break;
}
params.n_keep = std::stoi(argv[i]);
+ } else if (arg == "--draft") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.n_draft = std::stoi(argv[i]);
} else if (arg == "--chunks") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.model = argv[i];
+ } else if (arg == "-md" || arg == "--model-draft") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.model_draft = argv[i];
} else if (arg == "-a" || arg == "--alias") {
if (++i >= argc) {
invalid_param = true;
fprintf(stdout, " --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n");
fprintf(stdout, " --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
fprintf(stdout, " --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
+ fprintf(stdout, " --draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft);
fprintf(stdout, " --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
if (llama_mlock_supported()) {
fprintf(stdout, " --mlock force system to keep model in RAM rather than swapping or compressing\n");
fprintf(stdout, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
fprintf(stdout, " -m FNAME, --model FNAME\n");
fprintf(stdout, " model path (default: %s)\n", params.model.c_str());
+ fprintf(stdout, " -md FNAME, --model-draft FNAME\n");
+ fprintf(stdout, " draft model for speculative decoding (default: %s)\n", params.model.c_str());
fprintf(stdout, " -ld LOGDIR, --logdir LOGDIR\n");
fprintf(stdout, " path under which to save YAML logs (no logging if unset)\n");
fprintf(stdout, "\n");
return result;
}
+//
+// Sampling utils
+//
+
+llama_token llama_sample_token(
+ struct llama_context * ctx,
+ struct llama_context * ctx_guidance,
+ struct llama_grammar * grammar,
+ const struct gpt_params & params,
+ const std::vector<llama_token> & last_tokens,
+ std::vector<llama_token_data> & candidates,
+ int idx) {
+ const int n_ctx = llama_n_ctx(ctx);
+ const int n_vocab = llama_n_vocab(ctx);
+
+ const float temp = params.temp;
+ const int32_t top_k = params.top_k <= 0 ? n_vocab : params.top_k;
+ const float top_p = params.top_p;
+ const float tfs_z = params.tfs_z;
+ const float typical_p = params.typical_p;
+ const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
+ const float repeat_penalty = params.repeat_penalty;
+ const float alpha_presence = params.presence_penalty;
+ const float alpha_frequency = params.frequency_penalty;
+ const int mirostat = params.mirostat;
+ const float mirostat_tau = params.mirostat_tau;
+ const float mirostat_eta = params.mirostat_eta;
+ const bool penalize_nl = params.penalize_nl;
+
+ llama_token id = 0;
+
+ float * logits = llama_get_logits(ctx) + idx * n_vocab;
+
+ // Apply params.logit_bias map
+ for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
+ logits[it->first] += it->second;
+ }
+
+ candidates.clear();
+ for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+ candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
+ }
+
+ llama_token_data_array cur_p = { candidates.data(), candidates.size(), false };
+
+ if (ctx_guidance) {
+ llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale);
+ }
+
+ // apply penalties
+ if (!last_tokens.empty()) {
+ const float nl_logit = logits[llama_token_nl(ctx)];
+ const int last_n_repeat = std::min(std::min((int)last_tokens.size(), repeat_last_n), n_ctx);
+
+ llama_sample_repetition_penalty(ctx, &cur_p,
+ last_tokens.data() + last_tokens.size() - last_n_repeat,
+ last_n_repeat, repeat_penalty);
+ llama_sample_frequency_and_presence_penalties(ctx, &cur_p,
+ last_tokens.data() + last_tokens.size() - last_n_repeat,
+ last_n_repeat, alpha_frequency, alpha_presence);
+
+ if (!penalize_nl) {
+ for (size_t idx = 0; idx < cur_p.size; idx++) {
+ if (cur_p.data[idx].id == llama_token_nl(ctx)) {
+ cur_p.data[idx].logit = nl_logit;
+ break;
+ }
+ }
+ }
+ }
+
+ if (grammar != NULL) {
+ llama_sample_grammar(ctx, &cur_p, grammar);
+ }
+
+ if (temp <= 0) {
+ // Greedy sampling
+ id = llama_sample_token_greedy(ctx, &cur_p);
+ } else {
+ if (mirostat == 1) {
+ static float mirostat_mu = 2.0f * mirostat_tau;
+ const int mirostat_m = 100;
+ llama_sample_temperature(ctx, &cur_p, temp);
