Created
November 17, 2022 00:05
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Test for taichi + pytorch to use vector operations in kernel function without deepcopy.
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| import torch | |
| import taichi as ti | |
| ti.init(arch=ti.cuda) | |
| @ti.func | |
| def test_dev(x: ti.types.vector(2, ti.f32)): | |
| return 2*x | |
| @ti.kernel | |
| def test_kernel(x: ti.types.ndarray()): | |
| for i in range(x.shape[0]): | |
| v = ti.Vector([x[i, 0], x[i, 1]]) | |
| v = test_dev(v) | |
| x[i, 0] = v[0] | |
| x[i, 1] = v[1] | |
| x = torch.tensor([[2, 3]], device="cuda") | |
| test_kernel(x) | |
| print(x) | |
| # Out: | |
| # tensor([[4, 6]], device='cuda:0') |
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