diff --git a/models/tts/moss-tts-nano/coreml/.python-version b/models/tts/moss-tts-nano/coreml/.python-version new file mode 100644 index 0000000..e4fba21 --- /dev/null +++ b/models/tts/moss-tts-nano/coreml/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/models/tts/moss-tts-nano/coreml/README.md b/models/tts/moss-tts-nano/coreml/README.md new file mode 100644 index 0000000..97f2633 --- /dev/null +++ b/models/tts/moss-tts-nano/coreml/README.md @@ -0,0 +1,96 @@ +# MOSS-TTS-Nano → CoreML + +Conversion of [OpenMOSS-Team/MOSS-TTS-Nano-100M](https://huggingface.co/OpenMOSS-Team/MOSS-TTS-Nano-100M) +(0.1B multilingual streaming TTS with zero-shot voice cloning, 20 languages, native 48 kHz stereo) and +its codec [MOSS-Audio-Tokenizer-Nano](https://huggingface.co/OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano) +(22M, causal-transformer codec, 16×1024 RLFQ at 12.5 Hz) to CoreML. Apache-2.0 upstream. + +Published: [FluidInference/moss-tts-nano-coreml](https://huggingface.co/FluidInference/moss-tts-nano-coreml) +(mlpackage + mlmodelc, `config.json` with template ids, tokenizer, preset voices). Swift backend: +FluidAudio `Sources/FluidAudio/TTS/MossTtsNano` (`--backend moss-tts-nano`). + +## Pipeline + +``` +prompt wav ─CodecEncoder─► 16×T ref codes ─┐ +text ─SentencePiece─► ids ──────────────────┴─► rows [T,17] ─Prefill─► hidden + KV + │ + ┌────────────────────────────────────────────┘ + ▼ per 80 ms frame + Frame (1-layer local transformer + 16 heads, sampling in-graph) ─► 16 codes ─► CodecStep ─► 2×3840 samples + │ ▲ + Step (global GPT-2, KV update) ◄──── row [assistant_slot, 16 codes] ──┘ +``` + +Rows are `[text_token, code_0 … code_15]`; the audio pad id (1024) marks unused code slots. Prompt layout +(voice-clone mode) is upstream's `build_inference_input_ids`: `user … Reference: +{ref rows with user_slot=8} … Text: {text} assistant `. +Generation stops when the text head picks `audio_end` (7) instead of `assistant_slot` (9). + +## Models (`build/`) + +| mlpackage | I/O | Notes | +|---|---|---| +| `MossNano-Prefill-T512-M1024-fp16` | rows [1,512,17] int32 + len → hidden [1,768], kv_k/kv_v [12,1,12,1024,64] | right-padded prompt, causal + key mask | +| `MossNano-Step-M1024-fp16` | row [1,1,17] + kv in/out + cur_len → hidden | one-hot KV write at `cur_len` | +| `MossNano-Frame-fp16` | hidden + text_u [1] + audio_u [1,16] + temps + top_p + rep_penalty + seen [1,16,1024] + greedy → should_continue, frame [1,16] | 17 sequential local passes unrolled; inverse-CDF sampling from host uniforms (top-k 50 text / 25 audio fixed) | +| `MossNano-CodecStep-fp16` | codes [16,1,1] + frame_index + 24 KV caches → audio [1,2,3840] + caches | streaming; caches [1,4,{500,800,1200,1600},64] per layer | +| `MossNano-CodecDecoder-fp16` | codes [16,1,T≤125] (RangeDim) → audio [1,2,T·3840] | batch decode ≤10 s (full T×T attention) | +| `MossNano-CodecEncoder-fp32` | audio [1,2,S≤188·3840] (RangeDim, S % 3840 == 0) → codes [16,1,S/3840] | prompt encode; fp16 loses 34 % of codes | + +All targets macOS 14 / iOS 17 (no `StateType`). Weights: LM ~235 MB fp16 total, codec ~40 MB. + +## Commands + +`assets/en_2.wav` and `assets/zh_1.wav` are the upstream demo prompts (`MOSS-TTS-Nano/assets/audio/`, +gitignored here as `*.wav`); copy them in before running. + +```bash +uv sync +uv run python verify_pytorch.py # upstream reference (sampled) → build/ref_pytorch.wav + tokens +uv run python verify_pytorch.py --do-sample 0 --output build/ref_greedy.wav # deterministic parity target +uv run python convert_lm.py --fp16 +uv run python convert_codec.py --fp16 --skip-encoder && uv run python convert_codec.py --skip-decoder +uv run python convert_codec_step.py --fp16 +uv run python infer_coreml.py --replay 375 # teacher-forced greedy replay vs PyTorch +uv run python infer_coreml.py --codec-step build/codec/MossNano-CodecStep-fp16.mlpackage --text "…" +uv run python benchmark.py +``` + +## Parity (M5 Pro, macOS 26.7, coremltools 9.0, torch 2.7.0) + +- Wrappers vs upstream fp32: prefill/step hidden max|Δ| 7e-6 / 5e-6; frame greedy tokens 16/16; codec + decoder SNR 234 dB; encoder 1584/1584 codes; streaming step decoder vs full decode SNR 78.9 dB. +- CoreML fp16 vs wrappers: prefill hidden 4e-3, step hidden 1.1e-2; greedy replay of the 375-frame + reference **370/375 frames token-exact** (five near-tie flips); codec full decoder SNR 43.5 dB (T=57) + / 34.3 dB (T=125); streaming step SNR 56.6 dB on GPU, 40.6 dB CPU, 37.9 dB ANE; fp32 encoder exact. +- Intelligibility (Parakeet ASR via `fluidaudiocli tts-asr-verify --score-only`, two English phrases, + voice-clone prompt `assets/en_2.wav`): CoreML chain (LM fp16 + streaming codec) macro WER 8.3 % + (only "riverbank" → "river bank"), upstream PyTorch fp32 10.1 % (same split + one dropped word). +- Greedy decoding in upstream never emits the stop token (runs to max frames); sampling (defaults + text T=1.5, audio T=1.7 / top-p 0.8 / top-k 25) is the quality mode, greedy is only a parity oracle. + +## Latency (warm, ms per call) + +| model | ALL | ANE | GPU | CPU | +|---|---|---|---|---| +| Frame | 5.3 | 5.7 | 5.5 | 4.1 | +| Prefill T512 | 10.9 | 117 | 11.2 | 52.9 | +| Step M1024 | 7.6 | 15.7 | 7.8 | 58.8 | +| CodecStep | 5.2 | 7.8 | 5.3 | 5.5 | +| CodecDecoder (57 frames) | 6.8 | 241 | 7.6 | 89.8 | +| CodecEncoder fp32 (7.9 s prompt) | 22.5 | 589 | 32.5 | 247 | + +Per 80 ms frame the LM costs ≈ 13 ms (step + frame) and streaming codec ≈ 5 ms, i.e. ≈ 4.4× real time +with ≈ 30 ms compute to first audio after a ≈ 11 ms prefill. The Python driver measures ≈ 9 + 9 ms +(feed construction + 38 MB KV round trip per step); a stateful (`StateType`, iOS 18) step would remove +that copy. Prefill/Step/CodecDecoder fail ANE compilation (`ANECCompile FAILED`) and fall back — GPU is +the intended unit for those; Frame and CodecStep run on any unit. + +## Known gaps / follow-ups + +- Text normalisation: upstream runs WeTextProcessing + a robust normaliser before tokenising; not ported + (FluidAudio already ships English normalisers + NeMo ITN that can cover this). +- Prompt encode needs fp32 (residual LFQ amplifies fp16 error in deep codebooks). +- `nq < 16` (lower-bitrate) decoding not exposed; frame graph is fixed at 16 codebooks. +- No stateful KV variant yet; M=1024 caps prompt+generation at 1024 rows (≈ 66 s after a 200-row prompt). diff --git a/models/tts/moss-tts-nano/coreml/TRIALS.md b/models/tts/moss-tts-nano/coreml/TRIALS.md new file mode 100644 index 0000000..acb0022 --- /dev/null +++ b/models/tts/moss-tts-nano/coreml/TRIALS.md @@ -0,0 +1,23 @@ +# Trials + +## T1 — LM graphs (2026-09-13) +- Pass-through KV following the NeuTTS layout `[L,1,H,M,D]`; one-hot blend write at `cur_len`. +- Fused frame graph unrolls 17 local-transformer passes (no local KV — prefix ≤ 17 tokens, recompute is + cheaper than a cache round trip) and samples in-graph. `torch.clamp(max=)` lowers to `clip` with a + tensor `beta` that Core ML rejects → `torch.minimum`. +- Sampling: exact inverse-CDF in sorted space (top-k threshold → top-p keep on exclusive cumsum → + softmax → count(cdf < u)). Matches upstream in distribution, greedy path bit-matches (argmax). +- fp16 greedy replay: 370/375 exact over 30 s; mismatches are near-tie argmax flips (11/16, 10/16, …). + +## T2 — Codec (2026-09-13) +- HF remote code (3.3k lines) ≠ GitHub repo code: attention masks by `input_lengths`, so wrappers must + pass shape-derived lengths (`ones_like(x[:,0]).sum()`), not scalar 1 → first attempt gave −10 dB. +- Upstream `apply_rope` computes `2 / D` from a traced size → CoreML `inverse` on int32. Patched the + module-level function with a static-D version for export. +- Full decoder builds T×T masks per stage (last stage 32 tokens/frame): keep RangeDim ≤ 125 frames. +- Encoder: fp16 → 1044/1584 codes exact, degrading from 0.96 (codebook 0) to 0.5 (codebook 15); fp32 exact. +- Streaming step: shift-append caches sized to each stage's context (500/800/1200/1600), unrotated K + cached, RoPE applied with constant *relative* tables (q at cap−n+j, k at i) — identical dot products + to absolute positions, zero runtime trig, fp16-safe. Torch step vs full decode 78.9 dB; CoreML GPU 56.6 dB. +- Compute units: `ALL` splits the 26-input step graph across units and costs 69 ms/frame in the + per-frame loop vs 5–8 ms on a single unit — pin CodecStep to GPU (or ANE). diff --git a/models/tts/moss-tts-nano/coreml/benchmark.py b/models/tts/moss-tts-nano/coreml/benchmark.py new file mode 100644 index 0000000..013671a --- /dev/null +++ b/models/tts/moss-tts-nano/coreml/benchmark.py @@ -0,0 +1,89 @@ +"""Warm per-call latency of every MOSS-TTS-Nano CoreML model across compute units (M-series host).""" + +from __future__ import annotations + +import argparse +import time +from pathlib import Path + +import coremltools as ct +import numpy as np + +HERE = Path(__file__).resolve().parent +CU = {"all": ct.ComputeUnit.ALL, "ane": ct.ComputeUnit.CPU_AND_NE, "gpu": ct.ComputeUnit.CPU_AND_GPU, "cpu": ct.ComputeUnit.CPU_ONLY} + + +def feed_for(ml, seq_default: int | None = None) -> dict: + spec = ml.get_spec() + feed = {} + for i in spec.description.input: + t = i.type.multiArrayType + shape = [] + for k, d in enumerate(t.shape): + if t.shapeRange.sizeRanges and k < len(t.shapeRange.sizeRanges): + r = t.shapeRange.sizeRanges[k] + shape.append(seq_default if (seq_default and r.upperBound != r.lowerBound) else d) + else: + shape.append(d) + if t.dataType == t.INT32: + arr = np.zeros(shape, np.int32) + if i.name == "input_ids": + arr[..., 0] = 3 + arr[..., 1:] = 1024 + if i.name == "input_len": + arr[:] = 200 + if i.name == "cur_len": + arr[:] = 300 + else: + arr = np.zeros(shape, np.float32) + if i.name in ("text_temperature", "audio_temperature", "repetition_penalty"): + arr[:] = 1.0 + if i.name == "audio_top_p": + arr[:] = 0.8 + if i.name in ("text_u", "audio_u"): + arr[:] = 0.5 + if i.name == "audio": + arr = np.random.default_rng(0).standard_normal(shape).astype(np.float32) * 0.1 + feed[i.name] = arr + return feed + + +def bench(path: Path, units: list[str], iters: int, seq_default: int | None = None) -> None: + row = [path.stem] + for cu in units: + try: + ml = ct.models.MLModel(str(path), compute_units=CU[cu]) + feed = feed_for(ml, seq_default) + ml.predict(feed) + ml.predict(feed) + t0 = time.perf_counter() + for _ in range(iters): + ml.predict(feed) + row.append(f"{(time.perf_counter() - t0) * 1000 / iters:7.1f}") + except Exception as e: # noqa: BLE001 + row.append(f" err:{type(e).__name__[:8]}") + print(" | ".join(row)) + + +def main() -> None: + p = argparse.ArgumentParser() + p.add_argument("--lm-dir", default=str(HERE / "build" / "lm")) + p.add_argument("--codec-dir", default=str(HERE / "build" / "codec")) + p.add_argument("--units", default="all,ane,gpu,cpu") + p.add_argument("--iters", type=int, default=10) + args = p.parse_args() + units = args.units.split(",") + print("model | " + " | ".join(f"{u:>7}" for u in units) + " (ms per call, warm)") + for path in sorted(Path(args.lm_dir).glob("*.mlpackage")): + bench(path, units, args.iters) + for path in sorted(Path(args.codec_dir).glob("*.mlpackage")): + seq = None + if "CodecDecoder" in path.name: + seq = 57 # frames (4.6 s) + if "CodecEncoder" in path.name: + seq = 99 * 3840 # samples (7.9 s prompt) + bench(path, units, args.iters if seq is None else 3, seq) + + +if __name__ == "__main__": + main() diff --git a/models/tts/moss-tts-nano/coreml/convert_codec.py b/models/tts/moss-tts-nano/coreml/convert_codec.py new file mode 100644 index 0000000..66ea523 --- /dev/null +++ b/models/tts/moss-tts-nano/coreml/convert_codec.py @@ -0,0 +1,148 @@ +"""Convert MOSS-Audio-Tokenizer-Nano to CoreML. + + MossNano-CodecDecoder-{tag}.mlpackage codes [16,1,T≤max] → audio [1,2,T*3840] + MossNano-CodecEncoder-{tag}.mlpackage audio [1,2,S≤max*3840] → codes [16,1,S/3840] + +Parity: decoder vs upstream decode() on real generated tokens (SNR), encoder vs upstream +encode() on the bundled reference clip (exact code match rate). +""" + +from __future__ import annotations + +import argparse +import sys +import time +from pathlib import Path + +import coremltools as ct +import numpy as np +import soundfile as sf +import torch + +HERE = Path(__file__).resolve().parent +sys.path.insert(0, str(HERE)) + +from src.codec_coreml import MossCodecDecoder, MossCodecEncoder # noqa: E402 + +CODEC_REPO = "OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano" +FRAME = 3840 + + +def snr_db(got: np.ndarray, want: np.ndarray) -> float: + noise = np.sum((got - want) ** 2) + return float(10 * np.log10(np.sum(want**2) / max(noise, 1e-20))) + + +def precision(fp16: bool): + if not fp16: + return ct.precision.FLOAT32 + return ct.transform.FP16ComputePrecision(op_selector=lambda op: op.op_type not in {"softmax"}) + + +def load_prompt(path: Path) -> torch.Tensor: + wav, sr = sf.read(path, dtype="float32", always_2d=True) # [S, C] + wav = torch.from_numpy(wav.T) + if sr != 48000: + import torchaudio + + wav = torchaudio.functional.resample(wav, sr, 48000) + if wav.shape[0] == 1: + wav = wav.repeat(2, 1) + S = wav.shape[1] + pad = (-S) % FRAME + return torch.nn.functional.pad(wav, (0, pad))[None] # [1,2,S'] + + +def main() -> None: + p = argparse.ArgumentParser() + p.add_argument("--output-dir", default=str(HERE / "build" / "codec")) + p.add_argument("--fp16", action="store_true") + p.add_argument("--max-frames", type=int, default=125, help="decoder RangeDim upper bound in frames (10 s)") + p.add_argument("--max-prompt-frames", type=int, default=188, help="encoder RangeDim upper bound in frames (15 s)") + p.add_argument("--skip-decoder", action="store_true") + p.add_argument("--skip-encoder", action="store_true") + p.add_argument("--tokens", default=str(HERE / "build" / "ref_audio_token_ids.npy")) + p.add_argument("--long-tokens", default=str(HERE / "build" / "ref_greedy_audio_token_ids.npy")) + p.add_argument("--prompt-audio", default=str(HERE / "assets" / "en_2.wav")) + args = p.parse_args() + + out_dir = Path(args.output_dir) + out_dir.mkdir(parents=True, exist_ok=True) + tag = "fp16" if args.fp16 else "fp32" + target = ct.target.macOS14 + + from transformers import AutoModel + + print(f"[0] loading {CODEC_REPO}") + codec = AutoModel.from_pretrained(CODEC_REPO, trust_remote_code=True).eval() + codes = torch.from_numpy(np.load(args.tokens)).long().T[:, None, :] # [16,1,T] + long_codes = torch.from_numpy(np.load(args.long_tokens)).long().T[:, None, : args.max_frames] + with torch.no_grad(): + want = codec.decode(codes, return_dict=True).audio # [1,2,S] + want_long = codec.decode(long_codes, return_dict=True).audio + + if not args.skip_decoder: + print("[1] decoder") + dec = MossCodecDecoder(codec).eval() + with torch.no_grad(): + got = dec(codes.to(torch.int32)) + print(f" wrapper vs upstream: shape {tuple(got.shape)} SNR={snr_db(got.numpy(), want.numpy()):.1f} dB") + t0 = time.perf_counter() + with torch.no_grad(): + traced = torch.jit.trace(dec, (codes.to(torch.int32),), strict=False) + T = ct.RangeDim(lower_bound=1, upper_bound=args.max_frames, default=codes.shape[-1]) + ml = ct.convert( + traced, + inputs=[ct.TensorType(name="codes", shape=(16, 1, T), dtype=np.int32)], + outputs=[ct.TensorType(name="audio", dtype=np.float32)], + compute_precision=precision(args.fp16), + minimum_deployment_target=target, + convert_to="mlprogram", + ) + path = out_dir / f"MossNano-CodecDecoder-{tag}.mlpackage" + ml.save(str(path)) + print(f" saved {path.name} ({time.perf_counter() - t0:.0f}s)") + for name, c, w in (("T=57", codes, want), (f"T={long_codes.shape[-1]}", long_codes, want_long)): + t1 = time.perf_counter() + pred = ml.predict({"codes": c.numpy().astype(np.int32)})["audio"] + dt = time.perf_counter() - t1 + print(f" coreml {name}: shape {pred.shape} SNR={snr_db(pred, w.numpy()):.1f} dB {dt*1000:.0f} ms") + del ml, dec + + if not args.skip_encoder: + print("[2] encoder") + audio = load_prompt(Path(args.prompt_audio)) # [1,2,S] + with torch.no_grad(): + ref = codec.encode(audio, return_dict=True).audio_codes # [16,1,T] + enc = MossCodecEncoder(codec).eval() + with torch.no_grad(): + got = enc(audio) + n = ref.numel() + print(f" wrapper vs upstream: {tuple(got.shape)} vs {tuple(ref.shape)} " + f"exact={(got.long() == ref).sum().item()}/{n}") + t0 = time.perf_counter() + with torch.no_grad(): + traced = torch.jit.trace(enc, (audio,), strict=False) + S = ct.RangeDim(lower_bound=FRAME, upper_bound=args.max_prompt_frames * FRAME, default=audio.shape[-1]) + ml = ct.convert( + traced, + inputs=[ct.TensorType(name="audio", shape=(1, 2, S), dtype=np.float32)], + outputs=[ct.TensorType(name="codes", dtype=np.int32)], + compute_precision=precision(args.fp16), + minimum_deployment_target=target, + convert_to="mlprogram", + ) + path = out_dir / f"MossNano-CodecEncoder-{tag}.mlpackage" + ml.save(str(path)) + print(f" saved {path.name} ({time.perf_counter() - t0:.0f}s)") + t1 = time.perf_counter() + pred = torch.from_numpy(ml.predict({"audio": audio.numpy()})["codes"]).long() + dt = time.perf_counter() - t1 + per_cb = [(pred[i] == ref[i]).float().mean().item() for i in range(16)] + print(f" coreml: exact={(pred == ref).sum().item()}/{n} {dt*1000:.0f} ms " + f"per-codebook match={[round(v, 3) for v in per_cb]}") + print("[done]") + + +if __name__ == "__main__": + main() diff --git a/models/tts/moss-tts-nano/coreml/convert_codec_step.py b/models/tts/moss-tts-nano/coreml/convert_codec_step.py new file mode 100644 index 0000000..e27cbd7 --- /dev/null +++ b/models/tts/moss-tts-nano/coreml/convert_codec_step.py @@ -0,0 +1,118 @@ +"""Convert the per-frame streaming codec decoder to CoreML and verify against full decode. + + MossNano-CodecStep-{tag}.mlpackage codes [16,1,1] + frame_index + 24 KV caches → audio [1,2,3840] + caches +""" + +from __future__ import annotations + +import argparse +import sys +import time +from pathlib import Path + +import coremltools as ct +import numpy as np +import torch + +HERE = Path(__file__).resolve().parent +sys.path.insert(0, str(HERE)) + +from src.codec_coreml import MossCodecStepDecoder # noqa: E402 + +CODEC_REPO = "OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano" +CU = {"all": ct.ComputeUnit.ALL, "cpu": ct.ComputeUnit.CPU_ONLY, "gpu": ct.ComputeUnit.CPU_AND_GPU, "ane": ct.ComputeUnit.CPU_AND_NE} + + +def snr_db(got: np.ndarray, want: np.ndarray) -> float: + return float(10 * np.log10(np.sum(want**2) / max(np.sum((got - want) ** 2), 1e-20))) + + +def run_torch(step: MossCodecStepDecoder, codes: torch.Tensor) -> torch.Tensor: + caches = [torch.zeros(shape) for _, shape in step.cache_shapes()] + chunks = [] + with torch.no_grad(): + for t in range(codes.shape[-1]): + out = step(codes[:, :, t : t + 1].to(torch.int32), torch.tensor([t], dtype=torch.int32), *caches) + chunks.append(out[0]) + caches = list(out[1:]) + return torch.cat(chunks, dim=-1) + + +def run_coreml(ml, step: MossCodecStepDecoder, codes: np.ndarray) -> tuple[np.ndarray, float]: + names = [n for n, _ in step.cache_shapes()] + caches = {n: np.zeros(shape, np.float32) for n, shape in step.cache_shapes()} + chunks = [] + t_total = 0.0 + for t in range(codes.shape[-1]): + feed = {"codes": codes[:, :, t : t + 1].astype(np.int32), "frame_index": np.array([t], np.int32), **caches} + t0 = time.perf_counter() + out = ml.predict(feed) + t_total += time.perf_counter() - t0 + chunks.append(out["audio"]) + caches = {n: out[f"{n}_out"] for n in names} + return np.concatenate(chunks, axis=-1), t_total / codes.shape[-1] + + +def main() -> None: + p = argparse.ArgumentParser() + p.add_argument("--output-dir", default=str(HERE / "build" / "codec")) + p.add_argument("--fp16", action="store_true") + p.add_argument("--tokens", default=str(HERE / "build" / "ref_audio_token_ids.npy")) + p.add_argument("--skip-convert", action="store_true") + p.add_argument("--compute-units", default="all,ane,gpu,cpu") + args = p.parse_args() + out_dir = Path(args.output_dir) + out_dir.mkdir(parents=True, exist_ok=True) + tag = "fp16" if args.fp16 else "fp32" + + from transformers import AutoModel + + print(f"[0] loading {CODEC_REPO}") + codec = AutoModel.from_pretrained(CODEC_REPO, trust_remote_code=True).eval() + codes = torch.from_numpy(np.load(args.tokens)).long().T[:, None, :] + with torch.no_grad(): + want = codec.decode(codes, return_dict=True).audio.numpy() + + step = MossCodecStepDecoder(codec).eval() + print(" stages:", [(s["n"], s["context"], s["layers"]) for s in step.stage_specs]) + print(f" cache tensors: {len(step.cache_shapes())} " + f"elements: {sum(int(np.prod(sh)) for _, sh in step.cache_shapes())/1e6:.2f}M") + t0 = time.perf_counter() + got = run_torch(step, codes).numpy() + print(f"[1] torch step decoder vs upstream full decode: SNR={snr_db(got, want):.1f} dB " + f"({(time.perf_counter()-t0)*1000/codes.shape[-1]:.1f} ms/frame fp32 cpu)") + if args.skip_convert: + return + + example = [codes[:, :, :1].to(torch.int32), torch.tensor([0], dtype=torch.int32)] + example += [torch.zeros(shape) for _, shape in step.cache_shapes()] + with torch.no_grad(): + traced = torch.jit.trace(step, tuple(example), strict=False) + inputs = [ + ct.TensorType(name="codes", shape=(16, 1, 1), dtype=np.int32), + ct.TensorType(name="frame_index", shape=(1,), dtype=np.int32), + ] + [ct.TensorType(name=n, shape=shape, dtype=np.float32) for n, shape in step.cache_shapes()] + outputs = [ct.TensorType(name="audio", dtype=np.float32)] + [ + ct.TensorType(name=f"{n}_out", dtype=np.float32) for n, _ in step.cache_shapes() + ] + prec = ct.precision.FLOAT32 if not args.fp16 else ct.transform.FP16ComputePrecision( + op_selector=lambda op: op.op_type not in {"softmax"} + ) + t0 = time.perf_counter() + ml = ct.convert( + traced, inputs=inputs, outputs=outputs, compute_precision=prec, + minimum_deployment_target=ct.target.macOS14, convert_to="mlprogram", + ) + path = out_dir / f"MossNano-CodecStep-{tag}.mlpackage" + ml.save(str(path)) + print(f"[2] saved {path.name} ({time.perf_counter()-t0:.0f}s)") + for cu in args.compute_units.split(","): + m = ct.models.MLModel(str(path), compute_units=CU[cu]) + pred, per_frame = run_coreml(m, step, codes.numpy()) + print(f" coreml[{cu}]: SNR={snr_db(pred, want):.1f} dB {per_frame*1000:.2f} ms/frame " + f"(frame budget 80 ms → {80/(per_frame*1000):.0f}x RT)") + print("[done]") + + +if __name__ == "__main__": + main() diff --git a/models/tts/moss-tts-nano/coreml/convert_lm.py b/models/tts/moss-tts-nano/coreml/convert_lm.py new file mode 100644 index 0000000..634ad1f --- /dev/null +++ b/models/tts/moss-tts-nano/coreml/convert_lm.py @@ -0,0 +1,250 @@ +"""Convert the MOSS-TTS-Nano language model to CoreML. + +Emits into --output-dir: + MossNano-Prefill-T{t}-M{m}-{tag}.mlpackage rows [1,T,17] + len → hidden [1,768], kv_k/kv_v [12,1,12,M,64] + MossNano-Step-M{m}-{tag}.mlpackage row [1,1,17] + kv + cur_len → hidden, kv out + MossNano-Frame-{tag}.mlpackage hidden + sampling params → should_continue, frame [1,16] + +Every wrapper is checked against the upstream PyTorch model before tracing, and every +CoreML model is checked against its wrapper after conversion (greedy tokens must match). +""" + +from __future__ import annotations + +import argparse +import sys +import time +from pathlib import Path + +import coremltools as ct +import numpy as np +import torch + +HERE = Path(__file__).resolve().parent +sys.path.insert(0, str(HERE)) + +from src.lm_coreml import MossFrame, MossPrefill, MossStep # noqa: E402 + +TTS_REPO = "OpenMOSS-Team/MOSS-TTS-Nano-100M" +TEXT = "The quick brown fox jumps over the lazy dog near the riverbank." + + +def load_model(): + from transformers import AutoModelForCausalLM + + model = AutoModelForCausalLM.from_pretrained(TTS_REPO, trust_remote_code=True, dtype=torch.float32) + for module in model.modules(): + if hasattr(module, "attn_implementation"): + module.attn_implementation = "eager" + model.eval() + tokenizer = model._load_text_tokenizer(text_tokenizer=None, text_tokenizer_path=None) + return model, tokenizer + + +def precision(fp16: bool): + if not fp16: + return ct.precision.FLOAT32 + fp32_ops = {"softmax", "cumsum"} + return ct.transform.FP16ComputePrecision(op_selector=lambda op: op.op_type not in fp32_ops) + + +def report(name: str, got: torch.Tensor, want: torch.Tensor, tol: float) -> None: + diff = (got.float() - want.float()).abs().max().item() + ok = "ok" if diff <= tol else "FAIL" + print(f" {name}: max|Δ|={diff:.3e} (tol {tol:g}) {ok}") + if diff > tol: + raise SystemExit(f"parity failure: {name}") + + +def frame_inputs(hidden: torch.Tensor, greedy: bool, seen=None): + return { + "global_hidden": hidden.float(), + "text_u": torch.tensor([0.37]), + "audio_u": torch.linspace(0.05, 0.95, 16)[None, :], + "text_temperature": torch.tensor([1.5]), + "audio_temperature": torch.tensor([1.7]), + "audio_top_p": torch.tensor([0.8]), + "repetition_penalty": torch.tensor([1.0]), + "seen": torch.zeros(1, 16, 1024) if seen is None else seen, + "greedy": torch.tensor([1.0 if greedy else 0.0]), + } + + +def main() -> None: + p = argparse.ArgumentParser() + p.add_argument("--output-dir", default=str(HERE / "build" / "lm")) + p.add_argument("--t-prefill", type=int, default=512) + p.add_argument("--max-len", type=int, default=1024) + p.add_argument("--fp16", action="store_true") + p.add_argument("--skip-prefill", action="store_true") + p.add_argument("--skip-step", action="store_true") + p.add_argument("--skip-frame", action="store_true") + p.add_argument("--ref-tokens", default=str(HERE / "build" / "ref_greedy_audio_token_ids.npy")) + p.add_argument("--ref-prompt-tokens", default=str(HERE / "build" / "ref_greedy_prompt_audio_token_ids.npy")) + args = p.parse_args() + + out_dir = Path(args.output_dir) + out_dir.mkdir(parents=True, exist_ok=True) + tag = "fp16" if args.fp16 else "fp32" + T_pre, M = args.t_prefill, args.max_len + target = ct.target.macOS14 + + print(f"[0] loading {TTS_REPO}") + model, tokenizer = load_model() + cfg = model.config + prompt_codes = torch.from_numpy(np.load(args.ref_prompt_tokens)).long() # [Tp,16] + ref_frames = torch.from_numpy(np.load(args.ref_tokens)).long() # [Tg,16] + input_ids, attn = model.build_inference_input_ids( + text=TEXT, text_tokenizer=tokenizer, mode="voice_clone", prompt_audio_codes=prompt_codes, device="cpu" + ) + n_prompt = input_ids.shape[1] + print(f" prompt rows={n_prompt} ref frames={ref_frames.shape[0]} T_prefill={T_pre} M={M}") + if n_prompt > T_pre: + raise SystemExit("prompt longer than --t-prefill") + + # Upstream ground truth: hidden after prompt, and after prompt + first generated row. + row0 = model._build_generation_row(1, torch.device("cpu"), ref_frames[0][None]) # [1,1,17] + with torch.no_grad(): + hf_h0 = model(input_ids=input_ids, attention_mask=attn, use_cache=False).global_hidden_states[:, -1] + seq1 = torch.cat((input_ids, row0), dim=1) + hf_h1 = model(input_ids=seq1, use_cache=False).global_hidden_states[:, -1] + + padded = torch.full((1, T_pre, cfg.n_vq + 1), cfg.audio_pad_token_id, dtype=torch.int32) + padded[:, :, 0] = cfg.pad_token_id + padded[:, :n_prompt] = input_ids.to(torch.int32) + len_t = torch.tensor([n_prompt], dtype=torch.int32) + + # ---------------- prefill ---------------- + print("[1] prefill wrapper") + prefill = MossPrefill(model, max_len=M).eval() + with torch.no_grad(): + h0, kv_k, kv_v = prefill(padded, len_t) + report("wrapper hidden vs upstream", h0, hf_h0, 1e-3) + if not args.skip_prefill: + t0 = time.perf_counter() + with torch.no_grad(): + traced = torch.jit.trace(prefill, (padded, len_t), strict=False) + ml = ct.convert( + traced, + inputs=[ + ct.TensorType(name="input_ids", shape=(1, T_pre, cfg.n_vq + 1), dtype=np.int32), + ct.TensorType(name="input_len", shape=(1,), dtype=np.int32), + ], + outputs=[ + ct.TensorType(name="hidden", dtype=np.float32), + ct.TensorType(name="kv_k", dtype=np.float32), + ct.TensorType(name="kv_v", dtype=np.float32), + ], + compute_precision=precision(args.fp16), + minimum_deployment_target=target, + convert_to="mlprogram", + ) + path = out_dir / f"MossNano-Prefill-T{T_pre}-M{M}-{tag}.mlpackage" + ml.save(str(path)) + print(f" saved {path.name} ({time.perf_counter() - t0:.0f}s)") + pred = ml.predict({"input_ids": padded.numpy(), "input_len": len_t.numpy()}) + report("coreml prefill hidden", torch.from_numpy(pred["hidden"]), h0, 0.15 if args.fp16 else 1e-2) + report("coreml prefill kv_k", torch.from_numpy(pred["kv_k"]), kv_k, 0.25 if args.fp16 else 1e-2) + del ml + del prefill + + # ---------------- step ---------------- + print("[2] step wrapper") + step = MossStep(model, max_len=M).eval() + cur_len = torch.tensor([n_prompt], dtype=torch.int32) + with torch.no_grad(): + h1, kv_k1, kv_v1 = step(row0.to(torch.int32), kv_k, kv_v, cur_len) + report("wrapper hidden vs upstream", h1, hf_h1, 1e-3) + if not args.skip_step: + t0 = time.perf_counter() + with torch.no_grad(): + traced = torch.jit.trace(step, (row0.to(torch.int32), kv_k, kv_v, cur_len), strict=False) + L, _, H, _, Dh = kv_k.shape + ml = ct.convert( + traced, + inputs=[ + ct.TensorType(name="input_ids", shape=(1, 1, cfg.n_vq + 1), dtype=np.int32), + ct.TensorType(name="kv_k", shape=(L, 1, H, M, Dh), dtype=np.float32), + ct.TensorType(name="kv_v", shape=(L, 1, H, M, Dh), dtype=np.float32), + ct.TensorType(name="cur_len", shape=(1,), dtype=np.int32), + ], + outputs=[ + ct.TensorType(name="hidden", dtype=np.float32), + ct.TensorType(name="kv_k_out", dtype=np.float32), + ct.TensorType(name="kv_v_out", dtype=np.float32), + ], + compute_precision=precision(args.fp16), + minimum_deployment_target=target, + convert_to="mlprogram", + ) + path = out_dir / f"MossNano-Step-M{M}-{tag}.mlpackage" + ml.save(str(path)) + print(f" saved {path.name} ({time.perf_counter() - t0:.0f}s)") + pred = ml.predict( + { + "input_ids": row0.numpy().astype(np.int32), + "kv_k": kv_k.numpy(), + "kv_v": kv_v.numpy(), + "cur_len": cur_len.numpy(), + } + ) + report("coreml step hidden", torch.from_numpy(pred["hidden"]), h1, 0.15 if args.fp16 else 1e-2) + del ml + del step + + # ---------------- frame ---------------- + print("[3] frame wrapper") + frame = MossFrame(model).eval() + with torch.no_grad(): + cont, toks = frame(**frame_inputs(hf_h0, greedy=True)) + match = int((toks[0].long() == ref_frames[0]).sum()) + print(f" greedy frame0 vs upstream: continue={int(cont)} {match}/16 tokens match") + if match != 16 or int(cont) != 1: + raise SystemExit("frame wrapper greedy mismatch") + with torch.no_grad(): + cont_s, toks_s = frame(**frame_inputs(hf_h0, greedy=False)) + print(f" sampled frame0: continue={int(cont_s)} tokens={toks_s[0].tolist()}") + if not args.skip_frame: + t0 = time.perf_counter() + ex = frame_inputs(hf_h0, greedy=True) + with torch.no_grad(): + traced = torch.jit.trace(frame, tuple(ex.values()), strict=False) + ml = ct.convert( + traced, + inputs=[ + ct.TensorType(name="global_hidden", shape=(1, cfg.hidden_size), dtype=np.float32), + ct.TensorType(name="text_u", shape=(1,), dtype=np.float32), + ct.TensorType(name="audio_u", shape=(1, cfg.n_vq), dtype=np.float32), + ct.TensorType(name="text_temperature", shape=(1,), dtype=np.float32), + ct.TensorType(name="audio_temperature", shape=(1,), dtype=np.float32), + ct.TensorType(name="audio_top_p", shape=(1,), dtype=np.float32), + ct.TensorType(name="repetition_penalty", shape=(1,), dtype=np.float32), + ct.TensorType(name="seen", shape=(1, cfg.n_vq, cfg.audio_vocab_size), dtype=np.float32), + ct.TensorType(name="greedy", shape=(1,), dtype=np.float32), + ], + outputs=[ + ct.TensorType(name="should_continue", dtype=np.int32), + ct.TensorType(name="frame", dtype=np.int32), + ], + compute_precision=precision(args.fp16), + minimum_deployment_target=target, + convert_to="mlprogram", + ) + path = out_dir / f"MossNano-Frame-{tag}.mlpackage" + ml.save(str(path)) + print(f" saved {path.name} ({time.perf_counter() - t0:.0f}s)") + feed = {k: v.numpy().astype(np.float32) for k, v in ex.items()} + pred = ml.predict(feed) + got = torch.from_numpy(pred["frame"]).long()[0] + m = int((got == ref_frames[0]).sum()) + print(f" coreml greedy frame0: continue={int(pred['should_continue'].reshape(-1)[0])} {m}/16 match") + if m != 16: + raise SystemExit("coreml frame greedy mismatch") + feed["greedy"] = np.zeros(1, np.float32) + pred = ml.predict(feed) + print(f" coreml sampled frame0: {pred['frame'].reshape(-1).tolist()} (wrapper: {toks_s[0].tolist()})") + print("[done]") + + +if __name__ == "__main__": + main() diff --git a/models/tts/moss-tts-nano/coreml/infer_coreml.py b/models/tts/moss-tts-nano/coreml/infer_coreml.py new file mode 100644 index 0000000..ba3573c --- /dev/null +++ b/models/tts/moss-tts-nano/coreml/infer_coreml.py @@ -0,0 +1,228 @@ +"""End-to-end MOSS-TTS-Nano synthesis with the CoreML LM (prefill + step + frame). + +Modes: + --replay N greedy, teacher-forced replay of the PyTorch greedy reference: reports how many + of the first N frames the CoreML chain reproduces token-for-token. + default free-running sampled generation; decodes with the CoreML codec if --codec is + given, else the PyTorch codec, and writes a 48 kHz stereo WAV. +""" + +from __future__ import annotations + +import argparse +import sys +import time +from pathlib import Path + +import coremltools as ct +import numpy as np +import torch + +HERE = Path(__file__).resolve().parent +sys.path.insert(0, str(HERE)) + +TTS_REPO = "OpenMOSS-Team/MOSS-TTS-Nano-100M" +CODEC_REPO = "OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano" +TEXT = "The quick brown fox jumps over the lazy dog near the riverbank." + +CU = { + "all": ct.ComputeUnit.ALL, + "cpu": ct.ComputeUnit.CPU_ONLY, + "gpu": ct.ComputeUnit.CPU_AND_GPU, + "ane": ct.ComputeUnit.CPU_AND_NE, +} + + +class CoreMLMossLM: + def __init__(self, lm_dir: Path, tag: str, t_prefill: int, max_len: int, cu: dict[str, str]) -> None: + def load(name: str, unit: str): + path = lm_dir / name + t0 = time.perf_counter() + m = ct.models.MLModel(str(path), compute_units=CU[unit]) + print(f" loaded {path.name} on {unit} ({time.perf_counter() - t0:.1f}s)") + return m + + self.prefill = load(f"MossNano-Prefill-T{t_prefill}-M{max_len}-{tag}.mlpackage", cu["prefill"]) + self.step = load(f"MossNano-Step-M{max_len}-{tag}.mlpackage", cu["step"]) + self.frame = load(f"MossNano-Frame-{tag}.mlpackage", cu["frame"]) + self.t_prefill = t_prefill + self.max_len = max_len + + def run_prefill(self, input_ids: np.ndarray, pad_text: int, pad_audio: int): + n = input_ids.shape[1] + padded = np.full((1, self.t_prefill, input_ids.shape[2]), pad_audio, np.int32) + padded[:, :, 0] = pad_text + padded[:, :n] = input_ids + out = self.prefill.predict({"input_ids": padded, "input_len": np.array([n], np.int32)}) + return out["hidden"], out["kv_k"], out["kv_v"] + + def run_step(self, row: np.ndarray, kv_k, kv_v, cur_len: int): + out = self.step.predict( + {"input_ids": row.astype(np.int32), "kv_k": kv_k, "kv_v": kv_v, "cur_len": np.array([cur_len], np.int32)} + ) + return out["hidden"], out["kv_k_out"], out["kv_v_out"] + + def run_frame(self, hidden, rng, seen, greedy: bool, sampling: dict): + out = self.frame.predict( + { + "global_hidden": hidden.astype(np.float32), + "text_u": rng.random(1).astype(np.float32), + "audio_u": rng.random((1, 16)).astype(np.float32), + "text_temperature": np.array([sampling["text_temperature"]], np.float32), + "audio_temperature": np.array([sampling["audio_temperature"]], np.float32), + "audio_top_p": np.array([sampling["audio_top_p"]], np.float32), + "repetition_penalty": np.array([sampling["repetition_penalty"]], np.float32), + "seen": seen, + "greedy": np.array([1.0 if greedy else 0.0], np.float32), + } + ) + return int(out["should_continue"].reshape(-1)[0]), out["frame"].reshape(-1).astype(np.int64) + + +def main() -> None: + p = argparse.ArgumentParser() + p.add_argument("--lm-dir", default=str(HERE / "build" / "lm")) + p.add_argument("--tag", default="fp16") + p.add_argument("--t-prefill", type=int, default=512) + p.add_argument("--max-len", type=int, default=1024) + p.add_argument("--codec", default=None, help="CoreML full codec decoder mlpackage (default: PyTorch codec)") + p.add_argument("--codec-step", default=None, help="CoreML streaming codec step mlpackage (decode per frame)") + p.add_argument("--cu-codec", default="gpu") + p.add_argument("--text", default=TEXT) + p.add_argument("--prompt-audio", default=str(HERE / "assets" / "en_2.wav")) + p.add_argument("--prompt-tokens", default=str(HERE / "build" / "ref_greedy_prompt_audio_token_ids.npy")) + p.add_argument("--replay", type=int, default=0, help="greedy replay of the first N reference frames") + p.add_argument("--ref-tokens", default=str(HERE / "build" / "ref_greedy_audio_token_ids.npy")) + p.add_argument("--max-new-frames", type=int, default=375) + p.add_argument("--seed", type=int, default=0) + p.add_argument("--output", default=str(HERE / "build" / "coreml_out.wav")) + p.add_argument("--cu-prefill", default="all") + p.add_argument("--cu-step", default="all") + p.add_argument("--cu-frame", default="all") + p.add_argument("--text-temperature", type=float, default=1.5) + p.add_argument("--audio-temperature", type=float, default=1.7) + p.add_argument("--audio-top-p", type=float, default=0.8) + p.add_argument("--repetition-penalty", type=float, default=1.0) + args = p.parse_args() + + from transformers import AutoModelForCausalLM + + print("[0] loading upstream model for prompt construction") + model = AutoModelForCausalLM.from_pretrained(TTS_REPO, trust_remote_code=True, dtype=torch.float32).eval() + cfg = model.config + tokenizer = model._load_text_tokenizer(text_tokenizer=None, text_tokenizer_path=None) + prompt_codes = torch.from_numpy(np.load(args.prompt_tokens)).long() + input_ids, _ = model.build_inference_input_ids( + text=args.text, text_tokenizer=tokenizer, mode="voice_clone", prompt_audio_codes=prompt_codes, device="cpu" + ) + ids = input_ids.numpy().astype(np.int32) + n_prompt = ids.shape[1] + print(f" prompt rows={n_prompt}") + + print("[1] loading CoreML LM") + lm = CoreMLMossLM( + Path(args.lm_dir), args.tag, args.t_prefill, args.max_len, + {"prefill": args.cu_prefill, "step": args.cu_step, "frame": args.cu_frame}, + ) + sampling = { + "text_temperature": args.text_temperature, + "audio_temperature": args.audio_temperature, + "audio_top_p": args.audio_top_p, + "repetition_penalty": args.repetition_penalty, + } + rng = np.random.default_rng(args.seed) + seen = np.zeros((1, cfg.n_vq, cfg.audio_vocab_size), np.float32) + + t0 = time.perf_counter() + hidden, kv_k, kv_v = lm.run_prefill(ids, cfg.pad_token_id, cfg.audio_pad_token_id) + hidden, kv_k, kv_v = lm.run_prefill(ids, cfg.pad_token_id, cfg.audio_pad_token_id) # warm + t_prefill = time.perf_counter() - t0 + print(f" prefill: {t_prefill*1000/2:.0f} ms (avg of 2)") + + greedy = args.replay > 0 + ref = np.load(args.ref_tokens) if greedy else None + frames: list[np.ndarray] = [] + cur_len = n_prompt + t_step = t_frame = 0.0 + mismatched = 0 + limit = args.replay if greedy else args.max_new_frames + t_gen = time.perf_counter() + for i in range(limit): + t1 = time.perf_counter() + cont, frame = lm.run_frame(hidden, rng, seen, greedy, sampling) + t_frame += time.perf_counter() - t1 + if not cont: + print(f" stop token at frame {i}") + break + if greedy: + ok = np.array_equal(frame, ref[i]) + mismatched += 0 if ok else 1 + if not ok: + print(f" frame {i}: {int((frame == ref[i]).sum())}/16 match got={frame.tolist()}") + frame = ref[i] # teacher force + frames.append(frame) + seen[0, np.arange(cfg.n_vq), frame] = 1.0 + row = np.full((1, 1, cfg.n_vq + 1), cfg.audio_pad_token_id, np.int32) + row[0, 0, 0] = cfg.audio_assistant_slot_token_id + row[0, 0, 1:] = frame + t1 = time.perf_counter() + hidden, kv_k, kv_v = lm.run_step(row, kv_k, kv_v, cur_len) + t_step += time.perf_counter() - t1 + cur_len += 1 + n = len(frames) + t_total = time.perf_counter() - t_gen + print(f" frames={n} step {t_step*1000/max(n,1):.1f} ms frame {t_frame*1000/max(n,1):.1f} ms " + f"total {t_total:.2f}s ({n/12.5:.2f}s audio, LM RTFx={n/12.5/t_total:.2f})") + if greedy: + print(f" replay: {n - mismatched}/{n} frames match the PyTorch greedy reference exactly") + return + + tokens = np.stack(frames, 0) # [T,16] + codes = torch.from_numpy(tokens.T[:, None, :]).long() # [16,1,T] + t2 = time.perf_counter() + if args.codec_step: + from src.codec_coreml import MossCodecStepDecoder # geometry only; weights unused + + step_ml = ct.models.MLModel(args.codec_step, compute_units=CU[args.cu_codec]) + spec = step_ml.get_spec() + cache_names = [i.name for i in spec.description.input if i.name not in ("codes", "frame_index")] + caches = { + i.name: np.zeros([d for d in i.type.multiArrayType.shape], np.float32) + for i in spec.description.input + if i.name in cache_names + } + chunks = [] + t_first = None + for t in range(codes.shape[-1]): + feed = {"codes": codes[:, :, t : t + 1].numpy().astype(np.int32), + "frame_index": np.array([t], np.int32), **caches} + out = step_ml.predict(feed) + if t_first is None: + t_first = time.perf_counter() - t2 + chunks.append(out["audio"]) + caches = {n: out[f"{n}_out"] for n in cache_names} + audio = torch.from_numpy(np.concatenate(chunks, axis=-1)) + print(f" codec step[{args.cu_codec}]: first frame {t_first*1000:.0f} ms, " + f"{(time.perf_counter()-t2)*1000/codes.shape[-1]:.1f} ms/frame") + elif args.codec: + codec = ct.models.MLModel(args.codec, compute_units=ct.ComputeUnit.ALL) + audio = codec.predict({"codes": codes.numpy().astype(np.int32)})["audio"] + audio = torch.from_numpy(audio) + else: + from transformers import AutoModel + + codec = AutoModel.from_pretrained(CODEC_REPO, trust_remote_code=True).eval() + with torch.no_grad(): + audio = codec.decode(codes, return_dict=True).audio + t_codec = time.perf_counter() - t2 + import soundfile as sf + + wav = audio[0].T.numpy() + sf.write(args.output, wav, 48000) + dur = wav.shape[0] / 48000 + print(f" codec: {t_codec*1000:.0f} ms audio={dur:.2f}s wrote {args.output}") + print(f" end-to-end RTFx={dur/(t_total + t_codec + t_prefill/2):.2f}") + + +if __name__ == "__main__": + main() diff --git a/models/tts/moss-tts-nano/coreml/pyproject.toml b/models/tts/moss-tts-nano/coreml/pyproject.toml new file mode 100644 index 0000000..c50af89 --- /dev/null +++ b/models/tts/moss-tts-nano/coreml/pyproject.toml @@ -0,0 +1,16 @@ +[project] +name = "moss-tts-nano-coreml" +version = "0.1.0" +requires-python = ">=3.10" +dependencies = [ + "coremltools>=9.0", + "torch==2.7.0", + "torchaudio==2.7.0", + "transformers==4.57.1", + "sentencepiece>=0.1.99", + "soundfile", + "numpy<2", + "scipy", + "safetensors", + "onnxruntime>=1.20.0", +] diff --git a/models/tts/moss-tts-nano/coreml/src/__init__.py b/models/tts/moss-tts-nano/coreml/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/models/tts/moss-tts-nano/coreml/src/codec_coreml.py b/models/tts/moss-tts-nano/coreml/src/codec_coreml.py new file mode 100644 index 0000000..80df05d --- /dev/null +++ b/models/tts/moss-tts-nano/coreml/src/codec_coreml.py @@ -0,0 +1,239 @@ +"""Trace-friendly wrappers around MossAudioTokenizerModel (Nano) for CoreML export. + +MossCodecDecoder: codes [16,1,T] → stereo 48 kHz audio [1,2,T*3840] (full-utterance, flexible T) +MossCodecEncoder: audio [1,2,S] (S % 3840 == 0) → codes [16,1,S/3840] (voice-clone prompt encode) + +Channel interleave (stereo → one 96 kHz-rate mono stream) is replicated from +MossAudioTokenizerModel._flatten_channels_for_codec / _restore_channels_from_codec. +""" + +from __future__ import annotations + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def patch_rope(codec: nn.Module) -> None: + """Replace the HF module's apply_rope with a version whose head-dim math is a Python constant. + + Upstream computes ``2 / D`` from a traced tensor size, which torch.jit.trace records as an + integer division op that CoreML cannot lower (``inverse`` on int32). Only D is made static; + the time axis stays dynamic so flexible-length export still works. + """ + import math + import sys + + mod = sys.modules[type(codec).__module__] + + def apply_rope_static(q, k, offset, max_period=10_000, time_before_heads=False): + D = int(q.shape[-1]) + ds = torch.arange(D // 2, device=q.device, dtype=torch.float32) + freqs = torch.exp(ds * (-math.log(max_period) * 2.0 / float(D))) + T = q.shape[1] if time_before_heads else q.shape[2] + ts = offset.float().view(-1, 1) + torch.arange(T, device=q.device, dtype=torch.float32) + ts = ts.view(ts.shape[0], -1, 1, 1) if time_before_heads else ts.view(ts.shape[0], 1, -1, 1) + dims = q.shape[:-1] + q2 = q.reshape(*dims, D // 2, 2) + k2 = k.reshape(*dims, D // 2, 2) + qr, qi = q2[..., 0], q2[..., 1] + kr, ki = k2[..., 0], k2[..., 1] + rotr = torch.cos(freqs * ts) + roti = torch.sin(freqs * ts) + qo = torch.stack((qr * rotr - qi * roti, qr * roti + qi * rotr), dim=-1) + ko = torch.stack((kr * rotr - ki * roti, kr * roti + ki * rotr), dim=-1) + return qo.reshape(*dims, D), ko.reshape(*dims, D) + + mod.apply_rope = apply_rope_static + + +def strip_weight_norm(model: nn.Module) -> None: + for module in model.modules(): + if isinstance(module, nn.Conv1d): + try: + torch.nn.utils.parametrize.remove_parametrizations(module, "weight") + except ValueError: + pass + + +class MossCodecDecoder(nn.Module): + def __init__(self, codec: nn.Module) -> None: + super().__init__() + strip_weight_norm(codec) + patch_rope(codec) + self.quantizers = codec.quantizer.quantizers + self.output_proj = codec.quantizer.output_proj + self.decoder = codec.decoder + self.n_channels = int(codec.number_channels) + + def forward(self, codes: torch.Tensor) -> torch.Tensor: + codes = codes.long() + emb = None + for i, lfq in enumerate(self.quantizers): + z = lfq.out_proj(lfq.codebook(codes[i]).transpose(1, 2)) # [B, rvq_dim, T] + emb = z if emb is None else emb + z + x = self.output_proj(emb) # [B, 768, T] + lengths = torch.ones_like(x[:, 0, :]).sum(dim=-1).long() # [B] = T, shape-derived for flexible T + for module in self.decoder: + x, lengths = module(x, lengths) + # [B, 1, T*7680] interleaved → [B, 2, T*3840] + return x.squeeze(1).reshape(x.shape[0], -1, self.n_channels).transpose(1, 2) + + +class MossCodecEncoder(nn.Module): + def __init__(self, codec: nn.Module) -> None: + super().__init__() + strip_weight_norm(codec) + patch_rope(codec) + self.encoder = codec.encoder + self.input_proj = codec.quantizer.input_proj + self.quantizers = codec.quantizer.quantizers + self.n_channels = int(codec.number_channels) + + def forward(self, audio: torch.Tensor) -> torch.Tensor: + x = audio.transpose(1, 2).reshape(audio.shape[0], 1, -1) # interleave channels + lengths = torch.ones_like(x[:, 0, :]).sum(dim=-1).long() # [B] = 2*S + for module in self.encoder: + x, lengths = module(x, lengths) + residual = self.input_proj(x) # [B, 512, T] + codes = [] + for lfq in self.quantizers: + z_e = lfq.in_proj(residual) # [B, 8, T] + enc = F.normalize(z_e.transpose(1, 2).reshape(-1, z_e.shape[1]), dim=-1) # [B*T, 8] + cb = F.normalize(lfq.codebook.weight, dim=-1) # [1024, 8] + sim = torch.matmul(enc, cb.t()) # cosine similarity ⇔ -‖·‖² after normalisation + idx = sim.argmax(dim=-1) # [B*T] + z_q = lfq.out_proj(lfq.codebook(idx).reshape(z_e.shape[0], -1, z_e.shape[1]).transpose(1, 2)) + residual = residual - z_q + codes.append(idx.reshape(z_e.shape[0], -1)) + return torch.stack(codes, dim=0).to(torch.int32) # [16, B, T] + + +NEG = -1.0e4 + + +def _rope_tables(positions: torch.Tensor, head_dim: int, max_period: float) -> tuple[torch.Tensor, torch.Tensor]: + """cos/sin [len(positions), head_dim/2] for the codec's (real, imag) interleaved RoPE layout.""" + import math + + ds = torch.arange(head_dim // 2, dtype=torch.float32) + freqs = torch.exp(ds * (-math.log(max_period) * 2.0 / head_dim)) + ang = positions.float()[:, None] * freqs[None, :] + return ang.cos(), ang.sin() + + +def _rotate(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor: + """x [B,H,T,D] with pairs (re, im) interleaved along D; cos/sin broadcast to [T, D/2].""" + B, H, T, D = x.shape + x2 = x.reshape(B, H, T, D // 2, 2) + re, im = x2[..., 0], x2[..., 1] + out = torch.stack((re * cos - im * sin, re * sin + im * cos), dim=-1) + return out.reshape(B, H, T, D) + + +class MossCodecStepDecoder(nn.Module): + """One 80 ms frame per call: codes [16,1,1] → audio [1,2,3840], with explicit per-layer KV caches. + + Cache layout per transformer layer: k/v [1, heads, context, head_dim] holding the most recent + `context` positions in chronological order (shift-append, no ring indices). Keys are cached + *unrotated*; RoPE is applied at attention time with constant relative-position tables, which is + mathematically identical to upstream's absolute positions and keeps all trig out of the graph. + `frame_index` (0-based) only drives the warm-up mask for not-yet-written cache slots. + """ + + def __init__(self, codec: nn.Module) -> None: + super().__init__() + strip_weight_norm(codec) + self.quantizers = codec.quantizer.quantizers + self.output_proj = codec.quantizer.output_proj + self.decoder = codec.decoder + self.n_channels = int(codec.number_channels) + # Static per-stage geometry: tokens per frame, context, layer list. + self.stage_specs: list[dict] = [] + n = 1 + for module in codec.decoder: + if hasattr(module, "patch_size"): + n *= int(module.patch_size) + continue + tr = module.transformer + layer0 = tr.layers[0] + attn = layer0.self_attn + self.stage_specs.append( + { + "n": n, + "context": int(attn.context), + "heads": int(attn.num_heads), + "head_dim": int(attn.embed_dim // attn.num_heads), + "layers": len(tr.layers), + "max_period": float(tr.rope.max_period), + } + ) + for s, spec in enumerate(self.stage_specs): + cap, n, hd = spec["context"], spec["n"], spec["head_dim"] + k_cos, k_sin = _rope_tables(torch.arange(cap), hd, spec["max_period"]) + q_cos, q_sin = _rope_tables(torch.arange(cap - n, cap), hd, spec["max_period"]) + self.register_buffer(f"k_cos_{s}", k_cos, persistent=False) + self.register_buffer(f"k_sin_{s}", k_sin, persistent=False) + self.register_buffer(f"q_cos_{s}", q_cos, persistent=False) + self.register_buffer(f"q_sin_{s}", q_sin, persistent=False) + i = torch.arange(cap) + j = torch.arange(n) + delta = j[:, None] + (cap - n) - i[None, :] # query pos − key pos, [n, cap] + static_ok = (delta >= 0) & (delta < cap) + self.register_buffer(f"static_ok_{s}", static_ok, persistent=False) + self.register_buffer(f"slot_pos_{s}", (i + n - cap).float(), persistent=False) # + frame_index*n + + def cache_shapes(self) -> list[tuple[str, tuple[int, ...]]]: + shapes = [] + for s, spec in enumerate(self.stage_specs): + for l in range(spec["layers"]): + shape = (1, spec["heads"], spec["context"], spec["head_dim"]) + shapes.append((f"k{s}_{l}", shape)) + shapes.append((f"v{s}_{l}", shape)) + return shapes + + def _layer_step(self, layer, x, s: int, spec: dict, frame_index: torch.Tensor, k_cache, v_cache): + n, H, Dh, cap = spec["n"], spec["heads"], spec["head_dim"], spec["context"] + attn = layer.self_attn + h = layer.norm1(x) + proj = attn.in_proj(h).reshape(1, n, 3, H, Dh).permute(2, 0, 3, 1, 4) + q, k, v = proj[0], proj[1], proj[2] # [1,H,n,Dh] + k_all = torch.cat((k_cache[:, :, n:, :], k), dim=2) # [1,H,cap,Dh], unrotated + v_all = torch.cat((v_cache[:, :, n:, :], v), dim=2) + q_rot = _rotate(q, getattr(self, f"q_cos_{s}"), getattr(self, f"q_sin_{s}")) + k_rot = _rotate(k_all, getattr(self, f"k_cos_{s}"), getattr(self, f"k_sin_{s}")) + slot_pos = getattr(self, f"slot_pos_{s}") + frame_index.float() * float(n) # [cap] + ok = getattr(self, f"static_ok_{s}") & (slot_pos >= 0)[None, :] # [n, cap] + bias = torch.where(ok, torch.zeros((), dtype=x.dtype), torch.full((), NEG, dtype=x.dtype))[None, None] + scores = torch.matmul(q_rot, k_rot.transpose(-1, -2)) * (Dh**-0.5) + bias + out = torch.matmul(torch.softmax(scores, dim=-1), v_all) # [1,H,n,Dh] + out = out.transpose(1, 2).reshape(1, n, H * Dh) + x = x + layer.layer_scale_1(attn.out_proj(out)) + x = x + layer.layer_scale_2(layer.ffn(layer.norm2(x))) + return x, k_all, v_all + + def forward(self, codes: torch.Tensor, frame_index: torch.Tensor, *caches: torch.Tensor): + codes = codes.long() + emb = None + for i, lfq in enumerate(self.quantizers): + z = lfq.out_proj(lfq.codebook(codes[i]).transpose(1, 2)) + emb = z if emb is None else emb + z + x = self.output_proj(emb) # [1,768,1] + lengths = torch.ones(1, dtype=torch.long, device=x.device) + new_caches: list[torch.Tensor] = [] + ci = 0 + s = 0 + for module in self.decoder: + if hasattr(module, "patch_size"): + x, lengths = module(x, lengths) + continue + spec = self.stage_specs[s] + x = module.input_proj(x.transpose(1, 2)) # [1,n,d_model] + for layer in module.transformer.layers: + x, k_all, v_all = self._layer_step(layer, x, s, spec, frame_index, caches[ci], caches[ci + 1]) + new_caches.extend((k_all, v_all)) + ci += 2 + x = module.output_proj(x).transpose(1, 2) + s += 1 + audio = x.squeeze(1).reshape(1, -1, self.n_channels).transpose(1, 2) # [1,2,3840] + return (audio, *new_caches) diff --git a/models/tts/moss-tts-nano/coreml/src/lm_coreml.py b/models/tts/moss-tts-nano/coreml/src/lm_coreml.py new file mode 100644 index 0000000..d8519e5 --- /dev/null +++ b/models/tts/moss-tts-nano/coreml/src/lm_coreml.py @@ -0,0 +1,240 @@ +"""Trace-friendly wrappers around MossTTSNanoForCausalLM for CoreML export. + +Three graphs: + MossPrefill – prompt rows [1,T,17] → last hidden [1,768] + zero-padded KV [L,1,H,M,D] + MossStep – one row [1,1,17] + KV + cur_len → hidden [1,768] + updated KV + MossFrame – global hidden [1,768] → text stop decision + 16 audio tokens, with + temperature / top-k / top-p / repetition-penalty sampling done in-graph + from host-supplied uniform randoms (inverse-CDF; greedy flag → argmax). +""" + +from __future__ import annotations + +import torch +import torch.nn as nn +import torch.nn.functional as F + +NEG = -1.0e4 # finite stand-in for -inf that survives fp16 + + +def _rope_tables(positions: torch.Tensor, inv_freq: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + """positions [T] float → cos, sin [1, T, 1, D] laid out as (c0,c0,c1,c1,...) like repeat_interleave.""" + freqs = positions[:, None] * inv_freq[None, :] # [T, D/2] + cos = torch.stack((freqs.cos(), freqs.cos()), dim=-1).reshape(freqs.shape[0], -1) + sin = torch.stack((freqs.sin(), freqs.sin()), dim=-1).reshape(freqs.shape[0], -1) + return cos[None, :, None, :], sin[None, :, None, :] + + +def _rotate_half(x: torch.Tensor) -> torch.Tensor: + even = x[..., 0::2] + odd = x[..., 1::2] + return torch.stack((-odd, even), dim=-1).reshape(x.shape) + + +def _apply_rope(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor: + return x * cos + _rotate_half(x) * sin + + +class _GPT2Core(nn.Module): + """Explicit-KV re-implementation of MossTTSNanoGPT2Model.forward (eager attention).""" + + def __init__(self, transformer: nn.Module) -> None: + super().__init__() + self.tr = transformer + attn0 = transformer.h[0].attn + self.n_head = attn0.num_heads + self.head_dim = attn0.head_dim + self.scale = 1.0 / (self.head_dim**0.5) + self.register_buffer("inv_freq", attn0.rotary_emb.inv_freq.clone(), persistent=False) + + def qkv(self, block: nn.Module, x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor): + """x [B,T,C] → q,k,v [B,H,T,D] with RoPE applied to q,k.""" + B, T, _ = x.shape + qkv = block.attn.c_attn(block.ln_1(x)) + q, k, v = qkv.split(self.n_head * self.head_dim, dim=-1) + q = _apply_rope(q.view(B, T, self.n_head, self.head_dim), cos, sin).transpose(1, 2) + k = _apply_rope(k.view(B, T, self.n_head, self.head_dim), cos, sin).transpose(1, 2) + v = v.view(B, T, self.n_head, self.head_dim).transpose(1, 2) + return q, k, v + + def finish_block(self, block: nn.Module, x: torch.Tensor, q, k, v, bias: torch.Tensor) -> torch.Tensor: + """Attention over (k,v) with additive bias [.., Tq, Tk], then residual + MLP.""" + B, _, T, _ = q.shape + scores = torch.matmul(q, k.transpose(-1, -2)) * self.scale + bias + probs = torch.softmax(scores, dim=-1) + out = torch.matmul(probs, v).transpose(1, 2).reshape(B, T, self.n_head * self.head_dim) + x = x + block.attn.c_proj(out) + return x + block.mlp(block.ln_2(x)) + + +class MossEmbedder(nn.Module): + """Row [.., 17] int → summed text + audio embeddings [.., 768] (pad-masked, as upstream).""" + + def __init__(self, model: nn.Module) -> None: + super().__init__() + self.wte = model.transformer.wte + self.audio_embeddings = model.audio_embeddings + self.audio_pad = int(model.config.audio_pad_token_id) + + def forward(self, input_ids: torch.Tensor) -> torch.Tensor: + ids = input_ids.long() + x = self.wte(ids[..., 0]) + for c, emb in enumerate(self.audio_embeddings): + ch = ids[..., c + 1] + valid = ch.ne(self.audio_pad) + safe = torch.where(valid, ch, torch.zeros_like(ch)) + x = x + emb(safe) * valid.unsqueeze(-1).to(x.dtype) + return x + + +class MossPrefill(nn.Module): + def __init__(self, model: nn.Module, max_len: int) -> None: + super().__init__() + self.embed = MossEmbedder(model) + self.core = _GPT2Core(model.transformer) + self.max_len = max_len + + def forward(self, input_ids: torch.Tensor, input_len: torch.Tensor): + x = self.embed(input_ids) # [1,T,C] + T = x.shape[1] + ar = torch.arange(T, device=x.device) + cos, sin = _rope_tables(ar.float(), self.core.inv_freq) + key_valid = ar[None, :] < input_len.long()[:, None] # [1,T] + causal = ar[None, :] <= ar[:, None] # [T,T] + mask = causal[None, None] & key_valid[:, None, None, :] + bias = torch.where(mask, torch.zeros((), dtype=x.dtype), torch.full((), NEG, dtype=x.dtype)) + kv_keep = key_valid.to(x.dtype)[:, None, :, None] # [1,1,T,1] + ks, vs = [], [] + for block in self.core.tr.h: + q, k, v = self.core.qkv(block, x, cos, sin) + k = k * kv_keep + v = v * kv_keep + x = self.core.finish_block(block, x, q, k, v, bias) + ks.append(k) + vs.append(v) + x = self.core.tr.ln_f(x) + onehot = (ar[None, :] == (input_len.long() - 1)[:, None]).to(x.dtype) # [1,T] + hidden_last = (x * onehot[..., None]).sum(dim=1) # [1,C] + pad = self.max_len - T + kv_k = F.pad(torch.stack(ks, dim=0), (0, 0, 0, pad)) # [L,1,H,M,D] + kv_v = F.pad(torch.stack(vs, dim=0), (0, 0, 0, pad)) + return hidden_last, kv_k, kv_v + + +class MossStep(nn.Module): + def __init__(self, model: nn.Module, max_len: int) -> None: + super().