#include "arg.h"
#include "common.h"
#include "log.h"
#include "llama-cpp.h"
#include <clocale>
#include <random>
#include <vector>
struct llama_batch_ptr {
llama_batch batch;
llama_batch_ptr(int32_t n_tokens, int32_t embd, int32_t n_seq_max)
: batch{llama_batch_init(n_tokens, embd, n_seq_max)} {}
~llama_batch_ptr() { llama_batch_free(batch); }
llama_batch_ptr(const llama_batch_ptr &) = delete;
llama_batch_ptr & operator=(const llama_batch_ptr &) = delete;
llama_batch_ptr(llama_batch_ptr &&) = default;
llama_batch_ptr & operator=(llama_batch_ptr &&) = default;
llama_batch & get() { return batch; }
const llama_batch & get() const { return batch; }
};
static llama_tokens generate_tokens(llama_context * ctx, llama_sampler * smpl, int & n_past, int32_t n_predict, llama_seq_id seq_id) {
llama_tokens result;
llama_batch_ptr batch(1, 0, 1);
for (int i = 0; i < n_predict; i++) {
auto next_token = llama_sampler_sample(smpl, ctx, -1);
LOG("%d ", next_token);
result.push_back(next_token);
common_batch_clear(batch.get());
common_batch_add(batch.get(), next_token, n_past, {seq_id}, true);
if (llama_decode(ctx, batch.get())) {
LOG_ERR("\n%s: failed to evaluate\n", __func__);
return {};
}
n_past++;
}
return result;
}
// Test 1: baseline
// - decode all but the last token
// - save state to disk
// - decode the last token
// - generate n_predict tokens
static llama_tokens test_baseline(struct llama_model * model, const struct common_params & params, const llama_tokens & tokens) {
auto ctx = llama_context_ptr{llama_init_from_model(model, common_context_params_to_llama(params))};
auto sparams = llama_sampler_chain_default_params();
auto smpl = llama_sampler_ptr{llama_sampler_chain_init(sparams)};
llama_sampler_chain_add(smpl.get(), llama_sampler_init_dist(params.sampling.seed));
auto n_past = 0;
if (!common_prompt_batch_decode(ctx.get(), tokens, (int)tokens.size(), n_past, params.n_batch, params.out_file, true)) {
LOG_ERR("%s: failed to decode prompt\n", __func__);
return {};
}
LOG("\n=== Test 1: baseline ===\n");
auto result = generate_tokens(ctx.get(), smpl.get(), n_past, params.n_predict, 0);
if (result.empty()) {
return {};
}
LOG("\n");
return result;
}
// Test 2: sequence removal isolation
// - decode the same prefix into two sequences
// - remove sequence 0
// - verify that sequence 1 remains unchanged
static bool test_seq_rm_isolated(
struct llama_model * model,
const struct common_params & params,
const llama_tokens & tokens) {
auto params_ctx = common_context_params_to_llama(params);
params_ctx.n_ctx = 256;
params_ctx.n_seq_max = 2;
params_ctx.kv_unified = true;
auto ctx = llama_context_ptr{llama_init_from_model(model, params_ctx)};
if (!ctx) {
LOG_ERR("%s: failed to create context\n", __func__);
return false;
}
LOG("\n=== Test 2: sequence removal isolation ===\n");
const size_t n_tokens = tokens.size() < 128 ? tokens.size() : 128;
for (llama_seq_id seq_id = 0; seq_id < 2; ++seq_id) {
llama_batch_ptr batch(n_tokens, 0, 1);
for (size_t i = 0; i < n_tokens; ++i) {
common_batch_add(batch.get(), tokens[i], i, { seq_id }, false);
}
if (llama_decode(ctx.get(), batch.get())) {
LOG_ERR("%s: failed to decode prompt for sequence %d\n", __func__, seq_id);
return false;
}
}
const auto get_seq_state = [&](llama_seq_id seq_id, std::vector<uint8_t> & state) {
const size_t state_size = llama_state_seq_get_size(ctx.get(), seq_id);
if (state_size == 0) {
LOG_ERR("%s: sequence state is empty\n", __func__);
return false;
}
state.resize(state_size);
const size_t ncopy = llama_state_seq_get_data(ctx.get(), state.data(), state.size(), seq_id);
if (ncopy != state.size()) {
LOG_ERR("%s: sequence state length %zu does not match expected length %zu\n",
__func__, ncopy, state.size());
return false;
}
return true;
};
std::vector<uint8_t> state_before;
if (!get_seq_state(1, state_before)) {
return false;
}
if (!llama_memory_seq_rm(llama_get_memory(ctx.get()), 0, -1, -1)) {
LOG_ERR("%s: failed to remove sequence 0\n", __func__);
return false;
}
std::vector<uint8_t> state_after;
if (!get_seq_state(1, state_after)) {
return false;
}
if (state_before != state_after) {
LOG_ERR("%s: removing sequence 0 changed sequence 1\n", __func__);
return false;
}
LOG("PASS\n");
return true;
}
// Test 3: state load
// - create a new context
// - load state from file
// - replay the last prompt token
// - generate n_predict tokens and compare against expected result
static bool test_state_load(struct llama_model * model, const struct common_params & params, const llama_tokens & tokens, const llama_tokens & expected_result) {
auto ctx = llama_context_ptr{llama_init_from_model(model, common_context_params_to_llama(params))};
auto sparams = llama_sampler_chain_default_params();
auto smpl = llama_sampler_ptr{llama_sampler_chain_init(sparams)};
llama_sampler_chain_add(smpl.get(), llama_sampler_init_dist(params.sampling.seed));
LOG("\n=== Test 3: state load ===\n");
// Load state from file
llama_tokens unused_sts(tokens.size());
size_t n_token_count_out = 0;
if (!llama_state_load_file(ctx.get(), params.out_file.data(), unused_sts.data(), unused_sts.size(), &n_token_count_out)) {
LOG_ERR("\n%s: failed to load state\n", __func__);
return false;
}
LOG_TRC("%s: loaded state with %zu tokens\n", __func__, n_token_count_out);
// Replay last token
int n_past = (int) n_token_count_out - 1;
if (!common_replay_last_token(ctx.get(), tokens.back(), n_past)) {
return false;
}
n_past++;
// Generate tokens
auto result = generate_tokens(ctx.get(), smpl.get(), n_past, params.n_predict, 0);
if (result.empty()) {
return false;
}
if (result != expected_result) {
LOG_ERR("\n%s: error: generation differs from expected\n", __func__);
return false;
}
LOG("\nPASS\n");
return true;
}
// Test 4: seq copy (host)
// - create a multi-seq context
// - load state from file
// - replay the last prompt token
// - migrate KV cache from seq 0 to seq 1 via the CPU path
// - generate n_predict tokens on seq 1 and compare against expected result
static bool test_seq_cp_host(struct llama_model * model, const struct common_params & params, const llama_tokens & tokens, const llama_tokens & expected_result) {
auto params_ctx = common_context_params_to_llama(params);
params_ctx.n_seq_max = 2;
auto ctx = llama_context_ptr{llama_init_from_model(model, params_ctx)};
auto sparams = llama_sampler_chain_default_params();
auto smpl = llama_sampler_ptr{llama_sampler_chain_init(sparams)};
llama_sampler_chain_add(smpl.get(), llama_sampler_init_dist(params.sampling.seed));
LOG("\n=== Test 4: seq copy (host) ===\n");
// Load state from file
llama_tokens unused_sts(tokens.size());
size_t n_token_count_out = 0;
if (!llama_state_load_file(ctx.get(), params.out_file.data(), unused_sts.data(), unused_sts.size(), &n_token_count_out)) {
LOG_ERR("\n%s: failed to load state\n", __func__);
return false;
}
LOG_TRC("%s: loaded state with %zu tokens\n", __func__, n_token_count_out);
// Replay last token
int n_past = (int) n_token_count_out - 1;
if (!common_replay_last_token(ctx.get(), tokens.back(), n_past)) {
return false;
}
n_past++;
// Migrate KV cache from seq 0 to seq 1 (CPU path)
{
std::vector<uint8_t> seq_store(llama_state_seq_get_size(ctx.get(), 0));
const size_t ncopy = llama_state_seq_get_data(ctx.get(), seq_store.data(), seq_store.size(), 0);
if (ncopy != seq_store.size()) {
LOG_ERR("\n%s: seq copy data length %zd does not match expected length %zd\n", __func__, ncopy, seq_store.size());
return false;
}
LOG_TRC("%s: seq 0 copied, %zd bytes\n", __func__, ncopy);
llama_memory_clear(llama_get_memory(ctx.get()), true);
LOG_TRC("%s: kv cache cleared\n", __func__);
const size_t nset = llama_state_seq_set_data(ctx.get(), seq_store.data(), seq_store.size(), 1);
if (nset != seq_store.size()) {
LOG_ERR("\n%s: seq set data length %zd does not match expected length %zd\n", __func__, nset, seq_store.size());
return false;
}
LOG_TRC("%s: seq 1 restored, %zd bytes\n", __func__, nset);
}
// Generate tokens on seq 1
auto result = generate_tokens(ctx.get(), smpl.get(), n_past, params.n_predict, 1);
if (result.empty()) {
return false;
}
if (result != expected_result) {
LOG_ERR("\n%s: error: generation differs from expected\n", __func__);
return false;
}
LOG("\nPASS\n");
return true;
}
// Test 5: seq copy (device)
// - create a multi-seq context
// - load state from file
// - replay the last prompt token
// - migrate KV cache from seq 0 to seq 1 via the on-device path
// - generate n_predict tokens on seq 1 and compare against expected result
static bool test_seq_cp_device(struct llama_model * model, const struct common_params & params, const llama_tokens & tokens, const llama_tokens & expected_result) {
auto params_ctx = common_context_params_to_llama(params);
params_ctx.n_seq_max = 2;
auto ctx = llama_context_ptr{llama_init_from_model(model, params_ctx)};
auto sparams = llama_sampler_chain_default_params();
auto smpl = llama_sampler_ptr{llama_sampler_chain_init(sparams)};
llama_sampler_chain_add(smpl.get(), llama_sampler_init_dist(params.sampling.seed));
LOG("\n=== Test 5: seq copy (device) ===\n");
// Load state from file
llama_tokens unused_sts(tokens.size());
size_t n_token_count_out = 0;
if (!llama_state_load_file(ctx.get(), params.out_file.data(), unused_sts.data(), unused_sts.size(), &n_token_count_out)) {
LOG_ERR("\n%s: failed to load state\n", __func__);
return false;
}
LOG_TRC("%s: loaded state with %zu tokens\n", __func__, n_token_count_out);
// Replay last token
int n_past = (int) n_token_count_out - 1;
if (!common_replay_last_token(ctx.get(), tokens.back(), n_past)) {
return false;
}
n_past++;
// Migrate KV cache from seq 0 to seq 1 (on-device path)
{
std::vector<uint8_t> seq_store(llama_state_seq_get_size_ext(ctx.get(), 0, LLAMA_STATE_SEQ_FLAGS_ON_DEVICE));
const size_t ncopy = llama_state_seq_get_data_ext(ctx.get(), seq_store.data(), seq_store.size(), 0, LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
if (ncopy != seq_store.size()) {
LOG_ERR("\n%s: seq copy data length %zd does not match expected length %zd\n", __func__, ncopy, seq_store.size());
return false;
}
LOG_TRC("%s: seq 0 copied, %zd bytes\n", __func__, ncopy);
llama_memory_clear(llama_get_memory(ctx.get()), true);
LOG_TRC("%s: kv cache cleared\n", __func__);
const size_t nset = llama_state_seq_set_data_ext(ctx.get(), seq_store.data(), seq_store.size(), 1, LLAMA_STATE_SEQ_FLAGS_ON_DEVICE);
if (nset != seq_store.size()) {
LOG_ERR("\n%s: seq set data length %zd does not match expected length %zd\n", __func__, nset, seq_store.size());
return false;
}
LOG_TRC("%s: seq 1 restored, %zd bytes\n", __func__, nset);
}
// Generate tokens on seq 1
auto result = generate_tokens(ctx.get(), smpl.get(), n_past, params.n_predict, 1);
if (result.empty()) {
return false;
}
if (result != expected_result) {
LOG_ERR("\n%s: error: generation differs from expected\n", __func__);
return false;
}
LOG("\nPASS\n");
return true;
}
int main(int argc, char ** argv) {
std::setlocale(LC_NUMERIC, "C");
common_params params;
params.prompt = "";
params.n_batch = 100;
params.out_file = "dump_state.bin";
params.sampling.seed = 1234;
common_init();
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
}
if (params.n_parallel == 1) {
LOG_TRC("%s: n_parallel == 1, enabling unified kv cache\n", __func__);
params.kv_unified = true;
}
if (params.n_predict < 0) {
params.n_predict = 16;
}
ggml_backend_load_all();
auto llama_init = common_init_from_params(params, true);
auto * model = llama_init->model();
if (model == nullptr) {
LOG_ERR("%s: failed to init\n", __func__);
return 1;
}
GGML_ASSERT(llama_init->context() == nullptr);
// Tokenize prompt or generate random tokens
llama_tokens tokens;
if (params.prompt.empty()) {
const int n_prompt = params.n_batch;
// this path is useful for model files that do not have a tokenizer
LOG_INF("%s: no prompt provided, generating %d (n_batch) random tokens\n", __func__, n_prompt);
const auto * vocab = llama_model_get_vocab(model);
const auto n_vocab = llama_vocab_n_tokens(vocab);
std::mt19937 rng(params.sampling.seed);
std::uniform_int_distribution<llama_token> dist(0, n_vocab - 1);
for (int i = 0; i < n_prompt; i++) {
tokens.push_back(dist(rng));
}
} else {
LOG_INF("%s: tokenizing prompt '%s'\n", __func__, params.prompt.c_str());
auto ctx = llama_context_ptr{llama_init_from_model(model, common_context_params_to_llama(params))};
tokens = common_tokenize(ctx.get(), params.prompt, true);
}
LOG_INF("%s: the input prompt is %d tokens\n", __func__, (int)tokens.size());
// Test 1: baseline (saves state to disk)
auto result_baseline = test_baseline(model, params, tokens);
if (result_baseline.empty()) {
return 1;
}
// Test 2: sequence removal isolation
if (!test_seq_rm_isolated(model, params, tokens)) {
return 1;
}
// Test 3: state load
if (!test_state_load(model, params, tokens, result_baseline)) {
return 1;
}
// Test 4: seq copy (host)
if (!test_seq_cp_host(model, params, tokens, result_baseline)) {
return 1;
}
// Test 5: seq copy (device)
if (!test_seq_cp_device(model, params, tokens, result_baseline)) {
return 1;
}
LOG("\nAll tests passed.\n");
return 0;
}