541 lines
15 KiB
Python
541 lines
15 KiB
Python
"""
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||
# an AR + NAR model that handles:
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* inferencing the primary RVQ level in an autoregressive manner (AR)
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* inferencing the remaining RVQ levels in parallel (NAR)
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This model can fully handle being trained as a unified model (AR + NAR) or separate models (AR | NAR).
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It's recommended to train as a unified model, then "distill" knowledge of each tasks separately, just in case.
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"""
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from .base import Base, list_to_tensor, Categorical
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from ..config import cfg
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import torch
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from torch.nn.utils.rnn import pad_sequence
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import random
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import math
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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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from ..emb.qnt import trim
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class AR_NAR(Base):
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@property
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def capabilities(self) -> list[str]:
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if hasattr(self, "config") and self.config:
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return self.config.capabilities
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return cfg.model.capabilities
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@property
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def causal(self):
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return "ar" in self.capabilities
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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 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.model.arch_type
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@property
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def n_prom_levels(self) -> int:
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if hasattr(self, "config") and self.config:
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return self.config.prom_levels
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return cfg.model.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.model.resp_levels
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@property
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def n_max_levels(self) -> int:
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if hasattr(self, "config") and self.config:
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return self.config.max_levels
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return cfg.model.max_levels
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@property
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def n_tasks(self) -> int:
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if hasattr(self, "config") and self.config:
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return self.config.tasks
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return cfg.model.tasks
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@property
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def n_langs(self) -> int:
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if hasattr(self, "config") and self.config:
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return self.config.langs
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return cfg.model.langs
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@property
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def n_tones(self) -> int:
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if hasattr(self, "config") and self.config:
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return self.config.tones
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return cfg.model.tones
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@property
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def causal_size(self) -> int:
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# 1 for the stop token
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# governs how much to shift the logits by
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# 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
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return 1 # if self.causal else 0
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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 True
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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.model.version
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def _prune(self, l: Tensor, stop = None):
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if stop is None:
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stop = self.stop_token
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indices = (l == stop).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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resps_list: list[Tensor] | None = None,
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lang_list: list[Tensor] | None = None,
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tone_list: list[Tensor] | None = None,
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len_list: list[Tensor] | None = None,
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max_steps: int = 1000,
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max_levels: int = 0,
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max_resp_context: int = -1,
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sampling_temperature: float = 1.0,
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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,
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sampling_beam_width: int = 0,
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sampling_mirostat_tau: float = 0.0,
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sampling_mirostat_eta: float = 0.1,
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):
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device = text_list[0].device
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batch_size = len(text_list)
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# is training or NAR
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if resps_list is not None:
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n_levels_set = {r.shape[-1] for r in resps_list}
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n_levels = next(iter(n_levels_set))
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# is training
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if n_levels == self.n_resp_levels:
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# to-do: make this YAML configurable
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def sample_task():
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return "tts"
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# generate task list to train against
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task_list = [ sample_task() for _ in range(batch_size) ]
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# determines which RVQ level to target per batch
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quant_level_range = [ 0 if self.causal else 1, self.n_resp_levels ]
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if cfg.experimental:
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# makes higher levels less likely
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def generate( lo=0, hi=8 ):
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index = lo
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p = random.random()
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for i in range(lo, hi):
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if p < 1.0 / (2 ** i):
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index = i
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return int(index)
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quant_levels = [ generate(quant_level_range[0], quant_level_range[1]) for i in range(batch_size) ]
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else:
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# randomly select a target RVQ-bin level (0 being AR, 1+ being NAR)
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quant_levels = [ random.randint(quant_level_range[0], quant_level_range[1] - 1) for i in range(batch_size) ]
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resps_list = [r[..., 0] if l == 0 else r[..., :l+1] for r, l in zip(resps_list, quant_levels)]
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# append stop tokens for AR
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# could technically do it in the .inputs call
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for i in range(batch_size):
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# only apply stop token for RVQ level 0
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if quant_levels[i] > 0:
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continue
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resps_list[i] = torch.cat([resps_list[i], torch.Tensor([self.stop_token]).to(device=device, dtype=torch.int16) ])
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inputs = self.inputs(
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text_list=text_list,
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proms_list=proms_list,
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resps_list=resps_list,
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lang_list=lang_list,
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tone_list=tone_list,
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task_list=task_list,
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quant_levels=quant_levels,
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)
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return super().forward(
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inputs=inputs,
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quant_levels=quant_levels,
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)
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# is NAR
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if max_levels == 0:
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max_levels = self.n_resp_levels - 1
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# expand if given a raw 1D tensor
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for i, resp in enumerate(resps_list):
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if resp.dim() == 1:
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resps_list[i] = resp.unsqueeze(-1)
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prev_list = resps_list
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for n in trange( max_levels, desc="NAR" ):
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level = prev_list[0].shape[-1]
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if level >= max_levels + 1: # min(max_levels + 1, self.n_resp_levels): # commented out to experiment with exceeding trained levels
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break
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quant_levels = [ level for _ in range(batch_size) ] # torch.full((len(text_list),), level)
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inputs = self.inputs(
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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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tone_list=tone_list,
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quant_levels=quant_levels,
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)
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logits = super().forward(
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inputs=inputs,
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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=mirostat,
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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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# is AR
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sequence_list = [ torch.zeros(0, device=device).to(torch.int16) for _ in range(batch_size) ]
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stopped = torch.zeros(batch_size, device=device).bool()
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stop_token = self.stop_token
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task_list = [ "tts" for _ in range(batch_size) ]
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recurrent_state = [] if cfg.inference.recurrent_forward else None
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mirostat = [
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{"n": 1024, "tau": sampling_mirostat_tau, "eta": sampling_mirostat_eta, "max_surprise": sampling_mirostat_eta * 2, "error_surprise": 0, "running_total_surprise": 0}
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] * batch_size if sampling_mirostat_tau > 0.0 else None
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scores = [ 1.0 ] * sampling_beam_width
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if self.interleave:
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max_steps *= self.n_prom_levels
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# get next in sequence
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for n in trange(max_steps // max(1, self.causal_size), desc="AR"):
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# experimental rolling response to avoid too-long perplexity hits despite RetNet allegedly fixing this.
