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""" Normalization layers and wrappers
Norm layer definitions that support fast norm and consistent channel arg order (always first arg).
Hacked together by / Copyright 2022 Ross Wightman
"""
import numbers
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from .fast_norm import is_fast_norm, fast_group_norm, fast_layer_norm, fast_rms_norm, fast_simple_norm, simple_norm
try:
from torch.nn.functional import rms_norm
except ImportError:
from .fast_norm import rms_norm
class GroupNorm(nn.GroupNorm):
_fast_norm: torch.jit.Final[bool]
def __init__(self, num_channels, num_groups=32, eps=1e-5, affine=True):
# NOTE num_channels is swapped to first arg for consistency in swapping norm layers with BN
super().__init__(num_groups, num_channels, eps=eps, affine=affine)
self._fast_norm = is_fast_norm() # can't script unless we have these flags here (no globals)
def forward(self, x):
if self._fast_norm:
return fast_group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
else:
return F.group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
class GroupNorm1(nn.GroupNorm):
""" Group Normalization with 1 group.
Input: tensor in shape [B, C, *]
"""
_fast_norm: torch.jit.Final[bool]
def __init__(self, num_channels, **kwargs):
super().__init__(1, num_channels, **kwargs)
self._fast_norm = is_fast_norm() # can't script unless we have these flags here (no globals)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self._fast_norm:
return fast_group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
else:
return F.group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
class LayerNorm(nn.LayerNorm):
""" LayerNorm w/ fast norm option
"""
_fast_norm: torch.jit.Final[bool]
def __init__(self, num_channels, eps=1e-6, affine=True):
super().__init__(num_channels, eps=eps, elementwise_affine=affine)
self._fast_norm = is_fast_norm() # can't script unless we have these flags here (no globals)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self._fast_norm:
x = fast_layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
else:
x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
return x
class LayerNorm2d(nn.LayerNorm):
""" LayerNorm for channels of '2D' spatial NCHW tensors """
_fast_norm: torch.jit.Final[bool]
def __init__(self, num_channels, eps=1e-6, affine=True):
super().__init__(num_channels, eps=eps, elementwise_affine=affine)
self._fast_norm = is_fast_norm() # can't script unless we have these flags here (no globals)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x.permute(0, 2, 3, 1)
if self._fast_norm:
x = fast_layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
else:
x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
x = x.permute(0, 3, 1, 2)
return x
def _is_contiguous(tensor: torch.Tensor) -> bool:
# jit is oh so lovely :/
if torch.jit.is_scripting():
return tensor.is_contiguous()
else:
return tensor.is_contiguous(memory_format=torch.contiguous_format)
def _layer_norm_cf(x: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, eps: float):
s, u = torch.var_mean(x, dim=1, unbiased=False, keepdim=True)
x = (x - u) * torch.rsqrt(s + eps)
x = x * weight[:, None, None] + bias[:, None, None]
return x
def _layer_norm_cf_sqm(x: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, eps: float):
u = x.mean(dim=1, keepdim=True)
s = ((x * x).mean(dim=1, keepdim=True) - (u * u)).clamp(0)
x = (x - u) * torch.rsqrt(s + eps)
x = x * weight.view(1, -1, 1, 1) + bias.view(1, -1, 1, 1)
return x
class LayerNormExp2d(nn.LayerNorm):
""" LayerNorm for channels_first tensors with 2d spatial dimensions (ie N, C, H, W).
Experimental implementation w/ manual norm for tensors non-contiguous tensors.
This improves throughput in some scenarios (tested on Ampere GPU), esp w/ channels_last
layout. However, benefits are not always clear and can perform worse on other GPUs.
"""
def __init__(self, num_channels, eps=1e-6):
super().__init__(num_channels, eps=eps)
def forward(self, x) -> torch.Tensor:
if _is_contiguous(x):
x = F.layer_norm(
x.permute(0, 2, 3, 1), self.normalized_shape, self.weight, self.bias, self.eps).permute(0, 3, 1, 2)
else:
x = _layer_norm_cf(x, self.weight, self.bias, self.eps)
return x
class RmsNorm(nn.Module):
""" RmsNorm w/ fast (apex) norm if available
"""
__constants__ = ['normalized_shape', 'eps', 'elementwise_affine', '_fast_norm']
normalized_shape: Tuple[int, ...]
