f5_tts/model_new/backbones/dit.py

"""
ein notation:
b - batch
n - sequence
nt - text sequence
nw - raw wave length
d - dimension
"""

from __future__ import annotations

import torch
import torch.nn.functional as F
from torch import nn
from x_transformers.x_transformers import RotaryEmbedding

from f5_tts.model_new.modules import (AdaLayerNorm_Final, ConvNeXtV2Block,
                                      ConvPositionEmbedding, DiTBlock,
                                      TimestepEmbedding, get_pos_embed_indices,
                                      precompute_freqs_cis)

# Text embedding


class TextEmbedding(nn.Module):
    def __init__(
        self, text_num_embeds, text_dim, mask_padding=True, conv_layers=0, conv_mult=2
    ):
        super().__init__()
        self.text_embed = nn.Embedding(
            text_num_embeds + 1, text_dim
        )  # use 0 as filler token

        self.mask_padding = mask_padding  # mask filler and batch padding tokens or not

        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 self.mask_padding:
            text_mask = text == 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
            if self.mask_padding:
                text = text.masked_fill(
                    text_mask.unsqueeze(-1).expand(-1, -1, text.size(-1)), 0.0
                )
                for block in self.text_blocks:
                    text = block(text)
                    text = text.masked_fill(
                        text_mask.unsqueeze(-1).expand(-1, -1, text.size(-1)), 0.0
                    )
            else:
                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):
        super().__init__()
        self.proj = nn.Linear(mel_dim * 2 + text_dim, 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"],
        drop_audio_cond=False,
    ):  # noqa: F722
        if drop_audio_cond:  # cfg for cond audio
            cond = torch.zeros_like(cond)

        x = self.proj(torch.cat((x, cond, text_embed), 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,
        text_mask_padding=True,
        qk_norm=None,
        conv_layers=0,
        pe_attn_head=None,
        attn_backend="torch",  # "torch" | "flash_attn"
        attn_mask_enabled=False,
        long_skip_connection=False,
        checkpoint_activations=False,
    ):
        super().__init__()

        self.time_embed = TimestepEmbedding(dim)
        if text_dim is None:
            text_dim = mel_dim
        self.text_embed = TextEmbedding(
            text_num_embeds,
            text_dim,
            mask_padding=text_mask_padding,
            conv_layers=conv_layers,
        )
        self.text_cond, self.text_uncond = None, None  # text cache
        self.input_embed = InputEmbedding(mel_dim, text_dim, 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,
                    qk_norm=qk_norm,
                    pe_attn_head=pe_attn_head,
                    attn_backend=attn_backend,
                    attn_mask_enabled=attn_mask_enabled,
                )
                for _ in range(depth)
            ]
        )
        self.long_skip_connection = (
            nn.Linear(dim * 2, dim, bias=False) if long_skip_connection else None
        )

        self.norm_out = AdaLayerNorm_Final(dim)  # final modulation
        self.proj_out = nn.Linear(dim, mel_dim)

        self.checkpoint_activations = checkpoint_activations

        self.initialize_weights()

    def initialize_weights(self):
        # Zero-out AdaLN layers in DiT blocks:
        for block in self.transformer_blocks:
            nn.init.constant_(block.attn_norm.linear.weight, 0)
            nn.init.constant_(block.attn_norm.linear.bias, 0)

        # Zero-out output layers:
        nn.init.constant_(self.norm_out.linear.weight, 0)
        nn.init.constant_(self.norm_out.linear.bias, 0)
        nn.init.constant_(self.proj_out.weight, 0)
        nn.init.constant_(self.proj_out.bias, 0)

    def ckpt_wrapper(self, module):
        # https://github.com/chuanyangjin/fast-DiT/blob/main/models.py
        def ckpt_forward(*inputs):
            outputs = module(*inputs)
            return outputs

        return ckpt_forward

    def get_input_embed(
        self,
        x,  # b n d
        cond,  # b n d
        text,  # b nt
        drop_audio_cond: bool = False,
        drop_text: bool = False,
        cache: bool = True,
    ):
        seq_len = x.shape[1]
        if cache:
            if drop_text:
                if self.text_uncond is None:
                    self.text_uncond = self.text_embed(text, seq_len, drop_text=True)
                text_embed = self.text_uncond
            else:
                if self.text_cond is None:
                    self.text_cond = self.text_embed(text, seq_len, drop_text=False)
                text_embed = self.text_cond
        else:
            text_embed = self.text_embed(text, seq_len, drop_text=drop_text)

        x = self.input_embed(x, cond, text_embed, drop_audio_cond=drop_audio_cond)

        return x

    def clear_cache(self):
        self.text_cond, self.text_uncond = None, None

    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
        mask: bool["b n"] | None = None,  # noqa: F722
        drop_audio_cond: bool = False,  # cfg for cond audio
        drop_text: bool = False,  # cfg for text
        cfg_infer: bool = False,  # cfg inference, pack cond & uncond forward
        cache: bool = False,
    ):
        batch, seq_len = x.shape[0], x.shape[1]
        if time.ndim == 0:
            time = time.repeat(batch)

        # t: conditioning time, text: text, x: noised audio + cond audio + text
        t = self.time_embed(time)
        if cfg_infer:  # pack cond & uncond forward: b n d -> 2b n d
            x_cond = self.get_input_embed(
                x, cond, text, drop_audio_cond=False, drop_text=False, cache=cache
            )
            x_uncond = self.get_input_embed(
                x, cond, text, drop_audio_cond=True, drop_text=True, cache=cache
            )
            x = torch.cat((x_cond, x_uncond), dim=0)
            t = torch.cat((t, t), dim=0)
            mask = torch.cat((mask, mask), dim=0) if mask is not None else None
        else:
            x = self.get_input_embed(
                x,
                cond,
                text,
                drop_audio_cond=drop_audio_cond,
                drop_text=drop_text,
                cache=cache,
            )

        rope = self.rotary_embed.forward_from_seq_len(seq_len)

        if self.long_skip_connection is not None:
            residual = x

        for block in self.transformer_blocks:
            if self.checkpoint_activations:
                # https://pytorch.org/docs/stable/checkpoint.html#torch.utils.checkpoint.checkpoint
                x = torch.utils.checkpoint.checkpoint(
                    self.ckpt_wrapper(block), x, t, mask, rope, use_reentrant=False
                )
            else:
                x = block(x, t, mask=mask, rope=rope)

        if self.long_skip_connection is not None:
            x = self.long_skip_connection(torch.cat((x, residual), dim=-1))

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

        return output