File size: 4,627 Bytes
fb8ff9e | 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 | #!/usr/bin/env python3
"""35B-A3B grouped expert prefill GEMM package/native parity benchmark."""
from __future__ import annotations
import argparse
import importlib
import json
import sys
from pathlib import Path
import torch
PACKAGE = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(PACKAGE / "tests"))
from _source_loader import load_source_ops # noqa: E402
from test_grouped_moe_gemm import make_sf # noqa: E402
WORKLOADS = {
"35b_gate_up": (64, 1024, 2048, 16, 256),
"35b_down": (64, 2048, 512, 16, 256),
}
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 load_ops(backend: str, artifact: str | None):
if backend == "source":
return load_source_ops()
if artifact:
sys.path.insert(0, artifact)
try:
return importlib.import_module("grouped_moe_gemm")
finally:
if artifact:
sys.path.remove(artifact)
def make_case(tile_rows: int, n: int, k: int, tiles: int, experts: int):
rows = tile_rows * tiles
dev = "cuda"
packed = torch.randint(0, 256, (rows, k // 2), device=dev, dtype=torch.uint8)
weights = torch.randint(
0, 256, (experts, n, k // 2), device=dev, dtype=torch.uint8
)
input_scale, _ = make_sf(rows, k, dev)
weight_scales = torch.stack([make_sf(n, k, dev)[0] for _ in range(experts)])
alpha = torch.rand(experts, device=dev, dtype=torch.float32) + 0.5
tile_expert = torch.arange(tiles, device=dev, dtype=torch.int32) % experts
out = torch.empty(rows, n, device=dev, dtype=torch.bfloat16)
return packed, weights, input_scale, weight_scales, alpha, tile_expert, out
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument("--backend", choices=("source", "installed"), default="source")
parser.add_argument("--artifact")
parser.add_argument("--warmup", type=int, default=20)
parser.add_argument("--iters", type=int, default=100)
parser.add_argument("--json-out")
args = parser.parse_args()
torch.manual_seed(9102)
ops = load_ops(args.backend, args.artifact)
rows = []
for name, (tile_rows, n, k, tiles, experts) in WORKLOADS.items():
packed, weights, input_scale, weight_scales, alpha, tile_expert, out = make_case(
tile_rows, n, k, tiles, experts
)
kwargs = dict(
tile_rows=tile_rows,
weight_stride=weights[0].numel(),
weight_scale_stride=weight_scales[0].numel(),
)
def public_call():
return ops.grouped_nvfp4_gemm_bf16(
packed, weights, input_scale, weight_scales, alpha,
tile_expert, out=out, **kwargs
)
def raw_call():
ops.ops.grouped_nvfp4_gemm_bf16_out(
packed, weights, input_scale, weight_scales, alpha,
tile_expert, tile_rows, 0, kwargs["weight_stride"],
kwargs["weight_scale_stride"], out,
)
expected = public_call().clone()
raw_call()
torch.cuda.synchronize()
if not torch.equal(out, expected):
raise AssertionError(f"{name}: public/raw output mismatch")
public_us = time_us(public_call, args.warmup, args.iters)
raw_us = time_us(raw_call, args.warmup, args.iters)
ratio = public_us / raw_us
accepted = public_us - raw_us <= max(0.75, raw_us * 0.05)
row = {
"workload": name,
"tile_rows": tile_rows,
"rows": tile_rows * tiles,
"n": n,
"k": k,
"experts": experts,
"raw_native_us": raw_us,
"public_wrapper_us": public_us,
"wrapper_native": ratio,
"bit_exact": True,
"accepted": accepted,
}
rows.append(row)
print(
f"{name}: raw={raw_us:.3f}us public={public_us:.3f}us "
f"ratio={ratio:.4f} accepted={accepted}"
)
if args.json_out:
Path(args.json_out).parent.mkdir(parents=True, exist_ok=True)
Path(args.json_out).write_text(json.dumps(rows, indent=2) + "\n")
if not all(row["accepted"] for row in rows):
raise AssertionError("grouped MoE wrapper/native parity failed")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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