132 lines
5.5 KiB
Python
132 lines
5.5 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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 division
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import numpy as np
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from paddle import fluid
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import paddle.fluid.layers as F
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import paddle.fluid.dygraph as dg
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def compute_position_embedding(radians, speaker_position_rate):
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"""Compute sin/cos interleaved matrix from the radians.
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Arg:
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radians (Variable): shape(n_vocab, embed_dim), dtype float32, the radians matrix.
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speaker_position_rate (Variable): shape(B, ), speaker positioning rate.
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Returns:
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Variable: shape(B, n_vocab, embed_dim), the sin, cos interleaved matrix.
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"""
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_, embed_dim = radians.shape
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batch_size = speaker_position_rate.shape[0]
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speaker_position_rate = F.unsqueeze(speaker_position_rate, [1, 2])
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scaled_radians = speaker_position_rate * radians
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odd_mask = (np.arange(embed_dim) % 2).astype(np.float32)
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odd_mask = dg.to_variable(odd_mask)
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out = odd_mask * F.cos(scaled_radians) \
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+ (1 - odd_mask) * F.sin(scaled_radians)
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out = F.concat(
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[F.zeros((batch_size, 1, embed_dim), radians.dtype), out[:, 1:, :]],
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axis=1)
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return out
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def position_encoding_init(n_position,
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d_pos_vec,
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position_rate=1.0,
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padding_idx=None):
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"""Init the position encoding.
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Args:
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n_position (int): max position, vocab size for position embedding.
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d_pos_vec (int): position embedding size.
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position_rate (float, optional): position rate (this should only be used when all the utterances are from one speaker.). Defaults to 1.0.
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padding_idx (int, optional): padding index for the position embedding(it is set as 0 internally if not provided.). Defaults to None.
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Returns:
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[type]: [description]
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"""
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# init the position encoding table
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# keep idx 0 for padding token position encoding zero vector
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# CAUTION: it is radians here, sin and cos are not applied
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indices_range = np.expand_dims(np.arange(n_position), -1)
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embed_range = 2 * (np.arange(d_pos_vec) // 2)
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radians = position_rate \
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* indices_range \
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/ np.power(1.e4, embed_range / d_pos_vec)
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if padding_idx is not None:
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radians[padding_idx] = 0.
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return radians
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class PositionEmbedding(dg.Layer):
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def __init__(self, n_position, d_pos_vec, position_rate=1.0):
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"""Position Embedding for Deep Voice 3.
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Args:
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n_position (int): max position, vocab size for position embedding.
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d_pos_vec (int): position embedding size.
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position_rate (float, optional): position rate (this should only be used when all the utterances are from one speaker.). Defaults to 1.0.
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"""
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super(PositionEmbedding, self).__init__()
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self.weight = self.create_parameter((n_position, d_pos_vec))
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self.weight.set_value(
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position_encoding_init(n_position, d_pos_vec, position_rate)
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.astype("float32"))
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def forward(self, indices, speaker_position_rate=None):
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"""
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Args:
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indices (Variable): shape (B, T), dtype: int64, position
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indices, where B means the batch size, T means the time steps.
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speaker_position_rate (Variable | float, optional), position
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rate. It can be a float point number or a Variable with
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shape (1,), then this speaker_position_rate is used for every
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example. It can also be a Variable with shape (B, ), which
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contains a speaker position rate for each utterance.
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Returns:
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out (Variable): shape(B, T, C_pos), dtype float32, position embedding, where C_pos
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means position embedding size.
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"""
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batch_size, time_steps = indices.shape
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# convert speaker_position_rate to a Variable with shape(B, )
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if isinstance(speaker_position_rate, float):
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speaker_position_rate = dg.to_variable(
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np.array([speaker_position_rate]).astype("float32"))
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speaker_position_rate = F.expand(speaker_position_rate,
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[batch_size])
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elif isinstance(speaker_position_rate, fluid.framework.Variable) \
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and list(speaker_position_rate.shape) == [1]:
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speaker_position_rate = F.expand(speaker_position_rate,
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[batch_size])
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assert len(speaker_position_rate.shape) == 1 and \
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list(speaker_position_rate.shape) == [batch_size]
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weight = compute_position_embedding(self.weight,
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speaker_position_rate) # (B, V, C)
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# make indices for gather_nd
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batch_id = F.expand(
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F.unsqueeze(
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F.range(
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0, batch_size, 1, dtype="int64"), [1]), [1, time_steps])
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# (B, T, 2)
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gather_nd_id = F.stack([batch_id, indices], -1)
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out = F.gather_nd(weight, gather_nd_id)
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return out
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