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e8ff114 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 | """MayaTTS on MLX: the same engine (prompt, sampler, stop rule, guards, voices, text splitting, streaming Mimi), with
the model run by maya_mac.mlx_model instead of torch MPS.
What makes it faster than the torch engine, per 80 ms frame:
* the cb0 sampler, the 31-level depth decoder and the feedback embedding are one compiled Metal graph
(FrameStep.step): no per-op dispatch from Python, no host round trip between the backbone and depth;
* the frame loop is pipelined: frame t+1 is queued on the GPU before the host reads frame t (stop flag + codes, one
33-int read), so the host-side work (guards, Mimi hand-off) overlaps the GPU instead of stalling it. A
speculative frame is thrown away when frame t stops, or recomputed when the loop guard changes the AEOS bias;
* a voice's reference prefix (the ICL clip) is prefilled once and its KV reused by every later request;
* optional 8 / 4-bit weights (EngineConfig.backbone_bits / depth_bits).
Sampling draws from the same distributions as the torch engine but with MLX's RNG, so a given seed yields different
(equally likely) audio on the two backends.
"""
from __future__ import annotations
import json
import threading
import time
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
import mlx.core as mx
import numpy as np
import torch
from maya_mac import guards as G
from maya_mac import reference as S
from maya_mac.engine import HERE, EngineConfig, MayaTTS
from maya_mac.mlx_model import DTYPES, STOP_REASONS, Backbone, Composer, FrameStep, load_weights
from maya_mac.prompt import DEFAULT_BUILDER, MODES, Voice, VoiceRegistry, load_named_voices
from maya_mac.tts.tokens import N_Q
class MayaMLX(MayaTTS):
def __init__(self, model_dir, mimi_dir, ecapa_dir=None, device=None, config: EngineConfig = None, log=print):
from transformers import AutoTokenizer, MimiModel
from maya_mac.codec import StreamingMimi
from maya_mac.tts.data.packing_v2 import PackerV2
from maya_mac.tts.tokens import extend_tokenizer
self.device = "mlx"
self.cfg = cfg = config or EngineConfig()
self.log = log
t0 = time.perf_counter()
dtype = DTYPES[cfg.backbone_dtype]
self.dtype = dtype
bb = Path(model_dir) / "backbone"
# MLX streams are thread-local: every MLX call (load, compile, voices, frames) runs on this one thread, so the
# engine can be built on one thread and driven from another (the server loads on one and serves on another)
self._mlx = ThreadPoolExecutor(1, thread_name_prefix="maya-mlx")
self._mlx_thread = None
def build():
self._mlx_thread = threading.current_thread()
w = load_weights(model_dir)
self.backbone = Backbone(w, json.loads((bb / "config.json").read_text()), dtype=dtype,
bits=cfg.backbone_bits, capacity=cfg.capacity)
self.step = FrameStep(w, dtype=DTYPES[cfg.depth_dtype], bits=cfg.depth_bits, depth_temp=cfg.depth_temp,
depth_top_k=cfg.depth_top_k, rep_win=cfg.cb0_rep_win, fb_dtype=dtype)
self.compose = Composer(w, dtype=dtype)
self._on_mlx(build)
tokenizer, token_ids = extend_tokenizer(AutoTokenizer.from_pretrained(str(bb)))
self.packer = PackerV2(tokenizer, token_ids, max_tokens=8192)
self.prior = S.load_prior(str(HERE / "configs/text_frames_prior.json"))
self.dur = G.DurationPrior()
self.mimi = MimiModel.from_pretrained(str(mimi_dir)).to(cfg.codec_device).eval()
self.codec = StreamingMimi(self.mimi)
self._codec_pool = ThreadPoolExecutor(1, thread_name_prefix="maya-mimi")
self.ecapa_dir = ecapa_dir
self._ecapa = None
self._ecapa_device = "cpu"
self.registry = VoiceRegistry()
voices_json = Path(model_dir) / "voices" / "voices.json"
if voices_json.is_file():
load_named_voices(self.registry, str(voices_json))
if cfg.warmup: # compile the frame graph and the Metal kernels at load, not on the first request
list(self.stream("Hello, this is a warmup.", "en", max_frames=6, seed=1))
self.load_s = time.perf_counter() - t0
self._seed_rng = np.random.default_rng(cfg.seed)
self.last_info = None
def _on_mlx(self, fn, *args):
if threading.current_thread() is self._mlx_thread:
return fn(*args)
return self._mlx.submit(fn, *args).result()
def close(self):
super().close()
self._mlx.shutdown(wait=True)
# ------------------------------------------------------------------ voices
def prepare_voice(self, voice: Voice):
"""Pack the voice's reference prefix and compute its depth cross-attention constants (once per voice)."""
return self._on_mlx(self._prepare_voice, voice)
def _prepare_voice(self, voice):
with voice.lock:
if voice.mem is not None:
return voice
_, use_mem = MODES[voice.mode]
builder = voice.builder or DEFAULT_BUILDER
voice.prefix = builder.prefix(self.packer, voice)
voice.spk_refs = (np.asarray(voice.ecapa, np.float32).reshape(1, 192) if voice.ecapa is not None
else np.zeros((1, 192), np.float32))
voice.mem = self.step.cross(voice.spk_refs if use_mem else None)
mx.eval(voice.mem)
voice.ts = int(voice.prefix["tokens"].shape[0]) if voice.prefix is not None else 0
voice.kv = voice.emb = None
return voice
def _embed(self, w):
if self._ecapa is None:
if self.ecapa_dir is None:
raise ValueError("ecapa_dir is required for reference-audio voices")
from maya_mac.engine import Ecapa
self._ecapa = Ecapa(self.ecapa_dir, self._ecapa_device)
return self._ecapa(w)
# ------------------------------------------------------------------ synthesis
def _plan(self, text, lang, voice, max_frames):
from maya_mac.tts.data.packing_v2 import collate_v2
from maya_mac.tts.model.load_s3 import truncate_prompt_batch
cfg, g = self.cfg, self.cfg.guards
builder = voice.builder or DEFAULT_BUILDER
ex = builder.example(self.packer, voice, text, lang)
# reference.prompt_from_example, composed by MLX
b = collate_v2([ex], q_choices=(N_Q,), q_rng=np.random.default_rng(0))
kind = b["kind"][0]
a = (kind == 1).nonzero().flatten()
a = a[a >= int(ex.get("target_start", 0))]
assert a.numel(), "prompt has no target audio position"
prompt = self.compose(truncate_prompt_batch(b, int(a[0]), require_target_span=self.compose.anchor is not None))
ef, er = S.expected_frames(self.prior, lang, len(text))
loose = min(cfg.hard_cap, int(2.0 * (ef + er) + 40))
cap = loose
if g.enabled:
cap = min(cap, max(int(ef * 0.5) + 8, self.dur.cap_frames(text, lang, g.dur_k, g.dur_margin_s)))
if max_frames:
cap = min(cap, max(1, int(max_frames)))
minimum = max(4, int(0.5 * ef))
suspend = G.suspend_windows_v2(text, lang, self.dur) if (g.enabled and g.loop and g.aware_v2) else ()
return prompt, cap, loose, minimum, suspend
def _prefill(self, prompt, voice):
"""Run the prompt through the backbone, reusing the voice's cached reference-prefix KV when the prompt starts
with exactly that prefix."""
bb, ts = self.backbone, voice.ts
bb.reset()
if ts and ts < prompt.shape[1]:
head = prompt[:, :ts]
if voice.kv is not None and mx.array_equal(head, voice.emb).item():
bb.restore(voice.kv)
return bb(prompt[:, ts:])
bb(head)
voice.kv, voice.emb = bb.snapshot(ts), head
mx.eval(voice.kv, voice.emb) # materialise now, so later in-place cache writes don't have to copy the cache
return bb(prompt[:, ts:])
return bb(prompt)
def _frames(self, text, lang, voice, sampling, max_frames, info):
"""Yield the (32,) code tensor of each frame of one text piece; info receives the stop record. Each frame is
produced on the MLX thread."""
