Remove old, unused ablation models
These are the models that were used to ablate MQAN. They are worse than MQAN, and by removing them we can facilitate refactorings.
This commit is contained in:
Giovanni Campagna
parent
4cf9a1941f
commit
d927b33458
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@ -28,7 +28,4 @@
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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from .multitask_question_answering_network import MultitaskQuestionAnsweringNetwork
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from .multi_lingual_translation_model import MultiLingualTranslationModel
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from .coattentive_pointer_generator import CoattentivePointerGenerator
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from .self_attentive_pointer_generator import SelfAttentivePointerGenerator
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from .pointer_generator import PointerGenerator
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from .multi_lingual_translation_model import MultiLingualTranslationModel
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@ -1,261 +0,0 @@
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#
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# Copyright (c) 2018, Salesforce, Inc.
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# * Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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#
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# * Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# * Neither the name of the copyright holder nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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import os
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import math
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import numpy as np
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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 .common import positional_encodings_like, INF, EPSILON, TransformerEncoder, TransformerDecoder, PackedLSTM, LSTMDecoderAttention, LSTMDecoder, Embedding, Feedforward, mask, CoattentiveLayer
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class CoattentivePointerGenerator(nn.Module):
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def __init__(self, field, args):
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super().__init__()
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self.field = field
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self.args = args
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self.pad_idx = self.field.vocab.stoi[self.field.pad_token]
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self.encoder_embeddings = Embedding(field, args.dimension,
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dropout=args.dropout_ratio)
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self.decoder_embeddings = Embedding(field, args.dimension,
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dropout=args.dropout_ratio)
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self.bilstm_before_coattention = PackedLSTM(args.dimension, args.dimension,
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batch_first=True, dropout=args.dropout_ratio, bidirectional=True, num_layers=1)
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self.coattention = CoattentiveLayer(args.dimension, dropout=0.3)
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dim = 2*args.dimension + args.dimension + args.dimension
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self.context_bilstm_after_coattention = PackedLSTM(dim, args.dimension,
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batch_first=True, dropout=args.dropout_ratio, bidirectional=True,
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num_layers=args.rnn_layers)
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self.self_attentive_encoder_context = TransformerEncoder(args.dimension, args.transformer_heads, args.transformer_hidden, args.transformer_layers, args.dropout_ratio)
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self.bilstm_context = PackedLSTM(args.dimension, args.dimension,
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batch_first=True, dropout=args.dropout_ratio, bidirectional=True,
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num_layers=args.rnn_layers)
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self.self_attentive_decoder = TransformerDecoder(args.dimension, args.transformer_heads, args.transformer_hidden, args.transformer_layers, args.dropout_ratio)
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self.dual_ptr_rnn_decoder = DualPtrRNNDecoder(args.dimension, args.dimension,
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dropout=args.dropout_ratio, num_layers=args.rnn_layers)
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self.generative_vocab_size = min(len(field.vocab), args.max_generative_vocab)
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self.out = nn.Linear(args.dimension, self.generative_vocab_size)
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self.dropout = nn.Dropout(0.4)
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def set_embeddings(self, embeddings):
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self.encoder_embeddings.set_embeddings(embeddings)
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self.decoder_embeddings.set_embeddings(embeddings)
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def forward(self, batch):
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context, context_lengths, context_limited = batch.context, batch.context_lengths, batch.context_limited
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question, question_lengths, question_limited = batch.question, batch.question_lengths, batch.question_limited
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answer, answer_lengths, answer_limited = batch.answer, batch.answer_lengths, batch.answer_limited
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oov_to_limited_idx, limited_idx_to_full_idx = batch.oov_to_limited_idx, batch.limited_idx_to_full_idx
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def map_to_full(x):
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return limited_idx_to_full_idx[x]
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self.map_to_full = map_to_full
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context_embedded = self.encoder_embeddings(context)
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question_embedded = self.encoder_embeddings(question)
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context_encoded = self.bilstm_before_coattention(context_embedded, context_lengths)[0]
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question_encoded = self.bilstm_before_coattention(question_embedded, question_lengths)[0]
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context_padding = context.data == self.pad_idx
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question_padding = question.data == self.pad_idx
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coattended_context = self.coattention(context_encoded, question_encoded, context_padding, question_padding)
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context_summary = torch.cat([coattended_context, context_encoded, context_embedded], -1)
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condensed_context, _ = self.context_bilstm_after_coattention(context_summary, context_lengths)
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self_attended_context = self.self_attentive_encoder_context(condensed_context, padding=context_padding)
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final_context, (context_rnn_h, context_rnn_c) = self.bilstm_context(self_attended_context[-1], context_lengths)
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context_rnn_state = [self.reshape_rnn_state(x) for x in (context_rnn_h, context_rnn_c)]
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context_indices = context_limited if context_limited is not None else context
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answer_indices = answer_limited if answer_limited is not None else answer
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pad_idx = self.field.decoder_stoi[self.field.pad_token]
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context_padding = context_indices.data == pad_idx
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self.dual_ptr_rnn_decoder.applyMasks(context_padding)
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if self.training:
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answer_padding = answer_indices.data == pad_idx
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answer_embedded = self.decoder_embeddings(answer)
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self_attended_decoded = self.self_attentive_decoder(answer_embedded[:, :-1].contiguous(), self_attended_context, context_padding=context_padding, answer_padding=answer_padding[:, :-1], positional_encodings=True)
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decoder_outputs = self.dual_ptr_rnn_decoder(self_attended_decoded,
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final_context, hidden=context_rnn_state)
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rnn_output, context_attention, context_alignment, vocab_pointer_switch, rnn_state = decoder_outputs
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probs = self.probs(self.out, rnn_output, vocab_pointer_switch,
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context_attention,
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context_indices,
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oov_to_limited_idx)
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probs, targets = mask(answer_indices[:, 1:].contiguous(), probs.contiguous(), pad_idx=pad_idx)
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loss = F.nll_loss(probs.log(), targets)
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return loss, None
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else:
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return None, self.greedy(self_attended_context, final_context,
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context_indices,
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oov_to_limited_idx, rnn_state=context_rnn_state).data
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def reshape_rnn_state(self, h):
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return h.view(h.size(0) // 2, 2, h.size(1), h.size(2)) \
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.transpose(1, 2).contiguous() \
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.view(h.size(0) // 2, h.size(1), h.size(2) * 2).contiguous()
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def probs(self, generator, outputs, vocab_pointer_switches,
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context_attention,
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context_indices,
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oov_to_limited_idx):
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size = list(outputs.size())
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size[-1] = self.generative_vocab_size
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scores = generator(outputs.view(-1, outputs.size(-1))).view(size)
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p_vocab = F.softmax(scores, dim=scores.dim()-1)
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scaled_p_vocab = vocab_pointer_switches.expand_as(p_vocab) * p_vocab
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effective_vocab_size = self.generative_vocab_size + len(oov_to_limited_idx)
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if self.generative_vocab_size < effective_vocab_size:
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size[-1] = effective_vocab_size - self.generative_vocab_size
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buff = scaled_p_vocab.new_full(size, EPSILON)
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scaled_p_vocab = torch.cat([scaled_p_vocab, buff], dim=buff.dim()-1)
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p_context_ptr = scaled_p_vocab.new_full(scaled_p_vocab.size(), EPSILON)
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p_context_ptr.scatter_add_(p_context_ptr.dim()-1, context_indices.unsqueeze(1).expand_as(context_attention), context_attention)
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scaled_p_context_ptr = (1 - vocab_pointer_switches).expand_as(p_context_ptr) * p_context_ptr
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probs = scaled_p_vocab + scaled_p_context_ptr
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return probs
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def greedy(self, self_attended_context, context, context_indices, oov_to_limited_idx, rnn_state=None):
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B, TC, C = context.size()
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T = self.args.max_output_length
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outs = context.new_full((B, T), self.field.decoder_stoi['<pad>'], dtype=torch.long)
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hiddens = [self_attended_context[0].new_zeros((B, T, C))
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for l in range(len(self.self_attentive_decoder.layers) + 1)]
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hiddens[0] = hiddens[0] + positional_encodings_like(hiddens[0])
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eos_yet = context.data.new(B).byte().zero_()
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rnn_output, context_alignment = None, None
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for t in range(T):
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if t == 0:
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embedding = self.decoder_embeddings(
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self_attended_context[-1].new_full((B, 1), self.field.vocab.stoi['<init>'], dtype=torch.long), [1]*B)
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else:
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embedding = self.decoder_embeddings(outs[:, t - 1].unsqueeze(1), [1]*B)
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hiddens[0][:, t] = hiddens[0][:, t] + (math.sqrt(self.self_attentive_decoder.d_model) * embedding).squeeze(1)
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for l in range(len(self.self_attentive_decoder.layers)):
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hiddens[l + 1][:, t] = self.self_attentive_decoder.layers[l].feedforward(
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self.self_attentive_decoder.layers[l].attention(
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self.self_attentive_decoder.layers[l].selfattn(hiddens[l][:, t], hiddens[l][:, :t + 1], hiddens[l][:, :t + 1])
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, self_attended_context[l], self_attended_context[l]))
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decoder_outputs = self.dual_ptr_rnn_decoder(hiddens[-1][:, t].unsqueeze(1),
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context,
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context_alignment=context_alignment,
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hidden=rnn_state, output=rnn_output)
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rnn_output, context_attention, context_alignment, vocab_pointer_switch, rnn_state = decoder_outputs
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probs = self.probs(self.out, rnn_output, vocab_pointer_switch,
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context_attention,
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context_indices,
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oov_to_limited_idx)
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pred_probs, preds = probs.max(-1)
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preds = preds.squeeze(1)
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eos_yet = eos_yet | (preds == self.field.decoder_stoi['<eos>'])
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outs[:, t] = preds.cpu().apply_(self.map_to_full)
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if eos_yet.all():
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break
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return outs
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class DualPtrRNNDecoder(nn.Module):
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def __init__(self, d_in, d_hid, dropout=0.0, num_layers=1):
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super().__init__()
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self.d_hid = d_hid
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self.d_in = d_in
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self.num_layers = num_layers
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self.dropout = nn.Dropout(dropout)
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self.input_feed = True
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if self.input_feed:
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d_in += 1 * d_hid
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self.rnn = LSTMDecoder(self.num_layers, d_in, d_hid, dropout)
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self.context_attn = LSTMDecoderAttention(d_hid, dot=True)
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self.vocab_pointer_switch = nn.Sequential(Feedforward(2 * self.d_hid + d_in, 1), nn.Sigmoid())
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def forward(self, input, context, output=None, hidden=None, context_alignment=None):
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context_output = output.squeeze(1) if output is not None else self.make_init_output(context)
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context_alignment = context_alignment if context_alignment is not None else self.make_init_output(context)
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context_outputs, vocab_pointer_switches, context_attentions, context_alignments = [], [], [], []
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for emb_t in input.split(1, dim=1):
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emb_t = emb_t.squeeze(1)
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context_output = self.dropout(context_output)
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if self.input_feed:
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emb_t = torch.cat([emb_t, context_output], 1)
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dec_state, hidden = self.rnn(emb_t, hidden)
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context_output, context_attention, context_alignment = self.context_attn(dec_state, context)
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vocab_pointer_switch = self.vocab_pointer_switch(torch.cat([dec_state, context_output, emb_t], -1))
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context_output = self.dropout(context_output)
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context_outputs.append(context_output)
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vocab_pointer_switches.append(vocab_pointer_switch)
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context_attentions.append(context_attention)
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context_alignments.append(context_alignment)
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context_outputs, vocab_pointer_switches, context_attention = [self.package_outputs(x) for x in [context_outputs, vocab_pointer_switches, context_attentions]]
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return context_outputs, context_attention, context_alignment, vocab_pointer_switches, hidden
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def applyMasks(self, context_mask):
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self.context_attn.applyMasks(context_mask)
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def make_init_output(self, context):
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batch_size = context.size(0)
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h_size = (batch_size, self.d_hid)
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return context.new_zeros(h_size)
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def package_outputs(self, outputs):
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outputs = torch.stack(outputs, dim=1)
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return outputs
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@ -1,225 +0,0 @@
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#
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# Copyright (c) 2018, Salesforce, Inc.
