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#ifndef __FLUX_HPP__
#define __FLUX_HPP__
#include <vector>
#include "ggml_extend.hpp"
#include "model.h"
#define FLUX_GRAPH_SIZE 10240
namespace Flux {
struct MLPEmbedder : public UnaryBlock {
public:
MLPEmbedder(int64_t in_dim, int64_t hidden_dim) {
blocks["in_layer"] = std::shared_ptr<GGMLBlock>(new Linear(in_dim, hidden_dim, true));
blocks["out_layer"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_dim, hidden_dim, true));
}
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
// x: [..., in_dim]
// return: [..., hidden_dim]
auto in_layer = std::dynamic_pointer_cast<Linear>(blocks["in_layer"]);
auto out_layer = std::dynamic_pointer_cast<Linear>(blocks["out_layer"]);
x = in_layer->forward(ctx, x);
x = ggml_silu_inplace(ctx, x);
x = out_layer->forward(ctx, x);
return x;
}
};
class RMSNorm : public UnaryBlock {
protected:
int64_t hidden_size;
float eps;
void init_params(struct ggml_context* ctx, std::map<std::string, enum ggml_type>& tensor_types, const std::string prefix = "") {
ggml_type wtype = GGML_TYPE_F32; //(tensor_types.find(prefix + "scale") != tensor_types.end()) ? tensor_types[prefix + "scale"] : GGML_TYPE_F32;
params["scale"] = ggml_new_tensor_1d(ctx, wtype, hidden_size);
}
public:
RMSNorm(int64_t hidden_size,
float eps = 1e-06f)
: hidden_size(hidden_size),
eps(eps) {}
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
struct ggml_tensor* w = params["scale"];
x = ggml_rms_norm(ctx, x, eps);
x = ggml_mul(ctx, x, w);
return x;
}
};
struct QKNorm : public GGMLBlock {
public:
QKNorm(int64_t dim) {
blocks["query_norm"] = std::shared_ptr<GGMLBlock>(new RMSNorm(dim));
blocks["key_norm"] = std::shared_ptr<GGMLBlock>(new RMSNorm(dim));
}
struct ggml_tensor* query_norm(struct ggml_context* ctx, struct ggml_tensor* x) {
// x: [..., dim]
// return: [..., dim]
auto norm = std::dynamic_pointer_cast<RMSNorm>(blocks["query_norm"]);
x = norm->forward(ctx, x);
return x;
}
struct ggml_tensor* key_norm(struct ggml_context* ctx, struct ggml_tensor* x) {
// x: [..., dim]
// return: [..., dim]
auto norm = std::dynamic_pointer_cast<RMSNorm>(blocks["key_norm"]);
x = norm->forward(ctx, x);
return x;
}
};
__STATIC_INLINE__ struct ggml_tensor* apply_rope(struct ggml_context* ctx,
struct ggml_tensor* x,
struct ggml_tensor* pe) {
// x: [N, L, n_head, d_head]
// pe: [L, d_head/2, 2, 2]
int64_t d_head = x->ne[0];
int64_t n_head = x->ne[1];
int64_t L = x->ne[2];
int64_t N = x->ne[3];
x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3)); // [N, n_head, L, d_head]
x = ggml_reshape_4d(ctx, x, 2, d_head / 2, L, n_head * N); // [N * n_head, L, d_head/2, 2]
x = ggml_cont(ctx, ggml_permute(ctx, x, 3, 0, 1, 2)); // [2, N * n_head, L, d_head/2]
int64_t offset = x->nb[2] * x->ne[2];
auto x_0 = ggml_view_3d(ctx, x, x->ne[0], x->ne[1], x->ne[2], x->nb[1], x->nb[2], offset * 0); // [N * n_head, L, d_head/2]
auto x_1 = ggml_view_3d(ctx, x, x->ne[0], x->ne[1], x->ne[2], x->nb[1], x->nb[2], offset * 1); // [N * n_head, L, d_head/2]
x_0 = ggml_reshape_4d(ctx, x_0, 1, x_0->ne[0], x_0->ne[1], x_0->ne[2]); // [N * n_head, L, d_head/2, 1]
x_1 = ggml_reshape_4d(ctx, x_1, 1, x_1->ne[0], x_1->ne[1], x_1->ne[2]); // [N * n_head, L, d_head/2, 1]
auto temp_x = ggml_new_tensor_4d(ctx, x_0->type, 2, x_0->ne[1], x_0->ne[2], x_0->ne[3]);
x_0 = ggml_repeat(ctx, x_0, temp_x); // [N * n_head, L, d_head/2, 2]
x_1 = ggml_repeat(ctx, x_1, temp_x); // [N * n_head, L, d_head/2, 2]
pe = ggml_cont(ctx, ggml_permute(ctx, pe, 3, 0, 1, 2)); // [2, L, d_head/2, 2]
offset = pe->nb[2] * pe->ne[2];
auto pe_0 = ggml_view_3d(ctx, pe, pe->ne[0], pe->ne[1], pe->ne[2], pe->nb[1], pe->nb[2], offset * 0); // [L, d_head/2, 2]
auto pe_1 = ggml_view_3d(ctx, pe, pe->ne[0], pe->ne[1], pe->ne[2], pe->nb[1], pe->nb[2], offset * 1); // [L, d_head/2, 2]
auto x_out = ggml_add_inplace(ctx, ggml_mul(ctx, x_0, pe_0), ggml_mul(ctx, x_1, pe_1)); // [N * n_head, L, d_head/2, 2]
x_out = ggml_reshape_3d(ctx, x_out, d_head, L, n_head * N); // [N*n_head, L, d_head]
return x_out;
}
__STATIC_INLINE__ struct ggml_tensor* attention(struct ggml_context* ctx,
struct ggml_tensor* q,
struct ggml_tensor* k,
struct ggml_tensor* v,
struct ggml_tensor* pe,
bool flash_attn) {
// q,k,v: [N, L, n_head, d_head]
// pe: [L, d_head/2, 2, 2]
// return: [N, L, n_head*d_head]
q = apply_rope(ctx, q, pe); // [N*n_head, L, d_head]
k = apply_rope(ctx, k, pe); // [N*n_head, L, d_head]
