mmdit.py

### This file contains impls for MM-DiT, the core model component of SD3

import math
from typing import Dict, Optional
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange, repeat
from modules.models.sd3.other_impls import attention, Mlp


class PatchEmbed(nn.Module):
    """ 2D Image to Patch Embedding"""
    def __init__(
            self,
            img_size: Optional[int] = 224,
            patch_size: int = 16,
            in_chans: int = 3,
            embed_dim: int = 768,
            flatten: bool = True,
            bias: bool = True,
            strict_img_size: bool = True,
            dynamic_img_pad: bool = False,
            dtype=None,
            device=None,
    ):
        super().__init__()
        self.patch_size = (patch_size, patch_size)
        if img_size is not None:
            self.img_size = (img_size, img_size)
            self.grid_size = tuple([s // p for s, p in zip(self.img_size, self.patch_size)])
            self.num_patches = self.grid_size[0] * self.grid_size[1]
        else:
            self.img_size = None
            self.grid_size = None
            self.num_patches = None

        # flatten spatial dim and transpose to channels last, kept for bwd compat
        self.flatten = flatten
        self.strict_img_size = strict_img_size
        self.dynamic_img_pad = dynamic_img_pad

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device)

    def forward(self, x):
        B, C, H, W = x.shape
        x = self.proj(x)
        if self.flatten:
            x = x.flatten(2).transpose(1, 2)  # NCHW -> NLC
        return x


def modulate(x, shift, scale):
    if shift is None:
        shift = torch.zeros_like(scale)
    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)


#################################################################################
#                   Sine/Cosine Positional Embedding Functions                  #
#################################################################################


def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0, scaling_factor=None, offset=None):
    """
    grid_size: int of the grid height and width
    return:
    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
    """
    grid_h = np.arange(grid_size, dtype=np.float32)
    grid_w = np.arange(grid_size, dtype=np.float32)
    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
    grid = np.stack(grid, axis=0)
    if scaling_factor is not None:
        grid = grid / scaling_factor
    if offset is not None:
        grid = grid - offset
    grid = grid.reshape([2, 1, grid_size, grid_size])
    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
    if cls_token and extra_tokens > 0:
        pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
    return pos_embed


def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
    assert embed_dim % 2 == 0
    # use half of dimensions to encode grid_h
    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
    return emb


def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
    """
    embed_dim: output dimension for each position
    pos: a list of positions to be encoded: size (M,)
    out: (M, D)
    """
    assert embed_dim % 2 == 0
    omega = np.arange(embed_dim // 2, dtype=np.float64)
    omega /= embed_dim / 2.0
    omega = 1.0 / 10000**omega  # (D/2,)
    pos = pos.reshape(-1)  # (M,)
    out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product
    emb_sin = np.sin(out)  # (M, D/2)
    emb_cos = np.cos(out)  # (M, D/2)
    return np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)


#################################################################################
#               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, dtype=None, device=None):
        super().__init__()
        self.mlp = nn.Sequential(
            nn.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device),
            nn.SiLU(),
            nn.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
        )
        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.
        """
        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)
        if torch.is_floating_point(t):
            embedding = embedding.to(dtype=t.dtype)
        return embedding

    def forward(self, t, dtype, **kwargs):
        t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
        t_emb = self.mlp(t_freq)
        return t_emb


class VectorEmbedder(nn.Module):
    """Embeds a flat vector of dimension input_dim"""

    def __init__(self, input_dim: int, hidden_size: int, dtype=None, device=None):
        super().__init__()
        self.mlp = nn.Sequential(
            nn.Linear(input_dim, hidden_size, bias=True, dtype=dtype, device=device),
            nn.SiLU(),
            nn.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.mlp(x)


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


class QkvLinear(torch.nn.Linear):
    pass

def split_qkv(qkv, head_dim):
    qkv = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, -1, head_dim).movedim(2, 0)
    return qkv[0], qkv[1], qkv[2]

def optimized_attention(qkv, num_heads):
    return attention(qkv[0], qkv[1], qkv[2], num_heads)

class SelfAttention(nn.Module):
    ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug")

    def __init__(
        self,
        dim: int,
        num_heads: int = 8,
        qkv_bias: bool = False,
        qk_scale: Optional[float] = None,
        attn_mode: str = "xformers",
        pre_only: bool = False,
        qk_norm: Optional[str] = None,
        rmsnorm: bool = False,
        dtype=None,
        device=None,
    ):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = dim // num_heads

        self.qkv = QkvLinear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
        if not pre_only:
            self.proj = nn.Linear(dim, dim, dtype=dtype, device=device)
        assert attn_mode in self.ATTENTION_MODES
        self.attn_mode = attn_mode
        self.pre_only = pre_only

        if qk_norm == "rms":
            self.ln_q = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
            self.ln_k = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
        elif qk_norm == "ln":
            self.ln_q = nn.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
            self.ln_k = nn.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
        elif qk_norm is None:
            self.ln_q = nn.Identity()
            self.ln_k = nn.Identity()
        else:
            raise ValueError(qk_norm)

    def pre_attention(self, x: torch.Tensor):
        B, L, C = x.shape
        qkv = self.qkv(x)
        q, k, v = split_qkv(qkv, self.head_dim)
        q = self.ln_q(q).reshape(q.shape[0], q.shape[1], -1)
        k = self.ln_k(k).reshape(q.shape[0], q.shape[1], -1)
        return (q, k, v)

    def post_attention(self, x: torch.Tensor) -> torch.Tensor:
        assert not self.pre_only
        x = self.proj(x)
        return x

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        (q, k, v) = self.pre_attention(x)
        x = attention(q, k, v, self.num_heads)
        x = self.post_attention(x)
        return x


class RMSNorm(torch.nn.Module):
    def __init__(
        self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None
    ):
        """
        Initialize the RMSNorm normalization layer.
        Args:
            dim (int): The dimension of the input tensor.
            eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
        Attributes:
            eps (float): A small value added to the denominator for numerical stability.
            weight (nn.Parameter): Learnable scaling parameter.
        """
        super().__init__()
        self.eps = eps
        self.learnable_scale = elementwise_affine
        if self.learnable_scale:
            self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
        else:
            self.register_parameter("weight", None)

    def _norm(self, x):
        """
        Apply the RMSNorm normalization to the input tensor.
        Args:
            x (torch.Tensor): The input tensor.
        Returns:
            torch.Tensor: The normalized tensor.
        """
        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)

    def forward(self, x):
        """
        Forward pass through the RMSNorm layer.
        Args:
            x (torch.Tensor): The input tensor.
        Returns:
            torch.Tensor: The output tensor after applying RMSNorm.
        """
        x = self._norm(x)
        if self.learnable_scale:
            return x * self.weight.to(device=x.device, dtype=x.dtype)
        else:
            return x


class SwiGLUFeedForward(nn.Module):
    def __init__(
        self,
        dim: int,
        hidden_dim: int,
        multiple_of: int,
        ffn_dim_multiplier: Optional[float] = None,
    ):
        """
        Initialize the FeedForward module.

        Args:
            dim (int): Input dimension.
            hidden_dim (int): Hidden dimension of the feedforward layer.
            multiple_of (int): Value to ensure hidden dimension is a multiple of this value.
            ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None.

        Attributes:
            w1 (ColumnParallelLinear): Linear transformation for the first layer.
            w2 (RowParallelLinear): Linear transformation for the second layer.
            w3 (ColumnParallelLinear): Linear transformation for the third layer.

        """
        super().__init__()
        hidden_dim = int(2 * hidden_dim / 3)
        # custom dim factor multiplier
        if ffn_dim_multiplier is not None:
            hidden_dim = int(ffn_dim_multiplier * hidden_dim)
        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)

        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
        self.w3 = nn.Linear(dim, hidden_dim, bias=False)

    def forward(self, x):
        return self.w2(nn.functional.silu(self.w1(x)) * self.w3(x))


class DismantledBlock(nn.Module):
    """A DiT block with gated adaptive layer norm (adaLN) conditioning."""

    ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug")

    def __init__(
        self,
        hidden_size: int,
        num_heads: int,
        mlp_ratio: float = 4.0,
        attn_mode: str = "xformers",
        qkv_bias: bool = False,
        pre_only: bool = False,
        rmsnorm: bool = False,
        scale_mod_only: bool = False,
        swiglu: bool = False,
        qk_norm: Optional[str] = None,
        dtype=None,
        device=None,
        **block_kwargs,
    ):
        super().__init__()
        assert attn_mode in self.ATTENTION_MODES
        if not rmsnorm:
            self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
        else:
            self.norm1 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, attn_mode=attn_mode, pre_only=pre_only, qk_norm=qk_norm, rmsnorm=rmsnorm, dtype=dtype, device=device)
        if not pre_only:
            if not rmsnorm:
                self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
            else:
                self.norm2 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        mlp_hidden_dim = int(hidden_size * mlp_ratio)
        if not pre_only:
            if not swiglu:
                self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=nn.GELU(approximate="tanh"), dtype=dtype, device=device)
            else:
                self.mlp = SwiGLUFeedForward(dim=hidden_size, hidden_dim=mlp_hidden_dim, multiple_of=256)
        self.scale_mod_only = scale_mod_only
        if not scale_mod_only:
            n_mods = 6 if not pre_only else 2
        else:
            n_mods = 4 if not pre_only else 1
        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, n_mods * hidden_size, bias=True, dtype=dtype, device=device))
        self.pre_only = pre_only

