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# -*- coding: utf-8 -*-
# Copyright 2019 Shigeki Karita
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Positional Encoding Module."""
import logging
import math
import torch
from packaging.version import parse as V
# from espnet2.asr.frontend.cnn import dim_1_layer_norm
def dim_1_layer_norm(x, eps=1e-05, gamma=None, beta=None):
"""Functional version of Dim1LayerNorm."""
B, D, T = x.shape
mean = torch.mean(x, 1, keepdim=True)
variance = torch.mean((x - mean) ** 2, 1, keepdim=True)
x = (x - mean) * torch.rsqrt(variance + eps)
if gamma is not None:
x = x * gamma.view(1, -1, 1)
if beta is not None:
x = x + beta.view(1, -1, 1)
return x
def _pre_hook(
state_dict,
prefix,
local_metadata,
strict,
missing_keys,
unexpected_keys,
error_msgs,
):
"""Perform pre-hook in load_state_dict for backward compatibility.
Note:
We saved self.pe until v.0.5.2 but we have omitted it later.
Therefore, we remove the item "pe" from `state_dict` for backward compatibility.
"""
k = prefix + "pe"
if k in state_dict:
state_dict.pop(k)
class PositionalEncoding(torch.nn.Module):
"""Positional encoding.
Args:
d_model (int): Embedding dimension.
dropout_rate (float): Dropout rate.
max_len (int): Maximum input length.
reverse (bool): Whether to reverse the input position. Only for
the class LegacyRelPositionalEncoding. We remove it in the current
class RelPositionalEncoding.
"""
def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False):
"""Construct an PositionalEncoding object."""
super(PositionalEncoding, self).__init__()
self.d_model = d_model
self.reverse = reverse
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))
self._register_load_state_dict_pre_hook(_pre_hook)
def extend_pe(self, x):
"""Reset the positional encodings."""
if self.pe is not None:
if self.pe.size(1) >= x.size(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
pe = torch.zeros(x.size(1), self.d_model)
if self.reverse:
position = torch.arange(
x.size(1) - 1, -1, -1.0, dtype=torch.float32
).unsqueeze(1)
else:
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[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0)
self.pe = pe.to(device=x.device, dtype=x.dtype)
def forward(self, x: torch.Tensor):
"""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 + self.pe[:, : x.size(1)]
return self.dropout(x)
class ScaledPositionalEncoding(PositionalEncoding):
"""Scaled positional encoding module.
See Sec. 3.2 https://arxiv.org/abs/1809.08895
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):
"""Initialize class."""
super().__init__(d_model=d_model, dropout_rate=dropout_rate, max_len=max_len)
self.alpha = torch.nn.Parameter(torch.tensor(1.0))
def reset_parameters(self):
"""Reset parameters."""
self.alpha.data = torch.tensor(1.0)
def forward(self, x):
"""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.alpha * self.pe[:, : x.size(1)]
return self.dropout(x)
class LearnableFourierPosEnc(torch.nn.Module):
"""Learnable Fourier Features for Positional Encoding.
See https://arxiv.org/pdf/2106.02795.pdf
Args:
d_model (int): Embedding dimension.
dropout_rate (float): Dropout rate.
max_len (int): Maximum input length.
gamma (float): init parameter for the positional kernel variance
see https://arxiv.org/pdf/2106.02795.pdf.
apply_scaling (bool): Whether to scale the input before adding the pos encoding.
hidden_dim (int): if not None, we modulate the pos encodings with
an MLP whose hidden layer has hidden_dim neurons.
"""
def __init__(
self,
d_model,
dropout_rate=0.0,
max_len=5000,
gamma=1.0,
apply_scaling=False,
hidden_dim=None,
):
"""Initialize class."""
super(LearnableFourierPosEnc, self).__init__()
self.d_model = d_model
if apply_scaling:
self.xscale = math.sqrt(self.d_model)
else:
self.xscale = 1.0
self.dropout = torch.nn.Dropout(dropout_rate)
self.max_len = max_len
self.gamma = gamma
if self.gamma is None:
self.gamma = self.d_model // 2
assert (
d_model % 2 == 0
), "d_model should be divisible by two in order to use this layer."
self.w_r = torch.nn.Parameter(torch.empty(1, d_model // 2))
self._reset() # init the weights
self.hidden_dim = hidden_dim
if self.hidden_dim is not None:
self.mlp = torch.nn.Sequential(
torch.nn.Linear(d_model, hidden_dim),
torch.nn.GELU(),
torch.nn.Linear(hidden_dim, d_model),
)
def _reset(self):
self.w_r.data = torch.normal(
0, (1 / math.sqrt(self.gamma)), (1, self.d_model // 2)
)
def extend_pe(self, x):
"""Reset the positional encodings."""
position_v = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1).to(x)
cosine = torch.cos(torch.matmul(position_v, self.w_r))
sine = torch.sin(torch.matmul(position_v, self.w_r))
pos_enc = torch.cat((cosine, sine), -1)
pos_enc /= math.sqrt(self.d_model)
if self.hidden_dim is None:
return pos_enc.unsqueeze(0)
else:
return self.mlp(pos_enc.unsqueeze(0))
def forward(self, x: torch.Tensor):
"""Add positional encoding.
Args:
x (torch.Tensor): Input tensor (batch, time, `*`).
Returns:
torch.Tensor: Encoded tensor (batch, time, `*`).
"""
pe = self.extend_pe(x)
x = x * self.xscale + pe
return self.dropout(x)
class LegacyRelPositionalEncoding(PositionalEncoding):
"""Relative positional encoding module (old version).
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):
"""Initialize class."""
super().__init__(
d_model=d_model,
dropout_rate=dropout_rate,
max_len=max_len,
reverse=True,
)
def forward(self, x):
"""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.extend_pe(x)
x = x * self.xscale
pos_emb = self.pe[:, : x.size(1)]
return self.dropout(x), self.dropout(pos_emb)
class RelPositionalEncoding(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(RelPositionalEncoding, 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):
"""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.pe[
:,
self.pe.size(1) // 2 - x.size(1) + 1 : self.pe.size(1) // 2 + x.size(1),
]
return self.dropout(x), self.dropout(pos_emb)
class StreamPositionalEncoding(torch.nn.Module):
"""Streaming Positional encoding.
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(StreamPositionalEncoding, 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.tmp = torch.tensor(0.0).expand(1, max_len)
self.extend_pe(self.tmp.size(1), self.tmp.device, self.tmp.dtype)
self._register_load_state_dict_pre_hook(_pre_hook)
def extend_pe(self, length, device, dtype):
"""Reset the positional encodings."""
if self.pe is not None:
if self.pe.size(1) >= length:
if self.pe.dtype != dtype or self.pe.device != device:
self.pe = self.pe.to(dtype=dtype, device=device)
return
pe = torch.zeros(length, self.d_model)
position = torch.arange(0, length, 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[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0)
self.pe = pe.to(device=device, dtype=dtype)
def forward(self, x: torch.Tensor, start_idx: int = 0):
"""Add positional encoding.
Args:
x (torch.Tensor): Input tensor (batch, time, `*`).
Returns:
torch.Tensor: Encoded tensor (batch, time, `*`).
"""
self.extend_pe(x.size(1) + start_idx, x.device, x.dtype)
x = x * self.xscale + self.pe[:, start_idx : start_idx + x.size(1)]
return self.dropout(x)
class ConvolutionalPositionalEmbedding(torch.nn.Module):
"""Convolutional positional embedding.
Used in wav2vec2/HuBERT SSL models.
https://arxiv.org/abs/1904.11660
Args:
embed_dim (int): Feature dimension of the input Tensor.
dropout (float): unused
max_len (int): unused
num_layers (int): number of conv layers
kernel_size (int): The number of frames to be use.
groups (int): The number of groups in feature dimensions.
weight_norm (str): [new, legacy, none].
How to init conv weights. Recommended setting is
none if num_layers > 1.
"""
def __init__(
self,
embed_dim: int,
dropout: float,
max_len: int = 5000,
num_layers: int = 1,
kernel_size: int = 128,
groups: int = 16,
weight_norm: str = "new",
use_residual: bool = False,
):
"""Initialize Convoluational Positional Embedding."""
super().__init__()
self.embed_dim = embed_dim
self.kernel_size = kernel_size
self.weight_norm = weight_norm
convs = []
for layer in range(num_layers):
conv = torch.nn.Conv1d(
in_channels=embed_dim,
out_channels=embed_dim,
kernel_size=kernel_size,
padding=kernel_size // 2,
groups=groups,
)
if weight_norm != "none" and weight_norm is not None:
std = math.sqrt((4 * (1.0)) / (kernel_size * embed_dim))
torch.nn.init.normal_(conv.weight, mean=0, std=std)
torch.nn.init.constant_(conv.bias, 0)
# torch.nn.utils.weight_norm leads to weird behavior
# with copy.deepcopy(). Usually isnt needed,
# but its important for models that use EMA
if weight_norm == "new":
if V(torch.__version__) >= V("2.2.0"):
conv = torch.nn.utils.parametrizations.weight_norm(
conv, name="weight", dim=2
)
else:
weight_norm = "legacy"
logging.warning(
"torch.nn.utils.parametrizations.weight_norm is only "
+ "supported for pytorch versions >= 2.2.0. "
+ "Defaulting to torch.nn.utils.weight_norm."
)
if weight_norm == "legacy":
conv = torch.nn.utils.weight_norm(conv, name="weight", dim=2)
convs.append(conv)
self.convs = torch.nn.ModuleList(convs)
self.num_remove: int = 1 if kernel_size % 2 == 0 else 0
self.use_residual = use_residual
def __prepare_scriptable__(self):
"""Prepare Scriptable method."""
for hook in self.conv._forward_pre_hooks.values():
# The hook we want to remove is an instance of WeightNorm class, so
# normally we would do `if isinstance(...)` but this class is not accessible
# because of shadowing, so we check the module name directly.
# https://github.com/pytorch/pytorch/blob/be0ca00c5ce260eb5bcec3237357f7a30cc08983/torch/nn/utils/__init__.py#L3
if (
hook.__module__ == "torch.nn.utils.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
logging.warning("Removing weight_norm from %s", self.__class__.__name__)
torch.nn.utils.remove_weight_norm(self.conv)
return self
def forward(self, x):
"""Forward Method.
Args:
x (Tensor): shape ``[batch, frame, feature]``.
Returns:
Tensor: The resulting feature. Shape ``[batch, frame, feature]``.
"""
if self.use_residual:
residual = x
x = x.transpose(-2, -1)
for conv in self.convs:
x = conv(x)
# remove extra padding
if self.num_remove > 0:
x = x[..., : -self.num_remove]
x = torch.nn.functional.gelu(x)
# manually normalize if the conv is not parameterized
# with weight norm
if self.weight_norm is None or self.weight_norm == "none":
x = dim_1_layer_norm(x)
x = x.transpose(-2, -1)
if self.use_residual:
x = x + residual
return x
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