def run_trainer(
self,
eval_steps: int,
max_len: int,
model_name: str,
num_train_epochs: int,
learning_rate: float = 3e-3,
distributed: bool = False,
extra_args_str: str = None,
predict_with_generate: bool = True,
):
data_dir = self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
output_dir = self.get_auto_remove_tmp_dir()
args = f"""
--model_name_or_path {model_name}
--train_file {data_dir}/train.json
--validation_file {data_dir}/val.json
--test_file {data_dir}/test.json
--output_dir {output_dir}
--overwrite_output_dir
--max_train_samples 8
--max_val_samples 8
--max_source_length {max_len}
--max_target_length {max_len}
--val_max_target_length {max_len}
--do_train
--do_eval
--do_predict
--num_train_epochs {str(num_train_epochs)}
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--learning_rate {learning_rate}
--warmup_steps 8
--evaluation_strategy steps
--logging_steps 0
--eval_steps {str(eval_steps)}
--save_steps {str(eval_steps)}
--group_by_length
--label_smoothing_factor 0.1
--adafactor
--target_lang ro_RO
--source_lang en_XX
"""
if predict_with_generate:
args += "--predict_with_generate"
args = args.split()
if extra_args_str is not None:
args.extend(extra_args_str.split())
if distributed:
n_gpu = get_gpu_count()
distributed_args = f"""
-m torch.distributed.launch
--nproc_per_node={n_gpu}
{self.examples_dir_str}/pytorch/translation/run_translation.py
""".split()
cmd = [sys.executable] + distributed_args + args
execute_subprocess_async(cmd, env=self.get_env())
else:
testargs = ["run_translation.py"] + args
with patch.object(sys, "argv", testargs):
main()
return output_dir
def run_trainer(
self,
stage: str,
model_name: str,
eval_steps: int = 10,
num_train_epochs: int = 1,
do_train: bool = False,
do_eval: bool = True,
distributed: bool = True,
extra_args_str: str = None,
remove_args_str: str = None,
):
max_len = 32
data_dir = self.examples_dir / "test_data/wmt_en_ro"
output_dir = self.get_auto_remove_tmp_dir()
args = f"""
--model_name_or_path {model_name}
--train_file {data_dir}/train.json
--validation_file {data_dir}/val.json
--output_dir {output_dir}
--overwrite_output_dir
--max_source_length {max_len}
--max_target_length {max_len}
--val_max_target_length {max_len}
--warmup_steps 8
--predict_with_generate
--logging_steps 0
--save_steps 0
--eval_steps {eval_steps}
--group_by_length
--label_smoothing_factor 0.1
--adafactor
--source_lang en
--target_lang ro
""".split()
args.extend(["--source_prefix", '"translate English to Romanian: "'])
actions = 0
if do_train:
actions += 1
args.extend(
f"""
--do_train
--num_train_epochs {str(num_train_epochs)}
--max_train_samples 100
--per_device_train_batch_size 2
--learning_rate 3e-3
""".split()
)
if do_eval:
actions += 1
args.extend(
"""
--do_eval
--max_val_samples 100
--per_device_eval_batch_size 2
""".split()
)
assert actions > 0, "need at least do_train or do_eval for the test to run"
if extra_args_str is not None:
args.extend(extra_args_str.split())
# currently only works for bool args
if remove_args_str is not None:
remove_args = remove_args_str.split()
args = [x for x in args if x not in remove_args]
ds_args = f"--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json".split()
script = [f"{self.examples_dir_str}/seq2seq/run_translation.py"]
num_gpus = get_gpu_count() if distributed else 1
launcher = f"deepspeed --num_gpus {num_gpus}".split()
cmd = launcher + script + args + ds_args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(cmd, env=self.get_env())
return output_dir
def run_trainer(
self,
eval_steps: int,
max_len: str,
model_name: str,
num_train_epochs: int,
distributed: bool = False,
extra_args_str: str = None,
):
data_dir = self.examples_dir / "seq2seq/test_data/wmt_en_ro"
output_dir = self.get_auto_remove_tmp_dir()
args = f"""
--model_name_or_path {model_name}
--data_dir {data_dir}
--output_dir {output_dir}
--overwrite_output_dir
--n_train 8
--n_val 8
--max_source_length {max_len}
--max_target_length {max_len}
--val_max_target_length {max_len}
--do_train
--do_eval
--do_predict
--num_train_epochs {str(num_train_epochs)}
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--learning_rate 3e-3
--warmup_steps 8
--evaluation_strategy steps
--predict_with_generate
--logging_steps 0
--save_steps {str(eval_steps)}
--eval_steps {str(eval_steps)}
--sortish_sampler
--label_smoothing 0.1
--adafactor
--task translation
--tgt_lang ro_RO
--src_lang en_XX
""".split()
# --eval_beams 2
if extra_args_str is not None:
args.extend(extra_args_str.split())
if distributed:
n_gpu = get_gpu_count()
distributed_args = f"""
-m torch.distributed.launch
--nproc_per_node={n_gpu}
{self.test_file_dir}/finetune_trainer.py
""".split()
cmd = [sys.executable] + distributed_args + args
execute_subprocess_async(cmd, env=self.get_env())
else:
testargs = ["finetune_trainer.py"] + args
with patch.object(sys, "argv", testargs):
main()
return output_dir
