v1.0
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root
965
model_executor/models/clip.py
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965
model_executor/models/clip.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 collections.abc import Iterable, Mapping, Sequence
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from functools import cached_property
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from typing import Annotated, Literal
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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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CLIPConfig,
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CLIPProcessor,
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CLIPTextConfig,
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CLIPVisionConfig,
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)
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from vllm.attention import Attention
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from vllm.attention.layer import MultiHeadAttention
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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_fn
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from vllm.model_executor.layers.conv import Conv2dLayer
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from vllm.model_executor.layers.linear import (
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ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from vllm.model_executor.layers.pooler import DispatchPooler, Pooler
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.models.interfaces import SupportsQuant
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (
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MultiModalDataDict,
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MultiModalFieldConfig,
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MultiModalInputs,
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MultiModalKwargsItems,
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MultiModalUUIDDict,
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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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PromptIndexTargets,
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PromptReplacement,
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PromptUpdate,
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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 MultiModalEmbeddings, SupportsMultiModal
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from .interfaces_base import default_pooling_type
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from .utils import AutoWeightsLoader, maybe_prefix
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from .vision import (
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VisionEncoderInfo,
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VisionFeatureSelectStrategy,
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VisionFeatureSelectStrategyStr,
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get_num_selected_vision_tokens,
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resolve_visual_encoder_outputs,
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)
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class CLIPImagePixelInputs(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"]
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data: Annotated[torch.Tensor, TensorShape("bn", 3, "h", "w")]
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class CLIPEncoderInfo(VisionEncoderInfo[CLIPVisionConfig]):
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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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return self.get_patch_grid_length() ** 2 + 1
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def get_image_size(self) -> int:
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return self.vision_config.image_size
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def get_patch_size(self) -> int:
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return self.vision_config.patch_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()
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assert image_size % patch_size == 0
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return image_size // patch_size
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_POOLING_TYPE_TO_STRATEGY: dict[str, VisionFeatureSelectStrategyStr] = {
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"MEAN": "full",
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"ALL": "full",
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"CLS": "class",
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# This lets us use the same pooling type for both text and image
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"LAST": "class",
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}
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def _get_vision_feature_select_strategy(pooling_type: str):
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try:
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return _POOLING_TYPE_TO_STRATEGY[pooling_type]
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except KeyError:
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raise ValueError(
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f"No feature selection strategy is defined for "
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f"pooling_type: {pooling_type!r}"
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) from None
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class CLIPProcessingInfo(BaseProcessingInfo):
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def get_hf_config(self):
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return self.ctx.get_hf_config(CLIPConfig)
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def get_vision_encoder_info(self):
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return CLIPEncoderInfo(self.get_hf_config())
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def get_hf_processor(self, **kwargs: object):
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return self.ctx.get_hf_processor(CLIPProcessor, **kwargs)
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def get_supported_mm_limits(self) -> Mapping[str, int | None]:
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return {"image": 1}
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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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pooler_config = self.ctx.model_config.pooler_config
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assert pooler_config is not None
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return get_num_selected_vision_tokens(
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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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_get_vision_feature_select_strategy(pooler_config.pooling_type),
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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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def get_max_image_tokens(self) -> int:
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target_width, target_height = self.get_image_size_with_most_features()
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return self.get_num_image_tokens(
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image_width=target_width,
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image_height=target_height,
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)
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class CLIPDummyInputsBuilder(BaseDummyInputsBuilder[CLIPProcessingInfo]):
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def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
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return ""
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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 CLIPMultiModalProcessor(BaseMultiModalProcessor[CLIPProcessingInfo]):
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@cached_property
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def image_token_id(self) -> int:
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tokenizer = self.info.get_tokenizer()
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dummy_token_id = 0
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assert dummy_token_id not in tokenizer.all_special_ids
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return dummy_token_id
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def apply(
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self,
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prompt: str | list[int],
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mm_data: MultiModalDataDict,
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hf_processor_mm_kwargs: Mapping[str, object],
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tokenization_kwargs: Mapping[str, object] | None = None,
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*,
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mm_uuids: MultiModalUUIDDict | None = None,
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) -> MultiModalInputs:
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if prompt and mm_data:
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raise ValueError(
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"CLIP accepts text-only or image-only inputs, not both! "
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"Image-only inputs means passing an image with an empty text "
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"prompt."
