624 lines
18 KiB
Python
624 lines
18 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, encode_as_embedding
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from .lora import enable_lora
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def clamp(n, lo, hi):
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return max(lo, min(n, hi))
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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 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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if hasattr(self, "config") and self.config:
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return self.config.experimental.causal_size
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return cfg.model.experimental.causal_size
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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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task_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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training: bool | None = None,
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max_steps: int = 1000,
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max_levels: int = 0,
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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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sampling_dry_multiplier=0.0,
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sampling_dry_base=1.75,
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sampling_dry_allowed_length=2,
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disable_tqdm=False,
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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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# generate task list if not provided
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if task_list is None:
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task_list = [ "tts" for _ in range(batch_size) ]
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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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if training is None:
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training = n_levels == self.n_resp_levels
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# is training
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if training:
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# specifies how to sample probabilities of which RVQ levels to train against
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p_rvq_levels = self.config.experimental.p_rvq_levels if self.config is not None else "equal"
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# determines which RVQ level to target per batch
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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 - 1 ]
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# rate to perform token dropout errors
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token_dropout_error = self.config.experimental.token_dropout_error
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# RVQ levels to apply token dropout on
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token_dropout_rvq_levels = self.config.experimental.token_dropout_rvq_levels
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# implicitly set it to all levels
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if not token_dropout_rvq_levels:
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token_dropout_rvq_levels = [0, self.resp_levels - 1]
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# allow passing a specific distribution of RVQ levels
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p_rvq_levels = p_rvq_levels if isinstance(p_rvq_levels, list) else []
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if not p_rvq_levels:
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lo, hi = quant_level_range[0], quant_level_range[1] + 1
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# randomly select a target RVQ-bin level (0 being AR, 1+ being NAR)
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if p_rvq_levels == "equal":
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p_rvq_levels = [ i for i in range( lo, hi ) ]
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else:
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# yuck
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p_rvq_levels = sum([[i for _ in range(hi - i)] for i in range( lo, hi ) ], [])
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# input RVQ levels
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quant_levels = [ random.choice( p_rvq_levels ) for i in range(batch_size) ]
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# trim resps to only contain all levels below the target level
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resps_list = [r[..., :l+1] for r, l in zip(resps_list, quant_levels)]
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# tensor to cat for RVQ level 0
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stop_sequence = torch.Tensor([[self.stop_token] * 1]).to(device=device, dtype=torch.int16)
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# I hate python's value/reference semantics so much
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for i, quant_level, resps, proms in zip(range(batch_size), quant_levels, resps_list, proms_list):
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# cap quant_level if it exceeds its corresponding resp/prom
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if quant_level >= resps.shape[-1]:
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quant_levels[i] = resps.shape[-1] - 1
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# proms could be a Tensor, list[Tensor], or None
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if isinstance( proms, torch.Tensor ):
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if quant_level >= proms.shape[-1]:
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quant_levels[i] = proms.shape[-1] - 1
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elif isinstance( proms, list ):
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for j, prom in enumerate( proms ):
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if not isinstance( prom, torch.Tensor ):
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continue
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if quant_level >= prom.shape[-1]:
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quant_levels[i] = prom.shape[-1] - 1
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# apply token dropout error compensation
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if token_dropout_error > 0 and (token_dropout_rvq_levels[0] <= quant_level and quant_level <= token_dropout_rvq_levels[1]):
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steps = resps.shape[0]
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for l in range( quant_level ):
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for t in range( steps ):
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token = resps[t, l].item()
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if random.random() < token_dropout_error:
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offset = 1 * ( 1 if random.random() < 0.5 else -1 )
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resps_list[i][t, l] = clamp(token + offset, 1, 1022) # +- 1
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# only apply stop token for RVQ level 0
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if quant_level <= 0:
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# append stop tokens for AR
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resps_list[i] = torch.cat([ resps, stop_sequence ])
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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, # could technically just grab this from the above inputs since they're included as an RVQ level token
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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_max_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", disable=disable_tqdm ):
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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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if cfg.lora is not None:
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enable_lora( self, cfg.lora.active_level( level ) )
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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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if cfg.lora is not None:
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enable_lora( self )
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return prev_list
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# is AR
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if cfg.lora is not None:
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enable_lora( self, cfg.lora.active_level( 0 ) )
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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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state = 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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# get next in sequence
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for n in trange(max_steps // max(1, self.causal_size), desc="AR", disable=disable_tqdm):
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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 state is not None:
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logits, state = super().forward(
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inputs=inputs,
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state=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=state,
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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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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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dry_multiplier=sampling_dry_multiplier,
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dry_base=sampling_dry_base,
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dry_allowed_length=sampling_dry_allowed_length,
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)
