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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
import typing as tp
import warnings
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.utils import spectral_norm, weight_norm
CONV_NORMALIZATIONS = frozenset(['none', 'weight_norm', 'spectral_norm',
'time_group_norm'])
def apply_parametrization_norm(module: nn.Module, norm: str = 'none'):
assert norm in CONV_NORMALIZATIONS
if norm == 'weight_norm':
return weight_norm(module)
elif norm == 'spectral_norm':
return spectral_norm(module)
else:
# We already check was in CONV_NORMALIZATION, so any other choice
# doesn't need reparametrization.
return module
def get_norm_module(module: nn.Module, causal: bool = False, norm: str = 'none', **norm_kwargs):
"""Return the proper normalization module. If causal is True, this will ensure the returned
module is causal, or return an error if the normalization doesn't support causal evaluation.
"""
assert norm in CONV_NORMALIZATIONS
if norm == 'time_group_norm':
if causal:
raise ValueError("GroupNorm doesn't support causal evaluation.")
assert isinstance(module, nn.modules.conv._ConvNd)
return nn.GroupNorm(1, module.out_channels, **norm_kwargs)
else:
return nn.Identity()
def get_extra_padding_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int,
padding_total: int = 0) -> int:
"""See `pad_for_conv1d`.
"""
length = x.shape[-1]
n_frames = (length - kernel_size + padding_total) / stride + 1
ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
return ideal_length - length
def pad_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0):
"""Pad for a convolution to make sure that the last window is full.
Extra padding is added at the end. This is required to ensure that we can rebuild
an output of the same length, as otherwise, even with padding, some time steps
might get removed.
For instance, with total padding = 4, kernel size = 4, stride = 2:
0 0 1 2 3 4 5 0 0 # (0s are padding)
1 2 3 # (output frames of a convolution, last 0 is never used)
0 0 1 2 3 4 5 0 # (output of tr. conv., but pos. 5 is going to get removed as padding)
1 2 3 4 # once you removed padding, we are missing one time step !
"""
extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
return F.pad(x, (0, extra_padding))
def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'constant', value: float = 0.):
"""Tiny wrapper around F.pad, just to allow for reflect padding on small input.
If this is the case, we insert extra 0 padding to the right before the reflection happen.
"""
length = x.shape[-1]
padding_left, padding_right = paddings
assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
if mode == 'reflect':
max_pad = max(padding_left, padding_right)
extra_pad = 0
if length <= max_pad:
extra_pad = max_pad - length + 1
x = F.pad(x, (0, extra_pad))
padded = F.pad(x, paddings, mode, value)
end = padded.shape[-1] - extra_pad
return padded[..., :end]
else:
return F.pad(x, paddings, mode, value)
def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]):
"""Remove padding from x, handling properly zero padding. Only for 1d!
"""
padding_left, padding_right = paddings
assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
assert (padding_left + padding_right) <= x.shape[-1]
end = x.shape[-1] - padding_right
return x[..., padding_left: end]
class NormConv1d(nn.Module):
"""Wrapper around Conv1d and normalization applied to this conv
to provide a uniform interface across normalization approaches.
"""
def __init__(self, *args, causal: bool = False, norm: str = 'none',
norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
super().__init__()
self.conv = apply_parametrization_norm(nn.Conv1d(*args, **kwargs), norm)
self.norm = get_norm_module(self.conv, causal, norm, **norm_kwargs)
self.norm_type = norm
def forward(self, x):
x = self.conv(x)
x = self.norm(x)
return x
class NormConv2d(nn.Module):
"""Wrapper around Conv2d and normalization applied to this conv
to provide a uniform interface across normalization approaches.
"""
def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
super().__init__()
self.conv = apply_parametrization_norm(nn.Conv2d(*args, **kwargs), norm)
self.norm = get_norm_module(self.conv, causal=False, norm=norm, **norm_kwargs)
self.norm_type = norm
def forward(self, x):
x = self.conv(x)
x = self.norm(x)
return x
class NormConvTranspose1d(nn.Module):
"""Wrapper around ConvTranspose1d and normalization applied to this conv
to provide a uniform interface across normalization approaches.
"""
def __init__(self, *args, causal: bool = False, norm: str = 'none',
norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
super().__init__()
self.convtr = apply_parametrization_norm(nn.ConvTranspose1d(*args, **kwargs), norm)
self.norm = get_norm_module(self.convtr, causal, norm, **norm_kwargs)
self.norm_type = norm
def forward(self, x):
x = self.convtr(x)
x = self.norm(x)
return x
class NormConvTranspose2d(nn.Module):
"""Wrapper around ConvTranspose2d and normalization applied to this conv
to provide a uniform interface across normalization approaches.
"""
def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
super().__init__()
self.convtr = apply_parametrization_norm(nn.ConvTranspose2d(*args, **kwargs), norm)
self.norm = get_norm_module(self.convtr, causal=False, norm=norm, **norm_kwargs)
def forward(self, x):
x = self.convtr(x)
x = self.norm(x)
return x
class StreamableConv1d(nn.Module):
"""Conv1d with some builtin handling of asymmetric or causal padding
and normalization.
"""
def __init__(self, in_channels: int, out_channels: int,
kernel_size: int, stride: int = 1, dilation: int = 1,
groups: int = 1, bias: bool = True, causal: bool = False,
norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {},
pad_mode: str = 'reflect'):
super().__init__()
# warn user on unusual setup between dilation and stride
if stride > 1 and dilation > 1:
warnings.warn('StreamableConv1d has been initialized with stride > 1 and dilation > 1'
f' (kernel_size={kernel_size} stride={stride}, dilation={dilation}).')
self.conv = NormConv1d(in_channels, out_channels, kernel_size, stride,
dilation=dilation, groups=groups, bias=bias, causal=causal,
norm=norm, norm_kwargs=norm_kwargs)
self.causal = causal
self.pad_mode = pad_mode
def forward(self, x):
B, C, T = x.shape
kernel_size = self.conv.conv.kernel_size[0]
stride = self.conv.conv.stride[0]
dilation = self.conv.conv.dilation[0]
kernel_size = (kernel_size - 1) * dilation + 1 # effective kernel size with dilations
padding_total = kernel_size - stride
extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
if self.causal:
# Left padding for causal
x = pad1d(x, (padding_total, extra_padding), mode=self.pad_mode)
else:
# Asymmetric padding required for odd strides
padding_right = padding_total // 2
padding_left = padding_total - padding_right
x = pad1d(x, (padding_left, padding_right + extra_padding), mode=self.pad_mode)
return self.conv(x)
class StreamableConvTranspose1d(nn.Module):
"""ConvTranspose1d with some builtin handling of asymmetric or causal padding
and normalization.
"""
def __init__(self, in_channels: int, out_channels: int,
kernel_size: int, stride: int = 1, causal: bool = False,
norm: str = 'none', trim_right_ratio: float = 1.,
norm_kwargs: tp.Dict[str, tp.Any] = {}):
super().__init__()
self.convtr = NormConvTranspose1d(in_channels, out_channels, kernel_size, stride,
causal=causal, norm=norm, norm_kwargs=norm_kwargs)
self.causal = causal
self.trim_right_ratio = trim_right_ratio
assert self.causal or self.trim_right_ratio == 1., \
"`trim_right_ratio` != 1.0 only makes sense for causal convolutions"
assert self.trim_right_ratio >= 0. and self.trim_right_ratio <= 1.
def forward(self, x):
kernel_size = self.convtr.convtr.kernel_size[0]
stride = self.convtr.convtr.stride[0]
padding_total = kernel_size - stride
y = self.convtr(x)
# We will only trim fixed padding. Extra padding from `pad_for_conv1d` would be
# removed at the very end, when keeping only the right length for the output,
# as removing it here would require also passing the length at the matching layer
# in the encoder.
if self.causal:
# Trim the padding on the right according to the specified ratio
# if trim_right_ratio = 1.0, trim everything from right
padding_right = math.ceil(padding_total * self.trim_right_ratio)
padding_left = padding_total - padding_right
y = unpad1d(y, (padding_left, padding_right))
else:
# Asymmetric padding required for odd strides
padding_right = padding_total // 2
padding_left = padding_total - padding_right
y = unpad1d(y, (padding_left, padding_right))
return y
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