[Model] CLIP Embedding Support (#26010)
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
@@ -1,28 +1,63 @@
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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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"""Minimal implementation of CLIPVisionModel intended to be only used
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within a vision language model."""
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from collections.abc import Iterable
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from typing import Optional, Union
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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, Optional, Union
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import torch
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import torch.nn as nn
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from transformers import CLIPVisionConfig
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from transformers import (BatchFeature, CLIPConfig, CLIPProcessor,
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CLIPTextConfig, CLIPVisionConfig)
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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.linear import (ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear)
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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 (
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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 (MultiModalDataDict, MultiModalFieldConfig,
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MultiModalInputs, MultiModalKwargsItems,
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MultiModalUUIDDict)
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from vllm.multimodal.parse import (ImageProcessorItems, ImageSize,
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MultiModalDataItems)
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from vllm.multimodal.processing import (BaseMultiModalProcessor,
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BaseProcessingInfo, PromptIndexTargets,
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PromptReplacement, PromptUpdate)
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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 (VisionEncoderInfo, 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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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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@@ -45,7 +80,214 @@ class CLIPEncoderInfo(VisionEncoderInfo[CLIPVisionConfig]):
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return image_size // patch_size
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# Adapted from https://github.com/huggingface/transformers/blob/v4.39.0/src/transformers/models/clip/modeling_clip.py#L164 # noqa
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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(f"No feature selection strategy is defined for "
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f"pooling_type: {pooling_type!r}") 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, Optional[int]]:
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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: Optional[Mapping[str, BaseDummyOptions]] = 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 = \
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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":
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self._get_dummy_images(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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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: Union[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: Optional[Mapping[str, object]] = None,
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*,
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mm_uuids: Optional[MultiModalUUIDDict] = 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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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,
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embed_dim)
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self.position_embedding = VocabParallelEmbedding(
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config.max_position_embeddings, embed_dim)
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def forward(
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self,
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input_ids: Optional[torch.Tensor],
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position_ids: torch.Tensor,
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inputs_embeds: Optional[torch.Tensor] = 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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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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@@ -89,15 +331,17 @@ class CLIPVisionEmbeddings(nn.Module):
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class CLIPAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(
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self,
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config: CLIPVisionConfig,
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config: Union[CLIPTextConfig, CLIPVisionConfig],
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quant_config: Optional[QuantizationConfig] = None,
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*,
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prefix: str = "",
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):
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attn_cls: Union[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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@@ -127,8 +371,12 @@ class CLIPAttention(nn.Module):
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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 = MultiHeadAttention(self.num_heads_per_partition,
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self.head_dim, self.scale)
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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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@@ -148,7 +396,7 @@ class CLIPMLP(nn.Module):
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def __init__(
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self,
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config: CLIPVisionConfig,
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config: Union[CLIPTextConfig, CLIPVisionConfig],
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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@@ -178,15 +426,18 @@ class CLIPEncoderLayer(nn.Module):
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def __init__(
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self,
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config: CLIPVisionConfig,
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config: Union[CLIPTextConfig, CLIPVisionConfig],
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quant_config: Optional[QuantizationConfig] = None,
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*,
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prefix: str = "",
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attn_cls: Union[type[Attention], type[MultiHeadAttention]],
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) -> 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,
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eps=config.layer_norm_eps)
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@@ -223,10 +474,12 @@ class CLIPEncoder(nn.Module):
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def __init__(
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self,
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config: CLIPVisionConfig,
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config: Union[CLIPTextConfig, CLIPVisionConfig],
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quant_config: Optional[QuantizationConfig] = None,
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num_hidden_layers_override: Optional[int] = None,
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*,
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prefix: str = "",
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attn_cls: Union[type[Attention], type[MultiHeadAttention]],
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) -> None:
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super().__init__()
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@@ -239,12 +492,15 @@ class CLIPEncoder(nn.Module):
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self.layers = nn.ModuleList([
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CLIPEncoderLayer(config=config,
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quant_config=quant_config,
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prefix=f"{prefix}.layers.{layer_idx}")
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prefix=f"{prefix}.layers.{layer_idx}",
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attn_cls=attn_cls)
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for layer_idx in range(num_hidden_layers)
