# Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu)
# 2024 Alibaba Inc (Xiang Lyu)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Modified from ESPnet(https://github.com/espnet/espnet)
"""Positonal Encoding Module."""
import math
from typing import Tuple, Union
import torch
import torch.nn.functional as F
import numpy as np
class PositionalEncoding(torch.nn.Module):
"""Positional encoding.
:param int d_model: embedding dim
:param float dropout_rate: dropout rate
:param int max_len: maximum input length
PE(pos, 2i) = sin(pos/(10000^(2i/dmodel)))
PE(pos, 2i+1) = cos(pos/(10000^(2i/dmodel)))
"""
def __init__(self,
d_model: int,
dropout_rate: float,
max_len: int = 5000,
reverse: bool = False):
"""Construct an PositionalEncoding object."""
super().__init__()
self.d_model = d_model
self.xscale = math.sqrt(self.d_model)
self.dropout = torch.nn.Dropout(p=dropout_rate)
self.max_len = max_len
self.pe = torch.zeros(self.max_len, self.d_model)
position = torch.arange(0, self.max_len,
dtype=torch.float32).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, self.d_model, 2, dtype=torch.float32) *
-(math.log(10000.0) / self.d_model))
self.pe[:, 0::2] = torch.sin(position * div_term)
self.pe[:, 1::2] = torch.cos(position * div_term)
self.pe = self.pe.unsqueeze(0)
def forward(self,
x: torch.Tensor,
offset: Union[int, torch.Tensor] = 0) \
-> Tuple[torch.Tensor, torch.Tensor]:
"""Add positional encoding.
Args:
x (torch.Tensor): Input. Its shape is (batch, time, ...)
offset (int, torch.tensor): position offset
Returns:
torch.Tensor: Encoded tensor. Its shape is (batch, time, ...)
torch.Tensor: for compatibility to RelPositionalEncoding
"""
self.pe = self.pe.to(x.device)
pos_emb = self.position_encoding(offset, x.size(1), False)
x = x * self.xscale + pos_emb
return self.dropout(x), self.dropout(pos_emb)
def position_encoding(self,
offset: Union[int, torch.Tensor],
size: int,
apply_dropout: bool = True) -> torch.Tensor:
""" For getting encoding in a streaming fashion
Attention!!!!!
we apply dropout only once at the whole utterance level in a none
streaming way, but will call this function several times with
increasing input size in a streaming scenario, so the dropout will
be applied several times.
Args:
offset (int or torch.tensor): start offset
size (int): required size of position encoding
Returns:
torch.Tensor: Corresponding encoding
"""
# How to subscript a Union type:
# https://github.com/pytorch/pytorch/issues/69434
if isinstance(offset, int):
assert offset + size <= self.max_len
pos_emb = self.pe[:, offset:offset + size]
elif isinstance(offset, torch.Tensor) and offset.dim() == 0: # scalar
assert offset + size <= self.max_len
pos_emb = self.pe[:, offset:offset + size]
else: # for batched streaming decoding on GPU
assert torch.max(offset) + size <= self.max_len
index = offset.unsqueeze(1) + \
torch.arange(0, size).to(offset.device) # B X T
flag = index > 0
# remove negative offset
index = index * flag
pos_emb = F.embedding(index, self.pe[0]) # B X T X d_model
if apply_dropout:
pos_emb = self.dropout(pos_emb)
return pos_emb
class RelPositionalEncoding(PositionalEncoding):
"""Relative positional encoding module.
See : Appendix B in https://arxiv.org/abs/1901.02860
Args:
d_model (int): Embedding dimension.
dropout_rate (float): Dropout rate.
max_len (int): Maximum input length.
"""
def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000):
"""Initialize class."""
super().__init__(d_model, dropout_rate, max_len, reverse=True)
def forward(self,
x: torch.Tensor,
offset: Union[int, torch.Tensor] = 0) \
-> Tuple[torch.Tensor, torch.Tensor]:
"""Compute positional encoding.
Args:
x (torch.Tensor): Input tensor (batch, time, `*`).
Returns:
torch.Tensor: Encoded tensor (batch, time, `*`).
torch.Tensor: Positional embedding tensor (1, time, `*`).
