def main(cfg: MTBottleneckConfig) -> None:
# merge default config with user specified config
default_cfg = MTBottleneckConfig()
cfg = update_model_config(default_cfg, cfg)
logging.info("\n\n************** Experiment configuration ***********")
logging.info(f'Config: {OmegaConf.to_yaml(cfg)}')
# training is managed by PyTorch Lightning
trainer_cfg = OmegaConf.to_container(cfg.trainer)
trainer_cfg.pop('plugins', None)
trainer = Trainer(plugins=[NLPDDPPlugin(num_nodes=cfg.trainer.num_nodes)], **trainer_cfg)
# 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)
# experiment logs, checkpoints, and auto-resume are managed by exp_manager and PyTorch Lightning
exp_manager(trainer, cfg.exp_manager)
# everything needed to train translation models is encapsulated in the NeMo MTEncdDecModel
mt_model = MTBottleneckModel(cfg.model, trainer=trainer)
logging.info("\n\n************** Model parameters and their sizes ***********")
for name, param in mt_model.named_parameters():
print(name, param.size())
logging.info("***********************************************************\n\n")
if cfg.do_training:
trainer.fit(mt_model)
if cfg.do_testing:
trainer.test(mt_model)
def main(cfg: MTEncDecConfig) -> None:
# merge default config with user specified config
default_cfg = MTEncDecConfig()
cfg = update_model_config(default_cfg, cfg)
logging.info("\n\n************** Experiment configuration ***********")
logging.info(f'Config: {cfg.pretty()}')
# training is managed by PyTorch Lightning
trainer = Trainer(**cfg.trainer)
# tokenizers will be trained and and tarred training data will be created if needed
# model config is then updated
MTDataPreproc(cfg=cfg.model, trainer=trainer)
if cfg.do_training:
# experiment logs, checkpoints, and auto-resume are managed by exp_manager and PyTorch Lightning
exp_manager(trainer, cfg.exp_manager)
# everything needed to train translation models is encapsulated in the NeMo MTEncdDecModel
mt_model = MTEncDecModel(cfg.model, trainer=trainer)
logging.info("\n\n************** Model parameters and their sizes ***********")
for name, param in mt_model.named_parameters():
print(name, param.size())
logging.info("***********************************************************\n\n")
trainer.fit(mt_model)
def main(cfg: MTEncDecConfig) -> None:
# # merge default config with user specified config
default_cfg = MTEncDecConfig()
cfg = update_model_config(default_cfg, cfg)
logging.info("\n\n************** Experiment configuration ***********")
logging.info(f'Config: {cfg.pretty()}')
trainer = Trainer(**cfg.trainer)
exp_manager(trainer, cfg.exp_manager)
mt_model = MTEncDecModel(cfg.model, trainer=trainer)
logging.info(
"\n\n************** Model parameters and their sizes ***********")
for name, param in mt_model.named_parameters():
print(name, param.size())
logging.info(
"***********************************************************\n\n")
trainer.fit(mt_model)
def main(cfg):
# Generate default asr model config
asr_model_config = configs.EncDecCTCModelConfig()
# Merge hydra updates with model config
# `drop_missing_subconfig=True` is necessary here. Without it, while the data class will instantiate and be added
# to the config, it contains test_ds.sample_rate = MISSING and test_ds.labels = MISSING.
# This will raise a OmegaConf MissingMandatoryValue error when processing the dataloaders inside
# model_utils.resolve_test_dataloaders(model=self) (used for multi data loader support).
# In general, any operation that tries to use a DictConfig with MISSING in it will fail,
# other than explicit update operations to change MISSING to some actual value.
asr_model_config = update_model_config(asr_model_config,
cfg,
drop_missing_subconfigs=True)
# From here on out, its a general OmegaConf DictConfig, directly usable by our code.
trainer = pl.Trainer(**asr_model_config.trainer)
exp_manager(trainer, asr_model_config.get("exp_manager", None))
asr_model = EncDecCTCModel(cfg=asr_model_config.model, trainer=trainer)
trainer.fit(asr_model)
def test_dataclass_instantiation(self, asr_model):
model_cfg = configs.EncDecCTCModelConfig()
# Update mandatory values
vocabulary = asr_model.decoder.vocabulary
model_cfg.model.labels = vocabulary
# Update encoder
model_cfg.model.encoder.activation = 'relu'
model_cfg.model.encoder.feat_in = 64
model_cfg.model.encoder.jasper = [
nemo_asr.modules.conv_asr.JasperEncoderConfig(
filters=1024,
repeat=1,
kernel=[1],
stride=[1],
dilation=[1],
dropout=0.0,
residual=False,
se=True,
se_context_size=-1,
)
]
# Update decoder
model_cfg.model.decoder.feat_in = 1024
model_cfg.model.decoder.num_classes = 28
model_cfg.model.decoder.vocabulary = vocabulary
# Construct the model
asr_cfg = OmegaConf.create({'model': asr_model.cfg})
model_cfg_v1 = update_model_config(model_cfg, asr_cfg)
new_model = EncDecCTCModel(cfg=model_cfg_v1.model)
assert new_model.num_weights == asr_model.num_weights
# trainer and exp manager should be there
# assert 'trainer' in model_cfg_v1
# assert 'exp_manager' in model_cfg_v1
# datasets and optim/sched should not be there after ModelPT.update_model_dataclass()
assert 'train_ds' not in model_cfg_v1.model
assert 'validation_ds' not in model_cfg_v1.model
assert 'test_ds' not in model_cfg_v1.model
assert 'optim' not in model_cfg_v1.model
# Construct the model, without dropping additional keys
asr_cfg = OmegaConf.create({'model': asr_model.cfg})
model_cfg_v2 = update_model_config(model_cfg,
asr_cfg,
drop_missing_subconfigs=False)
# Assert all components are in config
# assert 'trainer' in model_cfg_v2
# assert 'exp_manager' in model_cfg_v2
assert 'train_ds' in model_cfg_v2.model
assert 'validation_ds' in model_cfg_v2.model
assert 'test_ds' in model_cfg_v2.model
assert 'optim' in model_cfg_v2.model
# Remove extra components (optim and sched can be kept without issue)
with open_dict(model_cfg_v2.model):
model_cfg_v2.model.pop('train_ds')
model_cfg_v2.model.pop('validation_ds')
model_cfg_v2.model.pop('test_ds')
new_model = EncDecCTCModel(cfg=model_cfg_v2.model)
assert new_model.num_weights == asr_model.num_weights
