KingIcyCreamProjects
51dd4805af
profile_print now emits 'expert-disk N.NNNs service / N.NNNs wait' but the harness regex still expected the old single expert-disk number, so every run died with 'benchmark output missing'. Accept both formats and report disk = service + wait. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
112 lines
3.9 KiB
Python
112 lines
3.9 KiB
Python
"""Reproducible CPU/CUDA A/B benchmark for tools/make_glm_bench_model.py output."""
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import argparse
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import json
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import os
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import re
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import statistics
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import subprocess
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from pathlib import Path
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SPEED_RE = re.compile(r"REPLAY decode:.*\| ([0-9.]+) tok/s")
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# accetta sia il formato storico "expert-disk 0.123s |" sia quello attuale
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# "expert-disk 0.123s service / 0.045s wait |" (glm.c profile_print)
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PROFILE_RE = re.compile(
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r"PROFILE: expert-disk ([0-9.]+)s(?: service / ([0-9.]+)s wait)? \| expert-matmul ([0-9.]+)s "
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r"\| attention ([0-9.]+)s .* lm_head ([0-9.]+)s \| other ([0-9.-]+)s"
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)
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PROFILE_KEYS = ("disk", "expert_matmul", "attention", "lm_head", "other")
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def parse_output(stdout: str, stderr: str = "") -> tuple[float, list[float]]:
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"""Extract throughput and profile timings from one engine run."""
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speed = SPEED_RE.search(stdout)
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profile = PROFILE_RE.search(stdout)
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if not speed or not profile:
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raise RuntimeError(f"benchmark output missing\nstdout:\n{stdout}\nstderr:\n{stderr}")
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service, wait, *rest = profile.groups()
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disk = float(service) + (float(wait) if wait else 0.0)
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return float(speed.group(1)), [disk] + [float(value) for value in rest]
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def execute(engine: str, env: dict[str, str]) -> tuple[float, list[float]]:
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run = subprocess.run(
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[engine, "4", "4", "4"], env=env, text=True, capture_output=True, check=True
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)
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return parse_output(run.stdout, run.stderr)
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def main() -> None:
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parser = argparse.ArgumentParser()
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parser.add_argument("--model", required=True)
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parser.add_argument("--engine", default="./glm")
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parser.add_argument("--gpu", default="0")
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parser.add_argument("--runs", type=int, default=7)
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parser.add_argument("--threads", type=int, default=os.cpu_count() or 1)
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parser.add_argument("--pin-gb", default="1")
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parser.add_argument("--cuda-expert-gb", default="2")
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args = parser.parse_args()
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model = Path(args.model).resolve()
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stats = model / "bench_stats.txt"
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base = os.environ.copy()
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for key in (
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"COLI_CUDA", "COLI_GPU", "COLI_GPUS", "CUDA_EXPERT_GB",
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"PIN", "PIN_GB", "STATS", "TF", "REPLAY", "CUDA_DENSE",
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):
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base.pop(key, None)
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base.update(
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SNAP=str(model),
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REF=str(model / "ref_glm.json"),
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REPLAY="1",
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OMP_NUM_THREADS=str(args.threads),
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OMP_PROC_BIND="spread",
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OMP_PLACES="cores",
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)
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execute(args.engine, base | {"STATS": str(stats)})
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modes = {
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"cpu_stream": {},
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"dense_cuda": {"COLI_CUDA": "1", "COLI_GPU": args.gpu, "CUDA_DENSE": "1"},
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"cpu_pin": {"PIN": str(stats), "PIN_GB": args.pin_gb},
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"cuda_pin": {
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"COLI_CUDA": "1", "COLI_GPU": args.gpu,
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"PIN": str(stats), "PIN_GB": args.pin_gb,
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"CUDA_EXPERT_GB": args.cuda_expert_gb,
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},
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"cuda_pin_dense": {
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"COLI_CUDA": "1", "COLI_GPU": args.gpu, "CUDA_DENSE": "1",
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"PIN": str(stats), "PIN_GB": args.pin_gb,
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"CUDA_EXPERT_GB": args.cuda_expert_gb,
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},
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}
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for extra in modes.values():
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execute(args.engine, base | extra) # warm-up
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speeds = {name: [] for name in modes}
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profiles = {name: [] for name in modes}
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names = list(modes)
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for run_index in range(args.runs):
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order = names[run_index % len(names):] + names[:run_index % len(names)]
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for name in order:
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speed, profile = execute(args.engine, base | modes[name])
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speeds[name].append(speed)
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profiles[name].append(profile)
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result = {}
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for name in names:
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result[name] = {
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"runs_tok_s": speeds[name],
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"median_tok_s": statistics.median(speeds[name]),
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"median_profile_s": {
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key: statistics.median(row[index] for row in profiles[name])
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for index, key in enumerate(PROFILE_KEYS)
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},
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}
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print(json.dumps(result, indent=2))
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if __name__ == "__main__":
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main()
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