    def pre_attention(self, x: torch.Tensor, c: torch.Tensor):
        assert x is not None, "pre_attention called with None input"
        if not self.pre_only:
            if not self.scale_mod_only:
                shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1)
            else:
                shift_msa = None
                shift_mlp = None
                scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(4, dim=1)
            qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))
            return qkv, (x, gate_msa, shift_mlp, scale_mlp, gate_mlp)
        else:
            if not self.scale_mod_only:
                shift_msa, scale_msa = self.adaLN_modulation(c).chunk(2, dim=1)
            else:
                shift_msa = None
                scale_msa = self.adaLN_modulation(c)
            qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))
            return qkv, None

    def post_attention(self, attn, x, gate_msa, shift_mlp, scale_mlp, gate_mlp):
        assert not self.pre_only
        x = x + gate_msa.unsqueeze(1) * self.attn.post_attention(attn)
        x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
        return x

    def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
        assert not self.pre_only
        (q, k, v), intermediates = self.pre_attention(x, c)
        attn = attention(q, k, v, self.attn.num_heads)
        return self.post_attention(attn, *intermediates)


def block_mixing(context, x, context_block, x_block, c):
    assert context is not None, "block_mixing called with None context"
    context_qkv, context_intermediates = context_block.pre_attention(context, c)

    x_qkv, x_intermediates = x_block.pre_attention(x, c)

    o = []
    for t in range(3):
        o.append(torch.cat((context_qkv[t], x_qkv[t]), dim=1))
    q, k, v = tuple(o)

    attn = attention(q, k, v, x_block.attn.num_heads)
    context_attn, x_attn = (attn[:, : context_qkv[0].shape[1]], attn[:, context_qkv[0].shape[1] :])

    if not context_block.pre_only:
        context = context_block.post_attention(context_attn, *context_intermediates)
    else:
        context = None
    x = x_block.post_attention(x_attn, *x_intermediates)
    return context, x


class JointBlock(nn.Module):
    """just a small wrapper to serve as a fsdp unit"""

    def __init__(self, *args, **kwargs):
        super().__init__()
        pre_only = kwargs.pop("pre_only")
        qk_norm = kwargs.pop("qk_norm", None)
        self.context_block = DismantledBlock(*args, pre_only=pre_only, qk_norm=qk_norm, **kwargs)
        self.x_block = DismantledBlock(*args, pre_only=False, qk_norm=qk_norm, **kwargs)

    def forward(self, *args, **kwargs):
        return block_mixing(*args, context_block=self.context_block, x_block=self.x_block, **kwargs)


class FinalLayer(nn.Module):
    """
    The final layer of DiT.
    """

    def __init__(self, hidden_size: int, patch_size: int, out_channels: int, total_out_channels: Optional[int] = None, dtype=None, device=None):
        super().__init__()
        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
        self.linear = (
            nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
            if (total_out_channels is None)
            else nn.Linear(hidden_size, total_out_channels, bias=True, dtype=dtype, device=device)
        )
        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device))

    def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
        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 MMDiT(nn.Module):
    """Diffusion model with a Transformer backbone."""

    def __init__(
        self,
        input_size: int = 32,
        patch_size: int = 2,
        in_channels: int = 4,
        depth: int = 28,
        mlp_ratio: float = 4.0,
        learn_sigma: bool = False,
        adm_in_channels: Optional[int] = None,
        context_embedder_config: Optional[Dict] = None,
        register_length: int = 0,
        attn_mode: str = "torch",
        rmsnorm: bool = False,
        scale_mod_only: bool = False,
        swiglu: bool = False,
        out_channels: Optional[int] = None,
        pos_embed_scaling_factor: Optional[float] = None,
        pos_embed_offset: Optional[float] = None,
        pos_embed_max_size: Optional[int] = None,
        num_patches = None,
        qk_norm: Optional[str] = None,
        qkv_bias: bool = True,
        dtype = None,
        device = None,
    ):
        super().__init__()
        self.dtype = dtype
        self.learn_sigma = learn_sigma
        self.in_channels = in_channels
        default_out_channels = in_channels * 2 if learn_sigma else in_channels
        self.out_channels = out_channels if out_channels is not None else default_out_channels
        self.patch_size = patch_size
        self.pos_embed_scaling_factor = pos_embed_scaling_factor
        self.pos_embed_offset = pos_embed_offset
        self.pos_embed_max_size = pos_embed_max_size

        # apply magic --> this defines a head_size of 64
        hidden_size = 64 * depth
        num_heads = depth

        self.num_heads = num_heads

        self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, bias=True, strict_img_size=self.pos_embed_max_size is None, dtype=dtype, device=device)
        self.t_embedder = TimestepEmbedder(hidden_size, dtype=dtype, device=device)

        if adm_in_channels is not None:
            assert isinstance(adm_in_channels, int)
            self.y_embedder = VectorEmbedder(adm_in_channels, hidden_size, dtype=dtype, device=device)

        self.context_embedder = nn.Identity()
        if context_embedder_config is not None:
            if context_embedder_config["target"] == "torch.nn.Linear":
                self.context_embedder = nn.Linear(**context_embedder_config["params"], dtype=dtype, device=device)

        self.register_length = register_length
        if self.register_length > 0:
            self.register = nn.Parameter(torch.randn(1, register_length, hidden_size, dtype=dtype, device=device))

        # num_patches = self.x_embedder.num_patches
        # Will use fixed sin-cos embedding:
        # just use a buffer already
        if num_patches is not None:
            self.register_buffer(
                "pos_embed",
                torch.zeros(1, num_patches, hidden_size, dtype=dtype, device=device),
            )
        else:
            self.pos_embed = None

        self.joint_blocks = nn.ModuleList(
            [
                JointBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, attn_mode=attn_mode, pre_only=i == depth - 1, rmsnorm=rmsnorm, scale_mod_only=scale_mod_only, swiglu=swiglu, qk_norm=qk_norm, dtype=dtype, device=device)
                for i in range(depth)
            ]
        )

        self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels, dtype=dtype, device=device)

    def cropped_pos_embed(self, hw):
        assert self.pos_embed_max_size is not None
        p = self.x_embedder.patch_size[0]
        h, w = hw
        # patched size
        h = h // p
        w = w // p
        assert h <= self.pos_embed_max_size, (h, self.pos_embed_max_size)
        assert w <= self.pos_embed_max_size, (w, self.pos_embed_max_size)
        top = (self.pos_embed_max_size - h) // 2
        left = (self.pos_embed_max_size - w) // 2
        spatial_pos_embed = rearrange(
            self.pos_embed,
            "1 (h w) c -> 1 h w c",
            h=self.pos_embed_max_size,
            w=self.pos_embed_max_size,
        )
        spatial_pos_embed = spatial_pos_embed[:, top : top + h, left : left + w, :]
        spatial_pos_embed = rearrange(spatial_pos_embed, "1 h w c -> 1 (h w) c")
        return spatial_pos_embed

    def unpatchify(self, x, hw=None):
        """
        x: (N, T, patch_size**2 * C)
        imgs: (N, H, W, C)
        """
        c = self.out_channels
        p = self.x_embedder.patch_size[0]
        if hw is None:
            h = w = int(x.shape[1] ** 0.5)
        else:
            h, w = hw
            h = h // p
            w = w // p
        assert h * w == x.shape[1]

        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
        x = torch.einsum("nhwpqc->nchpwq", x)
        imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
        return imgs

    def forward_core_with_concat(self, x: torch.Tensor, c_mod: torch.Tensor, context: Optional[torch.Tensor] = None) -> torch.Tensor:
        if self.register_length > 0:
            context = torch.cat((repeat(self.register, "1 ... -> b ...", b=x.shape[0]), context if context is not None else torch.Tensor([]).type_as(x)), 1)

        # context is B, L', D
        # x is B, L, D
        for block in self.joint_blocks:
            context, x = block(context, x, c=c_mod)

        x = self.final_layer(x, c_mod)  # (N, T, patch_size ** 2 * out_channels)
        return x

    def forward(self, x: torch.Tensor, t: torch.Tensor, y: Optional[torch.Tensor] = None, context: Optional[torch.Tensor] = None) -> torch.Tensor:
        """
        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
        """
        hw = x.shape[-2:]
        x = self.x_embedder(x) + self.cropped_pos_embed(hw)
        c = self.t_embedder(t, dtype=x.dtype)  # (N, D)
        if y is not None:
            y = self.y_embedder(y)  # (N, D)
            c = c + y  # (N, D)

        context = self.context_embedder(context)

        x = self.forward_core_with_concat(x, c, context)

        x = self.unpatchify(x, hw=hw)  # (N, out_channels, H, W)
        return x