eps: float
elementwise_affine: bool
_fast_norm: bool
def __init__(self, channels, eps=1e-6, affine=True, device=None, dtype=None) -> None:
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__()
normalized_shape = channels
if isinstance(normalized_shape, numbers.Integral):
# mypy error: incompatible types in assignment
normalized_shape = (normalized_shape,) # type: ignore[assignment]
self.normalized_shape = tuple(normalized_shape) # type: ignore[arg-type]
self.eps = eps
self.elementwise_affine = affine
self._fast_norm = is_fast_norm() # can't script unless we have these flags here (no globals)
if self.elementwise_affine:
self.weight = nn.Parameter(torch.empty(self.normalized_shape, **factory_kwargs))
else:
self.register_parameter('weight', None)
self.reset_parameters()
def reset_parameters(self) -> None:
if self.elementwise_affine:
nn.init.ones_(self.weight)
def forward(self, x: torch.Tensor) -> torch.Tensor:
# NOTE fast norm fallback needs our rms norm impl, so both paths through here.
# Since there is no built-in PyTorch impl, always use APEX RmsNorm if is installed.
if self._fast_norm:
x = fast_rms_norm(x, self.normalized_shape, self.weight, self.eps)
else:
x = rms_norm(x, self.normalized_shape, self.weight, self.eps)
return x
class RmsNorm2d(nn.Module):
""" RmsNorm w/ fast (apex) norm if available
"""
__constants__ = ['normalized_shape', 'eps', 'elementwise_affine', '_fast_norm']
normalized_shape: Tuple[int, ...]
eps: float
elementwise_affine: bool
_fast_norm: bool
def __init__(self, channels, eps=1e-6, affine=True, device=None, dtype=None) -> None:
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__()
normalized_shape = channels
if isinstance(normalized_shape, numbers.Integral):
# mypy error: incompatible types in assignment
normalized_shape = (normalized_shape,) # type: ignore[assignment]
self.normalized_shape = tuple(normalized_shape) # type: ignore[arg-type]
self.eps = eps
self.elementwise_affine = affine
self._fast_norm = is_fast_norm() # can't script unless we have these flags here (no globals)
if self.elementwise_affine:
self.weight = nn.Parameter(torch.empty(self.normalized_shape, **factory_kwargs))
else:
self.register_parameter('weight', None)
self.reset_parameters()
def reset_parameters(self) -> None:
if self.elementwise_affine:
nn.init.ones_(self.weight)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x.permute(0, 2, 3, 1)
# NOTE fast norm fallback needs our rms norm impl, so both paths through here.
# Since there is no built-in PyTorch impl, always use APEX RmsNorm if is installed.
if self._fast_norm:
x = fast_rms_norm(x, self.normalized_shape, self.weight, self.eps)
else:
x = rms_norm(x, self.normalized_shape, self.weight, self.eps)
x = x.permute(0, 3, 1, 2)
return x
class SimpleNorm(nn.Module):
""" SimpleNorm (x / std(x))
"""
__constants__ = ['normalized_shape', 'eps', 'elementwise_affine', '_fast_norm']
normalized_shape: Tuple[int, ...]
eps: float
elementwise_affine: bool
_fast_norm: bool
def __init__(self, channels, eps=1e-6, affine=True, device=None, dtype=None) -> None:
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__()
normalized_shape = channels
if isinstance(normalized_shape, numbers.Integral):
# mypy error: incompatible types in assignment
normalized_shape = (normalized_shape,) # type: ignore[assignment]
self.normalized_shape = tuple(normalized_shape) # type: ignore[arg-type]
self.eps = eps
self.elementwise_affine = affine
self._fast_norm = is_fast_norm() # can't script unless we have these flags here (no globals)
if self.elementwise_affine:
self.weight = nn.Parameter(torch.empty(self.normalized_shape, **factory_kwargs))
else:
self.register_parameter('weight', None)
self.reset_parameters()
def reset_parameters(self) -> None:
if self.elementwise_affine:
nn.init.ones_(self.weight)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self._fast_norm:
x = fast_simple_norm(x, self.normalized_shape, self.weight, self.eps)
else:
x = simple_norm(x, self.normalized_shape, self.weight, self.eps)
return x
class SimpleNorm2d(nn.Module):
""" SimpleNorm for NCHW tensors
"""
__constants__ = ['normalized_shape', 'eps', 'elementwise_affine', '_fast_norm']
normalized_shape: Tuple[int, ...]
eps: float
elementwise_affine: bool
_fast_norm: bool
def __init__(self, channels, eps=1e-6, affine=True, device=None, dtype=None) -> None:
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__()
normalized_shape = channels
if isinstance(normalized_shape, numbers.Integral):
# mypy error: incompatible types in assignment
normalized_shape = (normalized_shape,) # type: ignore[assignment]
self.normalized_shape = tuple(normalized_shape) # type: ignore[arg-type]
self.eps = eps
self.elementwise_affine = affine
self._fast_norm = is_fast_norm() # can't script unless we have these flags here (no globals)
if self.elementwise_affine:
self.weight = nn.Parameter(torch.empty(self.normalized_shape, **factory_kwargs))
else:
self.register_parameter('weight', None)
self.reset_parameters()
def reset_parameters(self) -> None:
if self.elementwise_affine:
nn.init.ones_(self.weight)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x.permute(0, 2, 3, 1)
if self._fast_norm:
x = fast_simple_norm(x, self.normalized_shape, self.weight, self.eps)
else:
x = simple_norm(x, self.normalized_shape, self.weight, self.eps)
x = x.permute(0, 3, 1, 2)
return x
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