auto x = ggml_nn_attention_ext(ctx, q, k, v, v->ne[1], NULL, false, true, flash_attn); // [N, L, n_head*d_head]
return x;
}
struct SelfAttention : public GGMLBlock {
public:
int64_t num_heads;
bool flash_attn;
public:
SelfAttention(int64_t dim,
int64_t num_heads = 8,
bool qkv_bias = false,
bool flash_attn = false)
: num_heads(num_heads) {
int64_t head_dim = dim / num_heads;
blocks["qkv"] = std::shared_ptr<GGMLBlock>(new Linear(dim, dim * 3, qkv_bias));
blocks["norm"] = std::shared_ptr<GGMLBlock>(new QKNorm(head_dim));
blocks["proj"] = std::shared_ptr<GGMLBlock>(new Linear(dim, dim));
}
std::vector<struct ggml_tensor*> pre_attention(struct ggml_context* ctx, struct ggml_tensor* x) {
auto qkv_proj = std::dynamic_pointer_cast<Linear>(blocks["qkv"]);
auto norm = std::dynamic_pointer_cast<QKNorm>(blocks["norm"]);
auto qkv = qkv_proj->forward(ctx, x);
auto qkv_vec = split_qkv(ctx, qkv);
int64_t head_dim = qkv_vec[0]->ne[0] / num_heads;
auto q = ggml_reshape_4d(ctx, qkv_vec[0], head_dim, num_heads, qkv_vec[0]->ne[1], qkv_vec[0]->ne[2]);
auto k = ggml_reshape_4d(ctx, qkv_vec[1], head_dim, num_heads, qkv_vec[1]->ne[1], qkv_vec[1]->ne[2]);
auto v = ggml_reshape_4d(ctx, qkv_vec[2], head_dim, num_heads, qkv_vec[2]->ne[1], qkv_vec[2]->ne[2]);
q = norm->query_norm(ctx, q);
k = norm->key_norm(ctx, k);
return {q, k, v};
}
struct ggml_tensor* post_attention(struct ggml_context* ctx, struct ggml_tensor* x) {
auto proj = std::dynamic_pointer_cast<Linear>(blocks["proj"]);
x = proj->forward(ctx, x); // [N, n_token, dim]
return x;
}
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, struct ggml_tensor* pe) {
// x: [N, n_token, dim]
// pe: [n_token, d_head/2, 2, 2]
// return [N, n_token, dim]
auto qkv = pre_attention(ctx, x); // q,k,v: [N, n_token, n_head, d_head]
x = attention(ctx, qkv[0], qkv[1], qkv[2], pe, flash_attn); // [N, n_token, dim]
x = post_attention(ctx, x); // [N, n_token, dim]
return x;
}
};
struct ModulationOut {
ggml_tensor* shift = NULL;
ggml_tensor* scale = NULL;
ggml_tensor* gate = NULL;
ModulationOut(ggml_tensor* shift = NULL, ggml_tensor* scale = NULL, ggml_tensor* gate = NULL)
: shift(shift), scale(scale), gate(gate) {}
};
struct Modulation : public GGMLBlock {
public:
bool is_double;
int multiplier;
public:
Modulation(int64_t dim, bool is_double)
: is_double(is_double) {
multiplier = is_double ? 6 : 3;
blocks["lin"] = std::shared_ptr<GGMLBlock>(new Linear(dim, dim * multiplier));
}
std::vector<ModulationOut> forward(struct ggml_context* ctx, struct ggml_tensor* vec) {
// x: [N, dim]
// return: [ModulationOut, ModulationOut]
auto lin = std::dynamic_pointer_cast<Linear>(blocks["lin"]);
auto out = ggml_silu(ctx, vec);
out = lin->forward(ctx, out); // [N, multiplier*dim]
auto m = ggml_reshape_3d(ctx, out, vec->ne[0], multiplier, vec->ne[1]); // [N, multiplier, dim]
m = ggml_cont(ctx, ggml_permute(ctx, m, 0, 2, 1, 3)); // [multiplier, N, dim]
int64_t offset = m->nb[1] * m->ne[1];
auto shift_0 = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 0); // [N, dim]
auto scale_0 = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 1); // [N, dim]
auto gate_0 = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 2); // [N, dim]
if (is_double) {
auto shift_1 = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 3); // [N, dim]
auto scale_1 = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 4); // [N, dim]
auto gate_1 = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 5); // [N, dim]
return {ModulationOut(shift_0, scale_0, gate_0), ModulationOut(shift_1, scale_1, gate_1)};
}
return {ModulationOut(shift_0, scale_0, gate_0), ModulationOut()};
}
};
__STATIC_INLINE__ struct ggml_tensor* modulate(struct ggml_context* ctx,
struct ggml_tensor* x,
struct ggml_tensor* shift,
struct ggml_tensor* scale) {
// x: [N, L, C]
// scale: [N, C]
// shift: [N, C]
scale = ggml_reshape_3d(ctx, scale, scale->ne[0], 1, scale->ne[1]); // [N, 1, C]
shift = ggml_reshape_3d(ctx, shift, shift->ne[0], 1, shift->ne[1]); // [N, 1, C]
x = ggml_add(ctx, x, ggml_mul(ctx, x, scale));
x = ggml_add(ctx, x, shift);
return x;
}
struct DoubleStreamBlock : public GGMLBlock {
bool flash_attn;
public:
DoubleStreamBlock(int64_t hidden_size,
int64_t num_heads,
float mlp_ratio,
bool qkv_bias = false,
bool flash_attn = false)
: flash_attn(flash_attn) {
int64_t mlp_hidden_dim = hidden_size * mlp_ratio;
blocks["img_mod"] = std::shared_ptr<GGMLBlock>(new Modulation(hidden_size, true));
blocks["img_norm1"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-6f, false));
