feat: implement OCR tool using llama.cpp multimodal API
- Support GLM-OCR model with -m (model) and --mmproj (mmproj) options - Custom prompt support via -p (default: OCR) - Greedy/temperature sampling with --temp flag - JSON output with --json flag - GPU acceleration via -ngl option - Use correct GLM-OCR chat template format for prompt construction
This commit is contained in:
wangjue
commit
cdcd1f7202
8
.gitignore
vendored
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8
.gitignore
vendored
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.idea
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.vscode
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DESIGN.md
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deps
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model
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build
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37
CMakeLists.txt
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37
CMakeLists.txt
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cmake_minimum_required(VERSION 3.15)
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project(ocr VERSION 1.0 LANGUAGES CXX)
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set(CMAKE_CXX_STANDARD 17)
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set(CMAKE_CXX_STANDARD_REQUIRED ON)
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set(DEPS_DIR "${CMAKE_SOURCE_DIR}/deps/llama.cpp")
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find_library(LLAMA_LIB llama PATHS "${DEPS_DIR}/lib" NO_DEFAULT_PATH)
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find_library(MTMD_LIB mtmd PATHS "${DEPS_DIR}/lib" NO_DEFAULT_PATH)
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find_library(GGML_LIB ggml PATHS "${DEPS_DIR}/lib" NO_DEFAULT_PATH)
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find_library(GGML_BASE ggml-base PATHS "${DEPS_DIR}/lib" NO_DEFAULT_PATH)
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find_library(GGML_CPU ggml-cpu PATHS "${DEPS_DIR}/lib" NO_DEFAULT_PATH)
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if(NOT LLAMA_LIB OR NOT MTMD_LIB)
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message(FATAL_ERROR "libraries not found in ${DEPS_DIR}/lib")
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endif()
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add_executable(ocr main.cpp)
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target_include_directories(ocr PRIVATE "${DEPS_DIR}/include")
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set(CUDA_DIR "${DEPS_DIR}/usr/local/cuda-12.2")
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set_target_properties(ocr PROPERTIES
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BUILD_RPATH "${DEPS_DIR}/lib;${CUDA_DIR}/targets/x86_64-linux/lib"
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INSTALL_RPATH "${DEPS_DIR}/lib;${CUDA_DIR}/targets/x86_64-linux/lib"
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)
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target_link_libraries(ocr PRIVATE
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${LLAMA_LIB}
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${MTMD_LIB}
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${GGML_LIB}
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${GGML_BASE}
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${GGML_CPU}
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pthread
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dl
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stdc++fs
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)
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322
main.cpp
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main.cpp
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#include "llama.h"
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#include "mtmd.h"
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#include "mtmd-helper.h"
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#include "ggml.h"
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#include <cstdio>
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#include <cstdlib>
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#include <cstring>
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#include <cmath>
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#include <string>
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#include <vector>
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#include <thread>
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#include <unistd.h>
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#include <getopt.h>
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#include <signal.h>
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#include <inttypes.h>
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static volatile bool g_interrupted = false;
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static void sigint_handler(int signo) {
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(void)signo;
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g_interrupted = true;
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}
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static void batch_clear(struct llama_batch & batch) {
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batch.n_tokens = 0;
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}
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static void batch_add(struct llama_batch & batch, llama_token id, llama_pos pos, const std::vector<llama_seq_id> & seq_ids, bool logits) {
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batch.token [batch.n_tokens] = id;
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batch.pos [batch.n_tokens] = pos;
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batch.n_seq_id[batch.n_tokens] = (int32_t)seq_ids.size();
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for (size_t i = 0; i < seq_ids.size(); ++i) {
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batch.seq_id[batch.n_tokens][i] = seq_ids[i];
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}
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batch.logits [batch.n_tokens] = logits ? 1 : 0;
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batch.n_tokens++;
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}
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static std::string token_to_piece(const struct llama_vocab * vocab, llama_token token) {
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std::string piece;
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piece.resize(256);
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int32_t n = llama_token_to_piece(vocab, token, &piece[0], (int32_t)piece.size(), 0, false);
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if (n < 0) {
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piece.resize(-n);
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n = llama_token_to_piece(vocab, token, &piece[0], (int32_t)piece.size(), 0, false);
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}
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piece.resize(std::max(0, n));
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return piece;
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}
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static std::string json_escape(const std::string & s) {
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std::string out;
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out.reserve(s.size() + 2);
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for (char c : s) {
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switch (c) {
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case '"': out += "\\\""; break;
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case '\\': out += "\\\\"; break;
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case '\n': out += "\\n"; break;
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case '\r': out += "\\r"; break;
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case '\t': out += "\\t"; break;
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default: out += c; break;
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}
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}
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return out;
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}
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static void print_usage(const char * prog) {
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fprintf(stderr,
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"Usage: %s -m <model> --mmproj <mmproj> [options] <image_path>\n"
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"\n"
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"Options:\n"
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" -m, --model <path> model file path (GGUF)\n"
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" --mmproj,--mm <path> mmproj file path\n"
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" -p <text> OCR prompt (default: OCR)\n"
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" -t <n> number of threads (default: cpu cores)\n"
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" --ngl <n> GPU layers (-1 = all, default: -1)\n"
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" -c <n> context size (default: 8192)\n"
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" --temp <f> sampling temperature (0 = greedy, default: 0)\n"
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" -s <seed> random seed\n"
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" --json output in JSON format\n"
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" -h show this help\n"
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"\n"
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"Example:\n"
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" %s -m model.gguf --mmproj mmproj.gguf -p \"OCR\" image.png\n",
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prog, prog);
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}
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int main(int argc, char ** argv) {
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std::string model_path;
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std::string mmproj_path;
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std::string image_path;
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std::string prompt = "OCR";
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int n_threads = (int)std::thread::hardware_concurrency();
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int n_gpu_layers = -1;
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int n_ctx = 8192;
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float temp = 0.0f;
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uint32_t seed = LLAMA_DEFAULT_SEED;
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bool json_output = false;
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// pre-process -mm to --mmproj so getopt handles it correctly
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for (int i = 1; i < argc; i++) {
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if (strcmp(argv[i], "-mm") == 0) {
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argv[i] = (char *)"--mmproj";
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}
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}
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while (1) {
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static struct option long_opts[] = {
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{"temp", required_argument, nullptr, 0},
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{"json", no_argument, nullptr, 1},
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{"mmproj", required_argument, nullptr, 2},
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{"mm", required_argument, nullptr, 2},
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{"ngl", required_argument, nullptr, 3},
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{"model", required_argument, nullptr, 'm'},
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{"help", no_argument, nullptr, 'h'},
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{nullptr, 0, nullptr, 0}
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};
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int idx = 0;
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int c = getopt_long(argc, argv, "m:p:t:c:s:h", long_opts, &idx);
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if (c == -1) break;
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switch (c) {
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case 0: temp = std::stof(optarg); break;
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case 1: json_output = true; break;
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case 2: mmproj_path = optarg; break;
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case 3: n_gpu_layers = std::stoi(optarg); break;
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case 'm': model_path = optarg; break;
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case 'p': prompt = optarg; break;
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case 't': n_threads = std::stoi(optarg); break;
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case 'c': n_ctx = std::stoi(optarg); break;
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case 's': seed = (uint32_t)std::stoul(optarg); break;
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case 'h': print_usage(argv[0]); return 0;
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default: print_usage(argv[0]); return 1;
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}
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}
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if ((optind + 1) != argc) {
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print_usage(argv[0]);
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return 1;
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}
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image_path = argv[optind];
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if (model_path.empty() || mmproj_path.empty()) {
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fprintf(stderr, "ERROR: -m/--model and --mmproj/--mm are required\n");
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return 1;
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}
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signal(SIGINT, sigint_handler);
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int64_t t_start_us = ggml_time_us();
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llama_backend_init();
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llama_model_params mparams = llama_model_default_params();
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mparams.n_gpu_layers = n_gpu_layers;
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llama_model * model = llama_model_load_from_file(model_path.c_str(), mparams);
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if (!model) {
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fprintf(stderr, "ERROR: failed to load model from %s\n", model_path.c_str());
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return 1;
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}
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llama_context_params cparams = llama_context_default_params();
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cparams.n_ctx = (uint32_t)n_ctx;
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cparams.n_batch = 512;
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cparams.n_ubatch = 512;
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llama_context * lctx = llama_init_from_model(model, cparams);
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if (!lctx) {
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fprintf(stderr, "ERROR: failed to create context\n");
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llama_model_free(model);
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return 1;
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}
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llama_set_n_threads(lctx, n_threads, n_threads);
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const struct llama_vocab * vocab = llama_model_get_vocab(model);
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mtmd_context_params mtmd_params = mtmd_context_params_default();
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mtmd_params.use_gpu = (n_gpu_layers != 0);
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mtmd_params.n_threads = n_threads;
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mtmd_context * ctx_vision = mtmd_init_from_file(mmproj_path.c_str(), model, mtmd_params);
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if (!ctx_vision) {
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fprintf(stderr, "ERROR: failed to load mmproj from %s\n", mmproj_path.c_str());
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llama_free(lctx);
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llama_model_free(model);
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return 1;
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}
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int64_t t_loaded_us = ggml_time_us();
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mtmd_bitmap * bmp = mtmd_helper_bitmap_init_from_file(ctx_vision, image_path.c_str());
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if (!bmp) {
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fprintf(stderr, "ERROR: failed to load image from %s\n", image_path.c_str());
