1.16.4

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 = "1.16.4"
 description = "Vision Transformer (ViT) - Pytorch"
 readme = { file = "README.md", content-type = "text/markdown" }
 license = { file = "LICENSE" }

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: