tests/python/relax/test_vm_multi_device.py

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"""Test eliminate common subexpr pass"""

from typing import List
import tvm
from tvm import relax
import tvm.testing
from tvm.ir.module import IRModule
from tvm.script.parser import ir as I, relax as R
from tvm.runtime import Device
import numpy as np


def compile(
    mod: IRModule,
    device: List[Device] = [
        tvm.cpu(),
    ],
) -> relax.VirtualMachine:
    # compile the model
    mod = relax.transform.RealizeVDevice()(mod)
    mod = relax.transform.LegalizeOps()(mod)
    mod = tvm.tir.transform.DefaultGPUSchedule()(mod)
    # no need to feed target argument for mult-target compilation
    ex = tvm.compile(mod)

    return relax.VirtualMachine(ex, device)


def test_multi_cpu():
    @I.ir_module
    class Example:
        I.module_attrs({"attr": 10})
        I.module_global_infos(
            {
                "vdevice": [
                    I.vdevice("llvm", 0),
                    I.vdevice("llvm", 1),
                ]
            }
        )

        @R.function
        def foo(
            x: R.Tensor((2, 3), "float32"),
            y: R.Tensor((3, 4), "float32"),
            z: R.Tensor((4, 5), "float32"),
        ) -> R.Tensor((2, 5), "float32"):
            with R.dataflow():
                lv0 = R.matmul(x, y)
                lv0 = R.hint_on_device(lv0, tvm.cpu(0))
                lv1: R.Tensor((2, 4), "float32", "llvm:1") = R.to_vdevice(  # noqa: F722
                    lv0, "llvm:1"
                )
                gv = R.matmul(lv1, z)
                R.output(gv)
            return gv

    devices = [tvm.cpu(0), tvm.cpu(1)]
    vm = compile(Example, devices)

    np_ipt0 = np.random.rand(2, 3).astype(np.float32)
    np_ipt1 = np.random.rand(3, 4).astype(np.float32)
    np_ipt2 = np.random.rand(4, 5).astype(np.float32)
    np_res = np.matmul(np.matmul(np_ipt0, np_ipt1), np_ipt2)

    ipt0 = tvm.runtime.tensor(np_ipt0, devices[0])
    ipt1 = tvm.runtime.tensor(np_ipt1, devices[0])
    ipt2 = tvm.runtime.tensor(np_ipt2, devices[1])
    res = vm["foo"](ipt0, ipt1, ipt2)
    tvm.testing.assert_allclose(res.numpy(), np_res)


@tvm.testing.requires_multi_gpu
def test_multi_gpu():
    @I.ir_module
    class Example:
        I.module_attrs({"attr": 10})
        I.module_global_infos(
            {
                "vdevice": [
                    I.vdevice("cuda", 1),
                    I.vdevice("cuda", 0),
                    I.vdevice("cuda", 2),
                ]
            }
        )

        @R.function
        def foo(
            a: R.Tensor((2, 3), "float32"),
            b: R.Tensor((3, 4), "float32"),
            c: R.Tensor((4, 5), "float32"),
            d: R.Tensor((5, 6), "float32"),
        ) -> R.Tensor((2, 6), "float32"):
            with R.dataflow():
                lv0: R.Tensor((2, 4), "float32", "cuda:0") = R.matmul(a, b)  # noqa: F722
                lv1: R.Tensor((2, 4), "float32", "cuda:1") = R.to_vdevice(  # noqa: F722
                    lv0,
                    "cuda:1",  # noqa: F722
                )
                lv2: R.Tensor((2, 5), "float32", "cuda:1") = R.matmul(lv1, c)  # noqa: F722
                lv3: R.Tensor((2, 5), "float32", "cuda:2") = R.to_vdevice(  # noqa: F722
                    lv2,
                    "cuda:2",  # noqa: F722
                )
                gv: R.Tensor((2, 6), "float32", "cuda:2") = R.matmul(lv3, d)  # noqa: F722
                R.output(gv)
            return gv

    # The number and ordering of devices should be identical with the vdevice list
    # defined in global_infos of ir_module
    devices = [tvm.cuda(1), tvm.cuda(0), tvm.cuda(2)]
    vm = compile(Example, devices)

    np_ipt0 = np.random.rand(2, 3).astype(np.float32)
    np_ipt1 = np.random.rand(3, 4).astype(np.float32)
    np_ipt2 = np.random.rand(4, 5).astype(np.float32)
    np_ipt3 = np.random.rand(5, 6).astype(np.float32)
    np_res = np.matmul(np.matmul(np.matmul(np_ipt0, np_ipt1), np_ipt2), np_ipt3)

    ipt0 = tvm.runtime.tensor(np_ipt0, devices[0])
    ipt1 = tvm.runtime.tensor(np_ipt1, devices[0])
    ipt2 = tvm.runtime.tensor(np_ipt2, devices[1])
    ipt3 = tvm.runtime.tensor(np_ipt3, devices[2])
    res = vm["foo"](ipt0, ipt1, ipt2, ipt3)
    tvm.testing.assert_allclose(res.numpy(), np_res)


@tvm.testing.requires_gpu
def test_multi_device():
    @I.ir_module
    class Example:
        I.module_attrs({"attr": 10})
        I.module_global_infos(
            {
                "vdevice": [
                    I.vdevice("cuda", 0),
                    I.vdevice("llvm"),
                ]
            }
        )

        @R.function
        def foo(
            x: R.Tensor((2, 3), "float32"),
            y: R.Tensor((3, 4), "float32"),
            z: R.Tensor((4, 5), "float32"),
        ) -> R.Tensor((2, 5), "float32"):
            with R.dataflow():
                lv0: R.Tensor((2, 4), "float32", "llvm") = R.matmul(x, y)
                lv1: R.Tensor((2, 4), "float32", "cuda") = R.to_vdevice(lv0, "cuda")
                gv: R.Tensor((2, 5), "float32", "cuda") = R.matmul(lv1, z)
                R.output(gv)
            return gv

    # The number and ordering of devices should be identical with the vdevice list
    # defined in global_infos of ir_module
    devices = [tvm.cuda(0), tvm.cpu(0)]
    vm = compile(Example, devices)

    np_ipt0 = np.random.rand(2, 3).astype(np.float32)
    np_ipt1 = np.random.rand(3, 4).astype(np.float32)
    np_ipt2 = np.random.rand(4, 5).astype(np.float32)
    np_res = np.matmul(np.matmul(np_ipt0, np_ipt1), np_ipt2)

    ipt0 = tvm.runtime.tensor(np_ipt0, devices[1])
    ipt1 = tvm.runtime.tensor(np_ipt1, devices[1])
    ipt2 = tvm.runtime.tensor(np_ipt2, devices[0])
    res = vm["foo"](ipt0, ipt1, ipt2)
    tvm.testing.assert_allclose(res.numpy(), np_res, rtol=1e-4, atol=1e-4)


if __name__ == "__main__":
    tvm.testing.main()