497 lines
18 KiB
Python
497 lines
18 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import math
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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
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import torch.nn as nn
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from transformers import (BatchFeature, LlavaNextVideoConfig,
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LlavaNextVideoProcessor)
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from vllm.config import VllmConfig
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
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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 MultiModalFieldConfig, MultiModalKwargs
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from vllm.multimodal.parse import (ImageSize, MultiModalDataItems,
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VideoEmbeddingItems, VideoProcessorItems)
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from vllm.multimodal.processing import (BaseMultiModalProcessor,
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BaseProcessingInfo, PromptReplacement,
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PromptUpdate)
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from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
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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 .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
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from .llava import init_vision_tower_for_llava
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from .siglip import SiglipVisionModel
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from .utils import (AutoWeightsLoader, init_vllm_registered_model,
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maybe_prefix, merge_multimodal_embeddings)
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from .vision import get_vision_encoder_info
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class LlavaNextVideoPixelInputs(TypedDict):
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type: Literal["pixel_values_videos"]
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data: Union[torch.Tensor, List[torch.Tensor]]
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"""
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Shape: `(batch_size, num_frames, num_channels, height, width)`
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Note that `num_frames` may be different for each batch, in which case
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the data is passed as a list instead of a batched tensor.
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Note that it only supports one video input for one batch.
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"""
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class LlavaNextVideoProcessingInfo(BaseProcessingInfo):
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def get_hf_config(self):
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return self.ctx.get_hf_config(LlavaNextVideoConfig)
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def get_vision_encoder_info(self):
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return get_vision_encoder_info(self.get_hf_config())
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def get_hf_processor(self, **kwargs: object):
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return self.ctx.get_hf_processor(LlavaNextVideoProcessor, **kwargs)
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def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
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return {"video": 1}
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def get_mm_max_tokens_per_item(
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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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) -> Mapping[str, int]:
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target_width, target_height = self.get_image_size_with_most_features()
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max_video_tokens = self.get_num_video_tokens(
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image_width=target_width,
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image_height=target_height,
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num_frames=self.get_num_frames_with_most_features(seq_len),
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)
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return {"video": max_video_tokens}
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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_num_frame_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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hf_config = self.get_hf_config()
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spatial_pool_stride = hf_config.spatial_pool_stride
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vision_encoder_info = self.get_vision_encoder_info()
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patch_grid_length = vision_encoder_info.get_patch_grid_length()
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pooled_grid_length = math.ceil(patch_grid_length / spatial_pool_stride)
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return pooled_grid_length * pooled_grid_length
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def get_num_video_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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num_frames: int,
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) -> int:
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num_frame_tokens = self._get_num_frame_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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return num_frame_tokens * num_frames
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def _get_max_video_frames(self, max_tokens: int) -> int:
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target_width, target_height = self.get_image_size_with_most_features()
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num_frames = 0
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while True:
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next_num_frames = num_frames + 1
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next_max_tokens = self.get_num_video_tokens(
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image_width=target_width,
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image_height=target_height,
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num_frames=next_num_frames,
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)
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if next_max_tokens > max_tokens:
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break
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num_frames = next_num_frames
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return num_frames
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def get_num_frames_with_most_features(self, seq_len: int) -> int:
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mm_config = self.ctx.get_mm_config()
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max_videos = mm_config.get_limit_per_prompt("video")
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max_total_frames = self._get_max_video_frames(seq_len)
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return max(max_total_frames // max(max_videos, 1), 1)
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class LlavaNextVideoDummyInputsBuilder(
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BaseDummyInputsBuilder[LlavaNextVideoProcessingInfo]):
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def get_dummy_processor_inputs(
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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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) -> ProcessorInputs:
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num_videos = mm_counts.get("video", 0)
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processor = self.info.get_hf_processor()
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video_token = processor.video_token
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target_width, target_height = \
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self.info.get_image_size_with_most_features()
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target_num_frames = \
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self.info.get_num_frames_with_most_features(seq_len)
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mm_data = {
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"video":
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self._get_dummy_videos(
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width=target_width,
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height=target_height,
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num_frames=target_num_frames,
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num_videos=num_videos,
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)
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}
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return ProcessorInputs(
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prompt_text=video_token * num_videos,
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mm_data=mm_data,
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)
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class LlavaNextVideoMultiModalProcessor(
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BaseMultiModalProcessor[LlavaNextVideoProcessingInfo]):
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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_videos=MultiModalFieldConfig.batched("video"))
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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: MultiModalKwargs,
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) -> Sequence[PromptUpdate]:
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hf_config = self.info.get_hf_config()
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video_token_id = hf_config.video_token_index
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def get_replacement(item_idx: int):
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videos = mm_items.get_items(
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"video", (VideoEmbeddingItems, VideoProcessorItems))
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if isinstance(videos, VideoEmbeddingItems):
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num_video_tokens = videos.get_feature_size(item_idx)
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else:
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image_size = videos.get_frame_size(item_idx)
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num_video_tokens = self.info.get_num_video_tokens(
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image_width=image_size.width,
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image_height=image_size.height,
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num_frames=videos.get_num_frames(item_idx),
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)
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return [video_token_id] * num_video_tokens
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return [
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PromptReplacement(
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modality="video",
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target=[video_token_id],
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replacement=get_replacement,
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),
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]
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# adopted from transformers modeling_llava_next_video.py
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class LlavaNextVideoPooler(nn.Module):
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def __init__(self, config: LlavaNextVideoConfig):
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super().__init__()
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mode = config.spatial_pool_mode
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stride = config.spatial_pool_stride
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image_size = config.vision_config.image_size
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patch_size = config.vision_config.patch_size
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self.image_size = image_size // patch_size**2
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if mode == "average":
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self.pool = nn.AvgPool2d(kernel_size=stride, stride=stride)
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elif mode == "max":
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self.pool = nn.MaxPool2d(kernel_size=stride, stride=stride)
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else:
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# TODO: Support Conv2d pooling layer, need to load weights
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raise ValueError(
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f"Unknown pooling mode: {mode}. Expected [`average`, `max`]")
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def forward(self, image_features: torch.Tensor):
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ori_width = int(
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math.sqrt(image_features.shape[1] * self.image_size //
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self.image_size))
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ori_height = int(ori_width * self.image_size // self.image_size)
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batch_size, _, dim = image_features.shape
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image_features_spatial = image_features \
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.view(batch_size, ori_height, ori_height, dim) \
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.permute(0, 3, 1, 2)
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image_features_spatial = self.pool(image_features_spatial)
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return image_features_spatial.flatten(2).transpose(1, 2).contiguous()
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class LlavaNextMultiModalProjector(nn.Module):
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def __init__(self, vision_hidden_size: int, text_hidden_size: int,
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projector_hidden_act: str, multimodal_projector_bias: bool):
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super().__init__()
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self.linear_1 = nn.Linear(vision_hidden_size,
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text_hidden_size,
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bias=multimodal_projector_bias)
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self.act = get_act_fn(projector_hidden_act)
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self.linear_2 = nn.Linear(text_hidden_size,
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text_hidden_size,
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bias=multimodal_projector_bias)
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def forward(self, image_features: torch.Tensor) -> torch.Tensor:
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hidden_states = self.linear_1(image_features)
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hidden_states = self.act(hidden_states)
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hidden_states = self.linear_2(hidden_states)
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return hidden_states
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@MULTIMODAL_REGISTRY.register_processor(
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LlavaNextVideoMultiModalProcessor,
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info=LlavaNextVideoProcessingInfo,
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dummy_inputs=LlavaNextVideoDummyInputsBuilder,
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)
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class LlavaNextVideoForConditionalGeneration(nn.Module, SupportsMultiModal,
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SupportsPP):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
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super().__init__()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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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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# Initialize the vision tower only up to the required feature layer
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self.vision_tower = init_vision_tower_for_llava(
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config,
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quant_config,
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require_post_norm=False,
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prefix=maybe_prefix(prefix, "vision_tower"))
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self.vision_resampler = LlavaNextVideoPooler(config)
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self.multi_modal_projector = LlavaNextMultiModalProjector(
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vision_hidden_size=config.vision_config.hidden_size,
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text_hidden_size=config.text_config.hidden_size,
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projector_hidden_act=config.projector_hidden_act,
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multimodal_projector_bias=config.multimodal_projector_bias)
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self.language_model = init_vllm_registered_model(
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vllm_config=vllm_config,
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hf_config=config.text_config,
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prefix=maybe_prefix(prefix, "language_model"),
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)
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self.make_empty_intermediate_tensors = (
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self.language_model.model.make_empty_intermediate_tensors)
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@cached_property
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def sampler(self):
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if hasattr(self.language_model, "sampler"):
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return self.language_model.sampler
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return get_sampler()
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def _validate_video_pixel_values(
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self, data: Union[torch.Tensor, List[torch.Tensor]]
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) -> Union[torch.Tensor, List[torch.Tensor]]:
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h = w = self.config.vision_config.image_size
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expected_dims = (3, h, w)
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def _validate_shape(d: torch.Tensor):
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actual_dims = tuple(d.shape[2:])
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if actual_dims != expected_dims:
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expected_expr = ("num_frames", *map(str, expected_dims))
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raise ValueError(
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"The expected shape of pixel values in each video frame "
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f"is {expected_expr}. You supplied {tuple(d.shape)}.")
