sigmoid.py

import torch
import time 
from torch.utils.cpp_extension import load
from typing import Optional
from functools import partial

torch.set_grad_enabled(False)

# Load the CUDA kernel as a python module
lib = load(name='sigmoid_lib', 
           sources=['sigmoid.cu'], 
           extra_cuda_cflags=[
               "-O3",
                "-U__CUDA_NO_HALF_OPERATORS__",
                "-U__CUDA_NO_HALF_CONVERSIONS__",
                "-U__CUDA_NO_HALF2_OPERATORS__",
                "-U__CUDA_NO_BFLOAT16_CONVERSIONS__",
                "--expt-relaxed-constexpr",
                "--expt-extended-lambda",
                "--use_fast_math",
            ], 
           extra_cflags=['-std=c++17'])


def run_benchmark(perf_func: callable, x: torch.Tensor, tag: str, 
                  out: Optional[torch.Tensor] = None, warmup: int = 10, 
                  iters: int = 1000, show_all: bool = False):
    if out is not None: 
        out.fill_(0)    
    # warmup
    if out is not None:
        for i in range(warmup):
            perf_func(x, out)
    else:
        for i in range(warmup):
            _ = perf_func(x) 
    torch.cuda.synchronize()

    start = time.time()
    # iters
    if out is not None:
        for i in range(iters):
            perf_func(x, out)
    else:
        for i in range(iters):
            out = perf_func(x) 
    torch.cuda.synchronize()
    end = time.time()
    total_time = (end - start) * 1000 # ms
    mean_time = total_time / iters
    out_info = f"out_{tag}"
    out_val = out.flatten().detach().cpu().numpy().tolist()[:2]
    out_val = [round(v, 8) for v in out_val]
    print(f"{out_info:>18}: {out_val}, time:{mean_time:.8f}ms")
    if show_all: print(out)
    return out, mean_time


Ss = [1024, 2048, 4096]
Ks = [1024, 2048, 4096]
SKs = [(S, K) for S in Ss for K in Ks]

for (S, K) in SKs:
    print("-" * 85)
    print(" " * 40 + f"S={S}, K={K}")
    x = torch.randn((S, K)).cuda().float().contiguous()
    y = torch.zeros_like(x).cuda().float().contiguous()
    run_benchmark(lib.sigmoid_f32,               x, "f32",   y)
    run_benchmark(lib.sigmoid_f32x4,             x, "f32x4", y)
    run_benchmark(partial(torch.sigmoid, out=y), x, "f32_th")

    print("-" * 85)
    x_f16 = x.half().contiguous()
    y_f16 = y.half().contiguous()
    run_benchmark(lib.sigmoid_f16,                   x_f16, "f16",       y_f16)
    run_benchmark(lib.sigmoid_f16x2,                 x_f16, "f16x2",     y_f16)
    run_benchmark(lib.sigmoid_f16x8,                 x_f16, "f16x8",     y_f16)
    run_benchmark(lib.sigmoid_f16x8_pack,            x_f16, "f16x8pack", y_f16)
    run_benchmark(partial(torch.sigmoid, out=y_f16), x_f16, "f16_th")
    print("-" * 85)