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"""
ein notation:
b - batch
n - sequence
nt - text sequence
nw - raw wave length
d - dimension
"""
from __future__ import annotations
import torch
from torch import nn
import torch
import torch.nn.functional as F
from x_transformers.x_transformers import RotaryEmbedding
from transformers.models.llama.modeling_llama import LlamaDecoderLayer
from transformers.models.llama import LlamaConfig
from torch.utils.checkpoint import checkpoint
from diffrhythm.model.modules import (
TimestepEmbedding,
ConvNeXtV2Block,
ConvPositionEmbedding,
DiTBlock,
AdaLayerNormZero_Final,
precompute_freqs_cis,
get_pos_embed_indices,
)
# Text embedding
class TextEmbedding(nn.Module):
def __init__(self, text_num_embeds, text_dim, conv_layers=0, conv_mult=2):
super().__init__()
self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim) # use 0 as filler token
if conv_layers > 0:
self.extra_modeling = True
self.precompute_max_pos = 4096 # ~44s of 24khz audio
self.register_buffer("freqs_cis", precompute_freqs_cis(text_dim, self.precompute_max_pos), persistent=False)
self.text_blocks = nn.Sequential(
*[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)]
)
else:
self.extra_modeling = False
def forward(self, text: int["b nt"], seq_len, drop_text=False): # noqa: F722
#text = text + 1 # use 0 as filler token. preprocess of batch pad -1, see list_str_to_idx()
#text = text[:, :seq_len] # curtail if character tokens are more than the mel spec tokens
batch, text_len = text.shape[0], text.shape[1]
#text = F.pad(text, (0, seq_len - text_len), value=0)
if drop_text: # cfg for text
text = torch.zeros_like(text)
text = self.text_embed(text) # b n -> b n d
# possible extra modeling
if self.extra_modeling:
# sinus pos emb
batch_start = torch.zeros((batch,), dtype=torch.long)
pos_idx = get_pos_embed_indices(batch_start, seq_len, max_pos=self.precompute_max_pos)
text_pos_embed = self.freqs_cis[pos_idx]
text = text + text_pos_embed
# convnextv2 blocks
text = self.text_blocks(text)
return text
# noised input audio and context mixing embedding
class InputEmbedding(nn.Module):
def __init__(self, mel_dim, text_dim, out_dim, cond_dim):
super().__init__()
self.proj = nn.Linear(mel_dim * 2 + text_dim + cond_dim * 2, out_dim)
self.conv_pos_embed = ConvPositionEmbedding(dim=out_dim)
def forward(self, x: float["b n d"], cond: float["b n d"], text_embed: float["b n d"], style_emb, time_emb, drop_audio_cond=False): # noqa: F722
if drop_audio_cond: # cfg for cond audio
cond = torch.zeros_like(cond)
style_emb = style_emb.unsqueeze(1).repeat(1, x.shape[1], 1)
time_emb = time_emb.unsqueeze(1).repeat(1, x.shape[1], 1)
# print(x.shape, cond.shape, text_embed.shape, style_emb.shape, time_emb.shape)
x = self.proj(torch.cat((x, cond, text_embed, style_emb, time_emb), dim=-1))
x = self.conv_pos_embed(x) + x
return x
# Transformer backbone using DiT blocks
class DiT(nn.Module):
def __init__(
self,
*,
dim,
depth=8,
heads=8,
dim_head=64,
dropout=0.1,
ff_mult=4,
mel_dim=100,
text_num_embeds=256,
text_dim=None,
conv_layers=0,
long_skip_connection=False,
use_style_prompt=False
):
super().__init__()
cond_dim = 512
self.time_embed = TimestepEmbedding(cond_dim)
self.start_time_embed = TimestepEmbedding(cond_dim)
if text_dim is None:
