def main(args):
quick_init_out = initialization.quick_init(args=args, verbose=True)
with quick_init_out.log_writer.log_context():
task, task_data = unsup_load_data.load_sup_and_unsup_data(
task_config_path=args.task_config_path,
unsup_task_config_path=args.unsup_task_config_path,
)
with distributed.only_first_process(local_rank=args.local_rank):
# load the model
model_class_spec = model_resolution.resolve_model_setup_classes(
model_type=args.model_type,
task_type=task.TASK_TYPE,
)
model_wrapper = model_setup.simple_model_setup(
model_type=args.model_type,
model_class_spec=model_class_spec,
config_path=args.model_config_path,
tokenizer_path=args.model_tokenizer_path,
task=task,
)
model_setup.simple_load_model_path(
model=model_wrapper.model,
model_load_mode=args.model_load_mode,
model_path=args.model_path,
)
model_wrapper.model.to(quick_init_out.device)
num_train_examples = len(task_data["sup"]["train"])
train_schedule = train_setup.get_train_schedule(
num_train_examples=num_train_examples,
max_steps=args.max_steps,
num_train_epochs=args.num_train_epochs,
gradient_accumulation_steps=args.gradient_accumulation_steps,
per_gpu_train_batch_size=args.train_batch_size,
n_gpu=quick_init_out.n_gpu,
)
print("t_total", train_schedule.t_total)
loss_criterion = train_setup.resolve_loss_function(
task_type=task.TASK_TYPE)
optimizer_scheduler = model_setup.create_optimizer(
model=model_wrapper.model,
learning_rate=args.learning_rate,
t_total=train_schedule.t_total,
warmup_steps=args.warmup_steps,
warmup_proportion=args.warmup_proportion,
optimizer_type=args.optimizer_type,
verbose=True,
)
model_setup.special_model_setup(
model_wrapper=model_wrapper,
optimizer_scheduler=optimizer_scheduler,
fp16=args.fp16,
fp16_opt_level=args.fp16_opt_level,
n_gpu=quick_init_out.n_gpu,
local_rank=args.local_rank,
)
rparams = uda_runner.RunnerParameters(
feat_spec=model_resolution.build_featurization_spec(
model_type=args.model_type,
max_seq_length=args.max_seq_length,
),
local_rank=args.local_rank,
n_gpu=quick_init_out.n_gpu,
fp16=args.fp16,
learning_rate=args.learning_rate,
eval_batch_size=args.eval_batch_size,
max_grad_norm=args.max_grad_norm,
)
uda_params = uda_runner.UDAParameters(
use_unsup=args.unsup_ratio != 0,
unsup_ratio=args.unsup_ratio,
tsa=not args.no_tsa,
tsa_schedule=args.tsa_schedule,
uda_softmax_temp=args.uda_softmax_temp,
uda_confidence_thresh=args.uda_confidence_thresh,
uda_coeff=args.uda_coeff,
)
runner = uda_runner.UDARunner(
task=task,
model_wrapper=model_wrapper,
optimizer_scheduler=optimizer_scheduler,
loss_criterion=loss_criterion,
device=quick_init_out.device,
rparams=rparams,
uda_params=uda_params,
train_schedule=train_schedule,
log_writer=quick_init_out.log_writer,
)
if args.do_train:
val_examples = task.get_val_examples()
uda_runner.train_val_save_every(
runner=runner,
task_data=task_data,
val_examples=
val_examples[:args.partial_eval_number], # quick and dirty
should_save_func=metarunner.get_should_save_func(
args.save_every_steps),
should_eval_func=metarunner.get_should_eval_func(
args.eval_every_steps),
output_dir=args.output_dir,
verbose=True,
save_best_model=args.do_save,
load_best_model=True,
log_writer=quick_init_out.log_writer,
)
if args.do_save:
torch.save(model_wrapper.model.state_dict(),
os.path.join(args.output_dir, "model.p"))
if args.do_val:
val_examples = task.get_val_examples()
results = runner.run_val(val_examples)
evaluate.write_val_results(
results=results,
output_dir=args.output_dir,
verbose=True,
)
if args.do_test:
test_examples = task.get_test_examples()
logits = runner.run_test(test_examples)
evaluate.write_preds(
logits=logits,
output_path=os.path.join(args.output_dir, "test_preds.csv"),
)