blocks["img_attn"] = std::shared_ptr<GGMLBlock>(new SelfAttention(hidden_size, num_heads, qkv_bias, flash_attn));
blocks["img_norm2"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-6f, false));
blocks["img_mlp.0"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, mlp_hidden_dim));
// img_mlp.1 is nn.GELU(approximate="tanh")
blocks["img_mlp.2"] = std::shared_ptr<GGMLBlock>(new Linear(mlp_hidden_dim, hidden_size));
blocks["txt_mod"] = std::shared_ptr<GGMLBlock>(new Modulation(hidden_size, true));
blocks["txt_norm1"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-6f, false));
blocks["txt_attn"] = std::shared_ptr<GGMLBlock>(new SelfAttention(hidden_size, num_heads, qkv_bias, flash_attn));
blocks["txt_norm2"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-6f, false));
blocks["txt_mlp.0"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, mlp_hidden_dim));
// img_mlp.1 is nn.GELU(approximate="tanh")
blocks["txt_mlp.2"] = std::shared_ptr<GGMLBlock>(new Linear(mlp_hidden_dim, hidden_size));
}
std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(struct ggml_context* ctx,
struct ggml_tensor* img,
struct ggml_tensor* txt,
struct ggml_tensor* vec,
struct ggml_tensor* pe) {
// img: [N, n_img_token, hidden_size]
// txt: [N, n_txt_token, hidden_size]
// pe: [n_img_token + n_txt_token, d_head/2, 2, 2]
// return: ([N, n_img_token, hidden_size], [N, n_txt_token, hidden_size])
auto img_mod = std::dynamic_pointer_cast<Modulation>(blocks["img_mod"]);
auto img_norm1 = std::dynamic_pointer_cast<LayerNorm>(blocks["img_norm1"]);
auto img_attn = std::dynamic_pointer_cast<SelfAttention>(blocks["img_attn"]);
auto img_norm2 = std::dynamic_pointer_cast<LayerNorm>(blocks["img_norm2"]);
auto img_mlp_0 = std::dynamic_pointer_cast<Linear>(blocks["img_mlp.0"]);
auto img_mlp_2 = std::dynamic_pointer_cast<Linear>(blocks["img_mlp.2"]);
auto txt_mod = std::dynamic_pointer_cast<Modulation>(blocks["txt_mod"]);
auto txt_norm1 = std::dynamic_pointer_cast<LayerNorm>(blocks["txt_norm1"]);
auto txt_attn = std::dynamic_pointer_cast<SelfAttention>(blocks["txt_attn"]);
auto txt_norm2 = std::dynamic_pointer_cast<LayerNorm>(blocks["txt_norm2"]);
auto txt_mlp_0 = std::dynamic_pointer_cast<Linear>(blocks["txt_mlp.0"]);
auto txt_mlp_2 = std::dynamic_pointer_cast<Linear>(blocks["txt_mlp.2"]);
auto img_mods = img_mod->forward(ctx, vec);
ModulationOut img_mod1 = img_mods[0];
ModulationOut img_mod2 = img_mods[1];
auto txt_mods = txt_mod->forward(ctx, vec);
ModulationOut txt_mod1 = txt_mods[0];
ModulationOut txt_mod2 = txt_mods[1];
// prepare image for attention
auto img_modulated = img_norm1->forward(ctx, img);
img_modulated = Flux::modulate(ctx, img_modulated, img_mod1.shift, img_mod1.scale);
auto img_qkv = img_attn->pre_attention(ctx, img_modulated); // q,k,v: [N, n_img_token, n_head, d_head]
auto img_q = img_qkv[0];
auto img_k = img_qkv[1];
auto img_v = img_qkv[2];
// prepare txt for attention
auto txt_modulated = txt_norm1->forward(ctx, txt);
txt_modulated = Flux::modulate(ctx, txt_modulated, txt_mod1.shift, txt_mod1.scale);
auto txt_qkv = txt_attn->pre_attention(ctx, txt_modulated); // q,k,v: [N, n_txt_token, n_head, d_head]
auto txt_q = txt_qkv[0];
auto txt_k = txt_qkv[1];
auto txt_v = txt_qkv[2];
// run actual attention
auto q = ggml_concat(ctx, txt_q, img_q, 2); // [N, n_txt_token + n_img_token, n_head, d_head]
auto k = ggml_concat(ctx, txt_k, img_k, 2); // [N, n_txt_token + n_img_token, n_head, d_head]
auto v = ggml_concat(ctx, txt_v, img_v, 2); // [N, n_txt_token + n_img_token, n_head, d_head]
auto attn = attention(ctx, q, k, v, pe, flash_attn); // [N, n_txt_token + n_img_token, n_head*d_head]
attn = ggml_cont(ctx, ggml_permute(ctx, attn, 0, 2, 1, 3)); // [n_txt_token + n_img_token, N, hidden_size]
auto txt_attn_out = ggml_view_3d(ctx,
attn,
attn->ne[0],
attn->ne[1],
txt->ne[1],
attn->nb[1],
attn->nb[2],
0); // [n_txt_token, N, hidden_size]
txt_attn_out = ggml_cont(ctx, ggml_permute(ctx, txt_attn_out, 0, 2, 1, 3)); // [N, n_txt_token, hidden_size]
auto img_attn_out = ggml_view_3d(ctx,
attn,
attn->ne[0],
attn->ne[1],
img->ne[1],
attn->nb[1],
attn->nb[2],
attn->nb[2] * txt->ne[1]); // [n_img_token, N, hidden_size]
img_attn_out = ggml_cont(ctx, ggml_permute(ctx, img_attn_out, 0, 2, 1, 3)); // [N, n_img_token, hidden_size]
// calculate the img bloks
img = ggml_add(ctx, img, ggml_mul(ctx, img_attn->post_attention(ctx, img_attn_out), img_mod1.gate));
