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llama.cpp
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llama.cpp/examples/llama.android/lib/src/main/cpp/ai_chat.cpp

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#include <android/log.h>
#include <jni.h>
#include <iomanip>
#include <cmath>
#include <string>
#include <unistd.h>
#include <sampling.h>
#include "chat.h"
#include "common.h"
#include "llama.h"
template<class T>
static std::string join(const std::vector<T> &values, const std::string &delim) {
std::ostringstream str;
for (size_t i = 0; i < values.size(); i++) {
str << values[i];
if (i < values.size() - 1) { str << delim; }
}
return str.str();
}
/**
* Logging utils
*/
#define TAG "ai-chat"
#define LOGv(...) __android_log_print(ANDROID_LOG_VERBOSE, TAG, __VA_ARGS__)
#define LOGd(...) __android_log_print(ANDROID_LOG_DEBUG, TAG, __VA_ARGS__)
#define LOGi(...) __android_log_print(ANDROID_LOG_INFO, TAG, __VA_ARGS__)
#define LOGw(...) __android_log_print(ANDROID_LOG_WARN, TAG, __VA_ARGS__)
#define LOGe(...) __android_log_print(ANDROID_LOG_ERROR, TAG, __VA_ARGS__)
/**
* LLama resources: context, model, batch and sampler
*/
constexpr int N_THREADS_MIN = 2;
constexpr int N_THREADS_MAX = 4;
constexpr int N_THREADS_HEADROOM = 2;
constexpr int DEFAULT_CONTEXT_SIZE = 8192;
constexpr int OVERFLOW_HEADROOM = 4;
constexpr int BATCH_SIZE = 512;
constexpr float DEFAULT_SAMPLER_TEMP = 0.3f;
static llama_model * g_model;
static llama_context * g_context;
static llama_batch g_batch;
static common_chat_templates_ptr g_chat_templates;
static common_sampler * g_sampler;
static void log_callback(ggml_log_level level, const char *fmt, void *data) {
int priority;
switch (level) {
case GGML_LOG_LEVEL_ERROR:
priority = ANDROID_LOG_ERROR;
break;
case GGML_LOG_LEVEL_WARN:
priority = GGML_LOG_LEVEL_WARN;
break;
case GGML_LOG_LEVEL_INFO:
priority = GGML_LOG_LEVEL_INFO;
break;
case GGML_LOG_LEVEL_DEBUG:
priority = GGML_LOG_LEVEL_DEBUG;
break;
default:
priority = ANDROID_LOG_DEFAULT;
break;
}
__android_log_print(priority, TAG, fmt, data);
}
extern "C"
JNIEXPORT void JNICALL
Java_com_arm_aichat_internal_InferenceEngineImpl_init(JNIEnv *env, jobject /*unused*/, jstring nativeLibDir) {
// Set llama log handler to Android
llama_log_set(log_callback, nullptr);
// Loading all CPU backend variants
const auto *path_to_backend = env->GetStringUTFChars(nativeLibDir, 0);
LOGi("Loading backends from %s", path_to_backend);
ggml_backend_load_all_from_path(path_to_backend);
env->ReleaseStringUTFChars(nativeLibDir, path_to_backend);
// Initialize backends
llama_backend_init();
LOGi("Backend initiated; Log handler set.");
}
extern "C"
JNIEXPORT jint JNICALL
Java_com_arm_aichat_internal_InferenceEngineImpl_load(JNIEnv *env, jobject, jstring jmodel_path) {
llama_model_params model_params = llama_model_default_params();
const auto *model_path = env->GetStringUTFChars(jmodel_path, 0);
LOGd("%s: Loading model from: \n%s\n", __func__, model_path);
auto *model = llama_model_load_from_file(model_path, model_params);
env->ReleaseStringUTFChars(jmodel_path, model_path);
if (!model) {
return 1;
}
g_model = model;
return 0;
}
static llama_context *init_context(llama_model *model, const int n_ctx = DEFAULT_CONTEXT_SIZE) {
if (!model) {
LOGe("%s: model cannot be null", __func__);
return nullptr;
}
// Multi-threading setup
const int n_threads = std::max(N_THREADS_MIN, std::min(N_THREADS_MAX,
(int) sysconf(_SC_NPROCESSORS_ONLN) -
N_THREADS_HEADROOM));
LOGi("%s: Using %d threads", __func__, n_threads);
// Context parameters setup
llama_context_params ctx_params = llama_context_default_params();
