phi3v.py 26.5 KB
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# Copyright 2024 The vLLM team.
# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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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 List, Literal, Optional, Set, Tuple, TypedDict, Union
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import torch
import torch.nn as nn
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from transformers import (BatchFeature, CLIPVisionConfig, PretrainedConfig,
                          ProcessorMixin)
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from vllm.attention import AttentionMetadata
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from vllm.config import VllmConfig
from vllm.inputs import InputContext
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from vllm.logger import init_logger
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
    VocabParallelEmbedding)
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from vllm.model_executor.models.clip import CLIPVisionModel
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
                                    MultiModalInputsV2, MultiModalKwargs,
                                    NestedTensors, PlaceholderRange)
from vllm.multimodal.parse import ImageProcessorItems
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from vllm.multimodal.processing import (BaseMultiModalProcessor,
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                                        MultiModalDataItems, ProcessorInputs,
                                        PromptReplacement,
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                                        _BoundPromptReplacement,
                                        _PlaceholderInfo)
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from vllm.sequence import IntermediateTensors
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from vllm.utils import is_list_of
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from .clip import dummy_image_for_clip
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from .interfaces import SupportsMultiModal, SupportsPP
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from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
                    init_vllm_registered_model, maybe_prefix,
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                    merge_multimodal_embeddings)
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logger = init_logger(__name__)

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# Cannot find the following 2 numbers from hf config.
_IMAGE_TOKEN_ID = 32044

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# Result in the max possible feature size (h:w = 16:1)
MAX_IMAGE_FEATURE_SIZE_HEIGHT = 8000
MAX_IMAGE_FEATURE_SIZE_WIDTH = 50

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CLIP_VIT_LARGE_PATCH14_336_CONFIG = CLIPVisionConfig(dropout=0.0,
                                                     hidden_act="quick_gelu",
                                                     hidden_size=1024,
                                                     image_size=336,
                                                     intermediate_size=4096,
                                                     num_attention_heads=16,
                                                     num_channels=3,
                                                     num_hidden_layers=24,
                                                     patch_size=14,
                                                     projection_dim=768)


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def _init_img_processor(hf_config: PretrainedConfig,
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                        quant_config: Optional[QuantizationConfig],
                        prefix: str = "") -> CLIPVisionModel:
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    clip_config = CLIP_VIT_LARGE_PATCH14_336_CONFIG
    layer_idx = hf_config.img_processor.get('layer_idx', -2)

    # Initialize the CLIP only up to the required feature layer
    if layer_idx < 0:
        num_hidden_layers = clip_config.num_hidden_layers + \
            layer_idx + 1
    else:
        num_hidden_layers = layer_idx + 1

    img_processor = CLIPVisionModel(
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        clip_config,
        quant_config,
        num_hidden_layers_override=num_hidden_layers,
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        prefix=prefix,
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    )
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    return img_processor


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class Phi3VImagePixelInputs(TypedDict):
    type: Literal["pixel_values"]
    data: Union[torch.Tensor, List[torch.Tensor]]
    """
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    Shape:
    `(batch_size * num_images, 1 + num_patches, num_channels, height, width)`
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    Note that `num_patches` may be different per batch and image,
    in which case the data is passed as a list instead of a batched tensor.
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    """

    image_sizes: torch.Tensor
    """
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    Shape: `(batch_size * num_images, 2)`
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    This should be in `(height, width)` format.
    """


class Phi3VImageEmbeddingInputs(TypedDict):
    type: Literal["image_embeds"]
    data: Union[torch.Tensor, List[torch.Tensor]]
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    """Shape: `(batch_size * num_images, image_feature_size, hidden_size)`
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    `hidden_size` must match the hidden size of language model backbone.
    """


Phi3VImageInputs = Union[Phi3VImagePixelInputs, Phi3VImageEmbeddingInputs]


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class Phi3ImageEmbeddingBase(nn.Module):

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    def __init__(self) -> None:
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        super().__init__()
        self.layer_idx: int
        self.type_feature: str
        self.img_processor: CLIPVisionModel

    def get_img_features(self,
                         img_embeds: torch.FloatTensor) -> torch.FloatTensor:
        TYPE_FEATURE = self.type_feature

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        # NOTE: we skip the step to select the vision feature layer since
        # this is already done inside the img_processor
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        img_feature = self.img_processor(img_embeds)
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        if TYPE_FEATURE == "patch":
            patch_feature = img_feature[:, 1:]
            return patch_feature

        if TYPE_FEATURE == "cls_patch":
            return img_feature

        raise NotImplementedError


# adapted from https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_embedding_phi3_v.py
class Phi3HDImageEmbedding(Phi3ImageEmbeddingBase):
    """Phi3 Image embedding with HD transform."""

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    def __init__(self,
                 config: PretrainedConfig,
                 quant_config: Optional[QuantizationConfig],
                 prefix: str = "") -> None:
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        super().__init__()
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        # n_embed or hidden_size
        hidden_size = config.n_embd if hasattr(
            config, 'n_embd') else config.hidden_size

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        self.img_processor = _init_img_processor(
            config, quant_config, prefix=f"{prefix}.img_processor")
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        image_dim_out = config.img_processor['image_dim_out']
        self.num_img_tokens = config.img_processor['num_img_tokens']

        self.image_dim_out = image_dim_out

        # global_gn and sub_gn for hd transform, serves as line separator
        self.use_hd_transform = config.embd_layer.get('use_hd_transform',
                                                      False)
        self.with_learnable_separator = config.embd_layer.get(
            'with_learnable_separator', False)
        self.hd_transform_order = config.embd_layer.get(
            'hd_transform_order', 'glb_sub')
        # with_hd_transform and with_learnable_separator should have same value
        assert self.use_hd_transform and self.with_learnable_separator

        # 1024 * 4, merge spatial to channel dimension
        self.glb_GN = nn.Parameter(torch.empty([1, 1, self.image_dim_out * 4]))
        self.sub_GN = nn.Parameter(
            torch.empty([1, 1, 1, self.image_dim_out * 4]))

        dim_projection = hidden_size
        depth = 2
        layers = [nn.Linear(image_dim_out * 4, dim_projection)]
        for _ in range(1, depth):
            layers.extend(
                [nn.GELU(),
                 nn.Linear(dim_projection, dim_projection)])
        self.img_projection = nn.Sequential(*layers)

        self.type_feature = config.img_processor.get('type_feature', 'patch')

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    def forward(self, pixel_values: torch.FloatTensor,
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                image_sizes: torch.Tensor) -> torch.FloatTensor:
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        """
        process image and return vision embeddings.

        pixel_values: (num_images, num_crops, c, h, w)
        output: (num_images, num_img_tokens, hidden_size)
        """
        num_images, num_crops, c, h, w = pixel_values.shape
        pixel_values = pixel_values.flatten(0, 1)
        img_features = self.get_img_features(pixel_values)
        img_features = img_features.reshape(num_images, num_crops, -1,
                                            self.image_dim_out)
        image_features_proj = self.hd_feature_transform(
            img_features, image_sizes)
        return image_features_proj

    def hd_feature_transform(self, image_features, image_sizes):
        """
        image_features: (num_images, num_crops+1, 24*24, 1024)
        """
        assert (
            self.hd_transform_order == 'sub_glb'
        ), f'hd_transform_order `{self.hd_transform_order}` not implemented'
        if isinstance(self.img_projection, nn.Sequential):
            target_device = self.img_projection[0].bias.device
            target_dtype = self.img_projection[0].bias.dtype
        else:  # It's a single nn.Linear layer
            target_device = self.img_projection.bias.device
            target_dtype = self.img_projection.bias.dtype

        global_image_features = image_features[:,
                                               0]  # (num_images, 24*24, 1024)
        # global feature can be viewed as a special HD case with num_crops 1x1
        global_image_features_hd = self.reshape_hd_patches_2x2merge(
            global_image_features, 1, 1)
        global_image_features_hd_newline = self.add_image_newline(
            global_image_features_hd)

