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Fix w.r.t. how cov stats are estimated: storing more stats, and now always zeroing the stats.

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Daniel Povey 2022-05-20 23:05:05 +08:00
parent 6f974b32f6
commit 11eac9089e
1 changed files with 9 additions and 14 deletions
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23
egs/librispeech/ASR/pruned_transducer_stateless4/optim.py
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@ -666,20 +666,17 @@ class Cain(Optimizer):
ndim = grad.ndim
numel = grad.numel()
step = state["step"]
for i in range(grad.ndim):
dim = (i + step) % grad.ndim
for dim in range(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 % 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 convolutional (odd). can revisit this later.
# we don't do any such co-ordinate changes in dims with sizes
# that are too small (no point) or large (too slow), or that are
# assumed convolutional (because they are odd). We can revisit
# this later.
continue
allow_store_stats = (i == grad.ndim - 1)
grad = self._change_coordinates_for_dim(grad, state, dim, forward,
allow_store_stats)
grad = self._change_coordinates_for_dim(grad, state, dim, forward)
return grad
@ -688,7 +685,6 @@ 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):
@ -710,7 +706,7 @@ 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,
allow_store_stats, orig_dim=dim)
orig_dim=dim)
return new_grad.reshape(new_shape).permute(*rev_dims_order)
# OK: grad.ndim == 2 and dim == 1
@ -719,8 +715,8 @@ class Cain(Optimizer):
orig_dim = dim
step = state["step"]
must_store_stats, must_zero_stats = self._must_store_stats(step)
if must_store_stats and allow_store_stats and forward:
# store stats for 100 iters preceding when we estimate the
if must_store_stats and forward:
# store stats for 200 iters preceding when we estimate the
# transform.
stats_name = f"cov_{orig_dim}"
if not stats_name in state:
@ -728,7 +724,6 @@ class Cain(Optimizer):
device=grad.device)
cov = state[stats_name]
if must_zero_stats:
#print("zero")
cov.zero_()
cov += torch.matmul(grad.t(), grad) * (1/grad.shape[0])