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Make cain average over more iters and use preconditioning on the other dims first

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Daniel Povey 2022-05-19 21:34:12 +08:00
parent ac5a9faafd
commit 6085ab64ef
1 changed files with 17 additions and 14 deletions
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31
egs/librispeech/ASR/pruned_transducer_stateless4/optim.py
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@ -598,19 +598,21 @@ class Cain(Optimizer):
"""
ndim = grad.ndim
numel = grad.numel()
for dim in range(grad.ndim):
step = state["step"]
for i in range(grad.ndim):
dim = (i + step) % grad.ndim
# see for each dim in turn whether we want to perform any changes in co-ordinates,
# or store any stats.
size = grad.shape[dim]
if size <= 3 or size > 1024 or size == numel:
if size <= 3 or size % 2 == 1 or size >= 2048 or size == numel:
# we don't do any such co-ordinate changes in dims that are too small (no point)
# or large (too slow)
# or large (too slow), or convolutional (odd). can revisit this later.
continue
if dim == 0:
# FOR NOW: don't do such co-ordinate changes on output
# dims, which will generally be dimension zero. We can revisit this later.
continue
grad = self._change_coordinates_for_dim(grad, state, dim, forward)
allow_store_stats = (i == grad.ndim - 1)
grad = self._change_coordinates_for_dim(grad, state, dim, forward,
allow_store_stats)
return grad
@ -619,6 +621,7 @@ class Cain(Optimizer):
state: dict,
dim: int,
forward: bool,
allow_store_stats: bool,
orig_dim: int = -1):
assert grad.ndim > 1
if not (grad.ndim == 2 and dim == 1):
@ -640,7 +643,7 @@ class Cain(Optimizer):
new_shape = new_grad.shape
new_grad = new_grad.reshape(-1, new_grad.shape[-1])
new_grad = self._change_coordinates_for_dim(new_grad, state, 1, forward,
orig_dim=dim)
allow_store_stats, orig_dim=dim)
return new_grad.reshape(new_shape).permute(*rev_dims_order)
# OK: grad.ndim == 2 and dim == 1
@ -649,8 +652,8 @@ class Cain(Optimizer):
orig_dim = dim
step = state["step"]
must_store_stats, must_zero_stats = self._must_store_stats(step)
if must_store_stats:
# store stats for 20 iters preceding when we estimate the
if must_store_stats and allow_store_stats:
# store stats for 100 iters preceding when we estimate the
# transform.
stats_name = f"cov_{orig_dim}"
if not stats_name in state:
@ -695,11 +698,11 @@ class Cain(Optimizer):
"""
if step < 4000:
if step < 2000:
return (step % 500 >= 480, step % 500 == 480)
return (step % 500 >= 400, step % 500 == 400)
else:
return (step % 1000 >= 980, step % 1000 == 980)
return (step % 1000 >= 800, step % 1000 == 800)
else:
return (step % 2000 >= 1980, step % 2000 == 1980)
return (step % 2000 >= 1800, step % 2000 == 1800)
def _must_estimate_transform(self, step: int) -> bool:
"""