1125 lines
36 KiB
Python
1125 lines
36 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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import time
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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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import logging
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_logger = logging.getLogger(__name__)
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from ..emb.qnt import trim, encode_as_embedding, get_silence
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from ..utils import get_devices, setup_logging, timer, clamp
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from .lora import enable_lora
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text_task = [ "stt" ]
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class AR_NAR(Base):
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def forward_train(
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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],
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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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):
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# deduce batch_size
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if text_list is not None:
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default_task = "tts"
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device = text_list[0].device
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batch_size = len(text_list)
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else:
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default_task = "stt"
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device = resps_list[0].device
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batch_size = len(resps_list)
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# specifies how to sample probabilities of which RVQ levels to train against
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rvq_levels_p = self.config.experimental.rvq_levels_p 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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# RVQ levels to apply masking training on
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masking_train_rvq_levels = self.config.experimental.masking_train_rvq_levels
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# force set mask training
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if "len" not in self.capabilities:
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masking_train_rvq_levels = 0.0
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elif "ar" not in self.capabilities:
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masking_train_rvq_levels = 1.0
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# CFG
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cfg_text_dropout_p = self.config.experimental.cfg_text_dropout_p if self.config is not None else 0.0
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cfg_cond_dropout_p = self.config.experimental.cfg_cond_dropout_p if self.config is not None else 0.0
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cfg_prom_dropout_p = self.config.experimental.cfg_prom_dropout_p if self.config is not None else 0.0
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# rate to train RVQ level AR-ly or NAR-ly
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masking_train_p = self.config.experimental.masking_train_p if self.config is not None else 0.5
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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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if not token_dropout_rvq_levels:
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token_dropout_rvq_levels = [0, 0]
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# allow passing a specific distribution of RVQ levels
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rvq_levels_p = rvq_levels_p if isinstance(rvq_levels_p, list) else []
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if not rvq_levels_p:
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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 rvq_levels_p == "equal":
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rvq_levels_p = [ i for i in range( lo, hi ) ]
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else:
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# yuck
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rvq_levels_p = 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( rvq_levels_p ) for i in range(batch_size) ]
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# timestep levels (for TTS NAR)
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timesteps = [ None for _ in range(batch_size) ]
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for i, task in enumerate( task_list ):
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lo, hi = masking_train_rvq_levels[0], masking_train_rvq_levels[1]
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if task in text_task:
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quant_levels[i] = 0 # self.n_resp_levels - 1
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elif lo <= quant_levels[i] and quant_levels[i] <= hi and random.random() < masking_train_p:
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timesteps[i] = random.random()
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# trim resps to only contain all levels below the target level
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resps_list = [r if t in text_task else r[..., :l+1] for r, l, t in zip(resps_list, quant_levels, task_list)]
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# tensor to cat for RVQ level 0
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text_stop_sequence = torch.tensor([2], device=device, dtype=torch.int16)
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text_start_stop_sequence = torch.tensor([1, 2], device=device, dtype=torch.int16)
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audio_stop_sequence = torch.tensor([[self.stop_token]], 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, task in zip(range(batch_size), quant_levels, resps_list, proms_list, task_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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if task in text_task:
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#text_list[i] = torch.cat([ resps, text_stop_sequence ])
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...
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else:
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resps_list[i] = torch.cat([ resps, audio_stop_sequence ])
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if task == "len":
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quant_levels[i] = 0
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# apply CFG (should probably only apply to NAR quant level 0)
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if task not in text_task + ["len"]:
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drop_text = False
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drop_audio = False
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if random.random() < cfg_prom_dropout_p:
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drop_audio = True
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if random.random() < cfg_cond_dropout_p:
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drop_audio = True
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drop_text = True
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if drop_text:
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text_list[i] = text_start_stop_sequence
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if drop_audio:
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proms_list[i] = None
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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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time_list=timesteps,
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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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def forward_nar(
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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 | int | None = None,
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max_steps: int = 1000,
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max_levels: int = 0,
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input_prompt_prefix: bool = False,
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prefix_silence: float = 1.0,
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denoise_start: float = 0.0,
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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_min_p: float = 0.0,
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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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sampling_entropix=False,
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sampling_layer_skip: bool = False,
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sampling_layer_skip_exit_layer: int = -1,
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sampling_layer_skip_entropy_threshold: float = -1,
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sampling_layer_skip_varentropy_threshold: float = -1,
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sampling_refine_on_stop: bool = False,
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disable_tqdm=False,
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use_lora=None,
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):
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# deduce batch_size
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if text_list is not None:
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default_task = "tts"
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device = text_list[0].device
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batch_size = len(text_list)
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else:
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default_task = "stt"
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device = resps_list[0].device
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batch_size = len(resps_list)
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if max_levels == 0:
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max_levels = self.n_max_levels - 1
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sampling_layer_skip_variables = {} if sampling_layer_skip else None
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if sampling_layer_skip:
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if sampling_layer_skip_entropy_threshold >= 0:
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sampling_layer_skip_variables["entropy_threshold"] = sampling_layer_skip_entropy_threshold
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if sampling_layer_skip_varentropy_threshold >= 0:
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sampling_layer_skip_variables["varentropy_threshold"] = sampling_layer_skip_varentropy_threshold
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if sampling_layer_skip_exit_layer >= 0:
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sampling_layer_skip_variables["max_layer"] = sampling_layer_skip_exit_layer
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# inference NAR level 0
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if len_list is not None:
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mask_token = torch.tensor([self.stop_token], dtype=torch.int16, device=device)
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prev_list = [ torch.concat([ mask_token for _ in range( resp_len ) ]) for resp_len in len_list ]
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# special "scheduling" to inference RVQ-level 0
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level = 0
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if cfg.lora is not None:
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enable_lora( self, cfg.lora.active_level( level ) if use_lora is None else use_lora )
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def log(x, eps = 1e-20):
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return torch.log(x.clamp(min = eps))
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def gumbel_sample(x, temperature = 1., dim = -1):
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return ((x / max(temperature, 1e-10)) + -log(-log(torch.zeros_like(x).uniform_(0, 1)))).argmax(dim = dim)
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_super = super()
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def demask_sampling( batch_index, seq_len ):
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# overrides
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max_steps = 10
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temperature = 0.3
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cfg_strength = 1.0
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sampling_repetition_penalty = 1.0 # force rep pen off, because this caused false positives due to how rep pen was being naively applied......
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sampling_top_p = 0.9 # a lot of demasking samplers use a top-k of seq_len * 0.9
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start_temperature = temperature
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start_noise = 0.0
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end_noise = 1.0
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# if we're denoising from an existing sequence
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if denoise_start > 0.0 and resps_list is not None:
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start_noise = denoise_start
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noise_p = math.cos( start_noise * math.pi * 0.5 )
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mask = torch.tensor( [ random.random() < noise_p for _ in range( seq_len ) ], dtype=torch.bool, device=device )
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input_ids = torch.where( mask, self.stop_token, resps_list[batch_index][:, 0] )
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else:
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input_ids = torch.ones((seq_len,), dtype=torch.int16, device=device) * self.stop_token
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scores = torch.zeros((seq_len,), dtype=torch.float32, device=device)
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quant_levels = [ level for _ in range(batch_size) ]
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prev_list = [ input_ids ]
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null_text = torch.tensor([1, 2], device=device, dtype=torch.int16)
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null_prom = None
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max_steps = math.floor(max_steps * (end_noise - start_noise))
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for timestep, steps_until_x0 in zip(torch.linspace(start_noise, end_noise, max_steps), reversed(range(max_steps))):
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# anneal temperature
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temperature = start_temperature * (steps_until_x0 / max_steps)
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# get noise level, per cosine scheduling
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noise_p = math.cos( timestep * math.pi * 0.5 )
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# number of tokens to mask off to "noise" the input sequence
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masked_tokens_n = max(int( noise_p * seq_len ), 1)
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# pick the worst scoring tokens to mask off
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masked_indices = scores.topk( masked_tokens_n, dim=-1 ).indices
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# mask off inputs
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input_ids = input_ids.scatter(0, masked_indices, self.stop_token)
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# boolean mask
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is_masked = input_ids == self.stop_token
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# setup inputs
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inputs = _super.inputs(
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text_list=text_list,
