fix eval mode for implicit neural codebooks and fix tests

Author: lucidrains

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "vector-quantize-pytorch"
-version = "1.16.0"
+version = "1.16.1"
 description = "Vector Quantization - Pytorch"
 authors = [
     { name = "Phil Wang", email = "lucidrains@gmail.com" }

tests/test_readme.py

@@ -71,14 +71,14 @@ def test_residual_vq(
     from vector_quantize_pytorch import ResidualVQ
 
     residual_vq = ResidualVQ(
-        dim = 256,
+        dim = 32,
         num_quantizers = 8,
-        codebook_size = 1024,
+        codebook_size = 128,
         implicit_neural_codebook = implicit_neural_codebook,
         use_cosine_sim = use_cosine_sim,
     )
 
-    x = torch.randn(1, 1024, 256)
+    x = torch.randn(1, 256, 32)
 
     quantized, indices, commit_loss = residual_vq(x)
     quantized, indices, commit_loss, all_codes = residual_vq(x, return_all_codes = True)

vector_quantize_pytorch/vector_quantize_pytorch.py

@@ -245,12 +245,6 @@ def kmeans(
 
     return means, bins
 
-def batched_embedding(indices, embeds):
-    batch, dim = indices.shape[1], embeds.shape[-1]
-    indices = repeat(indices, 'h b n -> h b n d', d = dim)
-    embeds = repeat(embeds, 'h c d -> h b c d', b = batch)
-    return embeds.gather(2, indices)
-
 # distributed helpers
 
 @cache
@@ -521,17 +515,22 @@ def forward(
 
         embed_ind = unpack_one(embed_ind, 'h *')
 
+        if exists(codebook_transform_fn):
+            transformed_embed = unpack_one(transformed_embed, 'h * c d')
+
         if self.training:
             unpacked_onehot = unpack_one(embed_onehot, 'h * c')
 
             if exists(codebook_transform_fn):
-                transformed_embed = unpack_one(transformed_embed, 'h * c d')
                 quantize = einsum('h b n c, h b n c d -> h b n d', unpacked_onehot, transformed_embed)
             else:
                 quantize = einsum('h b n c, h c d -> h b n d', unpacked_onehot, embed)
 
         else:
-            quantize = batched_embedding(embed_ind, embed)
+            if exists(codebook_transform_fn):
+                quantize = einx.get_at('h b n [c] d, h b n -> h b n d', transformed_embed, embed_ind)
+            else:
+                quantize = einx.get_at('h [c] d, h b n -> h b n d', embed, embed_ind)
 
         if self.training and self.ema_update and not freeze_codebook:
 
@@ -715,17 +714,22 @@ def forward(
         embed_ind, embed_onehot = self.gumbel_sample(dist, dim = -1, temperature = sample_codebook_temp, training = self.training)
         embed_ind = unpack_one(embed_ind, 'h *')
 
+        if exists(codebook_transform_fn):
+            transformed_embed = unpack_one(transformed_embed, 'h * c d')
+
         if self.training:
             unpacked_onehot = unpack_one(embed_onehot, 'h * c')
 
             if exists(codebook_transform_fn):
-                transformed_embed = unpack_one(transformed_embed, 'h * c d')
                 quantize = einsum('h b n c, h b n c d -> h b n d', unpacked_onehot, transformed_embed)
             else:
                 quantize = einsum('h b n c, h c d -> h b n d', unpacked_onehot, embed)
 
         else:
-            quantize = batched_embedding(embed_ind, embed)
+            if exists(codebook_transform_fn):
+                quantize = einx.get_at('h b n [c] d, h b n -> h b n d', transformed_embed, embed_ind)
+            else:
+                quantize = einx.get_at('h [c] d, h b n -> h b n d', embed, embed_ind)
 
         if self.training and self.ema_update and not freeze_codebook:
             if exists(mask):