mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-06-09 07:16:44 +02:00
Georgi Gerganov
65ef50a0a4
* 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
359 lines
12 KiB
C++
359 lines
12 KiB
C++
#include "arg.h"
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#include "common.h"
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#include "log.h"
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#include "llama-cpp.h"
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#include <clocale>
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#include <random>
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#include <vector>
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struct llama_batch_ptr {
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llama_batch batch;
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llama_batch_ptr(int32_t n_tokens, int32_t embd, int32_t n_seq_max)
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: batch{llama_batch_init(n_tokens, embd, n_seq_max)} {}
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~llama_batch_ptr() { llama_batch_free(batch); }
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llama_batch_ptr(const llama_batch_ptr &) = delete;
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llama_batch_ptr & operator=(const llama_batch_ptr &) = delete;
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llama_batch_ptr(llama_batch_ptr &&) = default;
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llama_batch_ptr & operator=(llama_batch_ptr &&) = default;
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llama_batch & get() { return batch; }
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const llama_batch & get() const { return batch; }
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};
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static llama_tokens generate_tokens(llama_context * ctx, llama_sampler * smpl, int & n_past, int32_t n_predict, llama_seq_id seq_id) {
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llama_tokens result;
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llama_batch_ptr batch(1, 0, 1);
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for (int i = 0; i < n_predict; i++) {
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auto next_token = llama_sampler_sample(smpl, ctx, -1);
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LOG("%d ", next_token);
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result.push_back(next_token);
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common_batch_clear(batch.get());
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common_batch_add(batch.get(), next_token, n_past, {seq_id}, true);
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if (llama_decode(ctx, batch.get())) {
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LOG_ERR("\n%s: failed to evaluate\n", __func__);
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return {};
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}
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n_past++;
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}
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return result;
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}
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// Test 1: baseline
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// - decode all but the last token
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// - save state to disk
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// - decode the last token
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// - generate n_predict tokens
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static llama_tokens test_baseline(struct llama_model * model, const struct common_params & params, const llama_tokens & tokens) {
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auto ctx = llama_context_ptr{llama_init_from_model(model, common_context_params_to_llama(params))};
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auto sparams = llama_sampler_chain_default_params();
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auto smpl = llama_sampler_ptr{llama_sampler_chain_init(sparams)};
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llama_sampler_chain_add(smpl.get(), llama_sampler_init_dist(params.sampling.seed));
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auto n_past = 0;
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if (!common_prompt_batch_decode(ctx.get(), tokens, (int)tokens.size(), n_past, params.n_batch, params.out_file, true)) {
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LOG_ERR("%s: failed to decode prompt\n", __func__);
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return {};
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}
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LOG("\n=== Test 1: baseline ===\n");
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auto result = generate_tokens(ctx.get(), smpl.get(), n_past, params.n_predict, 0);
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if (result.empty()) {
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return {};
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}
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LOG("\n");
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return result;
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}
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// Test 2: state load
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// - create a new context
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// - load state from file
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// - replay the last prompt token
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// - generate n_predict tokens and compare against expected result
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static bool test_state_load(struct llama_model * model, const struct common_params & params, const llama_tokens & tokens, const llama_tokens & expected_result) {
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auto ctx = llama_context_ptr{llama_init_from_model(model, common_context_params_to_llama(params))};
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auto sparams = llama_sampler_chain_default_params();
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auto smpl = llama_sampler_ptr{llama_sampler_chain_init(sparams)};
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llama_sampler_chain_add(smpl.get(), llama_sampler_init_dist(params.sampling.seed));
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LOG("\n=== Test 2: state load ===\n");
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// Load state from file
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llama_tokens unused_sts(tokens.size());
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size_t n_token_count_out = 0;
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if (!llama_state_load_file(ctx.get(), params.out_file.data(), unused_sts.data(), unused_sts.size(), &n_token_count_out)) {
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LOG_ERR("\n%s: failed to load state\n", __func__);
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return false;
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}
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LOG_TRC("%s: loaded state with %zu tokens\n", __func__, n_token_count_out);
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// Replay last token
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int n_past = (int) n_token_count_out - 1;
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if (!common_replay_last_token(ctx.get(), tokens.back(), n_past)) {
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return false;
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}
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n_past++;
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// Generate tokens
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auto result = generate_tokens(ctx.get(), smpl.get(), n_past, params.n_predict, 0);
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if (result.empty()) {
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return false;
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}
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if (result != expected_result) {
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LOG_ERR("\n%s: error: generation differs from expected\n", __func__);
