mirror of
https://github.com/nomic-ai/gpt4all.git
synced 2024-10-01 01:06:10 -04:00
396 lines
11 KiB
C++
396 lines
11 KiB
C++
#define LLAMAMODEL_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
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#include "llamamodel_impl.h"
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#include <cassert>
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <fstream>
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#include <map>
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#include <string>
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#include <vector>
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#include <iostream>
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#if defined(_WIN32) && defined(_MSC_VER)
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#define WIN32_LEAN_AND_MEAN
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#ifndef NOMINMAX
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#define NOMINMAX
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#endif
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#include <windows.h>
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#include <io.h>
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#include <stdio.h>
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#else
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#include <unistd.h>
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#endif
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#include <random>
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#include <thread>
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#include <unordered_set>
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#include <llama.h>
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#include <ggml.h>
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#ifdef GGML_USE_KOMPUTE
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#include "ggml-vulkan.h"
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#endif
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namespace {
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const char *modelType_ = "LLaMA";
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}
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struct gpt_params {
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int32_t seed = -1; // RNG seed
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int32_t n_keep = 0; // number of tokens to keep from initial prompt
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// sampling parameters
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float tfs_z = 1.0f; // 1.0 = disabled
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float typical_p = 1.0f; // 1.0 = disabled
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std::string prompt = "";
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bool memory_f16 = true; // use f16 instead of f32 for memory kv
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bool use_mmap = true; // use mmap for faster loads
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bool use_mlock = false; // use mlock to keep model in memory
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};
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static int llama_sample_top_p_top_k(
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llama_context *ctx,
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const llama_token *last_n_tokens_data,
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int last_n_tokens_size,
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int top_k,
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float top_p,
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float temp,
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float repeat_penalty) {
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auto logits = llama_get_logits(ctx);
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auto n_vocab = llama_n_vocab(ctx);
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// Populate initial list of all candidates
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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for (int 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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}
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llama_token_data_array candidates_p = {candidates.data(), candidates.size(), false};
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// Sample repeat penalty
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llama_sample_repetition_penalty(nullptr, &candidates_p, last_n_tokens_data, last_n_tokens_size, repeat_penalty);
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// Temperature sampling
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llama_sample_top_k(ctx, &candidates_p, top_k, 1);
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llama_sample_tail_free(ctx, &candidates_p, 1.0f, 1);
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llama_sample_typical(ctx, &candidates_p, 1.0f, 1);
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llama_sample_top_p(ctx, &candidates_p, top_p, 1);
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llama_sample_temperature(ctx, &candidates_p, temp);
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return llama_sample_token(ctx, &candidates_p);
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}
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struct LLamaPrivate {
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const std::string modelPath;
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bool modelLoaded;
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llama_context *ctx = nullptr;
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llama_context_params params;
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int64_t n_threads = 0;
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std::vector<LLModel::Token> end_tokens;
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};
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LLamaModel::LLamaModel()
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: d_ptr(new LLamaPrivate) {
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d_ptr->modelLoaded = false;
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}
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// default hparams (LLaMA 7B)
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struct llama_file_hparams {
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uint32_t n_vocab = 32000;
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uint32_t n_embd = 4096;
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uint32_t n_mult = 256;
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uint32_t n_head = 32;
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uint32_t n_layer = 32;
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uint32_t n_rot = 64;
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enum llama_ftype ftype = LLAMA_FTYPE_MOSTLY_F16;
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};
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size_t LLamaModel::requiredMem(const std::string &modelPath) {
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auto fin = std::ifstream(modelPath, std::ios::binary);
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fin.seekg(0, std::ios_base::end);
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size_t filesize = fin.tellg();
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fin.seekg(0, std::ios_base::beg);
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uint32_t magic = 0;
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fin.read(reinterpret_cast<char*>(&magic), sizeof(magic));
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if (magic != 0x67676a74) return 0;
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uint32_t version = 0;
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fin.read(reinterpret_cast<char*>(&version), sizeof(version));
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llama_file_hparams hparams;
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fin.read(reinterpret_cast<char*>(&hparams.n_vocab), sizeof(hparams.n_vocab));
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fin.read(reinterpret_cast<char*>(&hparams.n_embd), sizeof(hparams.n_embd));
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fin.read(reinterpret_cast<char*>(&hparams.n_head), sizeof(hparams.n_head));
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fin.read(reinterpret_cast<char*>(&hparams.n_layer), sizeof(hparams.n_layer));
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fin.read(reinterpret_cast<char*>(&hparams.n_rot), sizeof(hparams.n_rot));
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fin.read(reinterpret_cast<char*>(&hparams.ftype), sizeof(hparams.ftype));
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const size_t n_ctx = 2048;
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const size_t kvcache_element_size = 2; // fp16
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const size_t est_kvcache_size = hparams.n_embd * hparams.n_layer * 2u * n_ctx * kvcache_element_size;
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return filesize + est_kvcache_size;
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}
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bool LLamaModel::loadModel(const std::string &modelPath)
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{
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// load the model
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d_ptr->params = llama_context_default_params();
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gpt_params params;
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d_ptr->params.n_ctx = 2048;
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d_ptr->params.seed = params.seed;
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d_ptr->params.f16_kv = params.memory_f16;
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d_ptr->params.use_mmap = params.use_mmap;
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#if defined (__APPLE__)
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d_ptr->params.use_mlock = true;
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#else
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d_ptr->params.use_mlock = params.use_mlock;
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#endif
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#ifdef GGML_USE_METAL
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std::cerr << "llama.cpp: using Metal" << std::endl;
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// metal always runs the whole model if n_gpu_layers is not 0, at least
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// currently
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d_ptr->params.n_gpu_layers = 1;
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#endif
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#ifdef GGML_USE_KOMPUTE
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if (ggml_vk_has_device()) {
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// vulkan always runs the whole model if n_gpu_layers is not 0, at least
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// currently
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d_ptr->params.n_gpu_layers = 1;
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}
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#endif
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d_ptr->ctx = llama_init_from_file(modelPath.c_str(), d_ptr->params);
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if (!d_ptr->ctx) {
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#ifdef GGML_USE_KOMPUTE
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// Explicitly free the device so next load it doesn't use it
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ggml_vk_free_device();
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#endif
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std::cerr << "LLAMA ERROR: failed to load model from " << modelPath << std::endl;
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return false;
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}
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d_ptr->end_tokens = {llama_token_eos(d_ptr->ctx)};
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#ifdef GGML_USE_KOMPUTE
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if (ggml_vk_has_device()) {
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std::cerr << "llama.cpp: using Vulkan on " << ggml_vk_current_device().name << std::endl;
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}
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#endif
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d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
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d_ptr->modelLoaded = true;
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fflush(stderr);
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return true;
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}
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void LLamaModel::setThreadCount(int32_t n_threads) {
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d_ptr->n_threads = n_threads;
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}
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int32_t LLamaModel::threadCount() const {
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return d_ptr->n_threads;
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}
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LLamaModel::~LLamaModel()
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{
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if (d_ptr->ctx) {
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llama_free(d_ptr->ctx);
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}
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}
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bool LLamaModel::isModelLoaded() const
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{
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return d_ptr->modelLoaded;
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}
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size_t LLamaModel::stateSize() const
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{
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return llama_get_state_size(d_ptr->ctx);
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}
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size_t LLamaModel::saveState(uint8_t *dest) const
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{
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return llama_copy_state_data(d_ptr->ctx, dest);
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}
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size_t LLamaModel::restoreState(const uint8_t *src)
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{
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// const_cast is required, see: https://github.com/ggerganov/llama.cpp/pull/1540
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return llama_set_state_data(d_ptr->ctx, const_cast<uint8_t*>(src));
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}
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std::vector<LLModel::Token> LLamaModel::tokenize(PromptContext &ctx, const std::string &str) const
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{
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const bool useBOS = ctx.n_past == 0 && (ctx.tokens.empty() || ctx.tokens.front() != llama_token_bos(d_ptr->ctx));
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std::vector<LLModel::Token> fres(str.size()+4);
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auto fres_len = llama_tokenize(d_ptr->ctx, str.c_str(), str.length(), fres.data(), fres.size(), useBOS);
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fres.resize(fres_len);
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return fres;
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}
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std::string LLamaModel::tokenToString(Token id) const
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{
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return llama_token_to_str(d_ptr->ctx, id);
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}
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LLModel::Token LLamaModel::sampleToken(PromptContext &promptCtx) const
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{
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const size_t n_prev_toks = std::min((size_t) promptCtx.repeat_last_n, promptCtx.tokens.size());
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return llama_sample_top_p_top_k(d_ptr->ctx,
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promptCtx.tokens.data() + promptCtx.tokens.size() - n_prev_toks,
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n_prev_toks, promptCtx.top_k, promptCtx.top_p, promptCtx.temp,
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promptCtx.repeat_penalty);
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}
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bool LLamaModel::evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) const
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{
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return llama_eval(d_ptr->ctx, tokens.data(), tokens.size(), ctx.n_past, d_ptr->n_threads) == 0;
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}
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int32_t LLamaModel::contextLength() const
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{
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return llama_n_ctx(d_ptr->ctx);
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}
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const std::vector<LLModel::Token> &LLamaModel::endTokens() const
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{
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return d_ptr->end_tokens;
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}
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#if defined(GGML_USE_KOMPUTE)
