src/YingMusicSinger/models/modules.py

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

from __future__ import annotations

import math
from typing import Optional

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from librosa.filters import mel as librosa_mel_fn
from x_transformers.x_transformers import apply_rotary_pos_emb

from src.YingMusicSinger.utils.common import is_package_available

# raw wav to mel spec


mel_basis_cache = {}
hann_window_cache = {}


def get_bigvgan_mel_spectrogram(
    waveform,
    n_fft=1024,
    n_mel_channels=100,
    target_sample_rate=24000,
    hop_length=256,
    win_length=1024,
    fmin=0,
    fmax=None,
    center=False,
):  # Copy from https://github.com/NVIDIA/BigVGAN/tree/main
    device = waveform.device
    key = f"{n_fft}_{n_mel_channels}_{target_sample_rate}_{hop_length}_{win_length}_{fmin}_{fmax}_{device}"

    if key not in mel_basis_cache:
        mel = librosa_mel_fn(
            sr=target_sample_rate,
            n_fft=n_fft,
            n_mels=n_mel_channels,
            fmin=fmin,
            fmax=fmax,
        )
        mel_basis_cache[key] = (
            torch.from_numpy(mel).float().to(device)
        )  # TODO: why they need .float()?
        hann_window_cache[key] = torch.hann_window(win_length).to(device)

    mel_basis = mel_basis_cache[key]
    hann_window = hann_window_cache[key]

    padding = (n_fft - hop_length) // 2
    waveform = torch.nn.functional.pad(
        waveform.unsqueeze(1), (padding, padding), mode="reflect"
    ).squeeze(1)

    spec = torch.stft(
        waveform,
        n_fft,
        hop_length=hop_length,
        win_length=win_length,
        window=hann_window,
        center=center,
        pad_mode="reflect",
        normalized=False,
        onesided=True,
        return_complex=True,
    )
    spec = torch.sqrt(torch.view_as_real(spec).pow(2).sum(-1) + 1e-9)

    mel_spec = torch.matmul(mel_basis, spec)
    mel_spec = torch.log(torch.clamp(mel_spec, min=1e-5))

    return mel_spec


def get_vocos_mel_spectrogram(
    waveform,
    n_fft=1024,
    n_mel_channels=100,
    target_sample_rate=24000,
    hop_length=256,
    win_length=1024,
):
    mel_stft = torchaudio.transforms.MelSpectrogram(
        sample_rate=target_sample_rate,
        n_fft=n_fft,
        win_length=win_length,
        hop_length=hop_length,
        n_mels=n_mel_channels,
        power=1,
        center=True,
        normalized=False,
        norm=None,
    ).to(waveform.device)
    if len(waveform.shape) == 3:
        waveform = waveform.squeeze(1)  # 'b 1 nw -> b nw'

    assert len(waveform.shape) == 2

    mel = mel_stft(waveform)
    mel = mel.clamp(min=1e-5).log()
    return mel


class MelSpec(nn.Module):
    def __init__(
        self,
        n_fft=1024,
        hop_length=256,
        win_length=1024,
        n_mel_channels=100,
        target_sample_rate=24_000,
        mel_spec_type="vocos",
    ):
        super().__init__()
        assert mel_spec_type in ["vocos", "bigvgan"], print(
            "We only support two extract mel backend: vocos or bigvgan"
        )

        self.n_fft = n_fft
        self.hop_length = hop_length
        self.win_length = win_length
        self.n_mel_channels = n_mel_channels
        self.target_sample_rate = target_sample_rate

        if mel_spec_type == "vocos":
            self.extractor = get_vocos_mel_spectrogram
        elif mel_spec_type == "bigvgan":
            self.extractor = get_bigvgan_mel_spectrogram

        self.register_buffer("dummy", torch.tensor(0), persistent=False)

    def forward(self, wav):
        if self.dummy.device != wav.device:
            self.to(wav.device)

        mel = self.extractor(
            waveform=wav,
            n_fft=self.n_fft,
            n_mel_channels=self.n_mel_channels,
            target_sample_rate=self.target_sample_rate,
            hop_length=self.hop_length,
            win_length=self.win_length,
        )

        return mel


# sinusoidal position embedding


class SinusPositionEmbedding(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.dim = dim

    def forward(self, x, scale=1000):
        device = x.device
        half_dim = self.dim // 2
        emb = math.log(10000) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb)
        emb = scale * x.unsqueeze(1) * emb.unsqueeze(0)
        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
        return emb


# convolutional position embedding


class ConvPositionEmbedding(nn.Module):
    def __init__(self, dim, kernel_size=31, groups=16):
        super().__init__()
        assert kernel_size % 2 != 0
        self.conv1d = nn.Sequential(
            nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2),
            nn.Mish(),
            nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2),
            nn.Mish(),
        )

    def forward(self, x: float["b n d"], mask: bool["b n"] | None = None):
        if mask is not None:
            mask = mask[..., None]
            x = x.masked_fill(~mask, 0.0)

        x = x.permute(0, 2, 1)
        x = self.conv1d(x)
        out = x.permute(0, 2, 1)

        if mask is not None:
            out = out.masked_fill(~mask, 0.0)

        return out


# rotary positional embedding related


def precompute_freqs_cis(
    dim: int, end: int, theta: float = 10000.0, theta_rescale_factor=1.0
):
    # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
    # has some connection to NTK literature
    # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
    # https://github.com/lucidrains/rotary-embedding-torch/blob/main/rotary_embedding_torch/rotary_embedding_torch.py
    theta *= theta_rescale_factor ** (dim / (dim - 2))
    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
    t = torch.arange(end, device=freqs.device)  # type: ignore
    freqs = torch.outer(t, freqs).float()  # type: ignore
    freqs_cos = torch.cos(freqs)  # real part
    freqs_sin = torch.sin(freqs)  # imaginary part
    return torch.cat([freqs_cos, freqs_sin], dim=-1)


def get_pos_embed_indices(start, length, max_pos, scale=1.0):
    # length = length if isinstance(length, int) else length.max()
    scale = scale * torch.ones_like(
        start, dtype=torch.float32
    )  # in case scale is a scalar
    pos = (
        start.unsqueeze(1)
        + (
            torch.arange(length, device=start.device, dtype=torch.float32).unsqueeze(0)
            * scale.unsqueeze(1)
        ).long()
    )
    # avoid extra long error.
    pos = torch.where(pos < max_pos, pos, max_pos - 1)
    return pos


# Global Response Normalization layer (Instance Normalization ?)


class GRN(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.gamma = nn.Parameter(torch.zeros(1, 1, dim))
        self.beta = nn.Parameter(torch.zeros(1, 1, dim))

    def forward(self, x):
        Gx = torch.norm(x, p=2, dim=1, keepdim=True)
        Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
        return self.gamma * (x * Nx) + self.beta + x


# ConvNeXt-V2 Block https://github.com/facebookresearch/ConvNeXt-V2/blob/main/models/convnextv2.py
# ref: https://github.com/bfs18/e2_tts/blob/main/rfwave/modules.py#L108


class ConvNeXtV2Block(nn.Module):
    def __init__(
        self,
        dim: int,
        intermediate_dim: int,
        dilation: int = 1,
    ):
        super().__init__()
        padding = (dilation * (7 - 1)) // 2
        self.dwconv = nn.Conv1d(
            dim, dim, kernel_size=7, padding=padding, groups=dim, dilation=dilation
        )  # depthwise conv
        self.norm = nn.LayerNorm(dim, eps=1e-6)
        self.pwconv1 = nn.Linear(
            dim, intermediate_dim
        )  # pointwise/1x1 convs, implemented with linear layers
        self.act = nn.GELU()
        self.grn = GRN(intermediate_dim)
        self.pwconv2 = nn.Linear(intermediate_dim, dim)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        residual = x
        x = x.transpose(1, 2)  # b n d -> b d n
        x = self.dwconv(x)
        x = x.transpose(1, 2)  # b d n -> b n d
        x = self.norm(x)
        x = self.pwconv1(x)
        x = self.act(x)
        x = self.grn(x)
        x = self.pwconv2(x)
        return residual + x


