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import torch
import numpy as np
from torch import nn as nn
from torch.nn import init as init
import torch.distributed as dist
from collections import OrderedDict
class LayerNormFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, x, weight, bias, eps):
ctx.eps = eps
N, C, H, W = x.size()
mu = x.mean(1, keepdim=True)
var = (x - mu).pow(2).mean(1, keepdim=True)
y = (x - mu) / (var + eps).sqrt()
ctx.save_for_backward(y, var, weight)
y = weight.view(1, C, 1, 1) * y + bias.view(1, C, 1, 1)
return y
@staticmethod
def backward(ctx, grad_output):
eps = ctx.eps
N, C, H, W = grad_output.size()
y, var, weight = ctx.saved_variables
g = grad_output * weight.view(1, C, 1, 1)
mean_g = g.mean(dim=1, keepdim=True)
mean_gy = (g * y).mean(dim=1, keepdim=True)
gx = 1. / torch.sqrt(var + eps) * (g - y * mean_gy - mean_g)
return gx, (grad_output * y).sum(dim=3).sum(dim=2).sum(dim=0), grad_output.sum(dim=3).sum(dim=2).sum(
dim=0), None
class LayerNorm2d(nn.Module):
def __init__(self, channels, eps=1e-6):
super(LayerNorm2d, self).__init__()
self.register_parameter('weight', nn.Parameter(torch.ones(channels)))
self.register_parameter('bias', nn.Parameter(torch.zeros(channels)))
self.eps = eps
def forward(self, x):
return LayerNormFunction.apply(x, self.weight, self.bias, self.eps)
class CustomSequential(nn.Module):
'''
Similar to nn.Sequential, but it lets us introduce a second argument in the forward method
so adaptors can be considered in the inference.
'''
def __init__(self, *args):
super(CustomSequential, self).__init__()
self.modules_list = nn.ModuleList(args)
def forward(self, x, use_adapter=False):
for module in self.modules_list:
if hasattr(module, 'set_use_adapters'):
module.set_use_adapters(use_adapter)
x = module(x)
return x
if __name__ == '__main__':
pass |