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sdxl-lora-dataset / logs /20230805111330 /wandb /run-20230805_111355-hdfosgf0 /files /output.log
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hanungaddi
feat: upload jingliu_sdxl_20_ep dataset and logs
46c0d05
raw
history blame
11.7 kB
epoch 1/20
Traceback (most recent call last):
File "/workspace/kohya-trainer/sdxl_train.py", line 649, in <module>
train(args)
File "/workspace/kohya-trainer/sdxl_train.py", line 475, in train
accelerator.backward(loss)
File "/workspace/venv/lib/python3.10/site-packages/accelerate/accelerator.py", line 1743, in backward
self.scaler.scale(loss).backward(**kwargs)
File "/workspace/venv/lib/python3.10/site-packages/torch/_tensor.py", line 487, in backward
torch.autograd.backward(
File "/workspace/venv/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
File "/workspace/venv/lib/python3.10/site-packages/torch/autograd/function.py", line 274, in apply
return user_fn(self, *args)
File "/workspace/venv/lib/python3.10/site-packages/torch/utils/checkpoint.py", line 157, in backward
torch.autograd.backward(outputs_with_grad, args_with_grad)
File "/workspace/venv/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
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 26.00 MiB (GPU 0; 23.65 GiB total capacity; 18.73 GiB already allocated; 25.69 MiB free; 19.47 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
╭───────────────────── Traceback (most recent call last) ──────────────────────╮
│ /workspace/kohya-trainer/sdxl_train.py:649 in <module> │
│ │
│ 646 │ args = parser.parse_args() │
│ 647 │ args = train_util.read_config_from_file(args, parser) │
│ 648 │ │
│ ❱ 649 │ train(args) │
│ 650 │
│ │
│ /workspace/kohya-trainer/sdxl_train.py:475 in train │
│ │
│ 472 │ │ │ │ else: │
│ 473 │ │ │ │ │ loss = torch.nn.functional.mse_loss(noise_pred.flo │
│ 474 │ │ │ │ │
│ ❱ 475 │ │ │ │ accelerator.backward(loss) │
│ 476 │ │ │ │ if accelerator.sync_gradients and args.max_grad_norm ! │
│ 477 │ │ │ │ │ params_to_clip = [] │
│ 478 │ │ │ │ │ for m in training_models: │
│ │
│ /workspace/venv/lib/python3.10/site-packages/accelerate/accelerator.py:1743 │
│ in backward │
│ │
│ 1740 │ │ elif self.distributed_type == DistributedType.MEGATRON_LM: │
│ 1741 │ │ │ return │
│ 1742 │ │ elif self.scaler is not None: │
│ ❱ 1743 │ │ │ self.scaler.scale(loss).backward(**kwargs) │
│ 1744 │ │ else: │
│ 1745 │ │ │ loss.backward(**kwargs) │
│ 1746 │
│ │
│ /workspace/venv/lib/python3.10/site-packages/torch/_tensor.py:487 in │
│ backward │
│ │
│ 484 │ │ │ │ create_graph=create_graph, │
│ 485 │ │ │ │ inputs=inputs, │
│ 486 │ │ │ ) │
│ ❱  487 │ │ torch.autograd.backward( │
│ 488 │ │ │ self, gradient, retain_graph, create_graph, inputs=inputs │
│ 489 │ │ ) │
│ 490 │
│ │
│ /workspace/venv/lib/python3.10/site-packages/torch/autograd/__init__.py:200 │
│ in backward │
│ │
│ 197 │ # The reason we repeat same the comment below is that │
│ 198 │ # some Python versions print out the first line of a multi-line fu │
│ 199 │ # calls in the traceback and some print out the last line │
│ ❱ 200 │ Variable._execution_engine.run_backward( # Calls into the C++ eng │
│ 201 │ │ tensors, grad_tensors_, retain_graph, create_graph, inputs, │
│ 202 │ │ allow_unreachable=True, accumulate_grad=True) # Calls into th │
│ 203 │
│ │
│ /workspace/venv/lib/python3.10/site-packages/torch/autograd/function.py:274 │
│ in apply │
│ │
│ 271 │ │ │ │ │ │ │ "Function is not allowed. You should on │
│ 272 │ │ │ │ │ │ │ "of them.") │
│ 273 │ │ user_fn = vjp_fn if vjp_fn is not Function.vjp else backward_f │
│ ❱ 274 │ │ return user_fn(self, *args) │
│ 275 │ │
│ 276 │ def apply_jvp(self, *args): │
│ 277 │ │ # _forward_cls is defined by derived class │
│ │
│ /workspace/venv/lib/python3.10/site-packages/torch/utils/checkpoint.py:157 │
│ in backward │
│ │
│ 154 │ │ │ raise RuntimeError( │
│ 155 │ │ │ │ "none of output has requires_grad=True," │
│ 156 │ │ │ │ " this checkpoint() is not necessary") │
│ ❱ 157 │ │ torch.autograd.backward(outputs_with_grad, args_with_grad) │
│ 158 │ │ grads = tuple(inp.grad if isinstance(inp, torch.Tensor) else N │
│ 159 │ │ │ │ │ for inp in detached_inputs) │
│ 160 │
│ │
│ /workspace/venv/lib/python3.10/site-packages/torch/autograd/__init__.py:200 │
│ in backward │
│ │
│ 197 │ # The reason we repeat same the comment below is that │
│ 198 │ # some Python versions print out the first line of a multi-line fu │
│ 199 │ # calls in the traceback and some print out the last line │
│ ❱ 200 │ Variable._execution_engine.run_backward( # Calls into the C++ eng │
│ 201 │ │ tensors, grad_tensors_, retain_graph, create_graph, inputs, │
│ 202 │ │ allow_unreachable=True, accumulate_grad=True) # Calls into th │
│ 203 │
╰──────────────────────────────────────────────────────────────────────────────╯
OutOfMemoryError: CUDA out of memory. Tried to allocate 26.00 MiB (GPU 0; 23.65
GiB total capacity; 18.73 GiB already allocated; 25.69 MiB free; 19.47 GiB
reserved in total by PyTorch) If reserved memory is >> allocated memory try
setting max_split_size_mb to avoid fragmentation. See documentation for Memory
Management and PYTORCH_CUDA_ALLOC_CONF