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pytorch - RuntimeError：CUDA 错误：触发了设备端断言 - BART 模型

问题描述

我正在尝试为文本生成任务运行 BART 语言模型。

当我用于另一个编码器-解码器模型 (T5) 时，我的代码运行良好，但使用 bart 我收到此错误：

File "train_bart.py", line 89, in train
    outputs = model(input_ids = ids, attention_mask = mask, decoder_input_ids=y_ids, labels=lm_labels)                                                     cs-lab-host1" 12:39 10-Aug-21
  File ".../venv/tf_23/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File ".../venv/tf_23/lib/python3.6/site-packages/transformers/models/bart/modeling_bart.py", line 1308, in forward
    return_dict=return_dict,
  File ".../venv/tf_23/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File ".../venv/tf_23/lib/python3.6/site-packages/transformers/models/bart/modeling_bart.py", line 1196, in forward
    return_dict=return_dict,
  File ".../venv/tf_23/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File ".../venv/tf_23/lib/python3.6/site-packages/transformers/models/bart/modeling_bart.py", line 985, in forward
    attention_mask, input_shape, inputs_embeds, past_key_values_length
  File ".../venv/tf_23/lib/python3.6/site-packages/transformers/models/bart/modeling_bart.py", line 866, in _prepare_decoder_attent
ion_mask
    ).to(self.device)
RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.

这就是发生错误的地方：

for _, data in tqdm(enumerate(loader, 0), total=len(loader), desc='Processing batches..'):
    y = data['target_ids'].to(device, dtype = torch.long)
    y_ids = y[:, :-1].contiguous()
    lm_labels = y[:, 1:].clone().detach()
    lm_labels[y[:, 1:] == tokenizer.pad_token_id] = -100
    ids = data['source_ids'].to(device, dtype = torch.long)
    mask = data['source_mask'].to(device, dtype = torch.long)

    outputs = model(input_ids = ids, attention_mask = mask, decoder_input_ids=y_ids, labels=lm_labels)
    loss = outputs[0]

loader是标记化和处理过的数据。

标签： pytorchhuggingface-transformerslanguage-model

解决方案

我建议您将批量大小更改为 1 并临时在 CPU 中运行代码以获得更具描述性的回溯错误。

这将告诉您错误在哪里。

萨尔塔克

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