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if mirostat is not None:
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# 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 ]
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# we do it here because the sampler will already expand our logits list
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elif sampling_beam_width > 0:
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# expand tuple
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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
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proms_list = proms_list * sampling_beam_width
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sequence_list = sequence_list * sampling_beam_width
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stopped = torch.zeros(batch_size, device=device).bool()
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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):
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if stop_token in ri:
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stopped[i] = True
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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():
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break
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# 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] ]
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sequence_list = [self._prune(r, stop_token) for r in sequence_list]
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return sequence_list
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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_100
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from functools import partial
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from einops import repeat
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from tqdm import tqdm
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from ..emb.qnt import decode_to_file, unload_model, trim_random, repeat_extend_audio, concat_audio, merge_audio
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from ..engines import Engine, Engines
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from ..utils import wrapper as ml
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import numpy as np
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import re
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device = "cuda"
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# mamba seems to ONLY be used as an AR (any NAR attempts lobotomizes it)
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"""
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if "mamba" in cfg.model.arch_type:
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cfg.model.resp_levels = 1
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"""
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# cfg.model.loss_factors = {}
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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.resp_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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noise = _load_quants(f"./data/noise.{'dac' if cfg.audio_backend == 'dac' else 'enc'}")
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text_list = [
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tokenize("ˈaɪ wɪl nˌɑːt ˈæsk ɐ sˈɛkənd tˈaɪm").to(device),
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#tokenize("ˈaɪ wɪl nˌɑːt ˈæsk").to(device),
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]
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proms_list = [
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qnt[:cfg.dataset.frames_per_second, :].to(device),
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#qnt[:cfg.dataset.frames_per_second, :].to(device),
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]
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resps_list = [
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qnt[:, :].to(device),
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#qnt[:cfg.dataset.frames_per_second, :].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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resps_list = resps_list[:1]
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batch_size = len(text_list)
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# rentet-full is the only configuration with BitNet's BitLinear that converges despite the grad_norm saying otherwise
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kwargs = {
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'n_text_tokens': 256,
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'n_audio_tokens': 1024,
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'd_model': 1024, # 256, # 1024, # 1536
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'n_heads': 16, # 4, # 16, # 24
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'n_layers': 12, # 32
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'n_experts': 1,
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'p_dropout': 0.1,
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'l_padding': 8 if cfg.optimizations.fp8 else 0,
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'config': cfg.model
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}
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"""
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try:
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kwargs['config'] = cfg.model
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except Exception as e:
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pass
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"""
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bos_id, space_id, eos_id = cfg.tokenizer.encode( " " )
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tasks = cfg.dataset.tasks_list
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model = AR_NAR(**kwargs).to(device)
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steps = 150 * len(tasks) * cfg.model.experimental.causal_size
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optimizer = cfg.hyperparameters.optimizer.lower() if cfg.yaml_path is not None else "prodigy"
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scheduler = cfg.hyperparameters.scheduler.lower() if cfg.yaml_path is not None else ""
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learning_rate = cfg.hyperparameters.learning_rate if cfg.yaml_path is not None else None
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if cfg.optimizations.dadaptation:
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# do not combine the two
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if scheduler == "schedulefree":
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scheduler = ""
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learning_rate = 1.0
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if optimizer == "prodigy":
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if learning_rate is None:
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learning_rate = 1.0
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optimizer = ml.Prodigy
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elif optimizer == "adagrad":
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if learning_rate is None:
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learning_rate = 1.0e-2
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optimizer = ml.Adagrad
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elif optimizer == "adamw":
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if learning_rate is None:
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learning_rate = 1.0e-4
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optimizer = ml.AdamW
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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)
|
||
|
||
engines = Engines({"ar+nar": engine})
|
||
engines.setup()
|
||
|
||
"""
|
||
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.no_grad()
|
||
def sample_data(task=None):
|
||
texts = []
|
||
proms = []
|
||
resps = []
|
||
|
||
for i in range(batch_size):
|
||
if task is None:
|
||
task = random.choice(tasks)
|
||
|
||
text = text_list[i]
|
||
prom = proms_list[i]
|
||
resp = resps_list[i]
|
||
|
||
# do nothing
|
||
if task == "tts":
|
||
...
|
||
elif task == "tts-c":
|
||
trim_length = int(random.uniform(cfg.dataset.prompt_duration_range[0], cfg.dataset.prompt_duration_range[1]) * cfg.dataset.frames_per_second)
|
||
|
||
prom = resp[:trim_length]
|
||
resp = resp[trim_length:]
|
||
elif task == "ns" or task == "sr":
|
||
# extend the noise to fill the target audio
|
||
noise_ext = repeat_extend_audio( noise, resp.shape[0] )
|
||
# create the input prompt by merging the target audio with the noise
|
||
prom = merge_audio( resp.cpu(), noise_ext, scale=[1, cfg.dataset.noise_scale], device=cfg.dataset.reencode_device )
|
||
# set the target to just be the noise if <sr>
|
||
if task == "sr":
|
||
resp = noise_ext
|
||
|
||
# set the text prompt to empty to train without a guided text prompt
|
||
if random.random() < 0.5:
|
||
text = torch.tensor([bos_id, eos_id]).to(device=device, dtype=torch.uint8)
|
||
|
||
texts.append( text.to(device) )
|
||
proms.append( prom.to(device) )
|
||
resps.append( resp.to(device) )
|
||
|
||
return texts, proms, resps
|
||
|
||
@torch.inference_mode()
|
||
def sample( name, steps=1000, task=None ):
|
||
engine.eval()
|
||
|
||
texts, proms, resps = sample_data( task )
|
||
|
||
if "ar" in cfg.model.capabilities:
|
||
resps = engine( texts, proms, max_steps=steps, sampling_temperature=0.95 )
|
||
|
||
if "nar" in cfg.model.capabilities:
|
||
resps = engine( texts, proms, resps, sampling_temperature=0.2 )
|
||
|
||
for i, o in enumerate(resps):
|
||
_ = decode_to_file(o.to(dtype=torch.int32), f"data/{cfg.model.arch_type}.{cfg.audio_backend}.{i}.{task}.{name}.wav", device=device)
|
||
|
||
unload_model()
|
||
|
||
def train():
|
||
engine.train()
|
||
t = trange(steps)
|
||
for i in t:
|
||
texts, proms, resps = sample_data()
|
||
|
||
stats = {"step": i}
|
||
stats |= engine.traverse(text_list=texts, proms_list=proms, resps_list=resps)
|
||
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()
|
||
|
||
for task in tasks:
|
||
sample("final", task=task)
|
||
|
||
engines.quit()
|
||
|
||
if __name__ == "__main__":
|
||
example_usage() |