| 0%| | 0/2 [00:00<?, ?it/s]`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`... | |
| /opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None | |
| warnings.warn("None of the inputs have requires_grad=True. Gradients will be None") | |
| Traceback (most recent call last): | |
| File "/home/sagemaker-user/output-7b-26k-lora/../lora_finetuning_push_to_hub_save_local.py", line 236, in <module> | |
| train() | |
| File "/home/sagemaker-user/output-7b-26k-lora/../lora_finetuning_push_to_hub_save_local.py", line 223, in train | |
| trainer.train() | |
| File "/opt/conda/lib/python3.10/site-packages/transformers/trainer.py", line 1539, in train | |
| return inner_training_loop( | |
| File "/opt/conda/lib/python3.10/site-packages/transformers/trainer.py", line 1809, in _inner_training_loop | |
| tr_loss_step = self.training_step(model, inputs) | |
| File "/opt/conda/lib/python3.10/site-packages/transformers/trainer.py", line 2665, in training_step | |
| self.accelerator.backward(loss) | |
| File "/opt/conda/lib/python3.10/site-packages/accelerate/accelerator.py", line 1853, in backward | |
| loss.backward(**kwargs) | |
| File "/opt/conda/lib/python3.10/site-packages/torch/_tensor.py", line 487, in backward | |
| torch.autograd.backward( | |
| File "/opt/conda/lib/python3.10/site-packages/torch/autograd/__init__.py", line 200, in backward | |
| Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn |