# 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. from megatron_t5_seq2seq_finetune import load_from_checkpoint_dir, load_from_nemo, validate_checkpoint_loading_args from omegaconf.omegaconf import OmegaConf, open_dict from pytorch_lightning import Trainer from pytorch_lightning.plugins.environments import TorchElasticEnvironment from pytorch_lightning.plugins.precision.native_amp import NativeMixedPrecisionPlugin from nemo.collections.nlp.models.language_modeling.megatron_finetune_model import MegatronT5FinetuneModel from nemo.collections.nlp.models.language_modeling.megatron_glue_model import MegatronT5GLUEModel from nemo.collections.nlp.models.language_modeling.megatron_t0_model import MegatronT0Model from nemo.collections.nlp.parts.nlp_overrides import GradScaler, MegatronHalfPrecisionPlugin, NLPDDPStrategy from nemo.core.config import hydra_runner from nemo.utils import logging from nemo.utils.exp_manager import exp_manager def _modify_config(t5_cfg, cfg, add_cfg_to_tree=False): """ This function modifies the original t5 pre-training config (t5_cfg) with attributes from the finetuning config (cfg). The `add_cfg_to_tree` arg adds `cfg` to the top of the yaml tree which is needed for all `hparams.yaml` files when passed as an arg to `load_from_checkpoint()`. """ OmegaConf.set_struct(t5_cfg, True) with open_dict(t5_cfg): t5_cfg.precision = cfg.trainer.precision # Overwrite data configs if cfg.model.data.validation_ds.get('src_file_name', None) is not None: logging.info( 'Found validation_ds.src_file_name in the config file. Overriding the finetuned model config file with the values from the new config file.' ) t5_cfg.data.validation_ds.src_file_name = cfg.model.data.validation_ds.src_file_name if cfg.model.data.validation_ds.get('tgt_file_name', None) is not None: logging.info( 'Found validation_ds.tgt_file_name in the config file. Overriding the finetuned model config file with the values from the new config file.' ) t5_cfg.data.validation_ds.tgt_file_name = cfg.model.data.validation_ds.tgt_file_name if "write_predictions_to_file" in cfg.model.data.validation_ds: t5_cfg.data.validation_ds.write_predictions_to_file = ( cfg.model.data.validation_ds.write_predictions_to_file ) if "output_file_path_prefix" in cfg.model.data.validation_ds: t5_cfg.data.validation_ds.output_file_path_prefix = cfg.model.data.validation_ds.output_file_path_prefix t5_cfg.data.validation_ds.micro_batch_size = cfg.model.data.validation_ds.micro_batch_size t5_cfg.data.validation_ds.global_batch_size = cfg.model.data.validation_ds.global_batch_size # This is needed when modifying a hparam file directly to load `.ckpt` files. # This is not needed to modify the cfg in `.nemo` files. if add_cfg_to_tree: OmegaConf.resolve(t5_cfg) t5_cfg.cfg = t5_cfg return t5_cfg @hydra_runner(config_path="conf", config_name="megatron_t5_config_finetune_glue_eval") def main(cfg) -> None: logging.info("\n\n************** Experiment configuration ***********") logging.info(f'\n{OmegaConf.to_yaml(cfg)}') megatron_amp_o2 = cfg.model.get('megatron_amp_O2', False) plugins = [] strategy = NLPDDPStrategy( no_ddp_communication_hook=True, gradient_as_bucket_view=cfg.model.gradient_as_bucket_view, find_unused_parameters=False, ) if cfg.trainer.precision in [16, 'bf16']: scaler = None 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), ) if megatron_amp_o2: plugins.append(MegatronHalfPrecisionPlugin(precision=cfg.trainer.precision, device='cuda', scaler=scaler)) else: plugins.append(NativeMixedPrecisionPlugin(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) if hasattr(cfg.model.data.validation_ds, 'task_name'): if cfg.model.restore_from_path: t5_cfg = MegatronT5GLUEModel.restore_from( restore_path=cfg.model.restore_from_path, trainer=trainer, return_config=True ) model = load_from_nemo(MegatronT5GLUEModel, cfg, trainer, t5_cfg, modify_confg_fn=_modify_config) else: validate_checkpoint_loading_args(cfg.model.pretrained_checkpoint) model = load_from_checkpoint_dir(MegatronT5GLUEModel, cfg, trainer, modify_confg_fn=_modify_config) elif hasattr(cfg.model.data.validation_ds, 'file_names'): if cfg.model.restore_from_path: t5_cfg = MegatronT0Model.restore_from( restore_path=cfg.model.restore_from_path, trainer=trainer, return_config=True ) model = load_from_nemo(MegatronT0Model, cfg, trainer, t5_cfg, modify_confg_fn=_modify_config) else: validate_checkpoint_loading_args(cfg.model.pretrained_checkpoint) model = load_from_checkpoint_dir(MegatronT0Model, cfg, trainer, modify_confg_fn=_modify_config) else: if cfg.model.restore_from_path: t5_cfg = MegatronT5FinetuneModel.restore_from( restore_path=cfg.model.restore_from_path, trainer=trainer, return_config=True ) model = load_from_nemo(MegatronT5FinetuneModel, cfg, trainer, t5_cfg, modify_confg_fn=_modify_config) else: validate_checkpoint_loading_args(cfg.model.pretrained_checkpoint) model = load_from_checkpoint_dir(MegatronT5FinetuneModel, cfg, trainer, modify_confg_fn=_modify_config) model.freeze() trainer.validate(model) if hasattr(cfg.model.data, 'test_ds'): trainer.test(model) if __name__ == '__main__': main()