🐛 Describe the bug I get an internal assert failure when using fancy indexed assignment on CUDA in deterministic mode. This appears to be the same as #96724 (and #105819), which was closed when #105833 was merged. But the problem seems t...
🐛 Describe the bug On CUDA with use_deterministic_algorithms(True), advanced indexing assignment has no effect on target tensors with more than one effective dimension when the source tensor has one dimension. To reproduce import torch t...
upsample_bilinear2d_backward_out_cuda是PyTorch中的一个函数,用于在CUDA设备上执行双线性插值上采样的反向传播计算。双线性插值是一种常用的图像缩放技术,它通过计算四个最近邻像素的加权平均值来估计缩放后的像素值。 确定性实现问题: 在PyTorch中,torch.use_deterministic_algorithms(True)会启用确定性算法,以确保在...
Tests performed in a set of benchmark functions show that the algorithm is numerically accurate, with accelerations as high as 800X in CUDA and 300X in the OpenMP implementation both compared to a sequential multidimensional integration algorithm....
🚀 The feature, motivation and pitch I'm working on a multiclass classifier, and I would really like to have deterministic confusion matrices on cuda. Since that is implemented in torchmetrics using bincount_cuda, which doesn't currently ...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - Feature Request: deterministic CUDA cumsum · pytorch/pytorch@23c0d26
cumsumuse_deterministic_algorithms(True)and input is CUDA. 🔗 Helpful Links C++ docs built from this PR ❓ Need help or want to give feedback on the CI? Visit thebot commands wikior ouroffice hours Note: Links to docs will display an error until the docs builds have been completed. ...
type == 'cuda') @skipIfTorchInductor("https://github.com/pytorch/pytorch/issues/113707") @onlyCUDA def test_deterministic_cumsum(self, device): test_cases = [ # size, dim [(2, 3, 4), 0], [(2, 3, 4), 1], [(2, 3, 4), 2], [(1000, 10, 2), 0], ] for size, dim...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - Add deterministic path for CUDA `cumsum` · pytorch/pytorch@e6b9d31
🐛 Describe the bug Hi! I noticed some strange behavior of with torch.backends.cudnn.flags(deterministic=True) for ctc loss backward on CUDA. Main problem is that with torch.backends.cudnn.flags(deterministic=True) doesn't give an excepti...