122 lines
4.0 KiB
Python
122 lines
4.0 KiB
Python
import sys
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from typing import Dict
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from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
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from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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if is_torch_available():
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import torch
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from torch import nn
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from torch.utils.data.dataset import Dataset
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from transformers import Trainer
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class DummyDataset(Dataset):
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def __init__(self, length: int = 101):
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self.length = length
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def __len__(self):
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return self.length
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def __getitem__(self, i) -> int:
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return i
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class DummyDataCollator:
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def __call__(self, features):
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return {"input_ids": torch.tensor(features), "labels": torch.tensor(features)}
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class DummyModel(nn.Module):
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def __init__(self):
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super().__init__()
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# Add some (unused) params otherwise DDP will complain.
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self.fc = nn.Linear(120, 80)
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def forward(self, input_ids, labels=None):
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if labels is not None:
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return torch.tensor(0.0, device=input_ids.device), input_ids
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else:
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return input_ids
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class TestTrainerDistributed(TestCasePlus):
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@require_torch_multi_gpu
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def test_trainer(self):
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distributed_args = f"""
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-m torch.distributed.launch
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--nproc_per_node={torch.cuda.device_count()}
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{self.test_file_dir}/test_trainer_distributed.py
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""".split()
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output_dir = self.get_auto_remove_tmp_dir()
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args = f"--output_dir {output_dir}".split()
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cmd = [sys.executable] + distributed_args + args
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execute_subprocess_async(cmd, env=self.get_env())
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# successful return here == success - any errors would have caused an error in the sub-call
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if __name__ == "__main__":
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# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
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#
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# PYTHONPATH="src" python -m torch.distributed.launch --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py
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parser = HfArgumentParser((TrainingArguments,))
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training_args = parser.parse_args_into_dataclasses()[0]
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logger.warning(
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"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s",
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training_args.local_rank,
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training_args.device,
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training_args.n_gpu,
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training_args.local_rank != -1,
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)
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# Essentially, what we want to verify in the distributed case is that we get all samples back,
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# in the right order. (this is crucial for prediction for instance)
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for dataset_length in [101, 40, 7]:
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dataset = DummyDataset(dataset_length)
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def compute_metrics(p: EvalPrediction) -> Dict:
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sequential = list(range(len(dataset)))
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success = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
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return {"success": success}
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trainer = Trainer(
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model=DummyModel(),
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args=training_args,
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data_collator=DummyDataCollator(),
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eval_dataset=dataset,
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compute_metrics=compute_metrics,
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)
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metrics = trainer.evaluate()
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logger.info(metrics)
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if metrics["eval_success"] is not True:
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logger.error(metrics)
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exit(1)
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p = trainer.predict(dataset)
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logger.info(p.metrics)
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if p.metrics["eval_success"] is not True:
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logger.error(p.metrics)
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exit(1)
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trainer.args.eval_accumulation_steps = 2
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metrics = trainer.evaluate()
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logger.info(metrics)
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if metrics["eval_success"] is not True:
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logger.error(metrics)
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exit(1)
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p = trainer.predict(dataset)
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logger.info(p.metrics)
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if p.metrics["eval_success"] is not True:
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logger.error(p.metrics)
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exit(1)
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trainer.args.eval_accumulation_steps = None
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