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我正在处理一批不同长度的句子，因此我计划利用 gpt2 中的 padding + attention_mask 功能来实现这一点。

同时，对于每个句子我需要添加一个后缀短语并运行 N 个不同的推理。例如，给定句子“我喜欢喝可乐”，我可能需要运行两个不同的推论：“我喜欢喝可乐。可乐很好”和“我喜欢喝可乐。喝很好”。因此，我试图通过使用“过去”功能来改善推理时间：https ://huggingface.co/transformers/quickstart.html#using-the-past所以我只处理原始句子（例如“我like to drink coke") 一次，然后我以某种方式扩展结果以便能够与另外两个句子一起使用：“Coke is good”和“Drink is good”。

下面你会找到一个简单的代码，它试图代表我是如何尝试这样做的。为简单起见，我只是为每个句子添加一个后缀短语（......但我仍然希望我最初的想法是可能的）：

from transformers.tokenization_gpt2 import GPT2Tokenizer
from transformers.modeling_gpt2 import GPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained('gpt2', pad_token='<|endoftext|>')
model = GPT2LMHeadModel.from_pretrained('gpt2')

# Complete phrases are: "I like to drink soda without sugar" and "Go watch TV alone, I am not going"
docs = ["I like to drink soda", "Go watch TV"]
docs_tensors = tokenizer.batch_encode_plus(
    [d for d in docs], pad_to_max_length=True, return_tensors='pt')

docs_next = ["without sugar", "alone, I am not going"]
docs_next_tensors = tokenizer.batch_encode_plus(
    [d for d in docs_next], pad_to_max_length=True, return_tensors='pt')

# predicting the first part of each phrase
_, past = model(docs_tensors['input_ids'], attention_mask=docs_tensors['attention_mask'])

# predicting the rest of the phrase
logits, _ = model(docs_next_tensors['input_ids'], attention_mask=docs_next_tensors['attention_mask'], past=past)
logits = logits[:, -1]
_, top_indices_results = logits.topk(30)

我得到的错误如下：

Traceback (most recent call last):
  File "/Applications/PyCharm CE.app/Contents/plugins/python-ce/helpers/pydev/pydevd.py", line 1434, in _exec
    pydev_imports.execfile(file, globals, locals)  # execute the script
  File "/Applications/PyCharm CE.app/Contents/plugins/python-ce/helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "/Users/damiox/Workspace/xxLtd/yy/stress-test-withpast2.py", line 26, in <module>
    logits, _ = model(docs_next_tensors['input_ids'], attention_mask=docs_next_tensors['attention_mask'], past=past)
  File "/Users/damiox/.local/share/virtualenvs/yy-uMxmjV2h/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/Users/damiox/.local/share/virtualenvs/yy-uMxmjV2h/lib/python3.7/site-packages/transformers/modeling_gpt2.py", line 593, in forward
    inputs_embeds=inputs_embeds,
  File "/Users/damiox/.local/share/virtualenvs/yy-uMxmjV2h/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/Users/damiox/.local/share/virtualenvs/yy-uMxmjV2h/lib/python3.7/site-packages/transformers/modeling_gpt2.py", line 476, in forward
    hidden_states, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask[i]
  File "/Users/damiox/.local/share/virtualenvs/yy-uMxmjV2h/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/Users/damiox/.local/share/virtualenvs/yy-uMxmjV2h/lib/python3.7/site-packages/transformers/modeling_gpt2.py", line 226, in forward
    self.ln_1(x), layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask
  File "/Users/damiox/.local/share/virtualenvs/yy-uMxmjV2h/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/Users/damiox/.local/share/virtualenvs/yy-uMxmjV2h/lib/python3.7/site-packages/transformers/modeling_gpt2.py", line 189, in forward
    attn_outputs = self._attn(query, key, value, attention_mask, head_mask)
  File "/Users/damiox/.local/share/virtualenvs/yy-uMxmjV2h/lib/python3.7/site-packages/transformers/modeling_gpt2.py", line 150, in _attn
    w = w + attention_mask
RuntimeError: The size of tensor a (11) must match the size of tensor b (6) at non-singleton dimension 3

Process finished with exit code 1

最初我认为这与https://github.com/huggingface/transformers/issues/3031有关- 所以我重新构建了最新的 master 来尝试修复，但我仍然遇到这个问题。

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1 回答 1

1

为了使您当前的代码片段正常工作，您将结合以前的和新的注意掩码，如下所示：

from transformers.tokenization_gpt2 import GPT2Tokenizer
from transformers.modeling_gpt2 import GPT2LMHeadModel
import torch

tokenizer = GPT2Tokenizer.from_pretrained('gpt2', pad_token='<|endoftext|>')
model = GPT2LMHeadModel.from_pretrained('gpt2')

# Complete phrases are: "I like to drink soda without sugar" and "Go watch TV alone, I am not going"
docs = ["I like to drink soda", "Go watch TV"]
docs_tensors = tokenizer.batch_encode_plus(
    [d for d in docs], pad_to_max_length=True, return_tensors='pt')

docs_next = ["without sugar", "alone, I am not going"]
docs_next_tensors = tokenizer.batch_encode_plus(
    [d for d in docs_next], pad_to_max_length=True, return_tensors='pt')

# predicting the first part of each phrase
_, past = model(docs_tensors['input_ids'], attention_mask=docs_tensors['attention_mask'])

# predicting the rest of the phrase
attn_mask = torch.cat([docs_tensors['attention_mask'], docs_next_tensors['attention_mask']], dim=-1)
logits, _ = model(docs_next_tensors['input_ids'], attention_mask=attn_mask, past=past)
logits = logits[:, -1]
_, top_indices_results = logits.topk(30)

如果您想为一个句子开头测试两个可能的后缀，您可能必须克隆过去的变量，次数与拥有后缀的次数一样多。这意味着您的前缀 input_ids 的批量大小必须与您的后缀 input_ids 的批量大小相匹配才能使其正常工作。

如果您的前缀 input_ids 之一被填充，您还必须更改后缀 input_ids 的位置编码输入（GPT2 使用绝对位置编码）（上面的代码中没有显示 - 请查看https://github.com /huggingface/transformers/issues/3021看看它是如何完成的）。

于 2020-03-01T22:35:41.200 回答