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#
# 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
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#
"""
LayerNorm module.
"""
from aitemplate.compiler import ops
from aitemplate.frontend.nn.module import Module
from aitemplate.frontend.nn.parameter import Parameter
# pylint: disable=C0103
[docs]class LayerNorm(Module):
"""LayerNorm nn module"""
def __init__(
self,
normalized_shape,
eps=1e-5,
dtype="float16",
**kwargs,
):
"""Standalone layernorm op.
Applies Layer Normalization over a mini-batch of inputs as described in the
paper Layer Normalization. The mean and standard-deviation are calculated
over the last D dimensions, where D is the dimension of normalized_shape.
Input shape: [M0, M1, ..., Mp, N1, N2, ..., ND]
Normalized_shape: [N1, N2, ..., ND]
Gamma/Beta, if not None, have the same shape as normalized_shape.
"""
super().__init__()
self.eps = eps
self.dim = (
normalized_shape
if isinstance(normalized_shape, (tuple, list))
else (normalized_shape,)
)
self.weight = Parameter(shape=self.dim, dtype=dtype)
self.bias = Parameter(shape=self.dim, dtype=dtype)
self.op = ops.layernorm()
[docs] def forward(self, *args):
assert len(args) == 1
x = args[0]
y = self.op(x, self.weight.tensor(), self.bias.tensor(), self.dim, self.eps)
return y