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Cross-modal Connector Optimization

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noctis
2026-02-26 00:02:17 +08:00
parent 7a11c57f1b
commit 92f33eb431
3 changed files with 368 additions and 2 deletions
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2
benchmark.py
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@ -588,7 +588,7 @@ def main():
parser.add_argument("--model-path", type=str, default="./Qwen3-VL-2B-Instruct", help="Path to model weights")
parser.add_argument("--dataset-path", type=str, default="./data", help="Path to validation dataset")
parser.add_argument("--output", type=str, default="result.json", help="Output JSON file path")
parser.add_argument("--num-samples", type=int, default=100, help="Number of samples to evaluate (default: all)")
parser.add_argument("--num-samples", type=int, default=10, help="Number of samples to evaluate (default: all)")
parser.add_argument("--random-seed", type=int, default=None, help="Random seed for reproducibility")
args = parser.parse_args()

5
evaluation_wrapper.py
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@ -73,7 +73,7 @@ class VLMModel:
# self._optimize_kv_cache()
# 3. Cross-modal Connector Optimization
# self._optimize_cross_modal_connector()
self._optimize_cross_modal_connector()
# 4. Flash Attention Optimization
# self._enable_flash_attention()
@ -238,6 +238,9 @@ class VLMModel:
# 4. Replace: connector.cross_attention.forward = optimized_cross_attention
# 5. Test: Verify accuracy and performance improvements
from my_patch import patch_forward
self._model.model.__class__.forward = patch_forward
if 'cross_modal' not in self._optimizations_applied:
self._optimizations_applied.append('cross_modal')

363
my_patch.py Normal file
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@ -0,0 +1,363 @@
import numpy as np
import torch
from transformers.models.qwen3_vl.processing_qwen3_vl import Qwen3VLProcessor, Qwen3VLProcessorKwargs
from transformers.models.qwen3_vl.modeling_qwen3_vl import Qwen3VLModelOutputWithPast, BaseModelOutputWithDeepstackFeatures
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.processing_utils import Unpack
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
from transformers.utils import logging, TransformersKwargs, can_return_tuple
from transformers.video_utils import VideoInput
from transformers.cache_utils import Cache
from transformers.processing_utils import Unpack
logger = logging.get_logger(__name__)
class myQwen3VLProcessor(Qwen3VLProcessor):
def __init__(self, image_processor=None, tokenizer=None, video_processor=None, chat_template=None, **kwargs):
super().__init__(image_processor, tokenizer, video_processor, chat_template, **kwargs)
def __call__(
self,
images: ImageInput = None,
text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
videos: VideoInput = None,
**kwargs: Unpack[Qwen3VLProcessorKwargs],
) -> BatchFeature:
r"""
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
`None`).
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
- **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
- **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
- **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
"""
output_kwargs = self._merge_kwargs(
Qwen3VLProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
if images is not None:
image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
image_grid_thw = image_inputs["image_grid_thw"]
else:
image_inputs = {}
image_grid_thw = None
if videos is not None:
videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
video_grid_thw = videos_inputs["video_grid_thw"]
# If user has not requested video metadata, pop it
if not kwargs.get("return_metadata"):
video_metadata = videos_inputs.pop("video_metadata")
else:
video_metadata = videos_inputs["video_metadata"]
else:
videos_inputs = {}
video_grid_thw = None
if not isinstance(text, list):
text = [text]
text = text.copy() # below lines change text in-place
if image_grid_thw is not None:
merge_length = self.image_processor.merge_size**2
index = 0
for i in range(len(text)):
while self.image_token in text[i]:
# num_image_tokens = image_grid_thw[index].prod() // merge_length
num_image_tokens = 40
text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
index += 1
text[i] = text[i].replace("<|placeholder|>", self.image_token)
if video_grid_thw is not None:
merge_length = self.video_processor.merge_size**2
index = 0
for i in range(len(text)):
while self.video_token in text[i]:
metadata = video_metadata[index]
if metadata.fps is None:
logger.warning_once(
"Qwen3VL requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. "
"Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. "
"Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results."
)
metadata.fps = 24 if metadata.fps is None else metadata.fps
# if timestamps are not provided, calculate them
curr_timestamp = self._calculate_timestamps(
metadata.frames_indices,
metadata.fps,
self.video_processor.temporal_patch_size,
)
video_placeholder = ""
frame_seqlen = video_grid_thw[index][1:].prod() // merge_length
for frame_idx in range(video_grid_thw[index][0]):
curr_time = curr_timestamp[frame_idx]
video_placeholder += f"<{curr_time:.1f} seconds>"
video_placeholder += (
self.vision_start_token + "<|placeholder|>" * frame_seqlen + self.vision_end_token
)
if f"{self.vision_start_token}{self.video_token}{self.vision_end_token}" in text[i]:
text[i] = text[i].replace(
f"{self.vision_start_token}{self.video_token}{self.vision_end_token}", video_placeholder, 1
)
else:
# vllm may input video token directly
text[i] = text[i].replace(self.video_token, video_placeholder, 1)
index += 1
text[i] = text[i].replace("<|placeholder|>", self.video_token)
return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
if return_mm_token_type_ids:
array_ids = np.array(text_inputs["input_ids"])
mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
mm_token_type_ids[array_ids == self.image_token_id] = 1
text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)
def _sample_indices_uniform(idx: torch.LongTensor, keep_ratio: float, min_keep: int = 0):
"""
idx: 1D indices in original sequence (sorted)
keep_ratio: 0~1, keep uniformly spaced
"""
n = idx.numel()
if n == 0:
return idx
k = max(min_keep, int(torch.ceil(torch.tensor(n * keep_ratio)).item()))
k = min(k, n)
if k == n:
return idx
# uniform pick: linspace over [0, n-1]
pos = torch.linspace(0, n - 1, steps=k, device=idx.device)
pos = pos.round().long().clamp(0, n - 1)
return idx[pos]
def sparse_keep_and_gather(
inputs_embeds, # (B,S,D)
attention_mask, # (B,S)
position_ids, # (4,B,S)
visual_pos_masks, # (B,S) bool
deepstack_visual_embeds,# list[tensor] each (Nvis_total,D) OR None
keep_ratio: float = 0.25,
min_keep_per_vis: int = 0,
max_len: int | None = None,
):
"""
稀疏保留：保留全部文本 token；视觉 token 按 keep_ratio 均匀采样保留。
可选 max_len：如果最终还超长，再从视觉 token 里继续裁（不动文本）。
"""
device = inputs_embeds.device
B, S, D = inputs_embeds.shape
eff = attention_mask.bool()
keep_mask_token = torch.zeros((B, S), dtype=torch.bool, device=device)
for b in range(B):
eff_idx = eff[b].nonzero(as_tuple=False).squeeze(1) # 有效 token
if eff_idx.numel() == 0:
continue
vis_eff = visual_pos_masks[b, eff_idx] # 有效里哪些是视觉
text_idx = eff_idx[~vis_eff] # 全保留
vis_idx = eff_idx[vis_eff] # 待稀疏
# 视觉稀疏采样（删中间就靠这一步）
kept_vis = _sample_indices_uniform(vis_idx, keep_ratio, min_keep=min_keep_per_vis)
chosen = torch.cat([text_idx, kept_vis], dim=0)
chosen, _ = torch.sort(chosen) # 保持原序
# 如果还要控最大长度：优先继续裁视觉（不裁文本）
if max_len is not None and chosen.numel() > max_len:
# 已保留的视觉位置
chosen_vis = chosen[visual_pos_masks[b, chosen]]
chosen_txt = chosen[~visual_pos_masks[b, chosen]]
# 文本若已超 max_len，只能截文本（极少）
if chosen_txt.numel() >= max_len:
chosen = chosen_txt[:max_len]
else:
budget = max_len - chosen_txt.numel()
# 对视觉再均匀裁到 budget
chosen_vis = _sample_indices_uniform(chosen_vis, budget / max(chosen_vis.numel(), 1))
chosen = torch.cat([chosen_txt, chosen_vis], dim=0)
chosen, _ = torch.sort(chosen)
keep_mask_token[b, chosen] = True
# ===== gather + pad 到 batch 内最大长度 =====
keep_lens = keep_mask_token.sum(dim=1).tolist()
max_keep = max(keep_lens) if keep_lens else 0
new_inputs = inputs_embeds.new_zeros((B, max_keep, D))
new_attn = attention_mask.new_zeros((B, max_keep))
new_pos = position_ids.new_zeros((4, B, max_keep))
new_vis = visual_pos_masks.new_zeros((B, max_keep), dtype=torch.bool)
for b in range(B):
idx = keep_mask_token[b].nonzero(as_tuple=False).squeeze(1)
L = idx.numel()
if L == 0:
continue
new_inputs[b, :L, :] = inputs_embeds[b, idx, :]
new_attn[b, :L] = attention_mask[b, idx]
new_pos[:, b, :L] = position_ids[:, b, idx]
new_vis[b, :L] = visual_pos_masks[b, idx]
# ===== deepstack 同步裁剪（关键！）=====
new_deepstack = None
if deepstack_visual_embeds is not None:
# deepstack 的顺序 = visual_pos_masks flatten 后 True 的顺序
# 所以用 keep_mask_token 在这些位置的布尔值来裁剪
keep_vis_flat = keep_mask_token[visual_pos_masks] # 1D bool, length = Nvis_total
new_deepstack = [x[keep_vis_flat] for x in deepstack_visual_embeds]
return new_inputs, new_attn, new_pos, new_vis, new_deepstack
@can_return_tuple
def patch_forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: torch.Tensor | None = None,
position_ids: torch.LongTensor | None = None,
past_key_values: Cache | None = None,
inputs_embeds: torch.FloatTensor | None = None,
pixel_values: torch.Tensor | None = None,
pixel_values_videos: torch.FloatTensor | None = None,
image_grid_thw: torch.LongTensor | None = None,
video_grid_thw: torch.LongTensor | None = None,
cache_position: torch.LongTensor | None = None,
**kwargs: Unpack[TransformersKwargs],
) -> tuple | Qwen3VLModelOutputWithPast:
r"""
image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
The temporal, height and width of feature shape of each image in LLM.
video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
The temporal, height and width of feature shape of each video in LLM.