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return false;
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}
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LOG("\nPASS\n");
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return true;
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}
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// Test 3: seq copy (host)
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// - create a multi-seq context
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// - load state from file
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// - replay the last prompt token
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// - migrate KV cache from seq 0 to seq 1 via the CPU path
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// - generate n_predict tokens on seq 1 and compare against expected result
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static bool test_seq_cp_host(struct llama_model * model, const struct common_params & params, const llama_tokens & tokens, const llama_tokens & expected_result) {
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auto params_ctx = common_context_params_to_llama(params);
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params_ctx.n_seq_max = 2;
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auto ctx = llama_context_ptr{llama_init_from_model(model, params_ctx)};
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auto sparams = llama_sampler_chain_default_params();
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auto smpl = llama_sampler_ptr{llama_sampler_chain_init(sparams)};
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llama_sampler_chain_add(smpl.get(), llama_sampler_init_dist(params.sampling.seed));
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LOG("\n=== Test 3: seq copy (host) ===\n");
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// Load state from file
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llama_tokens unused_sts(tokens.size());
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size_t n_token_count_out = 0;
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if (!llama_state_load_file(ctx.get(), params.out_file.data(), unused_sts.data(), unused_sts.size(), &n_token_count_out)) {
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LOG_ERR("\n%s: failed to load state\n", __func__);
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return false;
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}
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LOG_TRC("%s: loaded state with %zu tokens\n", __func__, n_token_count_out);
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// Replay last token
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int n_past = (int) n_token_count_out - 1;
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if (!common_replay_last_token(ctx.get(), tokens.back(), n_past)) {
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return false;
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}
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n_past++;
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// Migrate KV cache from seq 0 to seq 1 (CPU path)
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{
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std::vector<uint8_t> seq_store(llama_state_seq_get_size(ctx.get(), 0));
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const size_t ncopy = llama_state_seq_get_data(ctx.get(), seq_store.data(), seq_store.size(), 0);
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if (ncopy != seq_store.size()) {
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LOG_ERR("\n%s: seq copy data length %zd does not match expected length %zd\n", __func__, ncopy, seq_store.size());
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return false;
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}
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LOG_TRC("%s: seq 0 copied, %zd bytes\n", __func__, ncopy);
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llama_memory_clear(llama_get_memory(ctx.get()), true);
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LOG_TRC("%s: kv cache cleared\n", __func__);
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const size_t nset = llama_state_seq_set_data(ctx.get(), seq_store.data(), seq_store.size(), 1);
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if (nset != seq_store.size()) {
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LOG_ERR("\n%s: seq set data length %zd does not match expected length %zd\n", __func__, nset, seq_store.size());
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return false;
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}
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LOG_TRC("%s: seq 1 restored, %zd bytes\n", __func__, nset);
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}
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// Generate tokens on seq 1
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auto result = generate_tokens(ctx.get(), smpl.get(), n_past, params.n_predict, 1);
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if (result.empty()) {
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return false;
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}
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if (result != expected_result) {
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LOG_ERR("\n%s: error: generation differs from expected\n", __func__);
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return false;
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}
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LOG("\nPASS\n");
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return true;
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}
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// Test 4: seq copy (device)
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// - create a multi-seq context
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// - load state from file
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// - replay the last prompt token
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// - migrate KV cache from seq 0 to seq 1 via the on-device path
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// - generate n_predict tokens on seq 1 and compare against expected result
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static bool test_seq_cp_device(struct llama_model * model, const struct common_params & params, const llama_tokens & tokens, const llama_tokens & expected_result) {
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auto params_ctx = common_context_params_to_llama(params);
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params_ctx.n_seq_max = 2;
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auto ctx = llama_context_ptr{llama_init_from_model(model, params_ctx)};
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auto sparams = llama_sampler_chain_default_params();
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auto smpl = llama_sampler_ptr{llama_sampler_chain_init(sparams)};
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llama_sampler_chain_add(smpl.get(), llama_sampler_init_dist(params.sampling.seed));
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LOG("\n=== Test 4: seq copy (device) ===\n");
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// Load state from file
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llama_tokens unused_sts(tokens.size());
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size_t n_token_count_out = 0;
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if (!llama_state_load_file(ctx.get(), params.out_file.data(), unused_sts.data(), unused_sts.size(), &n_token_count_out)) {
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LOG_ERR("\n%s: failed to load state\n", __func__);
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return false;
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}
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LOG_TRC("%s: loaded state with %zu tokens\n", __func__, n_token_count_out);
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// Replay last token
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int n_past = (int) n_token_count_out - 1;
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if (!common_replay_last_token(ctx.get(), tokens.back(), n_past)) {
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return false;
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}
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n_past++;
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// Migrate KV cache from seq 0 to seq 1 (on-device path)
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{
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std::vector<uint8_t> seq_store(llama_state_seq_get_size_ext(ctx.get(), 0, LLAMA_STATE_SEQ_FLAGS_ON_DEVICE));
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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);
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if (ncopy != seq_store.size()) {
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LOG_ERR("\n%s: seq copy data length %zd does not match expected length %zd\n", __func__, ncopy, seq_store.size());
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return false;
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}
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LOG_TRC("%s: seq 0 copied, %zd bytes\n", __func__, ncopy);
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llama_memory_clear(llama_get_memory(ctx.get()), true);
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LOG_TRC("%s: kv cache cleared\n", __func__);
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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);
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if (nset != seq_store.size()) {
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LOG_ERR("\n%s: seq set data length %zd does not match expected length %zd\n", __func__, nset, seq_store.size());
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return false;
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}
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LOG_TRC("%s: seq 1 restored, %zd bytes\n", __func__, nset);
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}
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// Generate tokens on seq 1
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auto result = generate_tokens(ctx.get(), smpl.get(), n_past, params.n_predict, 1);
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if (result.empty()) {
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return false;
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}
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if (result != expected_result) {
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LOG_ERR("\n%s: error: generation differs from expected\n", __func__);
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return false;
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}
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LOG("\nPASS\n");
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return true;
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}
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int main(int argc, char ** argv) {
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std::setlocale(LC_NUMERIC, "C");
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common_params params;
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params.prompt = "";
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params.n_batch = 100;
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params.out_file = "dump_state.bin";
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params.sampling.seed = 1234;
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common_init();
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if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
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return 1;
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}
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if (params.n_parallel == 1) {
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LOG_TRC("%s: n_parallel == 1, enabling unified kv cache\n", __func__);
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params.kv_unified = true;
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}
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if (params.n_predict < 0) {
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params.n_predict = 16;
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}
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ggml_backend_load_all();
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auto llama_init = common_init_from_params(params, true);
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auto * model = llama_init->model();
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if (model == nullptr) {
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LOG_ERR("%s: failed to init\n", __func__);
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return 1;
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}
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GGML_ASSERT(llama_init->context() == nullptr);
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// Tokenize prompt or generate random tokens
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llama_tokens tokens;
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if (params.prompt.empty()) {
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const int n_prompt = params.n_batch;
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// this path is useful for model files that do not have a tokenizer
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LOG_INF("%s: no prompt provided, generating %d (n_batch) random tokens\n", __func__, n_prompt);
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const auto * vocab = llama_model_get_vocab(model);
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const auto n_vocab = llama_vocab_n_tokens(vocab);
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std::mt19937 rng(params.sampling.seed);
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std::uniform_int_distribution<llama_token> dist(0, n_vocab - 1);
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for (int i = 0; i < n_prompt; i++) {
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tokens.push_back(dist(rng));
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}
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} else {
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LOG_INF("%s: tokenizing prompt '%s'\n", __func__, params.prompt.c_str());
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auto ctx = llama_context_ptr{llama_init_from_model(model, common_context_params_to_llama(params))};
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tokens = common_tokenize(ctx.get(), params.prompt, true);
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}
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LOG_INF("%s: the input prompt is %d tokens\n", __func__, (int)tokens.size());
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// Test 1: baseline (saves state to disk)
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auto result_baseline = test_baseline(model, params, tokens);
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if (result_baseline.empty()) {
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return 1;
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}
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// Test 2: state load
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if (!test_state_load(model, params, tokens, result_baseline)) {
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return 1;
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}
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// Test 3: seq copy (host)
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if (!test_seq_cp_host(model, params, tokens, result_baseline)) {
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return 1;
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}
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// Test 4: seq copy (device)
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if (!test_seq_cp_device(model, params, tokens, result_baseline)) {
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return 1;
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}
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LOG("\nAll tests passed.\n");
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return 0;
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}
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