move the rotation operation from rotation trick to a function for potential reuse for other research

Author: lucidrains

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "vector-quantize-pytorch"
-version = "1.19.4"
+version = "1.19.5"
 description = "Vector Quantization - Pytorch"
 authors = [
     { name = "Phil Wang", email = "lucidrains@gmail.com" }

vector_quantize_pytorch/vector_quantize_pytorch.py

@@ -76,19 +76,6 @@ def lens_to_mask(lens, max_length):
     seq = torch.arange(max_length, device = lens.device)
     return seq < lens[:, None]
 
-def efficient_rotation_trick_transform(u, q, e):
-    """
-    4.2 in https://arxiv.org/abs/2410.06424
-    """
-    e = rearrange(e, 'b d -> b 1 d')
-    w = l2norm(u + q, dim = 1).detach()
-
-    return (
-        e -
-        2 * (e @ rearrange(w, 'b d -> b d 1') @ rearrange(w, 'b d -> b 1 d')) +
-        2 * (e @ rearrange(u, 'b d -> b d 1').detach() @ rearrange(q, 'b d -> b 1 d').detach())
-    )
-
 def uniform_init(*shape):
     t = torch.empty(shape)
     nn.init.kaiming_uniform_(t)
@@ -248,6 +235,39 @@ def kmeans(
 
     return means, bins
 
+# rotation trick related
+
+def efficient_rotation_trick_transform(u, q, e):
+    """
+    4.2 in https://arxiv.org/abs/2410.06424
+    """
+    e = rearrange(e, 'b d -> b 1 d')
+    w = l2norm(u + q, dim = 1).detach()
+
+    return (
+        e -
+        2 * (e @ rearrange(w, 'b d -> b d 1') @ rearrange(w, 'b d -> b 1 d')) +
+        2 * (e @ rearrange(u, 'b d -> b d 1').detach() @ rearrange(q, 'b d -> b 1 d').detach())
+    )
+
+def rotate_from_to(src, tgt):
+    # rotation trick STE (https://arxiv.org/abs/2410.06424) to get gradients through VQ layer.
+    tgt, inverse = pack_one(tgt, '* d')
+    src, _ = pack_one(src, '* d')
+
+    norm_tgt = tgt.norm(dim = -1, keepdim = True)
+    norm_src = src.norm(dim = -1, keepdim = True)
+
+    rotated_src = efficient_rotation_trick_transform(
+        safe_div(tgt, norm_tgt),
+        safe_div(src, norm_src),
+        tgt
+    ).squeeze()
+
+    rotated = rotated_src * safe_div(norm_src, norm_tgt).detach()
+
+    return inverse(rotated)
+
 # distributed helpers
 
 @cache
@@ -1098,22 +1118,7 @@ def forward(
             commit_quantize = maybe_detach(quantize)
 
             if self.rotation_trick:
-                # rotation trick STE (https://arxiv.org/abs/2410.06424) to get gradients through VQ layer.
-                x, inverse = pack_one(x, '* d')
-                quantize, _ = pack_one(quantize, '* d')
-
-                norm_x = x.norm(dim = -1, keepdim = True)
-                norm_quantize = quantize.norm(dim = -1, keepdim = True)
-
-                rot_quantize = efficient_rotation_trick_transform(
-                    safe_div(x, norm_x),
-                    safe_div(quantize, norm_quantize),
-                    x
-                ).squeeze()
-
-                quantize = rot_quantize * safe_div(norm_quantize, norm_x).detach()
-
-                x, quantize = inverse(x), inverse(quantize)
+                quantize = rotate_from_to(quantize, x)
             else:
                 # standard STE to get gradients through VQ layer.
                 quantize = x + (quantize - x).detach()