F.cross_entropy erhöht "RunTimeError: CUDA-Fehler: Geräteseitiges Assert ausgelöst. Kompilieren Sie mit` Torch_USE_CUDA_ ⇐ Python

[Anonymous]

Ich habe diesen Fehler in der Verlustfunktion erfüllt. Ein Beispiel unten: < /p>

Code: Select all

import torch
device = "cuda" if torch.cuda.is_available() else "cpu"

a = torch.Tensor([[-10.3353, -28.4371,   2.0768,   -4.2789,  -8.6644,  -6.0815],
[-10.3353, -28.4371,   2.0768,   -4.2789,  -8.6644,  -6.0815],
[-10.3353, -28.4371,   2.0768,   -4.2789,  -8.6644,  -6.0815],
[-10.3353, -28.4371,   2.0768,   -4.2789,  -8.6644,  -6.0815]]).to(device)
b = torch.Tensor([ -100,  -1,  -100,  2456]).long().to(device)

loss = torch.nn.functional.cross_entropy(a,b,ignore_index=-1)
print(loss)
< /code>
Dann ist der Fehler: < /p>
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
 in ()
----> 1 loss = torch.nn.functional.cross_entropy(a,b,ignore_index=-1).to(device)
2 loss

/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction, label_smoothing)
3027     if size_average is not None or reduce is not None:
3028         reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 3029     return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing)
3030
3031

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.

Ich habe versucht, Export -Torch_USE_CUDA_DSA = 1 festzulegen, aber immer noch der gleiche Fehler.