backend/nn/flux.py

# Single File Implementation of Flux with aggressive optimizations, Copyright Forge 2024
# If used outside Forge, only non-commercial use is allowed.
# See also https://github.com/black-forest-labs/flux


import math
import torch

from torch import nn
from einops import rearrange, repeat
from backend.attention import attention_function
from backend.utils import fp16_fix


def attention(q, k, v, pe):
    q, k = apply_rope(q, k, pe)
    x = attention_function(q, k, v, q.shape[1], skip_reshape=True)
    return x


def rope(pos, dim, theta):
    scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
    omega = 1.0 / (theta ** scale)

    # out = torch.einsum("...n,d->...nd", pos, omega)
    out = pos.unsqueeze(-1) * omega.unsqueeze(0)

    cos_out = torch.cos(out)
    sin_out = torch.sin(out)
    out = torch.stack([cos_out, -sin_out, sin_out, cos_out], dim=-1)
    del cos_out, sin_out

    # out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
    b, n, d, _ = out.shape
    out = out.view(b, n, d, 2, 2)

    return out.float()


def apply_rope(xq, xk, freqs_cis):
    xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
    xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
    xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
    xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
    del xq_, xk_
    return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)


def timestep_embedding(t, dim, max_period=10000, time_factor=1000.0):
    t = time_factor * t
    half = dim // 2

    # TODO: Once A trainer for flux get popular, make timestep_embedding consistent to that trainer

    # Do not block CUDA steam, but having about 1e-4 differences with Flux official codes:
    freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half)

    # Block CUDA steam, but consistent with official codes:
    # freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(t.device)

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


class EmbedND(nn.Module):
    def __init__(self, dim, theta, axes_dim):
        super().__init__()
        self.dim = dim
        self.theta = theta
        self.axes_dim = axes_dim

    def forward(self, ids):
        n_axes = ids.shape[-1]
        emb = torch.cat(
            [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
            dim=-3,
        )
        del ids, n_axes
        return emb.unsqueeze(1)


class MLPEmbedder(nn.Module):
    def __init__(self, in_dim, hidden_dim):
        super().__init__()
        self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
        self.silu = nn.SiLU()
        self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)

    def forward(self, x):
        x = self.silu(self.in_layer(x))
        return self.out_layer(x)


class RMSNorm(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.scale = nn.Parameter(torch.ones(dim))

    def forward(self, x):
        to_args = dict(device=x.device, dtype=x.dtype)
        x = x.float()
        rrms = torch.rsqrt(torch.mean(x ** 2, dim=-1, keepdim=True) + 1e-6)
        return (x * rrms).to(**to_args) * self.scale.to(**to_args)


class QKNorm(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.query_norm = RMSNorm(dim)
        self.key_norm = RMSNorm(dim)

    def forward(self, q, k, v):
        del v
        q = self.query_norm(q)
        k = self.key_norm(k)
        return q.to(k), k.to(q)


class SelfAttention(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.norm = QKNorm(head_dim)
        self.proj = nn.Linear(dim, dim)

    def forward(self, x, pe):
        qkv = self.qkv(x)

        # q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
        B, L, _ = qkv.shape
        qkv = qkv.view(B, L, 3, self.num_heads, -1)
        q, k, v = qkv.permute(2, 0, 3, 1, 4)
        del qkv

        q, k = self.norm(q, k, v)

        x = attention(q, k, v, pe=pe)
        del q, k, v

        x = self.proj(x)
        return x


class Modulation(nn.Module):
    def __init__(self, dim, double):
        super().__init__()
        self.is_double = double
        self.multiplier = 6 if double else 3
        self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)

    def forward(self, vec):
        out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
        return out


class DoubleStreamBlock(nn.Module):
    def __init__(self, hidden_size, num_heads, mlp_ratio, qkv_bias=False):
        super().__init__()
        mlp_hidden_dim = int(hidden_size * mlp_ratio)
        self.num_heads = num_heads
        self.hidden_size = hidden_size
        self.img_mod = Modulation(hidden_size, double=True)
        self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
        self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.img_mlp = nn.Sequential(
            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
            nn.GELU(approximate="tanh"),
            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
        )
        self.txt_mod = Modulation(hidden_size, double=True)
        self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
        self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.txt_mlp = nn.Sequential(
            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
            nn.GELU(approximate="tanh"),
            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
        )