auto img_mlp_out = img_mlp_0->forward(ctx, Flux::modulate(ctx, img_norm2->forward(ctx, img), img_mod2.shift, img_mod2.scale));
img_mlp_out = ggml_gelu_inplace(ctx, img_mlp_out);
img_mlp_out = img_mlp_2->forward(ctx, img_mlp_out);
img = ggml_add(ctx, img, ggml_mul(ctx, img_mlp_out, img_mod2.gate));
// calculate the txt bloks
txt = ggml_add(ctx, txt, ggml_mul(ctx, txt_attn->post_attention(ctx, txt_attn_out), txt_mod1.gate));
auto txt_mlp_out = txt_mlp_0->forward(ctx, Flux::modulate(ctx, txt_norm2->forward(ctx, txt), txt_mod2.shift, txt_mod2.scale));
txt_mlp_out = ggml_gelu_inplace(ctx, txt_mlp_out);
txt_mlp_out = txt_mlp_2->forward(ctx, txt_mlp_out);
txt = ggml_add(ctx, txt, ggml_mul(ctx, txt_mlp_out, txt_mod2.gate));
return {img, txt};
}
};
struct SingleStreamBlock : public GGMLBlock {
public:
int64_t num_heads;
int64_t hidden_size;
int64_t mlp_hidden_dim;
bool flash_attn;
public:
SingleStreamBlock(int64_t hidden_size,
int64_t num_heads,
float mlp_ratio = 4.0f,
float qk_scale = 0.f,
bool flash_attn = false)
: hidden_size(hidden_size), num_heads(num_heads), flash_attn(flash_attn) {
int64_t head_dim = hidden_size / num_heads;
float scale = qk_scale;
if (scale <= 0.f) {
scale = 1 / sqrt((float)head_dim);
}
mlp_hidden_dim = hidden_size * mlp_ratio;
blocks["linear1"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, hidden_size * 3 + mlp_hidden_dim));
blocks["linear2"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size + mlp_hidden_dim, hidden_size));
blocks["norm"] = std::shared_ptr<GGMLBlock>(new QKNorm(head_dim));
blocks["pre_norm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-6f, false));
// mlp_act is nn.GELU(approximate="tanh")
blocks["modulation"] = std::shared_ptr<GGMLBlock>(new Modulation(hidden_size, false));
}
struct ggml_tensor* forward(struct ggml_context* ctx,
struct ggml_tensor* x,
struct ggml_tensor* vec,
struct ggml_tensor* pe) {
// x: [N, n_token, hidden_size]
// pe: [n_token, d_head/2, 2, 2]
// return: [N, n_token, hidden_size]
auto linear1 = std::dynamic_pointer_cast<Linear>(blocks["linear1"]);
auto linear2 = std::dynamic_pointer_cast<Linear>(blocks["linear2"]);
auto norm = std::dynamic_pointer_cast<QKNorm>(blocks["norm"]);
auto pre_norm = std::dynamic_pointer_cast<LayerNorm>(blocks["pre_norm"]);
auto modulation = std::dynamic_pointer_cast<Modulation>(blocks["modulation"]);
auto mods = modulation->forward(ctx, vec);
ModulationOut mod = mods[0];
auto x_mod = Flux::modulate(ctx, pre_norm->forward(ctx, x), mod.shift, mod.scale);
auto qkv_mlp = linear1->forward(ctx, x_mod); // [N, n_token, hidden_size * 3 + mlp_hidden_dim]
qkv_mlp = ggml_cont(ctx, ggml_permute(ctx, qkv_mlp, 2, 0, 1, 3)); // [hidden_size * 3 + mlp_hidden_dim, N, n_token]
auto qkv = ggml_view_3d(ctx,
qkv_mlp,
qkv_mlp->ne[0],
qkv_mlp->ne[1],
hidden_size * 3,
qkv_mlp->nb[1],
qkv_mlp->nb[2],
0); // [hidden_size * 3 , N, n_token]
qkv = ggml_cont(ctx, ggml_permute(ctx, qkv, 1, 2, 0, 3)); // [N, n_token, hidden_size * 3]
auto mlp = ggml_view_3d(ctx,
qkv_mlp,
qkv_mlp->ne[0],
qkv_mlp->ne[1],
mlp_hidden_dim,
qkv_mlp->nb[1],
qkv_mlp->nb[2],
qkv_mlp->nb[2] * hidden_size * 3); // [mlp_hidden_dim , N, n_token]
mlp = ggml_cont(ctx, ggml_permute(ctx, mlp, 1, 2, 0, 3)); // [N, n_token, mlp_hidden_dim]
auto qkv_vec = split_qkv(ctx, qkv); // q,k,v: [N, n_token, hidden_size]
int64_t head_dim = hidden_size / num_heads;
auto q = ggml_reshape_4d(ctx, qkv_vec[0], head_dim, num_heads, qkv_vec[0]->ne[1], qkv_vec[0]->ne[2]); // [N, n_token, n_head, d_head]
auto k = ggml_reshape_4d(ctx, qkv_vec[1], head_dim, num_heads, qkv_vec[1]->ne[1], qkv_vec[1]->ne[2]); // [N, n_token, n_head, d_head]
auto v = ggml_reshape_4d(ctx, qkv_vec[2], head_dim, num_heads, qkv_vec[2]->ne[1], qkv_vec[2]->ne[2]); // [N, n_token, n_head, d_head]
q = norm->query_norm(ctx, q);
k = norm->key_norm(ctx, k);
auto attn = attention(ctx, q, k, v, pe, flash_attn); // [N, n_token, hidden_size]
auto attn_mlp = ggml_concat(ctx, attn, ggml_gelu_inplace(ctx, mlp), 0); // [N, n_token, hidden_size + mlp_hidden_dim]
auto output = linear2->forward(ctx, attn_mlp); // [N, n_token, hidden_size]
output = ggml_add(ctx, x, ggml_mul(ctx, output, mod.gate));
return output;
}
};
struct LastLayer : public GGMLBlock {
public:
LastLayer(int64_t hidden_size,
int64_t patch_size,
int64_t out_channels) {
blocks["norm_final"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-06f, false));
blocks["linear"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, patch_size * patch_size * out_channels));
