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llama.cpp/examples/llava/clip.cpp
Matt Clayton e59ea539b8
llava: Fix cpu-only clip image encoding sefault (#12907) 
* llava: Fix cpu-only clip image encoding

* clip : no smart ptr for ggml_backend_t

* Fix for backend_ptr push_back

---------

Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
2025-04-12 07:29:03 +02:00

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// NOTE: This is modified from clip.cpp only for LLaVA,
// so there might be still unnecessary artifacts hanging around
// I'll gradually clean and extend it
// Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch
#include "clip.h"
#include "clip-impl.h"
#include "ggml.h"
#include "ggml-cpp.h"
#include "ggml-cpu.h"
#include "ggml-alloc.h"
#include "ggml-backend.h"
#include "gguf.h"
#define STB_IMAGE_IMPLEMENTATION
#include "stb_image.h"
#include <cassert>
#include <cmath>
#include <cstdlib>
#include <cstring>
#include <fstream>
#include <map>
#include <regex>
#include <stdexcept>
#include <unordered_set>
#include <vector>
#include <sstream>
#include <cinttypes>
#include <limits>
struct clip_logger_state g_logger_state = {GGML_LOG_LEVEL_CONT, clip_log_callback_default, NULL};
//#define CLIP_DEBUG_FUNCTIONS
#ifdef CLIP_DEBUG_FUNCTIONS
static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::string& filename) {
std::ofstream file(filename, std::ios::binary);
if (!file.is_open()) {
LOG_ERR("Failed to open file for writing: %s\n", filename.c_str());
return;
}
// PPM header: P6 format, width, height, and max color value
file << "P6\n" << img.nx << " " << img.ny << "\n255\n";
// Write pixel data
for (size_t i = 0; i < img.buf.size(); i += 3) {
// PPM expects binary data in RGB format, which matches our image buffer
file.write(reinterpret_cast<const char*>(&img.buf[i]), 3);
}
file.close();
}
static void clip_image_save_to_bmp(const clip_image_u8& img, const std::string& filename) {
std::ofstream file(filename, std::ios::binary);
if (!file.is_open()) {
LOG_ERR("Failed to open file for writing: %s\n", filename.c_str());
return;
}
int fileSize = 54 + 3 * img.nx * img.ny; // File header + info header + pixel data
int bytesPerPixel = 3;
int widthInBytes = img.nx * bytesPerPixel;
int paddingAmount = (4 - (widthInBytes % 4)) % 4;
int stride = widthInBytes + paddingAmount;
// Bitmap file header
unsigned char fileHeader[14] = {
'B','M', // Signature
0,0,0,0, // Image file size in bytes
0,0,0,0, // Reserved
54,0,0,0 // Start of pixel array
};
// Total file size
fileSize = 54 + (stride * img.ny);
fileHeader[2] = (unsigned char)(fileSize);
fileHeader[3] = (unsigned char)(fileSize >> 8);
fileHeader[4] = (unsigned char)(fileSize >> 16);
fileHeader[5] = (unsigned char)(fileSize >> 24);
// Bitmap information header (BITMAPINFOHEADER)
unsigned char infoHeader[40] = {
40,0,0,0, // Size of this header (40 bytes)
0,0,0,0, // Image width
0,0,0,0, // Image height
1,0, // Number of color planes
24,0, // Bits per pixel
0,0,0,0, // No compression
0,0,0,0, // Image size (can be 0 for no compression)
0,0,0,0, // X pixels per meter (not specified)
0,0,0,0, // Y pixels per meter (not specified)
0,0,0,0, // Total colors (color table not used)
0,0,0,0 // Important colors (all are important)
};
// Width and height in the information header
infoHeader[4] = (unsigned char)(img.nx);
infoHeader[5] = (unsigned char)(img.nx >> 8);
infoHeader[6] = (unsigned char)(img.nx >> 16);
infoHeader[7] = (unsigned char)(img.nx >> 24);
infoHeader[8] = (unsigned char)(img.ny);
infoHeader[9] = (unsigned char)(img.ny >> 8);
infoHeader[10] = (unsigned char)(img.ny >> 16);
infoHeader[11] = (unsigned char)(img.ny >> 24);
// Write file headers
file.write(reinterpret_cast<char*>(fileHeader), sizeof(fileHeader));
file.write(reinterpret_cast<char*>(infoHeader), sizeof(infoHeader));
// Pixel data
std::vector<unsigned char> padding(3, 0); // Max padding size to be added to each row
for (int y = img.ny - 1; y >= 0; --y) { // BMP files are stored bottom-to-top
for (int x = 0; x < img.nx; ++x) {
// Each pixel
size_t pixelIndex = (y * img.nx + x) * 3;
unsigned char pixel[3] = {
img.buf[pixelIndex + 2], // BMP stores pixels in BGR format
img.buf[pixelIndex + 1],
img.buf[pixelIndex]
};
file.write(reinterpret_cast<char*>(pixel), 3);
}
// Write padding for the row
file.write(reinterpret_cast<char*>(padding.data()), paddingAmount);
}
file.close();
}
// debug function to convert f32 to u8
static void clip_image_convert_f32_to_u8(const clip_image_f32& src, clip_image_u8& dst) {
dst.nx = src.nx;
dst.ny = src.ny;
dst.buf.resize(3 * src.nx * src.ny);
for (size_t i = 0; i < src.buf.size(); ++i) {
dst.buf[i] = static_cast<uint8_t>(std::min(std::max(int(src.buf[i] * 255.0f), 0), 255));
}
}
#endif
//
// clip layers
//
enum patch_merge_type {
PATCH_MERGE_FLAT,
PATCH_MERGE_SPATIAL_UNPAD,
};
struct clip_hparams {
int32_t image_size;
int32_t patch_size;
int32_t hidden_size;
int32_t n_intermediate;
int32_t projection_dim;
int32_t n_head;
int32_t n_layer;
patch_merge_type mm_patch_merge_type = PATCH_MERGE_FLAT;
float eps;
std::vector<int32_t> image_grid_pinpoints;
int32_t image_crop_resolution;
std::unordered_set<int32_t> vision_feature_layer;
};
struct clip_layer {
// attention
struct ggml_tensor * k_w = nullptr;
struct ggml_tensor * k_b = nullptr;
struct ggml_tensor * q_w = nullptr;
struct ggml_tensor * q_b = nullptr;
struct ggml_tensor * v_w = nullptr;
struct ggml_tensor * v_b = nullptr;
struct ggml_tensor * o_w = nullptr;
struct ggml_tensor * o_b = nullptr;
// layernorm 1
struct ggml_tensor * ln_1_w = nullptr;
struct ggml_tensor * ln_1_b = nullptr;
// ff
struct ggml_tensor * ff_i_w = nullptr;
struct ggml_tensor * ff_i_b = nullptr;
struct ggml_tensor * ff_o_w = nullptr;
struct ggml_tensor * ff_o_b = nullptr;
// layernorm 2
struct ggml_tensor * ln_2_w = nullptr;
struct ggml_tensor * ln_2_b = nullptr;
};
struct clip_vision_model {
struct clip_hparams hparams;
// embeddings
struct ggml_tensor * class_embedding = nullptr;
struct ggml_tensor * patch_embeddings_0 = nullptr;
struct ggml_tensor * patch_embeddings_1 = nullptr; // second Conv2D kernel when we decouple Conv3D along temproal dimension (Qwen2VL)
struct ggml_tensor * patch_bias = nullptr;
struct ggml_tensor * position_embeddings = nullptr;
struct ggml_tensor * pre_ln_w = nullptr;
struct ggml_tensor * pre_ln_b = nullptr;
std::vector<clip_layer> layers;
struct ggml_tensor * post_ln_w;
struct ggml_tensor * post_ln_b;
struct ggml_tensor * projection;
// LLaVA projection
struct ggml_tensor * mm_0_w = nullptr;
struct ggml_tensor * mm_0_b = nullptr;
struct ggml_tensor * mm_2_w = nullptr;
struct ggml_tensor * mm_2_b = nullptr;
struct ggml_tensor * image_newline = nullptr;
// Yi type models with mlp+normalization projection
struct ggml_tensor * mm_1_w = nullptr; // Yi type models have 0, 1, 3, 4
struct ggml_tensor * mm_1_b = nullptr;
struct ggml_tensor * mm_3_w = nullptr;
struct ggml_tensor * mm_3_b = nullptr;
struct ggml_tensor * mm_4_w = nullptr;
struct ggml_tensor * mm_4_b = nullptr;
//GLMV-Edge projection
struct ggml_tensor * mm_model_adapter_conv_w = nullptr;
struct ggml_tensor * mm_model_adapter_conv_b = nullptr;
struct ggml_tensor * boi_w = nullptr;
struct ggml_tensor * eoi_w = nullptr;
// MobileVLM projection
struct ggml_tensor * mm_model_mlp_1_w = nullptr;
struct ggml_tensor * mm_model_mlp_1_b = nullptr;
struct ggml_tensor * mm_model_mlp_3_w = nullptr;
struct ggml_tensor * mm_model_mlp_3_b = nullptr;
struct ggml_tensor * mm_model_block_1_block_0_0_w = nullptr;
struct ggml_tensor * mm_model_block_1_block_0_1_w = nullptr;
struct ggml_tensor * mm_model_block_1_block_0_1_b = nullptr;
struct ggml_tensor * mm_model_block_1_block_1_fc1_w = nullptr;
struct ggml_tensor * mm_model_block_1_block_1_fc1_b = nullptr;
struct ggml_tensor * mm_model_block_1_block_1_fc2_w = nullptr;
struct ggml_tensor * mm_model_block_1_block_1_fc2_b = nullptr;
struct ggml_tensor * mm_model_block_1_block_2_0_w = nullptr;
struct ggml_tensor * mm_model_block_1_block_2_1_w = nullptr;
struct ggml_tensor * mm_model_block_1_block_2_1_b = nullptr;
struct ggml_tensor * mm_model_block_2_block_0_0_w = nullptr;
struct ggml_tensor * mm_model_block_2_block_0_1_w = nullptr;
struct ggml_tensor * mm_model_block_2_block_0_1_b = nullptr;
struct ggml_tensor * mm_model_block_2_block_1_fc1_w = nullptr;
struct ggml_tensor * mm_model_block_2_block_1_fc1_b = nullptr;
struct ggml_tensor * mm_model_block_2_block_1_fc2_w = nullptr;
struct ggml_tensor * mm_model_block_2_block_1_fc2_b = nullptr;
struct ggml_tensor * mm_model_block_2_block_2_0_w = nullptr;
struct ggml_tensor * mm_model_block_2_block_2_1_w = nullptr;
struct ggml_tensor * mm_model_block_2_block_2_1_b = nullptr;
// MobileVLM_V2 projection
struct ggml_tensor * mm_model_mlp_0_w = nullptr;
struct ggml_tensor * mm_model_mlp_0_b = nullptr;
struct ggml_tensor * mm_model_mlp_2_w = nullptr;
struct ggml_tensor * mm_model_mlp_2_b = nullptr;
struct ggml_tensor * mm_model_peg_0_w = nullptr;
struct ggml_tensor * mm_model_peg_0_b = nullptr;
// MINICPMV projection
struct ggml_tensor * mm_model_pos_embed_k = nullptr;
struct ggml_tensor * mm_model_query = nullptr;
struct ggml_tensor * mm_model_proj = nullptr;
struct ggml_tensor * mm_model_kv_proj = nullptr;
struct ggml_tensor * mm_model_attn_q_w = nullptr;
struct ggml_tensor * mm_model_attn_q_b = nullptr;
struct ggml_tensor * mm_model_attn_k_w = nullptr;
struct ggml_tensor * mm_model_attn_k_b = nullptr;
struct ggml_tensor * mm_model_attn_v_w = nullptr;
struct ggml_tensor * mm_model_attn_v_b = nullptr;
struct ggml_tensor * mm_model_attn_o_w = nullptr;
struct ggml_tensor * mm_model_attn_o_b = nullptr;
struct ggml_tensor * mm_model_ln_q_w = nullptr;
struct ggml_tensor * mm_model_ln_q_b = nullptr;
struct ggml_tensor * mm_model_ln_kv_w = nullptr;
struct ggml_tensor * mm_model_ln_kv_b = nullptr;
struct ggml_tensor * mm_model_ln_post_w = nullptr;
struct ggml_tensor * mm_model_ln_post_b = nullptr;
// gemma3
struct ggml_tensor * mm_input_proj_w = nullptr;
struct ggml_tensor * mm_soft_emb_norm_w = nullptr;
};
struct clip_ctx {
bool has_text_encoder = false;
bool has_vision_encoder = false;
bool has_llava_projector = false;
bool has_minicpmv_projector = false;
bool has_glm_projector = false;
bool has_qwen2vl_merger = false;
int minicpmv_version = 2;
struct clip_vision_model vision_model;
projector_type proj_type = PROJECTOR_TYPE_MLP;
int32_t max_feature_layer; // unused in newer models like gemma3
float image_mean[3];
float image_std[3];
bool use_gelu = false;
bool use_silu = false;
gguf_context_ptr ctx_gguf;
ggml_context_ptr ctx_data;
std::vector<uint8_t> buf_compute_meta;
std::vector<ggml_backend_t> backend_ptrs;
