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Qwen2vl support cuda graph and disable radix cache (#1780)

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This commit is contained in:
yizhang2077
2024-10-25 22:45:17 +08:00
committed by GitHub
parent 86a2c473b7
commit def55bc876
5 changed files with 29 additions and 60 deletions
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63
python/sglang/srt/layers/rotary_embedding.py
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@@ -22,64 +22,33 @@ class MRotaryEmbedding:
@staticmethod
def get_input_positions(
input_tokens: List[int],
input_tokens: torch.Tensor,
image_grid_thw: Union[List[List[int]], torch.Tensor],
video_grid_thw: Union[List[List[int]], torch.Tensor],
image_token_id: int,
video_token_id: int,
vision_start_token_id: int,
vision_end_token_id: int,
spatial_merge_size: int,
context_len: int = 0,
extend_prefix_len: int = 0,
) -> Tuple[List[List[int]], int]:
"""Get mrope input positions and delta value."""
if isinstance(image_grid_thw, torch.Tensor):
image_grid_thw = image_grid_thw.tolist()
if isinstance(video_grid_thw, torch.Tensor):
video_grid_thw = video_grid_thw.tolist()
input_tokens_tensor = torch.tensor(input_tokens)
vision_start_indices = torch.argwhere(
input_tokens_tensor == vision_start_token_id
input_tokens == vision_start_token_id
).squeeze(1)
vision_tokens = input_tokens_tensor[vision_start_indices + 1]
image_nums = (vision_tokens == image_token_id).sum()
video_nums = (vision_tokens == video_token_id).sum()
image_indices = vision_start_indices + 1
image_nums = image_indices.shape[0]
llm_pos_ids_list: list = []
st = 0
remain_images, remain_videos = image_nums, video_nums
image_index, video_index = 0, 0
for _ in range(image_nums + video_nums):
if image_token_id in input_tokens and remain_images > 0:
ed_image = input_tokens.index(image_token_id, st)
else:
ed_image = len(input_tokens) + 1
if video_token_id in input_tokens and remain_videos > 0:
ed_video = input_tokens.index(video_token_id, st)
else:
ed_video = len(input_tokens) + 1
if ed_image < ed_video:
t, h, w = (
image_grid_thw[image_index][0],
image_grid_thw[image_index][1],
image_grid_thw[image_index][2],
)
image_index += 1
remain_images -= 1
ed = ed_image
else:
t, h, w = (
video_grid_thw[video_index][0],
video_grid_thw[video_index][1],
video_grid_thw[video_index][2],
)
video_index += 1
remain_videos -= 1
ed = ed_video
input_tokens_len = input_tokens.shape[0]
for image_index in range(image_nums):
ed = image_indices[image_index].item()
t, h, w = (
image_grid_thw[image_index][0],
image_grid_thw[image_index][1],
image_grid_thw[image_index][2],
)
llm_grid_t, llm_grid_h, llm_grid_w = (
t,
h // spatial_merge_size,
@@ -115,18 +84,16 @@ class MRotaryEmbedding:
)
st = ed + llm_grid_t * llm_grid_h * llm_grid_w
if st < len(input_tokens):
if st < input_tokens_len:
st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
text_len = len(input_tokens) - st
text_len = input_tokens_len - st
llm_pos_ids_list.append(
torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
)
llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
llm_positions = llm_positions[:, context_len:]
mrope_position_delta = (llm_positions.max() + 1 - len(input_tokens)).item()
llm_positions += extend_prefix_len
mrope_position_delta = (llm_positions.max() + 1 - input_tokens_len).item()
return llm_positions.tolist(), mrope_position_delta
@staticmethod