GPT_SoVITS/AR/modules/scaling.py

# 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
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# 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(),
    )