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https://github.com/babysor/MockingBird.git
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167 lines
4.7 KiB
Python
167 lines
4.7 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# Copyright 2019 Shigeki Karita
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Positonal Encoding Module."""
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import math
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import torch
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def _pre_hook(
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state_dict,
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prefix,
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local_metadata,
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strict,
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missing_keys,
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unexpected_keys,
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error_msgs,
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):
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"""Perform pre-hook in load_state_dict for backward compatibility.
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Note:
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We saved self.pe until v.0.5.2 but we have omitted it later.
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Therefore, we remove the item "pe" from `state_dict` for backward compatibility.
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"""
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k = prefix + "pe"
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if k in state_dict:
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state_dict.pop(k)
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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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:param reverse: whether to reverse the input position
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"""
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def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False):
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"""Construct an PositionalEncoding object."""
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super(PositionalEncoding, self).__init__()
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self.d_model = d_model
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self.reverse = reverse
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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.pe = None
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self.extend_pe(torch.tensor(0.0).expand(1, max_len))
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self._register_load_state_dict_pre_hook(_pre_hook)
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def extend_pe(self, x):
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"""Reset the positional encodings."""
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if self.pe is not None:
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if self.pe.size(1) >= x.size(1):
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if self.pe.dtype != x.dtype or self.pe.device != x.device:
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self.pe = self.pe.to(dtype=x.dtype, device=x.device)
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return
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pe = torch.zeros(x.size(1), self.d_model)
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if self.reverse:
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position = torch.arange(
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x.size(1) - 1, -1, -1.0, dtype=torch.float32
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).unsqueeze(1)
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else:
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position = torch.arange(0, x.size(1), 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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)
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pe[:, 0::2] = torch.sin(position * div_term)
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pe[:, 1::2] = torch.cos(position * div_term)
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pe = pe.unsqueeze(0)
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self.pe = pe.to(device=x.device, dtype=x.dtype)
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def forward(self, x: 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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Returns:
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torch.Tensor: Encoded tensor. Its shape is (batch, time, ...)
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"""
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self.extend_pe(x)
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x = x * self.xscale + self.pe[:, : x.size(1)]
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return self.dropout(x)
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class ScaledPositionalEncoding(PositionalEncoding):
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"""Scaled positional encoding module.
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See also: Sec. 3.2 https://arxiv.org/pdf/1809.08895.pdf
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"""
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def __init__(self, d_model, dropout_rate, max_len=5000):
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"""Initialize class.
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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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"""
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super().__init__(d_model=d_model, dropout_rate=dropout_rate, max_len=max_len)
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self.alpha = torch.nn.Parameter(torch.tensor(1.0))
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def reset_parameters(self):
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"""Reset parameters."""
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self.alpha.data = torch.tensor(1.0)
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def forward(self, x):
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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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Returns:
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torch.Tensor: Encoded tensor. Its shape is (batch, time, ...)
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"""
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self.extend_pe(x)
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x = x + self.alpha * self.pe[:, : x.size(1)]
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return self.dropout(x)
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class RelPositionalEncoding(PositionalEncoding):
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"""Relitive positional encoding module.
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See : Appendix B in https://arxiv.org/abs/1901.02860
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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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"""
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def __init__(self, d_model, dropout_rate, max_len=5000):
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"""Initialize class.
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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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"""
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super().__init__(d_model, dropout_rate, max_len, reverse=True)
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def forward(self, x):
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"""Compute positional encoding.
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Args:
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x (torch.Tensor): Input. Its shape is (batch, time, ...)
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Returns:
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torch.Tensor: x. Its shape is (batch, time, ...)
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torch.Tensor: pos_emb. Its shape is (1, time, ...)
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"""
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self.extend_pe(x)
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x = x * self.xscale
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pos_emb = self.pe[:, : x.size(1)]
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return self.dropout(x), self.dropout(pos_emb)
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