model/udit.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# GLIDE: https://github.com/openai/glide-text2im
# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
# --------------------------------------------------------

import torch
import torch.nn as nn
import numpy as np
import math
import warnings
import einops
import torch.utils.checkpoint
import yaml
import torch.nn.functional as F
from .attention import Attention


def _no_grad_trunc_normal_(tensor, mean, std, a, b):
    # Cut & paste from PyTorch official master until it's in a few official releases - RW
    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
    def norm_cdf(x):
        # Computes standard normal cumulative distribution function
        return (1. + math.erf(x / math.sqrt(2.))) / 2.

    if (mean < a - 2 * std) or (mean > b + 2 * std):
        warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
                      "The distribution of values may be incorrect.",
                      stacklevel=2)

    with torch.no_grad():
        # Values are generated by using a truncated uniform distribution and
        # then using the inverse CDF for the normal distribution.
        # Get upper and lower cdf values
        l = norm_cdf((a - mean) / std)
        u = norm_cdf((b - mean) / std)

        # Uniformly fill tensor with values from [l, u], then translate to
        # [2l-1, 2u-1].
        tensor.uniform_(2 * l - 1, 2 * u - 1)

        # Use inverse cdf transform for normal distribution to get truncated
        # standard normal
        tensor.erfinv_()

        # Transform to proper mean, std
        tensor.mul_(std * math.sqrt(2.))
        tensor.add_(mean)

        # Clamp to ensure it's in the proper range
        tensor.clamp_(min=a, max=b)
        return tensor


def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
    # type: (Tensor, float, float, float, float) -> Tensor
    r"""Fills the input Tensor with values drawn from a truncated
    normal distribution. The values are effectively drawn from the
    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
    with values outside :math:`[a, b]` redrawn until they are within
    the bounds. The method used for generating the random values works
    best when :math:`a \leq \text{mean} \leq b`.
    Args:
        tensor: an n-dimensional `torch.Tensor`
        mean: the mean of the normal distribution
        std: the standard deviation of the normal distribution
        a: the minimum cutoff value
        b: the maximum cutoff value
    Examples:
        >>> w = torch.empty(3, 5)
        >>> nn.init.trunc_normal_(w)
    """
    return _no_grad_trunc_normal_(tensor, mean, std, a, b)


class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x
    


class PositionalConvEmbedding(nn.Module):
    """
    Relative positional embedding used in HuBERT
    """

    def __init__(self, dim=768, kernel_size=128, groups=16):
        super().__init__()
        self.conv = nn.Conv1d(
            dim,
            dim,
            kernel_size=kernel_size,
            padding=kernel_size // 2,
            groups=groups,
            bias=True
        )
        self.conv = nn.utils.parametrizations.weight_norm(self.conv, name="weight", dim=2)

    def forward(self, x):
        x = x.transpose(2, 1)
        # B C T
        x = self.conv(x)
        x = F.gelu(x[:, :, :-1])
        x = x.transpose(2, 1)
        return x


class SinusoidalPositionalEncoding(nn.Module):
    def __init__(self, dim, length):
        super(SinusoidalPositionalEncoding, self).__init__()
        self.length = length
        self.dim = dim
        self.register_buffer('pe', self._generate_positional_encoding(length, dim))

    def _generate_positional_encoding(self, length, dim):
        pe = torch.zeros(length, dim)
        position = torch.arange(0, length, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, dim, 2).float() * (-math.log(10000.0) / dim))

        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)

        pe = pe.unsqueeze(0)
        return pe

    def forward(self, x):
        x = x + self.pe[:, :x.size(1)]
        return x


class PE_wrapper(nn.Module):
    def __init__(self, dim=768, method='none', length=None):
        super().__init__()
        self.method = method
        if method == 'abs':
            # init absolute pe like UViT
            self.length = length
            self.abs_pe = nn.Parameter(torch.zeros(1, length, dim))
            trunc_normal_(self.abs_pe, std=.02)
        elif method == 'conv':
            self.conv_pe = PositionalConvEmbedding(dim=dim)
        elif method == 'sinu':
            self.sinu_pe = SinusoidalPositionalEncoding(dim=dim, length=length)
        elif method == 'none':
            # skip pe
            self.id = nn.Identity()
        else:
            raise NotImplementedError

    def forward(self, x):
        if self.method == 'abs':
            _, L, _ = x.shape
            assert L <= self.length
            x = x + self.abs_pe[:, :L, :]
        elif self.method == 'conv':
            x = x + self.conv_pe(x)
        elif self.method == 'sinu':
            x = self.sinu_pe(x)
        elif self.method == 'none':
            x = self.id(x)
        else:
            raise NotImplementedError
        return x
    
    
def modulate(x, shift, scale):
    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)


#################################################################################
#               Embedding Layers for Timesteps and Class Labels                 #
#################################################################################

class TimestepEmbedder(nn.Module):
    """
    Embeds scalar timesteps into vector representations.
    """
    def __init__(self, hidden_size, frequency_embedding_size=256):
        super().__init__()
        self.mlp = nn.Sequential(
            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
            nn.SiLU(),
            nn.Linear(hidden_size, hidden_size, bias=True),
        )
        self.frequency_embedding_size = frequency_embedding_size

    @staticmethod
    def timestep_embedding(t, dim, max_period=10000):
        """
        Create sinusoidal timestep embeddings.
        :param t: a 1-D Tensor of N indices, one per batch element.
                          These may be fractional.
        :param dim: the dimension of the output.
        :param max_period: controls the minimum frequency of the embeddings.
        :return: an (N, D) Tensor of positional embeddings.
        """
        # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
        half = dim // 2
        freqs = torch.exp(
            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
        ).to(device=t.device)
        args = t[:, 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 forward(self, t):
        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
        t_emb = self.mlp(t_freq)
        return t_emb


class LabelEmbedder(nn.Module):
    """
    Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
    """
    def __init__(self, num_classes, hidden_size, dropout_prob):
        super().__init__()
        use_cfg_embedding = dropout_prob > 0
        self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
        self.num_classes = num_classes
        self.dropout_prob = dropout_prob

    def token_drop(self, labels, force_drop_ids=None):
        """
        Drops labels to enable classifier-free guidance.
        """
        if force_drop_ids is None:
            drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
        else:
            drop_ids = force_drop_ids == 1
        labels = torch.where(drop_ids, self.num_classes, labels)
        return labels

    def forward(self, labels, train, force_drop_ids=None):
        use_dropout = self.dropout_prob > 0
        if (train and use_dropout) or (force_drop_ids is not None):
            labels = self.token_drop(labels, force_drop_ids)
        embeddings = self.embedding_table(labels)
        return embeddings


#################################################################################
#                                 Core DiT Model                                #
#################################################################################

class DiTBlock(nn.Module):
    """
    A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
    """
    def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, skip=False, skip_norm=True, use_checkpoint=True, **block_kwargs):
        super().__init__()
        self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs)
        self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        mlp_hidden_dim = int(hidden_size * mlp_ratio)
        approx_gelu = lambda: nn.GELU(approximate="tanh")
        self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0)
        self.adaLN_modulation = nn.Sequential(
            nn.SiLU(),
            nn.Linear(hidden_size, 6 * hidden_size, bias=True)
        )
        self.skip_linear = nn.Linear(2 * hidden_size, hidden_size) if skip else None
        self.skip_norm =  nn.LayerNorm(2 * hidden_size, elementwise_affine=False, eps=1e-6) if skip_norm else nn.Identity()
        self.use_checkpoint = use_checkpoint
    
    def forward(self, x, c, skip=None):
        if self.use_checkpoint:
            return torch.utils.checkpoint.checkpoint(self._forward, x, c, skip)
        else:
            return self._forward(x, c, skip)

    def _forward(self, x, c, skip=None):
        if self.skip_linear is not None:
            cat = torch.cat([x, skip], dim=-1)
            cat = self.skip_norm(cat)
            x = self.skip_linear(cat)
        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1)
        x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa))
        x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
        return x


class FinalLayer(nn.Module):
    """
    The final layer of DiT.
    """
    def __init__(self, hidden_size, output_dim):
        super().__init__()
        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.linear = nn.Linear(hidden_size, output_dim, bias=True)
        self.adaLN_modulation = nn.Sequential(
            nn.SiLU(),
            nn.Linear(hidden_size, 2 * hidden_size, bias=True)
        )

    def forward(self, x, c):
        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
        x = modulate(self.norm_final(x), shift, scale)
        x = self.linear(x)
        return x


class UDiT(nn.Module):
    """
    Diffusion model with a Transformer backbone.
    """
    def __init__(
        self,
        input_dim=256,
        output_dim=128,
        pos_method='none',
        pos_length=500,
        timbre_dim=512,
        hidden_size=1152,
        depth=28,
        num_heads=16,
        mlp_ratio=4.0,
        use_checkpoint=True
    ):
        super().__init__()
        self.num_heads = num_heads
        self.input_proj = nn.Linear(input_dim, hidden_size, bias=True)
        self.t_embedder = TimestepEmbedder(hidden_size)
        self.pos_embed = PE_wrapper(dim=hidden_size, method=pos_method, length=pos_length)
        self.timbre_proj = nn.Linear(timbre_dim, hidden_size, bias=True)

        self.in_blocks = nn.ModuleList([
            DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio, use_checkpoint=use_checkpoint) for _ in range(depth // 2)
        ])
        self.mid_block = DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio, use_checkpoint=use_checkpoint)
        self.out_blocks = nn.ModuleList([
            DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio, skip=True, use_checkpoint=use_checkpoint) for _ in range(depth // 2)
        ])
        
        self.final_layer = FinalLayer(hidden_size, output_dim)
        self.initialize_weights()

    def initialize_weights(self):
        # Initialize transformer layers:
        def _basic_init(module):
            if isinstance(module, nn.Linear):
                torch.nn.init.xavier_uniform_(module.weight)
                if module.bias is not None:
                    nn.init.constant_(module.bias, 0)
        self.apply(_basic_init)

        # Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
        nn.init.normal_(self.input_proj.weight, std=0.02)
        nn.init.normal_(self.timbre_proj.weight, std=0.02)

        # Initialize timestep embedding MLP:
        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)

        # Zero-out adaLN modulation layers in DiT blocks:
        for block in self.in_blocks:
            nn.init.constant_(self.mid_block.adaLN_modulation[-1].weight, 0)
            nn.init.constant_(self.mid_block.adaLN_modulation[-1].bias, 0)
        
        nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
        nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
        
        for block in self.out_blocks:
            nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
            nn.init.constant_(block.adaLN_modulation[-1].bias, 0)

        # Zero-out output layers:
        nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
        nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
        nn.init.constant_(self.final_layer.linear.weight, 0)
        nn.init.constant_(self.final_layer.linear.bias, 0)

    def forward(self, x, timesteps, mixture, timbre):
        """
        Forward pass of DiT.
        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
        t: (N,) tensor of diffusion timesteps
        y: (N,) tensor of class labels
        """
        x = x.transpose(2,1)
        mixture = mixture.transpose(2,1)
        x = self.input_proj(torch.cat((x, mixture), dim=-1))
        x = self.pos_embed(x)
        if not torch.is_tensor(timesteps):
            timesteps = torch.tensor([timesteps], dtype=torch.long, device=x.device)
        elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
            timesteps = timesteps[None].to(x.device)
        t = self.t_embedder(timesteps)                   # (N, D)
        timbre = self.timbre_proj(timbre)
        c = t + timbre                                # (N, D)

        skips = []
        for blk in self.in_blocks:
            x = blk(x, c)
            skips.append(x)

        x = self.mid_block(x, c)

        for blk in self.out_blocks:
            x = blk(x, c, skips.pop())

        x = self.final_layer(x, c)                # (N, T, out_dim)
        x = x.transpose(2, 1)
        return x


#################################################################################
#                                   DiT Configs                                  #
#################################################################################

def DiT_XL_2(**kwargs):
    return DiT(depth=28, hidden_size=1152, patch_size=2, num_heads=16, **kwargs)

def DiT_XL_4(**kwargs):
    return DiT(depth=28, hidden_size=1152, patch_size=4, num_heads=16, **kwargs)

def DiT_XL_8(**kwargs):
    return DiT(depth=28, hidden_size=1152, patch_size=8, num_heads=16, **kwargs)

def DiT_L_2(**kwargs):
    return DiT(depth=24, hidden_size=1024, patch_size=2, num_heads=16, **kwargs)

def DiT_L_4(**kwargs):
    return DiT(depth=24, hidden_size=1024, patch_size=4, num_heads=16, **kwargs)

def DiT_L_8(**kwargs):
    return DiT(depth=24, hidden_size=1024, patch_size=8, num_heads=16, **kwargs)

def DiT_B_2(**kwargs):
    return DiT(depth=12, hidden_size=768, patch_size=2, num_heads=12, **kwargs)

def DiT_B_4(**kwargs):
    return DiT(depth=12, hidden_size=768, patch_size=4, num_heads=12, **kwargs)

def DiT_B_8(**kwargs):
    return DiT(depth=12, hidden_size=768, patch_size=8, num_heads=12, **kwargs)

def DiT_S_2(**kwargs):
    return DiT(depth=12, hidden_size=384, patch_size=2, num_heads=6, **kwargs)

def DiT_S_4(**kwargs):
    return DiT(depth=12, hidden_size=384, patch_size=4, num_heads=6, **kwargs)

def DiT_S_8(**kwargs):
    return DiT(depth=12, hidden_size=384, patch_size=8, num_heads=6, **kwargs)


DiT_models = {
    'DiT-XL/2': DiT_XL_2,  'DiT-XL/4': DiT_XL_4,  'DiT-XL/8': DiT_XL_8,
    'DiT-L/2':  DiT_L_2,   'DiT-L/4':  DiT_L_4,   'DiT-L/8':  DiT_L_8,
    'DiT-B/2':  DiT_B_2,   'DiT-B/4':  DiT_B_4,   'DiT-B/8':  DiT_B_8,
    'DiT-S/2':  DiT_S_2,   'DiT-S/4':  DiT_S_4,   'DiT-S/8':  DiT_S_8,
}

if __name__ == "__main__":
    with open('/export/corpora7/HW/DPMTSE-main/src/config/DiffTSE_udit_conv_v_b_1000.yaml', 'r') as fp:
        config = yaml.safe_load(fp)
    device = 'cuda'

    model = UDiT(
        **config['diffwrap']['UDiT']
    ).to(device)

    x = torch.rand((1, 128, 150)).to(device)
    t = torch.randint(0, 1000, (1, )).long().to(device)
    mixture = torch.rand((1, 128, 150)).to(device)
    timbre = torch.rand((1, 512)).to(device)

    y = model(x, t, mixture, timbre)
    print(y.shape)