text_dim = mel_dim
self.text_embed = TextEmbedding(text_num_embeds, text_dim, conv_layers=conv_layers)
self.input_embed = InputEmbedding(mel_dim, text_dim, dim, cond_dim=cond_dim)
#self.rotary_embed = RotaryEmbedding(dim_head)
self.dim = dim
self.depth = depth
#self.transformer_blocks = nn.ModuleList(
# [DiTBlock(dim=dim, heads=heads, dim_head=dim_head, ff_mult=ff_mult, dropout=dropout, use_style_prompt=use_style_prompt) for _ in range(depth)]
#)
llama_config = LlamaConfig(hidden_size=dim, intermediate_size=dim * ff_mult, hidden_act='silu')
llama_config._attn_implementation = 'sdpa'
self.transformer_blocks = nn.ModuleList(
[LlamaDecoderLayer(llama_config, layer_idx=i) for i in range(depth)]
)
self.long_skip_connection = nn.Linear(dim * 2, dim, bias=False) if long_skip_connection else None
self.text_fusion_linears = nn.ModuleList(
[
nn.Sequential(
nn.Linear(cond_dim, dim),
nn.SiLU()
) for i in range(depth // 2)
]
)
for layer in self.text_fusion_linears:
for p in layer.parameters():
p.detach().zero_()
self.norm_out = AdaLayerNormZero_Final(dim, cond_dim) # final modulation
self.proj_out = nn.Linear(dim, mel_dim)
# if use_style_prompt:
# self.prompt_rnn = nn.LSTM(64, cond_dim, 1, batch_first=True)
def forward(
self,
x: float["b n d"], # nosied input audio # noqa: F722
cond: float["b n d"], # masked cond audio # noqa: F722
text: int["b nt"], # text # noqa: F722
time: float["b"] | float[""], # time step # noqa: F821 F722
drop_audio_cond, # cfg for cond audio
drop_text, # cfg for text
drop_prompt=False,
style_prompt=None, # [b d t]
style_prompt_lens=None,
mask: bool["b n"] | None = None, # noqa: F722
grad_ckpt=False,
start_time=None,
):
batch, seq_len = x.shape[0], x.shape[1]
if time.ndim == 0:
time = time.repeat(batch)
# t: conditioning time, c: context (text + masked cond audio), x: noised input audio
t = self.time_embed(time)
s_t = self.start_time_embed(start_time)
c = t + s_t
text_embed = self.text_embed(text, seq_len, drop_text=drop_text)
# import pdb; pdb.set_trace()
if drop_prompt:
style_prompt = torch.zeros_like(style_prompt)
# if self.training:
# packed_style_prompt = torch.nn.utils.rnn.pack_padded_sequence(style_prompt.transpose(1, 2), style_prompt_lens.cpu(), batch_first=True, enforce_sorted=False)
# else:
# packed_style_prompt = style_prompt.transpose(1, 2)
#print(packed_style_prompt.shape)
# _, style_emb = self.prompt_rnn.forward(packed_style_prompt)
# _, (h_n, c_n) = self.prompt_rnn.forward(packed_style_prompt)
# style_emb = h_n.squeeze(0) # 1, B, dim -> B, dim
style_emb = style_prompt # [b, 512]
x = self.input_embed(x, cond, text_embed, style_emb, c, drop_audio_cond=drop_audio_cond)
if self.long_skip_connection is not None:
residual = x
pos_ids = torch.arange(x.shape[1], device=x.device)
pos_ids = pos_ids.unsqueeze(0).repeat(x.shape[0], 1)
for i, block in enumerate(self.transformer_blocks):
if not grad_ckpt:
x, *_ = block(x, position_ids=pos_ids)
else:
x, *_ = checkpoint(block, x, position_ids=pos_ids, use_reentrant=False)
if i < self.depth // 2:
x = x + self.text_fusion_linears[i](text_embed)
if self.long_skip_connection is not None:
x = self.long_skip_connection(torch.cat((x, residual), dim=-1))
x = self.norm_out(x, c)
output = self.proj_out(x)
return output
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