import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Variable
import math
import os
import sys
import numpy as np
import torch.nn as nn
from torch.nn.utils.rnn import pad_packed_sequence as unpack
from torch.nn.utils.rnn import pack_padded_sequence as pack
INF = 1e10
EPSILON = 1e-10
class LSTMDecoder(nn.Module):
def __init__(self, num_layers, input_size, rnn_size, dropout):
super(LSTMDecoder, self).__init__()
self.dropout = nn.Dropout(dropout)
self.num_layers = num_layers
self.layers = nn.ModuleList()
for i in range(num_layers):
self.layers.append(nn.LSTMCell(input_size, rnn_size))
input_size = rnn_size
def forward(self, input, hidden):
h_0, c_0 = hidden
h_1, c_1 = [], []
for i, layer in enumerate(self.layers):
input = self.dropout(input)
h_1_i, c_1_i = layer(input, (h_0[i], c_0[i]))
input = h_1_i
h_1 += [h_1_i]
c_1 += [c_1_i]
h_1 = torch.stack(h_1)
c_1 = torch.stack(c_1)
return input, (h_1, c_1)
def positional_encodings_like(x, t=None):
if t is None:
positions = torch.arange(0., x.size(1))
if x.is_cuda:
positions = positions.cuda(x.get_device())
else:
positions = t
encodings = torch.zeros(*x.size()[1:])
if x.is_cuda:
encodings = encodings.cuda(x.get_device())
for channel in range(x.size(-1)):
if channel % 2 == 0:
encodings[:, channel] = torch.sin(
positions / 10000 ** (channel / x.size(2)))
else:
encodings[:, channel] = torch.cos(
positions / 10000 ** ((channel - 1) / x.size(2)))
return Variable(encodings)
# torch.matmul can't do (4, 3, 2) @ (4, 2) -> (4, 3)
def matmul(x, y):
if x.dim() == y.dim():
return x @ y
if x.dim() == y.dim() - 1:
return (x.unsqueeze(-2) @ y).squeeze(-2)
return (x @ y.unsqueeze(-2)).squeeze(-2)
def pad_to_match(x, y):
x_len, y_len = x.size(1), y.size(1)
if x_len == y_len:
return x, y
extra = x.new_ones((x.size(0), abs(y_len - x_len)))
if x_len < y_len:
return torch.cat((x, extra), 1), y
return x, torch.cat((y, extra), 1)
class LayerNorm(nn.Module):
def __init__(self, d_model, eps=1e-6):
super().__init__()
self.gamma = nn.Parameter(torch.ones(d_model))
self.beta = nn.Parameter(torch.zeros(d_model))
self.eps = eps
def forward(self, x):
mean = x.mean(-1, keepdim=True)
std = x.std(-1, keepdim=True)
return self.gamma * (x - mean) / (std + self.eps) + self.beta
class ResidualBlock(nn.Module):
def __init__(self, layer, d_model, dropout_ratio):
super().__init__()
self.layer = layer
self.dropout = nn.Dropout(dropout_ratio)
self.layernorm = LayerNorm(d_model)
def forward(self, *x, padding=None):
return self.layernorm(x[0] + self.dropout(self.layer(*x, padding=padding)))
class Attention(nn.Module):
def __init__(self, d_key, dropout_ratio, causal):
super().__init__()
self.scale = math.sqrt(d_key)
self.dropout = nn.Dropout(dropout_ratio)
self.causal = causal
def forward(self, query, key, value, padding=None):
dot_products = matmul(query, key.transpose(1, 2))
if query.dim() == 3 and self.causal:
tri = key.new_ones((key.size(1), key.size(1))).triu(1) * INF
dot_products.sub_(tri.unsqueeze(0))
if not padding is None:
dot_products.masked_fill_(padding.unsqueeze(1).expand_as(dot_products), -INF)
return matmul(self.dropout(F.softmax(dot_products / self.scale, dim=-1)), value)
class MultiHead(nn.Module):
def __init__(self, d_key, d_value, n_heads, dropout_ratio, causal=False):
super().__init__()
self.attention = Attention(d_key, dropout_ratio, causal=causal)
self.wq = Linear(d_key, d_key, bias=False)
self.wk = Linear(d_key, d_key, bias=False)