std::vector<ggml_backend_buffer_type_t> backend_buft;
ggml_backend_t backend;
ggml_backend_t backend_cpu;
ggml_backend_buffer_ptr buf;
ggml_backend_sched_ptr sched;
clip_image_size load_image_size;
clip_ctx(clip_context_params & ctx_params) {
backend_cpu = ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, nullptr);
backend = ctx_params.use_gpu
? ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_GPU, nullptr)
: nullptr;
if (backend) {
LOG_INF("%s: CLIP using %s backend\n", __func__, ggml_backend_name(backend));
backend_ptrs.push_back(backend);
backend_buft.push_back(ggml_backend_get_default_buffer_type(backend));
} else {
backend = backend_cpu;
LOG_INF("%s: CLIP using CPU backend\n", __func__);
}
backend_ptrs.push_back(backend_cpu);
backend_buft.push_back(ggml_backend_get_default_buffer_type(backend_cpu));
sched.reset(
ggml_backend_sched_new(backend_ptrs.data(), backend_buft.data(), backend_ptrs.size(), 8192, false)
);
}
~clip_ctx() {
ggml_backend_free(backend);
if (backend != backend_cpu) {
ggml_backend_free(backend_cpu);
}
}
};
static ggml_cgraph * clip_image_build_graph_siglip(clip_ctx * ctx, const clip_image_f32_batch & imgs) {
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 patch_size = hparams.patch_size;
const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size));
const int hidden_size = hparams.hidden_size;
const int n_head = hparams.n_head;
const int d_head = hidden_size / n_head;
const int n_layer = hparams.n_layer;
const float eps = hparams.eps;
GGML_ASSERT(imgs.entries.size() == 1); // batch_size == 1
struct ggml_init_params params = {
/*.mem_size =*/ ctx->buf_compute_meta.size(),
/*.mem_buffer =*/ ctx->buf_compute_meta.data(),
/*.no_alloc =*/ true,
};
ggml_context_ptr ctx0_ptr(ggml_init(params));
auto ctx0 = ctx0_ptr.get();
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
// input raw
struct ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, image_size_width, image_size_height, 3);
ggml_set_name(inp_raw, "inp_raw");
ggml_set_input(inp_raw);
struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
inp = ggml_reshape_2d(ctx0, inp, num_patches, hidden_size);
inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp));
inp = ggml_add(ctx0, inp, model.patch_bias);
// position embeddings
struct ggml_tensor * embeddings = ggml_add(ctx0, inp, model.position_embeddings);
// loop over layers
for (int il = 0; il < n_layer; il++) {
struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states
// layernorm1
{
cur = ggml_norm(ctx0, cur, eps);
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_1_w), model.layers[il].ln_1_b);
}
// self-attention
{
struct ggml_tensor * Q =
ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].q_w, cur), model.layers[il].q_b);
Q = ggml_reshape_3d(ctx0, Q, d_head, n_head, num_patches);
Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3));
struct ggml_tensor * K =
ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].k_w, cur), model.layers[il].k_b);
K = ggml_reshape_3d(ctx0, K, d_head, n_head, num_patches);
K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3));
struct ggml_tensor * V =
ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].v_w, cur), model.layers[il].v_b);
V = ggml_reshape_3d(ctx0, V, d_head, n_head, num_patches);
V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3));
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f);
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ);
KQV = ggml_reshape_3d(ctx0, KQV, d_head, num_patches, n_head);
KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
cur = ggml_cont_2d(ctx0, KQV, hidden_size, num_patches);
}
// attention output
cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].o_w, cur), model.layers[il].o_b);
// re-add the layer input, e.g., residual
cur = ggml_add(ctx0, cur, embeddings);
embeddings = cur; // embeddings = residual, cur = hidden_states
// layernorm2
{
cur = ggml_norm(ctx0, cur, eps);
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_2_w), model.layers[il].ln_2_b);
}
cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);
cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b);
// siglip uses gelu
cur = ggml_gelu(ctx0, cur);
cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
cur = ggml_add(ctx0, cur, model.layers[il].ff_o_b);
// residual 2
cur = ggml_add(ctx0, embeddings, cur);
embeddings = cur;
}
// post-layernorm
if (model.post_ln_w) {
embeddings = ggml_norm(ctx0, embeddings, eps);
ggml_set_name(embeddings, "post_ln");
embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b);
}
if (ctx->proj_type == PROJECTOR_TYPE_GEMMA3) {
const int batch_size = 1;
const int mm_tokens_per_image = 256; // default value for gemma3
const int tokens_per_side = sqrt(mm_tokens_per_image);
const int patches_per_image = sqrt(num_patches);
const int kernel_size = patches_per_image / tokens_per_side;
embeddings = ggml_cont(ctx0, ggml_transpose(ctx0, embeddings));
embeddings = ggml_reshape_4d(ctx0, embeddings, patches_per_image, patches_per_image, hidden_size, batch_size);
// doing a pool2d to reduce the number of output tokens to 256
embeddings = ggml_pool_2d(ctx0, embeddings, GGML_OP_POOL_AVG, kernel_size, kernel_size, kernel_size, kernel_size, 0, 0);
embeddings = ggml_reshape_3d(ctx0, embeddings, embeddings->ne[0] * embeddings->ne[0], hidden_size, batch_size);
embeddings = ggml_cont(ctx0, ggml_transpose(ctx0, embeddings));
// apply norm before projection
embeddings = ggml_rms_norm(ctx0, embeddings, eps);
embeddings = ggml_mul(ctx0, embeddings, model.mm_soft_emb_norm_w);
// apply projection
embeddings = ggml_mul_mat(ctx0,
ggml_cont(ctx0, ggml_transpose(ctx0, model.mm_input_proj_w)),
embeddings);
}
// build the graph
ggml_build_forward_expand(gf, embeddings);
return gf;
}
static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_image_f32_batch & imgs, struct clip_image_size load_image_size, bool is_inf = false) {
if (!ctx->has_vision_encoder) {
LOG_ERR("This gguf file seems to have no vision encoder\n");
return nullptr;
}
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;
if (ctx->has_minicpmv_projector) {
LOG_DBG("%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.entries[0]->nx;
image_size_height = imgs.entries[0]->ny;
}
}
else if (ctx->has_qwen2vl_merger) {
// use the image's native resolution when image is avaible
if (is_inf) {
// if (imgs->data->nx && imgs->data->ny) {
image_size_width = imgs.entries[0]->nx;
image_size_height = imgs.entries[0]->ny;
}
}
const int patch_size = hparams.patch_size;
const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size));
const int patches_w = image_size_width / patch_size;
const int patches_h = image_size_height / patch_size;
const int num_positions = num_patches + (model.class_embedding ? 1 : 0);
const int num_position_ids = ctx->has_qwen2vl_merger ? num_positions * 4 : num_positions;
const int hidden_size = hparams.hidden_size;
const int n_head = hparams.n_head;
const int d_head = hidden_size / n_head;
const float eps = hparams.eps;
int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
const int batch_size = imgs.entries.size();
if (ctx->has_llava_projector || ctx->has_minicpmv_projector || ctx->has_glm_projector) {
GGML_ASSERT(batch_size == 1);
}
struct ggml_init_params params = {
/*.mem_size =*/ ctx->buf_compute_meta.size(),
/*.mem_buffer =*/ ctx->buf_compute_meta.data(),
/*.no_alloc =*/ true,
};
ggml_context_ptr ctx0_ptr(ggml_init(params));
auto ctx0 = ctx0_ptr.get();
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
struct ggml_tensor * inp_raw = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, image_size_width, image_size_height, 3, batch_size);
ggml_set_name(inp_raw, "inp_raw");
ggml_set_input(inp_raw);
struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
if (ctx->has_qwen2vl_merger) {
GGML_ASSERT(image_size_width % (patch_size * 2) == 0);
GGML_ASSERT(image_size_height % (patch_size * 2) == 0);
auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
inp = ggml_add(ctx0, inp, inp_1);
inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 2, 0, 3)); // [w, h, c, b] -> [c, w, h, b]
inp = ggml_reshape_4d(
ctx0, inp,
hidden_size * 2, patches_w / 2, patches_h, batch_size);
inp = ggml_reshape_4d(
ctx0, inp,
hidden_size * 2, patches_w / 2, 2, batch_size * (patches_h / 2));
inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 0, 2, 1, 3));
inp = ggml_reshape_3d(
ctx0, inp,
hidden_size, patches_w * patches_h, batch_size);
}
else {
inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size);
inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3));
}
if (model.patch_bias) {
// inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp));
inp = ggml_add(ctx0, inp, model.patch_bias);
}
struct ggml_tensor * embeddings = inp;
struct ggml_tensor * pos_embed = nullptr;
if (ctx->has_llava_projector) {
// concat class_embeddings and patch_embeddings
if (model.class_embedding) {
embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size);
ggml_set_name(embeddings, "embeddings");
ggml_set_input(embeddings);
embeddings = ggml_acc(ctx0, embeddings, model.class_embedding,
embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0);
embeddings = ggml_acc(ctx0, embeddings, inp,
embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]);
}
}
struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids);
ggml_set_name(positions, "positions");
ggml_set_input(positions);
if (!ctx->has_qwen2vl_merger) { // qwen2vl use rope position embedding
embeddings =
ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions));
}
if (ctx->has_minicpmv_projector) {
int pos_w = image_size_width/patch_size;
int pos_h = image_size_height/patch_size;
if (ctx->minicpmv_version == 2) {
pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 4096, pos_w * pos_h, 1);
}
else if (ctx->minicpmv_version == 3) {
pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 3584, pos_w * pos_h, 1);
}
else if (ctx->minicpmv_version == 4) {
pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 3584, pos_w * pos_h, 1);
}
ggml_set_name(pos_embed, "pos_embed");
ggml_set_input(pos_embed);
}
// pre-layernorm
if (model.pre_ln_w) {
embeddings = ggml_norm(ctx0, embeddings, eps);
ggml_set_name(embeddings, "pre_ln");
embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.pre_ln_w), model.pre_ln_b);
}
std::vector<struct ggml_tensor *> embedding_stack;
const auto & vision_feature_layer = hparams.vision_feature_layer;
// loop over layers
for (int il = 0; il < ctx->max_feature_layer; il++) {
struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states
// If this is an embedding feature layer, save the output.
// NOTE: 0 index here refers to the input to the encoder.
