mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-09-19 05:54:20 +00:00
Reduce debug probs
This commit is contained in:
parent
65bc964854
commit
35a51bc153
@ -370,8 +370,8 @@ param_rms_smooth1: Smoothing proportion for parameter matrix, if assumed rank of
|
|||||||
rank = numel // size
|
rank = numel // size
|
||||||
rms = self._smooth_param_rms(group, S.sqrt(), rank)
|
rms = self._smooth_param_rms(group, S.sqrt(), rank)
|
||||||
|
|
||||||
if random.random() < 0.05:
|
if random.random() < 0.0005:
|
||||||
logging.info(f"Shape={tuple(p.shape)}, dim={dim}, size={size}, rms={rms[::10]}")
|
logging.info(f"Shape={tuple(p.shape)}, dim={dim}, rank={rank}, size={size}, rms={rms[::10]}")
|
||||||
|
|
||||||
Q = state[f"Q_{dim}"]
|
Q = state[f"Q_{dim}"]
|
||||||
Q[:] = (U * rms).t()
|
Q[:] = (U * rms).t()
|
||||||
@ -405,7 +405,7 @@ param_rms_smooth1: Smoothing proportion for parameter matrix, if assumed rank of
|
|||||||
N_grad_cov = torch.matmul(Q, torch.matmul(grad_cov, Q.t()))
|
N_grad_cov = torch.matmul(Q, torch.matmul(grad_cov, Q.t()))
|
||||||
N_grad_cov = N_grad_cov + N_grad_cov.t() # ensure symmetric
|
N_grad_cov = N_grad_cov + N_grad_cov.t() # ensure symmetric
|
||||||
U, S, V = _svd(N_grad_cov)
|
U, S, V = _svd(N_grad_cov)
|
||||||
if random.random() < 0.1:
|
if random.random() < 0.001:
|
||||||
logging.info(f"Diagonalizing, shape={tuple(p.shape)}, dim={dim}, dispersion "
|
logging.info(f"Diagonalizing, shape={tuple(p.shape)}, dim={dim}, dispersion "
|
||||||
f"changed from {dispersion(N_grad_cov.diag())} to {dispersion(S)}")
|
f"changed from {dispersion(N_grad_cov.diag())} to {dispersion(S)}")
|
||||||
|
|
||||||
|
Loading…
x
Reference in New Issue
Block a user