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18f4d80 | 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 | from __future__ import annotations
import argparse
import json
import time
from pathlib import Path
from statistics import mean
import psutil
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from rotorquant_weights import load_quantized_package, dequantize_to_state_dict
from runtime_rotor_fused import load_fused_model
def rss_gb() -> float:
return psutil.Process().memory_info().rss / (1024 ** 3)
def make_inputs(tokenizer, prompt: str):
messages = [
{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
{"role": "user", "content": prompt},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
return tokenizer([text], return_tensors="pt")
def token_match(a: torch.Tensor, b: torch.Tensor) -> float:
n = min(a.numel(), b.numel())
if n == 0:
return 1.0
return (a[:n] == b[:n]).float().mean().item()
def run_metrics(model, tokenizer, prompts, max_new_tokens, baseline_gens=None):
tok_t, pre_t, first_t, gen_t, tps, matches = [], [], [], [], [], []
gens = []
with torch.no_grad():
for i, p in enumerate(prompts):
t0 = time.perf_counter()
inp = make_inputs(tokenizer, p)
tok_t.append(time.perf_counter() - t0)
t1 = time.perf_counter()
_ = model(**inp)
pre_t.append(time.perf_counter() - t1)
t2 = time.perf_counter()
_ = model.generate(**inp, max_new_tokens=1, min_new_tokens=1, do_sample=False)
first_t.append(time.perf_counter() - t2)
t3 = time.perf_counter()
out = model.generate(
**inp,
max_new_tokens=max_new_tokens,
min_new_tokens=max_new_tokens,
do_sample=False,
)
dt = time.perf_counter() - t3
gen_t.append(dt)
new_toks = out[:, inp["input_ids"].shape[1]:].reshape(-1).cpu()
gens.append(new_toks)
tps.append(new_toks.numel() / max(dt, 1e-9))
if baseline_gens is not None:
matches.append(token_match(new_toks, baseline_gens[i]))
return {
"tokenize_s": mean(tok_t),
"prefill_forward_s": mean(pre_t),
"first_token_latency_s": mean(first_t),
"generate_s": mean(gen_t),
"decode_tokens_per_s": mean(tps),
"token_match_vs_baseline": mean(matches) if matches else 1.0,
"gens": gens,
}
def load_baseline(model_id: str):
t0 = time.perf_counter()
m = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32, device_map=None, low_cpu_mem_usage=True).eval()
return m, time.perf_counter() - t0
def load_rotor(pkg_path: str):
t0 = time.perf_counter()
pkg = load_quantized_package(pkg_path)
model_id = pkg["model_id"]
m = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32, device_map=None, low_cpu_mem_usage=True).eval()
sd = dequantize_to_state_dict(pkg, dtype=torch.float32, device="cpu")
miss, unexp = m.load_state_dict(sd, strict=False)
if miss or unexp:
raise RuntimeError(f"State mismatch: missing={miss}, unexpected={unexp}")
return m, time.perf_counter() - t0
def load_dynamic_int8(path: str):
t0 = time.perf_counter()
m = torch.load(path, map_location="cpu", weights_only=False).eval()
return m, time.perf_counter() - t0
def scenario_result(name, load_s, metrics, rss_before, rss_after_load, rss_after_bench, baseline=None):
out = {
"scenario": name,
"load_s": load_s,
"tokenize_s": metrics["tokenize_s"],
"prefill_forward_s": metrics["prefill_forward_s"],
"first_token_latency_s": metrics["first_token_latency_s"],
"generate_s": metrics["generate_s"],
"decode_tokens_per_s": metrics["decode_tokens_per_s"],
"token_match_vs_baseline": metrics["token_match_vs_baseline"],
