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
GitHub
parent
8f429fa511
commit
47068e5170
6 changed files with 440 additions and 115 deletions
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@ -23,6 +23,7 @@ else()
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add_subdirectory(train-text-from-scratch)
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add_subdirectory(convert-llama2c-to-ggml)
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add_subdirectory(simple)
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add_subdirectory(speculative)
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add_subdirectory(embd-input)
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add_subdirectory(llama-bench)
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add_subdirectory(beam-search)
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@ -116,7 +116,7 @@ int main(int argc, char ** argv) {
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#ifndef LOG_DISABLE_LOGS
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log_set_target(log_filename_generator("main", "log"));
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LOG_TEE("Log start\n");
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log_dump_cmdline(argc,argv);
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log_dump_cmdline(argc, argv);
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#endif // LOG_DISABLE_LOGS
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// TODO: Dump params ?
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@ -425,8 +425,9 @@ int main(int argc, char ** argv) {
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LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
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LOG_TEE("\n\n");
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struct llama_grammar * grammar = NULL;
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grammar_parser::parse_state parsed_grammar;
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llama_grammar * grammar = NULL;
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if (!params.grammar.empty()) {
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parsed_grammar = grammar_parser::parse(params.grammar.c_str());
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// will be empty (default) if there are parse errors
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@ -450,8 +451,8 @@ int main(int argc, char ** argv) {
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}
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// TODO: replace with ring-buffer
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std::vector<llama_token> last_n_tokens(n_ctx);
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std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
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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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if (params.interactive) {
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const char *control_message;
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@ -492,6 +493,11 @@ int main(int argc, char ** argv) {
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std::vector<llama_token> embd;
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std::vector<llama_token> embd_guidance;
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const int n_vocab = llama_n_vocab(ctx);
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
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// predict
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if (embd.size() > 0) {
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@ -529,8 +535,8 @@ int main(int argc, char ** argv) {
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LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance);
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// insert n_left/2 tokens at the start of embd from last_n_tokens
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embd.insert(embd.begin(), last_n_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_n_tokens.end() - embd.size());
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// insert n_left/2 tokens at the start of embd from last_tokens
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embd.insert(embd.begin(), last_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_tokens.end() - embd.size());
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LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd));
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@ -629,20 +635,6 @@ int main(int argc, char ** argv) {
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embd_guidance.clear();
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if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
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const float temp = params.temp;
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const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
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const float top_p = params.top_p;
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const float tfs_z = params.tfs_z;
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const float typical_p = params.typical_p;
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const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
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const float repeat_penalty = params.repeat_penalty;
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const float alpha_presence = params.presence_penalty;
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const float alpha_frequency = params.frequency_penalty;
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const int mirostat = params.mirostat;
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const float mirostat_tau = params.mirostat_tau;
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const float mirostat_eta = params.mirostat_eta;
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const bool penalize_nl = params.penalize_nl;
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// optionally save the session on first sample (for faster prompt loading next time)
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if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) {
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need_to_save_session = false;
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@ -651,98 +643,12 @@ int main(int argc, char ** argv) {
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LOG("saved session to %s\n", path_session.c_str());
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}
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llama_token id = 0;
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const llama_token id = llama_sample_token(ctx, ctx_guidance, grammar, params, last_tokens, candidates);
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{
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auto logits = llama_get_logits(ctx);
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auto n_vocab = llama_n_vocab(ctx);
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last_tokens.erase(last_tokens.begin());
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last_tokens.push_back(id);
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// Apply params.logit_bias map
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for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
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logits[it->first] += it->second;
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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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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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if (ctx_guidance) {
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llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale);
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}
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// Apply penalties
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float nl_logit = logits[llama_token_nl(ctx)];
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auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
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llama_sample_repetition_penalty(ctx, &cur_p,
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last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
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last_n_repeat, repeat_penalty);
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llama_sample_frequency_and_presence_penalties(ctx, &cur_p,
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last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
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last_n_repeat, alpha_frequency, alpha_presence);
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if (!penalize_nl) {
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for (size_t idx = 0; idx < cur_p.size; idx++) {
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if (cur_p.data[idx].id == llama_token_nl(ctx)) {
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cur_p.data[idx].logit = nl_logit;
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break;
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}
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}
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}
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if (grammar != NULL) {
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llama_sample_grammar(ctx, &cur_p, grammar);
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}
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if (temp <= 0) {
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// Greedy sampling
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id = llama_sample_token_greedy(ctx, &cur_p);
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} else {
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if (mirostat == 1) {
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static float mirostat_mu = 2.0f * mirostat_tau;
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const int mirostat_m = 100;
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llama_sample_temperature(ctx, &cur_p, temp);
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id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
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} else if (mirostat == 2) {
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static float mirostat_mu = 2.0f * mirostat_tau;
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llama_sample_temperature(ctx, &cur_p, temp);
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id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu);
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} else {
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// Temperature sampling
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llama_sample_top_k (ctx, &cur_p, top_k, 1);
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llama_sample_tail_free (ctx, &cur_p, tfs_z, 1);
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llama_sample_typical (ctx, &cur_p, typical_p, 1);
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llama_sample_top_p (ctx, &cur_p, top_p, 1);
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llama_sample_temperature(ctx, &cur_p, temp);
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{
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const int n_top = 10;
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LOG("top %d candidates:\n", n_top);
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for (int i = 0; i < n_top; i++) {
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const llama_token id = cur_p.data[i].id;
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LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p);
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}
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}
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id = llama_sample_token(ctx, &cur_p);
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LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str());
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}
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}
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// printf("`%d`", candidates_p.size);
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if (grammar != NULL) {
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llama_grammar_accept_token(ctx, grammar, id);
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}
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last_n_tokens.erase(last_n_tokens.begin());
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last_n_tokens.push_back(id);
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LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, last_n_tokens));
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}
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LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, last_tokens));
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embd.push_back(id);
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@ -758,8 +664,8 @@ int main(int argc, char ** argv) {
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LOG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed);
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while ((int) embd_inp.size() > n_consumed) {
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embd.push_back(embd_inp[n_consumed]);
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last_n_tokens.erase(last_n_tokens.begin());
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last_n_tokens.push_back(embd_inp[n_consumed]);
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last_tokens.erase(last_tokens.begin());
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last_tokens.push_back(embd_inp[n_consumed]);
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++n_consumed;
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if ((int) embd.size() >= params.n_batch) {
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break;
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@ -792,7 +698,7 @@ int main(int argc, char ** argv) {
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// check for reverse prompt
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if (params.antiprompt.size()) {
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std::string last_output;
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for (auto id : last_n_tokens) {
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for (auto id : last_tokens) {
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last_output += llama_token_to_piece(ctx, id);
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}
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@ -823,7 +729,7 @@ int main(int argc, char ** argv) {
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}
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// deal with end of text token in interactive mode
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if (last_n_tokens.back() == llama_token_eos(ctx)) {
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if (last_tokens.back() == llama_token_eos(ctx)) {
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LOG("found EOS token\n");
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if (params.interactive) {
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@ -925,7 +831,7 @@ int main(int argc, char ** argv) {
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if (grammar != NULL) {
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llama_grammar_free(grammar);
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std::vector<const llama_grammar_element *> grammar_rules( parsed_grammar.c_rules());
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std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
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grammar = llama_grammar_init(
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grammar_rules.data(), grammar_rules.size(),
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parsed_grammar.symbol_ids.at("root"));
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8
examples/speculative/CMakeLists.txt
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8
examples/speculative/CMakeLists.txt
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@ -0,0 +1,8 @@
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set(TARGET speculative)
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add_executable(${TARGET} speculative.cpp)
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install(TARGETS ${TARGET} RUNTIME)
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target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
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target_compile_features(${TARGET} PRIVATE cxx_std_11)
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if(TARGET BUILD_INFO)
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add_dependencies(${TARGET} BUILD_INFO)
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endif()
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234
examples/speculative/speculative.cpp
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234
examples/speculative/speculative.cpp
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@ -0,0 +1,234 @@
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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});
|
||||
}
|
||||
|
||||
llama_token_data_array cur_p = { candidates.data(), candidates.size(), false };
|
||||
|
||||
// computes softmax and sorts the candidates
|
||||
llama_sample_softmax(ctx_dft, &cur_p);
|
||||
|
||||
for (int i = 0; i < 3; ++i) {
|
||||
LOG(" - draft candidate %d: %d (%.3f)\n", i, cur_p.data[i].id, cur_p.data[i].p);
|
||||
}
|
||||
|
||||
// too low probability, stop drafting
|
||||
if (cur_p.data[0].p < 2*cur_p.data[1].p) {
|
||||
break;
|
||||
}
|
||||
|
||||
drafted.push_back(cur_p.data[0].id);
|
||||
++n_drafted;
|
||||
|
||||
if (i < n_draft - 1) {
|
||||
// evaluate the drafted token on the draft model
|
||||
llama_eval(ctx_dft, &drafted.back(), 1, n_past_cur, params.n_threads);
|
||||
++n_past_cur;
|
||||
}
|
||||
}
|
||||
|
||||
// evaluate the target model on the drafted tokens
|
||||
llama_eval(ctx_tgt, drafted.data(), drafted.size(), n_past_tgt, params.n_threads);
|
||||
++n_past_tgt;
|
||||
|
||||
drafted.erase(drafted.begin());
|
||||
}
|
||||
|
||||
auto t_dec_end = ggml_time_us();
|
||||
|
||||
LOG_TEE("\n\n");
|
||||
|
||||
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));
|
||||
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));
|
||||
|
||||
// TODO: make sure these numbers are computed correctly
|
||||
LOG_TEE("\n");
|
||||
LOG_TEE("n_draft = %d\n", n_draft);
|
||||
LOG_TEE("n_predict = %d\n", n_predict);
|
||||
LOG_TEE("n_drafted = %d\n", n_drafted);
|
||||
LOG_TEE("n_accept = %d\n", n_accept);
|
||||
LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
|
||||
|
||||
LOG_TEE("\ndraft:\n");
|
||||
llama_print_timings(ctx_dft);
|
||||
|
||||
LOG_TEE("\ntarget:\n");
|
||||
llama_print_timings(ctx_tgt);
|
||||
|
||||
llama_free(ctx_tgt);
|
||||
llama_free_model(model_tgt);
|
||||
|
||||
llama_free(ctx_dft);
|
||||
llama_free_model(model_dft);
|
||||
|
||||
llama_backend_free();
|
||||
|
||||
fprintf(stderr, "\n\n");
|
||||
|
||||
return 0;
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue