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---------------------------------------------------------------------------
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RuntimeError                              Traceback (most recent call last)
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File /home/julyfun/Documents/GitHub/julyfun/how-to/notes/julyfun/技术学习/diffusion-models-class/unit2-02_class_conditioned_diffusion_model_example.py:3
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      1 # %%
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      2 @run
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----> 3 def func():
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      4     net = ClassConditionedUnet(num_classes=10).to(device)
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      5     img = torch.randn(3, 1, 28, 28).to(device)
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File ~/Documents/GitHub/julyfun/robotoy/robotoy/ziglike/test.py:58, in run(func)
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     57 def run(func):
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---> 58     func()
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File /home/julyfun/Documents/GitHub/julyfun/how-to/notes/julyfun/技术学习/diffusion-models-class/unit2-02_class_conditioned_diffusion_model_example.py:4
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      2 @run
51 collapsed lines
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      3 def func():
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----> 4     net = ClassConditionedUnet(num_classes=10).to(device)
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      5     img = torch.randn(3, 1, 28, 28).to(device)
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      6     cls = torch.tensor([0, 1, 9]).to(device)
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File ~/Documents/GitHub/diffusion-models-class/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1355, in Module.to(self, *args, **kwargs)
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   1352         else:
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   1353             raise
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-> 1355 return self._apply(convert)
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File ~/Documents/GitHub/diffusion-models-class/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:915, in Module._apply(self, fn, recurse)
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    913 if recurse:
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    914     for module in self.children():
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--> 915         module._apply(fn)
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    917 def compute_should_use_set_data(tensor, tensor_applied):
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    918     if torch._has_compatible_shallow_copy_type(tensor, tensor_applied):
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    919         # If the new tensor has compatible tensor type as the existing tensor,
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    920         # the current behavior is to change the tensor in-place using `.data =`,
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   (...)
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    925         # global flag to let the user control whether they want the future
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    926         # behavior of overwriting the existing tensor or not.
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File ~/Documents/GitHub/diffusion-models-class/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:942, in Module._apply(self, fn, recurse)
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    938 # Tensors stored in modules are graph leaves, and we don't want to
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    939 # track autograd history of `param_applied`, so we have to use
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    940 # `with torch.no_grad():`
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    941 with torch.no_grad():
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--> 942     param_applied = fn(param)
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    943 p_should_use_set_data = compute_should_use_set_data(param, param_applied)
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    945 # subclasses may have multiple child tensors so we need to use swap_tensors
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File ~/Documents/GitHub/diffusion-models-class/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1341, in Module.to.<locals>.convert(t)
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   1334     if convert_to_format is not None and t.dim() in (4, 5):
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   1335         return t.to(
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   1336             device,
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   1337             dtype if t.is_floating_point() or t.is_complex() else None,
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   1338             non_blocking,
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   1339             memory_format=convert_to_format,
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   1340         )
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-> 1341     return t.to(
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   1342         device,
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   1343         dtype if t.is_floating_point() or t.is_complex() else None,
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   1344         non_blocking,
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   1345     )
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   1346 except NotImplementedError as e:
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   1347     if str(e) == "Cannot copy out of meta tensor; no data!":
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RuntimeError: CUDA error: device-side assert triggered
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CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
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For debugging consider passing CUDA_LAUNCH_BLOCKING=1
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Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.