Reduce ConvNeXt parameters.

egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py

@@ -246,6 +246,7 @@ class CachingEvalFunction(torch.autograd.Function):
         # that matters in the forward pass), e.g. there should be no dropout.
         ctx.random_state = random.getstate()
         # we are inside torch.no_grad() here, so the following won't create the computation graph.
+        with torch.no_grad():
             y = m(x)
         ctx.save_for_backward(x, y)
         return y
@@ -254,6 +255,7 @@ class CachingEvalFunction(torch.autograd.Function):
     @custom_bwd
     def backward(ctx, y_grad: Tensor):
         x, y = ctx.saved_tensors
+        x = x.detach()
         x.requires_grad = ctx.x_requires_grad
         m = ctx.m  # e.g. a nn.Module
 
@@ -273,6 +275,14 @@ class CachingEvalFunction(torch.autograd.Function):
 
 
 def caching_eval(x: Tensor, m: nn.Module) -> Tensor:
+    if m.training:
+        # The purpose of this code is to make all parameters of m reachable in
+        # the computation graph, so that if we give find_unused_parameters=True
+        # to PyTorch's autograd code it does not assign them zero gradient.
+        tot = 0.0
+        for p in m.parameters():
+            tot = tot + 0.0 * p.flatten()[0]
+        x = x + tot  # tot will be 0, this does nothing.
     return CachingEvalFunction.apply(x, m)
 
 

egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py

@@ -1767,7 +1767,7 @@ class Conv2dSubsampling(nn.Module):
         out_channels: int,
         layer1_channels: int = 8,
         layer2_channels: int = 32,
-        layer3_channels: int = 128,
+        layer3_channels: int = 96,
         dropout: FloatLike = 0.1,
     ) -> None:
         """
@@ -1829,7 +1829,6 @@ class Conv2dSubsampling(nn.Module):
                                    ))
 
         self.convnext2 = nn.Sequential(ConvNeXt(layer3_channels),
-                                       ConvNeXt(layer3_channels),
                                        ConvNeXt(layer3_channels),
                                        BasicNorm(layer3_channels,
                                                  channel_dim=1))