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ollama/llama/common.cpp
Jeffrey Morgan 96efd9052f
Re-introduce the llama package (#5034) 
* Re-introduce the llama package

This PR brings back the llama package, making it possible to call llama.cpp and
ggml APIs from Go directly via CGo. This has a few advantages:

- C APIs can be called directly from Go without needing to use the previous
  "server" REST API
- On macOS and for CPU builds on Linux and Windows, Ollama can be built without
  a go generate ./... step, making it easy to get up and running to hack on
  parts of Ollama that don't require fast inference
- Faster build times for AVX,AVX2,CUDA and ROCM (a full build of all runners
  takes <5 min on a fast CPU)
- No git submodule making it easier to clone and build from source

This is a big PR, but much of it is vendor code except for:

- llama.go CGo bindings
- example/: a simple example of running inference
- runner/: a subprocess server designed to replace the llm/ext_server package
- Makefile an as minimal as possible Makefile to build the runner package for
  different targets (cpu, avx, avx2, cuda, rocm)

Co-authored-by: Jesse Gross <jesse@ollama.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>

* cache: Clear old KV cache entries when evicting a slot

When forking a cache entry, if no empty slots are available we
evict the least recently used one and copy over the KV entries
from the closest match. However, this copy does not overwrite
existing values but only adds new ones. Therefore, we need to
clear the old slot first.

This change fixes two issues:
 - The KV cache fills up and runs out of space even though we think
   we are managing it correctly
 - Performance gets worse over time as we use new cache entries that
   are not hot in the processor caches

* doc: explain golang objc linker warning (#6830)

* llama: gather transitive dependencies for rocm for dist packaging (#6848)

* Refine go server makefiles to be more DRY (#6924)

This breaks up the monolithic Makefile for the Go based runners into a
set of utility files as well as recursive Makefiles for the runners.
Files starting with the name "Makefile" are buildable, while files that
end with ".make" are utilities to include in other Makefiles.  This
reduces the amount of nearly identical targets and helps set a pattern
for future community contributions for new GPU runner architectures.

When we are ready to switch over to the Go runners, these files should
move to the top of the repo, and we should add targets for the main CLI,
as well as a helper "install" (put all the built binaries on the local
system in a runnable state) and "dist" target (generate the various
tar/zip files for distribution) for local developer use.

* llama: don't create extraneous directories (#6988)

* llama: Exercise the new build in CI (#6989)

Wire up some basic sanity testing in CI for the Go runner.  GPU runners are not covered yet.

* llama: Refine developer docs for Go server (#6842)

This enhances the documentation for development focusing on the new Go
server.  After we complete the transition further doc refinements
can remove the "transition" discussion.

* runner.go: Allocate batches for all sequences during init

We should tell the model that we could have full batches for all
sequences. We already do this when we allocate the batches but it was
missed during initialization.

* llama.go: Don't return nil from Tokenize on zero length input

Potentially receiving nil in a non-error condition is surprising to
most callers - it's better to return an empty slice.

* runner.go: Remove stop tokens from cache

If the last token is EOG then we don't return this and it isn't
present in the cache (because it was never submitted to Decode).
This works well for extending the cache entry with a new sequence.

However, for multi-token stop sequences, we won't return any of the
tokens but all but the last one will be in the cache. This means
when the conversation continues the cache will contain tokens that
don't overlap with the new prompt.

This works (we will pick up the portion where there is overlap) but
it causes unnecessary cache thrashing because we will fork the original
cache entry as it is not a perfect match.

By trimming the cache to the tokens that we actually return this
issue can be avoided.

* runner.go: Simplify flushing of pending tokens

* runner.go: Update TODOs

* runner.go: Don't panic when processing sequences

If there is an error processing a sequence, we should return a
clean HTTP error back to Ollama rather than panicing. This will
make us more resilient to transient failures.

Panics can still occur during startup as there is no way to serve
requests if that fails.

Co-authored-by: jmorganca <jmorganca@gmail.com>

* runner.go: More accurately capture timings

Currently prompt processing time doesn't capture the that it takes
to tokenize the input, only decoding time. We should capture the
full process to more accurately reflect reality. This is especially
true once we start processing images where the initial processing
can take significant time. This is also more consistent with the
existing C++ runner.

* runner.go: Support for vision models

In addition to bringing feature parity with the C++ runner, this also
incorporates several improvements:
 - Cache prompting works with images, avoiding the need to re-decode
   embeddings for every message in a conversation
 - Parallelism is supported, avoiding the need to restrict to one
   sequence at a time. (Though for now Ollama will not schedule
   them while we might need to fall back to the old runner.)

Co-authored-by: jmorganca <jmorganca@gmail.com>

* runner.go: Move Unicode checking code and add tests

* runner.go: Export external cache members

Runner and cache are in the same package so the change doesn't
affect anything but it is more internally consistent.

* runner.go: Image embedding cache

Generating embeddings from images can take significant time (on
my machine between 100ms and 8s depending on the model). Although
we already cache the result of decoding these images, the embeddings
need to be regenerated every time. This is not necessary if we get
the same image over and over again, for example, during a conversation.

This currently uses a very small cache with a very simple algorithm
but it is easy to improve as is warranted.

* llama: catch up on patches

Carry forward solar-pro and cli-unicode patches

* runner.go: Don't re-allocate memory for every batch

We can reuse memory allocated from batch to batch since batch
size is fixed. This both saves the cost of reallocation as well
keeps the cache lines hot.

