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
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SoundGame Dev
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#!/usr/bin/env python3
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"""GPTQ int8 for the SAME-L decoder FFN linears. ORT can't run the SWA plugin, so capture the
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per-linear input activations from the EAGER SAME-L autoencoder (hooks), build Hessians, GPTQ, and
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graft int8 (fp16 scale) onto the SAME-L onnx (attention proj kept fp32). Writes samel_w8_gptq.onnx."""
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import sys, os, re
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from pathlib import Path
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import numpy as np, torch, onnx
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from onnx import TensorProto, helper, numpy_helper
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sys.path.insert(0, "/weka2/cj/clod/sa3s/stable-audio-3/optimized/tensorRT/scripts")
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sys.path.insert(0, "/weka2/cj/clod/fp8_calib/build")
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from stable_audio_3.factory import create_autoencoder_from_config
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from stable_audio_3.loading_utils import copy_state_dict
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import json
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torch.set_grad_enabled(False)
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ML = "/weka2/cj/clod/sa3s/models/SAME-L"; AB = "/weka2/cj/clod/sames_fp8/decoder_ab"
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cfg = json.load(open(f"{ML}/SAME-L.json"))
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ae = create_autoencoder_from_config(cfg["model"], cfg["sample_rate"])
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ck = torch.load(f"{ML}/SAME-L.ckpt", map_location="cpu", weights_only=False)
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copy_state_dict(ae, ck.get("state_dict", ck) if isinstance(ck, dict) else ck)
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ae = ae.to("cuda").eval()
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# hook the decoder FFN linears (exclude attention proj). Normalize eager+onnx names to (block, role).
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def ekey(nm):
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m = re.search(r"transformers\.(\d+)\.ff\.ff\.0\.proj$", nm)
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if m: return f"b{m.group(1)}.proj"
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m = re.search(r"transformers\.(\d+)\.ff\.ff\.2$", nm)
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if m: return f"b{m.group(1)}.out"
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if re.search(r"latent_proj$", nm): return "latent_proj"
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return None
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def okey(nm):
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m = re.search(r"blocks\.(\d+)/ff/proj", nm)
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if m: return f"b{m.group(1)}.proj"
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m = re.search(r"blocks\.(\d+)/ff/out", nm)
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if m: return f"b{m.group(1)}.out"
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if re.search(r"latent_proj", nm): return "latent_proj"
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return None
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mods = {}
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for nm, mod in ae.named_modules():
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if hasattr(mod, "weight") and getattr(mod.weight, "ndim", 0) == 2:
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k = ekey(nm)
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if k: mods[k] = mod
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print(f"hooked {len(mods)} eager FFN linears: {sorted(mods)[:3]} ...", flush=True)
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H = {k: None for k in mods}
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def mk_hook(k):
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def hook(m, inp):
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A = inp[0].reshape(-1, inp[0].shape[-1]).double()
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h = (A.T @ A).cpu().numpy()
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H[k] = h if H[k] is None else H[k] + h
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return hook
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handles = [m.register_forward_pre_hook(mk_hook(k)) for k, m in mods.items()]
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# calibration latents: medium 2-min + 380s + the 10 real-song SAME-L latents
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lats = [np.load("/weka2/cj/clod/fp8_listening/latents_2min.npz")["bf16"]] # 1292 samples ...
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lats += [np.load(f) for f in sorted(Path(AB).glob("samel_lat_*.npy"))] # + 10×376 = ample for the Hessian
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for i, L in enumerate(lats):
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ae.decode(torch.tensor(L, device="cuda", dtype=torch.float32)); print(f" captured latent {i} ({L.shape[-1]})", flush=True)
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for h in handles: h.remove()
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def gptq(Wnp, Hnp, scale_np, pd=0.05, bs=128):
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dev = "cuda"
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W = torch.tensor(np.asarray(Wnp, np.float32), device=dev) # [out, in]
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H = torch.tensor(np.asarray(Hnp, np.float32), device=dev)
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sc = torch.tensor(scale_np.astype(np.float32), device=dev) # [out]
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cin = W.shape[1]; idx = torch.arange(cin, device=dev)
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H[~torch.isfinite(H)] = 0.0
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d = torch.diag(H).clone(); dead = d <= 0; d[dead] = 1.0; H[idx, idx] = d; W[:, dead] = 0.0
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H = (H + H.T) / 2; damp = pd * d.mean(); Hinv = None
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for k in range(6): # damped Cholesky of H⁻¹ (upper)
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try:
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L = torch.linalg.cholesky(H + torch.eye(cin, device=dev) * (damp * 4.0 ** k))
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Hinv = torch.linalg.cholesky(torch.cholesky_inverse(L), upper=True); break
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except Exception:
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continue
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if Hinv is None: Hinv = torch.diag(1.0 / torch.sqrt(d + damp))
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Q = torch.zeros_like(W)
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for i1 in range(0, cin, bs): # BLOCKED: small updates in-block, 1 matmul for the rest
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i2 = min(i1 + bs, cin); W1 = W[:, i1:i2].clone(); Q1 = torch.zeros_like(W1); Err = torch.zeros_like(W1)
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Hi = Hinv[i1:i2, i1:i2]
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for j in range(i2 - i1):
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w = W1[:, j]; dd = Hi[j, j].clamp_min(1e-12)
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qi = torch.clamp(torch.round(w / sc), -127, 127); err = (w - qi * sc) / dd
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Q1[:, j] = qi; Err[:, j] = err; W1[:, j:] -= err[:, None] * Hi[j, j:][None, :]
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Q[:, i1:i2] = Q1
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if i2 < cin: W[:, i2:] -= Err @ Hinv[i1:i2, i2:]
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return torch.clamp(torch.round(Q), -127, 127).to(torch.int8).cpu().numpy()
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# graft onto the onnx
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from huggingface_hub import hf_hub_download
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DEC = hf_hub_download("stabilityai/stable-audio-3-optimized", "onnx/same-l/dec_dynamic_triton_swa.onnx")
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m = onnx.load(DEC, load_external_data=True); g = m.graph
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inits = {i.name: i for i in g.initializer}; prod = {o: n for n in g.node for o in n.output}
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def warr(n):
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w = n.input[1]
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if w in inits: return w, numpy_helper.to_array(inits[w]), False, None
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p = prod.get(w)
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if p and p.op_type == "Transpose" and p.input and p.input[0] in inits: return p.input[0], numpy_helper.to_array(inits[p.input[0]]), True, p
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return None, None, None, None
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new_nodes, new_inits, done = [], [], 0
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for n in [x for x in g.node if x.op_type == "MatMul"]:
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key = okey(n.name)
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if key not in mods: continue # skip attn proj / non-FFN
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ws, W, vt, tn = warr(n)
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if ws is None: continue
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Wg = mods[key].weight.detach().double().cpu().numpy() # eager [out,in]
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scale = np.maximum(np.abs(Wg).max(axis=1) / 127.0, 1e-4).astype(np.float32)
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Qi = gptq(Wg, H[key], scale) # [out,in]
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qax = 0 if vt else 1
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inits[ws].CopyFrom(numpy_helper.from_array(Qi if vt else Qi.T, ws))
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pfx = n.name.strip("/").replace("/", "_")
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new_inits += [numpy_helper.from_array(scale.astype(np.float16), f"{pfx}_wsc"),
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helper.make_tensor(f"{pfx}_wzp", TensorProto.INT8, [scale.size], [0] * scale.size)]
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dq = f"{pfx}_wdq"
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new_nodes.append(helper.make_node("DequantizeLinear", [ws, f"{pfx}_wsc", f"{pfx}_wzp"], [dq], name=f"{pfx}_DQw", axis=qax))
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if vt:
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for i, inp in enumerate(tn.input):
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if inp == ws: tn.input[i] = dq
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else: n.input[1] = dq
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done += 1
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g.initializer.extend(new_inits); g.node.extend(new_nodes)
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avail = {i.name for i in g.initializer} | {i.name for i in g.input} | {""}
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order, rem = [], list(g.node)
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while rem:
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nx, prog = [], False
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for nd in rem:
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if all(i in avail for i in nd.input): order.append(nd); [avail.add(o) for o in nd.output]; prog = True
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else: nx.append(nd)
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rem = nx
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if not prog: raise RuntimeError("topo stuck")
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del g.node[:]; g.node.extend(order)
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OUT = "/weka2/cj/clod/sames_fp8/samel_w8_gptq.onnx"
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for f in (OUT, OUT + ".data"):
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if os.path.exists(f): os.remove(f)
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onnx.save(m, OUT, save_as_external_data=True, all_tensors_to_one_file=True, location=os.path.basename(OUT) + ".data", size_threshold=1024)
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print(f"GPTQ int8 grafted on {done} SAME-L FFN linears -> {OUT}", flush=True)
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