132 lines
3.9 KiB
Python
132 lines
3.9 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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# This test is meant to be run in on an instance with TPUs like this:
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#
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# python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
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#
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# Replace 8 with the number of TPU cores you have.
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#
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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.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 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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def main():
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parser = HfArgumentParser((TrainingArguments,))
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sys.argv += ["--output_dir", "./examples"]
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training_args = parser.parse_args_into_dataclasses()[0]
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logger.warning(
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f"Process rank: {training_args.local_rank}, device: {training_args.device}, "
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f"tpu_num_cores: {training_args.tpu_num_cores}",
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)
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# Essentially, what we want to verify in the distributed case is
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# that we get all samples back, in the right order.
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# (this is crucial for prediction for instance)
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for dataset_length in [1001, 256, 15]:
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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["test_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["test_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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logger.info("🔥 All distributed tests successful")
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def _mp_fn(index):
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# For xla_spawn (TPUs)
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main()
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if __name__ == "__main__":
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main()
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