# # Copyright (c) 2018, Salesforce, Inc. # The Board of Trustees of the Leland Stanford Junior University # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import os import math import numpy as np import json import torch from torch import nn from torch.nn import functional as F from ..util import get_trainable_params, set_seed from ..modules import expectedBLEU, expectedMultiBleu, matrixBLEU from cove import MTLSTM from allennlp.modules.elmo import Elmo, batch_to_ids from .common import * class MultitaskQuestionAnsweringNetwork(nn.Module): def __init__(self, field, args): super().__init__() self.field = field self.args = args self.pad_idx = self.field.vocab.stoi[self.field.pad_token] self.device = set_seed(args) def dp(args): return args.dropout_ratio if args.rnn_layers > 1 else 0. if self.args.glove_and_char: self.encoder_embeddings = Embedding(field, args.dimension, dropout=args.dropout_ratio, project=not args.cove) if self.args.cove or self.args.intermediate_cove: self.cove = MTLSTM(model_cache=args.embeddings, layer0=args.intermediate_cove, layer1=args.cove) cove_params = get_trainable_params(self.cove) for p in cove_params: p.requires_grad = False cove_dim = int(args.intermediate_cove) * 600 + int(args.cove) * 600 + 400 # the last 400 is for GloVe and char n-gram embeddings self.project_cove = Feedforward(cove_dim, args.dimension) if -1 not in self.args.elmo: options_file = "https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x4096_512_2048cnn_2xhighway/elmo_2x4096_512_2048cnn_2xhighway_options.json" weight_file = "https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x4096_512_2048cnn_2xhighway/elmo_2x4096_512_2048cnn_2xhighway_weights.hdf5" self.elmo = Elmo(options_file, weight_file, 3, dropout=0.0, do_layer_norm=False) elmo_params = get_trainable_params(self.elmo) for p in elmo_params: p.requires_grad = False elmo_dim = 1024 * len(self.args.elmo) self.project_elmo = Feedforward(elmo_dim, args.dimension) if self.args.glove_and_char: self.project_embeddings = Feedforward(2 * args.dimension, args.dimension, dropout=0.0) self.decoder_embeddings = Embedding(field, args.dimension, dropout=args.dropout_ratio, project=True) self.bilstm_before_coattention = PackedLSTM(args.dimension, args.dimension, batch_first=True, bidirectional=True, num_layers=1, dropout=0) self.coattention = CoattentiveLayer(args.dimension, dropout=0.3) dim = 2*args.dimension + args.dimension + args.dimension self.context_bilstm_after_coattention = PackedLSTM(dim, args.dimension, batch_first=True, dropout=dp(args), bidirectional=True, num_layers=args.rnn_layers) self.self_attentive_encoder_context = TransformerEncoder(args.dimension, args.transformer_heads, args.transformer_hidden, args.transformer_layers, args.dropout_ratio) self.bilstm_context = PackedLSTM(args.dimension, args.dimension, batch_first=True, dropout=dp(args), bidirectional=True, num_layers=args.rnn_layers) self.question_bilstm_after_coattention = PackedLSTM(dim, args.dimension, batch_first=True, dropout=dp(args), bidirectional=True, num_layers=args.rnn_layers) self.self_attentive_encoder_question = TransformerEncoder(args.dimension, args.transformer_heads, args.transformer_hidden, args.transformer_layers, args.dropout_ratio) self.bilstm_question = PackedLSTM(args.dimension, args.dimension, batch_first=True, dropout=dp(args), bidirectional=True, num_layers=args.rnn_layers) self.self_attentive_decoder = TransformerDecoder(args.dimension, args.transformer_heads, args.transformer_hidden, args.transformer_layers, args.dropout_ratio) self.dual_ptr_rnn_decoder = DualPtrRNNDecoder(args.dimension, args.dimension, dropout=args.dropout_ratio, num_layers=args.rnn_layers) self.generative_vocab_size = min(len(field.vocab), args.max_generative_vocab) self.out = nn.Linear(args.dimension, self.generative_vocab_size) self.dropout = nn.Dropout(0.4) def set_embeddings(self, embeddings): self.encoder_embeddings.set_embeddings(embeddings) self.decoder_embeddings.set_embeddings(embeddings) def