4063 lines
178 KiB
C++
4063 lines
178 KiB
C++
#include "clip.h"
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#include "clip-impl.h"
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#include "clip-model.h"
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#include "clip-graph.h"
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#include "models/models.h"
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#include "ggml.h"
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#include "ggml-cpp.h"
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#include "ggml-alloc.h"
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#include "ggml-backend.h"
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#include "gguf.h"
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#include <cassert>
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#include <cmath>
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#include <cstdlib>
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#include <cstring>
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#include <fstream>
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#include <map>
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#include <stdexcept>
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#include <unordered_set>
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#include <vector>
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#include <cinttypes>
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#include <limits>
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#include <array>
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#include <functional>
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#include <algorithm>
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struct clip_logger_state g_logger_state = {clip_log_callback_default, NULL};
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//#define CLIP_DEBUG_FUNCTIONS
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#ifdef CLIP_DEBUG_FUNCTIONS
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static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::string& filename) {
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std::ofstream file(filename, std::ios::binary);
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if (!file.is_open()) {
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LOG_ERR("Failed to open file for writing: %s\n", filename.c_str());
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return;
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}
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// PPM header: P6 format, width, height, and max color value
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file << "P6\n" << img.nx << " " << img.ny << "\n255\n";
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// Write pixel data
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for (size_t i = 0; i < img.buf.size(); i += 3) {
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// PPM expects binary data in RGB format, which matches our image buffer
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file.write(reinterpret_cast<const char*>(&img.buf[i]), 3);
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}
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file.close();
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}
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static void clip_image_save_to_bmp(const clip_image_u8& img, const std::string& filename) {
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std::ofstream file(filename, std::ios::binary);
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if (!file.is_open()) {
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LOG_ERR("Failed to open file for writing: %s\n", filename.c_str());
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return;
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}
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int fileSize = 54 + 3 * img.nx * img.ny; // File header + info header + pixel data
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int bytesPerPixel = 3;
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int widthInBytes = img.nx * bytesPerPixel;
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int paddingAmount = (4 - (widthInBytes % 4)) % 4;
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int stride = widthInBytes + paddingAmount;
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// Bitmap file header
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unsigned char fileHeader[14] = {
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'B','M', // Signature
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0,0,0,0, // Image file size in bytes
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0,0,0,0, // Reserved
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54,0,0,0 // Start of pixel array
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};
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// Total file size
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fileSize = 54 + (stride * img.ny);
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fileHeader[2] = (unsigned char)(fileSize);
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fileHeader[3] = (unsigned char)(fileSize >> 8);
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fileHeader[4] = (unsigned char)(fileSize >> 16);
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fileHeader[5] = (unsigned char)(fileSize >> 24);
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// Bitmap information header (BITMAPINFOHEADER)
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unsigned char infoHeader[40] = {
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40,0,0,0, // Size of this header (40 bytes)
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0,0,0,0, // Image width
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0,0,0,0, // Image height
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1,0, // Number of color planes
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24,0, // Bits per pixel
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0,0,0,0, // No compression
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0,0,0,0, // Image size (can be 0 for no compression)
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0,0,0,0, // X pixels per meter (not specified)
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0,0,0,0, // Y pixels per meter (not specified)
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0,0,0,0, // Total colors (color table not used)
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0,0,0,0 // Important colors (all are important)
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};
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// Width and height in the information header
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infoHeader[4] = (unsigned char)(img.nx);
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infoHeader[5] = (unsigned char)(img.nx >> 8);
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infoHeader[6] = (unsigned char)(img.nx >> 16);
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infoHeader[7] = (unsigned char)(img.nx >> 24);
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infoHeader[8] = (unsigned char)(img.ny);
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infoHeader[9] = (unsigned char)(img.ny >> 8);
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infoHeader[10] = (unsigned char)(img.ny >> 16);
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infoHeader[11] = (unsigned char)(img.ny >> 24);
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// Write file headers
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file.write(reinterpret_cast<char*>(fileHeader), sizeof(fileHeader));
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file.write(reinterpret_cast<char*>(infoHeader), sizeof(infoHeader));
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// Pixel data
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std::vector<unsigned char> padding(3, 0); // Max padding size to be added to each row
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for (int y = img.ny - 1; y >= 0; --y) { // BMP files are stored bottom-to-top
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for (int x = 0; x < img.nx; ++x) {
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// Each pixel
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size_t pixelIndex = (y * img.nx + x) * 3;
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unsigned char pixel[3] = {
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img.buf[pixelIndex + 2], // BMP stores pixels in BGR format
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img.buf[pixelIndex + 1],
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img.buf[pixelIndex]
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};
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file.write(reinterpret_cast<char*>(pixel), 3);
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}
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// Write padding for the row
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file.write(reinterpret_cast<char*>(padding.data()), paddingAmount);
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}
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file.close();
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}
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// debug function to convert f32 to u8
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static void clip_image_convert_f32_to_u8(const clip_image_f32& src, clip_image_u8& dst) {
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dst.nx = src.nx;
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dst.ny = src.ny;
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dst.buf.resize(3 * src.nx * src.ny);
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for (size_t i = 0; i < src.buf.size(); ++i) {
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dst.buf[i] = static_cast<uint8_t>(std::min(std::max(int(src.buf[i] * 255.0f), 0), 255));
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}
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}
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#endif
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struct clip_ctx {
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clip_model model;
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gguf_context_ptr ctx_gguf;
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ggml_context_ptr ctx_data;
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std::vector<uint8_t> buf_compute_meta;
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std::vector<ggml_backend_t> backend_ptrs;
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std::vector<ggml_backend_buffer_type_t> backend_buft;
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ggml_backend_t backend = nullptr;
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ggml_backend_t backend_cpu = nullptr;
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ggml_backend_buffer_ptr buf;
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int max_nodes = 8192;
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ggml_backend_sched_ptr sched;
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clip_flash_attn_type flash_attn_type = CLIP_FLASH_ATTN_TYPE_AUTO;
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bool is_allocated = false;
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// for debugging
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bool debug_graph = false;
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std::vector<ggml_tensor *> debug_print_tensors;
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clip_ctx(clip_context_params & ctx_params) {
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flash_attn_type = ctx_params.flash_attn_type;
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debug_graph = std::getenv("MTMD_DEBUG_GRAPH") != nullptr;
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backend_cpu = ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, nullptr);
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if (!backend_cpu) {
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throw std::runtime_error("failed to initialize CPU backend");
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}
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if (ctx_params.use_gpu) {
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auto backend_name = std::getenv("MTMD_BACKEND_DEVICE");
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if (backend_name != nullptr) {
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backend = ggml_backend_init_by_name(backend_name, nullptr);
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if (!backend) {
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LOG_WRN("%s: Warning: Failed to initialize \"%s\" backend, falling back to default GPU backend\n", __func__, backend_name);
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}
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}
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if (!backend) {
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backend = ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_GPU, nullptr);
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backend = backend ? backend : ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_IGPU, nullptr);
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}
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}
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if (backend) {
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LOG_INF("%s: CLIP using %s backend\n", __func__, ggml_backend_name(backend));
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backend_ptrs.push_back(backend);
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backend_buft.push_back(ggml_backend_get_default_buffer_type(backend));
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} else {
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backend = backend_cpu;
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LOG_INF("%s: CLIP using CPU backend\n", __func__);
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}
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if (ctx_params.image_min_tokens > 0) {
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model.hparams.custom_image_min_tokens = ctx_params.image_min_tokens;
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}
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if (ctx_params.image_max_tokens > 0) {
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model.hparams.custom_image_max_tokens = ctx_params.image_max_tokens;
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}
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backend_ptrs.push_back(backend_cpu);
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backend_buft.push_back(ggml_backend_get_default_buffer_type(backend_cpu));
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sched.reset(
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ggml_backend_sched_new(backend_ptrs.data(), backend_buft.data(), backend_ptrs.size(), 8192, false, true)
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);
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}
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~clip_ctx() {
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ggml_backend_free(backend);
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if (backend != backend_cpu) {
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ggml_backend_free(backend_cpu);
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}
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}
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// this function is added so that we don't change too much of the existing code
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projector_type proj_type() const {
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return model.proj_type;
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}
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};
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//
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// clip_graph
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//
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clip_graph::clip_graph(clip_ctx * ctx, const clip_image_f32 & img) :
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model(ctx->model),
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hparams(model.hparams),
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proj_type(ctx->proj_type()),
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img(img),
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patch_size(hparams.patch_size),
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n_patches_x(img.nx / patch_size),
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n_patches_y(img.ny / patch_size),
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n_patches(n_patches_x * n_patches_y),
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n_embd(hparams.n_embd),
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n_head(hparams.n_head),
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d_head(n_embd / n_head),
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n_layer(hparams.n_layer),
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n_mmproj_embd(clip_n_mmproj_embd(ctx)),
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eps(hparams.eps),
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kq_scale(1.0f / sqrtf((float)d_head)),
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flash_attn_type(ctx->flash_attn_type),
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debug_graph(ctx->debug_graph),
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debug_print_tensors(ctx->debug_print_tensors) {
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struct ggml_init_params params = {
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/*.mem_size =*/ ctx->buf_compute_meta.size(),
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/*.mem_buffer =*/ ctx->buf_compute_meta.data(),
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/*.no_alloc =*/ true,
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};
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ctx0_ptr.reset(ggml_init(params));
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ctx0 = ctx0_ptr.get();
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gf = ggml_new_graph_custom(ctx0, ctx->max_nodes, false);
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}
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void clip_graph::cb(ggml_tensor * cur0, const char * name, int il) const {
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if (debug_graph) {
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ggml_tensor * cur = ggml_cpy(ctx0, cur0, ggml_dup_tensor(ctx0, cur0));
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std::string cur_name = il >= 0 ? std::string(name) + "_" + std::to_string(il) : name;
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ggml_set_name(cur, cur_name.c_str());
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ggml_set_output(cur);
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ggml_build_forward_expand(gf, cur);
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debug_print_tensors.push_back(cur);
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}
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}
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// siglip2 naflex
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ggml_tensor * clip_graph::resize_position_embeddings(uint32_t interpolation_mode) {
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ggml_tensor * pos_embd = model.position_embeddings;
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const int height = img.ny / patch_size;
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const int width = img.nx / patch_size;
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const uint32_t mode = interpolation_mode;
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const int n_per_side = (int)std::sqrt(pos_embd->ne[1]);
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GGML_ASSERT(pos_embd);
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if (height == n_per_side && width == n_per_side) {
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return pos_embd;
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}
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pos_embd = ggml_reshape_3d(ctx0, pos_embd, n_embd, n_per_side, n_per_side); // -> (n_embd, n_per_side, n_per_side)
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pos_embd = ggml_permute(ctx0, pos_embd, 2, 0, 1, 3); // -> (n_per_side, n_per_side, n_embd)
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pos_embd = ggml_interpolate(ctx0, pos_embd, width, height, n_embd, 1, mode); // -> (width, height, n_embd)
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pos_embd = ggml_permute(ctx0, pos_embd, 1, 2, 0, 3); // -> (n_embd, width, height)
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pos_embd = ggml_cont_2d(ctx0, pos_embd, n_embd, width * height); // -> (n_embd, width * height)
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return pos_embd;
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}
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// build vision transformer (ViT) cgraph
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// this function should cover most of the models
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// if your model has specific features, you should probably duplicate this function
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ggml_tensor * clip_graph::build_vit(
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ggml_tensor * inp,
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int64_t n_pos,
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norm_type norm_t,
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ffn_op_type ffn_t,
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ggml_tensor * learned_pos_embd,
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std::function<ggml_tensor *(ggml_tensor *, const clip_layer &)> add_pos
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) {
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if (learned_pos_embd) {
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inp = ggml_add(ctx0, inp, learned_pos_embd);
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cb(inp, "pos_embed", -1);
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}
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ggml_tensor * inpL = inp;
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// pre-layernorm
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if (model.pre_ln_w) {
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inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1);
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cb(inpL, "pre_ln", -1);
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}
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// loop over layers
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for (int il = 0; il < n_layer; il++) {
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auto & layer = model.layers[il];
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ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states
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// layernorm1
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cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il);
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cb(cur, "layer_inp_normed", il);
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// self-attention
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{
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ggml_tensor * Qcur = nullptr;
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ggml_tensor * Kcur = nullptr;
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ggml_tensor * Vcur = nullptr;
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if (layer.qkv_w != nullptr) {
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// fused qkv
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cur = ggml_mul_mat(ctx0, layer.qkv_w, cur);
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if (layer.qkv_b != nullptr) {
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cur = ggml_add(ctx0, cur, layer.qkv_b);
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}
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Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
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/* nb1 */ ggml_row_size(cur->type, d_head),
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/* nb2 */ cur->nb[1],
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/* offset */ 0);
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Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
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/* nb1 */ ggml_row_size(cur->type, d_head),
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/* nb2 */ cur->nb[1],
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/* offset */ ggml_row_size(cur->type, n_embd));
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Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
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/* nb1 */ ggml_row_size(cur->type, d_head),
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/* nb2 */ cur->nb[1],
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/* offset */ ggml_row_size(cur->type, 2 * n_embd));
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// TODO: q/k norm requires row size == n_embd, while here it's d_head
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// we can add support in the future if needed
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GGML_ASSERT(layer.q_norm == nullptr && layer.k_norm == nullptr);
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} else {
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// separate q, k, v
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Qcur = ggml_mul_mat(ctx0, layer.q_w, cur);
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if (layer.q_b) {
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Qcur = ggml_add(ctx0, Qcur, layer.q_b);
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}
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Kcur = ggml_mul_mat(ctx0, layer.k_w, cur);
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if (layer.k_b) {
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Kcur = ggml_add(ctx0, Kcur, layer.k_b);
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}
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Vcur = ggml_mul_mat(ctx0, layer.v_w, cur);
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if (layer.v_b) {
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Vcur = ggml_add(ctx0, Vcur, layer.v_b);
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}
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if (layer.q_norm) {
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Qcur = build_norm(Qcur, layer.q_norm, NULL, norm_t, eps, il);
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cb(Qcur, "Qcur_norm", il);
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}
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if (layer.k_norm) {
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Kcur = build_norm(Kcur, layer.k_norm, NULL, norm_t, eps, il);
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cb(Kcur, "Kcur_norm", il);
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}
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Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos);
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Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos);
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Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos);
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}
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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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if (add_pos) {
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Qcur = add_pos(Qcur, layer);
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Kcur = add_pos(Kcur, layer);
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cb(Qcur, "Qcur_pos", il);
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cb(Kcur, "Kcur_pos", il);
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}
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cur = build_attn(layer.o_w, layer.o_b,
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Qcur, Kcur, Vcur, nullptr, kq_scale, il);
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cb(cur, "attn_out", il);
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}
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if (layer.ls_1_w) {
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cur = ggml_mul(ctx0, cur, layer.ls_1_w);
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cb(cur, "attn_out_scaled", il);
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}
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// re-add the layer input, e.g., residual
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cur = ggml_add(ctx0, cur, inpL);
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inpL = cur; // inpL = residual, cur = hidden_states
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cb(cur, "ffn_inp", il);
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// layernorm2
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cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il);
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cb(cur, "ffn_inp_normed", il);
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// ffn
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cur = build_ffn(cur,
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layer.ff_up_w, layer.ff_up_b,
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layer.ff_gate_w, layer.ff_gate_b,
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layer.ff_down_w, layer.ff_down_b,
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ffn_t, il);
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cb(cur, "ffn_out", il);
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if (layer.ls_2_w) {
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cur = ggml_mul(ctx0, cur, layer.ls_2_w);
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cb(cur, "ffn_out_scaled", il);
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}
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// residual 2
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cur = ggml_add(ctx0, inpL, cur);
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cb(cur, "layer_out", il);
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inpL = cur;
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}
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if (model.audio_has_avgpool()) {
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ggml_tensor * cur = inpL;
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cur = ggml_transpose(ctx0, cur);
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cur = ggml_cont(ctx0, cur);
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cur = ggml_pool_1d(ctx0, cur, GGML_OP_POOL_AVG, 2, 2, 0);
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cur = ggml_transpose(ctx0, cur);
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cur = ggml_cont(ctx0, cur);
|
||
inpL = cur;
|
||
}
|
||
|
||
// post-layernorm
|
||
if (model.post_ln_w) {
|
||
inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, -1);
|
||
}
|
||
return inpL;
|
||
}
|
||
|
||
// build the input after conv2d (inp_raw --> patches)
|
||
// returns tensor with shape [n_embd, n_patches]
|
||
ggml_tensor * clip_graph::build_inp() {
|
||
ggml_tensor * inp_raw = build_inp_raw();
|
||
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, n_patches, n_embd);
|
||
inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp));
|
||
if (model.patch_bias) {
|
||
inp = ggml_add(ctx0, inp, model.patch_bias);
|
||
cb(inp, "patch_bias", -1);
|
||
}
|
||
return inp;
|
||
}
|
||
|
||
ggml_tensor * clip_graph::build_inp_raw(int channels) {
|
||
ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, img.nx, img.ny, channels);
|
||
ggml_set_name(inp_raw, "inp_raw");
|
||
ggml_set_input(inp_raw);
|
||
return inp_raw;
|
||
}
|
||
|
||
ggml_tensor * clip_graph::build_norm(
|
||
ggml_tensor * cur,
|
||
ggml_tensor * mw,
|
||
ggml_tensor * mb,
|
||
norm_type type,
|
||
float norm_eps,
|
||
int il) const {
|
||
|
||
cur = type == NORM_TYPE_RMS
|
||
? ggml_rms_norm(ctx0, cur, norm_eps)
|
||
: ggml_norm(ctx0, cur, norm_eps);
|
||
|
||
if (mw) {
|
||
cur = ggml_mul(ctx0, cur, mw);
|
||
cb(cur, "norm_w", il);
|
||
}
|
||
|
||
if (mb) {
|
||
cur = ggml_add(ctx0, cur, mb);
|
||
cb(cur, "norm_b", il);
|
||
}
|
||
|
||
return cur;
|
||
}
|
||
|
||
ggml_tensor * clip_graph::build_ffn(
|
||
ggml_tensor * cur,
|
||
ggml_tensor * up,
|
||
ggml_tensor * up_b,
|
||
ggml_tensor * gate,
|
||
ggml_tensor * gate_b,
|
||
ggml_tensor * down,
|
||
ggml_tensor * down_b,
|
||
ffn_op_type type_op,
|
||
int il) const {
|
||
|
||
ggml_tensor * tmp = up ? ggml_mul_mat(ctx0, up, cur) : cur;
|
||
cb(tmp, "ffn_up", il);
|
||
|
||
if (up_b) {
|
||
tmp = ggml_add(ctx0, tmp, up_b);
|
||
cb(tmp, "ffn_up_b", il);
|
||
}
|
||
|
||
if (gate) {
|
||
cur = ggml_mul_mat(ctx0, gate, cur);
|
||
cb(cur, "ffn_gate", il);
|
||
|
||
if (gate_b) {
|
||
cur = ggml_add(ctx0, cur, gate_b);
|
||
cb(cur, "ffn_gate_b", il);
|
||
}
|
||
} else {
|
||
cur = tmp;
|
||
}
|
||
|
||
// we only support parallel ffn for now
|
||
switch (type_op) {
|
||
case FFN_SILU:
|
||
if (gate) {
|
||
cur = ggml_swiglu_split(ctx0, cur, tmp);
|
||
cb(cur, "ffn_swiglu", il);
|
||
} else {
|
||
cur = ggml_silu(ctx0, cur);
|
||
cb(cur, "ffn_silu", il);
|
||
} break;
|
||
case FFN_GELU:
|
||
if (gate) {
|
||
cur = ggml_geglu_split(ctx0, cur, tmp);
|
||
cb(cur, "ffn_geglu", il);
|
||
} else {
|
||
cur = ggml_gelu(ctx0, cur);
|
||
cb(cur, "ffn_gelu", il);
|
||
} break;
|
||
case FFN_GELU_ERF:
|
||
if (gate) {
|
||
cur = ggml_geglu_erf_split(ctx0, cur, tmp);
|
||
cb(cur, "ffn_geglu_erf", il);
|
||
} else {
|
||
cur = ggml_gelu_erf(ctx0, cur);
|
||
cb(cur, "ffn_gelu_erf", il);
|
||
} break;
|
||
case FFN_GELU_QUICK:
|
||
if (gate) {
|
||
cur = ggml_geglu_quick_split(ctx0, cur, tmp);
|
||
cb(cur, "ffn_geglu_quick", il);
|
||
} else {
|
||
cur = ggml_gelu_quick(ctx0, cur);
|
||
cb(cur, "ffn_gelu_quick", il);
|
||
} break;
|
||
}
|
||
|
||
if (down) {
|
||
cur = ggml_mul_mat(ctx0, down, cur);
|
||
}
|
||
|
||
if (down_b) {
|
||
cb(cur, "ffn_down", il);
|
||
}
|
||
|
||
if (down_b) {
|
||
cur = ggml_add(ctx0, cur, down_b);
|
||
}
|
||
|
||
return cur;
|
||
}
|
||
|
||
ggml_tensor * clip_graph::build_attn(
|
||
ggml_tensor * wo,
|
||
ggml_tensor * wo_b,
|
||
ggml_tensor * q_cur,
|
||
ggml_tensor * k_cur,
|
||
ggml_tensor * v_cur,
|
||
ggml_tensor * kq_mask,
|
||
float kq_scale,
|
||
int il) const {
|
||
// these nodes are added to the graph together so that they are not reordered
|
||
// by doing so, the number of splits in the graph is reduced
|
||
ggml_build_forward_expand(gf, q_cur);
|
||
ggml_build_forward_expand(gf, k_cur);
|
||
ggml_build_forward_expand(gf, v_cur);
|
||
|
||
ggml_tensor * q = ggml_permute(ctx0, q_cur, 0, 2, 1, 3);
|
||
//cb(q, "q", il);
|
||
|
||
ggml_tensor * k = ggml_permute(ctx0, k_cur, 0, 2, 1, 3);
|
||
//cb(k, "k", il);
|
||
|
||
ggml_tensor * cur;
|
||
|
||
if (flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) {
|
||
ggml_tensor * v = ggml_permute(ctx0, v_cur, 0, 2, 1, 3);
|
||
|
||
k = ggml_cast(ctx0, k, GGML_TYPE_F16);
|
||
v = ggml_cast(ctx0, v, GGML_TYPE_F16);
|
||
|
||
cur = ggml_flash_attn_ext(ctx0, q, k, v, kq_mask, kq_scale, 0.0f, 0.0f);
|
||
ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
|
||
|
||
cur = ggml_reshape_2d(ctx0, cur, cur->ne[0]*cur->ne[1], cur->ne[2]*cur->ne[3]);
|
||
|
||
} else {
|
||
ggml_tensor * v = ggml_permute(ctx0, v_cur, 1, 2, 0, 3);
|
||
v = ggml_cont(ctx0, v);
|
||
|
||
ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
|
||
// F32 may not needed for vision encoders?
|
||
// ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
|
||
|
||
kq = ggml_soft_max_ext(ctx0, kq, kq_mask, kq_scale, 0.0f);
|
||
|
||
ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq);
|
||
cur = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
|
||
cur = ggml_cont_2d(ctx0, cur, cur->ne[0] * cur->ne[1], cur->ne[2] * cur->ne[3]);
|
||
}
|
||
|
||
cb(cur, "kqv_out", il);
|
||
|
||
if (wo) {
|
||
cur = ggml_mul_mat(ctx0, wo, cur);
|
||
}
|
||
|
||
if (wo_b) {
|
||
cur = ggml_add(ctx0, cur, wo_b);
|
||
}
|
||
|
||
return cur;
|
||
}
|
||
|
||
// implementation of the 2D RoPE without adding a new op in ggml
|
||
// this is not efficient (use double the memory), but works on all backends
|
||
// TODO: there was a more efficient which relies on ggml_view and ggml_rope_ext_inplace, but the rope inplace does not work well with non-contiguous tensors ; we should fix that and revert back to the original implementation in https://github.com/ggml-org/llama.cpp/pull/13065
|
||
ggml_tensor * clip_graph::build_rope_2d(
|
||
ggml_context * ctx0,
|
||
ggml_tensor * cur,
|
||
ggml_tensor * pos_a, // first half
|
||
ggml_tensor * pos_b, // second half
|
||
const float freq_base,
|
||
const bool interleave_freq
|
||
) {
|
||
const int64_t n_dim = cur->ne[0];
|
||
const int64_t n_head = cur->ne[1];
|
||
const int64_t n_pos = cur->ne[2];
|
||
|
||
// for example, if we have cur tensor of shape (n_dim=8, n_head, n_pos)
|
||
// we will have a list of 4 inv_freq: 1e-0, 1e-1, 1e-2, 1e-3
|
||
// first half of cur will use 1e-0, 1e-2 (even)
|
||
// second half of cur will use 1e-1, 1e-3 (odd)
|
||
// the trick here is to rotate just half of n_dim, so inv_freq will automatically be even
|
||
// ^ don't ask me why, it's math! -2(2i) / n_dim == -2i / (n_dim/2)
|
||
// then for the second half, we use freq_scale to shift the inv_freq
|
||
// ^ why? replace (2i) with (2i+1) in the above equation
|
||
const float freq_scale_odd = interleave_freq
|
||
? std::pow(freq_base, (float)-2/n_dim)
|
||
: 1.0;
|
||
|
||
// first half
|
||
ggml_tensor * first;
|
||
{
|
||
first = ggml_view_3d(ctx0, cur,
|
||
n_dim/2, n_head, n_pos,
|
||
ggml_row_size(cur->type, n_dim),
|
||
ggml_row_size(cur->type, n_dim*n_head),
|
||
0);
|
||
first = ggml_rope_ext(
|
||
ctx0,
|
||
first,
|
||
pos_a, // positions
|
||
nullptr, // freq factors
|
||
n_dim/2, // n_dims
|
||
0, 0, freq_base,
|
||
1.0f, 0.0f, 1.0f, 0.0f, 0.0f
|
||
);
|
||
}
|
||
|
||
// second half
|
||
ggml_tensor * second;
|
||
{
|
||
second = ggml_view_3d(ctx0, cur,
|
||
n_dim/2, n_head, n_pos,
|
||
ggml_row_size(cur->type, n_dim),
|
||
ggml_row_size(cur->type, n_dim*n_head),
|
||
n_dim/2 * ggml_element_size(cur));
|
||
second = ggml_rope_ext(
|
||
ctx0,
|
||
second,
|
||
pos_b, // positions
|
||
nullptr, // freq factors
|
||
n_dim/2, // n_dims
|
||
0, 0, freq_base,
|
||
freq_scale_odd,
|
||
0.0f, 1.0f, 0.0f, 0.0f
|
||
);
|
||
}
|
||
|
||
cur = ggml_concat(ctx0, first, second, 0);
|
||
return cur;
|
||
}
|
||
|
||
// Generic function to stack frames for audio processing
|
||
// Abstracts out the StackAudioFrames logic used by ultravox
|
||
ggml_tensor * clip_graph::build_stack(ggml_tensor * cur, int32_t stack_factor, int32_t n_embed) {
|
||
if (stack_factor <= 1) {
|
||
return cur;
|
||
}
|
||
|
||
int64_t total_elements = ggml_nelements(cur);
|
||
int64_t stride = n_embed * stack_factor;
|
||
|
||
// Calculate padded length
|
||
int64_t padded_len = GGML_PAD(total_elements, stride);
|
||
int64_t pad = padded_len - total_elements;
|
||
|
||
if (pad > 0) {
|
||
// Pad the tensor to make it divisible by stride
|
||
cur = ggml_view_1d(ctx0, cur, total_elements, 0);
|
||
cur = ggml_pad(ctx0, cur, pad, 0, 0, 0);
|
||
}
|
||
|
||
// Reshape to [stride, padded_len / stride]
|
||
cur = ggml_view_2d(ctx0, cur, stride, padded_len / stride,
|
||
ggml_row_size(cur->type, stride), 0);
|
||
return cur;
|
||
}
|
||
|
||
// aka pixel_shuffle / pixel_unshuffle / patch_merger (Kimi-VL)
|
||
// support dynamic resolution
|
||
ggml_tensor * clip_graph::build_patch_merge_permute(ggml_tensor * cur, int scale_factor) {
|
||
GGML_ASSERT(scale_factor > 1);
|
||
|
||
const int n_embd = cur->ne[0];
|
||
int width = img.nx / patch_size;
|
||
int height = img.ny / patch_size;
|
||
|
||
// pad width and height to factor
|
||
const int64_t pad_width = CLIP_ALIGN(width, scale_factor) - width;
|
||
const int64_t pad_height = CLIP_ALIGN(height, scale_factor) - height;
|
||
cur = ggml_reshape_3d(ctx0, cur, n_embd, width, height);
|
||
if (pad_width || pad_height) {
|
||
cur = ggml_pad(ctx0, cur, 0, pad_width, pad_height, 0);
|
||
width += pad_width;
|
||
height += pad_height;
|
||
}
|
||
|
||
// unshuffle h
|
||
cur = ggml_reshape_3d(ctx0, cur, n_embd * scale_factor, width / scale_factor, height);
|
||
cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
|
||
|
||
// unshuffle w
|
||
cur = ggml_cont_3d(ctx0, cur, n_embd * scale_factor * scale_factor, height / scale_factor, width / scale_factor);
|
||
cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
|
||
|
||
cur = ggml_cont_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]);
|
||
cb(cur, "pixel_shuffle", -1);
|
||
|
||
return cur;
|
||
}
|
||
|
||
static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch & imgs) {
|
||
GGML_ASSERT(imgs.entries.size() == 1 && "n_batch > 1 is not supported");
|
||
|
||
const clip_image_f32 & img = *imgs.entries[0];
|
||
std::unique_ptr<clip_graph> builder;
|
||
|
||
switch (ctx->proj_type()) {
|
||
case PROJECTOR_TYPE_GEMMA3:
|
||
case PROJECTOR_TYPE_IDEFICS3:
|
||
case PROJECTOR_TYPE_LFM2:
|
||
case PROJECTOR_TYPE_JANUS_PRO:
|
||
{
|
||
builder = std::make_unique<clip_graph_siglip>(ctx, img);
|
||
} break;
|
||
case PROJECTOR_TYPE_PIXTRAL:
|
||
case PROJECTOR_TYPE_LIGHTONOCR:
|
||
{
|
||
builder = std::make_unique<clip_graph_pixtral>(ctx, img);
|
||
} break;
|
||
case PROJECTOR_TYPE_QWEN2VL:
|
||
case PROJECTOR_TYPE_QWEN25VL:
|
||
{
|
||
builder = std::make_unique<clip_graph_qwen2vl>(ctx, img);
|
||
} break;
|
||
case PROJECTOR_TYPE_QWEN3VL:
|
||
{
|
||
builder = std::make_unique<clip_graph_qwen3vl>(ctx, img);
|
||
} break;
|
||
case PROJECTOR_TYPE_MINICPMV:
|
||
{
|
||
builder = std::make_unique<clip_graph_minicpmv>(ctx, img);
|
||
} break;
|
||
case PROJECTOR_TYPE_INTERNVL:
|
||
{
|
||
builder = std::make_unique<clip_graph_internvl>(ctx, img);
|
||
} break;
|
||
case PROJECTOR_TYPE_LLAMA4:
|
||
{
|
||
builder = std::make_unique<clip_graph_llama4>(ctx, img);
|
||
} break;
|
||
case PROJECTOR_TYPE_ULTRAVOX:
|
||
case PROJECTOR_TYPE_VOXTRAL:
|
||
case PROJECTOR_TYPE_QWEN2A:
|
||
case PROJECTOR_TYPE_GLMA:
|
||
{
|
||
builder = std::make_unique<clip_graph_whisper_enc>(ctx, img);
|
||
} break;
|
||
case PROJECTOR_TYPE_KIMIVL:
|
||
{
|
||
builder = std::make_unique<clip_graph_kimivl>(ctx, img);
|
||
} break;
|
||
case PROJECTOR_TYPE_COGVLM:
|
||
{
|
||
builder = std::make_unique<clip_graph_cogvlm>(ctx, img);
|
||
} break;
|
||
case PROJECTOR_TYPE_MLP:
|
||
case PROJECTOR_TYPE_MLP_NORM:
|
||
case PROJECTOR_TYPE_LDP:
|
||
case PROJECTOR_TYPE_LDPV2:
|
||
case PROJECTOR_TYPE_GLM_EDGE:
|
||
{
|
||
builder = std::make_unique<clip_graph_llava>(ctx, img);
|
||
} break;
|
||
case PROJECTOR_TYPE_DEEPSEEKOCR:
|
||
{
|
||
builder = std::make_unique<clip_graph_deepseekocr>(ctx, img);
|
||
} break;
|
||
case PROJECTOR_TYPE_GLM4V:
|
||
{
|
||
builder = std::make_unique<clip_graph_glm4v>(ctx, img);
|
||
} break;
|
||
default:
|
||
GGML_ABORT("missing cgraph builder");
|
||
}
|
||
|
||
return builder->build();
|
||
}
|
||
|
||
//
|
||
// clip_model_loader
|
||
//
|
||
|
||
struct clip_model_loader {
|
||
ggml_context_ptr ctx_meta;
|
||
gguf_context_ptr ctx_gguf;
|
||
|
||
std::string fname;
|
||
|
||
size_t model_size = 0; // in bytes
|
||
|
||
bool has_vision = false;
|
||
bool has_audio = false;
|
||
|
||
// TODO @ngxson : we should not pass clip_ctx here, it should be clip_model
|
||
clip_model_loader(const char * fname) : 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");
|
||
}
|
||
|
||
// modalities
|
||
{
|
||
get_bool(KEY_HAS_VISION_ENC, has_vision, false);
|
||
get_bool(KEY_HAS_AUDIO_ENC, has_audio, false);
|
||
|
||
if (has_vision) {
|
||
LOG_INF("%s: has vision encoder\n", __func__);
|
||
}
|
||
if (has_audio) {
|
||
LOG_INF("%s: has audio encoder\n", __func__);
|
||
}
|
||
}
|
||
|
||
// 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);
|
||
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(clip_model & model, clip_modality modality) {
|
||
auto & hparams = model.hparams;
|
||
std::string log_ffn_op; // for logging
|
||
|
||
// sanity check
|
||
if (modality == CLIP_MODALITY_VISION) {
|
||
GGML_ASSERT(has_vision);
|
||
} else if (modality == CLIP_MODALITY_AUDIO) {
|
||
GGML_ASSERT(has_audio);
|
||
}
|
||
model.modality = modality;
|
||
|
||
|
||
// projector type
|
||
std::string proj_type;
|
||
{
|
||
// default key
|
||
get_string(KEY_PROJ_TYPE, proj_type, false);
|
||
|
||
// for models with mixed modalities
|
||
if (proj_type.empty()) {
|
||
if (modality == CLIP_MODALITY_VISION) {
|
||
get_string(KEY_VISION_PROJ_TYPE, proj_type, false);
|
||
} else if (modality == CLIP_MODALITY_AUDIO) {
|
||
get_string(KEY_AUDIO_PROJ_TYPE, proj_type, false);
|
||
} else {
|
||
GGML_ABORT("unknown modality");
|
||
}
|
||
}
|
||
|
||
model.proj_type = clip_projector_type_from_string(proj_type);
|
||
|
||
if (model.proj_type == PROJECTOR_TYPE_UNKNOWN) {
|
||
throw std::runtime_error(string_format("%s: unknown projector type: %s\n", __func__, proj_type.c_str()));
|
||
}
|
||
|
||
// correct arch for multimodal models (legacy method)
|
||
if (model.proj_type == PROJECTOR_TYPE_QWEN25O) {
|
||
model.proj_type = modality == CLIP_MODALITY_VISION
|
||
? PROJECTOR_TYPE_QWEN25VL
|
||
: PROJECTOR_TYPE_QWEN2A;
|
||
}
|
||
}
|
||
|
||
const bool is_vision = model.modality == CLIP_MODALITY_VISION;
|
||
const bool is_audio = model.modality == CLIP_MODALITY_AUDIO;
|
||
|
||
// other hparams
|
||
{
|
||
const char * prefix = is_vision ? "vision" : "audio";
|
||
get_u32(string_format(KEY_N_EMBD, prefix), hparams.n_embd);
|
||
get_u32(string_format(KEY_N_HEAD, prefix), hparams.n_head);
|
||
get_u32(string_format(KEY_N_FF, prefix), hparams.n_ff);
|
||
get_u32(string_format(KEY_N_BLOCK, prefix), hparams.n_layer);
|
||
get_u32(string_format(KEY_PROJ_DIM, prefix), hparams.projection_dim);
|
||
get_f32(string_format(KEY_LAYER_NORM_EPS, prefix), hparams.eps);
|
||
|
||
if (is_vision) {
|
||
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_i32(KEY_MINICPMV_VERSION, hparams.minicpmv_version, false); // legacy
|
||
get_u32(KEY_MINICPMV_QUERY_NUM, hparams.minicpmv_query_num, false);
|
||
if (hparams.minicpmv_query_num == 0) {
|
||
// Fallback to hardcoded values for legacy models
|
||
if (hparams.minicpmv_version == 3) {
|
||
hparams.minicpmv_query_num = 64;
|
||
} else if (hparams.minicpmv_version == 4) {
|
||
hparams.minicpmv_query_num = 64;
|
||
} else if (hparams.minicpmv_version == 5) {
|
||
hparams.minicpmv_query_num = 64;
|
||
} else if (hparams.minicpmv_version == 6) {
|
||
hparams.minicpmv_query_num = 64;
|
||
} else {
|
||
hparams.minicpmv_query_num = 96;
|
||
}
|
||
}
|
||
} else if (is_audio) {
|
||
get_u32(KEY_A_NUM_MEL_BINS, hparams.n_mel_bins);
|
||
// some hparams are unused, but still need to set to avoid issues
|
||
hparams.image_size = 0;
|
||
hparams.patch_size = 1;
|
||
|
||
} else {
|
||
GGML_ASSERT(false && "unknown modality");
|
||
}
|
||
|
||
// for pinpoints, we need to convert it into a list of resolution candidates
|
||
{
|
||
std::vector<int> pinpoints;
|
||
get_arr_int(KEY_IMAGE_GRID_PINPOINTS, pinpoints, false);
|
||
if (!pinpoints.empty()) {
|
||
for (size_t i = 0; i < pinpoints.size(); i += 2) {
|
||
hparams.image_res_candidates.push_back({
|
||
pinpoints[i],
|
||
pinpoints[i+1],
|
||
});
|
||
}
|
||
}
|
||
}
|
||
|
||
// default warmup value
|
||
hparams.warmup_image_size = hparams.image_size;
|
||
|
||
hparams.has_llava_projector = model.proj_type == PROJECTOR_TYPE_MLP
|
||
|| model.proj_type == PROJECTOR_TYPE_MLP_NORM
|
||
|| model.proj_type == PROJECTOR_TYPE_LDP
|
||
|| model.proj_type == PROJECTOR_TYPE_LDPV2;
|
||
|
||
{
|
||
bool use_gelu = false;
|
||
bool use_silu = false;
|
||
get_bool(KEY_USE_GELU, use_gelu, false);
|
||
get_bool(KEY_USE_SILU, use_silu, false);
|
||
if (use_gelu && use_silu) {
|
||
throw std::runtime_error(string_format("%s: both use_gelu and use_silu are set to true\n", __func__));
|
||
}
|
||
if (use_gelu) {
|
||
hparams.ffn_op = FFN_GELU;
|
||
log_ffn_op = "gelu";
|
||
} else if (use_silu) {
|
||
hparams.ffn_op = FFN_SILU;
|
||
log_ffn_op = "silu";
|
||
} else {
|
||
hparams.ffn_op = FFN_GELU_QUICK;
|
||
log_ffn_op = "gelu_quick";
|
||
}
|
||
}
|
||
|
||
{
|
||
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;
|
||
}
|
||
}
|
||
|
||
if (is_vision) {
|
||
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) {
|
||
hparams.image_mean[i] = mean_data[i];
|
||
hparams.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);
|
||
}
|
||
|
||
// model-specific params
|
||
switch (model.proj_type) {
|
||
case PROJECTOR_TYPE_MINICPMV:
|
||
{
|
||
if (hparams.minicpmv_version == 0) {
|
||
hparams.minicpmv_version = 2; // default to 2 if not set
|
||
}
|
||
} break;
|
||
case PROJECTOR_TYPE_INTERNVL:
|
||
{
|
||
get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
|
||
} break;
|
||
case PROJECTOR_TYPE_IDEFICS3:
|
||
{
|
||
get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
|
||
get_u32(KEY_PREPROC_IMAGE_SIZE, hparams.image_longest_edge, false);
|
||
} break;
|
||
case PROJECTOR_TYPE_LFM2:
|
||
{
|
||
get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
|
||
// ref: https://huggingface.co/LiquidAI/LFM2-VL-3B/blob/main/preprocessor_config.json
|
||
// config above specifies number of tokens after downsampling, while here it is before, relax lowerbound to 64
|
||
hparams.set_limit_image_tokens(64, 1024);
|
||
} break;
|
||
case PROJECTOR_TYPE_PIXTRAL:
|
||
case PROJECTOR_TYPE_LIGHTONOCR:
|
||
{
|
||
// ref: https://huggingface.co/mistral-community/pixtral-12b/blob/main/preprocessor_config.json
|
||
// TODO: verify the image_min_tokens
|
||
hparams.n_merge = 1; // the original pixtral does not use patch merging
|
||
hparams.rope_theta = 10000.0f;
|
||
get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false);
|
||
hparams.set_limit_image_tokens(8, 1024);
|
||
hparams.set_warmup_n_tokens(256); // avoid OOM on warmup
|
||
} break;
|
||
case PROJECTOR_TYPE_KIMIVL:
|
||
{
|
||
hparams.rope_theta = 10000.0f;
|
||
get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
|
||
// TODO: check kimivl preprocessor for exact values
|
||
hparams.set_limit_image_tokens(8, 1024);
|
||
hparams.set_warmup_n_tokens(256); // avoid OOM on warmup
|
||
} break;
|
||
case PROJECTOR_TYPE_GEMMA3:
|
||
{
|
||
// default value (used by all model sizes in gemma 3 family)
|
||
// number of patches for each **side** is reduced by a factor of 4
|
||
hparams.n_merge = 4;
|
||
// test model (tinygemma3) has a different value, we optionally read it
|
||
get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
|
||
} break;
|
||
case PROJECTOR_TYPE_QWEN2VL:
|
||
case PROJECTOR_TYPE_QWEN25VL:
|
||
case PROJECTOR_TYPE_QWEN3VL:
|
||
{
|
||
hparams.n_merge = 2; // default value for Qwen 2 and 2.5
|
||
get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false);
|
||
get_u32(KEY_WIN_ATTN_PATTERN, hparams.n_wa_pattern, model.proj_type == PROJECTOR_TYPE_QWEN25VL); // only 2.5 requires it
|
||
// ref: https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct/blob/main/preprocessor_config.json
|
||
hparams.set_limit_image_tokens(8, 4096);
|
||
hparams.set_warmup_n_tokens(46*46); // avoid OOM on warmup
|
||
const int warn_min_pixels = 1024 * hparams.n_merge * hparams.n_merge * hparams.patch_size * hparams.patch_size;
|
||
if (hparams.image_min_pixels < warn_min_pixels) {
|
||
LOG_WRN("%s: Qwen-VL models require at minimum 1024 image tokens to function correctly on grounding tasks\n", __func__);
|
||
LOG_WRN("%s: if you encounter problems with accuracy, try adding --image-min-tokens 1024\n", __func__);
|
||
LOG_WRN("%s: more info: https://github.com/ggml-org/llama.cpp/issues/16842\n\n", __func__);
|
||
}
|
||
} break;
|
||
case PROJECTOR_TYPE_GLM4V:
|
||
{
|
||
hparams.rope_theta = 10000.0f;
|
||
hparams.n_merge = 2; // default value for GLM4-V
|
||
get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false);
|
||
hparams.set_limit_image_tokens(8, 4096);
|
||
hparams.set_warmup_n_tokens(46*46); // avoid OOM on warmup
|
||
} break;
|
||
case PROJECTOR_TYPE_LLAMA4:
|
||
{
|
||
hparams.rope_theta = 10000.0f;
|
||
get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
|
||
set_llava_uhd_res_candidates(model, 3);
|
||
} break;
|
||
case PROJECTOR_TYPE_ULTRAVOX:
|
||
case PROJECTOR_TYPE_QWEN2A:
|
||
case PROJECTOR_TYPE_GLMA:
|
||
case PROJECTOR_TYPE_VOXTRAL:
|
||
{
|
||
bool require_stack = model.proj_type == PROJECTOR_TYPE_ULTRAVOX ||
|
||
model.proj_type == PROJECTOR_TYPE_VOXTRAL ||
|
||
model.proj_type == PROJECTOR_TYPE_GLMA;
|
||
get_u32(KEY_A_PROJ_STACK_FACTOR, hparams.proj_stack_factor, require_stack);
|
||
hparams.ffn_op = FFN_GELU_ERF;
|
||
log_ffn_op = "gelu_erf"; // temporary solution for logging
|
||
|
||
// audio preprocessing params
|
||
hparams.audio_chunk_len = 30; // in seconds
|
||
hparams.audio_sample_rate = 16000;
|
||
hparams.audio_n_fft = 400;
|
||
hparams.audio_window_len = 400;
|
||
hparams.audio_hop_len = 160;
|
||
} break;
|
||
case PROJECTOR_TYPE_DEEPSEEKOCR:
|
||
{
|
||
hparams.patch_size = 16;
|
||
hparams.image_size = 1024;
|
||
hparams.warmup_image_size = 1024;
|
||
|
||
get_u32(KEY_SAM_N_BLOCK, hparams.sam_n_layer, true);
|
||
get_u32(KEY_SAM_N_HEAD, hparams.sam_n_head, true);
|
||
get_u32(KEY_SAM_N_EMBD, hparams.sam_n_embd, true);
|
||
get_u32(KEY_ATTN_WINDOW_SIZE, hparams.attn_window_size, true);
|
||
} break;
|
||
default:
|
||
break;
|
||
}
|
||
|
||
// sanity check
|
||
{
|
||
if (hparams.image_max_pixels < hparams.image_min_pixels) {
|
||
throw std::runtime_error(string_format("%s: image_max_pixels (%d) is less than image_min_pixels (%d)\n", __func__, hparams.image_max_pixels, hparams.image_min_pixels));
|
||
}
|
||
}
|
||
|
||
LOG_INF("%s: projector: %s\n", __func__, proj_type.c_str());
|
||
LOG_INF("%s: n_embd: %d\n", __func__, hparams.n_embd);
|
||
LOG_INF("%s: n_head: %d\n", __func__, hparams.n_head);
|
||
LOG_INF("%s: n_ff: %d\n", __func__, hparams.n_ff);
|
||
LOG_INF("%s: n_layer: %d\n", __func__, hparams.n_layer);
|
||
LOG_INF("%s: ffn_op: %s\n", __func__, log_ffn_op.c_str());
|
||
LOG_INF("%s: projection_dim: %d\n", __func__, hparams.projection_dim);
|
||
if (is_vision) {
|
||
LOG_INF("\n--- vision hparams ---\n");
|
||
LOG_INF("%s: image_size: %d\n", __func__, hparams.image_size);
|
||
LOG_INF("%s: patch_size: %d\n", __func__, hparams.patch_size);
|
||
LOG_INF("%s: has_llava_proj: %d\n", __func__, hparams.has_llava_projector);
|
||
LOG_INF("%s: minicpmv_version: %d\n", __func__, hparams.minicpmv_version);
|
||
LOG_INF("%s: n_merge: %d\n", __func__, hparams.n_merge);
|
||
LOG_INF("%s: n_wa_pattern: %d\n", __func__, hparams.n_wa_pattern);
|
||
if (hparams.image_min_pixels > 0) {
|
||
LOG_INF("%s: image_min_pixels: %d%s\n", __func__, hparams.image_min_pixels, hparams.custom_image_min_tokens > 0 ? " (custom value)" : "");
|
||
}
|
||
if (hparams.image_max_pixels > 0) {
|
||
LOG_INF("%s: image_max_pixels: %d%s\n", __func__, hparams.image_max_pixels, hparams.custom_image_max_tokens > 0 ? " (custom value)" : "");
|
||
}
|
||
} else if (is_audio) {
|
||
LOG_INF("\n--- audio hparams ---\n");
|
||
LOG_INF("%s: n_mel_bins: %d\n", __func__, hparams.n_mel_bins);
|
||
LOG_INF("%s: proj_stack_factor: %d\n", __func__, hparams.proj_stack_factor);
|
||
LOG_INF("%s: audio_chunk_len: %d\n", __func__, hparams.audio_chunk_len);
|
||
LOG_INF("%s: audio_sample_rate: %d\n", __func__, hparams.audio_sample_rate);
|
||
LOG_INF("%s: audio_n_fft: %d\n", __func__, hparams.audio_n_fft);
|
||
LOG_INF("%s: audio_window_len: %d\n", __func__, hparams.audio_window_len);
|
||
LOG_INF("%s: audio_hop_len: %d\n", __func__, hparams.audio_hop_len);
|
||
}
|
||
LOG_INF("\n");
|
||
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(clip_ctx & ctx_clip) {
|
||
auto & model = ctx_clip.model;
|
||
auto & hparams = model.hparams;
|
||
std::map<std::string, size_t> tensor_offset;
|
||
std::vector<ggml_tensor *> tensors_to_load;
|
||
|
||
// TODO @ngxson : support both audio and video in the future
|
||
const char * prefix = model.modality == CLIP_MODALITY_AUDIO ? "a" : "v";
|
||
|
||
// 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 =*/ static_cast<size_t>(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) {
|
||
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
|
||
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;
|
||
};
|
||
|
||
model.class_embedding = get_tensor(TN_CLASS_EMBD, false);
|
||
|
||
model.pre_ln_w = get_tensor(string_format(TN_LN_PRE, prefix, "weight"), false);
|
||
model.pre_ln_b = get_tensor(string_format(TN_LN_PRE, prefix, "bias"), false);
|
||
|
||
model.post_ln_w = get_tensor(string_format(TN_LN_POST, prefix, "weight"), false);
|
||
model.post_ln_b = get_tensor(string_format(TN_LN_POST, prefix, "bias"), false);
|
||
|
||
model.patch_bias = get_tensor(TN_PATCH_BIAS, false);
|
||
model.patch_embeddings_0 = get_tensor(TN_PATCH_EMBD, false);
|
||
model.patch_embeddings_1 = get_tensor(TN_PATCH_EMBD_1, false);
|
||
|
||
model.norm_embd_w = get_tensor(string_format(TN_NORM_EMBD, "weight"), false);
|
||
model.norm_embd_b = get_tensor(string_format(TN_NORM_EMBD, "bias"), false);
|
||
|
||
model.position_embeddings = get_tensor(string_format(TN_POS_EMBD, prefix), false);
|
||
|
||
// layers
|
||
model.layers.resize(hparams.n_layer);
|
||
for (int il = 0; il < hparams.n_layer; ++il) {
|
||
auto & layer = model.layers[il];
|
||
layer.k_w = get_tensor(string_format(TN_ATTN_K, prefix, il, "weight"), false);
|
||
layer.q_w = get_tensor(string_format(TN_ATTN_Q, prefix, il, "weight"), false);
|
||
layer.v_w = get_tensor(string_format(TN_ATTN_V, prefix, il, "weight"), false);
|
||
layer.o_w = get_tensor(string_format(TN_ATTN_OUTPUT, prefix, il, "weight"));
|
||
layer.qkv_w = get_tensor(string_format(TN_ATTN_QKV, prefix, il, "weight"), false);
|
||
layer.k_norm = get_tensor(string_format(TN_ATTN_K_NORM, prefix, il, "weight"), false);
|
||
layer.q_norm = get_tensor(string_format(TN_ATTN_Q_NORM, prefix, il, "weight"), false);
|
||
layer.ln_1_w = get_tensor(string_format(TN_LN_1, prefix, il, "weight"), false);
|
||
layer.ln_2_w = get_tensor(string_format(TN_LN_2, prefix, il, "weight"), false);
|
||
layer.ls_1_w = get_tensor(string_format(TN_LS_1, prefix, il, "weight"), false); // no bias
|
||
layer.ls_2_w = get_tensor(string_format(TN_LS_2, prefix, il, "weight"), false); // no bias
|
||
|
||
layer.k_b = get_tensor(string_format(TN_ATTN_K, prefix, il, "bias"), false);
|
||
layer.q_b = get_tensor(string_format(TN_ATTN_Q, prefix, il, "bias"), false);
|
||
layer.v_b = get_tensor(string_format(TN_ATTN_V, prefix, il, "bias"), false);
|
||
layer.o_b = get_tensor(string_format(TN_ATTN_OUTPUT, prefix, il, "bias"), false);
|
||
layer.qkv_b = get_tensor(string_format(TN_ATTN_QKV, prefix, il, "bias"), false);
|
||
layer.ln_1_b = get_tensor(string_format(TN_LN_1, prefix, il, "bias"), false);
|
||
layer.ln_2_b = get_tensor(string_format(TN_LN_2, prefix, il, "bias"), false);
|
||
|
||
// ffn
|
||
layer.ff_up_w = get_tensor(string_format(TN_FFN_UP, prefix, il, "weight"));
|
||
layer.ff_up_b = get_tensor(string_format(TN_FFN_UP, prefix, il, "bias"), false);
|
||
layer.ff_gate_w = get_tensor(string_format(TN_FFN_GATE, prefix, il, "weight"), false);
|
||
layer.ff_gate_b = get_tensor(string_format(TN_FFN_GATE, prefix, il, "bias"), false);
|
||
layer.ff_down_w = get_tensor(string_format(TN_FFN_DOWN, prefix, il, "weight"));
|
||
layer.ff_down_b = get_tensor(string_format(TN_FFN_DOWN, prefix, il, "bias"), false);
|
||
|
||
|
||
// qwen3vl deepstack layer
|
||
layer.deepstack_norm_w = get_tensor(string_format(TN_DEEPSTACK_NORM, il, "weight"), false);
|
||
layer.deepstack_norm_b = get_tensor(string_format(TN_DEEPSTACK_NORM, il, "bias"), false);
|
||
layer.deepstack_fc1_w = get_tensor(string_format(TN_DEEPSTACK_FC1, il, "weight"), false);
|
||
layer.deepstack_fc1_b = get_tensor(string_format(TN_DEEPSTACK_FC1, il, "bias"), false);
|
||
layer.deepstack_fc2_w = get_tensor(string_format(TN_DEEPSTACK_FC2, il, "weight"), false);
|
||
layer.deepstack_fc2_b = get_tensor(string_format(TN_DEEPSTACK_FC2, il, "bias"), false);
|
||
if (layer.has_deepstack()) {
|
||
model.n_deepstack_layers++;
|
||
}
|
||
|
||
// some models already exported with legacy (incorrect) naming which is quite messy, let's fix it here
|
||
// note: Qwen model converted from the old surgery script has n_ff = 0, so we cannot use n_ff to check!
|
||
bool is_ffn_swapped = (
|
||
// only old models need this fix
|
||
model.proj_type == PROJECTOR_TYPE_MLP
|
||
|| model.proj_type == PROJECTOR_TYPE_MLP_NORM
|
||
|| model.proj_type == PROJECTOR_TYPE_LDP
|
||
|| model.proj_type == PROJECTOR_TYPE_LDPV2
|
||
|| model.proj_type == PROJECTOR_TYPE_QWEN2VL
|
||
|| model.proj_type == PROJECTOR_TYPE_QWEN25VL
|
||
|| model.proj_type == PROJECTOR_TYPE_GLM_EDGE
|
||
|| model.proj_type == PROJECTOR_TYPE_GEMMA3
|
||
|| model.proj_type == PROJECTOR_TYPE_IDEFICS3
|
||
|| model.proj_type == PROJECTOR_TYPE_MINICPMV
|
||
) && layer.ff_up_w && layer.ff_down_w && layer.ff_down_w->ne[0] == hparams.n_embd;
|
||
if (is_ffn_swapped) {
|
||
// swap up and down weights
|
||
ggml_tensor * tmp = layer.ff_up_w;
|
||
layer.ff_up_w = layer.ff_down_w;
|
||
layer.ff_down_w = tmp;
|
||
// swap up and down biases
|
||
tmp = layer.ff_up_b;
|
||
layer.ff_up_b = layer.ff_down_b;
|
||
layer.ff_down_b = tmp;
|
||
if (il == 0) {
|
||
LOG_WRN("%s: ffn up/down are swapped\n", __func__);
|
||
}
|
||
}
|
||
}
|
||
|
||
switch (model.proj_type) {
|
||
case PROJECTOR_TYPE_MLP:
|
||
case PROJECTOR_TYPE_MLP_NORM:
|
||
{
|
||
// LLaVA projection
|
||
model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight"), false);
|
||
model.mm_0_b = get_tensor(string_format(TN_LLAVA_PROJ, 0, "bias"), false);
|
||
// Yi-type llava
|
||
model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 1, "weight"), false);
|
||
model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 1, "bias"), false);
|
||
// missing in Yi-type llava
|
||
model.mm_2_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight"), false);
|
||
model.mm_2_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"), false);
|
||
// Yi-type llava
|
||
model.mm_3_w = get_tensor(string_format(TN_LLAVA_PROJ, 3, "weight"), false);
|
||
model.mm_3_b = get_tensor(string_format(TN_LLAVA_PROJ, 3, "bias"), false);
|
||
model.mm_4_w = get_tensor(string_format(TN_LLAVA_PROJ, 4, "weight"), false);
|
||
model.mm_4_b = get_tensor(string_format(TN_LLAVA_PROJ, 4, "bias"), false);
|
||
if (model.mm_3_w) {
|
||
// TODO: this is a hack to support Yi-type llava
|
||
model.proj_type = PROJECTOR_TYPE_MLP_NORM;
|
||
}
|
||
model.image_newline = get_tensor(TN_IMAGE_NEWLINE, false);
|
||
} break;
|
||
case PROJECTOR_TYPE_LDP:
|
||
{
|
||
// MobileVLM projection
|
||
model.mm_model_mlp_1_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 1, "weight"));
|
||
model.mm_model_mlp_1_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 1, "bias"));
|
||
model.mm_model_mlp_3_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "weight"));
|
||
model.mm_model_mlp_3_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "bias"));
|
||
model.mm_model_block_1_block_0_0_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 0, "0.weight"));
|
||
model.mm_model_block_1_block_0_1_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 0, "1.weight"));
|
||
model.mm_model_block_1_block_0_1_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 0, "1.bias"));
|
||
model.mm_model_block_1_block_1_fc1_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc1.weight"));
|
||
model.mm_model_block_1_block_1_fc1_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc1.bias"));
|
||
model.mm_model_block_1_block_1_fc2_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc2.weight"));
|
||
model.mm_model_block_1_block_1_fc2_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc2.bias"));
|
||
model.mm_model_block_1_block_2_0_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 2, "0.weight"));
|
||
model.mm_model_block_1_block_2_1_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 2, "1.weight"));
|
||
model.mm_model_block_1_block_2_1_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 1, 2, "1.bias"));
|
||
model.mm_model_block_2_block_0_0_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 0, "0.weight"));
|
||
model.mm_model_block_2_block_0_1_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 0, "1.weight"));
|
||
model.mm_model_block_2_block_0_1_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 0, "1.bias"));
|
||
model.mm_model_block_2_block_1_fc1_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc1.weight"));
|
||
model.mm_model_block_2_block_1_fc1_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc1.bias"));
|
||
model.mm_model_block_2_block_1_fc2_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc2.weight"));
|
||
model.mm_model_block_2_block_1_fc2_b = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc2.bias"));
|
||
model.mm_model_block_2_block_2_0_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 2, "0.weight"));
|
||
model.mm_model_block_2_block_2_1_w = get_tensor(string_format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.weight"));
|
||
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
|
||
model.mm_model_mlp_0_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 0, "weight"));
|
||
model.mm_model_mlp_0_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 0, "bias"));
|
||
model.mm_model_mlp_2_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 2, "weight"));
|
||
model.mm_model_mlp_2_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 2, "bias"));
|
||
model.mm_model_peg_0_w = get_tensor(string_format(TN_MVLM_PROJ_PEG, 0, "weight"));
|
||
model.mm_model_peg_0_b = get_tensor(string_format(TN_MVLM_PROJ_PEG, 0, "bias"));
|
||
} break;
|
||
case PROJECTOR_TYPE_MINICPMV:
|
||
{
|
||
// model.mm_model_pos_embed = get_tensor(new_clip->ctx_data, TN_MINICPMV_POS_EMBD);
|
||
model.mm_model_pos_embed_k = get_tensor(TN_MINICPMV_POS_EMBD_K);
|
||
model.mm_model_query = get_tensor(TN_MINICPMV_QUERY);
|
||
model.mm_model_proj = get_tensor(TN_MINICPMV_PROJ);
|
||
model.mm_model_kv_proj = get_tensor(TN_MINICPMV_KV_PROJ);
|
||
model.mm_model_attn_q_w = get_tensor(string_format(TN_MINICPMV_ATTN, "q", "weight"));
|
||
model.mm_model_attn_k_w = get_tensor(string_format(TN_MINICPMV_ATTN, "k", "weight"));
|
||
model.mm_model_attn_v_w = get_tensor(string_format(TN_MINICPMV_ATTN, "v", "weight"));
|
||
model.mm_model_attn_q_b = get_tensor(string_format(TN_MINICPMV_ATTN, "q", "bias"));
|
||
model.mm_model_attn_k_b = get_tensor(string_format(TN_MINICPMV_ATTN, "k", "bias"));
|
||
model.mm_model_attn_v_b = get_tensor(string_format(TN_MINICPMV_ATTN, "v", "bias"));
|
||
model.mm_model_attn_o_w = get_tensor(string_format(TN_MINICPMV_ATTN, "out", "weight"));
|
||
model.mm_model_attn_o_b = get_tensor(string_format(TN_MINICPMV_ATTN, "out", "bias"));
|
||
model.mm_model_ln_q_w = get_tensor(string_format(TN_MINICPMV_LN, "q", "weight"));
|
||
model.mm_model_ln_q_b = get_tensor(string_format(TN_MINICPMV_LN, "q", "bias"));
|
||
model.mm_model_ln_kv_w = get_tensor(string_format(TN_MINICPMV_LN, "kv", "weight"));
|
||
model.mm_model_ln_kv_b = get_tensor(string_format(TN_MINICPMV_LN, "kv", "bias"));
|
||
model.mm_model_ln_post_w = get_tensor(string_format(TN_MINICPMV_LN, "post", "weight"));
|
||
model.mm_model_ln_post_b = get_tensor(string_format(TN_MINICPMV_LN, "post", "bias"));
|
||
} break;
|
||
case PROJECTOR_TYPE_GLM_EDGE:
|
||
{
|
||
model.mm_model_adapter_conv_w = get_tensor(string_format(TN_GLM_ADAPER_CONV, "weight"));
|
||
model.mm_model_adapter_conv_b = get_tensor(string_format(TN_GLM_ADAPER_CONV, "bias"));
|
||
model.mm_model_mlp_0_w = get_tensor(string_format(TN_GLM_ADAPTER_LINEAR, "weight"));
|
||
model.mm_model_ln_q_w = get_tensor(string_format(TN_GLM_ADAPTER_NORM_1, "weight"));
|
||
model.mm_model_ln_q_b = get_tensor(string_format(TN_GLM_ADAPTER_NORM_1, "bias"));
|
||
model.mm_model_mlp_1_w = get_tensor(string_format(TN_GLM_ADAPTER_D_H_2_4H, "weight"));
|
||
model.mm_model_mlp_2_w = get_tensor(string_format(TN_GLM_ADAPTER_GATE, "weight"));
|
||
model.mm_model_mlp_3_w = get_tensor(string_format(TN_GLM_ADAPTER_D_4H_2_H, "weight"));
|
||
model.mm_boi = get_tensor(string_format(TN_TOK_GLM_BOI, "weight"));
|
||
model.mm_eoi = get_tensor(string_format(TN_TOK_GLM_EOI, "weight"));
|
||
} break;
|
||
case PROJECTOR_TYPE_QWEN2VL:
|
||
case PROJECTOR_TYPE_QWEN25VL:
|
||
{
|
||
model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight"));
|
||
model.mm_0_b = get_tensor(string_format(TN_LLAVA_PROJ, 0, "bias"));
|
||
model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight"));
|
||
model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"));
|
||
} break;
|
||
case PROJECTOR_TYPE_QWEN3VL:
|
||
{
|
||
model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight"));
|
||
model.mm_0_b = get_tensor(string_format(TN_LLAVA_PROJ, 0, "bias"));
|
||
model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight"));
|
||
model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"));
|
||
} break;
|
||
case PROJECTOR_TYPE_GLM4V:
|
||
{
|
||
model.fc_w = get_tensor(string_format(TN_MM_PROJECTOR, "weight"));
|
||
model.mm_ffn_up_w = get_tensor(string_format(TN_MM_UP, "weight"));
|
||
model.mm_ffn_up_b = get_tensor(string_format(TN_MM_UP, "bias"), false);
|
||
model.mm_ffn_gate_w = get_tensor(string_format(TN_MM_GATE, "weight"));
|
||
model.mm_ffn_gate_b = get_tensor(string_format(TN_MM_GATE, "bias"), false);
|
||
model.mm_ffn_down_w = get_tensor(string_format(TN_MM_DOWN, "weight"));
|
||
model.mm_ffn_down_b = get_tensor(string_format(TN_MM_DOWN, "bias"), false);
|
||
model.mm_post_norm_w = get_tensor(string_format(TN_MM_POST_NORM, "weight"));
|
||
model.mm_post_norm_b = get_tensor(string_format(TN_MM_POST_NORM, "bias"), false);
|
||
model.mm_patch_merger_w = get_tensor(string_format(TN_MM_PATCH_MERGER, "weight"));
|
||
model.mm_patch_merger_b = get_tensor(string_format(TN_MM_PATCH_MERGER, "bias"));
|
||
} break;
|
||
case PROJECTOR_TYPE_GEMMA3:
|
||
{
|
||
model.mm_input_proj_w = get_tensor(TN_MM_INP_PROJ);
|
||
model.mm_soft_emb_norm_w = get_tensor(TN_MM_SOFT_EMB_N);
|
||
} break;
|
||
case PROJECTOR_TYPE_IDEFICS3:
|
||
{
|
||
model.fc_w = get_tensor(string_format(TN_MM_PROJECTOR, "weight"));
|
||
} break;
|
||
case PROJECTOR_TYPE_LFM2:
|
||
case PROJECTOR_TYPE_KIMIVL:
|
||
{
|
||
model.mm_input_norm_w = get_tensor(TN_MM_INP_NORM);
|
||
model.mm_input_norm_b = get_tensor(TN_MM_INP_NORM_B);
|
||
model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 1, "weight"));
|
||
model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 1, "bias"));
|
||
model.mm_2_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight"));
|
||
model.mm_2_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"));
|
||
} break;
|
||
case PROJECTOR_TYPE_PIXTRAL:
|
||
{
|
||
model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 1, "weight"));
|
||
model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 1, "bias"), false);
|
||
model.mm_2_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight"));
|
||
model.mm_2_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"), false);
|
||
// [IMG_BREAK] token embedding
|
||
model.token_embd_img_break = get_tensor(TN_TOK_IMG_BREAK);
|
||
// for mistral small 3.1
|
||
model.mm_input_norm_w = get_tensor(TN_MM_INP_NORM, false);
|
||
model.mm_patch_merger_w = get_tensor(string_format(TN_MM_PATCH_MERGER, "weight"), false);
|
||
} break;
|
||
case PROJECTOR_TYPE_LIGHTONOCR:
|
||
{
|
||
model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 1, "weight"));
|
||
model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 1, "bias"), false);
|
||
model.mm_2_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight"));
|
||
model.mm_2_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"), false);
|
||
model.mm_input_norm_w = get_tensor(TN_MM_INP_NORM, false);
|
||
model.mm_patch_merger_w = get_tensor(string_format(TN_MM_PATCH_MERGER, "weight"), false);
|
||
} break;
|
||
case PROJECTOR_TYPE_ULTRAVOX:
|
||
{
|
||
model.conv1d_1_w = get_tensor(string_format(TN_CONV1D, 1, "weight"));
|
||
model.conv1d_1_b = get_tensor(string_format(TN_CONV1D, 1, "bias"));
|
||
model.conv1d_2_w = get_tensor(string_format(TN_CONV1D, 2, "weight"));
|
||
model.conv1d_2_b = get_tensor(string_format(TN_CONV1D, 2, "bias"));
|
||
model.mm_1_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "weight"));
|
||
model.mm_2_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "weight"));
|
||
model.mm_norm_pre_w = get_tensor(string_format(TN_MM_NORM_PRE, "weight"));
|
||
model.mm_norm_mid_w = get_tensor(string_format(TN_MM_NORM_MID, "weight"));
|
||
} break;
|
||
case PROJECTOR_TYPE_QWEN2A:
|
||
{
|
||
model.conv1d_1_w = get_tensor(string_format(TN_CONV1D, 1, "weight"));
|
||
model.conv1d_1_b = get_tensor(string_format(TN_CONV1D, 1, "bias"));
|
||
model.conv1d_2_w = get_tensor(string_format(TN_CONV1D, 2, "weight"));
|
||
model.conv1d_2_b = get_tensor(string_format(TN_CONV1D, 2, "bias"));
|
||
model.mm_fc_w = get_tensor(string_format(TN_MM_AUDIO_FC, "weight"));
|
||
model.mm_fc_b = get_tensor(string_format(TN_MM_AUDIO_FC, "bias"));
|
||
} break;
|
||
case PROJECTOR_TYPE_VOXTRAL:
|
||
{
|
||
model.conv1d_1_w = get_tensor(string_format(TN_CONV1D, 1, "weight"));
|
||
model.conv1d_1_b = get_tensor(string_format(TN_CONV1D, 1, "bias"));
|
||
model.conv1d_2_w = get_tensor(string_format(TN_CONV1D, 2, "weight"));
|
||
model.conv1d_2_b = get_tensor(string_format(TN_CONV1D, 2, "bias"));
|
||
model.mm_1_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "weight"));
|
||
model.mm_2_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "weight"));
|
||
} break;
|
||
case PROJECTOR_TYPE_INTERNVL:
|
||
{
|
||
model.mm_0_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 0, "weight"));
|
||
model.mm_0_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 0, "bias"));
|
||
model.mm_1_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 1, "weight"));
|
||
model.mm_1_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 1, "bias"));
|
||
model.mm_3_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "weight"));
|
||
model.mm_3_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "bias"));
|
||
} break;
|
||
case PROJECTOR_TYPE_GLMA:
|
||
{
|
||
model.conv1d_1_w = get_tensor(string_format(TN_CONV1D, 1, "weight"));
|
||
model.conv1d_1_b = get_tensor(string_format(TN_CONV1D, 1, "bias"));
|
||
model.conv1d_2_w = get_tensor(string_format(TN_CONV1D, 2, "weight"));
|
||
model.conv1d_2_b = get_tensor(string_format(TN_CONV1D, 2, "bias"));
|
||
model.mm_1_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "weight"));
|
||
model.mm_1_b = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "bias"));
|
||
model.mm_2_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "weight"));
|
||
model.mm_2_b = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "bias"));
|
||
model.mm_norm_pre_w = get_tensor(string_format(TN_MM_NORM_PRE, "weight"));
|
||
model.mm_norm_pre_b = get_tensor(string_format(TN_MM_NORM_PRE, "bias"));
|
||
model.mm_boi = get_tensor(string_format(TN_TOK_BOI, "weight"));
|
||
model.mm_eoi = get_tensor(string_format(TN_TOK_EOI, "weight"));
|
||
} break;
|
||
case PROJECTOR_TYPE_LLAMA4:
|
||
{
|
||
model.mm_model_proj = get_tensor(string_format(TN_MM_PROJECTOR, "weight"));
|
||
model.mm_model_mlp_1_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 1, "weight"));
|
||
model.mm_model_mlp_2_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 2, "weight"));
|
||
} break;
|
||
case PROJECTOR_TYPE_COGVLM:
|
||
{
|
||
model.mm_model_proj = get_tensor(string_format(TN_MM_PROJECTOR, "weight"));
|
||
model.mm_post_fc_norm_w = get_tensor(string_format(TN_MM_POST_FC_NORM, "weight"));
|
||
model.mm_post_fc_norm_b = get_tensor(string_format(TN_MM_POST_FC_NORM, "bias"));
|
||
model.mm_h_to_4h_w = get_tensor(string_format(TN_MM_H_TO_4H, "weight"));
|
||
model.mm_gate_w = get_tensor(string_format(TN_MM_GATE, "weight"));
|
||
model.mm_4h_to_h_w = get_tensor(string_format(TN_MM_4H_TO_H, "weight"));
|
||
model.mm_boi = get_tensor(TN_TOK_BOI);
|
||
model.mm_eoi = get_tensor(TN_TOK_EOI);
|
||
} break;
|
||
case PROJECTOR_TYPE_JANUS_PRO:
|
||
{
|
||
model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight"));
|
||
model.mm_0_b = get_tensor(string_format(TN_LLAVA_PROJ, 0, "bias"));
|
||
model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 1, "weight"));
|
||
model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 1, "bias"));
|
||
} break;
|
||
case PROJECTOR_TYPE_DEEPSEEKOCR:
|
||
{
|
||
model.pos_embed = get_tensor(TN_SAM_POS_EMBD);
|
||
model.patch_embed_proj_w = get_tensor(string_format(TN_SAM_PATCH_EMBD, "weight"));
|
||
model.patch_embed_proj_b = get_tensor(string_format(TN_SAM_PATCH_EMBD, "bias"));
|
||
model.sam_layers.resize(model.n_sam_layers);
|
||
for (int il = 0; il < model.n_sam_layers; ++il) {
|
||
auto & layer = model.sam_layers[il];
|
||
layer.qkv_w = get_tensor(string_format(TN_SAM_ATTN_QKV, il, "weight"));
|
||
layer.qkv_b = get_tensor(string_format(TN_SAM_ATTN_QKV, il, "bias"));
|
||
layer.o_w = get_tensor(string_format(TN_SAM_ATTN_OUT, il, "weight"));
|
||
layer.o_b = get_tensor(string_format(TN_SAM_ATTN_OUT, il, "bias"));
|
||
layer.ln_1_w = get_tensor(string_format(TN_SAM_PRE_NORM, il, "weight"));
|
||
layer.ln_1_b = get_tensor(string_format(TN_SAM_PRE_NORM, il, "bias"));
|
||
layer.ln_2_w = get_tensor(string_format(TN_SAM_POST_NORM, il, "weight"));
|
||
layer.ln_2_b = get_tensor(string_format(TN_SAM_POST_NORM, il, "bias"));
|
||
layer.rel_pos_h = get_tensor(string_format(TN_SAM_ATTN_POS_H, il));
|
||
layer.rel_pos_w = get_tensor(string_format(TN_SAM_ATTN_POS_W, il));
|
||
layer.ff_up_w = get_tensor(string_format(TN_SAM_FFN_UP, il, "weight"));
|
||
layer.ff_up_b = get_tensor(string_format(TN_SAM_FFN_UP, il, "bias"));
|
||
layer.ff_down_w = get_tensor(string_format(TN_SAM_FFN_DOWN, il, "weight"));
|
||
layer.ff_down_b = get_tensor(string_format(TN_SAM_FFN_DOWN, il, "bias"));
|
||
}
|
||
model.neck_0_w = get_tensor(string_format(TN_SAM_NECK, 0, "weight"));
|
||
model.neck_1_b = get_tensor(string_format(TN_SAM_NECK, 1, "bias"));
|
||
model.neck_1_w = get_tensor(string_format(TN_SAM_NECK, 1, "weight"));
|
||
model.neck_2_w = get_tensor(string_format(TN_SAM_NECK, 2, "weight"));
|
||
model.neck_3_b = get_tensor(string_format(TN_SAM_NECK, 3, "bias"));
|
||
model.neck_3_w = get_tensor(string_format(TN_SAM_NECK, 3, "weight"));
|
||
model.net_2 = get_tensor(string_format(TN_SAM_NET, 2, "weight"));
|
||
model.net_3 = get_tensor(string_format(TN_SAM_NET, 3, "weight"));
|
||
model.image_newline = get_tensor(TN_IMAGE_NEWLINE);
|
||
model.view_seperator = get_tensor(TN_IMAGE_SEPERATOR);
|
||
model.fc_w = get_tensor(string_format(TN_MM_PROJECTOR, "weight"));
|
||
model.fc_b = get_tensor(string_format(TN_MM_PROJECTOR, "bias"));
|
||
} 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) {
|
||
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());
|
||
}
|
||
}
|
||
|
||
struct support_info_op {
|
||
ggml_tensor * op;
|
||
|
||
// true if the op runs on the accelerated ctx_clip.backend
|
||
bool is_accel = true;
|
||
};
|
||
|
||
struct support_info_graph {
|
||
// whether the clip_ctx.backend supports flash attention
|
||
bool fattn = true;
|
||
ggml_tensor * fattn_op = nullptr; // for debugging
|
||
|
||
std::vector<support_info_op> ops;
|
||
};
|
||
|
||
static void warmup(clip_ctx & ctx_clip) {
|
||
// create a fake batch
|
||
const auto & hparams = ctx_clip.model.hparams;
|
||
clip_image_f32_batch batch;
|
||
clip_image_f32_ptr img(clip_image_f32_init());
|
||
if (ctx_clip.model.modality == CLIP_MODALITY_VISION) {
|
||
img->nx = hparams.warmup_image_size;
|
||
img->ny = hparams.warmup_image_size;
|
||
LOG_INF("%s: warmup with image size = %d x %d\n", __func__, img->nx, img->ny);
|
||
} else {
|
||
img->nx = hparams.warmup_audio_size;
|
||
img->ny = hparams.n_mel_bins;
|
||
LOG_INF("%s: warmup with audio size = %d\n", __func__, img->nx);
|
||
}
|
||
batch.entries.push_back(std::move(img));
|
||
warmup(ctx_clip, batch);
|
||
}
|
||
|
||
static void warmup(clip_ctx & ctx_clip, const clip_image_f32_batch & batch) {
|
||
support_info_graph info;
|
||
|
||
if (ctx_clip.flash_attn_type == CLIP_FLASH_ATTN_TYPE_AUTO) {
|
||
// try to enable flash attention to see if it's supported
|
||
ctx_clip.flash_attn_type = CLIP_FLASH_ATTN_TYPE_ENABLED;
|
||
info = alloc_compute_meta(ctx_clip, batch);
|
||
if (!info.fattn && info.fattn_op) {
|
||
auto op = info.fattn_op;
|
||
LOG_WRN("%s: *****************************************************************\n", __func__);
|
||
LOG_WRN("%s: WARNING: flash attention not supported by %s, memory usage will increase\n", __func__, ggml_backend_name(ctx_clip.backend));
|
||
LOG_WRN("%s: op params: \n", __func__);
|
||
static auto print_shape = [](const char * fn, const char * name, ggml_tensor * t) {
|
||
LOG_WRN("%s: %s: type = %s, ne = [%d %d %d %d], nb = [%d %d %d %d]\n", fn,
|
||
name, ggml_type_name(t->type),
|
||
t->ne[0], t->ne[1], t->ne[2], t->ne[3],
|
||
t->nb[0], t->nb[1], t->nb[2], t->nb[3]);
|
||
};
|
||
print_shape(__func__, " dst", op);
|
||
print_shape(__func__, "src0", op->src[0]);
|
||
print_shape(__func__, "src1", op->src[1]);
|
||
print_shape(__func__, "src2", op->src[2]);
|
||
LOG_WRN("%s: please report this on github as an issue\n", __func__);
|
||
LOG_WRN("%s: *****************************************************************\n", __func__);
|
||
ctx_clip.flash_attn_type = CLIP_FLASH_ATTN_TYPE_DISABLED;
|
||
alloc_compute_meta(ctx_clip, batch);
|
||
}
|
||
} else {
|
||
info = alloc_compute_meta(ctx_clip, batch);
|
||
if (!info.fattn && ctx_clip.flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) {
|
||
LOG_WRN("%s: flash attention is not supported by the current backend; falling back to CPU (performance will be degraded)\n", __func__);
|
||
}
|
||
}
|
||
|
||
ctx_clip.is_allocated = true; // mark buffers as allocated
|
||
|
||
LOG_INF("%s: flash attention is %s\n", __func__,
|
||
(ctx_clip.flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) ? "enabled" : "disabled");
|
||
|
||
// print ops that are not supported by the GPU backend (if there is one)
|
||
if (ctx_clip.backend && ctx_clip.backend != ctx_clip.backend_cpu) {
|
||
std::vector<support_info_op> unsupported_ops;
|
||
for (const auto & op : info.ops) {
|
||
if (!op.is_accel) {
|
||
unsupported_ops.push_back(op);
|
||
}
|
||
}
|
||
if (!unsupported_ops.empty()) {
|
||
LOG_WRN("%s: *****************************************************************\n", __func__);
|
||
LOG_WRN("%s: WARNING: the CLIP graph uses unsupported operators by the backend\n", __func__);
|
||
LOG_WRN("%s: the performance will be suboptimal \n", __func__);
|
||
LOG_WRN("%s: list of unsupported ops (backend=%s):\n", __func__, ggml_backend_name(ctx_clip.backend));
|
||
for (const auto & op : unsupported_ops) {
|
||
LOG_WRN("%s: %16s: type = %s, ne = [%d %d %d %d]\n", __func__,
|
||
ggml_op_name(op.op->op),
|
||
ggml_type_name(op.op->type),
|
||
op.op->ne[0], op.op->ne[1], op.op->ne[2], op.op->ne[3]);
|
||
}
|
||
LOG_WRN("%s: flash attention is %s\n", __func__,
|
||
(ctx_clip.flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) ? "enabled" : "disabled");
|
||
LOG_WRN("%s: please report this on github as an issue\n", __func__);
|
||
LOG_WRN("%s: ref: https://github.com/ggml-org/llama.cpp/pull/16837#issuecomment-3461676118\n", __func__);
|
||
LOG_WRN("%s: *****************************************************************\n", __func__);
|
||
}
|
||
}
|
||
}
|
||
|
||
static support_info_graph alloc_compute_meta(clip_ctx & ctx_clip, const clip_image_f32_batch & batch) {
|
||
ctx_clip.buf_compute_meta.resize(ctx_clip.max_nodes * ggml_tensor_overhead() + ggml_graph_overhead());
|
||
|
||
ggml_cgraph * gf = clip_image_build_graph(&ctx_clip, batch);
|
||
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);
|
||
}
|
||
}
|
||
|
||
const int n_splits = ggml_backend_sched_get_n_splits(ctx_clip.sched.get());
|
||
const int n_nodes = ggml_graph_n_nodes(gf);
|
||
|
||
LOG_INF("%s: graph splits = %d, nodes = %d\n", __func__, n_splits, n_nodes);
|
||
|
||
support_info_graph res {
|
||
/*.fattn = */ true,
|
||
/*.fattn_op = */ nullptr,
|
||
/*.ops = */ {},
|
||
};
|
||
|
||
// check op support
|
||
for (int i = 0; i < ggml_graph_n_nodes(gf); i++) {
|
||
ggml_tensor * node = ggml_graph_node(gf, i);
|
||
res.ops.push_back({node, true});
|
||
if (!ggml_backend_supports_op(ctx_clip.backend, node)) {
|
||
res.ops.back().is_accel = false;
|
||
if (node->op == GGML_OP_FLASH_ATTN_EXT) {
|
||
res.fattn = false;
|
||
res.fattn_op = node;
|
||
}
|
||
}
|
||
}
|
||
|
||
return res;
|
||
}
|
||
|
||
void get_bool(const std::string & key, bool & output, bool required = true) const {
|
||
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 {
|
||
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 {
|
||
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 {
|
||
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 {
|
||
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 {
|
||
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];
|
||
}
|
||
}
|
||
|
||
static void set_llava_uhd_res_candidates(clip_model & model, const int max_patches_per_side) {
|
||
auto & hparams = model.hparams;
|
||
for (int x = 1; x <= max_patches_per_side; x++) {
|
||
for (int y = 1; y <= max_patches_per_side; y++) {
|
||
if (x == 1 && y == 1) {
|
||
continue; // skip the first point
|
||
}
|
||
hparams.image_res_candidates.push_back(clip_image_size{
|
||
x*hparams.image_size,
|
||
y*hparams.image_size,
|
||
});
|
||
}
|
||
}
|
||
}
|
||
};
|
||
|
||
struct clip_init_result clip_init(const char * fname, struct clip_context_params ctx_params) {
|
||
clip_ctx * ctx_vision = nullptr;
|
||
clip_ctx * ctx_audio = nullptr;
|
||
|
||
try {
|
||
clip_model_loader loader(fname);
|
||
|
||
if (loader.has_vision) {
|
||
ctx_vision = new clip_ctx(ctx_params);
|
||
loader.load_hparams(ctx_vision->model, CLIP_MODALITY_VISION);
|
||
loader.load_tensors(*ctx_vision);
|
||
if (ctx_params.warmup) {
|
||
loader.warmup(*ctx_vision);
|
||
}
|
||
|
||
// clip_debug_encode(ctx_vision, 24*14, 24*14, 0.5f);
|
||
}
|
||
|
||
if (loader.has_audio) {
|
||
ctx_audio = new clip_ctx(ctx_params);
|
||
loader.load_hparams(ctx_audio->model, CLIP_MODALITY_AUDIO);
|
||
loader.load_tensors(*ctx_audio);
|
||
if (ctx_params.warmup) {
|
||
loader.warmup(*ctx_audio);
|
||
}
|
||
}
|
||
|
||
} catch (const std::exception & e) {
|
||
LOG_ERR("%s: failed to load model '%s': %s\n", __func__, fname, e.what());
|
||
|
||
delete ctx_vision;
|
||
delete ctx_audio;
|
||
|
||
return {nullptr, nullptr};
|
||
}
|
||
|
||
return {ctx_vision, ctx_audio};
|
||
}
|
||
|
||
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) { delete img; }
|
||
void clip_image_f32_free(struct clip_image_f32 * img) { delete img; }
|
||
void clip_image_u8_batch_free(struct clip_image_u8_batch * batch) { delete batch; }
|
||
void clip_image_f32_batch_free(struct clip_image_f32_batch * 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());
|
||
}
|
||
|
||
// 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];
|
||
}
|
||
}
|
||
|
||
// set of tools to manupulate images
|
||
// in the future, we can have HW acceleration by allowing this struct to access 3rd party lib like imagick or opencv
|
||
struct img_tool {
|
||
enum resize_algo {
|
||
RESIZE_ALGO_BILINEAR,
|
||
RESIZE_ALGO_BICUBIC,
|
||
RESIZE_ALGO_BICUBIC_PILLOW,
|
||
// RESIZE_ALGO_LANCZOS, // TODO
|
||
};
|
||
|
||
static void resize(
|
||
const clip_image_u8 & src,
|
||
clip_image_u8 & dst,
|
||
const clip_image_size & target_resolution,
|
||
resize_algo algo,
|
||
bool add_padding = true, // TODO: define the behavior for add_padding = false
|
||
std::array<uint8_t, 3> pad_color = {0, 0, 0}) {
|
||
dst.nx = target_resolution.width;
|
||
dst.ny = target_resolution.height;
|
||
dst.buf.resize(3 * dst.nx * dst.ny);
|
||
|
||
if (dst.nx == src.nx && dst.ny == src.ny) {
|
||
// no resize needed, simple copy
|
||
dst.buf = src.buf;
|
||
return;
|
||
}
|
||
|
||
if (!add_padding) {
|
||
// direct resize
|
||
switch (algo) {
|
||
case RESIZE_ALGO_BILINEAR:
|
||
resize_bilinear(src, dst, target_resolution.width, target_resolution.height);
|
||
break;
|
||
case RESIZE_ALGO_BICUBIC:
|
||
resize_bicubic(src, dst, target_resolution.width, target_resolution.height);
|
||
break;
|
||
case RESIZE_ALGO_BICUBIC_PILLOW:
|
||
resize_bicubic_pillow(src, dst, target_resolution.width, target_resolution.height);
|
||
break;
|
||
default:
|
||
throw std::runtime_error("Unsupported resize algorithm");
|
||
}
|
||
} else {
|
||
// resize with padding
|
||
clip_image_u8 resized_image;
|
||
float scale_w = static_cast<float>(target_resolution.width) / src.nx;
|
||
float scale_h = static_cast<float>(target_resolution.height) / src.ny;
|
||
float scale = std::min(scale_w, scale_h);
|
||
int new_width = std::min(static_cast<int>(std::ceil(src.nx * scale)), target_resolution.width);
|
||
int new_height = std::min(static_cast<int>(std::ceil(src.ny * scale)), target_resolution.height);
|
||
|
||
switch (algo) {
|
||
case RESIZE_ALGO_BILINEAR:
|
||
resize_bilinear(src, resized_image, new_width, new_height);
|
||
break;
|
||
case RESIZE_ALGO_BICUBIC:
|
||
resize_bicubic(src, resized_image, new_width, new_height);
|
||
break;
|
||
case RESIZE_ALGO_BICUBIC_PILLOW:
|
||
resize_bicubic_pillow(src, resized_image, new_width, new_height);
|
||
break;
|
||
default:
|
||
throw std::runtime_error("Unsupported resize algorithm");
|
||
}
|
||
|
||
// fill dst with pad_color
|
||
fill(dst, pad_color);
|
||
|
||
int offset_x = (target_resolution.width - new_width) / 2;
|
||
int offset_y = (target_resolution.height - new_height) / 2;
|
||
|
||
composite(dst, resized_image, offset_x, offset_y);
|
||
}
|
||
}
|
||
|
||
static void crop(const clip_image_u8 & image, clip_image_u8 & dst, int x, int y, int w, int h) {
|
||
dst.nx = w;
|
||
dst.ny = h;
|
||
dst.buf.resize(3 * w * h);
|
||
|
||
for (int i = 0; i < h; ++i) {
|
||
for (int j = 0; j < w; ++j) {
|
||
int src_idx = 3 * ((y + i)*image.nx + (x + j));
|
||
int dst_idx = 3 * (i*w + j);
|
||
dst.buf[dst_idx] = image.buf[src_idx];
|
||
dst.buf[dst_idx + 1] = image.buf[src_idx + 1];
|
||
dst.buf[dst_idx + 2] = image.buf[src_idx + 2];
|
||
}
|
||
}
|
||
}
|
||
|
||
// calculate the size of the **resized** image, while preserving the aspect ratio
|
||
// the calculated size will be aligned to the nearest multiple of align_size
|
||
// if H or W size is larger than longest_edge, it will be resized to longest_edge
|
||
static clip_image_size calc_size_preserved_ratio(const clip_image_size & inp_size, const int align_size, const int longest_edge) {
|
||
GGML_ASSERT(align_size > 0);
|
||
if (inp_size.width <= 0 || inp_size.height <= 0 || longest_edge <= 0) {
|
||
return {0, 0};
|
||
}
|
||
|
||
float scale = std::min(static_cast<float>(longest_edge) / inp_size.width,
|
||
static_cast<float>(longest_edge) / inp_size.height);
|
||
|
||
float target_width_f = static_cast<float>(inp_size.width) * scale;
|
||
float target_height_f = static_cast<float>(inp_size.height) * scale;
|
||
|
||
auto ceil_by_factor = [f = align_size](float x) { return static_cast<int>(std::ceil(x / static_cast<float>(f))) * f; };
|
||
int aligned_width = ceil_by_factor(target_width_f);
|
||
int aligned_height = ceil_by_factor(target_height_f);
|
||
|
||
return {aligned_width, aligned_height};
|
||
}
|
||
|
||
// calculate the size of the **resized** image, while preserving the aspect ratio
|
||
// the calculated size will have min_pixels <= W*H <= max_pixels
|
||
// this is referred as "smart_resize" in transformers code
|
||
static clip_image_size calc_size_preserved_ratio(const clip_image_size & inp_size, const int align_size, const int min_pixels, const int max_pixels) {
|
||
GGML_ASSERT(align_size > 0);
|
||
const int width = inp_size.width;
|
||
const int height = inp_size.height;
|
||
|
||
auto round_by_factor = [f = align_size](float x) { return static_cast<int>(std::round(x / static_cast<float>(f))) * f; };
|
||
auto ceil_by_factor = [f = align_size](float x) { return static_cast<int>(std::ceil(x / static_cast<float>(f))) * f; };
|
||
auto floor_by_factor = [f = align_size](float x) { return static_cast<int>(std::floor(x / static_cast<float>(f))) * f; };
|
||
|
||
// always align up first
|
||
int h_bar = std::max(align_size, round_by_factor(height));
|
||
int w_bar = std::max(align_size, round_by_factor(width));
|
||
|
||
if (h_bar * w_bar > max_pixels) {
|
||
const auto beta = std::sqrt(static_cast<float>(height * width) / max_pixels);
|
||
h_bar = std::max(align_size, floor_by_factor(height / beta));
|
||
w_bar = std::max(align_size, floor_by_factor(width / beta));
|
||
} else if (h_bar * w_bar < min_pixels) {
|
||
const auto beta = std::sqrt(static_cast<float>(min_pixels) / (height * width));
|
||
h_bar = ceil_by_factor(height * beta);
|
||
w_bar = ceil_by_factor(width * beta);
|
||
}
|
||
|
||
return {w_bar, h_bar};
|
||
}
|
||
|
||
// draw src image into dst image at offset (offset_x, offset_y)
|
||
static void composite(clip_image_u8 & dst, const clip_image_u8 & src, int offset_x, int offset_y) {
|
||
for (int y = 0; y < src.ny; ++y) {
|
||
for (int x = 0; x < src.nx; ++x) {
|
||
int dx = x + offset_x;
|
||
int dy = y + offset_y;
|
||
// skip pixels that would be out of bounds in the destination
|
||
if (dx < 0 || dy < 0 || dx >= dst.nx || dy >= dst.ny) {
|
||
continue;
|
||
}
|
||
size_t dst_idx = 3 * (static_cast<size_t>(dy) * dst.nx + static_cast<size_t>(dx));
|
||
size_t src_idx = 3 * (static_cast<size_t>(y) * src.nx + static_cast<size_t>(x));
|
||
dst.buf[dst_idx + 0] = src.buf[src_idx + 0];
|
||
dst.buf[dst_idx + 1] = src.buf[src_idx + 1];
|
||
dst.buf[dst_idx + 2] = src.buf[src_idx + 2];
|
||
}
|
||
}
|
||
}
|
||
|
||
// fill the image with a solid color
|
||
static void fill(clip_image_u8 & img, const std::array<uint8_t, 3> & color) {
|
||
for (size_t i = 0; i < img.buf.size(); i += 3) {
|
||
img.buf[i] = color[0];
|
||
img.buf[i + 1] = color[1];
|
||
img.buf[i + 2] = color[2];
|
||
}
|
||
}
|
||
|
||
private:
|
||
// Bilinear resize function
|
||
static void resize_bilinear(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 = 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 = 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>(lerp(top, bottom, y_lerp));
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
// Bicubic resize function
|
||
// part of image will be cropped if the aspect ratio is different
|
||
static bool resize_bicubic(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;
|
||
}
|
||
|
||
// Bicubic resize function using Pillow's ImagingResample algorithm
|
||
// Adapted from https://github.com/python-pillow/Pillow/blob/main/src/libImaging/Resample.c
|
||
//
|
||
// Key Difference with resize_bicubic:
|
||
// 1. Uses separable filtering: horizontal pass followed by vertical pass
|
||
// 2. Pre-computes normalized filter coefficients for each output pixel
|
||
// 3. Applies convolution using fixed-point integer arithmetic for performance
|
||
static bool resize_bicubic_pillow(const clip_image_u8 & img, clip_image_u8 & dst, int target_width, int target_height) {
|
||
// Fixed-point precision: 22 bits = 32 (int32_t) - 8 (uint8_t pixels) - 2 (headroom for accumulation)
|
||
// This allows encoding fractional weights as integers: weight * 2^22
|
||
const int PRECISION_BITS = 32 - 8 - 2;
|
||
|
||
// Bicubic filter function with a = -0.5 (Note that GGML/PyTorch takes a = -0.75)
|
||
// Returns filter weight for distance x from pixel center
|
||
// Support: [-2, 2], meaning the filter influences pixels within 2 units of distance
|
||
auto bicubic_filter = [](double x) -> double {
|
||
constexpr double a = -0.5;
|
||
if (x < 0.0) {
|
||
x = -x;
|
||
}
|
||
if (x < 1.0) {
|
||
return ((a + 2.0) * x - (a + 3.0)) * x * x + 1;
|
||
}
|
||
if (x < 2.0) {
|
||
return (((x - 5) * x + 8) * x - 4) * a;
|
||
}
|
||
return 0.0; // Zero outside [-2, 2]
|
||
};
|
||
|
||
// Filter support radius: bicubic extends 2 pixels in each direction
|
||
constexpr double filter_support = 2.0;
|
||
|
||
// Clipping function for 8-bit values
|
||
auto clip8 = [](int val) -> uint8_t {
|
||
if (val < 0) return 0;
|
||
if (val > 255) return 255;
|
||
return static_cast<uint8_t>(val);
|
||
};
|
||
|
||
// Precompute filter coefficients for ONE dimension (horizontal or vertical)
|
||
//
|
||
// Parameters:
|
||
// inSize - Number of pixels in input dimension (e.g., src_width or src_height)
|
||
// outSize - Number of pixels in output dimension (e.g., target_width or target_height)
|
||
// bounds - [OUTPUT] Array of size outSize*2 storing input pixel ranges:
|
||
// bounds[xx*2+0] = first input pixel index for output pixel xx (xmin)
|
||
// bounds[xx*2+1] = number of input pixels for output pixel xx (xcnt)
|
||
// weights - [OUTPUT] Array of size outSize*ksize storing fixed-point filter weights:
|
||
// kk[xx*ksize + x] = weight for input pixel x contributing to output pixel xx
|
||
//
|
||
// Returns: kernel size (ksize) - number of input pixels that contribute to each output pixel
|
||
auto precompute_weights = [&](int inSize, int outSize,
|
||
std::vector<int> & bounds, std::vector<int32_t> & weights) -> int {
|
||
double support, scale, filterscale;
|
||
double center, ww, ss;
|
||
int xx, x, ksize, xmin, xmax, xcnt;
|
||
|
||
// Calculate scaling factor: ratio of input range to output size
|
||
filterscale = scale = (double)inSize / outSize;
|
||
// For upsampling (scale < 1), keep filterscale = 1 to maintain filter sharpness
|
||
// For downsampling (scale > 1), widen filter to prevent aliasing
|
||
if (filterscale < 1.0) {
|
||
filterscale = 1.0;
|
||
}
|
||
|
||
// Determine filter support radius and kernel size
|
||
support = filter_support * filterscale; // Widen filter when downsampling
|
||
ksize = static_cast<int>(std::ceil(support)) * 2 + 1; // Total pixels in kernel
|
||
|
||
std::vector<double> pre_weights(outSize * ksize); // Temporary weights
|
||
bounds.resize(outSize * 2);
|
||
|
||
// For each output pixel, compute its filter coefficients
|
||
for (xx = 0; xx < outSize; xx++) {
|
||
// Calculate the center position in input space (pixel-center convention: +0.5)
|
||
center = (xx + 0.5) * scale;
|
||
ww = 0.0; // Sum of weights for normalization
|
||
ss = 1.0 / filterscale; // Scale factor for filter function
|
||
|
||
// Determine the range of input pixels that contribute to this output pixel
|
||
xmin = static_cast<int>(center - support + 0.5);
|
||
if (xmin < 0) {
|
||
xmin = 0;
|
||
}
|
||
|
||
xmax = static_cast<int>(center + support + 0.5);
|
||
if (xmax > inSize) {
|
||
xmax = inSize;
|
||
}
|
||
|
||
xcnt = xmax - xmin;
|
||
|
||
// Compute filter weights for each contributing input pixel
|
||
for (x = 0; x < xcnt; x++) {
|
||
// Distance from input pixel center to output pixel center in input space
|
||
double w = bicubic_filter((x + xmin - center + 0.5) * ss);
|
||
pre_weights[xx * ksize + x] = w;
|
||
ww += w; // Accumulate for normalization
|
||
}
|
||
|
||
// Normalize weights to sum to 1.0 (preserves brightness)
|
||
for (x = 0; x < xcnt; x++) {
|
||
if (ww != 0.0) {
|
||
pre_weights[xx * ksize + x] /= ww;
|
||
}
|
||
}
|
||
|
||
// Zero-pad remaining kernel positions
|
||
for (; x < ksize; x++) {
|
||
pre_weights[xx * ksize + x] = 0;
|
||
}
|
||
|
||
// Store input pixel range for this output pixel
|
||
bounds[xx * 2 + 0] = xmin;
|
||
bounds[xx * 2 + 1] = xcnt;
|
||
}
|
||
|
||
// Convert floating-point coefficients to fixed-point integers
|
||
// Formula: int32 = round(float * 2^PRECISION_BITS)
|
||
weights.resize(outSize * ksize);
|
||
for (int i = 0; i < outSize * ksize; i++) {
|
||
if (pre_weights[i] < 0) {
|
||
weights[i] = static_cast<int32_t>(-0.5 + pre_weights[i] * (1 << PRECISION_BITS));
|
||
} else {
|
||
weights[i] = static_cast<int32_t>(0.5 + pre_weights[i] * (1 << PRECISION_BITS));
|
||
}
|
||
}
|
||
|
||
return ksize;
|
||
};
|
||
|
||
// Horizontal resampling pass
|
||
// Resizes width from imIn.nx to imOut.nx, preserving height
|
||
auto resample_horizontal = [&](const clip_image_u8 & imIn, clip_image_u8 & imOut,
|
||
int ksize, const std::vector<int> & bounds, const std::vector<int32_t> & weights) {
|
||
imOut.ny = imIn.ny;
|
||
imOut.buf.resize(3 * imOut.nx * imOut.ny);
|
||
|
||
// Process each row independently
|
||
for (int yy = 0; yy < imOut.ny; yy++) {
|
||
// For each output pixel in this row
|
||
for (int xx = 0; xx < imOut.nx; xx++) {
|
||
// Get the range of input pixels and filter coefficients
|
||
int xmin = bounds[xx * 2 + 0]; // First input pixel index
|
||
int xcnt = bounds[xx * 2 + 1]; // Number of input pixels
|
||
|
||
// Initialize accumulators for RGB channels with rounding bias (0.5 in fixed-point)
|
||
int32_t ss0 = 1 << (PRECISION_BITS - 1);
|
||
int32_t ss1 = 1 << (PRECISION_BITS - 1);
|
||
int32_t ss2 = 1 << (PRECISION_BITS - 1);
|
||
|
||
// Convolve: sum weighted input pixels
|
||
for (int x = 0; x < xcnt; x++) {
|
||
int src_idx = ((yy * imIn.nx) + (x + xmin)) * 3;
|
||
ss0 += static_cast<uint8_t>(imIn.buf[src_idx + 0]) * weights[xx * ksize + x]; // R channel
|
||
ss1 += static_cast<uint8_t>(imIn.buf[src_idx + 1]) * weights[xx * ksize + x]; // G channel
|
||
ss2 += static_cast<uint8_t>(imIn.buf[src_idx + 2]) * weights[xx * ksize + x]; // B channel
|
||
}
|
||
|
||
// Convert back from fixed-point (divide by 2^PRECISION_BITS) and clamp to [0,255]
|
||
int dst_idx = (yy * imOut.nx + xx) * 3;
|
||
imOut.buf[dst_idx + 0] = clip8(ss0 >> PRECISION_BITS);
|
||
imOut.buf[dst_idx + 1] = clip8(ss1 >> PRECISION_BITS);
|
||
imOut.buf[dst_idx + 2] = clip8(ss2 >> PRECISION_BITS);
|
||
}
|
||
}
|
||
};
|
||
|
||
// Vertical resampling pass
|
||
// Resizes height from imIn.ny to imOut.ny, preserving width
|
||
auto resample_vertical = [&](const clip_image_u8 & imIn, clip_image_u8 & imOut,
|
||
int ksize, const std::vector<int> & bounds, const std::vector<int32_t> & weight) {
|
||
imOut.nx = imIn.nx;
|
||
imOut.buf.resize(3 * imOut.nx * imOut.ny);
|
||
|
||
// For each output row
|
||
for (int yy = 0; yy < imOut.ny; yy++) {
|
||
// Get the range of input rows and filter coefficients
|
||
int ymin = bounds[yy * 2 + 0]; // First input row index
|
||
int ycnt = bounds[yy * 2 + 1]; // Number of input rows
|
||
|
||
// Process each column in this output row
|
||
for (int xx = 0; xx < imOut.nx; xx++) {
|
||
// Initialize accumulators for RGB channels with rounding bias
|
||
int32_t ss0 = 1 << (PRECISION_BITS - 1);
|
||
int32_t ss1 = 1 << (PRECISION_BITS - 1);
|
||
int32_t ss2 = 1 << (PRECISION_BITS - 1);
|
||
|
||
// Convolve: sum weighted input pixels vertically
|
||
for (int y = 0; y < ycnt; y++) {
|
||
int src_idx = ((y + ymin) * imIn.nx + xx) * 3;
|
||
ss0 += static_cast<uint8_t>(imIn.buf[src_idx + 0]) * weight[yy * ksize + y]; // R channel
|
||
ss1 += static_cast<uint8_t>(imIn.buf[src_idx + 1]) * weight[yy * ksize + y]; // G channel
|
||
ss2 += static_cast<uint8_t>(imIn.buf[src_idx + 2]) * weight[yy * ksize + y]; // B channel
|
||
}
|
||
|
||
// Convert back from fixed-point and clamp to [0,255]
|
||
int dst_idx = (yy * imOut.nx + xx) * 3;
|
||
imOut.buf[dst_idx + 0] = clip8(ss0 >> PRECISION_BITS);
|
||
imOut.buf[dst_idx + 1] = clip8(ss1 >> PRECISION_BITS);
|
||
imOut.buf[dst_idx + 2] = clip8(ss2 >> PRECISION_BITS);
|
||
}
|
||
}
|
||
};
|
||
|
||
// Main resampling logic using separable two-pass approach
|
||
const int src_width = img.nx;
|
||
const int src_height = img.ny;
|
||
|
||
dst.nx = target_width;
|
||
dst.ny = target_height;
|
||
|
||
bool need_horizontal = (target_width != src_width);
|
||
bool need_vertical = (target_height != src_height);
|
||
|
||
// Precompute filter coefficients for both dimensions
|
||
std::vector<int> bounds_horiz, bounds_vert;
|
||
std::vector<int32_t> weights_horiz, weights_vert;
|
||
int ksize_horiz = 0, ksize_vert = 0;
|
||
|
||
if (need_horizontal) {
|
||
ksize_horiz = precompute_weights(src_width, target_width, bounds_horiz, weights_horiz);
|
||
}
|
||
|
||
if (need_vertical) {
|
||
ksize_vert = precompute_weights(src_height, target_height, bounds_vert, weights_vert);
|
||
}
|
||
|
||
// Perform two-pass resampling
|
||
if (need_horizontal && need_vertical) {
|
||
// Both horizontal and vertical
|
||
clip_image_u8 temp;
|
||
temp.nx = target_width;
|
||
resample_horizontal(img, temp, ksize_horiz, bounds_horiz, weights_horiz);
|
||
resample_vertical(temp, dst, ksize_vert, bounds_vert, weights_vert);
|
||
} else if (need_horizontal) {
|
||
// Only horizontal
|
||
resample_horizontal(img, dst, ksize_horiz, bounds_horiz, weights_horiz);
|
||
} else if (need_vertical) {
|
||
// Only vertical
|
||
resample_vertical(img, dst, ksize_vert, bounds_vert, weights_vert);
|
||
} else {
|
||
// No resizing needed - direct copy
|
||
dst.buf = img.buf;
|
||
}
|
||
|
||
return true;
|
||
}
|
||
|
||
static inline int clip(int x, int lower, int upper) {
|
||
return std::max(lower, std::min(x, upper));
|
||
}
|
||
|
||
// Linear interpolation between two points
|
||
static inline float lerp(float s, float e, float t) {
|
||
return s + (e - s) * t;
|
||
}
|
||
};
|
||
|
||
/**
|
||
* implementation of 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
|
||
*
|
||
* overview:
|
||
* - an image always have a single overview (downscaled image)
|
||
* - an image can have 0 or multiple slices, depending on the image size
|
||
* - each slice can then be considered as a separate image
|
||
*
|
||
* for example:
|
||
*
|
||
* [overview] --> [slice 1] --> [slice 2]
|
||
* | |
|
||
* +--> [slice 3] --> [slice 4]
|
||
*/
|
||
struct llava_uhd {
|
||
struct slice_coordinates {
|
||
int x;
|
||
int y;
|
||
clip_image_size size;
|
||
};
|
||
|
||
struct slice_instructions {
|
||
clip_image_size overview_size; // size of downscaled image
|
||
clip_image_size refined_size; // size of image right before slicing (must be multiple of slice size)
|
||
clip_image_size grid_size; // grid_size.width * grid_size.height = number of slices
|
||
std::vector<slice_coordinates> slices;
|
||
|
||
img_tool::resize_algo interpolation_overview = img_tool::RESIZE_ALGO_BILINEAR;
|
||
bool padding_overview = false; // if true, refine image will be padded to the grid size (e.g. llava-1.6)
|
||
std::array<uint8_t, 3> pad_color_overview = {0, 0, 0};
|
||
|
||
img_tool::resize_algo interpolation_refined = img_tool::RESIZE_ALGO_BICUBIC;
|
||
bool padding_refined = false; // if true, refine image will be padded to the grid size (e.g. llava-1.6)
|
||
std::array<uint8_t, 3> pad_color_refined = {0, 0, 0};
|
||
};
|
||
|
||
static slice_instructions get_slice_instructions(struct clip_ctx * ctx, const clip_image_size & original_size) {
|
||
slice_instructions res;
|
||
const int patch_size = clip_get_patch_size(ctx);
|
||
const int slice_size = clip_get_image_size(ctx);
|
||
const int original_width = original_size.width;
|
||
const int original_height = original_size.height;
|
||
|
||
const bool has_slices = original_size.width > slice_size || original_size.height > slice_size;
|
||
const bool has_pinpoints = !ctx->model.hparams.image_res_candidates.empty();
|
||
|
||
if (!has_slices) {
|
||
// skip slicing logic
|
||
res.overview_size = clip_image_size{slice_size, slice_size};
|
||
res.refined_size = clip_image_size{0, 0};
|
||
res.grid_size = clip_image_size{0, 0};
|
||
|
||
return res;
|
||
}
|
||
|
||
if (has_pinpoints) {
|
||
// has pinpoints, use them to calculate the grid size (e.g. llava-1.6)
