pytorch 奇怪报错记录 for grep
2025-06-17
0625julyfunnotespytorch 奇怪报错记录 for grep25.6.17---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
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
1 # %%
2 @run
----> 3 def func():
4 net = ClassConditionedUnet(num_classes=10).to(device)
5 img = torch.randn(3, 1, 28, 28).to(device)
File ~/Documents/GitHub/julyfun/robotoy/robotoy/ziglike/test.py:58, in run(func)
57 def run(func):
---> 58 func()
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
2 @run
3 def func():
----> 4 net = ClassConditionedUnet(num_classes=10).to(device)
5 img = torch.randn(3, 1, 28, 28).to(device)
6 cls = torch.tensor([0, 1, 9]).to(device)
File ~/Documents/GitHub/diffusion-models-class/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1355, in Module.to(self, *args, **kwargs)
1352 else:
1353 raise
-> 1355 return self._apply(convert)
File ~/Documents/GitHub/diffusion-models-class/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:915, in Module._apply(self, fn, recurse)
913 if recurse:
914 for module in self.children():
--> 915 module._apply(fn)
917 def compute_should_use_set_data(tensor, tensor_applied):
918 if torch._has_compatible_shallow_copy_type(tensor, tensor_applied):
919 # If the new tensor has compatible tensor type as the existing tensor,
920 # the current behavior is to change the tensor in-place using `.data =`,
(...)
925 # global flag to let the user control whether they want the future
926 # behavior of overwriting the existing tensor or not.
File ~/Documents/GitHub/diffusion-models-class/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:942, in Module._apply(self, fn, recurse)
938 # Tensors stored in modules are graph leaves, and we don't want to
939 # track autograd history of `param_applied`, so we have to use
940 # `with torch.no_grad():`
941 with torch.no_grad():
--> 942 param_applied = fn(param)
943 p_should_use_set_data = compute_should_use_set_data(param, param_applied)
945 # subclasses may have multiple child tensors so we need to use swap_tensors
File ~/Documents/GitHub/diffusion-models-class/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1341, in Module.to.<locals>.convert(t)
1334 if convert_to_format is not None and t.dim() in (4, 5):
1335 return t.to(
1336 device,
1337 dtype if t.is_floating_point() or t.is_complex() else None,
1338 non_blocking,
1339 memory_format=convert_to_format,
1340 )
-> 1341 return t.to(
1342 device,
1343 dtype if t.is_floating_point() or t.is_complex() else None,
1344 non_blocking,
1345 )
1346 except NotImplementedError as e:
1347 if str(e) == "Cannot copy out of meta tensor; no data!":
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.context see above.[ok, but why] 重启 kernel 运行就没问题了