def test_run_translation(self):
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
run_translation.py
--model_name_or_path sshleifer/student_marian_en_ro_6_1
--source_lang en
--target_lang ro
--train_file tests/fixtures/tests_samples/wmt16/sample.json
--validation_file tests/fixtures/tests_samples/wmt16/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--max_steps=50
--warmup_steps=8
--do_train
--do_eval
--learning_rate=3e-3
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
--source_lang en_XX
--target_lang ro_RO
""".split()
with patch.object(sys, "argv", testargs):
run_translation.main()
result = get_results(tmp_dir)
self.assertGreaterEqual(result["eval_bleu"], 30)
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.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
--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}/seq2seq/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,
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,
do_train: bool = True,
do_eval: bool = True,
do_predict: bool = True,
):
data_dir = self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
output_dir = self.get_auto_remove_tmp_dir()
args_train = 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_source_length {max_len}
--max_target_length {max_len}
--do_train
--num_train_epochs {str(num_train_epochs)}
--per_device_train_batch_size 4
--learning_rate {learning_rate}
--warmup_steps 8
--logging_steps 0
--logging_strategy no
--save_steps {str(eval_steps)}
--group_by_length
--label_smoothing_factor 0.1
--adafactor
--target_lang ro_RO
--source_lang en_XX
"""
args_eval = f"""
--do_eval
--per_device_eval_batch_size 4
--max_eval_samples 8
--val_max_target_length {max_len}
--evaluation_strategy steps
--eval_steps {str(eval_steps)}
"""
args_predict = """
--do_predict
"""
args = ""
if do_train:
args += args_train
if do_eval:
args += args_eval
if do_predict:
args += args_predict
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()
master_port = get_torch_dist_unique_port()
distributed_args = f"""
-m torch.distributed.launch
--nproc_per_node={n_gpu}
--master_port={master_port}
{self.examples_dir_str}/pytorch/translation/run_translation.py
""".split()
cmd = [sys.executable] + distributed_args + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(cmd, env=self.get_env())
else:
testargs = ["run_translation.py"] + args
with patch.object(sys, "argv", testargs):
main()
return output_dir
