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clip : style changes

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Georgi Gerganov 2024-08-06 11:44:29 +03:00 committed by GitHub
parent 65f7455cea
commit 6e299132e7
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2 changed files with 36 additions and 38 deletions
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73
examples/llava/clip.cpp
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@ -563,18 +563,18 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
const auto & model = ctx->vision_model;
const auto & hparams = model.hparams;
const int image_size = hparams.image_size;
int image_size_width = image_size;
int image_size_height = image_size;
const int image_size = hparams.image_size;
int image_size_width = image_size;
int image_size_height = image_size;
if (ctx->has_minicpmv_projector) {
if(load_image_size==nullptr){
load_image_size= clip_image_size_init();
if (load_image_size == nullptr) {
load_image_size = clip_image_size_init();
}
LOG_TEE("%s: %d %d\n", __func__, load_image_size->width, load_image_size->height);
image_size_width = load_image_size->width;
image_size_height = load_image_size->height;
if (is_inf){
image_size_width = imgs->data->nx;
image_size_width = load_image_size->width;
image_size_height = load_image_size->height;
if (is_inf) {
image_size_width = imgs->data->nx;
image_size_height = imgs->data->ny;
}
}
@ -618,7 +618,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
struct ggml_tensor * embeddings = inp;
struct ggml_tensor * pos_embed;
if(ctx->has_llava_projector){
if (ctx->has_llava_projector) {
// concat class_embeddings and patch_embeddings
if (ctx->has_class_embedding) {
embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size);
@ -638,7 +638,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
embeddings =
ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions));
if(ctx->has_minicpmv_projector){
if (ctx->has_minicpmv_projector) {
int pos_w = image_size_width/patch_size;
int pos_h = image_size_height/patch_size;
pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 4096, pos_w * pos_h, 1);
@ -655,7 +655,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
}
// loop over layers
if (ctx->has_minicpmv_projector){
if (ctx->has_minicpmv_projector) {
n_layer += 1;
}
for (int il = 0; il < n_layer - 1; il++) {
@ -747,8 +747,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
}
// llava projector
if(ctx->has_llava_projector)
{
if (ctx->has_llava_projector) {
embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]);
struct ggml_tensor * patches = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_patches);
@ -770,8 +769,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings);
embeddings = ggml_add(ctx0, embeddings, model.mm_2_b);
}
else if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) {
} else if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) {
embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
// ggml_tensor_printf(embeddings, "mm_0_w",0,true,false);
@ -931,7 +929,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
}
}
// minicpmv projector
else if(ctx->has_minicpmv_projector)
else if (ctx->has_minicpmv_projector)
{
if (ctx->proj_type == PROJECTOR_TYPE_RESAMPLER) {
struct ggml_tensor * q = model.mm_model_query;
@ -939,11 +937,12 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
q = ggml_norm(ctx0, q, eps);
q = ggml_add(ctx0, ggml_mul(ctx0, q, model.mm_model_ln_q_w), model.mm_model_ln_q_b);
}
struct ggml_tensor *k, *v = ggml_mul_mat(ctx0, model.mm_model_kv_proj, embeddings);
struct ggml_tensor * v = ggml_mul_mat(ctx0, model.mm_model_kv_proj, embeddings);
{ // layernorm
v = ggml_norm(ctx0, v, eps);
v = ggml_add(ctx0, ggml_mul(ctx0, v, model.mm_model_ln_kv_w), model.mm_model_ln_kv_b);
}
struct ggml_tensor * k;
{ // position
// q = ggml_add(ctx0, q, model.mm_model_pos_embed);
k = ggml_add(ctx0, v, pos_embed);
@ -1467,7 +1466,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
return new_clip;
}
void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size){
void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size) {
ctx_clip->load_image_size = load_image_size;
}
@ -1839,16 +1838,16 @@ static std::vector<std::vector<clip_image_u8 *>> uhd_slice_image(const clip_imag
LOG_TEE("%s: multiple %d\n", __func__, multiple);
images.push_back(std::vector<clip_image_u8 *>());
if(multiple <= 1){
if (multiple <= 1) {
auto best_size = uhd_find_best_resize(original_size, scale_resolution, patch_size, true);
clip_image_u8 *source_image = clip_image_u8_init();
clip_image_u8 * source_image = clip_image_u8_init();
bicubic_resize(*img, *source_image, best_size.first, best_size.second);
// source_image = image.resize(best_size, Image.Resampling.BICUBIC)
images[images.size()-1].push_back(source_image);
}
else if(multiple > 1){
