mirrors/icefall
1
0
Fork 0
mirror of https://github.com/k2-fsa/icefall.git synced 2025-09-19 05:54:20 +00:00
Code Issues Packages Projects Releases Wiki Activity

Lots of changes to how min and max are applied, use 1-norm for min in smooth_cov but not _apply_min_max_with_metric.

Browse Source
This commit is contained in:
Daniel Povey 2022-08-02 11:29:54 +08:00
parent 6ab4cf615d
commit 9473c7e23d
1 changed files with 20 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

27
egs/librispeech/ASR/pruned_transducer_stateless7/optim.py
View File

@ -164,7 +164,7 @@ param_rms_smooth1: Smoothing proportion for parameter matrix, if assumed rank of
betas=(0.9, 0.98),
size_lr_scale=0.1,
cov_min=(0.025, 0.0025, 0.02, 0.0001),
cov_max=(10.0, 10.0, 5.0, 20.0),
cov_max=(5.0, 20.0, 5.0, 40.0),
cov_pow=(1.0, 1.0, 1.0, 1.0),
param_rms_smooth0=0.4,
param_rms_smooth1=0.2,
@ -761,10 +761,14 @@ param_rms_smooth1: Smoothing proportion for parameter matrix, if assumed rank of
# we don't need to multiply `smooth` by anything, because at this point, P_prime should have
# diagonal elements close to 1.
P = P.clone()
P_diag = _diag(P)
P_diag_mean = _mean(P_diag, exclude_dims=[0], keepdim=True)
P_diag += smooth * P_diag_mean
P = self._smooth_cov(P,
max(smooth, group["cov_min"][0]),
group["cov_max"][0],
group["cov_pow"][0])
#P = P.clone()
#P_diag = _diag(P)
#P_diag_mean = _mean(P_diag, exclude_dims=[0], keepdim=True)
#P_diag += smooth * P_diag_mean
#G = G.clone()
#G_diag = _diag(G) # aliased
@ -818,9 +822,11 @@ param_rms_smooth1: Smoothing proportion for parameter matrix, if assumed rank of
eps = 1.0e-20
if power != 1.0:
U, S, _ = _svd(X)
def mean(Y):
return _mean(Y, exclude_dims=[0], keepdim=True)
def rms(Y):
return _mean(Y**2, exclude_dims=[0], keepdim=True).sqrt()
S = S + min_eig * rms(S) + eps
S = S + min_eig * mean(S) + eps
S = S / rms(S)
S = 1. / (1./S + 1./max_eig)
S = S ** power
@ -833,7 +839,7 @@ param_rms_smooth1: Smoothing proportion for parameter matrix, if assumed rank of
# rms of eigenvalues, or spectral 2-norm.
return (_sum(Y**2, exclude_dims=[0], keepdim=True) / size).sqrt()
diag = _diag(X).unsqueeze(-1) # Aliased with X
diag += (rms_eig(X) * min_eig + eps)
diag += (_mean(diag, exclude_dims=[0], keepdim=True) * min_eig + eps)
X /= rms_eig(X)
# eig_ceil is the maximum possible eigenvalue that X could possibly
@ -892,6 +898,11 @@ param_rms_smooth1: Smoothing proportion for parameter matrix, if assumed rank of
# size of the block-diagonal matrix..
size = X.shape[1] * X.shape[3]
# mean eig of M^{0.5} X M^{0.5} ...
def mean_eig(Y):
# rms of eigenvalues, or spectral 2-norm.
return _mean(_diag(Y), exclude_dims=[0], keepdim=True).unsqueeze(-1)
def rms_eig(Y):
# rms of eigenvalues, or spectral 2-norm.
return (_sum(Y**2, exclude_dims=[0], keepdim=True) / size).sqrt()
@ -903,6 +914,8 @@ param_rms_smooth1: Smoothing proportion for parameter matrix, if assumed rank of
X = X * (1.0-min_eig) + min_eig * M.inverse()
X = 0.5 * (X + X.transpose(-2, -1)) # make sure exactly symmetric.
X /= rms_eig(torch.matmul(X, M))
# eig_ceil is the maximum possible eigenvalue that X could possibly
# have at this time, equal to num_blocks * block_size.
eig_ceil = size ** 0.5