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276 lines
8.9 KiB
Python
276 lines
8.9 KiB
Python
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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import torch.nn as nn
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from torch import Tensor
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from typing import Tuple
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class Conv2dSubsampling(nn.Module):
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"""Convolutional 2D subsampling (to 1/4 length).
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Convert an input of shape (N, T, idim) to an output
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with shape (N, T', odim), where
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T' = ((T-1)//2 - 1)//2, which approximates T' == T//4
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It is based on
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https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/subsampling.py # noqa
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"""
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def __init__(self, idim: int, odim: int) -> None:
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"""
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Args:
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idim:
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Input dim. The input shape is (N, T, idim).
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Caution: It requires: T >=7, idim >=7
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odim:
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Output dim. The output shape is (N, ((T-1)//2 - 1)//2, odim)
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"""
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assert idim >= 7
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super().__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(
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in_channels=1, out_channels=odim, kernel_size=3, stride=2
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),
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nn.ReLU(),
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ExpScale(odim, 1, 1, speed=20.0),
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nn.Conv2d(
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in_channels=odim, out_channels=odim, kernel_size=3, stride=2
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),
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nn.ReLU(),
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ExpScale(odim, 1, 1, speed=20.0),
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)
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self.out = nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim)
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self.out_norm = nn.LayerNorm(odim, elementwise_affine=False)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""Subsample x.
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Args:
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x:
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Its shape is (N, T, idim).
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Returns:
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Return a tensor of shape (N, ((T-1)//2 - 1)//2, odim)
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"""
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# On entry, x is (N, T, idim)
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x = x.unsqueeze(1) # (N, T, idim) -> (N, 1, T, idim) i.e., (N, C, H, W)
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x = self.conv(x)
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# Now x is of shape (N, odim, ((T-1)//2 - 1)//2, ((idim-1)//2 - 1)//2)
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b, c, t, f = x.size()
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x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
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# Now x is of shape (N, ((T-1)//2 - 1))//2, odim)
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x = self.out_norm(x)
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return x
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class VggSubsampling(nn.Module):
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"""Trying to follow the setup described in the following paper:
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https://arxiv.org/pdf/1910.09799.pdf
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This paper is not 100% explicit so I am guessing to some extent,
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and trying to compare with other VGG implementations.
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Convert an input of shape (N, T, idim) to an output
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with shape (N, T', odim), where
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T' = ((T-1)//2 - 1)//2, which approximates T' = T//4
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"""
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def __init__(self, idim: int, odim: int) -> None:
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"""Construct a VggSubsampling object.
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This uses 2 VGG blocks with 2 Conv2d layers each,
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subsampling its input by a factor of 4 in the time dimensions.
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Args:
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idim:
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Input dim. The input shape is (N, T, idim).
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Caution: It requires: T >=7, idim >=7
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odim:
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Output dim. The output shape is (N, ((T-1)//2 - 1)//2, odim)
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"""
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super().__init__()
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cur_channels = 1
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layers = []
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block_dims = [32, 64]
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# The decision to use padding=1 for the 1st convolution, then padding=0
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# for the 2nd and for the max-pooling, and ceil_mode=True, was driven by
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# a back-compatibility concern so that the number of frames at the
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# output would be equal to:
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# (((T-1)//2)-1)//2.
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# We can consider changing this by using padding=1 on the
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# 2nd convolution, so the num-frames at the output would be T//4.
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for block_dim in block_dims:
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layers.append(
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torch.nn.Conv2d(
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in_channels=cur_channels,
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out_channels=block_dim,
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kernel_size=3,
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padding=1,
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stride=1,
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)
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)
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layers.append(torch.nn.ReLU())
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layers.append(
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torch.nn.Conv2d(
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in_channels=block_dim,
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out_channels=block_dim,
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kernel_size=3,
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padding=0,
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stride=1,
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)
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)
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layers.append(
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torch.nn.MaxPool2d(
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kernel_size=2, stride=2, padding=0, ceil_mode=True
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)
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)
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cur_channels = block_dim
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self.layers = nn.Sequential(*layers)
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self.out = nn.Linear(
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block_dims[-1] * (((idim - 1) // 2 - 1) // 2), odim
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""Subsample x.
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Args:
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x:
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Its shape is (N, T, idim).