# RMSNorm


class RMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))
        self.native_rms_norm = float(torch.__version__[:3]) >= 2.4

    def forward(self, x):
        if self.native_rms_norm:
            if self.weight.dtype in [torch.float16, torch.bfloat16]:
                x = x.to(self.weight.dtype)
            x = F.rms_norm(
                x, normalized_shape=(x.shape[-1],), weight=self.weight, eps=self.eps
            )
        else:
            variance = x.to(torch.float32).pow(2).mean(-1, keepdim=True)
            x = x * torch.rsqrt(variance + self.eps)
            if self.weight.dtype in [torch.float16, torch.bfloat16]:
                x = x.to(self.weight.dtype)
            x = x * self.weight

        return x


# AdaLayerNorm
# return with modulated x for attn input, and params for later mlp modulation


class AdaLayerNorm(nn.Module):
    def __init__(self, dim):
        super().__init__()

        self.silu = nn.SiLU()
        self.linear = nn.Linear(dim, dim * 6)

        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)

    def forward(self, x, emb=None):
        emb = self.linear(self.silu(emb))
        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(
            emb, 6, dim=1
        )

        x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp


# AdaLayerNorm for final layer
# return only with modulated x for attn input, cuz no more mlp modulation


class AdaLayerNorm_Final(nn.Module):
    def __init__(self, dim):
        super().__init__()

        self.silu = nn.SiLU()
        self.linear = nn.Linear(dim, dim * 2)

        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)

    def forward(self, x, emb):
        emb = self.linear(self.silu(emb))
        scale, shift = torch.chunk(emb, 2, dim=1)

        x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
        return x


# FeedForward


class FeedForward(nn.Module):
    def __init__(
        self, dim, dim_out=None, mult=4, dropout=0.0, approximate: str = "none"
    ):
        super().__init__()
        inner_dim = int(dim * mult)
        dim_out = dim_out if dim_out is not None else dim

        activation = nn.GELU(approximate=approximate)
        project_in = nn.Sequential(nn.Linear(dim, inner_dim), activation)
        self.ff = nn.Sequential(
            project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)
        )

    def forward(self, x):
        return self.ff(x)


# Attention with possible joint part
# modified from diffusers/src/diffusers/models/attention_processor.py


class Attention(nn.Module):
    def __init__(
        self,
        processor: JointAttnProcessor | AttnProcessor,
        dim: int,
        heads: int = 8,
        dim_head: int = 64,
        dropout: float = 0.0,
        context_dim: Optional[int] = None,  # if not None -> joint attention
        context_pre_only: bool = False,
        qk_norm: Optional[str] = None,
    ):
        super().__init__()

        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError(
                "Attention equires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
            )

        self.processor = processor

        self.dim = dim
        self.heads = heads
        self.inner_dim = dim_head * heads
        self.dropout = dropout

        self.context_dim = context_dim
        self.context_pre_only = context_pre_only

        self.to_q = nn.Linear(dim, self.inner_dim)
        self.to_k = nn.Linear(dim, self.inner_dim)
        self.to_v = nn.Linear(dim, self.inner_dim)

        if qk_norm is None:
            self.q_norm = None
            self.k_norm = None
        elif qk_norm == "rms_norm":
            self.q_norm = RMSNorm(dim_head, eps=1e-6)
            self.k_norm = RMSNorm(dim_head, eps=1e-6)
        else:
            raise ValueError(f"Unimplemented qk_norm: {qk_norm}")

        if self.context_dim is not None:
            self.to_q_c = nn.Linear(context_dim, self.inner_dim)
            self.to_k_c = nn.Linear(context_dim, self.inner_dim)
            self.to_v_c = nn.Linear(context_dim, self.inner_dim)
            if qk_norm is None:
                self.c_q_norm = None
                self.c_k_norm = None
            elif qk_norm == "rms_norm":
                self.c_q_norm = RMSNorm(dim_head, eps=1e-6)
                self.c_k_norm = RMSNorm(dim_head, eps=1e-6)

        self.to_out = nn.ModuleList([])
        self.to_out.append(nn.Linear(self.inner_dim, dim))
        self.to_out.append(nn.Dropout(dropout))

        if self.context_dim is not None and not self.context_pre_only:
            self.to_out_c = nn.Linear(self.inner_dim, context_dim)

    def forward(
        self,
        x: float["b n d"],  # noised input x
        c: float["b n d"] = None,  # context c
        mask: bool["b n"] | None = None,
        rope=None,  # rotary position embedding for x
        c_rope=None,  # rotary position embedding for c
    ) -> torch.Tensor:
        if c is not None:
            return self.processor(self, x, c=c, mask=mask, rope=rope, c_rope=c_rope)
        else:
            return self.processor(self, x, mask=mask, rope=rope)


# Attention processor

if is_package_available("flash_attn"):
    from flash_attn import flash_attn_func, flash_attn_varlen_func
    from flash_attn.bert_padding import pad_input, unpad_input


class AttnProcessor:
    def __init__(
        self,
        pe_attn_head: int
        | None = None,  # number of attention head to apply rope, None for all
        attn_backend: str = "torch",  # "torch" or "flash_attn"
        attn_mask_enabled: bool = True,
    ):
        if attn_backend == "flash_attn":
            assert is_package_available("flash_attn"), (
                "Please install flash-attn first."
            )

        self.pe_attn_head = pe_attn_head
        self.attn_backend = attn_backend
        self.attn_mask_enabled = attn_mask_enabled

    def __call__(
        self,
        attn: Attention,
        x: float["b n d"],  # noised input x
        mask: bool["b n"] | None = None,
        rope=None,  # rotary position embedding
    ) -> torch.FloatTensor:
        batch_size = x.shape[0]

        # `sample` projections
        query = attn.to_q(x)
        key = attn.to_k(x)
        value = attn.to_v(x)

        # attention
        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads
        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # qk norm
        if attn.q_norm is not None:
            query = attn.q_norm(query)
        if attn.k_norm is not None:
            key = attn.k_norm(key)

        # apply rotary position embedding
        if rope is not None:
            freqs, xpos_scale = rope
            q_xpos_scale, k_xpos_scale = (
                (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
            )

            if self.pe_attn_head is not None:
                pn = self.pe_attn_head
                query[:, :pn, :, :] = apply_rotary_pos_emb(
                    query[:, :pn, :, :], freqs, q_xpos_scale
                )
                key[:, :pn, :, :] = apply_rotary_pos_emb(
                    key[:, :pn, :, :], freqs, k_xpos_scale
                )
            else:
                query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
                key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)

        if self.attn_backend == "torch":
            # mask. e.g. inference got a batch with different target durations, mask out the padding
            if self.attn_mask_enabled and mask is not None:
                attn_mask = mask
                attn_mask = attn_mask.unsqueeze(1).unsqueeze(1)  # 'b n -> b 1 1 n'
                attn_mask = attn_mask.expand(
                    batch_size, attn.heads, query.shape[-2], key.shape[-2]
                )
            else:
                attn_mask = None
            x = F.scaled_dot_product_attention(
                query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False
            )
            x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)

        elif self.attn_backend == "flash_attn":
            query = query.transpose(1, 2)  # [b, h, n, d] -> [b, n, h, d]
            key = key.transpose(1, 2)
            value = value.transpose(1, 2)
            if self.attn_mask_enabled and mask is not None:
                query, indices, q_cu_seqlens, q_max_seqlen_in_batch, _ = unpad_input(
                    query, mask
                )
                key, _, k_cu_seqlens, k_max_seqlen_in_batch, _ = unpad_input(key, mask)
                value, _, _, _, _ = unpad_input(value, mask)
                x = flash_attn_varlen_func(
                    query,
                    key,
                    value,
                    q_cu_seqlens,
                    k_cu_seqlens,
                    q_max_seqlen_in_batch,
                    k_max_seqlen_in_batch,
                )
                x = pad_input(x, indices, batch_size, q_max_seqlen_in_batch)
                x = x.reshape(batch_size, -1, attn.heads * head_dim)
            else:
                x = flash_attn_func(query, key, value, dropout_p=0.0, causal=False)
                x = x.reshape(batch_size, -1, attn.heads * head_dim)

        x = x.to(query.dtype)

        # linear proj
        x = attn.to_out[0](x)
        # dropout
        x = attn.to_out[1](x)

        if mask is not None:
            mask = mask.unsqueeze(-1)
            x = x.masked_fill(~mask, 0.0)

        return x


# Joint Attention processor for MM-DiT
# modified from diffusers/src/diffusers/models/attention_processor.py


class JointAttnProcessor:
    def __init__(self):
        pass

    def __call__(
        self,
        attn: Attention,
        x: float["b n d"],  # noised input x
        c: float["b nt d"] = None,  # context c, here text
        mask: bool["b n"] | None = None,
        rope=None,  # rotary position embedding for x
        c_rope=None,  # rotary position embedding for c
    ) -> torch.FloatTensor:
        residual = x

        batch_size = c.shape[0]

        # `sample` projections
        query = attn.to_q(x)
        key = attn.to_k(x)
        value = attn.to_v(x)

        # `context` projections
        c_query = attn.to_q_c(c)
        c_key = attn.to_k_c(c)
        c_value = attn.to_v_c(c)

        # attention
        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads
        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        c_query = c_query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        c_key = c_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        c_value = c_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # qk norm
        if attn.q_norm is not None:
            query = attn.q_norm(query)
        if attn.k_norm is not None:
            key = attn.k_norm(key)
        if attn.c_q_norm is not None:
            c_query = attn.c_q_norm(c_query)
        if attn.c_k_norm is not None:
            c_key = attn.c_k_norm(c_key)

        # apply rope for context and noised input independently
        if rope is not None:
            freqs, xpos_scale = rope
            q_xpos_scale, k_xpos_scale = (
                (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
            )
            query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
            key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)
        if c_rope is not None:
            freqs, xpos_scale = c_rope
            q_xpos_scale, k_xpos_scale = (
                (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
            )
            c_query = apply_rotary_pos_emb(c_query, freqs, q_xpos_scale)
            c_key = apply_rotary_pos_emb(c_key, freqs, k_xpos_scale)

        # joint attention
        query = torch.cat([query, c_query], dim=2)
        key = torch.cat([key, c_key], dim=2)
        value = torch.cat([value, c_value], dim=2)

        # mask. e.g. inference got a batch with different target durations, mask out the padding
        if mask is not None:
            attn_mask = F.pad(mask, (0, c.shape[1]), value=True)  # no mask for c (text)
            attn_mask = attn_mask.unsqueeze(1).unsqueeze(1)  # 'b n -> b 1 1 n'
            attn_mask = attn_mask.expand(
                batch_size, attn.heads, query.shape[-2], key.shape[-2]
            )
        else:
            attn_mask = None

        x = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False
        )
        x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        x = x.to(query.dtype)

        # Split the attention outputs.
        x, c = (
            x[:, : residual.shape[1]],
            x[:, residual.shape[1] :],
        )

