mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-09-18 21:44:18 +00:00
Use exp_avg_sq as ref_exp_avg_sq
This commit is contained in:
Daniel Povey
parent
860322bf30
commit
f9bb745cf3
@ -49,7 +49,7 @@ class NeutralGradient(Optimizer):
|
||||
param_eps: An epsilon on the rms value of the parameter, such that when the parameter
|
||||
gets smaller than this we'll start using a fixed learning rate, not
|
||||
decreasing with the parameter norm.
|
||||
cond_eps: An epsilon that limits the condition number of gradient and parameter
|
||||
rel_eps: An epsilon that limits the condition number of gradient and parameter
|
||||
covariance matrices
|
||||
param_max: To prevent parameter tensors getting too large, we will clip elements to
|
||||
-param_max..param_max.
|
||||
@ -69,12 +69,12 @@ class NeutralGradient(Optimizer):
|
||||
scale_speed=0.1,
|
||||
eps=1e-8,
|
||||
param_eps=1.0e-05,
|
||||
cond_eps=1.0e-10,
|
||||
rel_eps=1.0e-10,
|
||||
param_max=10.0,
|
||||
min_diag_smooth=1.0,
|
||||
min_diag_smooth=0.2,
|
||||
max_size=1023,
|
||||
stats_period=1,
|
||||
estimate_period=200,
|
||||
estimate_period=50,
|
||||
):
|
||||
|
||||
if not 0.0 <= lr:
|
||||
@ -111,7 +111,7 @@ class NeutralGradient(Optimizer):
|
||||
eps=eps,
|
||||
scale_speed=scale_speed,
|
||||
param_eps=param_eps,
|
||||
cond_eps=cond_eps,
|
||||
rel_eps=rel_eps,
|
||||
min_diag_smooth=min_diag_smooth,
|
||||
param_max=param_max,
|
||||
max_size=max_size,
|
||||
@ -143,7 +143,7 @@ class NeutralGradient(Optimizer):
|
||||
scale_speed = group["scale_speed"]
|
||||
param_eps = group["param_eps"]
|
||||
eps = group["eps"]
|
||||
cond_eps = group["cond_eps"]
|
||||
rel_eps = group["rel_eps"]
|
||||
min_diag_smooth = group["min_diag_smooth"]
|
||||
param_max = group["param_max"]
|
||||
max_size = group["max_size"]
|
||||
@ -267,11 +267,11 @@ class NeutralGradient(Optimizer):
|
||||
self._estimate(p, state, beta3, max_size,
|
||||
stats_period, estimate_period,
|
||||
eps, param_eps,
|
||||
cond_eps, min_diag_smooth)
|
||||
rel_eps, min_diag_smooth)
|
||||
|
||||
# TEMP!! Override the setting inside _estimate.
|
||||
#state["ref_exp_avg_sq"][:] = ((exp_avg_sq/bias_correction2 + eps*eps) *
|
||||
# state["ref_exp_avg_sq"]).sqrt()
|
||||
state["ref_exp_avg_sq"][:] = (exp_avg_sq/bias_correction2 + eps*eps)
|
||||
|
||||
|
||||
ref_exp_avg_sq = state["ref_exp_avg_sq"] # computed in self._estimate()
|
||||
|
||||
@ -288,7 +288,7 @@ class NeutralGradient(Optimizer):
|
||||
|
||||
cur_grad = grad
|
||||
cur_grad = cur_grad * grad_scale
|
||||
cur_grad = self._precondition_grad(cur_grad, state)
|
||||
cur_grad = self._multiply_grad(cur_grad, state)
|
||||
cur_grad *= grad_scale
|
||||
|
||||
if random.random() < 0.004:
|
||||
@ -348,11 +348,16 @@ class NeutralGradient(Optimizer):
|
||||
grad_cov = state[f"grad_cov_{dim}"]
|
||||
if grad_cov.ndim == 1:
