pixtral.py 48.9 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 functools import cached_property
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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 mistral_common.tokens.tokenizers.multimodal import ImageEncoder
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from PIL import Image
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from transformers import BatchFeature, PixtralVisionConfig, TensorType
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from transformers.image_utils import ImageInput
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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 transformers.tokenization_utils_base import TextInput
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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,
    MultiModalUUIDDict,
    NestedTensors,
)
from vllm.multimodal.parse import ImageProcessorItems, ImageSize, MultiModalDataItems
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from vllm.multimodal.processing import BaseDummyInputsBuilder, ProcessorInputs
from vllm.multimodal.processing.processor import (
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    BaseMultiModalProcessor,
    BaseProcessingInfo,
    MultiModalProcessingInfo,
    PromptReplacement,
    PromptUpdate,
    PromptUpdateDetails,
)
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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.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 PixtralProcessorAdapter:
    """
    Provide a HF-compatible interface for
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    `mistral_common.tokens.tokenizers.multimodal.ImageEncoder`.
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    """
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    def __init__(self, tokenizer: MistralTokenizer) -> None:
        super().__init__()
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        self.tokenizer = tokenizer
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    @property
    def image_processor(self) -> ImageEncoder:
        image_encoder = self.tokenizer.instruct.mm_encoder
        assert isinstance(image_encoder, ImageEncoder)
        return image_encoder
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    @cached_property
    def image_break_id(self) -> int:
        return self.image_processor.special_ids.img_break
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    @cached_property
    def image_token_id(self) -> int:
        return self.image_processor.special_ids.img
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    @cached_property
    def image_end_id(self) -> int:
        return self.image_processor.special_ids.img_end
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    @cached_property
    def image_size(self) -> int:
        return self.image_processor.mm_config.max_image_size
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    @cached_property
    def patch_size(self) -> int:
        return self.image_processor.mm_config.image_patch_size

    def __call__(
        self,
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        text: TextInput | list[TextInput] | None = None,
        images: ImageInput | list[ImageInput] | None = None,
        return_tensors: str | TensorType | None = None,
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        **kwargs,
    ) -> Mapping[str, NestedTensors]:
        if text is None:
            text = []
        if not isinstance(text, list):
            text = [text]
        if images is None:
            images = []
        if not isinstance(images, list):
            images = [images]

        if not images:
            input_ids = self.tokenizer(text).input_ids

            return {"input_ids": torch.tensor(input_ids)}

        # Allow dummy text, which is used for profiling as well as token inputs
        if any(len(t) > 0 for t in text):
            raise ValueError(
                "You've passed text inputs instead of token inputs. "
                "Make sure to process your input via `mistral_common`'s "
                "tokenizer or pass a chat completion request. "
                "For more info, see: "
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                "https://github.com/vllm-project/vllm/issues/8411."
            )
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        images_processed = list[torch.Tensor]()
        images_tokens = list[torch.Tensor]()

        for image in images:
            image_inputs = self.image_processor(ImageChunk(image=image))
            image_processed = torch.tensor(image_inputs.image)
            image_tokens = torch.tensor(image_inputs.tokens)

            images_processed.append(image_processed)
            images_tokens.append(image_tokens)

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        return BatchFeature(
            {
                "input_ids": torch.cat(images_tokens)[None].expand(len(text), -1),
                "images": images_processed,
            }
        )
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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

    def get_hf_processor(self) -> PixtralProcessorAdapter:
        return PixtralProcessorAdapter(self.get_tokenizer())

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    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
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        return {"image": None}

    def get_vision_config(
        self,
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        processor: PixtralProcessorAdapter | None = None,
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    ):
        if processor is None:
            processor = self.get_hf_processor()

        return PixtralVisionConfig(
            image_size=processor.image_size,
            patch_size=processor.patch_size,
        )

