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gpt4all/gpt4all-backend/llamamodel.cpp
Jared Van Bortel 6518b33697
llamamodel: use greedy sampling when temp=0 (#2854) 
Signed-off-by: Jared Van Bortel <jared@nomic.ai>
2024-08-13 17:04:50 -04:00

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#define LLAMAMODEL_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
#include "llamamodel_impl.h"
#include "llmodel.h"
#include <ggml.h>
#include <llama.h>
#include <algorithm>
#include <cassert>
#include <cmath>
#include <cstdint>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <fstream>
#include <functional>
#include <initializer_list>
#include <iomanip>
#include <iostream>
#include <iterator>
#include <memory>
#include <numeric>
#include <optional>
#include <sstream>
#include <stdexcept>
#include <string>
#include <thread>
#include <vector>
#ifdef GGML_USE_KOMPUTE
# include <ggml-kompute.h>
#elif defined(GGML_USE_VULKAN)
# include <ggml-vulkan.h>
#elif defined(GGML_USE_CUDA)
# include <ggml-cuda.h>
#endif
using namespace std::string_literals;
// Maximum supported GGUF version
static constexpr int GGUF_VER_MAX = 3;
static const char * const modelType_ = "LLaMA";
// note: same order as LLM_ARCH_NAMES in llama.cpp
static const std::vector<const char *> KNOWN_ARCHES {
"llama",
"falcon",
// "grok", -- 314B parameters
"gpt2",
// "gptj", -- no inference code
"gptneox",
"mpt",
"baichuan",
"starcoder",
"refact",
"bert",
"nomic-bert",
// "jina-bert-v2", -- Assertion `i01 >= 0 && i01 < ne01' failed.
"bloom",
"stablelm",
"qwen",
"qwen2",
"qwen2moe",
"phi2",
"phi3",
// "plamo", -- https://github.com/ggerganov/llama.cpp/issues/5669
"codeshell",
"orion",
"internlm2",
// "minicpm", -- CUDA generates garbage
"gemma",
"gemma2",
"starcoder2",
// "mamba", -- CUDA missing SSM_CONV
"xverse",
"command-r",
// "dbrx", -- 16x12B parameters
"olmo",
"openelm",
// "arctic", -- 10B+128x3.66B parameters
"deepseek2",
"chatglm",
// "bitnet", -- tensor not within file bounds?
// "t5", -- seq2seq model
"jais",
};
static const std::vector<const char *> EMBEDDING_ARCHES {
"bert", "nomic-bert",
};
static bool is_embedding_arch(const std::string &arch)
{
return std::find(EMBEDDING_ARCHES.begin(), EMBEDDING_ARCHES.end(), arch) < EMBEDDING_ARCHES.end();
}
static bool llama_verbose()
{
const char* var = getenv("GPT4ALL_VERBOSE_LLAMACPP");
return var && *var;
}
static void llama_log_callback(enum ggml_log_level level, const char *text, void *userdata)
{
(void)userdata;
if (llama_verbose() || level <= GGML_LOG_LEVEL_ERROR) {
fputs(text, stderr);
}
}
#ifdef GGML_USE_CUDA
static void cuda_log_callback(enum ggml_log_level level, const char *text, void *userdata)
{
(void)userdata;
if (llama_verbose() || level <= GGML_LOG_LEVEL_WARN) {
fputs(text, stderr);
}
}
#endif
struct gpt_params {
int32_t seed = -1; // RNG seed
int32_t n_keep = 0; // number of tokens to keep from initial prompt
// sampling parameters
float tfs_z = 1.0f; // 1.0 = disabled
float typical_p = 1.0f; // 1.0 = disabled
std::string prompt = "";
enum ggml_type kv_type = GGML_TYPE_F16; // use f16 instead of f32 for memory kv
bool use_mmap = true; // use mmap for faster loads
bool use_mlock = false; // use mlock to keep model in memory
};
static llama_token llama_sample_top_p_top_k(
llama_context *ctx,
const llama_token *last_n_tokens_data,
int last_n_tokens_size,
int top_k,
float top_p,
float min_p,
float temp,
float repeat_penalty) {
auto logits = llama_get_logits_ith(ctx, -1);
auto n_vocab = llama_n_vocab(llama_get_model(ctx));
// Populate initial list of all candidates
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
for (int token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
}
llama_token_data_array candidates_p = {candidates.data(), candidates.size(), false};
// Sample repeat penalty
llama_sample_repetition_penalties(nullptr, &candidates_p, last_n_tokens_data, last_n_tokens_size, repeat_penalty, 0.0f, 0.0f);
llama_token id;
if (temp == 0.0) {
// greedy sampling, no probs
id = llama_sample_token_greedy(ctx, &candidates_p);
} else {
// temperature sampling
llama_sample_top_k(ctx, &candidates_p, top_k, 1);
llama_sample_tail_free(ctx, &candidates_p, 1.0f, 1);
llama_sample_typical(ctx, &candidates_p, 1.0f, 1);
llama_sample_top_p(ctx, &candidates_p, top_p, 1);
llama_sample_min_p(ctx, &candidates_p, min_p, 1);
llama_sample_temp(ctx, &candidates_p, temp);
id = llama_sample_token(ctx, &candidates_p);
}
return id;
}
const char *get_arch_name(gguf_context *ctx_gguf)
{
const int kid = gguf_find_key(ctx_gguf, "general.architecture");
if (kid == -1)
throw std::runtime_error("key not found in model: general.architecture");
enum gguf_type ktype = gguf_get_kv_type(ctx_gguf, kid);
if (ktype != GGUF_TYPE_STRING)
throw std::runtime_error("key general.architecture has wrong type");
return gguf_get_val_str(ctx_gguf, kid);
}
static gguf_context *load_gguf(const char *fname)
{
struct gguf_init_params params = {
/*.no_alloc = */ true,
/*.ctx = */ nullptr,
};
gguf_context *ctx = gguf_init_from_file(fname, params);
if (!ctx) {
std::cerr << __func__ << ": gguf_init_from_file failed\n";
return nullptr;
}
int gguf_ver = gguf_get_version(ctx);
if (gguf_ver > GGUF_VER_MAX) {
std::cerr << __func__ << ": unsupported gguf version: " << gguf_ver << "\n";
gguf_free(ctx);
return nullptr;
}
return ctx;
}
static int32_t get_arch_key_u32(std::string const &modelPath, std::string const &archKey)
{
int32_t value = -1;
std::string arch;
auto * ctx = load_gguf(modelPath.c_str());
if (!ctx)
goto cleanup;
try {
arch = get_arch_name(ctx);
} catch (const std::runtime_error &) {
goto cleanup; // cannot read key
}
{
auto key = arch + "." + archKey;
int keyidx = gguf_find_key(ctx, key.c_str());
if (keyidx != -1) {
value = gguf_get_val_u32(ctx, keyidx);
} else {
std::cerr << __func__ << ": " << key << "not found in " << modelPath << "\n";
}
}
cleanup:
gguf_free(ctx);
return value;
}
