mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-27 06:39:25 +01:00
10bce0450f
* llama : accept a list of devices to use to offload a model * accept `--dev none` to completely disable offloading * fix dev list with dl backends * rename env parameter to LLAMA_ARG_DEVICE for consistency
275 lines
8.7 KiB
C++
275 lines
8.7 KiB
C++
#include "arg.h"
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#include "common.h"
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#include "sampling.h"
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#include "speculative.h"
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#include "log.h"
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#include "llama.h"
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#include <cstdio>
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#include <cstring>
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#include <string>
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#include <vector>
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int main(int argc, char ** argv) {
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common_params params;
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if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_SPECULATIVE)) {
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return 1;
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}
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if (params.n_predict < -1) {
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LOG_ERR("%s: --n-predict must be >= -1\n", __func__);
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return 1;
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}
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common_init();
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if (params.speculative.model.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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// 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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common_init_result llama_init_tgt = common_init_from_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.devices = params.speculative.devices;
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params.model = params.speculative.model;
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params.n_ctx = params.speculative.n_ctx;
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params.n_batch = params.speculative.n_ctx > 0 ? params.speculative.n_ctx : params.n_batch;
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params.n_gpu_layers = params.speculative.n_gpu_layers;
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if (params.speculative.cpuparams.n_threads > 0) {
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params.cpuparams.n_threads = params.speculative.cpuparams.n_threads;
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}
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params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
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common_init_result llama_init_dft = common_init_from_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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if (!common_speculative_are_compatible(ctx_tgt, ctx_dft)) {
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return 1;
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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 = common_tokenize(ctx_tgt, params.prompt, true, true);
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if (llama_n_ctx(ctx_tgt) < (int) inp.size()) {
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LOG_ERR("%s: the prompt exceeds the context size (%d tokens, ctx %d)\n", __func__, (int) inp.size(), llama_n_ctx(ctx_tgt));
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return 1;
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}
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if (llama_n_batch(ctx_tgt) < (int) inp.size()) {
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LOG_ERR("%s: the prompt exceeds the batch size (%d tokens, batch %d)\n", __func__, (int) inp.size(), llama_n_batch(ctx_tgt));
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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", common_token_to_piece(ctx_tgt, id).c_str());
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}
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// how many tokens to draft each time
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int n_draft = params.speculative.n_max;
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int n_draft_min = params.speculative.n_min;
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float p_min = params.speculative.p_min;
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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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// used to determine end of generation
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bool has_eos = false;
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// ================================================
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// everything until here is standard initialization
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// the relevant stuff for speculative decoding starts here
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const auto t_enc_start = ggml_time_us();
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// target model sampling context
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struct common_sampler * smpl = common_sampler_init(model_tgt, params.sampling);
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// eval the prompt
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llama_decode(ctx_tgt, llama_batch_get_one(inp.data(), inp.size() - 1));
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// note: keep the last token separate!
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llama_token id_last = inp.back();
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// all tokens currently in the target context
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auto prompt_tgt = std::vector<llama_token>(inp.begin(), inp.end() - 1);
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int n_past = inp.size() - 1;
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// init the speculator
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struct common_speculative_params params_spec;
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params_spec.n_draft = n_draft;
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params_spec.n_reuse = llama_n_ctx(ctx_dft) - n_draft;
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params_spec.p_min = p_min;
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struct common_speculative * spec = common_speculative_init(ctx_dft);
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llama_batch batch_tgt = llama_batch_init(llama_n_batch(ctx_tgt), 0, 1);
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const auto t_enc_end = ggml_time_us();
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const auto t_dec_start = ggml_time_us();
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while (true) {
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// optionally, generate draft tokens that can be appended to the target batch
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//
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// this is the most important part of the speculation. the more probable tokens that are provided here
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// the better the performance will be. in theory, this computation can be performed asynchronously and even
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// offloaded to a remote device. it doesn't even have to be based on an LLM. instead, it can provide tokens
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// from a cache or lookup tables.
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//
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llama_tokens draft = common_speculative_gen_draft(spec, params_spec, prompt_tgt, id_last);
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//LOG_DBG("draft: %s\n", string_from(ctx_dft, draft).c_str());
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// always have a token to evaluate from before - id_last
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common_batch_clear(batch_tgt);
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common_batch_add (batch_tgt, id_last, n_past++, { 0 }, true);
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// evaluate the target model on [id_last, draft0, draft1, ..., draftN-1]
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{
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// do not waste time on small drafts
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if (draft.size() < n_draft_min) {
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draft.clear();
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}
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for (size_t i = 0; i < draft.size(); ++i) {
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common_batch_add(batch_tgt, draft[i], n_past + i, { 0 }, true);
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}
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//LOG_DBG("target batch: %s\n", string_from(ctx_tgt, batch_tgt).c_str());
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llama_decode(ctx_tgt, batch_tgt);
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}
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// sample from the full target batch and return the accepted tokens based on the target sampler
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//
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// for each token to be accepted, the sampler would have to sample that same token
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// in such cases, instead of decoding the sampled token as we normally do, we simply continue with the
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// available logits from the batch and sample the next token until we run out of logits or the sampler
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// disagrees with the draft
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//
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const auto ids = common_sampler_sample_and_accept_n(smpl, ctx_tgt, draft);
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//LOG_DBG("ids: %s\n", string_from(ctx_tgt, ids).c_str());
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GGML_ASSERT(ids.size() > 0); // there will always be at least one accepted token
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n_past += ids.size() - 1;
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n_drafted += batch_tgt.n_tokens - 1;
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n_accept += ids.size() - 1;
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// process the accepted tokens and update contexts
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//
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// this is the standard token post-processing that we normally do
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// in this case, we do it for a group of accepted tokens at once
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//
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{
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llama_token id;
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std::string token_str;
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for (size_t i = 0; i < ids.size(); ++i) {
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id = ids[i];
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++n_predict;
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if (llama_token_is_eog(model_tgt, id)) {
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has_eos = true;
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break;
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}
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token_str = common_token_to_piece(ctx_tgt, id);
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if (params.use_color && i + 1 < ids.size()) {
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LOG("\u001b[%dm%s\u001b[37m", (36 - 0 % 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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}
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if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) {
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break;
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}
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LOG_DBG("accepted %d/%d draft tokens, the last target token is: (%d, '%s')\n", (int) ids.size() - 1, (int) draft.size(), id, token_str.c_str());
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{
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LOG_DBG("clear kv cache from any extra tokens, n_past = %d\n", n_past);
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llama_kv_cache_seq_rm(ctx_tgt, 0, n_past, -1);
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}
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prompt_tgt.push_back(id_last);
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prompt_tgt.insert(prompt_tgt.end(), ids.begin(), ids.end() - 1);
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// remember the last accepted token for the next iteration
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id_last = id;
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}
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}
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auto t_dec_end = ggml_time_us();
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const int n_input = inp.size();
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LOG("\n\n");
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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));
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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));
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LOG_INF("\n");
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LOG_INF("n_draft = %d\n", n_draft);
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LOG_INF("n_predict = %d\n", n_predict);
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LOG_INF("n_drafted = %d\n", n_drafted);
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LOG_INF("n_accept = %d\n", n_accept);
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LOG_INF("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
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LOG_INF("\n");
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LOG_INF("draft:\n\n");
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llama_perf_context_print(ctx_dft);
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LOG_INF("\n");
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LOG_INF("target:\n\n");
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common_perf_print(ctx_tgt, smpl);
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common_sampler_free(smpl);
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common_speculative_free(spec);
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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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LOG("\n\n");
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return 0;
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}
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