+ id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
+ } else if (mirostat == 2) {
+ static float mirostat_mu = 2.0f * mirostat_tau;
+ llama_sample_temperature(ctx, &cur_p, temp);
+ id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu);
+ } else {
+ // Temperature sampling
+ llama_sample_top_k (ctx, &cur_p, top_k, 1);
+ llama_sample_tail_free (ctx, &cur_p, tfs_z, 1);
+ llama_sample_typical (ctx, &cur_p, typical_p, 1);
+ llama_sample_top_p (ctx, &cur_p, top_p, 1);
+ llama_sample_temperature(ctx, &cur_p, temp);
+
+ {
+ const int n_top = 10;
+ LOG("top %d candidates:\n", n_top);
+
+ for (int i = 0; i < n_top; i++) {
+ const llama_token id = cur_p.data[i].id;
+ LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p);
+ }
+ }
+
+ id = llama_sample_token(ctx, &cur_p);
+
+ LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str());
+ }
+ }
+ // printf("`%d`", candidates_p.size);
+
+ if (grammar != NULL) {
+ llama_grammar_accept_token(ctx, grammar, id);
+ }
+
+ return id;
+}
+
+//
+// YAML utils
+//
+
// returns true if successful, false otherwise
bool create_directory_with_parents(const std::string & path) {
#ifdef _WIN32
fprintf(stream, "mirostat_lr: %f # default: 0.1\n", params.mirostat_eta);
fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false");
fprintf(stream, "model: %s # default: models/7B/ggml-model.bin\n", params.model.c_str());
+ fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str());
fprintf(stream, "mtest: %s # default: false\n", params.mem_test ? "true" : "false");
fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
fprintf(stream, "n_gpu_layers: %d # default: 0\n", params.n_gpu_layers);
int32_t n_ctx = 512; // context size
int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_keep = 0; // number of tokens to keep from initial prompt
+ int32_t n_draft = 16; // number of tokens to draft during speculative decoding
int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
int32_t n_gpu_layers = 0; // number of layers to store in VRAM
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
float cfg_scale = 1.f; // How strong is guidance
std::string model = "models/7B/ggml-model-f16.gguf"; // model path
+ std::string model_draft = ""; // draft model for speculative decoding
std::string model_alias = "unknown"; // model alias
std::string prompt = "";
std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state
llama_context * ctx,
const std::vector<llama_token> & tokens);
+//
+// Sampling utils
+//
+
+// this is a common sampling function used across the examples for convenience
+// it can serve as a starting point for implementing your own sampling function
+//
+// required:
+// - ctx: context to use for sampling
+// - params: sampling parameters
+//
+// optional:
+// - ctx_guidance: context to use for classifier-free guidance, ignore if NULL
+// - grammar: grammar to use for sampling, ignore if NULL
+// - last_tokens: needed for repetition penalty, ignore if empty
+// - idx: sample from llama_get_logits(ctx) + idx * n_vocab
+//
+// returns:
+// - token: sampled token
+// - candidates: vector of candidate tokens
+//
+llama_token llama_sample_token(
+ struct llama_context * ctx,
+ struct llama_context * ctx_guidance,
+ struct llama_grammar * grammar,
+ const struct gpt_params & params,
+ const std::vector<llama_token> & last_tokens,
+ std::vector<llama_token_data> & candidates,
+ int idx = 0);
+
+//
+// YAML utils
+//
+
bool create_directory_with_parents(const std::string & path);
void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector<float> & data);
void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector<int> & data);
add_subdirectory(train-text-from-scratch)
add_subdirectory(convert-llama2c-to-ggml)
add_subdirectory(simple)
+ add_subdirectory(speculative)
add_subdirectory(embd-input)
add_subdirectory(llama-bench)
add_subdirectory(beam-search)
#ifndef LOG_DISABLE_LOGS
log_set_target(log_filename_generator("main", "log"));
LOG_TEE("Log start\n");
- log_dump_cmdline(argc,argv);
+ log_dump_cmdline(argc, argv);
#endif // LOG_DISABLE_LOGS
// TODO: Dump params ?
LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
LOG_TEE("\n\n");
+ struct llama_grammar * grammar = NULL;
grammar_parser::parse_state parsed_grammar;
- llama_grammar * grammar = NULL;
+
if (!params.grammar.empty()) {
parsed_grammar = grammar_parser::parse(params.grammar.c_str());
// will be empty (default) if there are parse errors
}
// TODO: replace with ring-buffer
- std::vector<llama_token> last_n_tokens(n_ctx);
- std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
+ std::vector<llama_token> last_tokens(n_ctx);
+ std::fill(last_tokens.begin(), last_tokens.end(), 0);
if (params.interactive) {
const char *control_message;
std::vector<llama_token> embd;
std::vector<llama_token> embd_guidance;
+ const int n_vocab = llama_n_vocab(ctx);
+
+ std::vector<llama_token_data> candidates;
+ candidates.reserve(n_vocab);
+
while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
// predict
if (embd.size() > 0) {
LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance);
- // insert n_left/2 tokens at the start of embd from last_n_tokens
- embd.insert(embd.begin(), last_n_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_n_tokens.end() - embd.size());
+ // insert n_left/2 tokens at the start of embd from last_tokens
+ embd.insert(embd.begin(), last_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_tokens.end() - embd.size());
LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd));
embd_guidance.clear();
if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
- const float temp = params.temp;
- const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
- const float top_p = params.top_p;
- const float tfs_z = params.tfs_z;
- const float typical_p = params.typical_p;
- const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
- const float repeat_penalty = params.repeat_penalty;
- const float alpha_presence = params.presence_penalty;
- const float alpha_frequency = params.frequency_penalty;
- const int mirostat = params.mirostat;
- const float mirostat_tau = params.mirostat_tau;
- const float mirostat_eta = params.mirostat_eta;
- const bool penalize_nl = params.penalize_nl;
-
// optionally save the session on first sample (for faster prompt loading next time)
if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) {
need_to_save_session = false;
LOG("saved session to %s\n", path_session.c_str());
}
- llama_token id = 0;
-
- {
- auto logits = llama_get_logits(ctx);
- auto n_vocab = llama_n_vocab(ctx);
-
- // Apply params.logit_bias map
- for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
- logits[it->first] += it->second;
- }
-
- std::vector<llama_token_data> candidates;
- candidates.reserve(n_vocab);
- for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
- candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
- }
-
- llama_token_data_array cur_p = { candidates.data(), candidates.size(), false };
-
- if (ctx_guidance) {
- llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale);
- }
-
- // Apply penalties
- float nl_logit = logits[llama_token_nl(ctx)];
- auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
- llama_sample_repetition_penalty(ctx, &cur_p,
- last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
- last_n_repeat, repeat_penalty);
- llama_sample_frequency_and_presence_penalties(ctx, &cur_p,
- last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
- last_n_repeat, alpha_frequency, alpha_presence);
- if (!penalize_nl) {
- for (size_t idx = 0; idx < cur_p.size; idx++) {
- if (cur_p.data[idx].id == llama_token_nl(ctx)) {
- cur_p.data[idx].logit = nl_logit;
- break;
- }
- }
- }
-
- if (grammar != NULL) {
- llama_sample_grammar(ctx, &cur_p, grammar);
- }
-
- if (temp <= 0) {
- // Greedy sampling
- id = llama_sample_token_greedy(ctx, &cur_p);
- } else {
- if (mirostat == 1) {
- static float mirostat_mu = 2.0f * mirostat_tau;
- const int mirostat_m = 100;
- llama_sample_temperature(ctx, &cur_p, temp);
- id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
- } else if (mirostat == 2) {
- static float mirostat_mu = 2.0f * mirostat_tau;
- llama_sample_temperature(ctx, &cur_p, temp);
- id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu);
- } else {
- // Temperature sampling
