From 143aee752605362206bbf9c7bdd773877aba45fa Mon Sep 17 00:00:00 2001 From: mrq Date: Sun, 3 Sep 2023 22:47:03 -0500 Subject: [PATCH] removed dedicated interleaved AR code --- vall_e/models/interleaved_ar.py | 578 -------------------------------- 1 file changed, 578 deletions(-) delete mode 100644 vall_e/models/interleaved_ar.py 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()