llama.cpp/examples/save-load-state/save-load-state.cpp
Georgi Gerganov df270ef745
llama : refactor sampling v2 (#9294)
- Add `struct llama_sampler` and `struct llama_sampler_i`
- Add `llama_sampler_` API
- Add `llama_sampler_chain_` API for chaining multiple samplers
- Remove `LLAMA_API_INTERNAL`
- Add `llama_perf_` API and remove old `llama_print_timings` and `llama_reset_timings`
2024-09-07 15:16:19 +03:00

257 lines
8.0 KiB
C++

#include "common.h"
#include "llama.h"
#include <vector>
#include <cstdio>
int main(int argc, char ** argv) {
gpt_params params;
params.prompt = "The quick brown fox";
params.sparams.seed = 1234;
if (!gpt_params_parse(argc, argv, params)) {
gpt_params_print_usage(argc, argv, params);
return 1;
}
print_build_info();
if (params.n_predict < 0) {
params.n_predict = 16;
}
auto n_past = 0;
std::string result0;
std::string result1;
std::string result2;
// init
llama_init_result llama_init = llama_init_from_gpt_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
if (model == nullptr || ctx == nullptr) {
fprintf(stderr, "%s : failed to init\n", __func__);
return 1;
}
auto sparams = llama_sampler_chain_default_params();
llama_sampler * smpl = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl, llama_sampler_init_softmax());
llama_sampler_chain_add(smpl, llama_sampler_init_dist(params.sparams.seed));
// tokenize prompt
auto tokens = llama_tokenize(ctx, params.prompt, true);
// evaluate prompt
llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), n_past, 0));
n_past += tokens.size();
// save state (rng, logits, embedding and kv_cache) to file
{
std::vector<uint8_t> state_mem(llama_state_get_size(ctx));
const size_t written = llama_state_get_data(ctx, state_mem.data(), state_mem.size());
FILE *fp_write = fopen("dump_state.bin", "wb");
fwrite(state_mem.data(), 1, written, fp_write);
fclose(fp_write);
fprintf(stderr, "%s : serialized state into %zd out of a maximum of %zd bytes\n", __func__, written, state_mem.size());
}
// save state (last tokens)
const auto n_past_saved = n_past;
// first run
printf("\nfirst run: %s", params.prompt.c_str());
for (auto i = 0; i < params.n_predict; i++) {
auto next_token = llama_sampler_sample(smpl, ctx, -1);
auto next_token_str = llama_token_to_piece(ctx, next_token);
llama_sampler_accept(smpl, next_token);
printf("%s", next_token_str.c_str());
result0 += next_token_str;
if (llama_decode(ctx, llama_batch_get_one(&next_token, 1, n_past, 0))) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_free(ctx);
llama_free_model(model);
return 1;
}
n_past += 1;
}
printf("\n\n");
// free old context
llama_free(ctx);
// make new context
auto * ctx2 = llama_new_context_with_model(model, llama_context_params_from_gpt_params(params));
llama_sampler * smpl2 = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl2, llama_sampler_init_softmax());
llama_sampler_chain_add(smpl2, llama_sampler_init_dist(params.sparams.seed));
printf("\nsecond run: %s", params.prompt.c_str());
// load state (rng, logits, embedding and kv_cache) from file
{
std::vector<uint8_t> state_mem;
FILE * fp_read = fopen("dump_state.bin", "rb");
fseek(fp_read, 0, SEEK_END);
state_mem.resize(ftell(fp_read));
fseek(fp_read, 0, SEEK_SET);
const size_t read = fread(state_mem.data(), 1, state_mem.size(), fp_read);
fclose(fp_read);
if (read != llama_state_set_data(ctx2, state_mem.data(), state_mem.size())) {
fprintf(stderr, "\n%s : failed to read state\n", __func__);
llama_free(ctx2);
llama_free_model(model);
return 1;
}
fprintf(stderr, "%s : deserialized state from %zd out of a maximum of %zd bytes\n", __func__, read, state_mem.size());