    def forward(self, img, txt, vec, pe):
        img_mod1_shift, img_mod1_scale, img_mod1_gate, img_mod2_shift, img_mod2_scale, img_mod2_gate = self.img_mod(vec)

        img_modulated = self.img_norm1(img)
        img_modulated = (1 + img_mod1_scale) * img_modulated + img_mod1_shift
        del img_mod1_shift, img_mod1_scale
        img_qkv = self.img_attn.qkv(img_modulated)
        del img_modulated

        # img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
        B, L, _ = img_qkv.shape
        H = self.num_heads
        D = img_qkv.shape[-1] // (3 * H)
        img_q, img_k, img_v = img_qkv.view(B, L, 3, H, D).permute(2, 0, 3, 1, 4)
        del img_qkv

        img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)

        txt_mod1_shift, txt_mod1_scale, txt_mod1_gate, txt_mod2_shift, txt_mod2_scale, txt_mod2_gate = self.txt_mod(vec)
        del vec

        txt_modulated = self.txt_norm1(txt)
        txt_modulated = (1 + txt_mod1_scale) * txt_modulated + txt_mod1_shift
        del txt_mod1_shift, txt_mod1_scale
        txt_qkv = self.txt_attn.qkv(txt_modulated)
        del txt_modulated

        # txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
        B, L, _ = txt_qkv.shape
        txt_q, txt_k, txt_v = txt_qkv.view(B, L, 3, H, D).permute(2, 0, 3, 1, 4)
        del txt_qkv

        txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)

        q = torch.cat((txt_q, img_q), dim=2)
        del txt_q, img_q
        k = torch.cat((txt_k, img_k), dim=2)
        del txt_k, img_k
        v = torch.cat((txt_v, img_v), dim=2)
        del txt_v, img_v

        attn = attention(q, k, v, pe=pe)
        del pe, q, k, v
        txt_attn, img_attn = attn[:, :txt.shape[1]], attn[:, txt.shape[1]:]
        del attn

        img = img + img_mod1_gate * self.img_attn.proj(img_attn)
        del img_attn, img_mod1_gate
        img = img + img_mod2_gate * self.img_mlp((1 + img_mod2_scale) * self.img_norm2(img) + img_mod2_shift)
        del img_mod2_gate, img_mod2_scale, img_mod2_shift

        txt = txt + txt_mod1_gate * self.txt_attn.proj(txt_attn)
        del txt_attn, txt_mod1_gate
        txt = txt + txt_mod2_gate * self.txt_mlp((1 + txt_mod2_scale) * self.txt_norm2(txt) + txt_mod2_shift)
        del txt_mod2_gate, txt_mod2_scale, txt_mod2_shift

        txt = fp16_fix(txt)

        return img, txt


class SingleStreamBlock(nn.Module):
    def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, qk_scale=None):
        super().__init__()
        self.hidden_dim = hidden_size
        self.num_heads = num_heads
        head_dim = hidden_size // num_heads
        self.scale = qk_scale or head_dim ** -0.5
        self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
        self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
        self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
        self.norm = QKNorm(head_dim)
        self.hidden_size = hidden_size
        self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.mlp_act = nn.GELU(approximate="tanh")
        self.modulation = Modulation(hidden_size, double=False)

    def forward(self, x, vec, pe):
        mod_shift, mod_scale, mod_gate = self.modulation(vec)
        del vec
        x_mod = (1 + mod_scale) * self.pre_norm(x) + mod_shift
        del mod_shift, mod_scale
        qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
        del x_mod

