Regularize how we apply the min and max to the eps of BasicNorm

egs/librispeech/ASR/pruned_transducer_stateless7/conformer.py

@@ -360,7 +360,11 @@ class ConformerEncoderLayer(nn.Module):
 
         delta = src - src_orig
         bypass_scale = self.bypass_scale
-        if random.random() > 0.1:
+        if self.training and random.random() < 0.25:
+            # with probability 0.25, in training mode, clamp bypass_scale to [
+            # 0.1, 1.0 ]; this will encourage it to learn parameters within this
+            # range by making parameters that are outside that range range
+            # noisy.
             bypass_scale = bypass_scale.clamp(min=0.1, max=1.0)
         src = src_orig + delta * self.bypass_scale
 

egs/librispeech/ASR/pruned_transducer_stateless7/optim.py

@@ -159,7 +159,7 @@ class ScaledAdam(BatchedOptimizer):
             eps=1.0e-08,
             param_min_rms=1.0e-05,
             param_max_rms=3.0,
-            scalar_max=5.0,
+            scalar_max=10.0,
             size_update_period=4,
             clipping_update_period=100,
     ):

egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py

@@ -348,6 +348,8 @@ class BasicNorm(torch.nn.Module):
           to indicate the connection with conventional LayerNorm.
        learn_eps: if true, we learn epsilon; if false, we keep it
          at the initial value.
+    eps_min: float
+    eps_max: float
     """
 
     def __init__(
@@ -356,6 +358,8 @@ class BasicNorm(torch.nn.Module):
         channel_dim: int = -1,  # CAUTION: see documentation.
         eps: float = 0.25,
         learn_eps: bool = True,
+        eps_min: float = -3.0,
+        eps_max: float = 3.0,
     ) -> None:
         super(BasicNorm, self).__init__()
         self.num_channels = num_channels
@@ -364,9 +368,21 @@ class BasicNorm(torch.nn.Module):
             self.eps = nn.Parameter(torch.tensor(eps).log().detach())
         else:
             self.register_buffer("eps", torch.tensor(eps).log().detach())
+        self.eps_min = eps_min
+        self.eps_max = eps_max
 
     def forward(self, x: Tensor) -> Tensor:
         assert x.shape[self.channel_dim] == self.num_channels
+        eps = self.eps
+        if self.training and random.random() < 0.25:
+            # with probability 0.25, in training mode, clamp eps between the min
+            # and max; this will encourage it to learn parameters within the
+            # allowed range by making parameters that are outside the allowed
+            # range noisy.
+
+            # gradients to allow the parameter to get back into the allowed
+            # region if it happens to exit it.
+            eps = eps.clamp(min=self.eps_min, max=self.eps_max)
         scales = (
             torch.mean(x ** 2, dim=self.channel_dim, keepdim=True)
             + self.eps.exp()