target_codegen_c_host

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

c_host add

source_dir = "/media/pc/data/lxw/ai/tvm"
kwargs = {
    "options" : [
        "-O2", "-std=c++17", 
        "-I" + f"{source_dir}/src/runtime/contrib", 
        "-I" + f"{source_dir}/include",
        "-I" + f"{source_dir}/3rdparty/dlpack/include"
    ]
}

nn = 1024
n = tvm.runtime.convert(nn)
A = te.placeholder((n,), name="A")
B = te.placeholder((n,), name="B")
C = te.compute(A.shape, lambda *i: A(*i) + B(*i), name="C")
s = te.create_schedule(C.op)

def check_c(kwargs):
    mhost = tvm.build(s, [A, B, C], "c", name="test_fadd")
    temp = tempdir()
    path_dso = temp.relpath("temp.so")
    mhost.export_library(path_dso, fcompile=False, **kwargs)
    m = tvm.runtime.load_module(path_dso)
    fadd = m["test_fadd"]
    dev = tvm.cpu(0)
    # launch the kernel.
    n = nn
    a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), dev)
    b = tvm.nd.array(np.random.uniform(size=n).astype(B.dtype), dev)
    c = tvm.nd.array(np.zeros(n, dtype=C.dtype), dev)
    fadd(a, b, c)
    np.testing.assert_allclose(c.numpy(), a.numpy() + b.numpy())

check_c(kwargs)

add_pipeline

nn = 1024
n = tvm.runtime.convert(nn)
A = te.placeholder((n,), name="A")
B = te.placeholder((n,), name="B")
AA = te.compute((n,), lambda *i: A(*i), name="A")
BB = te.compute((n,), lambda *i: B(*i), name="B")
T = te.compute(A.shape, lambda *i: AA(*i) + BB(*i), name="T")
C = te.compute(A.shape, lambda *i: T(*i), name="C")
s = te.create_schedule(C.op)
xo, xi = s[C].split(C.op.axis[0], factor=4)
xo1, xo2 = s[C].split(xo, factor=13)
s[C].parallel(xo2)
s[C].pragma(xo1, "parallel_launch_point")
s[C].pragma(xo2, "parallel_stride_pattern")
s[C].pragma(xo2, "parallel_barrier_when_finish")
# FIXME(tvm-team): vector operators are not supported for codegen to C yet
# s[C].vectorize(xi)

def check_c():
    # Specifically allow offset to test codepath when offset is available
    Ab = tvm.tir.decl_buffer(
        A.shape, A.dtype, elem_offset=te.size_var("Aoffset"), offset_factor=8, name="A"
    )
    binds = {A: Ab}
    # BUILD and invoke the kernel.
    f1 = tvm.lower(s, [A, B, C], name="test_fadd_pipeline")
    mhost = tvm.build(f1, target="c")

    temp = tempdir()
    path_dso = temp.relpath("temp.so")
    mhost.export_library(path_dso, fcompile=False, **kwargs)
    m = tvm.runtime.load_module(path_dso)
    fadd = m["test_fadd_pipeline"]
    dev = tvm.cpu(0)
    # launch the kernel.
    n = nn
    a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), dev)
    b = tvm.nd.array(np.random.uniform(size=n).astype(B.dtype), dev)
    c = tvm.nd.array(np.zeros(n, dtype=C.dtype), dev)
    fadd(a, b, c)
    np.testing.assert_allclose(c.numpy(), a.numpy() + b.numpy())

check_c()

reinterpret

nn = 1024
n = tvm.runtime.convert(nn)
A = te.placeholder((n,), name="A", dtype="int32")
B = te.compute(
    A.shape, lambda *i: tvm.tir.call_intrin("float32", "tir.reinterpret", 2 + A(*i)), name="B"
)
s = te.create_schedule(B.op)

def check_c():
    mhost = tvm.build(s, [A, B], "c", name="test_reinterpret")
    temp = tempdir()
    path_dso = temp.relpath("temp.so")
    mhost.export_library(path_dso, fcompile=False, **kwargs)
    m = tvm.runtime.load_module(path_dso)
    fadd = m["test_reinterpret"]
    dev = tvm.cpu(0)
    n = nn
    a = tvm.nd.array(np.random.randint(-(2**30), 2**30, size=n).astype(A.dtype), dev)
    b = tvm.nd.array(np.zeros(n, dtype=B.dtype), dev)
    fadd(a, b)
    np.testing.assert_allclose(b.numpy(), (2 + a.numpy()).view("float32"))

