BlenderBot-Distil-400M training fails if the input or target length exceeds a certain threshold, even when truncation and padding is on
### System Info

transformers version: 4.20.1, 4.21.0
Platform: Linux
Python version: 3.7.6
Huggingface_hub version: 0.8.1
PyTorch version (GPU?): 1.10.2 (Yes)
Tensorflow version (GPU?): not installed (NA)
Flax version (CPU?/GPU?/TPU?): not installed (NA)
Jax version: not installed
JaxLib version: not installed
Using GPU in script?: Yes (2+ Tesla V100)
Using distributed or parallel set-up in script?: No

### Who can help?

@patil-suraj

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)

### Reproduction

Run the following script with `python script_blenderbot_length.py`

```python
# The contents of script_blenderbot_length.py
# To make the code crash, set CRITICAL_NUMBER=64
# To make it pass, set CRITICAL_NUMBER=63
# The code fails if EITHER the input or the target is repeated 64+ times.

from __future__ import annotations
import functools
import typing as tp
import datasets
import transformers
from transformers import (
    DataCollatorForSeq2Seq,
    PreTrainedTokenizer,
    Seq2SeqTrainingArguments,
    Seq2SeqTrainer,
)


CRITICAL_NUMBER = 64


increment_en = [
    {"input": "One", "target": "Two"},
    {"input": "Three "*2, "target": "Four "*2},
    {"input": "Five "*4, "target": "Six "*4},
    {"input": "Seven "*8, "target": "Eight "*8},
    {"input": "Nine "*CRITICAL_NUMBER, "target": "Ten "*CRITICAL_NUMBER},
]
increment_en = increment_en * 100


def lod_to_dol(list_of_dicts: tp.List[tp.Dict[str, tp.Any]]) -> tp.Dict[str, list]:
    dict_of_lists = {
        key: [dct[key] for dct in list_of_dicts] for key in list_of_dicts[0]
    }
    return dict_of_lists


increment_en = lod_to_dol(increment_en)


def preprocess_function_(
    examples,
    tokenizer: PreTrainedTokenizer,
    max_input_length: int,
    max_target_length: int,
):
    inputs = examples["input"]
    targets = examples["target"]

    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)

    # Setup the tokenizer for targets
    with tokenizer.as_target_tokenizer():
        labels = tokenizer(targets, max_length=max_target_length, truncation=True)

    model_inputs["labels"] = labels["input_ids"]
    return model_inputs


def main():
    tokenizer = transformers.BlenderbotTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
    model = transformers.BlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill")

    args = Seq2SeqTrainingArguments(
        "script_debug",
        per_device_train_batch_size=4,
        per_device_eval_batch_size=4,
        fp16=True,
        push_to_hub=False,
        max_steps=10000,
        logging_steps=5000,
        save_steps=5000
    )

    data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, padding=True)

    dataset = datasets.DatasetDict(
        {
            "train": datasets.Dataset.from_dict(increment_en),
            "test": datasets.Dataset.from_dict(increment_en),
        }
    )

    preprocess_function = functools.partial(
        preprocess_function_,
        tokenizer=tokenizer,
        max_input_length=512,
        max_target_length=512
    )

    processed_ds = dataset.map(preprocess_function, batched=True)
    processed_ds.set_format(
        type="torch", columns=["input_ids", "attention_mask", "labels"]
    )

    trainer = Seq2SeqTrainer(
        model,
        args,
        train_dataset=processed_ds["train"],
        eval_dataset=processed_ds["test"],
        data_collator=data_collator,
        tokenizer=tokenizer,
    )
    trainer.train()


if __name__ == "__main__":
    main()
```

Running the code when `CRITICAL_NUMBER` is set to 64 or greater leads to the bizarre series of CUDA asserts:

```
<Similar messages appear above, which are omitted for brevity>
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [29,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [30,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [31,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [32,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [33,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [34,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [35,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [36,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [37,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [38,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [39,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [40,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [41,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [42,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [43,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [44,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [45,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [46,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [47,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [48,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [49,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [50,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [51,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [52,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [53,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [54,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [55,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [56,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [57,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [58,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [59,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [60,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [61,0,0] Assertion `srcIndex < srcSelectDimSi
ze` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [62,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1640811797118/work/aten/src/ATen/native/cuda/Indexing.cu:699: indexSelectLargeIndex: block: [2,0,0], thread: [63,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
  0%|                              | 0/10000 [00:07<?, ?it/s]
root@bolt-imq45r3c3y-8dfzr73qqa:/mnt/task_runtime# python script_blenderbot_length.py 
100%|██████████████████████████| 1/1 [00:00<00:00,  5.30ba/s]
100%|██████████████████████████| 1/1 [00:00<00:00,  5.72ba/s]
max_steps is given, it will override any value given in num_train_epochs
Using cuda_amp half precision backend
The following columns in the training set don't have a corresponding argument in `BlenderbotForConditionalGeneration.forward` and have been ignored: target, input. If target, input are not expected by `BlenderbotForConditionalGeneration.forward`,  you can safely ignore this message.
/miniconda/lib/python3.7/site-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
  FutureWarning,
***** Running training *****
  Num examples = 500
  Num Epochs = 313
  Instantaneous batch size per device = 4
  Total train batch size (w. parallel, distributed & accumulation) = 16
  Gradient Accumulation steps = 1
  Total optimization steps = 10000
  0%|                              | 0/10000 [00:00<?, ?it/s]Traceback (most recent call last):
  File "script_blenderbot_length.py", line 101, in <module>
    main()
  File "script_blenderbot_length.py", line 97, in main
    trainer.train()
  File "/miniconda/lib/python3.7/site-packages/transformers/trainer.py", line 1502, in train
    ignore_keys_for_eval=ignore_keys_for_eval,
  File "/miniconda/lib/python3.7/site-packages/transformers/trainer.py", line 1740, in _inner_training_loop
    tr_loss_step = self.training_step(model, inputs)
  File "/miniconda/lib/python3.7/site-packages/transformers/trainer.py", line 2470, in training_step
    loss = self.compute_loss(model, inputs)
  File "/miniconda/lib/python3.7/site-packages/transformers/trainer.py", line 2502, in compute_loss
    outputs = model(**inputs)
  File "/miniconda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/miniconda/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 168, in forward
    outputs = self.parallel_apply(replicas, inputs, kwargs)
  File "/miniconda/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 178, in parallel_apply
    return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
  File "/miniconda/lib/python3.7/site-packages/torch/nn/parallel/parallel_apply.py", line 86, in parallel_apply
    output.reraise()
  File "/miniconda/lib/python3.7/site-packages/torch/_utils.py", line 434, in reraise
    raise exception
RuntimeError: Caught RuntimeError in replica 0 on device 0.
Original Traceback (most recent call last):
  File "/miniconda/lib/python3.7/site-packages/torch/nn/parallel/parallel_apply.py", line 61, in _worker
    output = module(*input, **kwargs)
  File "/miniconda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/miniconda/lib/python3.7/site-packages/transformers/models/blenderbot/modeling_blenderbot.py", line 1340, in forward
    return_dict=return_dict,
  File "/miniconda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/miniconda/lib/python3.7/site-packages/transformers/models/blenderbot/modeling_blenderbot.py", line 1181, in forward
    return_dict=return_dict,
  File "/miniconda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/miniconda/lib/python3.7/site-packages/transformers/models/blenderbot/modeling_blenderbot.py", line 785, in forward
    output_attentions=output_attentions,
  File "/miniconda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/miniconda/lib/python3.7/site-packages/transformers/models/blenderbot/modeling_blenderbot.py", line 318, in forward
    output_attentions=output_attentions,
  File "/miniconda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/miniconda/lib/python3.7/site-packages/transformers/models/blenderbot/modeling_blenderbot.py", line 180, in forward
    query_states = self.q_proj(hidden_states) * self.scaling
  File "/miniconda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/miniconda/lib/python3.7/site-packages/torch/nn/modules/linear.py", line 103, in forward
    return F.linear(input, self.weight, self.bias)
  File "/miniconda/lib/python3.7/site-packages/torch/nn/functional.py", line 1848, in linear
    return torch._C._nn.linear(input, weight, bias)
RuntimeError: CUDA error: CUBLAS_STATUS_NOT_INITIALIZED when calling `cublasCreate(handle)`
```

