From 3b331c2c3a6334ae6377321162eeea5bd8271699 Mon Sep 17 00:00:00 2001 From: Alex-Wengg Date: Sat, 25 Jul 2026 13:45:45 -0400 Subject: [PATCH 1/5] Add NeuTTS-2E CoreML conversion (Qwen3 LM + NeuCodec decoder) Converts neuphonic/neutts-2e (emotional English TTS, Qwen3 236M backbone) and the NeuCodec FSQ decoder to CoreML: - LM-Prefill (macOS14), LM-Decode pass-through-KV (macOS14) and stateful StateType-KV (macOS15) mlpackages, fp16 with fp32 norm/softmax ops. Tied embedding/LM-head shared through one parameter so const-dedup stores the [217232, 512] vocab matrix once per package. - NeuCodec decoder with flexible code length (RangeDim). FSQ dequant is a precomputed [65536, 8] embedding gather (fp16 pass corrupts integer arithmetic above 2048). Replicates the upstream bs_roformer5 RoPE quirk (torchtune rope applied to [b, h, t, d] rotates by head index, constant over time), which is also what keeps the length axis flexible. Vocos same-padding ISTFT reimplemented as IDFT 1x1 convs + ConvTranspose1d overlap-add. - gen-pytorch-ref.py (seeded PyTorch reference), inference.py (full CoreML synthesis + teacher-forced parity replay). Parity: LM CoreML fp16 max-abs-diff 0.024/0.081 vs torch fp32 with argmax match; stateful decode bit-identical to pass-through. Codec CoreML SNR 40-47 dB vs PyTorch across lengths. Teacher-forced replay keeps 98.7% of reference tokens inside the CoreML top-50 support; final audio 41.5 dB vs the PyTorch waveform. Sampled decode 13 ms/token (~77 tok/s vs 50 needed for real-time), codec 2.7-9.6x RT on M5 Pro. torch pinned <2.8: newer torch traces shape unpacking, repeat_interleave and shape[-1]//2 into aten::Int/floor_divide nodes coremltools 9.0 cannot fold; wrappers use static ints + expand/reshape GQA for the same reason. --- models/tts/neutts-2e/coreml/.gitignore | 2 + models/tts/neutts-2e/coreml/.python-version | 1 + models/tts/neutts-2e/coreml/README.md | 98 + models/tts/neutts-2e/coreml/convert-codec.py | 129 ++ models/tts/neutts-2e/coreml/convert-lm.py | 273 +++ .../tts/neutts-2e/coreml/gen-pytorch-ref.py | 101 + models/tts/neutts-2e/coreml/inference.py | 232 +++ models/tts/neutts-2e/coreml/pyproject.toml | 23 + models/tts/neutts-2e/coreml/samples/emily.pt | Bin 0 -> 3163 bytes models/tts/neutts-2e/coreml/samples/emily.txt | 1 + models/tts/neutts-2e/coreml/samples/paul.pt | Bin 0 -> 2644 bytes models/tts/neutts-2e/coreml/samples/paul.txt | 1 + models/tts/neutts-2e/coreml/samples/sophie.pt | Bin 0 -> 2210 bytes .../tts/neutts-2e/coreml/samples/sophie.txt | 1 + models/tts/neutts-2e/coreml/samples/steven.pt | Bin 0 -> 2594 bytes .../tts/neutts-2e/coreml/samples/steven.txt | 1 + models/tts/neutts-2e/coreml/src/__init__.py | 0 .../tts/neutts-2e/coreml/src/codec_coreml.py | 239 +++ models/tts/neutts-2e/coreml/src/lm_coreml.py | 416 ++++ models/tts/neutts-2e/coreml/src/prompt.py | 95 + models/tts/neutts-2e/coreml/uv.lock | 1815 +++++++++++++++++ 21 files changed, 3428 insertions(+) create mode 100644 models/tts/neutts-2e/coreml/.gitignore create mode 100644 models/tts/neutts-2e/coreml/.python-version create mode 100644 models/tts/neutts-2e/coreml/README.md create mode 100644 models/tts/neutts-2e/coreml/convert-codec.py create mode 100644 models/tts/neutts-2e/coreml/convert-lm.py create mode 100644 models/tts/neutts-2e/coreml/gen-pytorch-ref.py create mode 100644 models/tts/neutts-2e/coreml/inference.py create mode 100644 models/tts/neutts-2e/coreml/pyproject.toml create mode 100644 models/tts/neutts-2e/coreml/samples/emily.pt create mode 100644 models/tts/neutts-2e/coreml/samples/emily.txt create mode 100644 models/tts/neutts-2e/coreml/samples/paul.pt create mode 100644 models/tts/neutts-2e/coreml/samples/paul.txt create mode 100644 models/tts/neutts-2e/coreml/samples/sophie.pt create mode 100644 models/tts/neutts-2e/coreml/samples/sophie.txt create mode 100644 models/tts/neutts-2e/coreml/samples/steven.pt create mode 100644 models/tts/neutts-2e/coreml/samples/steven.txt create mode 100644 models/tts/neutts-2e/coreml/src/__init__.py create mode 100644 models/tts/neutts-2e/coreml/src/codec_coreml.py create mode 100644 models/tts/neutts-2e/coreml/src/lm_coreml.py create mode 100644 models/tts/neutts-2e/coreml/src/prompt.py create mode 100644 models/tts/neutts-2e/coreml/uv.lock diff --git a/models/tts/neutts-2e/coreml/.gitignore b/models/tts/neutts-2e/coreml/.gitignore new file mode 100644 index 0000000..e7f9d25 --- /dev/null +++ b/models/tts/neutts-2e/coreml/.gitignore @@ -0,0 +1,2 @@ +build/ +.venv/ diff --git a/models/tts/neutts-2e/coreml/.python-version b/models/tts/neutts-2e/coreml/.python-version new file mode 100644 index 0000000..2c07333 --- /dev/null +++ b/models/tts/neutts-2e/coreml/.python-version @@ -0,0 +1 @@ +3.11 diff --git a/models/tts/neutts-2e/coreml/README.md b/models/tts/neutts-2e/coreml/README.md new file mode 100644 index 0000000..0c899cf --- /dev/null +++ b/models/tts/neutts-2e/coreml/README.md @@ -0,0 +1,98 @@ +# NeuTTS-2E → CoreML + +Conversion of [neuphonic/neutts-2e](https://huggingface.co/neuphonic/neutts-2e) +(emotional English TTS: Qwen3 236M backbone + [NeuCodec](https://huggingface.co/neuphonic/neucodec)) +to CoreML mlpackages. + +## Pipeline + +``` +text ──tokenizer──► prompt ids ──► LM-Prefill ──► logits + KV cache + │ + LM-Decode (top-k sampling loop, 50 codes/s) + │ <|speech_N|> tokens + NeuCodec-Decoder ──► 24 kHz waveform +``` + +Prompt layout (BPE, no phonemizer): +`<|TEXT_PROMPT_START|>{ref_text}[<|EMOTION|>]{text}<|TEXT_PROMPT_END|><|SPEECH_GENERATION_START|>{ref codes}`, +generate until `<|SPEECH_GENERATION_END|>` (temp 1.0, top-k 50, min 50 new tokens). +The four fixed speakers ship as pre-encoded NeuCodec code sequences +(`samples/*.pt`), so only the codec **decoder** is needed on-device. +Upstream additionally applies a Perth watermark to the output waveform in the +host app; that is postprocessing, not part of these models. + +## Models + +| mlpackage | I/O | Size | Target | +|---|---|---|---| +| `LM-Prefill-T768-M2048-fp16` | ids [1,768] + len → last-pos logits [1,217232], kv_k/kv_v [28,1,4,2048,128] | 456 MB | macOS 14+ | +| `LM-Decode-M2048-fp16` | id [1,1] + kv in/out + cur_len → logits | 451 MB | macOS 14+ | +| `LM-Decode-M2048-fp16-stateful` | id [1,1] + cur_len → logits, KV as `StateType` | 451 MB | macOS 15+ | +| `NeuCodec-Decoder-fp16` | codes [1,T≤2000] (RangeDim) → audio [1,T·480] | 372 MB | macOS 14+ | + +The tied embedding/LM-head weight ([217232, 512]) is used for both the input +gather and the output projection through one shared parameter, so CoreML +const-dedup stores it once per package. The stateful decode is bit-identical +to the pass-through variant and avoids round-tripping 117 MB of KV per step; +prefill KV seeds the state via `write_state` (float32 arrays only — the fp16 +state converts internally). + +## Commands + +```bash +uv sync +uv run python gen-pytorch-ref.py # PyTorch reference (bf16 LM + fp32 codec) +uv run python convert-lm.py --output-dir ./build/lm-fp16 --fp16 --stateful-decode +uv run python convert-codec.py --output-dir ./build/codec --fp16 +# End-to-end CoreML synthesis +uv run python inference.py --lm-dir ./build/lm-fp16 \ + --codec ./build/codec/NeuCodec-Decoder-fp16.mlpackage \ + --text "I can't believe it's finally here!" --speaker emily --emotion happy +# Deterministic parity replay of the PyTorch reference run +uv run python inference.py --lm-dir ./build/lm-fp16 \ + --codec ./build/codec/NeuCodec-Decoder-fp16.mlpackage --teacher-force +``` + +## Parity (M5 Pro, macOS 26) + +- LM wrappers vs HF fp32: max|Δ| 2.8e-5 (prefill), 3.1e-5 (decode), argmax match. +- CoreML fp16 vs torch fp32: max|Δ| 0.024 (prefill), 0.081 (decode), argmax match; + stateful ≡ pass-through exactly (two chained steps checked). +- Codec wrapper vs `neucodec.decode_code`: SNR 123.9 dB (fp32 torch); + CoreML fp16: SNR 46.6 / 41.7 / 40.1 dB at T = 125 / 300 / 800 codes. +- Teacher-forced replay of the 301-token PyTorch reference: 98.7 % of + reference tokens inside the CoreML top-50 sampling support; final audio + SNR 41.5 dB vs the PyTorch waveform. +- Sampled end-to-end run: 13 ms/token decode (≈77 tok/s vs 50 needed for + real-time), prefill 169 ms, codec 2.7–9.6× RT (ComputeUnit.ALL). + +## Conversion gotchas (also see git history) + +- **torch pin `>=2.7,<2.8`** — newer torch traces `x.shape` unpacking, + `repeat_interleave`, and `shape[-1] // 2` into `aten::Int`/`floor_divide` + nodes that coremltools 9.0 cannot fold ("only 0-dimensional arrays can be + converted to Python scalars"). The wrappers additionally use static ints and + expand+reshape for GQA so no shape-dependent ops remain. torchaudio must be + pinned to the same minor as torch (ABI). +- **fp16 integer hazard** — FSQ digit math (`floor(code/basis) % 4`) breaks + under the fp16 compute pass for codes > 2048; dequant is a precomputed + [65536, 8] embedding gather instead. +- **NeuCodec RoPE quirk** — `bs_roformer5.Attention` feeds `[b, h, t, d]` + into torchtune's rope (which expects `[b, s, h, d]`), so the pretrained + model rotates by *head index*, constant over time. Replicated with fixed + per-head cos/sin buffers; consequently the code-length axis stays flexible + (RangeDim) with no positional tables. +- **ISTFT** — vocos "same"-padding ISTFT reimplemented as real IDFT 1×1 convs + + `ConvTranspose1d` overlap-add with window-envelope division (verified vs + the library to 1e-3 before conversion). + +## Follow-ups + +- ANE profiling (`tools/coreml-cli`) and a fixed-shape codec variant if ANE + residency is worth it; LM decode currently runs GPU-dominant. +- Weight compression: 111 M of the 236 M params are the tied vocab matrix — + int8/palettized embedding would roughly halve both LM packages. +- Multifunction package (macOS 15+) to share weights between prefill and + decode (saves ~450 MB on disk). +- Swift host port (tokenizer + sampling loop + Perth watermark) for FluidAudio. diff --git a/models/tts/neutts-2e/coreml/convert-codec.py b/models/tts/neutts-2e/coreml/convert-codec.py new file mode 100644 index 0000000..1ba4474 --- /dev/null +++ b/models/tts/neutts-2e/coreml/convert-codec.py @@ -0,0 +1,129 @@ +"""Convert the NeuCodec decoder (codes → 24 kHz audio) to a CoreML mlpackage. + +The code-length axis is flexible (RangeDim up to --max-codes); the upstream +RoPE quirk rotates by head index only (see src.codec_coreml.AttentionRope), +so no time-dependent tables are needed. Flexible shapes keep the model off ANE, but the decoder runs once per +utterance on GPU/CPU, where exact-length decode gives bit-comparable parity +with the PyTorch reference. + +Pipeline: load neucodec fp32 → wrap (src.codec_coreml, which self-verifies +FSQ dequant + ISTFT against the library) → PyTorch parity on the reference +codes → trace → convert → CoreML parity at multiple lengths. + +Usage: + uv run python convert-codec.py --output-dir ./build/codec --fp16 +""" + +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path + +import coremltools as ct +import numpy as np +import torch + +HERE = Path(__file__).parent +sys.path.insert(0, str(HERE)) + +from src.codec_coreml import HOP, NeuCodecDecoder # noqa: E402 + +CODEC_REPO = "neuphonic/neucodec" + + +def _make_precision(fp16: bool): + if not fp16: + return ct.precision.FLOAT32 + # exp/cos/sin feed the ISTFT; softmax for range; norms for stability. + FP32_OPS = {"pow", "reduce_mean", "rsqrt", "softmax", "exp", "cos", "sin", "layer_norm"} + return ct.transform.FP16ComputePrecision(op_selector=lambda op: op.op_type not in FP32_OPS) + + +def load_codes(default_len: int = 300) -> list[int]: + ref = HERE / "build" / "ref" / "ref_codes.json" + if ref.exists(): + return json.loads(ref.read_text()) + rng = np.random.default_rng(0) + return rng.integers(0, 65_536, default_len).tolist() + + +def snr_db(got: np.ndarray, want: np.ndarray) -> float: + noise = got - want + return 10.0 * np.log10((want**2).sum() / max((noise**2).sum(), 1e-12)) + + +def main() -> None: + p = argparse.ArgumentParser() + p.add_argument("--output-dir", required=True) + p.add_argument("--fp16", action="store_true") + p.add_argument("--max-codes", type=int, default=2000, help="RangeDim upper bound (40s)") + p.add_argument("--trace-codes", type=int, default=500) + 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" + + print(f"[1/4] Loading {CODEC_REPO} (fp32, cpu)...") + from neucodec import NeuCodec + + codec = NeuCodec.from_pretrained(CODEC_REPO) + codec.eval() + + print("[2/4] Building wrapper (self-verifies FSQ + ISTFT vs library)...") + wrapper = NeuCodecDecoder(codec).eval() + + codes = load_codes() + codes_t = torch.tensor(codes, dtype=torch.int32)[None, :] + with torch.no_grad(): + got = wrapper(codes_t) + want = codec.decode_code(torch.tensor(codes, dtype=torch.long)[None, None, :]) + got_np = got[0].numpy() + want_np = want[0, 0, :].numpy() + assert got_np.shape == want_np.shape, (got_np.shape, want_np.shape) + print(f" PyTorch wrapper vs neucodec: max|Δ|={np.abs(got_np - want_np).max():.4e} " + f"SNR={snr_db(got_np, want_np):.1f} dB") + if snr_db(got_np, want_np) < 40: + raise SystemExit("wrapper parity too low") + + print("[3/4] Tracing + converting...") + t0 = args.trace_codes + trace_codes = torch.randint(0, 65_536, (1, t0), dtype=torch.int32) + with torch.no_grad(): + traced = torch.jit.trace(wrapper, (trace_codes,), strict=False) + + T = ct.RangeDim(lower_bound=2, upper_bound=args.max_codes, default=t0) + mlmodel = ct.convert( + traced, + inputs=[ + ct.TensorType(name="codes", shape=(1, T), dtype=np.int32), + ], + outputs=[ct.TensorType(name="audio", dtype=np.float32)], + compute_precision=_make_precision(args.fp16), + minimum_deployment_target=ct.target.macOS14, + convert_to="mlprogram", + ) + mlp = out_dir / f"NeuCodec-Decoder-{tag}.mlpackage" + mlmodel.save(str(mlp)) + print(f" saved: {mlp}") + + print("[4/4] CoreML parity at multiple lengths...") + for t in (125, len(codes), 800): + if t == len(codes): + test_codes = codes_t + ref_audio = want_np + else: + test_codes = torch.randint(0, 65_536, (1, t), dtype=torch.int32) + with torch.no_grad(): + ref_audio = codec.decode_code(test_codes.to(torch.long).unsqueeze(1))[0, 0].numpy() + pred = mlmodel.predict({"codes": test_codes.numpy().astype(np.int32)})["audio"][0] + print(f" T={t}: max|Δ|={np.abs(pred - ref_audio).max():.4e} " + f"SNR={snr_db(pred, ref_audio):.1f} dB") + + print("[done]") + + +if __name__ == "__main__": + main() diff --git a/models/tts/neutts-2e/coreml/convert-lm.py b/models/tts/neutts-2e/coreml/convert-lm.py new file mode 100644 index 0000000..432178c --- /dev/null +++ b/models/tts/neutts-2e/coreml/convert-lm.py @@ -0,0 +1,273 @@ +"""Convert the NeuTTS-2E Qwen3 backbone to CoreML mlpackages. + +Emits up to three models into --output-dir: + + LM-Prefill-T{t_prefill}-M{max_len}-{tag}.mlpackage (macOS 14+) + LM-Decode-M{max_len}-{tag}.mlpackage (macOS 14+, pass-through KV) + LM-Decode-M{max_len}-{tag}-stateful.mlpackage (macOS 15+, StateType KV) + +Pipeline: load HF fp32 → wrap (src.lm_coreml) → PyTorch parity vs HF → +torch.jit.trace → ct.convert → CoreML parity vs the torch wrappers. + +Usage: + uv run python convert-lm.py --output-dir ./build/lm --fp16 --stateful-decode +""" + +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path + +import coremltools as ct +import numpy as np +import torch + +HERE = Path(__file__).parent +sys.path.insert(0, str(HERE)) + +from src.lm_coreml import Qwen3Decode, Qwen3DecodeStateful, Qwen3Prefill # noqa: E402 + +BACKBONE_REPO = "neuphonic/neutts-2e" + + +def load_backbone(): + from transformers import AutoModelForCausalLM, AutoTokenizer + + tokenizer = AutoTokenizer.from_pretrained(BACKBONE_REPO) + model = AutoModelForCausalLM.from_pretrained(BACKBONE_REPO, dtype=torch.float32) + model.eval() + return model, tokenizer + + +def load_prompt_ids(tokenizer) -> list[int]: + ref = HERE / "build" / "ref" / "prompt_ids.json" + if ref.exists(): + return json.loads(ref.read_text()) + from src.prompt import build_prompt_ids + + return build_prompt_ids(tokenizer, "Hello there, this is a conversion smoke test.") + + +def _make_precision(fp16: bool): + """FP16 everywhere except RMSNorm internals and softmax (fp32 for range).""" + if not fp16: + return ct.precision.FLOAT32 + FP32_OPS = {"pow", "reduce_mean", "rsqrt", "softmax"} + return ct.transform.FP16ComputePrecision(op_selector=lambda op: op.op_type not in FP32_OPS) + + +def check(name: str, got: torch.Tensor, want: torch.Tensor, tol: float): + diff = (got.float() - want.float()).abs().max().item() + match = torch.argmax(got.reshape(-1)) == torch.argmax(want.reshape(-1)) + print(f" {name}: max|Δ|={diff:.4e} argmax match={bool(match)}") + if diff > tol: + raise SystemExit(f"parity failure: {name} max|Δ|={diff} > {tol}") + + +def main() -> None: + p = argparse.ArgumentParser() + p.add_argument("--output-dir", required=True) + p.add_argument("--t-prefill", type=int, default=768) + p.add_argument("--max-len", type=int, default=2048) + p.add_argument("--fp16", action="store_true") + p.add_argument("--skip-prefill", action="store_true") + p.add_argument("--skip-decode", action="store_true") + p.add_argument("--stateful-decode", action="store_true") + 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" + precision = _make_precision(args.fp16) + + print(f"[0/3] Loading {BACKBONE_REPO} (fp32, cpu)...") + hf_model, tokenizer = load_backbone() + prompt_ids = load_prompt_ids(tokenizer) + T_pre, M = args.t_prefill, args.max_len + if len(prompt_ids) > T_pre: + raise SystemExit(f"prompt ({len(prompt_ids)}) longer than --t-prefill ({T_pre})") + n_valid = len(prompt_ids) + print(f" prompt: {n_valid} tokens, T_prefill={T_pre}, max_len={M}") + + # HF ground truth: last-position logits for the prompt, and one decode step. + print("[0/3] HF reference forward...") + with torch.no_grad(): + hf_out = hf_model(torch.tensor(prompt_ids)[None, :]).logits + hf_last = hf_out[:, -1, :] # [1, V] + next_id = int(torch.argmax(hf_last, dim=-1)) + hf_out2 = hf_model(torch.tensor(prompt_ids + [next_id])[None, :]).logits + hf_step = hf_out2[:, -1, :] + + padded = prompt_ids + [0] * (T_pre - n_valid) + ids_t = torch.tensor(padded, dtype=torch.int32)[None, :] + len_t = torch.tensor([n_valid], dtype=torch.int32) + + # ---------------- prefill ---------------- + print("[1/3] Prefill wrapper...") + prefill = Qwen3Prefill(hf_model, max_len=M, t_prefill=T_pre).eval() + with torch.no_grad(): + logits_last, kv_k, kv_v = prefill(ids_t, len_t) + check("prefill logits vs HF", logits_last, hf_last, tol=2e-2) + + if not args.skip_prefill: + with torch.no_grad(): + traced = torch.jit.trace(prefill, (ids_t, len_t), strict=False) + mlmodel = ct.convert( + traced, + inputs=[ + ct.TensorType(name="input_ids", shape=(1, T_pre), dtype=np.int32), + ct.TensorType(name="input_len", shape=(1,), dtype=np.int32), + ], + outputs=[ + ct.TensorType(name="logits_last", dtype=np.float32), + ct.TensorType(name="kv_k", dtype=np.float32), + ct.TensorType(name="kv_v", dtype=np.float32), + ], + compute_precision=precision, + minimum_deployment_target=ct.target.macOS14, + convert_to="mlprogram", + ) + mlp = out_dir / f"LM-Prefill-T{T_pre}-M{M}-{tag}.mlpackage" + mlmodel.save(str(mlp)) + print(f" saved: {mlp}") + + pred = mlmodel.predict( + {"input_ids": ids_t.numpy().astype(np.int32), "input_len": len_t.numpy().astype(np.int32)} + ) + check( + "CoreML prefill logits", + torch.from_numpy(pred["logits_last"]), + logits_last, + tol=1.0 if args.fp16 else 1e-2, + ) + del prefill + + # ---------------- decode (pass-through KV) ---------------- + print("[2/3] Decode wrapper...") + decode = Qwen3Decode(hf_model, max_len=M).eval() + step_ids = torch.tensor([[next_id]], dtype=torch.int32) + cur_len = torch.tensor([n_valid], dtype=torch.int32) + with torch.no_grad(): + logits_step, kv_k2, kv_v2 = decode(step_ids, kv_k, kv_v, cur_len) + check("decode logits vs HF", logits_step, hf_step, tol=2e-2) + + if not args.skip_decode: + with torch.no_grad(): + traced = torch.jit.trace(decode, (step_ids, kv_k, kv_v, cur_len), strict=False) + L, _, Hkv, _, D = kv_k.shape + mlmodel = ct.convert( + traced, + inputs=[ + ct.TensorType(name="input_ids", shape=(1, 1), dtype=np.int32), + ct.TensorType(name="kv_k", shape=(L, 1, Hkv, M, D), dtype=np.float32), + ct.TensorType(name="kv_v", shape=(L, 1, Hkv, M, D), dtype=np.float32), + ct.TensorType(name="cur_len", shape=(1,), dtype=np.int32), + ], + outputs=[ + ct.TensorType(name="logits", dtype=np.float32), + ct.TensorType(name="kv_k_out", dtype=np.float32), + ct.TensorType(name="kv_v_out", dtype=np.float32), + ], + compute_precision=precision, + minimum_deployment_target=ct.target.macOS14, + convert_to="mlprogram", + ) + mlp = out_dir / f"LM-Decode-M{M}-{tag}.mlpackage" + mlmodel.save(str(mlp)) + print(f" saved: {mlp}") + + pred = mlmodel.predict( + { + "input_ids": step_ids.numpy().astype(np.int32), + "kv_k": kv_k.numpy().astype(np.float32), + "kv_v": kv_v.numpy().astype(np.float32), + "cur_len": cur_len.numpy().astype(np.int32), + } + ) + check( + "CoreML decode logits", + torch.from_numpy(pred["logits"]), + logits_step, + tol=1.0 if args.fp16 else 1e-2, + ) + del decode + + # ---------------- decode (stateful) ---------------- + if args.stateful_decode: + print("[3/3] Stateful decode wrapper (macOS 15+)...") + stateful = Qwen3DecodeStateful(hf_model, max_len=M).eval() + L, _, Hkv, _, D = kv_k.shape + + def seed_state(): + with torch.no_grad(): + for i in range(L): + getattr(stateful, f"kv_k_{i}").copy_(kv_k[i]) + getattr(stateful, f"kv_v_{i}").copy_(kv_v[i]) + + seed_state() + with torch.no_grad(): + logits_sf = stateful(step_ids, cur_len) + check("stateful vs pass-through", logits_sf, logits_step, tol=1e-4) + + seed_state() # warm-up mutated the buffers; restore before tracing + with torch.no_grad(): + traced = torch.jit.trace(stateful, (step_ids, cur_len), strict=False) + + state_dtype = np.float16 if args.fp16 else np.float32 + states = [] + for i in range(L): + states.append( + ct.StateType( + wrapped_type=ct.TensorType(shape=(1, Hkv, M, D), dtype=state_dtype), + name=f"kv_k_{i}", + ) + ) + states.append( + ct.StateType( + wrapped_type=ct.TensorType(shape=(1, Hkv, M, D), dtype=state_dtype), + name=f"kv_v_{i}", + ) + ) + mlmodel = ct.convert( + traced, + inputs=[ + ct.TensorType(name="input_ids", shape=(1, 1), dtype=np.int32), + ct.TensorType(name="cur_len", shape=(1,), dtype=np.int32), + ], + outputs=[ct.TensorType(name="logits", dtype=np.float32)], + states=states, + compute_precision=precision, + minimum_deployment_target=ct.target.macOS15, + convert_to="mlprogram", + ) + mlp = out_dir / f"LM-Decode-M{M}-{tag}-stateful.mlpackage" + mlmodel.save(str(mlp)) + print(f" saved: {mlp}") + + state = mlmodel.make_state() + # Seed CoreML state from prefill KV, then run one step. + # write_state only accepts float32 (converts to the fp16 state itself). + for i in range(L): + state.write_state(f"kv_k_{i}", kv_k[i].numpy().astype(np.float32)) + state.write_state(f"kv_v_{i}", kv_v[i].numpy().astype(np.float32)) + pred = mlmodel.predict( + { + "input_ids": step_ids.numpy().astype(np.int32), + "cur_len": cur_len.numpy().astype(np.int32), + }, + state=state, + ) + check( + "CoreML stateful logits", + torch.from_numpy(pred["logits"]), + logits_step, + tol=1.0 if args.fp16 else 1e-2, + ) + + print("[done]") + + +if __name__ == "__main__": + main() diff --git a/models/tts/neutts-2e/coreml/gen-pytorch-ref.py b/models/tts/neutts-2e/coreml/gen-pytorch-ref.py new file mode 100644 index 0000000..bde10c0 --- /dev/null +++ b/models/tts/neutts-2e/coreml/gen-pytorch-ref.py @@ -0,0 +1,101 @@ +"""Generate the PyTorch reference for NeuTTS-2E parity checks. + +Runs the upstream pipeline (transformers backbone + neucodec decode, no +watermark) with a fixed seed and saves everything the CoreML side needs to +compare against: + + build/ref/prompt_ids.json — full prompt token ids + build/ref/gen_ids.json — generated token ids (speech tokens + EOS) + build/ref/ref_codes.json — NeuCodec code indices actually decoded + build/ref/ref.wav — 24 kHz reference audio (bf16 backbone, fp32 codec) + +Usage: + uv run python gen-pytorch-ref.py [--text ...] [--speaker emily] [--emotion happy] +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np +import soundfile as sf +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +from src.prompt import MAX_CONTEXT, SAMPLE_RATE, build_prompt_ids, extract_speech_codes + +BACKBONE_REPO = "neuphonic/neutts-2e" +CODEC_REPO = "neuphonic/neucodec" + +DEFAULT_TEXT = "I can't believe it's finally here! The whole team worked so hard on this." + + +def main() -> None: + p = argparse.ArgumentParser() + p.add_argument("--text", default=DEFAULT_TEXT) + p.add_argument("--speaker", default="emily") + p.add_argument("--emotion", default="happy") + p.add_argument("--seed", type=int, default=1234) + p.add_argument("--temperature", type=float, default=1.0) + p.add_argument("--top-k", type=int, default=50) + p.add_argument("--output-dir", default="build/ref") + args = p.parse_args() + + out_dir = Path(args.output_dir) + out_dir.mkdir(parents=True, exist_ok=True) + + print(f"[1/4] Loading backbone {BACKBONE_REPO} (bf16, cpu)...") + tokenizer = AutoTokenizer.from_pretrained(BACKBONE_REPO) + backbone = AutoModelForCausalLM.from_pretrained(BACKBONE_REPO, dtype=torch.bfloat16) + backbone.eval() + + # Sanity: speech token ids must be contiguous (extract_speech_codes relies on it). + s0 = tokenizer.convert_tokens_to_ids("<|speech_0|>") + assert tokenizer.convert_tokens_to_ids("<|speech_1|>") == s0 + 1 + assert tokenizer.convert_tokens_to_ids("<|speech_65535|>") == s0 + 65_535 + + prompt_ids = build_prompt_ids(tokenizer, args.text, args.speaker, args.emotion) + print(f" prompt length: {len(prompt_ids)} tokens") + + print(f"[2/4] Generating (seed={args.seed}, temp={args.temperature}, top_k={args.top_k})...") + torch.manual_seed(args.seed) + speech_end_id = tokenizer.convert_tokens_to_ids("<|SPEECH_GENERATION_END|>") + prompt_tensor = torch.tensor(prompt_ids).unsqueeze(0) + with torch.no_grad(): + output = backbone.generate( + prompt_tensor, + max_length=MAX_CONTEXT, + eos_token_id=speech_end_id, + do_sample=True, + temperature=args.temperature, + top_k=args.top_k, + use_cache=True, + min_new_tokens=50, + ) + gen_ids = output[0, len(prompt_ids):].tolist() + codes = extract_speech_codes(tokenizer, gen_ids) + print(f" generated {len(gen_ids)} tokens -> {len(codes)} speech codes " + f"({len(codes) / 50.0:.2f}s audio)") + + print(f"[3/4] Decoding with {CODEC_REPO} (fp32, cpu)...") + from neucodec import NeuCodec + + codec = NeuCodec.from_pretrained(CODEC_REPO) + codec.eval() + with torch.no_grad(): + codes_t = torch.tensor(codes, dtype=torch.long)[None, None, :] + wav = codec.decode_code(codes_t).numpy()[0, 0, :] + + print(f"[4/4] Saving to {out_dir}/ ...") + (out_dir / "prompt_ids.json").write_text(json.dumps(prompt_ids)) + (out_dir / "gen_ids.json").write_text(json.dumps(gen_ids)) + (out_dir / "ref_codes.json").write_text(json.dumps(codes)) + (out_dir / "meta.json").write_text(json.dumps(vars(args))) + sf.write(out_dir / "ref.wav", wav.astype(np.float32), SAMPLE_RATE) + print(f" ref.wav: {len(wav) / SAMPLE_RATE:.2f}s, peak {np.abs(wav).max():.3f}") + + +if __name__ == "__main__": + main() diff --git a/models/tts/neutts-2e/coreml/inference.py b/models/tts/neutts-2e/coreml/inference.py new file mode 100644 index 0000000..34abb0c --- /dev/null +++ b/models/tts/neutts-2e/coreml/inference.py @@ -0,0 +1,232 @@ +"""End-to-end NeuTTS-2E inference on CoreML. + +tokenizer → LM prefill → sampled decode loop (stateful or pass-through KV) +→ NeuCodec decoder → 24 kHz wav. No watermark (upstream applies Perth +post-hoc; that is host-side postprocessing, not part of the models). + +Modes: + * default — sample with temperature/top-k like upstream + * --teacher-force — replay build/ref/gen_ids.json through the decode loop + and report next-token agreement vs the PyTorch run, + then decode the reference codes with the CoreML codec + (deterministic parity path) + +Usage: + uv run python inference.py \ + --lm-dir ./build/lm-fp16 --codec ./build/codec/NeuCodec-Decoder-fp16.mlpackage \ + --text "I can't believe it's finally here!" --speaker emily --emotion happy \ + --output ./build/out.wav +""" + +from __future__ import annotations + +import argparse +import json +import sys +import time +from pathlib import Path + +import coremltools as ct +import numpy as np +import soundfile as sf + +HERE = Path(__file__).parent +sys.path.insert(0, str(HERE)) + +from src.prompt import MAX_CONTEXT, SAMPLE_RATE, build_prompt_ids, extract_speech_codes # noqa: E402 + +BACKBONE_REPO = "neuphonic/neutts-2e" +MIN_NEW_TOKENS = 50 + + +def find_one(directory: Path, pattern: str) -> Path: + matches = sorted(directory.glob(pattern)) + if len(matches) != 1: + raise SystemExit(f"expected exactly one {pattern} in {directory}, found {matches}") + return matches[0] + + +def sample_top_k(logits: np.ndarray, temperature: float, top_k: int, rng) -> int: + logits = logits.astype(np.float64) / max(temperature, 1e-6) + top = np.argpartition(logits, -top_k)[-top_k:] + z = logits[top] - logits[top].max() + p = np.exp(z) / np.exp(z).sum() + return int(rng.choice(top, p=p)) + + +class StatefulDecoder: + def __init__(self, model: ct.models.MLModel, num_layers: int): + self.model = model + self.state = model.make_state() + self.num_layers = num_layers + + def seed(self, kv_k: np.ndarray, kv_v: np.ndarray) -> None: + # write_state only accepts float32 (converts to the fp16 state itself). + for i in range(self.num_layers): + self.state.write_state(f"kv_k_{i}", np.ascontiguousarray(kv_k[i], dtype=np.float32)) + self.state.write_state(f"kv_v_{i}", np.ascontiguousarray(kv_v[i], dtype=np.float32)) + + def step(self, token: int, cur_len: int) -> np.ndarray: + out = self.model.predict( + { + "input_ids": np.array([[token]], dtype=np.int32), + "cur_len": np.array([cur_len], dtype=np.int32), + }, + state=self.state, + ) + return out["logits"][0] + + +class PassthroughDecoder: + def __init__(self, model: ct.models.MLModel, kv_k: np.ndarray, kv_v: np.ndarray): + self.model = model + self.kv_k = kv_k.astype(np.float32) + self.kv_v = kv_v.astype(np.float32) + + def seed(self, kv_k: np.ndarray, kv_v: np.ndarray) -> None: + self.kv_k = kv_k.astype(np.float32) + self.kv_v = kv_v.astype(np.float32) + + def step(self, token: int, cur_len: int) -> np.ndarray: + out = self.model.predict( + { + "input_ids": np.array([[token]], dtype=np.int32), + "kv_k": self.kv_k, + "kv_v": self.kv_v, + "cur_len": np.array([cur_len], dtype=np.int32), + } + ) + self.kv_k = out["kv_k_out"] + self.kv_v = out["kv_v_out"] + return out["logits"][0] + + +def main() -> None: + p = argparse.ArgumentParser() + p.add_argument("--lm-dir", required=True) + p.add_argument("--codec", required=True) + p.add_argument("--text", default="I can't believe it's finally here!") + p.add_argument("--speaker", default="emily") + p.add_argument("--emotion", default="happy") + p.add_argument("--temperature", type=float, default=1.0) + p.add_argument("--top-k", type=int, default=50) + p.add_argument("--seed", type=int, default=1234) + p.add_argument("--output", default="build/out-coreml.wav") + p.add_argument("--passthrough-kv", action="store_true", + help="use the macOS14 pass-through-KV decode model instead of stateful") + p.add_argument("--teacher-force", action="store_true") + p.add_argument("--compute-units", default="ALL", + choices=["ALL", "CPU_AND_GPU", "CPU_ONLY", "CPU_AND_NE"]) + args = p.parse_args() + + cu = getattr(ct.ComputeUnit, args.compute_units) + lm_dir = Path(args.lm_dir) + + from transformers import AutoTokenizer + + tokenizer = AutoTokenizer.from_pretrained(BACKBONE_REPO) + if args.teacher_force: + # Replay must use the exact prompt the reference run saw. + prompt_ids = json.loads((HERE / "build" / "ref" / "prompt_ids.json").read_text()) + else: + prompt_ids = build_prompt_ids(tokenizer, args.text, args.speaker, args.emotion) + eos_id = tokenizer.convert_tokens_to_ids("<|SPEECH_GENERATION_END|>") + print(f"prompt: {len(prompt_ids)} tokens") + + print("loading prefill...") + prefill = ct.models.MLModel(str(find_one(lm_dir, "LM-Prefill-*.mlpackage")), compute_units=cu) + t_prefill = prefill.get_spec().description.input[0].type.multiArrayType.shape[1] + if len(prompt_ids) > t_prefill: + raise SystemExit(f"prompt ({len(prompt_ids)}) > prefill window ({t_prefill})") + + t0 = time.perf_counter() + padded = prompt_ids + [0] * (t_prefill - len(prompt_ids)) + out = prefill.predict( + { + "input_ids": np.array([padded], dtype=np.int32), + "input_len": np.array([len(prompt_ids)], dtype=np.int32), + } + ) + t_pre = time.perf_counter() - t0 + logits = out["logits_last"][0] + kv_k, kv_v = out["kv_k"], out["kv_v"] + num_layers = kv_k.shape[0] + print(f"prefill: {t_pre * 1000:.0f} ms ({num_layers} layers, kv {kv_k.shape})") + + print("loading decode...") + if args.passthrough_kv: + decode_path = find_one(lm_dir, "LM-Decode-M*[!l].mlpackage") + decoder = PassthroughDecoder(ct.models.MLModel(str(decode_path), compute_units=cu), kv_k, kv_v) + else: + decode_path = find_one(lm_dir, "LM-Decode-*-stateful.mlpackage") + decoder = StatefulDecoder(ct.models.MLModel(str(decode_path), compute_units=cu), num_layers) + decoder.seed(kv_k, kv_v) + + rng = np.random.default_rng(args.seed) + cur_len = len(prompt_ids) + gen_ids: list[int] = [] + + if args.teacher_force: + ref_ids = json.loads((HERE / "build" / "ref" / "gen_ids.json").read_text()) + agree_argmax = agree_topk = 0 + step_logits = logits + t0 = time.perf_counter() + for i, ref_tok in enumerate(ref_ids): + order = np.argsort(step_logits)[::-1] + agree_argmax += int(order[0] == ref_tok) + agree_topk += int(ref_tok in order[: args.top_k]) + if i + 1 < len(ref_ids): + step_logits = decoder.step(ref_tok, cur_len) + cur_len += 1 + dt = time.perf_counter() - t0 + n = len(ref_ids) + print(f"teacher-force: argmax agreement {agree_argmax}/{n} " + f"({100.0 * agree_argmax / n:.1f}%), ref token in CoreML top-{args.top_k}: " + f"{agree_topk}/{n} ({100.0 * agree_topk / n:.1f}%)") + print(f"decode: {1000.0 * dt / max(n - 1, 1):.1f} ms/token") + codes = json.loads((HERE / "build" / "ref" / "ref_codes.json").read_text()) + else: + t0 = time.perf_counter() + step_logits = logits + while cur_len < MAX_CONTEXT - 1: + if len(gen_ids) < MIN_NEW_TOKENS: + step_logits[eos_id] = -1e9 + tok = sample_top_k(step_logits, args.temperature, args.top_k, rng) + gen_ids.append(tok) + if tok == eos_id: + break + step_logits = decoder.step(tok, cur_len) + cur_len += 1 + dt = time.perf_counter() - t0 + codes = extract_speech_codes(tokenizer, gen_ids) + print(f"generated {len(gen_ids)} tokens ({len(codes)} codes, " + f"{len(codes) / 50.0:.2f}s) in {dt:.1f}s " + f"({1000.0 * dt / max(len(gen_ids), 1):.1f} ms/token)") + if not codes: + raise SystemExit("no speech codes generated") + + print("loading codec...") + codec = ct.models.MLModel(str(Path(args.codec)), compute_units=cu) + t0 = time.perf_counter() + audio = codec.predict({"codes": np.array([codes], dtype=np.int32)})["audio"][0] + t_dec = time.perf_counter() - t0 + dur = len(audio) / SAMPLE_RATE + print(f"codec: {t_dec * 1000:.0f} ms for {dur:.2f}s audio ({dur / t_dec:.1f}x RT)") + + out_path = Path(args.output) + out_path.parent.mkdir(parents=True, exist_ok=True) + sf.write(out_path, audio.astype(np.float32), SAMPLE_RATE) + print(f"wrote {out_path} ({dur:.2f}s, peak {np.abs(audio).max():.3f})") + + if args.teacher_force: + ref_wav_path = HERE / "build" / "ref" / "ref.wav" + if ref_wav_path.exists(): + ref_wav, _ = sf.read(ref_wav_path, dtype="float32") + n = min(len(ref_wav), len(audio)) + noise = audio[:n] - ref_wav[:n] + snr = 10.0 * np.log10((ref_wav[:n] ** 2).sum() / max((noise**2).sum(), 1e-12)) + print(f"audio vs PyTorch ref: SNR {snr:.1f} dB, max|Δ| {np.abs(noise).max():.4f}") + + +if __name__ == "__main__": + main() diff --git a/models/tts/neutts-2e/coreml/pyproject.toml b/models/tts/neutts-2e/coreml/pyproject.toml new file mode 100644 index 0000000..835bcea --- /dev/null +++ b/models/tts/neutts-2e/coreml/pyproject.toml @@ -0,0 +1,23 @@ +[project] +name = "neutts-2e-coreml" +version = "0.1.0" +description = "NeuTTS-2E (Qwen3 backbone + NeuCodec) CoreML conversion" +requires-python = ">=3.11,<3.13" +dependencies = [ + # 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So we just need to make the rest of the connection. \ No newline at end of file diff --git a/models/tts/neutts-2e/coreml/samples/steven.pt b/models/tts/neutts-2e/coreml/samples/steven.pt new file mode 100644 index 0000000000000000000000000000000000000000..e7ef98ffbb58fecf20dc32e8ae67353a7c8ac1d5 GIT binary patch literal 2594 zcmbtW4NR0}9RKls94M%y!)414%#VQM;J70(?ZO*$PzP!wQ_tggg^S~k_wEJpt5nd5 zLCwutI_J{NHC8RqS!+#mQv=gX*N2iRg&z~M54X(K`uIJ-oiAssvS<7A@IKG~SX~wRWFW zR_k(?+ZD;<_xkL0daaBpQ%Ym#-%m|Pg(Qddn`jF>%G~}XtsGt&Mh%@DQ5wP7SyL-V z7KMi?a#XPu{Aax!ZRMYsCa2Tuc1uph<@NZrPx#6upH!abaw=L`GqpsHRb*|69G7OZ z#%Sv2IlV8_mk`^KS&qi!K$dAsy}oLPVy}~Ye!91qCxj+DIQ71-3=!(xAI>!h_(Fyb zl?+ob)BqKBhuc-*sg^v7#hmpoqTLA~J4*nVwM@Pm&fr3Z29Su^0RUyo70D~uvzT-5 zDsBLY+odRONnlt%vnM&4@r4YHVHjowFo>>7*ICRn{ttkjs#UT9BI0NL30t%UeEd}W8pxp`J zLKpDcLEz_?fVa5!K|S#2a$sWt@HG9;@d1YnjI)^Y1)%F$pm7>dv=rF23212p;^))v zT3{0Ktz?;A+snBdn8u!Yn2bHZQ~1k%>f0&DfEm8+!coM&V1kN5NF6xv~ck%|{Ir>eb^fFE)^}BYHe;072nsLdM zo-qvcmAIWey-N9=e)a&(^wBY!M3JEi7F?+>Dz9CuV1LOx`TMQRO3mqrD7pn7iy|*f#E7c;7oMT zbfDovZPkV0-JlvJJf;I-|Gs5x*63&+nZ9vfL~77f{N55HFn2I}F>Vbk(5~;l4`ch+ R#kS2FMX16i2137I_b*uEQ{w;t literal 0 HcmV?d00001 diff --git a/models/tts/neutts-2e/coreml/samples/steven.txt b/models/tts/neutts-2e/coreml/samples/steven.txt new file mode 100644 index 0000000..1568251 --- /dev/null +++ b/models/tts/neutts-2e/coreml/samples/steven.txt @@ -0,0 +1 @@ +Meaning that my relationship with the other factions has been getting better, but there is still a long way to go. \ No newline at end of file diff --git a/models/tts/neutts-2e/coreml/src/__init__.py b/models/tts/neutts-2e/coreml/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/models/tts/neutts-2e/coreml/src/codec_coreml.py b/models/tts/neutts-2e/coreml/src/codec_coreml.py new file mode 100644 index 0000000..87a9099 --- /dev/null +++ b/models/tts/neutts-2e/coreml/src/codec_coreml.py @@ -0,0 +1,239 @@ +"""CoreML-friendly NeuCodec decoder (FSQ codes → 24 kHz audio). + +Reimplements only the ops coremltools cannot convert or that bake the +sequence length at trace time; everything else reuses the loaded neucodec +modules directly: + + * FSQ dequant — reimplemented (base-4 digit decomposition; the + vector-quantize-pytorch path is einops/int heavy) + * transformer RoPE — upstream misuses torchtune RoPE on [b, h, t, d] + (rotation by head index, constant over time); replicated + with fixed per-head buffers (see AttentionRope) + * ISTFT — irfft/complex → real IDFT matmul (1x1 convs) + + overlap-add via ConvTranspose1d, "same"-padding trim + +Reused as-is: embed conv, prior/post ResnetBlocks, attention/MLP weights, +final LayerNorm, fc_post_a, ISTFTHead.out linear. + +Wrapper I/O: + codes: [1, T] int32 NeuCodec indices + → audio: [1, T * 480] fp32 @ 24 kHz + +T is flexible (RangeDim): every reimplemented op is length-agnostic. +""" + +from __future__ import annotations + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +HOP = 480 +N_FFT = 1920 +ROPE_DIM = 64 # transformer head dim (pos_meb_dim) +ROPE_BASE = 10_000 + + +class FSQDequant(nn.Module): + """indices [1, T] → embeddings [1, T, 2048], matching + ``ResidualFSQ.get_output_from_indices`` for levels=[4]*8, 1 quantizer.""" + + def __init__(self, rfsq: nn.Module): + super().__init__() + levels = [4] * 8 + # Precompute all 65536 dequantized code vectors as an embedding table. + # Digit arithmetic (floor(code/basis) % level) is exact in fp32 but the + # fp16 compute pass corrupts integers > 2048; a gather is precision-safe. + basis = [1] + for lv in levels[:-1]: + basis.append(basis[-1] * lv) + idx = torch.arange(65_536, dtype=torch.float64).unsqueeze(-1) + digits = torch.floor(idx / torch.tensor(basis, dtype=torch.float64)) % torch.tensor( + levels, dtype=torch.float64 + ) + # vector-quantize-pytorch: half_width = level // 2 (integer), so level 4 + # dequantizes digits {0..3} to {-1, -0.5, 0, 0.5}. + half = torch.tensor([lv // 2 for lv in levels], dtype=torch.float64) + vals = (digits - half) / half + # ResidualFSQ per-quantizer, per-dim scales (quantizer 0 only here). + scales = rfsq.scales[0].detach().double().reshape(1, -1) if hasattr(rfsq, "scales") \ + else torch.ones(1, len(levels), dtype=torch.float64) + self.table = nn.Embedding(65_536, 8) + with torch.no_grad(): + self.table.weight.copy_((vals * scales).float()) + self.project_out = nn.Linear(8, 2048) + with torch.no_grad(): + self.project_out.weight.copy_(rfsq.project_out.weight.float()) + self.project_out.bias.copy_(rfsq.project_out.bias.float()) + + self._verify(rfsq) + + def forward(self, codes: torch.Tensor) -> torch.Tensor: + return self.project_out(self.table(codes.to(torch.long))) # [1, T, 2048] + + @torch.no_grad() + def _verify(self, rfsq: nn.Module) -> None: + idx = torch.randint(0, 65_536, (1, 173)) + want = rfsq.get_output_from_indices(idx.unsqueeze(-1)) # [1, T, 2048] + got = self.forward(idx.to(torch.int32)) + diff = (got - want.float()).abs().max().item() + if diff > 1e-4: + raise RuntimeError(f"FSQDequant mismatch vs vector-quantize-pytorch: {diff}") + + +class AttentionRope(nn.Module): + """bs_roformer5.Attention, replicating its RoPE quirk. + + Upstream calls torchtune's RotaryPositionalEmbeddings (which expects + ``[b, s, n_h, d]``) on tensors shaped ``[b, n_h, t, d]``, so the "position" + that gets rotated is the HEAD INDEX — a constant per head, identical at + every timestep. The pretrained weights bake this in, so we replicate it + with fixed [1, H, 1, D/2] cos/sin buffers. Upshot: no time-dependent + tables, and the sequence length stays fully flexible. + """ + + def __init__(self, hf_attn: nn.Module, n_heads: int, head_dim: int): + super().__init__() + self.n_heads = n_heads + self.head_dim = head_dim + self.c_attn = hf_attn.c_attn + self.c_proj = hf_attn.c_proj + + theta = 1.0 / ( + ROPE_BASE ** (torch.arange(0, head_dim, 2, dtype=torch.float32) / head_dim) + ) + pos = torch.arange(n_heads, dtype=torch.float32) # head index as "position" + freqs = torch.outer(pos, theta) # [H, D/2] + self.register_buffer("head_cos", freqs.cos().view(1, n_heads, 1, head_dim // 2)) + self.register_buffer("head_sin", freqs.sin().view(1, n_heads, 1, head_dim // 2)) + + def _rope(self, x: torch.Tensor) -> torch.Tensor: + # x: [B, H, T, D]; rotate interleaved pairs by per-head constants. + x1 = x[..., 0::2] + x2 = x[..., 1::2] + out = torch.stack( + [x1 * self.head_cos - x2 * self.head_sin, x2 * self.head_cos + x1 * self.head_sin], + dim=-1, + ) + return out.flatten(-2) + + def forward(self, x): + B = 1 + H, D = self.n_heads, self.head_dim + C = H * D + qkv = self.c_attn(x) # [1, T, 3*C] + q, k, v = qkv.split(C, dim=-1) + q = q.reshape(B, -1, H, D).transpose(1, 2) + k = k.reshape(B, -1, H, D).transpose(1, 2) + v = v.reshape(B, -1, H, D).transpose(1, 2) + q = self._rope(q) + k = self._rope(k) + y = F.scaled_dot_product_attention(q, k, v) + y = y.transpose(1, 2).reshape(B, -1, C) + return self.c_proj(y) + + +class TransformerBlockRope(nn.Module): + def __init__(self, hf_block: nn.Module, n_heads: int, head_dim: int): + super().__init__() + self.att_norm = hf_block.att_norm + self.ffn_norm = hf_block.ffn_norm + self.att = AttentionRope(hf_block.att, n_heads, head_dim) + self.mlp = hf_block.mlp + + def forward(self, x): + x = x + self.att(self.att_norm(x)) + x = x + self.mlp(self.ffn_norm(x)) + return x + + +class ISTFTSame(nn.Module): + """Vocos "same"-padding ISTFT via IDFT matmul + ConvTranspose1d overlap-add.""" + + def __init__(self): + super().__init__() + n_bins = N_FFT // 2 + 1 + n = np.arange(N_FFT)[:, None] + k = np.arange(n_bins)[None, :] + scale = np.ones(n_bins) + scale[1:-1] = 2.0 # hermitian doubling except DC and Nyquist + wr = (scale * np.cos(2 * np.pi * n * k / N_FFT) / N_FFT).astype(np.float32) + wi = (-scale * np.sin(2 * np.pi * n * k / N_FFT) / N_FFT).astype(np.float32) + window = torch.hann_window(N_FFT) + + # IDFT as 1x1 convs: [B, n_bins, T] → [B, N_FFT, T], window folded in. + w = window.numpy()[:, None] + self.register_buffer("idft_real", torch.from_numpy((wr * w))[:, :, None]) + self.register_buffer("idft_imag", torch.from_numpy((wi * w))[:, :, None]) + # Overlap-add: ConvTranspose1d, in=N_FFT, out=1, kernel=N_FFT, stride=HOP. + # Channel c contributes its value at kernel offset c: kernel[c, 0, c] = 1. + eye = torch.eye(N_FFT).reshape(N_FFT, 1, N_FFT) + self.register_buffer("ola_kernel", eye) + self.register_buffer("win_sq", (window * window).reshape(1, 1, N_FFT)) + self.pad = (N_FFT - HOP) // 2 + + self._verify() + + def forward(self, real: torch.Tensor, imag: torch.Tensor) -> torch.Tensor: + # real/imag: [B, n_bins, T] + frames = F.conv1d(real, self.idft_real) + F.conv1d(imag, self.idft_imag) + audio = F.conv_transpose1d(frames, self.ola_kernel, stride=HOP) # [B, 1, L] + ones = torch.ones_like(real[:, 0:1, :]) + envelope = F.conv_transpose1d(ones, self.win_sq, stride=HOP) + audio = audio[:, 0, self.pad : -self.pad] / envelope[:, 0, self.pad : -self.pad] + return audio # [B, T*HOP] + + @torch.no_grad() + def _verify(self) -> None: + from neucodec.codec_decoder_vocos import ISTFT + + ref = ISTFT(n_fft=N_FFT, hop_length=HOP, win_length=N_FFT, padding="same") + t = 37 + spec = torch.randn(1, N_FFT // 2 + 1, t, dtype=torch.complex64) + want = ref(spec) + got = self.forward(spec.real, spec.imag) + diff = (got - want).abs().max().item() + if diff > 1e-3: + raise RuntimeError(f"ISTFTSame mismatch vs neucodec ISTFT: {diff}") + + +class NeuCodecDecoder(nn.Module): + """codes [1, T] → audio [1, T*480].""" + + def __init__(self, codec: nn.Module): + super().__init__() + gen = codec.generator + self.fsq = FSQDequant(gen.quantizer) + self.fc_post_a = codec.fc_post_a + backbone = gen.backbone + self.embed = backbone.embed + self.prior_net = backbone.prior_net + self.blocks = nn.ModuleList( + [TransformerBlockRope(b, n_heads=16, head_dim=64) for b in backbone.transformers] + ) + self.final_layer_norm = backbone.final_layer_norm + self.post_net = backbone.post_net + self.head_out = gen.head.out + self.istft = ISTFTSame() + + def forward(self, codes: torch.Tensor): + x = self.fsq(codes) # [1, T, 2048] + x = self.fc_post_a(x) # [1, T, 1024] + x = x.transpose(1, 2) # [1, 1024, T] + x = self.embed(x) + x = self.prior_net(x) + x = x.transpose(1, 2) + for block in self.blocks: + x = block(x) + x = x.transpose(1, 2) + x = self.post_net(x) + x = x.transpose(1, 2) + x = self.final_layer_norm(x) + + x = self.head_out(x).transpose(1, 2) # [1, N_FFT+2, T] + mag, p = x.chunk(2, dim=1) + mag = torch.clip(torch.exp(mag), max=1e2) + real = mag * torch.cos(p) + imag = mag * torch.sin(p) + return self.istft(real, imag) # [1, T*480] diff --git a/models/tts/neutts-2e/coreml/src/lm_coreml.py b/models/tts/neutts-2e/coreml/src/lm_coreml.py new file mode 100644 index 0000000..1950306 --- /dev/null +++ b/models/tts/neutts-2e/coreml/src/lm_coreml.py @@ -0,0 +1,416 @@ +"""CoreML-friendly re-implementation of the NeuTTS-2E Qwen3 backbone. + +Same design as ``models/tts/cosyvoice3/coreml/src/llm_coreml.py`` (static KV +cache, three wrappers) with the Qwen3 differences: + + * per-head RMSNorm on q/k (``q_norm`` / ``k_norm``) before RoPE + * ``head_dim`` decoupled from ``hidden_size`` (128 vs 512/12) + * tied embeddings: one shared [V, C] weight used for both the input + gather and the LM head, so CoreML const-dedup stores it once per model + +Wrappers: + + * ``Qwen3Prefill`` — input_ids [1, T_pre] + input_len [1] → + logits_last [1, V] (logits at position input_len-1), + kv_k / kv_v [L, 1, Hkv, max_len, D] (positions [0, input_len) filled) + * ``Qwen3Decode`` — input_ids [1, 1] + kv_k + kv_v + cur_len [1] → + logits [1, V], kv_k_out, kv_v_out + * ``Qwen3DecodeStateful`` — input_ids [1, 1] + cur_len [1] → logits [1, V]; + KV lives in per-layer StateType buffers (macOS 15+ / iOS 18+), + seeded from prefill's kv_k / kv_v output by the host. +""" + +from __future__ import annotations + +from typing import List, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def _build_rope_inv_freq(head_dim: int, rope_theta: float) -> torch.Tensor: + return 1.0 / ( + rope_theta ** (torch.arange(0, head_dim, 2, dtype=torch.float32) / head_dim) + ) + + +def _resolve_rope_theta(cfg) -> float: + direct = getattr(cfg, "rope_theta", None) + if direct is not None: + return float(direct) + params = getattr(cfg, "rope_parameters", None) + if isinstance(params, dict): + for key in ("rope_theta", "base"): + if key in params and params[key] is not None: + return float(params[key]) + raise AttributeError("Config exposes neither rope_theta nor rope_parameters") + + +def _rope_cos_sin(positions: torch.Tensor, inv_freq: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + freqs = positions.float().unsqueeze(-1) * inv_freq.view(1, 1, -1) # [B, T, D/2] + emb = torch.cat([freqs, freqs], dim=-1) # [B, T, D] + return emb.cos(), emb.sin() + + +def _rotate_half(x: torch.Tensor, half: int) -> torch.Tensor: + # `half` must be a Python int: x.shape[-1] // 2 traces into + # aten::floor_divide/Int nodes the CoreML frontend cannot fold. + x1 = x[..., :half] + x2 = x[..., half:] + return torch.cat((-x2, x1), dim=-1) + + +def _apply_rope(q, k, cos, sin, half: int): + cos = cos.unsqueeze(1) + sin = sin.unsqueeze(1) + q_out = (q * cos) + (_rotate_half(q, half) * sin) + k_out = (k * cos) + (_rotate_half(k, half) * sin) + return q_out, k_out + + +class RMSNorm(nn.Module): + def __init__(self, weight: torch.Tensor, eps: float = 1e-6): + super().__init__() + self.weight = nn.Parameter(weight.detach().clone().to(torch.float32)) + self.eps = eps + + def forward(self, x: torch.Tensor) -> torch.Tensor: + var = x.float().pow(2).mean(-1, keepdim=True) + x = x.float() * torch.rsqrt(var + self.eps) + return self.weight * x + + +class _LinearLike(nn.Module): + def __init__(self, hf_lin: nn.Linear): + super().__init__() + self.weight = nn.Parameter(hf_lin.weight.detach().clone().to(torch.float32)) + if hf_lin.bias is not None: + self.bias = nn.Parameter(hf_lin.bias.detach().clone().to(torch.float32)) + else: + self.register_parameter("bias", None) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return F.linear(x, self.weight, self.bias) + + +class Qwen3MLP(nn.Module): + def __init__(self, hf_mlp: nn.Module): + super().__init__() + self.gate_proj = _LinearLike(hf_mlp.gate_proj) + self.up_proj = _LinearLike(hf_mlp.up_proj) + self.down_proj = _LinearLike(hf_mlp.down_proj) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x)) + + +class Qwen3AttnPrefill(nn.Module): + def __init__( + self, hf_attn: nn.Module, num_heads: int, num_kv_heads: int, head_dim: int, seq_len: int + ): + super().__init__() + self.q_proj = _LinearLike(hf_attn.q_proj) + self.k_proj = _LinearLike(hf_attn.k_proj) + self.v_proj = _LinearLike(hf_attn.v_proj) + self.o_proj = _LinearLike(hf_attn.o_proj) + self.q_norm = RMSNorm(hf_attn.q_norm.weight, eps=hf_attn.q_norm.variance_epsilon) + self.k_norm = RMSNorm(hf_attn.k_norm.weight, eps=hf_attn.k_norm.variance_epsilon) + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = head_dim + self.seq_len = seq_len + self.rep = num_heads // num_kv_heads + self.scale = head_dim**-0.5 + + def forward(self, x, cos, sin, attn_mask): + # Static shapes (torch>=2.10 traces x.shape unpacking into aten::Int + # nodes that coremltools' frontend cannot fold). + B, T = 1, self.seq_len + H, Hkv, D = self.num_heads, self.num_kv_heads, self.head_dim + + # Qwen3: per-head RMSNorm on q/k before RoPE. + q = self.q_norm(self.q_proj(x).view(B, T, H, D)).transpose(1, 2) + k = self.k_norm(self.k_proj(x).view(B, T, Hkv, D)).transpose(1, 2) + v = self.v_proj(x).view(B, T, Hkv, D).transpose(1, 2) + q, k = _apply_rope(q, k, cos, sin, D // 2) + + # GQA head expansion via expand+reshape (repeat_interleave traces into + # aten::floor_divide/Int nodes the CoreML frontend cannot fold). + k_rep = k.unsqueeze(2).expand(B, Hkv, self.rep, T, D).reshape(B, H, T, D) + v_rep = v.unsqueeze(2).expand(B, Hkv, self.rep, T, D).reshape(B, H, T, D) + + attn = torch.matmul(q, k_rep.transpose(-2, -1)) * self.scale + attn = attn + attn_mask + attn = F.softmax(attn, dim=-1) + out = torch.matmul(attn, v_rep) + out = out.transpose(1, 2).contiguous().view(B, T, H * D) + return self.o_proj(out), k, v + + +class Qwen3AttnDecode(nn.Module): + def __init__( + self, hf_attn: nn.Module, num_heads: int, num_kv_heads: int, head_dim: int, max_len: int + ): + super().__init__() + self.q_proj = _LinearLike(hf_attn.q_proj) + self.k_proj = _LinearLike(hf_attn.k_proj) + self.v_proj = _LinearLike(hf_attn.v_proj) + self.o_proj = _LinearLike(hf_attn.o_proj) + self.q_norm = RMSNorm(hf_attn.q_norm.weight, eps=hf_attn.q_norm.variance_epsilon) + self.k_norm = RMSNorm(hf_attn.k_norm.weight, eps=hf_attn.k_norm.variance_epsilon) + self.num_heads = num_heads + self.num_kv_heads = num_kv_heads + self.head_dim = head_dim + self.max_len = max_len + self.rep = num_heads // num_kv_heads + self.scale = head_dim**-0.5 + + def forward(self, x, cos, sin, k_cache, v_cache, update_mask, attn_mask): + B, T = 1, 1 # static: single-token decode step + H, Hkv, D = self.num_heads, self.num_kv_heads, self.head_dim + + q = self.q_norm(self.q_proj(x).view(B, T, H, D)).transpose(1, 2) + k = self.k_norm(self.k_proj(x).view(B, T, Hkv, D)).transpose(1, 2) + v = self.v_proj(x).view(B, T, Hkv, D).transpose(1, 2) + q, k = _apply_rope(q, k, cos, sin, D // 2) + + # Scatter (k, v) into the cache at position cur_len via one-hot mask. + k_full = k_cache * (1.0 - update_mask) + k * update_mask + v_full = v_cache * (1.0 - update_mask) + v * update_mask + + M = self.max_len + k_rep = k_full.unsqueeze(2).expand(B, Hkv, self.rep, M, D).reshape(B, H, M, D) + v_rep = v_full.unsqueeze(2).expand(B, Hkv, self.rep, M, D).reshape(B, H, M, D) + + attn = torch.matmul(q, k_rep.transpose(-2, -1)) * self.scale + attn = attn + attn_mask + attn = F.softmax(attn, dim=-1) + out = torch.matmul(attn, v_rep) + out = out.transpose(1, 2).contiguous().view(B, T, H * D) + return self.o_proj(out), k_full, v_full + + +class Qwen3LayerPrefill(nn.Module): + def __init__(self, hf_layer, num_heads, num_kv_heads, head_dim, seq_len): + super().__init__() + self.input_layernorm = RMSNorm(hf_layer.input_layernorm.weight) + self.post_attention_layernorm = RMSNorm(hf_layer.post_attention_layernorm.weight) + self.self_attn = Qwen3AttnPrefill(hf_layer.self_attn, num_heads, num_kv_heads, head_dim, seq_len) + self.mlp = Qwen3MLP(hf_layer.mlp) + + def forward(self, x, cos, sin, attn_mask): + h = self.input_layernorm(x) + a, k_new, v_new = self.self_attn(h, cos, sin, attn_mask) + x = x + a + h = self.post_attention_layernorm(x) + x = x + self.mlp(h) + return x, k_new, v_new + + +class Qwen3LayerDecode(nn.Module): + def __init__(self, hf_layer, num_heads, num_kv_heads, head_dim, max_len): + super().__init__() + self.input_layernorm = RMSNorm(hf_layer.input_layernorm.weight) + self.post_attention_layernorm = RMSNorm(hf_layer.post_attention_layernorm.weight) + self.self_attn = Qwen3AttnDecode(hf_layer.self_attn, num_heads, num_kv_heads, head_dim, max_len) + self.mlp = Qwen3MLP(hf_layer.mlp) + + def forward(self, x, cos, sin, k_cache, v_cache, update_mask, attn_mask): + h = self.input_layernorm(x) + a, k_full, v_full = self.self_attn(h, cos, sin, k_cache, v_cache, update_mask, attn_mask) + x = x + a + h = self.post_attention_layernorm(x) + x = x + self.mlp(h) + return x, k_full, v_full + + +class _Qwen3Base(nn.Module): + def _init_common(self, qwen_for_causal_lm: nn.Module, max_len: int): + qw = qwen_for_causal_lm + cfg = qw.config + self.num_layers = cfg.num_hidden_layers + self.num_heads = cfg.num_attention_heads + self.num_kv_heads = cfg.num_key_value_heads + self.head_dim = cfg.head_dim + self.hidden_size = cfg.hidden_size + self.vocab_size = cfg.vocab_size + self.rope_theta = _resolve_rope_theta(cfg) + self.max_len = max_len + + self.register_buffer( + "inv_freq", _build_rope_inv_freq(self.head_dim, self.rope_theta), persistent=False + ) + self.register_buffer( + "pos_ids", torch.arange(max_len, dtype=torch.int32), persistent=False + ) + + # Tied embedding / LM head weight [V, C]. Used twice (gather + linear); + # CoreML const-dedup collapses it to a single stored constant. + self.tok_weight = nn.Parameter( + qw.model.embed_tokens.weight.detach().clone().to(torch.float32) + ) + self.norm = RMSNorm(qw.model.norm.weight) + return qw + + def _embed(self, input_ids: torch.Tensor) -> torch.Tensor: + return F.embedding(input_ids.to(torch.long), self.tok_weight) + + def _logits(self, hidden: torch.Tensor) -> torch.Tensor: + return F.linear(hidden, self.tok_weight) + + +class Qwen3Prefill(_Qwen3Base): + """Static-shape prefill over token ids. + + Inputs: + input_ids: [1, T_pre] int32 (right-padded past input_len; pad value irrelevant) + input_len: [1] int32 — number of valid tokens + Outputs: + logits_last: [1, V] — logits at position input_len-1 + kv_k, kv_v: [L, 1, Hkv, max_len, D] — positions [0, input_len) filled + """ + + def __init__(self, qwen_for_causal_lm: nn.Module, max_len: int, t_prefill: int): + super().