Make alpha for LinearWithAuxLossFunction be in log space; simplify/rework NonlinAttentionModule, setup more like ConvModule now.

egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py

@@ -421,7 +421,7 @@ class LinearWithAuxLossFunction(torch.autograd.Function):
                 # recompute y as we need the gradient; this is easier to implement than
                 # saving y in the context.
                 y = torch.matmul(x, weight.t())
-                z = alpha * torch.matmul(y, weight)
+                z = alpha.exp() * torch.matmul(y, weight)
                 diff = x - z
                 dims_to_mean = tuple(range(x.ndim-1))
                 mean = diff.mean(dim=dims_to_mean)
@@ -452,7 +452,7 @@ class LinearWithAuxLoss(nn.Module):
       Suppose the input is x, and this layer computes:
           y = M x
      (the bias is applied separately), then we define:
-          z = alpha * M^T y
+          z = exp(alpha) * M^T y
       where alpha is learnable; and the auxiliary loss will be:
          aux_loss = normalize_mean(z - x)^2.
       (normalize_mean refers to subtracting the average value per channel,
@@ -485,7 +485,7 @@ class LinearWithAuxLoss(nn.Module):
                                      0.01 * initial_scale)
         else:
             self.register_parameter('bias', None)
-        self.alpha = nn.Parameter(torch.tensor(1.0))
+        self.alpha = nn.Parameter(torch.tensor(0.0))
 
 
     def forward(self,

egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py

@@ -343,7 +343,7 @@ def _whitening_schedule(x: float, ratio: float = 2.0) -> ScheduledFloat:
                           default=x)
 
 def _aux_grad_scale() -> float:
-    return 0.1
+    return 0.2
 def _aux_grad_prob() -> ScheduledFloat:
     return ScheduledFloat((0.0, 0.25), (1000.0, 0.0125))
 
@@ -1432,33 +1432,27 @@ class NonlinAttentionModule(nn.Module):
     ) -> None:
         super().__init__()
 
-        self.in_proj = LinearWithAuxLoss(channels, 2 * channels, bias=True,
+        self.in_proj = nn.Linear(channels, 2 * channels, bias=True)
-                                         aux_grad_scale=_aux_grad_scale(), prob=_aux_grad_prob())
 
-        # balancer that goes after the glu mechanism.
+        # balancer that goes before the sigmoid.
         self.balancer = ActivationBalancer(
             channels, channel_dim=-1,
-            min_positive=0.2, max_positive=0.8,
+            min_positive=0.05, max_positive=1.0,
-            min_abs=0.2, max_abs=10.0,
+            min_abs=0.2, max_abs=ScheduledFloat((0.0, 2.0),
-            min_prob=0.05,
+                                                (4000.0, 10.0),
+                                                default=1.0),
         )
-        self.pre_sigmoid = Identity()  # for diagnostics.
         self.sigmoid = nn.Sigmoid()
 
         self.activation = Identity()  # for diagnostics.
-        self.out_proj = LinearWithAuxLoss(channels, channels,
+        self.out_proj = ScaledLinear(channels, channels,
-                                          bias=True,
+                                     bias=True,
-                                          aux_grad_scale=_aux_grad_scale(), prob=_aux_grad_prob(),
+                                     initial_scale=0.05)
-                                          initial_scale=0.05)
 
-        self.whiten1 = Whiten(num_groups=1,
+        self.whiten = Whiten(num_groups=1,
-                              whitening_limit=_whitening_schedule(5.0),
+                             whitening_limit=_whitening_schedule(7.5),
-                              prob=(0.025, 0.25),
+                             prob=(0.025, 0.25),
-                              grad_scale=0.01)
+                             grad_scale=0.01)
-        self.whiten2 = Whiten(num_groups=1,
-                              whitening_limit=_whitening_schedule(7.5),
-                              prob=(0.025, 0.25),
-                              grad_scale=0.01)
 
 
 
@@ -1475,19 +1469,12 @@ attn_weights: a Tensor of shape (num_heads, batch_size, seq_len, seq_len)
         """
         x = self.in_proj(x)
 
-        v, s = x.chunk(2, dim=-1)
+        x, s = x.chunk(2, dim=-1)
 
-        if self.training and random.random() < 0.02:
+        s = self.balancer(s)
-            # prevent the inputs to the sigmoid from getting very large (this is
+        s = self.sigmoid(s)
-            # hopefully quite a rare phenomenon, so we are giving this path a
-            # very small probability to save time).
-            s = penalize_abs_values_gt(s, limit=20.0, penalty=1.0e-04)
 
-        v = self.whiten1(v)
+        x = x * s
-        # GLU mechanism
-        s = self.pre_sigmoid(s)
-        x = self.sigmoid(s) * v
-        x = self.balancer(x)
 
         (seq_len, batch_size, embed_dim) = x.shape
         num_heads = attn_weights.shape[0]
@@ -1500,7 +1487,7 @@ attn_weights: a Tensor of shape (num_heads, batch_size, seq_len, seq_len)
         x = x.permute(2, 1, 0, 3).reshape(seq_len, batch_size, -1)
 
         x = self.activation(x)  # diagnostics only, it's the identity.
-        x = self.whiten2(x)
+        x = self.whiten(x)
         x = self.out_proj(x)
         return x