tests/python/codegen/test_target_codegen_device.py

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import tvm
from tvm import te
from tvm.contrib import utils
import numpy as np
import tvm.testing
from tvm import tir


@tvm.testing.requires_gpu
def test_large_uint_imm():
    value = (1 << 63) + 123
    other = tvm.tir.const(3, "uint64")
    n = 12
    num_thread = 2

    A = te.compute((n,), lambda *i: tvm.tir.const(value, "uint64") + other, name="A")

    # Convert to TIR and create schedule
    mod = te.create_prim_func([A])
    sch = tir.Schedule(mod)

    # Get block and loop
    block = sch.get_block("A")
    loop = sch.get_loops(block)[0]

    # Split and bind
    xo, xi = sch.split(loop, factors=[None, num_thread])
    sch.bind(xi, "threadIdx.x")
    sch.bind(xo, "blockIdx.x")

    def check_target(device):
        if not tvm.testing.device_enabled(device):
            return
        dev = tvm.device(device, 0)
        f = tvm.compile(sch.mod, target=device)
        # launch the kernel.
        a = tvm.runtime.empty((n,), dtype=A.dtype, device=dev)
        f(a)
        assert a.numpy()[0] == value + 3

    check_target("cuda")
    check_target("vulkan -from_device=0")


@tvm.testing.requires_gpu
def test_add_pipeline():
    n = te.size_var("n")
    A = te.placeholder((n,), name="A")
    B = te.placeholder((), name="B")
    C = te.compute(A.shape, lambda *i: A(*i) + B(), name="C")
    D = te.compute(A.shape, lambda *i: C(*i) + 1, name="D")

    # Convert to TIR and create schedule
    mod = te.create_prim_func([A, B, D])
    sch = tir.Schedule(mod)

    # Get blocks and loops
    c_block = sch.get_block("C")
    d_block = sch.get_block("D")
    c_loop = sch.get_loops(c_block)[0]
    d_loop = sch.get_loops(d_block)[0]

    # GPU schedule have to split by gridIdx and threadIdx
    num_thread = 256

    # Schedule C
    c_xo, c_xi = sch.split(c_loop, factors=[None, num_thread])
    sch.bind(c_xi, "threadIdx.x")
    sch.bind(c_xo, "blockIdx.x")

    # Schedule D
    d_xo, d_xi = sch.split(d_loop, factors=[None, num_thread])
    sch.bind(d_xi, "threadIdx.x")
    sch.bind(d_xo, "blockIdx.x")

    def check_target(device, host):
        if not tvm.testing.device_enabled(device) or not tvm.testing.device_enabled(host):
            return
        dev = tvm.device(device, 0)
        target = tvm.target.Target(device, host)
        mhost = tvm.tir.build(sch.mod, target=target)
        f = mhost.main
        # launch the kernel.
        n = 1027
        a = tvm.runtime.tensor(np.random.uniform(size=n).astype(A.dtype), dev)
        b = tvm.runtime.tensor(np.random.uniform(size=()).astype(B.dtype), dev)
        d = tvm.runtime.tensor(np.zeros(n, dtype=D.dtype), dev)
        f(a, b, d)
        tvm.testing.assert_allclose(d.numpy(), a.numpy() + b.numpy() + 1)

    check_target("cuda", host="llvm")
    check_target("nvptx", host="llvm")
    check_target("vulkan", host="llvm")
    check_target("rocm", host="llvm")


if __name__ == "__main__":
    test_large_uint_imm()
    test_add_pipeline()