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# Copyright (c) 2022, 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 torch.multiprocessing as mp
from omegaconf.omegaconf import OmegaConf, open_dict
from pytorch_lightning import Trainer
from pytorch_lightning.plugins.environments import TorchElasticEnvironment
from nemo.collections.nlp.models.language_modeling.megatron_t5_prompt_learning_model import (
MegatronT5PromptLearningModel,
)
from nemo.collections.nlp.parts.nlp_overrides import (
GradScaler,
NLPDDPStrategy,
NLPSaveRestoreConnector,
PipelineMixedPrecisionPlugin,
)
from nemo.core.config import hydra_runner
from nemo.utils import logging
from nemo.utils.exp_manager import exp_manager
mp.set_start_method("spawn", force=True)
"""
This is an example of how to ptune/prompt-tune a pretrained T5 model.
Be sure to use a .nemo T5 model with this code. If you've downloaded
a model from NGC or are otherwise using a MegatronLM model, please use
either megatron_ckpt_to_nemo.py or megatron_lm_ckpt_to_nemo.py found
within this examples directory to convert your model to .nemo format.
"""
@hydra_runner(config_path="conf", config_name="megatron_t5_prompt_learning.yaml")
def main(cfg) -> None:
logging.info("\n\n************** Experiment configuration ***********")
logging.info(f'\n{OmegaConf.to_yaml(cfg)}')
plugins = []
strategy = NLPDDPStrategy(no_ddp_communication_hook=True, find_unused_parameters=False,)
if cfg.trainer.precision == 16:
scaler = GradScaler(
init_scale=cfg.model.get('native_amp_init_scale', 2 ** 32),
growth_interval=cfg.model.get('native_amp_growth_interval', 1000),
hysteresis=cfg.model.get('hysteresis', 2),
enabled=False
if cfg.model.pipeline_model_parallel_size > 1
else True, # turn off the grad scale for pipeline parallel LM model
)
plugins.append(PipelineMixedPrecisionPlugin(precision=cfg.trainer.precision, device='cuda', scaler=scaler))
if cfg.get('cluster_type', None) == 'BCP':
plugins.append(TorchElasticEnvironment())
trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer)
exp_manager(trainer, cfg.exp_manager)
# hydra interpolation does not work here as the interpolation key is lost when PTL saves hparams
with open_dict(cfg):
cfg.model.precision = cfg.trainer.precision
# load existing or init new soft prompt T5 model
if cfg.model.get("restore_path", None):
model = MegatronT5PromptLearningModel.restore_from(
cfg.model.restore_path, cfg.model, trainer=trainer, save_restore_connector=NLPSaveRestoreConnector()
)
else:
model = MegatronT5PromptLearningModel(cfg.model, trainer=trainer)
trainer.fit(model)
if __name__ == '__main__':
main()
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