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114 lines
3.5 KiB
Python
114 lines
3.5 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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"""
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Worker script for eval/predict ordering tests.
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Verifies that distributed eval/predict returns all samples in the correct order.
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Run via torchrun or accelerate launch.
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"""
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import torch
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import torch.nn as nn
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from torch.utils.data import Dataset
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from transformers import EvalPrediction, HfArgumentParser, Trainer, TrainingArguments
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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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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if __name__ == "__main__":
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parser = HfArgumentParser((TrainingArguments,))
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training_args = parser.parse_args_into_dataclasses()[0]
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for dataset_length in [49, 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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if not success and training_args.local_process_index == 0:
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logger.warning(
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"Predictions and/or labels do not match expected results:\n - predictions: "
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f"{p.predictions.tolist()}\n - labels: {p.label_ids.tolist()}\n - expected: {sequential}"
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)
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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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