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
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        processor: PixtralProcessorAdapter | None = None,
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    ) -> int:
        if processor is None:
            processor = self.get_hf_processor()

        ncols, nrows = processor.image_processor._image_to_num_tokens(
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            Image.new("RGB", (image_width, image_height))
        )
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        return ncols * nrows
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    def get_image_size_with_most_features(self) -> ImageSize:
        image_processor = self.get_hf_processor().image_processor
        max_image_size = image_processor.mm_config.max_image_size

        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] | None = None,
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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") if mm_options else None

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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] | None = None,
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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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        tokenization_kwargs = {"truncation": False}
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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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        return ProcessorInputs(
            prompt=dummy_tokens,
            mm_data=dummy_mm_data,
            tokenization_kwargs=tokenization_kwargs,
        )
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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)

            ncols, nrows = processor.image_processor._image_to_num_tokens(
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                Image.new("RGB", (image_size.width, image_size.height))
            )
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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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        prompt: str | list[int],
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        mm_data_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
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        tokenization_kwargs: Mapping[str, object],
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        mm_uuids: MultiModalUUIDDict | None = None,
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    ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
        prompt_ids, mm_info, _ = super()._cached_apply_hf_processor(
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            prompt=prompt,
            mm_data_items=mm_data_items,
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
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            tokenization_kwargs=tokenization_kwargs,
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            mm_uuids=mm_uuids,
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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:
1057
1058
            image_width = int(math.floor(image_width / ratio))
            image_height = int(math.floor(image_height / ratio))
1059
1060
1061
1062
1063
1064
1065

        nrows, ncols = _get_pixtral_hf_num_image_tokens(
            (image_height, image_width),
            (patch_height, patch_width),
        )  # type: ignore

        return ncols, nrows
1066
1067
1068


class PixtralHFMLP(nn.Module):
1069
1070
1071
    def __init__(
        self,
        config: PixtralVisionConfig,
1072
        quant_config: QuantizationConfig | None = None,
1073
1074
1075
        *,
        prefix: str = "",
    ) -> None:
1076
        super().__init__()
1077

1078
        use_data_parallel = is_vit_use_data_parallel()
1079

1080
        assert config.intermediate_size is not None
1081
1082
1083
1084
1085
        self.gate_up_proj = MergedColumnParallelLinear(
            input_size=config.hidden_size,
            output_sizes=[config.intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
1086
            prefix=f"{prefix}.gate_up_proj",
1087
            disable_tp=use_data_parallel,
1088
1089
1090
1091
1092
1093
1094
        )
        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",
1095
            disable_tp=use_data_parallel,
1096
        )
1097
        self.act_and_mul = get_act_and_mul_fn(config.hidden_act)
1098
1099

    def forward(self, x: torch.Tensor) -> torch.Tensor:
1100
1101
1102
1103
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_and_mul(gate_up)
        x, _ = self.down_proj(x)
        return x
1104
1105
1106


class PixtralHFAttention(nn.Module):
1107
1108
1109
    def __init__(
        self,
        config: PixtralVisionConfig,
1110
        quant_config: QuantizationConfig | None = None,
1111
1112
1113
        *,
        prefix: str = "",
    ) -> None:
1114
        super().__init__()
1115

1116
1117
        self.config = config
        assert not config.hidden_size % config.num_attention_heads
1118
        self.total_num_heads = config.num_attention_heads
1119
        self.head_dim = config.hidden_size // config.num_attention_heads
1120
        assert self.total_num_heads * self.head_dim == config.hidden_size
1121

1122
        use_data_parallel = is_vit_use_data_parallel()
1123
1124
1125
        self.qkv_proj = QKVParallelLinear(
            hidden_size=config.hidden_size,
            head_size=self.head_dim,
1126
            total_num_heads=self.total_num_heads,
1127
1128
1129
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
1130
            disable_tp=use_data_parallel,
1131
1132
1133
1134
1135
1136
1137
        )
        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",
1138
1139
1140
1141
1142
            disable_tp=use_data_parallel,
        )

        self.tp_size = (
            1 if use_data_parallel else get_tensor_model_parallel_world_size()
1143
        )
1144
        self.n_heads = divide(config.num_attention_heads, self.tp_size)
1145
1146
1147
1148

    def forward(
        self,
        hidden_states: torch.Tensor,
1149
        attention_mask: torch.Tensor,
1150
        position_embeddings: torch.Tensor,
1151
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
1152
        batch, patches, _ = hidden_states.size()
1153