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# * Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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#
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# * Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# * Neither the name of the copyright holder nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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import os
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import math
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import numpy as np
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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 .common import positional_encodings_like, INF, EPSILON, TransformerEncoder, TransformerDecoder, PackedLSTM, LSTMDecoderAttention, LSTMDecoder, Embedding, Feedforward, mask
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class PointerGenerator(nn.Module):
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def __init__(self, field, args):
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super().__init__()
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self.field = field
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self.args = args
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self.pad_idx = self.field.vocab.stoi[self.field.pad_token]
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self.encoder_embeddings = Embedding(field, args.dimension,
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dropout=args.dropout_ratio)
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self.decoder_embeddings = Embedding(field, args.dimension,
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dropout=args.dropout_ratio)
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self.bilstm_before_coattention = PackedLSTM(args.dimension, args.dimension,
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batch_first=True, dropout=args.dropout_ratio, bidirectional=True, num_layers=args.rnn_layers)
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self.dual_ptr_rnn_decoder = DualPtrRNNDecoder(args.dimension, args.dimension,
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dropout=args.dropout_ratio, num_layers=args.rnn_layers)
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self.generative_vocab_size = min(len(field.vocab), args.max_generative_vocab)
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self.out = nn.Linear(args.dimension, self.generative_vocab_size)
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self.dropout = nn.Dropout(0.4)
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def set_embeddings(self, embeddings):
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self.encoder_embeddings.set_embeddings(embeddings)
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self.decoder_embeddings.set_embeddings(embeddings)
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def forward(self, batch):
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context, context_lengths, context_limited = batch.context_question, batch.context_question_lengths, batch.context_question_limited
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answer, answer_lengths, answer_limited = batch.answer, batch.answer_lengths, batch.answer_limited
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oov_to_limited_idx, limited_idx_to_full_idx = batch.oov_to_limited_idx, batch.limited_idx_to_full_idx
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def map_to_full(x):
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return limited_idx_to_full_idx[x]
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self.map_to_full = map_to_full
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context_embedded = self.encoder_embeddings(context)
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context_encoded, (context_rnn_h, context_rnn_c) = self.bilstm_before_coattention(context_embedded, context_lengths)
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context_rnn_state = [self.reshape_rnn_state(x) for x in (context_rnn_h, context_rnn_c)]
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context_padding = context.data == self.pad_idx