### Expected behavior

The training code should not crash, especially when there are far fewer tokens than the tokenization limit.
Adding `padding=True` when tokenizing both the input and targets does not fix the issue.<|||||>Also, when running the script using the CPU only, I get this error:

```
root@pc:~ # CUDA_VISIBLE_DEVICES="" python script_blenderbot_length.py 
100%|██████████████████████████| 1/1 [00:00<00:00,  4.95ba/s]
100%|██████████████████████████| 1/1 [00:00<00:00,  5.46ba/s]
max_steps is given, it will override any value given in num_train_epochs
The following columns in the training set don't have a corresponding argument in `BlenderbotForConditionalGeneration.forward` and have been ignored: target, input. If target, input are not expected by `BlenderbotForConditionalGeneration.forward`,  you can safely ignore this message.
/miniconda/lib/python3.7/site-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
  FutureWarning,
***** Running training *****
  Num examples = 500
  Num Epochs = 80
  Instantaneous batch size per device = 4
  Total train batch size (w. parallel, distributed & accumulation) = 4
  Gradient Accumulation steps = 1
  Total optimization steps = 10000
  0%|                              | 0/10000 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "script_blenderbot_length.py", line 103, in <module>
    main()
  File "script_blenderbot_length.py", line 99, in main
    trainer.train()
  File "/miniconda/lib/python3.7/site-packages/transformers/trainer.py", line 1502, in train
    ignore_keys_for_eval=ignore_keys_for_eval,
  File "/miniconda/lib/python3.7/site-packages/transformers/trainer.py", line 1740, in _inner_training_loop
    tr_loss_step = self.training_step(model, inputs)
  File "/miniconda/lib/python3.7/site-packages/transformers/trainer.py", line 2470, in training_step
    loss = self.compute_loss(model, inputs)
  File "/miniconda/lib/python3.7/site-packages/transformers/trainer.py", line 2502, in compute_loss
    outputs = model(**inputs)
  File "/miniconda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/miniconda/lib/python3.7/site-packages/transformers/models/blenderbot/modeling_blenderbot.py", line 1340, in forward
    return_dict=return_dict,
  File "/miniconda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
File "/miniconda/lib/python3.7/site-packages/transformers/models/blenderbot/modeling_blenderbot.py", line 1181, in forward
    return_dict=return_dict,
  File "/miniconda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/miniconda/lib/python3.7/site-packages/transformers/models/blenderbot/modeling_blenderbot.py", line 738, in forward
    embed_pos = self.embed_positions(input_shape)
  File "/miniconda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/miniconda/lib/python3.7/site-packages/transformers/models/blenderbot/modeling_blenderbot.py", line 125, in forward
    return super().forward(positions)
  File "/miniconda/lib/python3.7/site-packages/torch/nn/modules/sparse.py", line 160, in forward
    self.norm_type, self.scale_grad_by_freq, self.sparse)
  File "/miniconda/lib/python3.7/site-packages/torch/nn/functional.py", line 2044, in embedding
    return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
IndexError: index out of range in self
  0%|                              | 0/10000 [00:00<?, ?it/s]
```<|||||>I've found out why the error seems to appear. I modified `transformers/src/transformers/models/blenderbot/modeling_blenderbot.py:BlenderbotLearnedPositionalEmbedding:forward` (approximately near line 125).

```diff
        positions = torch.arange(
            past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
        )
+        print(positions)
+        print(self.weight.shape)
        return super().forward(positions)
```

When running the script, I get this in the output:
```
tensor([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
         14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
         28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
         42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
         56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
         70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
         84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
         98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
        112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
        126, 127, 128, 129])
torch.Size([128, 1280])
```

Clearly, the positional embeddings are beyond the maximum range available. The question is why... Perhaps this can be configured in the constructor?<|||||>The length of the positions seems to be equal to `2*CRITICAL_NUMBER + 1`.<|||||>And... it goes to a maximum of the tokenizer's max_length-1, which is expected, I guess.<|||||>Ah. So the issue is that in the `BlenderbotConfig`, `max_position_embeddings` is set to 128. The publicly available weights only have position embeddings with those dimensions, so either I'd have to train from scratch or reduce the max tokenizer length to 128.<|||||>But seriously, this exception should be caught and re-raised with a more human-readable expression.<|||||>(I can contribute a fix after my internship ends, not before)<|||||>Catching and re-raising the exception during GPU training doesn't result in a more human-readable expression (It's still `RuntimeError: CUDA error: CUBLAS_STATUS_NOT_INITIALIZED when calling cublasCreate(handle)`, but at least the flood of CUDA asserts are gone). Getting a more human-readable exception seems to be only possible for CPU-only training.<|||||>cc @sgugger for usage with the `Trainer`!<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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