android : introduce starter project example (#4926)
* Introduce starter project for Android Based on examples/llama.swiftui. * Add github workflow * Set NDK version * Only build arm64-v8a in CI * Sync bench code * Rename CI prop to skip-armeabi-v7a * Remove unused tests
This commit is contained in:
Neuman Vong
GitHub
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41 changed files with 1807 additions and 0 deletions
394
examples/llama.android/app/src/main/cpp/llama-android.cpp
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394
examples/llama.android/app/src/main/cpp/llama-android.cpp
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#include <android/log.h>
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#include <jni.h>
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#include <iomanip>
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#include <math.h>
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#include <string>
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#include <unistd.h>
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#include "llama.h"
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#include "common/common.h"
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// Write C++ code here.
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//
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// Do not forget to dynamically load the C++ library into your application.
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//
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// For instance,
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//
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// In MainActivity.java:
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// static {
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// System.loadLibrary("llama-android");
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// }
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//
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// Or, in MainActivity.kt:
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// companion object {
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// init {
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// System.loadLibrary("llama-android")
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// }
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// }
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#define TAG "llama-android.cpp"
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#define LOGi(...) __android_log_print(ANDROID_LOG_INFO, TAG, __VA_ARGS__)
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#define LOGe(...) __android_log_print(ANDROID_LOG_ERROR, TAG, __VA_ARGS__)
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jclass la_int_var;
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jmethodID la_int_var_value;
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jmethodID la_int_var_inc;
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static void log_callback(ggml_log_level level, const char * fmt, void * data) {
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if (level == GGML_LOG_LEVEL_ERROR) __android_log_print(ANDROID_LOG_ERROR, TAG, fmt, data);
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else if (level == GGML_LOG_LEVEL_INFO) __android_log_print(ANDROID_LOG_INFO, TAG, fmt, data);
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else if (level == GGML_LOG_LEVEL_WARN) __android_log_print(ANDROID_LOG_WARN, TAG, fmt, data);
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else __android_log_print(ANDROID_LOG_DEFAULT, TAG, fmt, data);
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}
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extern "C"
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JNIEXPORT jlong JNICALL
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Java_com_example_llama_Llm_load_1model(JNIEnv *env, jobject, jstring filename) {
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llama_model_params model_params = llama_model_default_params();
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auto path_to_model = env->GetStringUTFChars(filename, 0);
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LOGi("Loading model from %s", path_to_model);
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auto model = llama_load_model_from_file(path_to_model, model_params);
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env->ReleaseStringUTFChars(filename, path_to_model);
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if (!model) {
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LOGe("load_model() failed");
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env->ThrowNew(env->FindClass("java/lang/IllegalStateException"), "load_model() failed");
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return 0;
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}
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return reinterpret_cast<jlong>(model);
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}
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extern "C"
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JNIEXPORT void JNICALL
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Java_com_example_llama_Llm_free_1model(JNIEnv *, jobject, jlong model) {
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llama_free_model(reinterpret_cast<llama_model *>(model));
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}
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extern "C"
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JNIEXPORT jlong JNICALL
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Java_com_example_llama_Llm_new_1context(JNIEnv *env, jobject, jlong jmodel) {
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auto model = reinterpret_cast<llama_model *>(jmodel);
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if (!model) {
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LOGe("new_context(): model cannot be null");
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env->ThrowNew(env->FindClass("java/lang/IllegalArgumentException"), "Model cannot be null");
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return 0;
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}
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int n_threads = std::max(1, std::min(8, (int) sysconf(_SC_NPROCESSORS_ONLN) - 2));
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LOGi("Using %d threads", n_threads);
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llama_context_params ctx_params = llama_context_default_params();
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ctx_params.seed = 1234;
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ctx_params.n_ctx = 2048;
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ctx_params.n_threads = n_threads;
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ctx_params.n_threads_batch = n_threads;
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llama_context * context = llama_new_context_with_model(model, ctx_params);
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if (!context) {
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LOGe("llama_new_context_with_model() returned null)");
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env->ThrowNew(env->FindClass("java/lang/IllegalStateException"),
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"llama_new_context_with_model() returned null)");
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return 0;
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}
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return reinterpret_cast<jlong>(context);
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}
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extern "C"
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JNIEXPORT void JNICALL
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Java_com_example_llama_Llm_free_1context(JNIEnv *, jobject, jlong context) {
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llama_free(reinterpret_cast<llama_context *>(context));
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}
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extern "C"
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JNIEXPORT void JNICALL
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Java_com_example_llama_Llm_backend_1free(JNIEnv *, jobject) {
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llama_backend_free();
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}
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extern "C"
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JNIEXPORT void JNICALL
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Java_com_example_llama_Llm_log_1to_1android(JNIEnv *, jobject) {
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llama_log_set(log_callback, NULL);
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}
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extern "C"
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JNIEXPORT jstring JNICALL
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Java_com_example_llama_Llm_bench_1model(
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JNIEnv *env,
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jobject,
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jlong context_pointer,
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jlong model_pointer,
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jlong batch_pointer,
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jint pp,
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jint tg,
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jint pl,
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jint nr
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) {
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auto pp_avg = 0.0;
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auto tg_avg = 0.0;
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auto pp_std = 0.0;
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auto tg_std = 0.0;
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const auto context = reinterpret_cast<llama_context *>(context_pointer);
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const auto model = reinterpret_cast<llama_model *>(model_pointer);
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const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
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const int n_ctx = llama_n_ctx(context);
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LOGi("n_ctx = %d", n_ctx);
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int i, j;
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int nri;
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for (nri = 0; nri < nr; nri++) {
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LOGi("Benchmark prompt processing (pp)");
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llama_batch_clear(*batch);
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const int n_tokens = pp;
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for (i = 0; i < n_tokens; i++) {
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llama_batch_add(*batch, 0, i, { 0 }, false);
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}
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batch->logits[batch->n_tokens - 1] = true;
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llama_kv_cache_clear(context);
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const auto t_pp_start = ggml_time_us();
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if (llama_decode(context, *batch) != 0) {
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LOGi("llama_decode() failed during prompt processing");
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}
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const auto t_pp_end = ggml_time_us();
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// bench text generation
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LOGi("Benchmark text generation (tg)");
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llama_kv_cache_clear(context);
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const auto t_tg_start = ggml_time_us();
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for (i = 0; i < tg; i++) {
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llama_batch_clear(*batch);
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for (j = 0; j < pl; j++) {
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llama_batch_add(*batch, 0, i, { j }, true);
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}
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LOGi("llama_decode() text generation: %d", i);
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if (llama_decode(context, *batch) != 0) {
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LOGi("llama_decode() failed during text generation");
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}
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}
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const auto t_tg_end = ggml_time_us();
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llama_kv_cache_clear(context);
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const auto t_pp = double(t_pp_end - t_pp_start) / 1000000.0;
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const auto t_tg = double(t_tg_end - t_tg_start) / 1000000.0;
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const auto speed_pp = double(pp) / t_pp;
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const auto speed_tg = double(pl * tg) / t_tg;
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pp_avg += speed_pp;
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tg_avg += speed_tg;
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pp_std += speed_pp * speed_pp;
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tg_std += speed_tg * speed_tg;
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LOGi("pp %f t/s, tg %f t/s", speed_pp, speed_tg);
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}
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pp_avg /= double(nr);
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tg_avg /= double(nr);
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if (nr > 1) {
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pp_std = sqrt(pp_std / double(nr - 1) - pp_avg * pp_avg * double(nr) / double(nr - 1));
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tg_std = sqrt(tg_std / double(nr - 1) - tg_avg * tg_avg * double(nr) / double(nr - 1));
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} else {
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pp_std = 0;
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tg_std = 0;
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}
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char model_desc[128];
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llama_model_desc(model, model_desc, sizeof(model_desc));
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const auto model_size = double(llama_model_size(model)) / 1024.0 / 1024.0 / 1024.0;
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const auto model_n_params = double(llama_model_n_params(model)) / 1e9;
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const auto backend = "(Android)"; // TODO: What should this be?
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std::stringstream result;
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result << std::setprecision(2);
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result << "| model | size | params | backend | test | t/s |\n";
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result << "| --- | --- | --- | --- | --- | --- |\n";
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result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | " << backend << " | pp " << pp << " | " << pp_avg << " ± " << pp_std << " |\n";
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result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | " << backend << " | tg " << tg << " | " << tg_avg << " ± " << tg_std << " |\n";
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return env->NewStringUTF(result.str().c_str());
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}
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extern "C"
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JNIEXPORT void JNICALL
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Java_com_example_llama_Llm_free_1batch(JNIEnv *, jobject, jlong batch_pointer) {
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llama_batch_free(*reinterpret_cast<llama_batch *>(batch_pointer));
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}
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extern "C"
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JNIEXPORT jlong JNICALL
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Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) {
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// Source: Copy of llama.cpp:llama_batch_init but heap-allocated.
