File size: 8,538 Bytes
3fd1a35 | 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 | """Dedicated-board regression for opt-in generated-state reuse; no concurrent inference."""
import argparse
import json
import time
import urllib.request
from pathlib import Path
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--base", required=True)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--budget-test", action="store_true")
args = parser.parse_args()
client = urllib.request.build_opener(urllib.request.ProxyHandler({}))
def open_request(path, body=None):
return client.open(urllib.request.Request(args.base + path,
data=None if body is None else json.dumps(body).encode(),
headers={"Content-Type": "application/json"}), timeout=180)
def call(path, body=None):
with open_request(path, body) as response:
return json.load(response)
def infer(messages, session="a", reuse=True, count=16, **extra):
body = {"model": "mindnano-ling3-tiny", "user": "generated-test", "messages": messages,
"temperature": 0, "max_tokens": count, "reuse_generated_state": reuse}
if session is not None:
body["session_id"] = session
return call("/v1/chat/completions", dict(body, **extra))
def equal(a, b):
assert a["choices"] == b["choices"], "identical numerical path changed output"
def follow(seed, result, question="请简短说明刚才的内容。"):
return seed + [result["choices"][0]["message"], {"role": "user", "content": question}]
def clear():
call("/v1/cache/clear", {})
results = {"cases": [], "passed": False}
assert not call("/v1/generation/status")["active"]
assert call("/health")["capabilities"]["generated_state_reuse"]
clear()
try:
seed = [{"role": "user", "content": "1" * 107}] # 128-token stable prefix
if args.budget_test:
first = infer(seed, count=64)
assert first["mindnano_metrics"]["continuation_skipped_budget"]
assert first["mindnano_metrics"]["continuation_stored"]
later = infer(follow(seed, first))
assert later["mindnano_metrics"]["generated_cached_tokens"] == 63
status = call("/v1/cache/status")
assert status["snapshot_bytes"] <= status["budget_bytes"]
# Fair single-session comparison: the old path also keeps its
# original prompt cache. Compare against it, not only cold prefill.
clear()
first = infer(seed, session=None, count=64)
messages = follow(seed, first)
incremental = infer(messages, session=None)
clear()
reference_seed = infer(seed, session=None, reuse=False, count=64)
equal(first, reference_seed)
baseline = infer(messages, session=None, reuse=False)
assert baseline["mindnano_metrics"]["cached_tokens"] == 128
assert incremental["mindnano_metrics"]["generated_cached_tokens"] == 63
results["cached_baseline"] = {"incremental": incremental, "original_prompt_cache": baseline,
"same_reply": incremental["choices"] == baseline["choices"]}
clear()
long_seed = infer(seed, session=None, count=512)
long_messages = follow(seed, long_seed)
long_incremental = infer(long_messages, session=None)
assert long_incremental["mindnano_metrics"]["generated_cached_tokens"] == 511
clear()
long_reference = infer(seed, session=None, reuse=False, count=512)
equal(long_seed, long_reference)
long_baseline = infer(long_messages, session=None, reuse=False)
assert long_baseline["mindnano_metrics"]["cached_tokens"] == 128
results["long_cached_baseline"] = {"incremental": long_incremental, "original_prompt_cache": long_baseline,
"same_reply": long_incremental["choices"] == long_baseline["choices"]}
results.update(passed=True, budget=status)
return
for session in (None, "a"):
clear()
first = infer(seed, session, count=64)
assert first["mindnano_metrics"]["continuation_stored"]
messages = follow(seed, first)
if session:
infer([{"role": "user", "content": "你好。"}], "other")
warm = infer(messages, session)
m = warm["mindnano_metrics"]
assert m["generated_cached_tokens"] == 63
assert m["cache_status"] == "generated_prefix_hit"
assert m["cached_tokens"] + m["prompt_evaluated_tokens"] == warm["usage"]["prompt_tokens"]
again = infer(messages, session)
equal(warm, again)
assert again["mindnano_metrics"]["prompt_evaluated_tokens"] == 0
# Turning it off must also reject exact caches derived from decode state.
strict = infer(messages, session, reuse=False)
assert strict["mindnano_metrics"]["cached_tokens"] == 0
cold = infer(messages, session, reuse=False, cache_prompt=False)
equal(strict, cold)
results["cases"].append({"session": session, "warm": warm, "cold": cold,
"same_reply_as_cold": warm["choices"] == cold["choices"]})
# Fork and cancel preserve the last committed generated prefix.
clear()
first = infer(seed, count=64)
call("/v1/cache/fork", {"user": "generated-test", "source_session_id": "a", "target_session_id": "fork"})
messages = follow(seed, first)
branch = infer(messages, "fork")
assert branch["mindnano_metrics"]["generated_cached_tokens"] == 63
body = {"model": "mindnano-ling3-tiny", "user": "generated-test", "session_id": "a",
"messages": [{"role": "user", "content": "讲个故事。"}], "stream": True,
"flow_control": "ack", "reuse_generated_state": True, "max_tokens": 64}
with open_request("/v1/chat/completions", body) as response:
for line in response:
if line.startswith(b"data:"):
event = json.loads(line[5:])
if event.get("mindnano_flow"):
call("/v1/cancel", {"request_id": event["id"]})
break
for line in response:
if line.strip() == b"data: [DONE]":
break
deadline = time.monotonic() + 10
while call("/v1/generation/status")["active"]:
assert time.monotonic() < deadline
time.sleep(.05)
resumed = infer(messages)
assert resumed["mindnano_metrics"]["generated_cached_tokens"] == 63
equal(branch, resumed)
# Changed/shorter histories and stop-filtered output cannot reuse hidden tokens.
for variant in ("edited", "shorter", "stop"):
clear()
first = infer(seed, count=64, **({"stop": "11111"} if variant == "stop" else {}))
messages = follow(seed, first)
if variant == "edited":
messages[1]["content"] = "修改过的回答。"
elif variant == "shorter":
messages = [{"role": "user", "content": "你好。"}]
response = infer(messages)
assert response["mindnano_metrics"]["generated_cached_tokens"] == 0
# Sample both language modes. Different cold output is measured, not hidden.
for think in (False, True):
clear()
seed = [{"role": "user", "content": "用中文介绍一次会议记录应包含什么。"}]
first = infer(seed, count=128, enable_thinking=think)
messages = follow(seed, first, "请简短总结。")
warm = infer(messages, enable_thinking=think)
cold = infer(messages, reuse=False, cache_prompt=False, enable_thinking=think)
results["cases"].append({"thinking": think, "warm": warm, "cold": cold,
"same_reply_as_cold": warm["choices"] == cold["choices"]})
results["passed"] = True
finally:
clear()
args.output.write_text(json.dumps(results, ensure_ascii=False, indent=2) + "\n")
print(json.dumps({"generated_cache": "PASS" if results["passed"] else "FAIL",
"results": str(args.output)}), flush=True)
if __name__ == "__main__":
main()
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