const int trained_context_size = llama_model_n_ctx_train(model);
if (n_ctx > trained_context_size) {
LOGw("%s: Model was trained with only %d context size! Enforcing %d context size...",
__func__, trained_context_size, n_ctx);
}
ctx_params.n_ctx = n_ctx;
ctx_params.n_batch = BATCH_SIZE;
ctx_params.n_ubatch = BATCH_SIZE;
ctx_params.n_threads = n_threads;
ctx_params.n_threads_batch = n_threads;
auto *context = llama_init_from_model(g_model, ctx_params);
if (context == nullptr) {
LOGe("%s: llama_new_context_with_model() returned null)", __func__);
}
return context;
}
static common_sampler *new_sampler(float temp) {
common_params_sampling sparams;
sparams.temp = temp;
return common_sampler_init(g_model, sparams);
}
extern "C"
JNIEXPORT jint JNICALL
Java_com_arm_aichat_internal_InferenceEngineImpl_prepare(JNIEnv * /*env*/, jobject /*unused*/) {
auto *context = init_context(g_model);
if (!context) { return 1; }
g_context = context;
g_batch = llama_batch_init(BATCH_SIZE, 0, 1);
g_chat_templates = common_chat_templates_init(g_model, "");
g_sampler = new_sampler(DEFAULT_SAMPLER_TEMP);
return 0;
}
static std::string get_backend() {
std::vector<std::string> backends;
for (size_t i = 0; i < ggml_backend_reg_count(); i++) {
auto *reg = ggml_backend_reg_get(i);
std::string name = ggml_backend_reg_name(reg);
if (name != "CPU") {
backends.push_back(ggml_backend_reg_name(reg));
}
}
return backends.empty() ? "CPU" : join(backends, ",");
}
extern "C"
JNIEXPORT jstring JNICALL
Java_com_arm_aichat_internal_InferenceEngineImpl_systemInfo(JNIEnv *env, jobject /*unused*/) {
return env->NewStringUTF(llama_print_system_info());
}
extern "C"
JNIEXPORT jstring JNICALL
Java_com_arm_aichat_internal_InferenceEngineImpl_benchModel(JNIEnv *env, jobject /*unused*/, jint pp, jint tg,
jint pl, jint nr) {
auto *context = init_context(g_model, pp);
if (!context) {
const auto *const err_msg = "Fail to init_context! Bench aborted.";
LOGe(err_msg);
return env->NewStringUTF(err_msg);
}
auto pp_avg = 0.0;
auto tg_avg = 0.0;
auto pp_std = 0.0;
auto tg_std = 0.0;
const uint32_t n_ctx = llama_n_ctx(context);
LOGi("n_ctx = %d", n_ctx);
int i, j;
int nri;
for (nri = 0; nri < nr; nri++) {
LOGi("Benchmark prompt processing (pp = %d)", pp);
common_batch_clear(g_batch);
const int n_tokens = pp;
for (i = 0; i < n_tokens; i++) {
common_batch_add(g_batch, 0, i, {0}, false);
}
g_batch.logits[g_batch.n_tokens - 1] = true;
llama_memory_clear(llama_get_memory(context), false);
const auto t_pp_start = ggml_time_us();
if (llama_decode(context, g_batch) != 0) {
LOGe("llama_decode() failed during prompt processing");
}
const auto t_pp_end = ggml_time_us();
// bench text generation
LOGi("Benchmark text generation (tg = %d)", tg);
llama_memory_clear(llama_get_memory(context), false);
const auto t_tg_start = ggml_time_us();
for (i = 0; i < tg; i++) {
common_batch_clear(g_batch);
for (j = 0; j < pl; j++) {
common_batch_add(g_batch, 0, i, {j}, true);
}
if (llama_decode(context, g_batch) != 0) {
LOGe("llama_decode() failed during text generation");
}
}
const auto t_tg_end = ggml_time_us();
llama_memory_clear(llama_get_memory(context), false);
const auto t_pp = double(t_pp_end - t_pp_start) / 1000000.0;
const auto t_tg = double(t_tg_end - t_tg_start) / 1000000.0;
const auto speed_pp = double(pp) / t_pp;
const auto speed_tg = double(pl * tg) / t_tg;
pp_avg += speed_pp;
tg_avg += speed_tg;
pp_std += speed_pp * speed_pp;
tg_std += speed_tg * speed_tg;
LOGi("pp %f t/s, tg %f t/s", speed_pp, speed_tg);
}
llama_free(context);
pp_avg /= double(nr);
tg_avg /= double(nr);
if (nr > 1) {
