icefall/egs/librispeech/ASR/pruned_transducer_stateless4/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
#
# 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 collections
from itertools import repeat
from typing import Optional, Tuple

import torch
import torch.nn as nn
from torch import Tensor
from torch.nn import Embedding as ScaledEmbedding


def _ntuple(n):
    def parse(x):
        if isinstance(x, collections.Iterable):
            return x
        return tuple(repeat(x, n))

    return parse


_single = _ntuple(1)
_pair = _ntuple(2)


class ActivationBalancerFunction(torch.autograd.Function):
    @staticmethod
    def forward(
        ctx,
        x: Tensor,
        channel_dim: int,
        min_positive: float,  # e.g. 0.05
        max_positive: float,  # e.g. 0.95
        max_factor: float,  # e.g. 0.01
        min_abs: float,  # e.g. 0.2
        max_abs: float,  # e.g. 100.0
    ) -> Tensor:
        if x.requires_grad:
            if channel_dim < 0:
                channel_dim += x.ndim
            sum_dims = [d for d in range(x.ndim) if d != channel_dim]
            xgt0 = x > 0
            proportion_positive = torch.mean(
                xgt0.to(x.dtype), dim=sum_dims, keepdim=True
            )
            factor1 = (
                (min_positive - proportion_positive).relu()
                * (max_factor / min_positive)
                if min_positive != 0.0
                else 0.0
            )
            factor2 = (
                (proportion_positive - max_positive).relu()
                * (max_factor / (max_positive - 1.0))
                if max_positive != 1.0
                else 0.0
            )
            factor = factor1 + factor2
            if isinstance(factor, float):
                factor = torch.zeros_like(proportion_positive)

            mean_abs = torch.mean(x.abs(), dim=sum_dims, keepdim=True)
            below_threshold = mean_abs < min_abs
            above_threshold = mean_abs > max_abs

            ctx.save_for_backward(
                factor, xgt0, below_threshold, above_threshold
            )
            ctx.max_factor = max_factor
            ctx.sum_dims = sum_dims
        return x

    @staticmethod
    def backward(
        ctx, x_grad: Tensor
    ) -> Tuple[Tensor, None, None, None, None, None, None]:
        factor, xgt0, below_threshold, above_threshold = ctx.saved_tensors
        dtype = x_grad.dtype
        scale_factor = (
            (below_threshold.to(dtype) - above_threshold.to(dtype))
            * (xgt0.to(dtype) - 0.5)
            * (ctx.max_factor * 2.0)
        )

        neg_delta_grad = x_grad.abs() * (factor + scale_factor)
        return x_grad - neg_delta_grad, None, None, None, None, None, None


class BasicNorm(torch.nn.Module):
    """
    This is intended to be a simpler, and hopefully cheaper, replacement for
    LayerNorm.  The observation this is based on, is that Transformer-type
    networks, especially with pre-norm, sometimes seem to set one of the
    feature dimensions to a large constant value (e.g. 50), which "defeats"
    the LayerNorm because the output magnitude is then not strongly dependent
    on the other (useful) features.  Presumably the weight and bias of the
    LayerNorm are required to allow it to do this.

    So the idea is to introduce this large constant value as an explicit
    parameter, that takes the role of the "eps" in LayerNorm, so the network
    doesn't have to do this trick.  We make the "eps" learnable.

    Args:
       num_channels: the number of channels, e.g. 512.
      channel_dim: the axis/dimension corresponding to the channel,
        interprted as an offset from the input's ndim if negative.
        shis is NOT the num_channels; it should typically be one of
        {-2, -1, 0, 1, 2, 3}.
       eps: the initial "epsilon" that we add as ballast in:
             scale = ((input_vec**2).mean() + epsilon)**-0.5
          Note: our epsilon is actually large, but we keep the name
          to indicate the connection with conventional LayerNorm.
       learn_eps: if true, we learn epsilon; if false, we keep it
         at the initial value.
    """

    def __init__(
        self,
        num_channels: int,
        channel_dim: int = -1,  # CAUTION: see documentation.
        eps: float = 0.25,
        learn_eps: bool = True,
    ) -> None:
        super(BasicNorm, self).__init__()
        self.num_channels = num_channels
        self.channel_dim = channel_dim
        if learn_eps:
            self.eps = nn.Parameter(torch.tensor(eps).log().detach())
        else:
            self.register_buffer("eps", torch.tensor(eps).log().detach())

    def forward(self, x: Tensor) -> Tensor:
        assert x.shape[self.channel_dim] == self.num_channels
        scales = (
            torch.mean(x ** 2, dim=self.channel_dim, keepdim=True)
            + self.eps.exp()
        ) ** -0.5
        return x * scales


class ScaledLinear(nn.Linear):
    """
    A modified version of nn.Linear that gives an easy way to set the
    default initial parameter scale.

