def run_experiment(args):
logger.info("\n***********************************************"
f"\n************* Experiment: {args.task.name} ************"
"\n************************************************")
ml_logger = MlLogger(tracking_uri=args.logging.mlflow_url)
ml_logger.init_experiment(
experiment_name=args.logging.mlflow_experiment,
run_name=args.logging.mlflow_run_name,
nested=args.logging.mlflow_nested,
)
validate_args(args)
distributed = bool(args.general.local_rank != -1)
# Init device and distributed settings
device, n_gpu = initialize_device_settings(
use_cuda=args.general.cuda,
local_rank=args.general.local_rank,
fp16=args.general.fp16,
)
args.parameter.batch_size = int(args.parameter.batch_size //
args.parameter.gradient_accumulation_steps)
if n_gpu > 1:
args.parameter.batch_size = args.parameter.batch_size * n_gpu
set_all_seeds(args.general.seed)
# Prepare Data
tokenizer = Tokenizer.load(args.parameter.model,
do_lower_case=args.parameter.lower_case)
processor = Processor.load(
tokenizer=tokenizer,
max_seq_len=args.parameter.max_seq_len,
data_dir=args.general.data_dir,
**args.task.toDict(
), # args is of type DotMap and needs conversion to std python dicts
)
data_silo = DataSilo(
processor=processor,
batch_size=args.parameter.batch_size,
distributed=distributed,
)
class_weights = None
if args.parameter.balance_classes:
task_names = list(processor.tasks.keys())
if len(task_names) > 1:
raise NotImplementedError(
f"Balancing classes is currently not supported for multitask experiments. Got tasks: {task_names} "
)
class_weights = data_silo.calculate_class_weights(
task_name=task_names[0])
model = get_adaptive_model(
lm_output_type=args.parameter.lm_output_type,
prediction_heads=args.parameter.prediction_head,
layer_dims=args.parameter.layer_dims,
model=args.parameter.model,
device=device,
class_weights=class_weights,
embeds_dropout_prob=args.parameter.embeds_dropout_prob,
)
# Init optimizer
# TODO: warmup linear is sometimes NONE depending on fp16 - is there a neater way to handle this?
optimizer, warmup_linear = initialize_optimizer(
model=model,
learning_rate=args.parameter.learning_rate,
warmup_proportion=args.parameter.warmup_proportion,
loss_scale=args.general.loss_scale,
fp16=args.general.fp16,
n_batches=len(data_silo.loaders["train"]),
grad_acc_steps=args.parameter.gradient_accumulation_steps,
n_epochs=args.parameter.epochs,
)
trainer = Trainer(
optimizer=optimizer,
data_silo=data_silo,
epochs=args.parameter.epochs,
n_gpu=n_gpu,
grad_acc_steps=args.parameter.gradient_accumulation_steps,
fp16=args.general.fp16,
local_rank=args.general.local_rank,
warmup_linear=warmup_linear,
evaluate_every=args.logging.eval_every,
device=device,
)
model = trainer.train(model)
model_name = (
f"{model.language_model.name}-{model.language_model.language}-{args.task.name}"
)
processor.save(f"{args.general.output_dir}/{model_name}")
model.save(f"{args.general.output_dir}/{model_name}")
def run_experiment(args):
logger.info("\n***********************************************"
f"\n************* Experiment: {args.task.name} ************"
"\n************************************************")
ml_logger = MlLogger(tracking_uri=args.logging.mlflow_url)
ml_logger.init_experiment(
experiment_name=args.logging.mlflow_experiment,
run_name=args.logging.mlflow_run_name,
nested=args.logging.mlflow_nested,
)
validate_args(args)
distributed = bool(args.general.local_rank != -1)
# Init device and distributed settings
device, n_gpu = initialize_device_settings(
use_cuda=args.general.cuda,
local_rank=args.general.local_rank,
use_amp=args.general.use_amp,
)
args.parameter.batch_size = int(args.parameter.batch_size //
args.parameter.gradient_accumulation_steps)
set_all_seeds(args.general.seed)
# Prepare Data
tokenizer = Tokenizer.load(args.parameter.model,
do_lower_case=args.parameter.lower_case)
processor = Processor.load(
tokenizer=tokenizer,
max_seq_len=args.parameter.max_seq_len,
data_dir=Path(args.general.data_dir),
**args.task.toDict(
), # args is of type DotMap and needs conversion to std python dicts
)
data_silo = DataSilo(
processor=processor,
batch_size=args.parameter.batch_size,
distributed=distributed,
)
class_weights = None
if args.parameter.balance_classes:
task_names = list(processor.tasks.keys())
if len(task_names) > 1:
raise NotImplementedError(
f"Balancing classes is currently not supported for multitask experiments. Got tasks: {task_names} "
)
class_weights = data_silo.calculate_class_weights(
task_name=task_names[0])
model = get_adaptive_model(
lm_output_type=args.parameter.lm_output_type,
prediction_heads=args.parameter.prediction_head,
layer_dims=args.parameter.layer_dims,
model=args.parameter.model,
device=device,
class_weights=class_weights,
embeds_dropout_prob=args.parameter.embeds_dropout_prob,
)
# Init optimizer
optimizer_opts = args.optimizer.optimizer_opts.toDict(
) if args.optimizer.optimizer_opts else None
schedule_opts = args.optimizer.schedule_opts.toDict(
) if args.optimizer.schedule_opts else None
model, optimizer, lr_schedule = initialize_optimizer(
model=model,
learning_rate=args.optimizer.learning_rate,
schedule_opts=schedule_opts,
optimizer_opts=optimizer_opts,
use_amp=args.general.use_amp,
n_batches=len(data_silo.loaders["train"]),
grad_acc_steps=args.parameter.gradient_accumulation_steps,
n_epochs=args.parameter.epochs,
device=device)
model_name = (
f"{model.language_model.name}-{model.language_model.language}-{args.task.name}"
)
# An early stopping instance can be used to save the model that performs best on the dev set
# according to some metric and stop training when no improvement is happening for some iterations.
if "early_stopping" in args:
early_stopping = EarlyStopping(
metric=args.task.metric,
mode=args.early_stopping.mode,
save_dir=Path(
f"{args.general.output_dir}/{model_name}_early_stopping"
), # where to save the best model
patience=args.early_stopping.
patience # number of evaluations to wait for improvement before terminating the training
)
else:
early_stopping = None
trainer = Trainer(
model=model,
optimizer=optimizer,
data_silo=data_silo,
epochs=args.parameter.epochs,
n_gpu=n_gpu,
grad_acc_steps=args.parameter.gradient_accumulation_steps,
use_amp=args.general.use_amp,
local_rank=args.general.local_rank,
lr_schedule=lr_schedule,
evaluate_every=args.logging.eval_every,
device=device,
early_stopping=early_stopping)
model = trainer.train()
processor.save(Path(f"{args.general.output_dir}/{model_name}"))
model.save(Path(f"{args.general.output_dir}/{model_name}"))
ml_logger.end_run()
def run_experiment(args):
validate_args(args)
directory_setup(output_dir=args.output_dir, do_train=args.do_train)
distributed = bool(args.local_rank != -1)
# Init device and distributed settings
device, n_gpu = initialize_device_settings(use_cuda=args.cuda,
local_rank=args.local_rank,
fp16=args.fp16)
args.batch_size = args.batch_size // args.gradient_accumulation_steps
if n_gpu > 1:
args.batch_size = args.batch_size * n_gpu
set_all_seeds(args.seed)
# Prepare Data
tokenizer = BertTokenizer.from_pretrained(args.model,
do_lower_case=args.lower_case)
processor = Processor.load(
processor_name=args.processor_name,
tokenizer=tokenizer,
max_seq_len=args.max_seq_len,
data_dir=args.data_dir,
)
data_silo = DataSilo(processor=processor,
batch_size=args.batch_size,
distributed=distributed)
class_weights = None
if args.balance_classes:
class_weights = data_silo.class_weights
model = get_adaptive_model(
lm_output_type=args.lm_output_type,
prediction_heads=args.prediction_head,
layer_dims=args.layer_dims,
model=args.model,
device=device,
class_weights=class_weights,
fp16=args.fp16,
embeds_dropout_prob=args.embeds_dropout_prob,
local_rank=args.local_rank,
n_gpu=n_gpu,
)