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Returns:
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Return a tensor of shape (N, ((T-1)//2 - 1)//2, odim)
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"""
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x = x.unsqueeze(1)
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x = self.layers(x)
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b, c, t, f = x.size()
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x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
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return x
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class PeLUFunction(torch.autograd.Function):
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"""
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Computes PeLU function (PeLUFunction.apply(x, cutoff, alpha)).
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The function is:
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x.relu() + alpha * (cutoff - x).relu()
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E.g. consider cutoff = -1, alpha = 0.01. This will tend to prevent die-off
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of neurons.
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"""
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@staticmethod
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def forward(ctx, x: Tensor, cutoff: float, alpha: float) -> Tensor:
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mask1 = (x >= 0) # >=, so there is deriv if x == 0.
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p = cutoff - x
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mask2 = (p >= 0)
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ctx.save_for_backward(mask1, mask2)
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ctx.alpha = alpha
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return x.relu() + alpha * p.relu()
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@staticmethod
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def backward(ctx, ans_grad: Tensor) -> Tuple[Tensor, None, None]:
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mask1, mask2 = ctx.saved_tensors
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return mask1 * ans_grad - (ctx.alpha * mask2) * ans_grad, None, None
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class PeLU(torch.nn.Module):
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def __init__(self, cutoff: float = -1.0, alpha: float = 0.01) -> None:
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super(PeLU, self).__init__()
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self.cutoff = cutoff
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self.alpha = alpha
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def forward(self, x: Tensor) -> Tensor:
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return PeLUFunction.apply(x, self.cutoff, self.alpha)
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class ExpScale(torch.nn.Module):
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def __init__(self, *shape, speed: float = 1.0):
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super(ExpScale, self).__init__()
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self.scale = nn.Parameter(torch.zeros(*shape))
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self.speed = speed
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def forward(self, x: Tensor) -> Tensor:
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return x * (self.scale * self.speed).exp()
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def _exp_scale_swish(x: Tensor, scale: Tensor, speed: float) -> Tensor:
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return (x * torch.sigmoid(x)) * (scale * speed).exp()
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def _exp_scale_swish_backward(x: Tensor, scale: Tensor, speed: float) -> Tensor:
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return (x * torch.sigmoid(x)) * (scale * speed).exp()
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class ExpScaleSwishFunction(torch.autograd.Function):
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@staticmethod
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def forward(ctx, x: Tensor, scale: Tensor, speed: float) -> Tensor:
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ctx.save_for_backward(x.detach(), scale.detach())
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ctx.speed = speed
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return _exp_scale_swish(x, scale, speed)
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@staticmethod
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def backward(ctx, y_grad: Tensor) -> Tensor:
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x, scale = ctx.saved_tensors
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x.requires_grad = True
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scale.requires_grad = True
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with torch.enable_grad():
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y = _exp_scale_swish(x, scale, ctx.speed)
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y.backward(gradient=y_grad)
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return x.grad, scale.grad, None
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class ExpScaleSwish(torch.nn.Module):
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# combines ExpScale an Swish
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# caution: need to specify name for speed, e.g. ExpScaleSwish(50, speed=4.0)
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def __init__(self, *shape, speed: float = 1.0):
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super(ExpScaleSwish, self).__init__()
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self.scale = nn.Parameter(torch.zeros(*shape))
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self.speed = speed
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def forward(self, x: Tensor) -> Tensor:
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return ExpScaleSwishFunction.apply(x, self.scale, self.speed)
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# return (x * torch.sigmoid(x)) * (self.scale * self.speed).exp()
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# return x * (self.scale * self.speed).exp()
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def _test_exp_scale_swish():
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class Swish(torch.nn.Module):
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def forward(self, x: Tensor) -> Tensor:
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"""Return Swich activation function."""
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return x * torch.sigmoid(x)
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x1 = torch.randn(50, 60).detach()
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x2 = x1.detach()
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m1 = ExpScaleSwish(50, 1, speed=4.0)
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m2 = torch.nn.Sequential(Swish(), ExpScale(50, 1, speed=4.0))
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x1.requires_grad = True
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x2.requires_grad = True
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y1 = m1(x1)
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y2 = m2(x2)
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assert torch.allclose(y1, y2)
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y1.sum().backward()
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y2.sum().backward()
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assert torch.allclose(x1.grad, x2.grad)
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if __name__ == '__main__':
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_test_exp_scale_swish()
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