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torch_attention.py
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torch_attention.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import *
import numpy as np
class layer_normalization(nn.Module):
def __init__(self, features, epsilon=1e-8):
'''Applies layer normalization.
Args:
epsilon: A floating number. A very small number for preventing ZeroDivision Error.
'''
super(layer_normalization, self).__init__()
self.epsilon = epsilon
self.gamma = nn.Parameter(torch.ones(features))
self.beta = nn.Parameter(torch.zeros(features))
def forward(self, x):
mean = x.mean(-1, keepdim=True)
std = x.std(-1, keepdim=True)
return self.gamma * (x - mean) / (std + self.epsilon) + self.beta
class positional_encoding(nn.Module):
def __init__(self, num_units, zeros_pad=True, scale=True):
'''Sinusoidal Positional_Encoding.
Args:
num_units: Output dimensionality
zero_pad: Boolean. If True, all the values of the first row (id = 0) should be constant zero
scale: Boolean. If True, the output will be multiplied by sqrt num_units(check details from paper)
'''
super(positional_encoding, self).__init__()
self.num_units = num_units
self.zeros_pad = zeros_pad
self.scale = scale
def forward(self, inputs):
# inputs: A 2d Tensor with shape of (N, T).
N, T = inputs.size()[0: 2]
# First part of the PE function: sin and cos argument
position_ind = Variable(torch.unsqueeze(torch.arange(0, T), 0).repeat(N, 1).long())
position_enc = torch.Tensor([
[pos / np.power(10000, 2. * i / self.num_units) for i in range(self.num_units)]
for pos in range(T)])
# Second part, apply the cosine to even columns and sin to odds.
position_enc[:, 0::2] = torch.sin(position_enc[:, 0::2]) # dim 2i
position_enc[:, 1::2] = torch.cos(position_enc[:, 1::2]) # dim 2i+1
# Convert to a Variable
lookup_table = Variable(position_enc)
if self.zeros_pad:
lookup_table = torch.cat((Variable(torch.zeros(1, self.num_units)),
lookup_table[1:, :]), 0)
padding_idx = 0
else:
padding_idx = -1
outputs = self._backend.Embedding.apply(
position_ind, lookup_table, padding_idx, None, 2, False, False) # copied from torch.nn.modules.sparse.py
if self.scale:
outputs = outputs * self.num_units ** 0.5
return outputs
class Multihead_Attention(nn.Module):
def __init__(self, num_units, num_heads=8, dropout=0, causality=False):
'''Applies multihead attention.
Args:
num_units: A scalar. Attention size.
dropout: A floating point number.
causality: Boolean. If true, units that reference the future are masked.
num_heads: An int. Number of heads.
'''
super(Multihead_Attention, self).__init__()
self.num_units = num_units
self.num_heads = num_heads
self.dropout_rate = dropout
self.causality = causality
self.Q_proj = nn.Sequential(nn.Linear(self.num_units, self.num_units), nn.ReLU())
self.K_proj = nn.Sequential(nn.Linear(self.num_units, self.num_units), nn.ReLU())
self.V_proj = nn.Sequential(nn.Linear(self.num_units, self.num_units), nn.ReLU())
self.output_dropout = nn.Dropout(p=self.dropout_rate)
self.normalization = layer_normalization(self.num_units)
def forward(self, mini_batch):
# keys, values: same shape of [N, T_k, C_k]
# queries: A 3d Variable with shape of [N, T_q, C_q]
if mini_batch.size()[-1] != self.num_units:
mini_batch = mini_batch.transpose(1, 2)
# Linear projections
Q = self.Q_proj(mini_batch) # (N, T_q, C)
K = self.K_proj(mini_batch) # (N, T_q, C)
V = self.V_proj(mini_batch) # (N, T_q, C)
# Split and concat
Q_ = torch.cat(torch.chunk(Q, self.num_heads, dim=2), dim=0) # (h*N, T_q, C/h)
