mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-15 07:19:53 +00:00
639 lines
24 KiB
C++
639 lines
24 KiB
C++
#include "arg.h"
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#include "common.h"
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#include "sampling.h"
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#include "log.h"
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#include "llama.h"
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#include <algorithm>
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#include <cstdio>
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#include <cstring>
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#include <random>
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#include <set>
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#include <string>
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#include <vector>
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#define SPEC_VOCAB_MAX_SIZE_DIFFERENCE 100
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#define SPEC_VOCAB_CHECK_START_TOKEN_ID 5
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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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std::vector<std::vector<llama_token_data>> dists;
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struct gpt_sampler * smpl = nullptr;
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};
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int main(int argc, char ** argv) {
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gpt_init();
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gpt_params params;
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if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_SPECULATIVE)) {
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return 1;
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}
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if (params.model_draft.empty()) {
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LOG_ERR("%s: --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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// probability threshold for splitting a draft branch (only for n_seq_dft > 1)
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const float p_split = params.p_split;
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std::default_random_engine rng(params.sparams.seed);
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std::uniform_real_distribution<> u_dist;
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// init llama.cpp
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llama_backend_init();
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llama_numa_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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llama_init_result llama_init_tgt = llama_init_from_gpt_params(params);
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model_tgt = llama_init_tgt.model;
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ctx_tgt = llama_init_tgt.context;
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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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if (params.draft_cpuparams.n_threads > 0) {
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params.cpuparams.n_threads = params.draft_cpuparams.n_threads;
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}
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params.cpuparams_batch.n_threads = params.draft_cpuparams_batch.n_threads;
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llama_init_result llama_init_dft = llama_init_from_gpt_params(params);
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model_dft = llama_init_dft.model;
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ctx_dft = llama_init_dft.context;
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const bool vocab_type_tgt = llama_vocab_type(model_tgt);
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LOG_DBG("vocab_type tgt: %d\n", vocab_type_tgt);
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const bool vocab_type_dft = llama_vocab_type(model_dft);
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LOG_DBG("vocab_type dft: %d\n", vocab_type_dft);
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if (vocab_type_tgt != vocab_type_dft) {
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LOG_ERR("%s: draft model vocab type must match target model to use speculation but ", __func__);
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LOG_ERR("vocab_type_dft = %d while vocab_type_tgt = %d\n", vocab_type_dft, vocab_type_tgt);
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return 1;
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}
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if (
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llama_add_bos_token(model_tgt) != llama_add_bos_token(model_dft) ||
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llama_add_eos_token(model_tgt) != llama_add_eos_token(model_dft) ||
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llama_token_bos(model_tgt) != llama_token_bos(model_dft) ||
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llama_token_eos(model_tgt) != llama_token_eos(model_dft)
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) {
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LOG_ERR("%s: draft model special tokens must match target model to use speculation\n", __func__);
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return 1;
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}
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{
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const int n_vocab_tgt = llama_n_vocab(model_tgt);
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const int n_vocab_dft = llama_n_vocab(model_dft);
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const int vocab_diff = n_vocab_tgt > n_vocab_dft
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? n_vocab_tgt - n_vocab_dft
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: n_vocab_dft - n_vocab_tgt;
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if (vocab_diff > SPEC_VOCAB_MAX_SIZE_DIFFERENCE) {
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LOG_ERR("%s: draft model vocab must closely match target model to use speculation but ", __func__);
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LOG_ERR("target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n",
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n_vocab_tgt, llama_n_vocab(model_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE);
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return 1;
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}
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for (int i = SPEC_VOCAB_CHECK_START_TOKEN_ID; i < std::min(n_vocab_tgt, n_vocab_dft); ++i) {
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const char * token_text_tgt = llama_token_get_text(model_tgt, i);
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const char * token_text_dft = llama_token_get_text(model_dft, i);
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if (std::strcmp(token_text_tgt, token_text_dft) != 0) {
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LOG_ERR("%s: draft model vocab must match target model to use speculation but ", __func__);
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LOG_ERR("token %d content differs - target '%s', draft '%s'\n", i,
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llama_token_to_piece(ctx_tgt, i).c_str(),
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llama_token_to_piece(ctx_dft, i).c_str());
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return 1;
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}
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}
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}
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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, 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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LOG_ERR("%s: 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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LOG("\n\n");
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for (auto id : inp) {
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LOG("%s", llama_token_to_piece(ctx_tgt, id).c_str());
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}
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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 (reuse the llama_context's sampling instance)
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struct gpt_sampler * smpl = gpt_sampler_init(model_tgt, params.sparams);
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struct llama_sampler * softmax = llama_sampler_init_softmax();
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// draft sequence data
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std::vector<seq_draft> drafts(n_seq_dft);
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for (int s = 0; s < n_seq_dft; ++s) {
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// allocate gpt_sampler for each draft sequence
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drafts[s].smpl = gpt_sampler_init(model_dft, 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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std::set<int> active_seqs = {};
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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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active_seqs.insert(s);
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const auto & tokens = drafts[s].tokens;
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LOG_DBG("draft %d: %s\n", s, string_from(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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llama_token token_id;
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std::string token_str;
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// loop until we fail to accept a drafted token or we run out of drafted tokens
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while (true) {
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// check if the target token matches any of the drafts
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// for stochastic sampling, attempt to match the token with the drafted tokens
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{
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bool accept = false;
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if (params.sparams.temp > 0) {
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// stochastic verification
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gpt_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft], true);
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auto & dist_tgt = *gpt_sampler_get_candidates(smpl);
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float p_tgt = 0.0f;
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float p_dft = 0.0f;
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while (active_seqs.size() > 0) {
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// randomly select a sequence to verify from active sequences
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std::uniform_int_distribution<unsigned int> u_int_dist(0, active_seqs.size() - 1);
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int s = *std::next(active_seqs.begin(), u_int_dist(rng));
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if (i_dft >= (int) drafts[s].tokens.size()) {
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drafts[s].active = false;
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active_seqs.erase(s);
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continue;
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}
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if (accept) {
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// if we already accepted a token, we can skip the rest
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if (drafts[s].tokens[i_dft] != drafts[s_keep].tokens[i_dft]) {
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drafts[s].active = false;
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active_seqs.erase(s);
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}
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continue;
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}
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LOG_DBG("verifying sequence #%d at pos #%d from %d active sequence(s)\n", s, i_dft, (int) active_seqs.size());
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float r = u_dist(rng);
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llama_token_data_array dist_dft = { drafts[s].dists[i_dft].data() , drafts[s].dists[i_dft].size(), LLAMA_TOKEN_NULL, true };
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//GGML_ASSERT(dist_tgt.size <= dist_dft.size);
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// acquire the token probabilities assigned by the draft and target models
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for (size_t i = 0; i < dist_tgt.size; i++) {
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if (dist_tgt.data[i].id == drafts[s].tokens[i_dft]) {
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p_tgt = dist_tgt.data[i].p;
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}
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if (dist_dft.data[i].id == drafts[s].tokens[i_dft]) {
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p_dft = dist_dft.data[i].p;
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}
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if (p_tgt && p_dft) {
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break;
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}
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}
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LOG_DBG("r = %f, p_dft = %f, p_tgt = %f\n", r, p_dft, p_tgt);
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if (r <= p_tgt / p_dft) {
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s_keep = s;
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accept = true;
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token_id = drafts[s].tokens[i_dft];
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token_str = llama_token_to_piece(ctx_tgt, token_id);
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gpt_sampler_accept(smpl, token_id, true);
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LOG_DBG("draft token %d of sequence %d (%d, '%s') accepted\n", i_dft, s, token_id, token_str.c_str());
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break;
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} else {
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LOG_DBG("draft token %d of sequence %d (%d, '%s') rejected\n", i_dft, s, drafts[s].tokens[i_dft], llama_token_to_piece(ctx_tgt, drafts[s].tokens[i_dft]).c_str());
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drafts[s].active = false;
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// calculate residual probability
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GGML_ASSERT(dist_tgt.sorted);
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GGML_ASSERT(dist_dft.sorted);
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// sort dist by id
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std::sort(dist_tgt.data, dist_tgt.data + dist_tgt.size, [](const llama_token_data &a, const llama_token_data &b) {
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return a.id < b.id;
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});
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std::sort(dist_dft.data, dist_dft.data + dist_dft.size, [](const llama_token_data &a, const llama_token_data &b) {
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return a.id < b.id;
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});
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float sum_probs = 0.0f;
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for (size_t i = 0; i < dist_tgt.size; i++) {
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if (i < dist_dft.size) {
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dist_tgt.data[i].p = std::max(0.0f, dist_tgt.data[i].p - dist_dft.data[i].p);
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} else {
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dist_tgt.data[i].p = std::max(0.0f, dist_tgt.data[i].p);
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}
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sum_probs += dist_tgt.data[i].p;
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}
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for (size_t i = 0; i < dist_tgt.size; i++) {
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dist_tgt.data[i].p /= sum_probs;
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}
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// sort dist_tgt by p desc
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std::sort(dist_tgt.data, dist_tgt.data + dist_tgt.size, [](const llama_token_data &a, const llama_token_data &b) {
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return a.p > b.p;
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});
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}
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active_seqs.erase(s);
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for(int i = 0; i < n_seq_dft; i++) {
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if (i == s) {
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continue;
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}
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if (drafts[i].tokens[i_dft] == drafts[s].tokens[i_dft]) {
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// synchronize active status for sequences with the same drafted token
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drafts[i].active = drafts[i].active && accept;
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if (!drafts[i].active) {
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active_seqs.erase(s);
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}
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}
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}
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}
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if (!accept) {
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// all drafted tokens were rejected
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// sample from the target model
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LOG_DBG("all drafted tokens were rejected, sampling from residual distribution\n");
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std::vector<float> probs(dist_tgt.size);
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for (size_t i = 0; i < dist_tgt.size; ++i) {
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probs[i] = dist_tgt.data[i].p;
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}
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std::discrete_distribution<> dist(probs.begin(), probs.end());
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const int idx = dist(rng);
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token_id = dist_tgt.data[idx].id;
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gpt_sampler_accept(smpl, token_id, true);
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token_str = llama_token_to_piece(ctx_tgt, token_id);
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}
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} else {
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// greedy verification
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// sample from the target model
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LOG_DBG("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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token_id = gpt_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft]);
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gpt_sampler_accept(smpl, token_id, true);
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token_str = llama_token_to_piece(ctx_tgt, token_id);
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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() && token_id == drafts[s].tokens[i_dft]) {
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LOG_DBG("the sampled target token matches the %dth drafted token of sequence %d (%d, '%s') - accepted\n", i_dft, s, token_id, token_str.c_str());
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s_keep = s;
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accept = 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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}
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if (llama_token_is_eog(model_tgt, token_id)) {
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has_eos = true;
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}
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++n_predict;
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if (accept) {
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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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if (params.use_color) {
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// Color token according to its origin sequence
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LOG("\u001b[%dm%s\u001b[37m", (36 - s_keep % 6), token_str.c_str());
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} else {
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LOG("%s", token_str.c_str());
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}
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continue;
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} else {
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LOG("%s", token_str.c_str());
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break;
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}
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}
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}
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{
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LOG_DBG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", token_id, token_str.c_str());
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// TODO: simplify
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{
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LOG_DBG("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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drafts[s].dists.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(token_id);
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drafts[0].dists.push_back(std::vector<llama_token_data>());
