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https://github.com/huggingface/transformers.git
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7caa57e85e
* enable trainer test cases on xpu Signed-off-by: Matrix Yao <matrix.yao@intel.com> * fix style Signed-off-by: Matrix Yao <matrix.yao@intel.com> --------- Signed-off-by: Matrix Yao <matrix.yao@intel.com>
92 lines
2.5 KiB
Python
92 lines
2.5 KiB
Python
import random
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import numpy as np
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import torch
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import torch.distributed as dist
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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 (
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HfArgumentParser,
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Trainer,
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TrainingArguments,
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set_seed,
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)
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from transformers.testing_utils import (
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TestCasePlus,
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backend_device_count,
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execute_subprocess_async,
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get_torch_dist_unique_port,
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require_torch_multi_accelerator,
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torch_device,
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)
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def gather_from_all_gpus(tensor, world_size):
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# Prepare a list to gather tensors from all processes
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gather_list = [torch.zeros_like(tensor) for _ in range(world_size)]
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dist.all_gather(gather_list, tensor)
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return gather_list # List of tensors from all ranks
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class DummyDataset(Dataset):
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def __init__(self):
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self.length = 64
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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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x = random.random()
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y = np.random.random()
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z = torch.rand([]).item()
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return {"x": torch.tensor([x, y, z])}
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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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self.fc = nn.Linear(3, 1)
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def forward(self, x):
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local_tensor = torch.tensor(x, device=torch_device)
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gathered = gather_from_all_gpus(local_tensor, dist.get_world_size())
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assert not all(torch.allclose(t, gathered[0]) for t in gathered[1:])
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y = self.fc(x)
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return (y.mean(), y)
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class TestTrainerDistributedWorkerSeed(TestCasePlus):
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@require_torch_multi_accelerator
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def test_trainer(self):
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device_count = backend_device_count(torch_device)
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output_dir = self.get_auto_remove_tmp_dir()
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distributed_args = f"""--nproc_per_node={device_count}
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--master_port={get_torch_dist_unique_port()}
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{self.test_file_dir}/test_trainer_distributed_worker_seed.py
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""".split()
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args = f"--output_dir {output_dir}".split()
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cmd = ["torchrun"] + distributed_args + args
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execute_subprocess_async(cmd, env=self.get_env())
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def run_distributed_training(training_args):
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set_seed(42)
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model = DummyModel()
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dataset = DummyDataset()
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training_args.max_steps = 10
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# dataloader_num_workers must be > 0 to enable worker_init_fn
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training_args.dataloader_num_workers = 2
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trainer = Trainer(
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model,
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training_args,
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train_dataset=dataset,
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)
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trainer.train()
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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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run_distributed_training(training_args)
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