blocks["adaLN_modulation.1"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, 2 * hidden_size));
}
struct ggml_tensor* forward(struct ggml_context* ctx,
struct ggml_tensor* x,
struct ggml_tensor* c) {
// x: [N, n_token, hidden_size]
// c: [N, hidden_size]
// return: [N, n_token, patch_size * patch_size * out_channels]
auto norm_final = std::dynamic_pointer_cast<LayerNorm>(blocks["norm_final"]);
auto linear = std::dynamic_pointer_cast<Linear>(blocks["linear"]);
auto adaLN_modulation_1 = std::dynamic_pointer_cast<Linear>(blocks["adaLN_modulation.1"]);
auto m = adaLN_modulation_1->forward(ctx, ggml_silu(ctx, c)); // [N, 2 * hidden_size]
m = ggml_reshape_3d(ctx, m, c->ne[0], 2, c->ne[1]); // [N, 2, hidden_size]
m = ggml_cont(ctx, ggml_permute(ctx, m, 0, 2, 1, 3)); // [2, N, hidden_size]
int64_t offset = m->nb[1] * m->ne[1];
auto shift = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 0); // [N, hidden_size]
auto scale = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 1); // [N, hidden_size]
x = Flux::modulate(ctx, norm_final->forward(ctx, x), shift, scale);
x = linear->forward(ctx, x);
return x;
}
};
struct FluxParams {
int64_t in_channels = 64;
int64_t vec_in_dim = 768;
int64_t context_in_dim = 4096;
int64_t hidden_size = 3072;
float mlp_ratio = 4.0f;
int64_t num_heads = 24;
int64_t depth = 19;
int64_t depth_single_blocks = 38;
std::vector<int> axes_dim = {16, 56, 56};
int64_t axes_dim_sum = 128;
int theta = 10000;
bool qkv_bias = true;
bool guidance_embed = true;
bool flash_attn = true;
};
struct Flux : public GGMLBlock {
public:
std::vector<float> linspace(float start, float end, int num) {
std::vector<float> result(num);
float step = (end - start) / (num - 1);
for (int i = 0; i < num; ++i) {
result[i] = start + i * step;
}
return result;
}
std::vector<std::vector<float>> transpose(const std::vector<std::vector<float>>& mat) {
int rows = mat.size();
int cols = mat[0].size();
std::vector<std::vector<float>> transposed(cols, std::vector<float>(rows));
for (int i = 0; i < rows; ++i) {
for (int j = 0; j < cols; ++j) {
transposed[j][i] = mat[i][j];
}
}
return transposed;
}
std::vector<float> flatten(const std::vector<std::vector<float>>& vec) {
std::vector<float> flat_vec;
for (const auto& sub_vec : vec) {
flat_vec.insert(flat_vec.end(), sub_vec.begin(), sub_vec.end());
}
return flat_vec;
}
std::vector<std::vector<float>> rope(const std::vector<float>& pos, int dim, int theta) {
assert(dim % 2 == 0);
int half_dim = dim / 2;
std::vector<float> scale = linspace(0, (dim * 1.0f - 2) / dim, half_dim);
std::vector<float> omega(half_dim);
for (int i = 0; i < half_dim; ++i) {
omega[i] = 1.0 / std::pow(theta, scale[i]);
}
int pos_size = pos.size();
std::vector<std::vector<float>> out(pos_size, std::vector<float>(half_dim));
for (int i = 0; i < pos_size; ++i) {
for (int j = 0; j < half_dim; ++j) {
out[i][j] = pos[i] * omega[j];
}
}
std::vector<std::vector<float>> result(pos_size, std::vector<float>(half_dim * 4));
for (int i = 0; i < pos_size; ++i) {
for (int j = 0; j < half_dim; ++j) {
result[i][4 * j] = std::cos(out[i][j]);
result[i][4 * j + 1] = -std::sin(out[i][j]);
result[i][4 * j + 2] = std::sin(out[i][j]);
result[i][4 * j + 3] = std::cos(out[i][j]);
}
}
return result;
}
// Generate IDs for image patches and text
std::vector<std::vector<float>> gen_ids(int h, int w, int patch_size, int bs, int context_len) {
int h_len = (h + (patch_size / 2)) / patch_size;
int w_len = (w + (patch_size / 2)) / patch_size;
std::vector<std::vector<float>> img_ids(h_len * w_len, std::vector<float>(3, 0.0));
std::vector<float> row_ids = linspace(0, h_len - 1, h_len);
std::vector<float> col_ids = linspace(0, w_len - 1, w_len);
for (int i = 0; i < h_len; ++i) {
for (int j = 0; j < w_len; ++j) {
img_ids[i * w_len + j][1] = row_ids[i];
img_ids[i * w_len + j][2] = col_ids[j];
}
}
std::vector<std::vector<float>> img_ids_repeated(bs * img_ids.size(), std::vector<float>(3));
for (int i = 0; i < bs; ++i) {
for (int j = 0; j < img_ids.size(); ++j) {
img_ids_repeated[i * img_ids.size() + j] = img_ids[j];
}
}
std::vector<std::vector<float>> txt_ids(bs * context_len, std::vector<float>(3, 0.0));
std::vector<std::vector<float>> ids(bs * (context_len + img_ids.size()), std::vector<float>(3));
for (int i = 0; i < bs; ++i) {
for (int j = 0; j < context_len; ++j) {
ids[i * (context_len + img_ids.size()) + j] = txt_ids[j];
}
for (int j = 0; j < img_ids.size(); ++j) {