This results in a roughly 1% performance improvement for token
generation with Nvidia GPUs on Linux.

* runner.go: Default to classic input cache policy

The input cache as part of the go runner implemented a cache
policy that aims to maximize hit rate in both single and multi-
user scenarios. When there is a cache hit, the response is
very fast.

However, performance is actually slower when there is an input
cache miss due to worse GPU VRAM locality. This means that
performance is generally better overall for multi-user scenarios
(better input cache hit rate, locality was relatively poor already).
But worse for single users (input cache hit rate is about the same,
locality is now worse).

This defaults the policy back to the old one to avoid a regression
but keeps the new one available through an environment variable
OLLAMA_MULTIUSER_CACHE. This is left undocumented as the goal is
to improve this in the future to get the best of both worlds
without user configuration.

For inputs that result in cache misses, on Nvidia/Linux this
change improves performance by 31% for prompt processing and
13% for token generation.

* runner.go: Increase size of response channel

Generally the CPU can easily keep up with handling reponses that
are generated but there's no reason not to let generation continue
and handle things in larger batches if needed.

* llama: Add CI to verify all vendored changes have patches (#7066)

Make sure we don't accidentally merge changes in the vendored code
that aren't also reflected in the patches.

* llama: adjust clip patch for mingw utf-16 (#7065)

* llama: adjust clip patch for mingw utf-16

* llama: ensure static linking of runtime libs

Avoid runtime dependencies on non-standard libraries

* runner.go: Enable llamafile (all platforms) and BLAS (Mac OS)

These are two features that are shown on llama.cpp's system info
that are currently different between the two runners. On my test
systems the performance difference is very small to negligible
but it is probably still good to equalize the features.

* llm: Don't add BOS/EOS for tokenize requests

This is consistent with what server.cpp currently does. It affects
things like token processing counts for embedding requests.

* runner.go: Don't cache prompts for embeddings

Our integration with server.cpp implicitly disables prompt caching
because it is not part of the JSON object being parsed, this makes
the Go runner behavior similarly.

Prompt caching has been seen to affect the results of text completions
on certain hardware. The results are not wrong either way but they
are non-deterministic. However, embeddings seem to be affected even
on hardware that does not show this behavior for completions. For
now, it is best to maintain consistency with the existing behavior.

* runner.go: Adjust debug log levels

Add system info printed at startup and quiet down noisier logging.

* llama: fix compiler flag differences (#7082)

Adjust the flags for the new Go server to more closely match the
generate flow

* llama: refine developer docs (#7121)

* llama: doc and example clean up (#7122)

* llama: doc and example clean up

* llama: Move new dockerfile into llama dir

Temporary home until we fully transition to the Go server

* llama: runner doc cleanup

* llama.go: Add description for Tokenize error case

---------

Co-authored-by: Jesse Gross <jesse@ollama.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
Co-authored-by: Daniel Hiltgen <dhiltgen@users.noreply.github.com>
2024-10-08 08:53:54 -07:00

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/**
* llama.cpp - commit 8962422b1c6f9b8b15f5aeaea42600bcc2d44177 - do not edit this file
*
* MIT License
*
* Copyright (c) 2023-2024 The ggml authors
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#if defined(_MSC_VER)
#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING
#endif
#include "common.h"
// Change JSON_ASSERT from assert() to GGML_ASSERT:
#define JSON_ASSERT GGML_ASSERT
#include "json.hpp"
#include "json-schema-to-grammar.h"
#include "llama.h"
#include <algorithm>
#include <cinttypes>
#include <cmath>
#include <codecvt>
#include <cstdarg>
#include <cstring>
#include <ctime>
#include <fstream>
#include <iostream>
#include <iterator>
#include <regex>
#include <sstream>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#if defined(__APPLE__) && defined(__MACH__)
#include <sys/types.h>
#include <sys/sysctl.h>
#endif
#if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
# define NOMINMAX
#endif
#include <locale>
#include <windows.h>
#include <fcntl.h>
#include <io.h>
#else
#include <sys/ioctl.h>
#include <sys/stat.h>
#include <unistd.h>
#endif
#if defined(LLAMA_USE_CURL)
#include <curl/curl.h>
#include <curl/easy.h>
#include <thread>
#include <future>
#endif
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL))
#define GGML_USE_CUDA_SYCL
#endif
#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)) || defined(GGML_USE_VULKAN)
#define GGML_USE_CUDA_SYCL_VULKAN
#endif
#if defined(LLAMA_USE_CURL)
#ifdef __linux__
#include <linux/limits.h>
#elif defined(_WIN32)
#define PATH_MAX MAX_PATH
#else
#include <sys/syslimits.h>
#endif
#define LLAMA_CURL_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
#endif // LLAMA_USE_CURL
using json = nlohmann::ordered_json;
//
// Environment variable utils
//
template<typename T>
static typename std::enable_if<std::is_same<T, std::string>::value, void>::type
get_env(std::string name, T & target) {
char * value = std::getenv(name.c_str());
target = value ? std::string(value) : target;
}
template<typename T>
static typename std::enable_if<!std::is_same<T, bool>::value && std::is_integral<T>::value, void>::type
get_env(std::string name, T & target) {
char * value = std::getenv(name.c_str());
target = value ? std::stoi(value) : target;
}
template<typename T>
static typename std::enable_if<std::is_floating_point<T>::value, void>::type
get_env(std::string name, T & target) {
char * value = std::getenv(name.c_str());
target = value ? std::stof(value) : target;
}
template<typename T>
static typename std::enable_if<std::is_same<T, bool>::value, void>::type
get_env(std::string name, T & target) {
char * value = std::getenv(name.c_str());
if (value) {
std::string val(value);
target = val == "1" || val == "true";
}
}
//
// CPU utils
//
int32_t cpu_get_num_physical_cores() {
#ifdef __linux__
// enumerate the set of thread siblings, num entries is num cores
std::unordered_set<std::string> siblings;
for (uint32_t cpu=0; cpu < UINT32_MAX; ++cpu) {
std::ifstream thread_siblings("/sys/devices/system/cpu/cpu"
+ std::to_string(cpu) + "/topology/thread_siblings");
if (!thread_siblings.is_open()) {
break; // no more cpus
}
std::string line;
if (std::getline(thread_siblings, line)) {
siblings.insert(line);
}
}
if (!siblings.empty()) {
return static_cast<int32_t>(siblings.size());
}
#elif defined(__APPLE__) && defined(__MACH__)
int32_t num_physical_cores;
size_t len = sizeof(num_physical_cores);
int result = sysctlbyname("hw.perflevel0.physicalcpu", &num_physical_cores, &len, NULL, 0);
if (result == 0) {
return num_physical_cores;
}
result = sysctlbyname("hw.physicalcpu", &num_physical_cores, &len, NULL, 0);
if (result == 0) {
return num_physical_cores;
}
#elif defined(_WIN32) && (_WIN32_WINNT >= 0x0601) && !defined(__MINGW64__) // windows 7 and later
// TODO: windows + arm64 + mingw64
unsigned int n_threads_win = std::thread::hardware_concurrency();
unsigned int default_threads = n_threads_win > 0 ? (n_threads_win <= 4 ? n_threads_win : n_threads_win / 2) : 4;
DWORD buffer_size = 0;
if (!GetLogicalProcessorInformationEx(RelationProcessorCore, nullptr, &buffer_size)) {
if (GetLastError() != ERROR_INSUFFICIENT_BUFFER) {
return default_threads;
}
}
std::vector<char> buffer(buffer_size);
if (!GetLogicalProcessorInformationEx(RelationProcessorCore, reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(buffer.data()), &buffer_size)) {
return default_threads;
}
int32_t num_physical_cores = 0;
PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX info = reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(buffer.data());
while (buffer_size > 0) {
if (info->Relationship == RelationProcessorCore) {
num_physical_cores += info->Processor.GroupCount;
}
buffer_size -= info->Size;
info = reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(reinterpret_cast<char*>(info) + info->Size);
