apps/topi_recipe/gemm/cuda_gemm_square.py

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"""Example code to do square matrix multiplication."""
import tvm
from tvm import te
import os
from tvm.contrib import nvcc
from tvm.contrib import spirv
import numpy as np

TASK = "gemm"
USE_MANUAL_CODE = False


@tvm.register_func
def tvm_callback_cuda_compile(code):
    ptx = nvcc.compile_cuda(code, target="ptx")
    return ptx


def write_code(code, fname):
    with open(fname, "w") as f:
        f.write(code)


@tvm.register_func
def tvm_callback_cuda_postproc(code):
    if not os.path.exists("perf"):
        os.mkdir("perf")
    write_code(code, "perf/%s_generated.cu" % TASK)
    if USE_MANUAL_CODE:
        code = open("perf/%s_manual.cu" % TASK).read()
    return code


def test_gemm():
    # graph
    nn = 2048
    n = te.var("n")
    n = tvm.runtime.convert(nn)
    m, l = n, n
    A = te.placeholder((l, n), name="A")
    B = te.placeholder((l, m), name="B")
    k = te.reduce_axis((0, l), name="k")
    C = te.compute((m, n), lambda ii, jj: te.sum(A[k, jj] * B[k, ii], axis=k), name="C")

    # schedule
    s = te.create_schedule(C.op)
    AA = s.cache_read(A, "shared", [C])
    BB = s.cache_read(B, "shared", [C])
    AL = s.cache_read(AA, "local", [C])
    BL = s.cache_read(BB, "local", [C])
    CC = s.cache_write(C, "local")

    scale = 8
    num_thread = 8
    block_factor = scale * num_thread
    block_x = te.thread_axis("blockIdx.x")
    thread_x = te.thread_axis((0, num_thread), "threadIdx.x")
    block_y = te.thread_axis("blockIdx.y")
    thread_y = te.thread_axis((0, num_thread), "threadIdx.y")
    thread_xz = te.thread_axis((0, 2), "vthread", name="vx")
    thread_yz = te.thread_axis((0, 2), "vthread", name="vy")

    by, yi = s[C].split(C.op.axis[0], factor=block_factor)
    bx, xi = s[C].split(C.op.axis[1], factor=block_factor)
    s[C].bind(by, block_y)
    s[C].bind(bx, block_x)
    s[C].reorder(by, bx, yi, xi)

    tyz, yi = s[C].split(yi, nparts=2)
    ty, yi = s[C].split(yi, nparts=num_thread)
    txz, xi = s[C].split(xi, nparts=2)
    tx, xi = s[C].split(xi, nparts=num_thread)
    s[C].bind(tyz, thread_yz)
    s[C].bind(txz, thread_xz)
    s[C].bind(ty, thread_y)
    s[C].bind(tx, thread_x)
    s[C].reorder(tyz, txz, ty, tx, yi, xi)
    s[CC].compute_at(s[C], tx)

    yo, xo = CC.op.axis
    ko, ki = s[CC].split(k, factor=8)
    kt, ki = s[CC].split(ki, factor=1)
    s[CC].reorder(ko, kt, ki, yo, xo)
    s[AA].compute_at(s[CC], ko)
    s[BB].compute_at(s[CC], ko)
    s[CC].unroll(kt)
    s[AL].compute_at(s[CC], kt)
    s[BL].compute_at(s[CC], kt)
    # Schedule for A's shared memory load
    ty, xi = s[AA].split(s[AA].op.axis[0], nparts=num_thread)
    _, xi = s[AA].split(s[AA].op.axis[1], factor=num_thread * 4)
    tx, xi = s[AA].split(xi, nparts=num_thread)
    s[AA].bind(ty, thread_y)
    s[AA].bind(tx, thread_x)
    s[AA].vectorize(xi)
    # Schedule for B' shared memory load
    ty, xi = s[BB].split(s[BB].op.axis[0], nparts=num_thread)
    _, xi = s[BB].split(s[BB].op.axis[1], factor=num_thread * 4)
    tx, xi = s[BB].split(xi, nparts=num_thread)
    s[BB].bind(ty, thread_y)
    s[BB].bind(tx, thread_x)
    s[BB].vectorize(xi)
    s[AA].double_buffer()
    s[BB].double_buffer()
    # correctness
    def check_device(device):
        ctx = tvm.context(device, 0)
        if not ctx.exist:
            print("Skip because %s is not enabled" % device)
            return
        print("Device %s" % device)
        f = tvm.build(s, [A, B, C], device)
        # launch the kernel.
        n, m, l = nn, nn, nn
        a_np = np.random.uniform(size=(n, l)).astype(A.dtype)
        b_np = np.random.uniform(size=(m, l)).astype(B.dtype)
        a = tvm.nd.array(a_np, ctx)
        b = tvm.nd.array(b_np, ctx)
        c = tvm.nd.array(np.zeros((n, m), dtype=C.dtype), ctx)
        for i in range(2):
            f(a, b, c)
        tvm.testing.assert_allclose(c.asnumpy(), np.dot(b_np.T, a_np), rtol=1e-5)

        num_flops = 2 * nn * nn * nn
        num_runs = 10
        timer_f = f.time_evaluator(f.entry_name, ctx, number=num_runs)
        t = timer_f(a, b, c).mean
        GFLOPS = num_flops / (t * 1e3) / 1e6
        print("average time cost of %d runs = %g ms, %g GFLOPS." % (num_runs, t * 1e3, GFLOPS))

    for device in ["cuda", "opencl", "rocm", "nvptx", "vulkan"]:
        with tvm.transform.PassContext(
            config={"tir.UnrollLoop": {"auto_max_step": 128, "explicit_unroll": device != "cuda"}}
        ):
            check_device(device)


if __name__ == "__main__":
    test_gemm()