1154
1155
        qkv_states, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv_states.chunk(3, dim=-1)
1156

1157
1158
1159
        # 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)
1160
        v = v.view(batch, patches, self.n_heads, self.head_dim)
1161
        cos, sin = position_embeddings
1162
        q, k = apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=0)
1163

1164
1165
1166
1167
        if USE_XFORMERS_OPS:
            # Transpose q and k back for attention
            q = q.transpose(1, 2).contiguous()
            k = k.transpose(1, 2).contiguous()
1168
            out = xops.memory_efficient_attention(q, k, v, attn_bias=attention_mask)
1169
        else:
1170
            v = v.transpose(1, 2)
1171
            out = nn.functional.scaled_dot_product_attention(
1172
1173
                q, k, v, attn_mask=attention_mask
            )
1174
            out = out.transpose(1, 2)
1175

1176
        out = out.reshape(batch, patches, self.n_heads * self.head_dim)
1177
        attn_output, _ = self.o_proj(out)
1178

1179
        return attn_output, None
1180
1181
1182


class PixtralHFTransformerBlock(nn.Module):
1183
1184
1185
    def __init__(
        self,
        config: PixtralVisionConfig,
1186
        quant_config: QuantizationConfig | None = None,
1187
1188
1189
        *,
        prefix: str = "",
    ) -> None:
1190
        super().__init__()
1191

1192
        self.attention_norm = RMSNorm(config.hidden_size, eps=1e-5)
1193
        self.attention = PixtralHFAttention(
1194
1195
1196
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.attention",
1197
1198
        )
        self.feed_forward = PixtralHFMLP(
1199
1200
1201
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.feed_forward",
1202
        )
1203
1204
1205
1206
1207
        self.ffn_norm = RMSNorm(config.hidden_size, eps=1e-5)

    def forward(
        self,
        hidden_states: torch.Tensor,
1208
        attention_mask: torch.Tensor,
1209
1210
        position_embeddings: torch.Tensor,
    ) -> torch.Tensor:
1211
1212
1213
1214
1215
        r, _ = self.attention.forward(
            self.attention_norm(hidden_states),
            attention_mask=attention_mask,
            position_embeddings=position_embeddings,
        )
1216
1217
1218
1219
1220
1221
1222
        h = hidden_states + r
        r = self.feed_forward.forward(self.ffn_norm(h))
        out = h + r
        return out


class PixtralHFTransformer(nn.Module):
1223
1224
1225
    def __init__(
        self,
        config: PixtralVisionConfig,
1226
        quant_config: QuantizationConfig | None = None,
1227
        *,
1228
        num_hidden_layers_override: int | None = None,
1229
1230
        prefix: str = "",
    ) -> None:
1231
        super().__init__()
1232
1233
1234
1235
1236
1237

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

1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
        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)
            ]
        )
1248
1249
1250
1251

    def forward(
        self,
        x: torch.Tensor,
1252
        attention_mask: torch.Tensor,
1253
        position_embeddings: torch.Tensor,
1254
        return_all_hidden_states: bool,
1255
    ) -> torch.Tensor:
1256
        hidden_states_pool = [x]
1257