ids[i * (context_len + img_ids.size()) + context_len + j] = img_ids_repeated[i * img_ids.size() + j];
}
}
return ids;
}
// Generate positional embeddings
std::vector<float> gen_pe(int h, int w, int patch_size, int bs, int context_len, int theta, const std::vector<int>& axes_dim) {
std::vector<std::vector<float>> ids = gen_ids(h, w, patch_size, bs, context_len);
std::vector<std::vector<float>> trans_ids = transpose(ids);
size_t pos_len = ids.size();
int num_axes = axes_dim.size();
for (int i = 0; i < pos_len; i++) {
// std::cout << trans_ids[0][i] << " " << trans_ids[1][i] << " " << trans_ids[2][i] << std::endl;
}
int emb_dim = 0;
for (int d : axes_dim)
emb_dim += d / 2;
std::vector<std::vector<float>> emb(bs * pos_len, std::vector<float>(emb_dim * 2 * 2, 0.0));
int offset = 0;
for (int i = 0; i < num_axes; ++i) {
std::vector<std::vector<float>> rope_emb = rope(trans_ids[i], axes_dim[i], theta); // [bs*pos_len, axes_dim[i]/2 * 2 * 2]
for (int b = 0; b < bs; ++b) {
for (int j = 0; j < pos_len; ++j) {
for (int k = 0; k < rope_emb[0].size(); ++k) {
emb[b * pos_len + j][offset + k] = rope_emb[j][k];
}
}
}
offset += rope_emb[0].size();
}
return flatten(emb);
}
public:
FluxParams params;
Flux() {}
Flux(FluxParams params)
: params(params) {
int64_t out_channels = params.in_channels;
int64_t pe_dim = params.hidden_size / params.num_heads;
blocks["img_in"] = std::shared_ptr<GGMLBlock>(new Linear(params.in_channels, params.hidden_size, true));
blocks["time_in"] = std::shared_ptr<GGMLBlock>(new MLPEmbedder(256, params.hidden_size));
blocks["vector_in"] = std::shared_ptr<GGMLBlock>(new MLPEmbedder(params.vec_in_dim, params.hidden_size));
if (params.guidance_embed) {
blocks["guidance_in"] = std::shared_ptr<GGMLBlock>(new MLPEmbedder(256, params.hidden_size));
}
blocks["txt_in"] = std::shared_ptr<GGMLBlock>(new Linear(params.context_in_dim, params.hidden_size, true));
for (int i = 0; i < params.depth; i++) {
blocks["double_blocks." + std::to_string(i)] = std::shared_ptr<GGMLBlock>(new DoubleStreamBlock(params.hidden_size,
params.num_heads,
params.mlp_ratio,
params.qkv_bias,
params.flash_attn));
}
for (int i = 0; i < params.depth_single_blocks; i++) {
blocks["single_blocks." + std::to_string(i)] = std::shared_ptr<GGMLBlock>(new SingleStreamBlock(params.hidden_size,
params.num_heads,
params.mlp_ratio,
0.f,
params.flash_attn));
}
blocks["final_layer"] = std::shared_ptr<GGMLBlock>(new LastLayer(params.hidden_size, 1, out_channels));
}
struct ggml_tensor* patchify(struct ggml_context* ctx,
struct ggml_tensor* x,
int64_t patch_size) {
// x: [N, C, H, W]
// return: [N, h*w, C * patch_size * patch_size]
int64_t N = x->ne[3];
int64_t C = x->ne[2];
int64_t H = x->ne[1];
int64_t W = x->ne[0];
int64_t p = patch_size;
int64_t h = H / patch_size;
int64_t w = W / patch_size;
GGML_ASSERT(h * p == H && w * p == W);
x = ggml_reshape_4d(ctx, x, p, w, p, h * C * N); // [N*C*h, p, w, p]
x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3)); // [N*C*h, w, p, p]
x = ggml_reshape_4d(ctx, x, p * p, w * h, C, N); // [N, C, h*w, p*p]
x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3)); // [N, h*w, C, p*p]
x = ggml_reshape_3d(ctx, x, p * p * C, w * h, N); // [N, h*w, C*p*p]
return x;
}
struct ggml_tensor* unpatchify(struct ggml_context* ctx,
struct ggml_tensor* x,
int64_t h,
int64_t w,
int64_t patch_size) {
// x: [N, h*w, C*patch_size*patch_size]
// return: [N, C, H, W]
int64_t N = x->ne[2];
int64_t C = x->ne[0] / patch_size / patch_size;
int64_t H = h * patch_size;
int64_t W = w * patch_size;
int64_t p = patch_size;
GGML_ASSERT(C * p * p == x->ne[0]);
x = ggml_reshape_4d(ctx, x, p * p, C, w * h, N); // [N, h*w, C, p*p]
x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3)); // [N, C, h*w, p*p]
x = ggml_reshape_4d(ctx, x, p, p, w, h * C * N); // [N*C*h, w, p, p]
x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3)); // [N*C*h, p, w, p]
x = ggml_reshape_4d(ctx, x, W, H, C, N); // [N, C, h*p, w*p]
return x;
}
struct ggml_tensor* forward_orig(struct ggml_context* ctx,
struct ggml_tensor* img,
struct ggml_tensor* txt,
struct ggml_tensor* timesteps,
struct ggml_tensor* y,
struct ggml_tensor* guidance,
struct ggml_tensor* pe,
std::vector<int> skip_layers = std::vector<int>()) {
auto img_in = std::dynamic_pointer_cast<Linear>(blocks["img_in"]);
auto time_in = std::dynamic_pointer_cast<MLPEmbedder>(blocks["time_in"]);
auto vector_in = std::dynamic_pointer_cast<MLPEmbedder>(blocks["vector_in"]);
auto txt_in = std::dynamic_pointer_cast<Linear>(blocks["txt_in"]);
auto final_layer = std::dynamic_pointer_cast<LastLayer>(blocks["final_layer"]);
img = img_in->forward(ctx, img);
auto vec = time_in->forward(ctx, ggml_nn_timestep_embedding(ctx, timesteps, 256, 10000, 1000.f));
if (params.guidance_embed) {
GGML_ASSERT(guidance != NULL);
auto guidance_in = std::dynamic_pointer_cast<MLPEmbedder>(blocks["guidance_in"]);
// bf16 and fp16 result is different
auto g_in = ggml_nn_timestep_embedding(ctx, guidance, 256, 10000, 1000.f);
vec = ggml_add(ctx, vec, guidance_in->forward(ctx, g_in));
}
vec = ggml_add(ctx, vec, vector_in->forward(ctx, y));
txt = txt_in->forward(ctx, txt);
for (int i = 0; i < params.depth; i++) {
if (skip_layers.size() > 0 && std::find(skip_layers.begin(), skip_layers.end(), i) != skip_layers.end()) {
continue;
}
auto block = std::dynamic_pointer_cast<DoubleStreamBlock>(blocks["double_blocks." + std::to_string(i)]);
auto img_txt = block->forward(ctx, img, txt, vec, pe);
img = img_txt.first; // [N, n_img_token, hidden_size]
txt = img_txt.second; // [N, n_txt_token, hidden_size]
}
auto txt_img = ggml_concat(ctx, txt, img, 1); // [N, n_txt_token + n_img_token, hidden_size]
for (int i = 0; i < params.depth_single_blocks; i++) {
if (skip_layers.size() > 0 && std::find(skip_layers.begin(), skip_layers.end(), i + params.depth) != skip_layers.end()) {
continue;
}
auto block = std::dynamic_pointer_cast<SingleStreamBlock>(blocks["single_blocks." + std::to_string(i)]);
txt_img = block->forward(ctx, txt_img, vec, pe);
}
txt_img = ggml_cont(ctx, ggml_permute(ctx, txt_img, 0, 2, 1, 3)); // [n_txt_token + n_img_token, N, hidden_size]
img = ggml_view_3d(ctx,
txt_img,
txt_img->ne[0],
txt_img->ne[1],
img->ne[1],
txt_img->nb[1],
txt_img->nb[2],
txt_img->nb[2] * txt->ne[1]); // [n_img_token, N, hidden_size]
img = ggml_cont(ctx, ggml_permute(ctx, img, 0, 2, 1, 3)); // [N, n_img_token, hidden_size]
img = final_layer->forward(ctx, img, vec); // (N, T, patch_size ** 2 * out_channels)
return img;
}
struct ggml_tensor* forward(struct ggml_context* ctx,
struct ggml_tensor* x,
struct ggml_tensor* timestep,
struct ggml_tensor* context,
struct ggml_tensor* y,
struct ggml_tensor* guidance,
struct ggml_tensor* pe,
std::vector<int> skip_layers = std::vector<int>()) {
// Forward pass of DiT.
// x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
// timestep: (N,) tensor of diffusion timesteps
// context: (N, L, D)
// y: (N, adm_in_channels) tensor of class labels
// guidance: (N,)
// pe: (L, d_head/2, 2, 2)
// return: (N, C, H, W)
GGML_ASSERT(x->ne[3] == 1);
int64_t W = x->ne[0];
int64_t H = x->ne[1];
int64_t patch_size = 2;
int pad_h = (patch_size - H % patch_size) % patch_size;
int pad_w = (patch_size - W % patch_size) % patch_size;
x = ggml_pad(ctx, x, pad_w, pad_h, 0, 0); // [N, C, H + pad_h, W + pad_w]
// img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
auto img = patchify(ctx, x, patch_size); // [N, h*w, C * patch_size * patch_size]
auto out = forward_orig(ctx, img, context, timestep, y, guidance, pe, skip_layers); // [N, h*w, C * patch_size * patch_size]
// rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)
out = unpatchify(ctx, out, (H + pad_h) / patch_size, (W + pad_w) / patch_size, patch_size); // [N, C, H + pad_h, W + pad_w]
return out;
}
};
struct FluxRunner : public GGMLRunner {
static std::map<std::string, enum ggml_type> empty_tensor_types;
public:
FluxParams flux_params;
Flux flux;
std::vector<float> pe_vec; // for cache
FluxRunner(ggml_backend_t backend,
std::map<std::string, enum ggml_type>& tensor_types = empty_tensor_types,
const std::string prefix = "",
bool flash_attn = false)
: GGMLRunner(backend) {
flux_params.flash_attn = flash_attn;
flux_params.guidance_embed = false;
flux_params.depth = 0;
flux_params.depth_single_blocks = 0;
for (auto pair : tensor_types) {
std::string tensor_name = pair.first;
if (tensor_name.find("model.diffusion_model.") == std::string::npos)
continue;
if (tensor_name.find("guidance_in.in_layer.weight") != std::string::npos) {
// not schnell
flux_params.guidance_embed = true;
}
size_t db = tensor_name.find("double_blocks.");
if (db != std::string::npos) {
tensor_name = tensor_name.substr(db); // remove prefix
int block_depth = atoi(tensor_name.substr(14, tensor_name.find(".", 14)).c_str());
if (block_depth + 1 > flux_params.depth) {
flux_params.depth = block_depth + 1;
}
}
size_t sb = tensor_name.find("single_blocks.");
if (sb != std::string::npos) {
