DarthAffe/stable-diffusion.cpp
1
0
Fork 0
mirror of https://github.com/leejet/stable-diffusion.cpp.git synced 2025-12-12 21:38:58 +00:00
Code Issues Packages Projects Releases Wiki Activity
stable-diffusion.cpp/rope.hpp
leejet 6103d86e2c
refactor: introduce GGMLRunnerContext (#928) 
* introduce GGMLRunnerContext

* add Flash Attention enable control through GGMLRunnerContext

* add conv2d_direct enable control through GGMLRunnerContext
2025-11-02 02:11:04 +08:00

409 lines
20 KiB
C++
Raw Blame History

#ifndef __ROPE_HPP__
#define __ROPE_HPP__
#include <vector>
#include "ggml_extend.hpp"
namespace Rope {
template <class T>
__STATIC_INLINE__ std::vector<T> linspace(T start, T end, int num) {
std::vector<T> result(num);
if (num == 1) {
result[0] = start;
return result;
}
T step = (end - start) / (num - 1);
for (int i = 0; i < num; ++i) {
result[i] = start + i * step;
}
return result;
}
__STATIC_INLINE__ 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;
}
__STATIC_INLINE__ 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;
}
__STATIC_INLINE__ 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.f, (dim * 1.f - 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
__STATIC_INLINE__ std::vector<std::vector<float>> gen_txt_ids(int bs, int context_len) {
return std::vector<std::vector<float>>(bs * context_len, std::vector<float>(3, 0.0));
}
__STATIC_INLINE__ std::vector<std::vector<float>> gen_img_ids(int h, int w, int patch_size, int bs, int index = 0, int h_offset = 0, int w_offset = 0) {
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<float>(h_offset, h_len - 1 + h_offset, h_len);
std::vector<float> col_ids = linspace<float>(w_offset, w_len - 1 + w_offset, w_len);
for (int i = 0; i < h_len; ++i) {
for (int j = 0; j < w_len; ++j) {
img_ids[i * w_len + j][0] = index;
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];
}
}
return img_ids_repeated;
}
__STATIC_INLINE__ std::vector<std::vector<float>> concat_ids(const std::vector<std::vector<float>>& a,
const std::vector<std::vector<float>>& b,
int bs) {
size_t a_len = a.size() / bs;
size_t b_len = b.size() / bs;
std::vector<std::vector<float>> ids(a.size() + b.size(), std::vector<float>(3));
for (int i = 0; i < bs; ++i) {
for (int j = 0; j < a_len; ++j) {
ids[i * (a_len + b_len) + j] = a[i * a_len + j];
}
for (int j = 0; j < b_len; ++j) {
ids[i * (a_len + b_len) + a_len + j] = b[i * b_len + j];
}
}
return ids;
}
__STATIC_INLINE__ std::vector<float> embed_nd(const std::vector<std::vector<float>>& ids,
int bs,
int theta,
const std::vector<int>& axes_dim) {
std::vector<std::vector<float>> trans_ids = transpose(ids);
size_t pos_len = ids.size() / bs;
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);
}
__STATIC_INLINE__ std::vector<std::vector<float>> gen_refs_ids(int patch_size,
int bs,
const std::vector<ggml_tensor*>& ref_latents,
bool increase_ref_index) {
std::vector<std::vector<float>> ids;
uint64_t curr_h_offset = 0;
uint64_t curr_w_offset = 0;
int index = 1;
for (ggml_tensor* ref : ref_latents) {
uint64_t h_offset = 0;
uint64_t w_offset = 0;
if (!increase_ref_index) {
if (ref->ne[1] + curr_h_offset > ref->ne[0] + curr_w_offset) {
w_offset = curr_w_offset;
} else {
h_offset = curr_h_offset;
}
}
auto ref_ids = gen_img_ids(ref->ne[1], ref->ne[0], patch_size, bs, index, h_offset, w_offset);
ids = concat_ids(ids, ref_ids, bs);
if (increase_ref_index) {
index++;
}
curr_h_offset = std::max(curr_h_offset, ref->ne[1] + h_offset);
curr_w_offset = std::max(curr_w_offset, ref->ne[0] + w_offset);
}
return ids;
}
__STATIC_INLINE__ std::vector<std::vector<float>> gen_flux_ids(int h,
int w,
int patch_size,
int bs,
int context_len,
const std::vector<ggml_tensor*>& ref_latents,
bool increase_ref_index) {
auto txt_ids = gen_txt_ids(bs, context_len);
auto img_ids = gen_img_ids(h, w, patch_size, bs);
auto ids = concat_ids(txt_ids, img_ids, bs);
if (ref_latents.size() > 0) {
auto refs_ids = gen_refs_ids(patch_size, bs, ref_latents, increase_ref_index);
ids = concat_ids(ids, refs_ids, bs);
}
return ids;
}
// Generate flux positional embeddings
__STATIC_INLINE__ std::vector<float> gen_flux_pe(int h,
int w,
int patch_size,
int bs,
int context_len,
const std::vector<ggml_tensor*>& ref_latents,
bool increase_ref_index,
int theta,
const std::vector<int>& axes_dim) {
std::vector<std::vector<float>> ids = gen_flux_ids(h, w, patch_size, bs, context_len, ref_latents, increase_ref_index);
return embed_nd(ids, bs, theta, axes_dim);
}
__STATIC_INLINE__ std::vector<std::vector<float>> gen_qwen_image_ids(int h,
int w,
int patch_size,
int bs,
int context_len,
const std::vector<ggml_tensor*>& ref_latents,
bool increase_ref_index) {
int h_len = (h + (patch_size / 2)) / patch_size;
int w_len = (w + (patch_size / 2)) / patch_size;
int txt_id_start = std::max(h_len, w_len);
auto txt_ids = linspace<float>(txt_id_start, context_len + txt_id_start, context_len);
std::vector<std::vector<float>> txt_ids_repeated(bs * context_len, std::vector<float>(3));
for (int i = 0; i < bs; ++i) {
for (int j = 0; j < txt_ids.size(); ++j) {
txt_ids_repeated[i * txt_ids.size() + j] = {txt_ids[j], txt_ids[j], txt_ids[j]};
}
}
auto img_ids = gen_img_ids(h, w, patch_size, bs);
auto ids = concat_ids(txt_ids_repeated, img_ids, bs);
if (ref_latents.size() > 0) {
auto refs_ids = gen_refs_ids(patch_size, bs, ref_latents, increase_ref_index);
ids = concat_ids(ids, refs_ids, bs);
}
return ids;
}
// Generate qwen_image positional embeddings
__STATIC_INLINE__ std::vector<float> gen_qwen_image_pe(int h,
int w,
int patch_size,
int bs,
int context_len,
const std::vector<ggml_tensor*>& ref_latents,
bool increase_ref_index,
int theta,
const std::vector<int>& axes_dim) {
std::vector<std::vector<float>> ids = gen_qwen_image_ids(h, w, patch_size, bs, context_len, ref_latents, increase_ref_index);
return embed_nd(ids, bs, theta, axes_dim);
}
__STATIC_INLINE__ std::vector<std::vector<float>> gen_vid_ids(int t,
int h,
int w,
int pt,
int ph,
int pw,
int bs,
int t_offset = 0,
int h_offset = 0,
int w_offset = 0) {
int t_len = (t + (pt / 2)) / pt;
int h_len = (h + (ph / 2)) / ph;
int w_len = (w + (pw / 2)) / pw;
std::vector<std::vector<float>> vid_ids(t_len * h_len * w_len, std::vector<float>(3, 0.0));
std::vector<float> t_ids = linspace<float>(t_offset, t_len - 1 + t_offset, t_len);
std::vector<float> h_ids = linspace<float>(h_offset, h_len - 1 + h_offset, h_len);
std::vector<float> w_ids = linspace<float>(w_offset, w_len - 1 + w_offset, w_len);
for (int i = 0; i < t_len; ++i) {
