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+_wandb: + desc: null + value: + python_version: 3.10.12 + cli_version: 0.15.0 + framework: huggingface + huggingface_version: 4.30.2 + is_jupyter_run: false + is_kaggle_kernel: false + start_time: 1694216942.928845 + t: + 1: + - 1 + - 2 + - 3 + - 11 + - 41 + - 49 + - 55 + - 71 + - 83 + 2: + - 1 + - 2 + - 3 + - 11 + - 41 + - 49 + - 55 + - 71 + - 83 + 3: + - 23 + 4: 3.10.12 + 5: 0.15.0 + 6: 4.30.2 + 8: + - 5 diff --git a/log/xl-loha20230908234553/wandb/run-20230908_234902-6uxsp12q/files/output.log b/log/xl-loha20230908234553/wandb/run-20230908_234902-6uxsp12q/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..593464386e0373639aaef57bfb3b4164f8ce921e --- /dev/null +++ b/log/xl-loha20230908234553/wandb/run-20230908_234902-6uxsp12q/files/output.log @@ -0,0 +1,221 @@ + +epoch 1/10 +Traceback (most recent call last): + File "/workspace/kohya_ss/./sdxl_train_network.py", line 176, in + trainer.train(args) + File "/workspace/kohya_ss/train_network.py", line 773, in train + noise_pred = self.call_unet( + File "/workspace/kohya_ss/./sdxl_train_network.py", line 156, in call_unet + noise_pred = unet(noisy_latents, timesteps, text_embedding, vector_embedding) + File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl + return forward_call(*args, **kwargs) + File "/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py", line 521, in forward + return model_forward(*args, **kwargs) + File "/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py", line 509, in __call__ + return convert_to_fp32(self.model_forward(*args, **kwargs)) + File "/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py", line 14, in decorate_autocast + return func(*args, **kwargs) + File "/workspace/kohya_ss/library/sdxl_original_unet.py", line 1088, in forward + h = call_module(module, h, emb, context) + File "/workspace/kohya_ss/library/sdxl_original_unet.py", line 1071, in call_module + x = layer(x, emb) + File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl + return forward_call(*args, **kwargs) + File "/workspace/kohya_ss/library/sdxl_original_unet.py", line 328, in forward + x = self.forward_body(x, emb) + File "/workspace/kohya_ss/library/sdxl_original_unet.py", line 309, in forward_body + h = self.in_layers(x) + File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl + return forward_call(*args, **kwargs) + File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py", line 217, in forward + input = module(input) + File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl + return forward_call(*args, **kwargs) + File "/workspace/kohya_ss/library/sdxl_original_unet.py", line 272, in forward + return super().forward(x) + File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/normalization.py", line 273, in forward + return F.group_norm( + File "/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py", line 2530, in group_norm + return torch.group_norm(input, num_groups, weight, bias, eps, torch.backends.cudnn.enabled) +torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 40.00 MiB (GPU 0; 19.71 GiB total capacity; 17.84 GiB already allocated; 6.62 MiB free; 18.10 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_ss/./sdxl_train_network.py:176 in  │ +│ │ +│ 173 │ args = train_util.read_config_from_file(args, parser) │ +│ 174 │ │ +│ 175 │ trainer = SdxlNetworkTrainer() │ +│ ❱ 176 │ trainer.train(args) │ +│ 177 │ +│ │ +│ /workspace/kohya_ss/train_network.py:773 in train │ +│ │ +│ 770 │ │ │ │ │ │ +│ 771 │ │ │ │ │ # Predict the noise residual │ +│ 772 │ │ │ │ │ with accelerator.autocast(): │ +│ ❱ 773 │ │ │ │ │ │ noise_pred = self.call_unet( │ +│ 774 │ │ │ │ │ │ │ args, accelerator, unet, noisy_latents, timesteps, text_enco │ +│ 775 │ │ │ │ │ │ ) │ +│ 776 │ +│ │ +│ /workspace/kohya_ss/./sdxl_train_network.py:156 in call_unet │ +│ │ +│ 153 │ │ vector_embedding = torch.cat([pool2, embs], dim=1).to(weight_dtype) │ +│ 154 │ │ text_embedding = torch.cat([encoder_hidden_states1, encoder_hidden_states2], dim │ +│ 155 │ │ │ +│ ❱ 156 │ │ noise_pred = unet(noisy_latents, timesteps, text_embedding, vector_embedding) │ +│ 157 │ │ return noise_pred │ +│ 158 │ │ +│ 159 │ def sample_images(self, accelerator, args, epoch, global_step, device, vae, tokenize │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1501 in _call_impl │ +│ │ +│ 1498 │ │ if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks │ +│ 1499 │ │ │ │ or _global_backward_pre_hooks or _global_backward_hooks │ +│ 1500 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ +│ ❱ 1501 │ │ │ return forward_call(*args, **kwargs) │ +│ 1502 │ │ # Do not call functions when jit is used │ +│ 1503 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ +│ 1504 │ │ backward_pre_hooks = [] │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py:521 in forward │ +│ │ +│ 518 │ model_forward = ConvertOutputsToFp32(model_forward) │ +│ 519 │ │ +│ 520 │ def forward(*args, **kwargs): │ +│ ❱ 521 │ │ return model_forward(*args, **kwargs) │ +│ 522 │ │ +│ 523 │ # To act like a decorator so that it can be popped when doing `extract_model_from_pa │ +│ 524 │ forward.