Hi Mo,
I presume it's still a draft, but d/rules creates `third_party/dlpack`, but does not create `third_party/jitify`. The d/copyright also does not yet seem to be complete.
On 2025-03-12 16:37, M. Zhou wrote:
cupy-rocm now compiles on amd64, arm64, and ppc64el locally,but I don't know whether it is really working. Could someonehelp me test it on real hardware before I upload it to NEW?https://salsa.debian.org/science-team/cupyI built and tested the current version in salsa and it seems to work on my Radeon VII.
Input:
# python3 <<EOFimport cupy as cpimport numpy as npx_cpu = np.array([1, 2, 3])x_gpu = cp.array([1, 2, 3])l2_cpu = np.linalg.norm(x_cpu)l2_gpu = cp.linalg.norm(x_gpu)print("Using Numpy: ", l2_cpu)print("\nUsing Cupy: ", l2_gpu)EOFOutput:
Using Numpy: 3.7416573867739413Using Cupy: 3.7416573867739413Input/Output:
>>> import cupy as cp>>> import numpy as np>>> ary_cpu = np.arange(10)>>> ary_gpu = cp.asarray(ary_cpu)>>> print('cpu:', ary_cpu)cpu: [0 1 2 3 4 5 6 7 8 9]>>> print('gpu:', ary_gpu)gpu: [0 1 2 3 4 5 6 7 8 9]>>> print(ary_gpu.device)<CUDA Device 0>>>> ary_cpu_returned = cp.asnumpy(ary_gpu)>>> print(repr(ary_cpu_returned))array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])>>> print(type(ary_cpu_returned))<class 'numpy.ndarray'>>>> print(ary_gpu * 2)[ 0 2 4 6 8 10 12 14 16 18]>>> print(cp.exp(-0.5 * ary_gpu**2))[1.00000000e+00 6.06530660e-01 1.35335283e-01 1.11089965e-023.35462628e-04 3.72665317e-06 1.52299797e-08 2.28973485e-111.26641655e-14 2.57675711e-18]>>> print(cp.linalg.norm(ary_gpu))16.881943016134134>>> print(cp.random.normal(loc=5, scale=2.0, size=10))[ 7.17178344 8.17284596 5.72956002 5.16859175 4.29981156 6.993455672.62313118 3.33248402 10.09166774 6.32673795]- Cory