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context_indices = context_limited if context_limited is not None else context
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answer_indices = answer_limited if answer_limited is not None else answer
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pad_idx = self.field.decoder_stoi[self.field.pad_token]
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context_padding = context_indices.data == pad_idx
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self.dual_ptr_rnn_decoder.applyMasks(context_padding)
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if self.training:
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answer_embedded = self.decoder_embeddings(answer)
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decoder_outputs = self.dual_ptr_rnn_decoder(answer_embedded[:, :-1].contiguous(),
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context_encoded, hidden=context_rnn_state)
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rnn_output, context_attention, context_alignment, vocab_pointer_switch, rnn_state = decoder_outputs
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|
||||
probs = self.probs(self.out, rnn_output, vocab_pointer_switch,
|
||||
context_attention,
|
||||
context_indices,
|
||||
oov_to_limited_idx)
|
||||
|
||||
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(context_encoded,
|
||||
context_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_attention,
|
||||
context_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.new_full(scaled_p_vocab.size(), EPSILON)
|
||||
p_context_ptr.scatter_add_(p_context_ptr.dim()-1, context_indices.unsqueeze(1).expand_as(context_attention), context_attention)
|
||||
scaled_p_context_ptr = (1 - vocab_pointer_switches).expand_as(p_context_ptr) * p_context_ptr
|
||||
probs = scaled_p_vocab + scaled_p_context_ptr
|
||||
return probs
|
||||
|
||||
|
||||
def greedy(self, context, context_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['<pad>'], dtype=torch.long)
|
||||
eos_yet = context.data.new(B).byte().zero_()
|
||||
|
||||
rnn_output, context_alignment = None, None
|
||||
for t in range(T):
|
||||
if t == 0:
|
||||
embedding = self.decoder_embeddings(
|
||||
context[-1].new_full((B, 1), self.field.vocab.stoi['<init>'], dtype=torch.long), [1]*B)
|
||||
|
||||
else:
|
||||
embedding = self.decoder_embeddings(outs[:, t - 1].unsqueeze(1), [1]*B)
|
||||
decoder_outputs = self.dual_ptr_rnn_decoder(embedding, #hiddens[-1][:, t].unsqueeze(1),
|
||||
context,
|
||||
context_alignment=context_alignment,
|
||||
hidden=rnn_state, output=rnn_output)
|
||||
|
||||
rnn_output, context_attention, context_alignment, vocab_pointer_switch, rnn_state = decoder_outputs
|
||||
probs = self.probs(self.out, rnn_output, vocab_pointer_switch,
|
||||
context_attention,
|
||||
context_indices,
|
||||
oov_to_limited_idx)
|
||||
pred_probs, preds = probs.max(-1)
|
||||
preds = preds.squeeze(1)
|
||||
eos_yet = eos_yet | (preds == self.field.decoder_stoi['<eos>'])
|
||||
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.vocab_pointer_switch = nn.Sequential(Feedforward(2 * self.d_hid + d_in, 1), nn.Sigmoid())
|
||||
|
||||
def forward(self, input, context, output=None, hidden=None, context_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)
|
||||
|
||||
context_outputs, vocab_pointer_switches, context_attentions, context_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)
|
||||
vocab_pointer_switch = self.vocab_pointer_switch(torch.cat([dec_state, context_output, emb_t], -1))
|
||||
context_output = self.dropout(context_output)
|
||||
context_outputs.append(context_output)
|
||||
vocab_pointer_switches.append(vocab_pointer_switch)
|
||||
context_attentions.append(context_attention)
|
||||
context_alignments.append(context_alignment)
|
||||
context_outputs, vocab_pointer_switches, context_attention = [self.package_outputs(x) for x in [context_outputs, vocab_pointer_switches, context_attentions]]
|
||||
return context_outputs, context_attention, context_alignment, vocab_pointer_switches, hidden
|
||||
|
||||
|
||||
def applyMasks(self, context_mask):
|
||||
self.context_attn.applyMasks(context_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