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llama_batch *batch = new llama_batch {
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0,
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nullptr,
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nullptr,
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nullptr,
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nullptr,
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nullptr,
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nullptr,
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0,
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0,
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0,
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};
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if (embd) {
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batch->embd = (float *) malloc(sizeof(float) * n_tokens * embd);
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} else {
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batch->token = (llama_token *) malloc(sizeof(llama_token) * n_tokens);
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}
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batch->pos = (llama_pos *) malloc(sizeof(llama_pos) * n_tokens);
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batch->n_seq_id = (int32_t *) malloc(sizeof(int32_t) * n_tokens);
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batch->seq_id = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * n_tokens);
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for (int i = 0; i < n_tokens; ++i) {
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batch->seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max);
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}
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batch->logits = (int8_t *) malloc(sizeof(int8_t) * n_tokens);
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return reinterpret_cast<jlong>(batch);
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}
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extern "C"
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JNIEXPORT void JNICALL
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Java_com_example_llama_Llm_backend_1init(JNIEnv *, jobject, jboolean numa) {
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llama_backend_init(numa);
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}
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extern "C"
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JNIEXPORT jstring JNICALL
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Java_com_example_llama_Llm_system_1info(JNIEnv *env, jobject) {
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return env->NewStringUTF(llama_print_system_info());
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}
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extern "C"
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JNIEXPORT jint JNICALL
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Java_com_example_llama_Llm_completion_1init(
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JNIEnv *env,
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jobject,
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jlong context_pointer,
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jlong batch_pointer,
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jstring jtext,
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jint n_len
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) {
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const auto text = env->GetStringUTFChars(jtext, 0);
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const auto context = reinterpret_cast<llama_context *>(context_pointer);
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const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
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const auto tokens_list = llama_tokenize(context, text, 1);
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auto n_ctx = llama_n_ctx(context);
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auto n_kv_req = tokens_list.size() + (n_len - tokens_list.size());
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LOGi("n_len = %d, n_ctx = %d, n_kv_req = %d", n_len, n_ctx, n_kv_req);
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if (n_kv_req > n_ctx) {
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LOGe("error: n_kv_req > n_ctx, the required KV cache size is not big enough");
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}
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for (auto id : tokens_list) {
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LOGi("%s", llama_token_to_piece(context, id).c_str());
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}
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llama_batch_clear(*batch);
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// evaluate the initial prompt
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for (auto i = 0; i < tokens_list.size(); i++) {
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llama_batch_add(*batch, tokens_list[i], i, { 0 }, false);
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}
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// llama_decode will output logits only for the last token of the prompt
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batch->logits[batch->n_tokens - 1] = true;
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if (llama_decode(context, *batch) != 0) {
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LOGe("llama_decode() failed");
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}
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env->ReleaseStringUTFChars(jtext, text);
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return batch->n_tokens;
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}
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extern "C"
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JNIEXPORT jstring JNICALL
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Java_com_example_llama_Llm_completion_1loop(
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JNIEnv * env,
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jobject,
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jlong context_pointer,
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jlong batch_pointer,
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jint n_len,
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jobject intvar_ncur
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) {
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const auto context = reinterpret_cast<llama_context *>(context_pointer);
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const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
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const auto model = llama_get_model(context);
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if (!la_int_var) la_int_var = env->GetObjectClass(intvar_ncur);
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if (!la_int_var_value) la_int_var_value = env->GetMethodID(la_int_var, "getValue", "()I");
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if (!la_int_var_inc) la_int_var_inc = env->GetMethodID(la_int_var, "inc", "()V");
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auto n_vocab = llama_n_vocab(model);
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auto logits = llama_get_logits_ith(context, batch->n_tokens - 1);
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
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candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
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}
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llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
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// sample the most likely token
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const auto new_token_id = llama_sample_token_greedy(context, &candidates_p);
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const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value);
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if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
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return env->NewStringUTF("");
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}
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auto new_token_chars = llama_token_to_piece(context, new_token_id);
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LOGi("new_token_chars: `%s`", new_token_chars.c_str());
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auto new_token = env->NewStringUTF(new_token_chars.c_str());
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llama_batch_clear(*batch);
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llama_batch_add(*batch, new_token_id, n_cur, { 0 }, true);
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env->CallVoidMethod(intvar_ncur, la_int_var_inc);
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if (llama_decode(context, *batch) != 0) {
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LOGe("llama_decode() returned null");
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}
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return new_token;
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}
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extern "C"
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JNIEXPORT void JNICALL
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Java_com_example_llama_Llm_kv_1cache_1clear(JNIEnv *, jobject, jlong context) {
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llama_kv_cache_clear(reinterpret_cast<llama_context *>(context));
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}
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