wandb: WARNING Saving files without folders. If you want to preserve subdirectories pass base_path to wandb.save, i.e. wandb.save("/mnt/folder/file.h5", base_path="/mnt")
  0%|                                                                                                              | 0/12382 [00:00<?, ?it/s]You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
  warnings.warn(
[rank0]:[2024-05-12 10:23:54,974] [22/0] torch._dynamo.variables.higher_order_ops: [WARNING] speculate_subgraph: while introspecting the user-defined autograd.Function, we were unable to trace function `trampoline_autograd_fwd` into a single graph. This means that Dynamo was unable to prove safety for this API and will fall back to eager-mode PyTorch, which could lead to a slowdown.
[rank0]:[2024-05-12 10:23:54,975] [22/0] torch._dynamo.variables.higher_order_ops: [ERROR] Tensor.data_ptr
[rank0]:[2024-05-12 10:23:54,989] [23/0] torch._dynamo.variables.higher_order_ops: [WARNING] speculate_subgraph: while introspecting the user-defined autograd.Function, we were unable to trace function `trampoline_autograd_fwd` into a single graph. This means that Dynamo was unable to prove safety for this API and will fall back to eager-mode PyTorch, which could lead to a slowdown.
[rank0]:[2024-05-12 10:23:54,989] [23/0] torch._dynamo.variables.higher_order_ops: [ERROR] Tensor.data_ptr
[2024-05-12 10:23:41,802] [INFO] [axolotl.callbacks.on_train_begin:770] [PID:41518] [RANK:0] The Axolotl config has been saved to the WandB run under files.
[2024-05-12 10:23:42,956] [INFO] [axolotl.utils.samplers.multipack._len_est:184] [PID:41518] [RANK:0] packing_efficiency_estimate: 0.92 total_num_tokens per device: 47141994
[2024-05-12 10:23:44,000] [INFO] [axolotl.utils.samplers.multipack._len_est:184] [PID:41518] [RANK:0] packing_efficiency_estimate: 0.92 total_num_tokens per device: 47141994
{'loss': 2.1033, 'grad_norm': 21.75, 'learning_rate': 8.787346221441124e-08, 'epoch': 0.0}
  0%|                                                                                                  | 1/12382 [00:34<115:27:20, 33.57s/it]Traceback (most recent call last):
  File "/root/miniconda3/envs/py3.10/lib/python3.10/runpy.py", line 196, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/root/miniconda3/envs/py3.10/lib/python3.10/runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "/workspace/disk1/axolotl/src/axolotl/cli/train.py", line 70, in <module>
    fire.Fire(do_cli)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/fire/core.py", line 143, in Fire
    component_trace = _Fire(component, args, parsed_flag_args, context, name)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/fire/core.py", line 477, in _Fire
    component, remaining_args = _CallAndUpdateTrace(
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/fire/core.py", line 693, in _CallAndUpdateTrace
    component = fn(*varargs, **kwargs)
  File "/workspace/disk1/axolotl/src/axolotl/cli/train.py", line 38, in do_cli
    return do_train(parsed_cfg, parsed_cli_args)
  File "/workspace/disk1/axolotl/src/axolotl/cli/train.py", line 66, in do_train
    return train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
  File "/workspace/disk1/axolotl/src/axolotl/train.py", line 170, in train
    trainer.train(resume_from_checkpoint=resume_from_checkpoint)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/transformers/trainer.py", line 1828, in train
    return inner_training_loop(
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/transformers/trainer.py", line 2256, in _inner_training_loop
    self._maybe_log_save_evaluate(tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/transformers/trainer.py", line 2640, in _maybe_log_save_evaluate
    metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/transformers/trainer.py", line 3445, in evaluate
    output = eval_loop(
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/transformers/trainer.py", line 3624, in evaluation_loop
    for step, inputs in enumerate(dataloader):
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/data_loader.py", line 452, in __iter__
    current_batch = next(dataloader_iter)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 630, in __next__
    data = self._next_data()
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 674, in _next_data
    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
  File "/workspace/disk1/axolotl/src/axolotl/monkeypatch/data/batch_dataset_fetcher.py", line 32, in fetch
    return self.collate_fn(data)
  File "/workspace/disk1/axolotl/src/axolotl/utils/collators.py", line 106, in __call__
    features = self.tokenizer.pad(
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 3274, in pad
    raise ValueError(
ValueError: You should supply an encoding or a list of encodings to this method that includes input_ids, but you provided ['labels']