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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
from functools import partial
import fiddle as fdl
import nemo_run as run
from nemo.collections import llm
from nemo.collections.llm.gpt.model.llama import Llama3Config, LlamaModel
from nemo.collections.llm.tools.auto_configurator import AutoConfigurator, generate_configs, get_results
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--model_type", type=str, choices=["llama", "bert", "t5"], help="Model type to run")
parser.add_argument("--run_number", type=int, help="Number of config to run")
parser.add_argument("--log_dir", type=str, help="Path where to save training logs")
parser.add_argument("--get_results", action="store_true")
parser.add_argument("--extra_metrics", action="store_true")
return parser.parse_args()
@dataclass
class Llama3Config145M(Llama3Config):
num_layers: int = 12
hidden_size: int = 768
num_attention_heads: int = 16
num_query_groups: int = 8
ffn_hidden_size: int = 2688
@run.cli.factory(target=llm.pretrain, name="llama3_145m")
def llama3_145m(num_nodes=1, num_gpus_per_node=1):
# Setup Llama3 145M config
recipe = partial(llm.llama3_8b.pretrain_recipe, num_nodes=num_nodes, num_gpus_per_node=num_gpus_per_node)()
recipe = run.Partial(
llm.pretrain,
model=run.Config(LlamaModel, config=run.Config(Llama3Config145M)),
trainer=recipe.trainer,
data=recipe.data,
log=recipe.log,
optim=recipe.optim,
resume=None,
)
return recipe
def train_config(args):
# This example will generate 3 configs.
# It is expected that this script will be run 3 times with changing --run_number flag for each run from 1 to 3.
# After all configurations are trained, please trigger the script using --get_results flag.
# This script provides only Auto Configurator example using only a single GPU.
# Get Auto Conf runner
calculate_model_size = False
if args.model_type == "llama":
recipe = partial(llama3_145m)()
recipe.data.seq_length = recipe.model.config.seq_length = 2048
elif args.model_type == "bert":
recipe = partial(llm.bert_110m.pretrain_recipe, num_nodes=1, num_gpus_per_node=1)()
elif args.model_type == "t5":
recipe = partial(llm.t5_220m.pretrain_recipe, num_nodes=1, num_gpus_per_node=1)()
# Set to False if you don't want Auto Configurator to calculate model size
calculate_model_size = True
else:
raise ValueError(f"Unsupported model type for this script: {args.model_type}")
recipe.data.global_batch_size = 16
runner = AutoConfigurator(
recipe=recipe,
gpu_memory_gb=40,
tensor_parallel_sizes=[1],
pipeline_parallel_sizes=[1],
micro_batch_sizes=[1, 2, 4],
max_training_days=1,
max_steps_per_run=10,
num_tokens_in_b=10,
vocab_size=32000,
path_to_logs=args.log_dir,
calculate_model_size=calculate_model_size,
)
base_cfg, configs = generate_configs(runner)
if not args.get_results:
# Get generated configs
partials = list(configs.values())
names = list(configs.keys())
# Run pre-training
pretrain_cfg = partials[args.run_number - 1]
if args.extra_metrics:
from nemo.lightning.pytorch.callbacks import (
MemoryMonitor,
OptimizerMonitor,
RuntimeEstimator,
SpeedMonitor,
)
# add callbacks
pretrain_cfg.trainer.callbacks.append(run.Config(SpeedMonitor, window_size=5))
pretrain_cfg.trainer.callbacks.append(run.Config(RuntimeEstimator))
pretrain_cfg.trainer.callbacks.append(run.Config(OptimizerMonitor))
pretrain_cfg.trainer.callbacks.append(run.Config(MemoryMonitor))
pretrain = fdl.build(pretrain_cfg)
pretrain()
else:
# # Get Auto Configurator results
get_results(base_cfg, runner, args.log_dir, log_file_prefix="nemo_error")
print(f"The results were successfully saved to {args.log_dir}.")
def main():
args = get_args()
train_config(args)
if __name__ == '__main__':
main()
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