435 lines
13 KiB
Python
435 lines
13 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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from .base import Base, list_to_tensor, Categorical
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from ..config import cfg
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import torch
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from torch.nn.utils.rnn import pad_sequence
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import random
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import math
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from einops import rearrange
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from torch import Tensor
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from tqdm import trange
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from ..emb.qnt import trim
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import logging
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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 | None = None,
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max_steps: int = 1000,
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max_levels: int = 0,
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max_resp_context: int = -1,
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sampling_temperature: float = 1.0,
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sampling_min_temperature: float = -1.0,
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sampling_top_k: int = -100,
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sampling_top_p: float = 1.0,
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sampling_repetition_penalty: float = 1.0,
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sampling_repetition_penalty_decay: float = 0.0,
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sampling_length_penalty: float = 0.0,
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sampling_beam_width: int = 0,
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sampling_mirostat_tau: float = 0.0,
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sampling_mirostat_eta: float = 0.1,
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disable_tqdm=False,
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):
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device = text_list[0].device
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batch_size = len(text_list)
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# is training
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if resps_list is not None:
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p_len_task = self.config.experimental.p_len_train 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() < p_len_task 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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p_rvq_levels = self.config.experimental.p_rvq_levels if self.config is not None else "equal"
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# determines which RVQ level to target per batch
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quant_level_range = self.config.experimental.rvq_level_range if self.config is not None and self.config.experimental.rvq_level_range else [ 0 if self.causal else 1, self.n_resp_levels - 1 ]
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# rate to perform token dropout errors
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token_dropout_error = self.config.experimental.token_dropout_error
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# RVQ levels to apply token dropout on
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token_dropout_rvq_levels = self.config.experimental.token_dropout_rvq_levels
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# implicitly set it to all levels
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if not token_dropout_rvq_levels:
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token_dropout_rvq_levels = [0, self.resp_levels - 1]
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# allow passing a specific distribution of RVQ levels
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p_rvq_levels = p_rvq_levels if isinstance(p_rvq_levels, list) else []
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if not p_rvq_levels:
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lo, hi = quant_level_range[0], quant_level_range[1] + 1
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# randomly select a target RVQ-bin level (0 being AR, 1+ being NAR)
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if p_rvq_levels == "equal":
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p_rvq_levels = [ i for i in range( lo, hi ) ]
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else:
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# yuck
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p_rvq_levels = sum([[i for _ in range(hi - i)] for i in range( lo, hi ) ], [])
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# input RVQ levels
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quant_levels = [ random.choice( p_rvq_levels ) for i in range(batch_size) ]
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# trim resps to only contain all levels below the target level
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resps_list = [r[..., :l+1] for r, l in zip(resps_list, quant_levels)]
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# I hate python's value/reference semantics so much
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for i, quant_level, resps, proms in zip(range(batch_size), quant_levels, resps_list, proms_list):
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# cap quant_level if it exceeds its corresponding resp/prom
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if quant_level >= resps.shape[-1]:
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quant_levels[i] = resps.shape[-1] - 1
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# proms could be a Tensor, list[Tensor], or None
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if isinstance( proms, torch.Tensor ):
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if quant_level >= proms.shape[-1]:
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quant_levels[i] = proms.shape[-1] - 1
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elif isinstance( proms, list ):
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for j, prom in enumerate( proms ):
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if not isinstance( prom, torch.Tensor ):
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continue
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if quant_level >= prom.shape[-1]:
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quant_levels[i] = prom.shape[-1] - 1
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# apply token dropout error compensation
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if token_dropout_error > 0 and (token_dropout_rvq_levels[0] <= quant_level and quant_level <= token_dropout_rvq_levels[1]):
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steps = resps.shape[0]
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for l in range( quant_level ):
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for t in range( steps ):
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token = resps[t, l].item()
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if random.random() < token_dropout_error:
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offset = 1 * ( 1 if random.random() < 0.5 else -1 )
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resps_list[i][t, l] = clamp(token + offset, 1, 1022) # +- 1
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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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# NAR
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if len_list is not None:
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# is NAR
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if max_levels == 0:
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max_levels = self.n_resp_levels
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# fill with mock tokens
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prev_list = [ torch.tensor([ self.stop_token for _ in range(resp_len) ], device=device, dtype=torch.int16) for resp_len in len_list ]
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start = True
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for n in trange( max_levels, desc="NAR", disable=disable_tqdm ):
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level = 0 if n == 0 else prev_list[0].shape[-1]
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if level >= max_levels + 1: # min(max_levels + 1, self.n_resp_levels): # commented out to experiment with exceeding trained levels
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break
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quant_levels = [ level for _ in range(batch_size) ] # torch.full((len(text_list),), level)
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inputs = self.inputs(
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text_list=text_list,
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proms_list=proms_list,
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resps_list=prev_list,
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lang_list=lang_list,
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tone_list=tone_list,
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quant_levels=quant_levels,
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)
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logits = super().forward(
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inputs=inputs,
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quant_levels=quant_levels,
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)
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"""
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resps_list = [ logit[-l:].argmax(dim=1) for logit, l in zip(logits, len_list) ]
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"""
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resps_list = super().sample(
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logits=logits,
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resps_list=prev_list,
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quant_levels=quant_levels,
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temperature=1.0 if n == 0 else sampling_temperature,
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min_temperature=sampling_min_temperature,
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top_p=sampling_top_p,
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top_k=sampling_top_k,
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repetition_penalty=sampling_repetition_penalty,
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repetition_penalty_decay=sampling_repetition_penalty_decay,
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#length_penalty=sampling_length_penalty,
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#beam_width=sampling_beam_width,
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#mirostat=mirostat,
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)
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if n == 0:
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prev_list = [ r.unsqueeze(-1).to(device) for r in resps_list ]
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else:
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prev_list = [ torch.cat([rs, r.unsqueeze(-1).to(device)], dim=-1) for rs, r in zip(prev_list, resps_list) ]
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return prev_list
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# is AR
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sequence_list = [ torch.tensor([0], device=device,dtype=torch.int16) for _ in range(batch_size) ]
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stopped = torch.zeros(batch_size, device=device).bool()
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stop_token = 10
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task_list = [ "len" for _ in range(batch_size) ]
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for n in trange(10, desc="AR", disable=disable_tqdm):
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len_list = sequence_list
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inputs = self.inputs(
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text_list=text_list,
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proms_list=proms_list,
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resps_list=resps_list,
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lang_list=lang_list,
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tone_list=tone_list,
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len_list=len_list,
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task_list=task_list,
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quant_levels=[ 0 for _ in range( max( batch_size, sampling_beam_width ) ) ]
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)
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logits = super().forward(
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inputs=inputs,
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)
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r = [ logit[-1:].argmax(dim=1) for logit in logits ]
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# sanitize
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for i, token in enumerate(r):
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if token > 10:
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r[i][0] = stop_token
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# append tokens
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for i, ri in enumerate(r):
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if stop_token in ri:
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stopped[i] = True
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sequence_list[i] = torch.cat([sequence_list[i], ri.to(device)])
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# stop token found
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stopped |= r == stop_token
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if stopped.all().item():
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break
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# convert tokens into int
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return [ int("".join([ str(token.item()) for token in r if token != stop_token ])) for r in sequence_list ]
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def example_usage():
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cfg.trainer.backend = "local"
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cfg.hyperparameters.gradient_accumulation_steps = 1
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if cfg.audio_backend == "dac":
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cfg.sample_rate = 44_100
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from functools import partial
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from einops import repeat
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from tqdm import tqdm
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from ..emb.qnt import decode_to_file, unload_model
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from ..engines import Engine
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from ..utils import wrapper as ml
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import numpy as np
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import re
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device = "cuda"
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# mamba seems to ONLY be used as an AR (any NAR attempts lobotomizes it)
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"""
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if "mamba" in cfg.model.arch_type:
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cfg.model.resp_levels = 1
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"""
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# cfg.model.loss_factors = {}
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def tokenize(content):
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return torch.tensor( cfg.tokenizer.encode(content) )
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def _load_quants(path) -> Tensor:
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qnt = np.load(path, allow_pickle=True)[()]
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return torch.from_numpy(qnt["codes"].astype(np.int16))[0, :cfg.model.resp_levels, :].t().to(torch.int16)
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qnt = _load_quants(f"./data/qnt.{'dac' if cfg.audio_backend == 'dac' else 'enc'}")
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text_list = [
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tokenize("ˈaɪ wɪl nˌɑːt ˈæsk ɐ sˈɛkənd tˈaɪm").to(device),
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#tokenize("ˈaɪ wɪl nˌɑːt ˈæsk").to(device),
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]
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proms_list = [
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qnt[:cfg.dataset.frames_per_second, :].to(device),
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#qnt[:cfg.dataset.frames_per_second, :].to(device),
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]
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resps_list = [
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qnt[:, :].to(device),
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#qnt[:cfg.dataset.frames_per_second, :].to(device),
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]
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text_list = text_list[:1]
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proms_list = proms_list[:1]
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resps_list = resps_list[:1]
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# rentet-full is the only configuration with BitNet's BitLinear that converges despite the grad_norm saying otherwise
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kwargs = {
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'n_text_tokens': 256,
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'n_audio_tokens': 1024,
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'd_model': 1024, # 256, # 1024, # 1536
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'n_heads': 16, # 4, # 16, # 24
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'n_layers': 12, # 32
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'n_experts': 1,
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'p_dropout': 0.1,
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'l_padding': 8 if cfg.optimizations.fp8 else 0,
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'config': cfg.model
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}
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"""
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try:
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kwargs['config'] = cfg.model
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except Exception as e:
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pass
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"""
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model = NAR(**kwargs).to(device)
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steps = 250
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optimizer = cfg.hyperparameters.optimizer.lower() if cfg.yaml_path is not None else "prodigy"
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scheduler = cfg.hyperparameters.scheduler.lower() if cfg.yaml_path is not None else ""
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learning_rate = cfg.hyperparameters.learning_rate if cfg.yaml_path is not None else None
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if cfg.optimizations.dadaptation:
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# do not combine the two
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if scheduler == "schedulefree":
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scheduler = ""
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learning_rate = 1.0
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if optimizer == "prodigy":
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if learning_rate is None:
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learning_rate = 1.0
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optimizer = ml.Prodigy
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elif optimizer == "adagrad":
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if learning_rate is None:
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learning_rate = 1.0e-2
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optimizer = ml.Adagrad
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elif optimizer == "adamw":
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if learning_rate is None:
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learning_rate = 1.0e-4
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optimizer = ml.AdamW
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elif optimizer == "sdg":
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if learning_rate is None:
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learning_rate = 1.0e-4
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optimizer = ml.SGD
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else:
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raise ValueError(f"Unrecognized optimizer: {optimizer}")
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_logger.info(f"Optimizer: {optimizer}\tLearning rate: {learning_rate}")
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optimizer = optimizer(model.parameters(), lr=learning_rate)
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if scheduler == "schedulefree":
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if isinstance(optimizer, ml.AdamW):
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scheduler = ml.schedulefree.AdamWScheduleFree
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elif isinstance(optimizer, ml.SGD):
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scheduler = ml.schedulefree.SGDScheduleFree
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else:
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scheduler = None
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if scheduler is not None:
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_logger.info(f"Scheduler: {scheduler}")
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optimizer = scheduler( model.parameters(), lr = learning_rate )
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if cfg.optimizations.replace and cfg.optimizations.linear:
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model = ml.replace_linear( model )
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if cfg.optimizations.replace and cfg.optimizations.embedding:
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model = ml.replace_embedding( model )
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engine = Engine(model=model, optimizer=optimizer)
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"""
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torch.save( {
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'module': model.state_dict()
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}, f"./data/{cfg.model.arch_type}.pth" )
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"""
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_logger.info(f"NAR parameter count: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
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@torch.inference_mode()
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def sample( name, steps=1000 ):
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if cfg.audio_backend == "dac" and name == "init":
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return
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engine.eval()
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len_list = engine(text_list, proms_list, max_steps=steps, sampling_temperature=0.95 )
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resps_list = engine( text_list, proms_list, len_list=len_list, sampling_temperature=0.2 )
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for i, o in enumerate(resps_list):
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_ = decode_to_file(o.to(dtype=torch.int32), f"data/{cfg.model.arch_type}.{cfg.audio_backend}.{i}.{name}.wav", device=device)
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unload_model()
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def train():
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engine.train()
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t = trange(steps)
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for i in t:
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stats = {"step": i}
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stats |= engine.traverse(text_list=text_list, proms_list=proms_list, resps_list=resps_list)
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stats |= {"grad_norm": engine.get_global_grad_norm()}
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tqdm.write(f"{stats}")
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"""
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torch.save( {
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'module': model.state_dict()
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}, f"./data/{cfg.model.arch_type}.pth" )
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"""
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#sample("init", 5)
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train()
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sample("final")
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if __name__ == "__main__":
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example_usage() |