K_ = torch.cat(torch.chunk(K, self.num_heads, dim=2), dim=0) # (h*N, T_q, C/h)
V_ = torch.cat(torch.chunk(V, self.num_heads, dim=2), dim=0) # (h*N, T_q, C/h)
# Multiplication
outputs = torch.bmm(Q_, K_.permute(0, 2, 1)) # (h*N, T_q, T_k)
# Scale
outputs = outputs / (K_.size()[-1] ** 0.5)
# Key Masking
key_masks = torch.sign(torch.abs(torch.sum(mini_batch, dim=-1))) # (N, T_k)
key_masks = key_masks.repeat(self.num_heads, 1) # (h*N, T_k)
key_masks = torch.unsqueeze(key_masks, 1).repeat(1, mini_batch.size()[1], 1) # (h*N, T_q, T_k)
padding = Variable(torch.ones(*outputs.size()).cuda() * (-2 ** 32 + 1))
condition = key_masks.eq(0.).float()
outputs = padding * condition + outputs * (1. - condition)
# Causality = Future blinding
if self.causality:
diag_vals = torch.ones(*outputs[0, :, :].size()) .cuda() # (T_q, T_k)
tril = torch.tril(diag_vals, diagonal=0) # (T_q, T_k)
# print(tril)
masks = Variable(torch.unsqueeze(tril, 0).repeat(outputs.size()[0], 1, 1)) # (h*N, T_q, T_k)
padding = Variable(torch.ones(*masks.size()).cuda() * (-2 ** 32 + 1))
condition = masks.eq(0.).float()
outputs = padding * condition + outputs * (1. - condition)
# Activation
outputs = F.softmax(outputs, dim=-1) # (h*N, T_q, T_k)
# Query Masking
query_masks = torch.sign(torch.abs(torch.sum(mini_batch, dim=-1))) # (N, T_q)
query_masks = query_masks.repeat(self.num_heads, 1) # (h*N, T_q)
query_masks = torch.unsqueeze(query_masks, 2).repeat(1, 1, mini_batch.size()[1]) # (h*N, T_q, T_k)
outputs = outputs * query_masks
# Dropouts
outputs = self.output_dropout(outputs) # (h*N, T_q, T_k)
# Weighted sum
outputs = torch.bmm(outputs, V_) # (h*N, T_q, C/h)
# Restore shape
outputs = torch.cat(torch.chunk(outputs, self.num_heads, dim=0), dim=2) # (N, T_q, C)
# Residual connection
outputs += mini_batch
# Normalize
outputs = self.normalization(outputs) # (N, T_q, C)
return outputs.transpose(2, 1)
class FeedForward(nn.Module):
def __init__(self, in_channels, num_units=[2048, 512]):
'''Point-wise feed forward net.
Args:
in_channels: a number of channels of inputs
num_units: A list of two integers.
'''
super(FeedForward, self).__init__()
self.in_channels = in_channels
self.num_units = num_units
# nn.Linear is faster than nn.Conv1d
self.conv = False
if self.conv:
params = {'in_channels': self.in_channels, 'out_channels': self.num_units[0],
'kernel_size': 1, 'stride': 1, 'bias': True}
self.conv1 = nn.Sequential(nn.Conv1d(**params), nn.ReLU())
params = {'in_channels': self.num_units[0], 'out_channels': self.num_units[1],
'kernel_size': 1, 'stride': 1, 'bias': True}
self.conv2 = nn.Conv1d(**params)
else:
self.conv1 = nn.Sequential(nn.Linear(self.in_channels, self.num_units[0]), nn.ReLU())
self.conv2 = nn.Linear(self.num_units[0], self.num_units[1])
self.normalization = layer_normalization(self.in_channels)
def forward(self, inputs):
if self.conv:
inputs = inputs.permute(0, 2, 1)
outputs = self.conv1(inputs)
outputs = self.conv2(outputs)
# Residual connection
outputs += inputs
# Layer normalization
if self.conv:
outputs = self.normalization(outputs.permute(0, 2, 1))
else:
outputs = self.normalization(outputs)
return outputs
class label_smoothing(nn.Module):
def __init__(self, epsilon=0.1):
'''Applies label smoothing. See https://arxiv.org/abs/1512.00567.
Args:
epsilon: Smoothing rate.
'''
super(label_smoothing, self).__init__()
self.epsilon = epsilon
def forward(self, inputs):
K = inputs.size()[-1]
return ((1 - self.epsilon) * inputs) + (self.epsilon / K)