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        batch_image_features_proj = []
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        # need a for loop to process each image because of different image sizes
        # (patch arrangement is different for each image)
        for i, img_size in enumerate(image_sizes):
            h, w = img_size
            h_crop = h // 336
            w_crop = w // 336
            num_crops = h_crop * w_crop

            # NOTE: real num_crops is padded
            # (num_crops, 24*24, 1024)
            sub_image_features = image_features[i, 1:1 + num_crops]
            sub_image_features_hd = self.reshape_hd_patches_2x2merge(
                sub_image_features, h_crop, w_crop)
            sub_image_features_hd_newline = self.add_image_newline(
                sub_image_features_hd)

            # [sub features, separator, global features]
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            image_embeddings = torch.cat([
                sub_image_features_hd_newline.squeeze(
                    0),  # (h_crop*12*(w_crop*12+1), 4096)
                self.glb_GN.squeeze(0),
                global_image_features_hd_newline[i],
            ])
            img_proj = self.img_projection(
                image_embeddings.to(target_device, target_dtype))
            batch_image_features_proj.append(img_proj)

        return batch_image_features_proj
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    def reshape_hd_patches_2x2merge(self, image_features, h_crop, w_crop):
        """
        image_features: (num_images*num_crops, 24*24, 1024)
        output: (num_images, h_crop*12, w_crop*12, 4096)
        where h_crop*w_crop == num_crops
        """
        N, L, C = image_features.shape
        assert L == 576 and C == 1024 and N % (h_crop * w_crop) == 0
        num_images = N // (h_crop * w_crop)
        H = int(L**0.5)
        image_features_hd = (
            image_features.reshape(N, H, H, C)  # N, 24, 24, 1024
            .reshape(N, H // 2, 2, H // 2, 2, C)  # N, 12, 2, 12, 2, 1024
            .permute(0, 1, 3, 2, 4, 5)  # N, 12, 12, 2, 2, 1024
            .reshape(N, -1, 4 * C)  # N, 144, 4096
            .reshape(num_images, h_crop, w_crop, H // 2, H // 2,
                     -1)  # n_img, h_crop, w_crop, 12, 12, 4096
            .permute(0, 1, 3, 2, 4, 5)  # n_img, h_crop, 12, w_crop, 12, 4096
            .reshape(num_images, h_crop * H // 2, w_crop * H // 2,
                     4 * C)  # n_img, h_crop*12, w_crop*12, 4096
        )
        return image_features_hd

    def add_image_newline(self, image_features_hd):
        """
        image_features_hd: (num_images, h_crop*12, w_crop*12, 4096)
        output: (num_images, (h_crop*12) * (w_crop*12+1), 4096)
        """
        num_images, h, w, hid_dim = image_features_hd.shape
        # add the newline token to the HD image feature patches
        newline_embeddings = self.sub_GN.expand(num_images, h, -1,
                                                -1)  # (n_img, h, 1, hid_dim)
        image_features_hd_newline = torch.cat(
            [image_features_hd, newline_embeddings],
            dim=2).reshape(num_images, -1, hid_dim)
        return image_features_hd_newline
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def get_max_phi3v_image_tokens(
    ctx: InputContext,
    *,
    num_crops: Optional[int] = None,
) -> int:
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    hf_processor_mm_kwargs = {}
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    if num_crops:
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        hf_processor_mm_kwargs["num_crops"] = num_crops
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    processor = ctx.get_hf_processor(**hf_processor_mm_kwargs)
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    return processor.calc_num_image_tokens_from_image_size(
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        width=MAX_IMAGE_FEATURE_SIZE_WIDTH,
        height=MAX_IMAGE_FEATURE_SIZE_HEIGHT,
    )
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class Phi3VMultiModalProcessor(BaseMultiModalProcessor):
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    def _get_hf_processor(
        self,
        *,
        num_crops: Optional[int] = None,
    ) -> ProcessorMixin:
        if num_crops is not None:
            return self.ctx.get_hf_processor(num_crops=num_crops)
        return self.ctx.get_hf_processor()

