# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
from ..functional import group_norm, layer_norm, rms_norm
from ..module import Module
from ..parameter import Parameter
[docs]
class LayerNorm(Module):
def __init__(self,
normalized_shape,
eps=1e-05,
elementwise_affine=True,
bias=True,
dtype=None,
tp_size=1,
tp_dim=-1):
super().__init__()
if isinstance(normalized_shape, int):
normalized_shape = (normalized_shape, )
self.normalized_shape = tuple(normalized_shape)
self.elementwise_affine = elementwise_affine
if self.elementwise_affine:
self.weight = Parameter(shape=self.normalized_shape, dtype=dtype)
if bias:
self.bias = Parameter(shape=self.normalized_shape, dtype=dtype)
else:
self.register_parameter('bias', None)
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
self.eps = eps
self.dtype = dtype
self.tp_size = tp_size
self.tp_dim = tp_dim
[docs]
def forward(self, x, normalized_shape=None):
weight = 1. if self.weight is None else self.weight.value
bias = 0. if self.bias is None else self.bias.value
if normalized_shape is None:
normalized_shape = self.normalized_shape
return layer_norm(x, normalized_shape, weight, bias, self.eps)
[docs]
class RmsNorm(Module):
def __init__(self,
normalized_shape,
num_groups=1,
eps=1e-06,
elementwise_affine=True,
dtype=None):
super().__init__()
if isinstance(normalized_shape, int):
normalized_shape = (normalized_shape, )
self.normalized_shape = tuple(normalized_shape)
self.elementwise_affine = elementwise_affine
self.num_groups = num_groups
num_channels = normalized_shape[-1]
if num_channels % num_groups != 0:
raise ValueError('num_channels must be divisible by num_groups')
if self.elementwise_affine:
self.weight = Parameter(shape=self.normalized_shape, dtype=dtype)
else:
self.register_parameter('weight', None)
self.eps = eps
self.dtype = dtype
[docs]
def forward(self, x, normalized_shape=None):
weight = None if self.weight is None else self.weight.value
if normalized_shape is None:
normalized_shape = self.normalized_shape
return rms_norm(x, normalized_shape, self.num_groups, weight, self.eps)
[docs]
class GroupNorm(Module):
def __init__(self,
num_groups,
num_channels,
eps=1e-05,
affine=True,
dtype=None):
super().__init__()
if num_channels % num_groups != 0:
raise ValueError('num_channels must be divisible by num_groups')
self.num_groups = num_groups
self.num_channels = num_channels
self.affine = affine
if self.affine:
self.weight = Parameter(shape=(self.num_channels, ), dtype=dtype)
self.bias = Parameter(shape=(self.num_channels, ), dtype=dtype)
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
self.eps = eps
[docs]
def forward(self, x):
weight = None if self.weight is None else self.weight.value
bias = None if self.bias is None else self.bias.value
return group_norm(x, self.num_groups, weight, bias, self.eps)