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9c0a4e6 | 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 | """Comprehensive GPU validation for the ASD custom generation method.
Covers:
1. Native strict baseline vs ASD(B=0) -> must be token-identical (gold check)
2. Native strict vs custom (no knobs) -> must be token-identical (lossless default)
3. ASD(B=2.0) relaxed -> runs, deterministic, >= strict acceptance
4. Determinism -> two identical relaxed runs, token-identical
5. Error handling -> do_sample / ensemble_weight / no draft source
6. Throughput + acceptance comparison -> strict vs relaxed
"""
import os
import sys
import time
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
REPO = os.environ.get("ASD_REPO", os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
TARGET = os.environ.get("ASD_TARGET", "Qwen/Qwen2.5-1.5B-Instruct")
DRAFT = os.environ.get("ASD_DRAFT", "Qwen/Qwen2.5-0.5B-Instruct")
MAX_NEW = 128
PROMPTS = [
"Give me a short introduction to large language models.",
"What is the capital of France? Answer in one sentence.",
"Write a Python function that computes the nth Fibonacci number.",
"Explain the difference between TCP and UDP briefly.",
"List three prime numbers greater than 10 and justify one of them.",
]
failures = []
def check(name, cond, detail=""):
status = "PASS" if cond else "FAIL"
print(f"[{status}] {name}" + (f" -- {detail}" if detail else ""))
if not cond:
failures.append(name)
def count_fwd(tok, out, prompt_len):
return out.shape[1] - prompt_len
def main():
tokenizer = AutoTokenizer.from_pretrained(TARGET)
model = AutoModelForCausalLM.from_pretrained(TARGET, dtype="auto", device_map="auto")
assistant = AutoModelForCausalLM.from_pretrained(DRAFT, dtype="auto", device_map="auto")
model.eval()
assistant.eval()
def run(prompt, **extra):
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
t0 = time.perf_counter()
out = model.generate(
**inputs, max_new_tokens=MAX_NEW, do_sample=False, **extra
)
dt = time.perf_counter() - t0
n = out.shape[1] - inputs["input_ids"].shape[1]
return out, n / dt
# ---------- 1&2. gold checks ----------
for prompt in PROMPTS:
strict, _ = run(prompt, assistant_model=assistant)
b0, _ = run(
prompt,
assistant_model=assistant,
custom_generate=REPO,
trust_remote_code=True,
assistant_asd_budget=0.0,
)
noknobs, _ = run(
prompt,
assistant_model=assistant,
custom_generate=REPO,
trust_remote_code=True,
)
check(f"gold B=0 identical ({prompt[:25]!r})", torch.equal(strict, b0))
check(f"gold no-knobs identical ({prompt[:25]!r})", torch.equal(strict, noknobs))
# ---------- 3&4. relaxed run + determinism ----------
for prompt in PROMPTS:
relaxed1, tps1 = run(
prompt,
assistant_model=assistant,
custom_generate=REPO,
trust_remote_code=True,
assistant_asd_budget=2.0,
assistant_asd_local_ratio=0.25,
assistant_asd_max_mismatches=2,
)
relaxed2, _ = run(
prompt,
assistant_model=assistant,
custom_generate=REPO,
trust_remote_code=True,
assistant_asd_budget=2.0,
assistant_asd_local_ratio=0.25,
assistant_asd_max_mismatches=2,
)
check(f"determinism ({prompt[:25]!r})", torch.equal(relaxed1, relaxed2))
check(f"relaxed non-empty ({prompt[:25]!r})", relaxed1.shape[1] > 0)
# ---------- 5. error handling ----------
inputs = tokenizer(PROMPTS[0], return_tensors="pt").to(model.device)
def expect_raise(name, **kw):
try:
model.generate(**inputs, max_new_tokens=8, **kw)
except ValueError:
check(f"raises {name}", True)
except Exception as e: # noqa: BLE001
check(f"raises {name}", False, f"wrong exception: {type(e).__name__}: {e}")
else:
check(f"raises {name}", False, "no exception")
expect_raise(
"do_sample=True",
assistant_model=assistant,
custom_generate=REPO,
trust_remote_code=True,
do_sample=True,
assistant_asd_budget=2.0,
)
expect_raise(
"ensemble_weight conflict",
assistant_model=assistant,
custom_generate=REPO,
trust_remote_code=True,
assistant_ensemble_weight=0.5,
assistant_asd_budget=2.0,
)
expect_raise(
"knobs without draft source",
custom_generate=REPO,
trust_remote_code=True,
assistant_asd_budget=2.0,
)
expect_raise(
"negative budget",
assistant_model=assistant,
custom_generate=REPO,
trust_remote_code=True,
assistant_asd_budget=-1.0,
)
# ---------- 6. throughput + acceptance ----------
print("\n=== throughput comparison ===")
tot_strict_tps = tot_relaxed_tps = 0.0
for prompt in PROMPTS:
strict, s_tps = run(prompt, assistant_model=assistant)
relaxed, r_tps = run(
prompt,
assistant_model=assistant,
custom_generate=REPO,
trust_remote_code=True,
assistant_asd_budget=2.0,
assistant_asd_local_ratio=0.25,
assistant_asd_max_mismatches=2,
)
tot_strict_tps += s_tps
tot_relaxed_tps += r_tps
overlap = sum(
1 for a, b in zip(strict[0].tolist(), relaxed[0].tolist()) if a == b
) / max(len(strict[0]), len(relaxed[0]))
print(
f" strict {s_tps:6.2f} tok/s | relaxed {r_tps:6.2f} tok/s "
f"({(r_tps / s_tps - 1) * 100:+5.1f}%) | prefix overlap {overlap:.2%}"
)
print(
f" MEAN strict {tot_strict_tps / len(PROMPTS):.2f} tok/s | "
f"relaxed {tot_relaxed_tps / len(PROMPTS):.2f} tok/s"
)
print("\n" + ("ALL PASS" if not failures else f"FAILURES: {failures}"))
sys.exit(1 if failures else 0)
if __name__ == "__main__":
main()
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