    Args:
        Accepts the standard args and kwargs that nn.Linear accepts
        e.g. in_features, out_features, bias=False.

        initial_scale: you can override this if you want to increase
           or decrease the initial magnitude of the module's output
           (affects the initialization of weight_scale and bias_scale).
           Another option, if you want to do something like this, is
           to re-initialize the parameters.
    """

    def __init__(
        self,
        *args,
        initial_scale: float = 1.0,
        **kwargs
    ):
        super(ScaledLinear, self).__init__(*args, **kwargs)
        with torch.no_grad():
            self.weight[:] *= initial_scale
            if self.bias is not None:
                torch.nn.init.uniform_(self.bias, -0.1, 0.1)

    def get_weight(self): # not needed any more but kept for back compatibility
        return self.weight

    def get_bias(self):
        return self.bias



class ScaledConv1d(nn.Conv1d):
    # See docs for ScaledLinear
    def __init__(
        self,
        *args,
        initial_scale: float = 1.0,
        **kwargs
    ):
        super(ScaledConv1d, self).__init__(*args, **kwargs)
        with torch.no_grad():
            self.weight[:] *= initial_scale
            if self.bias is not None:
                torch.nn.init.uniform_(self.bias, -0.1, 0.1)

    def get_weight(self):  # TODO: delete
        return self.weight

    def get_bias(self):  # TODO: delete
        return self.bias


class ScaledConv2d(nn.Conv2d):
    # See docs for ScaledLinear
    def __init__(
        self,
        *args,
        initial_scale: float = 1.0,
        **kwargs
    ):
        super(ScaledConv2d, self).__init__(*args, **kwargs)
        with torch.no_grad():
            self.weight[:] *= initial_scale
            if self.bias is not None:
                torch.nn.init.uniform_(self.bias, -0.1, 0.1)

    def get_weight(self):
        return self.weight

    def get_bias(self):
        return  self.bias



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:
           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].
           min_abs:  the minimum average-absolute-value per channel, which
              we allow, before we start to modify the derivatives to prevent
              this.
           max_abs:  the maximum average-absolute-value per channel, which
               we allow, before we start to modify the derivatives to prevent
               this.
    """

    def __init__(
        self,
        channel_dim: int,
        min_positive: float = 0.05,
        max_positive: float = 0.95,
        max_factor: float = 0.01,
        min_abs: float = 0.2,
        max_abs: float = 100.0,
    ):
        super(ActivationBalancer, self).__init__()
        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

    def forward(self, x: Tensor) -> Tensor:
        if torch.jit.is_scripting():
            return x

        return ActivationBalancerFunction.apply(
            x,
            self.channel_dim,
            self.min_positive,
            self.max_positive,
            self.max_factor,
            self.min_abs,
            self.max_abs,
        )


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:
        x = x.detach()
        s = torch.sigmoid(x - 1.0)
        y = x * s
        ctx.save_for_backward(s, y)
        return y

    @staticmethod
    def backward(ctx, y_grad: Tensor) -> Tensor:
        s, y = ctx.saved_tensors
        return (y * (1 - s) + s) * y_grad


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():
            return x * torch.sigmoid(x - 1.0)
        return DoubleSwishFunction.apply(x)




def _test_activation_balancer_sign():
    probs = torch.arange(0, 1, 0.01)
    N = 1000
    x = 1.0 * (torch.rand(probs.numel(), N) < probs.unsqueeze(-1))
    x = x.detach()
    x.requires_grad = True
    m = ActivationBalancer(
        channel_dim=0,
        min_positive=0.05,
        max_positive=0.95,
        max_factor=0.2,
        min_abs=0.0,
    )

    y_grad = torch.sign(torch.randn(probs.numel(), N))

    y = m(x)
    y.backward(gradient=y_grad)
    print("_test_activation_balancer_sign: x = ", x)
    print("_test_activation_balancer_sign: y grad = ", y_grad)
    print("_test_activation_balancer_sign: x grad = ", x.grad)


def _test_activation_balancer_magnitude():
    magnitudes = torch.arange(0, 1, 0.01)
    N = 1000
    x = torch.sign(torch.randn(magnitudes.numel(), N)) * magnitudes.unsqueeze(
        -1
    )
    x = x.detach()
    x.requires_grad = True
    m = ActivationBalancer(
        channel_dim=0,
        min_positive=0.0,
        max_positive=1.0,
        max_factor=0.2,
        min_abs=0.2,
        max_abs=0.8,
    )

    y_grad = torch.sign(torch.randn(magnitudes.numel(), N))

    y = m(x)
    y.backward(gradient=y_grad)
    print("_test_activation_balancer_magnitude: x = ", x)
    print("_test_activation_balancer_magnitude: y grad = ", y_grad)
    print("_test_activation_balancer_magnitude: x grad = ", x.grad)


def _test_basic_norm():
    num_channels = 128
    m = BasicNorm(num_channels=num_channels, channel_dim=1)

    x = torch.randn(500, num_channels)

    y = m(x)

    assert y.shape == x.shape
    x_rms = (x ** 2).mean().sqrt()
    y_rms = (y ** 2).mean().sqrt()
    print("x rms = ", x_rms)
    print("y rms = ", y_rms)
    assert y_rms < x_rms
    assert y_rms > 0.5 * x_rms


def _test_double_swish_deriv():
    x = torch.randn(10, 12, dtype=torch.double) * 0.5
    x.requires_grad = True
    m = DoubleSwish()
    torch.autograd.gradcheck(m, x)


if __name__ == "__main__":
    _test_activation_balancer_sign()
    _test_activation_balancer_magnitude()
    _test_basic_norm()
    _test_double_swish_deriv()