249
        out = self.transformer(
            patch_embeds,
            attention_mask,
            position_embedding,
1250
1251
            return_all_hidden_states=select_layers is not None,
        )
1252

1253
1254
1255
1256
1257
1258
1259
        out = resolve_visual_encoder_outputs(
            out,
            None,
            select_layers=select_layers,
            max_possible_layers=self.config.num_hidden_layers,
            feature_select_strategy=feature_select_strategy,
        )
1260

1261
        # squeeze dim 0 and split into separate tensors for each image
1262
        return torch.split(out.squeeze(0), embed_sizes)
1263
1264
1265

    # (TODO) Add prefix argument for filtering out weights to be loaded
    #        ref: https://github.com/vllm-project/vllm/pull/7186#discussion_r1734163986
1266
    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
1267
1268
1269
1270
1271
1272
1273
1274
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            (".qkv_proj", ".q_proj", "q"),
            (".qkv_proj", ".k_proj", "k"),
            (".qkv_proj", ".v_proj", "v"),
            (".gate_up_proj", ".gate_proj", 0),
            (".gate_up_proj", ".up_proj", 1),
        ]
1275
        params_dict = dict(self.named_parameters())
1276
        loaded_params: set[str] = set()
1277
        layer_count = len(self.transformer.layers)
1278
1279

        for name, loaded_weight in weights:
1280
1281
1282
1283
1284
1285
            # omit layers when num_hidden_layers_override is set
            if name.startswith("transformer.layers"):
                layer_idx = int(name.split(".")[2])
                if layer_idx >= layer_count:
                    continue

1286
            for param_name, weight_name, shard_id in stacked_params_mapping:
1287
1288
                if weight_name not in name:
                    continue
1289
1290
                name = name.replace(weight_name, param_name)
                param = params_dict[name]
1291
1292
1293
1294
1295
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                param = params_dict[name]
1296
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
1297
                weight_loader(param, loaded_weight)
1298
1299
            loaded_params.add(name)
        return loaded_params