File size: 10,341 Bytes
1a37c4f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 | #!/usr/bin/env python3
"""Benchmark flashrt-residual-norm-quant against PyTorch eager references."""
from __future__ import annotations
import argparse
import ctypes
import ctypes.util
import importlib
import json
import math
import os
import sys
from dataclasses import asdict, dataclass
from pathlib import Path
import torch
ROOT = Path(__file__).resolve().parents[2]
PACKAGE = ROOT / "flashrt-residual-norm-quant"
REGISTRATION_INCLUDE = (
ROOT.parent
/ "kernels"
/ "kernel-builder"
/ "src"
/ "pyproject"
/ "templates"
/ "torch"
)
SHAPES = {
"pi05_decoder": (10, 1024),
"pi05_vision": (512, 1152),
"groot_vl": (1024, 2048),
"video_prefill": (2520, 2048),
}
SHAPE_GROUPS = {
"smoke": ["pi05_decoder"],
"headline": ["pi05_decoder", "pi05_vision", "groot_vl"],
"all": list(SHAPES.keys()),
}
@dataclass
class Result:
shape: str
rows: int
dim: int
kernel: str
flashrt_us: float
torch_eager_us: float
speedup_vs_eager: float
max_abs: float
mean_abs: float
p99_abs: float
cosine: float
status: str
class SourceOps:
def __init__(self, namespace: str) -> None:
self._ops = getattr(torch.ops, namespace)
def rms_norm_quant_fp8_static_bf16(self, x, weight, scale, eps=1e-6, out=None):
if out is None:
out = torch.empty_like(x, dtype=torch.float8_e4m3fn)
self._ops.rms_norm_quant_fp8_static_bf16(x, weight, scale, float(eps), out)
return out
def residual_add_rms_norm_quant_fp8_static_bf16(
self, residual, x, weight, scale, eps=1e-6, out=None
):
if out is None:
out = torch.empty_like(x, dtype=torch.float8_e4m3fn)
self._ops.residual_add_rms_norm_quant_fp8_static_bf16(
residual, x, weight, scale, float(eps), out
)
return out
def _preload_cublaslt() -> None:
for parent in Path(torch.__file__).resolve().parents:
candidate = parent / "nvidia" / "cublas" / "lib" / "libcublasLt.so.12"
if candidate.exists():
ctypes.CDLL(str(candidate), mode=ctypes.RTLD_GLOBAL)
return
library = ctypes.util.find_library("cublasLt")
if library:
ctypes.CDLL(library, mode=ctypes.RTLD_GLOBAL)
def _current_arch_list() -> str:
major, minor = torch.cuda.get_device_capability(0)
return f"{major}.{minor}"
def load_source_ops() -> SourceOps:
from torch.utils.cpp_extension import load
if not REGISTRATION_INCLUDE.is_dir():
raise RuntimeError(f"missing kernel-builder registration include: {REGISTRATION_INCLUDE}")
_preload_cublaslt()
os.environ.setdefault("TORCH_CUDA_ARCH_LIST", _current_arch_list())
namespace = "flashrt_residual_norm_quant_benchmark"
load(
name=namespace,
sources=[
str(PACKAGE / "torch-ext" / "torch_binding.cpp"),
str(PACKAGE / "csrc" / "residual_norm_quant.cu"),
],
extra_include_paths=[str(PACKAGE / "csrc"), str(REGISTRATION_INCLUDE)],
extra_cflags=["-O3", "-DCUDA_KERNEL"],
extra_cuda_cflags=["-O3", "--expt-relaxed-constexpr", "-DCUDA_KERNEL"],
verbose=False,
)
return SourceOps(namespace)
def load_installed_ops(artifact: str | None):
if artifact:
sys.path.insert(0, artifact)
try:
return importlib.import_module("flashrt_residual_norm_quant")
finally:
if artifact:
sys.path.remove(artifact)
def quantize_fp8(x: torch.Tensor, scale: torch.Tensor) -> torch.Tensor:
return torch.clamp(x.float() / scale.float(), -448.0, 448.0).to(torch.float8_e4m3fn)
def torch_rms_norm(x: torch.Tensor, weight: torch.Tensor, eps: float) -> torch.Tensor:
rms = torch.rsqrt(torch.mean(x.float() * x.float(), dim=1, keepdim=True) + eps)
return x.float() * rms * weight.float()
def torch_rms_norm_quant(x, weight, scale, eps) -> torch.Tensor:
return quantize_fp8(torch_rms_norm(x, weight, eps), scale)
def torch_residual_add_rms_norm_quant(residual, x, weight, scale, eps) -> torch.Tensor:
added = residual.float() + x.float()
residual.copy_(added.to(torch.bfloat16))
rms = torch.rsqrt(torch.mean(added * added, dim=1, keepdim=True) + eps)
return quantize_fp8(residual.float() * rms * weight.float(), scale)
def make_case(rows: int, dim: int):
x = torch.randn((rows, dim), device="cuda", dtype=torch.bfloat16)
residual = torch.randn((rows, dim), device="cuda", dtype=torch.bfloat16)
weight = (1.0 + 0.1 * torch.randn((dim,), device="cuda", dtype=torch.bfloat16)).contiguous()
scale = torch.tensor([0.04], device="cuda", dtype=torch.float32)
out = torch.empty((rows, dim), device="cuda", dtype=torch.float8_e4m3fn)
return x, residual, weight, scale, out
def time_us(fn, warmup: int, iters: int) -> float:
for _ in range(warmup):
fn()
torch.cuda.synchronize()
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start.record()
