tests/python/contrib/test_dlpack.py

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import tvm
import tvm.testing
from tvm import te
import numpy as np
from tvm.contrib.dlpack import to_pytorch_func


def verify_torch_dlpack():
    a = np.random.randn(1337)
    tvm_a = tvm.runtime.tensor(a)
    np.testing.assert_equal(tvm.runtime.from_dlpack(tvm_a).numpy(), a)

    try:
        import torch
        import torch.utils.dlpack

        x = torch.rand(56, 56)
        tvm_x = tvm.runtime.from_dlpack(torch.utils.dlpack.to_dlpack(x))
        np.testing.assert_equal(x.numpy(), tvm_x.numpy())
        y = tvm.runtime.from_dlpack(tvm_x)
        np.testing.assert_equal(y.numpy(), tvm_x.numpy())
        np.testing.assert_equal(torch.utils.dlpack.from_dlpack(y).numpy(), tvm_x.numpy())

        n = tvm.runtime.convert(137)
        xx = torch.rand(137, 137)
        yy = torch.rand(137, 137)
        zz2 = torch.empty(137, 137)
        zz = xx.mm(yy)
        XX = te.placeholder((n, n), name="X")
        YY = te.placeholder((n, n), name="Y")

        k = te.reduce_axis((0, n), name="k")
        ZZ = te.compute((n, n), lambda i, j: te.sum(XX[i, k] * YY[k, j], axis=k))
        # No need to speficy target_host if it's llvm
        # Otherwise you will need to specify the target and target_host
        f = tvm.compile(te.create_prim_func([XX, YY, ZZ]))

        f_pytorch = to_pytorch_func(f)
        zz2 = torch.empty(137, 137)
        f_pytorch(xx, yy, zz2)
        tvm.testing.assert_allclose(zz.numpy(), zz2.numpy(), rtol=1e-4, atol=1e-4)

    except ImportError:
        pass


def test_torch_dlpack():
    # Run dlpack interoperability test a few times to make sure it's stable.
    for i in range(5):
        verify_torch_dlpack()


if __name__ == "__main__":
    test_torch_dlpack()