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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, token_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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// LOG_DBG("dft batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_dft, batch_dft).c_str());
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llama_decode(ctx_dft, batch_dft);
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++n_past_dft;
|
|
}
|
|
|
|
if (n_predict > params.n_predict || has_eos) {
|
|
break;
|
|
}
|
|
|
|
if (drafts[0].smpl) {
|
|
gpt_sampler_free(drafts[0].smpl);
|
|
}
|
|
drafts[0].smpl = gpt_sampler_clone(smpl);
|
|
|
|
int n_seq_cur = 1;
|
|
int n_past_cur = n_past_dft;
|
|
|
|
for (int s = 0; s < n_seq_dft; ++s) {
|
|
drafts[s].active = false;
|
|
drafts[s].drafting = false;
|
|
}
|
|
drafts[0].active = true;
|
|
drafts[0].drafting = true;
|
|
drafts[0].i_batch_dft = 0;
|
|
|
|
llama_batch_clear(batch_tgt);
|
|
llama_batch_add (batch_tgt, drafts[0].tokens[0], n_past_tgt, { 0 }, true);
|
|
|
|
// sample n_draft tokens from the draft model using tree-based sampling
|
|
for (int i = 0; i < n_draft; ++i) {
|
|
batch_dft.n_tokens = 0;
|
|
|
|
for (int s = 0; s < n_seq_dft; ++s) {
|
|
drafts[s].skip = false;
|
|
}
|
|
|
|
for (int s = 0; s < n_seq_dft; ++s) {
|
|
if (!drafts[s].drafting || drafts[s].skip) {
|
|
continue;
|
|
}
|
|
|
|
gpt_sampler_sample(drafts[s].smpl, ctx_dft, drafts[s].i_batch_dft, true);
|
|
|
|
const auto * cur_p = gpt_sampler_get_candidates(drafts[s].smpl);
|
|
|
|
for (int k = 0; k < std::min(n_seq_dft + 3, (int) cur_p->size); ++k) {
|
|
LOG_DBG(" - draft candidate %3d for seq %3d, pos %3d: %6d (%8.3f) '%s'\n",
|
|
k, s, i, cur_p->data[k].id, cur_p->data[k].p, llama_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
|
|
}
|
|
|
|
std::vector<int> sa(1, s);
|
|
|
|
// attempt to split the branch if the probability is high enough
|
|
for (int f = 1; f < 8; ++f) {
|
|
if (n_seq_cur < n_seq_dft && cur_p->data[f].p > p_split) {
|
|
LOG_DBG("splitting seq %3d into %3d\n", s, n_seq_cur);
|
|
|
|
llama_kv_cache_seq_rm(ctx_dft, n_seq_cur, -1, -1);
|
|
llama_kv_cache_seq_cp(ctx_dft, s, n_seq_cur, -1, -1);
|
|
|
|
// all previous tokens from this branch are now also part of the new branch
|
|
for (int t = 0; t < batch_tgt.n_tokens; ++t) {
|
|
for (int p = 0; p < batch_tgt.n_seq_id[t]; ++p) {
|
|
if (batch_tgt.seq_id[t][p] == s) {
|
|
batch_tgt.seq_id[t][batch_tgt.n_seq_id[t]] = n_seq_cur;
|
|
batch_tgt.n_seq_id[t]++;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
// copy the draft state
|
|
drafts[n_seq_cur].active = true;
|
|
drafts[n_seq_cur].drafting = true;
|
|
drafts[n_seq_cur].skip = true;
|
|
|
|
drafts[n_seq_cur].tokens = drafts[s].tokens;
|
|
drafts[n_seq_cur].dists = drafts[s].dists;
|
|
drafts[n_seq_cur].i_batch_dft = drafts[s].i_batch_dft;
|
|
drafts[n_seq_cur].i_batch_tgt = drafts[s].i_batch_tgt;
|
|
|
|
if (drafts[n_seq_cur].smpl) {
|
|
gpt_sampler_free(drafts[n_seq_cur].smpl);
|
|
}
|
|
drafts[n_seq_cur].smpl = gpt_sampler_clone(drafts[s].smpl);
|
|
|
|
sa.push_back(n_seq_cur);
|
|
|
|
n_seq_cur++;
|
|
} else {
|
|
break;
|
|
}
|
|
}
|
|
|
|
// add drafted token for each sequence
|
|
for (int is = 0; is < (int) sa.size(); ++is) {
|
|
const llama_token id = cur_p->data[is].id;
|
|
|
|
const int s = sa[is];
|
|
|
|
gpt_sampler_accept(drafts[s].smpl, id, true);
|
|
|
|
drafts[s].tokens.push_back(id);
|
|
// save cur_p.data into drafts[s].dists
|
|
drafts[s].dists.push_back({cur_p->data, cur_p->data + cur_p->size});
|
|
|
|
// add unique drafted tokens to the target batch
|
|
drafts[s].i_batch_tgt.push_back(batch_tgt.n_tokens);
|
|
|
|
llama_batch_add(batch_tgt, id, n_past_tgt + i + 1, { s }, true);
|
|
|
|
// add the token to the batch for batched decoding with the draft model
|
|
drafts[s].i_batch_dft = batch_dft.n_tokens;
|
|
|
|
llama_batch_add(batch_dft, id, n_past_cur, { s }, true);
|
|
|
|
if (batch_tgt.n_tokens > n_draft) {
|
|
drafts[s].drafting = false;
|
|
}
|
|
}
|
|
}
|
|
|
|
// no sequence is drafting anymore
|
|
if (batch_dft.n_tokens == 0) {
|
|
break;
|
|
}
|
|
|
|
// evaluate the drafted tokens on the draft model
|
|
llama_decode(ctx_dft, batch_dft);
|
|
++n_past_cur;
|
|
++n_drafted;
|
|
|
|
if (batch_tgt.n_tokens > n_draft) {
|
|
break;
|
|
}
|
|
}
|
|
|
|
// evaluate the target model on the drafted tokens
|
|
{
|
|
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);
|
|
}
|
|
|
|
// LOG_DBG("target batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_tgt, batch_tgt).c_str());
|
|
llama_decode(ctx_tgt, batch_tgt);
|
|
++n_past_tgt;
|
|
}
|
|
|
|
// the first token is always proposed by the target 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());
|
|
drafts[s].dists.erase(drafts[s].dists.begin());
|
|
}
|
|
}
|
|
|
|
auto t_dec_end = ggml_time_us();
|
|
|
|
LOG("\n\n");
|
|
|
|
LOG_INF("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_INF("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_INF("\n");
|
|
LOG_INF("n_draft = %d\n", n_draft);
|
|
LOG_INF("n_predict = %d\n", n_predict);
|
|
LOG_INF("n_drafted = %d\n", n_drafted);
|
|
LOG_INF("n_accept = %d\n", n_accept);
|
|
LOG_INF("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
|
|
|
|
LOG_INF("\n");
|
|
LOG_INF("draft:\n\n");
|
|
// TODO: print sampling/grammar timings for all drafts
|
|
llama_perf_context_print(ctx_dft);
|
|
|
|
LOG_INF("\n");
|
|
LOG_INF("target:\n\n");
|
|
gpt_perf_print(ctx_tgt, smpl);
|
|
|
|
gpt_sampler_free(smpl);
|
|
for (int s = 0; s < n_seq_dft; ++s) {
|
|
gpt_sampler_free(drafts[s].smpl);
|
|
}
|
|
|
|
llama_sampler_free(softmax);
|
|
llama_batch_free(batch_dft);
|
|
|
|
llama_free(ctx_tgt);
|
|
llama_free_model(model_tgt);
|
|
|
|
llama_free(ctx_dft);
|
|
llama_free_model(model_dft);
|
|
|
|
llama_backend_free();
|
|
|
|
LOG("\n\n");
|
|
|
|
return 0;
|
|
}
|