def main(args):
quick_init_out = initialization.quick_init(args=args, verbose=True)
with quick_init_out.log_writer.log_context():
task = tasks.create_task_from_config_path(
config_path=args.task_config_path,
verbose=True,
)
with distributed.only_first_process(local_rank=args.local_rank):
# load the model
model_class_spec = model_resolution.resolve_model_setup_classes(
model_type=args.model_type,
task_type=task.TASK_TYPE,
)
model_wrapper = model_setup.simple_model_setup(
model_type=args.model_type,
model_class_spec=model_class_spec,
config_path=args.model_config_path,
tokenizer_path=args.model_tokenizer_path,
task=task,
)
model_setup.simple_load_model_path(
model=model_wrapper.model,
model_load_mode=args.model_load_mode,
model_path=args.model_path,
verbose=True,
)
model_wrapper.model.to(quick_init_out.device)
train_examples = task.get_train_examples()
train_examples, _ = train_setup.maybe_subsample_train(
train_examples=train_examples,
train_examples_number=args.train_examples_number,
train_examples_fraction=args.train_examples_fraction,
)
num_train_examples = len(train_examples)
train_schedule = train_setup.get_train_schedule(
num_train_examples=num_train_examples,
max_steps=args.max_steps,
num_train_epochs=args.num_train_epochs,
gradient_accumulation_steps=args.gradient_accumulation_steps,
per_gpu_train_batch_size=args.train_batch_size,
n_gpu=quick_init_out.n_gpu,
)
quick_init_out.log_writer.write_entry(
"text", f"t_total: {train_schedule.t_total}", do_print=True)
loss_criterion = train_setup.resolve_loss_function(
task_type=task.TASK_TYPE)
optimizer_scheduler = model_setup.create_optimizer(
model=model_wrapper.model,
learning_rate=args.learning_rate,
t_total=train_schedule.t_total,
warmup_steps=args.warmup_steps,
warmup_proportion=args.warmup_proportion,
optimizer_type=args.optimizer_type,
verbose=True,
)
model_setup.special_model_setup(
model_wrapper=model_wrapper,
optimizer_scheduler=optimizer_scheduler,
fp16=args.fp16,
fp16_opt_level=args.fp16_opt_level,
n_gpu=quick_init_out.n_gpu,
local_rank=args.local_rank,
)
rparams = simple_runner.RunnerParameters(
feat_spec=model_resolution.build_featurization_spec(
model_type=args.model_type,
max_seq_length=args.max_seq_length,
),
local_rank=args.local_rank,
n_gpu=quick_init_out.n_gpu,
fp16=args.fp16,
learning_rate=args.learning_rate,
eval_batch_size=args.eval_batch_size,
max_grad_norm=args.max_grad_norm,
)
runner = simple_runner.SimpleTaskRunner(
task=task,
model_wrapper=model_wrapper,
optimizer_scheduler=optimizer_scheduler,
loss_criterion=loss_criterion,
device=quick_init_out.device,
rparams=rparams,
train_schedule=train_schedule,
log_writer=quick_init_out.log_writer,
)
if args.do_train:
val_examples = task.get_val_examples()
metarunner.MetaRunner(
runner=runner,
train_examples=train_examples,
val_examples=
val_examples[:args.partial_eval_number], # quick and dirty
should_save_func=metarunner.get_should_save_func(
args.save_every_steps),
should_eval_func=metarunner.get_should_eval_func(
args.eval_every_steps),
output_dir=args.output_dir,
verbose=True,
save_best_model=args.do_save,
load_best_model=True,
log_writer=quick_init_out.log_writer,
).train_val_save_every()
if args.do_save:
torch.save(model_wrapper.model.state_dict(),
os.path.join(args.output_dir, "model.p"))
if args.do_val:
val_examples = task.get_val_examples()
results = runner.run_val(val_examples)
evaluate.write_val_results(
results=results,
output_dir=args.output_dir,
verbose=True,
)
if args.do_test:
test_examples = task.get_test_examples()
logits = runner.run_test(test_examples)
evaluate.write_preds(
logits=logits,
output_path=os.path.join(args.output_dir, "test_preds.csv"),
)
def main(args):
quick_init_out = initialization.quick_init(args=args, verbose=False)
task = tasks.create_task_from_config_path(
config_path=args.task_config_path,
verbose=True,
)
with distributed.only_first_process(local_rank=args.local_rank):
model_class_spec = model_resolution.resolve_model_setup_classes(
model_type=args.model_type,
task_type=task.TASK_TYPE,
)
model_wrapper = model_setup.simple_model_setup(
model_type=args.model_type,
model_class_spec=model_class_spec,
config_path=args.model_config_path,
tokenizer_path=args.model_tokenizer_path,
task=task,
)
model_setup.simple_load_model_path(
model=model_wrapper.model,
model_load_mode=args.model_load_mode,
model_path=args.model_path,
)
adapter_weights_dict = multi_adapters.load_adapter_weights_dict_path(
args.adapter_weights_path)
multi_adapters.exclude_adapters(
adapter_weights_dict, exclude_list=args.adapter_exclude.split(","))
adapter_weights_dict = multi_adapters.isolate_adapter_weights_dict(
adapter_weights_dict=adapter_weights_dict,
model_type=args.model_type,
)
sub_module_name_list = list(adapter_weights_dict.keys())
modified_layers = multi_adapters.add_multi_adapters(
model=model_wrapper.model,
sub_module_name_list=sub_module_name_list,
adapter_config=adapters.AdapterConfig(),
include_base=args.adapter_include_base,
include_flex=args.adapter_include_flex,
num_weight_sets=args.adapter_num_weight_sets,
use_optimized=args.adapter_use_optimized,
)
multi_adapters.load_multi_adapter_weights(
model=model_wrapper.model,
modified_layers=modified_layers,
adapter_weights_dict=adapter_weights_dict,
)
model_wrapper.model.to(quick_init_out.device)
tunable_parameters = multi_adapters.get_tunable_parameters(
model=model_wrapper.model,
modified_layers=modified_layers,
ft_mode=args.adapter_ft_mode,
)
train_examples = task.get_train_examples()
train_examples, _ = train_setup.maybe_subsample_train(
train_examples=train_examples,
train_examples_number=args.train_examples_number,
train_examples_fraction=args.train_examples_fraction,
)
num_train_examples = len(train_examples)
train_schedule = train_setup.get_train_schedule(
num_train_examples=num_train_examples,
max_steps=args.max_steps,
num_train_epochs=args.num_train_epochs,
gradient_accumulation_steps=args.gradient_accumulation_steps,
per_gpu_train_batch_size=args.train_batch_size,
n_gpu=quick_init_out.n_gpu,
)
loss_criterion = train_setup.resolve_loss_function(
task_type=task.TASK_TYPE)
optimizer_scheduler = model_setup.create_optimizer_from_params(
named_parameters=tunable_parameters,
learning_rate=args.learning_rate,
t_total=train_schedule.t_total,
warmup_steps=args.warmup_steps,
warmup_proportion=args.warmup_proportion,
optimizer_type=args.optimizer_type,
verbose=True,
)
model_setup.special_model_setup(
model_wrapper=model_wrapper,
optimizer_scheduler=optimizer_scheduler,
fp16=args.fp16,
fp16_opt_level=args.fp16_opt_level,
n_gpu=quick_init_out.n_gpu,
local_rank=args.local_rank,
)
rparams = simple_runner.RunnerParameters(
feat_spec=model_resolution.build_featurization_spec(
model_type=args.model_type,
max_seq_length=args.max_seq_length,
),
local_rank=args.local_rank,
n_gpu=quick_init_out.n_gpu,
fp16=args.fp16,
learning_rate=args.learning_rate,
eval_batch_size=args.eval_batch_size,
max_grad_norm=args.max_grad_norm,
)
runner = simple_runner.SimpleTaskRunner(
task=task,
model_wrapper=model_wrapper,
optimizer_scheduler=optimizer_scheduler,
loss_criterion=loss_criterion,
device=quick_init_out.device,
rparams=rparams,
train_schedule=train_schedule,
log_writer=quick_init_out.log_writer,
)
if args.do_train:
val_examples = task.get_val_examples()
adapters_runner.AdapterMetaRunner(
runner=runner,
train_examples=train_examples,
val_examples=val_examples[:args.
