602 lines
19 KiB
Python
602 lines
19 KiB
Python
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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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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try:
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from ..ext.interleaver import (
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CodebooksPatternProvider,
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DelayedPatternProvider,
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MusicLMPattern,
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ParallelPatternProvider,
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UnrolledPatternProvider,
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VALLEPattern,
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)
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except Exception as e:
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pass
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from ..config import cfg
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def _get_pattern_provider( name ):
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return {
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'parallel': ParallelPatternProvider,
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'delay': DelayedPatternProvider,
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'unroll': UnrolledPatternProvider,
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'valle': VALLEPattern,
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'musiclm': MusicLMPattern,
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}[name]
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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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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.Embedding):
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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):
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super().__init__(max_n_levels, token_dim)
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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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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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w = self.weight
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padded_x_list = []
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for xi in 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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xi = F.pad(xi, (0, 0, 0, w.shape[0] - xi.shape[1])) # 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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class Base(nn.Module):
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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 use_stop_token(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"
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@property
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def arch_type(self) -> str:
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return "retnet"
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@property
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def n_prom_levels(self) -> int:
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return 4
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@property
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def n_resp_levels(self) -> int:
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return 4
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@property
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def n_max_levels(self) -> int:
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return 4
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@property
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def n_tasks(self) -> int:
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return 16
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@property
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def resp_loss_only(self) -> bool:
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return False
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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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@property
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def interleave_pattern(self) -> str | None:
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return "musiclm"
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@property
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def stop_token(self):
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return self.n_tokens + 0
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@property
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def interleaved_token(self):
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return self.n_tokens + 1
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@property
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def ignore_index(self):
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return -100 # self.interleaved_token
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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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@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 _interleave( self, codes ):
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if not self.interleave_pattern:
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return codes
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return codes.flatten()
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"""
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pattern_provider = _get_pattern_provider( self.interleave_pattern )( self.n_resp_levels )
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pattern = pattern_provider.get_pattern( codes.shape[0] )
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res, _, _ = pattern.build_pattern_sequence( codes.t()[None, :, :], self.interleaved_token, keep_only_valid_steps=True )
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return res[0].t().flatten()
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"""
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def _deinterleave( self, codes ):
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if not self.interleave_pattern:
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return codes
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return torch.unflatten( codes[:codes.shape[0] // self.n_resp_levels * self.n_resp_levels], 0, ( codes.shape[0] // self.n_resp_levels, self.n_resp_levels ) )
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"""
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if codes.dim() == 1:
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codes = torch.unflatten( codes[:codes.shape[0] // self.n_resp_levels * self.n_resp_levels], 0, ( codes.shape[0] // self.n_resp_levels, self.n_resp_levels ) )
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pattern_provider = _get_pattern_provider( self.interleave_pattern )( self.n_resp_levels )
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pattern = pattern_provider.get_pattern( codes.shape[0] )
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res, _, _ = pattern.revert_pattern_sequence( codes, special_token=self.interleaved_token)
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return res[0].t()
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"""
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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: dict | None = None
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):
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super().__init__()
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self._cfg = config
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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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# + tasks for each token they represent in the prom
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n_prom_tokens = n_tokens + (self.n_tasks - 1) + (1 if self.interleave_pattern else 0) # - 1 because tts is an inherent task
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# +1 to include the stop token + 1 to include interleave token
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n_resp_tokens = n_tokens + (1 if self.use_stop_token else 0) + (1 if self.interleave_pattern 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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self.proms_emb = MultiEmbedding(self.n_prom_levels, n_prom_tokens, d_model)
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self.resps_emb = MultiEmbedding(1, n_resp_tokens, 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=1,
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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_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=True,
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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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))
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# I imagine because each step returns `resp_level`s tokens at once, so we need to have a classifier for each level
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#self.classifier = nn.ModuleList([ nn.Linear(d_model, n_resp_tokens) for _ in range(self.n_resp_levels) ]) if self.interleave_pattern else nn.Linear(d_model, n_resp_tokens)
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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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@overload
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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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quant_levels: Tensor | None = None,
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shift_targ_list: bool = False,
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return_all: Literal[False] = False,
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return_all_resp: Literal[False] = False,
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sampling_temperature: float = 1.0,
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) -> Tensor:
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...
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@overload
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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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quant_levels: Tensor | None = None,
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shift_targ_list: bool = False,
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return_all: Literal[True] = True,
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return_all_resp: Literal[True] = True,
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sampling_temperature: float = 1.0,
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) -> list[Tensor]:
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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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quant_levels: Tensor | None = None,
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shift_targ_list: bool = False,
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return_all: bool = False,
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return_all_resp: bool = False,
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sampling_temperature: float = 1.0,
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state: dict | None = None,
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):
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"""
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Args:
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text_list: [t] * b
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proms_list: [t' l] * b, l quantization levels.
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resps_list: [t'' l] * b, l quantization levels.
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targ_list: [t''] * b, one quantization level only; when given, loss will be computed
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quant_levels: specify which quant_levels to feed forward, used in NAR mode.
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shift_targ_list: whether to shift target list when computing loss. True if AR.
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return_all_resp: True if NAR.
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sampling_temperature: a lower temperature makes the result more robust but less diverse.
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Returns:
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y: sampled tokens
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"""
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batch_size = len(text_list)
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x_list = self._samplewise_merge_tensors(
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self.text_emb(text_list),
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self.proms_emb(proms_list),
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self.resps_emb(resps_list),
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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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device = x.device
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if state is not None:
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# prefill
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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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if len(state) == 0:
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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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x = self.sin_emb.add_pe(x)
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for block in self.blocks:
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x = block(x, m, quant_levels)
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elif self.arch_type == "retnet":
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# to-do: actually make this work and verify it works with recurrent_forward / chunkwise_forward
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x, _ = self.retnet(x, incremental_state=state, token_embeddings=x, features_only=True)
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x = self.classifier(x) * m
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# Remove padding
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h_list = [hi[:li] for hi, li in zip(x, map(len, x_list))]
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if True:
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logits = [hi[:] for hi, li in zip(h_list, map(len, resps_list))]
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ret = [ Categorical(logits=hi / sampling_temperature).sample() for hi in logits ]
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print( [ r for r in ret ] )
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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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if any([l == 0 for l in map(len, targ_list)]):
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raise ValueError("Cannot compute loss given empty targ_list.")
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ignore_sep = torch.tensor(self.ignore_index, device=device)
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# ignore the prompt when computing loss
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prom_list = [
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torch.full_like(t[..., 0], self.ignore_index) for t in proms_list
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]
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# remake input with ignored input prompt
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text_prom_list = self._samplewise_merge_tensors(
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text_list, prom_list, sep=ignore_sep
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)
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for i in range(len(text_prom_list)):
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# ignore computing loss against text/prompt portion of input
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# the NAR doesn't need to compute the loss for it
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if self.resp_loss_only:
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text_prom_list[i][:] = self.ignore_index
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# roll the text/prompt for loss computing
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# the AR benefits from this, for some reason I'll figure out later
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else:
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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, roll by one and mark the ending with a stop token
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# this coerces the model into properly inferencing causally
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# why we don't just append a stop token in the dataloader, who knows
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if shift_targ_list:
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targ_list = [*targ_list]
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for i in range(len(targ_list)):
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targ_list[i] = targ_list[i].roll(-1, dims=0)
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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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y_list = self._samplewise_merge_tensors( text_prom_list, targ_list, sep=ignore_sep )
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self.loss = dict(
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nll=F.cross_entropy(
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torch.cat(h_list), # input / predicted logits
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torch.cat(y_list), # target / ground truth
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ignore_index=self.ignore_index,
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)
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)
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self.stats = dict(
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acc = self.accuracy_metric( torch.cat(h_list), torch.cat(y_list) ),
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precision = self.precision_metric( torch.cat(h_list), torch.cat(y_list) ),
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)
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# return the entire generated token string
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if return_all:
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logits = [hi[:] for hi, li in zip(h_list, map(len, resps_list))]
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# return the entire generated response
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elif return_all_resp:
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logits = [hi[-li:] for hi, li in zip(h_list, map(len, resps_list))]
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# return the last chunkwise piece
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elif self.causal and self.recurrent_chunk_size > 0:
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logits = [hi[-self.recurrent_chunk_size:] for hi, li in zip(h_list, map(len, resps_list))]
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# return just the last code
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else:
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logits = [ hi[-1:] for hi in h_list ]
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return [ Categorical(logits=hi / sampling_temperature).sample() for hi in logits ]
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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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max_steps: int = 1000,
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sampling_temperature: float = 1.0,
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):
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if resps_list is not None:
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resps_list = [self._interleave(r) for r in resps_list] # guarantees we only have the first levels
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return self._forward(
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text_list=text_list,
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proms_list=proms_list,
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resps_list=self._unsqueeze_list(resps_list),
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targ_list=resps_list,
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quant_levels=None,
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shift_targ_list=True,
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return_all_resp=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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resps_list: list[Tensor] = [ 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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state = {} if cfg.inference.recurrent_forward else None
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for n in range(max_steps // max(1, self.recurrent_chunk_size)):
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# get next in sequence
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r = self._forward(
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text_list,
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proms_list,
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self._unsqueeze_list(resps_list),
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sampling_temperature=sampling_temperature,
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state=state
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)
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# append tokens
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for i, ri in enumerate(r):
|
||
if self.stop_token in ri:
|
||
stopped[i] = True
|
||
resps_list[i] = torch.cat([resps_list[i], ri])
|
||
|
||
# stop token found
|
||
stopped |= r == self.stop_token
|
||
if stopped.all().item():
|
||
break
|
||
|
||
|
||
pruned = [self._prune(r) for r in resps_list]
|
||
print( [ r for r in pruned ] )
|
||
deinterleaved = [ self._deinterleave(r) for r in pruned ]
|
||
print( [ r for r in deinterleaved ] )
|
||
return deinterleaved
|
||
|
||
def example_usage():
|
||
from ..config import cfg
|
||
cfg.trainer.backend = "local"
|
||
cfg.trainer.check_for_oom = False
|
||
|
||
from functools import partial
|
||
|
||
from einops import repeat
|
||
|
||
from ..emb.qnt import decode_to_file
|
||
from ..engines import Engine, Engines
|
||
from tqdm import tqdm, trange
|
||
|
||
device = "cuda"
|
||
x8 = partial(repeat, pattern="t -> t l", l=cfg.models.prom_levels)
|
||
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': 18,
|
||
}
|
||
models = { "ar": Base(**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=torch.optim.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( filename, steps=450 * 4 ):
|
||
AR = None
|
||
|
||
engines.eval()
|
||
for name, engine in engines.items():
|
||
if name[:2] == "ar":
|
||
AR = engine
|
||
|
||
resps_list = AR(text_list, proms_list, max_steps=steps, sampling_temperature=1.0)
|
||
|
||
decode_to_file(resps_list[0].cpu(), f"./data/{filename}.wav", device="cpu")
|
||
|
||
if train:
|
||
sample("init", 15)
|
||
|
||
engines.train()
|
||
t = trange(100)
|
||
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()
|