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https://github.com/ggml-org/llama.cpp.git
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speculative : PoC for speeding-up inference via speculative sampling (#2926)
* speculative : initial example * speculative : print encoding speed * speculative : add --draft CLI arg
This commit is contained in:
Georgi Gerganov
234
examples/speculative/speculative.cpp
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234
examples/speculative/speculative.cpp
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#ifndef _GNU_SOURCE
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#define _GNU_SOURCE
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#endif
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#include "build-info.h"
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#include "common.h"
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#include "llama.h"
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#include <cmath>
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#include <cstdio>
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#include <string>
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#include <vector>
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int main(int argc, char ** argv) {
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gpt_params params;
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if (gpt_params_parse(argc, argv, params) == false) {
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return 1;
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}
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if (params.model_draft.empty()) {
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fprintf(stderr, "%s: error: --model-draft is required\n", __func__);
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return 1;
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}
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#ifndef LOG_DISABLE_LOGS
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log_set_target(log_filename_generator("speculative", "log"));
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LOG_TEE("Log start\n");
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log_dump_cmdline(argc, argv);
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#endif // LOG_DISABLE_LOGS
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// init llama.cpp
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llama_backend_init(params.numa);
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llama_model * model_tgt = NULL;
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llama_model * model_dft = NULL;
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llama_context * ctx_tgt = NULL;
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llama_context * ctx_dft = NULL;
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// load the target model
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params.perplexity = true; // HACK: enable logits_all = true
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std::tie(model_tgt, ctx_tgt) = llama_init_from_gpt_params(params);
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// load the draft model
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params.model = params.model_draft;
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std::tie(model_dft, ctx_dft) = llama_init_from_gpt_params(params);
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// tokenize the prompt
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std::vector<llama_token> inp;
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inp = ::llama_tokenize(ctx_tgt, params.prompt, true);
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const int max_context_size = llama_n_ctx(ctx_tgt);
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const int max_tokens_list_size = max_context_size - 4;
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if ((int) inp.size() > max_tokens_list_size) {
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fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size);
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return 1;
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}
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fprintf(stderr, "\n\n");
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for (auto id : inp) {
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fprintf(stderr, "%s", llama_token_to_piece(ctx_tgt, id).c_str());
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}
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fflush(stderr);
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const int n_input = inp.size();
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const auto t_enc_start = ggml_time_us();
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// eval the prompt with both models
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llama_eval(ctx_tgt, inp.data(), int(inp.size() - 1), 0, params.n_threads);
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llama_eval(ctx_tgt, &inp.back(), 1, inp.size() - 1, params.n_threads);
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llama_eval(ctx_dft, inp.data(), int(inp.size()), 0, params.n_threads);
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const auto t_enc_end = ggml_time_us();
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// the 2 models should have the same vocab
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const int n_ctx = llama_n_ctx(ctx_tgt);
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const int n_vocab = llama_n_vocab(ctx_tgt);
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//GGML_ASSERT(n_vocab == llama_n_vocab(ctx_dft));
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// how many tokens to draft each time
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const int n_draft = params.n_draft;
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int n_predict = 0;
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int n_drafted = 0;
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int n_accept = 0;
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int n_past_tgt = inp.size();
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int n_past_dft = inp.size();
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std::vector<llama_token> drafted;
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std::vector<llama_token> last_tokens(n_ctx);
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std::fill(last_tokens.begin(), last_tokens.end(), 0);
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for (auto & id : inp) {
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last_tokens.erase(last_tokens.begin());
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last_tokens.push_back(id);
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}
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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// used to determine end of generation
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bool has_eos = false;
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const auto t_dec_start = ggml_time_us();
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while (true) {
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LOG("drafted: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_dft, drafted));
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// sample from the drafted tokens if any
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int i_dft = 0;
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while (true) {
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const llama_token id = llama_sample_token(ctx_tgt, NULL, NULL, params, last_tokens, candidates, i_dft);
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last_tokens.erase(last_tokens.begin());
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last_tokens.push_back(id);
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//LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, last_tokens));
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const std::string token_str = llama_token_to_piece(ctx_tgt, id);
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printf("%s", token_str.c_str());
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fflush(stdout);
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if (id == llama_token_eos(ctx_tgt)) {
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has_eos = true;
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}
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++n_predict;
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if (i_dft < (int) drafted.size() && id == drafted[i_dft]) {
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LOG("drafted token %d accepted\n", id);
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++n_accept;
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++n_past_tgt;
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++n_past_dft;
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++i_dft;
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continue;
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}
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// the drafted token was rejected or we are out of drafted tokens
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llama_eval(ctx_dft, &id, 1, n_past_dft, params.n_threads);
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++n_past_dft;
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drafted.clear();
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drafted.push_back(id);
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break;
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}
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if (n_predict > params.n_predict || has_eos) {
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break;
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}
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// sample n_draft tokens from the draft model picking the best token
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int n_past_cur = n_past_dft;
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for (int i = 0; i < n_draft; ++i) {
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float * logits = llama_get_logits(ctx_dft);
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candidates.clear();
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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 cur_p = { candidates.data(), candidates.size(), false };
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// computes softmax and sorts the candidates
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llama_sample_softmax(ctx_dft, &cur_p);
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for (int i = 0; i < 3; ++i) {
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LOG(" - draft candidate %d: %d (%.3f)\n", i, cur_p.data[i].id, cur_p.data[i].p);
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}
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// too low probability, stop drafting
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if (cur_p.data[0].p < 2*cur_p.data[1].p) {
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break;
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}
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drafted.push_back(cur_p.data[0].id);
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++n_drafted;
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if (i < n_draft - 1) {
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// evaluate the drafted token on the draft model
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llama_eval(ctx_dft, &drafted.back(), 1, n_past_cur, params.n_threads);
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++n_past_cur;
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}
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}
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// evaluate the target model on the drafted tokens
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llama_eval(ctx_tgt, drafted.data(), drafted.size(), n_past_tgt, params.n_threads);
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++n_past_tgt;
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drafted.erase(drafted.begin());
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}
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auto t_dec_end = ggml_time_us();
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LOG_TEE("\n\n");
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LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
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LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f));
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// TODO: make sure these numbers are computed correctly
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LOG_TEE("\n");
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LOG_TEE("n_draft = %d\n", n_draft);
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LOG_TEE("n_predict = %d\n", n_predict);
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LOG_TEE("n_drafted = %d\n", n_drafted);
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LOG_TEE("n_accept = %d\n", n_accept);
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LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
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LOG_TEE("\ndraft:\n");
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llama_print_timings(ctx_dft);
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LOG_TEE("\ntarget:\n");
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llama_print_timings(ctx_tgt);
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llama_free(ctx_tgt);
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llama_free_model(model_tgt);
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llama_free(ctx_dft);
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llama_free_model(model_dft);
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llama_backend_free();
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fprintf(stderr, "\n\n");
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return 0;
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}
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