Thanks! I could reproduce this issue and guess an internal matmul kernel might run into an overflow.
As a workaround, use the CUDA10.2 binaries, which ship with cudnn7.6.5.32 or alternatively you could also build PyTorch from source using cudnn8.1.
I’ve forwarded the issue to the cudnn team, which will be looking into it. Since the latest cudnn release works fine, this issue might have been already fixed.