File size: 7,391 Bytes
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#include "common.h"
#include "llama.h"
#include <vector>
#include <cstdio>
int main(int argc, char ** argv) {
common_params params;
params.prompt = "The quick brown fox";
params.sampling.seed = 1234;
const std::string_view state_file = "dump_state.bin";
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
}
if (params.n_parallel == 1) {
// the example uses 2 sequences, so when n_parallel == 1, we need to enable unified kv cache
printf("%s: n_parallel == 1, enabling unified kv cache\n", __func__);
params.kv_unified = true;
}
common_init();
if (params.n_predict < 0) {
params.n_predict = 16;
}
auto n_past = 0;
std::string result0;
std::string result1;
std::string result2;
// init
auto llama_init = common_init_from_params(params);
auto * model = llama_init->model();
auto * 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_dist(params.sampling.seed));
// tokenize prompt
auto tokens = common_tokenize(ctx, params.prompt, true);
const bool save_state = true;
if (!common_prompt_batch_decode(ctx, tokens, n_past, params.n_batch, state_file, save_state)) {
return 1;
}
// first run
printf("\nfirst run: %s", params.prompt.c_str());
llama_batch batch = llama_batch_init(1, 0, 1);
for (auto i = 0; i < params.n_predict; i++) {
auto next_token = llama_sampler_sample(smpl, ctx, -1);
auto next_token_str = common_token_to_piece(ctx, next_token);
printf("%s", next_token_str.c_str());
result0 += next_token_str;
common_batch_clear(batch);
common_batch_add(batch, next_token, n_past, {0}, true);
if (llama_decode(ctx, batch)) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_batch_free(batch);
return 1;
}
n_past += 1;
}
printf("\n\n");
// make new context
llama_context * ctx2 = llama_init_from_model(model, common_context_params_to_llama(params));
llama_sampler * smpl2 = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl2, llama_sampler_init_dist(params.sampling.seed));
printf("\nsecond run: %s", params.prompt.c_str());
// load state from file
std::vector<llama_token> unused_sts(tokens.size()); // unused session tokens.
size_t n_token_count_out = 0;
if (!llama_state_load_file(ctx2, state_file.data(), unused_sts.data(), unused_sts.size(), &n_token_count_out)) {
fprintf(stderr, "\n%s : failed to load state\n", __func__);
return 1;
}
fprintf(stderr, "%s : loaded state with %zu tokens\n", __func__, n_token_count_out);
// restore state (last tokens)
n_past = n_token_count_out;
if (!common_replay_last_token(ctx2, tokens.back(), n_past)) {
return 1;
}
++n_past;
// second run
for (auto i = 0; i < params.n_predict; i++) {
auto next_token = llama_sampler_sample(smpl2, ctx2, -1);
auto next_token_str = common_token_to_piece(ctx2, next_token);
printf("%s", next_token_str.c_str());
result1 += next_token_str;
common_batch_clear(batch);
common_batch_add(batch, next_token, n_past, {0}, true);
if (llama_decode(ctx2, batch)) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_batch_free(batch);
return 1;
}
n_past += 1;
}
printf("\n\n");
if (result0 != result1) {
fprintf(stderr, "\n%s : error : the 2 generations are different\n", __func__);
return 1;
}
// make new context
auto params_ctx3 = common_context_params_to_llama(params);
params_ctx3.n_seq_max = 2;
llama_context * ctx3 = llama_init_from_model(model, params_ctx3);
llama_sampler * smpl3 = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl3, llama_sampler_init_dist(params.sampling.seed));
printf("\nsingle seq run: %s", params.prompt.c_str());
// load state (rng, logits, embedding and kv_cache) from file
n_token_count_out = 0;
if (!llama_state_load_file(ctx3, state_file.data(), unused_sts.data(), unused_sts.size(), &n_token_count_out)) {
fprintf(stderr, "\n%s : failed to load state\n", __func__);
return 1;
}
fprintf(stderr, "%s : loaded state with %zu tokens\n", __func__, n_token_count_out);
// restore state (last tokens)
n_past = n_token_count_out;
if (!common_replay_last_token(ctx3, tokens.back(), n_past)) {
return 1;
}
++n_past;
// 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());
return 1;
}
fprintf(stderr, "%s : seq 0 copied, %zd bytes\n", __func__, ncopy);
// erase whole kv
llama_memory_clear(llama_get_memory(ctx3), true);
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());
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 = common_token_to_piece(ctx3, next_token);
printf("%s", next_token_str.c_str());
result2 += next_token_str;
common_batch_clear(batch);
common_batch_add(batch, next_token, n_past, {1}, true);
if (llama_decode(ctx3, batch)) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_batch_free(batch);
return 1;
}
n_past += 1;
}
printf("\n");
llama_sampler_free(smpl);
llama_sampler_free(smpl2);
llama_sampler_free(smpl3);
llama_batch_free(batch);
// this one is managed by common_init_result
//llama_free(ctx);
llama_free(ctx2);
llama_free(ctx3);
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;
}
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