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)
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if mm_data:
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# For multi-modal data, the prompt after processing should
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# only contain the dummy image tokens
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tokenization_kwargs = {
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**(tokenization_kwargs or {}),
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"add_special_tokens": False,
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}
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return super().apply(
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prompt=prompt,
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mm_data=mm_data,
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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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)
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def _hf_processor_applies_updates(
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self,
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prompt_text: str,
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mm_items: MultiModalDataItems,
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hf_processor_mm_kwargs: Mapping[str, object],
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tokenization_kwargs: Mapping[str, object],
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) -> bool:
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return False
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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(pixel_values=MultiModalFieldConfig.batched("image"))
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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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image_token_id = self.image_token_id
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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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num_image_tokens = self.info.get_num_image_tokens(
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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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return [image_token_id] * num_image_tokens
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return [
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PromptReplacement(
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modality="image",
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target=PromptIndexTargets.start(),
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replacement=get_replacement,
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),
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]
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# Adapted from: https://github.com/huggingface/transformers/blob/v4.56.2/src/transformers/models/clip/modeling_clip.py
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class CLIPTextEmbeddings(nn.Module):
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def __init__(self, config: CLIPTextConfig):
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super().__init__()
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embed_dim = config.hidden_size
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self.token_embedding = VocabParallelEmbedding(config.vocab_size, embed_dim)
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self.position_embedding = VocabParallelEmbedding(
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config.max_position_embeddings, embed_dim
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)
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def forward(
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self,
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input_ids: torch.Tensor | None,
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position_ids: torch.Tensor,
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inputs_embeds: torch.Tensor | None = None,
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) -> torch.Tensor:
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if inputs_embeds is None:
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if input_ids is None:
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raise ValueError(
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"Either `input_ids` or `input_embeds` must be provided"
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)
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inputs_embeds = self.token_embedding(input_ids)
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position_embeddings = self.position_embedding(position_ids)
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embeddings = inputs_embeds + position_embeddings
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return embeddings
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class CLIPVisionEmbeddings(nn.Module):
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def __init__(self, config: CLIPVisionConfig):
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super().__init__()
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self.config = config
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self.embed_dim = config.hidden_size
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self.image_size = config.image_size
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self.patch_size = config.patch_size
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assert self.image_size % self.patch_size == 0
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self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
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self.patch_embedding = Conv2dLayer(
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in_channels=config.num_channels,
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out_channels=self.embed_dim,
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kernel_size=self.patch_size,
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stride=self.patch_size,
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bias=False,
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)
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self.num_patches = (self.image_size // self.patch_size) ** 2
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self.num_positions = self.num_patches + 1
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self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
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self.register_buffer(
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"position_ids",
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torch.arange(self.num_positions).expand((1, -1)),
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persistent=False,
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)
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def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
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batch_size = pixel_values.shape[0]
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target_dtype = self.patch_embedding.weight.dtype
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patch_embeds = self.patch_embedding(
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pixel_values.to(dtype=target_dtype)
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) # shape = [*, width, grid, grid]
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patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
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class_embeds = self.class_embedding.expand(batch_size, 1, -1)
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embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
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embeddings = embeddings + self.position_embedding(self.position_ids)
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return embeddings
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class CLIPAttention(nn.Module):
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def __init__(
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self,
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config: CLIPTextConfig | CLIPVisionConfig,
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quant_config: QuantizationConfig | None = None,
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*,
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prefix: str = "",
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attn_cls: type[Attention] | type[MultiHeadAttention],
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) -> None:
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super().__init__()
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self.config = config
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self.embed_dim = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = self.embed_dim // self.num_heads
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if self.head_dim * self.num_heads != self.embed_dim:
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raise ValueError(
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"embed_dim must be divisible by num_heads "
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f"(got `embed_dim`: {self.embed_dim} and `num_heads`:"
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f" {self.num_heads})."