__init__() + qw = self._init_common(qwen_for_causal_lm, max_len) + self.t_prefill = t_prefill + self.layers = nn.ModuleList( + [ + Qwen3LayerPrefill( + qw.model.layers[i], self.num_heads, self.num_kv_heads, self.head_dim, t_prefill + ) + for i in range(self.num_layers) + ] + ) + + def forward(self, input_ids: torch.Tensor, input_len: torch.Tensor): + B, T = 1, self.t_prefill + Hkv, D, M = self.num_kv_heads, self.head_dim, self.max_len + + positions = torch.arange(T, dtype=torch.int32).view(1, T) + cos, sin = _rope_cos_sin(positions, self.inv_freq) + + idx = torch.arange(T, dtype=torch.int32).view(1, 1, T, 1) + jdx = torch.arange(T, dtype=torch.int32).view(1, 1, 1, T) + causal = jdx <= idx + valid_key = jdx < input_len.view(1, 1, 1, 1).to(torch.int32) + attendable = causal & valid_key + neg_inf = torch.tensor(-1e4, dtype=torch.float32) # fp16-safe + attn_mask = torch.where(attendable, torch.zeros((), dtype=torch.float32), neg_inf) + + x = self._embed(input_ids) + pad = M - T + zero_pad = torch.zeros(B, Hkv, pad, D, dtype=x.dtype) + k_all: List[torch.Tensor] = [] + v_all: List[torch.Tensor] = [] + for layer in self.layers: + x, k_new, v_new = layer(x, cos, sin, attn_mask) + k_all.append(torch.cat([k_new, zero_pad], dim=2)) + v_all.append(torch.cat([v_new, zero_pad], dim=2)) + + x = self.norm(x) + + # Gather hidden state at the last valid position, then a single-row LM head. + last_idx = (input_len.view(1) - 1).to(torch.long) + h_last = x.index_select(1, last_idx) # [1, 1, C] + logits_last = self._logits(h_last).squeeze(1) # [1, V] + + kv_k = torch.stack(k_all, dim=0) + kv_v = torch.stack(v_all, dim=0) + return logits_last, kv_k, kv_v + + +class Qwen3Decode(_Qwen3Base): + """Single-step decode with pass-through KV cache (runs on macOS 14+).""" + + def __init__(self, qwen_for_causal_lm: nn.Module, max_len: int): + super().__init__() + qw = self._init_common(qwen_for_causal_lm, max_len) + self.layers = nn.ModuleList( + [ + Qwen3LayerDecode( + qw.model.layers[i], self.num_heads, self.num_kv_heads, self.head_dim, max_len + ) + for i in range(self.num_layers) + ] + ) + + def forward(self, input_ids, kv_k, kv_v, cur_len): + M = self.max_len + cur = cur_len.view(1).to(torch.int32) + + positions = cur.view(1, 1) + cos, sin = _rope_cos_sin(positions, self.inv_freq) + + pj = self.pos_ids.view(1, 1, M, 1) + update_mask = (pj == cur.view(1, 1, 1, 1)).to(torch.float32) + attendable = self.pos_ids.view(1, 1, 1, M) <= cur.view(1, 1, 1, 1) + neg_inf = torch.tensor(-1e4, dtype=torch.float32) + attn_mask = torch.where(attendable, torch.zeros((), dtype=torch.float32), neg_inf) + + x = self._embed(input_ids) + k_all: List[torch.Tensor] = [] + v_all: List[torch.Tensor] = [] + for i, layer in enumerate(self.layers): + x, k_full, v_full = layer(x, cos, sin, kv_k[i], kv_v[i], update_mask, attn_mask) + k_all.append(k_full) + v_all.append(v_full) + + x = self.norm(x) + logits = self._logits(x).squeeze(1) # [1, V] + return logits, torch.stack(k_all, dim=0), torch.stack(v_all, dim=0) + + +class Qwen3DecodeStateful(_Qwen3Base): + """Single-step decode with in-place StateType KV cache (macOS 15+ / iOS 18+). + + Per-layer buffers keep each state's read/write symmetric — the pattern + coremltools' stateful pass lowers cleanly (see cosyvoice3 notes). + """ + + def __init__(self, qwen_for_causal_lm: nn.Module, max_len: int): + super().__init__() + qw = self._init_common(qwen_for_causal_lm, max_len) + Hkv, D = self.num_kv_heads, self.head_dim + for i in range(self.num_layers): + self.register_buffer( + f"kv_k_{i}", torch.zeros(1, Hkv, max_len, D, dtype=torch.float32), persistent=False + ) + self.register_buffer( + f"kv_v_{i}", torch.zeros(1, Hkv, max_len, D, dtype=torch.float32), persistent=False + ) + self.layers = nn.ModuleList( + [ + Qwen3LayerDecode( + qw.model.layers[i], self.num_heads, self.num_kv_heads, self.head_dim, max_len + ) + for i in range(self.num_layers) + ] + ) + + def forward(self, input_ids, cur_len): + M = self.max_len + cur = cur_len.view(1).to(torch.int32) + + positions = cur.view(1, 1) + cos, sin = _rope_cos_sin(positions, self.inv_freq) + + pj = self.pos_ids.view(1, 1, M, 1) + update_mask = (pj == cur.view(1, 1, 1, 1)).to(torch.float32) + attendable = self.pos_ids.view(1, 1, 1, M) <= cur.view(1, 1, 1, 1) + neg_inf = torch.tensor(-1e4, dtype=torch.float32) + attn_mask = torch.where(attendable, torch.zeros((), dtype=torch.float32), neg_inf) + + x = self._embed(input_ids) + for i, layer in enumerate(self.layers): + k_i = getattr(self, f"kv_k_{i}") + v_i = getattr(self, f"kv_v_{i}") + x, k_full, v_full = layer(x, cos, sin, k_i, v_i, update_mask, attn_mask) + # [:] write pattern → coremltools generate_tensor_assignment_ops + # lowers each to an independent StateType update. + getattr(self, f"kv_k_{i}")[:] = k_full + getattr(self, f"kv_v_{i}")[:] = v_full + + x = self.norm(x) + return self._logits(x).squeeze(1) # [1, V] diff --git a/models/tts/neutts-2e/coreml/src/prompt.py b/models/tts/neutts-2e/coreml/src/prompt.py new file mode 100644 index 0000000..f51d588 --- /dev/null +++ b/models/tts/neutts-2e/coreml/src/prompt.py @@ -0,0 +1,95 @@ +"""Prompt construction for NeuTTS-2E (BPE input format). + +Reimplements ``neutts.NeuTTS._apply_chat_template`` for the 2e/BPE path so the +conversion project does not depend on the ``neutts`` package (which drags in +phonemizer/espeak that the BPE model never uses). + +Token layout produced (matches upstream exactly): + + <|TEXT_PROMPT_START|> {ref_text tokens} [<|EMOTION|>] {input_text tokens} + <|TEXT_PROMPT_END|> <|SPEECH_GENERATION_START|> {ref speech-code tokens} + +Generation then continues with sampled ``<|speech_N|>`` tokens until +``<|SPEECH_GENERATION_END|>``. +""" + +from __future__ import annotations + +import unicodedata +from pathlib import Path + +import torch + +SAMPLE_DIR = Path(__file__).parents[1] / "samples" +SPEAKERS = ("emily", "paul", "sophie", "steven") +EMOTIONS = ("angry", "disgusted", "fearful", "happy", "neutral", "sad", "surprised") + +MAX_CONTEXT = 2048 +SAMPLE_RATE = 24_000 +HOP_LENGTH = 480 # audio samples per speech code at 24 kHz (50 codes/s) + +_QUOTE_MAP = str.maketrans({"‘": "'", "’": "'", "“": '"', "”": '"'}) + + +def normalize_text(text: str) -> str: + return unicodedata.normalize("NFKC", text.translate(_QUOTE_MAP)) + + +def load_speaker(name: str) -> tuple[list[int], str]: + """Return (ref_codes, ref_text) for one of the four fixed speakers.""" + if name not in SPEAKERS: + raise ValueError(f"Unknown speaker '{name}'. Available: {list(SPEAKERS)}") + codes = torch.load(SAMPLE_DIR / f"{name}.pt", map_location="cpu", weights_only=True) + text = (SAMPLE_DIR / f"{name}.txt").read_text().strip() + return [int(c) for c in codes.reshape(-1).tolist()], text + + +def build_prompt_ids( + tokenizer, + text: str, + speaker: str = "emily", + emotion: str = "neutral", +) -> list[int]: + """Token ids for the full generation prompt (mirrors upstream).""" + if emotion not in EMOTIONS: + raise ValueError(f"Unknown emotion '{emotion}'. Supported: {list(EMOTIONS)}") + ref_codes, ref_text = load_speaker(speaker) + + text_prompt_start = tokenizer.convert_tokens_to_ids("<|TEXT_PROMPT_START|>") + text_prompt_end = tokenizer.convert_tokens_to_ids("<|TEXT_PROMPT_END|>") + speech_gen_start = tokenizer.convert_tokens_to_ids("<|SPEECH_GENERATION_START|>") + + ref_text = normalize_text(ref_text) + text = normalize_text(text) + if emotion == "neutral": + # Single-pass encode so BPE resolves the boundary the same way upstream does. + input_ids = tokenizer.encode(f"{ref_text} {text}", add_special_tokens=False) + else: + emotion_id = tokenizer.convert_tokens_to_ids(f"<|{emotion.upper()}|>") + input_ids = ( + tokenizer.encode(ref_text, add_special_tokens=False) + + [emotion_id] + + tokenizer.encode(text, add_special_tokens=False) + ) + + codes_str = "".join(f"<|speech_{i}|>" for i in ref_codes) + code_ids = tokenizer.encode(codes_str, add_special_tokens=False) + + return ( + [text_prompt_start] + + input_ids + + [text_prompt_end] + 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Also records the discovery that the hosted StyleTTS2 sized BERT/diffusion mlmodelc bundles are missing model.mil (long texts fail with corruptedModel). --- models/tts/neutts-2e/coreml/README.md | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/models/tts/neutts-2e/coreml/README.md b/models/tts/neutts-2e/coreml/README.md index 0c899cf..b424b96 100644 --- a/models/tts/neutts-2e/coreml/README.md +++ b/models/tts/neutts-2e/coreml/README.md @@ -87,6 +87,29 @@ uv run python inference.py --lm-dir ./build/lm-fp16 \ + `ConvTranspose1d` overlap-add with window-envelope division (verified vs the library to 1e-3 before conversion). +## Comparison vs FluidAudio TTS engines (M5 Pro, 2026-07-25) + +Same 14-word sentence ("I can't believe it's finally here! The whole team +worked so hard on this."), warm runs (mean of 3), WER via parakeet-tdt-v3 +round-trip. FluidAudio engines measured with +`fluidaudiocli tts --backend --metrics` (release build); NeuTTS-2E via +`inference.py` (Python coremltools host — a Swift host would shave per-step +overhead). + +| Engine | RTFx | WER | Disk | Notes | +|---|---|---|---|---| +| Supertonic-3 | 44× | ~5 % ("works"/"worked" in 2/3 runs) | 284 MB | 44.1 kHz, fastest | +| KokoroAne | 6.3× | 0 % | 782 MB | ANE-resident | +| PocketTTS | 4.8× | 2 % | 866 MB | streaming | +| **NeuTTS-2E (this)** | **1.06×** | **0 % (3/3 exact)** | 1.28 GB | only emotional-control engine; decode 11.9 ms/tok (84 tok/s vs 50 real-time), codec 3.2× RT | +| StyleTTS2 | 7.5× (short text only) | high | 452 MB | hosted sized BERT/diffusion mlmodelc are missing model.mil → texts beyond ~t32 fail (`corruptedModel`); needs re-upload | + +NeuTTS-2E is the slowest (autoregressive LM at 50 codes/s + full-sequence +vocoder) but the only engine with emotion control, and its intelligibility +matched the best engines. Batch RTFx 1.06× means marginally real-time; chunked +streaming decode (upstream's 25-frame windows) would cut time-to-first-audio +to well under a second. + ## Follow-ups - ANE profiling (`tools/coreml-cli`) and a fixed-shape codec variant if ANE From bac878652e5f23d14483f15a880b00ad81b43133 Mon Sep 17 00:00:00 2001 From: Alex-Wengg Date: Sat, 25 Jul 2026 15:47:26 -0400 Subject: [PATCH 3/5] Add streaming mode + round-2 benchmarks (ANE codec, TTFA 650ms) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - inference.py --stream: upstream 25-frame windowed overlap-add vocoding over the flexible-length CoreML codec, ref codes as initial lookback. TTFA ~650 ms at every text length, inter-chunk ~340 ms (500 ms budget), 0% WER (identical transcription to batch). - Compute-unit sweep: codec is ~2x faster on CPU_AND_NE (22-27x RT) than GPU despite RangeDim — corrected the docstring claim; LM decode best on GPU (9.2 ms/tok warm; CPU_AND_NE breaks the coremltools state API; CPU_ONLY still real-time). inference.py now defaults LM=ALL, codec=NE. - Multi-length benchmark matrix (5/14/53 words x 3 runs) vs KokoroAne, PocketTTS, Supertonic-3, StyleTTS2; NeuTTS-2E batch 1.3-1.7x RT, stream 1.4-1.5x RT, 0% WER on medium/long incl. the 23s paragraph. - StyleTTS2 corruptedModel