gen = self._frames_mlx(text, lang, voice, sampling, max_frames, info)
try:
while True:
try:
codes = self._on_mlx(next, gen)
except StopIteration:
return
yield codes
finally:
self._on_mlx(gen.close)
def _frames_mlx(self, text, lang, voice, sampling, max_frames, info):
cfg, g = self.cfg, self.cfg.guards
prompt, cap, loose, minimum, suspend = self._plan(text, lang, voice, max_frames)
seed = sampling.get("seed")
seed = int(seed) if seed is not None else int(self._seed_rng.integers(1, 2 ** 31 - 1))
if prompt.shape[1] + cap + 1 > cfg.capacity:
raise ValueError(f"prompt of {prompt.shape[1]} tokens + {cap} frames exceeds KV capacity {cfg.capacity}")
rp = sampling.get("repetition_penalty")
knobs = (mx.array(float(sampling.get("temperature") or cfg.cb0_temp)),
mx.array(min(int(sampling.get("top_k") or cfg.cb0_top_k), 2049)),
mx.array(float(sampling.get("top_p") or cfg.cb0_top_p)),
mx.array(float(rp if rp is not None else cfg.cb0_rep)))
cross = voice.mem
keys = iter(mx.random.split(mx.random.key(seed), cap + 1))
backbone = self.backbone
def issue(h, hazard, hist, frame, bias, key):
"""Queue one frame on the GPU; nothing is read back here."""
out, fb, hazard, hist = self.step.step(h, cross, hist, hazard, mx.array(frame >= minimum),
mx.array(bias, mx.float32), *knobs, key)
h = backbone(fb[None, None])
mx.async_eval(out, h, hazard, hist)
return out, h, hazard, hist, key
h = self._prefill(prompt, voice)
hist0 = mx.full((max(1, cfg.cb0_rep_win),), -1, mx.int32)
cur = issue(h, mx.array(0.0), hist0, 0, 0.0, next(keys))
cb0_hist = np.full(g.hist, -1, np.int64)
frames, reason, boost_at, n_boost, bias = 0, "cap", None, 0, 0.0
while True:
nxt = issue(*cur[1:4], frames + 1, bias, next(keys)) if frames + 1 < cap else None
out = np.array(cur[0]) # waits for this frame only; the next one is already queued
stop = int(out[0])
if stop:
reason = STOP_REASONS[stop]
break
codes = torch.from_numpy(out[1:].astype(np.int64))
yield codes
frames += 1
cb0_hist[:-1] = cb0_hist[1:]
cb0_hist[-1] = int(out[1])
if frames >= cap:
reason = "max_frames" if max_frames and frames >= max_frames else (
"cap_guard" if g.enabled and cap < loose else "cap")
break
new_bias = bias
if (g.enabled and g.loop and frames >= g.start and frames % g.every == 0
and not any(a <= frames <= b for a, b in suspend)):
hit = G.loop_detect_batch(cb0_hist[None], np.array([frames]), g)[0]
if hit is not None and g.mode == "boost":
if boost_at is None:
boost_at, n_boost = frames, n_boost + 1
new_bias = g.boost
if frames - boost_at >= g.backstop:
reason = "loop_backstop"
break
elif hit is not None:
reason = "loop_" + hit.split(":")[0]
break
elif boost_at is not None:
new_bias, boost_at = 0.0, None
if new_bias != bias: # the queued frame used the old AEOS bias: redo it (it rewrites the same KV slot)
bias = new_bias
nxt = issue(*cur[1:4], frames, bias, nxt[4])
cur = nxt
info.update({"reason": reason, "frames": frames, "cap": cap, "n_boost": n_boost})
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