pp_std = sqrt(pp_std / double(nr - 1) - pp_avg * pp_avg * double(nr) / double(nr - 1));
tg_std = sqrt(tg_std / double(nr - 1) - tg_avg * tg_avg * double(nr) / double(nr - 1));
} else {
pp_std = 0;
tg_std = 0;
}
char model_desc[128];
llama_model_desc(g_model, model_desc, sizeof(model_desc));
const auto model_size = double(llama_model_size(g_model)) / 1024.0 / 1024.0 / 1024.0;
const auto model_n_params = double(llama_model_n_params(g_model)) / 1e9;
const auto backend = get_backend();
std::stringstream result;
result << std::setprecision(3);
result << "| model | size | params | backend | test | t/s |\n";
result << "| --- | --- | --- | --- | --- | --- |\n";
result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | "
<< backend << " | pp " << pp << " | " << pp_avg << " ± " << pp_std << " |\n";
result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | "
<< backend << " | tg " << tg << " | " << tg_avg << " ± " << tg_std << " |\n";
return env->NewStringUTF(result.str().c_str());
}
/**
* Completion loop's long-term states:
* - chat management
* - position tracking
*/
constexpr const char *ROLE_SYSTEM = "system";
constexpr const char *ROLE_USER = "user";
constexpr const char *ROLE_ASSISTANT = "assistant";
static std::vector<common_chat_msg> chat_msgs;
static llama_pos system_prompt_position;
static llama_pos current_position;
static void reset_long_term_states(const bool clear_kv_cache = true) {
chat_msgs.clear();
system_prompt_position = 0;
current_position = 0;
if (clear_kv_cache)
llama_memory_clear(llama_get_memory(g_context), false);
}
/**
* TODO-hyin: implement sliding-window version as a better alternative
*
* Context shifting by discarding the older half of the tokens appended after system prompt:
* - take the [system_prompt_position] first tokens from the original prompt
* - take half of the last (system_prompt_position - system_prompt_position) tokens
* - recompute the logits in batches
*/
static void shift_context() {
const int n_discard = (current_position - system_prompt_position) / 2;
LOGi("%s: Discarding %d tokens", __func__, n_discard);
llama_memory_seq_rm(llama_get_memory(g_context), 0, system_prompt_position, system_prompt_position + n_discard);
llama_memory_seq_add(llama_get_memory(g_context), 0, system_prompt_position + n_discard, current_position, -n_discard);
current_position -= n_discard;
LOGi("%s: Context shifting done! Current position: %d", __func__, current_position);
}
static std::string chat_add_and_format(const std::string &role, const std::string &content) {
common_chat_msg new_msg;
new_msg.role = role;
new_msg.content = content;
auto formatted = common_chat_format_single(
g_chat_templates.get(), chat_msgs, new_msg, role == ROLE_USER, /* use_jinja */ false);
chat_msgs.push_back(new_msg);
LOGi("%s: Formatted and added %s message: \n%s\n", __func__, role.c_str(), formatted.c_str());
return formatted;
}
/**
* Completion loop's short-term states:
* - stop generation position
* - token chars caching
* - current assistant message being generated
*/
static llama_pos stop_generation_position;
static std::string cached_token_chars;
static std::ostringstream assistant_ss;
static void reset_short_term_states() {
stop_generation_position = 0;
cached_token_chars.clear();
assistant_ss.str("");
}
static int decode_tokens_in_batches(
llama_context *context,
llama_batch &batch,
const llama_tokens &tokens,
const llama_pos start_pos,
const bool compute_last_logit = false) {
// Process tokens in batches using the global batch
LOGd("%s: Decode %d tokens starting at position %d", __func__, (int) tokens.size(), start_pos);
for (int i = 0; i < (int) tokens.size(); i += BATCH_SIZE) {
const int cur_batch_size = std::min((int) tokens.size() - i, BATCH_SIZE);
common_batch_clear(batch);
LOGv("%s: Preparing a batch size of %d starting at: %d", __func__, cur_batch_size, i);
// Shift context if current batch cannot fit into the context
if (start_pos + i + cur_batch_size >= DEFAULT_CONTEXT_SIZE - OVERFLOW_HEADROOM) {
LOGw("%s: Current batch won't fit into context! Shifting...", __func__);
shift_context();
}
// Add tokens to the batch with proper positions
for (int j = 0; j < cur_batch_size; j++) {
const llama_token token_id = tokens[i + j];
const llama_pos position = start_pos + i + j;
const bool want_logit = compute_last_logit && (i + j == tokens.size() - 1);
common_batch_add(batch, token_id, position, {0}, want_logit);
}
// Decode this batch
const int decode_result = llama_decode(context, batch);
if (decode_result) {
LOGe("%s: llama_decode failed w/ %d", __func__, decode_result);
return 1;
}
}
return 0;
}
extern "C"
JNIEXPORT jint JNICALL
Java_com_arm_aichat_internal_InferenceEngineImpl_processSystemPrompt(
JNIEnv *env,
jobject /*unused*/,
jstring jsystem_prompt
) {
// Reset long-term & short-term states
reset_long_term_states();
reset_short_term_states();
// Obtain system prompt from JEnv
const auto *system_prompt = env->GetStringUTFChars(jsystem_prompt, nullptr);
LOGd("%s: System prompt received: \n%s", __func__, system_prompt);
std::string formatted_system_prompt(system_prompt);
env->ReleaseStringUTFChars(jsystem_prompt, system_prompt);
// Format system prompt if applicable
const bool has_chat_template = common_chat_templates_was_explicit(g_chat_templates.get());
if (has_chat_template) {
formatted_system_prompt = chat_add_and_format(ROLE_SYSTEM, system_prompt);
}
// Tokenize system prompt
const auto system_tokens = common_tokenize(g_context, formatted_system_prompt,
has_chat_template, has_chat_template);
for (auto id: system_tokens) {
LOGv("token: `%s`\t -> `%d`", common_token_to_piece(g_context, id).c_str(), id);
}
// Handle context overflow
const int max_batch_size = DEFAULT_CONTEXT_SIZE - OVERFLOW_HEADROOM;
if ((int) system_tokens.size() > max_batch_size) {
LOGe("%s: System prompt too long for context! %d tokens, max: %d",
__func__, (int) system_tokens.size(), max_batch_size);
return 1;
}
// Decode system tokens in batches
if (decode_tokens_in_batches(g_context, g_batch, system_tokens, current_position)) {
LOGe("%s: llama_decode() failed!", __func__);
return 2;
}
// Update position
system_prompt_position = current_position = (int) system_tokens.size();
return 0;
}
extern "C"
JNIEXPORT jint JNICALL
Java_com_arm_aichat_internal_InferenceEngineImpl_processUserPrompt(
JNIEnv *env,
jobject /*unused*/,
jstring juser_prompt,
jint n_predict
) {
// Reset short-term states
reset_short_term_states();
// Obtain and tokenize user prompt
const auto *const user_prompt = env->GetStringUTFChars(juser_prompt, nullptr);
LOGd("%s: User prompt received: \n%s", __func__, user_prompt);
std::string formatted_user_prompt(user_prompt);
env->ReleaseStringUTFChars(juser_prompt, user_prompt);
// Format user prompt if applicable
const bool has_chat_template = common_chat_templates_was_explicit(g_chat_templates.get());
if (has_chat_template) {
formatted_user_prompt = chat_add_and_format(ROLE_USER, user_prompt);
}
// Decode formatted user prompts
auto user_tokens = common_tokenize(g_context, formatted_user_prompt, has_chat_template, has_chat_template);
for (auto id: user_tokens) {
LOGv("token: `%s`\t -> `%d`", common_token_to_piece(g_context, id).c_str(), id);
}
// Ensure user prompt doesn't exceed the context size by truncating if necessary.
const int user_prompt_size = (int) user_tokens.size();
const int max_batch_size = DEFAULT_CONTEXT_SIZE - OVERFLOW_HEADROOM;
if (user_prompt_size > max_batch_size) {
const int skipped_tokens = user_prompt_size - max_batch_size;
user_tokens.resize(max_batch_size);
LOGw("%s: User prompt too long! Skipped %d tokens!", __func__, skipped_tokens);
}
// Decode user tokens in batches
if (decode_tokens_in_batches(g_context, g_batch, user_tokens, current_position, true)) {
LOGe("%s: llama_decode() failed!", __func__);
return 2;
}
// Update position
current_position += user_prompt_size;
stop_generation_position = current_position + user_prompt_size + n_predict;
return 0;
}
static bool is_valid_utf8(const char *string) {
if (!string) { return true; }
const auto *bytes = (const unsigned char *) string;
int num;
while (*bytes != 0x00) {
if ((*bytes & 0x80) == 0x00) {
// U+0000 to U+007F
num = 1;
} else if ((*bytes & 0xE0) == 0xC0) {
// U+0080 to U+07FF
num = 2;
} else if ((*bytes & 0xF0) == 0xE0) {
// U+0800 to U+FFFF
num = 3;
} else if ((*bytes & 0xF8) == 0xF0) {
// U+10000 to U+10FFFF
num = 4;
} else {
return false;
}
bytes += 1;
for (int i = 1; i < num; ++i) {
if ((*bytes & 0xC0) != 0x80) {
return false;
}
bytes += 1;
}
}
return true;
}
extern "C"
JNIEXPORT jstring JNICALL
Java_com_arm_aichat_internal_InferenceEngineImpl_generateNextToken(
JNIEnv *env,
jobject /*unused*/
) {
// Infinite text generation via context shifting
if (current_position >= DEFAULT_CONTEXT_SIZE - OVERFLOW_HEADROOM) {
LOGw("%s: Context full! Shifting...", __func__);
shift_context();
}
// Stop if reaching the marked position
if (current_position >= stop_generation_position) {
LOGw("%s: STOP: hitting stop position: %d", __func__, stop_generation_position);
return nullptr;
}
// Sample next token
const auto new_token_id = common_sampler_sample(g_sampler, g_context, -1);
common_sampler_accept(g_sampler, new_token_id, true);
// Populate the batch with new token, then decode
common_batch_clear(g_batch);
common_batch_add(g_batch, new_token_id, current_position, {0}, true);
if (llama_decode(g_context, g_batch) != 0) {
LOGe("%s: llama_decode() failed for generated token", __func__);
return nullptr;
}
// Update position
current_position++;
// Stop if next token is EOG
if (llama_vocab_is_eog(llama_model_get_vocab(g_model), new_token_id)) {
LOGd("id: %d,\tIS EOG!\nSTOP.", new_token_id);
chat_add_and_format(ROLE_ASSISTANT, assistant_ss.str());
return nullptr;
}
// If not EOG, convert to text
auto new_token_chars = common_token_to_piece(g_context, new_token_id);
cached_token_chars += new_token_chars;
// Create and return a valid UTF-8 Java string
jstring result = nullptr;
if (is_valid_utf8(cached_token_chars.c_str())) {
result = env->NewStringUTF(cached_token_chars.c_str());
LOGv("id: %d,\tcached: `%s`,\tnew: `%s`", new_token_id, cached_token_chars.c_str(), new_token_chars.c_str());
assistant_ss << cached_token_chars;
cached_token_chars.clear();
} else {
LOGv("id: %d,\tappend to cache", new_token_id);
result = env->NewStringUTF("");
}
return result;
}
extern "C"
JNIEXPORT void JNICALL
Java_com_arm_aichat_internal_InferenceEngineImpl_unload(JNIEnv * /*unused*/, jobject /*unused*/) {
// Reset long-term & short-term states
reset_long_term_states();
reset_short_term_states();
// Free up resources
common_sampler_free(g_sampler);
g_chat_templates.reset();
llama_batch_free(g_batch);
llama_free(g_context);
llama_model_free(g_model);
}
extern "C"
JNIEXPORT void JNICALL
Java_com_arm_aichat_internal_InferenceEngineImpl_shutdown(JNIEnv *env, jobject /*unused*/) {
llama_backend_free();
}
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