        # q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
        qkv = qkv.view(qkv.size(0), qkv.size(1), 3, self.num_heads, self.hidden_size // self.num_heads)
        q, k, v = qkv.permute(2, 0, 3, 1, 4)
        del qkv

        q, k = self.norm(q, k, v)
        attn = attention(q, k, v, pe=pe)
        del q, k, v, pe
        output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), dim=2))
        del attn, mlp

        x = x + mod_gate * output
        del mod_gate, output

        x = fp16_fix(x)

        return x


class LastLayer(nn.Module):
    def __init__(self, hidden_size, patch_size, out_channels):
        super().__init__()
        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))

    def forward(self, x, vec):
        shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
        del vec
        x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
        del scale, shift
        x = self.linear(x)
        return x


class IntegratedFluxTransformer2DModel(nn.Module):
    def __init__(self, in_channels: int, vec_in_dim: int, context_in_dim: int, hidden_size: int, mlp_ratio: float, num_heads: int, depth: int, depth_single_blocks: int, axes_dim: list[int], theta: int, qkv_bias: bool, guidance_embed: bool):
        super().__init__()

        self.guidance_embed = guidance_embed
        self.in_channels = in_channels * 4
        self.out_channels = self.in_channels

        if hidden_size % num_heads != 0:
            raise ValueError(f"Hidden size {hidden_size} must be divisible by num_heads {num_heads}")

        pe_dim = hidden_size // num_heads
        if sum(axes_dim) != pe_dim:
            raise ValueError(f"Got {axes_dim} but expected positional dim {pe_dim}")

        self.hidden_size = hidden_size
        self.num_heads = num_heads

        self.pe_embedder = EmbedND(dim=pe_dim, theta=theta, axes_dim=axes_dim)
        self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
        self.vector_in = MLPEmbedder(vec_in_dim, self.hidden_size)
        self.guidance_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if guidance_embed else nn.Identity()
        self.txt_in = nn.Linear(context_in_dim, self.hidden_size)

        self.double_blocks = nn.ModuleList(
            [
                DoubleStreamBlock(
                    self.hidden_size,
                    self.num_heads,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                )
                for _ in range(depth)
            ]
        )

        self.single_blocks = nn.ModuleList(
            [
                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=mlp_ratio)
                for _ in range(depth_single_blocks)
            ]
        )

        self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels)

    def inner_forward(self, img, img_ids, txt, txt_ids, timesteps, y, guidance=None):
        if img.ndim != 3 or txt.ndim != 3:
            raise ValueError("Input img and txt tensors must have 3 dimensions.")
        img = self.img_in(img)
        vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype))
        if self.guidance_embed:
            if guidance is None:
                raise ValueError("Didn't get guidance strength for guidance distilled model.")
            vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
        vec = vec + self.vector_in(y)
        txt = self.txt_in(txt)
        del y, guidance
        ids = torch.cat((txt_ids, img_ids), dim=1)
        del txt_ids, img_ids
        pe = self.pe_embedder(ids)
        del ids
        for block in self.double_blocks:
            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
        img = torch.cat((txt, img), 1)
        for block in self.single_blocks:
            img = block(img, vec=vec, pe=pe)
        del pe
        img = img[:, txt.shape[1]:, ...]
        del txt
        img = self.final_layer(img, vec)
        del vec
        return img

    def forward(self, x, timestep, context, y, guidance=None, **kwargs):
        bs, c, h, w = x.shape
        input_device = x.device
        input_dtype = x.dtype
        patch_size = 2
        pad_h = (patch_size - x.shape[-2] % patch_size) % patch_size
        pad_w = (patch_size - x.shape[-1] % patch_size) % patch_size
        x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode="circular")
        img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
        del x, pad_h, pad_w
        h_len = ((h + (patch_size // 2)) // patch_size)
        w_len = ((w + (patch_size // 2)) // patch_size)
        img_ids = torch.zeros((h_len, w_len, 3), device=input_device, dtype=input_dtype)
        img_ids[..., 1] = img_ids[..., 1] + torch.linspace(0, h_len - 1, steps=h_len, device=input_device, dtype=input_dtype)[:, None]
        img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=input_device, dtype=input_dtype)[None, :]
        img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
        txt_ids = torch.zeros((bs, context.shape[1], 3), device=input_device, dtype=input_dtype)
        del input_device, input_dtype
        out = self.inner_forward(img, img_ids, context, txt_ids, timestep, y, guidance)
        del img, img_ids, txt_ids, timestep, context
        out = rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:, :, :h, :w]
        del h_len, w_len, bs
        return out