236 lines
7.7 KiB
Python
Executable File
236 lines
7.7 KiB
Python
Executable File
from ..config import cfg
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from .base import Base
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import torch
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from torch import Tensor
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from tqdm import trange
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class NAR(Base):
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@property
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def causal(self):
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return False
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@property
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def arch_type(self) -> str:
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if hasattr(self, "config") and self.config:
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return self.config.arch_type
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return cfg.models.nar.arch_type
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@property
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def norm_type(self):
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return "ln" if self.n_resp_levels == 1 else "adaln"
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@property
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def n_prom_levels(self) -> int:
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return cfg.models.prom_levels
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@property
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def n_resp_levels(self) -> int:
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if hasattr(self, "config") and self.config:
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return self.config.resp_levels
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return cfg.models.nar.resp_levels
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@property
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def n_max_levels(self) -> int:
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return cfg.models.max_levels
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@property
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def n_tasks(self) -> int:
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return cfg.models.nar.tasks
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@property
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def n_langs(self) -> int:
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return cfg.models.nar.langs
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@property
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def version(self) -> int:
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if hasattr(self, "config") and self.config:
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return self.config.version
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return cfg.models.nar.version
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@property
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def recurrent_chunk_size(self) -> int:
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return 0
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"""
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@property
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def rotary_embedding_base(self) -> float:
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if hasattr(self, "config") and self.config:
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return self.config.rotary_embedding_base
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return cfg.models.nar.rotary_embedding_base
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"""
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@property
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def interleave(self) -> bool:
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return False
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@property
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def monolithic(self) -> bool:
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return False
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def forward(
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self,
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text_list: list[Tensor],
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proms_list: list[Tensor],
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resps_list: list[Tensor],
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lang_list: list[Tensor] | None = None,
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max_levels: int = 0,
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sampling_temperature: float = 0.2,
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sampling_min_temperature: float = -1.0,
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sampling_top_k: int = -100,
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sampling_top_p: float = 1.0,
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sampling_repetition_penalty: float = 1.0,
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sampling_repetition_penalty_decay: float = 0.0,
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sampling_length_penalty: float = 0.0, # unused
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sampling_beam_width: int = 0, # unused
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sampling_mirostat_tau: float = 0.0, # unused
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):
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"""
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Args:
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text_list: [t] * b
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proms_list: [t' l] * b, l=8
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resps_list: [t'' l] * b, l=1 or 8, 1 for testing and 8 for training.
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Returns:
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[t'' l], l=8 if testing. empty list will be returned during training.
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"""
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n_levels_set = {r.shape[-1] for r in resps_list}
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if len(n_levels_set) > 1:
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raise ValueError(f"Please give only one level, got {n_levels_set}.")
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n_levels = next(iter(n_levels_set))
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device = text_list[0].device
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if n_levels == self.n_resp_levels + 1:
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assert resps_list is not None
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quant_levels = torch.randint(0, self.n_resp_levels, (len(resps_list),))
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prev_list = [o[..., : l + 1] for o, l in zip(resps_list, quant_levels)]
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targ_list = [o[..., l + 1] for o, l in zip(resps_list, quant_levels)]
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#quant_levels = quant_levels.to(device=device)
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logits = super().forward(
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text_list=text_list,
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proms_list=proms_list,
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resps_list=prev_list,
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targ_list=targ_list,
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lang_list=lang_list,
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quant_levels=quant_levels,
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)
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prev_list = []
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else:
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prev_list = resps_list
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if max_levels == 0:
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max_levels = self.n_resp_levels
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while True:
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level = prev_list[0].shape[-1] - 1
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if level >= max_levels: # min(max_levels, self.n_resp_levels): # commented out to experiment with exceeding trained levels
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break
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quant_levels = torch.full((len(text_list),), level, device=device)
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logits = super().forward(
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text_list=text_list,
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proms_list=proms_list,
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resps_list=prev_list,
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lang_list=lang_list,
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quant_levels=quant_levels,
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)
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resps_list = super().sample(
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logits=logits,
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resps_list=prev_list,
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quant_levels=quant_levels,
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temperature=sampling_temperature,
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min_temperature=sampling_min_temperature,
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top_p=sampling_top_p,
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top_k=sampling_top_k,
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repetition_penalty=sampling_repetition_penalty,
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repetition_penalty_decay=sampling_repetition_penalty_decay,
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#length_penalty=sampling_length_penalty,
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#beam_width=sampling_beam_width,
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#mirostat_tau=sampling_mirostat_tau,
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#mirostat_state=mirostat_state,
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)
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prev_list = [ torch.cat([rs, r.unsqueeze(-1).to(device)], dim=-1) for rs, r in zip(prev_list, resps_list) ]
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return prev_list
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def example_usage():
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cfg.trainer.backend = "local"
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from functools import partial
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from einops import repeat
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from ..emb.qnt import decode_to_file
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from ..engines import Engine
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from tqdm import tqdm
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from ..utils import wrapper as ml
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device = "cuda"
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x8 = partial(repeat, pattern="t -> t l", l=cfg.models.prom_levels)
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symmap = {'<s>': 1, '</s>': 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}
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def tokenize(content, lang_marker="en"):
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split = content.split(" ")
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phones = [f"<s>"] + [ " " if not p else p for p in split ] + [f"</s>"]
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return torch.tensor([*map(symmap.get, phones)]).to()
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# to-do: unmangle this and the resp shit
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qnt = torch.load("data/qnt.pt")[0].t()[:, :cfg.models.prom_levels].to(device)
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text_list = [
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#torch.tensor([1, 2, 3], device=device),
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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),
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]
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proms_list = [
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x8(torch.tensor([2, 3], device=device)),
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]
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resps_list = [
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qnt.to(device),
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]
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kwargs = {
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'n_tokens': 1024,
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'd_model': 1024,
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'n_heads': 16,
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'n_layers': 12,
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}
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model = NAR(**kwargs).to(device)
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steps = 500
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optimizer = ml.Prodigy(model.parameters(), lr=1.0)
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engine = Engine(model=model, optimizer=optimizer)
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def sample( name ):
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engine.eval()
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codes = engine( text_list, proms_list, resps_list=[r[..., 0].unsqueeze(-1) for r in resps_list], sampling_temperature=0.2 )
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decode_to_file( codes[0], f"data/nar.{name}.wav", device )
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def train():
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engine.train()
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t = trange(steps)
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for i in t:
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stats = {"step": i}
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stats |= engine.traverse(text_list=text_list, proms_list=proms_list, resps_list=resps_list)
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tqdm.write(f"{stats}")
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sample("init")
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train()
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sample("final")
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if __name__ == "__main__":
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example_usage()
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