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for d in data:
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_validate_shape(d)
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return data
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def _parse_and_validate_video_input(
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self, **kwargs: object) -> Optional[LlavaNextVideoPixelInputs]:
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"""
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A legal video input should have the following dimensions:
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{
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"pixel_values_videos" :
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List[b, Tensor(nb_frames, nb_channels, height, width)]
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}
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"""
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pixel_values = kwargs.pop("pixel_values_videos", None)
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if pixel_values is None:
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return None
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if not (is_list_of(pixel_values,
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(torch.Tensor)) # different shape videos
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or isinstance(pixel_values,
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torch.Tensor)): # same shape videos
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raise ValueError("Incorrect type of pixel values. "
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f"Got type: {type(pixel_values)}")
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return LlavaNextVideoPixelInputs(
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type="pixel_values_videos",
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data=pixel_values,
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)
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def _select_image_features(self, image_features: torch.Tensor, *,
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strategy: str) -> torch.Tensor:
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if strategy == "default":
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return image_features[:, 1:]
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elif strategy == "full":
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return image_features
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raise ValueError(f"Unexpected select feature strategy: {strategy}")
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def _video_pixels_to_features(
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self,
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vision_tower: Union[CLIPVisionModel, SiglipVisionModel],
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pixel_values: torch.Tensor,
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) -> torch.Tensor:
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# NOTE: we skip the step to select the vision feature layer since
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# this is already done inside the vision tower
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image_features = vision_tower(pixel_values)
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image_features = self._select_image_features(
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image_features,
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strategy=self.config.vision_feature_select_strategy,
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)
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image_features = self.vision_resampler(image_features)
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image_features = self.multi_modal_projector(image_features)
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return image_features
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def _process_video_pixels(self, inputs: LlavaNextVideoPixelInputs):
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assert self.vision_tower is not None
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video_pixels = inputs["data"]
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if isinstance(video_pixels, torch.Tensor):
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# TODO: support multiple videos per input
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b, num_videos, num_frames, c, h, w = video_pixels.shape
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assert (num_videos == 1)
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stacked_pixels = video_pixels.view(b * num_videos * num_frames, c,
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h, w)
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stacked_embeddings = self._video_pixels_to_features(
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self.vision_tower, stacked_pixels)
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return stacked_embeddings.view(b, num_frames,
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*stacked_embeddings.shape[1:])
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elif is_list_of(video_pixels, torch.Tensor):
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frames_per_videos = [v.shape[0] for v in video_pixels]
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stacked_pixels = torch.cat(video_pixels, dim=0)
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stacked_embeddings = self._video_pixels_to_features(
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self.vision_tower, stacked_pixels)
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return torch.split(stacked_embeddings, frames_per_videos, dim=0)
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else:
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raise ValueError(
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f"Unsupported type of video input {type(video_pixels)}")
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def get_multimodal_embeddings(
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self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
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video_input = self._parse_and_validate_video_input(**kwargs)
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if video_input is None:
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return None
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vision_embeddings = self._process_video_pixels(video_input)
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return vision_embeddings
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def get_input_embeddings(
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self,
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input_ids: torch.Tensor,
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multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
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) -> torch.Tensor:
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inputs_embeds = self.language_model.get_input_embeddings(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.config.video_token_index)
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return inputs_embeds
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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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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) -> Union[torch.Tensor, IntermediateTensors]:
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"""Run forward pass for LlaVA-NeXT-Video.
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Args:
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input_ids: Flattened (concatenated) input_ids corresponding to a
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batch.
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pixel_values_videos: Pixels in each frames for each input videos.
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"""
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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
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# 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,
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vision_embeddings)
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input_ids = None
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hidden_states = self.language_model.model(input_ids,
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positions,
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intermediate_tensors,
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inputs_embeds=inputs_embeds)
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return hidden_states
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[torch.Tensor]:
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return self.language_model.compute_logits(hidden_states,
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sampling_metadata)
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def sample(
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self,
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> 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,
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torch.Tensor]]) -> Set[str]:
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loader = AutoWeightsLoader(
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self,
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# This model doesn't support images for now
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ignore_unexpected_prefixes=["image_newline"],
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)
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return loader.load_weights(weights)
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