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"""Measure batch-1 inference latency for baseline and LatentASR settings."""
from __future__ import annotations
import argparse
import json
import sys
import statistics
import time
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
import numpy as np
import torch
from datasets import Audio, load_dataset
from eval import (
build_base_model_bundle,
build_latent_bundle,
choose_device,
choose_dtype,
clean_prediction,
resolve_language_hint,
release_model,
)
from utils import set_seed
DATASETS = {
"fleurs": ("google/fleurs", "en_us", "test"),
"voxpopuli": ("facebook/voxpopuli", "en", "test"),
}
def collect_samples(
dataset_name: str,
config: str,
split: str,
n: int,
streaming: bool,
sampling_rate: int,
) -> List[Dict[str, Any]]:
ds = load_dataset(
dataset_name,
config,
split=split,
trust_remote_code=True,
streaming=streaming,
)
if not streaming:
ds = ds.cast_column("audio", Audio(sampling_rate=sampling_rate))
samples: List[Dict[str, Any]] = []
for sample in ds:
audio = sample.get("audio")
if isinstance(audio, dict) and "array" in audio and "sampling_rate" in audio:
samples.append(audio)
if len(samples) >= n:
break
return samples
def run_one(
model: Any,
processor: Any,
audio: Dict[str, Any],
use_baseline: bool,
threshold: float,
language_hint: Optional[str],
max_new_tokens: int,
) -> Tuple[str, Dict[str, Any]]:
prompt_text = (
f"Transcribe the {language_hint} audio into text."
if language_hint
else "Transcribe the audio into text."
)
target_dtype = model.thinker.dtype if hasattr(model.thinker, "dtype") else torch.float32
feat_out = processor.feature_extractor(
np.asarray(audio["array"], dtype=np.float64),
sampling_rate=audio["sampling_rate"],
return_attention_mask=True,
)
feats = torch.tensor(
feat_out.input_features[0],
dtype=target_dtype,
device=model.base_model.device,
).unsqueeze(0)
n_frames = feats.size(-1)
raw_mask = getattr(feat_out, "attention_mask", None)
if raw_mask is not None:
mask = torch.tensor(raw_mask[0], dtype=torch.long)
if mask.size(-1) < n_frames:
mask = torch.cat([mask, torch.zeros(n_frames - mask.size(-1), dtype=torch.long)])
else:
mask = mask[:n_frames]
feature_attention_mask = mask.to(model.base_model.device).unsqueeze(0)
else:
feature_attention_mask = torch.ones((1, n_frames), dtype=torch.long, device=model.base_model.device)
out = model.generate(
feats,
feature_attention_mask=feature_attention_mask,
max_new_tokens=max_new_tokens,
use_baseline=use_baseline,
return_thoughts=False,
return_stats=True,
do_sample=False,
eos_token_id=[151645, 151643],
num_beams=1,
language_hint=language_hint,
prompt_text=prompt_text,
dynamic_halt_threshold=threshold,
)
if isinstance(out, tuple):
gen_ids = out[0]
stats = out[1] if isinstance(out[1], dict) else {}
else:
gen_ids = out
stats = {}
text = clean_prediction(processor.tokenizer.decode(gen_ids[0], skip_special_tokens=True))
return text, stats
def measure_setting(
model: Any,
processor: Any,
samples: List[Dict[str, Any]],
use_baseline: bool,
threshold: float,
language_hint: Optional[str],
warmup: int,
measure: int,
max_new_tokens: int,
) -> Dict[str, Any]:
n_total = min(len(samples), warmup + measure)
if n_total <= warmup:
raise ValueError(f"Need > warmup samples, got {len(samples)}")
times: List[float] = []
steps: List[int] = []
for idx, audio in enumerate(samples[:n_total]):
if torch.cuda.is_available():
torch.cuda.synchronize()
t0 = time.perf_counter()
_, stats = run_one(
model=model,
processor=processor,
audio=audio,
use_baseline=use_baseline,
threshold=threshold,
language_hint=language_hint,
max_new_tokens=max_new_tokens,
)
if torch.cuda.is_available():
torch.cuda.synchronize()
dt_ms = (time.perf_counter() - t0) * 1000.0
if idx >= warmup:
times.append(dt_ms)
iters = stats.get("deq_iters")
if hasattr(iters, "item"):
steps.append(int(iters.item()))
return {
"mean_ms": statistics.mean(times),
"median_ms": statistics.median(times),
"std_ms": statistics.pstdev(times) if len(times) > 1 else 0.0,
"n": len(times),
"avg_steps": (sum(steps) / len(steps)) if steps else None,
}
def main() -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--model-id", default="Qwen/Qwen3-ASR-0.6B")
parser.add_argument("--latent-ckpt", default="latent_qwen_asr_best.pth")
parser.add_argument("--output-json", required=True)
parser.add_argument("--warmup", type=int, default=10)
parser.add_argument("--samples", type=int, default=100)
parser.add_argument("--max-new-tokens", type=int, default=128)
parser.add_argument("--streaming", action="store_true")
parser.add_argument("--device", default="auto", choices=["auto", "cuda", "cpu"])
parser.add_argument("--dtype", default="auto", choices=["auto", "float32", "float16", "bfloat16"])
args = parser.parse_args()
set_seed(42)
device = choose_device(args.device)
dtype = choose_dtype(args.dtype, device)
output: Dict[str, Any] = {
"model_id": args.model_id,
"latent_ckpt": args.latent_ckpt,
"device": device,
"dtype": str(dtype),
"gpu": torch.cuda.get_device_name(0) if torch.cuda.is_available() else None,
"warmup": args.warmup,
"samples": args.samples,
"results": {},
}
base_bundle = build_base_model_bundle(args.model_id, device=device, dtype=dtype)
latent_bundle = build_latent_bundle(
args.model_id,
checkpoint_path=args.latent_ckpt,
n_latent_override=-1,
device=device,
dtype=dtype,
)
try:
sampling_rate = int(getattr(base_bundle.processor.feature_extractor, "sampling_rate", 16000) or 16000)
for tag, (dataset_name, config, split) in DATASETS.items():
print(f"[latency] collect {tag}: {dataset_name}/{config}/{split}")
samples = collect_samples(
dataset_name=dataset_name,
config=config,
split=split,
n=args.warmup + args.samples,
streaming=args.streaming,
sampling_rate=sampling_rate,
)
language_hint = resolve_language_hint(dataset_name, config)
output["results"][tag] = {}
settings = [
("baseline", base_bundle.model, base_bundle.processor, True, 0.0),
("theta_full", latent_bundle.model, latent_bundle.processor, False, -2.0),
("theta_zero", latent_bundle.model, latent_bundle.processor, False, 0.0),
("theta_skip", latent_bundle.model, latent_bundle.processor, False, 0.5),
]
for name, model, processor, use_baseline, threshold in settings:
print(f"[latency] {tag} {name}")
output["results"][tag][name] = measure_setting(
model=model,
processor=processor,
samples=samples,
use_baseline=use_baseline,
threshold=threshold,
language_hint=language_hint,
warmup=args.warmup,
measure=args.samples,
max_new_tokens=args.max_new_tokens,
)
print(output["results"][tag][name])
finally:
release_model(latent_bundle)
release_model(base_bundle)
out_path = Path(args.output_json)
out_path.parent.mkdir(parents=True, exist_ok=True)
out_path.write_text(json.dumps(output, indent=2), encoding="utf-8")
print(out_path)
return 0
if __name__ == "__main__":
raise SystemExit(main())
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