tests : refactor test-save-load-state to accept token input (#24073)

* tests : refactor test-save-load-state to accept token input

- Default prompt is now empty; when not provided, generate n_batch
  random tokens (useful for models without a tokenizer)
- Tokenization happens once upfront; pass token vector to test functions
- generate_tokens prints token IDs instead of decoded pieces
- Use llama_model_get_vocab / llama_vocab_n_tokens API
- Upgrade log level from LOG_TRC to LOG_INF for visibility

Assisted-by: llama.cpp:local pi
* cont : use llama_tokens alias

diff --git a/tests/test-save-load-state.cpp b/tests/test-save-load-state.cpp
index 338bcde30971fe5823bd9367a0483f034ada5a81..b097d752ab71c05509ece90c4e86ac66d3c36ce3 100644 (file)
--- a/tests/test-save-load-state.cpp
+++ b/tests/test-save-load-state.cpp
@@ -4,6 +4,7 @@
 #include "llama-cpp.h"
 
 #include <clocale>
+#include <random>
 #include <vector>
 
 struct llama_batch_ptr {
@@ -23,16 +24,15 @@ struct llama_batch_ptr {
     const llama_batch & get() const { return batch; }
 };
 
-static std::string generate_tokens(llama_context * ctx, llama_sampler * smpl, int & n_past, int32_t n_predict, llama_seq_id seq_id) {
-    std::string result;
+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);
-        auto next_token_str = common_token_to_piece(ctx, next_token);
+        auto next_token = llama_sampler_sample(smpl, ctx, -1);
 
-        LOG("%s", next_token_str.c_str());
-        result += next_token_str;
+        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);
@@ -48,20 +48,17 @@ static std::string generate_tokens(llama_context * ctx, llama_sampler * smpl, in
 }
 
 // Test 1: baseline
-// - tokenize the prompt
 // - decode all but the last token
 // - save state to disk
 // - decode the last token
 // - generate n_predict tokens
-static std::string test_baseline(struct llama_model * model, const struct common_params & params) {
+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 tokens = common_tokenize(ctx.get(), params.prompt, true);
-
     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__);
@@ -69,7 +66,6 @@ static std::string test_baseline(struct llama_model * model, const struct common
     }
 
     LOG("\n=== Test 1: baseline ===\n");
-    LOG("%s", params.prompt.c_str());
 
     auto result = generate_tokens(ctx.get(), smpl.get(), n_past, params.n_predict, 0);
     if (result.empty()) {
@@ -87,20 +83,17 @@ static std::string test_baseline(struct llama_model * model, const struct common
 // - 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 std::string & 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));
 
-    auto tokens = common_tokenize(ctx.get(), params.prompt, true);
-
     LOG("\n=== Test 2: state load ===\n");
-    LOG("%s", params.prompt.c_str());
 
     // Load state from file
-    std::vector<llama_token> unused_sts(tokens.size());
+    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)) {
@@ -139,7 +132,7 @@ static bool test_state_load(struct llama_model * model, const struct common_para
 // - 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 std::string & 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)};
@@ -148,13 +141,10 @@ static bool test_seq_cp_host(struct llama_model * model, const struct common_par
     auto smpl = llama_sampler_ptr{llama_sampler_chain_init(sparams)};
     llama_sampler_chain_add(smpl.get(), llama_sampler_init_dist(params.sampling.seed));
 
-    auto tokens = common_tokenize(ctx.get(), params.prompt, true);
-
     LOG("\n=== Test 3: seq copy (host) ===\n");
-    LOG("%s", params.prompt.c_str());
 
     // Load state from file
-    std::vector<llama_token> unused_sts(tokens.size());
+    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)) {
@@ -214,7 +204,7 @@ static bool test_seq_cp_host(struct llama_model * model, const struct common_par
 // - 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 std::string & 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)};
@@ -223,13 +213,10 @@ static bool test_seq_cp_device(struct llama_model * model, const struct common_p
     auto smpl = llama_sampler_ptr{llama_sampler_chain_init(sparams)};
     llama_sampler_chain_add(smpl.get(), llama_sampler_init_dist(params.sampling.seed));
 
-    auto tokens = common_tokenize(ctx.get(), params.prompt, true);
-
     LOG("\n=== Test 4: seq copy (device) ===\n");
-    LOG("%s", params.prompt.c_str());
 
     // Load state from file
-    std::vector<llama_token> unused_sts(tokens.size());
+    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)) {
@@ -287,7 +274,8 @@ int main(int argc, char ** argv) {
     std::setlocale(LC_NUMERIC, "C");
 
     common_params params;
-    params.prompt = "The quick brown fox";
+    params.prompt = "";
+    params.n_batch = 100;
     params.out_file = "dump_state.bin";
     params.sampling.seed = 1234;
 
@@ -318,24 +306,49 @@ int main(int argc, char ** argv) {
 
     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);
+    auto result_baseline = test_baseline(model, params, tokens);
     if (result_baseline.empty()) {
         return 1;
     }
 
     // Test 2: state load
-    if (!test_state_load(model, params, result_baseline)) {
+    if (!test_state_load(model, params, tokens, result_baseline)) {
         return 1;
     }
 
     // Test 3: seq copy (host)
-    if (!test_seq_cp_host(model, params, result_baseline)) {
+    if (!test_seq_cp_host(model, params, tokens, result_baseline)) {
         return 1;
     }
 
     // Test 4: seq copy (device)
-    if (!test_seq_cp_device(model, params, result_baseline)) {
+    if (!test_seq_cp_device(model, params, tokens, result_baseline)) {
         return 1;
     }