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990 lines
32 KiB
C++
990 lines
32 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 <initializer_list>
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#include <iomanip>
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#include <iostream>
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#include <map>
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#include <numeric>
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#include <random>
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#include <sstream>
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#include <stdexcept>
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#include <string>
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#include <thread>
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#include <unordered_set>
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#include <vector>
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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-kompute.h>
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#endif
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using namespace std::string_literals;
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// Maximum supported GGUF version
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static constexpr int GGUF_VER_MAX = 3;
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static const char * const modelType_ = "LLaMA";
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static const std::vector<const char *> KNOWN_ARCHES {
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"baichuan", "bert", "bloom", "codeshell", "falcon", "gemma", "gpt2", "llama", "mpt", "nomic-bert", "orion",
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"persimmon", "phi2", "plamo", "qwen", "qwen2", "refact", "stablelm", "starcoder"
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};
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static const std::vector<const char *> EMBEDDING_ARCHES {
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"bert", "nomic-bert"
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};
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static bool is_embedding_arch(const std::string &arch) {
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return std::find(EMBEDDING_ARCHES.begin(), EMBEDDING_ARCHES.end(), arch) < EMBEDDING_ARCHES.end();
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}
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static bool llama_verbose() {
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const char* var = getenv("GPT4ALL_VERBOSE_LLAMACPP");
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return var && *var;
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}
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static void llama_log_callback(enum ggml_log_level level, const char *text, void *userdata) {
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(void)userdata;
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if (llama_verbose() || level <= GGML_LOG_LEVEL_ERROR) {
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fputs(text, stderr);
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}
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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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enum ggml_type kv_type = GGML_TYPE_F16; // 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 min_p,
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float temp,
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float repeat_penalty,
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int32_t pos) {
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auto logits = llama_get_logits_ith(ctx, pos);
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auto n_vocab = llama_n_vocab(llama_get_model(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_penalties(nullptr, &candidates_p, last_n_tokens_data, last_n_tokens_size, repeat_penalty, 0.0f, 0.0f);
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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_min_p(ctx, &candidates_p, min_p, 1);
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llama_sample_temp(ctx, &candidates_p, temp);
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return llama_sample_token(ctx, &candidates_p);
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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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static gguf_context *load_gguf(const char *fname) {
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struct gguf_init_params params = {
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/*.no_alloc = */ true,
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/*.ctx = */ nullptr,
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};
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gguf_context *ctx = gguf_init_from_file(fname, params);
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if (!ctx) {
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std::cerr << __func__ << ": gguf_init_from_file failed\n";
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return nullptr;
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}
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int gguf_ver = gguf_get_version(ctx);
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if (gguf_ver > GGUF_VER_MAX) {
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std::cerr << __func__ << ": unsupported gguf version: " << gguf_ver << "\n";
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gguf_free(ctx);
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return nullptr;
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}
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return ctx;
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}
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static int32_t get_arch_key_u32(std::string const &modelPath, std::string const &archKey) {
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auto * ctx = load_gguf(modelPath.c_str());
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if (!ctx)
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return -1;
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std::string arch = get_arch_name(ctx);
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int32_t value = -1;
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if (ctx) {
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auto key = arch + "." + archKey;
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int keyidx = gguf_find_key(ctx, key.c_str());
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if (keyidx != -1) {
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value = gguf_get_val_u32(ctx, keyidx);
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} else {
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std::cerr << __func__ << ": " << key << "not found in " << modelPath << "\n";
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}
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}
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gguf_free(ctx);
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return value;
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}
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struct LLamaPrivate {
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const std::string modelPath;
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bool modelLoaded = false;
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int device = -1;
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llama_model *model = nullptr;
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llama_context *ctx = nullptr;
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llama_model_params model_params;
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llama_context_params ctx_params;
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int64_t n_threads = 0;
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std::vector<LLModel::Token> end_tokens;
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const char *backend_name = nullptr;
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};
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LLamaModel::LLamaModel()
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: d_ptr(new LLamaPrivate) {}
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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, int n_ctx, int ngl) {
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// TODO(cebtenzzre): update to GGUF
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(void)ngl; // FIXME(cetenzzre): use this value
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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 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::isModelBlacklisted(const std::string &modelPath) const {