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#include "ggml-vulkan.h"
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#endif
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std::vector<LLModel::GPUDevice> LLamaModel::availableGPUDevices(size_t memoryRequired)
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{
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#if defined(GGML_USE_KOMPUTE)
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std::vector<ggml_vk_device> vkDevices = ggml_vk_available_devices(memoryRequired);
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std::vector<LLModel::GPUDevice> devices;
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for(const auto& vkDevice : vkDevices) {
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LLModel::GPUDevice device;
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device.index = vkDevice.index;
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device.type = vkDevice.type;
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device.heapSize = vkDevice.heapSize;
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device.name = vkDevice.name;
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device.vendor = vkDevice.vendor;
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devices.push_back(device);
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}
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return devices;
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#else
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return std::vector<LLModel::GPUDevice>();
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#endif
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}
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bool LLamaModel::initializeGPUDevice(size_t memoryRequired, const std::string& device)
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{
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#if defined(GGML_USE_KOMPUTE)
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return ggml_vk_init_device(memoryRequired, device);
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#else
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return false;
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#endif
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}
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bool LLamaModel::initializeGPUDevice(const LLModel::GPUDevice &device, std::string *unavail_reason)
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{
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bool result = false;
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#if defined(GGML_USE_KOMPUTE)
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ggml_vk_device vkDevice;
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vkDevice.index = device.index;
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vkDevice.type = device.type;
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vkDevice.heapSize = device.heapSize;
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vkDevice.name = device.name;
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vkDevice.vendor = device.vendor;
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result = ggml_vk_init_device(vkDevice);
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if (!result && unavail_reason) {
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*unavail_reason = "failed to init GPU";
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}
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#else
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if (unavail_reason) {
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*unavail_reason = "built without Kompute";
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}
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#endif
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return result;
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}
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bool LLamaModel::initializeGPUDevice(int device)
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{
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#if defined(GGML_USE_KOMPUTE)
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return ggml_vk_init_device(device);
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#else
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return false;
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#endif
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}
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bool LLamaModel::hasGPUDevice()
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{
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#if defined(GGML_USE_KOMPUTE)
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return ggml_vk_has_device();
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#else
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return false;
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#endif
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}
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bool LLamaModel::usingGPUDevice()
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{
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#if defined(GGML_USE_KOMPUTE)
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return ggml_vk_using_vulkan();
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#elif defined(GGML_USE_METAL)
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return true;
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#endif
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return false;
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}
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std::string get_arch_name(gguf_context *ctx_gguf) {
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std::string arch_name;
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const int kid = gguf_find_key(ctx_gguf, "general.architecture");
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enum gguf_type ktype = gguf_get_kv_type(ctx_gguf, kid);
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if (ktype != (GGUF_TYPE_STRING)) {
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throw std::runtime_error("ERROR: Can't get general architecture from gguf file.");
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}
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return gguf_get_val_str(ctx_gguf, kid);
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}
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#if defined(_WIN32)
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#define DLL_EXPORT __declspec(dllexport)
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#else
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#define DLL_EXPORT __attribute__ ((visibility ("default")))
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#endif
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extern "C" {
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DLL_EXPORT bool is_g4a_backend_model_implementation() {
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return true;
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}
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DLL_EXPORT const char *get_model_type() {
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return modelType_;
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}
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DLL_EXPORT const char *get_build_variant() {
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return GGML_BUILD_VARIANT;
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}
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DLL_EXPORT bool magic_match(const char * fname) {
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struct ggml_context * ctx_meta = NULL;
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struct gguf_init_params params = {
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/*.no_alloc = */ true,
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/*.ctx = */ &ctx_meta,
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};
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gguf_context *ctx_gguf = gguf_init_from_file(fname, params);
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if (!ctx_gguf)
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return false;
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bool isValid = gguf_get_version(ctx_gguf) <= 2;
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auto arch = get_arch_name(ctx_gguf);
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isValid = isValid && (arch == "llama" || arch == "starcoder" || arch == "falcon");
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gguf_free(ctx_gguf);
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return isValid;
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}
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DLL_EXPORT LLModel *construct() {
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return new LLamaModel;
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}
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}
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