for (int j = 0; j < h_len; ++j) {
for (int k = 0; k < w_len; ++k) {
int idx = i * h_len * w_len + j * w_len + k;
vid_ids[idx][0] = t_ids[i];
vid_ids[idx][1] = h_ids[j];
vid_ids[idx][2] = w_ids[k];
}
}
}
std::vector<std::vector<float>> vid_ids_repeated(bs * vid_ids.size(), std::vector<float>(3));
for (int i = 0; i < bs; ++i) {
for (int j = 0; j < vid_ids.size(); ++j) {
vid_ids_repeated[i * vid_ids.size() + j] = vid_ids[j];
}
}
return vid_ids_repeated;
}
// Generate wan positional embeddings
__STATIC_INLINE__ std::vector<float> gen_wan_pe(int t,
int h,
int w,
int pt,
int ph,
int pw,
int bs,
int theta,
const std::vector<int>& axes_dim) {
std::vector<std::vector<float>> ids = gen_vid_ids(t, h, w, pt, ph, pw, bs);
return embed_nd(ids, bs, theta, axes_dim);
}
__STATIC_INLINE__ std::vector<std::vector<float>> gen_qwen2vl_ids(int grid_h,
int grid_w,
int merge_size,
const std::vector<int>& window_index) {
std::vector<std::vector<float>> ids(grid_h * grid_w, std::vector<float>(2, 0.0));
int index = 0;
for (int ih = 0; ih < grid_h; ih += merge_size) {
for (int iw = 0; iw < grid_w; iw += merge_size) {
for (int iy = 0; iy < merge_size; iy++) {
for (int ix = 0; ix < merge_size; ix++) {
int inverse_index = window_index[index / (merge_size * merge_size)];
int i = inverse_index * (merge_size * merge_size) + index % (merge_size * merge_size);
GGML_ASSERT(i < grid_h * grid_w);
ids[i][0] = ih + iy;
ids[i][1] = iw + ix;
index++;
}
}
}
}
return ids;
}
// Generate qwen2vl positional embeddings
__STATIC_INLINE__ std::vector<float> gen_qwen2vl_pe(int grid_h,
int grid_w,
int merge_size,
const std::vector<int>& window_index,
int theta,
const std::vector<int>& axes_dim) {
std::vector<std::vector<float>> ids = gen_qwen2vl_ids(grid_h, grid_w, merge_size, window_index);
return embed_nd(ids, 1, theta, axes_dim);
}
__STATIC_INLINE__ struct ggml_tensor* apply_rope(struct ggml_context* ctx,
struct ggml_tensor* x,
struct ggml_tensor* pe,
bool rope_interleaved = true) {
// x: [N, L, n_head, d_head]
// pe: [L, d_head/2, 2, 2], [[cos, -sin], [sin, cos]]
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]
if (rope_interleaved) {
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]
} else {
x = ggml_reshape_4d(ctx, x, d_head / 2, 2, L, n_head * N); // [N * n_head, L, 2, d_head/2]
x = ggml_cont(ctx, ggml_ext_torch_permute(ctx, x, 0, 2, 3, 1)); // [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]
if (!rope_interleaved) {
x_out = ggml_cont(ctx, ggml_permute(ctx, x_out, 1, 0, 2, 3)); // [N * n_head, L, x, d_head/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(GGMLRunnerContext* ctx,
struct ggml_tensor* q,
struct ggml_tensor* k,
struct ggml_tensor* v,
struct ggml_tensor* pe,
struct ggml_tensor* mask,
float kv_scale = 1.0f,
bool rope_interleaved = true) {
// 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->ggml_ctx, q, pe, rope_interleaved); // [N*n_head, L, d_head]
k = apply_rope(ctx->ggml_ctx, k, pe, rope_interleaved); // [N*n_head, L, d_head]
auto x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, v->ne[1], mask, false, true, ctx->flash_attn_enabled, kv_scale); // [N, L, n_head*d_head]
return x;
}
}; // namespace Rope
#endif // __ROPE_HPP__
Reference in New Issue View Git Blame Copy Permalink