[Model] Siglip Embedding Support (#27324)
Signed-off-by: piood <2477084691@qq.com>
This commit is contained in:
Yu Jiaqi
@@ -4,13 +4,23 @@
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within a vision language model."""
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import math
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from collections.abc import Iterable
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from collections.abc import Iterable, Mapping
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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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from torch import nn
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from transformers import SiglipVisionConfig
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from transformers import (
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BatchFeature,
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SiglipConfig,
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SiglipProcessor,
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SiglipTextConfig,
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SiglipVisionConfig,
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)
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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 (
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@@ -18,20 +28,232 @@ from vllm.model_executor.layers.linear import (
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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 (
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default_weight_loader,
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maybe_remap_kv_scale_name,
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)
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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, SupportsQuant
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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 SiglipImagePixelInputs(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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_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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}
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def _get_vision_feature_select_strategy(
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pooling_type: str,
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) -> VisionFeatureSelectStrategyStr:
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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 SiglipProcessingInfo(BaseProcessingInfo):
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def get_hf_config(self):
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return self.ctx.get_hf_config(SiglipConfig)
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def get_vision_encoder_info(self):
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return SiglipEncoderInfo(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(SiglipProcessor, **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, image_height=target_height
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)
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class SiglipDummyInputsBuilder(BaseDummyInputsBuilder[SiglipProcessingInfo]):
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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 SiglipMultiModalProcessor(BaseMultiModalProcessor[SiglipProcessingInfo]):
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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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"Siglip 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 image token
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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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) -> list[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, 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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class SiglipEncoderInfo(VisionEncoderInfo[SiglipVisionConfig]):
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def get_num_image_tokens(
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self,
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@@ -151,8 +373,9 @@ class SiglipVisionEmbeddings(nn.Module):
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class SiglipAttention(nn.Module):
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def __init__(
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self,
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config: SiglipVisionConfig,
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config: SiglipVisionConfig | SiglipTextConfig,
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quant_config: QuantizationConfig | None = 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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@@ -195,12 +418,29 @@ class SiglipAttention(nn.Module):
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def forward(
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self,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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) -> tuple[torch.Tensor, None]:
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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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needs_unsqueeze = query_states.ndim == 2
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if needs_unsqueeze:
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query_states, key_states, value_states = (
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query_states.unsqueeze(0),
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key_states.unsqueeze(0),
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value_states.unsqueeze(0),
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)
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out = self.attn(query_states, key_states, value_states)
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if needs_unsqueeze:
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out, query_states, key_states, value_states = (
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out.squeeze(0),
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query_states.squeeze(0),
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key_states.squeeze(0),
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value_states.squeeze(0),
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)
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attn_output, _ = self.out_proj(out)
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return attn_output, None
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@@ -209,7 +449,7 @@ class SiglipAttention(nn.Module):
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class SiglipMLP(nn.Module):
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def __init__(
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self,
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config: SiglipVisionConfig,
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config: SiglipVisionConfig | SiglipTextConfig,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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) -> None:
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@@ -249,8 +489,9 @@ class SiglipMLP(nn.Module):
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class SiglipEncoderLayer(nn.Module):
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def __init__(
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self,
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config: SiglipVisionConfig,
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config: SiglipVisionConfig | SiglipTextConfig,
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quant_config: QuantizationConfig | None = 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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@@ -291,9 +532,10 @@ class SiglipEncoderLayer(nn.Module):
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class SiglipEncoder(nn.Module):
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def __init__(
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self,
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config: SiglipVisionConfig,
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config: SiglipVisionConfig | SiglipTextConfig,
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quant_config: QuantizationConfig | None = None,
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num_hidden_layers_override: int | None = 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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@@ -335,6 +577,76 @@ class SiglipEncoder(nn.Module):
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return hidden_states
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class SiglipTextTransformer(nn.Module):
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def __init__(
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self,
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config: SiglipTextConfig,
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quant_config: QuantizationConfig | None = 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 = SiglipTextEmbeddings(config)
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self.encoder = SiglipEncoder(
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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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)
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self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
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self.head = nn.Linear(embed_dim, config.projection_size)
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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: 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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hidden_states = self.embeddings(input_ids, position_ids, inputs_embeds)
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last_hidden_state = self.encoder(
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inputs_embeds=hidden_states, 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, 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", 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 SiglipMultiheadAttentionPoolingHead(nn.Module):
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"""Multihead Attention Pooling."""