- llama_sample_top_k (ctx, &cur_p, top_k, 1);
- llama_sample_tail_free (ctx, &cur_p, tfs_z, 1);
- llama_sample_typical (ctx, &cur_p, typical_p, 1);
- llama_sample_top_p (ctx, &cur_p, top_p, 1);
- llama_sample_temperature(ctx, &cur_p, temp);
-
- {
- const int n_top = 10;
- LOG("top %d candidates:\n", n_top);
-
- for (int i = 0; i < n_top; i++) {
- const llama_token id = cur_p.data[i].id;
- LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p);
- }
- }
-
- id = llama_sample_token(ctx, &cur_p);
+ const llama_token id = llama_sample_token(ctx, ctx_guidance, grammar, params, last_tokens, candidates);
- LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str());
- }
- }
- // printf("`%d`", candidates_p.size);
+ last_tokens.erase(last_tokens.begin());
+ last_tokens.push_back(id);
- if (grammar != NULL) {
- llama_grammar_accept_token(ctx, grammar, id);
- }
-
- last_n_tokens.erase(last_n_tokens.begin());
- last_n_tokens.push_back(id);
-
- LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, last_n_tokens));
- }
+ LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, last_tokens));
embd.push_back(id);
LOG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed);
while ((int) embd_inp.size() > n_consumed) {
embd.push_back(embd_inp[n_consumed]);
- last_n_tokens.erase(last_n_tokens.begin());
- last_n_tokens.push_back(embd_inp[n_consumed]);
+ last_tokens.erase(last_tokens.begin());
+ last_tokens.push_back(embd_inp[n_consumed]);
++n_consumed;
if ((int) embd.size() >= params.n_batch) {
break;
// check for reverse prompt
if (params.antiprompt.size()) {
std::string last_output;
- for (auto id : last_n_tokens) {
+ for (auto id : last_tokens) {
last_output += llama_token_to_piece(ctx, id);
}
}
// deal with end of text token in interactive mode
- if (last_n_tokens.back() == llama_token_eos(ctx)) {
+ if (last_tokens.back() == llama_token_eos(ctx)) {
LOG("found EOS token\n");
if (params.interactive) {
if (grammar != NULL) {
llama_grammar_free(grammar);
- std::vector<const llama_grammar_element *> grammar_rules( parsed_grammar.c_rules());
+ std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
grammar = llama_grammar_init(
grammar_rules.data(), grammar_rules.size(),
parsed_grammar.symbol_ids.at("root"));
--- /dev/null
+set(TARGET speculative)
+add_executable(${TARGET} speculative.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
+if(TARGET BUILD_INFO)
+ add_dependencies(${TARGET} BUILD_INFO)
+endif()
--- /dev/null
+#ifndef _GNU_SOURCE
+#define _GNU_SOURCE
+#endif
+
+#include "build-info.h"
+
+#include "common.h"
+#include "llama.h"
+
+#include <cmath>
+#include <cstdio>
+#include <string>
+#include <vector>
+
+int main(int argc, char ** argv) {
+ gpt_params params;
+
+ if (gpt_params_parse(argc, argv, params) == false) {
+ return 1;
+ }
+
+ if (params.model_draft.empty()) {
+ fprintf(stderr, "%s: error: --model-draft is required\n", __func__);
+ return 1;
+ }
+
+#ifndef LOG_DISABLE_LOGS
+ log_set_target(log_filename_generator("speculative", "log"));
+ LOG_TEE("Log start\n");
+ log_dump_cmdline(argc, argv);
+#endif // LOG_DISABLE_LOGS
+
+ // init llama.cpp
+ llama_backend_init(params.numa);
+
+ llama_model * model_tgt = NULL;
+ llama_model * model_dft = NULL;
+
+ llama_context * ctx_tgt = NULL;
+ llama_context * ctx_dft = NULL;
+
+ // load the target model
+ params.perplexity = true; // HACK: enable logits_all = true
+ std::tie(model_tgt, ctx_tgt) = llama_init_from_gpt_params(params);
+
+ // load the draft model
+ params.model = params.model_draft;
+ std::tie(model_dft, ctx_dft) = llama_init_from_gpt_params(params);
+
+ // tokenize the prompt
+ std::vector<llama_token> inp;
+ inp = ::llama_tokenize(ctx_tgt, params.prompt, true);
+
+ const int max_context_size = llama_n_ctx(ctx_tgt);
+ const int max_tokens_list_size = max_context_size - 4;
+
+ if ((int) inp.size() > max_tokens_list_size) {
+ fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size);
+ return 1;
+ }
+
+ fprintf(stderr, "\n\n");
+
+ for (auto id : inp) {
+ fprintf(stderr, "%s", llama_token_to_piece(ctx_tgt, id).c_str());
+ }
+
+ fflush(stderr);
+
+ const int n_input = inp.size();
+
+ const auto t_enc_start = ggml_time_us();
+
+ // eval the prompt with both models
