""" # an AR + NAR model that handles: * inferencing the primary RVQ level in an autoregressive manner (AR) * inferencing the remaining RVQ levels in parallel (NAR) This model can fully handle being trained as a unified model (AR + NAR) or separate models (AR | NAR). It's recommended to train as a unified model, then "distill" knowledge of each tasks separately, just in case. """ from .base import Base, list_to_tensor, Categorical from ..config import cfg import torch from torch.nn.utils.rnn import pad_sequence import random import math from einops import rearrange from torch import Tensor from tqdm import trange from ..emb.qnt import trim, encode_as_embedding from .lora import enable_lora class AR_NAR(Base): @property def capabilities(self) -> list[str]: if hasattr(self, "config") and self.config: return self.config.capabilities return cfg.model.capabilities @property def causal(self): return "ar" in self.capabilities @property def n_prom_levels(self) -> int: if hasattr(self, "config") and self.config: return self.config.prom_levels return cfg.model.prom_levels @property def n_resp_levels(self) -> int: if hasattr(self, "config") and self.config: return self.config.resp_levels return cfg.model.resp_levels @property def n_max_levels(self) -> int: if hasattr(self, "config") and self.config: return self.config.max_levels return cfg.model.max_levels @property def n_tasks(self) -> int: if hasattr(self, "config") and self.config: return self.config.tasks return cfg.model.tasks @property def n_langs(self) -> int: if hasattr(self, "config") and self.config: return self.config.langs return cfg.model.langs @property def n_tones(self) -> int: if hasattr(self, "config") and self.config: return self.config.tones return cfg.model.tones @property def causal_size(self) -> int: # 1 for the stop token # governs how much to shift the logits by # could *technically* make it work to where it can also predict *ALL* RVQ levels in one step, but experimental.py is the better way to go about it return 1 # if self.causal else 0 @property def version(self) -> int: if hasattr(self, "config") and self.config: return self.config.version return cfg.model.version def _prune(self, l: Tensor, stop = None): if stop is None: stop = self.stop_token indices = (l == stop).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] def forward( self, text_list: list[Tensor], proms_list: list[Tensor], resps_list: list[Tensor] | None = None, lang_list: list[Tensor] | None = None, tone_list: list[Tensor] | None = None, len_list: list[Tensor] | None = None, training: bool | None = None, max_steps: int = 1000, max_levels: int = 0, sampling_temperature: float = 1.0, sampling_min_temperature: float = -1.0, sampling_top_k: int = -100, sampling_top_p: float = 1.0, sampling_repetition_penalty: float = 1.0, sampling_repetition_penalty_decay: float = 0.0, sampling_length_penalty: float = 0.0, sampling_beam_width: int = 0, sampling_mirostat_tau: float = 0.0, sampling_mirostat_eta: float = 0.1, ): device = text_list[0].device batch_size = len(text_list) # is training or NAR if resps_list is not None: n_levels_set = {r.shape[-1] for r in resps_list} n_levels = next(iter(n_levels_set)) if training is None: training = n_levels == self.n_resp_levels # is training if training: # to-do: make this YAML configurable def sample_task(): return "tts" p_rvq_levels = self.config.experimental.p_rvq_levels if self.config is not None else "equal" # generate task list to train against task_list = [ sample_task() for _ in range(batch_size) ] # determines which RVQ level to target per batch quant_level_range = self.config.experimental.rvq_level_range if self.config is not None and self.config.experimental.rvq_level_range else [ 0 if self.causal else 1, self.n_resp_levels ] if p_rvq_levels == "equal": # randomly select a target RVQ-bin level (0 being AR, 1+ being NAR) quant_levels = [ random.randint(quant_level_range[0], quant_level_range[1] - 1) for i in range(batch_size) ] else: # if p_rvq_levels == "auto": # makes higher levels less likely def generate( lo=0, hi=8 ): index = lo p = random.random() for i in range(lo, hi): if p < 1.0 / (2 ** i): index = i return int(index) quant_levels = [ generate(quant_level_range[0], quant_level_range[1]) for i in range(batch_size) ] resps_list = [r[..., 0] if l == 0 else r[..., :l+1] for r, l in zip(resps_list, quant_levels)] for i in range(batch_size): # cap quant_level if it exceeds its corresponding resp/prom if quant_levels[i] >= resps_list[i].shape[-1]: quant_levels[i] = resps_list[i].shape[-1] - 1 if quant_levels[i] >= proms_list[i].shape[-1]: quant_levels[i] = proms_list[i].shape[-1] - 1 # only apply stop token for RVQ level 0 if quant_levels[i] > 0: continue # append stop tokens for AR # could technically do it in the .inputs call resps_list[i] = torch.cat([resps_list[i], torch.Tensor([self.stop_token]).to(device=device, dtype=torch.int16) ]) inputs = self.inputs( text_list=text_list, proms_list=proms_list, resps_list=resps_list, lang_list=lang_list, tone_list=tone_list, task_list=task_list, quant_levels=quant_levels, ) return super().forward( inputs=inputs, quant_levels=quant_levels, ) # is NAR if max_levels == 0: max_levels = self.n_resp_levels - 1 # expand if given a raw 1D tensor for i, resp in enumerate(resps_list): if resp.dim() == 1: resps_list[i] = resp.unsqueeze(-1) prev_list = resps_list for n in trange( max_levels, desc="NAR" ): level = prev_list[0].shape[-1] if level >= max_levels + 1: # min(max_levels + 1, self.n_resp_levels): # commented out to experiment with exceeding trained levels break if cfg.lora is not None: enable_lora( self, cfg.lora.active_level( level ) ) quant_levels = [ level for _ in range(batch_size) ] # torch.full((len(text_list),), level) inputs = self.inputs( text_list=text_list, proms_list=proms_list, resps_list=prev_list, lang_list=lang_list, tone_list=tone_list, quant_levels=quant_levels, ) logits = super().forward( inputs=inputs, quant_levels=quant_levels, ) resps_list = super().sample( logits=logits, resps_list=prev_list, quant_levels=quant_levels, temperature=sampling_temperature, min_temperature=sampling_min_temperature, top_p=sampling_top_p, top_k=sampling_top_k, repetition_penalty=sampling_repetition_penalty, repetition_penalty_decay=sampling_repetition_penalty_decay, #length_penalty=sampling_length_penalty, #beam_width=sampling_beam_width, #mirostat=mirostat, ) prev_list = [ torch.cat([rs, r.unsqueeze(-1).to(device)], dim=-1) for rs, r in zip(prev_list, resps_list) ] if cfg.lora is not None: enable_lora( self ) return prev_list # is AR if cfg.lora is not None: enable_lora( self, cfg.lora.active_level( 0 ) ) sequence_list = [ torch.zeros(0, device=device).to(torch.int16) for _ in range(batch_size) ] stopped = torch.zeros(batch_size, device=device).bool() stop_token = self.stop_token task_list = [ "tts" for _ in range(batch_size) ] state = None mirostat = [ {"n": 1024, "tau": sampling_mirostat_tau, "eta": sampling_mirostat_eta, "max_surprise": sampling_mirostat_eta * 2, "error_surprise": 0, "running_total_surprise": 0} ] * batch_size if sampling_mirostat_tau > 0.0 else None scores = [ 1.0 ] * sampling_beam_width # get next in sequence for n in trange(max_steps // max(1, self.causal_size), desc="AR"): resps_list = self._unsqueeze_list(sequence_list) inputs = self.inputs( text_list=text_list, proms_list=proms_list, resps_list=resps_list, lang_list=lang_list, tone_list=tone_list, len_list=len_list, task_list=task_list, quant_levels=[ 0 for _ in range( max( batch_size, sampling_beam_width ) ) ] ) if state is not None: logits, state = super().forward( inputs=inputs, state=state, ) else: logits = super().forward( inputs=inputs, state=state, ) r = super().sample( logits=logits, resps_list=resps_list, temperature=sampling_temperature, min_temperature=sampling_min_temperature, top_p=sampling_top_p, top_k=sampling_top_k, repetition_penalty=sampling_repetition_penalty, repetition_penalty_decay=sampling_repetition_penalty_decay, length_penalty=sampling_length_penalty, beam_width=sampling_beam_width, mirostat=mirostat, ) if mirostat is not None: # r is the state mirostat = r # extract token from state r = [ state["token"] for state in mirostat ] # we do it here because the sampler will already expand our logits list elif sampling_beam_width > 0: # expand tuple r, s = r # first step, expand batch if batch_size == 1: batch_size = sampling_beam_width text_list = text_list * sampling_beam_width proms_list = proms_list * sampling_beam_width sequence_list = sequence_list * sampling_beam_width stopped = torch.zeros(batch_size, device=device).bool() scores = [ scores[i] + score for i, score in enumerate(s) ] # append tokens for i, ri in enumerate(r): if stop_token in ri: stopped[i] = True sequence_list[i] = torch.cat([sequence_list[i], ri.to(device)]) # stop token found stopped |= r == stop_token if stopped.all().item(): break # pick the best scoring candidate # desu this is always going to be candidate 0 if sampling_beam_width: sequence_list = [ sequence_list[0] ] sequence_list = [self._prune(r, stop_token) for r in sequence_list] return sequence_list def example_usage(): cfg.trainer.backend = "local" cfg.hyperparameters.gradient_accumulation_steps = 1 if cfg.audio_backend == "dac": cfg.sample_rate = 44_000 from functools import partial from einops import repeat from tqdm import tqdm from ..emb.qnt import decode_to_file, unload_model from ..engines import Engine from ..utils import wrapper as ml import numpy as np import re device = "cuda" # mamba seems to ONLY be used as an AR (any NAR attempts lobotomizes it) """ if "mamba" in cfg.model.arch_type: cfg.model.prom_levels = 1 cfg.model.resp_levels = 1 """ # cfg.model.loss_factors = {} def tokenize(content): return torch.tensor( cfg.tokenizer.encode(content) ) def _load_quants(path) -> Tensor: qnt = np.load(path, allow_pickle=True)[()] return torch.from_numpy(qnt["codes"].astype(np.int16))[0, :cfg.model.prom_levels, :].t().to(torch.int16) qnt = _load_quants(f"./data/qnt.{'dac' if cfg.audio_backend == 'dac' else 'enc'}") text_list = [ tokenize("ˈaɪ wɪl nˌɑːt ˈæsk ɐ sˈɛkənd tˈaɪm").to(device), #tokenize("ˈaɪ wɪl nˌɑːt ˈæsk").to(device), ] proms_list = [ qnt[:cfg.dataset.frames_per_second, :].to(device), #qnt[:cfg.dataset.frames_per_second, :].to(device), ] resps_list = [ qnt[:, :].to(device), #qnt[:cfg.dataset.frames_per_second, :].to(device), ] text_list = text_list[:1] proms_list = proms_list[:1] resps_list = resps_list[:1] # rentet-full is the only configuration with BitNet's BitLinear that converges despite the grad_norm saying otherwise kwargs = { 'n_text_tokens': 256, 'n_audio_tokens': 1024, 'd_model': 1024, # 256, # 1024, # 1536 'n_heads': 16, # 4, # 16, # 24 'n_layers': 12, # 32 'n_experts': 1, 'p_dropout': 0.1, 'l_padding': 8 if cfg.optimizations.fp8 else 0, 'config': cfg.model } """ try: kwargs['config'] = cfg.model except Exception as e: pass """ model = AR_NAR(**kwargs).to(device) steps = 150 optimizer = cfg.hyperparameters.optimizer.lower() if cfg.yaml_path is not None else "prodigy" scheduler = cfg.hyperparameters.scheduler.lower() if cfg.yaml_path is not None else "" learning_rate = cfg.hyperparameters.learning_rate if cfg.yaml_path is not None else None if cfg.optimizations.dadaptation: # do not combine the two if scheduler == "schedulefree": scheduler = "" learning_rate = 1.0 if optimizer == "prodigy": if learning_rate is None: learning_rate = 1.0 optimizer = ml.Prodigy elif optimizer == "adagrad": if learning_rate is None: learning_rate = 1.0e-2 optimizer = ml.Adagrad elif optimizer == "adamw": if learning_rate is None: learning_rate = 1.0e-4 optimizer = ml.AdamW elif optimizer == "sdg": if learning_rate is None: learning_rate = 1.0e-4 optimizer = ml.SGD else: raise ValueError(f"Unrecognized optimizer: {optimizer}") print("Optimizer:", optimizer, "\tLearning rate:", learning_rate) optimizer = optimizer(model.parameters(), lr=learning_rate) if scheduler == "schedulefree": if isinstance(optimizer, ml.AdamW): scheduler = ml.schedulefree.AdamWScheduleFree elif isinstance(optimizer, ml.SGD): scheduler = ml.schedulefree.SGDScheduleFree else: scheduler = None if scheduler is not None: print("Scheduler:", scheduler) optimizer = scheduler( model.parameters(), lr = learning_rate ) if cfg.optimizations.replace and cfg.optimizations.linear: model = ml.replace_linear( model ) if cfg.optimizations.replace and cfg.optimizations.embedding: model = ml.replace_embedding( model ) engine = Engine(model=model, optimizer=optimizer) """ torch.save( { 'module': model.state_dict() }, f"./data/{cfg.model.arch_type}.pth" ) """ print(f"AR+NAR ({cfg.model.arch_type}, {cfg.audio_backend}) parameter count: {sum(p.numel() for p in model.parameters() if p.requires_grad)}") @torch.inference_mode() def sample( name, steps=1000 ): if cfg.audio_backend == "dac" and name == "init": return engine.eval() if "ar" in cfg.model.capabilities: resps_list = engine(text_list, proms_list, max_steps=steps, sampling_temperature=0.95 ) else: resps_list = [ qnt[:, 0].to( device ) ] if "nar" in cfg.model.capabilities: resps_list = engine( text_list, proms_list, resps_list=resps_list, sampling_temperature=0.2 ) for i, o in enumerate(resps_list): _ = decode_to_file(o.to(dtype=torch.int32), f"data/{cfg.model.arch_type}.{cfg.audio_backend}.{i}.{name}.wav", device=device) unload_model() def train(): engine.train() t = trange(steps) for i in t: stats = {"step": i} stats |= engine.traverse(text_list=text_list, proms_list=proms_list, resps_list=resps_list) stats |= {"grad_norm": engine.get_global_grad_norm()} tqdm.write(f"{stats}") """ torch.save( { 'module': model.state_dict() }, f"./data/{cfg.model.arch_type}.pth" ) """ #sample("init", 5) train() sample("final") if __name__ == "__main__": example_usage()