struct LLamaPrivate {
const std::string modelPath;
bool modelLoaded = false;
int device = -1;
std::string deviceName;
llama_model *model = nullptr;
llama_context *ctx = nullptr;
llama_model_params model_params;
llama_context_params ctx_params;
int64_t n_threads = 0;
std::vector<LLModel::Token> end_tokens;
const char *backend_name = nullptr;
};
LLamaModel::LLamaModel()
: d_ptr(new LLamaPrivate) {}
// default hparams (LLaMA 7B)
struct llama_file_hparams {
uint32_t n_vocab = 32000;
uint32_t n_embd = 4096;
uint32_t n_mult = 256;
uint32_t n_head = 32;
uint32_t n_layer = 32;
uint32_t n_rot = 64;
enum llama_ftype ftype = LLAMA_FTYPE_MOSTLY_F16;
};
size_t LLamaModel::requiredMem(const std::string &modelPath, int n_ctx, int ngl)
{
// TODO(cebtenzzre): update to GGUF
(void)ngl; // FIXME(cetenzzre): use this value
auto fin = std::ifstream(modelPath, std::ios::binary);
fin.seekg(0, std::ios_base::end);
size_t filesize = fin.tellg();
fin.seekg(0, std::ios_base::beg);
uint32_t magic = 0;
fin.read(reinterpret_cast<char*>(&magic), sizeof(magic));
if (magic != 0x67676a74) return 0;
uint32_t version = 0;
fin.read(reinterpret_cast<char*>(&version), sizeof(version));
llama_file_hparams hparams;
fin.read(reinterpret_cast<char*>(&hparams.n_vocab), sizeof(hparams.n_vocab));
fin.read(reinterpret_cast<char*>(&hparams.n_embd), sizeof(hparams.n_embd));
fin.read(reinterpret_cast<char*>(&hparams.n_head), sizeof(hparams.n_head));
fin.read(reinterpret_cast<char*>(&hparams.n_layer), sizeof(hparams.n_layer));
fin.read(reinterpret_cast<char*>(&hparams.n_rot), sizeof(hparams.n_rot));
fin.read(reinterpret_cast<char*>(&hparams.ftype), sizeof(hparams.ftype));
const size_t kvcache_element_size = 2; // fp16
const size_t est_kvcache_size = hparams.n_embd * hparams.n_layer * 2u * n_ctx * kvcache_element_size;
return filesize + est_kvcache_size;
}
bool LLamaModel::isModelBlacklisted(const std::string &modelPath) const
{
auto * ctx = load_gguf(modelPath.c_str());
if (!ctx) {
std::cerr << __func__ << ": failed to load " << modelPath << "\n";
return false;
}
auto get_key = [ctx, &modelPath](const char *name) {
int keyidx = gguf_find_key(ctx, name);
if (keyidx == -1) {
throw std::logic_error(name + " not found in "s + modelPath);
}
return keyidx;
};
bool res = false;
try {
std::string name(gguf_get_val_str(ctx, get_key("general.name")));
int token_idx = get_key("tokenizer.ggml.tokens");
int n_vocab = gguf_get_arr_n(ctx, token_idx);
// check for known bad models
if (name == "open-orca_mistral-7b-openorca"
&& n_vocab == 32002
&& gguf_get_arr_str(ctx, token_idx, 32000) == "<dummy32000>"s // should be <|im_end|>
) {
res = true;
}
} catch (const std::logic_error &e) {
std::cerr << __func__ << ": " << e.what() << "\n";
}
gguf_free(ctx);
return res;
}
bool LLamaModel::isEmbeddingModel(const std::string &modelPath) const
{
bool result = false;
std::string arch;
auto *ctx_gguf = load_gguf(modelPath.c_str());
if (!ctx_gguf) {