}
return num_physical_cores > 0 ? num_physical_cores : default_threads;
#endif
unsigned int n_threads = std::thread::hardware_concurrency();
return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
}
#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
#include <pthread.h>
static void cpuid(unsigned leaf, unsigned subleaf,
unsigned *eax, unsigned *ebx, unsigned *ecx, unsigned *edx) {
__asm__("movq\t%%rbx,%%rsi\n\t"
"cpuid\n\t"
"xchgq\t%%rbx,%%rsi"
: "=a"(*eax), "=S"(*ebx), "=c"(*ecx), "=d"(*edx)
: "0"(leaf), "2"(subleaf));
}
static int pin_cpu(int cpu) {
cpu_set_t mask;
CPU_ZERO(&mask);
CPU_SET(cpu, &mask);
return pthread_setaffinity_np(pthread_self(), sizeof(mask), &mask);
}
static bool is_hybrid_cpu(void) {
unsigned eax, ebx, ecx, edx;
cpuid(7, 0, &eax, &ebx, &ecx, &edx);
return !!(edx & (1u << 15));
}
static bool is_running_on_efficiency_core(void) {
unsigned eax, ebx, ecx, edx;
cpuid(0x1a, 0, &eax, &ebx, &ecx, &edx);
int intel_atom = 0x20;
int core_type = (eax & 0xff000000u) >> 24;
return core_type == intel_atom;
}
static int cpu_count_math_cpus(int n_cpu) {
int result = 0;
for (int cpu = 0; cpu < n_cpu; ++cpu) {
if (pin_cpu(cpu)) {
return -1;
}
if (is_running_on_efficiency_core()) {
continue; // efficiency cores harm lockstep threading
}
++cpu; // hyperthreading isn't useful for linear algebra
++result;
}
return result;
}
#endif // __x86_64__ && __linux__
/**
* Returns number of CPUs on system that are useful for math.
*/
int32_t cpu_get_num_math() {
#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
int n_cpu = sysconf(_SC_NPROCESSORS_ONLN);
if (n_cpu < 1) {
return cpu_get_num_physical_cores();
}
if (is_hybrid_cpu()) {
cpu_set_t affinity;
if (!pthread_getaffinity_np(pthread_self(), sizeof(affinity), &affinity)) {
int result = cpu_count_math_cpus(n_cpu);
pthread_setaffinity_np(pthread_self(), sizeof(affinity), &affinity);
if (result > 0) {
return result;
}
}
}
#endif
return cpu_get_num_physical_cores();
}
// Helper for setting process priority
#if defined(_WIN32)
bool set_process_priority(enum ggml_sched_priority prio) {
if (prio == GGML_SCHED_PRIO_NORMAL) {
return true;
}
DWORD p = NORMAL_PRIORITY_CLASS;
switch (prio) {
case GGML_SCHED_PRIO_NORMAL: p = NORMAL_PRIORITY_CLASS; break;
case GGML_SCHED_PRIO_MEDIUM: p = ABOVE_NORMAL_PRIORITY_CLASS; break;
case GGML_SCHED_PRIO_HIGH: p = HIGH_PRIORITY_CLASS; break;
case GGML_SCHED_PRIO_REALTIME: p = REALTIME_PRIORITY_CLASS; break;
}
if (!SetPriorityClass(GetCurrentProcess(), p)) {
fprintf(stderr, "warn: failed to set process priority class %d : (%d)\n", prio, (int) GetLastError());
return false;
}
return true;
}
#else // MacOS and POSIX
#include <sys/types.h>
#include <sys/resource.h>
bool set_process_priority(enum ggml_sched_priority prio) {
if (prio == GGML_SCHED_PRIO_NORMAL) {
return true;
}
int p = 0;
switch (prio) {
case GGML_SCHED_PRIO_NORMAL: p = 0; break;
case GGML_SCHED_PRIO_MEDIUM: p = -5; break;
case GGML_SCHED_PRIO_HIGH: p = -10; break;
case GGML_SCHED_PRIO_REALTIME: p = -20; break;
}
if (!setpriority(PRIO_PROCESS, 0, p)) {
fprintf(stderr, "warn: failed to set process priority %d : %s (%d)\n", prio, strerror(errno), errno);
return false;
}
return true;
}
#endif
//
// CLI argument parsing
//
void gpt_params_handle_model_default(gpt_params & params) {
if (!params.hf_repo.empty()) {
// short-hand to avoid specifying --hf-file -> default it to --model
if (params.hf_file.empty()) {
if (params.model.empty()) {
throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
}
params.hf_file = params.model;
} else if (params.model.empty()) {
params.model = fs_get_cache_file(string_split(params.hf_file, '/').back());
}
} else if (!params.model_url.empty()) {
if (params.model.empty()) {
auto f = string_split(params.model_url, '#').front();
f = string_split(f, '?').front();
params.model = fs_get_cache_file(string_split(f, '/').back());
}
} else if (params.model.empty()) {
params.model = DEFAULT_MODEL_PATH;
}
}
void postprocess_cpu_params(cpu_params& cpuparams, const cpu_params* role_model) {
int32_t n_set = 0;
if (cpuparams.n_threads < 0) {
// Assuming everything about cpuparams is invalid
if (role_model != nullptr) {
cpuparams = *role_model;
} else {
cpuparams.n_threads = cpu_get_num_math();
}
}
for (int32_t i = 0; i < GGML_MAX_N_THREADS; i++) {
if (cpuparams.cpumask[i]) {
n_set++;
}
}
if (n_set && n_set < cpuparams.n_threads) {
// Not enough set bits, may experience performance issues.
fprintf(stderr, "warn: Not enough set bits in CPU mask (%d) to satisfy requested thread count: %d\n", n_set, cpuparams.n_threads);
}
}
bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
bool invalid_param = false;
std::string arg;
const std::string arg_prefix = "--";
llama_sampling_params & sparams = params.sparams;
for (int i = 1; i < argc; i++) {
arg = argv[i];
if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