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    def _call_hf_processor(
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        self,
        prompt: str,
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        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
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    ) -> BatchFeature:
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        processed_outputs = super()._call_hf_processor(
            prompt=prompt,
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            mm_data=mm_data,
            mm_kwargs=mm_kwargs,
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        )

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        input_ids = processed_outputs["input_ids"]
        assert isinstance(input_ids, torch.Tensor)

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        # Phi3v processor has inserted -1, -2 etc as placeholder in prompt_ids,
        # which will cause OverflowError when decoding the prompt_ids.
        # Therefore, we need to do an early replacement here
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        input_ids.masked_fill_(input_ids < 0, _IMAGE_TOKEN_ID)
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        return processed_outputs

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    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        return dict(
            pixel_values=MultiModalFieldConfig.batched("image"),
            image_sizes=MultiModalFieldConfig.batched("image"),
            image_embeds=MultiModalFieldConfig.batched("image"),
        )

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    def _get_prompt_replacements(
        self,
        mm_items: MultiModalDataItems,
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        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargs,
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    ) -> list[PromptReplacement]:
        hf_processor = self._get_hf_processor()
        image_tokens: list[str] = hf_processor.img_tokens  # type: ignore
        image_processor = hf_processor.image_processor  # type: ignore

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        tokenizer = self._get_tokenizer()
        bos_token_id = tokenizer.bos_token_id
        assert isinstance(bos_token_id, int)
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        def get_replacement_phi3v(item_idx: int):
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            images = mm_items.get_items("image", ImageProcessorItems)
            image_size = images.get_image_size(item_idx)

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            num_tokens = image_processor.calc_num_image_tokens_from_image_size(
                width=image_size.width,
                height=image_size.height,
            )

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            return [_IMAGE_TOKEN_ID] * num_tokens + [bos_token_id]
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        num_images = mm_items.get_count("image", strict=False)

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        return [
            PromptReplacement(
                modality="image",
                target=image_token,
                replacement=get_replacement_phi3v,
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            ) for image_token in image_tokens[:num_images]
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        ]

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    def _apply_prompt_replacements(
        self,
        token_ids: list[int],
        prompt_repls: Sequence[_BoundPromptReplacement],
        mm_item_counts: Mapping[str, int],
    ) -> tuple[list[int], str, list[_PlaceholderInfo]]:
        token_ids, text, placeholders = super()._apply_prompt_replacements(
            token_ids=token_ids,
            prompt_repls=prompt_repls,
            mm_item_counts=mm_item_counts,
        )

        # Keep the behavior in line with HF processor
        if text.startswith("<s> <|image|>"):
            text = text.replace("<s> <|image|>", "<s><|image|>", 1)
            token_ids = [token_ids[0], *token_ids[2:]]
            placeholders = [
                _PlaceholderInfo(p.modality, p.start_idx - 1, p.replacement)
                for p in placeholders
            ]

        return token_ids, text, placeholders

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    def _get_dummy_mm_inputs(
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        self,
        mm_counts: Mapping[str, int],
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    ) -> ProcessorInputs:
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        num_images = mm_counts.get("image", 0)
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        data = dummy_image_for_clip(
            CLIP_VIT_LARGE_PATCH14_336_CONFIG,
            num_images,
            image_width_override=MAX_IMAGE_FEATURE_SIZE_WIDTH,
            image_height_override=MAX_IMAGE_FEATURE_SIZE_HEIGHT,
        )

        hf_processor = self._get_hf_processor()
        image_tokens: list[str] = hf_processor.img_tokens  # type: ignore

        return ProcessorInputs(
            prompt_text="".join(image_tokens[:num_images]),
            mm_data=data,
        )
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    def apply(
        self,
        prompt_text: str,
        mm_data: MultiModalDataDict,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> MultiModalInputsV2:
        result = super().apply(prompt_text, mm_data, hf_processor_mm_kwargs)

