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