std::cerr << __func__ << ": failed to load GGUF from " << modelPath << "\n";
goto cleanup;
}
try {
arch = get_arch_name(ctx_gguf);
} catch (const std::runtime_error &) {
goto cleanup; // cannot read key
}
result = is_embedding_arch(arch);
cleanup:
gguf_free(ctx_gguf);
return result;
}
bool LLamaModel::loadModel(const std::string &modelPath, int n_ctx, int ngl)
{
d_ptr->modelLoaded = false;
// clean up after previous loadModel()
if (d_ptr->model) {
llama_free_model(d_ptr->model);
d_ptr->model = nullptr;
}
if (d_ptr->ctx) {
llama_free(d_ptr->ctx);
d_ptr->ctx = nullptr;
}
if (n_ctx < 8) {
std::cerr << "warning: minimum context size is 8, using minimum size.\n";
n_ctx = 8;
}
// -- load the model --
gpt_params params;
d_ptr->model_params = llama_model_default_params();
d_ptr->model_params.use_mmap = params.use_mmap;
#if defined (__APPLE__)
d_ptr->model_params.use_mlock = true;
#else
d_ptr->model_params.use_mlock = params.use_mlock;
#endif
d_ptr->model_params.progress_callback = &LLModel::staticProgressCallback;
d_ptr->model_params.progress_callback_user_data = this;
d_ptr->backend_name = "cpu"; // default
#if defined(GGML_USE_KOMPUTE) || defined(GGML_USE_VULKAN) || defined(GGML_USE_CUDA)
if (d_ptr->device != -1) {
d_ptr->model_params.main_gpu = d_ptr->device;
d_ptr->model_params.n_gpu_layers = ngl;
d_ptr->model_params.split_mode = LLAMA_SPLIT_MODE_NONE;
} else {
#ifdef GGML_USE_CUDA
std::cerr << "Llama ERROR: CUDA loadModel was called without a device\n";
return false;
#endif // GGML_USE_CUDA
}
#elif defined(GGML_USE_METAL)
(void)ngl;
if (llama_verbose()) {
std::cerr << "llama.cpp: using Metal" << std::endl;
}
d_ptr->backend_name = "metal";
// always fully offload on Metal
// TODO(cebtenzzre): use this parameter to allow using more than 53% of system RAM to load a model
d_ptr->model_params.n_gpu_layers = 100;
#else // !KOMPUTE && !VULKAN && !CUDA && !METAL
(void)ngl;
#endif
d_ptr->model = llama_load_model_from_file(modelPath.c_str(), d_ptr->model_params);
if (!d_ptr->model) {
fflush(stdout);
#ifndef GGML_USE_CUDA
d_ptr->device = -1;
d_ptr->deviceName.clear();
#endif
std::cerr << "LLAMA ERROR: failed to load model from " << modelPath << std::endl;
return false;
}
// -- initialize the context --
d_ptr->ctx_params = llama_context_default_params();
bool isEmbedding = is_embedding_arch(llama_model_arch(d_ptr->model));
const int n_ctx_train = llama_n_ctx_train(d_ptr->model);
if (isEmbedding) {
d_ptr->ctx_params.n_batch = n_ctx;
d_ptr->ctx_params.n_ubatch = n_ctx;
} else {
if (n_ctx > n_ctx_train) {
std::cerr << "warning: model was trained on only " << n_ctx_train << " context tokens ("
<< n_ctx << " specified)\n";
}
}
d_ptr->ctx_params.n_ctx = n_ctx;
d_ptr->ctx_params.seed = params.seed;
d_ptr->ctx_params.type_k = params.kv_type;
d_ptr->ctx_params.type_v = params.kv_type;
// The new batch API provides space for n_vocab*n_tokens logits. Tell llama.cpp early
// that we want this many logits so the state serializes consistently.