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# UNTESTED. In theory it would be better to also adjust the text, but there's no way of correlating text to segment of audio without something like wav2vec2
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if max_resp_context > 0:
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resps_list = self._unsqueeze_list([ sequence[-max_resp_context:] for sequence in sequence_list ] )
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else:
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resps_list = self._unsqueeze_list(sequence_list)
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inputs = self.inputs(
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text_list=text_list,
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proms_list=proms_list,
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resps_list=resps_list,
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lang_list=lang_list,
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tone_list=tone_list,
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len_list=len_list,
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task_list=task_list,
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quant_levels=[ 0 for _ in range( max( batch_size, sampling_beam_width ) ) ]
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)
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if recurrent_state is not None:
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logits, recurrent_state = super().forward(
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inputs=inputs,
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state=recurrent_state,
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)
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else:
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logits = super().forward(
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inputs=inputs,
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state=recurrent_state,
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)
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||
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r = super().sample(
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||
logits=logits,
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resps_list=resps_list,
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||
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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,
|
||
top_k=sampling_top_k,
|
||
repetition_penalty=sampling_repetition_penalty,
|
||
repetition_penalty_decay=sampling_repetition_penalty_decay,
|
||
length_penalty=sampling_length_penalty,
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||
beam_width=sampling_beam_width,
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||
|
||
mirostat=mirostat,
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||
)
|
||
|
||
if mirostat is not None:
|
||
# r is the state
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||
mirostat = r
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||
# extract token from state
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||
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
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||
# first step, expand batch
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||
if batch_size == 1:
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||
batch_size = sampling_beam_width
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||
text_list = text_list * sampling_beam_width
|
||
proms_list = proms_list * sampling_beam_width
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||
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) ]
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||
|
||
# append tokens
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||
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)])
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||
|
||
# stop token found
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||
stopped |= r == stop_token
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||
if stopped.all().item():
|
||
break
|
||
|
||
# pick the best scoring candidate
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||
# desu this is always going to be candidate 0
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||
if sampling_beam_width:
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||
sequence_list = [ sequence_list[0] ]
|
||
|
||
sequence_list = [self._prune(r, stop_token) for r in sequence_list]
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||
return sequence_list
|
||
|
||
|
||
def example_usage():
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||
cfg.trainer.backend = "local"
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||
cfg.hyperparameters.gradient_accumulation_steps = 1
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||
if cfg.audio_backend == "dac":
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||
cfg.sample_rate = 44_000
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||
|
||
from functools import partial
|
||
from einops import repeat
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||
from tqdm import tqdm
|
||
|
||
from ..emb.qnt import decode_to_file, unload_model
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||
from ..engines import Engine
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||
from ..utils import wrapper as ml
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||
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||
import numpy as np
|
||
import re
|
||
|
||
device = "cuda"
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||
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||
# mamba seems to ONLY be used as an AR (any NAR attempts lobotomizes it)
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||
"""
|
||
if "mamba" in cfg.model.arch_type:
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||
cfg.model.prom_levels = 1
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||
cfg.model.resp_levels = 1
|
||
"""
|
||
# cfg.model.loss_factors = {}
|
||
|
||
def tokenize(content):
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||
return torch.tensor( cfg.tokenizer.encode(content) )
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||
|
||
def _load_quants(path) -> Tensor:
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||
qnt = np.load(path, allow_pickle=True)[()]
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||
return torch.from_numpy(qnt["codes"].astype(np.int16))[0, :cfg.model.prom_levels, :].t().to(torch.int16)
|
||
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qnt = _load_quants(f"./data/qnt.{'dac' if cfg.audio_backend == 'dac' else 'enc'}")
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||
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||
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text_list = [
|
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tokenize("ˈaɪ wɪl nˌɑːt ˈæsk ɐ sˈɛkənd tˈaɪm").to(device),
|
||
#tokenize("ˈaɪ wɪl nˌɑːt ˈæsk").to(device),
|
||
]
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||
proms_list = [
|
||
qnt[:cfg.dataset.frames_per_second, :].to(device),
|
||
#qnt[:cfg.dataset.frames_per_second, :].to(device),
|
||
]
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||
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 = 200
|
||
|
||
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() |