|
||
auto refine_size = llava_uhd::select_best_resolution(
|
||
original_size,
|
||
ctx->model.hparams.image_res_candidates);
|
||
res.overview_size = clip_image_size{slice_size, slice_size};
|
||
res.refined_size = refine_size;
|
||
res.grid_size = clip_image_size{0, 0};
|
||
res.padding_refined = true;
|
||
res.interpolation_refined = img_tool::RESIZE_ALGO_BILINEAR; // preserve old behavior when padding
|
||
|
||
LOG_DBG("%s: using pinpoints for slicing\n", __func__);
|
||
LOG_DBG("%s: original size: %d x %d, overview size: %d x %d, refined size: %d x %d\n",
|
||
__func__, original_width, original_height,
|
||
res.overview_size.width, res.overview_size.height,
|
||
res.refined_size.width, res.refined_size.height);
|
||
|
||
for (int y = 0; y < refine_size.height; y += slice_size) {
|
||
for (int x = 0; x < refine_size.width; x += slice_size) {
|
||
slice_coordinates slice;
|
||
slice.x = x;
|
||
slice.y = y;
|
||
slice.size.width = std::min(slice_size, refine_size.width - x);
|
||
slice.size.height = std::min(slice_size, refine_size.height - y);
|
||
res.slices.push_back(slice);
|
||
LOG_DBG("%s: slice %d: x=%d, y=%d, size=%dx%d\n",
|
||
__func__, (int)res.slices.size() - 1,
|
||
slice.x, slice.y, slice.size.width, slice.size.height);
|
||
}
|
||
}
|
||
|
||
res.grid_size.height = refine_size.height / slice_size;
|
||
res.grid_size.width = refine_size.width / slice_size;
|
||
LOG_DBG("%s: grid size: %d x %d\n", __func__, res.grid_size.width, res.grid_size.height);
|
||
|
||
return res;
|
||
}
|
||
|
||
// no pinpoints, dynamically calculate the grid size (e.g. minicpmv)
|
||
|
||
auto best_size = get_best_resize(original_size, slice_size, patch_size, !has_slices);
|
||
res.overview_size = best_size;
|
||
|
||
{
|
||
const int max_slice_nums = 9; // TODO: this is only used by minicpmv, maybe remove it
|
||
const float log_ratio = log((float)original_width / original_height);
|
||
const float ratio = (float)original_width * original_height / (slice_size * slice_size);
|
||
const int multiple = fmin(ceil(ratio), max_slice_nums);
|
||
|
||
auto best_grid = get_best_grid(max_slice_nums, multiple, log_ratio);
|
||
auto refine_size = get_refine_size(original_size, best_grid, slice_size, patch_size, true);
|
||
res.grid_size = best_grid;
|
||
res.refined_size = refine_size;
|
||
|
||
LOG_DBG("%s: original size: %d x %d, overview size: %d x %d, refined size: %d x %d, grid size: %d x %d\n",
|
||
__func__, original_width, original_height,
|
||
res.overview_size.width, res.overview_size.height,
|
||
res.refined_size.width, res.refined_size.height,
|
||
res.grid_size.width, res.grid_size.height);
|
||
|
||
int width = refine_size.width;
|
||
int height = refine_size.height;
|
||
int grid_x = int(width / best_grid.width);
|
||
int grid_y = int(height / best_grid.height);
|
||
for (int patches_y = 0, ic = 0;
|
||
patches_y < refine_size.height && ic < best_grid.height;
|
||
patches_y += grid_y, ic += 1) {
|
||
for (int patches_x = 0, jc = 0;
|
||
patches_x < refine_size.width && jc < best_grid.width;
|
||
patches_x += grid_x, jc += 1) {
|
||
slice_coordinates slice;
|
||
slice.x = patches_x;
|
||
slice.y = patches_y;
|
||
slice.size.width = grid_x;
|
||
slice.size.height = grid_y;
|
||
res.slices.push_back(slice);
|
||
LOG_DBG("%s: slice %d: x=%d, y=%d, size=%dx%d\n",
|
||
__func__, (int)res.slices.size() - 1,
|
||
slice.x, slice.y, slice.size.width, slice.size.height);
|
||
}
|
||
}
|
||
}
|
||
|
||
return res;
|
||
}
|
||
|
||
static std::vector<clip_image_u8_ptr> slice_image(const clip_image_u8 * img, const slice_instructions & inst) {
|
||
std::vector<clip_image_u8_ptr> output;
|
||
|
||
// resize to overview size
|
||
clip_image_u8_ptr resized_img(clip_image_u8_init());
|
||
img_tool::resize(*img, *resized_img, inst.overview_size, inst.interpolation_overview,
|
||
inst.padding_overview, inst.pad_color_overview);
|
||
output.push_back(std::move(resized_img));
|
||
|
||
if (inst.slices.empty()) {
|
||
// no slices, just return the resized image
|
||
return output;
|
||
}
|
||
|
||
// resize to refined size
|
||
clip_image_u8_ptr refined_img(clip_image_u8_init());
|
||
img_tool::resize(*img, *refined_img, inst.refined_size, inst.interpolation_refined,
|
||
inst.padding_refined, inst.pad_color_refined);
|
||
|
||
// create slices
|
||
for (const auto & slice : inst.slices) {
|
||
int x = slice.x;
|
||
int y = slice.y;
|
||
int w = slice.size.width;
|
||
int h = slice.size.height;
|
||
|
||
clip_image_u8_ptr img_slice(clip_image_u8_init());
|
||
img_tool::crop(*refined_img, *img_slice, x, y, w, h);
|
||
output.push_back(std::move(img_slice));
|
||
}
|
||
|
||
return output;
|
||
}
|
||
|
||
private:
|
||
static clip_image_size get_best_resize(const clip_image_size & original_size, int scale_resolution, int patch_size, bool allow_upscale = false) {
|
||
int width = original_size.width;
|
||
int height = original_size.height;
|
||
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);
|
||
}
|
||
clip_image_size res;
|
||
res.width = ensure_divide(width, patch_size);
|
||
res.height = ensure_divide(height, patch_size);
|
||
return res;
|
||
}
|
||
|
||
static clip_image_size resize_maintain_aspect_ratio(const clip_image_size & orig, const clip_image_size & target_max) {
|
||
float scale_width = static_cast<float>(target_max.width) / orig.width;
|
||
float scale_height = static_cast<float>(target_max.height) / orig.height;
|
||
float scale = std::min(scale_width, scale_height);
|
||
return clip_image_size{
|
||
static_cast<int>(orig.width * scale),
|
||
static_cast<int>(orig.height * scale),
|
||
};
|
||
}
|
||
|
||
/**
|
||
* Selects the best resolution from a list of possible resolutions based on the original size.
|
||
*
|
||
* For example, when given a list of resolutions:
|
||
* - 100x100
|
||
* - 200x100
|
||
* - 100x200
|
||
* - 200x200
|
||
*
|
||
* And an input image of size 111x200, then 100x200 is the best fit (least wasted resolution).
|
||
*
|
||
* @param original_size The original size of the image
|
||
* @param possible_resolutions A list of possible resolutions
|
||
* @return The best fit resolution
|
||
*/
|
||
static clip_image_size select_best_resolution(const clip_image_size & original_size, const std::vector<clip_image_size> & possible_resolutions) {
|
||
clip_image_size best_fit;
|
||
int min_wasted_area = std::numeric_limits<int>::max();
|
||
int max_effective_resolution = 0;
|
||
|
||
for (const clip_image_size & candidate : possible_resolutions) {
|
||
auto target_size = resize_maintain_aspect_ratio(original_size, candidate);
|
||
int effective_resolution = std::min(
|
||
target_size.width * target_size.height,
|
||
original_size.width * original_size.height);
|
||
int wasted_area = (candidate.width * candidate.height) - effective_resolution;
|
||
|
||
if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_area < min_wasted_area)) {
|
||
max_effective_resolution = effective_resolution;
|
||
min_wasted_area = wasted_area;
|
||
best_fit = candidate;
|
||
}
|
||
|
||
LOG_DBG("%s: candidate: %d x %d, target: %d x %d, wasted: %d, effective: %d\n", __func__, candidate.width, candidate.height, target_size.width, target_size.height, wasted_area, effective_resolution);
|
||
}
|
||
|
||
return best_fit;
|
||
}
|
||
|
||
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 clip_image_size get_refine_size(const clip_image_size & original_size, const clip_image_size & grid, int scale_resolution, int patch_size, bool allow_upscale = false) {
|
||
int width = original_size.width;
|
||
int height = original_size.height;
|
||
int grid_x = grid.width;
|
||
int grid_y = grid.height;
|
||
|
||
int refine_width = ensure_divide(width, grid_x);
|
||
int refine_height = ensure_divide(height, grid_y);
|
||
|
||
clip_image_size grid_size;
|
||
grid_size.width = refine_width / grid_x;
|
||
grid_size.height = refine_height / grid_y;
|
||
|
||
auto best_grid_size = get_best_resize(grid_size, scale_resolution, patch_size, allow_upscale);
|
||
int best_grid_width = best_grid_size.width;
|
||
int best_grid_height = best_grid_size.height;
|
||
|
||
clip_image_size refine_size;
|
||
refine_size.width = best_grid_width * grid_x;
|
||
refine_size.height = best_grid_height * grid_y;
|
||
return refine_size;
|
||
}
|
||
|
||
static clip_image_size get_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<clip_image_size> 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.push_back(clip_image_size{m, split_grids_nums / m});
|
||
}
|
||
++m;
|
||
}
|
||
}
|
||
|
||
clip_image_size 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.width / grid.height));
|
||
if (error < min_error) {
|
||
best_grid = grid;
|
||
min_error = error;
|
||
}
|
||
}
|
||
return best_grid;
|
||
}
|
||
};
|
||
|
||
static std::vector<std::pair<int, int>> ds_build_target_ratios(const int min_num, const int max_num) {
|
||
std::vector<std::pair<int, int>> ratios;
|
||
for (int n = min_num; n <= max_num; ++n) {
|
||
for (int i = 1; i <= n; ++i) {
|
||
for (int j = 1; j <= n; ++j) {
|
||
if (const int blocks = i * j; blocks >= min_num && blocks <= max_num) {
|
||
ratios.emplace_back(i, j); // (cols, rows)
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
// sort by total blocks like in Python (key=lambda x: x[0] * x[1])
|
||
std::sort(ratios.begin(), ratios.end(),
|
||
[](const auto &a, const auto &b) {
|
||
return (a.first * a.second) < (b.first * b.second);
|
||
});
|
||
|
||
// optional: dedup
|
||
ratios.erase(std::unique(ratios.begin(), ratios.end()), ratios.end());
|
||
return ratios;
|
||
}
|
||
|
||
static std::pair<int, int> ds_find_closest_ratio(
|
||
const float aspect_ratio,
|
||
const std::vector<std::pair<int, int>> &target_ratios,
|
||
const int width,
|
||
const int height,
|
||
const int image_size
|
||
) {
|
||
float best_diff = std::numeric_limits<float>::infinity();
|
||
std::pair<int, int> best_ratio = {1, 1};
|
||
const float area = static_cast<float>(width) * static_cast<float>(height);
|
||
|
||
for (const auto &r : target_ratios) {
|
||
const float target_ar = static_cast<float>(r.first) / static_cast<float>(r.second);
|
||
|
||
if (const float diff = std::fabs(aspect_ratio - target_ar); diff < best_diff) {
|
||
best_diff = diff;
|
||
best_ratio = r;
|
||
} else if (diff == best_diff) {
|
||
// same as python: prefer this ratio if the image area is “large enough”
|
||
if (const float needed_area = 0.5f * image_size * image_size * r.first * r.second; area > needed_area) {
|
||
best_ratio = r;
|
||
}
|
||
}
|
||
}
|
||
|
||
return best_ratio; // (cols, rows)
|
||
}
|
||
|
||
|
||
|
||
// 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) {
|
||
clip_image_size original_size{img->nx, img->ny};
|
||
auto & params = ctx->model.hparams;
|
||
|
||
switch (ctx->proj_type()) {
|
||
case PROJECTOR_TYPE_MINICPMV:
|
||
{
|
||
auto const inst = llava_uhd::get_slice_instructions(ctx, original_size);
|
||
std::vector<clip_image_u8_ptr> imgs = llava_uhd::slice_image(img, inst);
|
||
|
||
for (size_t i = 0; i < imgs.size(); ++i) {
|
||
// clip_image_save_to_bmp(*imgs[i], "slice_" + std::to_string(i) + ".bmp");
|
||
clip_image_f32_ptr res(clip_image_f32_init());
|
||
normalize_image_u8_to_f32(*imgs[i], *res, params.image_mean, params.image_std);
|
||
res_imgs->entries.push_back(std::move(res));
|
||
}
|
||
|
||
res_imgs->grid_x = inst.grid_size.width;
|
||
res_imgs->grid_y = inst.grid_size.height;
|
||
} break;
|
||
|
||
case PROJECTOR_TYPE_QWEN2VL:
|
||
case PROJECTOR_TYPE_QWEN25VL:
|
||
case PROJECTOR_TYPE_QWEN3VL:
|
||
case PROJECTOR_TYPE_GLM4V:
|
||
{
|
||
GGML_ASSERT(params.image_min_pixels > 0 && params.image_max_pixels > 0);
|
||
clip_image_u8 resized;
|
||
const clip_image_size new_size = img_tool::calc_size_preserved_ratio(
|
||
original_size,
|
||
params.patch_size * 2,
|
||
params.image_min_pixels,
|
||
params.image_max_pixels);
|
||
img_tool::resize(*img, resized, new_size, img_tool::RESIZE_ALGO_BILINEAR, false);
|
||
// clip_image_save_to_bmp(resized, "preproc.bmp");
|
||
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, params.image_mean, params.image_std);
|
||
// res_imgs->data[0] = *res;
|
||
res_imgs->entries.push_back(std::move(img_f32));
|
||
} break;
|
||
|
||
case PROJECTOR_TYPE_IDEFICS3:
|
||
{
|
||
// The refined size has two steps:
|
||
// 1. Resize w/ aspect-ratio preserving such that the longer side is
|
||
// the preprocessor longest size
|
||
// 2. Resize w/out preserving aspect ratio such that both sides are
|
||
// multiples of image_size (always rounding up)
|
||
//
|
||
// CITE: https://github.com/huggingface/transformers/blob/main/src/transformers/models/idefics3/image_processing_idefics3.py#L737
|
||
const clip_image_size refined_size = img_tool::calc_size_preserved_ratio(
|
||
original_size, params.image_size, params.image_longest_edge);
|
||
// LOG_INF("%s: original size: %d x %d, refined size: %d x %d\n",
|
||
// __func__, original_size.width, original_size.height,
|
||
// refined_size.width, refined_size.height);
|
||
|
||
llava_uhd::slice_instructions instructions;
|
||
instructions.overview_size = clip_image_size{params.image_size, params.image_size};
|
||
instructions.refined_size = refined_size;
|
||
instructions.grid_size = clip_image_size{
|
||
static_cast<int>(std::ceil(static_cast<float>(refined_size.width) / params.image_size)),
|
||
static_cast<int>(std::ceil(static_cast<float>(refined_size.height) / params.image_size)),
|
||
};
|
||
for (int y = 0; y < refined_size.height; y += params.image_size) {
|
||
for (int x = 0; x < refined_size.width; x += params.image_size) {
|
||
// LOG_INF("%s: adding slice at x=%d, y=%d\n", __func__, x, y);
|
||
instructions.slices.push_back(llava_uhd::slice_coordinates{
|
||
/* x */x,
|
||
/* y */y,
|
||
/* size */clip_image_size{
|
||
std::min(params.image_size, refined_size.width - x),
|
||
std::min(params.image_size, refined_size.height - y)
|
||
}
|
||
});
|
||
}
|
||
}
|
||
auto imgs = llava_uhd::slice_image(img, instructions);
|
||
|
||
// cast and normalize to f32
|
||
for (size_t i = 0; i < imgs.size(); ++i) {
|
||
// clip_image_save_to_bmp(*imgs[i], "slice_" + std::to_string(i) + ".bmp");
|
||
clip_image_f32_ptr res(clip_image_f32_init());
|
||
normalize_image_u8_to_f32(*imgs[i], *res, params.image_mean, params.image_std);
|
||
res_imgs->entries.push_back(std::move(res));
|
||
}
|
||
|
||
res_imgs->grid_x = instructions.grid_size.width;
|
||
res_imgs->grid_y = instructions.grid_size.height;
|
||
} break;
|
||
|
||
case PROJECTOR_TYPE_GLM_EDGE:
|
||
case PROJECTOR_TYPE_GEMMA3:
|
||
case PROJECTOR_TYPE_INTERNVL: // TODO @ngxson : support dynamic resolution
|
||
{
|
||
clip_image_u8 resized_image;
|
||
int sz = params.image_size;
|
||
img_tool::resize(*img, resized_image, {sz, sz}, img_tool::RESIZE_ALGO_BILINEAR);
|
||
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, params.image_mean, params.image_std);
|
||
res_imgs->entries.push_back(std::move(img_f32));
|
||
} break;
|
||
|
||
case PROJECTOR_TYPE_JANUS_PRO:
|
||
{
|
||
// Janus Pro preprocessing: pad to square with gray(127), resize to 384x384
|
||
const std::array<uint8_t, 3> pad_color = {127, 127, 127};
|
||
clip_image_u8 resized_image;
|
||
int sz = params.image_size;
|
||
img_tool::resize(*img, resized_image, {sz, sz}, img_tool::RESIZE_ALGO_BILINEAR, true, pad_color);
|
||
clip_image_f32_ptr img_f32(clip_image_f32_init());
|
||
normalize_image_u8_to_f32(resized_image, *img_f32, params.image_mean, params.image_std);
|
||
res_imgs->entries.push_back(std::move(img_f32));
|
||
} break;
|
||
|
||
case PROJECTOR_TYPE_PIXTRAL:
|
||
case PROJECTOR_TYPE_LIGHTONOCR:
|
||
{
|
||
GGML_ASSERT(params.image_min_pixels > 0 && params.image_max_pixels > 0);
|
||
clip_image_u8 resized_image;
|
||
// the original pixtral model doesn't have n_merge
|
||
const int cur_merge = params.n_merge == 0 ? 1 : params.n_merge;
|
||
const clip_image_size target_size = img_tool::calc_size_preserved_ratio(
|
||
original_size,
|
||
params.patch_size * cur_merge,
|
||
params.image_min_pixels,
|
||
params.image_max_pixels);
|
||
img_tool::resize(*img, resized_image, target_size, img_tool::RESIZE_ALGO_BILINEAR);
|
||
clip_image_f32_ptr img_f32(clip_image_f32_init());
|
||
normalize_image_u8_to_f32(resized_image, *img_f32, params.image_mean, params.image_std);
|
||
res_imgs->entries.push_back(std::move(img_f32));
|
||
} break;
|
||
|
||
case PROJECTOR_TYPE_LLAMA4:
|
||
{
|
||
GGML_ASSERT(!params.image_res_candidates.empty());
|
||
auto const inst = llava_uhd::get_slice_instructions(ctx, original_size);
|
||
std::vector<clip_image_u8_ptr> imgs = llava_uhd::slice_image(img, inst);
|
||
|
||
for (size_t i = 0; i < imgs.size(); ++i) {
|
||
clip_image_f32_ptr res(clip_image_f32_init());
|
||
normalize_image_u8_to_f32(*imgs[i], *res, params.image_mean, params.image_std);
|
||
res_imgs->entries.push_back(std::move(res));
|
||
}
|
||
|
||
res_imgs->grid_x = inst.grid_size.width;
|
||
res_imgs->grid_y = inst.grid_size.height;
|
||
} break;
|
||
|
||
case PROJECTOR_TYPE_LFM2:
|
||
case PROJECTOR_TYPE_KIMIVL:
|
||
{
|
||
GGML_ASSERT(params.image_min_pixels > 0 && params.image_max_pixels > 0);
|
||
const clip_image_size target_size = img_tool::calc_size_preserved_ratio(
|
||
original_size,
|
||
params.patch_size * params.n_merge,
|
||
params.image_min_pixels,
|
||
params.image_max_pixels);
|
||
const std::array<uint8_t, 3> pad_color = {122, 116, 104};
|
||
|
||
clip_image_u8 resized_img;
|
||
const bool pad = (ctx->proj_type() != PROJECTOR_TYPE_LFM2);
|
||
img_tool::resize(*img, resized_img, target_size, img_tool::RESIZE_ALGO_BILINEAR, pad, pad_color);
|
||
clip_image_f32_ptr res(clip_image_f32_init());
|
||
normalize_image_u8_to_f32(resized_img, *res, params.image_mean, params.image_std);
|
||
res_imgs->entries.push_back(std::move(res));
|
||
} break;
|
||
|
||
case PROJECTOR_TYPE_MLP:
|
||
case PROJECTOR_TYPE_MLP_NORM:
|
||
case PROJECTOR_TYPE_LDP:
|
||
case PROJECTOR_TYPE_LDPV2:
|
||
case PROJECTOR_TYPE_COGVLM: // TODO @ngxson : is this correct for cogvlm?
|
||
{
|
||
// TODO @ngxson : refactor the code below to avoid duplicated logic
|
||
|
||
// 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
|
||
|
||
// 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.image_res_candidates.empty()) { // pad_to_square
|
||
// for llava-1.5, we resize image to a square, and pad the shorter side with a background color
|
||
// see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156
|
||
const 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);
|
||
|
||
// background color in RGB from LLaVA (this is the mean rgb color * 255)
|
||
const std::array<uint8_t, 3> pad_color = {122, 116, 104};
|
||
|
||
// resize the image to the target_size
|
||
img_tool::resize(*img, *temp, clip_image_size{params.image_size, params.image_size}, img_tool::RESIZE_ALGO_BILINEAR, true, pad_color);
|
||
|
||
clip_image_f32_ptr res(clip_image_f32_init());
|
||
normalize_image_u8_to_f32(*temp, *res, params.image_mean, params.image_std);
|
||
res_imgs->entries.push_back(std::move(res));
|
||
|
||
} else {
|
||
// "spatial_unpad" with "anyres" processing for llava-1.6
|
||
auto const inst = llava_uhd::get_slice_instructions(ctx, original_size);
|
||
std::vector<clip_image_u8_ptr> imgs = llava_uhd::slice_image(img, inst);
|
||
|
||
for (size_t i = 0; i < imgs.size(); ++i) {
|
||
// clip_image_save_to_bmp(*imgs[i], "slice_" + std::to_string(i) + ".bmp");
|
||
clip_image_f32_ptr res(clip_image_f32_init());
|
||
normalize_image_u8_to_f32(*imgs[i], *res, params.image_mean, params.image_std);
|
||
res_imgs->entries.push_back(std::move(res));
|
||
}
|
||
}
|
||
} break;
|
||
case PROJECTOR_TYPE_DEEPSEEKOCR:
|
||
{
|
||
const std::vector native_resolutions = {
|
||
/*512 tiny , 640 small, */ 1024 /* base */, 1280 /* large */
|
||
};
|
||
// original image size
|
||
const int orig_w = original_size.width;
|
||
const int orig_h = original_size.height;
|
||
const int orig_area = orig_h * orig_w;
|
||
std::array<uint8_t, 3u> color;
|
||
|
||
for (int i = 0; i < 3; i++) {
|
||
color[i] = (int)(255 * params.image_mean[i]);
|
||
}
|
||
|
||
size_t mode_i = 0;
|
||
int min_diff = orig_area;
|
||
|
||
for (size_t i = 0; i < native_resolutions.size(); i++) {
|
||
int r = native_resolutions[i];
|
||
if (std::abs(orig_area - r * r) < min_diff) {
|
||
mode_i = i;
|
||
min_diff = std::abs(orig_area - r * r);
|
||
}
|
||
}
|
||
|
||
/* Native Resolution (Base/Large) */
|
||
const int image_size = native_resolutions[mode_i];
|
||
|
||
// Resize maintaining aspect ratio, then pad to square
|
||
float scale = std::min(
|
||
static_cast<float>(image_size) / orig_w,
|
||
static_cast<float>(image_size) / orig_h
|
||
);
|
||
int new_w = static_cast<int>(orig_w * scale);
|
||
int new_h = static_cast<int>(orig_h * scale);
|
||
|
||
clip_image_u8_ptr scaled_img(clip_image_u8_init());
|
||
img_tool::resize(*img, *scaled_img, clip_image_size{new_w, new_h},
|
||
img_tool::RESIZE_ALGO_BICUBIC_PILLOW, true, color);
|
||
|
||
// Use mean color for padding
|
||
unsigned char pad_r = static_cast<unsigned char>(params.image_mean[0] * 255.0f);
|
||
unsigned char pad_g = static_cast<unsigned char>(params.image_mean[1] * 255.0f);
|
||
unsigned char pad_b = static_cast<unsigned char>(params.image_mean[2] * 255.0f);
|
||
|
||
// Pad to image_size × image_size (center padding)
|
||
clip_image_u8_ptr padded_img(clip_image_u8_init());
|
||
padded_img->nx = image_size;
|
||
padded_img->ny = image_size;
|
||
padded_img->buf.resize(image_size * image_size * 3); // black padding
|
||
|
||
// Fill with mean color
|
||
for (int i = 0; i < image_size * image_size; ++i)
|
||
{
|
||
padded_img->buf[i * 3 + 0] = pad_r;
|
||
padded_img->buf[i * 3 + 1] = pad_g;
|
||
padded_img->buf[i * 3 + 2] = pad_b;
|
||
}
|
||
|
||
// Calculate padding offsets (center the image)
|
||
int pad_x = (image_size - new_w) / 2;
|
||
int pad_y = (image_size - new_h) / 2;
|
||
|
||
// Copy scaled image into padded canvas
|
||
for (int y = 0; y < new_h; ++y){
|
||
for (int x = 0; x < new_w; ++x){
|
||
int src_idx = (y * new_w + x) * 3;
|
||
int dst_idx = ((y + pad_y) * image_size + (x + pad_x)) * 3;
|
||
padded_img->buf[dst_idx + 0] = scaled_img->buf[src_idx + 0];
|
||
padded_img->buf[dst_idx + 1] = scaled_img->buf[src_idx + 1];
|
||
padded_img->buf[dst_idx + 2] = scaled_img->buf[src_idx + 2];
|
||
}
|
||
}
|
||
|
||
// Normalize and output
|
||
clip_image_f32_ptr res(clip_image_f32_init());
|
||
normalize_image_u8_to_f32(*padded_img, *res, params.image_mean, params.image_std);
|
||
res_imgs->entries.push_back(std::move(res));
|
||
|
||
res_imgs->grid_x = 1;
|
||
res_imgs->grid_y = 1;
|
||
} break;
|
||
|
||
default:
|
||
LOG_ERR("%s: unsupported projector type %d\n", __func__, ctx->proj_type());
|
||
return false;
|
||
}
|
||
|
||
return true;
|
||
}
|
||
|
||
ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx) {
|
||
return ctx->model.image_newline;
|
||
}
|
||
|
||
void clip_free(clip_ctx * ctx) {
|
||
if (ctx == nullptr) {
|
||
return;
|
||
}
|
||
delete ctx;
|
||
}
|
||
|
||
// deprecated
|
||
size_t clip_embd_nbytes(const struct clip_ctx * ctx) {
|
||
const int32_t nx = ctx->model.hparams.image_size;
|
||
const int32_t ny = ctx->model.hparams.image_size;
|
||
return clip_embd_nbytes_by_img(ctx, nx, ny);
|
||
}
|
||
|
||
size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_w, int img_h) {
|
||
clip_image_f32 img;
|
||
img.nx = img_w;
|
||
img.ny = img_h;
|
||
return clip_n_output_tokens(ctx, &img) * clip_n_mmproj_embd(ctx) * sizeof(float);
|
||
}
|
||
|
||
int32_t clip_get_image_size(const struct clip_ctx * ctx) {
|
||
return ctx->model.hparams.image_size;
|
||
}
|
||
|
||
int32_t clip_get_patch_size(const struct clip_ctx * ctx) {
|
||
return ctx->model.hparams.patch_size;
|
||
}
|
||
|
||
int32_t clip_get_hidden_size(const struct clip_ctx * ctx) {
|
||
return ctx->model.hparams.n_embd;
|
||
}
|
||
|
||
const char * clip_patch_merge_type(const struct clip_ctx * ctx) {
|
||
return ctx->model.hparams.mm_patch_merge_type == PATCH_MERGE_SPATIAL_UNPAD ? "spatial_unpad" : "flat";
|
||
}
|
||
|
||
int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 * img) {
|
||
const auto & params = ctx->model.hparams;
|
||
const int n_total = clip_n_output_tokens(ctx, img);
|
||
const auto & proj = ctx->proj_type();
|
||
switch (proj) {
|
||
case PROJECTOR_TYPE_QWEN2VL:
|
||
case PROJECTOR_TYPE_QWEN25VL:
|
||
case PROJECTOR_TYPE_QWEN3VL:
|
||
case PROJECTOR_TYPE_GLM4V:
|
||
return (img->nx / params.patch_size) / 2;
|
||
default:
|
||
break;
|
||
}
|
||
return n_total;
|
||
}
|
||
|
||
int clip_n_output_tokens_y(const struct clip_ctx * ctx, struct clip_image_f32 * img) {
|
||
const auto & params = ctx->model.hparams;
|
||
const auto & proj = ctx->proj_type();
|
||
switch (proj) {
|
||
case PROJECTOR_TYPE_QWEN2VL:
|
||
case PROJECTOR_TYPE_QWEN25VL:
|
||
case PROJECTOR_TYPE_QWEN3VL:
|
||
case PROJECTOR_TYPE_GLM4V:
|
||
return (img->ny / params.patch_size) / 2;
|
||
default:
|
||
break;
|
||
}
|
||
return 1;
|
||
}
|
||
|
||
int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * img) {
|
||
const auto & params = ctx->model.hparams;
|
||
|
||
// for models with fixed size image, the input image is already pre-processed and resized to square
|
||
int patch_size = params.patch_size;
|
||
int n_patches = (img->nx / patch_size) * (img->ny / patch_size);
|
||
|
||
projector_type proj = ctx->proj_type();
|
||
|
||
switch (proj) {
|
||
case PROJECTOR_TYPE_MLP:
|
||
case PROJECTOR_TYPE_MLP_NORM:
|
||
case PROJECTOR_TYPE_JANUS_PRO:
|
||
{
|
||
// do nothing
|
||
} break;
|
||
case PROJECTOR_TYPE_LDP:
|
||
case PROJECTOR_TYPE_LDPV2:
|
||
case PROJECTOR_TYPE_GLM_EDGE:
|
||
{
|
||
n_patches /= 4;
|
||
if (ctx->model.mm_boi) {
|
||
n_patches += 2; // for BOI and EOI token embeddings
|
||
}
|
||
} break;
|
||
case PROJECTOR_TYPE_MINICPMV:
|
||
{
|
||
// Use actual config value if available, otherwise fall back to hardcoded values
|
||
if (params.minicpmv_query_num > 0) {
|
||
n_patches = params.minicpmv_query_num;
|
||
} else {
|
||
// Fallback to hardcoded values for legacy models
|
||
if (params.minicpmv_version == 2) {
|
||
n_patches = 96;
|
||
} else if (params.minicpmv_version == 3) {
|
||
n_patches = 64;
|
||
} else if (params.minicpmv_version == 4) {
|
||
n_patches = 64;
|
||
} else if (params.minicpmv_version == 5) {
|
||
// MiniCPM-V 4.0
|
||
n_patches = 64;
|
||
} else if (params.minicpmv_version == 6) {
|
||
// MiniCPM-V 4.5
|
||
n_patches = 64;
|
||
} else {
|
||
GGML_ABORT("Unknown minicpmv version");
|
||
}
|
||
}
|
||
} break;
|
||
case PROJECTOR_TYPE_QWEN2VL:
|
||
case PROJECTOR_TYPE_QWEN25VL:
|
||
case PROJECTOR_TYPE_QWEN3VL:
|
||
case PROJECTOR_TYPE_GLM4V:
|
||
{
|
||
// dynamic size (2 conv, so double patch size)
|
||
int x_patch = img->nx / (params.patch_size * 2);
|
||
int y_patch = img->ny / (params.patch_size * 2);
|
||
n_patches = x_patch * y_patch;
|
||
} break;
|
||
case PROJECTOR_TYPE_GEMMA3:
|
||
case PROJECTOR_TYPE_IDEFICS3:
|
||
case PROJECTOR_TYPE_INTERNVL:
|
||
case PROJECTOR_TYPE_LLAMA4:
|
||
{
|
||
// both X and Y are downscaled by the scale factor
|
||
int scale_factor = ctx->model.hparams.n_merge;
|
||
n_patches /= (scale_factor * scale_factor);
|
||
} break;
|
||
case PROJECTOR_TYPE_LFM2:
|
||
case PROJECTOR_TYPE_KIMIVL:
|
||
{
|
||
// dynamic size
|
||
int out_patch_size = params.patch_size * ctx->model.hparams.n_merge;
|
||
int x_patch = CLIP_ALIGN(img->nx, out_patch_size) / out_patch_size;
|
||
int y_patch = CLIP_ALIGN(img->ny, out_patch_size) / out_patch_size;
|
||
n_patches = x_patch * y_patch;
|
||
} break;
|
||
case PROJECTOR_TYPE_PIXTRAL:
|
||
case PROJECTOR_TYPE_LIGHTONOCR:
|
||
{
|
||
// dynamic size
|
||
int n_merge = ctx->model.hparams.n_merge;
|
||
int n_patches_x = img->nx / patch_size / (n_merge > 0 ? n_merge : 1);
|
||
int n_patches_y = img->ny / patch_size / (n_merge > 0 ? n_merge : 1);
|
||
if (ctx->model.token_embd_img_break) {
|
||
n_patches = n_patches_y * n_patches_x + n_patches_y - 1; // + one [IMG_BREAK] per row, except the last row
|
||
} else {
|
||
n_patches = n_patches_y * n_patches_x;
|
||
}
|
||
} break;
|
||
case PROJECTOR_TYPE_VOXTRAL:
|
||
case PROJECTOR_TYPE_ULTRAVOX:
|
||
case PROJECTOR_TYPE_QWEN2A:
|
||
{
|
||
n_patches = img->nx;
|
||
|
||
const int proj_stack_factor = ctx->model.hparams.proj_stack_factor;
|
||
if (ctx->model.audio_has_stack_frames()) {
|
||
GGML_ASSERT(proj_stack_factor > 0);
|
||
const int n_len = CLIP_ALIGN(n_patches, proj_stack_factor);
|
||
n_patches = n_len / proj_stack_factor;
|
||
}
|
||
|
||
// whisper downscales input token by half after conv1d
|
||
n_patches /= 2;
|
||
|
||
if (ctx->model.audio_has_avgpool()) {
|
||
// divide by 2 because of nn.AvgPool1d(2, stride=2)
|
||
n_patches /= 2;
|
||
}
|
||
} break;
|
||
case PROJECTOR_TYPE_GLMA:
|
||
{
|
||
n_patches = img->nx;
|
||
// whisper downscales input token by half after conv1d
|
||
n_patches /= 2;
|
||
// reshape by merge_factor
|
||
n_patches /= ctx->model.hparams.proj_stack_factor;
|
||
// for BOI and EOI token embeddings
|
||
n_patches += 2;
|
||
} break;
|
||
case PROJECTOR_TYPE_COGVLM:
|
||
{
|
||
n_patches += 2; // for BOI and EOI token embeddings
|
||
} break;
|
||
case PROJECTOR_TYPE_DEEPSEEKOCR:
|
||
{
|
||
// SAM encoder applies two stride-2 convolutions (net_2 and net_3)
|
||
// which reduces spatial dimensions by 4x in each direction (16x total)
|
||
// E.g., 64x64 -> 16x16 patches
|
||
n_patches /= 16;
|
||
|
||
// build_global_local_features adds image newlines and view separator
|
||
// Formula: h*(w+1) + 1 where h = w = sqrt(n_patches)
|
||
int h = static_cast<int>(std::sqrt(static_cast<float>(n_patches)));
|
||
n_patches = h * (h + 1) + 1;
|
||
} break;
|
||
default:
|
||
GGML_ABORT("unsupported projector type");
|
||
}
|
||
|
||
return n_patches;
|
||
}
|
||
|
||
bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) {
|
||
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;
|
||
int batch_size = imgs.entries.size();
|
||
|
||
// TODO @ngxson : implement batch size > 1 as a loop
|
||
// we don't need true batching support because the cgraph will gonna be big anyway
|
||
if (batch_size != 1) {
|
||
return false; // only support batch size of 1
|
||
}
|
||
|
||
// if buffers are not allocated, we need to do a warmup run to allocate them
|
||
if (!ctx->is_allocated) {
|
||
clip_model_loader::warmup(*ctx, *imgs_c_ptr);
|
||
}
|
||
|
||
// build the inference graph
|
||
ctx->debug_print_tensors.clear();
|
||
ggml_backend_sched_reset(ctx->sched.get());
|
||
ggml_cgraph * gf = clip_image_build_graph(ctx, imgs);
|
||
ggml_backend_sched_alloc_graph(ctx->sched.get(), gf);
|
||
|
||
// set inputs
|
||
const auto & model = ctx->model;
|
||
const auto & hparams = model.hparams;
|
||
|
||
const int image_size_width = imgs.entries[0]->nx;
|
||
const int 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 n_pos = num_patches + (model.class_embedding ? 1 : 0);
|
||
const int pos_w = image_size_width / patch_size;
|
||
const int pos_h = image_size_height / patch_size;
|
||
|
||
const bool use_window_attn = hparams.n_wa_pattern > 0; // for qwen2.5vl
|
||
|
||
auto get_inp_tensor = [&gf](const char * name) {
|
||
ggml_tensor * inp = ggml_graph_get_tensor(gf, name);
|
||
if (inp == nullptr) {
|
||
GGML_ABORT("Failed to get tensor %s", name);
|
||
}
|
||
if (!(inp->flags & GGML_TENSOR_FLAG_INPUT)) {
|
||
GGML_ABORT("Tensor %s is not an input tensor", name);
|
||
}
|
||
return inp;
|
||
};
|
||
|
||
auto set_input_f32 = [&get_inp_tensor](const char * name, std::vector<float> & values) {
|
||
ggml_tensor * cur = get_inp_tensor(name);
|
||
GGML_ASSERT(cur->type == GGML_TYPE_F32);
|
||
GGML_ASSERT(ggml_nelements(cur) == (int64_t)values.size());
|
||
ggml_backend_tensor_set(cur, values.data(), 0, ggml_nbytes(cur));
|
||
};
|
||
|
||
auto set_input_i32 = [&get_inp_tensor](const char * name, std::vector<int32_t> & values) {
|
||
ggml_tensor * cur = get_inp_tensor(name);
|
||
GGML_ASSERT(cur->type == GGML_TYPE_I32);
|
||
GGML_ASSERT(ggml_nelements(cur) == (int64_t)values.size());
|
||
ggml_backend_tensor_set(cur, values.data(), 0, ggml_nbytes(cur));
|
||
};
|
||
|
||
// set input pixel values
|
||
if (!imgs.is_audio) {
|
||
size_t nelem = 0;
|
||
for (const auto & img : imgs.entries) {
|
||
nelem += img->nx * img->ny * 3;
|
||
}
|
||
std::vector<float> inp_raw(nelem);
|
||
|
||
// layout of data (note: the channel dim is unrolled to better visualize the layout):
|
||
//
|
||
// ┌──W──┐
|
||
// │ H │ channel = R
|
||
// ├─────┤ │
|
||
// │ H │ channel = G
|
||
// ├─────┤ │
|
||
// │ H │ channel = B
|
||
// └─────┘ │
|
||
// ──────┘ x B
|
||
|
||
for (size_t i = 0; i < imgs.entries.size(); i++) {
|
||
const int nx = imgs.entries[i]->nx;
|
||
const int ny = imgs.entries[i]->ny;
|
||
const int n = nx * ny;
|
||
|
||
for (int b = 0; b < batch_size; b++) {
|
||
float * batch_entry = inp_raw.data() + b * (3*n);
|
||
for (int y = 0; y < ny; y++) {
|
||
for (int x = 0; x < nx; x++) {
|
||
size_t base_src = 3*(y * nx + x); // idx of the first channel
|
||
size_t base_dst = y * nx + x; // idx of the first channel
|
||
batch_entry[ base_dst] = imgs.entries[b]->buf[base_src ];
|
||
batch_entry[1*n + base_dst] = imgs.entries[b]->buf[base_src + 1];
