545 lines
19 KiB
Python
Executable File
545 lines
19 KiB
Python
Executable File
import math
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import torch
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import torch.nn.functional as F
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import traceback
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import numpy as np
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from typing import Literal, overload
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from functools import partial
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from einops import rearrange
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from torch import Tensor, einsum, nn
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from torch.distributions import Categorical
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from torch.nn.utils.rnn import pad_sequence
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from torch.utils.checkpoint import checkpoint
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from torchmetrics.classification import BinaryAccuracy, MulticlassAccuracy, MulticlassPrecision
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from .retnet import RetNetDecoder, RetNetConfig
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from .transformer import SinusoidalEmbedding, Block as TransformerBlock
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from ..samplers import reptition_penalize, length_penalize, top_k_top_p_filtering, dynamic_temperature, top_k_logits_list, mirostat_sample
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def _create_mask(l, device):
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"""1 is valid region and 0 is invalid."""
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seq = torch.arange(max(l), device=device).unsqueeze(0) # (1 t)
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stop = torch.tensor(l, device=device).unsqueeze(1) # (b 1)
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return (seq < stop).float() # (b t)
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def _join(x: tuple[Tensor], sep: Tensor):
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"""
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Args:
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x: (k t d)
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sep: (d)
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"""
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ret = x[0]
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for i in range(1, len(x)):
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ret = torch.cat((ret, sep[None], x[i]), dim=0)
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return ret
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def list_to_tensor(x_list: list[Tensor], pattern="t b c -> b t c"):
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"""
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Args:
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x_list: [(t d)]
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Returns:
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x: (? ? ?)
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m: (? ? ?), same as x
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"""
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l = list(map(len, x_list))
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x = rearrange(pad_sequence(x_list), pattern)
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m = _create_mask(l, x_list[0].device)
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m = m.t().unsqueeze(-1) # (t b 1)
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m = rearrange(m, pattern)
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m = m.to(x)
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return x, m
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# automagically parses a batch-list and returns it as a list
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class Embedding(nn.Embedding):
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def forward(self, x_list: list[Tensor]) -> list[Tensor]:
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if len(x_list) == 0:
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return []
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return super().forward(torch.cat(x_list)).split([*map(len, x_list)])
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class MultiEmbedding(nn.Module):
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"""
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This embedding sums embeddings on different levels.
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"""
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def __init__(self, max_n_levels, n_tokens, token_dim, monolithic=False):
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super().__init__()
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self.monolithic = monolithic
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self.max_n_levels = max_n_levels
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self.n_tokens = n_tokens
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self.weight = nn.Parameter(torch.randn(max_n_levels, n_tokens, token_dim))
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# to-do: select quant level from given quant_levels tensor if given (i.e. through the resp_emb)
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# I imagine this is an oversight in the NAR.
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def forward(self, x_list: list[Tensor], quant_levels: Tensor | None = None) -> list[Tensor]:
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if len(x_list) == 0:
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return []
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# this "strategy" will reserve the weight[0] for te AR and weight[1:] for the NAR
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# the NAR cannot share RVQ-bin level 0 with the AR for the resp_emb
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if self.monolithic:
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w = self.weight[:1] if quant_levels is None else self.weight[1:]
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else:
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w = self.weight
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padded_x_list = []
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for i, xi in enumerate(x_list):
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xi = F.one_hot(xi.to(torch.int64), num_classes=self.n_tokens) # t l' k
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wi = w.shape[0] - xi.shape[1]
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xi = F.pad(xi, (0, 0, 0, wi)) # t l k
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padded_x_list.append(xi.to(w))
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x = torch.cat(padded_x_list) # n l k
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x = einsum("l k d, n l k -> n d", w, x)
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x_list = x.split([*map(len, x_list)])
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return x_list
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# Embedding that sums each RVQ-bin level within a given input acoustic prompt
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class AudioEmbedding(nn.Module):
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def __init__(self, l_tokens, token_dim):
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super().__init__()
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self.embeddings = nn.ModuleList([nn.Embedding(n_tokens, token_dim) for n_tokens in l_tokens])
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def forward(self, x_list: list[Tensor], quant_levels: Tensor | None = None ) -> list[Tensor]:
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res_list = []
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for i, xi in enumerate(x_list):
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# prom
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if quant_levels is None and xi.shape[-1] > 1:
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x = sum( [ self.embeddings[k]( xi[:, k] ) for k in range(xi.shape[-1]) ] )
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# AR resp
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elif quant_levels is None or quant_levels[i] == 0:
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x = self.embeddings[0]( xi[:, 0] )
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# NAR resp
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else:
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x = sum( [ self.embeddings[k+1]( xi[:, k] ) for k in range(xi.shape[-1]) ] )
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res_list.append(x)
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return res_list
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class Base(nn.Module):
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@property
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def causal(self) -> bool:
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raise NotImplementedError
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@property
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def arch_type(self) -> str:
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raise NotImplementedError
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@property
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def norm_type(self):
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raise NotImplementedError
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@property
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def n_prom_levels(self) -> int:
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raise NotImplementedError
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@property
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def n_resp_levels(self) -> int:
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raise NotImplementedError
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@property
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def n_max_levels(self) -> int:
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raise NotImplementedError
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@property
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def n_langs(self) -> int:
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raise NotImplementedError
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@property
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def n_tasks(self) -> int:
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raise NotImplementedError
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@property
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def recurrent_chunk_size(self) -> int:
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raise NotImplementedError
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@property
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def rotary_embedding_base(self) -> float:
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return 10000
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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 False
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@property
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def version(self) -> int:
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return 1
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@property
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def stop_token(self):
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if not self.causal:
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raise ValueError("Not using stop token!")
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return self.n_tokens
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@property
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def ignore_index(self):
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return -100
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@staticmethod
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def _samplewise_merge_tensors(*l, sep: Tensor | None):
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if sep is None:
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cat = torch.cat
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else:
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cat = partial(_join, sep=sep)
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return [*map(cat, zip(*l))]
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def __init__(
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self,
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n_tokens: int = 1024,
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d_model: int = 512,
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n_heads: int = 8,
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n_layers: int = 12,
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p_dropout: float = 0.1,
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config = None,
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):
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super().__init__()
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self.config = config
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self.activation_checkpointing = self.config.activation_checkpointing if self.config is not None else True
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self.n_tokens = n_tokens
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self.d_model = d_model
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self.n_heads = n_heads
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self.n_layers = n_layers
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# +1 to include the stop token
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# to-do: undo this dogshit mistake; tasks tokens should be delegated to its own embedding
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n_prom_tokens = n_tokens
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n_resp_tokens = n_tokens + (1 if self.causal else 0) # AR requires a stop token to... know when to stop
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self.text_emb = Embedding(n_tokens, d_model)
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if self.version == 1: # legacy
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n_prom_tokens += (self.n_tasks - 1) # old models have the task tokens in the prom
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self.proms_emb = MultiEmbedding(self.n_prom_levels, n_prom_tokens, d_model)
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self.resps_emb = MultiEmbedding(self.n_resp_levels, n_resp_tokens, d_model, monolithic=self.monolithic)
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else:
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# [1024] * 8
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self.proms_emb = AudioEmbedding([n_prom_tokens] * self.n_prom_levels, d_model)
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# [1025] + [1024] * 8
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self.resps_emb = AudioEmbedding([n_resp_tokens] + [n_resp_tokens - 1] * (self.n_resp_levels - 1), d_model)
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# self.langs_emb = Embedding(self.n_langs, d_model)
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# self.tasks_emb = Embedding(self.n_tasks, d_model)
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self.sep = nn.Parameter(torch.randn(d_model))
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if self.arch_type == "transformer":
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self.sin_emb = SinusoidalEmbedding(d_model)
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self.blocks = nn.ModuleList([TransformerBlock(
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d_model=d_model,
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n_heads=n_heads,
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p_dropout=p_dropout,
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causal=self.causal,
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norm_type=self.norm_type,
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n_levels=self.n_resp_levels,
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) for _ in range(n_layers) ])
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elif self.arch_type == "retnet":
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self.retnet = RetNetDecoder(RetNetConfig(
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vocab_size=n_tokens,
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decoder_embed_dim=d_model,
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decoder_value_embed_dim =d_model * 2,
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decoder_retention_heads=n_heads,
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decoder_ffn_embed_dim=d_model * 4,
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decoder_layers=n_layers,
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dropout=p_dropout,
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checkpoint_activations=self.activation_checkpointing,
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activation_fn="gelu",
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use_layernorm=True, # self.version < 3,
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use_biases=True, # self.version < 3,
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use_glu=False, # self.version >= 3,
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chunkwise_recurrent=self.causal and self.recurrent_chunk_size > 0,
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recurrent_chunkwise_size=self.recurrent_chunk_size if self.causal else 0,
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no_output_layer=True,
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decoder_normalize_before=True,
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rotary_embedding_base=self.rotary_embedding_base, # 10000
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))
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self.classifier = nn.Linear(d_model, n_resp_tokens)
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self.accuracy_metric = MulticlassAccuracy(
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n_resp_tokens,
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top_k=10,
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average="micro",
