address #162

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "vector-quantize-pytorch"
-version = "1.17.4"
+version = "1.17.5"
 description = "Vector Quantization - Pytorch"
 authors = [
     { name = "Phil Wang", email = "lucidrains@gmail.com" }

tests/test_readme.py

@@ -65,10 +65,12 @@ def test_vq_mask():
 @pytest.mark.parametrize('implicit_neural_codebook', (True, False))
 @pytest.mark.parametrize('use_cosine_sim', (True, False))
 @pytest.mark.parametrize('train', (True, False))
+@pytest.mark.parametrize('shared_codebook', (True, False))
 def test_residual_vq(
     implicit_neural_codebook,
     use_cosine_sim,
-    train
+    train,
+    shared_codebook
 ):
     from vector_quantize_pytorch import ResidualVQ
 
@@ -78,6 +80,7 @@ def test_residual_vq(
         codebook_size = 128,
         implicit_neural_codebook = implicit_neural_codebook,
         use_cosine_sim = use_cosine_sim,
+        shared_codebook = shared_codebook
     )
 
     x = torch.randn(1, 256, 32)

vector_quantize_pytorch/residual_vq.py

@@ -137,6 +137,11 @@ def __init__(
                 ema_update = False
             )
 
+        if shared_codebook:
+            vq_kwargs.update(
+                manual_ema_update = True
+            )
+
         self.layers = ModuleList([VectorQuantize(dim = codebook_dim, codebook_dim = codebook_dim, accept_image_fmap = accept_image_fmap, **vq_kwargs) for _ in range(num_quantizers)])
 
         assert all([not vq.has_projections for vq in self.layers])
@@ -157,6 +162,8 @@ def __init__(
 
         # sharing codebook logic
 
+        self.shared_codebook = shared_codebook
+
         if not shared_codebook:
             return
 
@@ -349,6 +356,11 @@ def forward(
             all_indices.append(embed_indices)
             all_losses.append(loss)
 
+        # if shared codebook, update ema only at end
+
+        if self.shared_codebook:
+            first(self.layers)._codebook.update_ema()
+
         # project out, if needed
 
         quantized_out = self.project_out(quantized_out)

vector_quantize_pytorch/vector_quantize_pytorch.py

@@ -280,6 +280,7 @@ def __init__(
         gumbel_sample = gumbel_sample,
         sample_codebook_temp = 1.,
         ema_update = True,
+        manual_ema_update = False,
         affine_param = False,
         sync_affine_param = False,
         affine_param_batch_decay = 0.99,
@@ -290,6 +291,7 @@ def __init__(
 
         self.decay = decay
         self.ema_update = ema_update
+        self.manual_ema_update = manual_ema_update
 
         init_fn = uniform_init if not kmeans_init else torch.zeros
         embed = init_fn(num_codebooks, codebook_size, dim)
@@ -458,6 +460,12 @@ def expire_codes_(self, batch_samples):
         batch_samples = rearrange(batch_samples, 'h ... d -> h (...) d')
         self.replace(batch_samples, batch_mask = expired_codes)
 
+    def update_ema(self):
+        cluster_size = laplace_smoothing(self.cluster_size, self.codebook_size, self.eps) * self.cluster_size.sum(dim = -1, keepdim = True)
+
+        embed_normalized = self.embed_avg / rearrange(cluster_size, '... -> ... 1')
+        self.embed.data.copy_(embed_normalized)
+
     @autocast('cuda', enabled = False)
     def forward(
         self,
@@ -551,11 +559,9 @@ def forward(
 
             ema_inplace(self.embed_avg.data, embed_sum, self.decay)
 
-            cluster_size = laplace_smoothing(self.cluster_size, self.codebook_size, self.eps) * self.cluster_size.sum(dim = -1, keepdim = True)
-
-            embed_normalized = self.embed_avg / rearrange(cluster_size, '... -> ... 1')
-            self.embed.data.copy_(embed_normalized)
-            self.expire_codes_(x)
+            if not self.manual_ema_update:
+                self.update_ema()
+                self.expire_codes_(x)
 
         if needs_codebook_dim:
             quantize, embed_ind = map(lambda t: rearrange(t, '1 ... -> ...'), (quantize, embed_ind))
@@ -582,11 +588,14 @@ def __init__(
         gumbel_sample = gumbel_sample,
         sample_codebook_temp = 1.,
         ema_update = True,
+        manual_ema_update = False
     ):
         super().__init__()
         self.transform_input = l2norm
 
         self.ema_update = ema_update
+        self.manual_ema_update = manual_ema_update
+
         self.decay = decay
 
         if not kmeans_init:
@@ -671,6 +680,14 @@ def expire_codes_(self, batch_samples):
         batch_samples = rearrange(batch_samples, 'h ... d -> h (...) d')
         self.replace(batch_samples, batch_mask = expired_codes)
 
+    def update_ema(self):
+        cluster_size = laplace_smoothing(self.cluster_size, self.codebook_size, self.eps) * self.cluster_size.sum(dim = -1, keepdim = True)
+
+        embed_normalized = self.embed_avg / rearrange(cluster_size, '... -> ... 1')
+        embed_normalized = l2norm(embed_normalized)
+
+        self.embed.data.copy_(embed_normalized)
+
     @autocast('cuda', enabled = False)
     def forward(
         self,
@@ -746,13 +763,9 @@ def forward(
 
             ema_inplace(self.embed_avg.data, embed_sum, self.decay)
 
-            cluster_size = laplace_smoothing(self.cluster_size, self.codebook_size, self.eps) * self.cluster_size.sum(dim = -1, keepdim = True)
-
-            embed_normalized = self.embed_avg / rearrange(cluster_size, '... -> ... 1')
-            embed_normalized = l2norm(embed_normalized)
-
-            self.embed.data.copy_(embed_normalized)
-            self.expire_codes_(x)
+            if not self.manual_ema_update:
+                self.update_ema()
+                self.expire_codes_(x)
 
         if needs_codebook_dim:
             quantize, embed_ind = map(lambda t: rearrange(t, '1 ... -> ...'), (quantize, embed_ind))
@@ -802,6 +815,7 @@ def __init__(
         sync_codebook = None,
         sync_affine_param = False,
         ema_update = True,
+        manual_ema_update = False,
         learnable_codebook = False,
         in_place_codebook_optimizer: Callable[..., Optimizer] = None, # Optimizer used to update the codebook embedding if using learnable_codebook
         affine_param = False,
@@ -881,7 +895,8 @@ def __init__(
             learnable_codebook = has_codebook_orthogonal_loss or learnable_codebook,
             sample_codebook_temp = sample_codebook_temp,
             gumbel_sample = gumbel_sample_fn,
-            ema_update = ema_update
+            ema_update = ema_update,
+            manual_ema_update = manual_ema_update
         )
 
         if affine_param: