490 lines
14 KiB
Python
490 lines
14 KiB
Python
from .base import Base, list_to_tensor, Categorical
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from ..config import cfg
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import torch
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from torch.nn.utils.rnn import pad_sequence
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import random
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import math
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from einops import rearrange
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from torch import Tensor
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from tqdm import trange
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from ..emb.qnt import trim
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class AR_NAR(Base):
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@property
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def causal(self):
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return True
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@property
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def norm_type(self):
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return "ln" # if self.n_resp_levels == 1 else "adaln"
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@property
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def arch_type(self) -> str:
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if hasattr(self, "config") and self.config:
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return self.config.arch_type
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return cfg.model.arch_type
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@property
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def n_prom_levels(self) -> int:
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return cfg.model.prom_levels
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@property
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def n_resp_levels(self) -> int:
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if hasattr(self, "config") and self.config:
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return self.config.resp_levels
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return cfg.model.resp_levels
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@property
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def n_max_levels(self) -> int:
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return cfg.model.max_levels
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@property
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def n_tasks(self) -> int:
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return cfg.model.tasks
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@property
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def n_langs(self) -> int:
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return cfg.model.langs
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@property
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def n_tones(self) -> int:
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return cfg.model.tones
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@property
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def recurrent_chunk_size(self) -> int:
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return 0
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"""
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@property
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def rotary_embedding_base(self) -> float:
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if hasattr(self, "config") and self.config:
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return self.config.rotary_embedding_base
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return cfg.model.rotary_embedding_base
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"""
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@property
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def interleave(self) -> bool:
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return False
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@property
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def monolithic(self) -> bool:
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return True
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@property
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def version(self) -> int:
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if hasattr(self, "config") and self.config:
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return self.config.version
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return cfg.model.version
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def _prune(self, l: Tensor):
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indices = (l == self.stop_token).nonzero()
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if len(indices) == 0:
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return l
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return l[: indices.min().item()]
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@staticmethod
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def _unsqueeze_list(x_list, axis=-1):
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return [x.unsqueeze(dim=axis) for x in x_list]
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def forward(
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self,
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text_list: list[Tensor],
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proms_list: list[Tensor],
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resps_list: list[Tensor] | None = None,
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lang_list: list[Tensor] | None = None,
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tone_list: list[Tensor] | None = None,
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max_steps: int = 1000,
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max_levels: int = 0,
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max_resp_context: int = -1,
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sampling_temperature: float = 1.0,
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sampling_min_temperature: float = -1.0,
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sampling_top_k: int = -100,
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sampling_top_p: float = 1.0,
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sampling_repetition_penalty: float = 1.0,
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sampling_repetition_penalty_decay: float = 0.0,
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sampling_length_penalty: float = 0.0,
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sampling_beam_width: int = 0,
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sampling_mirostat_tau: float = 0.0,
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sampling_mirostat_eta: float = 0.1,
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):
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device = text_list[0].device
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batch_size = len(text_list)
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# is training or NAR
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if resps_list is not None:
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n_levels_set = {r.shape[-1] for r in resps_list}
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n_levels = next(iter(n_levels_set))
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# is training
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if n_levels == self.n_resp_levels:
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# might be better to have this decided on the dataloader level
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if cfg.experimental:
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# makes higher levels less likely
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def generate( lo=0, hi=8 ):
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index = lo
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p = random.random()
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for i in range(lo, hi):
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if p < 1.0 / (2 ** i):
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index = i
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return int(index)
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quant_levels = torch.Tensor([ generate(0, self.n_resp_levels) for _ in range(batch_size) ]).to(dtype=torch.int16)
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else:
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quant_levels = torch.randint(0, self.n_resp_levels, (batch_size,)) # randomly select a target RVQ-bin level (0 being AR, 1+ being NAR)
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"""
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if cfg.model.p_ar_level == "auto" or cfg.model.p_ar_level is None:
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quant_levels = torch.randint(0, self.n_resp_levels, (batch_size,)) # randomly select a target RVQ-bin level (0 being AR, 1+ being NAR)
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else:
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quant_levels = torch.Tensor([ 0 if random.random() < cfg.model.p_ar_level else random.randint(1, self.n_resp_levels) for _ in range(batch_size) ])
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"""
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targ_list = [r[..., l] for r, l in zip(resps_list, quant_levels)] # ensures we only have 1 RVQ-bin (our target)
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resps_list = [r[..., 0] if l == 0 else r[..., :l] for r, l in zip(resps_list, quant_levels)] # r if l == 0 is technically correct since only r[:, 0] is passed through the embedding, but this should save some VRAM
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"""
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if cfg.experimental:
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proms_list = [ r if l == 0 else trim(r, cfg.dataset.frames_per_second * 3) for r, l in zip(proms_list, quant_levels) ] # trim input prompt to 3 seconds
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"""
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# append stop tokens for AR
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for i in range(batch_size):
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if quant_levels[i] > 0:
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continue
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resps_list[i] = torch.cat([resps_list[i], torch.Tensor([self.stop_token]).to(device=device, dtype=torch.int16) ])
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targ_list[i] = torch.cat([targ_list[i], torch.Tensor([self.stop_token]).to(device=device, dtype=torch.int16) ])
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inputs = self.inputs(
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text_list=text_list,
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proms_list=proms_list,
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resps_list=resps_list,
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targ_list=targ_list,
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lang_list=lang_list,
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tone_list=tone_list
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)
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return super().forward(
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inputs=inputs,
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quant_levels=quant_levels,
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)
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# is NAR
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if max_levels == 0:
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max_levels = self.n_resp_levels - 1
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prev_list = resps_list
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for n in trange( max_levels, desc="NAR" ):
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level = prev_list[0].shape[-1]
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if level >= max_levels + 1: # min(max_levels + 1, self.n_resp_levels): # commented out to experiment with exceeding trained levels
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break
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quant_levels = 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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)
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logits = super().forward(
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inputs=inputs,
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quant_levels=quant_levels,
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)
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resps_list = super().sample(
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logits=logits,
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resps_list=prev_list,
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quant_levels=quant_levels,
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temperature=sampling_temperature,
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min_temperature=sampling_min_temperature,
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top_p=sampling_top_p,
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top_k=sampling_top_k,
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repetition_penalty=sampling_repetition_penalty,
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repetition_penalty_decay=sampling_repetition_penalty_decay,
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#length_penalty=sampling_length_penalty,
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#beam_width=sampling_beam_width,
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#mirostat=mirostat,
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)
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prev_list = [ torch.cat([rs, r.unsqueeze(-1).to(device)], dim=-1) for rs, r in zip(prev_list, resps_list) ]
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return prev_list
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# is AR
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sequence_list = [ torch.zeros(0, device=device).to(torch.int16) for _ in text_list ]
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stopped = torch.zeros(batch_size, device=device).bool()
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recurrent_state = [] if cfg.inference.recurrent_forward else None
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mirostat = [
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{"n": 1024, "tau": sampling_mirostat_tau, "eta": sampling_mirostat_eta, "max_surprise": sampling_mirostat_eta * 2, "error_surprise": 0, "running_total_surprise": 0}
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] * batch_size if sampling_mirostat_tau > 0.0 else None
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scores = [ 1.0 ] * sampling_beam_width
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if self.interleave:
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max_steps *= self.n_prom_levels
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# get next in sequence
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for n in trange(max_steps // max(1, self.recurrent_chunk_size), desc="AR"):
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# experimental rolling response to avoid too-long perplexity hits despite RetNet allegedly fixing this.
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# UNTESTED. In theory it would be better to also adjust the text, but there's no way of correlating text to segment of audio without something like wav2vec2
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if max_resp_context > 0:
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resps_list = self._unsqueeze_list([ sequence[-max_resp_context:] for sequence in sequence_list ] )
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else:
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resps_list = self._unsqueeze_list(sequence_list)
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inputs = self.inputs(
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text_list=text_list,
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proms_list=proms_list,
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resps_list=resps_list,
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lang_list=lang_list,
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tone_list=tone_list,
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)
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if recurrent_state is not None:
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logits, recurrent_state = super().forward(
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inputs=inputs,
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state=recurrent_state,
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)
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else:
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logits = super().forward(
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inputs=inputs,
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state=recurrent_state,
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)
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r = super().sample(
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logits=logits,
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resps_list=resps_list,
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temperature=sampling_temperature,
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min_temperature=sampling_min_temperature,
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top_p=sampling_top_p,
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top_k=sampling_top_k,
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repetition_penalty=sampling_repetition_penalty,
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repetition_penalty_decay=sampling_repetition_penalty_decay,
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length_penalty=sampling_length_penalty,
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beam_width=sampling_beam_width,
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mirostat=mirostat,
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)
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if mirostat is not None:
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# r is the state
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mirostat = r
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# extract token from state
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r = [ state["token"] for state in mirostat ]
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# we do it here because the sampler will already expand our logits list
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elif sampling_beam_width > 0:
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# expand tuple
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r, s = r
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# first step, expand batch
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if batch_size == 1:
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batch_size = sampling_beam_width
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text_list = text_list * sampling_beam_width
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proms_list = proms_list * sampling_beam_width
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sequence_list = sequence_list * sampling_beam_width
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stopped = torch.zeros(batch_size, device=device).bool()
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scores = [ scores[i] + score for i, score in enumerate(s) ]
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# append tokens
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for i, ri in enumerate(r):
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if self.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 == self.stop_token
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if stopped.all().item():
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break
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# pick the best scoring candidate
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# desu this is always going to be candidate 0
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if sampling_beam_width:
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sequence_list = [ sequence_list[0] ]
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return [self._prune(r) 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_000
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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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x8 = partial(repeat, pattern="t -> t l", l=cfg.model.prom_levels)
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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.prom_levels, :].t().to(torch.int16)
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qnt = _load_quants(f"./data/qnt.{'dac' if cfg.audio_backend == 'dac' else 'enc'}")
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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_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': 8, # 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 = AR_NAR(**kwargs).to(device)
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steps = 50
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optimizer = cfg.hyperparameters.optimizer.lower() if cfg.cfg_path is not None else "prodigy"
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scheduler = cfg.hyperparameters.scheduler.lower() if cfg.cfg_path is not None else ""
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learning_rate = cfg.hyperparameters.learning_rate if cfg.cfg_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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print("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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print("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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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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print(f"AR+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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resps_list = engine(text_list, proms_list, max_steps=steps, sampling_temperature=0.95 )
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resps_list = [r.unsqueeze(-1) for r in resps_list]
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resps_list = engine( text_list, proms_list, resps_list=resps_list, sampling_temperature=0.2 )
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for i, o in enumerate(resps_list):
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_ = decode_to_file(o, 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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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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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()
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