mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-04 09:32:00 +00:00
5be6c803fa
* added `llama_model_token_*` variants to all the `llama_token_*` functions. * added `LLAMA_API` * formatting Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * removed old `llama_token` functions * changed 3 more functions to take in model - `llama_token_get_text` - `llama_token_get_score` - `llama_token_get_type` * added back docs * fixed main.cpp * changed token functions to use new model variants * changed token functions to use new model variants --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
427 lines
14 KiB
C++
427 lines
14 KiB
C++
#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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struct seq_draft {
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bool active = false;
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bool drafting = false;
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bool skip = false;
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int i_batch_dft = 0;
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std::vector<int> i_batch_tgt;
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std::vector<llama_token> tokens;
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struct llama_sampling_context * ctx_sampling;
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};
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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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// max number of parallel drafting sequences (i.e. tree branches)
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const int n_seq_dft = params.n_parallel;
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// TODO: make this configurable
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const float p_accept = 0.80f;
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const float p_split = 0.10f;
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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.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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params.n_gpu_layers = params.n_gpu_layers_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_decode(ctx_tgt, llama_batch_get_one( inp.data(), n_input - 1, 0, 0));
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llama_decode(ctx_tgt, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0));
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llama_decode(ctx_dft, llama_batch_get_one( inp.data(), n_input, 0, 0));
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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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//GGML_ASSERT(n_vocab == llama_n_vocab(model_dft));
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// how many tokens to draft each time
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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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// used to determine end of generation
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bool has_eos = false;
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// target model sampling context
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struct llama_sampling_context * ctx_sampling = llama_sampling_init(params.sparams);
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// draft sequence data
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std::vector<seq_draft> drafts(n_seq_dft);
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params.sparams.grammar.clear(); // the draft samplers will copy the target sampler's grammar
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params.sparams.temp = std::max(0.01f, params.sparams.temp);
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for (int s = 0; s < n_seq_dft; ++s) {
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drafts[s].ctx_sampling = llama_sampling_init(params.sparams);
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}
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llama_batch batch_dft = llama_batch_init(params.n_ctx, 0, 1);
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llama_batch batch_tgt = llama_batch_init(params.n_ctx, 0, n_seq_dft);
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const auto t_dec_start = ggml_time_us();
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// sample from the last token of the prompt
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drafts[0].i_batch_tgt.resize(1);
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drafts[0].i_batch_tgt[0] = 0;
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while (true) {
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// print current draft sequences
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for (int s = 0; s < n_seq_dft; ++s) {
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if (!drafts[s].active) {
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continue;
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}
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const auto & tokens = drafts[s].tokens;
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LOG("draft %d: %s\n", s, LOG_TOKENS_TOSTR_PRETTY(ctx_dft, tokens).c_str());
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}
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int i_dft = 0;
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int s_keep = 0;
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while (true) {
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LOG("sampling target: s_keep = %3d, i_dft = %3d, i_batch_tgt = %3d\n", s_keep, i_dft, drafts[s_keep].i_batch_tgt[i_dft]);
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// sample from the target model
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llama_token id = llama_sampling_sample(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]);
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llama_sampling_accept(ctx_sampling, ctx_tgt, id, true);
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//LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, ctx_sampling->prev).c_str());
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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(model_tgt)) {
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has_eos = true;
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}
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++n_predict;
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// check if the target token matches any of the drafts
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{
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bool matches = false;
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for (int s = 0; s < n_seq_dft; ++s) {
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if (!drafts[s].active) {
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continue;
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}
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if (i_dft < (int) drafts[s].tokens.size() && id == drafts[s].tokens[i_dft]) {
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LOG("the sampled target token matches the %dth drafted token of sequence %d (%d, '%s') - accepted\n", i_dft, s, id, token_str.c_str());
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s_keep = s;
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matches = true;
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} else {
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drafts[s].active = false;
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}
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}
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if (matches) {
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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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}
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LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str());
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// TODO: simplify
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{
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LOG("keeping sequence %d, n_past_tgt = %d, n_past_dft = %d\n", s_keep, n_past_tgt, n_past_dft);
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llama_kv_cache_seq_keep(ctx_dft, s_keep);
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llama_kv_cache_seq_cp (ctx_dft, s_keep, 0, -1, -1);
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llama_kv_cache_seq_keep(ctx_dft, 0);
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llama_kv_cache_seq_rm (ctx_tgt, s_keep, n_past_tgt, -1);
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llama_kv_cache_seq_keep(ctx_tgt, s_keep);
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llama_kv_cache_seq_cp (ctx_tgt, s_keep, 0, -1, -1);
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llama_kv_cache_seq_keep(ctx_tgt, 0);
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}
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for (int s = 0; s < n_seq_dft; ++s) {
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drafts[s].active = false;
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drafts[s].tokens.clear();
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drafts[s].i_batch_tgt.clear();
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}
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// note: will be erased after the speculation phase
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drafts[0].tokens.push_back(id);
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drafts[0].i_batch_tgt.push_back(0);
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llama_batch_clear(batch_dft);
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llama_batch_add (batch_dft, id, n_past_dft, { 0 }, true);
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llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, -1);
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llama_decode (ctx_dft, batch_dft);
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++n_past_dft;
