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# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# 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 argparse
import os
from dataclasses import dataclass
import torch
from megatron.core.optimizer import OptimizerConfig
from nemo import lightning as nl
from nemo.collections import llm
from nemo.collections.nlp.modules.common.tokenizer_utils import get_nmt_tokenizer
## NOTE: This script is present for github-actions testing only.
def get_args():
parser = argparse.ArgumentParser(description='Pretraining a small BERT model using NeMo 2.0')
parser.add_argument('--experiment_dir', type=str, help="directory to write results and checkpoints to")
parser.add_argument('--devices', type=int, default=1, help="number of devices")
parser.add_argument('--max_steps', type=int, default=3, help="number of devices")
parser.add_argument('--mbs', type=int, default=1, help="micro batch size")
parser.add_argument('--tp_size', type=int, default=1, help="tensor parallel size")
parser.add_argument('--pp_size', type=int, default=1, help="pipeline parallel size")
parser.add_argument('--type', type=str, default='huggingface')
return parser.parse_args()
if __name__ == '__main__':
args = get_args()
strategy = nl.MegatronStrategy(
tensor_model_parallel_size=args.tp_size,
pipeline_model_parallel_size=args.pp_size,
# Pipeline dtype is coupled with the bf16 mixed precision plugin
pipeline_dtype=torch.bfloat16,
ckpt_load_strictness="log_all", # Only for CI tests to use older versions of checkpoint
)
trainer = nl.Trainer(
devices=args.devices,
max_steps=args.max_steps,
accelerator="gpu",
strategy=strategy,
plugins=nl.MegatronMixedPrecision(precision="bf16-mixed"),
log_every_n_steps=1,
limit_val_batches=2,
val_check_interval=2,
num_sanity_val_steps=0,
)
ckpt = nl.ModelCheckpoint(
save_last=True,
monitor="reduced_train_loss",
save_top_k=1,
save_on_train_epoch_end=True,
save_optim_on_train_end=True,
)
logger = nl.NeMoLogger(
log_dir=args.experiment_dir,
use_datetime_version=False, # must be false if using auto resume
ckpt=ckpt,
)
adam = nl.MegatronOptimizerModule(
config=OptimizerConfig(
optimizer="adam",
lr=0.0001,
adam_beta2=0.98,
use_distributed_optimizer=True,
clip_grad=1.0,
bf16=True,
),
)
data = llm.BERTMockDataModule(
seq_length=512,
micro_batch_size=args.mbs,
global_batch_size=8,
num_workers=0,
)
tokenizer = get_nmt_tokenizer("megatron", "BertWordPieceLowerCase")
if args.type == 'huggingface':
print('Init HuggingFace Bert Base Model')
model = llm.BertModel(llm.HuggingFaceBertBaseConfig(), tokenizer=tokenizer)
elif args.type == 'megatron':
print('Init Megatron Bert Base Model')
model = llm.BertModel(llm.MegatronBertBaseConfig(), tokenizer=tokenizer)
else:
raise ValueError('Unknown type.')
resume = nl.AutoResume(
resume_if_exists=True,
resume_ignore_no_checkpoint=True,
)
llm.pretrain(model=model, data=data, trainer=trainer, log=logger, optim=adam, resume=resume)
print("Bert Pretraining Succeeded")
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