throw in an option to use code agnostic commit loss for LFQ, found em…

…pirically to work well by @MattMcPartlon

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "vector-quantize-pytorch"
-version = "1.14.12"
+version = "1.14.15"
 description = "Vector Quantization - Pytorch"
 authors = [
     { name = "Phil Wang", email = "[email protected]" }

vector_quantize_pytorch/lookup_free_quantization.py

@@ -62,8 +62,9 @@ def __init__(
         straight_through_activation = nn.Identity(),
         num_codebooks = 1,
         keep_num_codebooks_dim = None,
-        codebook_scale = 1.,            # for residual LFQ, codebook scaled down by 2x at each layer
-        frac_per_sample_entropy = 1.    # make less than 1. to only use a random fraction of the probs for per sample entropy
+        codebook_scale = 1.,                        # for residual LFQ, codebook scaled down by 2x at each layer
+        frac_per_sample_entropy = 1.,               # make less than 1. to only use a random fraction of the probs for per sample entropy
+        use_code_agnostic_commit_loss = False
     ):
         super().__init__()
 
@@ -110,6 +111,7 @@ def __init__(
         # commitment loss
 
         self.commitment_loss_weight = commitment_loss_weight
+        self.use_code_agnostic_commit_loss = use_code_agnostic_commit_loss
 
         # for no auxiliary loss, during inference
 
@@ -259,8 +261,19 @@ def forward(
 
         # commit loss
 
-        if self.training:
-            commit_loss = F.mse_loss(original_input, quantized.detach(), reduction = 'none')
+        if self.training and self.commitment_loss_weight > 0.:
+
+            if self.use_code_agnostic_commit_loss:
+                # credit goes to @MattMcPartlon for sharing this in https://github.com/lucidrains/vector-quantize-pytorch/issues/120#issuecomment-2095089337
+
+                commit_loss = F.mse_loss(
+                    original_input ** 2,
+                    codebook_value ** 2,
+                    reduction = 'none'
+                )
+
+            else:
+                commit_loss = F.mse_loss(original_input, quantized.detach(), reduction = 'none')
 
             if exists(mask):
                 commit_loss = commit_loss[mask]