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""" Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use
basic versions of the activations.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish_fwd(x):
return x.mul(torch.sigmoid(x))
def swish_bwd(x, grad_output):
x_sigmoid = torch.sigmoid(x)
return grad_output * (x_sigmoid * (1 + x * (1 - x_sigmoid)))
class SwishAutoFn(torch.autograd.Function):
""" optimised Swish w/ memory-efficient checkpoint
Inspired by conversation btw Jeremy Howard & Adam Pazske
https://twitter.com/jeremyphoward/status/1188251041835315200
"""
@staticmethod
def symbolic(g, x):
return g.op("Mul", x, g.op("Sigmoid", x))
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return swish_fwd(x)
@staticmethod
def backward(ctx, grad_output):
x = ctx.saved_tensors[0]
return swish_bwd(x, grad_output)
def swish_me(x, inplace=False):
return SwishAutoFn.apply(x)
class SwishMe(nn.Module):
def __init__(self, inplace: bool = False):
super(SwishMe, self).__init__()
def forward(self, x):
return SwishAutoFn.apply(x)
def mish_fwd(x):
return x.mul(torch.tanh(F.softplus(x)))
def mish_bwd(x, grad_output):
x_sigmoid = torch.sigmoid(x)
x_tanh_sp = F.softplus(x).tanh()
return grad_output.mul(x_tanh_sp + x * x_sigmoid * (1 - x_tanh_sp * x_tanh_sp))
class MishAutoFn(torch.autograd.Function):
""" Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
A memory efficient variant of Mish
"""
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return mish_fwd(x)
@staticmethod
def backward(ctx, grad_output):
x = ctx.saved_tensors[0]
return mish_bwd(x, grad_output)
def mish_me(x, inplace=False):
return MishAutoFn.apply(x)
class MishMe(nn.Module):
def __init__(self, inplace: bool = False):
super(MishMe, self).__init__()
def forward(self, x):
return MishAutoFn.apply(x)
def hard_sigmoid_fwd(x, inplace: bool = False):
return (x + 3).clamp(min=0, max=6).div(6.)
def hard_sigmoid_bwd(x, grad_output):
m = torch.ones_like(x) * ((x >= -3.) & (x <= 3.)) / 6.
return grad_output * m
class HardSigmoidAutoFn(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return hard_sigmoid_fwd(x)
@staticmethod
def backward(ctx, grad_output):
x = ctx.saved_tensors[0]
return hard_sigmoid_bwd(x, grad_output)
def hard_sigmoid_me(x, inplace: bool = False):
return HardSigmoidAutoFn.apply(x)
class HardSigmoidMe(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSigmoidMe, self).__init__()
def forward(self, x):
return HardSigmoidAutoFn.apply(x)
def hard_swish_fwd(x):
return x * (x + 3).clamp(min=0, max=6).div(6.)
def hard_swish_bwd(x, grad_output):
m = torch.ones_like(x) * (x >= 3.)
m = torch.where((x >= -3.) & (x <= 3.), x / 3. + .5, m)
return grad_output * m
class HardSwishAutoFn(torch.autograd.Function):
"""A memory efficient HardSwish activation"""
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return hard_swish_fwd(x)
@staticmethod
def backward(ctx, grad_output):
x = ctx.saved_tensors[0]
return hard_swish_bwd(x, grad_output)
@staticmethod
def symbolic(g, self):
input = g.op("Add", self, g.op('Constant', value_t=torch.tensor(3, dtype=torch.float)))
hardtanh_ = g.op("Clip", input, g.op('Constant', value_t=torch.tensor(0, dtype=torch.float)), g.op('Constant', value_t=torch.tensor(6, dtype=torch.float)))
hardtanh_ = g.op("Div", hardtanh_, g.op('Constant', value_t=torch.tensor(6, dtype=torch.float)))
return g.op("Mul", self, hardtanh_)
def hard_swish_me(x, inplace=False):
return HardSwishAutoFn.apply(x)
class HardSwishMe(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSwishMe, self).__init__()
def forward(self, x):
return HardSwishAutoFn.apply(x)
def hard_mish_fwd(x):
return 0.5 * x * (x + 2).clamp(min=0, max=2)
def hard_mish_bwd(x, grad_output):
m = torch.ones_like(x) * (x >= -2.)
m = torch.where((x >= -2.) & (x <= 0.), x + 1., m)
return grad_output * m
class HardMishAutoFn(torch.autograd.Function):
""" A memory efficient variant of Hard Mish
Experimental, based on notes by Mish author Diganta Misra at
https://github.com/digantamisra98/H-Mish/blob/0da20d4bc58e696b6803f2523c58d3c8a82782d0/README.md
"""
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return hard_mish_fwd(x)
@staticmethod
def backward(ctx, grad_output):
x = ctx.saved_tensors[0]
return hard_mish_bwd(x, grad_output)
def hard_mish_me(x, inplace: bool = False):
return HardMishAutoFn.apply(x)
class HardMishMe(nn.Module):
def __init__(self, inplace: bool = False):
super(HardMishMe, self).__init__()
def forward(self, x):
return HardMishAutoFn.apply(x)
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