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
commit f0f0a9b50e
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+import numpy as np
+import pytest
+
+pytest.importorskip("mlx.core")
+
+import mlx.core as mx
+
+from optimized.mlx.models.defs.training import (
+    rectified_flow_loss,
+    sample_training_timesteps,
+    shift_training_timesteps,
+)
+
+
+def test_truncated_logit_normal_sampler_matches_sa3_distribution():
+    values = sample_training_timesteps(
+        "trunc_logit_normal",
+        20_000,
+        rng=np.random.default_rng(17),
+    )
+
+    assert values.dtype == np.float32
+    assert np.all((values >= 0.0) & (values <= 1.0))
+    assert 0.52 < float(values.mean()) < 0.55
+    assert 0.52 < float(np.median(values)) < 0.56
+
+
+def test_full_training_shift_matches_sa3_defaults():
+    shifted = shift_training_timesteps(
+        [0.5],
+        507,
+        shift_type="full",
+    )
+
+    assert shifted[0] == pytest.approx(0.6323907626)
+
+
+def test_rectified_flow_loss_uses_velocity_target_and_mask():
+    clean = mx.array([[[1.0, 2.0, 3.0]]], dtype=mx.float32)
+    noise = mx.array([[[4.0, 5.0, 6.0]]], dtype=mx.float32)
+    timesteps = mx.array([0.25], dtype=mx.float32)
+    mask = mx.array([[True, False, True]])
+
+    def perfect_model(noised, timestep):
+        del noised, timestep
+        return noise - clean
+
+    def zero_model(noised, timestep):
+        del timestep
+        return mx.zeros_like(noised)
+
+    assert float(
+        rectified_flow_loss(
+            perfect_model,
+            clean,
+            timesteps,
+            noise=noise,
+            loss_mask=mask,
+        )
+    ) == pytest.approx(0.0)
+    assert float(
+        rectified_flow_loss(
+            zero_model,
+            clean,
+            timesteps,
+            noise=noise,
+            loss_mask=mask,
+        )
+    ) == pytest.approx(9.0)
+
+
+def test_rectified_flow_loss_averages_masked_loss_per_sample():
+    clean = mx.zeros((2, 1, 3), dtype=mx.float32)
+    noise = mx.array([[[1.0, 1.0, 1.0]], [[3.0, 3.0, 3.0]]], dtype=mx.float32)
+    timesteps = mx.array([0.25, 0.25], dtype=mx.float32)
+    mask = mx.array([[True, False, False], [True, True, True]])
+
+    def zero_model(noised, timestep):
+        del noised, timestep
+        return mx.zeros_like(clean)
+
+    loss = rectified_flow_loss(
+        zero_model,
+        clean,
+        timesteps,
+        noise=noise,
+        loss_mask=mask,
+    )
+
+    assert float(loss) == pytest.approx(5.0)