|
||
batch_entry[2*n + base_dst] = imgs.entries[b]->buf[base_src + 2];
|
||
}
|
||
}
|
||
}
|
||
}
|
||
set_input_f32("inp_raw", inp_raw);
|
||
|
||
} else {
|
||
// audio input
|
||
GGML_ASSERT(imgs.entries.size() == 1);
|
||
const auto & mel_inp = imgs.entries[0];
|
||
const int n_step = mel_inp->nx;
|
||
const int n_mel = mel_inp->ny;
|
||
std::vector<float> inp_raw(n_step * n_mel);
|
||
std::memcpy(inp_raw.data(), mel_inp->buf.data(), n_step * n_mel * sizeof(float));
|
||
set_input_f32("inp_raw", inp_raw);
|
||
}
|
||
|
||
// set input per projector
|
||
switch (ctx->model.proj_type) {
|
||
case PROJECTOR_TYPE_MINICPMV:
|
||
{
|
||
// 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
|
||
std::vector<int32_t> positions(pos_h * pos_w);
|
||
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[id++] = bucket_coords_h[i]*70 + bucket_coords_w[j];
|
||
}
|
||
}
|
||
set_input_i32("positions", positions);
|
||
|
||
// inputs for resampler projector
|
||
// set the 2D positions (using float for sinusoidal embedding)
|
||
int n_patches_per_col = image_size_width / patch_size;
|
||
std::vector<float> pos_data(n_pos);
|
||
// dimension H
|
||
for (int i = 0; i < n_pos; i++) {
|
||
pos_data[i] = static_cast<float>(i / n_patches_per_col);
|
||
}
|
||
set_input_f32("pos_h", pos_data);
|
||
// dimension W
|
||
for (int i = 0; i < n_pos; i++) {
|
||
pos_data[i] = static_cast<float>(i % n_patches_per_col);
|
||
}
|
||
set_input_f32("pos_w", pos_data);
|
||
// base frequency omega
|
||
const float base_freq = 10000.0f;
|
||
const int n_embd_proj = clip_n_mmproj_embd(ctx);
|
||
std::vector<float> omega(n_embd_proj / 4);
|
||
for (int i = 0; i < n_embd_proj / 4; ++i) {
|
||
omega[i] = 1.0f / std::pow(base_freq, static_cast<float>(i) / (n_embd_proj / 4));
|
||
}
|
||
set_input_f32("omega", omega);
|
||
} break;
|
||
case PROJECTOR_TYPE_QWEN2VL:
|
||
case PROJECTOR_TYPE_QWEN3VL:
|
||
case PROJECTOR_TYPE_GLM4V:
|
||
{
|
||
const int merge_ratio = hparams.n_merge;
|
||
const int pw = image_size_width / patch_size;
|
||
const int ph = image_size_height / patch_size;
|
||
std::vector<int> positions(n_pos * 4);
|
||
int ptr = 0;
|
||
for (int y = 0; y < ph; y += merge_ratio) {
|
||
for (int x = 0; x < pw; x += merge_ratio) {
|
||
for (int dy = 0; dy < 2; dy++) {
|
||
for (int dx = 0; dx < 2; dx++) {
|
||
positions[ ptr] = y + dy;
|
||
positions[ num_patches + ptr] = x + dx;
|
||
positions[2 * num_patches + ptr] = y + dy;
|
||
positions[3 * num_patches + ptr] = x + dx;
|
||
ptr++;
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
set_input_i32("positions", positions);
|
||
} break;
|
||
case PROJECTOR_TYPE_QWEN25VL:
|
||
{
|
||
// pw * ph = number of tokens output by ViT after apply patch merger
|
||
// ipw * ipw = number of vision token been processed inside ViT
|
||
const int merge_ratio = 2;
|
||
const int pw = image_size_width / patch_size / merge_ratio;
|
||
const int ph = image_size_height / patch_size / merge_ratio;
|
||
const int ipw = image_size_width / patch_size;
|
||
const int iph = image_size_height / patch_size;
|
||
|
||
std::vector<int> idx (ph * pw);
|
||
std::vector<int> inv_idx(ph * pw);
|
||
|
||
if (use_window_attn) {
|
||
const int attn_window_size = 112;
|
||
const int grid_window = attn_window_size / patch_size / merge_ratio;
|
||
int dst = 0;
|
||
// [num_vision_tokens, num_vision_tokens] attention mask tensor
|
||
std::vector<float> mask(pow(ipw * iph, 2), std::numeric_limits<float>::lowest());
|
||
int mask_row = 0;
|
||
|
||
for (int y = 0; y < ph; y += grid_window) {
|
||
for (int x = 0; x < pw; x += grid_window) {
|
||
const int win_h = std::min(grid_window, ph - y);
|
||
const int win_w = std::min(grid_window, pw - x);
|
||
const int dst_0 = dst;
|
||
// group all tokens belong to the same window togather (to a continue range)
|
||
for (int dy = 0; dy < win_h; dy++) {
|
||
for (int dx = 0; dx < win_w; dx++) {
|
||
const int src = (y + dy) * pw + (x + dx);
|
||
GGML_ASSERT(src < (int)idx.size());
|
||
GGML_ASSERT(dst < (int)inv_idx.size());
|
||
idx [src] = dst;
|
||
inv_idx[dst] = src;
|
||
dst++;
|
||
}
|
||
}
|
||
|
||
for (int r=0; r < win_h * win_w * merge_ratio * merge_ratio; r++) {
|
||
int row_offset = mask_row * (ipw * iph);
|
||
std::fill(
|
||
mask.begin() + row_offset + (dst_0 * merge_ratio * merge_ratio),
|
||
mask.begin() + row_offset + (dst * merge_ratio * merge_ratio),
|
||
0.0);
|
||
mask_row++;
|
||
}
|
||
}
|
||
}
|
||
|
||
set_input_i32("window_idx", idx);
|
||
set_input_i32("inv_window_idx", inv_idx);
|
||
set_input_f32("window_mask", mask);
|
||
} else {
|
||
for (int i = 0; i < ph * pw; i++) {
|
||
idx[i] = i;
|
||
}
|
||
}
|
||
|
||
const int mpow = merge_ratio * merge_ratio;
|
||
std::vector<int> positions(n_pos * 4);
|
||
|
||
int ptr = 0;
|
||
for (int y = 0; y < iph; y += merge_ratio) {
|
||
for (int x = 0; x < ipw; x += merge_ratio) {
|
||
for (int dy = 0; dy < 2; dy++) {
|
||
for (int dx = 0; dx < 2; dx++) {
|
||
auto remap = idx[ptr / mpow];
|
||
remap = (remap * mpow) + (ptr % mpow);
|
||
|
||
positions[ remap] = y + dy;
|
||
positions[ num_patches + remap] = x + dx;
|
||
positions[2 * num_patches + remap] = y + dy;
|
||
positions[3 * num_patches + remap] = x + dx;
|
||
ptr++;
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
set_input_i32("positions", positions);
|
||
} break;
|
||
case PROJECTOR_TYPE_PIXTRAL:
|
||
case PROJECTOR_TYPE_KIMIVL:
|
||
case PROJECTOR_TYPE_LIGHTONOCR:
|
||
{
|
||
// set the 2D positions
|
||
int n_patches_per_col = image_size_width / patch_size;
|
||
std::vector<int> pos_data(n_pos);
|
||
// dimension H
|
||
for (int i = 0; i < n_pos; i++) {
|
||
pos_data[i] = i / n_patches_per_col;
|
||
}
|
||
set_input_i32("pos_h", pos_data);
|
||
// dimension W
|
||
for (int i = 0; i < n_pos; i++) {
|
||
pos_data[i] = i % n_patches_per_col;
|
||
}
|
||
set_input_i32("pos_w", pos_data);
|
||
} break;
|
||
case PROJECTOR_TYPE_GLM_EDGE:
|
||
{
|
||
// llava and other models
|
||
std::vector<int32_t> positions(n_pos);
|
||
for (int i = 0; i < n_pos; i++) {
|
||
positions[i] = i;
|
||
}
|
||
set_input_i32("positions", positions);
|
||
} break;
|
||
case PROJECTOR_TYPE_MLP:
|
||
case PROJECTOR_TYPE_MLP_NORM:
|
||
case PROJECTOR_TYPE_LDP:
|
||
case PROJECTOR_TYPE_LDPV2:
|
||
{
|
||
// llava and other models
|
||
std::vector<int32_t> positions(n_pos);
|
||
for (int i = 0; i < n_pos; i++) {
|
||
positions[i] = i;
|
||
}
|
||
set_input_i32("positions", positions);
|
||
|
||
// 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;
|
||
std::vector<int32_t> patches(num_patches);
|
||
for (int i = 0; i < num_patches; i++) {
|
||
patches[i] = i + patch_offset;
|
||
}
|
||
set_input_i32("patches", patches);
|
||
} break;
|
||
case PROJECTOR_TYPE_DEEPSEEKOCR:
|
||
{
|
||
GGML_ASSERT(pos_w == pos_h);
|
||
|
||
const int window = hparams.attn_window_size;
|
||
const int pos = pos_w;
|
||
std::vector<int32_t> rel_pos_indices_local(window * window);
|
||
std::vector<int32_t> rel_pos_indices_global(pos * pos);
|
||
|
||
for (int q = 0; q < window; q++) {
|
||
for (int k = 0; k < window; k++) {
|
||
rel_pos_indices_local[q * window + k] = q - k + window - 1;
|
||
}
|
||
}
|
||
|
||
for (int q = 0; q < pos; q++) {
|
||
for (int k = 0; k < pos; k++) {
|
||
rel_pos_indices_global[q * pos + k] = q - k + pos - 1;
|
||
}
|
||
}
|
||
|
||
set_input_i32("rel_pos_indices_local", rel_pos_indices_local);
|
||
set_input_i32("rel_pos_indices_global", rel_pos_indices_global);
|
||
} break;
|
||
case PROJECTOR_TYPE_GEMMA3:
|
||
case PROJECTOR_TYPE_IDEFICS3:
|
||
case PROJECTOR_TYPE_INTERNVL:
|
||
case PROJECTOR_TYPE_QWEN2A:
|
||
case PROJECTOR_TYPE_GLMA:
|
||
case PROJECTOR_TYPE_ULTRAVOX:
|
||
case PROJECTOR_TYPE_LFM2:
|
||
case PROJECTOR_TYPE_VOXTRAL:
|
||
case PROJECTOR_TYPE_JANUS_PRO:
|
||
case PROJECTOR_TYPE_COGVLM:
|
||
{
|
||
// do nothing
|
||
} break;
|
||
case PROJECTOR_TYPE_LLAMA4:
|
||
{
|
||
// set the 2D positions
|
||
int n_patches_per_col = image_size_width / patch_size;
|
||
std::vector<int> pos_data(num_patches + 1, 0); // +1 for the [CLS] token
|
||
// last pos is always kept 0, it's for CLS
|
||
// dimension H
|
||
for (int i = 0; i < num_patches; i++) {
|
||
pos_data[i] = (i / n_patches_per_col) + 1;
|
||
}
|
||
set_input_i32("pos_h", pos_data);
|
||
// dimension W
|
||
for (int i = 0; i < num_patches; i++) {
|
||
pos_data[i] = (i % n_patches_per_col) + 1;
|
||
}
|
||
set_input_i32("pos_w", pos_data);
|
||
} break;
|
||
default:
|
||
GGML_ABORT("Unknown projector type");
|
||
}
|
||
|
||
// ggml_backend_cpu_set_n_threads(ctx->backend_cpu, n_threads);
|
||
ggml_backend_dev_t dev = ggml_backend_get_device(ctx->backend_cpu);
|
||
ggml_backend_reg_t reg = dev ? ggml_backend_dev_backend_reg(dev) : nullptr;
|
||
if (reg) {
|
||
auto ggml_backend_set_n_threads_fn = (ggml_backend_set_n_threads_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_set_n_threads");
|
||
if (ggml_backend_set_n_threads_fn) {
|
||
ggml_backend_set_n_threads_fn(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;
|
||
}
|
||
|
||
// print debug nodes
|
||
if (ctx->debug_graph) {
|
||
LOG_INF("\n\n---\n\n");
|
||
LOG_INF("\n\nDebug graph:\n\n");
|
||
for (ggml_tensor * t : ctx->debug_print_tensors) {
|
||
std::vector<uint8_t> data(ggml_nbytes(t));
|
||
ggml_backend_tensor_get(t, data.data(), 0, ggml_nbytes(t));
|
||
print_tensor_info(t);
|
||
print_tensor_shape(t);
|
||
print_tensor_sum(t, data.data(), 3);
|
||
std::string tname_s = std::string(t->name);
|
||
|
||
bool is_stored = false;
|
||
std::vector<std::string> patterns = {
|
||
/* Add tensor names here to dump (e.g. "sam_output") */
|
||
};
|
||
|
||
for (auto & p : patterns) {
|
||
if (tname_s == p) {
|
||
save_tensor_to_file(t, data.data());
|
||
is_stored = true;
|
||
break;
|
||
}
|
||
}
|
||
|
||
if (!is_stored) {
|
||
print_tensor_data(t, data.data(), 3);
|
||
}
|
||
}
|
||
}
|
||
|
||
// the last node is the embedding tensor
|
||
ggml_tensor * embeddings = ggml_graph_node(gf, -1);
|
||
|
||
// sanity check (only support batch size of 1 for now)
|
||
const int n_tokens_out = embeddings->ne[1];
|
||
const int expected_n_tokens_out = clip_n_output_tokens(ctx, imgs.entries[0].get());
|
||
if (n_tokens_out != expected_n_tokens_out) {
|
||
LOG_ERR("%s: expected output %d tokens, got %d\n", __func__, expected_n_tokens_out, n_tokens_out);
|
||
GGML_ABORT("Invalid number of output tokens");
|
||
}
|
||
|
||
// copy the embeddings to the location passed by the user
|
||
if (vec != nullptr) {
|
||
ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));
|
||
}
|
||
|
||
return true;
|
||
}
|
||
|
||
int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
|
||
switch (ctx->model.proj_type) {
|
||
case PROJECTOR_TYPE_LDP:
|
||
return ctx->model.mm_model_block_1_block_2_1_b->ne[0];
|
||
case PROJECTOR_TYPE_LDPV2:
|
||
return ctx->model.mm_model_peg_0_b->ne[0];
|
||
case PROJECTOR_TYPE_MLP:
|
||
case PROJECTOR_TYPE_PIXTRAL:
|
||
case PROJECTOR_TYPE_LIGHTONOCR:
|
||
return ctx->model.mm_2_w->ne[1];
|
||
case PROJECTOR_TYPE_MLP_NORM:
|
||
return ctx->model.mm_3_b->ne[0];
|
||
case PROJECTOR_TYPE_MINICPMV:
|
||
return ctx->model.mm_model_proj->ne[0];
|
||
case PROJECTOR_TYPE_GLM_EDGE:
|
||
return ctx->model.mm_model_mlp_3_w->ne[1];
|
||
case PROJECTOR_TYPE_QWEN2VL:
|
||
case PROJECTOR_TYPE_QWEN25VL:
|
||
case PROJECTOR_TYPE_JANUS_PRO:
|
||
return ctx->model.mm_1_b->ne[0];
|
||
case PROJECTOR_TYPE_QWEN3VL:
|
||
// main path + deepstack paths
|
||
return ctx->model.mm_1_b->ne[0] * (1 + ctx->model.n_deepstack_layers);
|
||
case PROJECTOR_TYPE_GEMMA3:
|
||
return ctx->model.mm_input_proj_w->ne[0];
|
||
case PROJECTOR_TYPE_IDEFICS3:
|
||
return ctx->model.fc_w->ne[1];
|
||
case PROJECTOR_TYPE_ULTRAVOX:
|
||
case PROJECTOR_TYPE_VOXTRAL:
|
||
return ctx->model.mm_2_w->ne[1];
|
||
case PROJECTOR_TYPE_INTERNVL:
|
||
return ctx->model.mm_3_w->ne[1];
|
||
case PROJECTOR_TYPE_LLAMA4:
|
||
return ctx->model.mm_model_proj->ne[1];
|
||
case PROJECTOR_TYPE_QWEN2A:
|
||
return ctx->model.mm_fc_w->ne[1];
|
||
case PROJECTOR_TYPE_GLMA:
|
||
return ctx->model.mm_2_w->ne[1];
|
||
case PROJECTOR_TYPE_LFM2:
|
||
case PROJECTOR_TYPE_KIMIVL:
|
||
return ctx->model.mm_2_w->ne[1];
|
||
case PROJECTOR_TYPE_COGVLM:
|
||
return ctx->model.mm_4h_to_h_w->ne[1];
|
||
case PROJECTOR_TYPE_DEEPSEEKOCR:
|
||
return ctx->model.fc_w->ne[1];
|
||
case PROJECTOR_TYPE_GLM4V:
|
||
return ctx->model.mm_ffn_down_w->ne[1];
|
||
default:
|
||
GGML_ABORT("Unknown projector type");
|
||
}
|
||
}
|
||
|
||
int clip_is_minicpmv(const struct clip_ctx * ctx) {
|
||
if (ctx->proj_type() == PROJECTOR_TYPE_MINICPMV) {
|
||
return ctx->model.hparams.minicpmv_version;
|
||
}
|
||
return 0;
|
||
}
|
||
|
||
bool clip_is_glm(const struct clip_ctx * ctx) {
|
||
return ctx->proj_type() == PROJECTOR_TYPE_GLM_EDGE;
|
||
}
|
||
|
||
bool clip_is_mrope(const struct clip_ctx * ctx) {
|
||
return ctx->proj_type() == PROJECTOR_TYPE_QWEN2VL
|
||
|| ctx->proj_type() == PROJECTOR_TYPE_QWEN25VL
|
||
|| ctx->proj_type() == PROJECTOR_TYPE_QWEN3VL
|
||
|| ctx->proj_type() == PROJECTOR_TYPE_GLM4V;
|
||
}
|
||
|
||
bool clip_is_llava(const struct clip_ctx * ctx) {
|
||
return ctx->model.hparams.has_llava_projector;
|
||
}
|
||
|
||
bool clip_is_gemma3(const struct clip_ctx * ctx) {
|
||
return ctx->proj_type() == PROJECTOR_TYPE_GEMMA3;
|
||
}
|
||
|
||
bool clip_is_deepseekocr(const struct clip_ctx * ctx) {
|
||
return ctx->proj_type() == PROJECTOR_TYPE_DEEPSEEKOCR;
|
||
}
|
||
|
||
bool clip_has_vision_encoder(const struct clip_ctx * ctx) {
|
||
return ctx->model.modality == CLIP_MODALITY_VISION;
|
||
}
|
||
|
||
bool clip_has_audio_encoder(const struct clip_ctx * ctx) {
|
||
return ctx->model.modality == CLIP_MODALITY_AUDIO;
|
||
}
|
||
|
||
bool clip_has_whisper_encoder(const struct clip_ctx * ctx) {
|
||
return ctx->proj_type() == PROJECTOR_TYPE_ULTRAVOX
|
||
|| ctx->proj_type() == PROJECTOR_TYPE_QWEN2A
|
||
|| ctx->proj_type() == PROJECTOR_TYPE_GLMA
|
||
|| ctx->proj_type() == PROJECTOR_TYPE_VOXTRAL;
|
||
}
|
||
|
||
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();
|
||
}
|
||
|
||
void clip_image_f32_batch_add_mel(struct clip_image_f32_batch * batch, int n_mel, int n_frames, float * mel) {
|
||
clip_image_f32 * audio = new clip_image_f32;
|
||
audio->nx = n_frames;
|
||
audio->ny = n_mel;
|
||
audio->buf.resize(n_frames * n_mel);
|
||
std::memcpy(audio->buf.data(), mel, n_frames * n_mel * sizeof(float));
|
||
|
||
batch->entries.push_back(clip_image_f32_ptr(audio));
|
||
batch->is_audio = true;
|
||
}
|
||
|
||
const clip_hparams * clip_get_hparams(const struct clip_ctx * ctx) {
|
||
return &ctx->model.hparams;
|
||
}
|
||
|
||
//
|
||
// API for debugging
|
||
//
|
||
|
||
void clip_debug_encode(clip_ctx * ctx, int h, int w, float fill_value) {
|
||
clip_image_f32 img;
|
||
img.nx = w;
|
||
img.ny = h;
|
||
img.buf.resize(h * w * 3);
|
||
for (int i = 0; i < h * w * 3; i++) {
|
||
img.buf[i] = static_cast<float>(fill_value);
|
||
}
|
||
bool cur_debug_graph = ctx->debug_graph;
|
||
ctx->debug_graph = true;
|
||
clip_image_encode(ctx, 1, &img, nullptr);
|
||
ctx->debug_graph = cur_debug_graph;
|
||
GGML_ASSERT(img.buf.empty() && "expected, always stop here");
|
||
}
|