}
// restore state (last tokens)
n_past = n_past_saved;
// second run
for (auto i = 0; i < params.n_predict; i++) {
auto next_token = llama_sampler_sample(smpl2, ctx2, -1);
auto next_token_str = llama_token_to_piece(ctx2, next_token);
llama_sampler_accept(smpl2, next_token);
printf("%s", next_token_str.c_str());
result1 += next_token_str;
if (llama_decode(ctx2, llama_batch_get_one(&next_token, 1, n_past, 0))) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_free(ctx2);
llama_free_model(model);
return 1;
}
n_past += 1;
}
printf("\n\n");
llama_free(ctx2);
if (result0 != result1) {
fprintf(stderr, "\n%s : error : the 2 generations are different\n", __func__);
return 1;
}
// make new context
auto * ctx3 = llama_new_context_with_model(model, llama_context_params_from_gpt_params(params));
llama_sampler * smpl3 = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl3, llama_sampler_init_softmax());
llama_sampler_chain_add(smpl3, llama_sampler_init_dist(params.sparams.seed));
printf("\nsingle seq run: %s", params.prompt.c_str());
// load state (rng, logits, embedding and kv_cache) from file
{
std::vector<uint8_t> state_mem;
FILE * fp_read = fopen("dump_state.bin", "rb");
fseek(fp_read, 0, SEEK_END);
state_mem.resize(ftell(fp_read));
fseek(fp_read, 0, SEEK_SET);
const size_t read = fread(state_mem.data(), 1, state_mem.size(), fp_read);
fclose(fp_read);
if (read != llama_state_set_data(ctx3, state_mem.data(), state_mem.size())) {
fprintf(stderr, "\n%s : failed to read state\n", __func__);
llama_free(ctx3);
llama_free_model(model);
return 1;
}
fprintf(stderr, "%s : deserialized state from %zd out of a maximum of %zd bytes\n", __func__, read, state_mem.size());
}
// restore state (last tokens)
n_past = n_past_saved;
// save seq 0 and load into seq 1
{
// save kv of seq 0
std::vector<uint8_t> seq_store(llama_state_seq_get_size(ctx3, 0));
const size_t ncopy = llama_state_seq_get_data(ctx3, seq_store.data(), seq_store.size(), 0);
if (ncopy != seq_store.size()) {
fprintf(stderr, "\n%s : seq copy data length %zd does not match expected length %zd\n", __func__, ncopy, seq_store.size());
llama_free(ctx3);
llama_free_model(model);
return 1;
}
fprintf(stderr, "%s : seq 0 copied, %zd bytes\n", __func__, ncopy);
// erase whole kv
llama_kv_cache_clear(ctx3);
fprintf(stderr, "%s : kv cache cleared\n", __func__);
// restore kv into seq 1
const size_t nset = llama_state_seq_set_data(ctx3, seq_store.data(), seq_store.size(), 1);
if (nset != seq_store.size()) {
fprintf(stderr, "\n%s : seq set data length %zd does not match expected length %zd\n", __func__, nset, seq_store.size());
llama_free(ctx3);
llama_free_model(model);
return 1;
}
fprintf(stderr, "%s : seq 1 restored, %zd bytes\n", __func__, nset);
}
// third run with seq 1 instead of 0
for (auto i = 0; i < params.n_predict; i++) {
auto next_token = llama_sampler_sample(smpl3, ctx3, -1);
auto next_token_str = llama_token_to_piece(ctx3, next_token);
llama_sampler_accept(smpl3, next_token);
printf("%s", next_token_str.c_str());
result2 += next_token_str;
if (llama_decode(ctx3, llama_batch_get_one(&next_token, 1, n_past, 1))) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_free(ctx3);
llama_free_model(model);
return 1;
}
n_past += 1;
}
printf("\n");
llama_sampler_free(smpl);
llama_sampler_free(smpl2);
llama_sampler_free(smpl3);
llama_free(ctx3);
llama_free_model(model);
if (result0 != result2) {
fprintf(stderr, "\n%s : error : the seq restore generation is different\n", __func__);
return 1;
}
fprintf(stderr, "\n%s : success\n", __func__);
return 0;
}