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rms_norm.py
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rms_norm.py
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import torch
import time
from torch.utils.cpp_extension import load
from functools import partial
from typing import Optional
torch.set_grad_enabled(False)
# Load the CUDA kernel as a python module
lib = load(name='rms_norm_lib',
sources=['rms_norm.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'])
# un-fused naive rms norm
def naive_rms_norm(x: torch.Tensor, g: float):
# y'=x/rms(x) 1/rms(x) = rsqrtf(sum(x^2)/K)
s_rms = torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True))
y = (x * s_rms) * g
return y
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):
g = 1.0
if out is not None:
out.fill_(0)
if out is not None:
for i in range(warmup):
perf_func(x, out, g)
else:
for i in range(warmup):
_ = perf_func(x, g)
torch.cuda.synchronize()
start = time.time()
# iters
if out is not None:
for i in range(iters):
perf_func(x, out, g)
else:
for i in range(iters):
out = perf_func(x, g)
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()[:3]
out_val = [round(v, 8) for v in out_val]
out_val = [f"{v:<12}" for v in out_val]
print(f"{out_info:>17}: {out_val}, time:{mean_time:.8f}ms")
if show_all: print(out)
return out, mean_time
print("-" * 85)
N, K = 4096, 512
print(" " * 40 + f"N={N}, K={K}")
x = torch.randn((N, K)).cuda().float().contiguous()
out = torch.zeros_like(x).cuda().float().contiguous()
run_benchmark(lib.rms_norm_f32, x, "f32", out)
run_benchmark(lib.rms_norm_f32x4, x, "f32x4", out)
run_benchmark(naive_rms_norm, x, "f32_th")
print("-" * 85)
x_f16 = x.half()
out_f16 = out.half()
run_benchmark(lib.rms_norm_f16_f16, x_f16, "f16f16", out_f16)
run_benchmark(lib.rms_norm_f16_f32, x_f16, "f16f32", out_f16)
run_benchmark(lib.rms_norm_f16x2_f16, x_f16, "f16x2f16", out_f16)
run_benchmark(lib.rms_norm_f16x8_f16, x_f16, "f16x8f16", out_f16)
run_benchmark(lib.rms_norm_f16x8_f32, x_f16, "f16x8f32", out_f16)
run_benchmark(lib.rms_norm_f16x8_pack_f16, x_f16, "f16x8packf16", out_f16)
run_benchmark(lib.rms_norm_f16x8_pack_f32, x_f16, "f16x8packf32", out_f16)
run_benchmark(naive_rms_norm, x_f16, "f16_th")
print("-" * 85)
print("-" * 85)
N, K = 4096, 1024
print(" " * 40 + f"N={N}, K={K}")
x = torch.randn((N, K)).cuda().float().contiguous()
out = torch.zeros_like(x).cuda().float().contiguous()
run_benchmark(lib.rms_norm_f32, x, "f32", out)
run_benchmark(lib.rms_norm_f32x4, x, "f32x4", out)
run_benchmark(naive_rms_norm, x, "f32_th")
print("-" * 85)
x_f16 = x.half()
out_f16 = out.half()
run_benchmark(lib.rms_norm_f16_f16, x_f16, "f16f16", out_f16)
run_benchmark(lib.rms_norm_f16_f32, x_f16, "f16f32", out_f16)
run_benchmark(lib.rms_norm_f16x2_f16, x_f16, "f16x2f16", out_f16)
run_benchmark(lib.rms_norm_f16x8_f16, x_f16, "f16x8f16", out_f16)
run_benchmark(lib.rms_norm_f16x8_f32, x_f16, "f16x8f32", out_f16)
run_benchmark(lib.rms_norm_f16x8_pack_f16, x_f16, "f16x8packf16", out_f16)
run_benchmark(lib.rms_norm_f16x8_pack_f32, x_f16, "f16x8packf32", out_f16)
run_benchmark(naive_rms_norm, x_f16, "f16_th")
print("-" * 85)
print("-" * 85)
N, K = 4096, 2048
print(" " * 40 + f"N={N}, K={K}")
x = torch.randn((N, K)).cuda().float().contiguous()
out = torch.zeros_like(x).cuda().float().contiguous()
run_benchmark(lib.rms_norm_f32x4, x, "f32x4", out)
run_benchmark(naive_rms_norm, x, "f32_th")
print("-" * 85)
x_f16 = x.half()
out_f16 = out.half()
run_benchmark(lib.rms_norm_f16x2_f16, x_f16, "f16x2f16", out_f16)
run_benchmark(lib.rms_norm_f16x8_f16, x_f16, "f16x8f16", out_f16)
run_benchmark(lib.rms_norm_f16x8_f32, x_f16, "f16x8f32", out_f16)
run_benchmark(lib.rms_norm_f16x8_pack_f16, x_f16, "f16x8packf16", out_f16)
run_benchmark(lib.rms_norm_f16x8_pack_f32, x_f16, "f16x8packf32", out_f16)
run_benchmark(naive_rms_norm, x_f16, "f16_th")
print("-" * 85)
print("-" * 85)
N, K = 4096, 4096
print(" " * 40 + f"N={N}, K={K}")
x = torch.randn((N, K)).cuda().float().contiguous()
out = torch.zeros_like(x).cuda().float().contiguous()
run_benchmark(lib.rms_norm_f32x4, x, "f32x4", out)
run_benchmark(naive_rms_norm, x, "f32_th")
print("-" * 85)
x_f16 = x.half()
out_f16 = out.half()
run_benchmark(lib.rms_norm_f16x8_f16, x_f16, "f16x8f16", out_f16)
run_benchmark(lib.rms_norm_f16x8_f32, x_f16, "f16x8f32", out_f16)
run_benchmark(lib.rms_norm_f16x8_pack_f16, x_f16, "f16x8packf16", out_f16)
run_benchmark(lib.rms_norm_f16x8_pack_f32, x_f16, "f16x8packf32", out_f16)
run_benchmark(naive_rms_norm, x_f16, "f16_th")
print("-" * 85)
print("-" * 85)
N, K = 4096, 8192
print(" " * 40 + f"N={N}, K={K}")
x_f16 = torch.randn((N, K)).cuda().half().contiguous()
out_f16 = torch.zeros_like(x_f16).cuda().half().contiguous()
run_benchmark(lib.rms_norm_f16x8_f16, x_f16, "f16x8f16", out_f16)
run_benchmark(lib.rms_norm_f16x8_f32, x_f16, "f16x8f32", out_f16)
run_benchmark(lib.rms_norm_f16x8_pack_f16, x_f16, "f16x8packf16", out_f16)
run_benchmark(lib.rms_norm_f16x8_pack_f32, x_f16, "f16x8packf32", out_f16)
run_benchmark(naive_rms_norm, x_f16, "f16_th")
print("-" * 85)
print("-" * 85)
N, K = 8192, 8192
print(" " * 40 + f"N={N}, K={K}")
x_f16 = torch.randn((N, K)).cuda().half().contiguous()
out_f16 = torch.zeros_like(x_f16).cuda().half().contiguous()
run_benchmark(lib.rms_norm_f16x8_f16, x_f16, "f16x8f16", out_f16)
run_benchmark(lib.rms_norm_f16x8_f32, x_f16, "f16x8f32", out_f16)
run_benchmark(lib.rms_norm_f16x8_pack_f16, x_f16, "f16x8packf16", out_f16)
run_benchmark(lib.rms_norm_f16x8_pack_f32, x_f16, "f16x8packf32", out_f16)
run_benchmark(naive_rms_norm, x_f16, "f16_th")
print("-" * 85)