if (vision_feature_layer.find(il) != vision_feature_layer.end()) {
embedding_stack.push_back(embeddings);
}
//const size_t nb_q_w = model.layers[il].q_w->nb[0];
// layernorm1
{
cur = ggml_norm(ctx0, cur, eps);
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_1_w),
model.layers[il].ln_1_b);
}
// self-attention
{
struct ggml_tensor * Q =
ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].q_w, cur), model.layers[il].q_b);
Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_positions, batch_size);
if (ctx->has_qwen2vl_merger) {
Q = ggml_rope_multi(
ctx0, Q, positions, nullptr,
d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
}
Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3));
Q = ggml_reshape_3d(ctx0, Q, d_head, num_positions, n_head * batch_size);
struct ggml_tensor * K =
ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].k_w, cur), model.layers[il].k_b);
K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size);
if (ctx->has_qwen2vl_merger) {
K = ggml_rope_multi(
ctx0, K, positions, nullptr,
d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
}
K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3));
K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size);
struct ggml_tensor * V =
ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].v_w, cur), model.layers[il].v_b);
V = ggml_reshape_4d(ctx0, V, d_head, n_head, num_positions, batch_size);
V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3));
V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size);
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f);
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ);
KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_positions, n_head, batch_size);
KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
cur = ggml_cont_3d(ctx0, KQV, hidden_size, num_positions, batch_size);
}
// attention output
cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].o_w, cur), model.layers[il].o_b);
// re-add the layer input, e.g., residual
cur = ggml_add(ctx0, cur, embeddings);
embeddings = cur; // embeddings = residual, cur = hidden_states
// layernorm2
{
cur = ggml_norm(ctx0, cur, eps);
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_2_w), model.layers[il].ln_2_b);
}
cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);
cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b);
if (ctx->use_gelu) {
cur = ggml_gelu_inplace(ctx0, cur);
} else if (ctx->use_silu) {
cur = ggml_silu_inplace(ctx0, cur);
} else {
cur = ggml_gelu_quick_inplace(ctx0, cur);
}
cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
cur = ggml_add(ctx0, cur, model.layers[il].ff_o_b);
// residual 2
cur = ggml_add(ctx0, embeddings, cur);
embeddings = cur;
}
// post-layernorm
if (model.post_ln_w) {
embeddings = ggml_norm(ctx0, embeddings, eps);
ggml_set_name(embeddings, "post_ln");
embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b);
}
// final layer is a vision feature layer
if (vision_feature_layer.find(ctx->max_feature_layer) != vision_feature_layer.end()) {
embedding_stack.push_back(embeddings);
}
// If feature layers are explicitly set, stack them (if we have multiple)
if (!embedding_stack.empty()) {
embeddings = embedding_stack[0];
for (size_t i = 1; i < embedding_stack.size(); i++) {
embeddings = ggml_concat(ctx0, embeddings, embedding_stack[i], 0);
}
}
// 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);
ggml_set_name(patches, "patches");
ggml_set_input(patches);
// shape [1, 576, 1024]
// ne is whcn, ne = [1024, 576, 1, 1]
embeddings = ggml_get_rows(ctx0, embeddings, patches);
// print_tensor_info(embeddings, "embeddings");
// llava projector
if (ctx->proj_type == PROJECTOR_TYPE_MLP) {
embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
embeddings = ggml_gelu(ctx0, embeddings);
if (model.mm_2_w) {
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) {
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);
// First LayerNorm
embeddings = ggml_norm(ctx0, embeddings, eps);
embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_1_w),
model.mm_1_b);
// GELU activation
embeddings = ggml_gelu(ctx0, embeddings);
// Second linear layer
embeddings = ggml_mul_mat(ctx0, model.mm_3_w, embeddings);
embeddings = ggml_add(ctx0, embeddings, model.mm_3_b);
// Second LayerNorm
embeddings = ggml_norm(ctx0, embeddings, eps);
embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_4_w),
model.mm_4_b);
}
else if (ctx->proj_type == PROJECTOR_TYPE_LDP) {
// MobileVLM projector
int n_patch = 24;
struct ggml_tensor * mlp_1 = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w, embeddings);
mlp_1 = ggml_add(ctx0, mlp_1, model.mm_model_mlp_1_b);
mlp_1 = ggml_gelu(ctx0, mlp_1);
struct ggml_tensor * mlp_3 = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, mlp_1);
mlp_3 = ggml_add(ctx0, mlp_3, model.mm_model_mlp_3_b);
// mlp_3 shape = [1, 576, 2048], ne = [2048, 576, 1, 1]
// block 1
struct ggml_tensor * block_1 = nullptr;
{
// transpose from [1, 576, 2048] --> [1, 2048, 576] --> [1, 2048, 24, 24]
mlp_3 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_3, 1, 0, 2, 3));
mlp_3 = ggml_reshape_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]);
// stride = 1, padding = 1, bias is nullptr
block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1);
// layer norm
// // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3));
// block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1]
block_1 = ggml_norm(ctx0, block_1, eps);
block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_0_1_w), model.mm_model_block_1_block_0_1_b);
block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3));
// block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
// hardswish
struct ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1);
block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0);
// block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1]
// pointwise conv
block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]);
block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc1_w, block_1);
block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc1_b);
block_1 = ggml_relu(ctx0, block_1);
block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc2_w, block_1);
block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc2_b);
block_1 = ggml_hardsigmoid(ctx0, block_1);
// block_1_hw shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1], block_1 shape = [1, 2048], ne = [2048, 1, 1, 1]
block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]);
block_1 = ggml_mul(ctx0, block_1_hw, block_1);
int w = block_1->ne[0], h = block_1->ne[1];
block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]);
block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3));
// block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1]
block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_2_0_w, block_1);
block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]);
// block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1]
block_1 = ggml_norm(ctx0, block_1, eps);
block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_2_1_w), model.mm_model_block_1_block_2_1_b);
block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3));
// block1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
// residual
block_1 = ggml_add(ctx0, mlp_3, block_1);
}
// block_2
{
// stride = 2
block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1);
// block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1]
// layer norm
block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3));
// block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1]
block_1 = ggml_norm(ctx0, block_1, eps);
block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_0_1_w), model.mm_model_block_2_block_0_1_b);
block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3));
// block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1]
// hardswish
struct ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1);
// not sure the parameters is right for globalAvgPooling
block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0);
// block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1]
// pointwise conv
block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]);
block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc1_w, block_1);
block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc1_b);
block_1 = ggml_relu(ctx0, block_1);
block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc2_w, block_1);
block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc2_b);
block_1 = ggml_hardsigmoid(ctx0, block_1);
// block_1_hw shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1], block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1]
block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]);
block_1 = ggml_mul(ctx0, block_1_hw, block_1);
int w = block_1->ne[0], h = block_1->ne[1];
block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]);
block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3));
// block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1]
block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_2_0_w, block_1);
block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]);
// block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1]
block_1 = ggml_norm(ctx0, block_1, eps);
block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_2_1_w), model.mm_model_block_2_block_2_1_b);
block_1 = ggml_reshape_3d(ctx0, block_1, block_1->ne[0], block_1->ne[1] * block_1->ne[2], block_1->ne[3]);
// block_1 shape = [1, 144, 2048], ne = [2048, 144, 1]
}
embeddings = block_1;
}
else if (ctx->proj_type == PROJECTOR_TYPE_LDPV2)
{
int n_patch = 24;
struct ggml_tensor * mlp_0 = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings);
mlp_0 = ggml_add(ctx0, mlp_0, model.mm_model_mlp_0_b);
mlp_0 = ggml_gelu(ctx0, mlp_0);
struct ggml_tensor * mlp_2 = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, mlp_0);
mlp_2 = ggml_add(ctx0, mlp_2, model.mm_model_mlp_2_b);
// mlp_2 ne = [2048, 576, 1, 1]
// // AVG Pool Layer 2*2, strides = 2
mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 0, 2, 3));
// mlp_2 ne = [576, 2048, 1, 1]
mlp_2 = ggml_reshape_4d(ctx0, mlp_2, n_patch, n_patch, mlp_2->ne[1], mlp_2->ne[2]);
// mlp_2 ne [24, 24, 2048, 1]
mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0);
// weight ne = [3, 3, 2048, 1]
struct ggml_tensor * peg_0 = ggml_conv_2d_dw(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1);
peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3));
peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b);
mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 2, 0, 3));
peg_0 = ggml_add(ctx0, peg_0, mlp_2);
peg_0 = ggml_reshape_3d(ctx0, peg_0, peg_0->ne[0], peg_0->ne[1] * peg_0->ne[2], peg_0->ne[3]);
embeddings = peg_0;
}
else {
GGML_ABORT("fatal error");
}
}
// minicpmv projector
else if (ctx->has_minicpmv_projector)
{
if (ctx->proj_type == PROJECTOR_TYPE_RESAMPLER) {
struct ggml_tensor * q = model.mm_model_query;
{ // layernorm
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 * 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);
}
{ // attention
int hidden_size = 4096;
const int d_head = 128;
int n_head = hidden_size/d_head;
int num_query = 96;
if (ctx->minicpmv_version == 2) {
hidden_size = 4096;
n_head = hidden_size/d_head;
num_query = 96;
}
else if (ctx->minicpmv_version == 3) {
hidden_size = 3584;
n_head = hidden_size/d_head;
num_query = 64;
}
else if (ctx->minicpmv_version == 4) {
hidden_size = 3584;
n_head = hidden_size/d_head;
num_query = 64;
}
struct ggml_tensor * Q = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_q_w, q), model.mm_model_attn_q_b);
struct ggml_tensor * K = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_k_w, k), model.mm_model_attn_k_b);
struct ggml_tensor * V = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_v_w, v), model.mm_model_attn_v_b);
// permute
Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_query, batch_size);
Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3));
Q = ggml_reshape_3d(ctx0, Q, d_head, num_query, n_head * batch_size);
K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size);
K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3));
K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size);
V = ggml_reshape_4d(ctx0, V, d_head, n_head, num_positions, batch_size);
V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3));
V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size);
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f);
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ);
KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_query, n_head, batch_size);
KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
KQV = ggml_cont_3d(ctx0, KQV, hidden_size, num_query, batch_size);
embeddings = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_o_w, KQV), model.mm_model_attn_o_b);
}
{ // layernorm
embeddings = ggml_norm(ctx0, embeddings, eps);
embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_post_w), model.mm_model_ln_post_b);
}
embeddings = ggml_mul_mat(ctx0, model.mm_model_proj, embeddings);
}
else {
GGML_ASSERT(false);
}
}
// glm projector
else if (ctx->has_glm_projector) {
if (ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) {
size_t gridsz = (size_t)sqrt(embeddings->ne[1]);
embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings,1,0,2,3));
embeddings = ggml_reshape_3d(ctx0, embeddings, gridsz, gridsz, embeddings->ne[1]);
embeddings = ggml_conv_2d(ctx0, model.mm_model_adapter_conv_w, embeddings, 2, 2, 0, 0, 1, 1);
embeddings = ggml_reshape_3d(ctx0, embeddings,embeddings->ne[0]*embeddings->ne[1] , embeddings->ne[2], batch_size);
embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings, 1, 0, 2, 3));
embeddings = ggml_add(ctx0, embeddings, model.mm_model_adapter_conv_b);
//GLU
{
embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings);
embeddings = ggml_norm(ctx0, embeddings, eps);
embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_q_w), model.mm_model_ln_q_b);
embeddings = ggml_gelu_inplace(ctx0, embeddings);
struct ggml_tensor * x = embeddings;
embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, embeddings);
x = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w,x);
embeddings = ggml_silu_inplace(ctx0, embeddings);
embeddings = ggml_mul(ctx0, embeddings,x);
embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, embeddings);
}
} else {
GGML_ABORT("fatal error");
}
}
else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size * 4, num_positions / 4, batch_size);
embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
// GELU activation
embeddings = ggml_gelu(ctx0, embeddings);
// Second linear layer
embeddings = ggml_mul_mat(ctx0, model.mm_1_w, embeddings);
embeddings = ggml_add(ctx0, embeddings, model.mm_1_b);
}
// build the graph
ggml_build_forward_expand(gf, embeddings);
return gf;
}
static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch & imgs, struct clip_image_size load_image_size, bool is_inf = false) {
if (ctx->proj_type == PROJECTOR_TYPE_GEMMA3) {
return clip_image_build_graph_siglip(ctx, imgs);
} else {
// TODO: we should have one build_* function per model
return clip_image_build_graph_legacy(ctx, imgs, load_image_size, is_inf);
}
}
struct clip_model_loader {
ggml_context_ptr ctx_meta;
gguf_context_ptr ctx_gguf;
clip_ctx & ctx_clip;
std::string fname;
size_t model_size; // in bytes
// TODO @ngxson : we should not pass clip_ctx here, it should be clip_vision_model
clip_model_loader(const char * fname, clip_ctx & ctx_clip) : ctx_clip(ctx_clip), fname(fname) {
struct ggml_context * meta = nullptr;
struct gguf_init_params params = {
/*.no_alloc = */ true,
/*.ctx = */ &meta,
};
ctx_gguf = gguf_context_ptr(gguf_init_from_file(fname, params));
if (!ctx_gguf.get()) {
throw std::runtime_error(string_format("%s: failed to load CLIP model from %s. Does this file exist?\n", __func__, fname));
}
ctx_meta.reset(meta);
const int n_tensors = gguf_get_n_tensors(ctx_gguf.get());
// print gguf info
{
std::string name;
get_string(KEY_NAME, name, false);
std::string description;
get_string(KEY_DESCRIPTION, description, false);
LOG_INF("%s: model name: %s\n", __func__, name.c_str());
LOG_INF("%s: description: %s\n", __func__, description.c_str());
LOG_INF("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx_gguf.get()));
LOG_INF("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx_gguf.get()));
LOG_INF("%s: n_tensors: %d\n", __func__, n_tensors);
LOG_INF("%s: n_kv: %d\n", __func__, (int)gguf_get_n_kv(ctx_gguf.get()));
LOG_INF("\n");
}
// tensors
{
for (int i = 0; i < n_tensors; ++i) {
const char * name = gguf_get_tensor_name(ctx_gguf.get(), i);
const size_t offset = gguf_get_tensor_offset(ctx_gguf.get(), i);
enum ggml_type type = gguf_get_tensor_type(ctx_gguf.get(), i);
struct ggml_tensor * cur = ggml_get_tensor(meta, name);
size_t tensor_size = ggml_nbytes(cur);
model_size += tensor_size;
LOG_DBG("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n",
__func__, i, ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_type_name(type));
}
}
}
void load_hparams() {
// projector type
{
std::string proj_type;
get_string(KEY_PROJ_TYPE, proj_type, false);
if (!proj_type.empty()) {
ctx_clip.proj_type = clip_projector_type_from_string(proj_type);
}
if (ctx_clip.proj_type == PROJECTOR_TYPE_UNKNOWN) {
throw std::runtime_error(string_format("%s: unknown projector type: %s\n", __func__, proj_type.c_str()));
}
}
// other hparams
{
get_bool(KEY_HAS_TEXT_ENC, ctx_clip.has_text_encoder, false);
get_bool(KEY_HAS_VIS_ENC, ctx_clip.has_vision_encoder, false);
GGML_ASSERT(ctx_clip.has_vision_encoder);
GGML_ASSERT(!ctx_clip.has_text_encoder);
// legacy keys, use KEY_PROJ_TYPE instead
get_bool(KEY_HAS_LLAVA_PROJ, ctx_clip.has_llava_projector, false);
get_bool(KEY_HAS_MINICPMV_PROJ, ctx_clip.has_minicpmv_projector, false);
get_i32(KEY_MINICPMV_VERSION, ctx_clip.minicpmv_version, false);
get_bool(KEY_HAS_GLM_PROJ, ctx_clip.has_glm_projector, false);
get_bool(KEY_HAS_QWEN2VL_MERGER, ctx_clip.has_qwen2vl_merger, false);
// !!! do NOT extend the list above, use KEY_PROJ_TYPE instead
get_bool(KEY_USE_GELU, ctx_clip.use_gelu, false);
get_bool(KEY_USE_SILU, ctx_clip.use_silu, false);
auto & hparams = ctx_clip.vision_model.hparams;
get_u32(string_format(KEY_N_EMBD, "vision"), hparams.hidden_size);
get_u32(string_format(KEY_N_HEAD, "vision"), hparams.n_head);
get_u32(string_format(KEY_N_FF, "vision"), hparams.n_intermediate);
get_u32(string_format(KEY_N_BLOCK, "vision"), hparams.n_layer);
get_u32(string_format(KEY_PROJ_DIM, "vision"), hparams.projection_dim);
get_f32(string_format(KEY_LAYER_NORM_EPS, "vision"), hparams.eps);
get_u32(KEY_IMAGE_SIZE, hparams.image_size);
get_u32(KEY_PATCH_SIZE, hparams.patch_size);
get_u32(KEY_IMAGE_CROP_RESOLUTION, hparams.image_crop_resolution, false);
get_arr_int(KEY_IMAGE_GRID_PINPOINTS, hparams.image_grid_pinpoints, false);
{
std::string mm_patch_merge_type;
get_string(KEY_MM_PATCH_MERGE_TYPE, mm_patch_merge_type, false);
if (mm_patch_merge_type == "spatial_unpad") {
hparams.mm_patch_merge_type = PATCH_MERGE_SPATIAL_UNPAD;
}
}
{
int idx_mean = gguf_find_key(ctx_gguf.get(), KEY_IMAGE_MEAN);
int idx_std = gguf_find_key(ctx_gguf.get(), KEY_IMAGE_STD);
GGML_ASSERT(idx_mean >= 0 && "image_mean not found");
GGML_ASSERT(idx_std >= 0 && "image_std not found");
const float * mean_data = (const float *) gguf_get_arr_data(ctx_gguf.get(), idx_mean);
const float * std_data = (const float *) gguf_get_arr_data(ctx_gguf.get(), idx_std);
for (int i = 0; i < 3; ++i) {
ctx_clip.image_mean[i] = mean_data[i];
ctx_clip.image_std[i] = std_data[i];
}
}
// Load the vision feature layer indices if they are explicitly provided;
// if multiple vision feature layers are present, the values will be concatenated
// to form the final visual features.
