fix distill

Use lru_cache to cache unique (ph, pw, device) position grids, avoiding
redundant computation when multiple images share the same patch
dimensions. Cache persists across forward passes.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "vit-pytorch"
-version = "1.16.3"
+version = "1.16.5"
 description = "Vision Transformer (ViT) - Pytorch"
 readme = { file = "README.md", content-type = "text/markdown" }
 license = { file = "LICENSE" }

vit_pytorch/distill.py

@@ -25,12 +25,12 @@ class DistillMixin:
         x = self.to_patch_embedding(img)
         b, n, _ = x.shape
 
-        cls_tokens = repeat(self.cls_token, '1 n d -> b n d', b = b)
+        cls_tokens = repeat(self.cls_token, 'n d -> b n d', b = b)
         x = torch.cat((cls_tokens, x), dim = 1)
-        x += self.pos_embedding[:, :(n + 1)]
+        x += self.pos_embedding[:(n + 1)]
 
         if distilling:
-            distill_tokens = repeat(distill_token, '1 n d -> b n d', b = b)
+            distill_tokens = repeat(distill_token, 'n d -> b n d', b = b)
             x = torch.cat((x, distill_tokens), dim = 1)
 
         x = self._attend(x)
@@ -125,7 +125,7 @@ class DistillWrapper(Module):
         self.alpha = alpha
         self.hard = hard
 
-        self.distillation_token = nn.Parameter(torch.randn(1, 1, dim))
+        self.distillation_token = nn.Parameter(torch.randn(1, dim))
 
         self.distill_mlp = nn.Sequential(
             nn.LayerNorm(dim) if mlp_layernorm else nn.Identity(),

vit_pytorch/na_vit.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from functools import partial
+from functools import partial, lru_cache
 from typing import List
 
 import torch
@@ -27,6 +27,12 @@ def pair(t):
 def divisible_by(numer, denom):
     return (numer % denom) == 0
 
+@lru_cache(maxsize=128)
+def posemb_grid(ph, pw, device):
+    h_idx = torch.arange(ph, device=device).repeat_interleave(pw)
+    w_idx = torch.arange(pw, device=device).repeat(ph)
+    return torch.stack([h_idx, w_idx], dim=-1)
+
 # auto grouping images
 
 def group_images_by_max_seq_len(
@@ -293,12 +299,8 @@ class NaViT(nn.Module):
             # extract patches for all images
             sequences = [rearrange(img, 'c (h p1) (w p2) -> (h w) (c p1 p2)', p1=p, p2=p) for img in images]
 
-            # compute positions using repeat_interleave (faster than meshgrid per image)
-            positions = []
-            for ph, pw in patch_dims:
-                h_idx = arange(ph).repeat_interleave(pw)
-                w_idx = arange(pw).repeat(ph)
-                positions.append(torch.stack([h_idx, w_idx], dim=-1))
+            # compute positions - uses lru_cache to avoid redundant computation across forward passes
+            positions = [posemb_grid(ph, pw, device) for ph, pw in patch_dims]
 
             # handle token dropout
             if has_token_dropout: