246 lines
7.4 KiB
C++
246 lines
7.4 KiB
C++
#include "arg.h"
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#include "common.h"
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#include "llama.h"
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#include <filesystem>
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#include <vector>
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#include <cstdio>
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static bool replay_last_token(llama_context * ctx, llama_token last_token, int & n_past) {
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llama_batch batch = llama_batch_get_one(&last_token, 1);
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int pos = n_past;
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batch.pos = &pos;
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if (llama_decode(ctx, batch)) {
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fprintf(stderr, "%s: failed to replay last token after loading state\n", __func__);
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return false;
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}
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++n_past;
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return true;
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}
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int main(int argc, char ** argv) {
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common_params params;
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params.prompt = "The quick brown fox";
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params.sampling.seed = 1234;
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std::filesystem::path state_file = "dump_state.bin";
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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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// the example uses 2 sequences, so when n_parallel == 1, we need to enable unified kv cache
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printf("%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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common_init();
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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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auto n_past = 0;
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std::string result0;
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std::string result1;
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std::string result2;
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// init
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auto llama_init = common_init_from_params(params);
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auto * model = llama_init->model();
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auto * ctx = llama_init->context();
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if (model == nullptr || ctx == nullptr) {
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fprintf(stderr, "%s : failed to init\n", __func__);
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return 1;
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}
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auto sparams = llama_sampler_chain_default_params();
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llama_sampler * smpl = llama_sampler_chain_init(sparams);
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llama_sampler_chain_add(smpl, llama_sampler_init_dist(params.sampling.seed));
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// tokenize prompt
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auto tokens = common_tokenize(ctx, params.prompt, true);
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const bool save_state = true;
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if (!common_prompt_batch_decode(ctx, tokens, n_past, params.n_batch, state_file, save_state)) {
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return 1;
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}
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// first run
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printf("\nfirst run: %s", params.prompt.c_str());
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llama_batch batch = llama_batch_init(1, 0, 1);
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for (auto i = 0; i < params.n_predict; i++) {
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auto next_token = llama_sampler_sample(smpl, ctx, -1);
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auto next_token_str = common_token_to_piece(ctx, next_token);
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printf("%s", next_token_str.c_str());
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result0 += next_token_str;
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common_batch_clear(batch);
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common_batch_add(batch, next_token, n_past, {0}, true);
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if (llama_decode(ctx, batch)) {
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fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
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llama_batch_free(batch);
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return 1;
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}
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n_past += 1;
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}
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printf("\n\n");
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// make new context
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llama_context * ctx2 = llama_init_from_model(model, common_context_params_to_llama(params));
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llama_sampler * smpl2 = llama_sampler_chain_init(sparams);
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llama_sampler_chain_add(smpl2, llama_sampler_init_dist(params.sampling.seed));
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printf("\nsecond run: %s", params.prompt.c_str());
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// load state from file
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std::vector<llama_token> unused_sts(tokens.size()); // unused session tokens.
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size_t n_token_count_out = 0;
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if (!llama_state_load_file(ctx2, state_file.string().c_str(), unused_sts.data(), unused_sts.size(), &n_token_count_out)) {
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fprintf(stderr, "\n%s : failed to load state\n", __func__);
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return 1;
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}
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fprintf(stderr, "%s : loaded state with %zu tokens\n", __func__, n_token_count_out);
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// restore state (last tokens)
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n_past = n_token_count_out;
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if (!replay_last_token(ctx2, tokens.back(), n_past)) {
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return 1;
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}
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// second run
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for (auto i = 0; i < params.n_predict; i++) {
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auto next_token = llama_sampler_sample(smpl2, ctx2, -1);
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auto next_token_str = common_token_to_piece(ctx2, next_token);
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printf("%s", next_token_str.c_str());
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result1 += next_token_str;
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common_batch_clear(batch);
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common_batch_add(batch, next_token, n_past, {0}, true);
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if (llama_decode(ctx2, batch)) {
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fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
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llama_batch_free(batch);
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return 1;
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}
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n_past += 1;
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}
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printf("\n\n");
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if (result0 != result1) {
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fprintf(stderr, "\n%s : error : the 2 generations are different\n", __func__);
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return 1;
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}
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// make new context
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auto params_ctx3 = common_context_params_to_llama(params);
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params_ctx3.n_seq_max = 2;
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llama_context * ctx3 = llama_init_from_model(model, params_ctx3);
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llama_sampler * smpl3 = llama_sampler_chain_init(sparams);
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llama_sampler_chain_add(smpl3, llama_sampler_init_dist(params.sampling.seed));
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printf("\nsingle seq run: %s", params.prompt.c_str());
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// load state (rng, logits, embedding and kv_cache) from file
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n_token_count_out = 0;
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if (!llama_state_load_file(ctx3, state_file.string().c_str(), unused_sts.data(), unused_sts.size(), &n_token_count_out)) {
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fprintf(stderr, "\n%s : failed to load state\n", __func__);
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return 1;
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}
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fprintf(stderr, "%s : loaded state with %zu tokens\n", __func__, n_token_count_out);
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// restore state (last tokens)
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n_past = n_token_count_out;
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if (!replay_last_token(ctx3, tokens.back(), n_past)) {
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return 1;
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}
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// save seq 0 and load into seq 1
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{
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// save kv of seq 0
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std::vector<uint8_t> seq_store(llama_state_seq_get_size(ctx3, 0));
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const size_t ncopy = llama_state_seq_get_data(ctx3, seq_store.data(), seq_store.size(), 0);
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if (ncopy != seq_store.size()) {
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fprintf(stderr, "\n%s : seq copy data length %zd does not match expected length %zd\n", __func__, ncopy, seq_store.size());
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return 1;
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}
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fprintf(stderr, "%s : seq 0 copied, %zd bytes\n", __func__, ncopy);
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// erase whole kv
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llama_memory_clear(llama_get_memory(ctx3), true);
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fprintf(stderr, "%s : kv cache cleared\n", __func__);
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// restore kv into seq 1
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const size_t nset = llama_state_seq_set_data(ctx3, seq_store.data(), seq_store.size(), 1);
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if (nset != seq_store.size()) {
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fprintf(stderr, "\n%s : seq set data length %zd does not match expected length %zd\n", __func__, nset, seq_store.size());
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return 1;
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}
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fprintf(stderr, "%s : seq 1 restored, %zd bytes\n", __func__, nset);
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}
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// third run with seq 1 instead of 0
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for (auto i = 0; i < params.n_predict; i++) {
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auto next_token = llama_sampler_sample(smpl3, ctx3, -1);
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auto next_token_str = common_token_to_piece(ctx3, next_token);
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printf("%s", next_token_str.c_str());
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result2 += next_token_str;
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common_batch_clear(batch);
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common_batch_add(batch, next_token, n_past, {1}, true);
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if (llama_decode(ctx3, batch)) {
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fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
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llama_batch_free(batch);
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return 1;
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}
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n_past += 1;
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}
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printf("\n");
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llama_sampler_free(smpl);
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llama_sampler_free(smpl2);
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llama_sampler_free(smpl3);
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llama_batch_free(batch);
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// this one is managed by common_init_result
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//llama_free(ctx);
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llama_free(ctx2);
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llama_free(ctx3);
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if (result0 != result2) {
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fprintf(stderr, "\n%s : error : the seq restore generation is different\n", __func__);
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return 1;
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}
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fprintf(stderr, "\n%s : success\n", __func__);
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return 0;
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}
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