Implement limits on parameter values a different way.

egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py

@@ -158,47 +158,6 @@ class ActivationScaleBalancerFunction(torch.autograd.Function):
         return x_grad - neg_delta_grad, None, None, None,
 
 
-class RandomClampFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(
-            ctx,
-            x: Tensor,
-            min: Optional[float],
-            max: Optional[float],
-            prob: float,
-            reflect: float) -> Tensor:
-        kwargs = {}
-        if min is not None:
-            kwargs['min'] = min
-        if max is not None:
-            kwargs['max'] = max
-        x_clamped = torch.clamp(x, **kwargs)
-        mask = torch.rand_like(x) < prob
-        ans = torch.where(mask, x_clamped, x)
-        if x.requires_grad:
-            ctx.save_for_backward(ans == x)
-            ctx.reflect = reflect
-        if reflect != 0.0:
-            ans = ans * (1.0 + reflect) - (x * reflect)
-        return ans
-
-    @staticmethod
-    def backward(ctx, ans_grad: Tensor) -> Tuple[Tensor, None, None, None, None]:
-        is_same, = ctx.saved_tensors
-        x_grad = ans_grad * is_same.to(ans_grad.dtype)
-        reflect = ctx.reflect
-        if reflect !=  0.0:
-            x_grad = x_grad * (1.0 + reflect) - (ans_grad * reflect)
-        return x_grad, None, None, None, None
-
-def random_clamp(x: Tensor,
-                 min: Optional[float] = None,
-                 max: Optional[float] = None,
-                 prob: float = 0.5,
-                 reflect: float = 0.0):
-    return RandomClampFunction.apply(x, min, max, prob, reflect)
-
-
 def random_cast_to_half(x: Tensor,
                         min_abs: float = 5.0e-06) -> Tensor:
     """
@@ -757,6 +716,38 @@ def with_loss(x, y):
     return WithLoss.apply(x, y)
 
 
+class LimitParamValue(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x: Tensor, min: float, max: float):
+        ctx.save_for_backward(x)
+        ctx.min = min
+        ctx.max = max
+        return x
+    @staticmethod
+    def backward(ctx, x_grad: Tensor):
+        x, = ctx.saved_tensors
+        # where x < ctx.min, ensure all grads are negative (this will tend to make
+        # x more positive).
+        x_grad *= torch.where(torch.logical_and(x_grad > 0, x < ctx.min), -1.0, 1.0)
+        # where x > ctx.max, ensure all grads are positive (this will tend to make
+        # x more negative).
+        x_grad *= torch.where(torch.logical_and(x_grad < 0, x > ctx.max), -1.0, 1.0)
+        return x_grad, None, None
+
+def limit_param_value(x: Tensor,
+                      min: float, max: float,
+                      prob: float = 0.2):
+    # You apply this to (typically) an nn.Parameter during training to ensure that its
+    # (elements mostly) stays within a supplied range.  This is done by modifying the
+    # gradients in backprop.
+    # It's not necessary to do this on every batch: do it only some of the time,
+    # to save a little time.
+    if random.random() < prob:
+        return LimitParamValue.apply(x, min, max)
+    else:
+        return x
+
+
 def _no_op(x: Tensor) -> Tensor:
     if (torch.jit.is_scripting()):
         return x

egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py

@@ -34,11 +34,11 @@ from scaling import (
     Whiten,
     Identity,
     _diag,
-    random_clamp,
     penalize_abs_values_gt,
     softmax,
     ScheduledFloat,
     FloatLike,
+    limit_param_value,
 )
 from torch import Tensor, nn
 
@@ -435,13 +435,12 @@ class ZipformerEncoderLayer(nn.Module):
                              grad_scale=0.01)
 
     def get_bypass_scale(self):
-        if torch.jit.is_scripting() or not self.training or random.random() < 0.5:
-            # the random.random() part is to ensure we get grads if self.bypass_scale becomes out of range
+        if torch.jit.is_scripting() or not self.training:
             return self.bypass_scale
-
-        return self.bypass_scale.clamp(min=float(self.bypass_clamp_min),
-                                       max=float(self.bypass_clamp_max))
-
+        else:
+            return limit_param_value(self.bypass_scale,
+                                     min=float(self.bypass_clamp_min),
+                                     max=float(self.bypass_clamp_max))
 
     def forward(
         self,
@@ -860,10 +859,10 @@ class SimpleCombiner(torch.nn.Module):
 
 
         weight1 = self.weight1
-        if self.training and random.random() < 0.5 and self.min_weight != (0., 0.):
-            weight1 = weight1.clamp(min=self.min_weight[0],
-                                    max=1.0-self.min_weight[1])
-
+        if self.training:
+            weight1 = limit_param_value(weight1,
+                                        min=self.min_weight[0],
+                                        max=1.0-self.min_weight[1])
 
         src1_dim = src1.shape[-1]
         src2_dim = src2.shape[-1]