pixtral.py 46.4 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
from vllm.multimodal.processing import (
    BaseMultiModalProcessor,
    BaseProcessingInfo,
    MultiModalProcessingInfo,
    PromptReplacement,
    PromptUpdate,
    PromptUpdateDetails,
)
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from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
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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.transformers_utils.tokenizer import (
    MistralTokenizer,
    cached_tokenizer_from_config,
)
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from vllm.utils.tensor_schema import TensorSchema, TensorShape
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from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
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from .utils import init_vllm_registered_model, maybe_prefix
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from .vision import (
    VisionEncoderInfo,
    VisionFeatureSelectStrategy,
    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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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:
        tokenizer = cached_tokenizer_from_config(self.ctx.model_config)
        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,
)
class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
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    merge_by_field_config = True

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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
        self.language_model = init_vllm_registered_model(
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            vllm_config=vllm_config,
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            hf_config=config.text_config,
            prefix=maybe_prefix(prefix, "language_model"),
        )
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        self.vision_encoder = VisionTransformer(self.vision_args)
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        if self.vision_args.add_pre_mm_projector_layer_norm:
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            self.pre_mm_projector_norm = RMSNorm(self.vision_args.hidden_size, eps=1e-5)
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        if self.vision_args.mm_projector_id == PATCH_MERGE:
            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,
            )

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        self.vision_language_adapter = VisionLanguageAdapter(
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            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)
        if self.vision_args.add_pre_mm_projector_layer_norm:
            image_features = self.pre_mm_projector_norm(image_features)
        if self.vision_args.mm_projector_id == PATCH_MERGE:
            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 get_language_model(self) -> torch.nn.Module:
        return self.language_model

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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,
        input_ids: torch.Tensor,
        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")

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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")

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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())
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        patch_merger_dict = (
            dict(self.patch_merger.named_parameters())
            if self.vision_args.mm_projector_id == PATCH_MERGE
            else dict()
        )
        pre_mm_projector_norm_dict = (
            dict(self.pre_mm_projector_norm.named_parameters())
            if self.vision_args.add_pre_mm_projector_layer_norm
            else dict()
        )
        vision_lang_adapter_dict = dict(self.vision_language_adapter.named_parameters())
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        def llm_weights_generator():
            # Single pass over weights
            for name, w in weights:
                if is_vision_encoder_weights((name, w)):
                    # Load vision encoder weights directly
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                    trimmed_name = ".".join(name.split(".")[1:])
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                    param = vision_encoder_dict[trimmed_name]
                    with torch.no_grad():
                        default_weight_loader(param, w)
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                elif is_patch_merger((name, w)):
                    # 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)):
                    # 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)):
                    # 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[trimmed_name]
                    with torch.no_grad():
                        default_weight_loader(param, w)
                else:
                    # LLM weights: yield them to be loaded
                    # by language_model.load_weights
                    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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# 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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        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",
        )
        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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        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
1048
1049
1050


class PixtralHFAttention(nn.Module):
1051
1052
1053
    def __init__(
        self,
        config: PixtralVisionConfig,
1054
        quant_config: QuantizationConfig | None = None,
1055
1056
1057
        *,
        prefix: str = "",
    ) -> None:
1058
        super().__init__()
1059

1060
1061
        self.config = config
        assert not config.hidden_size % config.num_attention_heads
1062
1063
1064
        self.total_num_heads = config.num_attention_heads
        tp_size = get_tensor_model_parallel_world_size()
        self.n_heads = divide(config.num_attention_heads, tp_size)
1065
1066
        self.head_dim = config.hidden_size // config.num_attention_heads

1067
1068
1069
        self.qkv_proj = QKVParallelLinear(
            hidden_size=config.hidden_size,
            head_size=self.head_dim,
1070
            total_num_heads=self.total_num_heads,
1071
1072
1073
1074
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )
1075
        assert self.total_num_heads * self.head_dim == config.hidden_size
1076
1077
1078
1079
1080
1081
1082
        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",
        )
1083
1084
1085
1086

    def forward(
        self,
        hidden_states: torch.Tensor,
1087
        attention_mask: torch.Tensor,
1088
        position_embeddings: torch.Tensor,
1089
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
1090
        batch, patches, _ = hidden_states.size()
1091

