Sync from v0.13
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635
vllm/model_executor/models/mistral3.py
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635
vllm/model_executor/models/mistral3.py
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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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from abc import abstractmethod
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from collections.abc import Iterable, Mapping, Sequence
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from typing import Annotated, Final, Literal, Protocol, TypeVar
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import torch
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import torch.nn as nn
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from transformers import (
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BatchFeature,
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Mistral3Config,
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PixtralVisionConfig,
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PretrainedConfig,
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)
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from transformers.models.pixtral import PixtralProcessor
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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.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import ColumnParallelLinear, RowParallelLinear
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.models.module_mapping import MultiModelKeys
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.cache import BaseMultiModalProcessorCache
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from vllm.multimodal.inputs import (
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MultiModalDataDict,
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MultiModalFieldConfig,
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MultiModalKwargsItems,
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)
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from vllm.multimodal.parse import ImageProcessorItems, ImageSize, MultiModalDataItems
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from vllm.multimodal.processing import (
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BaseMultiModalProcessor,
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BaseProcessingInfo,
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InputProcessingContext,
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PromptReplacement,
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PromptUpdate,
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PromptUpdateDetails,
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)
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from vllm.multimodal.profiling import BaseDummyInputsBuilder
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from vllm.sequence import IntermediateTensors
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from vllm.utils.tensor_schema import TensorSchema, TensorShape
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from .interfaces import (
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MultiModalEmbeddings,
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SupportsLoRA,
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SupportsMultiModal,
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SupportsPP,
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)
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from .pixtral import PixtralHFEncoderInfo, PixtralHFVisionModel
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from .utils import (
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AutoWeightsLoader,
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WeightsMapper,
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init_vllm_registered_model,
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maybe_prefix,
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)
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from .vision import get_vision_encoder_info
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class Mistral3ImagePixelInputs(TensorSchema):
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"""
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Dimensions:
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- bn: Batch size * number of images
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- c: Number of channels (3)
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- h: Height of each image
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- w: Width of each image
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"""
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type: Literal["pixel_values_pixtral"] = "pixel_values_pixtral"
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# Note that `height` or `width` may be different per batch and image,
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# in which case the data is passed as a list instead of a batched tensor.
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pixel_values: 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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]
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class Mistral3PatchMerger(nn.Module):
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"""
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Learned merging of spatial_merge_size ** 2 patches
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"""
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def __init__(
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self, vision_hidden_size: int, spatial_merge_size: int, patch_size: int
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):
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super().__init__()
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self.vision_hidden_size = vision_hidden_size
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self.spatial_merge_size = spatial_merge_size
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self.patch_size = patch_size
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self.merging_layer = nn.Linear(
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vision_hidden_size * self.spatial_merge_size**2,
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vision_hidden_size,
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bias=False,
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)
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def forward(
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self, image_features: torch.Tensor, image_sizes: torch.Tensor
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) -> torch.Tensor:
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image_sizes = [
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(image_size[0] // self.patch_size, image_size[1] // self.patch_size)
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for image_size in image_sizes
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]
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tokens_per_image = [h * w for h, w in image_sizes]
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d = image_features.shape[-1]
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permuted_tensor = []
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for image_index, image_tokens in enumerate(
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image_features.split(tokens_per_image)
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):
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# Reshape image_tokens into a 2D grid
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h, w = image_sizes[image_index]
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image_grid = image_tokens.view(h, w, d).permute(2, 0, 1).unsqueeze(0)
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grid = torch.nn.functional.unfold(
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image_grid,
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kernel_size=self.spatial_merge_size,
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stride=self.spatial_merge_size,
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)
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grid = grid.view(d * self.spatial_merge_size**2, -1).t()
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permuted_tensor.append(grid)
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image_features = torch.cat(permuted_tensor, dim=0)
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image_features = self.merging_layer(image_features)
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return image_features
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class Mistral3MultiModalProjector(nn.Module):
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def __init__(
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self,
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vision_hidden_size: int,
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text_hidden_size: int,
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spatial_merge_size: int,
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patch_size: int,
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projector_hidden_act: str,
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multimodal_projector_bias: bool,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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):
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super().__init__()
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self.norm = RMSNorm(vision_hidden_size, eps=1e-5)
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self.patch_merger = Mistral3PatchMerger(
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vision_hidden_size=vision_hidden_size,
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spatial_merge_size=spatial_merge_size,
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patch_size=patch_size,
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)
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self.linear_1 = ColumnParallelLinear(
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vision_hidden_size,
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text_hidden_size,
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bias=multimodal_projector_bias,
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quant_config=quant_config,
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prefix=f"{prefix}.linear_1",
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)
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self.act = get_act_fn(projector_hidden_act)
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self.linear_2 = RowParallelLinear(
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text_hidden_size,
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text_hidden_size,
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bias=multimodal_projector_bias,
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quant_config=quant_config,
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prefix=f"{prefix}.linear_2",
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)
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def forward(
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self, image_features: torch.Tensor, image_sizes: torch.Tensor
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) -> torch.Tensor:
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image_features = self.norm(image_features)
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image_features = self.patch_merger(image_features, image_sizes)
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hidden_states, _ = self.linear_1(image_features)
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hidden_states = self.act(hidden_states)
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hidden_states, _ = self.linear_2(hidden_states)
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return hidden_states
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class LlavaLikeConfig(Protocol):
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vision_config: Final[PretrainedConfig]
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image_token_index: Final[int]
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vision_feature_select_strategy: Final[str]
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vision_feature_layer: Final[int | list[int]]
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class LlavaLikeProcessor(Protocol):
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image_token: Final[str]
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class BaseLlavaProcessingInfo(BaseProcessingInfo):
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def get_hf_config(self) -> LlavaLikeConfig:
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return self.ctx.get_hf_config(Mistral3Config)
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def get_vision_encoder_info(self):
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return get_vision_encoder_info(self.get_hf_config())
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@abstractmethod
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def get_hf_processor(self, **kwargs: object) -> LlavaLikeProcessor:
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raise NotImplementedError
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def get_supported_mm_limits(self) -> Mapping[str, int | None]:
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return {"image": None}
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def get_num_image_tokens(
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self,
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*,
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image_width: int,
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image_height: int,
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) -> int:
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vision_encoder_info = self.get_vision_encoder_info()
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return vision_encoder_info.get_num_image_tokens(
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image_width=image_width,
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image_height=image_height,
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)
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def get_image_size_with_most_features(self) -> ImageSize:
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vision_encoder_info = self.get_vision_encoder_info()
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width = height = vision_encoder_info.get_image_size()
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return ImageSize(width=width, height=height)
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_I = TypeVar("_I", bound=BaseLlavaProcessingInfo)
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class Mistral3DummyInputsBuilder(BaseDummyInputsBuilder[_I]):
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def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
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num_images = mm_counts.get("image", 0)
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processor = self.info.get_hf_processor()
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image_token = processor.image_token
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return image_token * num_images
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def get_dummy_mm_data(
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self,
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seq_len: int,
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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(
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width=target_width,
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height=target_height,
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num_images=num_images,
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overrides=image_overrides,
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)
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}
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class Mistral3ProcessingInfo(BaseLlavaProcessingInfo):
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def get_hf_processor(self, **kwargs: object):
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return self.ctx.get_hf_processor(PixtralProcessor, **kwargs)
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class Mistral3MultiModalProcessor(BaseMultiModalProcessor[Mistral3ProcessingInfo]):
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def _call_hf_processor(
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self,
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prompt: str,
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mm_data: Mapping[str, object],
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mm_kwargs: Mapping[str, object],
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tok_kwargs: Mapping[str, object],
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) -> BatchFeature:
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processed_outputs = super()._call_hf_processor(
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prompt=prompt,
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mm_data=mm_data,
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mm_kwargs=mm_kwargs,
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tok_kwargs=tok_kwargs,
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)
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pixel_values = processed_outputs.get("pixel_values")
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if pixel_values is not None:
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# Avoid padding since we need the output for each image to be
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# independent of other images for the cache to work correctly
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image_sizes = processed_outputs["image_sizes"]
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assert len(pixel_values) == len(image_sizes)
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processed_outputs["pixel_values"] = [
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p[:, :h, :w] for p, (h, w) in zip(pixel_values, image_sizes)
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]
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return processed_outputs
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def _get_mm_fields_config(
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self,
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hf_inputs: BatchFeature,
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hf_processor_mm_kwargs: Mapping[str, object],
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) -> Mapping[str, MultiModalFieldConfig]:
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return dict(
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pixel_values=MultiModalFieldConfig.batched("image"),
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image_embeds=MultiModalFieldConfig.batched("image"),
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)
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def _get_prompt_updates(
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self,
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mm_items: MultiModalDataItems,
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hf_processor_mm_kwargs: Mapping[str, object],
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out_mm_kwargs: MultiModalKwargsItems,
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) -> Sequence[PromptUpdate]:
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processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
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hf_config = self.info.get_hf_config()
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tokenizer = self.info.get_tokenizer()
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vocab = tokenizer.get_vocab()
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image_break_id = vocab[processor.image_break_token]
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image_token_id = hf_config.image_token_index
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image_end_id = vocab[processor.image_end_token]
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assert isinstance(hf_config.vision_config, PixtralVisionConfig)
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encoder_info = PixtralHFEncoderInfo(hf_config)
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def get_replacement(item_idx: int):
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images = mm_items.get_items("image", ImageProcessorItems)
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image_size = images.get_image_size(item_idx)
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ncols, nrows = encoder_info.get_patch_grid_size(
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image_width=image_size.width,
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image_height=image_size.height,
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)
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tokens = ([image_token_id] * ncols + [image_break_id]) * nrows
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tokens[-1] = image_end_id
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return PromptUpdateDetails.select_token_id(tokens, image_token_id)
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return [
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PromptReplacement(
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modality="image",
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target=[image_token_id],
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replacement=get_replacement,
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),
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]
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def _build_mistral3_info(
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ctx: InputProcessingContext,
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) -> BaseLlavaProcessingInfo:
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hf_config = ctx.get_hf_config(Mistral3Config)
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assert isinstance(hf_config.vision_config, PixtralVisionConfig)
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return Mistral3ProcessingInfo(ctx)
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def _build_mistral3_processor(
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info: _I,
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dummy_inputs: BaseDummyInputsBuilder[_I],
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*,
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cache: BaseMultiModalProcessorCache | None = None,
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) -> BaseMultiModalProcessor:
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assert isinstance(info, Mistral3ProcessingInfo)
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return Mistral3MultiModalProcessor(
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info,
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dummy_inputs, # type: ignore
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cache=cache,
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)
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def _get_num_hidden_layers(hf_config: LlavaLikeConfig) -> int:
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"""Determine the number of hidden layers to initialize up to in the
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visual encoder.
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Args:
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hf_config: Model config with vision feature layer(s).
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"""
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feature_layers = hf_config.vision_feature_layer
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num_hidden_layers = hf_config.vision_config.num_hidden_layers
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# If we have one feature layer, initialize up to that layer
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if isinstance(feature_layers, int):
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return _get_layer_index(feature_layers, num_hidden_layers)
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# If we have multiple feature layers, initialize up to the deepest one
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elif isinstance(feature_layers, (list, tuple)):
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return max(_get_layer_index(idx, num_hidden_layers) for idx in feature_layers)
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raise TypeError(
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f"vision_layer_feature type: {type(feature_layers)} is not supported"
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)
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def _get_layer_index(feature_layer_index: int, num_hidden_layers: int) -> int:
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"""Given a signed vision feature layer, get the number of hidden layers
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needed to leverage it.
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Args:
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feature_layer_index: Index of a required layer in the visual encoder.
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num_hidden_layers: The total number of hidden layers in the visual
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encoder.