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@@ -357,8 +669,9 @@ class SiglipMultiheadAttentionPoolingHead(nn.Module):
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)
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def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
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batch_size = hidden_state.shape[0]
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probe = self.probe.repeat(batch_size, 1, 1)
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batch_size = hidden_state.size(0)
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probe = self.probe.expand(batch_size, -1, -1)
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hidden_state = self.attention(probe, hidden_state, hidden_state)[0]
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@@ -367,7 +680,9 @@ class SiglipMultiheadAttentionPoolingHead(nn.Module):
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hidden_state = self.mlp(hidden_state)
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hidden_state += residual
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return hidden_state[:, 0]
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pooled = hidden_state[:, 0]
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return pooled.unsqueeze(1)
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class SiglipVisionTransformer(nn.Module):
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@@ -420,6 +735,14 @@ class SiglipVisionTransformer(nn.Module):
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prefix=f"{prefix}.head",
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)
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@property
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def dtype(self):
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return next(self.parameters()).dtype
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@property
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def device(self):
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return next(self.parameters()).device
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def forward(
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self,
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pixel_values: torch.Tensor,
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@@ -432,7 +755,6 @@ class SiglipVisionTransformer(nn.Module):
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pixel_values,
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interpolate_pos_encoding=interpolate_pos_encoding,
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)
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# Produces either the last layer output or all of the hidden states,
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# depending on if we have select_layers or not
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encoder_outputs = self.encoder(
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@@ -440,21 +762,60 @@ class SiglipVisionTransformer(nn.Module):
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return_all_hidden_states=select_layers is not None,
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)
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# Handle post-norm (if applicable) and stacks feature layers if needed
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if self.post_layernorm is not None:
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encoder_outputs = self.post_layernorm(encoder_outputs)
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if self.use_head:
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encoder_outputs = self.head(encoder_outputs)
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# stacks feature layers if needed
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encoder_outputs = resolve_visual_encoder_outputs(
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encoder_outputs,
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self.post_layernorm,
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None,
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select_layers=select_layers,
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max_possible_layers=self.config.num_hidden_layers,
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feature_select_strategy=feature_select_strategy,
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)
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# TODO: add this back when pooled_output is used in inference.