1092
1093
        qkv_states, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv_states.chunk(3, dim=-1)
1094

1095
1096
1097
        # 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)
1098
        v = v.view(batch, patches, self.n_heads, self.head_dim)
1099
        cos, sin = position_embeddings
1100
        q, k = apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=0)
1101

1102
1103
1104
1105
        if USE_XFORMERS_OPS:
            # Transpose q and k back for attention
            q = q.transpose(1, 2).contiguous()
            k = k.transpose(1, 2).contiguous()
1106
            out = xops.memory_efficient_attention(q, k, v, attn_bias=attention_mask)
1107
        else:
1108
            v = v.transpose(1, 2)
1109
            out = nn.functional.scaled_dot_product_attention(
1110
1111
                q, k, v, attn_mask=attention_mask
            )
1112
            out = out.transpose(1, 2)
1113

1114
        out = out.reshape(batch, patches, self.n_heads * self.head_dim)
1115
        attn_output, _ = self.o_proj(out)
1116

1117
        return attn_output, None
1118
1119
1120


class PixtralHFTransformerBlock(nn.Module):
1121
1122
1123
    def __init__(
        self,
        config: PixtralVisionConfig,
1124
        quant_config: QuantizationConfig | None = None,
1125
1126
1127
        *,
        prefix: str = "",
    ) -> None:
1128
        super().__init__()
1129

1130
        self.attention_norm = RMSNorm(config.hidden_size, eps=1e-5)
1131
1132
1133
1134
1135
1136
        self.attention = PixtralHFAttention(
            config, quant_config=quant_config, prefix=f"{prefix}.attention"
        )
        self.feed_forward = PixtralHFMLP(
            config, quant_config=quant_config, prefix=f"{prefix}.feed_forward"
        )
1137
1138
1139
1140
1141
        self.ffn_norm = RMSNorm(config.hidden_size, eps=1e-5)

    def forward(
        self,
        hidden_states: torch.Tensor,
1142
        attention_mask: torch.Tensor,
1143
1144
        position_embeddings: torch.Tensor,
    ) -> torch.Tensor:
1145
1146
1147
1148
1149
        r, _ = self.attention.forward(
            self.attention_norm(hidden_states),
            attention_mask=attention_mask,
            position_embeddings=position_embeddings,
        )
1150
1151
1152
1153
1154
1155
1156
        h = hidden_states + r
        r = self.feed_forward.forward(self.ffn_norm(h))
        out = h + r
        return out


class PixtralHFTransformer(nn.Module):
1157
1158
1159
    def __init__(
        self,
        config: PixtralVisionConfig,
1160
        quant_config: QuantizationConfig | None = None,
1161
        *,
1162
        num_hidden_layers_override: int | None = None,
1163
1164
        prefix: str = "",
    ) -> None:
1165
        super().__init__()
1166
1167
1168
1169
1170
1171

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

1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
        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)
            ]
        )
1182
1183
1184
1185

    def forward(
        self,
        x: torch.Tensor,
1186
        attention_mask: torch.Tensor,
1187
        position_embeddings: torch.Tensor,
1188
        return_all_hidden_states: bool,
1189
    ) -> torch.Tensor:
1190
        hidden_states_pool = [x]
1191

1192
1193
        for layer in self.layers:
            x = layer(x, attention_mask, position_embeddings)
1194
1195
1196
1197
1198
1199
            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
1200
1201
1202
1203
        return x


class PixtralHFVisionModel(nn.Module):
1204
1205
1206
    def __init__(
        self,
        config: PixtralVisionConfig,
1207
        quant_config: QuantizationConfig | None = None,
1208
        *,
1209
1210
        num_hidden_layers_override: int | None = None,
        require_post_norm: bool | None = None,
1211
1212
        prefix: str = "",
    ) -> None:
1213
1214
1215
        super().__init__()

        self.config = config
1216

1217
        self.patch_conv = Conv2dLayer(
1218
1219
1220
1221
1222
1223
1224
            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)
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
        self.transformer = PixtralHFTransformer(
            config,
            quant_config,
            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)} "
1237
1238
                "layers."
            )
1239
1240
1241
1242
1243

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

1244
1245
        self.dtype = next(self.parameters()).dtype
        self.device = next(self.parameters()).device
1246
        self.patch_positional_embedding = PixtralRotaryEmbedding(config, self.device)
1247
1248
1249

    def forward(
        self,
1250
        pixel_values: list[torch.Tensor],
1251
        *,
1252
1253
        select_layers: list[int] | None = None,
        feature_select_strategy: VisionFeatureSelectStrategy | None = None,
1254
    ) -> tuple[torch.Tensor, ...]:
1255
1256
        """
        Args:
1257
1258
1259
1260
            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
1261
            select_layers: Layer indices whose features should be
1262
1263
                concatenated and used as the visual encoder output. If none
                are provided, the last layer is used.
1264

1265
1266
1267
1268
1269
1270
        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 = [
1271
            self.patch_conv(img.unsqueeze(0).to(self.dtype)) for img in pixel_values
1272
1273
        ]

1274
        patch_embeds = [p.flatten(2).permute(0, 2, 1) for p in patch_embeds_list]
1275
1276
        embed_sizes = [p.shape[1] for p in patch_embeds]

1277
        # flatten to a single sequence
1278
        patch_embeds = torch.cat(patch_embeds, dim=1)
1279
1280
1281
1282
1283
        patch_embeds = self.ln_pre(patch_embeds)

        # positional embeddings
        position_ids = position_ids_in_meshgrid(
            patch_embeds_list,
1284
1285
1286
            max_width=self.config.image_size // self.config.patch_size,
        ).to(self.device)
        position_embedding = self.patch_positional_embedding(patch_embeds, position_ids)
1287
1288
1289

        if USE_XFORMERS_OPS:
            attention_mask = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens(
1290
1291
                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list],
            )
1292
1293
        else:
            from transformers.models.pixtral.modeling_pixtral import (
1294
1295
1296
                generate_block_attention_mask,
            )

1297
            attention_mask = generate_block_attention_mask(
1298
1299
                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], patch_embeds
            )
1300

1301
1302
1303
1304
        out = self.transformer(
            patch_embeds,
            attention_mask,
            position_embedding,
1305
1306
            return_all_hidden_states=select_layers is not None,
        )
1307

1308
1309
1310
1311
1312
1313
1314
        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,
        )
1315

1316
        # squeeze dim 0 and split into separate tensors for each image
1317
        return torch.split(out.squeeze(0), embed_sizes)
1318
1319
1320

    # (TODO) Add prefix argument for filtering out weights to be loaded
    #        ref: https://github.com/vllm-project/vllm/pull/7186#discussion_r1734163986
1321
    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
1322
1323
1324
1325
1326
1327
1328
1329
        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),
        ]
1330
        params_dict = dict(self.named_parameters())
1331
        loaded_params: set[str] = set()
1332
        layer_count = len(self.transformer.layers)
1333
1334

        for name, loaded_weight in weights:
1335
1336
1337
1338
1339
1340
            # 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

1341
            for param_name, weight_name, shard_id in stacked_params_mapping:
1342
1343
                if weight_name not in name:
                    continue
1344
1345
                name = name.replace(weight_name, param_name)
                param = params_dict[name]
1346
1347
1348
1349
1350
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                param = params_dict[name]
1351
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
1352
                weight_loader(param, loaded_weight)
1353
1354
            loaded_params.add(name)
        return loaded_params