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"""
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if feature_layer_index < 0:
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return num_hidden_layers + feature_layer_index + 1
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return feature_layer_index
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def init_vision_tower_for_llava(
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hf_config: LlavaLikeConfig,
|
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quant_config: QuantizationConfig | None,
|
||||
*,
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require_post_norm: bool | None = None,
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prefix: str = "",
|
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) -> PixtralHFVisionModel:
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vision_config = hf_config.vision_config
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# Initialize the vision tower only up to the deepest required feature layer
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num_hidden_layers = _get_num_hidden_layers(hf_config)
|
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assert isinstance(vision_config, PixtralVisionConfig)
|
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return PixtralHFVisionModel(
|
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vision_config,
|
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quant_config=quant_config,
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num_hidden_layers_override=num_hidden_layers,
|
||||
require_post_norm=require_post_norm,
|
||||
prefix=prefix,
|
||||
)
|
||||
|
||||
|
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@MULTIMODAL_REGISTRY.register_processor(
|
||||
_build_mistral3_processor,
|
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info=_build_mistral3_info,
|
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dummy_inputs=Mistral3DummyInputsBuilder,
|
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)
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class Mistral3ForConditionalGeneration(
|
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nn.Module, SupportsLoRA, SupportsMultiModal, SupportsPP
|
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):
|
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packed_modules_mapping = {
|
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"qkv_proj": ["q_proj", "k_proj", "v_proj"],
|
||||
"gate_up_proj": ["gate_proj", "up_proj"],
|
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}
|
||||
|
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hf_to_vllm_mapper = WeightsMapper(
|
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orig_to_new_prefix={
|
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# mapping for new names in checkpoint saved after transformers v4.52
|
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"model.language_model.": "language_model.model.",
|
||||
"model.vision_tower.": "vision_tower.",
|
||||
"model.multi_modal_projector.": "multi_modal_projector.",
|
||||
"lm_head.": "language_model.lm_head.",
|
||||
}
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def get_placeholder_str(cls, modality: str, i: int) -> str | None:
|
||||
if modality.startswith("image"):
|
||||
return None
|
||||
|
||||
raise ValueError("Only image modality is supported")
|
||||
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
|
||||
super().__init__()
|
||||
|
||||
config = vllm_config.model_config.hf_config
|
||||
quant_config = vllm_config.quant_config
|
||||
multimodal_config = vllm_config.model_config.multimodal_config
|
||||
|
||||
self.config = config
|
||||
self.multimodal_config = multimodal_config
|
||||
|
||||
# NOTE: These are special cases for Pixtral-12B in the HF-format
|
||||
# https://huggingface.co/mistral-community/pixtral-12b/blob/main/config.json # noqa
|
||||
if (
|
||||
config.text_config.architectures is None
|
||||
and config.text_config.model_type == "mistral"
|
||||
):
|
||||
config.text_config.architectures = ["MistralForCausalLM"]
|
||||
if (
|
||||
config.projector_hidden_act is None
|
||||
and config.vision_config.hidden_act == "gelu"
|
||||
):
|
||||
config.projector_hidden_act = "gelu"
|
||||
|
||||
# TODO: Optionally initializes this for supporting embeddings.
|
||||
if multimodal_config.get_limit_per_prompt("image"):
|
||||
self.vision_tower = init_vision_tower_for_llava(
|
||||
config,
|
||||
quant_config,
|
||||
require_post_norm=False,
|
||||
prefix=maybe_prefix(prefix, "vision_tower"),
|
||||
)
|
||||
self.multi_modal_projector = Mistral3MultiModalProjector(
|
||||
vision_hidden_size=config.vision_config.hidden_size,
|
||||
text_hidden_size=config.text_config.hidden_size,
|
||||
projector_hidden_act=config.projector_hidden_act,
|
||||
spatial_merge_size=config.spatial_merge_size,
|
||||
patch_size=config.vision_config.patch_size,
|
||||
multimodal_projector_bias=config.multimodal_projector_bias,
|
||||
quant_config=quant_config,
|
||||
prefix=maybe_prefix(prefix, "multi_modal_projector"),
|
||||
)
|
||||
else:
|
||||
self.vision_tower = None
|
||||
self.multi_modal_projector = None
|
||||
|
||||
self.language_model = init_vllm_registered_model(
|
||||
vllm_config=vllm_config,
|
||||
hf_config=config.text_config,
|
||||
prefix=maybe_prefix(prefix, "language_model"),
|
||||
)
|
||||
|
||||
self.make_empty_intermediate_tensors = (
|
||||
self.language_model.make_empty_intermediate_tensors
|
||||
)
|
||||
|
||||
def _parse_and_validate_image_input(
|
||||
self, **kwargs: object
|
||||
) -> Mistral3ImagePixelInputs | None:
|
||||
pixel_values = kwargs.pop("pixel_values", None)
|
||||
image_embeds = kwargs.pop("image_embeds", None)
|
||||
|
||||
if pixel_values is None and image_embeds is None:
|
||||
return None
|
||||
|
||||
return Mistral3ImagePixelInputs(
|
||||
type="pixel_values_pixtral",
|
||||
pixel_values=pixel_values,
|
||||
)
|
||||
|
||||
def _process_image_input(
|
||||
self,
|
||||
image_input: Mistral3ImagePixelInputs,
|
||||
) -> torch.Tensor | tuple[torch.Tensor, ...]:
|
||||
if image_input["type"] == "image_embeds":
|
||||
return image_input["data"]
|
||||
|
||||
image_sizes = [
|
||||
(img.shape[-2], img.shape[-1]) for img in image_input["pixel_values"]
|
||||
]
|
||||
|
||||
image_features = self.vision_tower(image_input["pixel_values"])
|
||||
|
||||
if isinstance(image_features, torch.Tensor):
|
||||
return self.multi_modal_projector(image_features, image_sizes)
|
||||
|
||||
feature_sizes = [
|
||||
image_feature.shape[0] // self.config.spatial_merge_size**2
|
||||
for image_feature in image_features
|
||||
]
|
||||
|
||||
image_embeds = self.multi_modal_projector(
|
||||
torch.cat(image_features), image_sizes
|
||||
)
|
||||
if len(feature_sizes) > 1:
|
||||
image_embeds = torch.split(image_embeds, feature_sizes)
|
||||
else:
|
||||
image_embeds = (image_embeds,)
|
||||
return image_embeds
|
||||
|
||||
def get_language_model(self) -> torch.nn.Module:
|
||||
return self.language_model
|
||||
|
||||
def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
|
||||
image_input = self._parse_and_validate_image_input(**kwargs)
|
||||
if image_input is None:
|
||||
return []
|
||||
|
||||
vision_embeddings = self._process_image_input(image_input)
|
||||
|
||||
return vision_embeddings
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None = None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
**kwargs: object,
|
||||
) -> torch.Tensor | IntermediateTensors:
|
||||
"""Run forward pass for Mistral3.