+ llama_eval(ctx_tgt, inp.data(), int(inp.size() - 1), 0, params.n_threads);
+ llama_eval(ctx_tgt, &inp.back(), 1, inp.size() - 1, params.n_threads);
+ llama_eval(ctx_dft, inp.data(), int(inp.size()), 0, params.n_threads);
+
+ const auto t_enc_end = ggml_time_us();
+
+ // the 2 models should have the same vocab
+ const int n_ctx = llama_n_ctx(ctx_tgt);
+ const int n_vocab = llama_n_vocab(ctx_tgt);
+ //GGML_ASSERT(n_vocab == llama_n_vocab(ctx_dft));
+
+ // how many tokens to draft each time
+ const int n_draft = params.n_draft;
+
+ int n_predict = 0;
+ int n_drafted = 0;
+ int n_accept = 0;
+
+ int n_past_tgt = inp.size();
+ int n_past_dft = inp.size();
+
+ std::vector<llama_token> drafted;
+
+ std::vector<llama_token> last_tokens(n_ctx);
+ std::fill(last_tokens.begin(), last_tokens.end(), 0);
+
+ for (auto & id : inp) {
+ last_tokens.erase(last_tokens.begin());
+ last_tokens.push_back(id);
+ }
+
+ std::vector<llama_token_data> candidates;
+ candidates.reserve(n_vocab);
+
+ // used to determine end of generation
+ bool has_eos = false;
+
+ const auto t_dec_start = ggml_time_us();
+
+ while (true) {
+ LOG("drafted: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_dft, drafted));
+
+ // sample from the drafted tokens if any
+ int i_dft = 0;
+ while (true) {
+ const llama_token id = llama_sample_token(ctx_tgt, NULL, NULL, params, last_tokens, candidates, i_dft);
+
+ last_tokens.erase(last_tokens.begin());
+ last_tokens.push_back(id);
+
+ //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, last_tokens));
+
+ const std::string token_str = llama_token_to_piece(ctx_tgt, id);
+ printf("%s", token_str.c_str());
+ fflush(stdout);
+
+ if (id == llama_token_eos(ctx_tgt)) {
+ has_eos = true;
+ }
+
+ ++n_predict;
+
+ if (i_dft < (int) drafted.size() && id == drafted[i_dft]) {
+ LOG("drafted token %d accepted\n", id);
+ ++n_accept;
+ ++n_past_tgt;
+ ++n_past_dft;
+ ++i_dft;
+
+ continue;
+ }
+
+ // the drafted token was rejected or we are out of drafted tokens
+ llama_eval(ctx_dft, &id, 1, n_past_dft, params.n_threads);
+ ++n_past_dft;
+
+ drafted.clear();
+ drafted.push_back(id);
+
+ break;
+ }
+
+ if (n_predict > params.n_predict || has_eos) {
+ break;
+ }
+
+ // sample n_draft tokens from the draft model picking the best token
+ int n_past_cur = n_past_dft;
+ for (int i = 0; i < n_draft; ++i) {
+ float * logits = llama_get_logits(ctx_dft);
+
+ candidates.clear();
+ for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+ candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
+ }
+
+ llama_token_data_array cur_p = { candidates.data(), candidates.size(), false };
+
+ // computes softmax and sorts the candidates
+ llama_sample_softmax(ctx_dft, &cur_p);
+
+ for (int i = 0; i < 3; ++i) {
+ LOG(" - draft candidate %d: %d (%.3f)\n", i, cur_p.data[i].id, cur_p.data[i].p);
+ }
+
+ // too low probability, stop drafting
+ if (cur_p.data[0].p < 2*cur_p.data[1].p) {
+ break;
+ }
+
+ drafted.push_back(cur_p.data[0].id);
+ ++n_drafted;
+
+ if (i < n_draft - 1) {
+ // evaluate the drafted token on the draft model
+ llama_eval(ctx_dft, &drafted.back(), 1, n_past_cur, params.n_threads);
+ ++n_past_cur;
+ }
+ }
+
+ // evaluate the target model on the drafted tokens
+ llama_eval(ctx_tgt, drafted.data(), drafted.size(), n_past_tgt, params.n_threads);
+ ++n_past_tgt;
+
+ drafted.erase(drafted.begin());
+ }
+
+ auto t_dec_end = ggml_time_us();
+
+ LOG_TEE("\n\n");
+
+ LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
+ LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f));
+
+ // TODO: make sure these numbers are computed correctly
+ LOG_TEE("\n");
+ LOG_TEE("n_draft = %d\n", n_draft);
+ LOG_TEE("n_predict = %d\n", n_predict);
+ LOG_TEE("n_drafted = %d\n", n_drafted);
+ LOG_TEE("n_accept = %d\n", n_accept);
+ LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
+
+ LOG_TEE("\ndraft:\n");
+ llama_print_timings(ctx_dft);
+
+ LOG_TEE("\ntarget:\n");
+ llama_print_timings(ctx_tgt);
+
+ llama_free(ctx_tgt);
+ llama_free_model(model_tgt);
+
+ llama_free(ctx_dft);
+ llama_free_model(model_dft);
+
+ llama_backend_free();
+
+ fprintf(stderr, "\n\n");
+
+ return 0;
+}
overview / pkg / ggml / sources / llama.cpp / commitdiff