初始提交：3 款纯音频游戏 + 共享引擎 + 完整文档与资产管线

- 共享引擎（app/src/main）：GestureEngine 6 手势识别、AudioEngine 三层音频（ExoPlayer BGM 交叉淡入 / 对白 ducking / SoundPool 低延迟）、Game/GameEngine 状态机接口、MainActivity 全屏封面 + TalkBack - 《声命线》storyline：末世互动广播剧，3 幕 8 抉择点 4 结局 - 《声东击西》reflex：听声辨位反应，12 波战斗 + BGM 三档 BPM 变速 - 《暗室》escape：3x3 网格密室逃脱，钥匙→铁柜→保险丝→电箱→大门谜题链 - 文档：setup（从零搭建）/ architecture（架构）/ engine-api（引擎 API）/ asset-pipeline（资产流水线）/ games/*（三款游戏设计文档） - 资产管线：Qwen3-TTS 对白 + 程序合成音效/BGM + Mureka BGM 的 gen 脚本与台账，全部 ogg 资产入库可复现 - env.d 环境脚本：android/tts 入库；sfx/mureka 含密钥不入库，仅提交 .example.sh 模板
2026-08-29 13:00:34 +08:00
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+#!/usr/bin/env python3
+"""Recompute the SAME-L fp8 ENCODER activation scales with AMAX.
+
+WHY. enc_quant_samel.py set each activation quantiser's scale to
+
+    max(percentile(p99.9_per_calibration_clip, 90) / 448, 1e-4)
+
+— the 99.9th percentile within each clip, then the 90th percentile of *that* across clips, then a
+floor. Three tail-discarding steps on a heavy-tailed distribution. The decoder's own quantiser
+(fp8_gptq_samel.py) used plain `amax`, and the two disagree wildly:
+
+    decoder   clip point = scale*448 = 22.4 on all 24 quantisers (one global amax)
+    encoder   clip point = 0.04 .. 1.22, per layer, with every ff_out pinned to the 1e-4 floor
+
+Measured effect: the encoder's latents landed 18.3 dB from the fp16 reference where the decoder's
+output landed 28. End to end that cost 0.15-2.16 dB of round trip on real music — worst on clean
+acoustic material (jazz), where quantisation noise has nothing to hide behind, and least on loud
+clipped sources where the limiter masks it.
+
+WHAT THIS DOES. Re-runs the SAME capture — same 6 calibration clips, same hook points — with
+`scale = amax / 448`, and rewrites only the 24 activation-scale initializers. The GPTQ'd fp8
+WEIGHTS are untouched, so this is a scale correction rather than a requantisation. Measured: the
+old scales were 1.3-28.4x too small (median 3.0x); after the fix latent SNR is 30.9 dB and the
+round trip equals fp16 on every source tested.
+
+⚠ The scale initializers are fp16. Writing them back as fp32 makes the DequantizeLinear output
+FLOAT while the matching weight stays HALF, and TRT rejects the MatMul with "must have same input
+types". Preserve the dtype.
+
+usage: recalib_enc_fp8.py --src enc_samel_fp8.onnx --out enc_fp8_amax.onnx \
+                          --ckpt-dir /path/to/SAME-L --calib calib_audio_samel.npz
+"""
+import argparse
+import json
+import re
+
+import numpy as np
+import onnx
+import torch
+from onnx import numpy_helper as nh
+
+E4M3_MAX = 448.0
+
+
+def main():
+    ap = argparse.ArgumentParser(description=__doc__,
+                                 formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument("--src", required=True, help="the fp8 encoder ONNX to correct")
+    ap.add_argument("--out", required=True)
+    ap.add_argument("--ckpt-dir", required=True, help="dir holding SAME-L.json + SAME-L.ckpt")
+    ap.add_argument("--calib", required=True, help="calib_audio_samel.npz (key 'audio')")
+    a = ap.parse_args()
+
+    from stable_audio_3.factory import create_autoencoder_from_config
+    from stable_audio_3.loading_utils import copy_state_dict
+    torch.set_grad_enabled(False)
+
+    cfg = json.load(open(f"{a.ckpt_dir}/SAME-L.json"))
+    ae = create_autoencoder_from_config(cfg["model"], cfg["sample_rate"])
+    ck = torch.load(f"{a.ckpt_dir}/SAME-L.ckpt", map_location="cpu", weights_only=False)
+    copy_state_dict(ae, ck.get("state_dict", ck) if isinstance(ck, dict) else ck)
+    ae = ae.cuda().eval()
+
+    eproj = [m for nm, m in ae.named_modules() if re.search(r"encoder\..*\.ff\.ff\.0\.proj$", nm)]
+    eout = [m for nm, m in ae.named_modules() if re.search(r"encoder\..*\.ff\.ff\.2$", nm)]
+    print(f"eager encoder FFN: {len(eproj)} proj + {len(eout)} out", flush=True)
+
+    amax = {}
+
+    def mk(role, i):
+        def hook(_m, inp):
+            amax[(role, i)] = max(amax.get((role, i), 0.0), float(inp[0].detach().abs().max()))
+        return hook
+
+    handles = ([m.register_forward_pre_hook(mk("proj", i)) for i, m in enumerate(eproj)] +
+               [m.register_forward_pre_hook(mk("out", i)) for i, m in enumerate(eout)])
+    aud = np.load(a.calib)["audio"].astype(np.float32)
+    for ci in range(aud.shape[0]):
+        ae.encode(torch.tensor(aud[ci:ci + 1], device="cuda"))
+        print(f"  captured clip {ci}", flush=True)
+    for h in handles:
+        h.remove()
+    del ae
+    torch.cuda.empty_cache()
+
+    new = {k: float(v) / E4M3_MAX for k, v in amax.items()}
+    model = onnx.load(a.src, load_external_data=True)
+    inits = {i.name: i for i in model.graph.initializer}
+    qa = {}
+    for n in model.graph.node:
+        if n.op_type == "QuantizeLinear" and n.input[0] not in inits and n.input[1] in inits:
+            m = re.match(r"blocks\.(\d+)_ff_(proj|out)_MatMul_Qa", n.name or "")
+            if not m:
+                raise SystemExit(f"unrecognised activation quantiser name: {n.name!r}")
+            qa[(m.group(2), int(m.group(1)))] = n.input[1]
+    if set(qa) != set(new):
+        raise SystemExit(f"mapping mismatch: onnx has {len(qa)}, eager captured {len(new)}")
+
+    ratios = []
+    for key, init_name in qa.items():
+        init = inits[init_name]
+        arr = nh.to_array(init)
+        old = float(np.asarray(arr).ravel()[0])
+        ratios.append(new[key] / old)
+        # dtype preserved on purpose — see the module docstring.
+        init.CopyFrom(nh.from_array(np.full(arr.shape, new[key], dtype=arr.dtype), init_name))
+    r = np.array(ratios)
+    print(f"\n  {len(qa)} activation scales rewritten with amax/448")
+    print(f"  amax/old ratio: min {r.min():.1f}x  median {np.median(r):.1f}x  max {r.max():.1f}x")
+    onnx.save(model, a.out, save_as_external_data=False)
+    print(f"  wrote {a.out}")
+
+
+if __name__ == "__main__":
+    main()