__init__() + self.embed = MossEmbedder(model) + self.core = _GPT2Core(model.transformer) + self.max_len = max_len + + def forward(self, input_ids: torch.Tensor, kv_k: torch.Tensor, kv_v: torch.Tensor, cur_len: torch.Tensor): + x = self.embed(input_ids) # [1,1,C] + pos = cur_len.long() + cos, sin = _rope_tables(pos.float(), self.core.inv_freq) # [1,1,1,D] + j = torch.arange(self.max_len, device=x.device) + key_ok = j[None, :] <= pos[:, None] # [1,M] + bias = torch.where(key_ok, torch.zeros((), dtype=x.dtype), torch.full((), NEG, dtype=x.dtype)) + bias = bias[:, None, None, :] # [1,1,1,M] + onehot = (j[None, :] == pos[:, None]).to(x.dtype)[:, None, :, None] # [1,1,M,1] + ks, vs = [], [] + for layer, block in enumerate(self.core.tr.h): + q, k_new, v_new = self.core.qkv(block, x, cos, sin) # [1,H,1,D] + k = kv_k[layer] * (1.0 - onehot) + k_new * onehot + v = kv_v[layer] * (1.0 - onehot) + v_new * onehot + x = self.core.finish_block(block, x, q, k, v, bias) + ks.append(k) + vs.append(v) + hidden = self.core.tr.ln_f(x)[:, 0, :] + return hidden, torch.stack(ks, dim=0), torch.stack(vs, dim=0) + + +def _sample( + scores: torch.Tensor, + u: torch.Tensor, + temperature: torch.Tensor, + top_k: int, + top_p: torch.Tensor, + greedy: torch.Tensor, +) -> torch.Tensor: + """scores [1,V] → token [1] (int64). Mirrors upstream _sample_next_token; multinomial → inverse CDF.""" + greedy_tok = scores.argmax(dim=-1) + s = scores / temperature + V = s.shape[-1] + if top_k < V: + thr = torch.topk(s, top_k, dim=-1).values[..., -1:] + s = torch.where(s < thr, torch.full_like(s, NEG), s) + sorted_s, sorted_idx = torch.sort(s, dim=-1, descending=True) + probs = torch.softmax(sorted_s, dim=-1) + excl = torch.cumsum(probs, dim=-1) - probs # mass strictly before each entry + keep = excl <= top_p # first entry always kept + sorted_s = torch.where(keep, sorted_s, torch.full_like(sorted_s, NEG)) + probs = torch.softmax(sorted_s, dim=-1) + cdf = torch.cumsum(probs, dim=-1) + pick = (cdf < u[:, None]).to(torch.int32).sum(dim=-1) # [1] + pick = torch.minimum(pick, torch.full_like(pick, V - 1)) + sampled = torch.gather(sorted_idx, 1, pick.long()[:, None])[:, 0] + return torch.where(greedy > 0.5, greedy_tok, sampled) + + +class MossFrame(nn.Module): + TEXT_TOP_K = 50 + AUDIO_TOP_K = 25 + + def __init__(self, model: nn.Module) -> None: + super().__init__() + cfg = model.config + self.local = _GPT2Core(model.local_transformer) + self.wte = model.transformer.wte + self.audio_embeddings = model.audio_embeddings + self.audio_heads = model.audio_lm_heads + self.n_vq = int(cfg.n_vq) + self.slot_id = int(cfg.audio_assistant_slot_token_id) + self.end_id = int(cfg.audio_end_token_id) + self.register_buffer("cand_ids", torch.tensor([self.slot_id, self.end_id]), persistent=False) + + def _local_last(self, embeds: torch.Tensor) -> torch.Tensor: + T = embeds.shape[1] + ar = torch.arange(T, device=embeds.device) + cos, sin = _rope_tables(ar.float(), self.local.inv_freq) + causal = ar[None, :] <= ar[:, None] + bias = torch.where(causal, torch.zeros((), dtype=embeds.dtype), torch.full((), NEG, dtype=embeds.dtype)) + bias = bias[None, None] + x = embeds + for block in self.local.tr.h: + q, k, v = self.local.qkv(block, x, cos, sin) + x = self.local.finish_block(block, x, q, k, v, bias) + return self.local.tr.ln_f(x)[:, -1, :] + + def forward( + self, + global_hidden: torch.Tensor, # [1,768] + text_u: torch.Tensor, # [1] + audio_u: torch.Tensor, # [1,16] + text_temperature: torch.Tensor, # [1] + audio_temperature: torch.Tensor, # [1] + audio_top_p: torch.Tensor, # [1] + repetition_penalty: torch.Tensor, # [1] + seen: torch.Tensor, # [1,16,1024] 0/1 float + greedy: torch.Tensor, # [1] 0/1 float + ): + embeds = global_hidden[:, None, :] + h = self._local_last(embeds) + cand_logits = F.linear(h, self.wte.weight[self.cand_ids]) # [1,2] + one = torch.ones_like(text_temperature) + cand = _sample(cand_logits, text_u, text_temperature, self.TEXT_TOP_K, one, greedy) # 0 → continue + should_continue = (cand == 0).to(torch.int32) + text_tok = torch.where(cand == 0, torch.full_like(cand, self.slot_id), torch.full_like(cand, self.end_id)) + cur = self.wte(text_tok) # [1,768] + toks = [] + for c in range(self.n_vq): + embeds = torch.cat((embeds, cur[:, None, :]), dim=1) + h = 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0000000..8a01e36 --- /dev/null +++ b/models/tts/moss-tts-nano/coreml/verify_codec_streaming.py @@ -0,0 +1,50 @@ +"""Check that per-frame streaming codec decode matches full-utterance decode in PyTorch. + +Streaming decode with chunk_duration = one 12.5 Hz frame (0.08 s) is the mode the +CoreML step decoder will replicate, so it has to match the non-streaming path first. +""" + +from __future__ import annotations + +import argparse +import time +from pathlib import Path + +import numpy as np +import torch +from transformers import AutoModel + +CODEC_REPO = "OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano" +HERE = Path(__file__).resolve().parent + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--tokens", default=str(HERE / "build" / "ref_pytorch_audio_token_ids.npy")) + parser.add_argument("--chunk-frames", type=int, default=1) + args = parser.parse_args() + + codec = AutoModel.from_pretrained(CODEC_REPO, trust_remote_code=True).eval() + tokens = torch.from_numpy(np.load(args.tokens)).long() # (T, nq) + codes = tokens.T.unsqueeze(1) # (nq, 1, T) + frame_sec = codec.downsample_rate / codec.sampling_rate + print(f"frames={codes.shape[-1]} nq={codes.shape[0]} frame={frame_sec*1000:.0f}ms downsample={codec.downsample_rate}") + + with torch.no_grad(): + t0 = time.perf_counter() + full = codec.decode(codes, return_dict=True).audio + t_full = time.perf_counter() - t0 + t0 = time.perf_counter() + stream = codec.decode(codes, return_dict=True, chunk_duration=frame_sec * args.chunk_frames).audio + t_stream = time.perf_counter() - t0 + + print(f"full: {tuple(full.shape)} in {t_full*1000:.0f}ms stream: {tuple(stream.shape)} in {t_stream*1000:.0f}ms") + n = min(full.shape[-1], stream.shape[-1]) + diff = (full[..., :n] - stream[..., :n]).abs() + print(f"max|diff|={diff.max().item():.3e} mean|diff|={diff.mean().item():.3e} peak={full.abs().max().item():.3f}") + samples_per_frame = full.shape[-1] // codes.shape[-1] + print(f"samples/frame/channel={samples_per_frame} channels={full.shape[1]}") + + +if __name__ == "__main__": + main() diff --git a/models/tts/moss-tts-nano/coreml/verify_pytorch.py b/models/tts/moss-tts-nano/coreml/verify_pytorch.py new file mode 100644 index 0000000..429314f --- /dev/null +++ b/models/tts/moss-tts-nano/coreml/verify_pytorch.py @@ -0,0 +1,74 @@ +"""PyTorch reference synthesis for MOSS-TTS-Nano (voice clone mode). + +Produces the baseline WAV that CoreML outputs are compared against. +""" + +from __future__ import annotations + +import argparse +import time +from pathlib import Path + +import torch +from transformers import AutoModelForCausalLM + +TTS_REPO = "OpenMOSS-Team/MOSS-TTS-Nano-100M" +CODEC_REPO = "OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano" +HERE = Path(__file__).resolve().parent + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--text", default="The quick brown fox jumps over the lazy dog near the riverbank.") + parser.add_argument("--prompt-audio", default=str(HERE / "assets" / "en_2.wav")) + parser.add_argument("--output", default=str(HERE / "build" / "ref_pytorch.wav")) + parser.add_argument("--device", default="cpu") + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--nq", type=int, default=None) + parser.add_argument("--do-sample", type=int, default=1) + parser.add_argument("--max-new-frames", type=int, default=375) + args = parser.parse_args() + + torch.manual_seed(args.seed) + t0 = time.perf_counter() + model = AutoModelForCausalLM.from_pretrained(TTS_REPO, trust_remote_code=True, dtype=torch.float32) + model.config.attn_implementation = "eager" + model.config.local_transformer_attn_implementation = "eager" + for module in model.modules(): + if hasattr(module, "attn_implementation"): + module.attn_implementation = "eager" + model.eval() + print(f"load: {time.perf_counter() - t0:.2f}s params={sum(p.numel() for p in model.parameters())/1e6:.1f}M") + + t1 = time.perf_counter() + result = model.inference( + text=args.text, + output_audio_path=args.output, + mode="voice_clone", + prompt_audio_path=args.prompt_audio, + audio_tokenizer_type="moss-audio-tokenizer-nano", + audio_tokenizer_pretrained_name_or_path=CODEC_REPO, + device=args.device, + nq=args.nq, + do_sample=bool(args.do_sample), + max_new_frames=args.max_new_frames, + voice_clone_max_text_tokens=0, + ) + dt = time.perf_counter() - t1 + keys = {k: (tuple(v.shape) if hasattr(v, "shape") else v) for k, v in result.items() if k != "waveform"} + print("result:", keys) + import numpy as np + + out_dir = Path(args.output).parent + stem = Path(args.output).stem + np.save(out_dir / f"{stem}_audio_token_ids.npy", np.asarray(result["audio_token_ids"])) + np.save(out_dir / f"{stem}_prompt_audio_token_ids.npy", np.asarray(result["reference_audio_token_ids"])) + import soundfile as sf + + audio, sr = sf.read(args.output) + dur = audio.shape[0] / sr + print(f"synth: {dt:.2f}s audio={dur:.2f}s @ {sr}Hz shape={audio.shape} RTFx={dur/dt:.2f}") + + +if __name__ == "__main__": + main()