455 lines
17 KiB
Python
455 lines
17 KiB
Python
import math
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from functools import cached_property
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from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
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TypedDict, Union)
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import numpy as np
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import torch
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import torch.nn as nn
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from transformers import (CLIPVisionConfig, LlavaNextVideoConfig,
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SiglipVisionConfig)
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from vllm.attention import AttentionMetadata
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from vllm.config import VllmConfig
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from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData,
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InputContext, token_inputs)
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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.utils import (cached_get_tokenizer,
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repeat_and_pad_placeholder_tokens)
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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, dummy_seq_data_for_clip
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from .interfaces import SupportsMultiModal, SupportsPP
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from .llava import init_vision_tower_for_llava
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from .siglip import (SiglipVisionModel, dummy_image_for_siglip,
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dummy_seq_data_for_siglip)
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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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# For profile run
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_MAX_FRAMES_PER_VIDEO = 32
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_MAX_NUM_VIDEOS = 1
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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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def get_llava_next_video_frame_feature_size(
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hf_config: LlavaNextVideoConfig) -> int:
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# Support both CLIPVisionConfig and SiglipVisionConfig
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image_size = hf_config.vision_config.image_size
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patch_size = hf_config.vision_config.patch_size
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spatial_pool_stride = hf_config.spatial_pool_stride
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return int((image_size / patch_size / spatial_pool_stride)**2)
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def _get_max_llm_tokens(ctx: InputContext) -> int:
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"""
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Calculated from the maximum video frames under the context length
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constraints of the language model.
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"""
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hf_text_config = ctx.model_config.hf_text_config
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model_config = ctx.model_config
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max_tokens = model_config.max_model_len
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rope_scaling = model_config.rope_scaling
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if rope_scaling:
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rope_scaling_factor = hf_text_config.rope_scaling["factor"]
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else:
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rope_scaling_factor = 1
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max_tokens *= rope_scaling_factor
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return max_tokens
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def get_max_llava_next_video_tokens(ctx: InputContext) -> int:
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# Currently set to 32 frames
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# TODO: max_tokens = _get_max_llm_tokens(ctx)
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hf_config = ctx.get_hf_config(LlavaNextVideoConfig)
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tokens_per_frame = get_llava_next_video_frame_feature_size(hf_config)
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return _MAX_FRAMES_PER_VIDEO * tokens_per_frame
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def dummy_data_for_llava_next_video(ctx: InputContext, seq_len: int,
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mm_counts: Mapping[str, int]):
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hf_config = ctx.get_hf_config(LlavaNextVideoConfig)
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vision_config = hf_config.vision_config
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# TODO: support multiple videos
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num_videos = mm_counts["video"]
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if num_videos != _MAX_NUM_VIDEOS:
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raise NotImplementedError(
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f"Only {_MAX_NUM_VIDEOS} videos are supported")
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# TODO: support configuring the number of frames
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frames_per_video = _MAX_FRAMES_PER_VIDEO
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# num_images = num_videos * frames_per_video
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# fills the sequence with as longer video data as possible
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tokens_per_frame = get_llava_next_video_frame_feature_size(hf_config)
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video_feature_size = frames_per_video * tokens_per_frame
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if isinstance(vision_config, CLIPVisionConfig):
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seq_data, ranges = dummy_seq_data_for_clip(
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vision_config,
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seq_len,
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num_videos,
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image_token_id=hf_config.video_token_index,
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image_feature_size_override=video_feature_size,
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mm_key="video",
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)
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pil_frame = dummy_image_for_clip(vision_config, num_images=1)
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np_frame = np.array(pil_frame["image"])
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mm_data_per_video = np.repeat([np_frame], frames_per_video, axis=0)
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mm_data = {"video": mm_data_per_video}
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return DummyData(seq_data, mm_data, ranges)
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elif isinstance(vision_config, SiglipVisionConfig):
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seq_data, ranges = dummy_seq_data_for_siglip(
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vision_config,
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seq_len,
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num_videos,
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image_token_id=hf_config.video_token_index,
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image_feature_size_override=video_feature_size,
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mm_key="video",
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)
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pil_frame = dummy_image_for_siglip(vision_config, num_images=1)
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np_frame = np.array(pil_frame["image"])
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mm_data_per_video = np.repeat([np_frame], frames_per_video, axis=0)
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mm_data = {"video": mm_data_per_video}
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return DummyData(seq_data, mm_data, ranges)
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msg = f"Unsupported vision config: {type(vision_config)}"
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raise NotImplementedError(msg)
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def input_processor_for_llava_next_video(ctx: InputContext,
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inputs: DecoderOnlyInputs):
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multi_modal_data = inputs.get("multi_modal_data")
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if multi_modal_data is None or "video" not in multi_modal_data:
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return inputs
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if "multi_modal_placeholders" in inputs and "video" in inputs[
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"multi_modal_placeholders"]:
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# The inputs already have placeholders.
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return inputs
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video_data = multi_modal_data["video"]
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model_config = ctx.model_config
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hf_config = ctx.get_hf_config(LlavaNextVideoConfig)
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vision_config = hf_config.vision_config
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if isinstance(video_data, np.ndarray):
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# Supports both CLIP and Siglip
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num_frames = video_data.shape[0]
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frame_feature_size = \
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get_llava_next_video_frame_feature_size(hf_config)
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video_feature_size = num_frames * frame_feature_size
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tokenizer = cached_get_tokenizer(model_config.tokenizer)
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new_prompt, new_token_ids, ranges = repeat_and_pad_placeholder_tokens(
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tokenizer,
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inputs.get("prompt"),
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inputs["prompt_token_ids"],
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placeholder_token_id=hf_config.video_token_index,
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repeat_count=video_feature_size,
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)
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return token_inputs(prompt_token_ids=new_token_ids,
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prompt=new_prompt,
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multi_modal_data=multi_modal_data,
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multi_modal_placeholders={"video": ranges})
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elif is_list_of(video_data, np.ndarray):
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raise NotImplementedError(
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"Processing multiple videos is not supported")
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msg = f"Unsupported vision config: {type(vision_config)}"
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raise NotImplementedError(msg)
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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):
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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):
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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):
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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=True)
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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=True)
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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_input_mapper("video")
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@MULTIMODAL_REGISTRY.register_max_multimodal_tokens(
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"video", get_max_llava_next_video_tokens)
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@INPUT_REGISTRY.register_dummy_data(dummy_data_for_llava_next_video)
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@INPUT_REGISTRY.register_input_processor(input_processor_for_llava_next_video)
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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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self.language_model = init_vllm_registered_model(
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config.text_config,
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vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "language_model"))
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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 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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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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intermediate_tensors: Optional[IntermediateTensors] = 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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input_ids = None
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inputs_embeds = None
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else:
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video_input = self._parse_and_validate_video_input(**kwargs)
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if video_input is not None:
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video_embeddings = self._process_video_pixels(video_input)
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inputs_embeds = self.language_model \
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.model.get_input_embeddings(input_ids)
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inputs_embeds = merge_multimodal_embeddings(
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input_ids, inputs_embeds, video_embeddings,
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self.config.video_token_index)
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input_ids = None
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else:
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inputs_embeds = None
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hidden_states = self.language_model.model(input_ids,
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positions,
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kv_caches,
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attn_metadata,
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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, torch.Tensor]]):
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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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loader.load_weights(weights)
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