4747e2f4
 tangwang
embedding perform...
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parser.add_argument("--paragraph-min-chars", type=int, default=250)
parser.add_argument("--target-doc-chars", type=int, default=4500)
parser.add_argument("--min-doc-chars", type=int, default=2400)
parser.add_argument("--runs", type=int, default=3)
parser.add_argument("--batch-size", type=int, default=64)
parser.add_argument("--ct2-inter-threads", type=int, default=4)
parser.add_argument("--ct2-max-queued-batches", type=int, default=32)
parser.add_argument("--ct2-batch-type", default="examples")
parser.add_argument("--max-new-tokens", type=int, default=64)
parser.add_argument("--ct2-decoding-length-mode", default="source")
parser.add_argument("--ct2-decoding-length-extra", type=int, default=8)
parser.add_argument("--ct2-decoding-length-min", type=int, default=32)
return parser.parse_args()
def build_long_document(args: argparse.Namespace) -> str:
source_path = (PROJECT_ROOT / args.source_md).resolve()
text = source_path.read_text(encoding="utf-8")
paragraphs = []
for raw in text.split("\n\n"):
normalized = " ".join(line.strip() for line in raw.splitlines() if line.strip())
if len(normalized) >= args.paragraph_min_chars and not normalized.startswith("```"):
paragraphs.append(normalized)
parts = []
total = 0
for paragraph in paragraphs:
parts.append(paragraph)
total += len(paragraph) + 2
if total >= args.target_doc_chars:
break
document = "\n\n".join(parts)
if len(document) < args.min_doc_chars:
raise ValueError(
f"Prepared long document is too short: {len(document)} chars < {args.min_doc_chars}"
)
return document
def build_service(args: argparse.Namespace) -> TranslationService:
config = copy.deepcopy(get_translation_config())
for name, capability in config["capabilities"].items():
capability["enabled"] = name == args.model
capability = config["capabilities"][args.model]
capability["use_cache"] = False
capability["batch_size"] = args.batch_size
capability["ct2_inter_threads"] = args.ct2_inter_threads
capability["ct2_max_queued_batches"] = args.ct2_max_queued_batches
capability["ct2_batch_type"] = args.ct2_batch_type
capability["max_new_tokens"] = args.max_new_tokens
capability["ct2_decoding_length_mode"] = args.ct2_decoding_length_mode
capability["ct2_decoding_length_extra"] = args.ct2_decoding_length_extra
capability["ct2_decoding_length_min"] = args.ct2_decoding_length_min
config["default_model"] = args.model
return TranslationService(config)
def percentile(values: list[float], p: float) -> float:
if not values:
return 0.0
ordered = sorted(values)
if len(ordered) == 1:
return float(ordered[0])
index = min(len(ordered) - 1, max(0, round((len(ordered) - 1) * p)))
return float(ordered[index])
def main() -> None:
args = parse_args()
logging.getLogger().setLevel(logging.WARNING)
document = build_long_document(args)
load_started = time.perf_counter()
service = build_service(args)
backend = service.get_backend(args.model)
load_seconds = time.perf_counter() - load_started
safe_input_limit = compute_safe_input_token_limit(
max_input_length=backend.max_input_length,
max_new_tokens=backend.max_new_tokens,
decoding_length_mode=backend.ct2_decoding_length_mode,
decoding_length_extra=backend.ct2_decoding_length_extra,
)
segments = backend._split_text_if_needed(
document,
target_lang=args.target_lang,
source_lang=args.source_lang,
)
# Warm up once before measurements.
_ = service.translate(
document,
source_lang=args.source_lang,
target_lang=args.target_lang,
model=args.model,
scene=args.scene,
)
if torch.cuda.is_available():
torch.cuda.synchronize()
latencies_ms: list[float] = []
output_chars = 0
for _ in range(args.runs):
started = time.perf_counter()
output = service.translate(
document,
source_lang=args.source_lang,
target_lang=args.target_lang,
model=args.model,
scene=args.scene,
)
if torch.cuda.is_available():
torch.cuda.synchronize()
latencies_ms.append((time.perf_counter() - started) * 1000)
output_chars += len(output or "")
total_seconds = sum(latencies_ms) / 1000.0
payload = {
"model": args.model,
"source_lang": args.source_lang,
"target_lang": args.target_lang,
"doc_chars": len(document),
"runs": args.runs,
"load_seconds": round(load_seconds, 3),
"batch_size": backend.batch_size,
"ct2_inter_threads": backend.ct2_inter_threads,
"ct2_max_queued_batches": backend.ct2_max_queued_batches,
"ct2_batch_type": backend.ct2_batch_type,
"max_new_tokens": backend.max_new_tokens,
"ct2_decoding_length_mode": backend.ct2_decoding_length_mode,
"ct2_decoding_length_extra": backend.ct2_decoding_length_extra,
"ct2_decoding_length_min": backend.ct2_decoding_length_min,
"safe_input_limit": safe_input_limit,
"segment_count": len(segments),
"segment_char_lengths": {
"min": min(len(segment) for segment in segments),
"max": max(len(segment) for segment in segments),
"avg": round(statistics.fmean(len(segment) for segment in segments), 1),
},
"latency_avg_ms": round(statistics.fmean(latencies_ms), 2),
"latency_p50_ms": round(percentile(latencies_ms, 0.50), 2),
"latency_p95_ms": round(percentile(latencies_ms, 0.95), 2),
"latency_max_ms": round(max(latencies_ms), 2),
"input_chars_per_second": round((len(document) * args.runs) / total_seconds, 2),
"output_chars_per_second": round(output_chars / total_seconds, 2),
}
print(json.dumps(payload, ensure_ascii=False))
if __name__ == "__main__":
main()
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