Refactor patch and pos embed resampling based on feedback from https://github.com/stas-sl

timm/layers/patch_embed.py

@@ -270,47 +270,33 @@ def _compute_resize_matrix(
 
     eye_matrix = torch.eye(old_total, device=device, dtype=dtype)
     basis_vectors_batch = eye_matrix.reshape(old_total, 1, old_h, old_w)
-
     resized_basis_vectors_batch = F.interpolate(
         basis_vectors_batch,
         size=new_size,
         mode=interpolation,
         antialias=antialias,
         align_corners=False
     ) # Output shape: (old_total, 1, new_h, new_w)
-
-    resize_matrix = resized_basis_vectors_batch.squeeze(1).reshape(old_total, new_total).T
+    resize_matrix = resized_basis_vectors_batch.squeeze(1).permute(1, 2, 0).reshape(new_total, old_total)
     return resize_matrix # Shape: (new_total, old_total)
 
 
-def _compute_pinv_for_resampling(resize_matrix: torch.Tensor) -> torch.Tensor:
-    """Calculates the pseudoinverse matrix used for the resampling operation."""
-    pinv_matrix = torch.linalg.pinv(resize_matrix.T) # Shape: (new_total, old_total)
-    return pinv_matrix
-
-
 def _apply_resampling(
     patch_embed: torch.Tensor,
     pinv_matrix: torch.Tensor,
     new_size_tuple: Tuple[int, int],
     orig_dtype: torch.dtype,
     intermediate_dtype: torch.dtype = DTYPE_INTERMEDIATE
 ) -> torch.Tensor:
-    """Applies the precomputed pinv_matrix to resample the patch_embed tensor."""
-    try:
-        from torch import vmap
-    except ImportError:
-        from functorch import vmap
-
-    def resample_kernel(kernel: torch.Tensor) -> torch.Tensor:
-        kernel_flat = kernel.reshape(-1).to(intermediate_dtype)
-        resampled_kernel_flat = pinv_matrix @ kernel_flat
-        return resampled_kernel_flat.reshape(new_size_tuple)
-
-    resample_kernel_vmap = vmap(vmap(resample_kernel, in_dims=0, out_dims=0), in_dims=0, out_dims=0)
-    patch_embed_float = patch_embed.to(intermediate_dtype)
-    resampled_patch_embed = resample_kernel_vmap(patch_embed_float)
-    return resampled_patch_embed.to(orig_dtype)
+    """ Simplified resampling w/o vmap use.
+    As proposed by https://github.com/stas-sl
+    """
+    c_out, c_in, *_ = patch_embed.shape
+    patch_embed = patch_embed.reshape(c_out, c_in, -1).to(dtype=intermediate_dtype)
+    pinv_matrix = pinv_matrix.to(dtype=intermediate_dtype)
+    resampled_patch_embed = patch_embed @ pinv_matrix  # (C_out, C_in, P_old * P_old) @ (P_old * P_old, P_new * P_new)
+    resampled_patch_embed = resampled_patch_embed.reshape(c_out, c_in, *new_size_tuple).to(dtype=orig_dtype)
+    return resampled_patch_embed
 
 
 def resample_patch_embed(
@@ -336,7 +322,7 @@ def resample_patch_embed(
     resize_mat = _compute_resize_matrix(
         old_size_tuple, new_size_tuple, interpolation, antialias, device, DTYPE_INTERMEDIATE
     )
-    pinv_matrix = _compute_pinv_for_resampling(resize_mat)
+    pinv_matrix = torch.linalg.pinv(resize_mat)  # Calculates the pseudoinverse matrix used for resampling
     resampled_patch_embed = _apply_resampling(
         patch_embed, pinv_matrix, new_size_tuple, orig_dtype, DTYPE_INTERMEDIATE
     )
@@ -388,7 +374,7 @@ def _get_or_create_pinv_matrix(
         resize_mat = _compute_resize_matrix(
             self.orig_size, new_size, self.interpolation, self.antialias, device, dtype
         )
-        pinv_matrix = _compute_pinv_for_resampling(resize_mat)
+        pinv_matrix = torch.linalg.pinv(resize_mat)  # Calculates the pseudoinverse matrix used for resampling
 
         # Cache using register_buffer
         buffer_name = f"pinv_{new_size[0]}x{new_size[1]}"