mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-10-31 23:28:51 +01:00
1442677f92
* common : gpt_params_parse do not print usage * common : rework usage print (wip) * common : valign * common : rework print_usage * infill : remove cfg support * common : reorder args * server : deduplicate parameters ggml-ci * common : add missing header ggml-ci * common : remote --random-prompt usages ggml-ci * examples : migrate to gpt_params ggml-ci * batched-bench : migrate to gpt_params * retrieval : migrate to gpt_params * common : change defaults for escape and n_ctx * common : remove chatml and instruct params ggml-ci * common : passkey use gpt_params
248 lines
8.0 KiB
C++
248 lines
8.0 KiB
C++
#include "common.h"
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#include "llama.h"
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#include <vector>
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#include <cstdio>
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#include <chrono>
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int main(int argc, char ** argv) {
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gpt_params params;
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params.prompt = "The quick brown fox";
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if (!gpt_params_parse(argc, argv, params)) {
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gpt_params_print_usage(argc, argv, params);
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return 1;
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}
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print_build_info();
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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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llama_model * model;
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llama_context * ctx;
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std::tie(model, ctx) = llama_init_from_gpt_params(params);
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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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// tokenize prompt
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auto tokens = llama_tokenize(ctx, params.prompt, true);
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// evaluate prompt
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llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), n_past, 0));
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n_past += tokens.size();
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// save state (rng, logits, embedding and kv_cache) to file
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{
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std::vector<uint8_t> state_mem(llama_state_get_size(ctx));
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const size_t written = llama_state_get_data(ctx, state_mem.data());
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FILE *fp_write = fopen("dump_state.bin", "wb");
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fwrite(state_mem.data(), 1, written, fp_write);
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fclose(fp_write);
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fprintf(stderr, "%s : serialized state into %zd out of a maximum of %zd bytes\n", __func__, written, state_mem.size());
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}
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// save state (last tokens)
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const auto n_past_saved = n_past;
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// first run
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printf("\nfirst run: %s", params.prompt.c_str());
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for (auto i = 0; i < params.n_predict; i++) {
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auto * logits = llama_get_logits(ctx);
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auto n_vocab = llama_n_vocab(model);
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
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candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
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}
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llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
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auto next_token = llama_sample_token(ctx, &candidates_p);
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auto next_token_str = llama_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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if (llama_decode(ctx, llama_batch_get_one(&next_token, 1, n_past, 0))) {
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fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
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llama_free(ctx);
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llama_free_model(model);
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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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// free old context
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llama_free(ctx);
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// make new context
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auto * ctx2 = llama_new_context_with_model(model, llama_context_params_from_gpt_params(params));
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printf("\nsecond run: %s", params.prompt.c_str());
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// load state (rng, logits, embedding and kv_cache) from file
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{
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std::vector<uint8_t> state_mem(llama_state_get_size(ctx2));
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FILE * fp_read = fopen("dump_state.bin", "rb");
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const size_t read = fread(state_mem.data(), 1, state_mem.size(), fp_read);
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fclose(fp_read);
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if (read != llama_state_set_data(ctx2, state_mem.data())) {
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fprintf(stderr, "\n%s : failed to read state\n", __func__);
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llama_free(ctx2);
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llama_free_model(model);
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return 1;
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}
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fprintf(stderr, "%s : deserialized state from %zd out of a maximum of %zd bytes\n", __func__, read, state_mem.size());
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}
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// restore state (last tokens)
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n_past = n_past_saved;
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// second run
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for (auto i = 0; i < params.n_predict; i++) {
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auto * logits = llama_get_logits(ctx2);
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auto n_vocab = llama_n_vocab(model);
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
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candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
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}
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llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
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auto next_token = llama_sample_token(ctx2, &candidates_p);
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auto next_token_str = llama_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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if (llama_decode(ctx2, llama_batch_get_one(&next_token, 1, n_past, 0))) {
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fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
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llama_free(ctx2);
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llama_free_model(model);
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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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llama_free(ctx2);
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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* ctx3 = llama_new_context_with_model(model, llama_context_params_from_gpt_params(params));
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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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{
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std::vector<uint8_t> state_mem(llama_state_get_size(ctx3));
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FILE * fp_read = fopen("dump_state.bin", "rb");
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const size_t read = fread(state_mem.data(), 1, state_mem.size(), fp_read);
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fclose(fp_read);
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if (read != llama_state_set_data(ctx3, state_mem.data())) {
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fprintf(stderr, "\n%s : failed to read state\n", __func__);
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llama_free(ctx3);
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llama_free_model(model);
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return 1;
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}
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fprintf(stderr, "%s : deserialized state from %zd out of a maximum of %zd bytes\n", __func__, read, state_mem.size());
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}
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// restore state (last tokens)
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n_past = n_past_saved;
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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(), 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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llama_free(ctx3);
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llama_free_model(model);
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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_kv_cache_clear(ctx3);
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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(), 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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llama_free(ctx3);
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llama_free_model(model);
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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 * logits = llama_get_logits(ctx3);
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auto n_vocab = llama_n_vocab(model);
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
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candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
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}
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llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
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auto next_token = llama_sample_token(ctx3, &candidates_p);
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auto next_token_str = llama_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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if (llama_decode(ctx3, llama_batch_get_one(&next_token, 1, n_past, 1))) {
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fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
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llama_free(ctx3);
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llama_free_model(model);
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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_free(ctx3);
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llama_free_model(model);
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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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