|
||||
# We are treating this dimension diagonally because it is too big.
|
||||
# note: other_dims is nonempty because we know ndim != 1 when we get
|
||||
# here. dim=[] to torch mean() does not work as you would expect.
|
||||
other_dims = [ i for i in range(ndim) if i != dim]
|
||||
grad_cov.mul_(beta3).add_((grad**2).mean(dim=other_dims))
|
||||
grad_cov.mul_(beta3).add_((grad**2).mean(dim=other_dims),
|
||||
alpha=(1.0-beta3))
|
||||
else:
|
||||
# full-covariance stats.
|
||||
this_beta3 = self._get_this_beta3(beta3, grad.numel(), size)
|
||||
if this_beta3 != beta3 and random.random() < 0.01:
|
||||
print("this_beta3=", this_beta3)
|
||||
grad_cov.mul_(this_beta3).add_(self._get_cov(grad, dim),
|
||||
alpha=(1.0-this_beta3))
|
||||
|
||||
@ -365,7 +370,7 @@ class NeutralGradient(Optimizer):
|
||||
estimate_period: int,
|
||||
eps: float,
|
||||
param_eps: float,
|
||||
cond_eps: float,
|
||||
rel_eps: float,
|
||||
min_diag_smooth: float
|
||||
) -> Tensor:
|
||||
"""
|
||||
@ -379,6 +384,7 @@ class NeutralGradient(Optimizer):
|
||||
step = state["step"]
|
||||
assert step % stats_period == 0
|
||||
norm_step = step // stats_period
|
||||
param_rel_eps = 1.0e-04
|
||||
|
||||
scale_change = True
|
||||
|
||||
@ -411,13 +417,25 @@ class NeutralGradient(Optimizer):
|
||||
other_dims = [ i for i in range(ndim) if i != dim]
|
||||
bias_correction3 = 1 - beta3 ** (norm_step + 1)
|
||||
# _smoothed means we have the eps terms.
|
||||
param_var_smoothed = (p**2).mean(dim=other_dims) + param_eps*param_eps
|
||||
param_var_smoothed = (p**2).mean(dim=other_dims)
|
||||
param_var_smoothed.add_(param_eps*param_eps + param_rel_eps * param_var_smoothed.mean())
|
||||
if bias_correction3 < 0.9999:
|
||||
grad_cov = grad_cov / bias_correction3
|
||||
grad_var_smoothed = grad_cov + eps*eps
|
||||
grad_var_smoothed = grad_cov + (eps*eps + rel_eps * grad_cov.mean())
|
||||
ref_exp_avg_sq = update_ref_exp_avg_sq(ref_exp_avg_sq,
|
||||
grad_var_smoothed,
|
||||
dim, scale_change)
|
||||
|
||||
if True:
|
||||
def check_close(a, b):
|
||||
assert ((a-b).abs().sum() < 0.01 * (a.abs()+b.abs()).sum())
|
||||
param_cov_smoothed = self._estimate_and_smooth_param_cov(p, dim,
|
||||
param_eps,
|
||||
rel_eps=param_rel_eps,
|
||||
min_diag_smooth=min_diag_smooth)
|
||||
check_close(param_var_smoothed, param_cov_smoothed.diag())
|
||||
|
||||
|
||||
if scale_change:
|
||||
scale_change = False # only use the scale change once
|
||||
else:
|
||||
@ -427,15 +445,16 @@ class NeutralGradient(Optimizer):
|
||||
# to count the overall change in scale once.
|
||||
grad_var_smoothed *= (param_var_smoothed.sum() / grad_var_smoothed.sum())
|
||||
proj[:] = (param_var_smoothed / grad_var_smoothed).sqrt()
|
||||
|
||||
else:
|
||||
param_cov_smoothed = self._estimate_and_smooth_param_cov(p, dim,
|
||||
param_eps,
|
||||
cond_eps=1.0e-04,
|
||||
rel_eps=param_rel_eps,
|
||||
min_diag_smooth=min_diag_smooth)
|
||||
grad_cov_smoothed = self._smooth_grad_cov(p, grad_cov,
|
||||
eps, norm_step,
|
||||
eps, norm_step+100000, # TEMp
|
||||
beta3,
|
||||
cond_eps=cond_eps,
|
||||
rel_eps=rel_eps,
|
||||
min_diag_smooth=min_diag_smooth)
|
||||
ref_exp_avg_sq = update_ref_exp_avg_sq(ref_exp_avg_sq,
|
||||
grad_cov_smoothed.diag(),
|
||||
@ -473,9 +492,9 @@ class NeutralGradient(Optimizer):
|
||||
return ans
|
||||
|
||||
|
||||
def _precondition_grad(self,
|
||||
grad: Tensor,
|
||||
state: dict) -> Tensor:
|
||||
def _multiply_grad(self,
|
||||
grad: Tensor,
|
||||
state: dict) -> Tensor:
|
||||
"""
|
||||
Multiply the grad by a positive-semidefinite matrix for each dimension, to
|
||||
try to make its covariance the same as that of the parameters (up to a
|
||||
@ -511,7 +530,7 @@ class NeutralGradient(Optimizer):
|
||||
|
||||
def _estimate_and_smooth_param_cov(self, p: Tensor, dim: int,
|
||||
param_eps: float,
|
||||
cond_eps: float = 1.0e-10,
|
||||
rel_eps: float = 1.0e-10,
|
||||
min_diag_smooth: float = 0.2) -> Tensor:
|
||||
"""
|
||||
Compute a smoothed version of a covariance matrix for one dimension of
|
||||
@ -522,7 +541,7 @@ class NeutralGradient(Optimizer):
|
||||
dim: The dimenion that we want the covariances for.