|
||||
|
||||
One key thing to understand is the `input_ids` already accounts for the
|
||||
positions of the to-be-inserted image embeddings.
|
||||
|
||||
Concretely, consider a text prompt:
|
||||
`"USER: <image>\\nWhat's the content of the image?\\nASSISTANT:"`.
|
||||
|
||||
Tokenizer outputs:
|
||||
`[1, 3148, 1001, 29901, 29871, 32000, 29871, 13, 5618, 29915, 29879,
|
||||
278, 2793, 310, 278, 1967, 29973, 13, 22933, 9047, 13566, 29901]`.
|
||||
|
||||
To reserve space in KV cache, we have to insert placeholder tokens
|
||||
before they are inputted to the model, so the input processor prepends
|
||||
additional image tokens (denoted as `32000`), resulting in:
|
||||
`[1, 3148, 1001, 29901, 29871, 32000, ..., 32000, 29871, 13, 5618,
|
||||
29915, 29879, 278, 2793, 310, 278, 1967, 29973, 13, 22933, 9047, 13566,
|
||||
29901]`.
|
||||
|
||||
We insert 575 tokens so that including the original image token in the
|
||||
input, there are a total of 576 (24 * 24) image tokens, which
|
||||
corresponds to the number of image tokens inputted to the language
|
||||
model, i.e. the number of image tokens outputted by the visual encoder.
|
||||
|
||||
This way, the `positions` and `attn_metadata` are consistent
|
||||
with the `input_ids`.
|
||||
|
||||
Args:
|
||||
input_ids: Flattened (concatenated) input_ids corresponding to a
|
||||
batch.
|
||||
positions: Position indices for the input tokens.
|
||||
intermediate_tensors: Intermediate tensors from prior forward pass.
|
||||
inputs_embeds: Optional tensor of input embeddings.
|
||||
|
||||
Info:
|
||||
[`Mistral3ImagePixelInputs`][vllm.model_executor.models.mistral3.Mistral3ImagePixelInputs]
|
||||
"""
|
||||
if intermediate_tensors is not None:
|
||||
inputs_embeds = None
|
||||
|
||||
hidden_states = self.language_model.model(
|
||||
input_ids, positions, intermediate_tensors, inputs_embeds=inputs_embeds
|
||||
)
|
||||
|
||||
return hidden_states
|
||||
|
||||
def compute_logits(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
) -> torch.Tensor | None:
|
||||
return self.language_model.compute_logits(hidden_states)
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
||||
skip_prefixes = []
|
||||
if self.vision_tower is None and self.multi_modal_projector is None:
|
||||
skip_prefixes = ["vision_tower.", "multi_modal_projector."]
|
||||
|
||||
loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
|
||||
return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
|
||||
|
||||
def get_mm_mapping(self) -> MultiModelKeys:
|
||||
"""
|
||||
Get the module prefix in multimodal models
|
||||
"""
|
||||
return MultiModelKeys.from_string_field(
|
||||
language_model="language_model",
|
||||
connector="multi_modal_projector",
|
||||
tower_model="vision_tower",
|
||||
)
|
||||
Reference in New Issue
Block a user