// NOTE: gguf conversions should standardize the values of the vision feature layer to
// be non-negative, since we use -1 to mark values as unset here.
std::vector<int> vision_feature_layer;
get_arr_int(KEY_FEATURE_LAYER, vision_feature_layer, false);
// convert std::vector to std::unordered_set
for (auto & layer : vision_feature_layer) {
hparams.vision_feature_layer.insert(layer);
}
// Calculate the deepest feature layer based on hparams and projector type
ctx_clip.max_feature_layer = get_deepest_feature_layer(&ctx_clip);
LOG_INF("%s: text_encoder: %d\n", __func__, ctx_clip.has_text_encoder);
LOG_INF("%s: vision_encoder: %d\n", __func__, ctx_clip.has_vision_encoder);
LOG_INF("%s: llava_projector: %d\n", __func__, ctx_clip.has_llava_projector);
LOG_INF("%s: minicpmv_projector: %d\n", __func__, ctx_clip.has_minicpmv_projector);
LOG_INF("%s: minicpmv_version: %d\n", __func__, ctx_clip.minicpmv_version);
LOG_INF("%s: glm_projector: %d\n", __func__, ctx_clip.has_glm_projector);
LOG_INF("%s: model size: %.2f MiB\n", __func__, model_size / 1024.0 / 1024.0);
LOG_INF("%s: metadata size: %.2f MiB\n", __func__, ggml_get_mem_size(ctx_meta.get()) / 1024.0 / 1024.0);
}
}
void load_tensors() {
std::map<std::string, size_t> tensor_offset;
std::vector<ggml_tensor *> tensors_to_load;
// get offsets
for (int64_t i = 0; i < gguf_get_n_tensors(ctx_gguf.get()); ++i) {
const char * name = gguf_get_tensor_name(ctx_gguf.get(), i);
tensor_offset[name] = gguf_get_data_offset(ctx_gguf.get()) + gguf_get_tensor_offset(ctx_gguf.get(), i);
}
// create data context
struct ggml_init_params params = {
/*.mem_size =*/ (gguf_get_n_tensors(ctx_gguf.get()) + 1) * ggml_tensor_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
ctx_clip.ctx_data.reset(ggml_init(params));
if (!ctx_clip.ctx_data) {
throw std::runtime_error(string_format("%s: failed to init ggml context\n", __func__));
}
// helper function
auto get_tensor = [&](const std::string & name, bool required = true) {
struct ggml_tensor * cur = ggml_get_tensor(ctx_meta.get(), name.c_str());
if (!cur && required) {
throw std::runtime_error(string_format("%s: unable to find tensor %s\n", __func__, name.c_str()));
}
if (cur) {
tensors_to_load.push_back(cur);
// add tensors to context
struct ggml_tensor * data_tensor = ggml_dup_tensor(ctx_clip.ctx_data.get(), cur);
ggml_set_name(data_tensor, cur->name);
cur = data_tensor;
}
return cur;
};
auto & vision_model = ctx_clip.vision_model;
vision_model.class_embedding = get_tensor(TN_CLASS_EMBD, false);
vision_model.pre_ln_w = get_tensor(string_format(TN_LN_PRE, "v", "weight"), false);
vision_model.pre_ln_b = get_tensor(string_format(TN_LN_PRE, "v", "bias"), false);
vision_model.post_ln_w = get_tensor(string_format(TN_LN_POST, "v", "weight"), false);
vision_model.post_ln_b = get_tensor(string_format(TN_LN_POST, "v", "bias"), false);
vision_model.patch_bias = get_tensor(TN_PATCH_BIAS, false);
vision_model.patch_embeddings_0 = get_tensor(TN_PATCH_EMBD, false);
vision_model.patch_embeddings_1 = get_tensor(TN_PATCH_EMBD_1, false);
if (vision_model.patch_embeddings_1 == nullptr) {
ctx_clip.has_qwen2vl_merger = false;
}
vision_model.position_embeddings = get_tensor(string_format(TN_POS_EMBD, "v"), false);
// layers
vision_model.layers.resize(vision_model.hparams.n_layer);
for (int il = 0; il < vision_model.hparams.n_layer; ++il) {
auto & layer = vision_model.layers[il];
layer.k_w = get_tensor(string_format(TN_ATTN_K, "v", il, "weight"));
layer.q_w = get_tensor(string_format(TN_ATTN_Q, "v", il, "weight"));
layer.v_w = get_tensor(string_format(TN_ATTN_V, "v", il, "weight"));
layer.o_w = get_tensor(string_format(TN_ATTN_OUTPUT, "v", il, "weight"));
layer.ln_1_w = get_tensor(string_format(TN_LN_1, "v", il, "weight"), false);
layer.ln_2_w = get_tensor(string_format(TN_LN_2, "v", il, "weight"), false);
layer.ff_i_w = get_tensor(string_format(TN_FFN_DOWN, "v", il, "weight"));
layer.ff_o_w = get_tensor(string_format(TN_FFN_UP, "v", il, "weight"));
layer.k_b = get_tensor(string_format(TN_ATTN_K, "v", il, "bias"), false);
layer.q_b = get_tensor(string_format(TN_ATTN_Q, "v", il, "bias"), false);
layer.v_b = get_tensor(string_format(TN_ATTN_V, "v", il, "bias"), false);
layer.o_b = get_tensor(string_format(TN_ATTN_OUTPUT, "v", il, "bias"), false);
layer.ln_1_b = get_tensor(string_format(TN_LN_1, "v", il, "bias"), false);
layer.ln_2_b = get_tensor(string_format(TN_LN_2, "v", il, "bias"), false);
layer.ff_i_b = get_tensor(string_format(TN_FFN_DOWN, "v", il, "bias"), false);
layer.ff_o_b = get_tensor(string_format(TN_FFN_UP, "v", il, "bias"), false);
}
switch (ctx_clip.proj_type) {
case PROJECTOR_TYPE_MLP:
case PROJECTOR_TYPE_MLP_NORM:
{
// LLaVA projection
vision_model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight"), false);
vision_model.mm_0_b = get_tensor(string_format(TN_LLAVA_PROJ, 0, "bias"), false);
// Yi-type llava
vision_model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 1, "weight"), false);
vision_model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 1, "bias"), false);
// missing in Yi-type llava
vision_model.mm_2_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight"), false);
vision_model.mm_2_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"), false);
// Yi-type llava
vision_model.mm_3_w = get_tensor(string_format(TN_LLAVA_PROJ, 3, "weight"), false);
vision_model.mm_3_b = get_tensor(string_format(TN_LLAVA_PROJ, 3, "bias"), false);
vision_model.mm_4_w = get_tensor(string_format(TN_LLAVA_PROJ, 4, "weight"), false);
vision_model.mm_4_b = get_tensor(string_format(TN_LLAVA_PROJ, 4, "bias"), false);
if (vision_model.mm_3_w) {
// TODO: this is a hack to support Yi-type llava
ctx_clip.proj_type = PROJECTOR_TYPE_MLP_NORM;
}
vision_model.image_newline = get_tensor(TN_IMAGE_NEWLINE, false);
} break;
case PROJECTOR_TYPE_LDP:
{
// MobileVLM projection
vision_model.mm_model_mlp_1_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 1, "weight"));
vision_model.mm_model_mlp_1_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 1, "bias"));
vision_model.mm_model_mlp_3_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "weight"));
vision_model.mm_model_mlp_3_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "bias"));
vision_model.mm_model_block_1_block_0_0_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 0, "0.weight"));
vision_model.mm_model_block_1_block_0_1_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 0, "1.weight"));
vision_model.mm_model_block_1_block_0_1_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 0, "1.bias"));
vision_model.mm_model_block_1_block_1_fc1_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc1.weight"));
vision_model.mm_model_block_1_block_1_fc1_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc1.bias"));
vision_model.mm_model_block_1_block_1_fc2_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc2.weight"));
vision_model.mm_model_block_1_block_1_fc2_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc2.bias"));
vision_model.mm_model_block_1_block_2_0_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 2, "0.weight"));
vision_model.mm_model_block_1_block_2_1_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 2, "1.weight"));
vision_model.mm_model_block_1_block_2_1_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 2, "1.bias"));
vision_model.mm_model_block_2_block_0_0_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 0, "0.weight"));
vision_model.mm_model_block_2_block_0_1_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 0, "1.weight"));
vision_model.mm_model_block_2_block_0_1_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 0, "1.bias"));
vision_model.mm_model_block_2_block_1_fc1_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc1.weight"));
vision_model.mm_model_block_2_block_1_fc1_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc1.bias"));
vision_model.mm_model_block_2_block_1_fc2_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc2.weight"));
vision_model.mm_model_block_2_block_1_fc2_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc2.bias"));
vision_model.mm_model_block_2_block_2_0_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 2, "0.weight"));
vision_model.mm_model_block_2_block_2_1_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.weight"));
vision_model.mm_model_block_2_block_2_1_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.bias"));
} break;
case PROJECTOR_TYPE_LDPV2:
{
// MobilVLM_V2 projection
vision_model.mm_model_mlp_0_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 0, "weight"));
vision_model.mm_model_mlp_0_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 0, "bias"));
vision_model.mm_model_mlp_2_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 2, "weight"));
vision_model.mm_model_mlp_2_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 2, "bias"));
vision_model.mm_model_peg_0_w = get_tensor(string_format(TN_MVLM_PROJ_PEG, 0, "weight"));
vision_model.mm_model_peg_0_b = get_tensor(string_format(TN_MVLM_PROJ_PEG, 0, "bias"));
} break;
case PROJECTOR_TYPE_RESAMPLER:
{
// vision_model.mm_model_pos_embed = get_tensor(new_clip->ctx_data, TN_MINICPMV_POS_EMBD);
vision_model.mm_model_pos_embed_k = get_tensor(TN_MINICPMV_POS_EMBD_K);
vision_model.mm_model_query = get_tensor(TN_MINICPMV_QUERY);
vision_model.mm_model_proj = get_tensor(TN_MINICPMV_PROJ);
vision_model.mm_model_kv_proj = get_tensor(TN_MINICPMV_KV_PROJ);
vision_model.mm_model_attn_q_w = get_tensor(string_format(TN_MINICPMV_ATTN, "q", "weight"));
vision_model.mm_model_attn_k_w = get_tensor(string_format(TN_MINICPMV_ATTN, "k", "weight"));
vision_model.mm_model_attn_v_w = get_tensor(string_format(TN_MINICPMV_ATTN, "v", "weight"));
vision_model.mm_model_attn_q_b = get_tensor(string_format(TN_MINICPMV_ATTN, "q", "bias"));
vision_model.mm_model_attn_k_b = get_tensor(string_format(TN_MINICPMV_ATTN, "k", "bias"));
vision_model.mm_model_attn_v_b = get_tensor(string_format(TN_MINICPMV_ATTN, "v", "bias"));
vision_model.mm_model_attn_o_w = get_tensor(string_format(TN_MINICPMV_ATTN, "out", "weight"));
vision_model.mm_model_attn_o_b = get_tensor(string_format(TN_MINICPMV_ATTN, "out", "bias"));
vision_model.mm_model_ln_q_w = get_tensor(string_format(TN_MINICPMV_LN, "q", "weight"));
vision_model.mm_model_ln_q_b = get_tensor(string_format(TN_MINICPMV_LN, "q", "bias"));
vision_model.mm_model_ln_kv_w = get_tensor(string_format(TN_MINICPMV_LN, "kv", "weight"));
vision_model.mm_model_ln_kv_b = get_tensor(string_format(TN_MINICPMV_LN, "kv", "bias"));
vision_model.mm_model_ln_post_w = get_tensor(string_format(TN_MINICPMV_LN, "post", "weight"));
vision_model.mm_model_ln_post_b = get_tensor(string_format(TN_MINICPMV_LN, "post", "bias"));
} break;
case PROJECTOR_TYPE_GLM_EDGE:
{
vision_model.mm_model_adapter_conv_w = get_tensor(string_format(TN_GLM_ADAPER_CONV, "weight"));
vision_model.mm_model_adapter_conv_b = get_tensor(string_format(TN_GLM_ADAPER_CONV, "bias"));
vision_model.mm_model_mlp_0_w = get_tensor(string_format(TN_GLM_ADAPTER_LINEAR,"weight"));
vision_model.mm_model_ln_q_w = get_tensor(string_format(TN_GLM_ADAPTER_NORM_1,"weight"));
vision_model.mm_model_ln_q_b = get_tensor(string_format(TN_GLM_ADAPTER_NORM_1,"bias"));
vision_model.mm_model_mlp_1_w = get_tensor(string_format(TN_GLM_ADAPTER_D_H_2_4H,"weight"));
vision_model.mm_model_mlp_2_w = get_tensor(string_format(TN_GLM_ADAPTER_GATE,"weight"));
vision_model.mm_model_mlp_3_w = get_tensor(string_format(TN_GLM_ADAPTER_D_4H_2_H,"weight"));
vision_model.boi_w = get_tensor(TN_GLM_BOI_W);
vision_model.eoi_w = get_tensor(TN_GLM_EOI_W);
} break;
case PROJECTOR_TYPE_MERGER:
{
vision_model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight"));
vision_model.mm_0_b = get_tensor(string_format(TN_LLAVA_PROJ, 0, "bias"));
vision_model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight"));
vision_model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"));
} break;
case PROJECTOR_TYPE_GEMMA3:
{
vision_model.mm_input_proj_w = get_tensor(TN_MM_INP_PROJ);
vision_model.mm_soft_emb_norm_w = get_tensor(TN_MM_SOFT_EMB_N);
} break;
default:
GGML_ASSERT(false && "unknown projector type");
}
// load data
{
std::vector<uint8_t> read_buf;
auto fin = std::ifstream(fname, std::ios::binary);
if (!fin) {
throw std::runtime_error(string_format("%s: failed to open %s\n", __func__, fname.c_str()));
}
// alloc memory and offload data
ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(ctx_clip.backend);
ctx_clip.buf.reset(ggml_backend_alloc_ctx_tensors_from_buft(ctx_clip.ctx_data.get(), buft));
ggml_backend_buffer_set_usage(ctx_clip.buf.get(), GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
for (auto & t : tensors_to_load) {
struct ggml_tensor * cur = ggml_get_tensor(ctx_clip.ctx_data.get(), t->name);
const size_t offset = tensor_offset[t->name];
fin.seekg(offset, std::ios::beg);
if (!fin) {
throw std::runtime_error(string_format("%s: failed to seek for tensor %s\n", __func__, t->name));
}
size_t num_bytes = ggml_nbytes(cur);
if (ggml_backend_buft_is_host(buft)) {
// for the CPU and Metal backend, we can read directly into the tensor
fin.read(reinterpret_cast<char *>(cur->data), num_bytes);
} else {
// read into a temporary buffer first, then copy to device memory
read_buf.resize(num_bytes);
fin.read(reinterpret_cast<char *>(read_buf.data()), num_bytes);
ggml_backend_tensor_set(cur, read_buf.data(), 0, num_bytes);
}
}
fin.close();
LOG_DBG("%s: loaded %zu tensors from %s\n", __func__, tensors_to_load.size(), fname.c_str());
}
}
void alloc_compute_meta() {
ctx_clip.buf_compute_meta.resize(GGML_DEFAULT_GRAPH_SIZE * ggml_tensor_overhead() + ggml_graph_overhead());
// create a fake batch
clip_image_f32_batch batch;
clip_image_f32_ptr img(clip_image_f32_init());
clip_image_size image_size;
image_size.width = clip_get_image_size(&ctx_clip);
image_size.height = clip_get_image_size(&ctx_clip);
int n_patches = clip_get_image_size(&ctx_clip) / image_size.width;
img->nx = n_patches;
img->ny = n_patches;
img->buf.resize(n_patches * image_size.width * image_size.height * 3);
batch.entries.push_back(std::move(img));
ggml_cgraph * gf = clip_image_build_graph(&ctx_clip, batch, image_size, false);
ggml_backend_sched_reserve(ctx_clip.sched.get(), gf);
for (size_t i = 0; i < ctx_clip.backend_ptrs.size(); ++i) {
ggml_backend_t backend = ctx_clip.backend_ptrs[i];
ggml_backend_buffer_type_t buft = ctx_clip.backend_buft[i];
size_t size = ggml_backend_sched_get_buffer_size(ctx_clip.sched.get(), backend);
if (size > 1) {
LOG_INF("%s: %10s compute buffer size = %8.2f MiB\n", __func__,
ggml_backend_buft_name(buft),
size / 1024.0 / 1024.0);
}
}
}
void get_bool(const std::string & key, bool & output, bool required = true) {
const int i = gguf_find_key(ctx_gguf.get(), key.c_str());
if (i < 0) {
if (required) throw std::runtime_error("Key not found: " + key);
return;
}
output = gguf_get_val_bool(ctx_gguf.get(), i);
}
void get_i32(const std::string & key, int & output, bool required = true) {
const int i = gguf_find_key(ctx_gguf.get(), key.c_str());
if (i < 0) {
if (required) throw std::runtime_error("Key not found: " + key);
return;
}
output = gguf_get_val_i32(ctx_gguf.get(), i);
}
void get_u32(const std::string & key, int & output, bool required = true) {
const int i = gguf_find_key(ctx_gguf.get(), key.c_str());
if (i < 0) {
if (required) throw std::runtime_error("Key not found: " + key);
return;
}
output = gguf_get_val_u32(ctx_gguf.get(), i);
}
void get_f32(const std::string & key, float & output, bool required = true) {
const int i = gguf_find_key(ctx_gguf.get(), key.c_str());
if (i < 0) {
if (required) throw std::runtime_error("Key not found: " + key);
return;
}
output = gguf_get_val_f32(ctx_gguf.get(), i);
}
void get_string(const std::string & key, std::string & output, bool required = true) {
const int i = gguf_find_key(ctx_gguf.get(), key.c_str());
if (i < 0) {
if (required) throw std::runtime_error("Key not found: " + key);
return;
}
output = std::string(gguf_get_val_str(ctx_gguf.get(), i));
}
void get_arr_int(const std::string & key, std::vector<int> & output, bool required = true) {
const int i = gguf_find_key(ctx_gguf.get(), key.c_str());
if (i < 0) {
if (required) throw std::runtime_error("Key not found: " + key);
return;
}
int n = gguf_get_arr_n(ctx_gguf.get(), i);
output.resize(n);
const int32_t * values = (const int32_t *)gguf_get_arr_data(ctx_gguf.get(), i);
for (int i = 0; i < n; ++i) {
output[i] = values[i];
}
}
};
// read and create ggml_context containing the tensors and their data
struct clip_ctx * clip_model_load(const char * fname, const int verbosity) {
return clip_init(fname, clip_context_params{
/* use_gpu */ true,
/* verbosity */ static_cast<ggml_log_level>(verbosity),
});
}
struct clip_ctx * clip_init(const char * fname, struct clip_context_params ctx_params) {
g_logger_state.verbosity_thold = ctx_params.verbosity;
clip_ctx * ctx_clip = new clip_ctx(ctx_params);
try {
clip_model_loader loader(fname, *ctx_clip);
loader.load_hparams();
loader.load_tensors();
loader.alloc_compute_meta();
} catch (const std::exception & e) {
LOG_ERR("%s: failed to load model '%s': %s\n", __func__, fname, e.what());
delete ctx_clip;
return nullptr;
}
return ctx_clip;
}
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; // copy
}
struct clip_image_size * clip_get_load_image_size(struct clip_ctx * ctx_clip) {
return &ctx_clip->load_image_size;
}
struct clip_image_size * clip_image_size_init() {
struct clip_image_size * load_image_size = new struct clip_image_size();
load_image_size->width = 448;
load_image_size->height = 448;
return load_image_size;
}
struct clip_image_u8 * clip_image_u8_init() {
return new clip_image_u8();
}
struct clip_image_f32 * clip_image_f32_init() {
return new clip_image_f32();
}
struct clip_image_f32_batch * clip_image_f32_batch_init() {
return new clip_image_f32_batch();
}
unsigned char * clip_image_u8_get_data(struct clip_image_u8 * img, uint32_t * nx, uint32_t * ny) {
if (nx) *nx = img->nx;
if (ny) *ny = img->ny;
return img->buf.data();
}
void clip_image_size_free(struct clip_image_size * load_image_size) {
if (load_image_size == nullptr) {
return;
}
delete load_image_size;
}
void clip_image_u8_free(struct clip_image_u8 * img) { if (img) delete img; }
void clip_image_f32_free(struct clip_image_f32 * img) { if (img) delete img; }
void clip_image_u8_batch_free(struct clip_image_u8_batch * batch) { if (batch) delete batch; }
void clip_image_f32_batch_free(struct clip_image_f32_batch * batch) { if (batch) delete batch; }
size_t clip_image_f32_batch_n_images(const struct clip_image_f32_batch * batch) {
return batch->entries.size();
}
size_t clip_image_f32_batch_nx(const struct clip_image_f32_batch * batch, int idx) {
if (idx < 0 || idx >= (int)batch->entries.size()) {
LOG_ERR("%s: invalid index %d\n", __func__, idx);
return 0;
}
return batch->entries[idx]->nx;
}
size_t clip_image_f32_batch_ny(const struct clip_image_f32_batch * batch, int idx) {
if (idx < 0 || idx >= (int)batch->entries.size()) {
LOG_ERR("%s: invalid index %d\n", __func__, idx);
return 0;
}
return batch->entries[idx]->ny;
}
clip_image_f32 * clip_image_f32_get_img(const struct clip_image_f32_batch * batch, int idx) {
if (idx < 0 || idx >= (int)batch->entries.size()) {
LOG_ERR("%s: invalid index %d\n", __func__, idx);
return nullptr;
}
return batch->entries[idx].get();
}
void clip_build_img_from_pixels(const unsigned char * rgb_pixels, int nx, int ny, clip_image_u8 * img) {
img->nx = nx;
img->ny = ny;
img->buf.resize(3 * nx * ny);
memcpy(img->buf.data(), rgb_pixels, img->buf.size());
}
bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) {
int nx, ny, nc;
auto * data = stbi_load(fname, &nx, &ny, &nc, 3);
if (!data) {
LOG_ERR("%s: failed to load image '%s'\n", __func__, fname);
return false;
}
clip_build_img_from_pixels(data, nx, ny, img);
stbi_image_free(data);
return true;
}
bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img) {
int nx, ny, nc;
auto * data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3);
if (!data) {
LOG_ERR("%s: failed to decode image bytes\n", __func__);
return false;
}
clip_build_img_from_pixels(data, nx, ny, img);
stbi_image_free(data);
return true;
}
// Linear interpolation between two points
inline float clip_lerp(float s, float e, float t) {
return s + (e - s) * t;
}
// Bilinear resize function
static void bilinear_resize(const clip_image_u8& src, clip_image_u8& dst, int target_width, int target_height) {
dst.nx = target_width;
dst.ny = target_height;
dst.buf.resize(3 * target_width * target_height);
float x_ratio = static_cast<float>(src.nx - 1) / target_width;
float y_ratio = static_cast<float>(src.ny - 1) / target_height;
for (int y = 0; y < target_height; y++) {
for (int x = 0; x < target_width; x++) {
float px = x_ratio * x;
float py = y_ratio * y;
int x_floor = static_cast<int>(px);
int y_floor = static_cast<int>(py);
float x_lerp = px - x_floor;
float y_lerp = py - y_floor;
for (int c = 0; c < 3; c++) {
float top = clip_lerp(
static_cast<float>(src.buf[3 * (y_floor * src.nx + x_floor) + c]),
static_cast<float>(src.buf[3 * (y_floor * src.nx + (x_floor + 1)) + c]),
x_lerp
);
float bottom = clip_lerp(
static_cast<float>(src.buf[3 * ((y_floor + 1) * src.nx + x_floor) + c]),
static_cast<float>(src.buf[3 * ((y_floor + 1) * src.nx + (x_floor + 1)) + c]),
x_lerp
);
dst.buf[3 * (y * target_width + x) + c] = static_cast<uint8_t>(clip_lerp(top, bottom, y_lerp));
}
}
}
}
// Normalize image to float32 - careful with pytorch .to(model.device, dtype=torch.float16) - this sometimes reduces precision (32>16>32), sometimes not
static void normalize_image_u8_to_f32(const clip_image_u8 & src, clip_image_f32 & dst, const float mean[3], const float std[3]) {
dst.nx = src.nx;
dst.ny = src.ny;
dst.buf.resize(src.buf.size());
// TODO @ngxson : seems like this could be done more efficiently on cgraph
for (size_t i = 0; i < src.buf.size(); ++i) {
int c = i % 3; // rgb
dst.buf[i] = (static_cast<float>(src.buf[i]) / 255.0f - mean[c]) / std[c];
}
}
inline int clip(int x, int lower, int upper) {
return std::max(lower, std::min(x, upper));
}
static bool bicubic_resize(const clip_image_u8 & img, clip_image_u8 & dst, int target_width, int target_height) {
const int nx = img.nx;
const int ny = img.ny;
dst.nx = target_width;
dst.ny = target_height;
dst.buf.resize(3 * target_width * target_height);
float Cc;
float C[5];
float d0, d2, d3, a0, a1, a2, a3;
int i, j, k, jj;
int x, y;
float dx, dy;
float tx, ty;
tx = (float)nx / (float)target_width;
ty = (float)ny / (float)target_height;
// Bicubic interpolation; adapted from ViT.cpp, inspired from :
// -> https://github.com/yglukhov/bicubic-interpolation-image-processing/blob/master/libimage.c#L36
// -> https://en.wikipedia.org/wiki/Bicubic_interpolation
for (i = 0; i < target_height; i++) {
for (j = 0; j < target_width; j++) {
x = (int)(tx * j);
y = (int)(ty * i);
dx = tx * j - x;
dy = ty * i - y;
for (k = 0; k < 3; k++) {
for (jj = 0; jj <= 3; jj++) {
d0 = img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x - 1, 0, nx - 1)) * 3 + k] - img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x, 0, nx - 1)) * 3 + k];
d2 = img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x + 1, 0, nx - 1)) * 3 + k] - img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x, 0, nx - 1)) * 3 + k];
d3 = img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x + 2, 0, nx - 1)) * 3 + k] - img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x, 0, nx - 1)) * 3 + k];
a0 = img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x, 0, nx - 1)) * 3 + k];
a1 = -1.0 / 3 * d0 + d2 - 1.0 / 6 * d3;
a2 = 1.0 / 2 * d0 + 1.0 / 2 * d2;
a3 = -1.0 / 6 * d0 - 1.0 / 2 * d2 + 1.0 / 6 * d3;
C[jj] = a0 + a1 * dx + a2 * dx * dx + a3 * dx * dx * dx;
d0 = C[0] - C[1];
d2 = C[2] - C[1];
d3 = C[3] - C[1];
a0 = C[1];
a1 = -1.0 / 3 * d0 + d2 - 1.0 / 6 * d3;
a2 = 1.0 / 2 * d0 + 1.0 / 2 * d2;
a3 = -1.0 / 6 * d0 - 1.0 / 2 * d2 + 1.0 / 6 * d3;
Cc = a0 + a1 * dy + a2 * dy * dy + a3 * dy * dy * dy;
const uint8_t Cc2 = std::min(std::max(std::round(Cc), 0.0f), 255.0f);
dst.buf[(i * target_width + j) * 3 + k] = float(Cc2);
}
}
}
}
return true;
}
// llava-1.6 type of resize_and_pad (black)
static void resize_and_pad_image(const clip_image_u8& image, clip_image_u8 &image_output, const std::pair<int, int>& target_resolution) {
int target_width = target_resolution.first;
int target_height = target_resolution.second;
float scale_w = static_cast<float>(target_width) / image.nx;
float scale_h = static_cast<float>(target_height) / image.ny;
int new_width, new_height;
if (scale_w < scale_h) {
new_width = target_width;
new_height = std::min(static_cast<int>(std::ceil(image.ny * scale_w)), target_height);
} else {
new_height = target_height;
new_width = std::min(static_cast<int>(std::ceil(image.nx * scale_h)), target_width);
}
clip_image_u8 resized_image;
// bilinear_resize(image, resized_image, new_width, new_height);
bicubic_resize(image, resized_image, new_width, new_height);
clip_image_u8 padded_image;
padded_image.nx = target_width;
padded_image.ny = target_height;
padded_image.buf.resize(3 * target_width * target_height, 0); // Initialize with black
// Calculate padding offsets
int pad_x = (target_width - new_width) / 2;
int pad_y = (target_height - new_height) / 2;
// Copy the resized image into the center of the padded buffer
for (int y = 0; y < new_height; ++y) {
for (int x = 0; x < new_width; ++x) {
for (int c = 0; c < 3; ++c) {
padded_image.buf[3 * ((y + pad_y) * target_width + (x + pad_x)) + c] = resized_image.buf[3 * (y * new_width + x) + c];
}
}
}
image_output = std::move(padded_image);
}
/**
* Selects the best resolution from a list of possible resolutions based on the original size.
