Changes to avoid bug in backward hooks, affecting diagnostics.

egs/librispeech/ASR/pruned_transducer_stateless7/conformer.py

@@ -32,6 +32,7 @@ from scaling import (
     ScaledConv1d,
     ScaledLinear,  # not as in other dirs.. just scales down initial parameter values.
     Whiten,
+    Identity,
     _diag,
 )
 from torch import Tensor, nn
@@ -864,8 +865,8 @@ class RelPositionMultiheadAttention(nn.Module):
                                        initial_scale=0.05)
 
         # the following are for diagnosics only, see --print-diagnostics option
-        self.copy_pos_query = nn.Identity()
-        self.copy_query = nn.Identity()
+        self.copy_pos_query = Identity()
+        self.copy_query = Identity()
 
         self.in_balancer = ActivationBalancer(3 * attention_dim,
                                               channel_dim=-1, max_abs=5.0)

egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py

@@ -382,7 +382,7 @@ class ActivationBalancer(torch.nn.Module):
 
     def forward(self, x: Tensor) -> Tensor:
         if torch.jit.is_scripting() or not x.requires_grad:
-            return x
+            return _no_op(x)
 
         count = self.cpu_count
         self.cpu_count += 1
@@ -418,7 +418,7 @@ class ActivationBalancer(torch.nn.Module):
                 x, scale_factor, sign_factor, self.channel_dim,
             )
         else:
-            return x
+            return _no_op(x)
 
 
 
@@ -567,7 +567,7 @@ class Whiten(nn.Module):
         and nothing will happen in backprop.
         """
         if not x.requires_grad or random.random() > self.prob or self.grad_scale == 0:
-            return x
+            return _no_op(x)
         else:
             if hasattr(self, 'min_prob') and random.random() < 0.25:
                 # occasionally switch between min_prob and max_prob, based on whether
@@ -583,9 +583,22 @@ class Whiten(nn.Module):
                                                   self.whitening_limit,
                                                   self.grad_scale)
 
+def _no_op(x: Tensor) -> Tensor:
+    if (torch.jit.is_scripting()):
+        return x
+    else:
+        # a no-op function that will have a node in the autograd graph,
+        # to avoid certain bugs relating to backward hooks
+        return x.chunk(1, dim=-1)[0]
 
 
 
+class Identity(torch.nn.Module):
+    def __init__(self):
+        super(Identity, self).__init__()
+
+    def forward(self, x):
+        return _no_op(x)
 
 class MaxEig(torch.nn.Module):
     """
@@ -643,7 +656,7 @@ class MaxEig(torch.nn.Module):
         if (torch.jit.is_scripting() or
             self.max_var_per_eig <= 0 or
             random.random() > self.cur_prob):
-            return x
+            return _no_op(x)
 
         with torch.cuda.amp.autocast(enabled=False):
             eps = 1.0e-20