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[Perf] Further optimization for Qwen3-VL fast_pos_embed_interpolate (#25347)

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Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>
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Isotr0py 2025-09-22 04:12:45 +08:00 committed by GitHub
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1 changed files with 30 additions and 16 deletions
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vllm/model_executor/models/qwen3_vl.py
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@ -405,25 +405,39 @@ class Qwen3_VisionTransformer(nn.Module):
dh = h_idxs - h_floor dh = h_idxs - h_floor
dw = w_idxs - w_floor dw = w_idxs - w_floor
w00 = ((1 - dh)[:, None] * (1 - dw)[None, :]).reshape(-1) # Create meshgrid view for all h, w vars
w01 = ((1 - dh)[:, None] * dw[None, :]).reshape(-1) dh_grid, dw_grid = torch.meshgrid(dh, dw, indexing='ij')
w10 = (dh[:, None] * (1 - dw)[None, :]).reshape(-1) h_floor_grid, w_floor_grid = torch.meshgrid(h_floor,
w11 = (dh[:, None] * dw[None, :]).reshape(-1) w_floor,
indexing='ij')
h_ceil_grid, w_ceil_grid = torch.meshgrid(h_ceil,
w_ceil,
indexing='ij')
h_floor_grid_idx = h_floor_grid * num_grid_per_side
h_ceil_grid_idx = h_ceil_grid * num_grid_per_side
idx00 = (h_floor[:, None] * num_grid_per_side + # original computation of weights
w_floor[None, :]).reshape(-1) # w00 = (1 - dh_grid) * (1 - dw_grid)
idx01 = (h_floor[:, None] * num_grid_per_side + # w01 = (1 - dh_grid) * dw_grid
w_ceil[None, :]).reshape(-1) # w10 = dh_grid * (1 - dw_grid)
idx10 = (h_ceil[:, None] * num_grid_per_side + # w11 = dh_grid * dw_grid
w_floor[None, :]).reshape(-1) # we reuse w11 here to avoid duplicate
idx11 = (h_ceil[:, None] * num_grid_per_side + # dh_grid * dw_grid computation
w_ceil[None, :]).reshape(-1) w11 = dh_grid * dw_grid
w10 = dh_grid - w11
w01 = dw_grid - w11
w00 = 1 - dh_grid - dw_grid + w11
indices = torch.stack([idx00, idx01, idx10, idx11], dim=0) idx00 = h_floor_grid_idx + w_floor_grid
idx01 = h_floor_grid_idx + w_ceil_grid
idx10 = h_ceil_grid_idx + w_floor_grid
idx11 = h_ceil_grid_idx + w_ceil_grid
indices = torch.stack([idx00, idx01, idx10, idx11],
dim=0).reshape(4, -1)
weights = torch.stack([w00, w01, w10, w11], weights = torch.stack([w00, w01, w10, w11],
dim=0).to(dtype=self.dtype, dim=0).reshape(4, -1, 1)
device=self.device) weights = weights.to(dtype=self.dtype, device=self.device)
weights = weights.unsqueeze(-1)
embeds = self.pos_embed(indices) embeds = self.pos_embed(indices)
weighted_embeds = embeds * weights weighted_embeds = embeds * weights