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proms_list=proms_list,
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resps_list=[ input_ids ],
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lang_list=lang_list,
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tone_list=tone_list,
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time_list=[ timestep ],
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quant_levels=quant_levels,
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)
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output = _super.forward(
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inputs=inputs,
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quant_levels=quant_levels,
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#layer_skip_variables=sampling_layer_skip_variables,
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)
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logits = output.logits
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if cfg_strength > 0:
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null_inputs = _super.inputs(
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text_list=[ null_text ],
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proms_list=[ null_prom ],
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resps_list=[ input_ids ],
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lang_list=lang_list,
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tone_list=tone_list,
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time_list=[ timestep ],
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quant_levels=quant_levels,
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)
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null_output = _super.forward(
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inputs=null_inputs,
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quant_levels=quant_levels,
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#layer_skip_variables=sampling_layer_skip_variables,
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)
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for logit, null_logits in zip(output.logits, null_output.logits):
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logit[-seq_len:] = logit[-seq_len:] + ( logit[-seq_len:] - null_logits[-seq_len:] ) * cfg_strength
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# sample with sampler settings
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filtered_sampled = _super.sample(
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logits=logits,
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prev_list=prev_list,
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quant_levels=quant_levels,
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temperature=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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min_p=sampling_min_p,
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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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)
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# retrieves unfiltered logits
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unfiltered_sampled = _super.sample(
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logits=logits,
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prev_list=prev_list,
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quant_levels=quant_levels,
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temperature=0.0,
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)
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# update previous list of tokens
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prev_list = [ input_ids ]
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# extract logits
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filtered_logits = filtered_sampled.logits[0]
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unfiltered_logits = unfiltered_sampled.logits[0]
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# extract scores
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filtered_scores = filtered_sampled.scores[0]
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unfiltered_scores = unfiltered_sampled.scores[0]
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# extract sampled tokens
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filtered_tokens = filtered_sampled[0][0]
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unfiltered_tokens = unfiltered_sampled[0][0]
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# sample with gumbelnoise
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# I actually feel like this doesn't matter? it's hard to judge with a partially trained NAR-len model
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sampled_ids = gumbel_sample( filtered_logits, temperature=temperature, dim=-1 )
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#sampled_ids = filtered_tokens
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# keep unmasked tokens
