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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import copy
import numbers
from typing import Any, List, Tuple, Union
import torch
from torch import Tensor, nn
from torch.nn import functional as F
from modules.general.scaling import ActivationBalancer
from modules.general.scaling import BasicNorm as _BasicNorm
_shape_t = Union[int, List[int], torch.Size]
class LayerNorm(nn.Module):
__constants__ = ["normalized_shape", "eps", "elementwise_affine"]
normalized_shape: Tuple[int, ...]
eps: float
elementwise_affine: bool
def __init__(
self,
normalized_shape: _shape_t,
eps: float = 1e-5,
elementwise_affine: bool = True,
device=None,
dtype=None,
) -> None:
factory_kwargs = {"device": device, "dtype": dtype}
super(LayerNorm, self).__init__()
if isinstance(normalized_shape, numbers.Integral):
normalized_shape = (normalized_shape,)
self.normalized_shape = tuple(normalized_shape)
self.eps = eps
self.elementwise_affine = elementwise_affine
if self.elementwise_affine:
self.weight = nn.Parameter(
torch.empty(self.normalized_shape, **factory_kwargs)
)
self.bias = nn.Parameter(
torch.empty(self.normalized_shape, **factory_kwargs)
)
else:
self.register_parameter("weight", None)
self.register_parameter("bias", None)
self.reset_parameters()
def reset_parameters(self) -> None:
if self.elementwise_affine:
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
if isinstance(input, tuple):
input, embedding = input
output = F.layer_norm(input, self.normalized_shape, self.weight, self.bias, self.eps)
return output, embedding
assert embedding is None
return F.layer_norm(input, self.normalized_shape, self.weight, self.bias, self.eps)
def extra_repr(self) -> str:
return (
"{normalized_shape}, eps={eps}, "
"elementwise_affine={elementwise_affine}".format(**self.__dict__)
)
class AdaptiveLayerNorm(nn.Module):
r"""Adaptive Layer Normalization"""
def __init__(self, d_model, norm) -> None:
super(AdaptiveLayerNorm, self).__init__()
self.project_layer = nn.Linear(d_model, 2 * d_model)
self.norm = norm
self.d_model = d_model
self.eps = self.norm.eps
def forward(self, input: Tensor, embedding: Tensor = None) -> Tensor:
if isinstance(input, tuple):
input, embedding = input
weight, bias = torch.split(
self.project_layer(embedding),
split_size_or_sections=self.d_model,
dim=-1,
)
return (weight * self.norm(input) + bias, embedding)
weight, bias = torch.split(
self.project_layer(embedding),
split_size_or_sections=self.d_model,
dim=-1,
)
return weight * self.norm(input) + bias
class BasicNorm(_BasicNorm):
def __init__(
self,
d_model: int,
eps: float = 1e-5,
device=None,
dtype=None,
):
super(BasicNorm, self).__init__(d_model, eps=eps)
def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
if isinstance(input, tuple):
input, embedding = input
return (
super(BasicNorm, self).forward(input),
embedding,
)
assert embedding is None
return super(BasicNorm, self).forward(input)
class BalancedBasicNorm(nn.Module):
def __init__(
self,
d_model: int,
eps: float = 1e-5,
device=None,
dtype=None,
):
super(BalancedBasicNorm, self).__init__()
self.balancer = ActivationBalancer(
d_model,
channel_dim=-1,
min_positive=0.45,
max_positive=0.55,
max_abs=6.0,
)
self.norm = BasicNorm(d_model, eps, device=device, dtype=dtype)
def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
if isinstance(input, tuple):
input, embedding = input
return self.norm((self.balancer(input), embedding))
assert embedding is None
return self.norm(self.balancer(input))
class IdentityNorm(nn.Module):
def __init__(
self,
d_model: int,
eps: float = 1e-5,
device=None,
dtype=None,
) -> None:
super(IdentityNorm, self).__init__()
def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
if isinstance(input, tuple):
return input
assert embedding is None
return input
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