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auto * ctx = load_gguf(modelPath.c_str());
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if (!ctx) {
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std::cerr << __func__ << ": failed to load " << modelPath << "\n";
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return false;
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}
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auto get_key = [ctx, &modelPath](const char *name) {
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int keyidx = gguf_find_key(ctx, name);
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if (keyidx == -1) {
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throw std::logic_error(name + " not found in "s + modelPath);
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}
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return keyidx;
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};
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bool res = false;
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try {
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std::string name(gguf_get_val_str(ctx, get_key("general.name")));
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int token_idx = get_key("tokenizer.ggml.tokens");
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int n_vocab = gguf_get_arr_n(ctx, token_idx);
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// check for known bad models
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if (name == "open-orca_mistral-7b-openorca"
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&& n_vocab == 32002
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&& gguf_get_arr_str(ctx, token_idx, 32000) == "<dummy32000>"s // should be <|im_end|>
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) {
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res = true;
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}
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} catch (const std::logic_error &e) {
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std::cerr << __func__ << ": " << e.what() << "\n";
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}
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gguf_free(ctx);
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return res;
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}
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bool LLamaModel::isEmbeddingModel(const std::string &modelPath) const {
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auto *ctx_gguf = load_gguf(modelPath.c_str());
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if (!ctx_gguf) {
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std::cerr << __func__ << ": failed to load GGUF from " << modelPath << "\n";
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return false;
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}
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std::string arch = get_arch_name(ctx_gguf);
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gguf_free(ctx_gguf);
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return is_embedding_arch(arch);
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}
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bool LLamaModel::loadModel(const std::string &modelPath, int n_ctx, int ngl)
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{
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d_ptr->modelLoaded = false;
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// clean up after previous loadModel()
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if (d_ptr->model) {
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llama_free_model(d_ptr->model);
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d_ptr->model = nullptr;
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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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d_ptr->ctx = nullptr;
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}
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if (n_ctx < 8) {
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std::cerr << "warning: minimum context size is 8, using minimum size.\n";
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n_ctx = 8;
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}
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// -- load the model --
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gpt_params params;
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d_ptr->model_params = llama_model_default_params();
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d_ptr->model_params.use_mmap = params.use_mmap;
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#if defined (__APPLE__)
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d_ptr->model_params.use_mlock = true;
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#else
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d_ptr->model_params.use_mlock = params.use_mlock;
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#endif
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d_ptr->model_params.progress_callback = &LLModel::staticProgressCallback;
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d_ptr->model_params.progress_callback_user_data = this;
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d_ptr->backend_name = "cpu"; // default
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#ifdef GGML_USE_KOMPUTE
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if (d_ptr->device != -1) {
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d_ptr->model_params.main_gpu = d_ptr->device;
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d_ptr->model_params.n_gpu_layers = ngl;
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}
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#elif defined(GGML_USE_METAL)
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(void)ngl;
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if (llama_verbose()) {
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std::cerr << "llama.cpp: using Metal" << std::endl;
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}
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d_ptr->backend_name = "metal";
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// always fully offload on Metal
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// TODO(cebtenzzre): use this parameter to allow using more than 53% of system RAM to load a model
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d_ptr->model_params.n_gpu_layers = 100;
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#else
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(void)ngl;
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#endif
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d_ptr->model = llama_load_model_from_file_gpt4all(modelPath.c_str(), &d_ptr->model_params);
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if (!d_ptr->model) {
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fflush(stdout);
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d_ptr->device = -1;
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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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// -- initialize the context --
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d_ptr->ctx_params = llama_context_default_params();
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bool isEmbedding = is_embedding_arch(llama_model_arch(d_ptr->model));
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const int n_ctx_train = llama_n_ctx_train(d_ptr->model);
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if (isEmbedding) {
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d_ptr->ctx_params.n_batch = n_ctx;
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} else {
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if (n_ctx > n_ctx_train) {
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std::cerr << "warning: model was trained on only " << n_ctx_train << " context tokens ("
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<< n_ctx << " specified)\n";
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}
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}
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d_ptr->ctx_params.n_ctx = n_ctx;
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d_ptr->ctx_params.seed = params.seed;
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d_ptr->ctx_params.type_k = params.kv_type;
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d_ptr->ctx_params.type_v = params.kv_type;
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// The new batch API provides space for n_vocab*n_tokens logits. Tell llama.cpp early
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// that we want this many logits so the state serializes consistently.
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d_ptr->ctx_params.logits_all = true;
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d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
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d_ptr->ctx_params.n_threads = d_ptr->n_threads;
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d_ptr->ctx_params.n_threads_batch = d_ptr->n_threads;
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if (isEmbedding)
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d_ptr->ctx_params.embeddings = true;
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d_ptr->ctx = llama_new_context_with_model(d_ptr->model, d_ptr->ctx_params);
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if (!d_ptr->ctx) {
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fflush(stdout);
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std::cerr << "LLAMA ERROR: failed to init context for model " << modelPath << std::endl;
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llama_free_model(d_ptr->model);
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d_ptr->model = nullptr;
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d_ptr->device = -1;
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return false;
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}
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d_ptr->end_tokens = {llama_token_eos(d_ptr->model)};
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#ifdef GGML_USE_KOMPUTE
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if (usingGPUDevice()) {
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if (llama_verbose()) {
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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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d_ptr->backend_name = "kompute";
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}
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#endif
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m_supportsEmbedding = isEmbedding;
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m_supportsCompletion = !isEmbedding;
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fflush(stdout);
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d_ptr->modelLoaded = true;
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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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llama_set_n_threads(d_ptr->ctx, 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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llama_free_model(d_ptr->model);
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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, bool special) const
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{
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const bool wantBOS = ctx.n_past == 0 && ctx.tokens.empty();
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const bool useBOS = wantBOS && shouldAddBOS();
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auto strCat = wantBOS && !special ? " " + str : str; // insert leading space ourselves, llama.cpp fork doesn't anymore
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std::vector<LLModel::Token> fres(strCat.size()+4);
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auto fres_len = llama_tokenize(d_ptr->model, strCat.c_str(), strCat.length(), fres.data(), fres.size(), useBOS, special);
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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_piece(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.min_p, promptCtx.temp,
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promptCtx.repeat_penalty, promptCtx.n_last_batch_tokens - 1);
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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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llama_kv_cache_seq_rm(d_ptr->ctx, 0, ctx.n_past, -1);
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llama_batch batch = llama_batch_init(tokens.size(), 0, 1);
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batch.n_tokens = tokens.size();
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ctx.n_last_batch_tokens = tokens.size();
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for (int32_t i = 0; i < batch.n_tokens; i++) {
|
|
batch.token [i] = tokens[i];
|
|
batch.pos [i] = ctx.n_past + i;
|
|
batch.n_seq_id[i] = 1;
|
|
batch.seq_id [i][0] = 0;
|
|
batch.logits [i] = false;
|
|
}
|
|
|
|
// llama_decode will output logits only for the last token of the prompt
|
|
batch.logits[batch.n_tokens - 1] = true;
|
|
|
|
int res = llama_decode(d_ptr->ctx, batch);
|
|
llama_batch_free(batch);
|
|
return res == 0;
|
|
}
|
|
|
|
int32_t LLamaModel::contextLength() const
|
|
{
|
|
return llama_n_ctx(d_ptr->ctx);
|
|
}
|
|
|
|
const std::vector<LLModel::Token> &LLamaModel::endTokens() const
|
|
{
|
|
return d_ptr->end_tokens;
|
|
}
|
|
|
|
bool LLamaModel::shouldAddBOS() const
|
|
{
|
|
int add_bos = llama_add_bos_token(d_ptr->model);
|
|
if (add_bos != -1) { return add_bos; }
|
|
auto vocab_type = llama_vocab_type(d_ptr->model);
|
|
return vocab_type == LLAMA_VOCAB_TYPE_SPM || vocab_type == LLAMA_VOCAB_TYPE_WPM;
|
|
}
|
|
|
|
int32_t LLamaModel::maxContextLength(std::string const &modelPath) const
|
|
{
|
|
return get_arch_key_u32(modelPath, "context_length");
|
|
}
|
|
|
|
int32_t LLamaModel::layerCount(std::string const &modelPath) const
|
|
{
|
|
return get_arch_key_u32(modelPath, "block_count");
|
|
}
|
|
|
|
std::vector<LLModel::GPUDevice> LLamaModel::availableGPUDevices(size_t memoryRequired) const
|
|
{
|
|
#ifdef GGML_USE_KOMPUTE
|
|
size_t count = 0;
|
|
auto * vkDevices = ggml_vk_available_devices(memoryRequired, &count);
|
|
|
|
if (vkDevices) {
|
|
std::vector<LLModel::GPUDevice> devices;
|
|
devices.reserve(count);
|
|
|
|
for (size_t i = 0; i < count; ++i) {
|
|
auto & dev = vkDevices[i];
|
|
devices.emplace_back(
|
|
/* index = */ dev.index,
|
|
/* type = */ dev.type,
|
|
/* heapSize = */ dev.heapSize,
|
|
/* name = */ dev.name,
|
|
/* vendor = */ dev.vendor
|
|
);
|
|
ggml_vk_device_destroy(&dev);
|
|
}
|
|
|
|
free(vkDevices);
|
|
return devices;
|
|
}
|
|
#else
|
|
(void)memoryRequired;
|
|
std::cerr << __func__ << ": built without Kompute\n";
|
|
#endif
|
|
|
|
return {};
|
|
}
|
|
|
|
bool LLamaModel::initializeGPUDevice(size_t memoryRequired, const std::string &name) const
|
|
{
|
|
#if defined(GGML_USE_KOMPUTE)
|
|
ggml_vk_device device;
|
|
bool ok = ggml_vk_get_device(&device, memoryRequired, name.c_str());
|
|
if (ok) {
|
|
d_ptr->device = device.index;
|
|
return true;
|
|
}
|
|
#else
|
|
(void)memoryRequired;
|
|
(void)name;
|
|
#endif
|
|
return false;
|
|
}
|
|
|
|
bool LLamaModel::initializeGPUDevice(int device, std::string *unavail_reason) const
|
|
{
|
|
#if defined(GGML_USE_KOMPUTE)
|
|
(void)unavail_reason;
|
|
d_ptr->device = device;
|
|
return true;
|
|
#else
|
|
(void)device;
|
|
if (unavail_reason) {
|
|
*unavail_reason = "built without Kompute";
|
|
}
|
|
return false;
|
|
#endif
|
|
}
|
|
|
|
bool LLamaModel::hasGPUDevice() const
|
|
{
|
|
#if defined(GGML_USE_KOMPUTE)
|
|
return d_ptr->device != -1;
|
|
#else
|
|
return false;
|
|
#endif
|
|
}
|
|
|
|
bool LLamaModel::usingGPUDevice() const
|
|
{
|
|
#if defined(GGML_USE_KOMPUTE)
|
|
bool hasDevice = hasGPUDevice() && d_ptr->model_params.n_gpu_layers > 0;
|
|
