BART fine tuning on gpu issue
# ❓ Questions & Help

I am fine tuning BART by executing transformers/examples/seq2seq/finetune.sh as instructed.
I am getting the following error:

```
Traceback (most recent call last):
  File "finetune.py", line 346, in <module>
    main(args)
  File "finetune.py", line 324, in main
    logger=logger,
  File "/data/t-angand/transformers/examples/lightning_base.py", line 330, in generic_train
    trainer.fit(model)
  File "/home/angand/.conda/envs/bart_env/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 928, in fit
    self.single_gpu_train(model)
  File "/home/angand/.conda/envs/bart_env/lib/python3.6/site-packages/pytorch_lightning/trainer/distrib_parts.py", line 183, in single_gpu_train
    self.run_pretrain_routine(model)
  File "/home/angand/.conda/envs/bart_env/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 1086, in run_pretrain_routine
    False)
  File "/home/angand/.conda/envs/bart_env/lib/python3.6/site-packages/pytorch_lightning/trainer/evaluation_loop.py", line 291, in _evaluate
    output = self.evaluation_forward(model, batch, batch_idx, dataloader_idx, test_mode)
  File "/home/angand/.conda/envs/bart_env/lib/python3.6/site-packages/pytorch_lightning/trainer/evaluation_loop.py", line 462, in evaluation_forward
    output = model.validation_step(*args)
  File "finetune.py", line 136, in validation_step
    return self._generative_step(batch)
  File "finetune.py", line 163, in _generative_step
    generated_ids = self.model.generate(input_ids=source_ids, attention_mask=source_mask, use_cache=True,)
  File "/home/angand/.conda/envs/bart_env/lib/python3.6/site-packages/torch/autograd/grad_mode.py", line 15, in decorate_context
    return func(*args, **kwargs)
  File "/data/t-angand/transformers/src/transformers/modeling_utils.py", line 1159, in generate
    encoder_outputs: tuple = encoder(input_ids, attention_mask=attention_mask)
  File "/home/angand/.conda/envs/bart_env/lib/python3.6/site-packages/torch/nn/modules/module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "/data/t-angand/transformers/src/transformers/modeling_bart.py", line 303, in forward
    inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
  File "/home/angand/.conda/envs/bart_env/lib/python3.6/site-packages/torch/nn/modules/module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/angand/.conda/envs/bart_env/lib/python3.6/site-packages/torch/nn/modules/sparse.py", line 114, in forward
    self.norm_type, self.scale_grad_by_freq, self.sparse)
  File "/home/angand/.conda/envs/bart_env/lib/python3.6/site-packages/torch/nn/functional.py", line 1724, in embedding
    return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
RuntimeError: Expected object of device type cuda but got device type cpu for argument #3 'index' in call to _th_index_select
```

Thanks is advance.
Have you checked if your setup is working properly and if 
`torch.cuda.is_available()`
returns True?<|||||>Yeah
It gives initial output as:
Some weights of the model checkpoint at facebook/bart-large were not used when initializing BartForConditionalGeneration: ['encoder.version', 'decoder.version']
- This IS expected if you are initializing BartForConditionalGeneration from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPretraining model).
- This IS NOT expected if you are initializing BartForConditionalGeneration from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BartForConditionalGeneration were not initialized from the model checkpoint at facebook/bart-large and are newly initialized: ['final_logits_bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
CUDA_VISIBLE_DEVICES: [0]
Using APEX 16bit precision.
Selected optimization level O1:  Insert automatic casts around Pytorch functions and Tensor methods.

Defaults for this optimization level are:
enabled                : True
opt_level              : O1
cast_model_type        : None
patch_torch_functions  : True
keep_batchnorm_fp32    : None
master_weights         : None
loss_scale             : dynamic
Processing user overrides (additional kwargs that are not None)...
After processing overrides, optimization options are:
enabled                : True
opt_level              : O1
cast_model_type        : None
patch_torch_functions  : True
keep_batchnorm_fp32    : None
master_weights         : None
loss_scale             : dynamic
Warning:  multi_tensor_applier fused unscale kernel is unavailable, possibly because apex was installed without --cuda_ext --cpp_ext. Using Python fallback.  Original ImportError was: ModuleNotFoundError("No module named 'amp_C'",)
Validation sanity check: 0it [00:00, ?it/s]
Then fails.<|||||>What's the output of `transformers-cli env`?<|||||>Sorry I didnt get what you are asking for.
<|||||>Can you run the command `transformers-cli env` in terminal and paste the output here in code format (using ```)<|||||>It says command not found.<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