*
* @param original_size The original size of the image in the format (width, height).
* @param possible_resolutions A list of possible resolutions in the format [(width1, height1), (width2, height2), ...].
* @return The best fit resolution in the format (width, height).
*/
static std::pair<int, int> select_best_resolution(const std::pair<int, int> & original_size, const std::vector<std::pair<int, int>> & possible_resolutions) {
int original_width = original_size.first;
int original_height = original_size.second;
std::pair<int, int> best_fit;
int max_effective_resolution = 0;
int min_wasted_resolution = std::numeric_limits<int>::max();
for (const auto& resolution : possible_resolutions) {
int width = resolution.first;
int height = resolution.second;
float scale = std::min(static_cast<float>(width) / original_width, static_cast<float>(height) / original_height);
int downscaled_width = static_cast<int>(original_width * scale);
int downscaled_height = static_cast<int>(original_height * scale);
int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
int wasted_resolution = (width * height) - effective_resolution;
// LOG_INF("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
max_effective_resolution = effective_resolution;
min_wasted_resolution = wasted_resolution;
best_fit = resolution;
}
}
return best_fit;
}
static std::vector<clip_image_u8_ptr> divide_to_patches_u8(const clip_image_u8 & image, int patch_size) {
std::vector<clip_image_u8_ptr> patches;
int width = image.nx;
int height = image.ny;
for (int i = 0; i < height; i += patch_size) {
for (int j = 0; j < width; j += patch_size) {
clip_image_u8_ptr patch(clip_image_u8_init());
patch->nx = std::min(patch_size, width - j);
patch->ny = std::min(patch_size, height - i);
patch->buf.resize(3 * patch->nx * patch->ny);
for (int y = 0; y < patch->ny; ++y) {
for (int x = 0; x < patch->nx; ++x) {
for (int c = 0; c < 3; ++c) {
patch->buf[3 * (y * patch->nx + x) + c] = image.buf[3 * ((i + y) * width + (j + x)) + c];
}
}
}
patches.push_back(std::move(patch));
}
}
return patches;
}
static int ensure_divide(int length, int patch_size) {
return std::max(static_cast<int>(std::round(static_cast<float>(length) / patch_size) * patch_size), patch_size);
}
static std::pair<int, int> uhd_find_best_resize(std::pair<int, int> original_size, int scale_resolution, int patch_size, bool allow_upscale = false) {
int width = original_size.first;
int height = original_size.second;
if ((width * height > scale_resolution * scale_resolution) || allow_upscale) {
float r = static_cast<float>(width) / height;
height = static_cast<int>(scale_resolution / std::sqrt(r));
width = static_cast<int>(height * r);
}
int best_width = ensure_divide(width, patch_size);
int best_height = ensure_divide(height, patch_size);
return std::make_pair(best_width, best_height);
}
static std::pair<int, int> uhd_get_refine_size(std::pair<int, int> original_size, std::pair<int, int> grid, int scale_resolution, int patch_size, bool allow_upscale = false) {
int width, height;
std::tie(width, height) = original_size;
int grid_x, grid_y;
std::tie(grid_x, grid_y) = grid;
int refine_width = ensure_divide(width, grid_x);
int refine_height = ensure_divide(height, grid_y);
int grid_width = refine_width / grid_x;
int grid_height = refine_height / grid_y;
// auto best_grid_size = find_best_resize(std::make_tuple(grid_width, grid_height), scale_resolution, patch_size, allow_upscale); (old line)
auto best_grid_size = uhd_find_best_resize(std::make_pair(grid_width, grid_height), scale_resolution, patch_size, allow_upscale); // (new line) => fixes conversion for make_tuple to make_pair
int best_grid_width, best_grid_height;
std::tie(best_grid_width, best_grid_height) = best_grid_size;
// std::pair<int, int> refine_size = std::make_tuple(best_grid_width * grid_x, best_grid_height * grid_y); (old line)
std::pair<int, int> refine_size = std::make_pair(best_grid_width * grid_x, best_grid_height * grid_y); // (new line)
return refine_size;
}
static std::pair<int, int> uhd_best_grid(const int max_slice_nums, const int multiple, const float log_ratio) {
std::vector<int> candidate_split_grids_nums;
for (int i : {multiple - 1, multiple, multiple + 1}) {
if (i == 1 || i > max_slice_nums) {
continue;
}
candidate_split_grids_nums.push_back(i);
}
std::vector<std::pair<int, int>> candidate_grids;
for (int split_grids_nums : candidate_split_grids_nums) {
int m = 1;
while (m <= split_grids_nums) {
if (split_grids_nums % m == 0) {
candidate_grids.emplace_back(m, split_grids_nums / m);
}
++m;
}
}
std::pair<int, int> best_grid{1, 1};
float min_error = std::numeric_limits<float>::infinity();
for (const auto& grid : candidate_grids) {
float error = std::abs(log_ratio - std::log(1.0 * grid.first / grid.second));
if (error < min_error) {
best_grid = grid;
min_error = error;
}
}
return best_grid;
}
// inspired from LLaVA-UHD:
// -> https://arxiv.org/pdf/2403.11703
// -> https://github.com/thunlp/LLaVA-UHD
// -> https://github.com/thunlp/LLaVA-UHD/blob/302301bc2175f7e717fb8548516188e89f649753/llava_uhd/train/llava-uhd/slice_logic.py#L118
static std::vector<std::vector<clip_image_u8_ptr>> uhd_slice_image(const clip_image_u8 * img, const int max_slice_nums=9, const int scale_resolution=448, const int patch_size=14) {
const std::pair<int, int> original_size={img->nx,img->ny};
const int original_width = img->nx;
const int original_height = img->ny;
const float log_ratio = log(1.0*original_width/original_height);
const float ratio = 1.0 * original_width * original_height/ (scale_resolution * scale_resolution);
const int multiple = fmin(ceil(ratio), max_slice_nums);
std::vector<std::vector<clip_image_u8_ptr>> images;
LOG_DBG("%s: multiple %d\n", __func__, multiple);
images.push_back(std::vector<clip_image_u8_ptr>());
if (multiple <= 1) {
auto best_size = uhd_find_best_resize(original_size, scale_resolution, patch_size, true);
clip_image_u8_ptr 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.back().push_back(std::move(source_image));
}
else if (multiple > 1) {
auto best_size = uhd_find_best_resize(original_size, scale_resolution, patch_size);
clip_image_u8_ptr 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_DBG("%s: image_size: %d %d; source_image size: %d %d\n", __func__, img->nx, img->ny, best_size.first, best_size.second);
images.back().push_back(std::move(source_image));
std::pair<int, int> best_grid = uhd_best_grid(max_slice_nums, multiple, log_ratio);
LOG_DBG("%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_ptr refine_image(clip_image_u8_init());
bicubic_resize(*img, *refine_image, refine_size.first, refine_size.second);
LOG_DBG("%s: refine_image_size: %d %d; refine_size: %d %d\n", __func__, refine_image->nx, refine_image->ny, refine_size.first, refine_size.second);
// split_to_patches
int width = refine_image->nx;
int height = refine_image->ny;
int grid_x = int(width / best_grid.first);
int grid_y = int(height / best_grid.second);
for (int patches_i = 0, ic = 0; patches_i < height && ic < best_grid.second; patches_i += grid_y, ic += 1){
images.push_back(std::vector<clip_image_u8_ptr>());
for(int patches_j = 0, jc = 0; patches_j < width && jc < best_grid.first; patches_j += grid_x, jc += 1){
clip_image_u8_ptr patch(clip_image_u8_init());
patch->nx = grid_x;
patch->ny = grid_y;
patch->buf.resize(3 * patch->nx * patch->ny);
for (int y = patches_i; y < patches_i + grid_y; ++y) {
for (int x = patches_j; x < patches_j + grid_x; ++x) {
const int i = 3 * (y * refine_image->nx + x);
const int j = 3 * ((y-patches_i) * patch->nx + (x-patches_j));
patch->buf[j] = refine_image->buf[i];
patch->buf[j+1] = refine_image->buf[i+1];
patch->buf[j+2] = refine_image->buf[i+2];
}
}
images.back().push_back(std::move(patch));
}
}
}
return images;
}
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;
const int original_height = ctx_clip->load_image_size.height;
const float log_ratio = log(1.0*original_width/original_height);
const float ratio = 1.0 * original_width * original_height/ (scale_resolution * scale_resolution);
const int multiple = fmin(ceil(ratio), max_slice_nums);
std::pair<int, int> best_grid = uhd_best_grid(max_slice_nums, multiple, log_ratio);
return best_grid.first;
}
// 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, struct clip_image_f32_batch * res_imgs) {
if (clip_is_minicpmv(ctx)) {
int max_slice_nums = 9;
std::vector<std::vector<clip_image_u8_ptr>> imgs = uhd_slice_image(img, max_slice_nums);
for (size_t i = 0; i < imgs.size(); ++i) {
for (size_t j = 0; j < imgs[i].size(); ++j) {
LOG_DBG("%s: %d %d\n", __func__,imgs[i][j]->nx,imgs[i][j]->ny);
clip_image_f32_ptr res(clip_image_f32_init());
normalize_image_u8_to_f32(*imgs[i][j], *res, ctx->image_mean, ctx->image_std);
res_imgs->entries.push_back(std::move(res));
}
}
return true;
}
else if (ctx->has_qwen2vl_merger) {
clip_image_u8 resized;
auto patch_size = clip_get_patch_size(ctx) * 2;
int nx = ceil((float)img->nx / patch_size) * patch_size;
int ny = ceil((float)img->ny / patch_size) * patch_size;
bicubic_resize(*img, resized, nx, ny);
clip_image_f32_ptr img_f32(clip_image_f32_init());
// clip_image_f32_ptr res(clip_image_f32_init());
normalize_image_u8_to_f32(resized, *img_f32, ctx->image_mean, ctx->image_std);
// res_imgs->data[0] = *res;
res_imgs->entries.push_back(std::move(img_f32));
return true;
}
if (ctx->has_glm_projector || ctx->proj_type == PROJECTOR_TYPE_GEMMA3) {
clip_image_u8 resized_image;
int32_t sz=ctx->vision_model.hparams.image_size;
bicubic_resize(*img, resized_image,sz,sz);
clip_image_f32_ptr img_f32(clip_image_f32_init());
//clip_image_save_to_bmp(resized_image, "resized.bmp");
normalize_image_u8_to_f32(resized_image, *img_f32, ctx->image_mean, ctx->image_std);
res_imgs->entries.push_back(std::move(img_f32));
return true;
}
bool pad_to_square = true;
if (!ctx->has_vision_encoder) {
LOG_ERR("%s: This gguf file seems to have no vision encoder\n", __func__);
return false;
}
auto & params = ctx->vision_model.hparams;
// The model config actually contains all we need to decide on how to preprocess, here we automatically switch to the new llava-1.6 preprocessing
if (params.mm_patch_merge_type == PATCH_MERGE_SPATIAL_UNPAD) {
pad_to_square = false;
}
// free the previous res_imgs if any set
res_imgs->entries.clear();
// the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104)
// see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156
clip_image_u8_ptr temp(clip_image_u8_init()); // we will keep the input image data here temporarily
if (pad_to_square && img->nx != img->ny) {
int longer_side = std::max(img->nx, img->ny);
temp->nx = longer_side;
temp->ny = longer_side;
temp->buf.resize(3 * longer_side * longer_side);
const uint8_t bc[3] = {122, 116, 104}; // background color in RGB from LLaVA (this is the mean rgb color * 255)
// fill with background color
for (size_t i = 0; i < temp->buf.size(); i++) {
temp->buf[i] = bc[i % 3];
}
// copy from the input image
for (int y = 0; y < img->ny; y++) {
for (int x = 0; x < img->nx; x++) {
const int i = 3 * (y * img->nx + x);
