from ..config import cfg from .base import Base, list_to_tensor, Categorical import torch from torch.nn.utils.rnn import pad_sequence from einops import rearrange from torch import Tensor from tqdm import trange class AR(Base): @property def causal(self): return True @property def norm_type(self): return "ln" @property def arch_type(self) -> str: if hasattr(self, "config") and self.config: return self.config.arch_type return cfg.models.ar.arch_type @property def n_prom_levels(self) -> int: return cfg.models.prom_levels @property def n_resp_levels(self) -> int: if hasattr(self, "config") and self.config: return self.config.resp_levels return cfg.models.ar.resp_levels @property def n_max_levels(self) -> int: return cfg.models.max_levels @property def n_tasks(self) -> int: return cfg.models.tasks @property def recurrent_chunk_size(self) -> int: if cfg.mode == "training": return 0 return cfg.inference.recurrent_chunk_size @property def interleave(self) -> bool: if hasattr(self, "config") and self.config: return self.config.interleave return False def _prune(self, l: Tensor): indices = (l == self.stop_token).nonzero() if len(indices) == 0: return l return l[: indices.min().item()] def _interleave( self, codes ): if not self.interleave: return codes return codes.flatten() def _deinterleave( self, codes, length = 0 ): if not self.interleave: return codes return torch.unflatten( codes[:codes.shape[0] // self.n_prom_levels * self.n_prom_levels], 0, ( codes.shape[0] // self.n_prom_levels, self.n_prom_levels ) ) @staticmethod def _unsqueeze_list(x_list, axis=-1): return [x.unsqueeze(dim=axis) for x in x_list] def forward( self, text_list: list[Tensor], proms_list: list[Tensor], resps_list: list[Tensor] | None = None, max_steps: int = 1000, sampling_temperature: float = 1.0, ): if resps_list is not None: if self.interleave: resps_list = [self._interleave(r) for r in resps_list] else: resps_list = [r[..., 0] for r in resps_list] # guarantees we only have the first levels return super().forward( text_list=text_list, proms_list=proms_list, resps_list=self._unsqueeze_list(resps_list), targ_list=resps_list, quant_levels=None, ) device = text_list[0].device batch_size = len(text_list) resps_list: list[Tensor] = [ torch.zeros(0, device=device).to(torch.int16) for _ in text_list ] stopped = torch.zeros(batch_size, device=device).bool() state = {} if cfg.inference.recurrent_forward else None if self.interleave: max_steps *= self.n_prom_levels for n in trange(max_steps // max(1, self.recurrent_chunk_size)): # get next in sequence r = super().forward( text_list=text_list, proms_list=proms_list, resps_list=self._unsqueeze_list(resps_list), quant_levels=None, sampling_temperature=sampling_temperature, state=state ) # append tokens for i, ri in enumerate(r): if self.stop_token in ri: stopped[i] = True resps_list[i] = torch.cat([resps_list[i], ri]) # stop token found stopped |= r == self.stop_token if stopped.all().item(): break res = [self._prune(r) for r in resps_list] if self.interleave: res = [self._deinterleave(r) for r in res] return res def example_usage(): cfg.trainer.backend = "local" from functools import partial from einops import repeat from ..emb.qnt import decode_to_file from ..engines import Engine from tqdm import tqdm device = "cuda" x8 = partial(repeat, pattern="t -> t l", l=cfg.models.prom_levels) symmap = {'': 1, '': 2, ' ': 3, '.': 4, ',': 5, '!': 6, '?': 7, 'p': 7, 'iː': 8, 'ɚ': 9, 'ˌ': 10, 'dˌ': 11, 'mˌ': 12, 'd': 13, 'ɹ': 14, 'tˈ': 15, 'pˌ': 16, 'uː': 17, 'l': 18, 'æ': 19, 'ɛ': 20, 'ɪ': 21, 'j': 22, 'ʊ': 23, 't': 24, 'n': 25, 'v': 26, 'a': 27, 'o': 28, 'ŋ': 29, 'w': 30, 'ʌ': 31, 'hˈ': 32, 'ɡˈ': 33, 'ə': 34, 'θˈ': 35, 'dˈ': 36, 'wˌ': 37, 'h': 38, 'z': 39, 'k': 40, 'ð': 41, 'ɡˌ': 42, 'ˈ': 43, 'fˈ': 44, 'i': 45, 's': 46, 'ʃ': 47, 'wˈ': 48, 'ðˈ': 49, 'ɹˈ': 50, 'lˈ': 51, 'ɡ': 52, 'oː': 53, 'mˈ': 54, 'e': 55, 'ɑː': 56, 'nˈ': 57, 'm': 58, 'θˌ': 59, 'sˈ': 60, 'f': 61, 'ɔː': 62, 'hˌ': 63, 'b': 64, 'jˈ': 65, 'ɐ': 66, 'ʒˈ': 67, 'θ': 68, 'bˈ': 69, 'ɾ': 70, 'ɜː': 71, 'ʌˈ': 72, 'ʃˌ': 73, 'bˌ': 74, 'kˈ': 75, 'ɔ': 76, 'zˈ': 77, 'ᵻ': 78, 'kˌ': 79, 'vˈ': 80, 'fˌ': 81, 'ʒ': 82, 'ʃˈ': 83, 'ɹˌ': 84, 'tˌ': 85, 'pˈ': 86, 'ðˌ': 87, 'sˌ': 88, 'nˌ': 89, 'lˌ': 90, '̩': 91, 'ʔ': 92, 'vˌ': 93, 'ɪˈ': 94, '"': 95, 'ɪˌ': 96, 'ʒˌ': 97, 'uːˌ': 98, 'ʊˈ': 99, 'jˌ': 100, 'uːˈ': 101, 'iːˈ': 102, 'zˌ': 103, '.ˈ': 104, '…': 105, 'ŋˌ': 106, 'ɐˌ': 107, '—ˈ': 108, 'iˌ': 109, 'iːˌ': 110, 'ɛː': 111, ')': 112, ')ˈ': 113, '(': 114, 'u': 115, '-': 116, 'ɖˈ': 117, 'iˈ': 118, 'ʰˈ': 119, 'ɟˈ': 120, '̃': 121, 'eː': 122, 'ɾˈ': 123, 'r': 124, 'ʰ': 125, '-ˌ': 126, 'ɫ': 127, 'q': 128, '—': 129, 'ʊˌ': 130, 'aː': 131, 'cˈ': 132, '…ˈ': 133, 'c': 134, 'ɳ': 135, 'ɐˈ': 136, 'x': 137, 'ʔˌ': 138, '.ˌ': 139, 'ɑ': 140, '?ˈ': 141, '̩ˈ': 142, '"ˈ': 143, ',ˈ': 144, 'ŋˈ': 145, 'əˌ': 146, '!ˈ': 147, '"ˌ': 148, '?ˌ': 149, ',ˌ': 150, '—ˌ': 151, '̩ˌ': 152, 'əˈ': 153, '!ˌ': 154, 'ɬ': 155, 'ʲ': 156, '¡': 157, 'ɯ': 158, 'qˌ': 159, 'ʑ': 160, 'ʑˈ': 161, '¿': 162, 'ɑːˈ': 163, 'iːː': 164, 'ɛˈ': 165, '¡ˈ': 166, 'æˈ': 167, 'ç': 168, 'ɾˌ': 169, 'ᵻˈ': 170, 'xˈ': 171, 'ɔːˈ': 172, ';': 173, 'ɬˌ': 174, ':': 175, 'ʔˈ': 176, 'ɑːˌ': 177, 'ɬˈ': 178} def tokenize(content, lang_marker="en"): split = content.split(" ") phones = [f""] + [ " " if not p else p for p in split ] + [f""] return torch.tensor([*map(symmap.get, phones)]).to() qnt = torch.load("data/qnt.pt")[0].t()[:, :cfg.models.prom_levels].to(device) text_list = [ #torch.tensor([1, 2, 3], device=device), tokenize("ˈ a ɪ w ɪ l nˌ ɑː t ˈ æ s k ɐ sˈ ɛ k ə n d tˈ a ɪ m").to(device), ] proms_list = [ #x8(torch.tensor([1, 2, 3], device=device)), qnt.to(device), ] resps_list = [ qnt.to(device), ] text_list = text_list[:1] proms_list = proms_list[:1] resps_list = resps_list[:1] kwargs = { 'n_tokens': 1024, 'd_model': 1024, 'n_heads': 16, 'n_layers': 24, } try: kwargs['config'] = cfg.models.ar except Exception as e: pass model = AR(**kwargs).to(device) engine = Engine(model=model, optimizer=torch.optim.SGD(model.parameters(), lr=0.1)) def sample( name, steps=400 ): engine.eval() out = engine(text_list, proms_list, max_steps=steps) for i, o in enumerate(out): wav, sr = decode_to_file(o, f"data/ar.{i}.{name}.wav", device=device) def train(): engine.train() t = trange(60) for i in t: stats = {"step": i} stats |= engine.traverse(text_list=text_list, proms_list=proms_list, resps_list=resps_list) tqdm.write(f"{stats}") sample("init", 75) train() sample("final") if __name__ == "__main__": example_usage()