698 lines
22 KiB
Python
698 lines
22 KiB
Python
"""
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A (mostly) NAR model that handles inferencing all RVQ levels in parallel (NAR).
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I believe Meta's Voicebox does this too (predict the utterance length, then decode in parallel)
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It *does* have to inference the initial length in an autoregresssive-ish manner (it can technically also be done in parallel)
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Initial experiments show this only really "works" for the a few brief seconds before going to silence. I imagine I need to read more papers or just need to train longer.
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"""
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import random
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import math
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import numpy as np
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import logging
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import torch
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from torch.nn.utils.rnn import pad_sequence
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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 .base import Base, list_to_tensor, Categorical, _dropout_mask
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from ..config import cfg
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from ..emb.qnt import trim, repeat_extend_audio
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def clamp(n, lo, hi):
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return max(lo, min(n, hi))
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_logger = logging.getLogger(__name__)
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class NAR(Base):
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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 | 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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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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text_task = [ "stt" ]
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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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# generate task list if not provided
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if task_list is None:
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task_list = [ default_task for _ in range(batch_size) ]
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has_none = resps_list is None or text_list is None
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if not has_none:
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for i, task in enumerate( task_list ):
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if resps_list[i] is None or text_list[i] is None:
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has_none = True
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break
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# is training or NAR
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if not has_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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# implicit
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if training is None:
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training = 0 if n_levels == self.n_resp_levels else None
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# is training
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if training is not None:
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len_train_p = self.config.experimental.len_train_p if self.config is not None else 0.05
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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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# assert n_levels == self.n_resp_levels
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# to-do: make this YAML configurable
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def sample_task():
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return "len" if random.random() < len_train_p else "tts"
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# generate task list to train against
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task_list = [ sample_task() for _ in range(batch_size) ]
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# 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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# 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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# 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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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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for i, task in enumerate( task_list ):
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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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# 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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# empty string for CFG
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text_start_stop_sequence = torch.tensor([1, 2], 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, text, resps, proms, task in zip(range(batch_size), quant_levels, text_list, 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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...
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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:
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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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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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if len_list is not None:
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sampling_layer_skip_variables = {} if sampling_layer_skip else None
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if max_levels == 0:
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max_levels = self.n_max_levels - 1
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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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# initial condition
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len_list = [ clamp(1, 75*3, l) for l in len_list ]
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metrics = []
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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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test_artifact = None
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# nasty hardcode to load a reference file and have that as the input target
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# to-do: expose a way to provide the initial sequence instead through CLI
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"""
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if False:
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path = "./data/00_part2_success-1.enc"
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test_artifact = np.load(path, allow_pickle=True)[()]
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text_list = [ torch.tensor( cfg.tokenizer.encode( test_artifact["metadata"]["phonemes"] ) ).to(dtype=torch.uint8, device=device) ]
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resps_list = [ torch.from_numpy(test_artifact["codes"].astype(np.int16))[0, :, :].t().to(dtype=torch.int16, device=device) ]
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proms_list = [ resps[:75*3, :] for resps in resps_list ]
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#proms_list = [ resps for resps in resps_list ]