assert(!hasDevice || ggml_vk_has_device());
|
|
return hasDevice;
|
|
#elif defined(GGML_USE_METAL)
|
|
return true;
|
|
#else
|
|
return false;
|
|
#endif
|
|
}
|
|
|
|
const char *LLamaModel::backendName() const {
|
|
return d_ptr->backend_name;
|
|
}
|
|
|
|
const char *LLamaModel::gpuDeviceName() const {
|
|
#if defined(GGML_USE_KOMPUTE)
|
|
if (usingGPUDevice()) {
|
|
return ggml_vk_current_device().name;
|
|
}
|
|
#endif
|
|
return nullptr;
|
|
}
|
|
|
|
void llama_batch_add(
|
|
struct llama_batch & batch,
|
|
llama_token id,
|
|
llama_pos pos,
|
|
const std::vector<llama_seq_id> & seq_ids,
|
|
bool logits) {
|
|
batch.token [batch.n_tokens] = id;
|
|
batch.pos [batch.n_tokens] = pos;
|
|
batch.n_seq_id[batch.n_tokens] = seq_ids.size();
|
|
for (size_t i = 0; i < seq_ids.size(); ++i) {
|
|
batch.seq_id[batch.n_tokens][i] = seq_ids[i];
|
|
}
|
|
batch.logits [batch.n_tokens] = logits;
|
|
|
|
batch.n_tokens++;
|
|
}
|
|
|
|
static void batch_add_seq(llama_batch &batch, const std::vector<LLModel::Token> &tokens, int seq_id) {
|
|
for (unsigned i = 0; i < tokens.size(); i++) {
|
|
llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1);
|
|
}
|
|
}
|
|
|
|
size_t LLamaModel::embeddingSize() const {
|
|
return llama_n_embd(d_ptr->model);
|
|
}
|
|
|
|
struct EmbModelSpec {
|
|
const char *docPrefix;
|
|
const char *queryPrefix;
|
|
std::vector<const char *> otherPrefixes = {};
|
|
bool matryoshkaCapable = false;
|
|
const char *recommendedDims = nullptr;
|
|
};
|
|
|
|
struct EmbModelGroup {
|
|
EmbModelSpec spec;
|
|
std::vector<const char *> names;
|
|
};
|
|
|
|
static const EmbModelSpec NOPREFIX_SPEC {"", ""};
|
|
static const EmbModelSpec NOMIC_SPEC {"search_document", "search_query", {"clustering", "classification"}};
|
|
static const EmbModelSpec E5_SPEC {"passage", "query"};
|
|
|
|
static const EmbModelSpec NOMIC_1_5_SPEC {
|
|
"search_document", "search_query", {"clustering", "classification"}, true, "[768, 512, 384, 256, 128]",
|
|
};
|
|
static const EmbModelSpec LLM_EMBEDDER_SPEC {
|
|
"Represent this document for retrieval",
|
|
"Represent this query for retrieving relevant documents",
|
|
};
|
|
static const EmbModelSpec BGE_SPEC {
|
|
"", "Represent this sentence for searching relevant passages",
|
|
};
|
|
static const EmbModelSpec E5_MISTRAL_SPEC {
|
|
"", "Instruct: Given a query, retrieve relevant passages that answer the query\nQuery",
|
|
};
|
|
|
|
static const EmbModelGroup EMBEDDING_MODEL_SPECS[] {
|
|
{NOPREFIX_SPEC, {"all-MiniLM-L6-v1", "all-MiniLM-L12-v1", "all-MiniLM-L6-v2", "all-MiniLM-L12-v2"}},
|
|
{NOMIC_SPEC, {"nomic-embed-text-v1", "nomic-embed-text-v1-ablated", "nomic-embed-text-v1-unsupervised"}},
|
|
{NOMIC_1_5_SPEC, {"nomic-embed-text-v1.5"}},
|
|
{LLM_EMBEDDER_SPEC, {"llm-embedder"}},
|
|
{BGE_SPEC, {"bge-small-en", "bge-base-en", "bge-large-en",
|
|
"bge-small-en-v1.5", "bge-base-en-v1.5", "bge-large-en-v1.5"}},
|
|
// NOTE: E5 Mistral is not yet implemented in llama.cpp, so it's not in EMBEDDING_ARCHES
|
|
{E5_SPEC, {"e5-small", "e5-base", "e5-large",
|
|
"e5-small-unsupervised", "e5-base-unsupervised", "e5-large-unsupervised",
|
|
"e5-small-v2", "e5-base-v2", "e5-large-v2"}},
|
|
{E5_MISTRAL_SPEC, {"e5-mistral-7b-instruct",
|
|
"multilingual-e5-small", "multilingual-e5-base", "multilingual-e5-large",
|
|
"multilingual-e5-large-instruct"}},
|
|
};
|
|
|
|
static const EmbModelSpec *getEmbedSpec(const std::string &modelName) {
|
|
static const auto &specs = EMBEDDING_MODEL_SPECS;
|
|
auto it = std::find_if(specs, std::end(specs),
|
|
[&modelName](auto &spec) {
|
|
auto &names = spec.names;
|
|
return std::find(names.begin(), names.end(), modelName) < names.end();
|
|
}
|
|
);
|
|
return it < std::end(specs) ? &it->spec : nullptr;