        # Only <|image|> tokens should be considered as placeholders,
        # so we ignore the trailing bos_token_id
        result["mm_placeholders"] = {
            modality: [
                PlaceholderRange(offset=p["offset"], length=p["length"] - 1)
                for p in ps
            ]
            for modality, ps in result["mm_placeholders"].items()
        }

        return result

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@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_phi3v_image_tokens)
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@MULTIMODAL_REGISTRY.register_processor(Phi3VMultiModalProcessor)
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class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
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    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_prefix={
            "model.vision_embed_tokens.wte": "embed_tokens",
            "model.vision_embed_tokens.": "vision_embed_tokens.",
            "lm_head.": "language_model.lm_head.",
            "model.": "language_model.model.",
        })
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    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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        super().__init__()
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        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        multimodal_config = vllm_config.model_config.multimodal_config
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        self.config = config
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        self.multimodal_config = multimodal_config
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        self.image_token_id = _IMAGE_TOKEN_ID
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        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
            org_num_embeddings=config.vocab_size,
            quant_config=quant_config,
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            prefix=maybe_prefix(prefix, "model.embed_tokens"),
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        )

        # TODO: Optionally initializes this for supporting input embeddings.
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        self.vision_embed_tokens = Phi3HDImageEmbedding(
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            config,
            quant_config,
            prefix=maybe_prefix(prefix, "model.vision_embed_tokens"))
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        self.language_model = init_vllm_registered_model(
            vllm_config=vllm_config,
            # The prefix is empty intentionally because default prefix of
            # LlamaForCausalLM is "model"
            prefix="",
            # We don't directly initialize vLLM's LlamaForCausalLM so we
            # can automatically apply embedding wrapper if this model is
            # initialized as an embedding model
            architectures=["LlamaForCausalLM"],
        )

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        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors)

    @cached_property
    def sampler(self):
        if hasattr(self.language_model, "sampler"):
            return self.language_model.sampler

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        return get_sampler()
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    def _validate_image_sizes(self, data: torch.Tensor) -> torch.Tensor:
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        expected_dims = (2, )

        def _validate_shape(d: torch.Tensor):
            actual_dims = tuple(d.shape)

            if actual_dims != expected_dims:
                expected_expr = str(expected_dims)
                raise ValueError(
                    f"The expected shape of image sizes per image per batch "
                    f"is {expected_expr}. You supplied {tuple(d.shape)}.")

        for d in data:
            _validate_shape(d)
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        return data

    def _validate_pixel_values(
        self, data: Union[torch.Tensor, List[torch.Tensor]]
    ) -> Union[torch.Tensor, List[torch.Tensor]]:

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        h = w = CLIP_VIT_LARGE_PATCH14_336_CONFIG.image_size
        expected_dims = (3, h, w)

        def _validate_shape(d: torch.Tensor):
            actual_dims = tuple(d.shape[1:])

            if actual_dims != expected_dims:
                expected_expr = ("num_patches", *map(str, expected_dims))
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                raise ValueError(
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                    "The expected shape of pixel values per image per batch "
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                    f"is {expected_expr}. You supplied {tuple(d.shape)}.")
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        for d in data:
            _validate_shape(d)
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        return data

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    def _parse_and_validate_image_input(
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            self, **kwargs: object) -> Optional[Phi3VImageInputs]:
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        pixel_values = kwargs.pop("pixel_values", None)
        image_sizes = kwargs.pop("image_sizes", None)
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        image_embeds = kwargs.pop("image_embeds", None)
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        if pixel_values is None and image_embeds is None:
            return None

        if pixel_values is not None:
            if not isinstance(pixel_values, (torch.Tensor, list)):
                raise ValueError("Incorrect type of pixel values. "
                                 f"Got type: {type(pixel_values)}")