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# if self.use_head:
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# pooled_output = self.head(encoder_outputs)
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return encoder_outputs
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def load_weights(self, weights: Iterable[tuple[str, 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"),
|
||||
("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 SiglipVisionTransformer
|
||||
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 SiglipVisionModel(nn.Module):
|
||||
config_class = SiglipVisionConfig
|
||||
@@ -484,7 +845,11 @@ class SiglipVisionModel(nn.Module):
|
||||
|
||||
@property
|
||||
def dtype(self):
|
||||
return self.get_input_embeddings().weight.dtype
|
||||
return self.vision_model.dtype
|
||||
|
||||
@property
|
||||
def device(self):
|
||||
return self.vision_model.device
|
||||
|
||||
def forward(
|
||||
self,
|
||||
@@ -555,3 +920,214 @@ class SiglipVisionModel(nn.Module):
|
||||
weight_loader(param, loaded_weight)
|
||||
loaded_params.add(name)
|
||||
return loaded_params
|
||||
|
||||
|
||||
# Adapted from: https://github.com/huggingface/transformers/blob/v4.54.1/src/transformers/models/siglip/modeling_siglip.py#L200
|
||||
class SiglipTextEmbeddings(nn.Module):
|
||||
def __init__(self, config: SiglipTextConfig):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
|
||||
self.token_embedding = VocabParallelEmbedding(
|
||||
config.vocab_size, config.hidden_size
|
||||
)
|
||||
|
||||
self.position_embedding = VocabParallelEmbedding(
|
||||
config.max_position_embeddings, config.hidden_size
|
||||
)
|
||||
|
||||
self.register_buffer(
|
||||
"position_ids",
|
||||
torch.arange(config.max_position_embeddings).expand((1, -1)),
|
||||
persistent=False,
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor | None,
|
||||
position_ids: torch.Tensor,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
) -> torch.Tensor:
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.token_embedding(input_ids)
|
||||
|
||||
position_embeddings = self.position_embedding(position_ids)
|
||||
embeddings = inputs_embeds + position_embeddings
|
||||
return embeddings
|
||||
|
||||
|
||||
# Assume EOS token corresponds to CLS token in text model
|
||||
@default_pooling_type("CLS")
|
||||
@MULTIMODAL_REGISTRY.register_processor(
|
||||
SiglipMultiModalProcessor,
|
||||
info=SiglipProcessingInfo,
|
||||
dummy_inputs=SiglipDummyInputsBuilder,
|
||||
)
|
||||
class SiglipEmbeddingModel(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: SiglipConfig = 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
|
||||
|
||||
if hasattr(config, "num_labels"):
|
||||
config.num_labels = 0
|
||||
|
||||
text_config = config.text_config
|
||||
vision_config = config.vision_config
|
||||
|
||||
self.text_embed_dim = text_config.hidden_size
|
||||
self.vision_embed_dim = vision_config.hidden_size
|
||||
|
||||
self.text_model = SiglipTextTransformer(
|
||||
text_config,
|
||||
quant_config=quant_config,
|
||||
prefix=maybe_prefix(prefix, "text_model"),
|
||||
)
|
||||
self.vision_model = SiglipVisionTransformer(
|
||||
vision_config,
|
||||
quant_config=quant_config,
|
||||
prefix=maybe_prefix(prefix, "vision_model"),
|
||||
)
|
||||
|
||||
self.text_projection_size = text_config.projection_size
|
||||
|
||||
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),
|
||||
}
|
||||
)
|
||||
|
||||
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:
|
||||
last_hidden_state = self.text_model(
|
||||
input_ids=input_ids,
|
||||
position_ids=position_ids,
|
||||
inputs_embeds=inputs_embeds,
|
||||
)
|
||||
text_features = self.text_model.head(last_hidden_state)
|
||||
# Flip to extract CLS token (first token after reversal) for pooling
|
||||
text_features = text_features.flip(0)
|
||||
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,
|
||||
)
|
||||
|
||||
return pooled_output
|
||||
|
||||
def _parse_and_validate_image_input(
|
||||
self, **kwargs: object
|
||||
) -> SiglipImagePixelInputs | 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 SiglipImagePixelInputs(
|
||||
type="pixel_values",
|
||||
data=pixel_values,
|
||||
resolve_bindings={"h": expected_h, "w": expected_w},
|
||||
)
|
||||
|
||||
def _process_image_inputs(self, inputs: SiglipImagePixelInputs) -> 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: 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
|
||||
)
|
||||
|
||||
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: 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 (image embeddings)
|
||||
if not self._is_text_input:
|
||||
return inputs_embeds
|
||||
|
||||
return self.get_text_features(input_ids, positions, inputs_embeds)
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
|
||||
loader = AutoWeightsLoader(
|
||||
self,
|
||||
skip_substrs=[".position_ids"],
|
||||
ignore_unexpected_prefixes=["logit_scale.", "logit_bias."],
|
||||
)
|
||||
|
||||
return loader.load_weights(weights)
|
||||
|
||||
Reference in New Issue
Block a user