error while using onnx runtime

[Bagherihaali]

Hi,
i want t use onnx-runtime to speed-up question generation as you suggested here
but i got some errors.

i am on ubuntu 20
installed packages from requirement files
because of onnx dependency errors replaced protobuf with protobuf==3.20.1

my code :

from pipelines import pipeline
nlp = pipeline("question-generation",onnx=True, onnx_path='./onnx')

stack trace :

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In [3], line 1
----> 1 nlp = pipeline("question-generation",onnx=True, onnx_path='./onnx')

File ~/Desktop/virtproj/suraj-onnx-support/pipelines.py:376, in pipeline(task, model, tokenizer, qg_format, ans_model, ans_tokenizer, use_cuda, onnx, onnx_path, **kwargs)
    374     return task_class(model=model, tokenizer=tokenizer, **extra_pipeline_args)
    375 elif task == "question-generation":
--> 376     return task_class(
    377         model=model,
    378         tokenizer=tokenizer,
    379         ans_model=ans_model,
    380         ans_tokenizer=ans_tokenizer,
    381         qg_format=qg_format,
    382         **extra_pipeline_args,
    383     )
    384 else:
    385     return task_class(
    386         model=model,
    387         tokenizer=tokenizer,
   (...)
    391         **extra_pipeline_args,
    392     )

File ~/Desktop/virtproj/suraj-onnx-support/pipelines.py:38, in QGPipeline.__init__(self, model, tokenizer, ans_model, ans_tokenizer, qg_format, use_cuda, onnx, onnx_path)
     35 self.device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu"
     37 if self.onnx:
---> 38     self.model = OnnxT5(model, onnx_path)
     39     self.ans_model = self.model if model == ans_model else OnnxT5(ans_model, onnx_path)
     40 else:

File ~/Desktop/virtproj/suraj-onnx-support/onnx_t5.py:188, in OnnxT5.__init__(self, model_name_or_path, onnx_path)
    185 decoder = T5ForConditionalGeneration.from_pretrained(model_name_or_path).decoder
    186 self.decoder = T5Decoder(decoder, self.config).eval()
--> 188 self._warmup_onnx_graph()

File ~/Desktop/virtproj/suraj-onnx-support/onnx_t5.py:277, in OnnxT5._warmup_onnx_graph(self)
    272 for _ in range(10):
    273     encoder_outputs = self._encoder_forward(
    274         input_ids=input_ids, attention_mask=attention_mask
    275     ).last_hidden_state
--> 277 decoder_output, _ = self.decoder(input_ids, encoder_outputs, attention_mask)
    278 inputs = {"decoder_output": decoder_output.cpu().detach().numpy()}
    279 for _ in range(10):

File ~/Desktop/virtproj/suraj-onnx-support/surajonnx-venv/lib/python3.8/site-packages/torch/nn/modules/module.py:722, in Module._call_impl(self, *input, **kwargs)
    720     result = self._slow_forward(*input, **kwargs)
    721 else:
--> 722     result = self.forward(*input, **kwargs)
    723 for hook in itertools.chain(
    724         _global_forward_hooks.values(),
    725         self._forward_hooks.values()):
    726     hook_result = hook(self, input, result)

File ~/Desktop/virtproj/suraj-onnx-support/onnx_t5.py:146, in T5Decoder.forward(self, input_ids, encoder_hidden_states, attention_mask, past_key_values)
    140 past_arg_key = (
    141     "past_key_value_states"
    142     if "past_key_value_states" in inspect.getargspec(self.decoder.forward)[0]
    143     else past_key_values
    144 )
    145 past_arg = {past_arg_key: past_key_values}
--> 146 decoder_output = self.decoder(
    147     input_ids=input_ids,
    148     encoder_attention_mask=attention_mask,
    149     encoder_hidden_states=encoder_hidden_states,
    150     use_cache=True,
    151     return_dict=True,
    152     **past_arg,
    153 )
    154 past_key_values = decoder_output.past_key_values
    155 sequence_output = decoder_output.last_hidden_state

TypeError: _call_impl() keywords must be strings