|
||||
|
|
@ -1,253 +0,0 @@
|
|||
#
|
||||
# Copyright (c) 2018, Salesforce, Inc.
|
||||
# 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 torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
from .common import positional_encodings_like, INF, EPSILON, TransformerEncoder, TransformerDecoder, PackedLSTM, LSTMDecoderAttention, LSTMDecoder, Embedding, Feedforward, mask
|
||||
|
||||
|
||||
class SelfAttentivePointerGenerator(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.encoder_embeddings = Embedding(field, args.dimension,
|
||||
dropout=args.dropout_ratio)
|
||||
self.decoder_embeddings = Embedding(field, args.dimension,
|
||||
dropout=args.dropout_ratio)
|
||||
|
||||
|
||||
self.bilstm_before_coattention = PackedLSTM(args.dimension, args.dimension,
|
||||
batch_first=True, dropout=args.dropout_ratio, bidirectional=True, num_layers=1)
|
||||
dim = args.dimension + args.dimension
|
||||
|
||||
self.context_bilstm_after_coattention = PackedLSTM(dim, args.dimension,
|
||||
batch_first=True, dropout=args.dropout_ratio, 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=args.dropout_ratio, 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):
|
||||
context, context_lengths, context_limited = batch.context_question, batch.context_question_lengths, batch.context_question_limited
|
||||
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
|
||||
|
||||
context_embedded = self.encoder_embeddings(context)
|
||||
|
||||
context_encoded = self.bilstm_before_coattention(context_embedded, context_lengths)[0]
|
||||
|
||||
context_padding = context.data == self.pad_idx
|
||||
|
||||
context_summary = torch.cat([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)]
|
||||
|
||||
context_indices = context_limited if context_limited is not None else context
|
||||
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
|
||||
|
||||
self.dual_ptr_rnn_decoder.applyMasks(context_padding)
|
||||
|
||||
if self.training:
|
||||
answer_padding = answer_indices.data == pad_idx
|
||||
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[:, :-1], positional_encodings=True)
|
||||
decoder_outputs = self.dual_ptr_rnn_decoder(self_attended_decoded,
|
||||
final_context, hidden=context_rnn_state)
|
||||
rnn_output, context_attention, context_alignment, vocab_pointer_switch, rnn_state = decoder_outputs
|
||||
|
||||
probs = self.probs(self.out, rnn_output, vocab_pointer_switch,
|
||||
context_attention,
|
||||
context_indices,
|
||||
oov_to_limited_idx)
|
||||
|
||||
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,
|
||||
context_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_attention,
|
||||
context_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.new_full(scaled_p_vocab.size(), EPSILON)
|
||||
p_context_ptr.scatter_add_(p_context_ptr.dim()-1, context_indices.unsqueeze(1).expand_as(context_attention), context_attention)
|
||||
scaled_p_context_ptr = (1 - vocab_pointer_switches).expand_as(p_context_ptr) * p_context_ptr
|
||||
|
||||
probs = scaled_p_vocab + scaled_p_context_ptr
|
||||
return probs
|
||||
|
||||
|
||||
def greedy(self, self_attended_context, context, context_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['<pad>'], 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.data.new(B).byte().zero_()
|
||||
|
||||
rnn_output, context_alignment = 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['<init>'], 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,
|
||||
context_alignment=context_alignment,
|
||||
hidden=rnn_state, output=rnn_output)
|
||||
|
||||
rnn_output, context_attention, context_alignment, vocab_pointer_switch, rnn_state = decoder_outputs
|
||||
probs = self.probs(self.out, rnn_output, vocab_pointer_switch,
|
||||
context_attention,
|
||||
context_indices,
|
||||
oov_to_limited_idx)
|
||||
pred_probs, preds = probs.max(-1)
|
||||
preds = preds.squeeze(1)
|
||||
eos_yet = eos_yet | (preds == self.field.decoder_stoi['<eos>'])
|
||||
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.vocab_pointer_switch = nn.Sequential(Feedforward(2 * self.d_hid + d_in, 1), nn.Sigmoid())
|
||||
|
||||
def forward(self, input, context, output=None, hidden=None, context_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)
|
||||
|
||||
context_outputs, vocab_pointer_switches, context_attentions, context_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)
|
||||
vocab_pointer_switch = self.vocab_pointer_switch(torch.cat([dec_state, context_output, emb_t], -1))
|
||||
context_output = self.dropout(context_output)
|
||||
context_outputs.append(context_output)
|
||||
vocab_pointer_switches.append(vocab_pointer_switch)
|
||||
context_attentions.append(context_attention)
|
||||
context_alignments.append(context_alignment)
|
||||
context_outputs, vocab_pointer_switches, context_attention = [self.package_outputs(x) for x in [context_outputs, vocab_pointer_switches, context_attentions]]
|
||||
return context_outputs, context_attention, context_alignment, vocab_pointer_switches, hidden
|
||||
|
||||
|
||||
def applyMasks(self, context_mask):
|
||||
self.context_attn.applyMasks(context_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
|
||||
Loading…
Reference in New Issue