TypeError: forward() got an unexpected keyword argument 'output_attentions'

[yihp]

Hi! Thanks for your contribution.

When I trained config/train/single_tf.yaml: the following error occurred：

Traceback (most recent call last):
  File "/usr/local/bin/dlhpcstarter", line 8, in <module>
    sys.exit(main())
  File "/usr/local/lib/python3.8/site-packages/dlhpcstarter/__main__.py", line 126, in main
    submit(args, cmd_line_args, stages_fnc)
  File "/usr/local/lib/python3.8/site-packages/dlhpcstarter/__main__.py", line 21, in submit
    stages_fnc(args)
  File "/home/tools/stages.py", line 85, in stages
    trainer.fit(model, ckpt_path=ckpt_path)
  File "/usr/local/lib/python3.8/site-packages/lightning/pytorch/trainer/trainer.py", line 543, in fit
    call._call_and_handle_interrupt(
  File "/usr/local/lib/python3.8/site-packages/lightning/pytorch/trainer/call.py", line 43, in _call_and_handle_interrupt
    return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/lightning/pytorch/strategies/launchers/subprocess_script.py", line 105, in launch
    return function(*args, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/lightning/pytorch/trainer/trainer.py", line 579, in _fit_impl
    self._run(model, ckpt_path=ckpt_path)
  File "/usr/local/lib/python3.8/site-packages/lightning/pytorch/trainer/trainer.py", line 986, in _run
    results = self._run_stage()
  File "/usr/local/lib/python3.8/site-packages/lightning/pytorch/trainer/trainer.py", line 1030, in _run_stage
    self.fit_loop.run()
  File "/usr/local/lib/python3.8/site-packages/lightning/pytorch/loops/fit_loop.py", line 205, in run
    self.advance()
  File "/usr/local/lib/python3.8/site-packages/lightning/pytorch/loops/fit_loop.py", line 363, in advance
    self.epoch_loop.run(self._data_fetcher)
  File "/usr/local/lib/python3.8/site-packages/lightning/pytorch/loops/training_epoch_loop.py", line 141, in run
    self.on_advance_end(data_fetcher)
  File "/usr/local/lib/python3.8/site-packages/lightning/pytorch/loops/training_epoch_loop.py", line 295, in on_advance_end
    self.val_loop.run()
  File "/usr/local/lib/python3.8/site-packages/lightning/pytorch/loops/utilities.py", line 182, in _decorator
    return loop_run(self, *args, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/lightning/pytorch/loops/evaluation_loop.py", line 135, in run
    self._evaluation_step(batch, batch_idx, dataloader_idx, dataloader_iter)
  File "/usr/local/lib/python3.8/site-packages/lightning/pytorch/loops/evaluation_loop.py", line 396, in _evaluation_step
    output = call._call_strategy_hook(trainer, hook_name, *step_args)
  File "/usr/local/lib/python3.8/site-packages/lightning/pytorch/trainer/call.py", line 311, in _call_strategy_hook
    output = fn(*args, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/lightning/pytorch/strategies/strategy.py", line 410, in validation_step
    return self._forward_redirection(self.model, self.lightning_module, "validation_step", *args, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/lightning/pytorch/strategies/strategy.py", line 640, in __call__
    wrapper_output = wrapper_module(*args, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1519, in forward
    else self._run_ddp_forward(*inputs, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1355, in _run_ddp_forward
    return self.module(*inputs, **kwargs)  # type: ignore[index]
  File "/usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/lightning/pytorch/strategies/strategy.py", line 633, in wrapped_forward
    out = method(*_args, **_kwargs)
  File "/home/modules/lightning_modules/single.py", line 455, in validation_step
    output_ids = self.encoder_decoder.generate(
  File "/usr/local/lib/python3.8/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/transformers/generation/utils.py", line 1597, in generate
    model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(
  File "/usr/local/lib/python3.8/site-packages/transformers/generation/utils.py", line 523, in _prepare_encoder_decoder_kwargs_for_generation
    model_kwargs["encoder_outputs"]: ModelOutput = encoder(**encoder_kwargs)
  File "/usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
TypeError: forward() got an unexpected keyword argument 'output_attentions'

But I trained other models config/train/multi_tf.yaml without any problem.
I did not find out the reason. I tried switching the transformer version, but it didn't work.

[yihp]

I found the problem. The forward() of the class CvtWithProjectionHead in modelling_single.py should add the parameter output_attentions: Optional[bool] = None, or do not pass this parameter.

class CvtWithProjectionHead(transformers.CvtPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.cvt = transformers.CvtModel(config, add_pooling_layer=False)
        self.projection_head = CvtProjectionHead(config)

        # Initialize weights and apply final processing:
        self.post_init()

    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
    ) -> Union[Tuple, ModelOutput]:

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.cvt(
            pixel_values,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        projection = self.projection_head(
            torch.permute(torch.flatten(outputs.last_hidden_state, 2), [0, 2, 1]),
        )

        if not return_dict:
            return projection

        return ModelOutput(
            last_hidden_state=projection,
        )

[anicolson]

Thanks for that, this started occurring in later transformers packages. I will fix this up. Did I fix this up on the Hugging Face Hub with aehrc/cxrmate-single-tf? I might have just missed it in this repo...