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input_ids = torch.where( is_masked, sampled_ids, input_ids )
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# update scores (conjugated to put the worst scores at the top)
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scores = 1.0 - torch.tensor([score for score in unfiltered_scores], device=device)
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if cfg.experimental and max_steps > 0:
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print( timestep, steps_until_x0, noise_p, masked_tokens_n, input_ids, scores )
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return input_ids
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# perform demasked sampling (mock diffusion)
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resps_list = [ demask_sampling( batch_index=i, seq_len=l ) for i, l in enumerate( len_list ) ]
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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 ) if use_lora is None else use_lora )
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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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output = super().forward(
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inputs=inputs,
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quant_levels=quant_levels,
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#layer_skip_variables=sampling_layer_skip_variables,
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)
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logits, state = output.logits, output.state
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sampled = super().sample(
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logits=logits,
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prev_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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#min_p=sampling_min_p,
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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,
|
|
#mirostat=mirostat,
|
|
)
|
|
|
|
resps_list = sampled[0]
|
|
prev_list = [ torch.cat([rs, r.unsqueeze(-1).to(device=device)], dim=-1) for rs, r in zip(prev_list, resps_list) ]
|
|
|
|
return prev_list
|
|
|
|
def forward_ar(
|
|
self,
|
|
|
|
text_list: list[Tensor],
|
|
proms_list: list[Tensor],
|
|
resps_list: list[Tensor] | None = None,
|
|
|
|
task_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 | int | None = None,
|
|
|
|
max_steps: int = 1000,
|
|
max_levels: int = 0,
|
|
|
|
input_prompt_prefix: bool = False,
|
|
prefix_silence: float = 1.0,
|
|
denoise_start: float = 0.0,
|
|
|
|
sampling_temperature: float = 1.0,
|
|
sampling_min_temperature: float = -1.0,
|
|
sampling_top_k: int = -100,
|
|
sampling_top_p: float = 1.0,
|
|
sampling_min_p: float = 0.0,
|
|
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,
|
|
sampling_dry_multiplier=0.0,
|
|
sampling_dry_base=1.75,
|
|
sampling_dry_allowed_length=2,
|
|
sampling_entropix=False,
|
|
|
|
sampling_layer_skip: bool = False,
|
|
sampling_layer_skip_exit_layer: int = -1,
|
|
sampling_layer_skip_entropy_threshold: float = -1,
|
|
sampling_layer_skip_varentropy_threshold: float = -1,
|
|
|
|
sampling_refine_on_stop: bool = False,
|
|
|
|
disable_tqdm=False,
|
|
use_lora=None,
|
|
):
|
|
# deduce batch_size
|
|
if text_list is not None:
|
|
default_task = "tts"
|
|
device = text_list[0].device
|
|
batch_size = len(text_list)
|
|
else:
|
|
default_task = "stt"
|
|
device = resps_list[0].device
|
|
batch_size = len(resps_list)
|
|
|
|
if cfg.lora is not None:
|
|
enable_lora( self, cfg.lora.active_level( 0 ) if use_lora is None else use_lora )
|
|
|
|
# inference len
|
|
if task_list is not None and task_list[0] == "len":
|
|
sequence_list = [ torch.tensor([0], device=device,dtype=torch.int16) for _ in range(batch_size) ]
|
|
stopped = torch.zeros(batch_size, device=device).bool()
|
|
|
|
stop_token = 10
|
|
task_list = [ "len" for _ in range(batch_size) ]
|
|
quant_levels = [ 0 for _ in range( max( batch_size, sampling_beam_width ) ) ]
|
|
|
|
for n in trange(10, desc="AR", disable=disable_tqdm):
|
|
len_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=quant_levels,
|
|
)
|
|
|
|
output = super().forward(
|
|
inputs=inputs,
|
|
quant_levels=quant_levels,
|
|
)
|
|
logits = output.logits
|
|
|
|
r = [ logit[-1:].argmax(dim=1) for logit in logits ]
|
|
# sanitize
|
|
for i, token in enumerate(r):
|
|
if token > 10:
|
|
r[i][0] = stop_token
|
|
|
|
# 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
|
|
|
|
# convert tokens into int
|
|
return [ int("".join([ str(token.item()) for token in r if token != stop_token ])) for r in sequence_list ]
|
|
|
|
# STT
|
|
start_slice = [ 0 for _ in range(batch_size) ]
|
|
sequence_list = [ torch.zeros(0, device=device).to(torch.int16) for _ in range(batch_size) ]
|
|
stopped = torch.zeros(batch_size, device=device).bool()
|
|
|
|
audio_stop_token = self.stop_token
|
|
text_stop_token = 2
|
|
|
|
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
|
|
metrics = []
|
|
|
|
# ick
|
|
"""
|
|
low_temperature = False # sampling_temperature < 0.6 # sampling_repetition_penalty == 1.0 and sampling_temperature == 0.0 #
|
|
low_temperature_range = cfg.dataset.frames_per_second * 5
|
|
|
|
original_sampling_temperature = sampling_temperature
|
|
original_sampling_repetition_penalty = sampling_repetition_penalty
|
|
original_sampling_repetition_penalty_decay = sampling_repetition_penalty_decay
|
|
"""
|
|
|
|
sampling_layer_skip_variables = {} if sampling_layer_skip else None
|
|
|
|
if sampling_layer_skip:
|
|
if sampling_layer_skip_entropy_threshold >= 0:
|
|
sampling_layer_skip_variables["entropy_threshold"] = sampling_layer_skip_entropy_threshold
|
|
if sampling_layer_skip_varentropy_threshold >= 0:
|
|
sampling_layer_skip_variables["varentropy_threshold"] = sampling_layer_skip_varentropy_threshold
|
|
if sampling_layer_skip_exit_layer >= 0:
|
|
sampling_layer_skip_variables["max_layer"] = sampling_layer_skip_exit_layer
|
|
|
|
for i, sequence in enumerate( sequence_list ):
|
|
# add <bos> to text for STT
|
|
if task_list[i] in text_task:
|
|
start_slice[i] = 1
|
|
sequence_list[i] = torch.cat([sequence_list[i], torch.tensor([1], dtype=torch.int16, device=device)])
|
|
# treat input prompt as initial resp (by prefixing with the prompt instead)
|
|
elif input_prompt_prefix:
|
|
start_slice[i] = proms_list[i].shape[0]
|
|
sequence_list[i], proms_list[i] = proms_list[i][:, 0], sequence_list[i]
|
|
elif prefix_silence > 0:
|
|
sequence_list[i] = get_silence(prefix_silence, device=sequence_list[i].device)
|
|
sequence_list[i] = sequence_list[i][:, 0]
|
|
# start_slice[i] = sequence_list[i].shape[0]
|
|
|
|
# get next in sequence
|
|
for n in trange(max_steps // max(1, self.causal_size), desc="AR", disable=disable_tqdm):
|
|
# it would technically be faster to just append the new token's embedding to the inputs, but there's a VERY small performance gain from doing it, so it's not worth it
|
|
text_list = [ sequence_list[i] if task in text_task else text_list[i] for i, task in enumerate(task_list) ]
|
|
resps_list = [ sequence_list[i] if task not in text_task else resps_list[i] for i, task in enumerate(task_list) ]
|
|
|
|
# greedy sampling in the AR *does* work, but requires some quasi-exotic sampling to work around the initial burst of garbage from polluting the rest of the sequence
|
|
# naturally, rep pen wrangles this initial burst of noise, but naively relying on rep_pen is no good, as it fails after ~6 seconds of audio
|
|
# however, switching to a default sampling temperature with "clean greedy sampled codes" will make the rest of sequence sound as if it were greedy sampled
|
|
# to-do: tune these values, maybe have it factor based on confidence scores or something
|
|
"""
|
|
if low_temperature:
|
|
enabled = n < low_temperature_range
|
|
sampling_repetition_penalty = 1.125 if enabled else 1.25
|
|
#sampling_repetition_penalty_decay = 0.0 if enabled else original_sampling_repetition_penalty_decay
|
|
#sampling_temperature = original_sampling_temperature if enabled else 1.0
|
|
"""
|
|
|
|
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 ) ) ]
|
|
)
|
|
|
|
# to-do: find an elegant way to write this
|
|
output = super().forward(
|
|
inputs=inputs,
|
|
state=state,
|
|
#layer_skip_variables=sampling_layer_skip_variables,
|
|
output_attentions=sampling_entropix,
|
|
)
|
|
logits, state = output.logits, output.state
|
|
|
|
sampled = super().sample(
|
|
logits=logits,
|
|
prev_list=None if sampling_repetition_penalty == 1.0 and sampling_length_penalty == 0.0 else [ resps_list[i] if task not in text_task else text_list[i] for i, task in enumerate( task_list ) ],
|
|
|
|
temperature=sampling_temperature,
|
|
min_temperature=sampling_min_temperature,
|
|
top_p=sampling_top_p,
|
|
top_k=sampling_top_k,
|
|
min_p=sampling_min_p,
|
|
repetition_penalty=sampling_repetition_penalty,
|
|
repetition_penalty_decay=sampling_repetition_penalty_decay,
|
|
length_penalty=sampling_length_penalty,
|
|
beam_width=sampling_beam_width,
|
|
|
|
mirostat=mirostat,
|
|
|
|
dry_multiplier=sampling_dry_multiplier,
|
|
dry_base=sampling_dry_base,
|
|
dry_allowed_length=sampling_dry_allowed_length,
|
|
|
|
attentions=output.attentions if sampling_entropix else None,
|
|
)
|
|
|
|
r = sampled[0]
|
|
|
|
if cfg.experimental:
|
|
if sampled.entropy:
|
|
metrics.append( sampled.entropy )
|
|
elif sampled.scores:
|
|
#metrics.append( [ { "p": p[0], "exited_layer": output.exited_layer } for p in sampled.scores ] )
|
|
metrics.append( [ { "p": p[0] } for p in sampled.scores ] )
|
|
|
|
if mirostat is not None:
|
|
mirostat = sampled.scores
|
|
elif sampling_beam_width > 0:
|
|
# expand tuple
|
|
s = sampled.scores
|
|
# 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
|
|
task_list = task_list * sampling_beam_width
|
|
start_slice = start_slice * 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):
|
|
task = task_list[i]
|
|
stop_token = audio_stop_token if task not in text_task else text_stop_token
|
|
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
|
|
|
|
# to-do for layerskip / speculative sampling: rerun the last sequence again at max depth
|
|
|
|
if metrics:
|
|
from ..plot import plot_sample_metrics
|
|