        # linear proj
        x = attn.to_out[0](x)
        # dropout
        x = attn.to_out[1](x)
        if not attn.context_pre_only:
            c = attn.to_out_c(c)

        if mask is not None:
            mask = mask.unsqueeze(-1)
            x = x.masked_fill(~mask, 0.0)
            # c = c.masked_fill(~mask, 0.)  # no mask for c (text)

        return x, c


# DiT Block


class DiTBlock(nn.Module):
    def __init__(
        self,
        dim,
        heads,
        dim_head,
        ff_mult=4,
        dropout=0.1,
        qk_norm=None,
        pe_attn_head=None,
        attn_backend="torch",  # "torch" or "flash_attn"
        attn_mask_enabled=True,
    ):
        super().__init__()

        self.attn_norm = AdaLayerNorm(dim)
        self.attn = Attention(
            processor=AttnProcessor(
                pe_attn_head=pe_attn_head,
                attn_backend=attn_backend,
                attn_mask_enabled=attn_mask_enabled,
            ),
            dim=dim,
            heads=heads,
            dim_head=dim_head,
            dropout=dropout,
            qk_norm=qk_norm,
        )

        self.ff_norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
        self.ff = FeedForward(
            dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh"
        )

    def forward(self, x, t, mask=None, rope=None):  # x: noised input, t: time embedding
        # pre-norm & modulation for attention input
        norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attn_norm(x, emb=t)

        # attention
        attn_output = self.attn(x=norm, mask=mask, rope=rope)

        # process attention output for input x
        x = x + gate_msa.unsqueeze(1) * attn_output

        norm = self.ff_norm(x) * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
        ff_output = self.ff(norm)
        x = x + gate_mlp.unsqueeze(1) * ff_output

        return x


# MMDiT Block https://arxiv.org/abs/2403.03206


class MMDiTBlock(nn.Module):
    r"""
    modified from diffusers/src/diffusers/models/attention.py

    notes.
    _c: context related. text, cond, etc. (left part in sd3 fig2.b)
    _x: noised input related. (right part)
    context_pre_only: last layer only do prenorm + modulation cuz no more ffn
    """

    def __init__(
        self,
        dim,
        heads,
        dim_head,
        ff_mult=4,
        dropout=0.1,
        context_dim=None,
        context_pre_only=False,
        qk_norm=None,
    ):
        super().__init__()
        if context_dim is None:
            context_dim = dim
        self.context_pre_only = context_pre_only

        self.attn_norm_c = (
            AdaLayerNorm_Final(context_dim)
            if context_pre_only
            else AdaLayerNorm(context_dim)
        )
        self.attn_norm_x = AdaLayerNorm(dim)
        self.attn = Attention(
            processor=JointAttnProcessor(),
            dim=dim,
            heads=heads,
            dim_head=dim_head,
            dropout=dropout,
            context_dim=context_dim,
            context_pre_only=context_pre_only,
            qk_norm=qk_norm,
        )

        if not context_pre_only:
            self.ff_norm_c = nn.LayerNorm(
                context_dim, elementwise_affine=False, eps=1e-6
            )
            self.ff_c = FeedForward(
                dim=context_dim, mult=ff_mult, dropout=dropout, approximate="tanh"
            )
        else:
            self.ff_norm_c = None
            self.ff_c = None
        self.ff_norm_x = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
        self.ff_x = FeedForward(
            dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh"
        )

    def forward(
        self, x, c, t, mask=None, rope=None, c_rope=None
    ):  # x: noised input, c: context, t: time embedding
        # pre-norm & modulation for attention input
        if self.context_pre_only:
            norm_c = self.attn_norm_c(c, t)
        else:
            norm_c, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.attn_norm_c(
                c, emb=t
            )
        norm_x, x_gate_msa, x_shift_mlp, x_scale_mlp, x_gate_mlp = self.attn_norm_x(
            x, emb=t
        )

        # attention
        x_attn_output, c_attn_output = self.attn(
            x=norm_x, c=norm_c, mask=mask, rope=rope, c_rope=c_rope
        )

        # process attention output for context c
        if self.context_pre_only:
            c = None
        else:  # if not last layer
            c = c + c_gate_msa.unsqueeze(1) * c_attn_output

            norm_c = (
                self.ff_norm_c(c) * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
            )
            c_ff_output = self.ff_c(norm_c)
            c = c + c_gate_mlp.unsqueeze(1) * c_ff_output

        # process attention output for input x
        x = x + x_gate_msa.unsqueeze(1) * x_attn_output

        norm_x = self.ff_norm_x(x) * (1 + x_scale_mlp[:, None]) + x_shift_mlp[:, None]
        x_ff_output = self.ff_x(norm_x)
        x = x + x_gate_mlp.unsqueeze(1) * x_ff_output

        return c, x


# time step conditioning embedding


# class TimestepEmbedding(nn.Module):
#     def __init__(self, dim, freq_embed_dim=256):
#         super().__init__()
#         self.time_embed = SinusPositionEmbedding(freq_embed_dim)
#         self.time_mlp = nn.Sequential(nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim))

#     def forward(self, timestep: float["b"]):
#         time_hidden = self.time_embed(timestep)
#         time_hidden = time_hidden.to(timestep.dtype)
#         time = self.time_mlp(time_hidden)  # b d
#         return time


def zipvoice_timestep_embedding(timesteps, dim, max_period=10000):
    """Create sinusoidal timestep embeddings.

    :param timesteps: shape of (N) or (N, T)
    :param dim: the dimension of the output.
    :param max_period: controls the minimum frequency of the embeddings.
    :return: an Tensor of positional embeddings. shape of (N, dim) or (T, N, dim)
    """
    half = dim // 2
    freqs = torch.exp(
        -math.log(max_period)
        * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device)
        / half
    )

    if timesteps.dim() == 2:
        timesteps = timesteps.transpose(0, 1)  # (N, T) -> (T, N)

    args = timesteps[..., None].float() * freqs[None]
    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
    if dim % 2:
        embedding = torch.cat([embedding, torch.zeros_like(embedding[..., :1])], dim=-1)
    return embedding


def ScaledLinear(*args, initial_scale: float = 1.0, **kwargs) -> nn.Linear:
    """
    Behaves like a constructor of a modified version of nn.Linear
    that gives an easy way to set the default initial parameter scale.

    Args:
        Accepts the standard args and kwargs that nn.Linear accepts
        e.g. in_features, out_features, bias=False.

        initial_scale: you can override this if you want to increase
           or decrease the initial magnitude of the module's output
           (affects the initialization of weight_scale and bias_scale).
           Another option, if you want to do something like this, is
           to re-initialize the parameters.
    """
    ans = nn.Linear(*args, **kwargs)
    with torch.no_grad():
        ans.weight[:] *= initial_scale
        if ans.bias is not None:
            torch.nn.init.uniform_(ans.bias, -0.1 * initial_scale, 0.1 * initial_scale)
    return ans


# 在蒸馏的时候使用！
class TimestepGuidanceEmbedding(nn.Module):
    def __init__(
        self,
        dim,
        freq_embed_dim=256,
        use_guidance_scale_embed=False,
        guidance_scale_embed_dim=192,
    ):
        super().__init__()
        self.time_embed = SinusPositionEmbedding(freq_embed_dim)
        self.time_mlp = nn.Sequential(
            nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim)
        )
        if use_guidance_scale_embed:
            self.guidance_scale_embed = ScaledLinear(
                guidance_scale_embed_dim,
                freq_embed_dim,
                bias=False,
                initial_scale=0.1,
            )
            self.guidance_scale_embed_dim = guidance_scale_embed_dim
        else:
            self.guidance_scale_embed = None

    def forward(self, timestep: float["b"], guidance_scale=None):
        # import pdb

        # pdb.set_trace()
        time_hidden = self.time_embed(timestep)

        if self.guidance_scale_embed:
            assert guidance_scale is not None
            guidance_scale_emb = self.guidance_scale_embed(
                zipvoice_timestep_embedding(
                    guidance_scale, self.guidance_scale_embed_dim
                )
            )
            time_hidden = time_hidden + guidance_scale_emb
        else:
            assert guidance_scale is None

        time_hidden = time_hidden.to(timestep.dtype)
        time = self.time_mlp(time_hidden)  # b d
        return time