partial_eval_number], # quick and dirty
should_save_func=metarunner.get_should_save_func(
args.save_every_steps),
should_eval_func=metarunner.get_should_eval_func(
args.eval_every_steps),
output_dir=args.output_dir,
verbose=True,
save_best_model=args.do_save,
load_best_model=True,
log_writer=quick_init_out.log_writer,
modified_layers=modified_layers,
).train_val_save_every()
if args.do_save:
torch.save(model_wrapper.model.state_dict(),
os.path.join(args.output_dir, "model.p"))
if args.do_val:
val_examples = task.get_val_examples()
results = runner.run_val(val_examples)
evaluate.write_val_results(
results=results,
output_dir=args.output_dir,
verbose=True,
)
if args.do_test:
test_examples = task.get_test_examples()
logits = runner.run_test(test_examples)
evaluate.write_preds(
logits=logits,
output_path=os.path.join(args.output_dir, "test_preds.csv"),
)
def main(args):
quick_init_out = initialization.quick_init(args=args, verbose=True)
task, uda_task_data = uda_load_data.load_task_data_from_path(
args.uda_task_config_path)
with distributed.only_first_process(local_rank=args.local_rank):
# load the model
model_wrapper = llp_model_setup.setup_model(
model_type=args.model_type,
task=task,
llp_embedding_dim=args.llp_embedding_dim,
config_path=args.model_config_path,
tokenizer_path=args.model_tokenizer_path,
)
llp_model_setup.load_model(
model=model_wrapper.model,
state_dict=torch.load(args.model_path),
load_mode=args.model_load_mode,
)
model_wrapper.model.to(quick_init_out.device)
# === Train Data Setup [START] === #
labeled_examples = uda_task_data["sup"]["train"]
unlabeled_examples, indices = train_setup.maybe_subsample_train(
train_examples=uda_task_data["unsup"]["orig"],
train_examples_number=args.unlabeled_train_examples_number,
train_examples_fraction=args.unlabeled_train_examples_fraction,
)
if indices is not None:
write_json(indices,
os.path.join(args.output_dir, "sampled_indices.json"))
train_examples = labeled_examples + unlabeled_examples
num_train_examples = len(train_examples)
# === Train Data Setup [END] === #
train_schedule = train_setup.get_train_schedule(
num_train_examples=num_train_examples,
max_steps=args.max_steps,
num_train_epochs=args.num_train_epochs,
gradient_accumulation_steps=args.gradient_accumulation_steps,
per_gpu_train_batch_size=args.train_batch_size,
n_gpu=quick_init_out.n_gpu,
)
print("t_total", train_schedule.t_total)
loss_criterion = train_setup.resolve_loss_function(
task_type=task.TASK_TYPE)
optimizer_scheduler = shared_model_setup.create_optimizer(
model=model_wrapper.model,
learning_rate=args.learning_rate,
t_total=train_schedule.t_total,
warmup_steps=args.warmup_steps,
warmup_proportion=args.warmup_proportion,
optimizer_type=args.optimizer_type,
verbose=True,
)
# I don't think this works for LLP...
shared_model_setup.special_model_setup(
model_wrapper=model_wrapper,
optimizer_scheduler=optimizer_scheduler,
fp16=args.fp16,
fp16_opt_level=args.fp16_opt_level,
n_gpu=quick_init_out.n_gpu,
local_rank=args.local_rank,
)
rparams = llp_runner.RunnerParameters(
feat_spec=model_resolution.build_featurization_spec(
model_type=args.model_type,
max_seq_length=args.max_seq_length,
),
local_rank=args.local_rank,
n_gpu=quick_init_out.n_gpu,
fp16=args.fp16,
learning_rate=args.learning_rate,
eval_batch_size=args.eval_batch_size,
max_grad_norm=args.max_grad_norm,
)
llp_params = llp_runner.LlpParameters(
num_labeled=len(labeled_examples),
llp_embedding_dim=args.llp_embedding_dim,
llp_const_k=args.llp_const_k,
llp_const_t=args.llp_const_t,
llp_const_tau=args.llp_const_tau,
llp_prop_chunk_size=args.llp_prop_chunk_size,
llp_mem_bank_t=args.llp_mem_bank_t,
llp_rep_global_agg_loss_lambda=args.llp_rep_global_agg_loss_lambda,
llp_embedding_norm_loss=args.llp_embedding_norm_loss,
llp_compute_global_agg_loss_mode=args.llp_compute_global_agg_loss_mode,
)
llpuda_params = uda_llp_runner.LLPUDAParameters(
uda_coeff=args.uda_coeff,
use_unsup=args.unsup_ratio != 0,
unsup_ratio=args.unsup_ratio,
)
with quick_init_out.log_writer.log_context():
runner = uda_llp_runner.UDALLPRunner(
task=task,
model_wrapper=model_wrapper,
optimizer_scheduler=optimizer_scheduler,
loss_criterion=loss_criterion,
device=quick_init_out.device,
rparams=rparams,
llp_params=llp_params,
llpuda_params=llpuda_params,
train_schedule=train_schedule,
log_writer=quick_init_out.log_writer,
)
if args.do_train:
runner.init_llp_state(train_examples)
runner.run_train(train_examples, uda_task_data)
if args.do_save:
torch.save(model_wrapper.model.state_dict(),
os.path.join(args.output_dir, "model.p"))
if args.do_val:
val_examples = task.get_val_examples()
results = runner.run_val(val_examples)
evaluate.write_val_results(
results=results,
output_dir=args.output_dir,
verbose=True,
)
if args.do_test:
test_examples = task.get_test_examples()
logits = runner.run_test(test_examples)
evaluate.write_preds(
logits=logits,
output_path=os.path.join(args.output_dir, "test_preds.csv"),
)
def main(args):
quick_init_out = initialization.quick_init(args=args, verbose=True)
task = tasks.create_task_from_config_path(
config_path=args.task_config_path)
for phase in ["train", "val", "test"]:
if phase in task.path_dict:
print(task.path_dict[phase])
with distributed.only_first_process(local_rank=args.local_rank):
# load the model
model_class_spec = model_resolution.resolve_model_setup_classes(
model_type=args.model_type,
task_type=task.TASK_TYPE,
)
model_wrapper = model_setup.simple_model_setup(
model_type=args.model_type,
model_class_spec=model_class_spec,
config_path=args.model_config_path,
tokenizer_path=args.model_tokenizer_path,
task=task,
)
model_setup.safe_load_model(model=model_wrapper.model,
state_dict=torch.load(args.model_path))
model_wrapper.model.to(quick_init_out.device)
train_examples = task.get_train_examples()
for train_path in args.extra_train_paths:
print(f"Extra: {train_path}")
extra_task = task.__class__(task.name, {"train": train_path})
train_examples += extra_task.get_train_examples()
num_train_examples = len(train_examples)
train_schedule = train_setup.get_train_schedule(
num_train_examples=num_train_examples,
max_steps=args.max_steps,
num_train_epochs=args.num_train_epochs,
gradient_accumulation_steps=args.gradient_accumulation_steps,
per_gpu_train_batch_size=args.train_batch_size,
n_gpu=quick_init_out.n_gpu,
)
print("t_total", train_schedule.t_total)
loss_criterion = train_setup.resolve_loss_function(
task_type=task.TASK_TYPE)
optimizer_scheduler = model_setup.create_optimizer(
model=model_wrapper.model,
learning_rate=args.learning_rate,
t_total=train_schedule.t_total,
warmup_steps=args.warmup_steps,
warmup_proportion=args.warmup_proportion,
optimizer_type=args.optimizer_type,
verbose=True,
)
model_setup.special_model_setup(
model_wrapper=model_wrapper,
optimizer_scheduler=optimizer_scheduler,
fp16=args.fp16,
fp16_opt_level=args.fp16_opt_level,
n_gpu=quick_init_out.n_gpu,
local_rank=args.local_rank,
)
rparams = simple_runner.RunnerParameters(
feat_spec=model_resolution.build_featurization_spec(
model_type=args.model_type,
max_seq_length=args.max_seq_length,
),
local_rank=args.local_rank,
n_gpu=quick_init_out.n_gpu,
fp16=args.fp16,
learning_rate=args.learning_rate,
eval_batch_size=args.eval_batch_size,
max_grad_norm=args.max_grad_norm,
)
runner = simple_runner.SimpleTaskRunner(
task=task,
model_wrapper=model_wrapper,
optimizer_scheduler=optimizer_scheduler,
loss_criterion=loss_criterion,
device=quick_init_out.device,
rparams=rparams,
train_schedule=train_schedule,
log_writer=quick_init_out.log_writer,
)
with quick_init_out.log_writer.log_context():
if args.do_train:
runner.run_train(train_examples)
if args.do_save:
torch.save(model_wrapper.model.state_dict(),
os.path.join(args.output_dir, "model.p"))
if args.do_val:
val_examples = task.get_val_examples()
results = runner.run_val(val_examples)
evaluate.write_val_results(
results=results,
output_dir=args.output_dir,
verbose=True,
)
if args.do_test:
test_examples = task.get_test_examples()
logits = runner.run_test(test_examples)
evaluate.write_preds(
logits=logits,
output_path=os.path.join(args.output_dir, "test_preds.csv"),
)