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)
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self.scale = self.head_dim**-0.5
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self.qkv_proj = QKVParallelLinear(
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hidden_size=self.embed_dim,
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head_size=self.head_dim,
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total_num_heads=self.num_heads,
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quant_config=quant_config,
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prefix=f"{prefix}.qkv_proj",
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)
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self.out_proj = RowParallelLinear(
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input_size=self.embed_dim,
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output_size=self.embed_dim,
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quant_config=quant_config,
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prefix=f"{prefix}.out_proj",
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)
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self.tp_size = get_tensor_model_parallel_world_size()
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self.num_heads_per_partition = divide(self.num_heads, self.tp_size)
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self.attn = attn_cls(
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self.num_heads_per_partition,
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self.head_dim,
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self.scale,
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prefix=f"{prefix}.attn",
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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):
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"""Input shape: Batch x Time x Channel"""
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qkv_states, _ = self.qkv_proj(hidden_states)
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query_states, key_states, value_states = qkv_states.chunk(3, dim=-1)
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out = self.attn(query_states, key_states, value_states)
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attn_output, _ = self.out_proj(out)
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return attn_output, None
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class CLIPMLP(nn.Module):
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def __init__(
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self,
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config: CLIPTextConfig | CLIPVisionConfig,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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self.activation_fn = get_act_fn(config.hidden_act)
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self.fc1 = ColumnParallelLinear(
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config.hidden_size,
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config.intermediate_size,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.fc1",
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)
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self.fc2 = RowParallelLinear(
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config.intermediate_size,
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config.hidden_size,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.fc2",
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)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states, _ = self.fc1(hidden_states)
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hidden_states = self.activation_fn(hidden_states)
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hidden_states, _ = self.fc2(hidden_states)
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return hidden_states
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class CLIPEncoderLayer(nn.Module):
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def __init__(
|
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self,
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config: CLIPTextConfig | CLIPVisionConfig,
|
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quant_config: QuantizationConfig | None = None,
|
||||
*,
|
||||
prefix: str = "",
|
||||
attn_cls: type[Attention] | type[MultiHeadAttention],
|
||||
) -> None:
|
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super().__init__()
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self.self_attn = CLIPAttention(
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config,
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quant_config=quant_config,
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prefix=f"{prefix}.self_attn",
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attn_cls=attn_cls,
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)
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self.layer_norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.mlp = CLIPMLP(config, quant_config=quant_config, prefix=f"{prefix}.mlp")
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self.layer_norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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residual = hidden_states
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hidden_states = self.layer_norm1(hidden_states)
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hidden_states, _ = self.self_attn(hidden_states=hidden_states)
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hidden_states = residual + hidden_states
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residual = hidden_states
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hidden_states = self.layer_norm2(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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return hidden_states
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class CLIPEncoder(nn.Module):
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||||
"""
|
||||
Transformer encoder consisting of `config.num_hidden_layers` self
|
||||
attention layers. Each layer is a [`CLIPEncoderLayer`].
|
||||
|
||||
Args:
|
||||
config: CLIPConfig
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: CLIPTextConfig | CLIPVisionConfig,
|
||||
quant_config: QuantizationConfig | None = None,
|
||||
num_hidden_layers_override: int | None = None,
|
||||
*,
|
||||
prefix: str = "",
|
||||
attn_cls: type[Attention] | type[MultiHeadAttention],
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.config = config
|
||||
|
||||
if num_hidden_layers_override is None:
|
||||
num_hidden_layers = config.num_hidden_layers
|
||||
else:
|
||||
num_hidden_layers = num_hidden_layers_override
|
||||
self.layers = nn.ModuleList(
|
||||
[
|
||||
CLIPEncoderLayer(
|
||||
config=config,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.layers.{layer_idx}",
|
||||
attn_cls=attn_cls,
|
||||
)
|
||||
for layer_idx in range(num_hidden_layers)
|
||||
]
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
inputs_embeds: torch.Tensor,
|
||||
return_all_hidden_states: bool,
|
||||
) -> torch.Tensor | list[torch.Tensor]:
|
||||
hidden_states_pool = [inputs_embeds]
|
||||
hidden_states = inputs_embeds
|
||||
|
||||
for encoder_layer in self.layers:
|
||||
hidden_states = encoder_layer(hidden_states)
|
||||
if return_all_hidden_states:
|
||||
hidden_states_pool.append(hidden_states)
|
||||
# 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
|
||||
return hidden_states
|
||||
|
||||
|
||||
class CLIPTextTransformer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
config: CLIPTextConfig,
|
||||
quant_config: QuantizationConfig | None = None,
|
||||
*,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.config = config
|
||||
embed_dim = config.hidden_size
|
||||
|
||||
self.embeddings = CLIPTextEmbeddings(config)
|
||||
|
||||
self.encoder = CLIPEncoder(
|
||||
config=config,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.encoder",
|
||||
attn_cls=Attention,
|
||||
)
|
||||
|
||||
self.final_layer_norm = nn.LayerNorm(
|
||||
embed_dim,
|
||||
eps=config.layer_norm_eps,
|
||||
)
|
||||
|
||||
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.embeddings.token_embedding(input_ids)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor | None,
|
||||
position_ids: torch.Tensor,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
) -> torch.Tensor:
|
||||
hidden_states = self.embeddings(
|
||||
input_ids=input_ids,
|
||||
position_ids=position_ids,
|
||||
inputs_embeds=inputs_embeds,
|
||||
)
|
||||
|
||||
last_hidden_state = self.encoder(
|
||||
inputs_embeds=hidden_states,
|
||||
return_all_hidden_states=False,
|
||||
)
|
||||
last_hidden_state = self.final_layer_norm(last_hidden_state)
|
||||
|
||||
return last_hidden_state
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
||||
stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
("qkv_proj", "q_proj", "q"),
|
||||
("qkv_proj", "k_proj", "k"),
|
||||
("qkv_proj", "v_proj", "v"),
|
||||
]
|
||||
params_dict = dict(self.named_parameters())
|
||||
loaded_params: set[str] = set()
|
||||
|
||||
for name, loaded_weight in weights:
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
||||
weight_loader(param, loaded_weight)
|
||||
loaded_params.add(name)
|
||||
return loaded_params
|
||||
|
||||
|
||||
class CLIPVisionTransformer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
config: CLIPVisionConfig,
|
||||
quant_config: QuantizationConfig | None = None,
|
||||
*,
|
||||
num_hidden_layers_override: int | None = None,
|
||||
require_post_norm: bool | None = None,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.config = config
|
||||
embed_dim = config.hidden_size
|
||||
|
||||
self.embeddings = CLIPVisionEmbeddings(config)
|
||||
|
||||
# NOTE: This typo of "layrnorm" is not fixed on purpose to match
|
||||
# the original transformers code and name of the model weights.
|
||||
self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
|
||||
|
||||
self.encoder = CLIPEncoder(
|
||||
config=config,
|
||||
quant_config=quant_config,
|
||||
num_hidden_layers_override=num_hidden_layers_override,
|
||||
prefix=f"{prefix}.encoder",
|
||||
attn_cls=MultiHeadAttention,
|
||||
)
|
||||
|
||||
num_hidden_layers = config.num_hidden_layers
|
||||
if len(self.encoder.layers) > config.num_hidden_layers:
|
||||
raise ValueError(
|
||||
f"The original encoder only has {num_hidden_layers} "
|
||||
f"layers, but you requested {len(self.encoder.layers)} layers."
|
||||
)
|
||||
|
||||
# If possible, skip post_layernorm to conserve memory
|
||||
if require_post_norm is None:
|
||||
require_post_norm = len(self.encoder.layers) == num_hidden_layers
|
||||
|
||||
if require_post_norm:
|
||||
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
|
||||
else:
|
||||
self.post_layernorm = None
|
||||
|
||||
@property
|
||||
def dtype(self):
|
||||
return next(self.parameters()).dtype
|
||||
|
||||
@property
|
||||
def device(self):
|
||||
return next(self.parameters()).device
|
||||
|
||||
def forward(
|
||||
self,
|
||||
pixel_values: torch.Tensor,
|
||||
*,
|
||||
select_layers: list[int] | None = None,
|
||||
feature_select_strategy: VisionFeatureSelectStrategy | None = None,
|
||||
) -> torch.Tensor:
|
||||
hidden_states = self.embeddings(pixel_values)
|
||||
hidden_states = self.pre_layrnorm(hidden_states)
|
||||
|
||||
# Produces either the last layer output or all of the hidden states,
|
||||
# depending on if we have select_layers or not
|
||||
encoder_outputs = self.encoder(
|
||||
inputs_embeds=hidden_states,
|
||||
return_all_hidden_states=select_layers is not None,
|
||||
)
|
||||
|
||||
# Handle post-norm (if applicable) and stacks feature layers if needed
|
||||
encoder_outputs = resolve_visual_encoder_outputs(
|
||||
encoder_outputs,
|
||||
self.post_layernorm,
|
||||
select_layers=select_layers,
|
||||
max_possible_layers=self.config.num_hidden_layers,
|
||||
feature_select_strategy=feature_select_strategy,
|
||||
)
|
||||
|
||||
return encoder_outputs
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
||||
stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
("qkv_proj", "q_proj", "q"),
|
||||
("qkv_proj", "k_proj", "k"),
|
||||
("qkv_proj", "v_proj", "v"),
|
||||
]
|
||||
params_dict = dict(self.named_parameters())
|
||||
loaded_params: set[str] = set()
|
||||
layer_count = len(self.encoder.layers)
|
||||
|
||||
for name, loaded_weight in weights:
|
||||
# post_layernorm is not needed in CLIPVisionModel
|
||||
if name.startswith("post_layernorm") and self.post_layernorm is None:
|
||||
continue
|
||||
|
||||
# omit layers when num_hidden_layers_override is set
|
||||
if name.startswith("encoder.layers"):
|
||||
layer_idx = int(name.split(".")[2])
|
||||
if layer_idx >= layer_count:
|
||||
continue
|
||||
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
||||
weight_loader(param, loaded_weight)
|
||||
loaded_params.add(name)
|
||||
return loaded_params
|
||||
|
||||
|
||||
class CLIPVisionModel(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
config: CLIPVisionConfig,
|
||||
quant_config: QuantizationConfig | None = None,
|
||||
*,
|
||||
num_hidden_layers_override: int | None = None,
|
||||
require_post_norm: bool | None = None,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.vision_model = CLIPVisionTransformer(
|
||||
config=config,
|
||||
quant_config=quant_config,
|
||||
num_hidden_layers_override=num_hidden_layers_override,
|
||||
require_post_norm=require_post_norm,
|
||||
prefix=f"{prefix}.vision_model",
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
pixel_values: torch.Tensor,
|
||||
select_layers: list[int] | None = None,
|
||||
feature_select_strategy: VisionFeatureSelectStrategy | None = None,
|
||||
) -> torch.Tensor:
|
||||
return self.vision_model(
|
||||
pixel_values,
|
||||
select_layers=select_layers,
|
||||
feature_select_strategy=feature_select_strategy,
|
||||
)
|
||||
|
||||
@property
|
||||
def dtype(self):
|
||||
return self.vision_model.dtype
|
||||
|
||||
@property
|
||||
def device(self):
|
||||
return self.vision_model.device
|
||||
|
||||
|
||||
# Assume EOS token corresponds to LAST token in text model
|
||||
@default_pooling_type("LAST")
|
||||
@MULTIMODAL_REGISTRY.register_processor(
|
||||
CLIPMultiModalProcessor,
|
||||
info=CLIPProcessingInfo,
|
||||
dummy_inputs=CLIPDummyInputsBuilder,
|
||||
)
|
||||
class CLIPEmbeddingModel(nn.Module, SupportsMultiModal, SupportsQuant):
|
||||
is_pooling_model = True
|
||||
|
||||
packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
|
||||
merge_by_field_config = True
|
||||
|
||||
@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 = ""):
|
||||
super().__init__()
|
||||
|
||||
config: CLIPConfig = 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
|
||||
|
||||
text_config = config.text_config
|
||||
vision_config = config.vision_config
|
||||
|
||||
self.projection_dim = config.projection_dim
|
||||
self.text_embed_dim = text_config.hidden_size
|
||||
self.vision_embed_dim = vision_config.hidden_size
|
||||
|
||||
self.text_model = CLIPTextTransformer(
|
||||
text_config,
|
||||
quant_config=quant_config,
|
||||
prefix=maybe_prefix(prefix, "text_model"),
|
||||
)
|
||||
self.vision_model = CLIPVisionTransformer(
|
||||
vision_config,
|
||||
quant_config=quant_config,
|
||||
prefix=maybe_prefix(prefix, "vision_model"),
|
||||
)
|
||||
|
||||
self.visual_projection = nn.Linear(
|
||||
self.vision_embed_dim,
|
||||
self.projection_dim,
|
||||
bias=False,
|
||||
)
|
||||
self.text_projection = nn.Linear(
|
||||
self.text_embed_dim,
|
||||
self.projection_dim,
|
||||
bias=False,
|
||||
)
|
||||
|
||||
pooler_config = vllm_config.model_config.pooler_config
|
||||
assert pooler_config is not None
|
||||
self.pooler_config = pooler_config
|
||||
|
||||
self.pooler = DispatchPooler(
|
||||
{
|
||||
"token_embed": Pooler.for_token_embed(pooler_config),
|
||||
"embed": Pooler.for_embed(pooler_config),
|
||||
}
|
||||
)
|
||||
|
||||
# Assumes that self.forward is called after self.embed_input_ids
|
||||
self._is_text_input = True
|
||||
|
||||
def get_text_features(
|
||||
self,
|
||||
input_ids: torch.Tensor | None,
|
||||
position_ids: torch.Tensor,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
) -> torch.Tensor:
|
||||
pooled_output = self.text_model(
|
||||
input_ids=input_ids,
|
||||
position_ids=position_ids,
|
||||
inputs_embeds=inputs_embeds,
|
||||
)
|
||||
|
||||
text_features = self.text_projection(pooled_output)
|
||||
|
||||
return text_features
|
||||
|
||||
def get_image_features(
|
||||
self,
|
||||
pixel_values: torch.Tensor,
|
||||
feature_select_strategy: VisionFeatureSelectStrategy | None = None,
|
||||
) -> torch.Tensor:
|
||||
if feature_select_strategy is None:
|
||||
feature_select_strategy = _get_vision_feature_select_strategy(
|
||||
self.pooler_config.pooling_type
|
||||
)
|
||||
|
||||
pooled_output = self.vision_model(
|
||||
pixel_values=pixel_values,
|
||||
select_layers=None,
|
||||
feature_select_strategy=feature_select_strategy,
|
||||
)
|
||||
|
||||
image_features = self.visual_projection(pooled_output)
|
||||
|
||||
return image_features
|
||||
|
||||
def _parse_and_validate_image_input(
|
||||
self, **kwargs: object
|
||||
) -> CLIPImagePixelInputs | None:
|
||||
pixel_values = kwargs.pop("pixel_values", None)
|
||||
if pixel_values is None:
|
||||
return None
|
||||
|
||||
expected_h = expected_w = self.config.vision_config.image_size
|
||||
return CLIPImagePixelInputs(
|
||||
type="pixel_values",
|
||||
data=pixel_values,
|
||||
resolve_bindings={"h": expected_h, "w": expected_w},
|
||||
)
|
||||
|
||||
def _process_image_inputs(self, inputs: CLIPImagePixelInputs) -> torch.Tensor:
|
||||
pixel_values = inputs["data"]
|
||||
|
||||
return self.get_image_features(pixel_values)
|
||||
|
||||
def get_language_model(self) -> torch.nn.Module:
|
||||
return self.text_model
|
||||
|
||||
def embed_input_ids(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
multimodal_embeddings: MultiModalEmbeddings | None = None,
|
||||
*,
|
||||
is_multimodal: torch.Tensor | None = None,
|
||||
handle_oov_mm_token: bool = False,
|
||||
) -> torch.Tensor:
|
||||
self._is_text_input = (
|
||||
multimodal_embeddings is None or len(multimodal_embeddings) == 0
|
||||
)
|
||||
|
||||
# This is to satisfy the type checker for each overload
|
||||
if multimodal_embeddings is None or is_multimodal is None:
|
||||
return super().embed_input_ids(input_ids)
|
||||
|
||||
return super().embed_input_ids(
|
||||
input_ids,
|
||||
multimodal_embeddings=multimodal_embeddings,
|
||||
is_multimodal=is_multimodal,
|
||||
handle_oov_mm_token=handle_oov_mm_token,
|
||||
)
|
||||
|
||||
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_inputs(image_input)
|
||||
return vision_embeddings
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor | None,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None = None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
**kwargs: object,
|
||||
) -> torch.Tensor:
|
||||
if intermediate_tensors is not None:
|
||||
raise RuntimeError("PP is not supported for this model")
|
||||
|
||||
# Multimodal inputs
|
||||
if not self._is_text_input:
|
||||
return inputs_embeds
|
||||
|
||||
# Text inputs
|
||||
return self.get_text_features(
|
||||
input_ids=input_ids, position_ids=positions, inputs_embeds=inputs_embeds
|
||||
)
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
|
||||
loader = AutoWeightsLoader(
|
||||
self,
|
||||
skip_substrs=[".position_ids"],
|
||||
ignore_unexpected_prefixes=["logit_scale."],
|
||||
)
|
||||
|
||||
return loader.load_weights(weights)
|
||||
Reference in New Issue
Block a user