check_c()

ceil

nn = 1024
n = tvm.runtime.convert(nn)
A = te.placeholder((n,), name="A", dtype="float32")
B = te.compute(A.shape, lambda *i: tvm.tir.call_intrin("float32", "tir.ceil", A(*i)), name="B")
s = te.create_schedule(B.op)

def check_c():
    mhost = tvm.build(s, [A, B], "c", name="test_ceil")
    temp = tempdir()
    path_dso = temp.relpath("temp.so")
    mhost.export_library(path_dso, fcompile=False, **kwargs)
    m = tvm.runtime.load_module(path_dso)
    fceil = m["test_ceil"]
    dev = tvm.cpu(0)
    n = nn
    a = tvm.nd.array(np.random.rand(n).astype(A.dtype), dev)
    b = tvm.nd.array(np.zeros(n, dtype=B.dtype), dev)
    fceil(a, b)
    np.testing.assert_allclose(b.numpy(), (np.ceil(a.numpy()).view("float32")))

check_c()

floor

nn = 1024
n = tvm.runtime.convert(nn)
A = te.placeholder((n,), name="A", dtype="float32")
B = te.compute(A.shape, lambda *i: tvm.tir.call_intrin("float32", "tir.floor", A(*i)), name="B")
s = te.create_schedule(B.op)

def check_c():
    mhost = tvm.build(s, [A, B], "c", name="test_floor")
    temp = tempdir()
    path_dso = temp.relpath("temp.so")
    mhost.export_library(path_dso, fcompile=False, **kwargs)
    m = tvm.runtime.load_module(path_dso)
    ffloor = m["test_floor"]
    dev = tvm.cpu(0)
    n = nn
    a = tvm.nd.array(np.random.rand(n).astype(A.dtype), dev)
    b = tvm.nd.array(np.zeros(n, dtype=B.dtype), dev)
    ffloor(a, b)
    np.testing.assert_allclose(b.numpy(), (np.floor(a.numpy()).view("float32")))

check_c()

round

nn = 1024
n = tvm.runtime.convert(nn)
A = te.placeholder((n,), name="A", dtype="float32")
B = te.compute(A.shape, lambda *i: tvm.tir.call_intrin("float32", "tir.round", A(*i)), name="B")
s = te.create_schedule(B.op)

def check_c():
    mhost = tvm.build(s, [A, B], "c", name="test_round")
    temp = tempdir()
    path_dso = temp.relpath("temp.so")
    mhost.export_library(path_dso, fcompile=False, **kwargs)
    m = tvm.runtime.load_module(path_dso)
    fround = m["test_round"]
    dev = tvm.cpu(0)
    n = nn
    a = tvm.nd.array(np.random.rand(n).astype(A.dtype), dev)
    b = tvm.nd.array(np.zeros(n, dtype=B.dtype), dev)
    fround(a, b)
    np.testing.assert_allclose(b.numpy(), (np.round(a.numpy()).view("float32")))

check_c()

call_packed

def fake_func(fname="fake.func", name="A"):
    ib = tvm.tir.ir_builder.create()
    A = ib.pointer("float32", name=name)
    fake_func1 = tvm.tir.call_packed(fname, A[0])

    ib.emit(fake_func1)
    body = ib.get()
    return A, body

def check_global_packed_func():
    fname = "fake.func"
    A, body = fake_func(fname)
    func1 = tvm.tir.PrimFunc([A], body).with_attr("global_symbol", "func1")
    B, body = fake_func()
    func2 = tvm.tir.PrimFunc([B], body).with_attr("global_symbol", "func2")
    mod = tvm.IRModule({"fake_func1": func1, "fake_func2": func2})
    mod.show()
    fcode = tvm.build(mod, None, "c")
    src = fcode.get_source()
    # print(src)
    # there are two locations calling the packed func
    assert src.count(fname) == 2

    suffix = "_packed"
    packed_func_name = fname + suffix
    # func name will be standardized by GetUniqueName and not exists anymore
    assert src.find(packed_func_name) == -1

    packed_func_real_name = "_".join(fname.split(".")) + suffix
    func_declaration = "static void* %s = NULL;" % packed_func_real_name
    # src only has 1 valid declaration
    assert src.count(func_declaration) == 1

check_global_packed_func()