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multidim_average="global",
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ignore_index=self.ignore_index,
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)
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self.precision_metric = MulticlassPrecision(
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n_resp_tokens,
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top_k=10,
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average="micro",
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multidim_average="global",
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ignore_index=self.ignore_index,
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)
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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],
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targ_list: list[Tensor] | None = None,
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#langs_list: list[Tensor] | None = None,
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#tasks_list: list[Tensor] | None = None,
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quant_levels: Tensor | None = None,
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state: dict | None = None,
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):
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x_list = self._samplewise_merge_tensors(
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self.text_emb(text_list),
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#self.langs_emb(langs_list),
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self.proms_emb(proms_list),
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#self.tasks_emb(tasks_list),
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self.resps_emb(resps_list, quant_levels),
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sep=self.sep,
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)
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x, m = list_to_tensor(x_list)
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batch_size = len(text_list)
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device = x.device
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if state is not None and self.arch_type == "retnet":
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# prefill
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if len(state) == 0:
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prefill_size = x.shape[1]
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# run the initial prompt to fill the KV cache
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for n in range(prefill_size):
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xi = x[:, n, :].unsqueeze(1)
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self.retnet(xi, incremental_state=state, token_embeddings=xi, features_only=True)
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# grab last token(s)
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x = x[:, -1, :].unsqueeze(1)
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if self.arch_type == "transformer":
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# ensures we specify a quant_level for the transformer implementation's AdaLN
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l = torch.zeros((batch_size,), dtype=torch.int32) if quant_levels is None else quant_levels
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l = l.to(device)
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# inject position information
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x = self.sin_emb.add_pe(x)
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# pass our inputs through the transformer
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for block in self.blocks:
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x = block(x, m, l)
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elif self.arch_type == "retnet":
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# pass our inputs through the RetNet
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x, _ = self.retnet(x, incremental_state=state, token_embeddings=x, features_only=True)
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# output projection layer with masking
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x = self.classifier(x) * m
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# Remove padding
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logits = [ hi[:li] for hi, li in zip(x, map(len, x_list)) ]
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# compute loss if the target is given
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if targ_list is not None:
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ignore_sep = torch.tensor(self.ignore_index, device=device)
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# create a tensor sequence with one RVQ-bin of the input prompt, but with `ignore_index`, as the prompt is not neeeded for computing the loss against
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prom_list = [ torch.full_like(t[..., 0], self.ignore_index) for t in proms_list ]
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# remake input sequence
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text_prom_list = self._samplewise_merge_tensors( text_list, prom_list, sep=ignore_sep )
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# process each batch
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for i in range(len(text_prom_list)):
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# for the AR and NAR, shift the text/input prompt into the future by 1, and ignore the rolled back token
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text_prom_list[i] = text_prom_list[i].roll(-1, dims=0)
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text_prom_list[i][-1] = self.ignore_index
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# for the AR, shift the target response into the future by 1, and ignore the rolled back text token
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if quant_levels is None or quant_levels[i] == 0:
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targ_list[i] = targ_list[i].clone().roll(-1, dims=0) # clone ensures it's not an aliased copy/view of resps