filename = "metrics"
|
|
if sampling_entropix:
|
|
filename += f'[entropix]'
|
|
if sampling_layer_skip_exit_layer >= 0:
|
|
filename += f'[{sampling_layer_skip_exit_layer+1}]'
|
|
|
|
plot_sample_metrics( metrics, filename=f'{filename}.png' )
|
|
|
|
# pick the best scoring candidate
|
|
# desu this is always going to be candidate 0
|
|
if sampling_beam_width:
|
|
sequence_list = sequence_list[:1]
|
|
task_list = task_list[:1]
|
|
|
|
# remove stop token
|
|
sequence_list = [self._prune(r, audio_stop_token if task_list[i] not in text_task else text_stop_token) for i, r in enumerate(sequence_list)]
|
|
# remove <bos>
|
|
sequence_list = [ sequence_list[i][start_slice[i]:] for i, task in enumerate( task_list ) ]
|
|
|
|
if sampling_refine_on_stop:
|
|
# get how much we need to slice from the end
|
|
slice_lengths = [ sequence.shape[-1] for sequence in sequence_list ]
|
|
# -1 for the stop token
|
|
logits = [ logit[-length-1:-1] for logit, length in zip(logits, slice_lengths) ]
|
|
# greedy sample from the sequence
|
|
refined_list = [ logit.argmax(dim=-1) for logit in logits ]
|
|
# to-do: compare scores
|
|
# set the "refined" list as the output
|
|
sequence_list = refined_list
|
|
|
|
return sequence_list
|
|
|
|
def forward(
|
|
self,
|
|
text_list: list[Tensor],
|
|
proms_list: list[Tensor],
|
|
resps_list: list[Tensor] | None = None,
|
|
|
|
task_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 | int | None = None,
|
|
|
|
max_steps: int = 1000,
|
|
max_levels: int = 0,
|
|
|
|
input_prompt_prefix: bool = False,
|
|
prefix_silence: float = 1.0,
|
|
denoise_start: float = 0.0,
|
|
|
|
sampling_temperature: float = 1.0,
|
|
sampling_min_temperature: float = -1.0,
|
|
sampling_top_k: int = -100,
|
|
sampling_top_p: float = 1.0,
|
|
sampling_min_p: float = 0.0,
|
|
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,
|
|
sampling_dry_multiplier=0.0,
|
|
sampling_dry_base=1.75,
|
|
sampling_dry_allowed_length=2,
|
|
sampling_entropix=False,
|
|
|
|
sampling_layer_skip: bool = False,
|
|
sampling_layer_skip_exit_layer: int = -1,
|
|
sampling_layer_skip_entropy_threshold: float = -1,
|
|
sampling_layer_skip_varentropy_threshold: float = -1,
|
|
|
|
sampling_refine_on_stop: bool = False,
|
|
|
|
disable_tqdm=False,
|
|
use_lora=None,
|
|
):
|
|
kwargs = dict(
|
|
max_steps=max_steps,
|
|
max_levels=max_levels,
|
|
input_prompt_prefix=input_prompt_prefix,
|
|
prefix_silence=prefix_silence,
|
|
denoise_start=denoise_start,
|
|
sampling_temperature=sampling_temperature,
|
|
sampling_min_temperature=sampling_min_temperature,
|
|
sampling_top_k=sampling_top_k,
|
|
sampling_top_p=sampling_top_p,
|
|
sampling_min_p=sampling_min_p,
|
|
sampling_repetition_penalty=sampling_repetition_penalty,
|
|
sampling_repetition_penalty_decay=sampling_repetition_penalty_decay,
|
|
sampling_length_penalty=sampling_length_penalty,
|
|
sampling_beam_width=sampling_beam_width,
|
|
sampling_mirostat_tau=sampling_mirostat_tau,
|
|
sampling_mirostat_eta=sampling_mirostat_eta,
|
|
sampling_dry_multiplier=sampling_dry_multiplier,
|
|
sampling_dry_base=sampling_dry_base,
|
|
sampling_dry_allowed_length=sampling_dry_allowed_length,
|
|
sampling_entropix=sampling_entropix,
|
|
sampling_layer_skip=sampling_layer_skip,
|
|
sampling_layer_skip_exit_layer=sampling_layer_skip_exit_layer,
|
|
sampling_layer_skip_entropy_threshold=sampling_layer_skip_entropy_threshold,
|
|
sampling_layer_skip_varentropy_threshold=sampling_layer_skip_varentropy_threshold,
|
|
sampling_refine_on_stop=sampling_refine_on_stop,
|
|
disable_tqdm=disable_tqdm,
|
|
use_lora=use_lora,
|
|
)
|
|
|
|
# deduce batch_size
|
|
if text_list is not None:
|
|
default_task = "tts"
|
|
device = text_list[0].device
|
|
batch_size = len(text_list)
|
|
else:
|
|
default_task = "stt"
|
|
device = resps_list[0].device
|
|
batch_size = len(resps_list)
|
|
|
|
# generate task list if not provided
|
|
if task_list is None:
|
|
task_list = [ default_task for _ in range(batch_size) ]
|
|
|
|
# implicitly set for training
|
|
if training is None and text_list is not None and resps_list is not None:
|
|
n_levels_set = {r.shape[-1] for r in resps_list}
|
|
n_levels = next(iter(n_levels_set))
|
|
|
|
training = n_levels == self.n_resp_levels
|
|
|
|
# is training
|
|
if training:
|
|
return self.forward_train(
|
|
text_list=text_list,
|
|
proms_list=proms_list,
|
|
resps_list=resps_list,
|
|
task_list=task_list,
|
|
lang_list=lang_list,
|
|
tone_list=tone_list,
|
|
len_list=len_list,
|
|
)
|
|
|
|
# is NAR
|
|
if (len_list is not None or resps_list is not None) and text_list is not None:
|
|
return self.forward_nar(
|
|
text_list=text_list,
|
|
proms_list=proms_list,
|
|
resps_list=resps_list,
|
|
task_list=task_list,
|
|
lang_list=lang_list,
|
|
tone_list=tone_list,
|
|
len_list=len_list,
|
|
**kwargs,
|
|
)
|
|
|
|
# is AR
|
|
return self.forward_ar(
|
|
text_list=text_list,
|
|
proms_list=proms_list,
|
|
resps_list=resps_list,
|
|
task_list=task_list,
|
|
lang_list=lang_list,
|
|
tone_list=tone_list,
|
|
len_list=len_list,
|
|
**kwargs,
|
|
)
|
|
|
|
|
|
def example_usage():
|
|
cfg.device = "cuda"
|
|
cfg.trainer.backend = "local"
|
|
if cfg.audio_backend == "dac":
|
|
cfg.sample_rate = 44_100
|
|
|
|
from functools import partial
|
|
from einops import repeat
|
|
from tqdm import tqdm
|
|
|
|
from ..emb.qnt import decode_to_file, unload_model, trim_random, repeat_extend_audio, concat_audio, merge_audio
|
|
from ..engines import Engine, Engines
|
|
from ..utils import wrapper as ml
|
|
from ..utils import setup_logging
|
|
|
|
import numpy as np
|
|
import re
|
|
|
|
setup_logging()
|
|
|
|
def load_artifact( path ):
|
|
artifact = np.load(path, allow_pickle=True)[()]
|
|
|
|
text = torch.tensor( cfg.tokenizer.encode( artifact["metadata"]["phonemes"] ) ).to(dtype=torch.uint8, device=cfg.device)
|
|
audio = torch.from_numpy(artifact["codes"].astype(np.int16))[0, :, :].t().to(dtype=torch.int16, device=cfg.device)
|
|
|
|
return text, audio
|
|
|
|
text, audio = load_artifact(f"./data/qnt.{'dac' if cfg.audio_backend == 'dac' else 'enc'}")
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|
batch_size = cfg.hyperparameters.batch_size
|
|
cfg.model.experimental.masking_train_p = 0.5
|
|
|
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text_list = [ text ] * batch_size
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proms_list = [ audio[:cfg.dataset.frames_per_second, :] ] * batch_size
|
|
resps_list = [ audio ] * batch_size
|
|
|
|
kwargs = {
|
|
'n_text_tokens': 256,
|
|
'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 if not cfg.model else cfg.model.experts,
|
|
|
|
'p_dropout': 0.1,
|
|
|
|
'l_padding': 8 if cfg.optimizations.fp8 else 0,
|
|
|
|
'config': cfg.model
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|
}
|
|
|
|
bos_id, space_id, eos_id = cfg.tokenizer.encode( " " )
|
|
available_tasks = ["tts-ar", "tts-nar"]
|
|
|
|
model = AR_NAR(**kwargs).to(cfg.device)
|
|
steps = 500 // batch_size
|
|
|
|
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}")
|
|
|
|
_logger.info(f"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:
|
|
_logger.info(f"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 )
|
|
|
|
"""
|
|
cfg.optimizations.model_offloading = {
|
|
"devices": ["cuda:0", "cpu"],
|
|
# "limits": [ 0.9, -1 ],
|
|
"assign": [[ f'layers.{i}.' for i in range(0,10) ], [ f'layers.{i}.' for i in range(11,12) ] + [ "model.norm" ]],
|
|
# "limits": [ 256 * (1024 ** 2), -1 ]
|
|
}
|
|
"""
|
|
|
|
engine = Engine(model=model, optimizer=optimizer)
|
|
engines = Engines({"ar+nar": engine})
|
|
engines.setup()
|
|
|
|
"""
|
|
if cfg.optimizations.model_offloading:
|
|
model = ml.offload_model( model, policy=cfg.optimizations.model_offloading )
|
|
"""
|
|
|
|
"""
|
|
torch.save( {
|
|
'module': model.state_dict()
|
|
}, f"./data/{cfg.model.arch_type}.pth" )
|
|
"""
|
|
|
|
_logger.info(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(t=None):
|
|
if isinstance(t, list):
|
|
tasks = t
|
|
texts = [ text_list[0].to(cfg.device) if task not in text_task else None for i, task in enumerate( tasks ) ]
|
|
proms = [ proms_list[0].to(cfg.device) if task not in text_task else [ "stt" ] for i, task in enumerate( tasks ) ]
|
|
resps = [ None if task not in text_task else resps_list[0].to(cfg.device) for i, task in enumerate( tasks ) ]
|
|
|
|
return texts, proms, resps, tasks
|
|
|
|
texts = []
|
|
proms = []
|
|
resps = []
|
|
tasks = []
|
|
|
|
for i in range(batch_size):
|
|
task = random.choice(available_tasks) if t is None else t
|
|
|
|
text = text_list[i].to(cfg.device)
|
|
prom = proms_list[i].to(cfg.device)
|
|
resp = resps_list[i].to(cfg.device)
|
|
|
|
# do nothing
|
|
if task == "stt":
|
|
prom = [ task ]
|
|
else:
|
|
task = "tts" if random.random() > 0.1 else "len"
|
|
|
|
texts.append( text )
|
|
proms.append( prom )
|
|
resps.append( resp )
|
|
tasks.append( task )
|
|
|
|
return texts, proms, resps, tasks
|
|
|
|
@torch.inference_mode()
|
|
def sample( name, steps=500, task=None ):
|
|
engine.eval()
|
|
|
|
text_list, proms_list, resp_list, task_list = sample_data( task )
|
|
|
|
if task == "tts-nar":
|
|
len_list = engine(text_list, proms_list, task_list=["len"], max_steps=5, sampling_temperature=0.0 )
|
|
len_list = [ resp_list[0].shape[0] for l in len_list ]
|
|
resps_list = engine( text_list, proms_list, len_list=len_list, sampling_temperature=0.0 )
|
|
else:
|
|
resps_list = engine( text_list, proms_list, task_list=["tts"], max_steps=steps, sampling_temperature=1.0 )
|
|
resps_list = engine( text_list, proms_list, resps_list=resps_list, sampling_temperature=0.0 )
|
|
|
|
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}.{task}.wav", device=cfg.device)
|
|
|
|
unload_model()
|
|
|
|
def train():
|
|
engine.train()
|
|
t = trange(steps)
|
|
for i in t:
|
|
texts, proms, resps, tasks = sample_data()
|
|
|
|
stats = {"step": i}
|
|
stats |= engine.traverse(text_list=texts, proms_list=proms, resps_list=resps, task_list=tasks, training=True)
|
|
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()
|
|
|
|
"""
|
|
if cfg.optimizations.compile:
|
|
model = ml.compile_model(model, backend=cfg.optimizations.compile)
|
|
"""
|
|
|
|
for task in available_tasks:
|
|
sample("final", task=task)
|
|
|
|
engines.quit()
|
|
|
|
if __name__ == "__main__":
|
|
example_usage() |