self.wv = Linear(d_value, d_value, bias=False)
self.n_heads = n_heads
def forward(self, query, key, value, padding=None):
query, key, value = self.wq(query), self.wk(key), self.wv(value)
query, key, value = (
x.chunk(self.n_heads, -1) for x in (query, key, value))
return torch.cat([self.attention(q, k, v, padding=padding)
for q, k, v in zip(query, key, value)], -1)
class LinearReLU(nn.Module):
def __init__(self, d_model, d_hidden):
super().__init__()
self.feedforward = Feedforward(d_model, d_hidden, activation='relu')
self.linear = Linear(d_hidden, d_model)
def forward(self, x, padding=None):
return self.linear(self.feedforward(x))
class TransformerEncoderLayer(nn.Module):
def __init__(self, dimension, n_heads, hidden, dropout):
super().__init__()
self.selfattn = ResidualBlock(
MultiHead(
dimension, dimension, n_heads, dropout),
dimension, dropout)
self.feedforward = ResidualBlock(
LinearReLU(dimension, hidden),
dimension, dropout)
def forward(self, x, padding=None):
return self.feedforward(self.selfattn(x, x, x, padding=padding))
class TransformerEncoder(nn.Module):
def __init__(self, dimension, n_heads, hidden, num_layers, dropout):
super().__init__()
self.layers = nn.ModuleList(
[TransformerEncoderLayer(dimension, n_heads, hidden, dropout) for i in range(num_layers)])
self.dropout = nn.Dropout(dropout)
def forward(self, x, padding=None):
x = self.dropout(x)
encoding = [x]
for layer in self.layers:
x = layer(x, padding=padding)
encoding.append(x)
return encoding
class TransformerDecoderLayer(nn.Module):
def __init__(self, dimension, n_heads, hidden, dropout, causal=True):
super().__init__()
self.selfattn = ResidualBlock(
MultiHead(dimension, dimension, n_heads,
dropout, causal),
dimension, dropout)
self.attention = ResidualBlock(
MultiHead(dimension, dimension, n_heads,
dropout),
dimension, dropout)
self.feedforward = ResidualBlock(
LinearReLU(dimension, hidden),
dimension, dropout)
def forward(self, x, encoding, context_padding=None, answer_padding=None):
x = self.selfattn(x, x, x, padding=answer_padding)
return self.feedforward(self.attention(x, encoding, encoding, padding=context_padding))
class TransformerDecoder(nn.Module):
def __init__(self, dimension, n_heads, hidden, num_layers, dropout, causal=True):
super().__init__()
self.layers = nn.ModuleList(
[TransformerDecoderLayer(dimension, n_heads, hidden, dropout, causal=causal) for i in range(num_layers)])
self.dropout = nn.Dropout(dropout)
self.d_model = dimension
def forward(self, x, encoding, context_padding=None, positional_encodings=True, answer_padding=None):
if positional_encodings:
x = x + positional_encodings_like(x)
x = self.dropout(x)
for layer, enc in zip(self.layers, encoding[1:]):
x = layer(x, enc, context_padding=context_padding, answer_padding=answer_padding)
return x
def mask(targets, out, squash=True, pad_idx=1):
mask = (targets != pad_idx)
out_mask = mask.unsqueeze(-1).expand_as(out).contiguous()
out_after = out[out_mask].contiguous().view(-1, out.size(-1))
targets_after = targets[mask]
return out_after, targets_after
class Highway(torch.nn.Module):
def __init__(self, d_in, activation='relu', n_layers=1):
super(Highway, self).__init__()
self.d_in = d_in
self._layers = torch.nn.ModuleList([Linear(d_in, 2 * d_in) for _ in range(n_layers)])
for layer in self._layers:
layer.bias[d_in:].fill_(1)
self.activation = getattr(F, activation)
def forward(self, inputs):
current_input = inputs
for layer in self._layers:
projected_input = layer(current_input)
linear_part = current_input
nonlinear_part = projected_input[:, :self.d_in] if projected_input.dim() == 2 else projected_input[:, :, :self.d_in]
nonlinear_part = self.activation(nonlinear_part)
gate = projected_input[:, self.d_in:(2 * self.d_in)] if projected_input.dim() == 2 else projected_input[:, :, self.d_in:(2 * self.d_in)]
gate = F.sigmoid(gate)
current_input = gate * linear_part + (1 - gate) * nonlinear_part
return current_input
class LinearFeedforward(nn.Module):
def __init__(self, d_in, d_hid, d_out, activation='relu'):
super().__init__()
self.feedforward = Feedforward(d_in, d_hid, activation=activation)
self.linear = Linear(d_hid, d_out)
self.dropout = nn.Dropout(0.2)
def forward(self, x):
return self.dropout(self.linear(self.feedforward(x)))
class PackedLSTM(nn.Module):
def __init__(self, d_in, d_out, bidirectional=False, num_layers=1,
dropout=0.0, batch_first=True):
"""A wrapper class that packs input sequences and unpacks output sequences"""
super().__init__()
if bidirectional:
d_out = d_out // 2
self.rnn = nn.LSTM(d_in, d_out,
num_layers=num_layers,
dropout=dropout,
bidirectional=bidirectional,
batch_first=batch_first)
self.batch_first = batch_first
def forward(self, inputs, lengths, hidden=None):
lens, indices = torch.sort(inputs.new_tensor(lengths, dtype=torch.long), 0, True)
inputs = inputs[indices] if self.batch_first else inputs[:, indices]
outputs, (h, c) = self.rnn(pack(inputs, lens.tolist(),
batch_first=self.batch_first), hidden)
outputs = unpack(outputs, batch_first=self.batch_first)[0]
_, _indices = torch.sort(indices, 0)
outputs = outputs[_indices] if self.batch_first else outputs[:, _indices]
h, c = h[:, _indices, :], c[:, _indices, :]
return outputs, (h, c)
class Linear(nn.Linear):
def forward(self, x):
size = x.size()
return super().forward(
x.contiguous().view(-1, size[-1])).view(*size[:-1], -1)
class Feedforward(nn.Module):
def __init__(self, d_in, d_out, activation=None, bias=True, dropout=0.2):
super().__init__()
if activation is not None:
self.activation = getattr(torch, activation)
else:
self.activation = lambda x: x
self.linear = Linear(d_in, d_out, bias=bias)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
return self.activation(self.linear(self.dropout(x)))
class Embedding(nn.Module):
def __init__(self, field, trained_dimension, dropout=0.0, project=True):
super().__init__()
self.field = field
self.project = project
dimension = 0
pretrained_dimension = field.vocab.vectors.size(-1)
self.pretrained_embeddings = [nn.Embedding(len(field.vocab), pretrained_dimension)]
self.pretrained_embeddings[0].weight.data = field.vocab.vectors
self.pretrained_embeddings[0].weight.requires_grad = False
dimension += pretrained_dimension
if self.project:
self.projection = Feedforward(dimension, trained_dimension)
dimension = trained_dimension
self.dropout = nn.Dropout(dropout)
self.dimension = dimension
def forward(self, x, lengths=None, device=-1):
pretrained_embeddings = self.pretrained_embeddings[0](x.cpu()).to(x.device).detach()
return self.projection(pretrained_embeddings) if self.project else pretrained_embeddings
def set_embeddings(self, w):
self.pretrained_embeddings[0].weight.data = w
self.pretrained_embeddings[0].weight.requires_grad = False
class SemanticFusionUnit(nn.Module):
def __init__(self, d, l):
super().__init__()
self.r_hat = Feedforward(d*l, d, 'tanh')
self.g = Feedforward(d*l, d, 'sigmoid')
self.dropout = nn.Dropout(0.2)
def forward(self, x):
c = self.dropout(torch.cat(x, -1))
r_hat = self.r_hat(c)
g = self.g(c)
o = g * r_hat + (1 - g) * x[0]
return o
class LSTMDecoderAttention(nn.Module):
def __init__(self, dim, dot=False):
super().__init__()
self.linear_in = nn.Linear(dim, dim, bias=False)
self.linear_out = nn.Linear(2 * dim, dim, bias=False)
self.tanh = nn.Tanh()
self.mask = None
self.dot = dot
def applyMasks(self, context_mask):
self.context_mask = context_mask
def forward(self, input, context):
if not self.dot:
targetT = self.linear_in(input).unsqueeze(2) # batch x dim x 1
else:
targetT = input.unsqueeze(2)
context_scores = torch.bmm(context, targetT).squeeze(2)
context_scores.masked_fill_(self.context_mask, -float('inf'))
context_attention = F.softmax(context_scores, dim=-1) + EPSILON
context_alignment = torch.bmm(context_attention.unsqueeze(1), context).squeeze(1)
combined_representation = torch.cat([input, context_alignment], 1)
output = self.tanh(self.linear_out(combined_representation))
return output, context_attention, context_alignment
class CoattentiveLayer(nn.Module):
def __init__(self, d, dropout=0.2):
super().__init__()
self.proj = Feedforward(d, d, dropout=0.0)
self.embed_sentinel = nn.Embedding(2, d)
self.dropout = nn.Dropout(dropout)
def forward(self, context, question, context_padding, question_padding):
context_padding = torch.cat([context.new_zeros((context.size(0), 1), dtype=torch.long)==1, context_padding], 1)
question_padding = torch.cat([question.new_zeros((question.size(0), 1), dtype=torch.long)==1, question_padding], 1)
context_sentinel = self.embed_sentinel(context.new_zeros((context.size(0), 1), dtype=torch.long))
context = torch.cat([context_sentinel, self.dropout(context)], 1) # batch_size x (context_length + 1) x features
question_sentinel = self.embed_sentinel(question.new_ones((question.size(0), 1), dtype=torch.long))
question = torch.cat([question_sentinel, question], 1) # batch_size x (question_length + 1) x features
question = torch.tanh(self.proj(question)) # batch_size x (question_length + 1) x features
affinity = context.bmm(question.transpose(1,2)) # batch_size x (context_length + 1) x (question_length + 1)
attn_over_context = self.normalize(affinity, context_padding) # batch_size x (context_length + 1) x 1
attn_over_question = self.normalize(affinity.transpose(1,2), question_padding) # batch_size x (question_length + 1) x 1
sum_of_context = self.attn(attn_over_context, context) # batch_size x (question_length + 1) x features
sum_of_question = self.attn(attn_over_question, question) # batch_size x (context_length + 1) x features
coattn_context = self.attn(attn_over_question, sum_of_context) # batch_size x (context_length + 1) x features
coattn_question = self.attn(attn_over_context, sum_of_question) # batch_size x (question_length + 1) x features
return torch.cat([coattn_context, sum_of_question], 2)[:, 1:], torch.cat([coattn_question, sum_of_context], 2)[:, 1:]
@staticmethod
def attn(weights, candidates):
w1, w2, w3 = weights.size()
c1, c2, c3 = candidates.size()
return weights.unsqueeze(3).expand(w1, w2, w3, c3).mul(candidates.unsqueeze(2).expand(c1, c2, w3, c3)).sum(1).squeeze(1)
@staticmethod
def normalize(original, padding):
raw_scores = original.clone()
raw_scores.masked_fill_(padding.unsqueeze(-1).expand_as(raw_scores), -INF)
return F.softmax(raw_scores, dim=1)