__wrapped__ = model_forward │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py:509 in __call__ │ +│ │ +│ 506 │ │ update_wrapper(self, model_forward) │ +│ 507 │ │ +│ 508 │ def __call__(self, *args, **kwargs): │ +│ ❱ 509 │ │ return convert_to_fp32(self.model_forward(*args, **kwargs)) │ +│ 510 │ │ +│ 511 │ def __getstate__(self): │ +│ 512 │ │ raise pickle.PicklingError( │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:14 in decorate_autocast │ +│ │ +│ 11 │ @functools.wraps(func) │ +│ 12 │ def decorate_autocast(*args, **kwargs): │ +│ 13 │ │ with autocast_instance: │ +│ ❱  14 │ │ │ return func(*args, **kwargs) │ +│ 15 │ decorate_autocast.__script_unsupported = '@autocast() decorator is not supported in  │ +│ 16 │ return decorate_autocast │ +│ 17 │ +│ │ +│ /workspace/kohya_ss/library/sdxl_original_unet.py:1088 in forward │ +│ │ +│ 1085 │ │ │ +│ 1086 │ │ for module in self.output_blocks: │ +│ 1087 │ │ │ h = torch.cat([h, hs.pop()], dim=1) │ +│ ❱ 1088 │ │ │ h = call_module(module, h, emb, context) │ +│ 1089 │ │ │ +│ 1090 │ │ h = h.type(x.dtype) │ +│ 1091 │ │ h = call_module(self.out, h, emb, context) │ +│ │ +│ /workspace/kohya_ss/library/sdxl_original_unet.py:1071 in call_module │ +│ │ +│ 1068 │ │ │ for layer in module: │ +│ 1069 │ │ │ │ # print(layer.__class__.__name__, x.dtype, emb.dtype, context.dtype if c │ +│ 1070 │ │ │ │ if isinstance(layer, ResnetBlock2D): │ +│ ❱ 1071 │ │ │ │ │ x = layer(x, emb) │ +│ 1072 │ │ │ │ elif isinstance(layer, Transformer2DModel): │ +│ 1073 │ │ │ │ │ x = layer(x, context) │ +│ 1074 │ │ │ │ else: │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1501 in _call_impl │ +│ │ +│ 1498 │ │ if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks │ +│ 1499 │ │ │ │ or _global_backward_pre_hooks or _global_backward_hooks │ +│ 1500 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ +│ ❱ 1501 │ │ │ return forward_call(*args, **kwargs) │ +│ 1502 │ │ # Do not call functions when jit is used │ +│ 1503 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ +│ 1504 │ │ backward_pre_hooks = [] │ +│ │ +│ /workspace/kohya_ss/library/sdxl_original_unet.py:328 in forward │ +│ │ +│ 325 │ │ │ │ +│ 326 │ │ │ x = torch.utils.checkpoint.checkpoint(create_custom_forward(self.forward_bod │ +│ 327 │ │ else: │ +│ ❱  328 │ │ │ x = self.forward_body(x, emb) │ +│ 329 │ │ │ +│ 330 │ │ return x │ +│ 331 │ +│ │ +│ /workspace/kohya_ss/library/sdxl_original_unet.py:309 in forward_body │ +│ │ +│ 306 │ │ self.gradient_checkpointing = False │ +│ 307 │ │ +│ 308 │ def forward_body(self, x, emb): │ +│ ❱  309 │ │ h = self.in_layers(x) │ +│ 310 │ │ emb_out = self.emb_layers(emb).type(h.dtype) │ +│ 311 │ │ h = h + emb_out[:, :, None, None] │ +│ 312 │ │ h = self.out_layers(h) │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1501 in _call_impl │ +│ │ +│ 1498 │ │ if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks │ +│ 1499 │ │ │ │ or _global_backward_pre_hooks or _global_backward_hooks │ +│ 1500 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ +│ ❱ 1501 │ │ │ return forward_call(*args, **kwargs) │ +│ 1502 │ │ # Do not call functions when jit is used │ +│ 1503 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ +│ 1504 │ │ backward_pre_hooks = [] │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py:217 in forward │ +│ │ +│ 214 │ # with Any as TorchScript expects a more precise type │ +│ 215 │ def forward(self, input): │ +│ 216 │ │ for module in self: │ +│ ❱ 217 │ │ │ input = module(input) │ +│ 218 │ │ return input │ +│ 219 │ │ +│ 220 │ def append(self, module: Module) -> 'Sequential': │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1501 in _call_impl │ +│ │ +│ 1498 │ │ if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks │ +│ 1499 │ │ │ │ or _global_backward_pre_hooks or _global_backward_hooks │ +│ 1500 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ +│ ❱ 1501 │ │ │ return forward_call(*args, **kwargs) │ +│ 1502 │ │ # Do not call functions when jit is used │ +│ 1503 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ +│ 1504 │ │ backward_pre_hooks = [] │ +│ │ +│ /workspace/kohya_ss/library/sdxl_original_unet.py:272 in forward │ +│ │ +│ 269 class GroupNorm32(nn.GroupNorm): │ +│ 270 │ def forward(self, x): │ +│ 271 │ │ if self.weight.dtype != torch.float32: │ +│ ❱  272 │ │ │ return super().forward(x) │ +│ 273 │ │ return super().forward(x.float()).type(x.dtype) │ +│ 274 │ +│ 275 │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/normalization.py:273 in forward │ +│ │ +│ 270 │ │ │ init.zeros_(self.bias) │ +│ 271 │ │ +│ 272 │ def forward(self, input: Tensor) -> Tensor: │ +│ ❱ 273 │ │ return F.group_norm( │ +│ 274 │ │ │ input, self.num_groups, self.weight, self.bias, self.eps) │ +│ 275 │ │ +│ 276 │ def extra_repr(self) -> str: │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/nn/functional.py:2530 in group_norm │ +│ │ +│ 2527 │ if input.dim() < 2: │ +│ 2528 │ │ raise RuntimeError(f"Expected at least 2 dimensions for input tensor but receive │ +│ 2529 │ _verify_batch_size([input.size(0) * input.size(1) // num_groups, num_groups] + list( │ +│ ❱ 2530 │ return torch.group_norm(input, num_groups, weight, bias, eps, torch.backends.cudnn.e │ +│ 2531 │ +│ 2532 │ +│ 2533 def local_response_norm(input: Tensor, size: int, alpha: float = 1e-4, beta: float = 0.7 │ +╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ +OutOfMemoryError: CUDA out of memory. Tried to allocate 40.00 MiB (GPU 0; 19.71 GiB total capacity; 17.84 GiB already allocated; 6.62 MiB free; 18.10 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 \ No newline at end of file diff --git a/log/xl-loha20230908234553/wandb/run-20230908_234902-6uxsp12q/files/requirements.txt b/log/xl-loha20230908234553/wandb/run-20230908_234902-6uxsp12q/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..22aa8c32bc07c7b9665aa7a47c798246fef52fbb --- /dev/null +++ b/log/xl-loha20230908234553/wandb/run-20230908_234902-6uxsp12q/files/requirements.txt @@ -0,0 +1,266 @@ +absl-py==1.4.0 +accelerate==0.19.0 +aiofiles==23.2.1 +aiohttp==3.8.5 +aiosignal==1.3.1 +aiosqlite==0.19.0 +altair==4.2.2 +annotated-types==0.5.0 +anyio==3.7.1 +appdirs==1.4.4 +argon2-cffi-bindings==21.2.0 +argon2-cffi==21.3.0 +arrow==1.2.3 +asttokens==2.2.1 +astunparse==1.6.3 +async-timeout==4.0.3 +attrs==23.1.0 +babel==2.12.1 +backcall==0.2.0 +beautifulsoup4==4.12.2 +bitsandbytes==0.41.1 +bleach==6.0.0 +blinker==1.4 +cachetools==5.3.1 +certifi==2022.12.7 +cffi==1.15.1 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1 + - 2 + - 3 + - 11 + - 41 + - 49 + - 55 + - 71 + - 83 + 2: + - 1 + - 2 + - 3 + - 11 + - 41 + - 49 + - 55 + - 71 + - 83 + 3: + - 23 + 4: 3.10.12 + 5: 0.15.0 + 6: 4.30.2 + 8: + - 5 diff --git a/log/xl-loha20230908234952/wandb/run-20230908_235006-1nmqkt8m/files/output.log b/log/xl-loha20230908234952/wandb/run-20230908_235006-1nmqkt8m/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..549cbb2541dd9f2e59531736643cbc4190fa2389 --- /dev/null +++ b/log/xl-loha20230908234952/wandb/run-20230908_235006-1nmqkt8m/files/output.log @@ -0,0 +1,209 @@ + +epoch 1/10 +Traceback (most recent call last): + File "/workspace/kohya_ss/./sdxl_train_network.py", line 176, in + trainer.train(args) + File "/workspace/kohya_ss/train_network.py", line 773, in train + noise_pred = self.call_unet( + File "/workspace/kohya_ss/./sdxl_train_network.py", line 156, in call_unet + noise_pred = unet(noisy_latents, timesteps, text_embedding, vector_embedding) + File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl + return forward_call(*args, **kwargs) + File "/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py", line 521, in forward + return model_forward(*args, **kwargs) + File "/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py", line 509, in __call__ + return convert_to_fp32(self.model_forward(*args, **kwargs)) + File "/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py", line 14, in decorate_autocast + return func(*args, **kwargs) + File "/workspace/kohya_ss/library/sdxl_original_unet.py", line 1088, in forward + h = call_module(module, h, emb, context) + File "/workspace/kohya_ss/library/sdxl_original_unet.py", line 1071, in call_module + x = layer(x, emb) + File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl + return forward_call(*args, **kwargs) + File "/workspace/kohya_ss/library/sdxl_original_unet.py", line 328, in forward + x = self.forward_body(x, emb) + File "/workspace/kohya_ss/library/sdxl_original_unet.py", line 309, in forward_body + h = self.in_layers(x) + File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl + return forward_call(*args, **kwargs) + File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py", line 217, in forward + input = module(input) + File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl + return forward_call(*args, **kwargs) + File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py", line 463, in forward + return self._conv_forward(input, self.weight, self.bias) + File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py", line 459, in _conv_forward + return F.conv2d(input, weight, bias, self.stride, +torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 30.00 MiB (GPU 0; 19.71 GiB total capacity; 17.83 GiB already allocated; 2.62 MiB free; 18.10 