for _ in range(iters):
fn()
end.record()
torch.cuda.synchronize()
return start.elapsed_time(end) * 1000.0 / iters
def percentile(x: torch.Tensor, q: float) -> torch.Tensor:
flat = x.flatten()
k = max(1, min(flat.numel(), math.ceil(q * flat.numel())))
return flat.kthvalue(k).values
def metrics(got: torch.Tensor, expected: torch.Tensor):
diff = (got.float() - expected.float()).abs().flatten()
cosine = torch.nn.functional.cosine_similarity(
got.float().flatten(), expected.float().flatten(), dim=0
)
return {
"max_abs": float(diff.max().item()),
"mean_abs": float(diff.mean().item()),
"p99_abs": float(percentile(diff, 0.99).item()),
"cosine": float(cosine.item()),
}
def run_one(ops, name: str, rows: int, dim: int, args) -> list[Result]:
x, residual, weight, scale, out = make_case(rows, dim)
eps = args.eps
results = []
got = ops.rms_norm_quant_fp8_static_bf16(x, weight, scale, eps, out)
expected = torch_rms_norm_quant(x, weight, scale, eps)
m = metrics(got, expected)
kernel_us = time_us(
lambda: ops.rms_norm_quant_fp8_static_bf16(x, weight, scale, eps, out),
args.warmup,
args.iters,
)
torch_us = time_us(lambda: torch_rms_norm_quant(x, weight, scale, eps), args.warmup, args.iters)
results.append(
Result(
shape=name,
rows=rows,
dim=dim,
kernel="rms_norm_quant_fp8_static_bf16",
flashrt_us=kernel_us,
torch_eager_us=torch_us,
speedup_vs_eager=torch_us / kernel_us,
status="PASS" if m["p99_abs"] <= args.p99_abs_limit else "FAIL",
**m,
)
)
residual0 = residual.clone()
residual_kernel = residual0.clone()
got = ops.residual_add_rms_norm_quant_fp8_static_bf16(
residual_kernel, x, weight, scale, eps, out
)
residual_ref = residual0.clone()
expected = torch_residual_add_rms_norm_quant(residual_ref, x, weight, scale, eps)
m = metrics(got, expected)
residual_kernel = residual0.clone()
residual_ref = residual0.clone()
kernel_us = time_us(
lambda: ops.residual_add_rms_norm_quant_fp8_static_bf16(
residual_kernel, x, weight, scale, eps, out
),
args.warmup,
args.iters,
)
torch_us = time_us(
lambda: torch_residual_add_rms_norm_quant(residual_ref, x, weight, scale, eps),
args.warmup,
args.iters,
)
results.append(
Result(
shape=name,
rows=rows,
dim=dim,
kernel="residual_add_rms_norm_quant_fp8_static_bf16",
flashrt_us=kernel_us,
torch_eager_us=torch_us,
speedup_vs_eager=torch_us / kernel_us,
status="PASS" if m["p99_abs"] <= args.p99_abs_limit else "FAIL",
**m,
)
)
return results
def write_markdown(path: Path, results: list[Result]) -> None:
lines = [
"| Shape | Rows,Dim | Kernel | FlashRT us | Eager us | vs eager | Max abs | Mean abs | P99 abs | Cosine | Status |",
"|---|---:|---|---:|---:|---:|---:|---:|---:|---:|---|",
]
for r in results:
lines.append(
f"| {r.shape} | {r.rows},{r.dim} | {r.kernel} | {r.flashrt_us:.3f} | "
f"{r.torch_eager_us:.3f} | {r.speedup_vs_eager:.2f}x | "
f"{r.max_abs:.6f} | {r.mean_abs:.6f} | {r.p99_abs:.6f} | "
f"{r.cosine:.8f} | {r.status} |"
)
path.write_text("\n".join(lines) + "\n")
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--backend", choices=["source", "installed"], default="source")
parser.add_argument("--artifact", default=None)
parser.add_argument("--shapes", choices=sorted(SHAPE_GROUPS), default="smoke")
parser.add_argument("--warmup", type=int, default=5)
parser.add_argument("--iters", type=int, default=20)
parser.add_argument("--eps", type=float, default=1e-6)
parser.add_argument("--p99-abs-limit", type=float, default=0.5)
parser.add_argument("--output", default=None)
parser.add_argument("--markdown", default=None)
args = parser.parse_args()
if not torch.cuda.is_available():
raise SystemExit("CUDA is required")
torch.manual_seed(29)
ops = load_source_ops() if args.backend == "source" else load_installed_ops(args.artifact)
results = []
for name in SHAPE_GROUPS[args.shapes]:
rows, dim = SHAPES[name]
results.extend(run_one(ops, name, rows, dim, args))
for r in results:
print(
f"{r.status} {r.shape}/{r.kernel}: flashrt={r.flashrt_us:.3f}us "
f"eager={r.torch_eager_us:.3f}us speedup={r.speedup_vs_eager:.2f}x "
f"p99_abs={r.p99_abs:.6f} cosine={r.cosine:.8f}"
)
if args.output:
Path(args.output).parent.mkdir(parents=True, exist_ok=True)
Path(args.output).write_text(json.dumps([asdict(r) for r in results], indent=2) + "\n")
if args.markdown:
Path(args.markdown).parent.mkdir(parents=True, exist_ok=True)
write_markdown(Path(args.markdown), results)
if any(r.status != "PASS" for r in results):
raise SystemExit(1)
if __name__ == "__main__":
main()
|