# Init optimizer
# TODO: warmup linear is sometimes NONE depending on fp16 - is there a neater way to handle this?
optimizer, warmup_linear = initialize_optimizer(
model=model,
learning_rate=args.learning_rate,
warmup_proportion=args.warmup_proportion,
loss_scale=args.loss_scale,
fp16=args.fp16,
n_examples=data_silo.n_samples("train"),
batch_size=args.batch_size,
grad_acc_steps=args.gradient_accumulation_steps,
n_epochs=args.epochs,
)
trainer = Trainer(
optimizer=optimizer,
data_silo=data_silo,
epochs=args.epochs,
n_gpu=n_gpu,
grad_acc_steps=args.gradient_accumulation_steps,
fp16=args.fp16,
warmup_linear=warmup_linear,
evaluate_every=args.eval_every,
device=device,
)
model = trainer.train(model)
model_name = (
f"{model.language_model.name}-{model.language_model.language}-{args.name}"
)
processor.save(f"saved_models/{model_name}")
model.save(f"saved_models/{model_name}")
def run_experiment(args):
validate_args(args)
distributed = bool(args.general.local_rank != -1)
# Init device and distributed settings
device, n_gpu = initialize_device_settings(
use_cuda=args.general.cuda,
local_rank=args.general.local_rank,
fp16=args.general.fp16,
)
args.parameter.batch_size = int(args.parameter.batch_size //
args.parameter.gradient_accumulation_steps)
if n_gpu > 1:
args.parameter.batch_size = args.parameter.batch_size * n_gpu
set_all_seeds(args.general.seed)
# Prepare Data
tokenizer = BertTokenizer.from_pretrained(
args.parameter.model, do_lower_case=args.parameter.lower_case)
# processor = Processor.load(
# tokenizer=tokenizer,
# max_seq_len=args.parameter.max_seq_len,
# data_dir=args.general.data_dir,
# train_filename=args.task.train_filename,
# dev_filename=args.task.dev_filename,
# test_filename=args.task.test_filename,
# dev_split=args.task.dev_split,
# metrics=args.task.metrics,
# **args.task.toDict(), # args is of type DotMap and needs conversion to std python dicts
# )
processor = Processor.load(
tokenizer=tokenizer,
max_seq_len=args.parameter.max_seq_len,
data_dir=args.general.data_dir,
**args.task.toDict(
), # args is of type DotMap and needs conversion to std python dicts
)
data_silo = DataSilo(
processor=processor,
batch_size=args.parameter.batch_size,
distributed=distributed,
)
class_weights = None
if args.parameter.balance_classes:
class_weights = data_silo.class_weights
model = get_adaptive_model(
lm_output_type=args.parameter.lm_output_type,
prediction_heads=args.parameter.prediction_head,
layer_dims=args.parameter.layer_dims,
model=args.parameter.model,
device=device,
class_weights=class_weights,
embeds_dropout_prob=args.parameter.embeds_dropout_prob,
)
# Init optimizer
# TODO: warmup linear is sometimes NONE depending on fp16 - is there a neater way to handle this?
optimizer, warmup_linear = initialize_optimizer(
model=model,
learning_rate=args.parameter.learning_rate,
warmup_proportion=args.parameter.warmup_proportion,
loss_scale=args.general.loss_scale,
fp16=args.general.fp16,
n_batches=len(data_silo.loaders["train"]),
grad_acc_steps=args.parameter.gradient_accumulation_steps,
n_epochs=args.parameter.epochs,
)
trainer = Trainer(
optimizer=optimizer,
data_silo=data_silo,
epochs=args.parameter.epochs,
n_gpu=n_gpu,
grad_acc_steps=args.parameter.gradient_accumulation_steps,
fp16=args.general.fp16,
local_rank=args.general.local_rank,
warmup_linear=warmup_linear,
evaluate_every=args.logging.eval_every,
device=device,
)
model = trainer.train(model)
model_name = (
f"{model.language_model.name}-{model.language_model.language}-{args.task.name}"
)
processor.save(f"{args.general.output_dir}/{model_name}")
model.save(f"{args.general.output_dir}/{model_name}")