const int j = 3 * (y * temp->nx + x);
temp->buf[j] = img->buf[i];
temp->buf[j+1] = img->buf[i+1];
temp->buf[j+2] = img->buf[i+2];
}
}
} else {
if (!params.image_grid_pinpoints.empty()) {
// "spatial_unpad" with "anyres" processing for llava-1.6
std::vector<std::pair<int, int>> possible_resolutions;
for (size_t i = 0; i < params.image_grid_pinpoints.size(); i+=2) {
possible_resolutions.push_back({params.image_grid_pinpoints[i], params.image_grid_pinpoints[i+1]});
}
std::pair<int, int> best_resolution = select_best_resolution({img->nx, img->ny}, possible_resolutions);
// clip_image_save_to_bmp(*img, "input.bmp");
resize_and_pad_image(*img, *temp, best_resolution); // we do not pad with mean-bg color anymore in llava-1.6
// clip_image_save_to_bmp(*temp, "resized.bmp");
// visually verify normalized image:
// normalize_image_u8_to_f32(*temp, *res, ctx->image_mean, ctx->image_std);
// {
// clip_image_u8 * temp2 = clip_image_u8_init();
// clip_image_convert_f32_to_u8(*res, *temp2);
// clip_image_save_to_bmp(*temp2, "resized_normalized_f32.bmp");
// clip_image_u8_free(temp2);
// }
std::vector<clip_image_u8_ptr> patches = divide_to_patches_u8(*temp, params.image_size); // prepare spatial sorted main patches of image_size each (336 in llava-1.6)
clip_image_u8_ptr image_original_resize(clip_image_u8_init());
// bilinear_resize(*img, *image_original_resize, params.image_size, params.image_size); // in python this is "shortest_edge", but all CLIP are square
bicubic_resize(*img, *image_original_resize, params.image_size, params.image_size); // in python this is "shortest_edge", but all CLIP are square
patches.insert(patches.begin(), std::move(image_original_resize));
for (auto & patch : patches) {
clip_image_f32_ptr res(clip_image_f32_init());
normalize_image_u8_to_f32(*patch, *res, ctx->image_mean, ctx->image_std);
res_imgs->entries.push_back(std::move(res));
}
return true;
} else {
temp->nx = img->nx;
temp->ny = img->ny;
temp->buf.resize(img->buf.size());
memcpy(temp->buf.data(), img->buf.data(), temp->buf.size());
}
}
const int nx = temp->nx;
const int ny = temp->ny;
// clip_image_save_to_bmp(*temp, "resized_vanilla.bmp");
const int nx2 = ctx->vision_model.hparams.image_size;
const int ny2 = ctx->vision_model.hparams.image_size;
clip_image_f32_ptr res(clip_image_f32_init());
res->nx = nx2;
res->ny = ny2;
res->buf.resize(3 * nx2 * ny2);
const float scale = std::max(nx, ny) / (float)ctx->vision_model.hparams.image_size;
const int nx3 = int(nx / scale + 0.5f);
const int ny3 = int(ny / scale + 0.5f);
const auto & m3 = ctx->image_mean; // {0.48145466f, 0.4578275f, 0.40821073f};
const auto & s3 = ctx->image_std; // {0.26862954f, 0.26130258f, 0.27577711f};
for (int y = 0; y < ny3; y++) {
for (int x = 0; x < nx3; x++) {
for (int c = 0; c < 3; c++) {
// linear interpolation
const float sx = (x + 0.5f) * scale - 0.5f;
const float sy = (y + 0.5f) * scale - 0.5f;
const int x0 = std::max(0, (int)std::floor(sx));
const int y0 = std::max(0, (int)std::floor(sy));
const int x1 = std::min(x0 + 1, nx - 1);
const int y1 = std::min(y0 + 1, ny - 1);
const float dx = sx - x0;
const float dy = sy - y0;
const int j00 = 3 * (y0 * nx + x0) + c;
const int j01 = 3 * (y0 * nx + x1) + c;
const int j10 = 3 * (y1 * nx + x0) + c;
const int j11 = 3 * (y1 * nx + x1) + c;
const float v00 = temp->buf[j00];
const float v01 = temp->buf[j01];
const float v10 = temp->buf[j10];
const float v11 = temp->buf[j11];
const float v0 = v00 * (1.0f - dx) + v01 * dx;
const float v1 = v10 * (1.0f - dx) + v11 * dx;
const float v = v0 * (1.0f - dy) + v1 * dy;
const uint8_t v2 = std::min(std::max(std::round(v), 0.0f), 255.0f);
const int i = 3 * (y * nx3 + x) + c;
res->buf[i] = ((float(v2) / 255.0f) - m3[c]) / s3[c];
}
}
}
// {
// clip_image_u8 * temp2 = clip_image_u8_init();
// clip_image_convert_f32_to_u8(*res, *temp2);
// clip_image_save_to_bmp(*temp2, "resized_normalized_f32_vanilla.bmp");
// clip_image_u8_free(temp2);
// }
// res_imgs.push_back(res);
res_imgs->entries.push_back(std::move(res));
return true;
}
ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx) {
return ctx->vision_model.image_newline;
}
void clip_free(clip_ctx * ctx) {
if (ctx == nullptr) {
return;
}
delete ctx;
}
size_t clip_embd_nbytes(const struct clip_ctx * ctx) {
int extra_tokens = ctx->has_glm_projector ? 2 : 0;
return (clip_n_patches(ctx) + extra_tokens) * clip_n_mmproj_embd(ctx) * sizeof(float);
}
size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w) {
clip_image_f32 img;
img.nx = img_w;
img.ny = img_h;
return clip_n_patches_by_img(ctx, &img) * clip_n_mmproj_embd(ctx) * sizeof(float);
}
int32_t clip_get_image_size(const struct clip_ctx * ctx) {
return ctx->vision_model.hparams.image_size;
}
int32_t clip_get_patch_size(const struct clip_ctx * ctx) {
return ctx->vision_model.hparams.patch_size;
}
int32_t clip_get_hidden_size(const struct clip_ctx * ctx) {
return ctx->vision_model.hparams.hidden_size;
}
const char * clip_patch_merge_type(const struct clip_ctx * ctx) {
return ctx->vision_model.hparams.mm_patch_merge_type == PATCH_MERGE_SPATIAL_UNPAD ? "spatial_unpad" : "flat";
}
const int32_t * clip_image_grid(const struct clip_ctx * ctx) {
if (ctx->vision_model.hparams.image_grid_pinpoints.size()) {
return &ctx->vision_model.hparams.image_grid_pinpoints.front();
}
return nullptr;
}
size_t get_clip_image_grid_size(const struct clip_ctx * ctx) {
return ctx->vision_model.hparams.image_grid_pinpoints.size();
}
int clip_n_patches(const struct clip_ctx * ctx) {
clip_image_f32 img;
img.nx = ctx->vision_model.hparams.image_size;
img.ny = ctx->vision_model.hparams.image_size;
return clip_n_patches_by_img(ctx, &img);
}
int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * img) {
const auto & params = ctx->vision_model.hparams;
int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2 || ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) {
n_patches /= 4;
} else if (ctx->proj_type == PROJECTOR_TYPE_RESAMPLER) {
if (ctx->minicpmv_version == 2) {
n_patches = 96;
}
else if (ctx->minicpmv_version == 3) {
n_patches = 64;
}
else if (ctx->minicpmv_version == 4) {
n_patches = 64;
}
} else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
int patch_size = params.patch_size * 2;
int x_patch = img->nx / patch_size + (int)(img->nx % patch_size > 0);
int y_patch = img->ny / patch_size + (int)(img->ny % patch_size > 0);
n_patches = x_patch * y_patch;
} else if (ctx->proj_type == PROJECTOR_TYPE_GEMMA3) {
n_patches = 256;
}
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) {
assert(embed_dim % 2 == 0);
int H = pos.size();
int W = pos[0].size();
std::vector<float> omega(embed_dim / 2);
for (int i = 0; i < embed_dim / 2; ++i) {
omega[i] = 1.0 / pow(10000.0, static_cast<float>(i) / (embed_dim / 2));
}
std::vector<std::vector<std::vector<float>>> emb(H, std::vector<std::vector<float>>(W, std::vector<float>(embed_dim)));
for (int h = 0; h < H; ++h) {
for (int w = 0; w < W; ++w) {
for (int d = 0; d < embed_dim / 2; ++d) {
float out_value = pos[h][w] * omega[d];
emb[h][w][d] = sin(out_value);
emb[h][w][d + embed_dim / 2] = cos(out_value);
}
}
}
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) {
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)
int H = emb_h.size();
int W = emb_h[0].size();
std::vector<std::vector<std::vector<float>>> emb(H, std::vector<std::vector<float>>(W, std::vector<float>(embed_dim)));
for (int h = 0; h < H; ++h) {
for (int w = 0; w < W; ++w) {
for (int d = 0; d < embed_dim / 2; ++d) {
emb[h][w][d] = emb_h[h][w][d];
emb[h][w][d + embed_dim / 2] = emb_w[h][w][d];
}
}
}
return emb;
}
static std::vector<std::vector<float>> get_2d_sincos_pos_embed(int embed_dim, const std::pair<int, int> image_size) {
int grid_h_size = image_size.first;
int grid_w_size = image_size.second;
std::vector<float> grid_h(grid_h_size);
std::vector<float> grid_w(grid_w_size);
for (int i = 0; i < grid_h_size; ++i) {
grid_h[i] = static_cast<float>(i);
}
for (int i = 0; i < grid_w_size; ++i) {
grid_w[i] = static_cast<float>(i);
}
std::vector<std::vector<float>> grid(grid_h_size, std::vector<float>(grid_w_size));
for (int h = 0; h < grid_h_size; ++h) {
for (int w = 0; w < grid_w_size; ++w) {
grid[h][w] = grid_w[w];
}
}
std::vector<std::vector<std::vector<float>>> grid_2d = {grid, grid};
for (int h = 0; h < grid_h_size; ++h) {
for (int w = 0; w < grid_w_size; ++w) {
grid_2d[0][h][w] = grid_h[h];
grid_2d[1][h][w] = grid_w[w];
}
}
std::vector<std::vector<std::vector<float>>> pos_embed_3d = get_2d_sincos_pos_embed_from_grid(embed_dim, grid_2d);
int H = image_size.first;
int W = image_size.second;
std::vector<std::vector<float>> pos_embed_2d(H * W, std::vector<float>(embed_dim));
for (int h = 0; h < H; ++h) {
for (int w = 0; w < W; ++w) {
pos_embed_2d[w * H + h] = pos_embed_3d[h][w];
}
}
return pos_embed_2d;
}
bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) {
if (!ctx->has_vision_encoder) {
LOG_ERR("%s: This gguf file seems to have no vision encoder\n", __func__);
return false;
}
clip_image_f32_batch imgs;
clip_image_f32_ptr img_copy(clip_image_f32_init());
*img_copy = *img;
imgs.entries.push_back(std::move(img_copy));
return clip_image_batch_encode(ctx, n_threads, &imgs, vec);
}
bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs_c_ptr, float * vec) {
const clip_image_f32_batch & imgs = *imgs_c_ptr;
if (!ctx->has_vision_encoder) {
LOG_ERR("%s: This gguf file seems to have no vision encoder\n", __func__);
return false;
}
int batch_size = imgs.entries.size();
if (ctx->has_llava_projector) {
GGML_ASSERT(batch_size == 1); // TODO: support multiple images
}
if (ctx->has_minicpmv_projector) {
GGML_ASSERT(batch_size == 1);
}
if (ctx->has_glm_projector) {
GGML_ASSERT(batch_size == 1);
ggml_tensor * boi = ctx->vision_model.boi_w;
ggml_backend_tensor_get(boi,vec,0,ggml_nbytes(boi));
vec = (float*)(vec+ggml_nelements(boi)); //offset for boi
}
// build the inference graph
ggml_backend_sched_reset(ctx->sched.get());
ggml_cgraph * gf = clip_image_build_graph(ctx, imgs, ctx->load_image_size, true);
ggml_backend_sched_alloc_graph(ctx->sched.get(), gf);
// set inputs
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;
if (ctx->has_minicpmv_projector | ctx->has_qwen2vl_merger) {
image_size_width = imgs.entries[0]->nx;
image_size_height = imgs.entries[0]->ny;
}
const int patch_size = hparams.patch_size;
const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size));
const int num_positions = num_patches + (model.class_embedding ? 1 : 0);
const int pos_w = ctx->load_image_size.width / patch_size;
const int pos_h = ctx->load_image_size.height / patch_size;
{
struct ggml_tensor * inp_raw = ggml_graph_get_tensor(gf, "inp_raw");
float * data = (float *)malloc(ggml_nbytes(inp_raw));
for (size_t i = 0; i < imgs.entries.size(); i++) {
const int nx = imgs.entries[i]->nx;
const int ny = imgs.entries[i]->ny;
if (!(ctx->has_minicpmv_projector | ctx->has_qwen2vl_merger)) {
GGML_ASSERT(nx == image_size && ny == image_size);
}
const int n = nx * ny;
for (int b = 0; b < batch_size; b++) {
for (int k = 0; k < 3; k++) {
for (int y = 0; y < ny; y++) {
