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Audio-WestlakeU / NBSS

The official repo of NBC & SpatialNet for multichannel speech separation, denoising, and dereverberation
MIT License
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Error in WHAMR! Training #18

Open zx1292982431 opened 6 months ago

zx1292982431 commented 6 months ago

I tried to train SpatialNet on WHAMR! dataset by the script python SharedTrainer.py fit --config=configs/SpatialNet.yaml --config=configs/datasets/whamr.yaml --model.arch.dim_input=12 --model.arch.dim_output=4 --model.arch.num_freqs=129 --trainer.precision=bf16-mixed --model.compile=True --data.batch_size=[2,4] --trainer.devices=0,1,2,3, --trainer.max_epochs=100, but I got an error：

Traceback (most recent call last):
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/pytorch_lightning/trainer/call.py", line 43, in _call_and_handle_interrupt
    return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 102, in launch
    return function(*args, **kwargs)
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 580, in _fit_impl
    self._run(model, ckpt_path=ckpt_path)
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 989, in _run
    results = self._run_stage()
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1033, in _run_stage
    self._run_sanity_check()
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1062, in _run_sanity_check
    val_loop.run()
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/pytorch_lightning/loops/utilities.py", line 182, in _decorator
    return loop_run(self, *args, **kwargs)
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/pytorch_lightning/loops/evaluation_loop.py", line 134, in run
    self._evaluation_step(batch, batch_idx, dataloader_idx, dataloader_iter)
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/pytorch_lightning/loops/evaluation_loop.py", line 391, in _evaluation_step
    output = call._call_strategy_hook(trainer, hook_name, *step_args)
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/pytorch_lightning/trainer/call.py", line 309, in _call_strategy_hook
    output = fn(*args, **kwargs)
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/pytorch_lightning/strategies/strategy.py", line 402, in validation_step
    return self._forward_redirection(self.model, self.lightning_module, "validation_step", *args, **kwargs)
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/pytorch_lightning/strategies/strategy.py", line 633, in __call__
    wrapper_output = wrapper_module(*args, **kwargs)
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1519, in forward
    else self._run_ddp_forward(*inputs, **kwargs)
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1355, in _run_ddp_forward
    return self.module(*inputs, **kwargs)  # type: ignore[index]
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/pytorch_lightning/strategies/strategy.py", line 626, in wrapped_forward
    out = method(*_args, **_kwargs)
  File "/data/lzx/SpatialNet/NBSS/SharedTrainer.py", line 153, in validation_step
    yr_hat = self.forward(x)
  File "/data/lzx/SpatialNet/NBSS/SharedTrainer.py", line 107, in forward
    X, stft_paras = self.stft.stft(x[:, self.channels])  # [B,C,F,T], complex
  File "/data/lzx/SpatialNet/NBSS/models/io/stft.py", line 55, in stft
    X = torch.stft(x, n_fft=self.n_fft, hop_length=self.n_hop, win_length=self.win_len, window=self.window, return_complex=True)
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/torch/functional.py", line 648, in stft
    input = F.pad(input.view(extended_shape), [pad, pad], pad_mode)
RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/data/lzx/SpatialNet/NBSS/SharedTrainer.py", line 335, in <module>
    cli = TrainCLI(
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/pytorch_lightning/cli.py", line 386, in __init__
    self._run_subcommand(self.subcommand)
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/pytorch_lightning/cli.py", line 677, in _run_subcommand
    fn(**fn_kwargs)
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 544, in fit
    call._call_and_handle_interrupt(
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/pytorch_lightning/trainer/call.py", line 68, in _call_and_handle_interrupt
    trainer._teardown()
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1012, in _teardown
    self.strategy.teardown()
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/pytorch_lightning/strategies/ddp.py", line 405, in teardown
    super().teardown()
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/pytorch_lightning/strategies/parallel.py", line 127, in teardown
    super().teardown()
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/pytorch_lightning/strategies/strategy.py", line 528, in teardown
    self.lightning_module.cpu()
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/lightning_fabric/utilities/device_dtype_mixin.py", line 79, in cpu
    return super().cpu()
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/torch/nn/modules/module.py", line 967, in cpu
    return self._apply(lambda t: t.cpu())
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/torch/nn/modules/module.py", line 810, in _apply
    module._apply(fn)
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/torch/nn/modules/module.py", line 810, in _apply
    module._apply(fn)
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/torch/nn/modules/module.py", line 810, in _apply
    module._apply(fn)
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/torch/nn/modules/module.py", line 833, in _apply
    param_applied = fn(param)
  File "/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/torch/nn/modules/module.py", line 967, in <lambda>
    return self._apply(lambda t: t.cpu())
RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

/opt/anaconda3/envs/lzx/lib/python3.8/site-packages/torch/lib/../../../.././libstdc++.so.6(_ZN9__gnu_cxx27__verbose_terminate_handlerEv+0xc0)[0x7fc55453ff00]
[ubuntu:39556] [ 4] /opt/anaconda3/envs/lzx/lib/python3.8/site-packages/torch/lib/../../../.././libstdc++.so.6(+0xb643c)[0x7fc55453e43c]
[ubuntu:39556] [ 5] /opt/anaconda3/envs/lzx/lib/python3.8/site-packages/torch/lib/../../../.././libstdc++.so.6(+0xb648e)[0x7fc55453e48e]
[ubuntu:39556] [ 6] /opt/anaconda3/envs/lzx/lib/python3.8/site-packages/torch/lib/../../../.././libstdc++.so.6(+0xb6435)[0x7fc55453e435]
[ubuntu:39556] [ 7] /opt/anaconda3/envs/lzx/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so(+0xcc8475)[0x7fc4b780f475]
[ubuntu:39556] [ 8] /opt/anaconda3/envs/lzx/lib/python3.8/site-packages/torch/lib/../../../.././libstdc++.so.6(+0xd3e95)[0x7fc55455be95]
[ubuntu:39556] [ 9] /lib/x86_64-linux-gnu/libpthread.so.0(+0x8609)[0x7fc5583c9609]
[ubuntu:39556] [10] /lib/x86_64-linux-gnu/libc.so.6(clone+0x43)[0x7fc558194353]
[ubuntu:39556] *** End of error message ***
Aborted (core dumped)

May I ask if you have encountered the similar problem and how to fix it?

quancs commented 6 months ago
  1. Use single-gpu training or not compile the model (there is an error related to Torch ddp & compile, see https://github.com/pytorch/pytorch/issues/111279)
  2. Modify the model.channels and model.arch.dim_input to suit the channels of the dataset, where model.arch.dim_input=2*len(model.channels)
  3. The following command is tested OK on our server. You can try it on your server: 
    python SharedTrainer.py fit --config=configs/SpatialNet.yaml --config=configs/datasets/whamr.yaml --model.channels=[0,1] --model.arch.dim_input=4 --model.arch.dim_output=4 --model.arch.num_freqs=129 --trainer.precision=bf16-mixed --model.compile=True --data.batch_size=[2,4] --trainer.devices=0, --trainer.max_epochs=100
zx1292982431 commented 6 months ago

It worked, Thank you so much for your kindness and support. I truly appreciate it!🌹

quancs commented 6 months ago

Happy to help