# Copyright 2023 OpenNLPLab | |
# | |
# 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 logging | |
import os | |
import sys | |
import torch | |
from torch import nn | |
logging.basicConfig( | |
format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", | |
datefmt="%Y-%m-%d %H:%M:%S", | |
level=os.environ.get("LOGLEVEL", "INFO").upper(), | |
stream=sys.stdout, | |
) | |
logger = logging.getLogger("srmsnorm") | |
class SimpleRMSNorm(nn.Module): | |
def __init__(self, dim: int, eps: float = 1e-6): | |
super().__init__() | |
self.eps = eps | |
def _norm(self, x): | |
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) | |
def forward(self, x): | |
output = self._norm(x.float()).type_as(x) | |
return output | |