else if (multiple > 1) {
auto best_size = uhd_find_best_resize(original_size, scale_resolution, patch_size);
clip_image_u8 *source_image = clip_image_u8_init();
clip_image_u8 * source_image = clip_image_u8_init();
bicubic_resize(*img, *source_image, best_size.first, best_size.second);
// source_image = image.copy().resize(best_resize, Image.Resampling.BICUBIC)
LOG_TEE("%s: image_size: %d %d; source_image size: %d %d\n", __func__, img->nx, img->ny, best_size.first, best_size.second);
@ -1858,7 +1857,7 @@ static std::vector<std::vector<clip_image_u8 *>> uhd_slice_image(const clip_imag
LOG_TEE("%s: image_size: %d %d; best_grid: %d %d\n", __func__, img->nx, img->ny, best_grid.first, best_grid.second);
auto refine_size = uhd_get_refine_size(original_size, best_grid, scale_resolution, patch_size, true);
clip_image_u8 *refine_image = clip_image_u8_init();
clip_image_u8 * refine_image = clip_image_u8_init();
bicubic_resize(*img, *refine_image, refine_size.first, refine_size.second);
LOG_TEE("%s: refine_image_size: %d %d; refine_size: %d %d\n", __func__, refine_image->nx, refine_image->ny, refine_size.first, refine_size.second);
@ -1891,7 +1890,7 @@ static std::vector<std::vector<clip_image_u8 *>> uhd_slice_image(const clip_imag
return images;
}
int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip){
int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip) {
const int max_slice_nums=9;
const int scale_resolution=448;
const int original_width = ctx_clip->load_image_size->width;
@ -1906,16 +1905,15 @@ int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip){
// returns the normalized float tensor for llava-1.5, for spatial_unpad with anyres processing for llava-1.6 it returns the normalized image patch tensors as a vector
// res_imgs memory is being allocated here, previous allocations will be freed if found
bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch * res_imgs) {
if(clip_is_minicpmv(ctx)){
if (clip_is_minicpmv(ctx)) {
std::vector<std::vector<clip_image_u8 *>> imgs = uhd_slice_image(img);
res_imgs->size = 0;
for (size_t i = 0; i < imgs.size(); ++i){
for (size_t i = 0; i < imgs.size(); ++i) {
res_imgs->size += imgs[i].size();
}
res_imgs->data = new clip_image_f32[res_imgs->size];
int idx = 0;
for (size_t i = 0; i < imgs.size(); ++i){
for (size_t i = 0; i < imgs.size(); ++i) {
for (size_t j = 0; j < imgs[i].size(); ++j) {
LOG_TEE("%s: %d %d\n", __func__,imgs[i][j]->nx,imgs[i][j]->ny);
clip_image_f32 * res = clip_image_f32_init();
@ -2149,7 +2147,7 @@ int clip_n_patches(const struct clip_ctx * ctx) {
return n_patches;
}
static std::vector<std::vector<std::vector<float>>> get_1d_sincos_pos_embed_from_grid_new(int embed_dim, const std::vector<std::vector<float>>& pos) {
static std::vector<std::vector<std::vector<float>>> get_1d_sincos_pos_embed_from_grid_new(int embed_dim, const std::vector<std::vector<float>> & pos) {
assert(embed_dim % 2 == 0);
int H = pos.size();
int W = pos[0].size();
@ -2173,7 +2171,7 @@ static std::vector<std::vector<std::vector<float>>> get_1d_sincos_pos_embed_from
return emb;
}
static std::vector<std::vector<std::vector<float>>> get_2d_sincos_pos_embed_from_grid(int embed_dim, const std::vector<std::vector<std::vector<float>>>& grid) {
static std::vector<std::vector<std::vector<float>>> get_2d_sincos_pos_embed_from_grid(int embed_dim, const std::vector<std::vector<std::vector<float>>> & grid) {
assert(embed_dim % 2 == 0);
std::vector<std::vector<std::vector<float>>> emb_h = get_1d_sincos_pos_embed_from_grid_new(embed_dim / 2, grid[0]); // (H, W, D/2)
std::vector<std::vector<std::vector<float>>> emb_w = get_1d_sincos_pos_embed_from_grid_new(embed_dim / 2, grid[1]); // (H, W, D/2)
@ -2269,12 +2267,12 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
const auto & model = ctx->vision_model;
const auto & hparams = model.hparams;
const int image_size = hparams.image_size;
int image_size_width = image_size;
int image_size_height = image_size;
const int image_size = hparams.image_size;
int image_size_width = image_size;
int image_size_height = image_size;
if (ctx->has_minicpmv_projector) {
image_size_width = imgs->data[0].nx;;
image_size_height = imgs->data[0].ny;
image_size_width = imgs->data[0].nx;
image_size_height = imgs->data[0].ny;
}
const int patch_size = hparams.patch_size;
const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size));
@ -2343,8 +2341,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
ggml_backend_tensor_set(pos_embed, pos_embed_data, 0, ggml_nbytes(pos_embed));
free(pos_embed_data);
}
}
else{
} else {
{
if (ctx->has_class_embedding) {
struct ggml_tensor * embeddings = ggml_graph_get_tensor(gf, "embeddings");

1
examples/llava/clip.h
View File

@ -30,6 +30,7 @@ struct clip_image_size {
int width;
int height;
};
struct clip_image_u8_batch {
struct clip_image_u8 * data;
size_t size;