diff --git a/vall_e/models/interleaved_ar.py b/vall_e/models/interleaved_ar.py
deleted file mode 100644
index d2644cf..0000000
--- a/vall_e/models/interleaved_ar.py
+++ /dev/null
@@ -1,578 +0,0 @@
-import math
-import torch
-import torch.nn.functional as F
-import traceback
-
-from typing import Literal, overload
-from functools import partial
-from einops import rearrange
-
-from torch import Tensor, einsum, nn
-from torch.distributions import Categorical
-from torch.nn.utils.rnn import pad_sequence
-from torch.utils.checkpoint import checkpoint
-from torchmetrics.classification import BinaryAccuracy, MulticlassAccuracy, MulticlassPrecision
-
-from .retnet import RetNetDecoder, RetNetConfig
-from .transformer import SinusoidalEmbedding, Block as TransformerBlock
-
-try:
- from ..ext.interleaver import (
- CodebooksPatternProvider,
- DelayedPatternProvider,
- MusicLMPattern,
- ParallelPatternProvider,
- UnrolledPatternProvider,
- VALLEPattern,
- )
-except Exception as e:
- pass
-
-from ..config import cfg
-
-def _get_pattern_provider( name ):
- return {
- 'parallel': ParallelPatternProvider,
- 'delay': DelayedPatternProvider,
- 'unroll': UnrolledPatternProvider,
- 'valle': VALLEPattern,
- 'musiclm': MusicLMPattern,
- }[name]
-
-def _create_mask(l, device):
- """1 is valid region and 0 is invalid."""
- seq = torch.arange(max(l), device=device).unsqueeze(0) # (1 t)
- stop = torch.tensor(l, device=device).unsqueeze(1) # (b 1)
- return (seq < stop).float() # (b t)
-
-def _join(x: tuple[Tensor], sep: Tensor):
- """
- Args:
- x: (k t d)
- sep: (d)
- """
- ret = x[0]
- for i in range(1, len(x)):
- ret = torch.cat((ret, sep[None], x[i]), dim=0)
- return ret
-
-def list_to_tensor(x_list: list[Tensor], pattern="t b c -> b t c"):
- """
- Args:
- x_list: [(t d)]
- Returns:
- x: (? ? ?)
- m: (? ? ?), same as x
- """
- l = list(map(len, x_list))
- x = rearrange(pad_sequence(x_list), pattern)
- m = _create_mask(l, x_list[0].device)
- m = m.t().unsqueeze(-1) # (t b 1)
- m = rearrange(m, pattern)
- m = m.to(x)
- return x, m
-
-class Embedding(nn.Embedding):
- def forward(self, x_list: list[Tensor]) -> list[Tensor]:
- if len(x_list) == 0:
- return []
-
- return super().forward(torch.cat(x_list)).split([*map(len, x_list)])
-
-
-class MultiEmbedding(nn.Embedding):
- """
- This embedding sums embeddings on different levels.
- """
-
- def __init__(self, max_n_levels, n_tokens, token_dim):
- super().__init__(max_n_levels, token_dim)
- self.max_n_levels = max_n_levels
- self.n_tokens = n_tokens
- self.weight = nn.Parameter(torch.randn(max_n_levels, n_tokens, token_dim))
-
- def forward(self, x_list: list[Tensor]) -> list[Tensor]:
- if len(x_list) == 0:
- return []
-
- w = self.weight
-
- padded_x_list = []
-
- for xi in x_list:
- xi = F.one_hot(xi.to(torch.int64), num_classes=self.n_tokens) # t l' k
- xi = F.pad(xi, (0, 0, 0, w.shape[0] - xi.shape[1])) # t l k
- padded_x_list.append(xi.to(w))
-
- x = torch.cat(padded_x_list) # n l k
- x = einsum("l k d, n l k -> n d", w, x)
-
- x_list = x.split([*map(len, x_list)])
-
- return x_list
-
-
-class Base(nn.Module):
- @property
- def causal(self):
- return True
-
- @property
- def use_stop_token(self):
- return True
-
- @property
- def norm_type(self):
- return "ln"
-
- @property
- def arch_type(self) -> str:
- return "retnet"
-
- @property
- def n_prom_levels(self) -> int:
- return 4
-
- @property
- def n_resp_levels(self) -> int:
- return 1
-
- @property
- def n_max_levels(self) -> int:
- return 4
-
- @property
- def n_tasks(self) -> int:
- return 16
-
- @property
- def resp_loss_only(self) -> bool:
- return False
-
- @property
- def recurrent_chunk_size(self) -> int:
- return 0
-
- @property
- def interleave_pattern(self) -> str | None:
- return "flatten"
-
- @property
- def stop_token(self):
- return self.n_tokens + 0
-
- @property
- def interleaved_token(self):
- return self.n_tokens + 1
-
- @property
- def ignore_index(self):
- return -100 # self.interleaved_token
-
- def _prune(self, l: Tensor):
- indices = (l == self.stop_token).nonzero()
- if len(indices) == 0:
- return l
- return l[: indices.min().item()]
-
- @staticmethod
- def _unsqueeze_list(x_list, axis=-1):
- return [x.unsqueeze(dim=axis) for x in x_list]
-
- @staticmethod
- def _samplewise_merge_tensors(*l, sep: Tensor | None):
- if sep is None:
- cat = torch.cat
- else:
- cat = partial(_join, sep=sep)
- return [*map(cat, zip(*l))]
-
- def _interleave( self, codes ):
- if not self.interleave_pattern:
- return codes
-
- return codes.flatten()
-
- def _deinterleave( self, codes, length = 0 ):
- if not self.interleave_pattern:
- 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 ) )
-
- def __init__(
- self,
- n_tokens: int = 1024,
- d_model: int = 512,
- n_heads: int = 8,
- n_layers: int = 12,
- p_dropout: float = 0.1,
-
- config: dict | None = None
- ):
- super().__init__()
- self._cfg = config
- self.n_tokens = n_tokens
- self.d_model = d_model
- self.n_heads = n_heads
- self.n_layers = n_layers
-
- # + tasks for each token they represent in the prom
- n_prom_tokens = n_tokens + (self.n_tasks - 1) # - 1 because tts is an inherent task
- # +1 to include the stop token + 1 to include interleave token
- n_resp_tokens = n_tokens + (1 if self.use_stop_token else 0) # AR requires a stop token to... know when to stop
-
- self.text_emb = Embedding(n_tokens, d_model)
- self.proms_emb = MultiEmbedding(self.n_prom_levels, n_prom_tokens, d_model)
- self.resps_emb = MultiEmbedding(self.n_resp_levels, n_resp_tokens, d_model)
-
- self.sep = nn.Parameter(torch.randn(d_model))
-
- if self.arch_type == "transformer":
- self.sin_emb = SinusoidalEmbedding(d_model)
- self.blocks = nn.ModuleList([TransformerBlock(
- d_model=d_model,
- n_heads=n_heads,
- p_dropout=p_dropout,
- causal=self.causal,
- norm_type=self.norm_type,
- n_levels=1,
- ) for _ in range(n_layers) ])
-
- elif self.arch_type == "retnet":
- self.retnet = RetNetDecoder(RetNetConfig(
- vocab_size=n_tokens,
- decoder_embed_dim=d_model,
- decoder_retention_heads=n_heads,
- decoder_ffn_embed_dim=d_model * 4,
- decoder_layers=n_layers,
- dropout=p_dropout,
- checkpoint_activations=True,
-
- chunkwise_recurrent=self.causal and self.recurrent_chunk_size > 0,
- recurrent_chunkwise_size=self.recurrent_chunk_size if self.causal else 0,
- no_output_layer=True,
- decoder_normalize_before=True,
- ))
-
- # I imagine because each step returns `resp_level`s tokens at once, so we need to have a classifier for each level
- self.classifier = nn.Linear(d_model, n_resp_tokens)
-
- self.accuracy_metric = MulticlassAccuracy(
- n_resp_tokens,
- top_k=10,
- average="micro",
- multidim_average="global",
- ignore_index=self.ignore_index,
- )
-
- self.precision_metric = MulticlassPrecision(
- n_resp_tokens,
- top_k=10,
- average="micro",
- multidim_average="global",
- ignore_index=self.ignore_index,
- )
-
- @overload
- def forward(
- self,
- text_list: list[Tensor],
- proms_list: list[Tensor],
- resps_list: list[Tensor],
- targ_list: list[Tensor] | None = None,
- quant_levels: Tensor | None = None,
- shift_targ_list: bool = False,
- return_all: Literal[False] = False,
- return_all_resp: Literal[False] = False,
- sampling_temperature: float = 1.0,
- ) -> Tensor:
- ...
