Pre-trained model raises IndexError: index out of range in self #85
Description
I tried to test the performance of the pre-trained model using the codes below:
from transformers import BertTokenizer, BartModel
model = BartModel.from_pretrained("/path/to/generated/pretrained/model/")
tokenizer = BertTokenizer.from_pretrained("/path/to/generated/pretrained/model/")
inputs = tokenizer("今天天氣很好", return_tensors="pt")
outputs = model(**inputs)
It raises:
TypeError: BartModel.forward() got an unexpected keyword argument 'token_type_ids'
Then, it uses the following line to remove the token_type_ids and retry:
del(inputs['token_type_ids'])
outputs = model(**inputs)
Then, it shows:
IndexError: index out of range in self
How do I resolve this issue?
Traceback is as follows:
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ in <module>:1 │
│ │
│ /home/jupyter-raptor/.local/lib/python3.10/site-packages/torch/nn/modules/module.py:1553 in │
│ _wrapped_call_impl │
│ │
│ 1550 │ │ if self._compiled_call_impl is not None: │
│ 1551 │ │ │ return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc] │
│ 1552 │ │ else: │
│ ❱ 1553 │ │ │ return self._call_impl(*args, **kwargs) │
│ 1554 │ │
│ 1555 │ def _call_impl(self, *args, **kwargs): │
│ 1556 │ │ forward_call = (self._slow_forward if torch._C._get_tracing_state() else self.fo │
│ │
│ /home/jupyter-raptor/.local/lib/python3.10/site-packages/torch/nn/modules/module.py:1562 in │
│ _call_impl │
│ │
│ 1559 │ │ if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks │
│ 1560 │ │ │ │ or _global_backward_pre_hooks or _global_backward_hooks │
│ 1561 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │
│ ❱ 1562 │ │ │ return forward_call(*args, **kwargs) │
│ 1563 │ │ │
│ 1564 │ │ try: │
│ 1565 │ │ │ result = None │
│ │
│ /home/jupyter-raptor/.local/lib/python3.10/site-packages/transformers/models/bart/modeling_bart. │
│ py:1222 in forward │
│ │
│ 1219 │ │ return_dict = return_dict if return_dict is not None else self.config.use_return │
│ 1220 │ │ │
│ 1221 │ │ if encoder_outputs is None: │
│ ❱ 1222 │ │ │ encoder_outputs = self.encoder( │
│ 1223 │ │ │ │ input_ids=input_ids, │
│ 1224 │ │ │ │ attention_mask=attention_mask, │
│ 1225 │ │ │ │ head_mask=head_mask, │
│ │
│ /home/jupyter-raptor/.local/lib/python3.10/site-packages/torch/nn/modules/module.py:1553 in │
│ _wrapped_call_impl │
│ │
│ 1550 │ │ if self._compiled_call_impl is not None: │
│ 1551 │ │ │ return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc] │
│ 1552 │ │ else: │
│ ❱ 1553 │ │ │ return self._call_impl(*args, **kwargs) │
│ 1554 │ │
│ 1555 │ def _call_impl(self, *args, **kwargs): │
│ 1556 │ │ forward_call = (self._slow_forward if torch._C._get_tracing_state() else self.fo │
│ │
│ /home/jupyter-raptor/.local/lib/python3.10/site-packages/torch/nn/modules/module.py:1562 in │
│ _call_impl │
│ │
│ 1559 │ │ if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks │
│ 1560 │ │ │ │ or _global_backward_pre_hooks or _global_backward_hooks │
│ 1561 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │
│ ❱ 1562 │ │ │ return forward_call(*args, **kwargs) │
│ 1563 │ │ │
│ 1564 │ │ try: │
│ 1565 │ │ │ result = None │
│ │
│ /home/jupyter-raptor/.local/lib/python3.10/site-packages/transformers/models/bart/modeling_bart. │
│ py:799 in forward │
│ │
│ 796 │ │ │ raise ValueError("You have to specify either input_ids or inputs_embeds") │
│ 797 │ │ │
│ 798 │ │ if inputs_embeds is None: │
│ ❱ 799 │ │ │ inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale │
│ 800 │ │ │
│ 801 │ │ embed_pos = self.embed_positions(input_shape) │
│ 802 │
│ │
│ /home/jupyter-raptor/.local/lib/python3.10/site-packages/torch/nn/modules/module.py:1553 in │
│ _wrapped_call_impl │
│ │
│ 1550 │ │ if self._compiled_call_impl is not None: │
│ 1551 │ │ │ return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc] │
│ 1552 │ │ else: │
│ ❱ 1553 │ │ │ return self._call_impl(*args, **kwargs) │
│ 1554 │ │
│ 1555 │ def _call_impl(self, *args, **kwargs): │
│ 1556 │ │ forward_call = (self._slow_forward if torch._C._get_tracing_state() else self.fo │
│ │
│ /home/jupyter-raptor/.local/lib/python3.10/site-packages/torch/nn/modules/module.py:1562 in │
│ _call_impl │
│ │
│ 1559 │ │ if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks │
│ 1560 │ │ │ │ or _global_backward_pre_hooks or _global_backward_hooks │
│ 1561 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │
│ ❱ 1562 │ │ │ return forward_call(*args, **kwargs) │
│ 1563 │ │ │
│ 1564 │ │ try: │
│ 1565 │ │ │ result = None │
│ │
│ /home/jupyter-raptor/.local/lib/python3.10/site-packages/torch/nn/modules/sparse.py:164 in │
│ forward │
│ │
│ 161 │ │ │ │ self.weight[self.padding_idx].fill_(0) │
│ 162 │ │
│ 163 │ def forward(self, input: Tensor) -> Tensor: │
│ ❱ 164 │ │ return F.embedding( │
│ 165 │ │ │ input, self.weight, self.padding_idx, self.max_norm, │
│ 166 │ │ │ self.norm_type, self.scale_grad_by_freq, self.sparse) │
│ 167 │
│ │
│ /home/jupyter-raptor/.local/lib/python3.10/site-packages/torch/nn/functional.py:2267 in │
│ embedding │
│ │
│ 2264 │ │ # torch.embedding_renorm_ │
│ 2265 │ │ # remove once script supports set_grad_enabled │
│ 2266 │ │ _no_grad_embedding_renorm_(weight, input, max_norm, norm_type) │
│ ❱ 2267 │ return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) │
│ 2268 │
│ 2269 │
│ 2270 def embedding_bag( │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
IndexError: index out of range in self