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RVC-Boss
2024-01-16 17:38:48 +08:00
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# Copyright 2022 Xiaomi Corp. (authors: Daniel Povey)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import math
import random
from typing import Optional
from typing import Tuple
from typing import Union
import torch
import torch.nn as nn
from torch import Tensor
class DoubleSwishFunction(torch.autograd.Function):
"""
double_swish(x) = x * torch.sigmoid(x-1)
This is a definition, originally motivated by its close numerical
similarity to swish(swish(x)), where swish(x) = x * sigmoid(x).
Memory-efficient derivative computation:
double_swish(x) = x * s, where s(x) = torch.sigmoid(x-1)
double_swish'(x) = d/dx double_swish(x) = x * s'(x) + x' * s(x) = x * s'(x) + s(x).
Now, s'(x) = s(x) * (1-s(x)).
double_swish'(x) = x * s'(x) + s(x).
= x * s(x) * (1-s(x)) + s(x).
= double_swish(x) * (1-s(x)) + s(x)
... so we just need to remember s(x) but not x itself.
"""
@staticmethod
def forward(ctx, x: Tensor) -> Tensor:
requires_grad = x.requires_grad
x_dtype = x.dtype
if x.dtype == torch.float16:
x = x.to(torch.float32)
s = torch.sigmoid(x - 1.0)
y = x * s
if requires_grad:
deriv = y * (1 - s) + s
# notes on derivative of x * sigmoid(x - 1):
# https://www.wolframalpha.com/input?i=d%2Fdx+%28x+*+sigmoid%28x-1%29%29
# min \simeq -0.043638. Take floor as -0.043637 so it's a lower bund
# max \simeq 1.1990. Take ceil to be 1.2 so it's an upper bound.
# the combination of "+ torch.rand_like(deriv)" and casting to torch.uint8 (which
# floors), should be expectation-preserving.
floor = -0.043637
ceil = 1.2
d_scaled = (deriv - floor) * (255.0 / (ceil - floor)
) + torch.rand_like(deriv)
if __name__ == "__main__":
# for self-testing only.
assert d_scaled.min() >= 0.0
assert d_scaled.max() < 256.0
d_int = d_scaled.to(torch.uint8)
ctx.save_for_backward(d_int)
if x.dtype == torch.float16 or torch.is_autocast_enabled():
y = y.to(torch.float16)
return y
@staticmethod
def backward(ctx, y_grad: Tensor) -> Tensor:
(d, ) = ctx.saved_tensors
# the same constants as used in forward pass.
floor = -0.043637
ceil = 1.2
d = d * ((ceil - floor) / 255.0) + floor
return y_grad * d
class DoubleSwish(torch.nn.Module):
def forward(self, x: Tensor) -> Tensor:
"""Return double-swish activation function which is an approximation to Swish(Swish(x)),
that we approximate closely with x * sigmoid(x-1).
"""
if torch.jit.is_scripting() or torch.jit.is_tracing():
return x * torch.sigmoid(x - 1.0)
return DoubleSwishFunction.apply(x)
class ActivationBalancerFunction(torch.autograd.Function):
@staticmethod
def forward(
ctx,
x: Tensor,
scale_factor: Tensor,
sign_factor: Optional[Tensor],
channel_dim: int, ) -> Tensor:
if channel_dim < 0:
channel_dim += x.ndim
ctx.channel_dim = channel_dim
xgt0 = x > 0
if sign_factor is None:
ctx.save_for_backward(xgt0, scale_factor)
else:
ctx.save_for_backward(xgt0, scale_factor, sign_factor)
return x
@staticmethod
def backward(ctx, x_grad: Tensor) -> Tuple[Tensor, None, None, None]:
if len(ctx.saved_tensors) == 3:
xgt0, scale_factor, sign_factor = ctx.saved_tensors
for _ in range(ctx.channel_dim, x_grad.ndim - 1):
scale_factor = scale_factor.unsqueeze(-1)
sign_factor = sign_factor.unsqueeze(-1)
factor = sign_factor + scale_factor * (xgt0.to(x_grad.dtype) - 0.5)
else:
xgt0, scale_factor = ctx.saved_tensors
for _ in range(ctx.channel_dim, x_grad.ndim - 1):
scale_factor = scale_factor.unsqueeze(-1)
factor = scale_factor * (xgt0.to(x_grad.dtype) - 0.5)
neg_delta_grad = x_grad.abs() * factor
return (x_grad - neg_delta_grad, None, None, None, )
def _compute_scale_factor(
x: Tensor,
channel_dim: int,
min_abs: float,
max_abs: float,
gain_factor: float,
max_factor: float, ) -> Tensor:
if channel_dim < 0:
channel_dim += x.ndim
sum_dims = [d for d in range(x.ndim) if d != channel_dim]
x_abs_mean = torch.mean(x.abs(), dim=sum_dims).to(torch.float32)
if min_abs == 0.0:
below_threshold = 0.0
else:
# below_threshold is 0 if x_abs_mean > min_abs, can be at most max_factor if
# x_abs)_mean , min_abs.
below_threshold = (
(min_abs - x_abs_mean) * (gain_factor / min_abs)).clamp(
min=0, max=max_factor)
above_threshold = ((x_abs_mean - max_abs) * (gain_factor / max_abs)).clamp(
min=0, max=max_factor)
return below_threshold - above_threshold
def _compute_sign_factor(
x: Tensor,
channel_dim: int,
min_positive: float,
max_positive: float,
gain_factor: float,
max_factor: float, ) -> Tensor:
if channel_dim < 0:
channel_dim += x.ndim
sum_dims = [d for d in range(x.ndim) if d != channel_dim]
proportion_positive = torch.mean((x > 0).to(torch.float32), dim=sum_dims)
if min_positive == 0.0:
factor1 = 0.0
else:
# 0 if proportion_positive >= min_positive, else can be
# as large as max_factor.
factor1 = ((min_positive - proportion_positive) *
(gain_factor / min_positive)).clamp_(
min=0, max=max_factor)
if max_positive == 1.0:
factor2 = 0.0
else:
# 0 if self.proportion_positive <= max_positive, else can be
# as large as -max_factor.
factor2 = ((proportion_positive - max_positive) *
(gain_factor / (1.0 - max_positive))).clamp_(
min=0, max=max_factor)
sign_factor = factor1 - factor2
# require min_positive != 0 or max_positive != 1:
assert not isinstance(sign_factor, float)
return sign_factor
class ActivationBalancer(torch.nn.Module):
"""
Modifies the backpropped derivatives of a function to try to encourage, for
each channel, that it is positive at least a proportion `threshold` of the
time. It does this by multiplying negative derivative values by up to
(1+max_factor), and positive derivative values by up to (1-max_factor),
interpolated from 1 at the threshold to those extremal values when none
of the inputs are positive.
Args:
num_channels: the number of channels
channel_dim: the dimension/axis corresponding to the channel, e.g.
-1, 0, 1, 2; will be interpreted as an offset from x.ndim if negative.
min_positive: the minimum, per channel, of the proportion of the time
that (x > 0), below which we start to modify the derivatives.
max_positive: the maximum, per channel, of the proportion of the time
that (x > 0), above which we start to modify the derivatives.
max_factor: the maximum factor by which we modify the derivatives for
either the sign constraint or the magnitude constraint;
e.g. with max_factor=0.02, the the derivatives would be multiplied by
values in the range [0.98..1.02].
sign_gain_factor: determines the 'gain' with which we increase the
change in gradient once the constraints on min_positive and max_positive
are violated.
scale_gain_factor: determines the 'gain' with which we increase the
change in gradient once the constraints on min_abs and max_abs
are violated.
min_abs: the minimum average-absolute-value difference from the mean
value per channel, which we allow, before we start to modify
the derivatives to prevent this.
max_abs: the maximum average-absolute-value difference from the mean
value per channel, which we allow, before we start to modify
the derivatives to prevent this.
min_prob: determines the minimum probability with which we modify the
gradients for the {min,max}_positive and {min,max}_abs constraints,
on each forward(). This is done randomly to prevent all layers
from doing it at the same time. Early in training we may use
higher probabilities than this; it will decay to this value.
"""
def __init__(
self,
num_channels: int,
channel_dim: int,
min_positive: float=0.05,
max_positive: float=0.95,
max_factor: float=0.04,
sign_gain_factor: float=0.01,
scale_gain_factor: float=0.02,
min_abs: float=0.2,
max_abs: float=100.0,
min_prob: float=0.1, ):
super(ActivationBalancer, self).__init__()
self.num_channels = num_channels
self.channel_dim = channel_dim
self.min_positive = min_positive
self.max_positive = max_positive
self.max_factor = max_factor
self.min_abs = min_abs
self.max_abs = max_abs
self.min_prob = min_prob
self.sign_gain_factor = sign_gain_factor
self.scale_gain_factor = scale_gain_factor
# count measures how many times the forward() function has been called.
# We occasionally sync this to a tensor called `count`, that exists to
# make sure it is synced to disk when we load and save the model.
self.cpu_count = 0
self.register_buffer("count", torch.tensor(0, dtype=torch.int64))
def forward(self, x: Tensor) -> Tensor:
if (torch.jit.is_scripting() or not x.requires_grad or
torch.jit.is_tracing()):
return _no_op(x)
count = self.cpu_count
self.cpu_count += 1
if random.random() < 0.01:
# Occasionally sync self.cpu_count with self.count.
# count affects the decay of 'prob'. don't do this on every iter,
# because syncing with the GPU is slow.
self.cpu_count = max(self.cpu_count, self.count.item())
self.count.fill_(self.cpu_count)
# the prob of doing some work exponentially decreases from 0.5 till it hits
# a floor at min_prob (==0.1, by default)
prob = max(self.min_prob, 0.5**(1 + (count / 4000.0)))
if random.random() < prob:
sign_gain_factor = 0.5
if self.min_positive != 0.0 or self.max_positive != 1.0:
sign_factor = _compute_sign_factor(
x,
self.channel_dim,
self.min_positive,
self.max_positive,
gain_factor=self.sign_gain_factor / prob,
max_factor=self.max_factor, )
else:
sign_factor = None
scale_factor = _compute_scale_factor(
x.detach(),
self.channel_dim,
min_abs=self.min_abs,
max_abs=self.max_abs,
gain_factor=self.scale_gain_factor / prob,
max_factor=self.max_factor, )
return ActivationBalancerFunction.apply(
x,
scale_factor,
sign_factor,
self.channel_dim, )
else:
return _no_op(x)
def BalancedDoubleSwish(d_model, channel_dim=-1, max_abs=10.0,
min_prob=0.25) -> nn.Sequential:
"""
ActivationBalancer -> DoubleSwish
"""
balancer = ActivationBalancer(
d_model, channel_dim=channel_dim, max_abs=max_abs, min_prob=min_prob)
return nn.Sequential(
balancer,
DoubleSwish(), )