forward(self, batch, iteration): context, context_lengths, context_limited, context_elmo = batch.context, batch.context_lengths, batch.context_limited, batch.context_elmo question, question_lengths, question_limited, question_elmo = batch.question, batch.question_lengths, batch.question_limited, batch.question_elmo answer, answer_lengths, answer_limited = batch.answer, batch.answer_lengths, batch.answer_limited oov_to_limited_idx, limited_idx_to_full_idx = batch.oov_to_limited_idx, batch.limited_idx_to_full_idx def map_to_full(x): return limited_idx_to_full_idx[x] self.map_to_full = map_to_full if -1 not in self.args.elmo: def elmo(z, layers, device): e = self.elmo(batch_to_ids(z).to(device))['elmo_representations'] return torch.cat([e[x] for x in layers], -1) context_elmo = self.project_elmo(elmo(context_elmo, self.args.elmo, context.device).detach()) question_elmo = self.project_elmo(elmo(question_elmo, self.args.elmo, question.device).detach()) if self.args.glove_and_char: context_embedded = self.encoder_embeddings(context) question_embedded = self.encoder_embeddings(question) if self.args.cove: context_embedded = self.project_cove(torch.cat([self.cove(context_embedded[:, :, -300:], context_lengths), context_embedded], -1).detach()) question_embedded = self.project_cove(torch.cat([self.cove(question_embedded[:, :, -300:], question_lengths), question_embedded], -1).detach()) if -1 not in self.args.elmo: context_embedded = self.project_embeddings(torch.cat([context_embedded, context_elmo], -1)) question_embedded = self.project_embeddings(torch.cat([question_embedded, question_elmo], -1)) else: context_embedded, question_embedded = context_elmo, question_elmo context_encoded = self.bilstm_before_coattention(context_embedded, context_lengths)[0] question_encoded = self.bilstm_before_coattention(question_embedded, question_lengths)[0] context_padding = context.data == self.pad_idx question_padding = question.data == self.pad_idx coattended_context, coattended_question = self.coattention(context_encoded, question_encoded, context_padding, question_padding) context_summary = torch.cat([coattended_context, context_encoded, context_embedded], -1) condensed_context, _ = self.context_bilstm_after_coattention(context_summary, context_lengths) self_attended_context = self.self_attentive_encoder_context(condensed_context, padding=context_padding) final_context, (context_rnn_h, context_rnn_c) = self.bilstm_context(self_attended_context[-1], context_lengths) context_rnn_state = [self.reshape_rnn_state(x) for x in (context_rnn_h, context_rnn_c)] question_summary = torch.cat([coattended_question, question_encoded, question_embedded], -1) condensed_question, _ = self.question_bilstm_after_coattention(question_summary, question_lengths) self_attended_question = self.self_attentive_encoder_question(condensed_question, padding=question_padding) final_question, (question_rnn_h, question_rnn_c) = self.bilstm_question(self_attended_question[-1], question_lengths) question_rnn_state = [self.reshape_rnn_state(x) for x in (question_rnn_h, question_rnn_c)] context_indices = context_limited if context_limited is not None else context question_indices = question_limited if question_limited is not None else question answer_indices = answer_limited if answer_limited is not None else answer pad_idx = self.field.decoder_stoi[self.field.pad_token] context_padding = context_indices.data == pad_idx question_padding = question_indices.data == pad_idx self.dual_ptr_rnn_decoder.applyMasks(context_padding, question_padding) if self.training: answer_padding = (answer_indices.data == pad_idx)[:, :-1] answer_embedded = self.decoder_embeddings(answer) self_attended_decoded = self.self_attentive_decoder(answer_embedded[:, :-1].contiguous(), self_attended_context, context_padding=context_padding, answer_padding=answer_padding, positional_encodings=True) decoder_outputs = self.dual_ptr_rnn_decoder(self_attended_decoded, final_context, final_question, hidden=context_rnn_state) rnn_output, context_attention, question_attention, context_alignment, question_alignment, vocab_pointer_switch, context_question_switch, rnn_state = decoder_outputs probs = self.probs(self.out, rnn_output, vocab_pointer_switch, context_question_switch, context_attention, question_attention, context_indices, question_indices, oov_to_limited_idx) if self.args.use_bleu_loss and iteration >= self.args.loss_switch * max(self.args.train_iterations): max_order = 4 targets = answer_indices[:, 1:].contiguous() batch_size = targets.size(0) reference_lengths = [l-1 for l in answer_lengths] translation_len = max(reference_lengths) translation_lengths = torch.tensor([translation_len] * batch_size, device=self.device) bleu_loss_smoothed = expectedMultiBleu.bleu(probs, targets, translation_lengths, reference_lengths, max_order=max_order, smooth=True) loss = -1 * bleu_loss_smoothed[0] elif self.args.use_maxmargin_loss: targets = answer_indices[:, 1:].contiguous() loss = max_margin_loss(probs, targets, pad_idx=pad_idx) else: probs, targets = mask(answer_indices[:, 1:].contiguous(), probs.contiguous(), pad_idx=pad_idx) loss = F.nll_loss(probs.log(), targets) return loss, None else: return None, self.greedy(self_attended_context, final_context, final_question, context_indices, question_indices, oov_to_limited_idx, rnn_state=context_rnn_state).data def reshape_rnn_state(self, h): return h.view(h.size(0) // 2, 2, h.size(1), h.size(2)) \ .transpose(1, 2).contiguous() \ .view(h.size(0) // 2, h.size(1), h.size(2) * 2).contiguous() def probs(self, generator, outputs, vocab_pointer_switches, context_question_switches, context_attention, question_attention, context_indices, question_indices, oov_to_limited_idx): size = list(outputs.size()) size[-1] = self.generative_vocab_size scores = generator(outputs.view(-1, outputs.size(-1))).view(size) p_vocab = F.softmax(scores, dim=scores.dim()-1) scaled_p_vocab = vocab_pointer_switches.expand_as(p_vocab) * p_vocab effective_vocab_size = self.generative_vocab_size + len(oov_to_limited_idx) if self.generative_vocab_size < effective_vocab_size: size[-1] = effective_vocab_size - self.generative_vocab_size buff = scaled_p_vocab.new_full(size, EPSILON) scaled_p_vocab = torch.cat([scaled_p_vocab, buff], dim=buff.dim()-1) # p_context_ptr scaled_p_vocab.scatter_add_(scaled_p_vocab.dim()-1, context_indices.unsqueeze(1).expand_as(context_attention), (context_question_switches * (1 - vocab_pointer_switches)).expand_as(context_attention) * context_attention) # p_question_ptr scaled_p_vocab.scatter_add_(scaled_p_vocab.dim()-1, question_indices.unsqueeze(1).expand_as(question_attention), ((1 - context_question_switches) * (1 - vocab_pointer_switches)).expand_as(question_attention) * question_attention) return scaled_p_vocab def greedy(self, self_attended_context, context, question, context_indices, question_indices, oov_to_limited_idx, rnn_state=None): B, TC, C = context.size() T = self.args.max_output_length outs = context.new_full((B, T), self.field.decoder_stoi[''], dtype=torch.long) hiddens = [self_attended_context[0].new_zeros((B, T, C)) for l in range(len(self.self_attentive_decoder.layers) + 1)] hiddens[0] = hiddens[0] + positional_encodings_like(hiddens[0]) eos_yet = context.new_zeros((B, )).byte() rnn_output, context_alignment, question_alignment = None, None, None for t in range(T): if t == 0: embedding = self.decoder_embeddings( self_attended_context[-1].new_full((B, 1), self.field.vocab.stoi[''], dtype=torch.long), [1]*B) else: embedding = self.decoder_embeddings(outs[:, t - 1].unsqueeze(1), [1]*B) hiddens[0][:, t] = hiddens[0][:, t] + (math.sqrt(self.self_attentive_decoder.d_model) * embedding).squeeze(1) for l in range(len(self.self_attentive_decoder.layers)): hiddens[l + 1][:, t] = self.self_attentive_decoder.layers[l].feedforward( self.self_attentive_decoder.layers[l].attention( self.self_attentive_decoder.layers[l].selfattn(hiddens[l][:, t], hiddens[l][:, :t + 1], hiddens[l][:, :t + 1]) , self_attended_context[l], self_attended_context[l])) decoder_outputs = self.dual_ptr_rnn_decoder(hiddens[-1][:, t].unsqueeze(1), context, question, context_alignment=context_alignment, question_alignment=question_alignment, hidden=rnn_state, output=rnn_output) rnn_output, context_attention, question_attention, context_alignment, question_alignment, vocab_pointer_switch, context_question_switch, rnn_state = decoder_outputs probs = self.probs(self.out, rnn_output, vocab_pointer_switch, context_question_switch, context_attention, question_attention, context_indices, question_indices, oov_to_limited_idx) pred_probs, preds = probs.max(-1) preds = preds.squeeze(1) eos_yet = eos_yet | (preds == self.field.decoder_stoi['']) outs[:, t] = preds.cpu().apply_(self.map_to_full) if eos_yet.all(): break return outs class DualPtrRNNDecoder(nn.Module): def __init__(self, d_in, d_hid, dropout=0.0, num_layers=1): super().__init__() self.d_hid = d_hid self.d_in = d_in self.num_layers = num_layers self.dropout = nn.Dropout(dropout) self.input_feed = True if self.input_feed: d_in += 1 * d_hid self.rnn = LSTMDecoder(self.num_layers, d_in, d_hid, dropout) self.context_attn = LSTMDecoderAttention(d_hid, dot=True) self.question_attn = LSTMDecoderAttention(d_hid, dot=True) self.vocab_pointer_switch = nn.Sequential(Feedforward(2 * self.d_hid + d_in, 1), nn.Sigmoid()) self.context_question_switch = nn.Sequential(Feedforward(2 * self.d_hid + d_in, 1), nn.Sigmoid()) def forward(self, input, context, question, output=None, hidden=None, context_alignment=None, question_alignment=None): context_output = output.squeeze(1) if output is not None else self.make_init_output(context) context_alignment = context_alignment if context_alignment is not None else self.make_init_output(context) question_alignment = question_alignment if question_alignment is not None else self.make_init_output(question) context_outputs, vocab_pointer_switches, context_question_switches, context_attentions, question_attentions, context_alignments, question_alignments = [], [], [], [], [], [], [] for emb_t in input.split(1, dim=1): emb_t = emb_t.squeeze(1) context_output = self.dropout(context_output) if self.input_feed: emb_t = torch.cat([emb_t, context_output], 1) dec_state, hidden = self.rnn(emb_t, hidden) context_output, context_attention, context_alignment = self.context_attn(dec_state, context) question_output, question_attention, question_alignment = self.question_attn(dec_state, question) vocab_pointer_switch = self.vocab_pointer_switch(torch.cat([dec_state, context_output, emb_t], -1)) context_question_switch = self.context_question_switch(torch.cat([dec_state, question_output, emb_t], -1)) context_output = self.dropout(context_output) context_outputs.append(context_output) vocab_pointer_switches.append(vocab_pointer_switch) context_question_switches.append(context_question_switch) context_attentions.append(context_attention) context_alignments.append(context_alignment) question_attentions.append(question_attention) question_alignments.append(question_alignment) context_outputs, vocab_pointer_switches, context_question_switches, context_attention, question_attention = [self.package_outputs(x) for x in [context_outputs, vocab_pointer_switches, context_question_switches, context_attentions, question_attentions]] return context_outputs, context_attention, question_attention, context_alignment, question_alignment, vocab_pointer_switches, context_question_switches, hidden def applyMasks(self, context_mask, question_mask): self.context_attn.applyMasks(context_mask) self.question_attn.applyMasks(question_mask) def make_init_output(self, context): batch_size = context.size(0) h_size = (batch_size, self.d_hid) return context.new_zeros(h_size) def package_outputs(self, outputs): outputs = torch.stack(outputs, dim=1) return outputs