d_ptr->ctx_params.logits_all = true;
d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
d_ptr->ctx_params.n_threads = d_ptr->n_threads;
d_ptr->ctx_params.n_threads_batch = d_ptr->n_threads;
if (isEmbedding)
d_ptr->ctx_params.embeddings = true;
d_ptr->ctx = llama_new_context_with_model(d_ptr->model, d_ptr->ctx_params);
if (!d_ptr->ctx) {
fflush(stdout);
std::cerr << "LLAMA ERROR: failed to init context for model " << modelPath << std::endl;
llama_free_model(d_ptr->model);
d_ptr->model = nullptr;
#ifndef GGML_USE_CUDA
d_ptr->device = -1;
d_ptr->deviceName.clear();
#endif
return false;
}
d_ptr->end_tokens = {llama_token_eos(d_ptr->model)};
if (usingGPUDevice()) {
#ifdef GGML_USE_KOMPUTE
if (llama_verbose()) {
std::cerr << "llama.cpp: using Vulkan on " << d_ptr->deviceName << std::endl;
}
d_ptr->backend_name = "kompute";
#elif defined(GGML_USE_VULKAN)
d_ptr->backend_name = "vulkan";
#elif defined(GGML_USE_CUDA)
d_ptr->backend_name = "cuda";
#endif
}
m_supportsEmbedding = isEmbedding;
m_supportsCompletion = !isEmbedding;
fflush(stdout);
d_ptr->modelLoaded = true;
return true;
}
void LLamaModel::setThreadCount(int32_t n_threads)
{
d_ptr->n_threads = n_threads;
llama_set_n_threads(d_ptr->ctx, n_threads, n_threads);
}
int32_t LLamaModel::threadCount() const
{
return d_ptr->n_threads;
}
LLamaModel::~LLamaModel()
{
if (d_ptr->ctx) {
llama_free(d_ptr->ctx);
}
llama_free_model(d_ptr->model);
}
bool LLamaModel::isModelLoaded() const
{
return d_ptr->modelLoaded;
}
size_t LLamaModel::stateSize() const
{
return llama_get_state_size(d_ptr->ctx);
}
size_t LLamaModel::saveState(uint8_t *dest) const
{
return llama_copy_state_data(d_ptr->ctx, dest);
}
size_t LLamaModel::restoreState(const uint8_t *src)
{
// const_cast is required, see: https://github.com/ggerganov/llama.cpp/pull/1540
return llama_set_state_data(d_ptr->ctx, const_cast<uint8_t*>(src));
}
std::vector<LLModel::Token> LLamaModel::tokenize(PromptContext &ctx, const std::string &str, bool special)
{
bool atStart = m_tokenize_last_token == -1;
bool insertSpace = atStart || isSpecialToken(m_tokenize_last_token);
std::vector<LLModel::Token> fres(str.length() + 4);
int32_t fres_len = llama_tokenize_gpt4all(
d_ptr->model, str.c_str(), str.length(), fres.data(), fres.size(), /*add_special*/ atStart,
/*parse_special*/ special, /*insert_space*/ insertSpace
);
fres.resize(fres_len);
if (fres_len)
m_tokenize_last_token = fres.back();
return fres;
}
bool LLamaModel::isSpecialToken(Token id) const
{
return llama_token_get_attr(d_ptr->model, id)
& (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_USER_DEFINED | LLAMA_TOKEN_ATTR_UNKNOWN);
}
std::string LLamaModel::tokenToString(Token id) const
{
std::vector<char> result(8, 0);
const int n_tokens = llama_token_to_piece(d_ptr->model, id, result.data(), result.size(), 0, true);
if (n_tokens < 0) {
result.resize(-n_tokens);
int check = llama_token_to_piece(d_ptr->model, id, result.data(), result.size(), 0, true);
GGML_ASSERT(check == -n_tokens);
}
else {
result.resize(n_tokens);
}
return std::string(result.data(), result.size());
}
LLModel::Token LLamaModel::sampleToken(PromptContext &promptCtx) const
{
const size_t n_prev_toks = std::min((size_t) promptCtx.repeat_last_n, promptCtx.tokens.size());
return llama_sample_top_p_top_k(d_ptr->ctx,
promptCtx.tokens.data() + promptCtx.tokens.size() - n_prev_toks,
n_prev_toks, promptCtx.top_k, promptCtx.top_p, promptCtx.min_p, promptCtx.temp,
promptCtx.repeat_penalty);
