365 lines
17 KiB
Python
365 lines
17 KiB
Python
import numpy as np
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import torch
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from transformers.models.qwen3_vl.processing_qwen3_vl import Qwen3VLProcessor, Qwen3VLProcessorKwargs
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from transformers.models.qwen3_vl.modeling_qwen3_vl import Qwen3VLModelOutputWithPast, BaseModelOutputWithDeepstackFeatures
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from transformers.feature_extraction_utils import BatchFeature
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from transformers.image_utils import ImageInput
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from transformers.processing_utils import Unpack
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from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
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from transformers.utils import logging, TransformersKwargs, can_return_tuple
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from transformers.video_utils import VideoInput
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from transformers.cache_utils import Cache
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from transformers.processing_utils import Unpack
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logger = logging.get_logger(__name__)
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class myQwen3VLProcessor(Qwen3VLProcessor):
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def __init__(self, image_processor=None, tokenizer=None, video_processor=None, chat_template=None, **kwargs):
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super().__init__(image_processor, tokenizer, video_processor, chat_template, **kwargs)
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def __call__(
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self,
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images: ImageInput = None,
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text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
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videos: VideoInput = None,
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**kwargs: Unpack[Qwen3VLProcessorKwargs],
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) -> BatchFeature:
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r"""
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Returns:
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[`BatchFeature`]: A [`BatchFeature`] with the following fields:
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- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
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- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
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`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
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`None`).
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- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
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- **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
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- **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
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- **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
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"""
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output_kwargs = self._merge_kwargs(
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Qwen3VLProcessorKwargs,
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tokenizer_init_kwargs=self.tokenizer.init_kwargs,
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**kwargs,
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)
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if images is not None:
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image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
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image_grid_thw = image_inputs["image_grid_thw"]
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else:
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image_inputs = {}
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image_grid_thw = None
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if videos is not None:
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videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
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video_grid_thw = videos_inputs["video_grid_thw"]
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# If user has not requested video metadata, pop it
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if not kwargs.get("return_metadata"):
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video_metadata = videos_inputs.pop("video_metadata")
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else:
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video_metadata = videos_inputs["video_metadata"]
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else:
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videos_inputs = {}
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video_grid_thw = None
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if not isinstance(text, list):
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text = [text]
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text = text.copy() # below lines change text in-place
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if image_grid_thw is not None:
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merge_length = self.image_processor.merge_size**2
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index = 0
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for i in range(len(text)):
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while self.image_token in text[i]:
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# num_image_tokens = image_grid_thw[index].prod() // merge_length
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num_image_tokens = 40
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text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
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index += 1
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text[i] = text[i].replace("<|placeholder|>", self.image_token)
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if video_grid_thw is not None:
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merge_length = self.video_processor.merge_size**2
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index = 0
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for i in range(len(text)):
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while self.video_token in text[i]:
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metadata = video_metadata[index]
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if metadata.fps is None:
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logger.warning_once(
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"Qwen3VL requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. "
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"Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. "
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"Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results."
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)
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metadata.fps = 24 if metadata.fps is None else metadata.fps
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# if timestamps are not provided, calculate them
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curr_timestamp = self._calculate_timestamps(
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metadata.frames_indices,
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metadata.fps,
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self.video_processor.temporal_patch_size,
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)
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video_placeholder = ""
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frame_seqlen = video_grid_thw[index][1:].prod() // merge_length
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for frame_idx in range(video_grid_thw[index][0]):
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curr_time = curr_timestamp[frame_idx]
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video_placeholder += f"<{curr_time:.1f} seconds>"
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video_placeholder += (
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self.vision_start_token + "<|placeholder|>" * frame_seqlen + self.vision_end_token
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)
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if f"{self.vision_start_token}{self.video_token}{self.vision_end_token}" in text[i]:
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text[i] = text[i].replace(
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f"{self.vision_start_token}{self.video_token}{self.vision_end_token}", video_placeholder, 1
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)
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else:
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# vllm may input video token directly
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text[i] = text[i].replace(self.video_token, video_placeholder, 1)
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index += 1
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text[i] = text[i].replace("<|placeholder|>", self.video_token)
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return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
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return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
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text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
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self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
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if return_mm_token_type_ids:
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array_ids = np.array(text_inputs["input_ids"])
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mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
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mm_token_type_ids[array_ids == self.image_token_id] = 1
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text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
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return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)
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def _sample_indices_uniform(idx: torch.LongTensor, keep_ratio: float, min_keep: int = 0):
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"""
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idx: 1D indices in original sequence (sorted)
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keep_ratio: 0~1, keep uniformly spaced
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"""
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n = idx.numel()
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if n == 0:
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return idx
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k = max(min_keep, int(torch.ceil(torch.tensor(n * keep_ratio)).item()))
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k = min(k, n)
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if k == n:
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return idx
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# uniform pick: linspace over [0, n-1]
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pos = torch.linspace(0, n - 1, steps=k, device=idx.device)
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pos = pos.round().long().clamp(0, n - 1)
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return idx[pos]
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def sparse_keep_and_gather(
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inputs_embeds, # (B,S,D)
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attention_mask, # (B,S)
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position_ids, # (4,B,S)
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visual_pos_masks, # (B,S) bool
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deepstack_visual_embeds,# list[tensor] each (Nvis_total,D) OR None
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keep_ratio: float = 0.25,
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min_keep_per_vis: int = 0,
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max_len: int | None = None,
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):
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"""
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稀疏保留：保留全部文本 token；视觉 token 按 keep_ratio 均匀采样保留。
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可选 max_len：如果最终还超长，再从视觉 token 里继续裁（不动文本）。
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"""
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device = inputs_embeds.device
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B, S, D = inputs_embeds.shape
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eff = attention_mask.bool()
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keep_mask_token = torch.zeros((B, S), dtype=torch.bool, device=device)
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for b in range(B):
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eff_idx = eff[b].nonzero(as_tuple=False).squeeze(1) # 有效 token
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if eff_idx.numel() == 0:
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continue
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vis_eff = visual_pos_masks[b, eff_idx] # 有效里哪些是视觉
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text_idx = eff_idx[~vis_eff] # 全保留
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vis_idx = eff_idx[vis_eff] # 待稀疏
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# 视觉稀疏采样（删中间就靠这一步）
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kept_vis = _sample_indices_uniform(vis_idx, keep_ratio, min_keep=min_keep_per_vis)
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chosen = torch.cat([text_idx, kept_vis], dim=0)
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chosen, _ = torch.sort(chosen) # 保持原序
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# 如果还要控最大长度：优先继续裁视觉（不裁文本）
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if max_len is not None and chosen.numel() > max_len:
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# 已保留的视觉位置
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chosen_vis = chosen[visual_pos_masks[b, chosen]]
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chosen_txt = chosen[~visual_pos_masks[b, chosen]]
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# 文本若已超 max_len，只能截文本（极少）
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if chosen_txt.numel() >= max_len:
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chosen = chosen_txt[:max_len]
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else:
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budget = max_len - chosen_txt.numel()
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# 对视觉再均匀裁到 budget
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chosen_vis = _sample_indices_uniform(chosen_vis, budget / max(chosen_vis.numel(), 1))
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chosen = torch.cat([chosen_txt, chosen_vis], dim=0)
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chosen, _ = torch.sort(chosen)
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keep_mask_token[b, chosen] = True
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# ===== gather + pad 到 batch 内最大长度 =====
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keep_lens = keep_mask_token.sum(dim=1).tolist()
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max_keep = max(keep_lens) if keep_lens else 0
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new_inputs = inputs_embeds.new_zeros((B, max_keep, D))
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new_attn = attention_mask.new_zeros((B, max_keep))
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new_pos = position_ids.new_zeros((4, B, max_keep))
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new_vis = visual_pos_masks.new_zeros((B, max_keep), dtype=torch.bool)
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for b in range(B):
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idx = keep_mask_token[b].nonzero(as_tuple=False).squeeze(1)
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L = idx.numel()
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if L == 0:
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continue
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new_inputs[b, :L, :] = inputs_embeds[b, idx, :]
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new_attn[b, :L] = attention_mask[b, idx]
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new_pos[:, b, :L] = position_ids[:, b, idx]
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new_vis[b, :L] = visual_pos_masks[b, idx]
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# ===== deepstack 同步裁剪（关键！）=====
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new_deepstack = None
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if deepstack_visual_embeds is not None:
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# deepstack 的顺序 = visual_pos_masks flatten 后 True 的顺序
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# 所以用 keep_mask_token 在这些位置的布尔值来裁剪
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keep_vis_flat = keep_mask_token[visual_pos_masks] # 1D bool, length = Nvis_total
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new_deepstack = [x[keep_vis_flat] for x in deepstack_visual_embeds]
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return new_inputs, new_attn, new_pos, new_vis, new_deepstack
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@can_return_tuple
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def patch_forward(
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self,
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input_ids: torch.LongTensor = None,
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attention_mask: torch.Tensor | None = None,
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position_ids: torch.LongTensor | None = None,
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past_key_values: Cache | None = None,
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inputs_embeds: torch.FloatTensor | None = None,
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pixel_values: torch.Tensor | None = None,
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pixel_values_videos: torch.FloatTensor | None = None,
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image_grid_thw: torch.LongTensor | None = None,
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video_grid_thw: torch.LongTensor | None = None,
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cache_position: torch.LongTensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple | Qwen3VLModelOutputWithPast:
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r"""
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image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
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The temporal, height and width of feature shape of each image in LLM.
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video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
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The temporal, height and width of feature shape of each video in LLM.
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"""
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if (input_ids is None) ^ (inputs_embeds is not None):
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raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
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if inputs_embeds is None:
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inputs_embeds = self.get_input_embeddings()(input_ids)
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image_mask = None
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video_mask = None
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if pixel_values is not None:
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image_outputs: BaseModelOutputWithDeepstackFeatures = self.get_image_features(
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pixel_values, image_grid_thw, return_dict=True
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)
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image_embeds = image_outputs.pooler_output
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deepstack_image_embeds = image_outputs.deepstack_features
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image_embeds = torch.cat(image_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
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image_mask, _ = self.get_placeholder_mask(
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input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds
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)
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inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
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if pixel_values_videos is not None:
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video_outputs: BaseModelOutputWithDeepstackFeatures = self.get_video_features(
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pixel_values_videos, video_grid_thw, return_dict=True
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)
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video_embeds = video_outputs.pooler_output
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deepstack_video_embeds = video_outputs.deepstack_features
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video_embeds = torch.cat(video_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
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_, video_mask = self.get_placeholder_mask(
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input_ids, inputs_embeds=inputs_embeds, video_features=video_embeds
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)
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inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)
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visual_pos_masks = None
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deepstack_visual_embeds = None
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if image_mask is not None and video_mask is not None:
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# aggregate visual_pos_masks and deepstack_visual_embeds
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image_mask = image_mask[..., 0]
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video_mask = video_mask[..., 0]
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visual_pos_masks = image_mask | video_mask
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deepstack_visual_embeds = []
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image_mask_joint = image_mask[visual_pos_masks]
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video_mask_joint = video_mask[visual_pos_masks]
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for img_embed, vid_embed in zip(deepstack_image_embeds, deepstack_video_embeds):
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embed_joint = img_embed.new_zeros(visual_pos_masks.sum(), img_embed.shape[-1]).to(img_embed.device)
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embed_joint[image_mask_joint, :] = img_embed
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embed_joint[video_mask_joint, :] = vid_embed
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deepstack_visual_embeds.append(embed_joint)
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elif image_mask is not None:
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image_mask = image_mask[..., 0]
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visual_pos_masks = image_mask
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deepstack_visual_embeds = deepstack_image_embeds
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elif video_mask is not None:
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video_mask = video_mask[..., 0]
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visual_pos_masks = video_mask
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deepstack_visual_embeds = deepstack_video_embeds
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if position_ids is None:
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position_ids = self.compute_3d_position_ids(
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input_ids=input_ids,
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image_grid_thw=image_grid_thw,
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video_grid_thw=video_grid_thw,
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inputs_embeds=inputs_embeds,
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attention_mask=attention_mask,
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past_key_values=past_key_values,
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)
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# ====== 稀疏采样裁剪：只在 prefill 做（past_key_values is None）=====
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if past_key_values.get_seq_length() == 0 and visual_pos_masks is not None:
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# 这些参数你可以通过 kwargs 传入
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keep_ratio = kwargs.pop("visual_keep_ratio", 0.1) # 只保留 25% 视觉 token
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min_keep = kwargs.pop("min_keep_per_vis", 0) # 每段视觉最少保留多少（可设比如 16）
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max_len = kwargs.pop("truncate_max_len", None) # 总长度上限（可选）
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inputs_embeds, attention_mask, position_ids, visual_pos_masks, deepstack_visual_embeds = sparse_keep_and_gather(
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inputs_embeds=inputs_embeds,
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attention_mask=attention_mask,
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position_ids=position_ids,
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visual_pos_masks=visual_pos_masks,
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deepstack_visual_embeds=deepstack_visual_embeds,
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keep_ratio=keep_ratio,
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min_keep_per_vis=min_keep,
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max_len=max_len,
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)
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# cache_position 建议重建为 0..L-1（避免对齐问题）
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cache_position = torch.arange(
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inputs_embeds.shape[1], device=inputs_embeds.device, dtype=torch.long
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).unsqueeze(0).expand(inputs_embeds.shape[0], -1)
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# rope_deltas 建议也按裁剪后的序列重算（防止不一致）
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eff_len = attention_mask.sum(dim=1).to(torch.long) # (B,)
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max_pos = position_ids.max(dim=0).values.max(dim=1).values # (B,)
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self.rope_deltas = (max_pos + 1 - eff_len).unsqueeze(1)
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# ====== 裁剪结束 ======
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outputs = self.language_model(
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input_ids=None,
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position_ids=position_ids,
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attention_mask=attention_mask,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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cache_position=cache_position,
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visual_pos_masks=visual_pos_masks,
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deepstack_visual_embeds=deepstack_visual_embeds,
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**kwargs,
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)
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return Qwen3VLModelOutputWithPast(
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**outputs,
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rope_deltas=self.rope_deltas,
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)
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