speculative : add tree-based sampling example (#3624)
* sampling : one sequence per sampling context ggml-ci * speculative : add tree-based sampling support ggml-ci * speculative : reuse the n_parallel CLI param * speculative : refactor sampling * examples : fix build after sampling refactoring ggml-ci * batched : fix n_seq_id * sampling : fix malloc ggml-ci * swift : fix build ggml-ci * swift : try to fix build ggml-ci * prompts : add assistant.txt * common : add llama_batch_add() and llama_batch_clear() helpers * speculative : minor refactor ggml-ci * minor : comments + rename ggml-ci * speculative : fix off-by-one for n_drafted * speculative : fix the n_drafted fix + p constants
This commit is contained in:
Georgi Gerganov
GitHub
parent
c67fe68e41
commit
0e89203b51
21 changed files with 737 additions and 578 deletions
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@ -2,13 +2,25 @@
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#include "common.h"
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#include "llama.h"
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#include "grammar-parser.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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@ -21,6 +33,13 @@ int main(int argc, char ** argv) {
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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 = 0.4f;
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const float p_split = 0.3f;
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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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@ -77,8 +96,6 @@ int main(int argc, char ** argv) {
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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(model_tgt);
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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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@ -91,60 +108,58 @@ int main(int argc, char ** argv) {
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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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// grammar stuff
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struct llama_grammar * grammar_dft = NULL;
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struct llama_grammar * grammar_tgt = NULL;
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// target model sampling context
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struct llama_sampling_context * ctx_sampling = llama_sampling_init(params);
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grammar_parser::parse_state parsed_grammar;
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// draft sequence data
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std::vector<seq_draft> drafts(n_seq_dft);
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// if requested - load the grammar, error checking is omitted for brevity
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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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if (parsed_grammar.rules.empty()) {
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return 1;
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}
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params.grammar.clear(); // the draft samplers will copy the target sampler's grammar
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params.sampling_params.temp = 1.0f; // the draft samplers use default temperature
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std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
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grammar_tgt = llama_grammar_init(grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
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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);
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}
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llama_sampling_context ctx_sampling = llama_sampling_context_init(params, grammar_tgt);
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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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while (true) {
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LOG("drafted: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_dft, drafted));
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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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int i_dft = 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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// sample from the target model
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llama_token id = llama_sampling_sample(ctx_tgt, NULL, ctx_sampling, last_tokens, candidates, i_dft);
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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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// remember which tokens were sampled - used for repetition penalties during sampling
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last_tokens.erase(last_tokens.begin());
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last_tokens.push_back(id);
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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);
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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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@ -154,53 +169,67 @@ int main(int argc, char ** argv) {
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++n_predict;
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// check if the draft matches the target
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if (i_dft < (int) drafted.size() && id == drafted[i_dft]) {
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LOG("the sampled target token matches the %dth drafted token (%d, '%s') - accepted\n", i_dft, id, token_str.c_str());
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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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if (i_dft < (int) drafted.size()) {
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LOG("the %dth drafted token (%d, '%s') does not match the sampled target token (%d, '%s') - rejected\n",
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i_dft, drafted[i_dft], llama_token_to_piece(ctx_dft, drafted[i_dft]).c_str(), id, token_str.c_str());
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} else {
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LOG("out of drafted tokens\n");
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}
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llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, -1);
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llama_decode(ctx_dft, llama_batch_get_one(&id, 1, n_past_dft, 0));
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++n_past_dft;
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// heuristic for n_draft
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// check if the target token matches any of the drafts
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{
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const int n_draft_cur = (int) drafted.size();
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const bool all_accepted = i_dft == n_draft_cur;
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bool matches = false;
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LOG("n_draft = %d\n", n_draft);
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LOG("n_draft_cur = %d\n", n_draft_cur);
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LOG("i_dft = %d\n", i_dft);
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LOG("all_accepted = %d\n", all_accepted);
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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 (all_accepted && n_draft == n_draft_cur) {
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LOG(" - max drafted tokens accepted - n_draft += 8\n");
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n_draft = std::min(30, n_draft + 8);
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} else if (all_accepted) {
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LOG(" - partially drafted tokens accepted - no change\n");
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} else {
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LOG(" - drafted token rejected - n_draft -= 1\n");
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n_draft = std::max(2, n_draft - 1);
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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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drafted.clear();
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drafted.push_back(id);
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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", s_keep);
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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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@ -209,78 +238,158 @@ int main(int argc, char ** argv) {
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break;
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}
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if (grammar_tgt) {
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if (grammar_dft) {
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llama_grammar_free(grammar_dft);
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}
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// Note: Hardcoded to sequence id 0, if this ever supports parallel generation
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// that will need to change.
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auto it = ctx_sampling.sequence_contexts.find(0);
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GGML_ASSERT(it != ctx_sampling.sequence_contexts.end());
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// This is necessary because each sequence id in sequence_contexts
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// uses a copy of the original grammar.
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grammar_dft = llama_grammar_copy(it->second.grammar);
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llama_sampling_cp(ctx_sampling, drafts[0].ctx_sampling);
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LOG("copied target grammar to draft grammar\n");
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}
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// sample n_draft tokens from the draft model using greedy decoding
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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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float * logits = llama_get_logits(ctx_dft);
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batch_dft.n_tokens = 0;
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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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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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llama_token_data_array cur_p = { candidates.data(), candidates.size(), false };
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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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if (grammar_dft != NULL) {
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llama_sample_grammar(ctx_dft, &cur_p, grammar_dft);
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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 < 2*%.3f\n", s, cur_p[0].p, cur_p[1].p);
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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);
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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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// no need to evaluate the last drafted token, since we won't use the result
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if (batch_tgt.n_tokens > n_draft) {
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drafts[s].drafting = false;
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continue;
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}
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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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}
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}
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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 %3d: %6d (%8.3f) '%s'\n", i, cur_p.data[i].id, cur_p.data[i].p, llama_token_to_piece(ctx_dft, cur_p.data[i].id).c_str());
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}
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// TODO: better logic?
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if (cur_p.data[0].p < 2*cur_p.data[1].p) {
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LOG("stopping drafting, probability too low: %.3f < 2*%.3f\n", cur_p.data[0].p, cur_p.data[1].p);
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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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// drafted token
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const llama_token id = cur_p.data[0].id;
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drafted.push_back(id);
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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;
|
||||
++n_drafted;
|
||||
|
||||
// no need to evaluate the last drafted token, since we won't use the result
|
||||
if (i == n_draft - 1) {
|
||||
if (batch_tgt.n_tokens > n_draft) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
// evaluate the drafted token on the draft model
|
||||
llama_kv_cache_seq_rm(ctx_dft, 0, n_past_cur, -1);
|
||||
llama_decode(ctx_dft, llama_batch_get_one(&drafted.back(), 1, n_past_cur, 0));
|
||||
++n_past_cur;
|
||||
|
||||
if (grammar_dft != NULL) {
|
||||
llama_grammar_accept_token(ctx_dft, grammar_dft, id);
|
||||
}
|
||||
// account for the last drafted token that we didn't evaluate
|
||||
if (batch_tgt.n_tokens > n_draft) {
|
||||
++n_drafted;
|
||||
}
|
||||
|
||||
// evaluate the target model on the drafted tokens
|
||||
llama_kv_cache_seq_rm(ctx_tgt, 0, n_past_tgt, -1);
|
||||
llama_decode(ctx_tgt, llama_batch_get_one(drafted.data(), drafted.size(), n_past_tgt, 0));
|
||||
++n_past_tgt;
|
||||
{
|
||||
llama_kv_cache_seq_keep(ctx_tgt, 0);
|
||||
for (int s = 1; s < n_seq_dft; ++s) {
|
||||
llama_kv_cache_seq_cp(ctx_tgt, 0, s, -1, -1);
|
||||
}
|
||||
|
||||
// the first token is always proposed by the traget model before the speculation loop
|
||||
drafted.erase(drafted.begin());
|
||||
//LOG("target batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_tgt, batch_tgt));
|
||||
llama_decode(ctx_tgt, batch_tgt);
|
||||
++n_past_tgt;
|
||||
}
|
||||
|
||||
// the first token is always proposed by the traget model before the speculation loop so we erase it here
|
||||
for (int s = 0; s < n_seq_dft; ++s) {
|
||||
if (!drafts[s].active) {
|
||||
continue;
|
||||
}
|
||||
|
||||
drafts[s].tokens.erase(drafts[s].tokens.begin());
|
||||
}
|
||||
}
|
||||
|
||||
auto t_dec_end = ggml_time_us();
|
||||
|
|
@ -288,9 +397,8 @@ int main(int argc, char ** argv) {
|
|||
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));
|
||||
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);
|
||||
|
|
@ -304,16 +412,19 @@ int main(int argc, char ** argv) {
|
|||
LOG_TEE("\ntarget:\n");
|
||||
llama_print_timings(ctx_tgt);
|
||||
|
||||
llama_sampling_free(ctx_sampling);
|
||||
for (int s = 0; s < n_seq_dft; ++s) {
|
||||
llama_sampling_free(drafts[s].ctx_sampling);
|
||||
}
|
||||
|
||||
llama_batch_free(batch_dft);
|
||||
|
||||
llama_free(ctx_tgt);
|
||||
llama_free_model(model_tgt);
|
||||
|
||||
llama_free(ctx_dft);
|
||||
llama_free_model(model_dft);
|
||||
|
||||
if (grammar_dft != NULL) {
|
||||
llama_grammar_free(grammar_dft);
|
||||
llama_grammar_free(grammar_tgt);
|
||||
}
|
||||
llama_backend_free();
|
||||
|
||||
fprintf(stderr, "\n\n");
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue