203 lines
7.7 KiB
Python
203 lines
7.7 KiB
Python
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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import numpy as np
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class AttentionHead(nn.Layer):
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def __init__(self, in_channels, out_channels, hidden_size, **kwargs):
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super(AttentionHead, self).__init__()
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self.input_size = in_channels
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self.hidden_size = hidden_size
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self.num_classes = out_channels
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self.attention_cell = AttentionGRUCell(
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in_channels, hidden_size, out_channels, use_gru=False)
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self.generator = nn.Linear(hidden_size, out_channels)
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def _char_to_onehot(self, input_char, onehot_dim):
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input_ont_hot = F.one_hot(input_char, onehot_dim)
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return input_ont_hot
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def forward(self, inputs, targets=None, batch_max_length=25):
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batch_size = paddle.shape(inputs)[0]
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num_steps = batch_max_length
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hidden = paddle.zeros((batch_size, self.hidden_size))
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output_hiddens = []
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if targets is not None:
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for i in range(num_steps):
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char_onehots = self._char_to_onehot(
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targets[:, i], onehot_dim=self.num_classes)
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(outputs, hidden), alpha = self.attention_cell(hidden, inputs,
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char_onehots)
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output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
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output = paddle.concat(output_hiddens, axis=1)
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probs = self.generator(output)
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else:
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targets = paddle.zeros(shape=[batch_size], dtype="int32")
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probs = None
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char_onehots = None
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outputs = None
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alpha = None
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for i in range(num_steps):
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char_onehots = self._char_to_onehot(
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targets, onehot_dim=self.num_classes)
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(outputs, hidden), alpha = self.attention_cell(hidden, inputs,
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char_onehots)
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probs_step = self.generator(outputs)
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if probs is None:
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probs = paddle.unsqueeze(probs_step, axis=1)
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else:
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probs = paddle.concat(
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[probs, paddle.unsqueeze(
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probs_step, axis=1)], axis=1)
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next_input = probs_step.argmax(axis=1)
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targets = next_input
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return probs
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class AttentionGRUCell(nn.Layer):
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def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False):
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super(AttentionGRUCell, self).__init__()
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self.i2h = nn.Linear(input_size, hidden_size, bias_attr=False)
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self.h2h = nn.Linear(hidden_size, hidden_size)
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self.score = nn.Linear(hidden_size, 1, bias_attr=False)
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self.rnn = nn.GRUCell(
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input_size=input_size + num_embeddings, hidden_size=hidden_size)
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self.hidden_size = hidden_size
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def forward(self, prev_hidden, batch_H, char_onehots):
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batch_H_proj = self.i2h(batch_H)
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prev_hidden_proj = paddle.unsqueeze(self.h2h(prev_hidden), axis=1)
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res = paddle.add(batch_H_proj, prev_hidden_proj)
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res = paddle.tanh(res)
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e = self.score(res)
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alpha = F.softmax(e, axis=1)
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alpha = paddle.transpose(alpha, [0, 2, 1])
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context = paddle.squeeze(paddle.mm(alpha, batch_H), axis=1)
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concat_context = paddle.concat([context, char_onehots], 1)
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cur_hidden = self.rnn(concat_context, prev_hidden)
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return cur_hidden, alpha
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class AttentionLSTM(nn.Layer):
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def __init__(self, in_channels, out_channels, hidden_size, **kwargs):
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super(AttentionLSTM, self).__init__()
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self.input_size = in_channels
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self.hidden_size = hidden_size
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self.num_classes = out_channels
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self.attention_cell = AttentionLSTMCell(
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in_channels, hidden_size, out_channels, use_gru=False)
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self.generator = nn.Linear(hidden_size, out_channels)
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def _char_to_onehot(self, input_char, onehot_dim):
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input_ont_hot = F.one_hot(input_char, onehot_dim)
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return input_ont_hot
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def forward(self, inputs, targets=None, batch_max_length=25):
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batch_size = inputs.shape[0]
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num_steps = batch_max_length
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hidden = (paddle.zeros((batch_size, self.hidden_size)), paddle.zeros(
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(batch_size, self.hidden_size)))
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output_hiddens = []
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if targets is not None:
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for i in range(num_steps):
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# one-hot vectors for a i-th char
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char_onehots = self._char_to_onehot(
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targets[:, i], onehot_dim=self.num_classes)
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hidden, alpha = self.attention_cell(hidden, inputs,
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char_onehots)
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hidden = (hidden[1][0], hidden[1][1])
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output_hiddens.append(paddle.unsqueeze(hidden[0], axis=1))
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output = paddle.concat(output_hiddens, axis=1)
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probs = self.generator(output)
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else:
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targets = paddle.zeros(shape=[batch_size], dtype="int32")
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probs = None
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for i in range(num_steps):
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char_onehots = self._char_to_onehot(
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targets, onehot_dim=self.num_classes)
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hidden, alpha = self.attention_cell(hidden, inputs,
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char_onehots)
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probs_step = self.generator(hidden[0])
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hidden = (hidden[1][0], hidden[1][1])
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if probs is None:
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probs = paddle.unsqueeze(probs_step, axis=1)
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else:
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probs = paddle.concat(
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[probs, paddle.unsqueeze(
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probs_step, axis=1)], axis=1)
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next_input = probs_step.argmax(axis=1)
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targets = next_input
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return probs
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class AttentionLSTMCell(nn.Layer):
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def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False):
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super(AttentionLSTMCell, self).__init__()
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self.i2h = nn.Linear(input_size, hidden_size, bias_attr=False)
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self.h2h = nn.Linear(hidden_size, hidden_size)
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self.score = nn.Linear(hidden_size, 1, bias_attr=False)
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if not use_gru:
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self.rnn = nn.LSTMCell(
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input_size=input_size + num_embeddings, hidden_size=hidden_size)
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else:
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self.rnn = nn.GRUCell(
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input_size=input_size + num_embeddings, hidden_size=hidden_size)
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self.hidden_size = hidden_size
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def forward(self, prev_hidden, batch_H, char_onehots):
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batch_H_proj = self.i2h(batch_H)
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prev_hidden_proj = paddle.unsqueeze(self.h2h(prev_hidden[0]), axis=1)
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res = paddle.add(batch_H_proj, prev_hidden_proj)
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res = paddle.tanh(res)
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e = self.score(res)
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alpha = F.softmax(e, axis=1)
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alpha = paddle.transpose(alpha, [0, 2, 1])
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context = paddle.squeeze(paddle.mm(alpha, batch_H), axis=1)
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concat_context = paddle.concat([context, char_onehots], 1)
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cur_hidden = self.rnn(concat_context, prev_hidden)
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return cur_hidden, alpha
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