|
|
}
|
|
|
|
void LLamaModel::embed(
|
|
const std::vector<std::string> &texts, float *embeddings, bool isRetrieval, int dimensionality, size_t *tokenCount,
|
|
bool doMean, bool atlas
|
|
) {
|
|
const EmbModelSpec *spec;
|
|
std::optional<std::string> prefix;
|
|
if (d_ptr->model && (spec = getEmbedSpec(llama_model_name(d_ptr->model))))
|
|
prefix = isRetrieval ? spec->queryPrefix : spec->docPrefix;
|
|
|
|
embed(texts, embeddings, prefix, dimensionality, tokenCount, doMean, atlas);
|
|
}
|
|
|
|
void LLamaModel::embed(
|
|
const std::vector<std::string> &texts, float *embeddings, std::optional<std::string> prefix, int dimensionality,
|
|
size_t *tokenCount, bool doMean, bool atlas, LLModel::EmbedCancelCallback *cancelCb
|
|
) {
|
|
if (!d_ptr->model)
|
|
throw std::logic_error("no model is loaded");
|
|
|
|
const char *modelName = llama_model_name(d_ptr->model);
|
|
if (!m_supportsEmbedding)
|
|
throw std::logic_error("not an embedding model: "s + modelName);
|
|
|
|
auto *spec = getEmbedSpec(modelName);
|
|
if (!spec)
|
|
std::cerr << __func__ << ": warning: unknown model " << modelName << "\n";
|
|
|
|
const int32_t n_embd = llama_n_embd(d_ptr->model);
|
|
if (dimensionality < 0) {
|
|
dimensionality = n_embd;
|
|
} else if (spec && dimensionality != n_embd) {
|
|
auto msg = [dimensionality, modelName]() {
|
|
return "unsupported dimensionality " + std::to_string(dimensionality) + " for model " + modelName;
|
|
};
|
|
if (!spec->matryoshkaCapable)
|
|
throw std::out_of_range(msg() + " (supported: " + std::to_string(n_embd) + ")");
|
|
if (dimensionality == 0 || dimensionality > n_embd)
|
|
throw std::out_of_range(msg() + " (recommended: " + spec->recommendedDims + ")");
|
|
}
|
|
|
|
if (!prefix) {
|
|
if (!spec)
|
|
throw std::invalid_argument("unknown model "s + modelName + ", specify a prefix if applicable or an empty string");
|
|
prefix = spec->docPrefix;
|
|
} else if (spec && prefix != spec->docPrefix && prefix != spec->queryPrefix &&
|
|
std::find(spec->otherPrefixes.begin(), spec->otherPrefixes.end(), *prefix) == spec->otherPrefixes.end())
|
|
{
|
|
std::stringstream ss;
|
|
ss << std::quoted(*prefix) << " is not a valid task type for model " << modelName;
|
|
throw std::invalid_argument(ss.str());
|
|
}
|
|
|
|
embedInternal(texts, embeddings, *prefix, dimensionality, tokenCount, doMean, atlas, cancelCb, spec);
|
|
}
|
|
|
|
// MD5 hash of "nomic empty"
|
|
static const char EMPTY_PLACEHOLDER[] = "24df574ea1c998de59d5be15e769658e";
|
|
|
|
auto product(double a) -> std::function<double(double)> {
|
|
return [a](double b) { return a * b; };
|
|
}
|
|
|
|
template <typename T>
|
|
double getL2NormScale(T *start, T *end) {
|
|
double magnitude = std::sqrt(std::inner_product(start, end, start, 0.0));
|
|
return 1.0 / std::max(magnitude, 1e-12);
|
|
}
|
|
|
|
void LLamaModel::embedInternal(
|
|
const std::vector<std::string> &texts, float *embeddings, std::string prefix, int dimensionality,
|
|
size_t *tokenCount, bool doMean, bool atlas, LLModel::EmbedCancelCallback *cancelCb, const EmbModelSpec *spec
|
|
) {
|
|
typedef std::vector<LLModel::Token> TokenString;
|
|
static constexpr int32_t atlasMaxLength = 8192;
|
|
static constexpr int chunkOverlap = 8; // Atlas overlaps chunks of input by 8 tokens
|
|
|
|
const llama_token bos_token = llama_token_bos(d_ptr->model);
|
|
const llama_token eos_token = llama_token_eos(d_ptr->model);
|
|
|
|
bool useBOS = shouldAddBOS();
|
|
bool useEOS = llama_vocab_type(d_ptr->model) == LLAMA_VOCAB_TYPE_WPM;
|
|
|
|
// no EOS, optional BOS
|
|
auto tokenize = [this, useBOS, useEOS, eos_token](std::string text, TokenString &tokens, bool wantBOS) {
|
|
if (!text.empty() && text[0] != ' ') {
|
|
text = ' ' + text; // normalize for SPM - our fork of llama.cpp doesn't add a space prefix
|
|
}
|
|
wantBOS &= useBOS;
|
|
|
|
tokens.resize(text.length()+4);
|
|
int32_t n_tokens = llama_tokenize(d_ptr->model, text.c_str(), text.length(), tokens.data(), tokens.size(), wantBOS, false);
|
|
if (n_tokens) {
|
|
(void)eos_token;
|
|
assert(useEOS == (eos_token != -1 && tokens[n_tokens - 1] == eos_token));
|
|
tokens.resize(n_tokens - useEOS); // erase EOS/SEP
|
|
} else {
|
|
tokens.clear();
|
|
}
|
|
};
|
|
|
|
// tokenize the texts
|
|
std::vector<TokenString> inputs;
|
|
for (unsigned i = 0; i < texts.size(); i++) {
|
|
auto &text = texts[i];
|
|
auto &inp = inputs.emplace_back();
|
|
tokenize(text, inp, false);
|
|
if (atlas && inp.size() > atlasMaxLength) {
|
|
if (doMean) {
|
|
throw std::length_error(
|
|
"length of text at index " + std::to_string(i) + " is " + std::to_string(inp.size()) +
|
|
" tokens which exceeds limit of " + std::to_string(atlasMaxLength)
|
|
);
|
|
}
|
|
inp.resize(atlasMaxLength);
|
|
} else if (inp.empty()) {
|
|
if (!atlas || !text.empty()) {
|
|
std::cerr << __func__ << ": warning: chunking tokenized text at index " << std::to_string(i)
|
|
<< " into zero tokens\n";
|
|
}
|
|
tokenize(EMPTY_PLACEHOLDER, inp, false);
|
|
}
|
|
}
|
|
|
|
// tokenize the prefix
|
|
TokenString prefixTokens;
|
|
if (prefix.empty()) {
|
|
prefixTokens.push_back(bos_token);
|
|
} else {
|
|
tokenize(prefix + ':', prefixTokens, true);
|
|
}
|
|
|
|
// n_ctx_train: max sequence length of model (RoPE scaling not implemented)
|
|
const uint32_t n_ctx_train = llama_n_ctx_train(d_ptr->model);
|
|
// n_batch (equals n_ctx): max tokens per call to llama_decode (one more more sequences)
|
|
const uint32_t n_batch = llama_n_batch(d_ptr->ctx);
|
|
|
|
// effective sequence length minus prefix and SEP token
|
|
const uint32_t max_len = std::min(n_ctx_train, n_batch) - (prefixTokens.size() + useEOS);
|
|
if (max_len <= chunkOverlap) {
|
|
throw std::logic_error("max chunk length of " + std::to_string(max_len) + " is smaller than overlap of " +
|
|
std::to_string(chunkOverlap) + " tokens");
|
|
}
|
|
|
|
// split into max_len-sized chunks
|
|
struct split_batch { unsigned idx; TokenString batch; };
|
|
std::vector<split_batch> batches;
|
|
size_t totalTokens = 0;
|
|
for (unsigned i = 0; i < inputs.size(); i++) {
|
|
auto &input = inputs[i];
|
|
for (auto it = input.begin(); it < input.end(); it += max_len) {
|
|
if (it > input.begin()) { it -= chunkOverlap; }
|
|
auto end = std::min(it + max_len, input.end());
|
|
batches.push_back({ i, {} });
|
|
auto &batch = batches.back().batch;
|
|
batch = prefixTokens;
|
|
batch.insert(batch.end(), it, end);
|
|
totalTokens += end - it;
|
|
batch.push_back(eos_token);
|
|
if (!doMean) { break; /* limit text to one chunk */ }
|
|
}
|
|
}
|
|
inputs.clear();
|
|
|
|
if (cancelCb) {
|
|
// copy of batching code below, but just count tokens instead of running inference
|
|
unsigned nBatchTokens = 0;
|
|
std::vector<unsigned> batchSizes;
|
|
for (const auto &inp: batches) {
|
|
if (nBatchTokens + inp.batch.size() > n_batch) {
|
|
batchSizes.push_back(nBatchTokens);
|
|
nBatchTokens = 0;
|
|
}
|
|
nBatchTokens += inp.batch.size();
|
|
}
|
|
batchSizes.push_back(nBatchTokens);
|
|
if (cancelCb(batchSizes.data(), batchSizes.size(), d_ptr->backend_name)) {
|
|
throw std::runtime_error("operation was canceled");
|
|
}
|
|
}
|
|
|
|
// initialize batch
|
|
struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
|
|
|
|
// n_texts x n_embd matrix
|
|
const int32_t n_embd = llama_n_embd(d_ptr->model);
|
|
std::vector<double> embeddingsSum(texts.size() * n_embd);
|
|
std::vector<int> embeddingsSumTotal(texts.size());
|
|
std::vector<int> queued_indices; // text indices of batches to be processed
