File size: 5,418 Bytes
b386992 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 |
# 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 nemo_run as run
from nemo.collections import avlm
def configure_recipe(
nodes: int = 1,
gpus_per_node: int = 8,
pretrain=False,
language_model_from_pretrained=None,
checkpoint_path=None,
output_dir=None,
freeze_modules=None,
):
"""Configure the recipe"""
if pretrain:
recipe = avlm.avlm_8b.pretrain_recipe(
dir=output_dir, # Path to store checkpoints
name="avlm_pretrain",
num_nodes=nodes,
num_gpus_per_node=gpus_per_node,
language_model_from_pretrained=language_model_from_pretrained,
freeze_modules=freeze_modules,
)
else:
recipe = avlm.avlm_8b.finetune_recipe(
checkpoint_path=checkpoint_path,
dir=output_dir, # Path to store checkpoints
name="avlm_finetune",
num_nodes=nodes,
num_gpus_per_node=gpus_per_node,
freeze_modules=freeze_modules,
peft_scheme="none",
)
recipe.trainer.max_steps = 20
recipe.trainer.val_check_interval = 20
return recipe
def local_executor_torchrun(nodes: int = 1, devices: int = 8) -> run.LocalExecutor:
# pylint: disable=C0115,C0116
# Env vars for jobs are configured here
env_vars = {}
executor = run.LocalExecutor(ntasks_per_node=devices, launcher="torchrun", env_vars=env_vars)
return executor
def run_pretraining(language_model_from_pretrained=None, checkpoint_path=None, output_dir=None, freeze_modules=None):
# pylint: disable=C0115,C0116
recipe = configure_recipe(
pretrain=True,
language_model_from_pretrained=language_model_from_pretrained,
checkpoint_path=checkpoint_path,
output_dir=output_dir,
freeze_modules=freeze_modules,
)
executor = local_executor_torchrun(nodes=recipe.trainer.num_nodes, devices=recipe.trainer.devices)
run.run(recipe, executor=executor)
def run_finetuning(checkpoint_path=None, output_dir=None, freeze_modules=None):
# pylint: disable=C0115,C0116
recipe = configure_recipe(
pretrain=False, checkpoint_path=checkpoint_path, output_dir=output_dir, freeze_modules=freeze_modules
)
executor = local_executor_torchrun(nodes=recipe.trainer.num_nodes, devices=recipe.trainer.devices)
run.run(recipe, executor=executor)
# This condition is necessary for the script to be compatible with Python's multiprocessing module.
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Script with two optional arguments.")
parser.add_argument(
"--training_mode",
type=str,
required=True,
choices=["pretrain", "finetune"],
help="Training mode - either 'pretrain' or 'finetune'",
)
parser.add_argument(
"--language_model_from_pretrained",
type=str,
default=None,
required=False,
help="Path to pretrained language model (optional).",
)
parser.add_argument(
"--checkpoint_path", type=str, default=None, required=False, help="Path to checkpoint (optional)."
)
parser.add_argument(
"--output_dir", type=str, default="./outputs/checkpoints/avlm", help="Path to store checkpoints (optional)."
)
parser.add_argument("--unfreeze_language_model", action="store_true", help="Unfreeze language model (optional).")
parser.add_argument("--unfreeze_vision_model", action="store_true", help="Unfreeze vision model (optional).")
parser.add_argument("--unfreeze_audio_model", action="store_true", help="Unfreeze audio model (optional).")
parser.add_argument(
"--unfreeze_vision_projection", action="store_true", help="Unfreeze vision projection (optional)."
)
parser.add_argument(
"--unfreeze_audio_projection", action="store_true", help="Unfreeze audio projection (optional)."
)
args = parser.parse_args()
# run nemo_run
freeze_modules = {
"freeze_language_model": not args.unfreeze_language_model,
"freeze_vision_model": not args.unfreeze_vision_model,
"freeze_audio_model": not args.unfreeze_audio_model,
"freeze_vision_projection": not args.unfreeze_vision_projection,
"freeze_audio_projection": not args.unfreeze_audio_projection,
}
if args.training_mode == "pretrain":
run_pretraining(
language_model_from_pretrained=args.language_model_from_pretrained,
checkpoint_path=args.checkpoint_path,
output_dir=args.output_dir,
freeze_modules=freeze_modules,
)
elif args.training_mode == "finetune":
run_finetuning(
checkpoint_path=args.checkpoint_path,
output_dir=args.output_dir,
freeze_modules=freeze_modules,
)
|