root cause: FluidAudio downloader drops model.mil from sized mlmodelc bundles (HF assets are complete); cache patched via curl for the benchmark. --- models/tts/neutts-2e/coreml/README.md | 66 ++++++---- models/tts/neutts-2e/coreml/inference.py | 153 +++++++++++++++++++++-- 2 files changed, 189 insertions(+), 30 deletions(-) diff --git a/models/tts/neutts-2e/coreml/README.md b/models/tts/neutts-2e/coreml/README.md index b424b96..6806ce6 100644 --- a/models/tts/neutts-2e/coreml/README.md +++ b/models/tts/neutts-2e/coreml/README.md @@ -64,8 +64,15 @@ uv run python inference.py --lm-dir ./build/lm-fp16 \ - Teacher-forced replay of the 301-token PyTorch reference: 98.7 % of reference tokens inside the CoreML top-50 sampling support; final audio SNR 41.5 dB vs the PyTorch waveform. -- Sampled end-to-end run: 13 ms/token decode (≈77 tok/s vs 50 needed for - real-time), prefill 169 ms, codec 2.7–9.6× RT (ComputeUnit.ALL). +- Sampled end-to-end run (warm): decode 9.2 ms/token (109 tok/s vs 50 needed + for real-time; LM on GPU), prefill 33 ms, codec 12.7–27× RT on ANE. + Compute units: LM decode best on ALL/CPU_AND_GPU (CPU_AND_NE breaks the + coremltools state API; CPU_ONLY still real-time at 58 tok/s); codec ~2× + faster on CPU_AND_NE than GPU. `inference.py` defaults to this split. +- Streaming (`--stream`, upstream 25-frame windowed overlap-add over the + flexible-length codec): TTFA ≈ 650 ms at every text length, steady-state + inter-chunk ≈ 340 ms against the 500 ms budget, 1.36–1.47× RT overall, + 0 % WER (transcribes identically to batch). ## Conversion gotchas (also see git history) @@ -89,26 +96,41 @@ uv run python inference.py --lm-dir ./build/lm-fp16 \ ## Comparison vs FluidAudio TTS engines (M5 Pro, 2026-07-25) -Same 14-word sentence ("I can't believe it's finally here! The whole team -worked so hard on this."), warm runs (mean of 3), WER via parakeet-tdt-v3 -round-trip. FluidAudio engines measured with -`fluidaudiocli tts --backend --metrics` (release build); NeuTTS-2E via -`inference.py` (Python coremltools host — a Swift host would shave per-step -overhead). - -| Engine | RTFx | WER | Disk | Notes | -|---|---|---|---|---| -| Supertonic-3 | 44× | ~5 % ("works"/"worked" in 2/3 runs) | 284 MB | 44.1 kHz, fastest | -| KokoroAne | 6.3× | 0 % | 782 MB | ANE-resident | -| PocketTTS | 4.8× | 2 % | 866 MB | streaming | -| **NeuTTS-2E (this)** | **1.06×** | **0 % (3/3 exact)** | 1.28 GB | only emotional-control engine; decode 11.9 ms/tok (84 tok/s vs 50 real-time), codec 3.2× RT | -| StyleTTS2 | 7.5× (short text only) | high | 452 MB | hosted sized BERT/diffusion mlmodelc are missing model.mil → texts beyond ~t32 fail (`corruptedModel`); needs re-upload | - -NeuTTS-2E is the slowest (autoregressive LM at 50 codes/s + full-sequence -vocoder) but the only engine with emotion control, and its intelligibility -matched the best engines. Batch RTFx 1.06× means marginally real-time; chunked -streaming decode (upstream's 25-frame windows) would cut time-to-first-audio -to well under a second. +Three text lengths — S: 5 words (~2 s), M: 14 words (~5 s), L: 53-word +paragraph (~23 s) — warm runs (mean of 3 per cell), WER via parakeet-tdt-v3 +round-trip. FluidAudio engines via `fluidaudiocli tts --metrics` (release); +NeuTTS-2E via `inference.py` (LM on GPU, codec on ANE). + +RTFx (inference speed vs audio duration): + +| Engine | S | M | L | WER S/M/L | Disk | TTFA | +|---|---|---|---|---|---|---| +| Supertonic-3 | 20× | 43× | 87× | 0 / 4.8 / 1.2 % | 284 MB | ≈ batch (fast) | +| KokoroAne | 1.9× | 6.5× | 19× | 0 / 0 / 0 % | 782 MB | — | +| StyleTTS2 | 4.6× | 11.3× | 18× | 80 / 0 / 12.7 % | 452 MB | — | +| PocketTTS | 3.4× | 5.7× | 6.2× | 0 / 0 / 0 % | 866 MB | streaming-capable | +| **NeuTTS-2E batch** | 1.3× | 1.5× | 1.7× | 6.7 / 0 / 0 % | 1.28 GB | n/a | +| **NeuTTS-2E stream** | 1.4× | 1.4× | 1.5× | 0 / 0 / 0 % | 1.28 GB | **650 ms** | + +Notes: + +- NeuTTS-2E is the only engine with emotion control, and (with KokoroAne and + PocketTTS) one of three that stayed fully intelligible on the long + paragraph. Its S-column WER is one sampled insertion in one of three seeds + ("…finally here **and**") — autoregressive variance, not corruption. +- The streaming mode holds TTFA at ~650 ms independent of text length while + batch latency grows with duration; steady-state chunk cadence 340 ms vs the + 500 ms real-time budget. +- StyleTTS2 initially failed all M/L texts with `corruptedModel`: the + FluidAudio downloader materializes the sized `bert_fp16_t*` / + `fused_diffusion_sampler_fp16_t*` mlmodelc **without `model.mil`** (the + hosted files in `FluidInference/StyleTTS-2-coreml/iteration_3/compiled/` + are complete — likely the HF listing pagination gap from the download + refactor, issue #765). Benchmarked after curling the six missing + `model.mil` files into the cache. Its 80 % WER at S ("Elon is Mahir") is + the unsized short-window path, unrelated to that bug. +- Cross-host caveat: NeuTTS-2E timings are from the Python coremltools host; + the others ran the release Swift CLI. ## Follow-ups diff --git a/models/tts/neutts-2e/coreml/inference.py b/models/tts/neutts-2e/coreml/inference.py index 34abb0c..6fdef62 100644 --- a/models/tts/neutts-2e/coreml/inference.py +++ b/models/tts/neutts-2e/coreml/inference.py @@ -101,6 +101,89 @@ def step(self, token: int, cur_len: int) -> np.ndarray: return out["logits"][0] +def linear_overlap_add(frames: list[np.ndarray], stride: int) -> np.ndarray: + """Upstream neutts._linear_overlap_add (triangular weights).""" + assert len(frames) + total_size = 0 + for i, frame in enumerate(frames): + total_size = max(total_size, stride * i + frame.shape[-1]) + sum_weight = np.zeros(total_size, dtype=frames[0].dtype) + out = np.zeros(total_size, dtype=frames[0].dtype) + offset = 0 + for frame in frames: + n = frame.shape[-1] + t = np.linspace(0, 1, n + 2, dtype=frames[0].dtype)[1:-1] + weight = 0.5 - np.abs(t - 0.5) + out[offset : offset + n] += weight * frame + sum_weight[offset : offset + n] += weight + offset += stride + assert sum_weight.min() > 0 + return out / sum_weight + + +class StreamingVocoder: + """Upstream _infer_stream_ggml windowing over the CoreML codec. + + The speaker's reference codes provide lookback context for the first + windows, exactly as upstream does; emitted samples cover only newly + generated codes. + """ + + CHUNK = 25 # frames yielded per step (0.5 s) + LOOKBACK = 50 + LOOKFORWARD = 5 + OVERLAP = 1 + + def __init__(self, codec: ct.models.MLModel, ref_codes: list[int]): + self.codec = codec + self.stride = self.CHUNK * 480 + self.token_cache: list[int] = list(ref_codes) + self.n_decoded_tokens = len(ref_codes) + self.n_decoded_samples = 0 + self.audio_cache: list[np.ndarray] = [] + # One-time warmup at the window size used per chunk — the first + # predict at a new RangeDim size pays a specialization cost. + w = self.LOOKBACK + self.OVERLAP + self.CHUNK + self.LOOKFORWARD + self.OVERLAP + t0 = time.perf_counter() + self._decode((self.token_cache * ((w // len(ref_codes)) + 1))[:w]) + print(f"vocoder warmup: {(time.perf_counter() - t0) * 1000:.0f} ms (one-time)") + + def _decode(self, window: list[int]) -> np.ndarray: + return self.codec.predict({"codes": np.array([window], dtype=np.int32)})["audio"][0] + + def push(self, code: int) -> np.ndarray | None: + self.token_cache.append(code) + if len(self.token_cache) - self.n_decoded_tokens < self.CHUNK + self.LOOKFORWARD: + return None + start = max(self.n_decoded_tokens - self.LOOKBACK - self.OVERLAP, 0) + end = self.n_decoded_tokens + self.CHUNK + self.LOOKFORWARD + self.OVERLAP + sample_start = (self.n_decoded_tokens - start) * 480 + sample_end = sample_start + (self.CHUNK + 2 * self.OVERLAP) * 480 + recon = self._decode(self.token_cache[start:end])[sample_start:sample_end] + self.audio_cache.append(recon) + processed = linear_overlap_add(self.audio_cache, self.stride) + new_end = len(self.audio_cache) * self.stride + out = processed[self.n_decoded_samples : new_end] + self.n_decoded_samples = new_end + self.n_decoded_tokens += self.CHUNK + return out + + def flush(self) -> np.ndarray | None: + remaining = len(self.token_cache) - self.n_decoded_tokens + if remaining <= 0: + return None + start = max( + len(self.token_cache) - (self.LOOKBACK + self.OVERLAP + remaining), 0 + ) + sample_start = ( + len(self.token_cache) - start - remaining - self.OVERLAP + ) * 480 + recon = self._decode(self.token_cache[start:])[sample_start:] + self.audio_cache.append(recon) + processed = linear_overlap_add(self.audio_cache, self.stride) + return processed[self.n_decoded_samples :] + + def main() -> None: p = argparse.ArgumentParser() p.add_argument("--lm-dir", required=True) @@ -115,11 +198,18 @@ def main() -> None: p.add_argument("--passthrough-kv", action="store_true", help="use the macOS14 pass-through-KV decode model instead of stateful") p.add_argument("--teacher-force", action="store_true") + p.add_argument("--stream", action="store_true", + help="windowed streaming vocoder (upstream 25-frame chunks); reports TTFA") p.add_argument("--compute-units", default="ALL", - choices=["ALL", "CPU_AND_GPU", "CPU_ONLY", "CPU_AND_NE"]) + choices=["ALL", "CPU_AND_GPU", "CPU_ONLY", "CPU_AND_NE"], + help="LM compute units (stateful decode cannot use CPU_AND_NE)") + p.add_argument("--codec-compute-units", default="CPU_AND_NE", + choices=["ALL", "CPU_AND_GPU", "CPU_ONLY", "CPU_AND_NE"], + help="codec compute units (ANE is ~2x faster than GPU here)") args = p.parse_args() cu = getattr(ct.ComputeUnit, args.compute_units) + codec_cu = getattr(ct.ComputeUnit, args.codec_compute_units) lm_dir = Path(args.lm_dir) from transformers import AutoTokenizer @@ -185,6 +275,52 @@ def main() -> None: f"{agree_topk}/{n} ({100.0 * agree_topk / n:.1f}%)") print(f"decode: {1000.0 * dt / max(n - 1, 1):.1f} ms/token") codes = json.loads((HERE / "build" / "ref" / "ref_codes.json").read_text()) + elif args.stream: + from src.prompt import load_speaker + + print("loading codec (streaming)...") + codec = ct.models.MLModel(str(Path(args.codec)), compute_units=codec_cu) + ref_codes, _ = load_speaker(args.speaker) + voc = StreamingVocoder(codec, ref_codes) + speech_0 = tokenizer.convert_tokens_to_ids("<|speech_0|>") + + pieces: list[np.ndarray] = [] + chunk_times: list[float] = [] + t_start = t_gen0 = time.perf_counter() + ttfa = None + step_logits = logits + while cur_len < MAX_CONTEXT - 1: + if len(gen_ids) < MIN_NEW_TOKENS: + step_logits[eos_id] = -1e9 + tok = sample_top_k(step_logits, args.temperature, args.top_k, rng) + gen_ids.append(tok) + if tok == eos_id: + break + if speech_0 <= tok < speech_0 + 65_536: + piece = voc.push(tok - speech_0) + if piece is not None: + now = time.perf_counter() + if ttfa is None: + ttfa = now - t_start + chunk_times.append(now) + pieces.append(piece) + step_logits = decoder.step(tok, cur_len) + cur_len += 1 + tail = voc.flush() + if tail is not None: + pieces.append(tail) + dt = time.perf_counter() - t_gen0 + codes = extract_speech_codes(tokenizer, gen_ids) + audio = np.concatenate(pieces) if pieces else np.zeros(0, dtype=np.float32) + dur = len(audio) / SAMPLE_RATE + gaps = np.diff(chunk_times) if len(chunk_times) > 1 else np.array([0.0]) + # TTFA includes prefill, which happened before t_start; add it back. + print(f"stream: TTFA {(t_pre + (ttfa or 0)) * 1000:.0f} ms " + f"(prefill {t_pre * 1000:.0f} + gen-to-first-chunk {(ttfa or 0) * 1000:.0f}), " + f"{len(pieces)} chunks, inter-chunk mean {gaps.mean() * 1000:.0f} ms " + f"(budget 500 ms), max {gaps.max() * 1000:.0f} ms") + print(f"generated {len(codes)} codes ({dur:.2f}s) in {dt:.1f}s wall " + f"(incl. interleaved vocoding) → {dur / dt:.2f}x RT overall") else: t0 = time.perf_counter() step_logits = logits @@ -205,13 +341,14 @@ def main() -> None: if not codes: raise SystemExit("no speech codes generated") - print("loading codec...") - codec = ct.models.MLModel(str(Path(args.codec)), compute_units=cu) - t0 = time.perf_counter() - audio = codec.predict({"codes": np.array([codes], dtype=np.int32)})["audio"][0] - t_dec = time.perf_counter() - t0 - dur = len(audio) / SAMPLE_RATE - print(f"codec: {t_dec * 1000:.0f} ms for {dur:.2f}s audio ({dur / t_dec:.1f}x RT)") + if not args.stream: + print("loading codec...") + codec = ct.models.MLModel(str(Path(args.codec)), compute_units=codec_cu) + t0 = time.perf_counter() + audio = codec.predict({"codes": np.array([codes], dtype=np.int32)})["audio"][0] + t_dec = time.perf_counter() - t0 + dur = len(audio) / SAMPLE_RATE + print(f"codec: {t_dec * 1000:.0f} ms for {dur:.2f}s audio ({dur / t_dec:.1f}x RT)") out_path = Path(args.output) out_path.parent.mkdir(parents=True, exist_ok=True) From 77d294f390a8dada016cc949e5de35d9f70ae38d Mon Sep 17 00:00:00 2001 From: Alex-Wengg Date: Sat, 25 Jul 2026 16:24:58 -0400 Subject: [PATCH 4/5] Speed up LM decode 23% via M=1024 cache variant; document dead-ends convert-lm.py --max-len 1024 pair: stateful decode 7.0 ms/tok pure (143 tok/s) vs 9.1 at M=2048; end-to-end batch ~2.0x RT, streaming TTFA 554 ms / inter-chunk 303 ms, verbatim ASR. inference.py caps generation at the decode state capacity; compress-lm.py added (int8 halves disk, no speed change). Measured dead-ends recorded in README: int8 no speedup (decode is dispatch-latency-bound, not bandwidth-bound); pure-fp16 RMSNorm overflows (0/50 top-50 overlap); in-model top-k head is net slower and hits a CoreML kernel bug (topk over >2^17-wide intermediate returns indices mod 131072, values correct, fp16+fp32, chunking doesn't help; op is correct on a standalone input-fed model). ANE rejects the decode graph (ANECCompile -14), matching the Qwen3-0.6B finding. --- models/tts/neutts-2e/coreml/README.md | 46 ++++++++++----- models/tts/neutts-2e/coreml/compress-lm.py | 66 ++++++++++++++++++++++ models/tts/neutts-2e/coreml/inference.py | 5 +- 3 files changed, 101 insertions(+), 16 deletions(-) create mode 100644 models/tts/neutts-2e/coreml/compress-lm.py diff --git a/models/tts/neutts-2e/coreml/README.md b/models/tts/neutts-2e/coreml/README.md index 6806ce6..00c10cf 100644 --- a/models/tts/neutts-2e/coreml/README.md +++ b/models/tts/neutts-2e/coreml/README.md @@ -64,15 +64,34 @@ uv run python inference.py --lm-dir ./build/lm-fp16 \ - Teacher-forced replay of the 301-token PyTorch reference: 98.7 % of reference tokens inside the CoreML top-50 sampling support; final audio SNR 41.5 dB vs the PyTorch waveform. -- Sampled end-to-end run (warm): decode 9.2 ms/token (109 tok/s vs 50 needed - for real-time; LM on GPU), prefill 33 ms, codec 12.7–27× RT on ANE. - Compute units: LM decode best on ALL/CPU_AND_GPU (CPU_AND_NE breaks the - coremltools state API; CPU_ONLY still real-time at 58 tok/s); codec ~2× - faster on CPU_AND_NE than GPU. `inference.py` defaults to this split. +- Sampled end-to-end run (warm): decode 9.1 ms/token at M=2048, **7.0 ms/token + (143 tok/s) at M=1024** (LM on GPU), prefill 33–40 ms, codec 12.7–27× RT on + ANE. Compute units: LM decode best on ALL/CPU_AND_GPU (ANE rejects the + decode graph outright, ANECCompile error -14, same as Qwen3-0.6B; CPU_ONLY + still real-time at 58 tok/s); codec ~2× faster on CPU_AND_NE than GPU. + `inference.py` defaults to this split. - Streaming (`--stream`, upstream 25-frame windowed overlap-add over the - flexible-length codec): TTFA ≈ 650 ms at every text length, steady-state - inter-chunk ≈ 340 ms against the 500 ms budget, 1.36–1.47× RT overall, - 0 % WER (transcribes identically to batch). + flexible-length codec): with the M=1024 pair, TTFA ≈ 554 ms, steady-state + inter-chunk ≈ 303 ms against the 500 ms budget, 1.6× RT overall; batch ≈ + 2.0× RT. 0 % WER (transcribes identically to batch). M=1024 caps + prompt+generation at 1024 tokens (~11 s of audio after the emily prompt) — + hosts should pick the M=2048 pair for longer utterances. + +### Speed dead-ends (measured, don't retry) + +- **int8 weight quantization: zero speedup** (9.5 ms/tok either way) — the + one-token step is dispatch-latency-bound (hundreds of small GPU ops), not + weight-bandwidth-bound. int8 still halves disk (451→227 MB) at a small + quality cost (48/50 top-50 overlap); `compress-lm.py` kept for that. +- **Pure fp16 (no fp32 op islands): catastrophically wrong** — fp16 RMSNorm + overflows on Qwen3 activation outliers (top-50 overlap 0/50). The fp32 + pow/reduce_mean/rsqrt/softmax islands are load-bearing. +- **In-model top-k head: slower** (+1.0 ms/tok — topk over 217 232 costs more + than shipping the 870 KB logits), and it exposed a CoreML kernel bug: topk + over a >2^17-wide *intermediate* tensor returns indices modulo 131072 + (values correct; both fp16 and fp32; chunked two-stage topk gives the same + corruption; the op is fine in a standalone model fed by an input, so it is + layout-dependent on the big matmul output). ## Conversion gotchas (also see git history) @@ -109,8 +128,8 @@ RTFx (inference speed vs audio duration): | KokoroAne | 1.9× | 6.5× | 19× | 0 / 0 / 0 % | 782 MB | — | | StyleTTS2 | 4.6× | 11.3× | 18× | 80 / 0 / 12.7 % | 452 MB | — | | PocketTTS | 3.4× | 5.7× | 6.2× | 0 / 0 / 0 % | 866 MB | streaming-capable | -| **NeuTTS-2E batch** | 1.3× | 1.5× | 1.7× | 6.7 / 0 / 0 % | 1.28 GB | n/a | -| **NeuTTS-2E stream** | 1.4× | 1.4× | 1.5× | 0 / 0 / 0 % | 1.28 GB | **650 ms** | +| **NeuTTS-2E batch** | 1.3× | 1.5× (2.0× @M1024) | 1.7× | 6.7 / 0 / 0 % | 1.28 GB | n/a | +| **NeuTTS-2E stream** | 1.4× | 1.4× (1.6× @M1024) | 1.5× | 0 / 0 / 0 % | 1.28 GB | **650 ms (554 @M1024)** | Notes: @@ -134,10 +153,9 @@ Notes: ## Follow-ups -- ANE profiling (`tools/coreml-cli`) and a fixed-shape codec variant if ANE - residency is worth it; LM decode currently runs GPU-dominant. -- Weight compression: 111 M of the 236 M params are the tied vocab matrix — - int8/palettized embedding would roughly halve both LM packages. +- LM decode is dispatch-latency-bound; the remaining levers are a Swift host + (per-step Python overhead ~1–2 ms) and multi-token/speculative decode. + ANE rejects the decode graph (-14) and int8 doesn't help speed (see above). - Multifunction package (macOS 15+) to share weights between prefill and decode (saves ~450 MB on disk). - Swift host port (tokenizer + sampling loop + Perth watermark) for FluidAudio. diff --git a/models/tts/neutts-2e/coreml/compress-lm.py b/models/tts/neutts-2e/coreml/compress-lm.py new file mode 100644 index 0000000..fd4c5ce --- /dev/null +++ b/models/tts/neutts-2e/coreml/compress-lm.py @@ -0,0 +1,66 @@ +"""Post-training weight compression for the NeuTTS-2E CoreML LM. + +The decode step is memory-bandwidth-bound (every token streams ~470 MB of +fp16 weights), so weight compression is the main speed lever. Produces +variants of the prefill + stateful-decode mlpackages: + + int8 — linear_symmetric per-channel (W8A16) + int4 — 4-bit palettization (kmeans LUT, per-grouped-channel) + +Usage: + uv run python compress-lm.py --lm-dir ./build/lm-fp16 --output-dir ./build/lm-int8 --mode int8 + uv run python compress-lm.py --lm-dir ./build/lm-fp16 --output-dir ./build/lm-int4 --mode int4 +""" + +from __future__ import annotations + +import argparse +from pathlib import Path + +import coremltools as ct +import coremltools.optimize as cto + + +def compress(src: Path, dst: Path, mode: str) -> None: + print(f"[{mode}] {src.name} ...") + model = ct.models.MLModel(str(src), skip_model_load=True) + if mode == "int8": + cfg = cto.coreml.OptimizationConfig( + global_config=cto.coreml.OpLinearQuantizerConfig( + mode="linear_symmetric", dtype="int8", granularity="per_channel" + ) + ) + compressed = cto.coreml.linear_quantize_weights(model, cfg) + elif mode == "int4": + cfg = cto.coreml.OptimizationConfig( + global_config=cto.coreml.OpPalettizerConfig( + mode="kmeans", nbits=4, granularity="per_grouped_channel", group_size=16 + ) + ) + compressed = cto.coreml.palettize_weights(model, cfg) + else: + raise SystemExit(f"unknown mode {mode}") + compressed.save(str(dst)) + import subprocess + + size = subprocess.run(["du", "-sh", str(dst)], capture_output=True, text=True).stdout.split()[0] + print(f" saved {dst.name} ({size})") + + +def main() -> None: + p = argparse.ArgumentParser() + p.add_argument("--lm-dir", required=True) + p.add_argument("--output-dir", required=True) + p.add_argument("--mode", choices=["int8", "int4"], required=True) + args = p.parse_args() + + lm_dir, out_dir = Path(args.lm_dir), Path(args.output_dir) + out_dir.mkdir(parents=True, exist_ok=True) + for src in sorted(lm_dir.glob("*.mlpackage")): + if "-M2048-fp16.mlpackage" in src.name and "Decode" in src.name: + continue # skip the pass-through decode; stateful is the fast path + compress(src, out_dir / src.name.replace("fp16", args.mode), args.mode) + + +if __name__ == "__main__": + main() diff --git a/models/tts/neutts-2e/coreml/inference.py b/models/tts/neutts-2e/coreml/inference.py index 6fdef62..11c82ea 100644 --- a/models/tts/neutts-2e/coreml/inference.py +++ b/models/tts/neutts-2e/coreml/inference.py @@ -241,6 +241,7 @@ def main() -> None: logits = out["logits_last"][0] kv_k, kv_v = out["kv_k"], out["kv_v"] num_layers = kv_k.shape[0] + max_len = kv_k.shape[3] # decode state capacity; generation must stop here print(f"prefill: {t_pre * 1000:.0f} ms ({num_layers} layers, kv {kv_k.shape})") print("loading decode...") @@ -289,7 +290,7 @@ def main() -> None: t_start = t_gen0 = time.perf_counter() ttfa = None step_logits = logits - while cur_len < MAX_CONTEXT - 1: + while cur_len < min(MAX_CONTEXT, max_len) - 1: if len(gen_ids) < MIN_NEW_TOKENS: step_logits[eos_id] = -1e9 tok = sample_top_k(step_logits, args.temperature, args.top_k, rng) @@ -324,7 +325,7 @@ def main() -> None: else: t0 = time.perf_counter() step_logits = logits - while cur_len < MAX_CONTEXT - 1: + while cur_len < min(MAX_CONTEXT, max_len) - 1: if len(gen_ids) < MIN_NEW_TOKENS: step_logits[eos_id] = -1e9 tok = sample_top_k(step_logits, args.temperature, args.top_k, rng) From 68bd7bf690666b3996b6c80966dbc4f9c30cc0cc Mon Sep 17 00:00:00 2001 From: Alex-Wengg Date: Sat, 25 Jul 2026 16:50:29 -0400 Subject: [PATCH 5/5] Link published HF repo FluidInference/neutts-2e-coreml in README --- models/tts/neutts-2e/coreml/README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/models/tts/neutts-2e/coreml/README.md b/models/tts/neutts-2e/coreml/README.md index 00c10cf..89d36d2 100644 --- a/models/tts/neutts-2e/coreml/README.md +++ b/models/tts/neutts-2e/coreml/README.md @@ -4,6 +4,9 @@ Conversion of [neuphonic/neutts-2e](https://huggingface.co/neuphonic/neutts-2e) (emotional English TTS: Qwen3 236M backbone + [NeuCodec](https://huggingface.co/neuphonic/neucodec)) to CoreML mlpackages. +Published: [FluidInference/neutts-2e-coreml](https://huggingface.co/FluidInference/neutts-2e-coreml) +(all six mlpackages + speaker refs + upstream NeuTTS Open License v1.0). + ## Pipeline ```