std::replace(arg.begin(), arg.end(), '_', '-');
}
if (!gpt_params_find_arg(argc, argv, arg, params, i, invalid_param)) {
throw std::invalid_argument("error: unknown argument: " + arg);
}
if (invalid_param) {
throw std::invalid_argument("error: invalid parameter for argument: " + arg);
}
}
postprocess_cpu_params(params.cpuparams, nullptr);
postprocess_cpu_params(params.cpuparams_batch, &params.cpuparams);
postprocess_cpu_params(params.draft_cpuparams, &params.cpuparams);
postprocess_cpu_params(params.draft_cpuparams_batch, &params.cpuparams_batch);
if (params.prompt_cache_all && (params.interactive || params.interactive_first)) {
throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
}
gpt_params_handle_model_default(params);
if (params.hf_token.empty()) {
get_env("HF_TOKEN", params.hf_token);
}
if (params.escape) {
string_process_escapes(params.prompt);
string_process_escapes(params.input_prefix);
string_process_escapes(params.input_suffix);
string_process_escapes(sparams.cfg_negative_prompt);
for (auto & antiprompt : params.antiprompt) {
string_process_escapes(antiprompt);
}
}
if (!params.kv_overrides.empty()) {
params.kv_overrides.emplace_back();
params.kv_overrides.back().key[0] = 0;
}
return true;
}
void gpt_params_parse_from_env(gpt_params & params) {
// we only care about server-related params for now
get_env("LLAMA_ARG_MODEL", params.model);
get_env("LLAMA_ARG_MODEL_URL", params.model_url);
get_env("LLAMA_ARG_MODEL_ALIAS", params.model_alias);
get_env("LLAMA_ARG_HF_REPO", params.hf_repo);
get_env("LLAMA_ARG_HF_FILE", params.hf_file);
get_env("LLAMA_ARG_THREADS", params.cpuparams.n_threads);
get_env("LLAMA_ARG_CTX_SIZE", params.n_ctx);
get_env("LLAMA_ARG_N_PARALLEL", params.n_parallel);
get_env("LLAMA_ARG_BATCH", params.n_batch);
get_env("LLAMA_ARG_UBATCH", params.n_ubatch);
get_env("LLAMA_ARG_N_GPU_LAYERS", params.n_gpu_layers);
get_env("LLAMA_ARG_THREADS_HTTP", params.n_threads_http);
get_env("LLAMA_ARG_CHAT_TEMPLATE", params.chat_template);
get_env("LLAMA_ARG_N_PREDICT", params.n_predict);
get_env("LLAMA_ARG_ENDPOINT_METRICS", params.endpoint_metrics);
get_env("LLAMA_ARG_ENDPOINT_SLOTS", params.endpoint_slots);
get_env("LLAMA_ARG_EMBEDDINGS", params.embedding);
get_env("LLAMA_ARG_FLASH_ATTN", params.flash_attn);
get_env("LLAMA_ARG_DEFRAG_THOLD", params.defrag_thold);
get_env("LLAMA_ARG_CONT_BATCHING", params.cont_batching);
get_env("LLAMA_ARG_HOST", params.hostname);
get_env("LLAMA_ARG_PORT", params.port);
}
bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
const auto params_org = params; // the example can modify the default params
try {
if (!gpt_params_parse_ex(argc, argv, params) || params.usage) {
params = params_org;
params.usage = true;
return false;
}
} catch (const std::invalid_argument & ex) {
fprintf(stderr, "%s\n", ex.what());
params = params_org;
return false;
}
return true;
}
bool parse_cpu_range(const std::string & range, bool (&boolmask)[GGML_MAX_N_THREADS]) {
size_t dash_loc = range.find('-');
if (dash_loc == std::string::npos) {
fprintf(stderr, "Format of CPU range is invalid! Expected [<start>]-[<end>].\n");
return false;
}
size_t start_i;
size_t end_i;
if (dash_loc == 0) {
start_i = 0;
} else {
start_i = std::stoull(range.substr(0, dash_loc));
if (start_i >= GGML_MAX_N_THREADS) {
fprintf(stderr, "Start index out of bounds!\n");
return false;
}
}
if (dash_loc == range.length() - 1) {
end_i = GGML_MAX_N_THREADS - 1;
} else {
end_i = std::stoull(range.substr(dash_loc + 1));
if (end_i >= GGML_MAX_N_THREADS) {
fprintf(stderr, "End index out of bounds!\n");
return false;
}
}
for (size_t i = start_i; i <= end_i; i++) {
boolmask[i] = true;
}
return true;
}
bool parse_cpu_mask(const std::string & mask, bool (&boolmask)[GGML_MAX_N_THREADS]) {
// Discard potential 0x prefix
size_t start_i = 0;
if (mask.length() >= 2 && mask.substr(0, 2) == "0x") {
start_i = 2;
}
size_t num_digits = mask.length() - start_i;
if (num_digits > 128) num_digits = 128;
size_t end_i = num_digits + start_i;
for (size_t i = start_i, n = (num_digits*4 - 1); i < end_i; i++, n-=4) {
char c = mask.at(i);
int8_t id = c;
if ((c >= '0' && c <= '9')) {
id -= '0';
} else if (c >= 'a' && c <= 'f') {
id -= 'a' - 10;
} else if (c >= 'A' && c <= 'F') {
id -= 'A' - 10;
} else {
fprintf(stderr, "Invalid hex character '%c' at position %d\n", c, int32_t(i));
return false;
}
boolmask[ n ] = boolmask[ n ] || ((id & 8) != 0);
boolmask[n - 1] = boolmask[n - 1] || ((id & 4) != 0);
boolmask[n - 2] = boolmask[n - 2] || ((id & 2) != 0);
boolmask[n - 3] = boolmask[n - 3] || ((id & 1) != 0);
}
return true;
}
#define CHECK_ARG if (++i >= argc) { invalid_param = true; return true; }
bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) {
const char split_delim = ',';
llama_sampling_params & sparams = params.sparams;
if (arg == "-s" || arg == "--seed") {
CHECK_ARG