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            if not isinstance(image_sizes, (torch.Tensor, list)):
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                raise ValueError("Incorrect type of image sizes. "
                                 f"Got type: {type(image_sizes)}")

            return Phi3VImagePixelInputs(
                type="pixel_values",
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                data=self._validate_pixel_values(flatten_bn(pixel_values)),
                image_sizes=self._validate_image_sizes(
                    flatten_bn(image_sizes, concat=True)))
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        if image_embeds is not None:
            if not isinstance(image_embeds, torch.Tensor):
                raise ValueError("Incorrect type of image embeddings. "
                                 f"Got type: {type(image_embeds)}")
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            return Phi3VImageEmbeddingInputs(
                type="image_embeds",
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                data=flatten_bn(image_embeds),
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            )

        raise AssertionError("This line should be unreachable.")

    def _process_image_input(
        self,
        image_input: Phi3VImageInputs,
    ) -> torch.Tensor:

        if image_input["type"] == "image_embeds":
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            image_data = image_input["data"]
            if is_list_of(image_data, torch.Tensor):
                # it's already a list of tensors
                return image_data
            if len(image_data.shape) == 3:
                # 3D tensor
                return list(torch.unbind(image_data, dim=0))
            raise ValueError(
                "We expect batched 2D tensors;"
                "this can be either a list of 2D tensors or a single 3D tensor."
            )
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        assert self.vision_embed_tokens is not None
        image_embeds = self.vision_embed_tokens(image_input["data"],
                                                image_input["image_sizes"])
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        return image_embeds
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    def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]:
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        image_input = self._parse_and_validate_image_input(**kwargs)
        if image_input is None:
            return None
        vision_embeddings = self._process_image_input(image_input)
        return vision_embeddings

    def get_input_embeddings(
        self,
        input_ids: torch.Tensor,
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        multimodal_embeddings: Optional[NestedTensors] = None,
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    ) -> torch.Tensor:
        inputs_embeds = self.embed_tokens(input_ids)
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        if multimodal_embeddings is not None:
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            inputs_embeds = merge_multimodal_embeddings(
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                input_ids, inputs_embeds, multimodal_embeddings,
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                self.image_token_id)
        return inputs_embeds

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    def forward(self,
                input_ids: torch.Tensor,
                positions: torch.Tensor,
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                kv_caches: List[torch.Tensor],
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                attn_metadata: AttentionMetadata,
                intermediate_tensors: Optional[IntermediateTensors] = None,
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                inputs_embeds: Optional[torch.Tensor] = None,
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                **kwargs: object):
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        if intermediate_tensors is not None:
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            inputs_embeds = None
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        # NOTE: In v1, inputs_embeds is always generated at model runner, this
        # condition is for v0 compatibility
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        elif inputs_embeds is None:
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            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
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            inputs_embeds = self.get_input_embeddings(input_ids,
                                                      vision_embeddings)
            input_ids = None
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        hidden_states = self.language_model.model(input_ids,
                                                  positions,
                                                  kv_caches,
                                                  attn_metadata,
                                                  intermediate_tensors,
                                                  inputs_embeds=inputs_embeds)
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        return hidden_states

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    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[torch.Tensor]:
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        return self.language_model.compute_logits(hidden_states,
                                                  sampling_metadata)
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    def sample(
        self,
        logits: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
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        return self.language_model.sample(logits, sampling_metadata)
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    def load_weights(self, weights: Iterable[Tuple[str,
                                                   torch.Tensor]]) -> Set[str]:
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        loader = AutoWeightsLoader(self)
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        autoloaded_weights = loader.load_weights(weights,
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                                                 mapper=self.hf_to_vllm_mapper)
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        # The HF config doesn't specify whether these are tied,
        # so we detect it this way
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        if "embed_tokens.weight" not in autoloaded_weights:
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            self.embed_tokens = self.language_model.model.embed_tokens
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            autoloaded_weights.add("embed_tokens.weight")
        return autoloaded_weights