"rss_before_load_gb": rss_before,
"rss_after_load_gb": rss_after_load,
"rss_after_bench_gb": rss_after_bench,
}
if baseline is not None:
out["delta_vs_baseline"] = {
"load_s": out["load_s"] - baseline["load_s"],
"prefill_forward_s": out["prefill_forward_s"] - baseline["prefill_forward_s"],
"first_token_latency_s": out["first_token_latency_s"] - baseline["first_token_latency_s"],
"generate_s": out["generate_s"] - baseline["generate_s"],
"decode_tokens_per_s": out["decode_tokens_per_s"] - baseline["decode_tokens_per_s"],
"rss_after_load_gb": out["rss_after_load_gb"] - baseline["rss_after_load_gb"],
}
return out
def parse_args():
p = argparse.ArgumentParser(description="Benchmark baseline vs RotorQuant vs runtime INT8")
p.add_argument("--model-id", default="Qwen/Qwen2.5-0.5B-Instruct")
p.add_argument("--rotor-pkg", default="artifacts/qwen2.5-0.5b-rotorq3-mlp-only.pt")
p.add_argument("--fused-pkg", default="artifacts/qwen2.5-0.5b-rotorq3-rowwise-skipemb.pt")
p.add_argument("--int8-model", default="artifacts/qwen2.5-0.5b-dynamic-int8.pt")
p.add_argument("--max-new-tokens", type=int, default=64)
p.add_argument("--out", default="artifacts/runtime_benchmark.json")
return p.parse_args()
def main():
args = parse_args()
prompts = [
"Explain quantization in one paragraph.",
"Write a Python function for binary search.",
"Summarize why weight quantization helps deployment.",
"Give 3 practical tips for reducing LLM latency.",
]
tokenizer = AutoTokenizer.from_pretrained(args.model_id)
_ = make_inputs(tokenizer, "warmup")
results = {}
rb = rss_gb()
baseline, load_b = load_baseline(args.model_id)
ral = rss_gb()
met_b = run_metrics(baseline, tokenizer, prompts, args.max_new_tokens)
rab = rss_gb()
results["baseline_fp32"] = scenario_result("baseline_fp32", load_b, met_b, rb, ral, rab)
base_ref = results["baseline_fp32"]
base_gens = met_b["gens"]
del baseline
rr0 = rss_gb()
rotor, load_r = load_rotor(args.rotor_pkg)
rr1 = rss_gb()
met_r = run_metrics(rotor, tokenizer, prompts, args.max_new_tokens, baseline_gens=base_gens)
rr2 = rss_gb()
results["rotorquant_pkg"] = scenario_result("rotorquant_pkg", load_r, met_r, rr0, rr1, rr2, baseline=base_ref)
del rotor
rf0 = rss_gb()
fused, _, load_f = load_fused_model(args.fused_pkg, out_chunk_size=64)
rf1 = rss_gb()
met_f = run_metrics(fused, tokenizer, prompts, args.max_new_tokens, baseline_gens=base_gens)
rf2 = rss_gb()
results["rotorquant_fused_runtime"] = scenario_result("rotorquant_fused_runtime", load_f, met_f, rf0, rf1, rf2, baseline=base_ref)
del fused
ri0 = rss_gb()
int8m, load_i = load_dynamic_int8(args.int8_model)
ri1 = rss_gb()
met_i = run_metrics(int8m, tokenizer, prompts, args.max_new_tokens, baseline_gens=base_gens)
ri2 = rss_gb()
results["runtime_dynamic_int8"] = scenario_result("runtime_dynamic_int8", load_i, met_i, ri0, ri1, ri2, baseline=base_ref)
del int8m
out = Path(args.out)
out.parent.mkdir(parents=True, exist_ok=True)
out.write_text(json.dumps(results, indent=2), encoding="utf-8")
print(f"Saved: {out}")
for k, v in results.items():
print(
f"- {k}: load={v['load_s']:.3f}s, first={v['first_token_latency_s']:.3f}s, "
f"gen={v['generate_s']:.3f}s, tok/s={v['decode_tokens_per_s']:.2f}, "
f"rss_load={v['rss_after_load_gb']:.2f}GB, match={v['token_match_vs_baseline']:.4f}"
)
if __name__ == "__main__":
main()
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