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Change the discretization of the sigmoid to be expectation preserving.

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This commit is contained in:
Daniel Povey 2022-10-23 17:11:35 +08:00
parent 09cbc9fdab
commit e586cc319c
1 changed files with 5 additions and 5 deletions
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10
egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py
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@ -918,9 +918,9 @@ class DoubleSwishFunction(torch.autograd.Function):
x = x.detach()
s = torch.sigmoid(x - 1.0)
y = x * s
# discretize s. Note: .to(torch.uint8) rounds down. We'll correct for this
# in an amortized way by adding 0.5 when we convert back to float.
s = (s * 255.999).to(torch.uint8)
# discretize s. This should be expectation-preserving if we just divide the
# result by 255.
s = ((s * 255) + torch.randn_like(s)).to(torch.uint8)
ctx.save_for_backward(s, y)
return y
@ -928,7 +928,7 @@ class DoubleSwishFunction(torch.autograd.Function):
def backward(ctx, y_grad: Tensor) -> Tensor:
s, y = ctx.saved_tensors
# converts back to float.
s = (s.to(y_grad.dtype) + 0.5) * (1.0 / 255.999)
s = s.to(y_grad.dtype) * (1.0 / 255)
return (y * (1 - s) + s) * y_grad
@ -1075,7 +1075,7 @@ def _test_double_swish_deriv():
x = torch.randn(10, 12, dtype=torch.double) * 0.5
x.requires_grad = True
m = DoubleSwish()
torch.autograd.gradcheck(m, x, atol=0.01)
torch.autograd.gradcheck(m, x, atol=0.02)
def _test_softmax():