Hi,
Thanks for the awesome framework. Recently, I’ve been using pytorch for linear algebra and found a weird memory leak in torch.qr
.
I’m using torch.__version__
0.2.0_4 on anaconda python 3.6 CUDA 8.0 using the standard conda installation.
import torch
B = torch.rand(4000, 1000).cuda() # nvidia-smi output 298MiB
_ = torch.qr(B) # 388MiB
for i in range(100): torch.qr(B) # Constantly increases until 1941MiB
compared to
import torch
B = torch.rand(4000, 1000).cuda() # nvidia-smi output 298MiB
_ = torch.svd(B) # 399MiB
for i in range(100): torch.svd(B) # fluctuates between 414 to 432 and back to 414
Aside from that, is there a reason for using Magma instead of cuBLAS?
Thanks!