"""
self.pe = self.pe.to(x.device)
x = x * self.xscale
pos_emb = self.position_encoding(offset, x.size(1), False)
return self.dropout(x), self.dropout(pos_emb)
class WhisperPositionalEncoding(PositionalEncoding):
""" Sinusoids position encoding used in openai-whisper.encoder
"""
def __init__(self, d_model: int, dropout_rate: float, max_len: int = 1500):
super().__init__(d_model, dropout_rate, max_len)
self.xscale = 1.0
log_timescale_increment = np.log(10000) / (d_model // 2 - 1)
inv_timescales = torch.exp(-log_timescale_increment *
torch.arange(d_model // 2))
scaled_time = torch.arange(max_len)[:, np.newaxis] * \
inv_timescales[np.newaxis, :]
pe = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
delattr(self, "pe")
self.register_buffer("pe", pe.unsqueeze(0))
class LearnablePositionalEncoding(PositionalEncoding):
""" Learnable position encoding used in openai-whisper.decoder
"""
def __init__(self, d_model: int, dropout_rate: float, max_len: int = 448):
super().__init__(d_model, dropout_rate, max_len)
# NOTE(xcsong): overwrite self.pe & self.xscale
self.pe = torch.nn.Parameter(torch.empty(1, max_len, d_model))
self.xscale = 1.0
class NoPositionalEncoding(torch.nn.Module):
""" No position encoding
"""
def __init__(self, d_model: int, dropout_rate: float):
super().__init__()
self.d_model = d_model
self.dropout = torch.nn.Dropout(p=dropout_rate)
def forward(self,
x: torch.Tensor,
offset: Union[int, torch.Tensor] = 0) \
-> Tuple[torch.Tensor, torch.Tensor]:
""" Just return zero vector for interface compatibility
"""
pos_emb = torch.zeros(1, x.size(1), self.d_model).to(x.device)
return self.dropout(x), pos_emb
def position_encoding(self, offset: Union[int, torch.Tensor],
size: int) -> torch.Tensor:
return torch.zeros(1, size, self.d_model)
class EspnetRelPositionalEncoding(torch.nn.Module):
"""Relative positional encoding module (new implementation).
Details can be found in https://github.com/espnet/espnet/pull/2816.
See : Appendix B in https://arxiv.org/abs/1901.02860
Args:
d_model (int): Embedding dimension.
dropout_rate (float): Dropout rate.
max_len (int): Maximum input length.
"""
def __init__(self, d_model, dropout_rate, max_len=5000):
"""Construct an PositionalEncoding object."""
super(EspnetRelPositionalEncoding, self).__init__()
self.d_model = d_model
self.xscale = math.sqrt(self.d_model)
self.dropout = torch.nn.Dropout(p=dropout_rate)
self.pe = None
self.extend_pe(torch.tensor(0.0).expand(1, max_len))
def extend_pe(self, x):
"""Reset the positional encodings."""
if self.pe is not None:
# self.pe contains both positive and negative parts
# the length of self.pe is 2 * input_len - 1
if self.pe.size(1) >= x.size(1) * 2 - 1:
if self.pe.dtype != x.dtype or self.pe.device != x.device:
self.pe = self.pe.to(dtype=x.dtype, device=x.device)
return
# Suppose `i` means to the position of query vecotr and `j` means the
# position of key vector. We use position relative positions when keys
# are to the left (i>j) and negative relative positions otherwise (i<j).
pe_positive = torch.zeros(x.size(1), self.d_model)
pe_negative = torch.zeros(x.size(1), self.d_model)
position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, self.d_model, 2, dtype=torch.float32)
* -(math.log(10000.0) / self.d_model)
)
pe_positive[:, 0::2] = torch.sin(position * div_term)
pe_positive[:, 1::2] = torch.cos(position * div_term)
pe_negative[:, 0::2] = torch.sin(-1 * position * div_term)
pe_negative[:, 1::2] = torch.cos(-1 * position * div_term)
# Reserve the order of positive indices and concat both positive and
# negative indices. This is used to support the shifting trick
# as in https://arxiv.org/abs/1901.02860
pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0)
pe_negative = pe_negative[1:].unsqueeze(0)
pe = torch.cat([pe_positive, pe_negative], dim=1)
self.pe = pe.to(device=x.device, dtype=x.dtype)
def forward(self, x: torch.Tensor, offset: Union[int, torch.Tensor] = 0):
"""Add positional encoding.
Args:
x (torch.Tensor): Input tensor (batch, time, `*`).
Returns:
torch.Tensor: Encoded tensor (batch, time, `*`).
"""
self.extend_pe(x)
x = x * self.xscale
pos_emb = self.position_encoding(size=x.size(1), offset=offset)
return self.dropout(x), self.dropout(pos_emb)
def position_encoding(self,
offset: Union[int, torch.Tensor],
size: int) -> torch.Tensor:
""" For getting encoding in a streaming fashion
Attention!!!!!
we apply dropout only once at the whole utterance level in a none
streaming way, but will call this function several times with
increasing input size in a streaming scenario, so the dropout will
be applied several times.
Args:
offset (int or torch.tensor): start offset
size (int): required size of position encoding
Returns:
torch.Tensor: Corresponding encoding
"""
pos_emb = self.pe[
:,
self.pe.size(1) // 2 - size + 1 : self.pe.size(1) // 2 + size,
]
return pos_emb