|
||||
param_eps: A small epsilon value that represents the minimum root-mean-square
|
||||
parameter value that we'll estimate.
|
||||
cond_eps: An epsilon value that limits the condition number of the resulting
|
||||
rel_eps: An epsilon value that limits the condition number of the resulting
|
||||
matrix. Caution: this applies to the variance, not the rms value,
|
||||
so should be quite small.
|
||||
|
||||
@ -540,7 +559,7 @@ class NeutralGradient(Optimizer):
|
||||
#diag_smooth = min_diag_smooth
|
||||
diag_smooth = 0.4
|
||||
diag = param_cov.diag()
|
||||
extra_diag = (diag * diag_smooth) + (diag.max() * cond_eps +
|
||||
extra_diag = (diag * diag_smooth) + (diag.mean() * rel_eps +
|
||||
param_eps * param_eps)
|
||||
param_cov.mul_(1-diag_smooth).add_(extra_diag.diag())
|
||||
return param_cov
|
||||
@ -551,7 +570,7 @@ class NeutralGradient(Optimizer):
|
||||
eps: float,
|
||||
norm_step: int,
|
||||
beta3: float,
|
||||
cond_eps: float = 1.0e-10,
|
||||
rel_eps: float = 1.0e-10,
|
||||
min_diag_smooth: float = 0.2) -> Tensor:
|
||||
"""
|
||||
Compute a smoothed version of a covariance matrix for one dimension of
|
||||
@ -569,7 +588,7 @@ class NeutralGradient(Optimizer):
|
||||
is just step divided by stats_perio.
|
||||
beta3: The user-supplied beta value for decaying the gradient covariance
|
||||
stats
|
||||
cond_eps: An epsilon value that limits the condition number of the resulting
|
||||
rel_eps: An epsilon value that limits the condition number of the resulting
|
||||
matrix. Caution: this applies to the variance, not the rms value,
|
||||
so should be quite small.
|
||||
min_diag_smooth: A minimum proportion by which we smooth the covariance
|
||||
@ -584,7 +603,7 @@ class NeutralGradient(Optimizer):
|
||||
if bias_correction3 < 0.9999:
|
||||
grad_cov = grad_cov / bias_correction3
|
||||
|
||||
rank_per_iter = p.numel() // size # maximum rank of each iteration's covaraince
|
||||
rank_per_iter = p.numel() // size # maximum rank of each iteration's covariance
|
||||
# the second part of the following formula roughly represents the number of
|
||||
# frames that have a "large" weight in the stats.
|
||||
num_iters_in_stats = min(norm_step + 1, 1.0 / (1 - beta3))
|
||||
@ -598,8 +617,8 @@ class NeutralGradient(Optimizer):
|
||||
print(f"grad diag_smooth = {diag_smooth}, shape={p.shape}")
|
||||
|
||||
diag = grad_cov.diag()
|
||||
extra_diag = (diag * diag_smooth) + (diag.max() * cond_eps +
|
||||
eps * eps)
|
||||
extra_diag = (diag * diag_smooth).add_(diag.mean() * rel_eps +
|
||||
eps * eps)
|
||||
grad_cov = (grad_cov * (1-diag_smooth)).add_(extra_diag.diag())
|
||||
return grad_cov
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user