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targ_list[i][-1] = self.stop_token
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# create the new target sequence to compute the loss against
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target = torch.cat( self._samplewise_merge_tensors( text_prom_list, targ_list, sep=ignore_sep ) )
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inputs = torch.cat( logits )
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self.loss = dict(
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# "nll" was in the original implementation and should actually just be called something else
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nll = F.cross_entropy( inputs, target, ignore_index=self.ignore_index )
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)
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self.stats = dict(
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acc = self.accuracy_metric( inputs, target ),
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precision = self.precision_metric( inputs, target ),
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)
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return logits
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def sample(
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self,
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logits: list[Tensor],
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resps_list: list[Tensor],
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quant_levels: Tensor | None = None,
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temperature: float = 1.0,
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min_temperature: float = -1.0,
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top_k: int = -100,
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top_p: float = 1.0,
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repetition_penalty: float = 1.0,
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repetition_penalty_decay: float = 0.0,
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length_penalty: float = 0.0,
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beam_width: int = 0,
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mirostat: list[dict] | None = None,
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):
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if min_temperature < 0:
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min_temperature = temperature
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# (NAR) return the entire generated response
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if quant_levels is not None:
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logits = [ logit[-l:] for logit, l in zip(logits, map(len, resps_list)) ]
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# (AR chunkwise) return the last chunkwise piece
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elif self.causal and self.recurrent_chunk_size > 0:
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logits = [ logit[-l:] for logit, l in zip(logits, self.recurrent_chunk_size) ]
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# (AR) return just the last code
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else:
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logits = [ logit[-1:] for logit in logits ]
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devices = [ logit.device for logit in logits ]
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logits = [ logit.cpu() for logit in logits ]
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# perform repetition penalizing
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logits = [ reptition_penalize(logit, previous=resps[:, -1], factor=repetition_penalty, decay=repetition_penalty_decay) for logit, resps in zip( logits, resps_list ) ]
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# (AR) perform length penalizing
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if quant_levels is None and self.causal:
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logits = [ length_penalize(logit, length=l + 1, factor=length_penalty, token=self.stop_token) for logit, l in zip( logits, map(len, resps_list) ) ]
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# perform top_k/top_p filtering of our logits
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if top_k > 0 or top_p < 1.0:
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logits = [ top_k_top_p_filtering(logit, top_k=top_k, top_p=top_p) for logit in logits ]
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# trigger dynamic temperature sampling if the minimum temperature is not the same as the sampling temperature
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# epsilon float comparison because I don't trust Python
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if abs(temperature - min_temperature) >= 0.001:
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logits = [ dynamic_temperature(logit, temperature=temperature, min_temperature=min_temperature) for logit in logits ]
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else:
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logits = [ logit / temperature for logit in logits ]
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# do mirostat sampling
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# currently incompatible with beam searching with the way the two are implemented, perhaps a night of brain bashing can make the two work
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if mirostat is not None:
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# mirostat sampling
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return [ mirostat_sample(logit, state=state) for logit, state in zip(logits, mirostat) ]
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# do beam search (naive implementation)
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# picks the top-k across all batches, and re-batches those resultant tokens
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# returns the logit scores as well to be P-concatted with the previous scores
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# to-do: not naively implement beam searching
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if beam_width > 1:
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candidates = top_k_logits_list( logits, beam_width )
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res = [ torch.tensor(token, dtype=torch.int16).unsqueeze(dim=-1) for batch, token in candidates ]
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scores = [ logits[batch].flatten()[token] for batch, token in candidates ]
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return res, scores
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# and sample
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return [ Categorical(logits=logit).sample() for logit in logits ]
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def example_usage():
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from ..config import cfg
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cfg.trainer.backend = "local"
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cfg.trainer.check_for_oom = False
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from functools import partial
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from einops import repeat
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from ..emb.qnt import decode_to_file
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from ..engines import Engine, Engines
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from tqdm import tqdm, trange
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from ..utils import wrapper as ml
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from .ar import AR
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from .nar import NAR
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device = "cuda"
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x8 = partial(repeat, pattern="t -> t l", l=cfg.models.prom_levels)
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symmap = {'<s>': 1, '</s>': 2, ' ': 3, '.': 4, ',': 5, '!': 6, '?': 7, 'p': 7, 'iː': 8, 'ɚ': 9, 'ˌ': 10, 'dˌ': 11, 'mˌ': 12, 'd': 13, 'ɹ': 14, 'tˈ': 15, 'pˌ': 16, 'uː': 17, 'l': 18, 'æ': 19, 'ɛ': 20, 'ɪ': 21, 'j': 22, 'ʊ': 23, 't': 24, 'n': 25, 'v': 26, 'a': 27, 'o': 28, 'ŋ': 29, 'w': 30, 'ʌ': 31, 'hˈ': 32, 'ɡˈ': 33, 'ə': 34, 'θˈ': 35, 'dˈ': 36, 'wˌ': 37, 'h': 38, 'z': 39, 'k': 40, 'ð': 41, 'ɡˌ': 42, 'ˈ': 43, 'fˈ': 44, 'i': 45, 's': 46, 'ʃ': 47, 'wˈ': 48, 'ðˈ': 49, 'ɹˈ': 50, 'lˈ': 51, 'ɡ': 52, 'oː': 53, 'mˈ': 54, 'e': 55, 'ɑː': 56, 'nˈ': 57, 'm': 58, 'θˌ': 59, 'sˈ': 60, 'f': 61, 'ɔː': 62, 'hˌ': 63, 'b': 64, 'jˈ': 65, 'ɐ': 66, 'ʒˈ': 67, 'θ': 68, 'bˈ': 69, 'ɾ': 70, 'ɜː': 71, 'ʌˈ': 72, 'ʃˌ': 73, 'bˌ': 74, 'kˈ': 75, 'ɔ': 76, 'zˈ': 77, 'ᵻ': 78, 'kˌ': 79, 'vˈ': 80, 'fˌ': 81, 'ʒ': 82, 'ʃˈ': 83, 'ɹˌ': 84, 'tˌ': 85, 'pˈ': 86, 'ðˌ': 87, 'sˌ': 88, 'nˌ': 89, 'lˌ': 90, '̩': 91, 'ʔ': 92, 'vˌ': 93, 'ɪˈ': 94, '"': 95, 'ɪˌ': 96, 'ʒˌ': 97, 'uːˌ': 98, 'ʊˈ': 99, 'jˌ': 100, 'uːˈ': 101, 'iːˈ': 102, 'zˌ': 103, '.ˈ': 104, '…': 105, 'ŋˌ': 106, 'ɐˌ': 107, '—ˈ': 108, 'iˌ': 109, 'iːˌ': 110, 'ɛː': 111, ')': 112, ')ˈ': 113, '(': 114, 'u': 115, '-': 116, 'ɖˈ': 117, 'iˈ': 118, 'ʰˈ': 119, 'ɟˈ': 120, '̃': 121, 'eː': 122, 'ɾˈ': 123, 'r': 124, 'ʰ': 125, '-ˌ': 126, 'ɫ': 127, 'q': 128, '—': 129, 'ʊˌ': 130, 'aː': 131, 'cˈ': 132, '…ˈ': 133, 'c': 134, 'ɳ': 135, 'ɐˈ': 136, 'x': 137, 'ʔˌ': 138, '.ˌ': 139, 'ɑ': 140, '?ˈ': 141, '̩ˈ': 142, '"ˈ': 143, ',ˈ': 144, 'ŋˈ': 145, 'əˌ': 146, '!ˈ': 147, '"ˌ': 148, '?ˌ': 149, ',ˌ': 150, '—ˌ': 151, '̩ˌ': 152, 'əˈ': 153, '!ˌ': 154, 'ɬ': 155, 'ʲ': 156, '¡': 157, 'ɯ': 158, 'qˌ': 159, 'ʑ': 160, 'ʑˈ': 161, '¿': 162, 'ɑːˈ': 163, 'iːː': 164, 'ɛˈ': 165, '¡ˈ': 166, 'æˈ': 167, 'ç': 168, 'ɾˌ': 169, 'ᵻˈ': 170, 'xˈ': 171, 'ɔːˈ': 172, ';': 173, 'ɬˌ': 174, ':': 175, 'ʔˈ': 176, 'ɑːˌ': 177, 'ɬˈ': 178}
|
||
def tokenize(content, lang_marker="en"):
|
||
split = content.split(" ")
|
||
phones = [f"<s>"] + [ " " if not p else p for p in split ] + [f"</s>"]
|
||
return torch.tensor([*map(symmap.get, phones)]).to()
|
||
|
||
kwargs = {
|
||
'n_tokens': 1024,
|
||
'd_model': 1024,
|
||
'n_heads': 16,
|
||
'n_layers': 12,
|
||
}
|
||
models = { "ar": AR(**kwargs).to(device), "nar": NAR(**kwargs).to(device) }
|
||
|
||
for name, model in models.items():
|
||
print(f"{name} parameter count: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
|
||
|
||
engines = Engines({ name: Engine(model=model, optimizer=ml.AdamW(model.parameters(), lr=1e-4)) for name, model in models.items() })
|
||
|
||
train = True
|
||
|
||
qnt = torch.load("data/qnt.pt")[0].t()[:, :cfg.models.prom_levels].to(device)
|
||
text_list = [
|
||
tokenize("ˈ a ɪ w ɪ l nˌ ɑː t ˈ æ s k ɐ sˈ ɛ k ə n d tˈ a ɪ m").to(device),
|
||
#tokenize("ˌ ɔ n ɡˌ o ʊ ɪ ŋ hˈ o ʊ m ð ə tˈ uː f ɹˈ ɛ n d z fˈ a ʊ n d ɐ lˈ ɛ ɾ ɚ f ɹ ʌ m ˈ æ θ o ʊ z , hˌ uː d ɪ zˈ a ɪ ɚ d ðˌ ɛ m t ə mˈ iː t hˌ ɪ m æ t ð ə ɡ ɹˈ æ n d t ʃˈ ɑː ɹ l ɪ mˌ æ ɡ n i ɔ n ð ə fˈ ɑː l o ʊ ɪ ŋ dˈ e ɪ .").to(device),
|
||
]
|
||
|
||
proms_list = [
|
||
qnt.to(device),
|
||
]
|
||
resps_list = [
|
||
qnt.to(device),
|
||
]
|
||
|
||
def sample( name, steps=600 ):
|
||
AR = None
|
||
NAR = None
|
||
|
||
engines.eval()
|
||
for name, engine in engines.items():
|
||
if name[:2] == "ar":
|
||
AR = engine
|
||
elif name[:3] == "nar":
|
||
NAR = engine
|
||
|
||
resps_list = AR(text_list, proms_list, max_steps=steps, sampling_temperature=1.0)
|
||
resps_list = [r.unsqueeze(-1) for r in resps_list]
|
||
codes = NAR( text_list, proms_list, resps_list=resps_list, sampling_temperature=0.2 )
|
||
|
||
decode_to_file(resps_list[0], f"./data/ar.{name}.wav", device=device)
|
||
decode_to_file(codes[0], f"./data/ar+nar.{name}.wav", device=device)
|
||
|
||
if train:
|
||
sample("init", 15)
|
||
|
||
engines.train()
|
||
t = trange(500)
|
||
for i in t:
|
||
stats = {"step": i}
|
||
"""
|
||
for name, engine in engines.items():
|
||
stats |= engine.traverse(text_list=text_list, proms_list=proms_list, resps_list=resps_list)
|
||
"""
|
||
stats = engines.step({"text_list": text_list, "proms_list": proms_list, "resps_list": resps_list})
|
||
tqdm.write(f"{stats}")
|
||
else:
|
||
for name, engine in engines.items():
|
||
engine.module.load_state_dict(torch.load(f"./data/{name}.pth"))
|
||
|
||
sample("final")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
example_usage()
|