for (int x = 0; x < nx; x++) {
data[(b * 3 * n) + k * n + y * nx + x] = imgs.entries[b]->buf[3 * (y * nx + x) + k];
}
}
}
}
}
ggml_backend_tensor_set(inp_raw, data, 0, ggml_nbytes(inp_raw));
free(data);
}
if (ctx->has_minicpmv_projector) {
{
// inspired from siglip:
// -> https://huggingface.co/HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit
// -> https://huggingface.co/HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit/blob/d66538faeba44480d0bfaa42145eef26f9423199/modeling_siglip.py#L316
struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");
int* positions_data = (int*)malloc(ggml_nbytes(positions));
int bucket_coords_h[1024];
int bucket_coords_w[1024];
for (int i = 0; i < pos_h; i++){
bucket_coords_h[i] = std::floor(70.0*i/pos_h);
}
for (int i = 0; i < pos_w; i++){
bucket_coords_w[i] = std::floor(70.0*i/pos_w);
}
for (int i = 0, id = 0; i < pos_h; i++){
for (int j = 0; j < pos_w; j++){
positions_data[id++] = bucket_coords_h[i]*70 + bucket_coords_w[j];
}
}
ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
free(positions_data);
}
{
// inspired from resampler of Qwen-VL:
// -> https://huggingface.co/Qwen/Qwen-VL/tree/main
// -> https://huggingface.co/Qwen/Qwen-VL/blob/0547ed36a86561e2e42fecec8fd0c4f6953e33c4/visual.py#L23
struct ggml_tensor * pos_embed = ggml_graph_get_tensor(gf, "pos_embed");
int embed_dim = 4096;
if (ctx->minicpmv_version == 2) {
embed_dim = 4096;
}
else if (ctx->minicpmv_version == 3) {
embed_dim = 3584;
}
else if (ctx->minicpmv_version == 4) {
embed_dim = 3584;
}
auto pos_embed_t = get_2d_sincos_pos_embed(embed_dim, std::make_pair(pos_w, pos_h));
float * pos_embed_data = (float *)malloc(ggml_nbytes(pos_embed));
for(int i=0;i < pos_w * pos_h; ++i){
for(int j=0; j < embed_dim; ++j){
pos_embed_data[i * embed_dim + j] = pos_embed_t[i][j];
}
}
ggml_backend_tensor_set(pos_embed, pos_embed_data, 0, ggml_nbytes(pos_embed));
free(pos_embed_data);
}
}
else {
if (model.class_embedding) {
struct ggml_tensor * embeddings = ggml_graph_get_tensor(gf, "embeddings");
void* zero_mem = malloc(ggml_nbytes(embeddings));
memset(zero_mem, 0, ggml_nbytes(embeddings));
ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings));
free(zero_mem);
}
if (ctx->has_qwen2vl_merger) {
struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");
const int pw = image_size_width / patch_size;
const int ph = image_size_height / patch_size;
int* positions_data = (int*)malloc(ggml_nbytes(positions));
int ptr = 0;
for (int y = 0; y < ph; y+=2)
{
for (int x = 0; x < pw; x+=2)
{
for (int dy = 0; dy < 2; dy++) {
for (int dx = 0; dx < 2; dx++) {
positions_data[ptr] = y + dy;
positions_data[num_patches + ptr] = x + dx;
positions_data[num_patches * 2 + ptr] = y + dy;
positions_data[num_patches * 3 + ptr] = x + dx;
ptr++;
}
}
}
}
ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
free(positions_data);
}
else if (ctx->proj_type == PROJECTOR_TYPE_GEMMA3) {
// do nothing
}
else {
struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");
int* positions_data = (int*)malloc(ggml_nbytes(positions));
for (int i = 0; i < num_positions; i++) {
positions_data[i] = i;
}
ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
free(positions_data);
if (!ctx->has_glm_projector) {
struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches");
// The patches vector is used to get rows to index into the embeds with;
// we should skip dim 0 only if we have CLS to avoid going out of bounds
// when retrieving the rows.
int patch_offset = model.class_embedding ? 1 : 0;
int* patches_data = (int*)malloc(ggml_nbytes(patches));
for (int i = 0; i < num_patches; i++) {
patches_data[i] = i + patch_offset;
}
ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches));
free(patches_data);
}
}
}
ggml_backend_cpu_set_n_threads(ctx->backend_cpu, n_threads);
auto status = ggml_backend_sched_graph_compute(ctx->sched.get(), gf);
if (status != GGML_STATUS_SUCCESS) {
LOG_ERR("%s: ggml_backend_sched_graph_compute failed with error %d\n", __func__, status);
return false;
}
// the last node is the embedding tensor
struct ggml_tensor * embeddings = ggml_graph_node(gf, -1);
// copy the embeddings to the location passed by the user
ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));
if (ctx->has_glm_projector) {
//eoi
ggml_tensor * eoi = ctx->vision_model.eoi_w;
int offset = ggml_nelements(embeddings);
ggml_backend_tensor_get(eoi, vec+offset, 0, ggml_nbytes(eoi));
}
return true;
}
bool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype) {
assert(itype < GGML_TYPE_COUNT);
ggml_type type = static_cast<ggml_type>(itype);
auto * ctx_clip = clip_init(fname_inp, clip_context_params{
/* use_gpu */ false,
/* verbosity */ GGML_LOG_LEVEL_ERROR,
});
const auto & ctx_src = ctx_clip->ctx_gguf.get();
const auto & ctx_data = ctx_clip->ctx_data.get();
auto * ctx_out = gguf_init_empty();
gguf_set_kv(ctx_out, ctx_src);
gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
gguf_set_val_u32(ctx_out, "general.file_type", itype);
auto fout = std::ofstream(fname_out, std::ios::binary);
const int n_tensors = gguf_get_n_tensors(ctx_src);
for (int i = 0; i < n_tensors; ++i) {
const char * name = gguf_get_tensor_name(ctx_src, i);
struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name);
gguf_add_tensor(ctx_out, cur);
}
const size_t meta_size = gguf_get_meta_size(ctx_out);
for (size_t i = 0; i < meta_size; ++i) {
fout.put(0);
}
// regexes of tensor names to be quantized
const std::vector<std::string> k_names = {
".*weight",
};
std::vector<uint8_t> work(512);
std::vector<float> conv_buf(512);
size_t total_size_org = 0;
size_t total_size_new = 0;
for (int i = 0; i < n_tensors; ++i) {
const std::string name = gguf_get_tensor_name(ctx_src, i);
struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name.c_str());
enum ggml_type new_type;
void * new_data;
size_t new_size;
bool quantize = false;
for (const auto & s : k_names) {
if (std::regex_match(name, std::regex(s))) {
quantize = true;
break;
}
}
// quantize only 2D tensors and bigger than block size
quantize &= (ggml_n_dims(cur) == 2) && cur->ne[0] > ggml_blck_size(type);
if (quantize) {
new_type = type;
if (new_type >= GGML_TYPE_Q2_K && name.find("embd") != std::string::npos) {
new_type = GGML_TYPE_Q8_0; // ggml_get_rows needs non K type
// LOG_ERR("%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type));
}
const size_t n_elms = ggml_nelements(cur);
float * f32_data;
switch (cur->type) {
case GGML_TYPE_F32:
f32_data = (float *)cur->data;
break;
case GGML_TYPE_F16:
if (conv_buf.size() < n_elms) {
conv_buf.resize(n_elms);
}
for (size_t j = 0; j < n_elms; ++j) {
conv_buf[j] = ggml_fp16_to_fp32(((ggml_fp16_t *)cur->data)[j]);
}
f32_data = (float *)conv_buf.data();
break;
default:
LOG_ERR("%s: Please use an input file in f32 or f16\n", __func__);
gguf_free(ctx_out);
return false;
}
if (work.size() < n_elms * 4) {
work.resize(n_elms * 4);
}
new_data = work.data();
new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, n_elms/cur->ne[0], cur->ne[0], nullptr);
} else {
new_type = cur->type;
new_data = cur->data;
new_size = ggml_nbytes(cur);
}
const size_t orig_size = ggml_nbytes(cur);
total_size_org += orig_size;
total_size_new += new_size;
gguf_set_tensor_type(ctx_out, name.c_str(), new_type);
GGML_ASSERT(gguf_get_tensor_size(ctx_out, gguf_find_tensor(ctx_out, name.c_str())) == new_size);
gguf_set_tensor_data(ctx_out, name.c_str(), new_data);
fout.write((const char *)new_data, new_size);
size_t pad = GGML_PAD(new_size, gguf_get_alignment(ctx_out)) - new_size;
for (size_t j = 0; j < pad; ++j) {
fout.put(0);
}
LOG_INF("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize,
orig_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
}
// go back to beginning of file and write the updated metadata
fout.seekp(0, std::ios::beg);
std::vector<uint8_t> meta(meta_size);
gguf_get_meta_data(ctx_out, meta.data());
fout.write((const char *)meta.data(), meta_size);
fout.close();
clip_free(ctx_clip);
gguf_free(ctx_out);
{
LOG_INF("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
LOG_INF("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0);
}
return true;
}
int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
if (ctx->proj_type == PROJECTOR_TYPE_LDP) {
return ctx->vision_model.mm_model_block_1_block_2_1_b->ne[0];
}
if (ctx->proj_type == PROJECTOR_TYPE_LDPV2) {
return ctx->vision_model.mm_model_peg_0_b->ne[0];
}
if (ctx->proj_type == PROJECTOR_TYPE_MLP) {
return ctx->vision_model.mm_2_b->ne[0];
}
if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) {
return ctx->vision_model.mm_3_b->ne[0];
}
if (ctx->proj_type == PROJECTOR_TYPE_RESAMPLER) {
if (ctx->minicpmv_version == 2) {
return 4096;
}
else if (ctx->minicpmv_version == 3) {
return 3584;
}
else if (ctx->minicpmv_version == 4) {
return 3584;
}
}
if (ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE){
return ctx->vision_model.mm_model_mlp_3_w->ne[1];
}
if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
return ctx->vision_model.mm_1_b->ne[0];
}
if (ctx->proj_type == PROJECTOR_TYPE_GEMMA3) {
return ctx->vision_model.mm_input_proj_w->ne[0];
}
std::string proj_type = PROJECTOR_TYPE_NAMES[ctx->proj_type];
throw std::runtime_error(string_format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
}
int clip_is_minicpmv(const struct clip_ctx * ctx) {
if (ctx->has_minicpmv_projector) {
return ctx->minicpmv_version;
}
return 0;
}
bool clip_is_glm(const struct clip_ctx * ctx) {
return ctx->has_glm_projector;
}
bool clip_is_qwen2vl(const struct clip_ctx * ctx) {
return ctx->has_qwen2vl_merger;
}
bool clip_is_llava(const struct clip_ctx * ctx) {
return ctx->has_llava_projector;
}
bool clip_is_gemma3(const struct clip_ctx * ctx) {
return ctx->proj_type == PROJECTOR_TYPE_GEMMA3;
}
// Determine the number of encoder layers to iterate over
int get_deepest_feature_layer(const struct clip_ctx * ctx) {
// Get the index of the second to last layer; this is the
// default for models that have a llava projector
const auto & hparams = ctx->vision_model.hparams;
int n_layer = hparams.n_layer - 1;
int deepest_feature_layer = -1;
// Handle other projectors; incrementing here indicates that we
// should use the last encoder layer for the vision features.
if (ctx->has_minicpmv_projector || ctx->has_glm_projector || ctx->has_qwen2vl_merger) {
n_layer += 1;
}
// If we set explicit vision feature layers, only go up to the deepest one
for (const auto & feature_layer : hparams.vision_feature_layer) {
if (feature_layer > deepest_feature_layer) {
deepest_feature_layer = feature_layer;
}
}
return deepest_feature_layer < 0 ? n_layer : deepest_feature_layer;
}
bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec) {
clip_image_f32 clip_img;
clip_img.buf.resize(h * w * 3);
for (int i = 0; i < h*w*3; i++)
{
clip_img.buf[i] = img[i];
}
clip_img.nx = w;
clip_img.ny = h;
clip_image_encode(ctx, n_threads, &clip_img, vec);
return true;
}
//
// API used internally with mtmd
//
projector_type clip_get_projector_type(const struct clip_ctx * ctx) {
return ctx->proj_type;
}
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