132 lines
4.5 KiB
Python
132 lines
4.5 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# Copyright 2019 Mobvoi Inc. All Rights Reserved.
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# Author: di.wu@mobvoi.com (DI WU)
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"""Positonal Encoding Module."""
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import math
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from typing import Tuple
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import torch
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class PositionalEncoding(torch.nn.Module):
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"""Positional encoding.
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:param int d_model: embedding dim
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:param float dropout_rate: dropout rate
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:param int max_len: maximum input length
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PE(pos, 2i) = sin(pos/(10000^(2i/dmodel)))
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PE(pos, 2i+1) = cos(pos/(10000^(2i/dmodel)))
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"""
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def __init__(self,
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d_model: int,
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dropout_rate: float,
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max_len: int = 5000,
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reverse: bool = False):
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"""Construct an PositionalEncoding object."""
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super().__init__()
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self.d_model = d_model
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self.xscale = math.sqrt(self.d_model)
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self.dropout = torch.nn.Dropout(p=dropout_rate)
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self.max_len = max_len
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self.pe = torch.zeros(self.max_len, self.d_model)
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position = torch.arange(0, self.max_len,
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dtype=torch.float32).unsqueeze(1)
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div_term = torch.exp(
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torch.arange(0, self.d_model, 2, dtype=torch.float32) *
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-(math.log(10000.0) / self.d_model))
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self.pe[:, 0::2] = torch.sin(position * div_term)
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self.pe[:, 1::2] = torch.cos(position * div_term)
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self.pe = self.pe.unsqueeze(0)
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def forward(self,
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x: torch.Tensor,
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offset: int = 0) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Add positional encoding.
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Args:
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x (torch.Tensor): Input. Its shape is (batch, time, ...)
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offset (int): position offset
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Returns:
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torch.Tensor: Encoded tensor. Its shape is (batch, time, ...)
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torch.Tensor: for compatibility to RelPositionalEncoding
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"""
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assert offset + x.size(1) < self.max_len
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self.pe = self.pe.to(x.device)
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pos_emb = self.pe[:, offset:offset + x.size(1)]
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x = x * self.xscale + pos_emb
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return self.dropout(x), self.dropout(pos_emb)
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def position_encoding(self, offset: int, size: int) -> torch.Tensor:
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""" For getting encoding in a streaming fashion
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Attention!!!!!
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we apply dropout only once at the whole utterance level in a none
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streaming way, but will call this function several times with
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increasing input size in a streaming scenario, so the dropout will
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be applied several times.
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Args:
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offset (int): start offset
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size (int): requried size of position encoding
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Returns:
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torch.Tensor: Corresponding encoding
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"""
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assert offset + size < self.max_len
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return self.dropout(self.pe[:, offset:offset + size])
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class RelPositionalEncoding(PositionalEncoding):
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"""Relative positional encoding module.
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See : Appendix B in https://arxiv.org/abs/1901.02860
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Args:
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d_model (int): Embedding dimension.
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dropout_rate (float): Dropout rate.
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max_len (int): Maximum input length.
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"""
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def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000):
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"""Initialize class."""
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super().__init__(d_model, dropout_rate, max_len, reverse=True)
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def forward(self,
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x: torch.Tensor,
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offset: int = 0) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Compute positional encoding.
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Args:
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x (torch.Tensor): Input tensor (batch, time, `*`).
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Returns:
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torch.Tensor: Encoded tensor (batch, time, `*`).
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torch.Tensor: Positional embedding tensor (1, time, `*`).
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"""
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assert offset + x.size(1) < self.max_len
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self.pe = self.pe.to(x.device)
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x = x * self.xscale
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pos_emb = self.pe[:, offset:offset + x.size(1)]
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return self.dropout(x), self.dropout(pos_emb)
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class NoPositionalEncoding(torch.nn.Module):
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""" No position encoding
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"""
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def __init__(self, d_model: int, dropout_rate: float):
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super().__init__()
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self.d_model = d_model
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self.dropout = torch.nn.Dropout(p=dropout_rate)
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def forward(self,
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x: torch.Tensor,
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offset: int = 0) -> Tuple[torch.Tensor, torch.Tensor]:
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""" Just return zero vector for interface compatibility
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"""
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pos_emb = torch.zeros(1, x.size(1), self.d_model).to(x.device)
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return self.dropout(x), pos_emb
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def position_encoding(self, offset: int, size: int) -> torch.Tensor:
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return torch.zeros(1, size, self.d_model)
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