Apply x row scaling with grad

egs/librispeech/ASR/pruned_transducer_stateless2/scaling.py

@@ -713,25 +713,6 @@ class GaussProjDrop(torch.nn.Module):
             x = (x_next * self.rand_scale + x_bypass)
             return x
 
-class PseudoNormalizeFunction(torch.autograd.Function):
-    """
-    Function object that is the identity function in the forward pass; and, in the
-    backward pass, removes the component of the derivative in the direction of x itself
-    (as if it had gone through some kind of normalization layer
-    """
-    @staticmethod
-    def forward(ctx, x: Tensor) -> Tensor:
-        ctx.save_for_backward(x)
-        return x
-
-    @staticmethod
-    def backward(ctx, x_grad: Tensor) -> Tensor:
-        x, = ctx.saved_tensors
-        eps = 1.0e-20
-        x_sumsq = (x**2).sum() + eps
-        grad_x_sum = (x_grad * x).sum()
-        return x_grad - x * (grad_x_sum / x_sumsq)
-
 
 def _compute_correlation_loss(cov: Tensor,
                               eps: float) -> Tensor:
@@ -759,7 +740,6 @@ def _update_cov_stats(cov: Tensor,
         x: Tensor of features/activations, of shape (num_frames, num_channels)
      beta: The decay constant for the stats, e.g. 0.8.
     """
-    x = PseudoNormalizeFunction.apply(x)
     new_cov = torch.matmul(x.t(), x)
     return cov * beta + new_cov * (1-beta)
 
@@ -818,18 +798,19 @@ class DecorrelateFunction(torch.autograd.Function):
         # the computation.
 
         x_grad_old_sqnorm = (x_grad ** 2).sum(dim=1)
-        x_sqnorm = (x.detach() ** 2).sum(dim=1)
-
-        x_desired_sqscale = x_grad_old_sqnorm ** 0.5 # desired scale of x*x in sum for cov
-        x_desired_sqscale /= (x_desired_sqscale.sum() + 1.0e-20) # sum-to-one scales
-        x_desired_sqscale_is_inf = (x_desired_sqscale - x_desired_sqscale != 0)
-        # if grads are inf, use equal scales for frames (can happen due to GradScaler, in half
-        # precision)
-        x_desired_sqscale.masked_fill_(x_desired_sqscale_is_inf, 1.0 / x_desired_sqscale.numel())
-
-        x_factor = (x_desired_sqscale * num_channels / (x_sqnorm + ctx.eps)) ** 0.5
 
         with torch.enable_grad():
+            x_sqnorm = (x ** 2).sum(dim=1)
+
+            x_desired_sqscale = x_grad_old_sqnorm ** 0.5 # desired scale of x*x in sum for cov
+            x_desired_sqscale /= (x_desired_sqscale.sum() + 1.0e-20) # sum-to-one scales
+            x_desired_sqscale_is_inf = (x_desired_sqscale - x_desired_sqscale != 0)
+            # if grads are inf, use equal scales for frames (can happen due to GradScaler, in half
+            # precision)
+            x_desired_sqscale.masked_fill_(x_desired_sqscale_is_inf, 1.0 / x_desired_sqscale.numel())
+
+            x_factor = (x_desired_sqscale * num_channels / (x_sqnorm + ctx.eps)) ** 0.5
+
             scaled_x = x * x_factor.unsqueeze(-1)
             cov = _update_cov_stats(old_cov, scaled_x, ctx.beta)
             assert old_cov.dtype != torch.float16
@@ -1014,13 +995,6 @@ def _test_gauss_proj_drop():
             m1.eval()
             m2.eval()
 
-def _test_pseudo_normalize():
-    x = torch.randn(3, 4)
-    x.requires_grad = True
-    y = PseudoNormalizeFunction.apply(x)
-    l = (y**2).sum()
-    l.backward()
-    assert (x.grad * x).sum().abs() < 0.1
 
 def _test_decorrelate():
     D = 384
@@ -1049,7 +1023,6 @@ if __name__ == "__main__":
     logging.getLogger().setLevel(logging.INFO)
     torch.set_num_threads(1)
     torch.set_num_interop_threads(1)
-    _test_pseudo_normalize()
     _test_decorrelate()
     _test_gauss_proj_drop()
     _test_activation_balancer_sign()