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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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llama_sampling_cp(ctx_sampling, drafts[0].ctx_sampling);
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int n_seq_cur = 1;
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int n_past_cur = n_past_dft;
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for (int s = 0; s < n_seq_dft; ++s) {
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drafts[s].active = false;
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drafts[s].drafting = false;
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}
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drafts[0].active = true;
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drafts[0].drafting = true;
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drafts[0].i_batch_dft = 0;
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llama_batch_clear(batch_tgt);
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llama_batch_add (batch_tgt, drafts[0].tokens[0], n_past_tgt, { 0 }, true);
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// sample n_draft tokens from the draft model using tree-based sampling
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for (int i = 0; i < n_draft; ++i) {
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batch_dft.n_tokens = 0;
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for (int s = 0; s < n_seq_dft; ++s) {
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drafts[s].skip = false;
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}
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for (int s = 0; s < n_seq_dft; ++s) {
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if (!drafts[s].drafting || drafts[s].skip) {
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continue;
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}
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llama_sampling_sample(drafts[s].ctx_sampling, ctx_dft, NULL, drafts[s].i_batch_dft);
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const auto & cur_p = drafts[s].ctx_sampling->cur;
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for (int k = 0; k < std::min(n_seq_dft + 3, (int) cur_p.size()); ++k) {
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LOG(" - draft candidate %3d for seq %3d, pos %3d: %6d (%8.3f) '%s'\n",
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k, s, i, cur_p[k].id, cur_p[k].p, llama_token_to_piece(ctx_dft, cur_p[k].id).c_str());
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}
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if (cur_p[0].p < p_accept) {
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LOG("stopping drafting for seq %3d, probability too low: %.3f < %.3f\n", s, cur_p[0].p, p_accept);
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drafts[s].drafting = false;
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continue;
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}
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std::vector<int> sa(1, s);
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// attempt to split the branch if the probability is high enough
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for (int f = 1; f < 8; ++f) {
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if (n_seq_cur < n_seq_dft && cur_p[f].p > p_split) {
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LOG("splitting seq %3d into %3d\n", s, n_seq_cur);
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llama_kv_cache_seq_rm(ctx_dft, n_seq_cur, -1, -1);
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llama_kv_cache_seq_cp(ctx_dft, s, n_seq_cur, -1, -1);
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// all previous tokens from this branch are now also part of the new branch
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for (int t = 0; t < batch_tgt.n_tokens; ++t) {
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for (int p = 0; p < batch_tgt.n_seq_id[t]; ++p) {
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if (batch_tgt.seq_id[t][p] == s) {
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batch_tgt.seq_id[t][batch_tgt.n_seq_id[t]] = n_seq_cur;
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batch_tgt.n_seq_id[t]++;
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break;
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}
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}
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}
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// copy the draft state
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drafts[n_seq_cur].active = true;
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drafts[n_seq_cur].drafting = true;
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drafts[n_seq_cur].skip = true;
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drafts[n_seq_cur].tokens = drafts[s].tokens;
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drafts[n_seq_cur].i_batch_dft = drafts[s].i_batch_dft;
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drafts[n_seq_cur].i_batch_tgt = drafts[s].i_batch_tgt;
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llama_sampling_cp(drafts[s].ctx_sampling, drafts[n_seq_cur].ctx_sampling);
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sa.push_back(n_seq_cur);
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n_seq_cur++;
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} else {
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break;
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}
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}
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// add drafted token for each sequence
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for (int is = 0; is < (int) sa.size(); ++is) {
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const llama_token id = cur_p[is].id;
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const int s = sa[is];
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llama_sampling_accept(drafts[s].ctx_sampling, ctx_dft, id, true);
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drafts[s].tokens.push_back(id);
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// add unique drafted tokens to the target batch
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drafts[s].i_batch_tgt.push_back(batch_tgt.n_tokens);
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llama_batch_add(batch_tgt, id, n_past_tgt + i + 1, { s }, true);
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// add the token to the batch for batched decoding with the draft model
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drafts[s].i_batch_dft = batch_dft.n_tokens;
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llama_batch_add(batch_dft, id, n_past_cur, { s }, true);
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if (batch_tgt.n_tokens > n_draft) {
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drafts[s].drafting = false;
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}
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}
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}
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// no sequence is drafting anymore
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if (batch_dft.n_tokens == 0) {
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break;
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}
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// evaluate the drafted tokens on the draft model
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llama_decode(ctx_dft, batch_dft);
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++n_past_cur;
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++n_drafted;
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if (batch_tgt.n_tokens > n_draft) {
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break;
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}
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}
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// evaluate the target model on the drafted tokens
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{
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llama_kv_cache_seq_keep(ctx_tgt, 0);
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for (int s = 1; s < n_seq_dft; ++s) {
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llama_kv_cache_seq_cp(ctx_tgt, 0, s, -1, -1);
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}
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//LOG("target batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_tgt, batch_tgt));
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llama_decode(ctx_tgt, batch_tgt);
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++n_past_tgt;
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}
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// the first token is always proposed by the traget model before the speculation loop so we erase it here
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for (int s = 0; s < n_seq_dft; ++s) {
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if (!drafts[s].active) {
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continue;
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}
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drafts[s].tokens.erase(drafts[s].tokens.begin());
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}
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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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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_sampling_free(ctx_sampling);
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for (int s = 0; s < n_seq_dft; ++s) {
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llama_sampling_free(drafts[s].ctx_sampling);
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}
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llama_batch_free(batch_dft);
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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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