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_ss/./sdxl_train_network.py:176 in  │ +│ │ +│ 173 │ args = train_util.read_config_from_file(args, parser) │ +│ 174 │ │ +│ 175 │ trainer = SdxlNetworkTrainer() │ +│ ❱ 176 │ trainer.train(args) │ +│ 177 │ +│ │ +│ /workspace/kohya_ss/train_network.py:773 in train │ +│ │ +│ 770 │ │ │ │ │ │ +│ 771 │ │ │ │ │ # Predict the noise residual │ +│ 772 │ │ │ │ │ with accelerator.autocast(): │ +│ ❱ 773 │ │ │ │ │ │ noise_pred = self.call_unet( │ +│ 774 │ │ │ │ │ │ │ args, accelerator, unet, noisy_latents, timesteps, text_enco │ +│ 775 │ │ │ │ │ │ ) │ +│ 776 │ +│ │ +│ /workspace/kohya_ss/./sdxl_train_network.py:156 in call_unet │ +│ │ +│ 153 │ │ vector_embedding = torch.cat([pool2, embs], dim=1).to(weight_dtype) │ +│ 154 │ │ text_embedding = torch.cat([encoder_hidden_states1, encoder_hidden_states2], dim │ +│ 155 │ │ │ +│ ❱ 156 │ │ noise_pred = unet(noisy_latents, timesteps, text_embedding, vector_embedding) │ +│ 157 │ │ return noise_pred │ +│ 158 │ │ +│ 159 │ def sample_images(self, accelerator, args, epoch, global_step, device, vae, tokenize │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1501 in _call_impl │ +│ │ +│ 1498 │ │ if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks │ +│ 1499 │ │ │ │ or _global_backward_pre_hooks or _global_backward_hooks │ +│ 1500 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ +│ ❱ 1501 │ │ │ return forward_call(*args, **kwargs) │ +│ 1502 │ │ # Do not call functions when jit is used │ +│ 1503 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ +│ 1504 │ │ backward_pre_hooks = [] │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py:521 in forward │ +│ │ +│ 518 │ model_forward = ConvertOutputsToFp32(model_forward) │ +│ 519 │ │ +│ 520 │ def forward(*args, **kwargs): │ +│ ❱ 521 │ │ return model_forward(*args, **kwargs) │ +│ 522 │ │ +│ 523 │ # To act like a decorator so that it can be popped when doing `extract_model_from_pa │ +│ 524 │ forward.__wrapped__ = model_forward │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py:509 in __call__ │ +│ │ +│ 506 │ │ update_wrapper(self, model_forward) │ +│ 507 │ │ +│ 508 │ def __call__(self, *args, **kwargs): │ +│ ❱ 509 │ │ return convert_to_fp32(self.model_forward(*args, **kwargs)) │ +│ 510 │ │ +│ 511 │ def __getstate__(self): │ +│ 512 │ │ raise pickle.PicklingError( │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:14 in decorate_autocast │ +│ │ +│ 11 │ @functools.wraps(func) │ +│ 12 │ def decorate_autocast(*args, **kwargs): │ +│ 13 │ │ with autocast_instance: │ +│ ❱  14 │ │ │ return func(*args, **kwargs) │ +│ 15 │ decorate_autocast.__script_unsupported = '@autocast() decorator is not supported in  │ +│ 16 │ return decorate_autocast │ +│ 17 │ +│ │ +│ /workspace/kohya_ss/library/sdxl_original_unet.py:1088 in forward │ +│ │ +│ 1085 │ │ │ +│ 1086 │ │ for module in self.output_blocks: │ +│ 1087 │ │ │ h = torch.cat([h, hs.pop()], dim=1) │ +│ ❱ 1088 │ │ │ h = call_module(module, h, emb, context) │ +│ 1089 │ │ │ +│ 1090 │ │ h = h.type(x.dtype) │ +│ 1091 │ │ h = call_module(self.out, h, emb, context) │ +│ │ +│ /workspace/kohya_ss/library/sdxl_original_unet.py:1071 in call_module │ +│ │ +│ 1068 │ │ │ for layer in module: │ +│ 1069 │ │ │ │ # print(layer.__class__.__name__, x.dtype, emb.dtype, context.dtype if c │ +│ 1070 │ │ │ │ if isinstance(layer, ResnetBlock2D): │ +│ ❱ 1071 │ │ │ │ │ x = layer(x, emb) │ +│ 1072 │ │ │ │ elif isinstance(layer, Transformer2DModel): │ +│ 1073 │ │ │ │ │ x = layer(x, context) │ +│ 1074 │ │ │ │ else: │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1501 in _call_impl │ +│ │ +│ 1498 │ │ if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks │ +│ 1499 │ │ │ │ or _global_backward_pre_hooks or _global_backward_hooks │ +│ 1500 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ +│ ❱ 1501 │ │ │ return forward_call(*args, **kwargs) │ +│ 1502 │ │ # Do not call functions when jit is used │ +│ 1503 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ +│ 1504 │ │ backward_pre_hooks = [] │ +│ │ +│ /workspace/kohya_ss/library/sdxl_original_unet.py:328 in forward │ +│ │ +│ 325 │ │ │ │ +│ 326 │ │ │ x = torch.utils.checkpoint.checkpoint(create_custom_forward(self.forward_bod │ +│ 327 │ │ else: │ +│ ❱  328 │ │ │ x = self.forward_body(x, emb) │ +│ 329 │ │ │ +│ 330 │ │ return x │ +│ 331 │ +│ │ +│ /workspace/kohya_ss/library/sdxl_original_unet.py:309 in forward_body │ +│ │ +│ 306 │ │ self.gradient_checkpointing = False │ +│ 307 │ │ +│ 308 │ def forward_body(self, x, emb): │ +│ ❱  309 │ │ h = self.in_layers(x) │ +│ 310 │ │ emb_out = self.emb_layers(emb).type(h.dtype) │ +│ 311 │ │ h = h + emb_out[:, :, None, None] │ +│ 312 │ │ h = self.out_layers(h) │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1501 in _call_impl │ +│ │ +│ 1498 │ │ if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks │ +│ 1499 │ │ │ │ or _global_backward_pre_hooks or _global_backward_hooks │ +│ 1500 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ +│ ❱ 1501 │ │ │ return forward_call(*args, **kwargs) │ +│ 1502 │ │ # Do not call functions when jit is used │ +│ 1503 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ +│ 1504 │ │ backward_pre_hooks = [] │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py:217 in forward │ +│ │ +│ 214 │ # with Any as TorchScript expects a more precise type │ +│ 215 │ def forward(self, input): │ +│ 216 │ │ for module in self: │ +│ ❱ 217 │ │ │ input = module(input) │ +│ 218 │ │ return input │ +│ 219 │ │ +│ 220 │ def append(self, module: Module) -> 'Sequential': │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1501 in _call_impl │ +│ │ +│ 1498 │ │ if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks │ +│ 1499 │ │ │ │ or _global_backward_pre_hooks or _global_backward_hooks │ +│ 1500 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ +│ ❱ 1501 │ │ │ return forward_call(*args, **kwargs) │ +│ 1502 │ │ # Do not call functions when jit is used │ +│ 1503 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ +│ 1504 │ │ backward_pre_hooks = [] │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py:463 in forward │ +│ │ +│ 460 │ │ │ │ │ │ self.padding, self.dilation, self.groups) │ +│ 461 │ │ +│ 462 │ def forward(self, input: Tensor) -> Tensor: │ +│ ❱  463 │ │ return self._conv_forward(input, self.weight, self.bias) │ +│ 464 │ +│ 465 class Conv3d(_ConvNd): │ +│ 466 │ __doc__ = r"""Applies a 3D convolution over an input signal composed of several inpu │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py:459 in _conv_forward │ +│ │ +│ 456 │ │ │ return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=sel │ +│ 457 │ │ │ │ │ │ │ weight, bias, self.stride, │ +│ 458 │ │ │ │ │ │ │ _pair(0), self.dilation, self.groups) │ +│ ❱  459 │ │ return F.conv2d(input, weight, bias, self.stride, │ +│ 460 │ │ │ │ │ │ self.padding, self.dilation, self.groups) │ +│ 461 │ │ +│ 462 │ def forward(self, input: Tensor) -> Tensor: │ +╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ +OutOfMemoryError: CUDA out of memory. Tried to allocate 30.00 MiB (GPU 0; 19.71 GiB total capacity; 17.83 GiB already allocated; 2.62 MiB free; 18.10 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 \ No newline at end of file diff --git a/log/xl-loha20230908234952/wandb/run-20230908_235006-1nmqkt8m/files/requirements.txt b/log/xl-loha20230908234952/wandb/run-20230908_235006-1nmqkt8m/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..22aa8c32bc07c7b9665aa7a47c798246fef52fbb --- /dev/null +++ b/log/xl-loha20230908234952/wandb/run-20230908_235006-1nmqkt8m/files/requirements.txt @@ -0,0 +1,266 @@ +absl-py==1.4.0 +accelerate==0.19.0 +aiofiles==23.2.1 +aiohttp==3.8.5 +aiosignal==1.3.1 +aiosqlite==0.19.0 +altair==4.2.2 +annotated-types==0.5.0 +anyio==3.7.1 +appdirs==1.4.4 +argon2-cffi-bindings==21.2.0 +argon2-cffi==21.3.0 +arrow==1.2.3 +asttokens==2.2.1 +astunparse==1.6.3 +async-timeout==4.0.3 +attrs==23.1.0 +babel==2.12.1 +backcall==0.2.0 +beautifulsoup4==4.12.2 +bitsandbytes==0.41.1 +bleach==6.0.0 +blinker==1.4 +cachetools==5.3.1 +certifi==2022.12.7 +cffi==1.15.1 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[wandb_setup.py:_flush():76] Loading settings from /root/.config/wandb/settings +2023-09-08 23:50:06,158 INFO MainThread:2222 [wandb_setup.py:_flush():76] Loading settings from /workspace/kohya_ss/wandb/settings +2023-09-08 23:50:06,158 INFO MainThread:2222 [wandb_setup.py:_flush():76] Loading settings from environment variables: {'root_dir': '/workspace/kohya_ss/dataset/kohya-training/log/xl-loha20230908234952'} +2023-09-08 23:50:06,159 INFO MainThread:2222 [wandb_setup.py:_flush():76] Inferring run settings from compute environment: {'program_relpath': 'sdxl_train_network.py', 'program': '/workspace/kohya_ss/./sdxl_train_network.py'} +2023-09-08 23:50:06,159 INFO MainThread:2222 [wandb_setup.py:_flush():76] Applying login settings: {'api_key': '***REDACTED***'} +2023-09-08 23:50:06,159 INFO MainThread:2222 [wandb_setup.py:_flush():76] Applying login settings: {'api_key': '***REDACTED***'} +2023-09-08 23:50:06,161 INFO MainThread:2222 [wandb_init.py:_log_setup():507] Logging user logs to /workspace/kohya_ss/dataset/kohya-training/log/xl-loha20230908234952/wandb/run-20230908_235006-1nmqkt8m/logs/debug.log +2023-09-08 23:50:06,162 INFO MainThread:2222 [wandb_init.py:_log_setup():508] Logging internal logs to /workspace/kohya_ss/dataset/kohya-training/log/xl-loha20230908234952/wandb/run-20230908_235006-1nmqkt8m/logs/debug-internal.log +2023-09-08 23:50:06,162 INFO MainThread:2222 [wandb_init.py:init():547] calling init triggers +2023-09-08 23:50:06,163 INFO MainThread:2222 [wandb_init.py:init():554] wandb.init called with sweep_config: {} +config: {} +2023-09-08 23:50:06,163 INFO MainThread:2222 [wandb_init.py:init():595] starting backend +2023-09-08 23:50:06,164 INFO MainThread:2222 [wandb_init.py:init():599] setting up manager +2023-09-08 23:50:06,173 INFO MainThread:2222 [backend.py:_multiprocessing_setup():106] multiprocessing start_methods=fork,spawn,forkserver, using: spawn +2023-09-08 23:50:06,182 INFO MainThread:2222 [wandb_init.py:init():605] backend started 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To upgrade, please run:\n $ pip install wandb --upgrade" + +2023-09-08 23:50:07,121 INFO MainThread:2222 [wandb_init.py:init():782] starting run threads in backend +2023-09-08 23:50:07,303 INFO MainThread:2222 [wandb_run.py:_console_start():2157] atexit reg +2023-09-08 23:50:07,304 INFO MainThread:2222 [wandb_run.py:_redirect():2012] redirect: SettingsConsole.WRAP_RAW +2023-09-08 23:50:07,304 INFO MainThread:2222 [wandb_run.py:_redirect():2077] Wrapping output streams. +2023-09-08 23:50:07,305 INFO MainThread:2222 [wandb_run.py:_redirect():2102] Redirects installed. +2023-09-08 23:50:07,307 INFO MainThread:2222 [wandb_init.py:init():824] run started, returning control to user process +2023-09-08 23:50:17,964 WARNING MsgRouterThr:2222 [router.py:message_loop():77] message_loop has been closed diff --git a/log/xl-loha20230908234952/wandb/run-20230908_235006-1nmqkt8m/run-1nmqkt8m.wandb b/log/xl-loha20230908234952/wandb/run-20230908_235006-1nmqkt8m/run-1nmqkt8m.wandb new file mode 100644 index 0000000000000000000000000000000000000000..54683a06e9a4dd1e3a79d29d1c948606123c1901 Binary files /dev/null and b/log/xl-loha20230908234952/wandb/run-20230908_235006-1nmqkt8m/run-1nmqkt8m.wandb differ diff --git a/log/xl-loha20230908235202/wandb/debug-internal.log b/log/xl-loha20230908235202/wandb/debug-internal.log new file mode 120000 index 0000000000000000000000000000000000000000..82b9843a9d91910bc42f26fb352c49f8e4772ef9 --- /dev/null +++ b/log/xl-loha20230908235202/wandb/debug-internal.log @@ -0,0 +1 @@ +run-20230908_235214-o8t1pxng/logs/debug-internal.log \ No newline at end of file diff --git a/log/xl-loha20230908235202/wandb/debug.log b/log/xl-loha20230908235202/wandb/debug.log new file mode 120000 index 0000000000000000000000000000000000000000..a1529f1f2d96e9e1e0bf8bf982ab25b85ca894c1 --- /dev/null +++ b/log/xl-loha20230908235202/wandb/debug.log @@ -0,0 +1 @@ +run-20230908_235214-o8t1pxng/logs/debug.log \ No newline at end of file diff --git a/log/xl-loha20230908235202/wandb/latest-run b/log/xl-loha20230908235202/wandb/latest-run new file mode 120000 index 0000000000000000000000000000000000000000..9d625bdddb2f55b14d913f492a7f4470872e92d6 --- /dev/null +++ b/log/xl-loha20230908235202/wandb/latest-run @@ -0,0 +1 @@ +run-20230908_235214-o8t1pxng \ No newline at end of file diff --git a/log/xl-loha20230908235202/wandb/run-20230908_235214-o8t1pxng/files/config.yaml b/log/xl-loha20230908235202/wandb/run-20230908_235214-o8t1pxng/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..022c96bed06d1f775833bb2c016144204286ba54 --- /dev/null +++ b/log/xl-loha20230908235202/wandb/run-20230908_235214-o8t1pxng/files/config.yaml @@ -0,0 +1,40 @@ +wandb_version: 1 + +_wandb: + desc: null + value: + python_version: 3.10.12 + cli_version: 0.15.0 + framework: huggingface + huggingface_version: 4.30.2 + is_jupyter_run: false + is_kaggle_kernel: false + start_time: 1694217135.018819 + t: + 1: + - 1 + - 2 + - 3 + - 11 + - 41 + - 49 + - 55 + - 71 + - 83 + 2: + - 1 + - 2 + - 3 + - 11 + - 41 + - 49 + - 55 + - 71 + - 83 + 3: + - 23 + 4: 3.10.12 + 5: 0.15.0 + 6: 4.30.2 + 8: + - 5 diff --git a/log/xl-loha20230908235202/wandb/run-20230908_235214-o8t1pxng/files/output.log b/log/xl-loha20230908235202/wandb/run-20230908_235214-o8t1pxng/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..e648673f751ee2f8f91f7389cd5c5b01b028c3ac --- /dev/null +++ b/log/xl-loha20230908235202/wandb/run-20230908_235214-o8t1pxng/files/output.log @@ -0,0 +1,209 @@ + +epoch 1/10 +Traceback (most recent call last): + File "/workspace/kohya_ss/./sdxl_train_network.py", line 176, in + trainer.train(args) + File "/workspace/kohya_ss/train_network.py", line 773, in train + noise_pred = self.call_unet( + File "/workspace/kohya_ss/./sdxl_train_network.py", line 156, in call_unet + noise_pred = unet(noisy_latents, timesteps, text_embedding, vector_embedding) + File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl + return forward_call(*args, **kwargs) + File "/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py", line 521, in forward + return model_forward(*args, **kwargs) + File "/usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py", line 509, in __call__ + return convert_to_fp32(self.model_forward(*args, **kwargs)) + File "/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py", line 14, in decorate_autocast + return func(*args, **kwargs) + File "/workspace/kohya_ss/library/sdxl_original_unet.py", line 1088, in forward + h = call_module(module, h, emb, context) + File "/workspace/kohya_ss/library/sdxl_original_unet.py", line 1071, in call_module + x = layer(x, emb) + File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl + return forward_call(*args, **kwargs) + File "/workspace/kohya_ss/library/sdxl_original_unet.py", line 328, in forward + x = self.forward_body(x, emb) + File "/workspace/kohya_ss/library/sdxl_original_unet.py", line 309, in forward_body + h = self.in_layers(x) + File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl + return forward_call(*args, **kwargs) + File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py", line 217, in forward + input = module(input) + File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl + return forward_call(*args, **kwargs) + File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py", line 463, in forward + return self._conv_forward(input, self.weight, self.bias) + File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py", line 459, in _conv_forward + return F.conv2d(input, weight, bias, self.stride, +torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 30.00 MiB (GPU 0; 19.71 GiB total capacity; 17.81 GiB already allocated; 16.62 MiB free; 18.09 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_ss/./sdxl_train_network.py:176 in  │ +│ │ +│ 173 │ args = train_util.read_config_from_file(args, parser) │ +│ 174 │ │ +│ 175 │ trainer = SdxlNetworkTrainer() │ +│ ❱ 176 │ trainer.train(args) │ +│ 177 │ +│ │ +│ /workspace/kohya_ss/train_network.py:773 in train │ +│ │ +│ 770 │ │ │ │ │ │ +│ 771 │ │ │ │ │ # Predict the noise residual │ +│ 772 │ │ │ │ │ with accelerator.autocast(): │ +│ ❱ 773 │ │ │ │ │ │ noise_pred = self.call_unet( │ +│ 774 │ │ │ │ │ │ │ args, accelerator, unet, noisy_latents, timesteps, text_enco │ +│ 775 │ │ │ │ │ │ ) │ +│ 776 │ +│ │ +│ /workspace/kohya_ss/./sdxl_train_network.py:156 in call_unet │ +│ │ +│ 153 │ │ vector_embedding = torch.cat([pool2, embs], dim=1).to(weight_dtype) │ +│ 154 │ │ text_embedding = torch.cat([encoder_hidden_states1, encoder_hidden_states2], dim │ +│ 155 │ │ │ +│ ❱ 156 │ │ noise_pred = unet(noisy_latents, timesteps, text_embedding, vector_embedding) │ +│ 157 │ │ return noise_pred │ +│ 158 │ │ +│ 159 │ def sample_images(self, accelerator, args, epoch, global_step, device, vae, tokenize │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1501 in _call_impl │ +│ │ +│ 1498 │ │ if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks │ +│ 1499 │ │ │ │ or _global_backward_pre_hooks or _global_backward_hooks │ +│ 1500 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ +│ ❱ 1501 │ │ │ return forward_call(*args, **kwargs) │ +│ 1502 │ │ # Do not call functions when jit is used │ +│ 1503 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ +│ 1504 │ │ backward_pre_hooks = [] │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py:521 in forward │ +│ │ +│ 518 │ model_forward = ConvertOutputsToFp32(model_forward) │ +│ 519 │ │ +│ 520 │ def forward(*args, **kwargs): │ +│ ❱ 521 │ │ return model_forward(*args, **kwargs) │ +│ 522 │ │ +│ 523 │ # To act like a decorator so that it can be popped when doing `extract_model_from_pa │ +│ 524 │ forward.__wrapped__ = model_forward │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/accelerate/utils/operations.py:509 in __call__ │ +│ │ +│ 506 │ │ update_wrapper(self, model_forward) │ +│ 507 │ │ +│ 508 │ def __call__(self, *args, **kwargs): │ +│ ❱ 509 │ │ return convert_to_fp32(self.model_forward(*args, **kwargs)) │ +│ 510 │ │ +│ 511 │ def __getstate__(self): │ +│ 512 │ │ raise pickle.PicklingError( │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py:14 in decorate_autocast │ +│ │ +│ 11 │ @functools.wraps(func) │ +│ 12 │ def decorate_autocast(*args, **kwargs): │ +│ 13 │ │ with autocast_instance: │ +│ ❱  14 │ │ │ return func(*args, **kwargs) │ +│ 15 │ decorate_autocast.__script_unsupported = '@autocast() decorator is not supported in  │ +│ 16 │ return decorate_autocast │ +│ 17 │ +│ │ +│ /workspace/kohya_ss/library/sdxl_original_unet.py:1088 in forward │ +│ │ +│ 1085 │ │ │ +│ 1086 │ │ for module in self.output_blocks: │ +│ 1087 │ │ │ h = torch.cat([h, hs.pop()], dim=1) │ +│ ❱ 1088 │ │ │ h = call_module(module, h, emb, context) │ +│ 1089 │ │ │ +│ 1090 │ │ h = h.type(x.dtype) │ +│ 1091 │ │ h = call_module(self.out, h, emb, context) │ +│ │ +│ /workspace/kohya_ss/library/sdxl_original_unet.py:1071 in call_module │ +│ │ +│ 1068 │ │ │ for layer in module: │ +│ 1069 │ │ │ │ # print(layer.__class__.__name__, x.dtype, emb.dtype, context.dtype if c │ +│ 1070 │ │ │ │ if isinstance(layer, ResnetBlock2D): │ +│ ❱ 1071 │ │ │ │ │ x = layer(x, emb) │ +│ 1072 │ │ │ │ elif isinstance(layer, Transformer2DModel): │ +│ 1073 │ │ │ │ │ x = layer(x, context) │ +│ 1074 │ │ │ │ else: │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1501 in _call_impl │ +│ │ +│ 1498 │ │ if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks │ +│ 1499 │ │ │ │ or _global_backward_pre_hooks or _global_backward_hooks │ +│ 1500 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ +│ ❱ 1501 │ │ │ return forward_call(*args, **kwargs) │ +│ 1502 │ │ # Do not call functions when jit is used │ +│ 1503 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ +│ 1504 │ │ backward_pre_hooks = [] │ +│ │ +│ /workspace/kohya_ss/library/sdxl_original_unet.py:328 in forward │ +│ │ +│ 325 │ │ │ │ +│ 326 │ │ │ x = torch.utils.checkpoint.checkpoint(create_custom_forward(self.forward_bod │ +│ 327 │ │ else: │ +│ ❱  328 │ │ │ x = self.forward_body(x, emb) │ +│ 329 │ │ │ +│ 330 │ │ return x │ +│ 331 │ +│ │ +│ /workspace/kohya_ss/library/sdxl_original_unet.py:309 in forward_body │ +│ │ +│ 306 │ │ self.gradient_checkpointing = False │ +│ 307 │ │ +│ 308 │ def forward_body(self, x, emb): │ +│ ❱  309 │ │ h = self.in_layers(x) │ +│ 310 │ │ emb_out = self.emb_layers(emb).type(h.dtype) │ +│ 311 │ │ h = h + emb_out[:, :, None, None] │ +│ 312 │ │ h = self.out_layers(h) │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1501 in _call_impl │ +│ │ +│ 1498 │ │ if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks │ +│ 1499 │ │ │ │ or _global_backward_pre_hooks or _global_backward_hooks │ +│ 1500 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ +│ ❱ 1501 │ │ │ return forward_call(*args, **kwargs) │ +│ 1502 │ │ # Do not call functions when jit is used │ +│ 1503 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ +│ 1504 │ │ backward_pre_hooks = [] │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py:217 in forward │ +│ │ +│ 214 │ # with Any as TorchScript expects a more precise type │ +│ 215 │ def forward(self, input): │ +│ 216 │ │ for module in self: │ +│ ❱ 217 │ │ │ input = module(input) │ +│ 218 │ │ return input │ +│ 219 │ │ +│ 220 │ def append(self, module: Module) -> 'Sequential': │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1501 in _call_impl │ +│ │ +│ 1498 │ │ if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks │ +│ 1499 │ │ │ │ or _global_backward_pre_hooks or _global_backward_hooks │ +│ 1500 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ +│ ❱ 1501 │ │ │ return forward_call(*args, **kwargs) │ +│ 1502 │ │ # Do not call functions when jit is used │ +│ 1503 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ +│ 1504 │ │ backward_pre_hooks = [] │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py:463 in forward │ +│ │ +│ 460 │ │ │ │ │ │ self.padding, self.dilation, self.groups) │ +│ 461 │ │ +│ 462 │ def forward(self, input: Tensor) -> Tensor: │ +│ ❱  463 │ │ return self._conv_forward(input, self.weight, self.bias) │ +│ 464 │ +│ 465 class Conv3d(_ConvNd): │ +│ 466 │ __doc__ = r"""Applies a 3D convolution over an input signal composed of several inpu │ +│ │ +│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py:459 in _conv_forward │ +│ │ +│ 456 │ │ │ return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=sel │ +│ 457 │ │ │ │ │ │ │ weight, bias, self.stride, │ +│ 458 │ │ │ │ │ │ │ _pair(0), self.dilation, self.groups) │ +│ ❱  459 │ │ return F.conv2d(input, weight, bias, self.stride, │ +│ 460 │ │ │ │ │ │ self.padding, self.dilation, self.groups) │ +│ 461 │ │ +│ 462 │ def forward(self, input: Tensor) -> Tensor: │ +╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ +OutOfMemoryError: CUDA out of memory. Tried to allocate 30.00 MiB (GPU 0; 19.71 GiB total capacity; 17.81 GiB already allocated; 16.62 MiB free; 18.09 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 \ No newline at end of file diff --git a/log/xl-loha20230908235202/wandb/run-20230908_235214-o8t1pxng/files/requirements.txt b/log/xl-loha20230908235202/wandb/run-20230908_235214-o8t1pxng/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..22aa8c32bc07c7b9665aa7a47c798246fef52fbb --- /dev/null +++ b/log/xl-loha20230908235202/wandb/run-20230908_235214-o8t1pxng/files/requirements.txt @@ -0,0 +1,266 @@ +absl-py==1.4.0 +accelerate==0.19.0 +aiofiles==23.2.1 +aiohttp==3.8.5 +aiosignal==1.3.1 +aiosqlite==0.19.0 +altair==4.2.2 +annotated-types==0.5.0 +anyio==3.7.1 +appdirs==1.4.4 +argon2-cffi-bindings==21.2.0 +argon2-cffi==21.3.0 +arrow==1.2.3 +asttokens==2.2.1 +astunparse==1.6.3 +async-timeout==4.0.3 +attrs==23.1.0 +babel==2.12.1 +backcall==0.2.0 +beautifulsoup4==4.12.2 +bitsandbytes==0.41.1 +bleach==6.0.0 +blinker==1.4 +cachetools==5.3.1 +certifi==2022.12.7 +cffi==1.15.1 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