Spaces:
Build error
Build error
File size: 2,670 Bytes
b6af722 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 |
# 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 math
from typing import List
import torch
class WarmupLambdaLR(torch.optim.lr_scheduler.LambdaLR):
def __init__(self, optimizer, warmup, last_epoch=-1, verbose=False):
# Define the lambda function based on the warmup period
self.warmup = warmup
def lr_lambda(epoch):
# Increase lr linearly for the first 'warmup' epochs
if epoch < warmup:
return float(epoch + 1) / warmup
# After 'warmup' epochs, keep lr constant
return 1.0
# Initialize the parent class with the generated lr_lambda
super(WarmupLambdaLR, self).__init__(optimizer, lr_lambda, last_epoch, verbose)
# cosine lr decay scheduler with warmup from https://github.com/karpathy/nanoGPT/blob/master/train.py#L228
class WarmupCosineLR(torch.optim.lr_scheduler.LRScheduler):
def __init__(
self,
optimizer: torch.optim.Optimizer,
warmup_iters: int,
lr_decay_iters: int,
min_lr: float,
last_epoch: int = -1,
):
self.warmup_iters = warmup_iters
self.lr_decay_iters = lr_decay_iters
self.min_lr = min_lr
super().__init__(optimizer, last_epoch)
def get_lr(self) -> List[float]:
# 1) linear warmup for warmup_iters steps
if self.last_epoch < self.warmup_iters:
return [base_lr * self.last_epoch / self.warmup_iters for base_lr in self.base_lrs]
# 2) if it > lr_decay_iters, return min learning rate
if self.last_epoch > self.lr_decay_iters:
return [self.min_lr for _ in self.base_lrs]
# 3) in between, use cosine decay down to min learning rate
decay_ratio = (self.last_epoch - self.warmup_iters) / (self.lr_decay_iters - self.warmup_iters)
assert 0 <= decay_ratio <= 1
coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) # coeff ranges 0..1
return [self.min_lr + coeff * (base_lr - self.min_lr) for base_lr in self.base_lrs]
|