model/backbones/mmdit.py

"""
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

from einops import repeat

from x_transformers.x_transformers import RotaryEmbedding

from model.modules import (
    TimestepEmbedding,
    ConvPositionEmbedding,
    MMDiTBlock,
    AdaLayerNormZero_Final,
    precompute_freqs_cis, get_pos_embed_indices,
)


# text embedding

class TextEmbedding(nn.Module):
    def __init__(self, out_dim, text_num_embeds):
        super().__init__()
        self.text_embed = nn.Embedding(text_num_embeds + 1, out_dim)  # will use 0 as filler token

        self.precompute_max_pos = 1024
        self.register_buffer("freqs_cis", precompute_freqs_cis(out_dim, self.precompute_max_pos), persistent=False)

    def forward(self, text: int['b nt'], drop_text = False) -> int['b nt d']:
        text = text + 1
        if drop_text:
            text = torch.zeros_like(text)
        text = self.text_embed(text)

        # sinus pos emb
        batch_start = torch.zeros((text.shape[0],), dtype=torch.long)
        batch_text_len = text.shape[1]
        pos_idx = get_pos_embed_indices(batch_start, batch_text_len, max_pos=self.precompute_max_pos)
        text_pos_embed = self.freqs_cis[pos_idx]

        text = text + text_pos_embed

        return text


# noised input & masked cond audio embedding

class AudioEmbedding(nn.Module):
    def __init__(self, in_dim, out_dim):
        super().__init__()
        self.linear = nn.Linear(2 * in_dim, out_dim)
        self.conv_pos_embed = ConvPositionEmbedding(out_dim)

    def forward(self, x: float['b n d'], cond: float['b n d'], drop_audio_cond = False):
        if drop_audio_cond:
            cond = torch.zeros_like(cond)
        x = torch.cat((x, cond), dim = -1)
        x = self.linear(x)
        x = self.conv_pos_embed(x) + x
        return x
    

# Transformer backbone using MM-DiT blocks

class MMDiT(nn.Module):
    def __init__(self, *, 
                 dim, depth = 8, heads = 8, dim_head = 64, dropout = 0.1, ff_mult = 4,
                 text_num_embeds = 256, mel_dim = 100,
    ):
        super().__init__()

        self.time_embed = TimestepEmbedding(dim)
        self.text_embed = TextEmbedding(dim, text_num_embeds)
        self.audio_embed = AudioEmbedding(mel_dim, dim)

        self.rotary_embed = RotaryEmbedding(dim_head)

        self.dim = dim
        self.depth = depth
        
        self.transformer_blocks = nn.ModuleList(
            [
                MMDiTBlock(
                    dim = dim,
                    heads = heads,
                    dim_head = dim_head,
                    dropout = dropout,
                    ff_mult = ff_mult,
                    context_pre_only = i == depth - 1,
                )
                for i in range(depth)
            ]
        )
        self.norm_out = AdaLayerNormZero_Final(dim)  # final modulation
        self.proj_out = nn.Linear(dim, mel_dim)

    def forward(
        self,
        x: float['b n d'],     # nosied input audio
        cond: float['b n d'],  # masked cond audio
        text: int['b nt'],     # text
        time: float['b'] | float[''],  # time step
        drop_audio_cond,  # cfg for cond audio
        drop_text,        # cfg for text
        mask: bool['b n'] | None = None,
    ):
        batch = x.shape[0]
        if time.ndim == 0:
            time = repeat(time, ' -> b', b = batch)

        # t: conditioning (time), c: context (text + masked cond audio), x: noised input audio
        t = self.time_embed(time)
        c = self.text_embed(text, drop_text = drop_text)
        x = self.audio_embed(x, cond, drop_audio_cond = drop_audio_cond)

        seq_len = x.shape[1]
        text_len = text.shape[1]
        rope_audio = self.rotary_embed.forward_from_seq_len(seq_len)
        rope_text = self.rotary_embed.forward_from_seq_len(text_len)
        
        for block in self.transformer_blocks:
            c, x = block(x, c, t, mask = mask, rope = rope_audio, c_rope = rope_text)

        x = self.norm_out(x, t)
        output = self.proj_out(x)

        return output