"""
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.get_input_embeddings()(input_ids)
image_mask = None
video_mask = None
if pixel_values is not None:
image_outputs: BaseModelOutputWithDeepstackFeatures = self.get_image_features(
pixel_values, image_grid_thw, return_dict=True
)
image_embeds = image_outputs.pooler_output
deepstack_image_embeds = image_outputs.deepstack_features
image_embeds = torch.cat(image_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
image_mask, _ = self.get_placeholder_mask(
input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds
)
inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
if pixel_values_videos is not None:
video_outputs: BaseModelOutputWithDeepstackFeatures = self.get_video_features(
pixel_values_videos, video_grid_thw, return_dict=True
)
video_embeds = video_outputs.pooler_output
deepstack_video_embeds = video_outputs.deepstack_features
video_embeds = torch.cat(video_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
_, video_mask = self.get_placeholder_mask(
input_ids, inputs_embeds=inputs_embeds, video_features=video_embeds
)
inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)
visual_pos_masks = None
deepstack_visual_embeds = None
if image_mask is not None and video_mask is not None:
# aggregate visual_pos_masks and deepstack_visual_embeds
image_mask = image_mask[..., 0]
video_mask = video_mask[..., 0]
visual_pos_masks = image_mask | video_mask
deepstack_visual_embeds = []
image_mask_joint = image_mask[visual_pos_masks]
video_mask_joint = video_mask[visual_pos_masks]
for img_embed, vid_embed in zip(deepstack_image_embeds, deepstack_video_embeds):
embed_joint = img_embed.new_zeros(visual_pos_masks.sum(), img_embed.shape[-1]).to(img_embed.device)
embed_joint[image_mask_joint, :] = img_embed
embed_joint[video_mask_joint, :] = vid_embed
deepstack_visual_embeds.append(embed_joint)
elif image_mask is not None:
image_mask = image_mask[..., 0]
visual_pos_masks = image_mask
deepstack_visual_embeds = deepstack_image_embeds
elif video_mask is not None:
video_mask = video_mask[..., 0]
visual_pos_masks = video_mask
deepstack_visual_embeds = deepstack_video_embeds
if position_ids is None:
position_ids = self.compute_3d_position_ids(
input_ids=input_ids,
image_grid_thw=image_grid_thw,
video_grid_thw=video_grid_thw,
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
past_key_values=past_key_values,
)
# ====== 稀疏采样裁剪：只在 prefill 做（past_key_values is None）=====
if past_key_values.get_seq_length() == 0 and visual_pos_masks is not None:
# 这些参数你可以通过 kwargs 传入
keep_ratio = kwargs.pop("visual_keep_ratio", 0.1) # 只保留 25% 视觉 token
min_keep = kwargs.pop("min_keep_per_vis", 0) # 每段视觉最少保留多少（可设比如 16）
max_len = kwargs.pop("truncate_max_len", None) # 总长度上限（可选）
inputs_embeds, attention_mask, position_ids, visual_pos_masks, deepstack_visual_embeds = sparse_keep_and_gather(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
position_ids=position_ids,
visual_pos_masks=visual_pos_masks,
deepstack_visual_embeds=deepstack_visual_embeds,
keep_ratio=keep_ratio,
min_keep_per_vis=min_keep,
max_len=max_len,
)
# cache_position 建议重建为 0..L-1（避免对齐问题）
cache_position = torch.arange(
inputs_embeds.shape[1], device=inputs_embeds.device, dtype=torch.long
).unsqueeze(0).expand(inputs_embeds.shape[0], -1)
# rope_deltas 建议也按裁剪后的序列重算（防止不一致）
eff_len = attention_mask.sum(dim=1).to(torch.long) # (B,)
max_pos = position_ids.max(dim=0).values.max(dim=1).values # (B,)
self.rope_deltas = (max_pos + 1 - eff_len).unsqueeze(1)
# ====== 裁剪结束 ======
outputs = self.language_model(
input_ids=None,
position_ids=position_ids,
attention_mask=attention_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
cache_position=cache_position,
visual_pos_masks=visual_pos_masks,
deepstack_visual_embeds=deepstack_visual_embeds,
**kwargs,
)
return Qwen3VLModelOutputWithPast(
**outputs,
rope_deltas=self.rope_deltas,
)