-
- @overload
- def forward(
- self,
- text_list: list[Tensor],
- proms_list: list[Tensor],
- resps_list: list[Tensor],
- targ_list: list[Tensor] | None = None,
- quant_levels: Tensor | None = None,
- shift_targ_list: bool = False,
- return_all: Literal[True] = True,
- return_all_resp: Literal[True] = True,
- sampling_temperature: float = 1.0,
- ) -> list[Tensor]:
- ...
-
- def _forward(
- self,
- text_list: list[Tensor],
- proms_list: list[Tensor],
- resps_list: list[Tensor],
- targ_list: list[Tensor] | None = None,
- quant_levels: Tensor | None = None,
- shift_targ_list: bool = False,
- return_all: bool = False,
- return_all_resp: bool = False,
- sampling_temperature: float = 1.0,
-
- state: dict | None = None,
- ):
- """
- Args:
- text_list: [t] * b
- proms_list: [t' l] * b, l quantization levels.
- resps_list: [t'' l] * b, l quantization levels.
- targ_list: [t''] * b, one quantization level only; when given, loss will be computed
- quant_levels: specify which quant_levels to feed forward, used in NAR mode.
- shift_targ_list: whether to shift target list when computing loss. True if AR.
- return_all_resp: True if NAR.
- sampling_temperature: a lower temperature makes the result more robust but less diverse.
- Returns:
- y: sampled tokens
- """
-
- batch_size = len(text_list)
-
- x_list = self._samplewise_merge_tensors(
- self.text_emb(text_list),
- self.proms_emb(proms_list),
- self.resps_emb(resps_list),
- sep=self.sep,
- )
-
- x, m = list_to_tensor(x_list)
- device = x.device
-
- if state is not None:
- # prefill
- prefill_size = x.shape[1]
-
- # run the initial prompt to fill the KV cache
- if len(state) == 0:
- for n in range(prefill_size):
- xi = x[:, n, :].unsqueeze(1)
- self.retnet(xi, incremental_state=state, token_embeddings=xi, features_only=True)
-
- # grab last token(s)
- x = x[:, -1, :].unsqueeze(1)
-
- if self.arch_type == "transformer":
- x = self.sin_emb.add_pe(x)
- for block in self.blocks:
- x = block(x, m, quant_levels)
- elif self.arch_type == "retnet":
- # to-do: actually make this work and verify it works with recurrent_forward / chunkwise_forward
- x, _ = self.retnet(x, incremental_state=state, token_embeddings=x, features_only=True)
-
- x = self.classifier(x) * m
-
- # Remove padding
- h_list = [hi[:li] for hi, li in zip(x, map(len, x_list))]
-
- # compute loss if the target is given
- if targ_list is not None:
- if any([l == 0 for l in map(len, targ_list)]):
- raise ValueError("Cannot compute loss given empty targ_list.")
-
- ignore_sep = torch.tensor(self.ignore_index, device=device)
-
- # ignore the prompt when computing loss
- prom_list = [
- torch.full_like(t[..., 0], self.ignore_index) for t in proms_list
- ]
- # remake input with ignored input prompt
- text_prom_list = self._samplewise_merge_tensors(
- text_list, prom_list, sep=ignore_sep
- )
-
- for i in range(len(text_prom_list)):
- # ignore computing loss against text/prompt portion of input
- # the NAR doesn't need to compute the loss for it
- if self.resp_loss_only:
- text_prom_list[i][:] = self.ignore_index
-
- # roll the text/prompt for loss computing
- # the AR benefits from this, for some reason I'll figure out later
- else:
- text_prom_list[i] = text_prom_list[i].roll(-1, dims=0)
- text_prom_list[i][-1] = self.ignore_index
-
- # for the AR, roll by one and mark the ending with a stop token
- # this coerces the model into properly inferencing causally
-
- # why we don't just append a stop token in the dataloader, who knows
- if shift_targ_list:
- targ_list = [*targ_list]
- for i in range(len(targ_list)):
- targ_list[i] = targ_list[i].roll(-1, dims=0)
- targ_list[i][-1] = self.stop_token
-
- # create the new target sequence to compute the loss against
- y_list = self._samplewise_merge_tensors( text_prom_list, targ_list, sep=ignore_sep )
-
- self.loss = dict(
- nll=F.cross_entropy(
- torch.cat(h_list), # input / predicted logits
- torch.cat(y_list), # target / ground truth
- ignore_index=self.ignore_index,
- )
- )
- self.stats = dict(
- acc = self.accuracy_metric( torch.cat(h_list), torch.cat(y_list) ),
- precision = self.precision_metric( torch.cat(h_list), torch.cat(y_list) ),
- )
-
- # return the entire generated token string
- if return_all:
- logits = [hi[:] for hi, li in zip(h_list, map(len, resps_list))]
- # return the entire generated response
- elif return_all_resp:
- logits = [hi[-li:] for hi, li in zip(h_list, map(len, resps_list))]
- # return the last chunkwise piece
- elif self.causal and self.recurrent_chunk_size > 0:
- logits = [hi[-self.recurrent_chunk_size:] for hi, li in zip(h_list, map(len, resps_list))]
- # return just the last code
- else:
- logits = [ hi[-1:] for hi in h_list ]
-
- return [ Categorical(logits=hi / sampling_temperature).sample() for hi in logits ]
-
- 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:
- resps_list = [self._interleave(r) for r in resps_list] # guarantees we only have the first levels
-
- return self._forward(
- text_list=text_list,
- proms_list=proms_list,
- resps_list=self._unsqueeze_list(resps_list),
- targ_list=resps_list,
- quant_levels=None,
- shift_targ_list=True,
- return_all_resp=False,
- )
-
- 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
-
- max_steps *= self.n_prom_levels
-
- for n in range(max_steps // max(1, self.recurrent_chunk_size)):
- # get next in sequence
-
- r = self._forward(
- text_list,
- proms_list,
- self._unsqueeze_list(resps_list),
- 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
-
- return [self._deinterleave(self._prune(r)) for r in resps_list]
-
-def example_usage():
- from ..config import cfg
- cfg.trainer.backend = "local"
- cfg.trainer.check_for_oom = False
-
- from functools import partial
-
- from einops import repeat
-
- from ..emb.qnt import decode_to_file
- from ..engines import Engine, Engines
- from tqdm import tqdm, trange
-
- 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()
-
- kwargs = {
- 'n_tokens': 1024,
- 'd_model': 1024,
- 'n_heads': 16,
- 'n_layers': 18,
- }
- models = { "ar": Base(**kwargs).to(device) }
-
- for name, model in models.items():
- print(f"{name} parameter count: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
-
- engines = Engines({ name: Engine(model=model, optimizer=torch.optim.AdamW(model.parameters(), lr=5e-5)) for name, model in models.items() })
-
- train = True
-
- qnt = torch.load("data/qnt.pt")[0].t()[:, :cfg.models.prom_levels].to(device)
- text_list = [
- 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),
- #tokenize("ˌ ɔ n ɡˌ o ʊ ɪ ŋ hˈ o ʊ m ð ə tˈ uː f ɹˈ ɛ n d z fˈ a ʊ n d ɐ lˈ ɛ ɾ ɚ f ɹ ʌ m ˈ æ θ o ʊ z , hˌ uː d ɪ zˈ a ɪ ɚ d ðˌ ɛ m t ə mˈ iː t hˌ ɪ m æ t ð ə ɡ ɹˈ æ n d t ʃˈ ɑː ɹ l ɪ mˌ æ ɡ n i ɔ n ð ə fˈ ɑː l o ʊ ɪ ŋ dˈ e ɪ .").to(device),
- ]
-
- proms_list = [
- qnt.to(device),
- ]
- resps_list = [
- qnt.to(device),
- ]
-
- def sample( filename, steps=450 ):
- AR = None
-
- engines.eval()
- for name, engine in engines.items():
- if name[:2] == "ar":
- AR = engine
-
- resps_list = AR(text_list, proms_list, max_steps=steps, sampling_temperature=1.0)
-
- decode_to_file(resps_list[0].cpu(), f"./data/{filename}.wav", device="cpu")
-
- if train:
- sample("init", 75 )
-
- engines.train()
- t = trange(500)
- for i in t:
- stats = {"step": i}
- """
- for name, engine in engines.items():
- stats |= engine.traverse(text_list=text_list, proms_list=proms_list, resps_list=resps_list)
- """
- stats = engines.step({"text_list": text_list, "proms_list": proms_list, "resps_list": resps_list})
- tqdm.write(f"{stats}")
- else:
- for name, engine in engines.items():
- engine.module.load_state_dict(torch.load(f"./data/{name}.pth"))
-
- sample("final")
-
-
-if __name__ == "__main__":
- example_usage()