ValueError: You have to specify either decoder_input_ids or decoder_inputs_embeds

Question

尝试将 question-generation t5 模型转换为 torchscript model，同时运行出现此错误

ValueError：您必须指定 decoder_input_ids 或 decoder_inputs_embeds

这是我运行在 colab 上的代码。

!pip install -U transformers==3.0.0
!python -m nltk.downloader punkt

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model = AutoModelForSeq2SeqLM.from_pretrained('valhalla/t5-base-qg-hl')

t_input =  'Python is a programming language. It is developed by <hl> Guido Van Rossum <hl>. </s>'

tokenizer = AutoTokenizer.from_pretrained('valhalla/t5-base-qg-hl', return_tensors = 'pt')

def _tokenize(
    inputs,
    padding=True,
    truncation=True,
    add_special_tokens=True,
    max_length=64
):
    inputs = tokenizer.batch_encode_plus(
        inputs, 
        max_length=max_length,
        add_special_tokens=add_special_tokens,
        truncation=truncation,
        padding="max_length" if padding else False,
        pad_to_max_length=padding,
        return_tensors="pt"
    )
    return inputs

token = _tokenize(t_input, padding=True, truncation=True)


traced_model = torch.jit.trace(model, [token['input_ids'], token['attention_mask']] )
torch.jit.save(traced_model, "traced_t5.pt")

收到此错误

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-1-f9b449524ef1> in <module>()
     32 
     33 
---> 34 traced_model = torch.jit.trace(model, [token['input_ids'], token['attention_mask']] )
     35 torch.jit.save(traced_model, "traced_t5.pt")

7 frames
/usr/local/lib/python3.6/dist-packages/transformers/modeling_t5.py in forward(self, input_ids, attention_mask, encoder_hidden_states, encoder_attention_mask, inputs_embeds, head_mask, past_key_value_states, use_cache, output_attentions, output_hidden_states)
    682         else:
    683             if self.is_decoder:
--> 684                 raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
    685             else:
    686                 raise ValueError("You have to specify either input_ids or inputs_embeds")

ValueError: You have to specify either decoder_input_ids or decoder_inputs_embeds

如何解决这个问题？还是有更好的方法将 t5 模型转换为 torchscript.

谢谢。

Answer 1

更新: 参考 answer and if you are exporting t5 to onnx, it can be done easily using the fastT5库

我知道是什么导致了这个问题。由于上面的模型是sequential，它既有编码器又有解码器。我们需要将特征传递给编码器，将标签（目标）传递给解码器。

traced_model = torch.jit.trace(model, 
                               (input_ids, attention_mask, decoder_input_ids, decoder_attention_mask)
                               )
torch.jit.save(traced_model, "qg_model.pt")

decoder_input_ids 是问题的标记化 ID（这里的问题是标签）。

即使创建了 torchscript 模型，它也没有像 huggingface' t5 那样的 generate() 方法。

ValueError: You have to specify either decoder_input_ids or decoder_inputs_embeds

ValueError: You have to specify either decoder_input_ids or decoder_inputs_embeds

python

deep-learning

torchscript

huggingface-transformers