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

Signed-off-by: Isotr0py <[email protected]>

Author: Isotr0py

vllm/model_executor/models/qwen3_vl.py

@@ -405,25 +405,39 @@ def fast_pos_embed_interpolate(self,
             dh = h_idxs - h_floor
             dw = w_idxs - w_floor
 
-            w00 = ((1 - dh)[:, None] * (1 - dw)[None, :]).reshape(-1)
-            w01 = ((1 - dh)[:, None] * dw[None, :]).reshape(-1)
-            w10 = (dh[:, None] * (1 - dw)[None, :]).reshape(-1)
-            w11 = (dh[:, None] * dw[None, :]).reshape(-1)
-
-            idx00 = (h_floor[:, None] * num_grid_per_side +
-                     w_floor[None, :]).reshape(-1)
-            idx01 = (h_floor[:, None] * num_grid_per_side +
-                     w_ceil[None, :]).reshape(-1)
-            idx10 = (h_ceil[:, None] * num_grid_per_side +
-                     w_floor[None, :]).reshape(-1)
-            idx11 = (h_ceil[:, None] * num_grid_per_side +
-                     w_ceil[None, :]).reshape(-1)
-
-            indices = torch.stack([idx00, idx01, idx10, idx11], dim=0)
+            # Create meshgrid view for all h, w vars
+            dh_grid, dw_grid = torch.meshgrid(dh, dw, indexing='ij')
+            h_floor_grid, w_floor_grid = torch.meshgrid(h_floor,
+                                                        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
+
+            # original computation of weights
+            # w00 = (1 - dh_grid) * (1 - dw_grid)
+            # w01 = (1 - dh_grid) * dw_grid
+            # w10 = dh_grid * (1 - dw_grid)
+            # w11 = dh_grid * dw_grid
+            # we reuse w11 here to avoid duplicate
+            # dh_grid * dw_grid computation
+            w11 = dh_grid * dw_grid
+            w10 = dh_grid - w11
+            w01 = dw_grid - w11
+            w00 = 1 - dh_grid - dw_grid + w11
+
+            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],
-                                  dim=0).to(dtype=self.dtype,
-                                            device=self.device)
-            weights = weights.unsqueeze(-1)
+                                  dim=0).reshape(4, -1, 1)
+            weights = weights.to(dtype=self.dtype, device=self.device)
 
             embeds = self.pos_embed(indices)
             weighted_embeds = embeds * weights