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5a76465 | 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 | #!/usr/bin/env python3
"""RTX benchmark for grouped W4A4 expert projection launch aggregation."""
from __future__ import annotations
import argparse
import importlib
import sys
from pathlib import Path
import torch
PACKAGE = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(PACKAGE / "tests"))
from test_grouped_moe_gemv import load_source_ops, sfb_bytes # noqa: E402
def elapsed_us(fn, warmup: int = 20, repeats: int = 100) -> 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(repeats):
fn()
end.record()
end.synchronize()
return start.elapsed_time(end) * 1000.0 / repeats
def load_ops(backend: str, artifact: str | None):
if backend == "source":
return load_source_ops()
if artifact:
sys.path.insert(0, artifact)
return importlib.import_module("grouped_moe_gemv")
def run_case(ops, label: str, m: int, top_k: int, n: int, k: int) -> dict:
experts = 8
x = torch.randn((m, k), device="cuda", dtype=torch.bfloat16) * 0.2
packed = torch.empty((m, k // 2), device="cuda", dtype=torch.uint8)
sfa = torch.empty((sfb_bytes(m, k),), device="cuda", dtype=torch.uint8)
weights = torch.full(
(experts, n, k // 2), 0x11, device="cuda", dtype=torch.uint8
)
sfb = torch.full(
(experts, sfb_bytes(n, k)), 0x38, device="cuda", dtype=torch.uint8
)
alpha = torch.ones((experts,), device="cuda", dtype=torch.float32)
idx = (
torch.arange(m * top_k, device="cuda", dtype=torch.int32)
.reshape(m, top_k)
.remainder(experts)
.contiguous()
)
out = torch.empty((m, top_k, n), device="cuda", dtype=torch.bfloat16)
routed_x = x[:, None, :].expand(m, top_k, k).reshape(m * top_k, k).contiguous()
w4a16_out = torch.empty((m * top_k, n), device="cuda", dtype=torch.bfloat16)
def grouped_region():
ops.quantize_activations_nvfp4_bf16(x, packed=packed, sfa=sfa)
ops.grouped_w4a4_gemv_bf16(
packed, weights, sfa, sfb, alpha, idx, out=out
)
def grouped_kernel():
ops.grouped_w4a4_gemv_bf16(
packed, weights, sfa, sfb, alpha, idx, out=out
)
route_out = torch.empty((m, 1, n), device="cuda", dtype=torch.bfloat16)
route_idx = [idx[:, route : route + 1].contiguous() for route in range(top_k)]
def route_loop():
ops.quantize_activations_nvfp4_bf16(x, packed=packed, sfa=sfa)
for route in range(top_k):
ops.grouped_w4a4_gemv_bf16(
packed,
weights,
sfa,
sfb,
alpha,
route_idx[route],
out=route_out,
)
pair_packed = torch.empty((1, k // 2), device="cuda", dtype=torch.uint8)
pair_sfa = torch.empty((sfb_bytes(1, k),), device="cuda", dtype=torch.uint8)
pair_out = torch.empty((1, 1, n), device="cuda", dtype=torch.bfloat16)
pair_idx = [
idx[token : token + 1, route : route + 1].contiguous()
for token in range(m)
for route in range(top_k)
]
def pair_loop():
pair = 0
for token in range(m):
for _route in range(top_k):
ops.quantize_activations_nvfp4_bf16(
x[token : token + 1], packed=pair_packed, sfa=pair_sfa
)
ops.grouped_w4a4_gemv_bf16(
pair_packed,
weights,
pair_sfa,
sfb,
alpha,
pair_idx[pair],
out=pair_out,
)
pair += 1
grouped_us = elapsed_us(grouped_region)
grouped_kernel_us = elapsed_us(grouped_kernel)
def w4a16_region():
ops.grouped_w4a16_gemv_bf16(
routed_x,
weights,
sfb,
alpha,
idx.reshape(-1),
w_stride=n * k // 2,
sfb_stride=sfb.shape[1],
n=n,
out=w4a16_out,
)
w4a16_us = elapsed_us(w4a16_region)
route_us = elapsed_us(route_loop) if top_k > 1 else grouped_us
pair_repeats = 30 if m * top_k >= 32 else 100
pair_us = elapsed_us(pair_loop, warmup=5, repeats=pair_repeats)
return {
"label": label,
"M": m,
"top_k": top_k,
"pairs": m * top_k,
"N": n,
"K": k,
"grouped_us": grouped_us,
"grouped_kernel_us": grouped_kernel_us,
"route_loop_us": route_us,
"pair_loop_us": pair_us,
"w4a16_us": w4a16_us,
"vs_route_loop": route_us / grouped_us,
"vs_pair_loop": pair_us / grouped_us,
"vs_w4a16": w4a16_us / grouped_us,
}
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument("--backend", choices=["source", "installed"], default="source")
parser.add_argument("--artifact")
args = parser.parse_args()
ops = load_ops(args.backend, args.artifact)
cases = [
("gate_up_decode", 1, 8, 1024, 2048),
("gate_up_verify", 7, 8, 1024, 2048),
("down_decode", 8, 1, 2048, 512),
("down_verify", 56, 1, 2048, 512),
]
print("label,M,top_k,pairs,N,K,w4a4_region_us,w4a4_kernel_us,w4a16_us,route_loop_us,pair_loop_us,vs_w4a16,vs_route,vs_pair")
for case in cases:
result = run_case(ops, *case)
print(
f"{result['label']},{result['M']},{result['top_k']},{result['pairs']},"
f"{result['N']},{result['K']},{result['grouped_us']:.3f},"
f"{result['grouped_kernel_us']:.3f},{result['w4a16_us']:.3f},"
f"{result['route_loop_us']:.3f},"
f"{result['pair_loop_us']:.3f},{result['vs_w4a16']:.2f}x,"
f"{result['vs_route_loop']:.2f}x,{result['vs_pair_loop']:.2f}x"
)
return 0
if __name__ == "__main__":
raise SystemExit(main())
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