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Simplified gradient scaling [no scaling]; only use 1k first iters; beta =0.8

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Daniel Povey 2022-06-10 17:16:05 +08:00
parent cecd52155c
commit 9d4633facf
1 changed files with 14 additions and 14 deletions
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28
egs/librispeech/ASR/pruned_transducer_stateless2/scaling.py
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@ -796,32 +796,32 @@ class DecorrelateFunction(torch.autograd.Function):
# to have magnitudes proportional to the norm of the gradient on that
# frame; the goal is to exclude "don't-care" frames such as padding frames from
# the computation.
x_grad_old_sqnorm = (x_grad ** 2).sum(dim=1)
#x_grad_old_sqnorm = (x_grad ** 2).sum(dim=1)
with torch.enable_grad():
x_sqnorm = (x ** 2).sum(dim=1)
#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)
#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_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
#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)
#scaled_x = x * x_factor.unsqueeze(-1)
cov = _update_cov_stats(old_cov, x, ctx.beta)
assert old_cov.dtype != torch.float16
old_cov[:] = cov # update the stats outside! This is not really
# how backprop is supposed to work, but this input
# is not differentiable..
loss = _compute_correlation_loss(cov, ctx.eps)
assert loss.dtype == torch.float32
#print(f"x_sqnorm mean = {x_sqnorm.mean().item()}, x_sqnorm_mean={x_sqnorm.mean().item()}, x_desired_sqscale_sum={x_desired_sqscale.sum()}, x_grad_old_sqnorm mean = {x_grad_old_sqnorm.mean().item()}, x**2_mean = {(x**2).mean().item()}, scaled_x**2_mean = {(scaled_x**2).mean().item()}, (cov-abs-mean)={cov.abs().mean().item()}, old_cov_abs_mean={old_cov.abs().mean().item()}, loss = {loss}")
if random.random() < 0.01:
#if random.random() < 0.01:
if random.random() < 0.05:
logging.info(f"Decorrelate: loss = {loss}")
loss.backward()
@ -862,9 +862,9 @@ class Decorrelate(torch.nn.Module):
def __init__(self,
num_channels: int,
scale: float = 0.1,
apply_steps: int = 3000,
apply_steps: int = 1000,
eps: float = 1.0e-05,
beta: float = 0.95,
beta: float = 0.8,
channel_dim: int = -1):
super(Decorrelate, self).__init__()
self.scale = scale