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Running
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Zero
File size: 2,055 Bytes
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# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
# //
# // 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 typing import Callable, Optional
from diffusers.models.normalization import RMSNorm
from torch import nn
# (dim: int, eps: float, elementwise_affine: bool)
norm_layer_type = Callable[[int, float, bool], nn.Module]
def get_norm_layer(norm_type: Optional[str]) -> norm_layer_type:
def _norm_layer(dim: int, eps: float, elementwise_affine: bool):
if norm_type is None:
return nn.Identity()
if norm_type == "layer":
return nn.LayerNorm(
normalized_shape=dim,
eps=eps,
elementwise_affine=elementwise_affine,
)
if norm_type == "rms":
return RMSNorm(
dim=dim,
eps=eps,
elementwise_affine=elementwise_affine,
)
if norm_type == "fusedln":
from apex.normalization import FusedLayerNorm
return FusedLayerNorm(
normalized_shape=dim,
elementwise_affine=elementwise_affine,
eps=eps,
)
if norm_type == "fusedrms":
from apex.normalization import FusedRMSNorm
return FusedRMSNorm(
normalized_shape=dim,
elementwise_affine=elementwise_affine,
eps=eps,
)
raise NotImplementedError(f"{norm_type} is not supported")
return _norm_layer
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