|
|
|
|
auto decode = [this, &queued_indices, n_embd, &batch, &embeddingsSum, &embeddingsSumTotal, spec, dimensionality]() {
|
|
if (llama_decode(d_ptr->ctx, batch) < 0)
|
|
throw std::runtime_error("llama_decode failed");
|
|
|
|
for (int i = 0; i < batch.n_tokens; ++i) {
|
|
if (!batch.logits[i]) { continue; }
|
|
int i_prompt = queued_indices[batch.seq_id[i][0]];
|
|
auto *out = &embeddingsSum[i_prompt * n_embd];
|
|
|
|
// sequence embeddings aren't available when pooling_type is NONE
|
|
auto *embd = llama_get_embeddings_seq(d_ptr->ctx, batch.seq_id[i][0]);
|
|
if (!embd) { embd = llama_get_embeddings_ith(d_ptr->ctx, i); }
|
|
assert(embd);
|
|
|
|
auto *embd_end = embd + n_embd;
|
|
|
|
// layer normalization for nomic-embed-text-v1.5
|
|
if (spec && spec->matryoshkaCapable) {
|
|
// normalize mean
|
|
double mean = std::accumulate(embd, embd_end, 0.0) / n_embd;
|
|
std::transform(embd, embd_end, embd, [mean](double f){ return f - mean; });
|
|
|
|
// unbiased sample variance, with Bessel's correction
|
|
double variance = std::inner_product(embd, embd_end, embd, 0.0) / (n_embd - 1);
|
|
|
|
// trim to matryoshka dim
|
|
embd_end = embd + dimensionality;
|
|
|
|
// normalize variance
|
|
std::transform(embd, embd_end, embd, product(1.0 / std::sqrt(variance + 1e-5)));
|
|
}
|
|
|
|
// L2 norm
|
|
auto scale = getL2NormScale(embd, embd_end);
|
|
std::transform(embd, embd_end, out, out, [scale](double e, double o){ return o + scale * e; });
|
|
embeddingsSumTotal[i_prompt]++;
|
|
}
|
|
};
|
|
|
|
// break into batches
|
|
for (const auto &inp: batches) {
|
|
// encode if at capacity
|
|
if (batch.n_tokens + inp.batch.size() > n_batch) {
|
|
decode();
|
|
batch.n_tokens = 0;
|
|
queued_indices.clear();
|
|
}
|
|
|
|
// add to batch
|
|
batch_add_seq(batch, inp.batch, queued_indices.size());
|
|
queued_indices.push_back(inp.idx);
|
|
}
|
|
|
|
// final batch
|
|
decode();
|
|
|
|
for (unsigned i = 0; i < texts.size(); i++) {
|
|
auto *embd = &embeddingsSum[i * n_embd];
|
|
auto *embd_end = embd + dimensionality;
|
|
int total = embeddingsSumTotal[i];
|
|
|
|
// average over chunks
|
|
std::transform(embd, embd_end, embd, product(1.0 / total));
|
|
|
|
// L2 norm and copy
|
|
auto scale = getL2NormScale(embd, embd_end);
|
|
std::transform(embd, embd_end, embeddings, product(scale));
|
|
embeddings += dimensionality;
|
|
}
|
|
|
|
if (tokenCount) { *tokenCount = totalTokens; }
|
|
}
|
|
|
|
#if defined(_WIN32)
|
|
#define DLL_EXPORT __declspec(dllexport)
|
|
#else
|
|
#define DLL_EXPORT __attribute__ ((visibility ("default")))
|
|
#endif
|
|
|
|
extern "C" {
|
|
DLL_EXPORT bool is_g4a_backend_model_implementation() {
|
|
return true;
|
|
}
|
|
|
|
DLL_EXPORT const char *get_model_type() {
|
|
return modelType_;
|
|
}
|
|
|
|
DLL_EXPORT const char *get_build_variant() {
|
|
return GGML_BUILD_VARIANT;
|
|
}
|
|
|
|
DLL_EXPORT bool magic_match(const char *fname) {
|
|
auto * ctx = load_gguf(fname);
|
|
std::string arch = get_arch_name(ctx);
|
|
|
|
bool valid = true;
|
|
|
|
if (std::find(KNOWN_ARCHES.begin(), KNOWN_ARCHES.end(), arch) == KNOWN_ARCHES.end()) {
|
|
// not supported by this version of llama.cpp
|
|
if (arch != "gptj") { // we support this via another module
|
|
std::cerr << __func__ << ": unsupported model architecture: " << arch << "\n";
|
|
}
|
|
valid = false;
|
|
}
|
|
|
|
if (valid && is_embedding_arch(arch) && gguf_find_key(ctx, (arch + ".pooling_type").c_str()) < 0)
|
|
valid = false; // old pre-llama.cpp embedding model, e.g. all-MiniLM-L6-v2-f16.gguf
|
|
|
|
gguf_free(ctx);
|
|
return valid;
|
|
}
|
|
|
|
DLL_EXPORT LLModel *construct() {
|
|
llama_log_set(llama_log_callback, nullptr);
|
|
return new LLamaModel;
|
|
}
|
|
}
|