1258
1259
        for layer in self.layers:
            x = layer(x, attention_mask, position_embeddings)
1260
1261
1262
1263
1264
1265
            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
1266
1267
1268
1269
        return x


class PixtralHFVisionModel(nn.Module):
1270
1271
1272
    def __init__(
        self,
        config: PixtralVisionConfig,
1273
        quant_config: QuantizationConfig | None = None,
1274
        *,
1275
1276
        num_hidden_layers_override: int | None = None,
        require_post_norm: bool | None = None,
1277
1278
        prefix: str = "",
    ) -> None:
1279
1280
1281
        super().__init__()

        self.config = config
1282

1283
        self.patch_conv = Conv2dLayer(
1284
1285
1286
1287
1288
1289
1290
            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)
1291
1292
        self.transformer = PixtralHFTransformer(
            config,
1293
            quant_config=quant_config,
1294
1295
1296
1297
1298
1299
1300
1301
1302
            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)} "
1303
1304
                "layers."
            )
1305
1306
1307
1308
1309

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

1310
1311
        self.dtype = next(self.parameters()).dtype
        self.device = next(self.parameters()).device
1312
        self.patch_positional_embedding = PixtralRotaryEmbedding(config, self.device)
1313
1314
1315

    def forward(
        self,
1316
        pixel_values: list[torch.Tensor],
1317
        *,
1318
1319
        select_layers: list[int] | None = None,
        feature_select_strategy: VisionFeatureSelectStrategy | None = None,
1320
    ) -> tuple[torch.Tensor, ...]:
1321
1322
        """
        Args:
1323
1324
1325
1326
            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
1327
            select_layers: Layer indices whose features should be
1328
1329
                concatenated and used as the visual encoder output. If none
                are provided, the last layer is used.
1330

1331
1332
1333
1334
1335
1336
        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 = [
1337
            self.patch_conv(img.unsqueeze(0).to(self.dtype)) for img in pixel_values
1338
1339
        ]

1340
        patch_embeds = [p.flatten(2).permute(0, 2, 1) for p in patch_embeds_list]
1341
1342
        embed_sizes = [p.shape[1] for p in patch_embeds]

1343
        # flatten to a single sequence
1344
        patch_embeds = torch.cat(patch_embeds, dim=1)
1345
1346
1347
1348
1349
        patch_embeds = self.ln_pre(patch_embeds)

        # positional embeddings
        position_ids = position_ids_in_meshgrid(
            patch_embeds_list,
1350
1351
1352
            max_width=self.config.image_size // self.config.patch_size,
        ).to(self.device)
        position_embedding = self.patch_positional_embedding(patch_embeds, position_ids)
1353
1354
1355

        if USE_XFORMERS_OPS:
            attention_mask = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens(
1356
1357
                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list],
            )
1358
1359
        else:
            from transformers.models.pixtral.modeling_pixtral import (
1360
1361
1362
                generate_block_attention_mask,
            )

1363
            attention_mask = generate_block_attention_mask(
1364
1365
                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], patch_embeds
            )
1366

1367
1368
1369
1370
        out = self.transformer(
            patch_embeds,
            attention_mask,
            position_embedding,
1371
1372
            return_all_hidden_states=select_layers is not None,
        )
1373

1374
1375
1376
1377
1378
1379
1380
        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,
        )
1381

1382
        # squeeze dim 0 and split into separate tensors for each image
1383
        return torch.split(out.squeeze(0), embed_sizes)
1384
1385
1386

    # (TODO) Add prefix argument for filtering out weights to be loaded
    #        ref: https://github.com/vllm-project/vllm/pull/7186#discussion_r1734163986
1387
    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
1388
1389
1390
1391
1392
1393
1394
1395
        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),
        ]
1396
        params_dict = dict(self.named_parameters())
1397
        loaded_params: set[str] = set()
1398
        layer_count = len(self.transformer.layers)
1399
1400

        for name, loaded_weight in weights:
1401
1402
1403
1404
1405
1406
            # 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

1407
            for param_name, weight_name, shard_id in stacked_params_mapping:
1408
1409
                if weight_name not in name:
                    continue
1410
1411
                name = name.replace(weight_name, param_name)
                param = params_dict[name]
1412
1413
1414
1415
1416
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                param = params_dict[name]
1417
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
1418
                weight_loader(param, loaded_weight)
1419
1420
            loaded_params.add(name)
        return loaded_params