}
bool LLamaModel::evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) const
{
llama_kv_cache_seq_rm(d_ptr->ctx, 0, ctx.n_past, -1);
llama_batch batch = llama_batch_init(tokens.size(), 0, 1);
batch.n_tokens = tokens.size();
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;
}
void LLamaModel::shiftContext(PromptContext &promptCtx)
{
// infinite text generation via context shifting
// erase up to n_ctx*contextErase tokens
int n_keep = shouldAddBOS();
int n_past = promptCtx.n_past;
int n_discard = std::min(n_past - n_keep, int(promptCtx.n_ctx * promptCtx.contextErase));
assert(n_discard > 0);
if (n_discard <= 0)
return;
std::cerr << "Llama: context full, swapping: n_past = " << n_past << ", n_keep = " << n_keep
<< ", n_discard = " << n_discard << "\n";
// erase the first n_discard tokens from the context
llama_kv_cache_seq_rm (d_ptr->ctx, 0, n_keep, n_keep + n_discard);
llama_kv_cache_seq_add(d_ptr->ctx, 0, n_keep + n_discard, n_past, -n_discard);
promptCtx.tokens.erase(promptCtx.tokens.begin() + n_keep, promptCtx.tokens.begin() + n_keep + n_discard);
promptCtx.n_past = promptCtx.tokens.size();
}
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
{
return llama_add_bos_token(d_ptr->model);
}
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");
}
#ifdef GGML_USE_VULKAN
static const char *getVulkanVendorName(uint32_t vendorID)
{
switch (vendorID) {
case 0x10DE: return "nvidia";
case 0x1002: return "amd";
case 0x8086: return "intel";
default: return "unknown";
}
}
#endif
std::vector<LLModel::GPUDevice> LLamaModel::availableGPUDevices(size_t memoryRequired) const
{
#if defined(GGML_USE_KOMPUTE) || defined(GGML_USE_VULKAN) || defined(GGML_USE_CUDA)
size_t count = 0;
#ifdef GGML_USE_KOMPUTE
auto *lcppDevices = ggml_vk_available_devices(memoryRequired, &count);
#elif defined(GGML_USE_VULKAN)
(void)memoryRequired; // hasn't been used since GGUF was added
auto *lcppDevices = ggml_vk_available_devices(&count);
#else // defined(GGML_USE_CUDA)
(void)memoryRequired;
auto *lcppDevices = ggml_cuda_available_devices(&count);
#endif
if (lcppDevices) {
std::vector<LLModel::GPUDevice> devices;
devices.reserve(count);
for (size_t i = 0; i < count; ++i) {
auto & dev = lcppDevices[i];
devices.emplace_back(
#ifdef GGML_USE_KOMPUTE
/* backend = */ "kompute",
/* index = */ dev.index,
/* type = */ dev.type,
/* heapSize = */ dev.heapSize,
/* name = */ dev.name,
/* vendor = */ dev.vendor
#elif defined(GGML_USE_VULKAN)
/* backend = */ "vulkan",
/* index = */ dev.index,
/* type = */ dev.type,
/* heapSize = */ dev.heapSize,
/* name = */ dev.name,
/* vendor = */ getVulkanVendorName(dev.vendorID)
#else // defined(GGML_USE_CUDA)
/* backend = */ "cuda",
/* index = */ dev.index,
/* type = */ 2, // vk::PhysicalDeviceType::eDiscreteGpu
/* heapSize = */ dev.heapSize,
/* name = */ dev.name,
/* vendor = */ "nvidia"
#endif
);
#ifndef GGML_USE_CUDA
ggml_vk_device_destroy(&dev);
#else
ggml_cuda_device_destroy(&dev);
#endif
}
free(lcppDevices);
return devices;
}
#else
(void)memoryRequired;
std::cerr << __func__ << ": built without a GPU backend\n";
#endif
return {};
}
bool LLamaModel::initializeGPUDevice(size_t memoryRequired, const std::string &name) const
{
#if defined(GGML_USE_VULKAN) || defined(GGML_USE_CUDA)
auto devices = availableGPUDevices(memoryRequired);
auto dev_it = devices.begin();
#ifndef GGML_USE_CUDA
if (name == "amd" || name == "nvidia" || name == "intel") {
