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 = [
NLPDDPPlugin(
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(PipelineMixedPrecisionPlugin(precision=cfg.trainer.precision, device='cuda', scaler=scaler))
if cfg.get('cluster_type', None) == 'BCP':
plugins.append(TorchElasticEnvironment())
trainer = Trainer(plugins=plugins, **cfg.trainer, callbacks=[ModelSummary(max_depth=3)])
exp_manager(trainer, cfg.exp_manager)
# update resume from checkpoint found by exp_manager
if cfg.model.resume_from_checkpoint is not None:
resume_from_checkpoint = cfg.model.resume_from_checkpoint
else:
resume_from_checkpoint = trainer._checkpoint_connector.resume_from_checkpoint_fit_path
logging.info(f'Resuming training from checkpoint: {resume_from_checkpoint}')
trainer._checkpoint_connector = CheckpointConnector(trainer, resume_from_checkpoint=resume_from_checkpoint)
# Override timer callback to a stateless one
for idx, callback in enumerate(trainer.callbacks):
if isinstance(callback, Timer):
trainer.callbacks[idx] = StatelessTimer(cfg.trainer.max_time,)
# 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
model = MegatronBARTModel(cfg.model, trainer)
trainer.fit(model)
def main(cfg) -> None:
logging.info("\n\n************** Experiment configuration ***********")
logging.info(f'\n{OmegaConf.to_yaml(cfg)}')
plugins = [
NLPDDPPlugin(
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),
)
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, **cfg.trainer)
exp_manager(trainer, cfg.exp_manager)
# Override timer callback to a stateless one
for idx, callback in enumerate(trainer.callbacks):
if isinstance(callback, Timer):
trainer.callbacks[idx] = StatelessTimer(cfg.trainer.max_time, )
# 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 GPT model
if cfg.model.get("restore_path", None):
model = MegatronGPTPromptLearningModel.restore_from(
cfg.model.restore_path,
cfg.model,
trainer=trainer,
save_restore_connector=NLPSaveRestoreConnector())
else:
model = MegatronGPTPromptLearningModel(cfg.model, trainer=trainer)
trainer.fit(model)
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 = [
NLPDDPPlugin(
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(
PipelineMixedPrecisionPlugin(precision=cfg.trainer.precision,
device='cuda',
scaler=scaler))
if cfg.get('cluster_type', None) == 'BCP':
plugins.append(TorchElasticEnvironment())
trainer = Trainer(plugins=plugins,
**cfg.trainer,
callbacks=[ModelSummary(max_depth=3)])
# tokenizers will be trained and and tarred training data will be created if needed
# model config is then updated
if cfg.model.preproc_out_dir is not None:
MTDataPreproc(cfg=cfg.model, trainer=trainer)
exp_manager(trainer, cfg.exp_manager)
# update resume from checkpoint found by exp_manager
if cfg.model.resume_from_checkpoint is not None:
resume_from_checkpoint = cfg.model.resume_from_checkpoint
else:
resume_from_checkpoint = trainer._checkpoint_connector.resume_from_checkpoint_fit_path
logging.info(
f'Resuming training from checkpoint: {resume_from_checkpoint}')
trainer._checkpoint_connector = CheckpointConnector(
trainer, resume_from_checkpoint=resume_from_checkpoint)
# Override timer callback to a stateless one
for idx, callback in enumerate(trainer.callbacks):
if isinstance(callback, Timer):
trainer.callbacks[idx] = StatelessTimer(cfg.trainer.max_time, )
# 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
if hasattr(cfg.model, 'pretrained_model_path'
) and cfg.model.pretrained_model_path is not None:
if not hasattr(cfg.model, 'pretrained_model_type'):
raise ValueError(f"Pretrained model type must be in [T5, BART].")
assert cfg.model.pretrained_model_type in ['T5', 'BART']
if cfg.model.pretrained_model_type == 'T5':
pretrained_cfg = MegatronT5Model.restore_from(
cfg.model.pretrained_model_path,
trainer=trainer,
return_config=True)
else:
pretrained_cfg = MegatronBARTModel.restore_from(
cfg.model.pretrained_model_path,
trainer=trainer,
return_config=True)
OmegaConf.set_struct(pretrained_cfg, True)
with open_dict(pretrained_cfg):
pretrained_cfg.masked_softmax_fusion = False
# Set source and target language/multilingual
pretrained_cfg.src_language = cfg.model.src_language
pretrained_cfg.tgt_language = cfg.model.tgt_language
pretrained_cfg.multilingual = cfg.model.multilingual
pretrained_cfg.shared_tokenizer = True
# Max generation delta
pretrained_cfg.max_generation_delta = cfg.model.max_generation_delta
# Set label smoothing
pretrained_cfg.label_smoothing = cfg.model.label_smoothing
# Set tokenizer paths:
pretrained_cfg.encoder_tokenizer = pretrained_cfg.tokenizer
pretrained_cfg.decoder_tokenizer = pretrained_cfg.tokenizer
# Pre-trained models should use the legacy sentencepiece tokenizer ex: mT5
pretrained_cfg.encoder_tokenizer.sentencepiece_legacy = True
pretrained_cfg.decoder_tokenizer.sentencepiece_legacy = True
# Override dropout
pretrained_cfg.hidden_dropout = cfg.model.hidden_dropout
pretrained_cfg.attention_dropout = cfg.model.attention_dropout
# Override precision
pretrained_cfg.precision = cfg.model.precision # Set above from trainer.precision