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mtmd_free(ctx_vision);
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llama_free(lctx);
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llama_model_free(model);
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return 1;
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}
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// Construct prompt using GLM-OCR chat template format
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// The model expects: [gMASK]<sop><|user|>\n<__media__>PROMPT\n<|assistant|>\n
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std::string full_prompt = "[gMASK]<sop><|user|>\n";
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full_prompt += mtmd_default_marker();
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full_prompt += prompt;
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full_prompt += "\n<|assistant|>\n";
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mtmd_input_chunks * chunks = mtmd_input_chunks_init();
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mtmd_input_text in_text;
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in_text.text = full_prompt.c_str();
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in_text.add_special = false; // template already includes [gMASK]/<sop>
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in_text.parse_special = true;
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const mtmd_bitmap * bitmaps[1] = { bmp };
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int32_t res = mtmd_tokenize(ctx_vision, chunks, &in_text, bitmaps, 1);
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if (res != 0) {
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fprintf(stderr, "ERROR: mtmd_tokenize failed (%d)\n", res);
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mtmd_input_chunks_free(chunks);
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mtmd_bitmap_free(bmp);
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mtmd_free(ctx_vision);
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llama_free(lctx);
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llama_model_free(model);
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return 1;
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}
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llama_pos n_past = 0;
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res = mtmd_helper_eval_chunks(ctx_vision, lctx, chunks, n_past, 0, 512, true, &n_past);
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if (res != 0) {
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fprintf(stderr, "ERROR: mtmd_helper_eval_chunks failed (%d)\n", res);
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mtmd_input_chunks_free(chunks);
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mtmd_bitmap_free(bmp);
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mtmd_free(ctx_vision);
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llama_free(lctx);
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llama_model_free(model);
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return 1;
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}
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mtmd_input_chunks_free(chunks);
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mtmd_bitmap_free(bmp);
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struct llama_sampler * smpl = nullptr;
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if (temp <= 0.0f) {
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struct llama_sampler_chain_params sparams = llama_sampler_chain_default_params();
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smpl = llama_sampler_chain_init(sparams);
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llama_sampler_chain_add(smpl, llama_sampler_init_greedy());
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} else {
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struct llama_sampler_chain_params sparams = llama_sampler_chain_default_params();
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smpl = llama_sampler_chain_init(sparams);
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llama_sampler_chain_add(smpl, llama_sampler_init_top_k(40));
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llama_sampler_chain_add(smpl, llama_sampler_init_top_p(0.9f, 1));
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llama_sampler_chain_add(smpl, llama_sampler_init_temp(temp));
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llama_sampler_chain_add(smpl, llama_sampler_init_dist(seed));
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}
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struct llama_batch batch = llama_batch_init(1, 0, 1);
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int64_t t_infer_start_us = ggml_time_us();
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std::string ocr_text;
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int n_generated = 0;
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for (int i = 0; i < 2048; i++) {
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if (g_interrupted) break;
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llama_token token_id = llama_sampler_sample(smpl, lctx, -1);
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llama_sampler_accept(smpl, token_id);
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if (llama_vocab_is_eog(vocab, token_id)) break;
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std::string piece = token_to_piece(vocab, token_id);
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ocr_text += piece;
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if (!json_output) {
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printf("%s", piece.c_str());
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fflush(stdout);
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}
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batch_clear(batch);
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batch_add(batch, token_id, n_past++, {0}, true);
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if (llama_decode(lctx, batch) != 0) {
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fprintf(stderr, "\nERROR: llama_decode failed\n");
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break;
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}
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n_generated++;
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}
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int64_t t_end_us = ggml_time_us();
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if (!json_output) {
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printf("\n");
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}
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llama_batch_free(batch);
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llama_sampler_free(smpl);
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mtmd_free(ctx_vision);
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llama_free(lctx);
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llama_model_free(model);
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llama_backend_free();
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if (json_output) {
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printf("{\n");
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printf(" \"text\": \"%s\",\n", json_escape(ocr_text).c_str());
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printf(" \"meta\": {\n");
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printf(" \"model\": \"%s\",\n", json_escape(model_path).c_str());
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printf(" \"mmproj\": \"%s\",\n", json_escape(mmproj_path).c_str());
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printf(" \"prompt\": \"%s\",\n", json_escape(prompt).c_str());
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printf(" \"n_prompt_tokens\": %" PRId64 ",\n", (int64_t)n_past);
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printf(" \"n_generated\": %d,\n", n_generated);
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printf(" \"timings_ms\": {\n");
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printf(" \"load\": %" PRId64 ",\n", (t_loaded_us - t_start_us) / 1000);
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printf(" \"inference\": %" PRId64 "\n", (t_end_us - t_infer_start_us) / 1000);
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printf(" }\n");
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printf(" }\n");
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printf("}\n");
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}
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return g_interrupted ? 130 : 0;
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}
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