222 lines
6.8 KiB
Python
222 lines
6.8 KiB
Python
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from ..config import cfg
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from .base import Base, list_to_tensor, Categorical
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import torch
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from einops import rearrange
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from torch import Tensor
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from tqdm import trange
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class AR(Base):
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@property
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def n_resp_levels(self) -> int:
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return cfg.models.ar.resp_levels
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@property
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def causal(self):
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return True
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@property
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def use_stop_token(self):
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return True
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@property
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def norm_type(self):
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return "ln"
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@property
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def arch_type(self) -> bool:
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return cfg.models.ar.arch_type
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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 resp_loss_only(self):
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return False
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def _prune(self, l: Tensor):
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indices = (l == self.stop_token).nonzero()
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if len(indices) == 0:
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return l
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return l[: indices.min().item()]
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@staticmethod
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def _unsqueeze_list(x_list, axis=-1):
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return [x.unsqueeze(dim=axis) for x in x_list]
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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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resp_list: list[Tensor] | None = None,
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max_steps: int = 1000,
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sampling_temperature: float = 1.0,
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naive: bool = True,
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):
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if resp_list is not None:
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return super().forward(
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text_list,
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proms_list,
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self._unsqueeze_list(resp_list),
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resp_list,
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quant_levels=None,
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shift_targ_list=True,
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return_all_resp=False,
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)
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else:
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return self._generate(
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text_list,
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proms_list,
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max_steps,
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sampling_temperature,
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naive=naive,
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)
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def _generate(
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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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max_steps: int,
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sampling_temperature: float,
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naive: bool = True,
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):
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device = text_list[0].device
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resp_list: list[Tensor] = [
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torch.zeros(0, device=device).to(torch.int16) for _ in text_list
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]
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stopped = torch.zeros(len(text_list), device=device).bool()
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if self.arch_type == "transformer":
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naive = True
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chunk_size = 1 # don't really know what to do about this desu
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state = None
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start = 0
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# prefill
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if self.arch_type == "retnet/local":
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# pre-process
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state = [
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[
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torch.zeros(self.retnet.hidden_dim // self.retnet.heads, self.retnet.v_dim // self.retnet.heads, device=device).unsqueeze(0).repeat(len(text_list), 1, 1)
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for _ in range(self.retnet.heads)
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] for _ in range(self.retnet.layers)
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]
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resps_list = self._unsqueeze_list(resp_list)
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x_list = self._samplewise_merge_tensors(
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self.text_emb(text_list),
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self.proms_emb(proms_list),
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self.resps_emb(resps_list),
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sep=self.sep,
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)
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x, m = list_to_tensor(x_list)
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start = x.shape[1]
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for i in trange(start-1):
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_, state = self.retnet.forward_recurrent( x[:, i:i+1, :], state, i+1 )
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for n in trange(max_steps // chunk_size):
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# get next in sequence
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r, state = super().forward(
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text_list,
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proms_list,
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self._unsqueeze_list(resp_list),
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sampling_temperature=sampling_temperature,
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state=state,
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)
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# append outputted token
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for i, ri in enumerate(r):
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resp_list[i] = torch.cat([resp_list[i], ri[None]])
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# stop token found
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stopped |= r == self.stop_token
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if stopped.all().item():
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break
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pruned = [self._prune(r) for r in resp_list]
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return pruned
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def example_usage():
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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 ..utils import gather_attribute
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device = "cpu"
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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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qnt = torch.load("data/qnt.pt")[0, 0].to(device)
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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 = AR(**kwargs).to(device)
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x8 = partial(repeat, pattern="t -> t l", l=2)
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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([1, 2, 3], device=device)),
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#qnt.to(device),
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]
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resp_list = [
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qnt.to(device),
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]
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text_list = text_list[:1]
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proms_list = proms_list[:1]
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resp_list = resp_list[:1]
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model.eval()
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out = model(text_list, proms_list, max_steps=75)[0]
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print("qnt:", qnt.shape, qnt)
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print("out:", out.shape, out)
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wav, sr = decode_to_file(out, "data/test/test.ar.init.wav", device=device)
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optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
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model.train()
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for i in trange(60):
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optimizer.zero_grad()
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_ = model(text_list, proms_list, resp_list)
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losses = gather_attribute(model, "loss")
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loss = sum(losses.values())
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loss.backward()
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optimizer.step()
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if i % 20 == 0:
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print(f"iter={i}, {losses}.")
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model.eval()
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out = model(text_list, proms_list, max_steps=400)
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print("qnt:", qnt.shape, qnt)
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for i, o in enumerate(out):
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print("out:", i, o.shape, o)
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wav, sr = decode_to_file(o, f"data/test/test.ar.{i}.wav", device=device)
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if __name__ == "__main__":
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example_usage()
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