mirror of
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138 lines
4.7 KiB
Python
138 lines
4.7 KiB
Python
# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import os
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import unittest
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from transformers.integrations import is_deepspeed_available
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from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
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from transformers.trainer_callback import TrainerState
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from transformers.trainer_utils import set_seed
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set_seed(42)
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MBART_TINY = "sshleifer/tiny-mbart"
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def load_json(path):
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with open(path) as f:
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return json.load(f)
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# a candidate for testing_utils
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def require_deepspeed(test_case):
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"""
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Decorator marking a test that requires deepspeed
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"""
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if not is_deepspeed_available():
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return unittest.skip("test requires deepspeed")(test_case)
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else:
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return test_case
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@require_deepspeed
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class TestDeepSpeed(TestCasePlus):
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# XXX: need to do better validation beyond just that the run was successful
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def run_quick(self, distributed=None, extra_args_str=None, remove_args_str=None):
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output_dir = self.run_trainer(1, "12", MBART_TINY, 1, distributed, extra_args_str, remove_args_str)
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logs = TrainerState.load_from_json(os.path.join(output_dir, "trainer_state.json")).log_history
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eval_metrics = [log for log in logs if "eval_loss" in log.keys()]
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first_step_stats = eval_metrics[0]
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assert "eval_bleu" in first_step_stats
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def run_quick_no_train(self, distributed=None, extra_args_str=None):
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remove_args_str = "--do_train"
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output_dir = self.run_trainer(1, "12", MBART_TINY, 1, distributed, extra_args_str, remove_args_str)
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val_metrics = load_json(os.path.join(output_dir, "val_results.json"))
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assert "val_bleu" in val_metrics
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test_metrics = load_json(os.path.join(output_dir, "test_results.json"))
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assert "test_bleu" in test_metrics
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@require_torch_multi_gpu
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def test_basic(self):
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self.run_quick()
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@require_torch_multi_gpu
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def test_grad_acum(self):
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self.run_quick(extra_args_str="--gradient_accumulation_steps 2")
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@require_torch_multi_gpu
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def test_no_train(self):
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# we should not fail if train is skipped
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self.run_quick_no_train()
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def run_trainer(
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self,
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eval_steps: int,
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max_len: str,
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model_name: str,
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num_train_epochs: int,
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distributed: bool = False,
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extra_args_str: str = None,
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remove_args_str: str = None,
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):
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data_dir = self.examples_dir / "seq2seq/test_data/wmt_en_ro"
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output_dir = self.get_auto_remove_tmp_dir()
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args = f"""
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--model_name_or_path {model_name}
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--data_dir {data_dir}
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--output_dir {output_dir}
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--overwrite_output_dir
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--n_train 8
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--n_val 8
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--max_source_length {max_len}
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--max_target_length {max_len}
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--val_max_target_length {max_len}
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--do_train
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--do_eval
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--do_predict
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--num_train_epochs {str(num_train_epochs)}
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--per_device_train_batch_size 4
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--per_device_eval_batch_size 4
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--learning_rate 3e-3
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--warmup_steps 8
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--evaluation_strategy steps
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--predict_with_generate
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--logging_steps 0
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--save_steps {str(eval_steps)}
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--eval_steps {str(eval_steps)}
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--group_by_length
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--label_smoothing_factor 0.1
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--adafactor
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--task translation
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--tgt_lang ro_RO
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--src_lang en_XX
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""".split()
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# --eval_beams 2
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if extra_args_str is not None:
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args.extend(extra_args_str.split())
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if remove_args_str is not None:
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remove_args = remove_args_str.split()
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args = [x for x in args if x not in remove_args]
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ds_args = f"--deepspeed {self.test_file_dir_str}/ds_config.json".split()
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distributed_args = f"""
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{self.test_file_dir}/../../seq2seq/finetune_trainer.py
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""".split()
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cmd = ["deepspeed"] + distributed_args + args + ds_args
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# keep for quick debug
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# print(" ".join(cmd)); die
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execute_subprocess_async(cmd, env=self.get_env())
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return output_dir
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