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[Model][Qwen3VL] Simplify get_mrope_input_positions using numpy #28302

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Nov 12, 2025
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[Model][Qwen3VL] Simplify get_mrope_input_positions using numpy #28302

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44 changes: 11 additions & 33 deletions vllm/model_executor/models/qwen3_vl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1432,13 +1432,11 @@ def get_mrope_input_positions(
vision_start_token_id = hf_config.vision_start_token_id
spatial_merge_size = hf_config.vision_config.spatial_merge_size

input_tokens_tensor = torch.tensor(input_tokens)
vision_start_indices = torch.argwhere(
input_tokens_tensor == 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()
input_tokens_array = np.array(input_tokens)
vision_start_mask = input_tokens_array == vision_start_token_id
vision_tokens = input_tokens_array[vision_start_mask.nonzero()[0] + 1]
image_nums = np.count_nonzero(vision_tokens == image_token_id)
video_nums = np.count_nonzero(vision_tokens == video_token_id)
llm_pos_ids_list: list = []

st = 0
Expand Down Expand Up @@ -1474,43 +1472,23 @@ def get_mrope_input_positions(

st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
llm_pos_ids_list.append(
torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
np.broadcast_to(np.arange(text_len), (3, text_len)) + st_idx
)

t_index = (
torch.arange(llm_grid_t)
.view(-1, 1)
.expand(-1, llm_grid_h * llm_grid_w)
.flatten()
)
h_index = (
torch.arange(llm_grid_h)
.view(1, -1, 1)
.expand(llm_grid_t, -1, llm_grid_w)
.flatten()
)
w_index = (
torch.arange(llm_grid_w)
.view(1, 1, -1)
.expand(llm_grid_t, llm_grid_h, -1)
.flatten()
)
llm_pos_ids_list.append(
torch.stack([t_index, h_index, w_index]) + text_len + st_idx
)
grid_indices = np.indices((llm_grid_t, llm_grid_h, llm_grid_w))
llm_pos_ids_list.append(grid_indices.reshape(3, -1) + text_len + st_idx)
st = ed + llm_grid_t * llm_grid_h * llm_grid_w

if st < len(input_tokens):
st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
text_len = len(input_tokens) - st
llm_pos_ids_list.append(
torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
np.broadcast_to(np.arange(text_len), (3, text_len)) + st_idx
)

llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
llm_positions = np.concatenate(llm_pos_ids_list, axis=1).reshape(3, -1)
mrope_position_delta = (llm_positions.max() + 1 - len(input_tokens)).item()

return llm_positions, mrope_position_delta
return torch.from_numpy(llm_positions), mrope_position_delta

def get_language_model(self) -> torch.nn.Module:
return self.language_model
Expand Down