if mask is passed into VQ, output 0 for padding

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "vector-quantize-pytorch"
-version = "1.15.4"
+version = "1.15.5"
 description = "Vector Quantization - Pytorch"
 authors = [
     { name = "Phil Wang", email = "[email protected]" }

tests/test_readme.py

@@ -59,7 +59,7 @@ def test_vq_mask():
     assert torch.allclose(quantized, mask_quantized[:, :512])
     assert torch.allclose(indices, mask_indices[:, :512])
 
-    assert torch.allclose(mask_quantized[:, 512:], x[:, 512:])
+    assert (mask_quantized[:, 512:] == 0.).all()
     assert (mask_indices[:, 512:] == -1).all()
 
 def test_residual_vq():

vector_quantize_pytorch/vector_quantize_pytorch.py

@@ -759,6 +759,7 @@ def __init__(
         affine_param_batch_decay = 0.99,
         affine_param_codebook_decay = 0.9, 
         sync_update_v = 0., # the v that controls optimistic vs pessimistic update for synchronous update rule (21) https://minyoungg.github.io/vqtorch/assets/draft_050523.pdf
+        return_zeros_for_masked_padding = True
     ):
         super().__init__()
         self.dim = dim
@@ -855,6 +856,9 @@ def __init__(
 
         self.register_buffer('zero', torch.tensor(0.), persistent = False)
 
+        # for variable lengthed sequences, whether to take care of masking out the padding to 0 (or return the original input)
+        self.return_zeros_for_masked_padding = return_zeros_for_masked_padding
+
     @property
     def codebook(self):
         codebook = self._codebook.embed
@@ -1133,11 +1137,16 @@ def calculate_ce_loss(codes):
         # if masking, only return quantized for where mask has True
 
         if exists(mask):
+            masked_out_value = orig_input
+
+            if self.return_zeros_for_masked_padding:
+                masked_out_value = torch.zeros_like(orig_input)
+
             quantize = einx.where(
                 'b n, b n d, b n d -> b n d',
                 mask,
                 quantize,
-                orig_input
+                masked_out_value
             )
 
             embed_ind = einx.where(