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])
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def forward(
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self, inputs_embeds: torch.Tensor, return_all_hidden_states: bool
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self,
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inputs_embeds: torch.Tensor,
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return_all_hidden_states: bool,
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) -> Union[torch.Tensor, list[torch.Tensor]]:
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hidden_states_pool = [inputs_embeds]
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hidden_states = inputs_embeds
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@@ -260,6 +516,87 @@ class CLIPEncoder(nn.Module):
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return hidden_states
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class CLIPTextTransformer(nn.Module):
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def __init__(
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self,
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config: CLIPTextConfig,
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quant_config: Optional[QuantizationConfig] = None,
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*,
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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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embed_dim = config.hidden_size
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self.embeddings = CLIPTextEmbeddings(config)
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self.encoder = CLIPEncoder(
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config=config,
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quant_config=quant_config,
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prefix=f"{prefix}.encoder",
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attn_cls=Attention,
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)
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self.final_layer_norm = nn.LayerNorm(
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embed_dim,
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eps=config.layer_norm_eps,
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)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embeddings.token_embedding(input_ids)
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def forward(
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self,
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input_ids: Optional[torch.Tensor],
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position_ids: torch.Tensor,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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hidden_states = self.embeddings(
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input_ids=input_ids,
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position_ids=position_ids,
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inputs_embeds=inputs_embeds,
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)
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last_hidden_state = self.encoder(
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inputs_embeds=hidden_states,
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return_all_hidden_states=False,
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)
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last_hidden_state = self.final_layer_norm(last_hidden_state)
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return last_hidden_state
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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]
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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for (param_name, weight_name, shard_id) in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
|
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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return loaded_params
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class CLIPVisionTransformer(nn.Module):
|
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|
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def __init__(
|
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@@ -287,6 +624,7 @@ class CLIPVisionTransformer(nn.Module):
|
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quant_config=quant_config,
|
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num_hidden_layers_override=num_hidden_layers_override,
|
||||
prefix=f"{prefix}.encoder",
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attn_cls=MultiHeadAttention,
|
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)
|
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|
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num_hidden_layers = config.num_hidden_layers
|
||||
@@ -306,6 +644,14 @@ class CLIPVisionTransformer(nn.Module):
|
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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,
|
||||
@@ -335,47 +681,6 @@ class CLIPVisionTransformer(nn.Module):
|
||||
|
||||
return encoder_outputs
|
||||
|
||||
|
||||
class CLIPVisionModel(nn.Module, SupportsQuant):
|
||||
config_class = CLIPVisionConfig
|
||||
main_input_name = "pixel_values"
|
||||
packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: CLIPVisionConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
*,
|
||||
num_hidden_layers_override: Optional[int] = None,
|
||||
require_post_norm: Optional[bool] = 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: Optional[list[int]] = None,
|
||||
feature_select_strategy: Optional[VisionFeatureSelectStrategy] = None,
|
||||
) -> torch.Tensor:
|
||||
return self.vision_model(
|
||||
pixel_values,
|
||||
select_layers=select_layers,
|
||||
feature_select_strategy=feature_select_strategy,
|
||||
)
|
||||
|
||||
@property
|
||||
def device(self):
|
||||
return next(self.parameters()).device
|
||||
|
||||
# (TODO) Add prefix argument for filtering out weights to be loaded
|
||||
# ref: https://github.com/vllm-project/vllm/pull/7186#discussion_r1734163986
|
||||
def load_weights(self, weights: Iterable[tuple[str,
|
||||
torch.Tensor]]) -> set[str]:
|
||||
stacked_params_mapping = [
|
||||
@@ -386,17 +691,17 @@ class CLIPVisionModel(nn.Module, SupportsQuant):
|
||||
]
|
||||
params_dict = dict(self.named_parameters())
|
||||
loaded_params: set[str] = set()
|
||||
layer_count = len(self.vision_model.encoder.layers)
|
||||
layer_count = len(self.encoder.layers)
|
||||
|
||||
for name, loaded_weight in weights:
|
||||
# post_layernorm is not needed in CLIPVisionModel
|
||||
if (name.startswith("vision_model.post_layernorm")
|
||||
and self.vision_model.post_layernorm is None):
|
||||
if (name.startswith("post_layernorm")
|
||||
and self.post_layernorm is None):
|
||||
continue
|
||||
|
||||
# omit layers when num_hidden_layers_override is set
|
||||
if name.startswith("vision_model.encoder.layers"):
|
||||
layer_idx = int(name.split(".")[3])
|
||||
if name.startswith("encoder.layers"):
|
||||
layer_idx = int(name.split(".")[2])
|
||||
if layer_idx >= layer_count:
|
||||
continue
|
||||
|
||||
@@ -416,3 +721,233 @@ class CLIPVisionModel(nn.Module, SupportsQuant):
|
||||
weight_loader(param, loaded_weight)
|
||||
loaded_params.add(name)
|
||||
return loaded_params
|
||||
|
||||
|
||||
class CLIPVisionModel(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: CLIPVisionConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
*,
|
||||
num_hidden_layers_override: Optional[int] = None,
|
||||
require_post_norm: Optional[bool] = 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: Optional[list[int]] = None,
|
||||
feature_select_strategy: Optional[VisionFeatureSelectStrategy] = 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) -> Optional[str]:
|
||||
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({
|
||||
"encode": Pooler.for_encode(pooler_config),
|
||||
"embed": Pooler.for_embed(pooler_config),
|
||||
})
|
||||
|
||||
# Assumes that self.forward is called after self.get_input_embeddings
|
||||
self._is_text_input = True
|
||||
|
||||
def get_text_features(
|
||||
self,
|
||||
input_ids: Optional[torch.Tensor],
|
||||
position_ids: torch.Tensor,
|
||||
inputs_embeds: Optional[torch.Tensor] = 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: Optional[VisionFeatureSelectStrategy] = 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) -> Optional[CLIPImagePixelInputs]:
|
||||
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 get_input_embeddings(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
|
||||
*,
|
||||
is_multimodal: Optional[torch.Tensor] = 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().get_input_embeddings(input_ids)
|
||||
|
||||
return super().get_input_embeddings(
|
||||
input_ids,
|
||||
multimodal_embeddings=multimodal_embeddings,
|
||||
is_multimodal=is_multimodal,
|
||||
handle_oov_mm_token=handle_oov_mm_token,
|
||||
)
|
||||
|
||||
def get_multimodal_embeddings(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: Optional[torch.Tensor],
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: Optional[IntermediateTensors] = None,
|
||||
inputs_embeds: Optional[torch.Tensor] = 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