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# SPDX-FileCopyrightText: Copyright (c) 2025 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.
import torch
import torch.nn as nn
def create_norm(norm_type: str, dim: int, eps: float = 1e-6):
"""
Creates the specified normalization layer based on the norm_type.
Adopted from TorchTriton: https://github.com/pytorch/torchtitan/blob/main/torchtitan/models/norms.py
Args:
norm_type (str): The type of normalization layer to create.
Supported types: 1. rmsnorm 2. fused_rmsnorm 3. layernorm 4. np_layernorm
dim (int): The dimension of the normalization layer.
eps (float, optional): The epsilon value for numerical stability. Defaults to 1e-6.
Returns:
The created normalization layer.
Raises:
NotImplementedError: If an unknown norm_type is provided.
"""
norm_type = norm_type.lower() # Normalize to lowercase
if norm_type == "layernorm":
return nn.LayerNorm(dim, eps=eps, bias=False)
elif norm_type == "np_layernorm":
return nn.LayerNorm(dim, eps=eps, elementwise_affine=False, bias=False)
elif norm_type == "rmsnorm":
return RMSNorm(dim, eps=eps, compile=False)
elif norm_type == "compiled_rmsnorm":
return RMSNorm(dim, eps=eps, compile=True)
elif norm_type == "fused_rmsnorm":
raise NotImplementedError("Fused RMSNorm is not supported yet.")
else:
raise NotImplementedError(f"Unknown norm_type: '{norm_type}'")
class RMSNorm(nn.Module):
"""
Initialize the RMSNorm normalization layer.
Reference implementation: https://github.com/pytorch/torchtitan/blob/main/torchtitan/models/norms.py
Args:
dim (int): The dimension of the input tensor.
eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
compile (bool, optional): Whether to compile the forward function. Default is False.
Attributes:
eps (float): A small value added to the denominator for numerical stability.
weight (nn.Parameter): Learnable scaling parameter.
"""
def __init__(self, dim: int, eps: float = 1e-6, compile: bool = False):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
self.rmsnorm_fn = torch.compile(self.compute_rmsnorm, fullgraph=True) if compile else self.compute_rmsnorm
@staticmethod
def compute_rmsnorm(x: torch.Tensor, weight: torch.Tensor, eps: float):
def _norm(x, eps):
# Computes the root-mean-square norm of the input tensor.
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + eps)
output = _norm(x.float(), eps).type_as(x)
return output * weight
def forward(self, x: torch.Tensor):
return self.rmsnorm_fn(x, self.weight, self.eps)
def reset_parameters(self):
torch.nn.init.ones_(self.weight)
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