tensor_name = tensor_name.substr(sb); // remove prefix
int block_depth = atoi(tensor_name.substr(14, tensor_name.find(".", 14)).c_str());
if (block_depth + 1 > flux_params.depth_single_blocks) {
flux_params.depth_single_blocks = block_depth + 1;
}
}
}
LOG_INFO("Flux blocks: %d double, %d single", flux_params.depth, flux_params.depth_single_blocks);
if (!flux_params.guidance_embed) {
LOG_INFO("Flux guidance is disabled (Schnell mode)");
}
flux = Flux(flux_params);
flux.init(params_ctx, tensor_types, prefix);
}
std::string get_desc() {
return "flux";
}
void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
flux.get_param_tensors(tensors, prefix);
}
struct ggml_cgraph* build_graph(struct ggml_tensor* x,
struct ggml_tensor* timesteps,
struct ggml_tensor* context,
struct ggml_tensor* y,
struct ggml_tensor* guidance,
std::vector<int> skip_layers = std::vector<int>()) {
GGML_ASSERT(x->ne[3] == 1);
struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, FLUX_GRAPH_SIZE, false);
x = to_backend(x);
context = to_backend(context);
y = to_backend(y);
timesteps = to_backend(timesteps);
if (flux_params.guidance_embed) {
guidance = to_backend(guidance);
}
pe_vec = flux.gen_pe(x->ne[1], x->ne[0], 2, x->ne[3], context->ne[1], flux_params.theta, flux_params.axes_dim);
int pos_len = pe_vec.size() / flux_params.axes_dim_sum / 2;
// LOG_DEBUG("pos_len %d", pos_len);
auto pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, flux_params.axes_dim_sum / 2, pos_len);
// pe->data = pe_vec.data();
// print_ggml_tensor(pe);
// pe->data = NULL;
set_backend_tensor_data(pe, pe_vec.data());
struct ggml_tensor* out = flux.forward(compute_ctx,
x,
timesteps,
context,
y,
guidance,
pe,
skip_layers);
ggml_build_forward_expand(gf, out);
return gf;
}
void compute(int n_threads,
struct ggml_tensor* x,
struct ggml_tensor* timesteps,
struct ggml_tensor* context,
struct ggml_tensor* y,
struct ggml_tensor* guidance,
struct ggml_tensor** output = NULL,
struct ggml_context* output_ctx = NULL,
std::vector<int> skip_layers = std::vector<int>()) {
// x: [N, in_channels, h, w]
// timesteps: [N, ]
// context: [N, max_position, hidden_size]
// y: [N, adm_in_channels] or [1, adm_in_channels]
// guidance: [N, ]
auto get_graph = [&]() -> struct ggml_cgraph* {
return build_graph(x, timesteps, context, y, guidance, skip_layers);
};
GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
}
void test() {
struct ggml_init_params params;
params.mem_size = static_cast<size_t>(20 * 1024 * 1024); // 20 MB
params.mem_buffer = NULL;
params.no_alloc = false;
struct ggml_context* work_ctx = ggml_init(params);
GGML_ASSERT(work_ctx != NULL);
{
// cpu f16:
// cuda f16: nan
// cuda q8_0: pass
auto x = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 16, 16, 16, 1);
ggml_set_f32(x, 0.01f);
// print_ggml_tensor(x);
std::vector<float> timesteps_vec(1, 999.f);
auto timesteps = vector_to_ggml_tensor(work_ctx, timesteps_vec);
std::vector<float> guidance_vec(1, 3.5f);
auto guidance = vector_to_ggml_tensor(work_ctx, guidance_vec);
auto context = ggml_new_tensor_3d(work_ctx, GGML_TYPE_F32, 4096, 256, 1);
ggml_set_f32(context, 0.01f);
// print_ggml_tensor(context);
auto y = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 768, 1);
ggml_set_f32(y, 0.01f);
// print_ggml_tensor(y);
struct ggml_tensor* out = NULL;
int t0 = ggml_time_ms();
compute(8, x, timesteps, context, y, guidance, &out, work_ctx);
int t1 = ggml_time_ms();
print_ggml_tensor(out);
LOG_DEBUG("flux test done in %dms", t1 - t0);
}
}
static void load_from_file_and_test(const std::string& file_path) {
// ggml_backend_t backend = ggml_backend_cuda_init(0);
ggml_backend_t backend = ggml_backend_cpu_init();
ggml_type model_data_type = GGML_TYPE_Q8_0;
std::shared_ptr<FluxRunner> flux = std::shared_ptr<FluxRunner>(new FluxRunner(backend));
{
LOG_INFO("loading from '%s'", file_path.c_str());
flux->alloc_params_buffer();
std::map<std::string, ggml_tensor*> tensors;
flux->get_param_tensors(tensors, "model.diffusion_model");
ModelLoader model_loader;
if (!model_loader.init_from_file(file_path, "model.diffusion_model.")) {
LOG_ERROR("init model loader from file failed: '%s'", file_path.c_str());
return;
}
bool success = model_loader.load_tensors(tensors, backend);
if (!success) {
LOG_ERROR("load tensors from model loader failed");
return;
}
LOG_INFO("flux model loaded");
}
flux->test();
}
};
} // namespace Flux
#endif // __FLUX_HPP__
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