dev_it = std::find_if(dev_it, devices.end(), [&name](auto &dev) { return dev.vendor == name; });
} else
#endif
if (name != "gpu") {
dev_it = std::find_if(dev_it, devices.end(), [&name](auto &dev) { return dev.name == name; });
}
if (dev_it < devices.end()) {
d_ptr->device = dev_it->index;
d_ptr->deviceName = dev_it->name;
return true;
}
return false;
#elif 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;
d_ptr->deviceName = device.name;
ggml_vk_device_destroy(&device);
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) || defined(GGML_USE_VULKAN) || defined(GGML_USE_CUDA)
(void)unavail_reason;
auto devices = availableGPUDevices();
auto it = std::find_if(devices.begin(), devices.end(), [device](auto &dev) { return dev.index == device; });
d_ptr->device = device;
d_ptr->deviceName = it < devices.end() ? it->name : "(unknown)";
return true;
#else
(void)device;
if (unavail_reason) {
*unavail_reason = "built without a GPU backend";
}
return false;
#endif
}
bool LLamaModel::usingGPUDevice() const
{
if (!d_ptr->model)
return false;
bool usingGPU = llama_model_using_gpu(d_ptr->model);
#ifdef GGML_USE_KOMPUTE
assert(!usingGPU || ggml_vk_has_device());
#endif
return usingGPU;
}
const char *LLamaModel::backendName() const
{
return d_ptr->backend_name;
}
const char *LLamaModel::gpuDeviceName() const
{
if (usingGPUDevice()) {
#if defined(GGML_USE_KOMPUTE) || defined(GGML_USE_VULKAN) || defined(GGML_USE_CUDA)
return d_ptr->deviceName.c_str();
#elif defined(GGML_USE_METAL)
return "Metal";
#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 = llama_add_bos_token(d_ptr->model);
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
}
tokens.resize(text.length()+4);
int32_t n_tokens = llama_tokenize_gpt4all(
d_ptr->model, text.c_str(), text.length(), tokens.data(), tokens.size(), /*add_special*/ wantBOS,
/*parse_special*/ false, /*insert_space*/ false
);
if (n_tokens) {
(void)eos_token;
(void)useBOS;
assert((useEOS && wantBOS && useBOS) == (eos_token != -1 && tokens[n_tokens - 1] == eos_token));
if (useEOS && wantBOS)
n_tokens--; // erase EOS/SEP
}
tokens.resize(n_tokens);
};
// 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 (unsigned j = 0; j < input.size(); j += max_len) {
if (j) { j -= chunkOverlap; }
unsigned end = std::min(j + max_len, unsigned(input.size()));
batches.push_back({ i, {} });
auto &batch = batches.back().batch;
batch = prefixTokens;
batch.insert(batch.end(), input.begin() + j, input.begin() + end);
totalTokens += end - j;
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; }
llama_batch_free(batch);
}
#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 char *get_file_arch(const char *fname)
{
char *arch = nullptr;
std::string archStr;
auto *ctx = load_gguf(fname);
if (!ctx)
goto cleanup;
try {
archStr = get_arch_name(ctx);
} catch (const std::runtime_error &) {
goto cleanup; // cannot read key
}
if (is_embedding_arch(archStr) && gguf_find_key(ctx, (archStr + ".pooling_type").c_str()) < 0) {
// old bert.cpp embedding model
} else {
arch = strdup(archStr.c_str());
}
cleanup:
gguf_free(ctx);
return arch;
}
DLL_EXPORT bool is_arch_supported(const char *arch)
{
return std::find(KNOWN_ARCHES.begin(), KNOWN_ARCHES.end(), std::string(arch)) < KNOWN_ARCHES.end();
}
DLL_EXPORT LLModel *construct()
{
llama_log_set(llama_log_callback, nullptr);
#ifdef GGML_USE_CUDA
ggml_backend_cuda_log_set_callback(cuda_log_callback, nullptr);
#endif
return new LLamaModel;
}
}
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