Spaces:
Running
on
A100
Running
on
A100
File size: 3,424 Bytes
174ae06 |
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 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 |
# Copyright (c) 2025 NVIDIA CORPORATION.
# Licensed under the MIT license.
# Adapted from https://github.com/NVlabs/VILA/tree/main under the Apache 2.0 license.
# LICENSE is in incl_licenses directory.
# Copyright 2024 NVIDIA CORPORATION & 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.
#
# SPDX-License-Identifier: Apache-2.0
import datetime
import logging
import logging.handlers
import os
import sys
import time
import warnings
import requests
import torch
import transformers
from transformers.trainer_callback import TrainerCallback, TrainerControl, TrainerState, TrainingArguments
from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, get_last_checkpoint
def get_rank():
if not torch.distributed.is_initialized():
return 0
return torch.distributed.get_rank()
def get_local_rank():
if not torch.distributed.is_initialized():
return 0
num_gpus = torch.cuda.device_count()
return get_rank() % num_gpus
def get_world_size():
if not torch.distributed.is_initialized():
return 1
return torch.distributed.get_world_size()
class Timer:
def __init__(self):
self.start_time = None
self.elapsed_time = 0
def start(self):
self.start_time = time.time()
def reset(self):
self.start_time = None
self.elapsed_time = 0
def get_elapsed_time(self):
if self.start_time is not None:
return self.elapsed_time + (time.time() - self.start_time)
timer = Timer()
def set_timer():
timer.start()
def rank_print(*s):
if not torch.distributed.is_initialized():
rank = 0
else:
rank = torch.distributed.get_rank()
current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print(f"[{current_time}] Rank {rank}:", *s)
class TimeoutTerminateCallback(transformers.TrainerCallback):
def __init__(self, total_time_limit=240, pre_terminate_time=10):
self.total_time_limit = total_time_limit
self.pre_terminate_time = pre_terminate_time
elapsed_time = timer.get_elapsed_time()
rank_print(
f"Timer for terminate callback has been set.\nTotal limit: {total_time_limit}min\nPre terminate time: {pre_terminate_time}min elapsed_time: {elapsed_time}s"
)
self.time_to_kill = (total_time_limit - pre_terminate_time) * 60
def on_step_end(self, args, state, control, model, **kwargs):
elapsed_time = timer.get_elapsed_time()
if elapsed_time is None:
# no timer has been set
return control
if elapsed_time > self.time_to_kill:
rank_print("Timeout, start to save checkpoint....")
control.should_save = True
control.should_training_stop = True
return control
def on_train_end(self, args, state, control, **kwargs):
if state.global_step < state.max_steps:
exit(124)
|