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len_list = [ resps.shape[0] for resps in resps_list ]
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"""
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_super = super()
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def demask_sampling( seq_len, max_steps=5, temperature=1.0 ):
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starting_temperature = temperature
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input_ids = torch.ones((seq_len,), dtype=torch.long, 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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start_noise = 0.0
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end_noise = 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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# use hardcoded reference file to test inference capabilities
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if test_artifact is not None:
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# because we "set" it later on, it's not implicitly captured
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nonlocal resps_list
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start_noise = 0.5
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noise_p = math.cos( start_noise * math.pi * 0.5 )
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input_ids = torch.tensor( [ self.stop_token if random.random() < noise_p else token for _, token in enumerate( resps_list[0][:, 0] ) ], dtype=torch.int16, device=device )
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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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cfg_strength = 1.0
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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 = starting_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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resps_list = [ input_ids ]
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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=resps_list,
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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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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=resps_list,
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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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logits = [ logits + ( logits - null_logits ) * cfg_strength for logits, null_logits in zip(output.logits, null_output.logits) ]
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else:
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logits = output.logits
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# sample with sampler settings
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sampling_top_p = 0.9
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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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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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prev_list = [ demask_sampling( seq_len=l ) for l in len_list ]
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# expand if given a raw 1D tensor
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for i, resp in enumerate(prev_list):
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if resp.dim() == 1:
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prev_list[i] = resp.unsqueeze(-1)
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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=0.0, # 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,
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#mirostat=mirostat,
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)
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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
|
||
|
||
# is AR
|
||
if cfg.lora is not None:
|
||
enable_lora( self, cfg.lora.active_level( 0 ) if use_lora is None else use_lora )
|
||
|
||
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) ]
|
||
|
||
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=[ 0 for _ in range( max( batch_size, sampling_beam_width ) ) ]
|
||
)
|
||
|
||
output = super().forward(
|
||
inputs=inputs,
|
||
)
|
||
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 ]
|
||
|
||
|
||
def example_usage():
|
||
cfg.trainer.backend = "local"
|
||
cfg.hyperparameters.gradient_accumulation_steps = 1
|
||
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
|
||
from ..engines import Engine
|
||
from ..utils import wrapper as ml
|
||
|
||
import numpy as np
|
||
import re
|
||
|
||
device = "cuda"
|
||
|
||
# mamba seems to ONLY be used as an AR (any NAR attempts lobotomizes it)
|
||
"""
|
||
if "mamba" in cfg.model.arch_type:
|
||
cfg.model.resp_levels = 1
|
||
"""
|
||
# cfg.model.loss_factors = {}
|
||
|
||
def tokenize(content):
|
||
return torch.tensor( cfg.tokenizer.encode(content) )
|
||
|
||
def _load_quants(path) -> Tensor:
|
||
qnt = np.load(path, allow_pickle=True)[()]
|
||
return torch.from_numpy(qnt["codes"].astype(np.int16))[0, :cfg.model.resp_levels, :].t().to(torch.int16)
|
||
|
||
qnt = _load_quants(f"./data/qnt.{'dac' if cfg.audio_backend == 'dac' else 'enc'}")
|
||
|
||
text_list = [
|
||
tokenize("ˈaɪ wɪl nˌɑːt ˈæsk ɐ sˈɛkənd tˈaɪm").to(device),
|
||
#tokenize("ˈaɪ wɪl nˌɑːt ˈæsk").to(device),
|
||
]
|
||
proms_list = [
|
||
qnt[:cfg.dataset.frames_per_second, :].to(device),
|
||
#qnt[:cfg.dataset.frames_per_second, :].to(device),
|
||
]
|
||
resps_list = [
|
||
qnt[:, :].to(device),
|
||
#qnt[:cfg.dataset.frames_per_second, :].to(device),
|
||
]
|
||
|
||
text_list = text_list[:1]
|
||
proms_list = proms_list[:1]
|
||
resps_list = resps_list[:1]
|
||
|
||
# rentet-full is the only configuration with BitNet's BitLinear that converges despite the grad_norm saying otherwise
|
||
kwargs = {
|
||
'n_text_tokens': 256,
|
||
'n_audio_tokens': 1024,
|
||
|
||
'd_model': 1024, # 256, # 1024, # 1536
|
||
'n_heads': 16, # 4, # 16, # 24
|
||
'n_layers': 12, # 32
|
||
'n_experts': 1,
|
||
|
||
'p_dropout': 0.1,
|
||
|
||
'l_padding': 8 if cfg.optimizations.fp8 else 0,
|
||
|
||
'config': cfg.model
|
||
}
|
||
|
||
"""
|
||
try:
|
||
kwargs['config'] = cfg.model
|
||
except Exception as e:
|
||
pass
|
||
"""
|
||
|
||
model = NAR(**kwargs).to(device)
|
||
steps = 250
|
||
|
||
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 )
|
||
|
||
engine = Engine(model=model, optimizer=optimizer)
|
||
|
||
"""
|
||
torch.save( {
|
||
'module': model.state_dict()
|
||
}, f"./data/{cfg.model.arch_type}.pth" )
|
||
"""
|
||
|
||
_logger.info(f"NAR parameter count: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
|
||
|
||
@torch.inference_mode()
|
||
def sample( name, steps=1000 ):
|
||
if cfg.audio_backend == "dac" and name == "init":
|
||
return
|
||
|
||
engine.eval()
|
||
|
||
len_list = engine(text_list, proms_list, max_steps=steps, sampling_temperature=0.95 )
|
||
resps_list = engine( text_list, proms_list, len_list=len_list, sampling_temperature=0.2 )
|
||
|
||
len_list = [ min(l, 500) for l in len_list ]
|
||
|
||
for i, o in enumerate(resps_list):
|
||
_ = decode_to_file(o.to(dtype=torch.int32), f"data/{cfg.model.arch_type}.{cfg.audio_backend}.{i}.{name}.wav", device=device)
|
||
|
||
unload_model()
|
||
|
||
def train():
|
||
engine.train()
|
||
t = trange(steps)
|
||
for i in t:
|
||
stats = {"step": i}
|
||
stats |= engine.traverse(text_list=text_list, proms_list=proms_list, resps_list=resps_list)
|
||
stats |= {"grad_norm": engine.get_global_grad_norm()}
|
||
|
||
tqdm.write(f"{stats}")
|
||
|
||
"""
|
||
torch.save( {
|
||
'module': model.state_dict()
|
||
}, f"./data/{cfg.model.arch_type}.pth" )
|
||
"""
|
||
|
||
#sample("init", 5)
|
||
train()
|
||
sample("final")
|
||
|
||
if __name__ == "__main__":
|
||
example_usage() |