# Override data and global/micro batch size.
pretrained_cfg.train_ds = cfg.model.train_ds
pretrained_cfg.validation_ds = cfg.model.validation_ds
pretrained_cfg.test_ds = cfg.model.test_ds
pretrained_cfg.micro_batch_size = cfg.model.micro_batch_size
pretrained_cfg.global_batch_size = cfg.model.global_batch_size
# Class target for the new class being restored.
pretrained_cfg.target = (
"nemo.collections.nlp.models.machine_translation.megatron_nmt_model.MegatronNMTModel"
)
# Optimizer overrides.
pretrained_cfg.optim = cfg.model.optim
model = MegatronNMTModel.restore_from(
cfg.model.pretrained_model_path,
trainer=trainer,
override_config_path=pretrained_cfg,
save_restore_connector=NLPSaveRestoreConnector(),
)
else:
model = MegatronNMTModel(cfg.model, trainer)
if cfg.do_training:
trainer.fit(model)
if cfg.do_testing:
trainer.test(model)
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 = [
NLPDDPPlugin(
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(PipelineMixedPrecisionPlugin(precision=cfg.trainer.precision, device='cuda', scaler=scaler))
if cfg.get('cluster_type', None) == 'BCP':
plugins.append(TorchElasticEnvironment())
trainer = Trainer(plugins=plugins, **cfg.trainer)
exp_manager(trainer, cfg.exp_manager)
# update resume from checkpoint found by exp_manager
if cfg.model.resume_from_checkpoint is not None:
resume_from_checkpoint = cfg.model.resume_from_checkpoint
else:
resume_from_checkpoint = trainer._checkpoint_connector.resume_from_checkpoint_fit_path
logging.info(f'Resuming training from checkpoint: {resume_from_checkpoint}')
trainer._checkpoint_connector = CheckpointConnector(trainer, resume_from_checkpoint=resume_from_checkpoint)
# Override timer callback to a stateless one
for idx, callback in enumerate(trainer.callbacks):
if isinstance(callback, Timer):
trainer.callbacks[idx] = StatelessTimer(cfg.trainer.max_time,)
# Get the T5 Base configuration.
t5_cfg = MegatronT5FinetuneModel.restore_from(
restore_path=cfg.model.restore_from_path, trainer=trainer, return_config=True
)
# Override the T5 configuration with the one from the config file.
OmegaConf.set_struct(t5_cfg, True)
with open_dict(t5_cfg):
t5_cfg.masked_softmax_fusion = False
t5_cfg.megatron_amp_O2 = cfg.model.get('megatron_amp_O2', False)
t5_cfg.hidden_dropout = cfg.model.get('hidden_dropout', 0.1)
t5_cfg.attention_dropout = cfg.model.get('attention_dropout', 0.1)
t5_cfg.data = cfg.model.data
t5_cfg.precision = cfg.trainer.precision
t5_cfg.optim = cfg.model.optim
t5_cfg.micro_batch_size = cfg.model.data.train_ds.micro_batch_size
t5_cfg.global_batch_size = cfg.model.data.train_ds.global_batch_size
# XNLI has eval languages in the yaml config.
if hasattr(cfg.model, 'eval_languages'):
t5_cfg.eval_languages = cfg.model.eval_languages
if hasattr(cfg.model.data.train_ds, 'task_name'):
model = MegatronT5GLUEModel.restore_from(
restore_path=cfg.model.restore_from_path,
trainer=trainer,
override_config_path=t5_cfg,
save_restore_connector=NLPSaveRestoreConnector(),
)
else:
model = MegatronT5FinetuneModel.restore_from(
restore_path=cfg.model.restore_from_path,
trainer=trainer,
override_config_path=t5_cfg,
save_restore_connector=NLPSaveRestoreConnector(),
)
trainer.fit(model)
trainer.validate(model)
if hasattr(cfg.model.data, 'test_ds'):
trainer.test(model)
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 = [
NLPDDPPlugin(
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(
PipelineMixedPrecisionPlugin(precision=cfg.trainer.precision,
device='cuda',
scaler=scaler))
if cfg.get('cluster_type', None) == 'BCP':
plugins.append(TorchElasticEnvironment())
trainer = Trainer(plugins=plugins, **cfg.trainer)
exp_manager(trainer, cfg.exp_manager)
app_state = AppState()
if cfg.model.tensor_model_parallel_size > 1 or cfg.model.pipeline_model_parallel_size > 1:
app_state.model_parallel_size = cfg.model.tensor_model_parallel_size * cfg.model.pipeline_model_parallel_size
(
app_state.tensor_model_parallel_rank,
app_state.pipeline_model_parallel_rank,
app_state.model_parallel_size,
_,
) = fake_initialize_model_parallel(
world_size=app_state.model_parallel_size,
rank=trainer.global_rank,
tensor_model_parallel_size_=cfg.model.tensor_model_parallel_size,
pipeline_model_parallel_size_=cfg.model.
pipeline_model_parallel_size,
)
# Override timer callback to a stateless one
for idx, callback in enumerate(trainer.callbacks):
if isinstance(callback, Timer):
trainer.callbacks[idx] = StatelessTimer(cfg.trainer.max_time, )
# 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
model = MegatronGPTModel.restore_from(cfg.restore_from_path,
cfg.model,
trainer=trainer)
trainer.fit(model)