// TODO: this is temporary, in the future the sampling state will be moved fully to llama_sampling_context.
params.seed = std::stoul(argv[i]);
sparams.seed = std::stoul(argv[i]);
return true;
}
if (arg == "-t" || arg == "--threads") {
CHECK_ARG
params.cpuparams.n_threads = std::stoi(argv[i]);
if (params.cpuparams.n_threads <= 0) {
params.cpuparams.n_threads = std::thread::hardware_concurrency();
}
return true;
}
if (arg == "-C" || arg == "--cpu-mask") {
CHECK_ARG
std::string mask = argv[i];
params.cpuparams.mask_valid = true;
invalid_param = !parse_cpu_mask(mask, params.cpuparams.cpumask);
return true;
}
if (arg == "-Cr" || arg == "--cpu-range") {
CHECK_ARG
std::string range = argv[i];
params.cpuparams.mask_valid = true;
invalid_param = !parse_cpu_range(range, params.cpuparams.cpumask);
return true;
}
if (arg == "--prio") {
CHECK_ARG
params.cpuparams.priority = (enum ggml_sched_priority) std::stoul(argv[i]);
return true;
}
if (arg == "--cpu-strict") {
CHECK_ARG
params.cpuparams.strict_cpu = std::stoul(argv[i]);
return true;
}
if (arg == "--poll") {
CHECK_ARG
params.cpuparams.poll = std::stoul(argv[i]);
return true;
}
if (arg == "-tb" || arg == "--threads-batch") {
CHECK_ARG
params.cpuparams_batch.n_threads = std::stoi(argv[i]);
if (params.cpuparams_batch.n_threads <= 0) {
params.cpuparams_batch.n_threads = std::thread::hardware_concurrency();
}
return true;
}
if (arg == "-Cb" || arg == "--cpu-mask-batch") {
CHECK_ARG
std::string mask = argv[i];
params.cpuparams_batch.mask_valid = true;
invalid_param = !parse_cpu_mask(mask, params.cpuparams_batch.cpumask);
return true;
}
if (arg == "-Crb" || arg == "--cpu-range_batch") {
CHECK_ARG
std::string range = argv[i];
params.cpuparams_batch.mask_valid = true;
invalid_param = !parse_cpu_range(range, params.cpuparams_batch.cpumask);
return true;
}
if (arg == "--prio-batch") {
CHECK_ARG
params.cpuparams_batch.priority = (enum ggml_sched_priority) std::stoul(argv[i]);
return true;
}
if (arg == "--cpu-strict-batch") {
params.cpuparams_batch.strict_cpu = true;
return true;
}
if (arg == "--poll-batch") {
CHECK_ARG
params.cpuparams_batch.poll = std::stoul(argv[i]);
return true;
}
if (arg == "-td" || arg == "--threads-draft") {
CHECK_ARG
params.draft_cpuparams.n_threads = std::stoi(argv[i]);
if (params.draft_cpuparams.n_threads <= 0) {
params.draft_cpuparams.n_threads = std::thread::hardware_concurrency();
}
return true;
}
if (arg == "-Cd" || arg == "--cpu-mask-draft") {
CHECK_ARG
std::string mask = argv[i];
params.draft_cpuparams.mask_valid = true;
invalid_param = !parse_cpu_mask(mask, params.draft_cpuparams.cpumask);
return true;
}
if (arg == "-Crd" || arg == "--cpu-range-draft") {
CHECK_ARG
std::string range = argv[i];
params.draft_cpuparams.mask_valid = true;
invalid_param = !parse_cpu_range(range, params.draft_cpuparams.cpumask);
return true;
}
if (arg == "--prio-draft") {
CHECK_ARG
params.draft_cpuparams.priority = (enum ggml_sched_priority) std::stoul(argv[i]);
return true;
}
if (arg == "--cpu-strict-draft") {
params.draft_cpuparams.strict_cpu = true;
return true;
}
if (arg == "--poll-draft") {
CHECK_ARG
params.draft_cpuparams.poll = std::stoul(argv[i]);
return true;
}
if (arg == "-tbd" || arg == "--threads-batch-draft") {
CHECK_ARG
params.draft_cpuparams_batch.n_threads = std::stoi(argv[i]);
if (params.draft_cpuparams_batch.n_threads <= 0) {
params.draft_cpuparams_batch.n_threads = std::thread::hardware_concurrency();
}
return true;
}
if (arg == "-Crbd" || arg == "--cpu-range-batch-draft") {
CHECK_ARG
std::string range = argv[i];
params.draft_cpuparams_batch.mask_valid = true;
invalid_param = !parse_cpu_range(range, params.draft_cpuparams_batch.cpumask);
return true;
}
if (arg == "--prio-batch-draft") {
CHECK_ARG
params.draft_cpuparams_batch.priority = (enum ggml_sched_priority) std::stoul(argv[i]);
return true;
}
if (arg == "--cpu-strict-batch-draft") {
params.draft_cpuparams_batch.strict_cpu = true;
return true;
}
if (arg == "--poll-batch-draft") {
CHECK_ARG
params.draft_cpuparams_batch.poll = std::stoul(argv[i]);
return true;
}
if (arg == "-p" || arg == "--prompt") {
CHECK_ARG
params.prompt = argv[i];
return true;
}
if (arg == "-e" || arg == "--escape") {
params.escape = true;
return true;
}
if (arg == "--no-escape") {
params.escape = false;
return true;
}
if (arg == "--prompt-cache") {
CHECK_ARG
params.path_prompt_cache = argv[i];
return true;
}
if (arg == "--prompt-cache-all") {
params.prompt_cache_all = true;
return true;
}
if (arg == "--prompt-cache-ro") {
params.prompt_cache_ro = true;
return true;
}
if (arg == "-bf" || arg == "--binary-file") {
CHECK_ARG
std::ifstream file(argv[i], std::ios::binary);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
// store the external file name in params
params.prompt_file = argv[i];
std::ostringstream ss;
ss << file.rdbuf();
params.prompt = ss.str();
fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), argv[i]);
return true;
}
if (arg == "-f" || arg == "--file") {
CHECK_ARG
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
// store the external file name in params
params.prompt_file = argv[i];
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
if (!params.prompt.empty() && params.prompt.back() == '\n') {
params.prompt.pop_back();
}
return true;
}
if (arg == "--in-file") {
CHECK_ARG
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
params.in_files.push_back(argv[i]);
return true;
}
if (arg == "-n" || arg == "--predict" || arg == "--n-predict") {
CHECK_ARG
params.n_predict = std::stoi(argv[i]);
return true;
}
if (arg == "--top-k") {
CHECK_ARG
sparams.top_k = std::stoi(argv[i]);
return true;
}
if (arg == "-c" || arg == "--ctx-size") {
CHECK_ARG
params.n_ctx = std::stoi(argv[i]);
return true;
}
if (arg == "--grp-attn-n" || arg == "-gan") {
CHECK_ARG
params.grp_attn_n = std::stoi(argv[i]);
return true;
}
if (arg == "--grp-attn-w" || arg == "-gaw") {
CHECK_ARG
params.grp_attn_w = std::stoi(argv[i]);
return true;
}
if (arg == "--rope-freq-base") {
CHECK_ARG
params.rope_freq_base = std::stof(argv[i]);
return true;
}
if (arg == "--rope-freq-scale") {
CHECK_ARG
params.rope_freq_scale = std::stof(argv[i]);
return true;
}
if (arg == "--rope-scaling") {
CHECK_ARG
std::string value(argv[i]);
/**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
else { invalid_param = true; }
return true;
}
if (arg == "--rope-scale") {
CHECK_ARG
params.rope_freq_scale = 1.0f / std::stof(argv[i]);
return true;
}
if (arg == "--yarn-orig-ctx") {
CHECK_ARG
params.yarn_orig_ctx = std::stoi(argv[i]);
return true;
}
if (arg == "--yarn-ext-factor") {
CHECK_ARG
params.yarn_ext_factor = std::stof(argv[i]);
return true;
}
if (arg == "--yarn-attn-factor") {
CHECK_ARG
params.yarn_attn_factor = std::stof(argv[i]);
return true;
}
if (arg == "--yarn-beta-fast") {
CHECK_ARG
params.yarn_beta_fast = std::stof(argv[i]);
return true;
}
if (arg == "--yarn-beta-slow") {
CHECK_ARG
params.yarn_beta_slow = std::stof(argv[i]);
return true;
}
if (arg == "--pooling") {
CHECK_ARG
std::string value(argv[i]);
/**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
else if (value == "last") { params.pooling_type = LLAMA_POOLING_TYPE_LAST; }
else { invalid_param = true; }
return true;
}
if (arg == "--attention") {
CHECK_ARG
std::string value(argv[i]);
/**/ if (value == "causal") { params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL; }
else if (value == "non-causal") { params.attention_type = LLAMA_ATTENTION_TYPE_NON_CAUSAL; }
else { invalid_param = true; }
return true;
}
if (arg == "--defrag-thold" || arg == "-dt") {
CHECK_ARG
params.defrag_thold = std::stof(argv[i]);
return true;
}
if (arg == "--samplers") {
CHECK_ARG
const auto sampler_names = string_split(argv[i], ';');
sparams.samplers_sequence = llama_sampling_types_from_names(sampler_names, true);
return true;
}
if (arg == "--sampling-seq") {
CHECK_ARG
sparams.samplers_sequence = llama_sampling_types_from_chars(argv[i]);
return true;
}
if (arg == "--top-p") {
CHECK_ARG
sparams.top_p = std::stof(argv[i]);
return true;
}
if (arg == "--min-p") {
CHECK_ARG
sparams.min_p = std::stof(argv[i]);
return true;
}
if (arg == "--temp") {
CHECK_ARG
sparams.temp = std::stof(argv[i]);
sparams.temp = std::max(sparams.temp, 0.0f);
return true;
}
if (arg == "--tfs") {
CHECK_ARG
sparams.tfs_z = std::stof(argv[i]);
return true;
}
if (arg == "--typical") {
CHECK_ARG
sparams.typical_p = std::stof(argv[i]);
return true;
}
if (arg == "--repeat-last-n") {
CHECK_ARG
sparams.penalty_last_n = std::stoi(argv[i]);
sparams.n_prev = std::max(sparams.n_prev, sparams.penalty_last_n);
return true;
}
if (arg == "--repeat-penalty") {
CHECK_ARG
sparams.penalty_repeat = std::stof(argv[i]);
return true;
}
if (arg == "--frequency-penalty") {
CHECK_ARG
sparams.penalty_freq = std::stof(argv[i]);
return true;
}
if (arg == "--presence-penalty") {
CHECK_ARG
sparams.penalty_present = std::stof(argv[i]);
return true;
}
if (arg == "--dynatemp-range") {
CHECK_ARG
sparams.dynatemp_range = std::stof(argv[i]);
return true;
}
if (arg == "--dynatemp-exp") {
CHECK_ARG
sparams.dynatemp_exponent = std::stof(argv[i]);
return true;
}
if (arg == "--mirostat") {
CHECK_ARG
sparams.mirostat = std::stoi(argv[i]);
return true;
}
if (arg == "--mirostat-lr") {
CHECK_ARG
sparams.mirostat_eta = std::stof(argv[i]);
return true;
}
if (arg == "--mirostat-ent") {
CHECK_ARG
sparams.mirostat_tau = std::stof(argv[i]);
return true;
}
if (arg == "--cfg-negative-prompt") {
CHECK_ARG
sparams.cfg_negative_prompt = argv[i];
return true;
}
if (arg == "--cfg-negative-prompt-file") {
CHECK_ARG
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(sparams.cfg_negative_prompt));
if (!sparams.cfg_negative_prompt.empty() && sparams.cfg_negative_prompt.back() == '\n') {
sparams.cfg_negative_prompt.pop_back();
}
return true;
}
if (arg == "--cfg-scale") {
CHECK_ARG
sparams.cfg_scale = std::stof(argv[i]);
return true;
}
if (arg == "-b" || arg == "--batch-size") {
CHECK_ARG
params.n_batch = std::stoi(argv[i]);
return true;
}
if (arg == "-ub" || arg == "--ubatch-size") {
CHECK_ARG
params.n_ubatch = std::stoi(argv[i]);
return true;
}
if (arg == "--keep") {
CHECK_ARG
params.n_keep = std::stoi(argv[i]);
return true;
}
if (arg == "--draft") {
CHECK_ARG
params.n_draft = std::stoi(argv[i]);
return true;
}
if (arg == "--chunks") {
CHECK_ARG
params.n_chunks = std::stoi(argv[i]);
return true;
}
if (arg == "-np" || arg == "--parallel") {
CHECK_ARG
params.n_parallel = std::stoi(argv[i]);
return true;
}
if (arg == "-ns" || arg == "--sequences") {
CHECK_ARG
params.n_sequences = std::stoi(argv[i]);
return true;
}
if (arg == "--p-split" || arg == "-ps") {
CHECK_ARG
params.p_split = std::stof(argv[i]);
return true;
}
if (arg == "-m" || arg == "--model") {
CHECK_ARG
params.model = argv[i];
return true;
}
if (arg == "-md" || arg == "--model-draft") {
CHECK_ARG
params.model_draft = argv[i];
return true;
}
if (arg == "-a" || arg == "--alias") {
CHECK_ARG
params.model_alias = argv[i];
return true;
}
if (arg == "-mu" || arg == "--model-url") {
CHECK_ARG
params.model_url = argv[i];
return true;
}
if (arg == "-hft" || arg == "--hf-token") {
if (++i >= argc) {
invalid_param = true;
return true;
}
params.hf_token = argv[i];
return true;
}
if (arg == "-hfr" || arg == "--hf-repo") {
CHECK_ARG
params.hf_repo = argv[i];
return true;
}
if (arg == "-hff" || arg == "--hf-file") {
CHECK_ARG
params.hf_file = argv[i];
return true;
}
if (arg == "--lora") {
CHECK_ARG
params.lora_adapters.push_back({
std::string(argv[i]),
1.0,
});
return true;
}
if (arg == "--lora-scaled") {
CHECK_ARG
std::string lora_adapter = argv[i];
CHECK_ARG
params.lora_adapters.push_back({
lora_adapter,
std::stof(argv[i]),
});
return true;
}
if (arg == "--lora-init-without-apply") {
params.lora_init_without_apply = true;
return true;
}
if (arg == "--control-vector") {
CHECK_ARG
params.control_vectors.push_back({ 1.0f, argv[i], });
return true;
}
if (arg == "--control-vector-scaled") {
CHECK_ARG
const char* fname = argv[i];
CHECK_ARG
params.control_vectors.push_back({ std::stof(argv[i]), fname, });
return true;
}
if (arg == "--control-vector-layer-range") {
CHECK_ARG
params.control_vector_layer_start = std::stoi(argv[i]);
CHECK_ARG
params.control_vector_layer_end = std::stoi(argv[i]);
return true;
}
if (arg == "--mmproj") {
CHECK_ARG
params.mmproj = argv[i];
return true;
}
if (arg == "--image") {
CHECK_ARG
params.image.emplace_back(argv[i]);
return true;
}
if (arg == "-i" || arg == "--interactive") {
params.interactive = true;
return true;
}
if (arg == "-sp" || arg == "--special") {
params.special = true;
return true;
}
if (arg == "--embedding" || arg == "--embeddings") {
params.embedding = true;
return true;
}
if (arg == "--embd-normalize") {
CHECK_ARG
params.embd_normalize = std::stoi(argv[i]);
return true;
}
if (arg == "--embd-output-format") {
CHECK_ARG
params.embd_out = argv[i];
return true;
}
if (arg == "--embd-separator") {
CHECK_ARG
params.embd_sep = argv[i];
return true;
}
if (arg == "-if" || arg == "--interactive-first") {
params.interactive_first = true;
return true;
}
if (arg == "-cnv" || arg == "--conversation") {
params.conversation = true;
return true;
}
if (arg == "--infill") {
params.infill = true;
return true;
}
if (arg == "-dkvc" || arg == "--dump-kv-cache") {
params.dump_kv_cache = true;
return true;
}
if (arg == "-nkvo" || arg == "--no-kv-offload") {
params.no_kv_offload = true;
return true;
}
if (arg == "-ctk" || arg == "--cache-type-k") {
params.cache_type_k = argv[++i];
return true;
}
if (arg == "-ctv" || arg == "--cache-type-v") {
params.cache_type_v = argv[++i];
return true;
}
if (arg == "-mli" || arg == "--multiline-input") {
params.multiline_input = true;
return true;
}
if (arg == "--simple-io") {
params.simple_io = true;
return true;
}
if (arg == "-cb" || arg == "--cont-batching") {
params.cont_batching = true;
return true;
}
if (arg == "-nocb" || arg == "--no-cont-batching") {
params.cont_batching = false;
return true;
}
if (arg == "-fa" || arg == "--flash-attn") {
params.flash_attn = true;
return true;
}
if (arg == "-co" || arg == "--color") {
params.use_color = true;
return true;
}
if (arg == "--mlock") {
params.use_mlock = true;
return true;
}
if (arg == "-ngl" || arg == "--gpu-layers" || arg == "--n-gpu-layers") {
CHECK_ARG
params.n_gpu_layers = std::stoi(argv[i]);
if (!llama_supports_gpu_offload()) {
fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers option will be ignored\n");
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
}
return true;
}
if (arg == "-ngld" || arg == "--gpu-layers-draft" || arg == "--n-gpu-layers-draft") {
CHECK_ARG
params.n_gpu_layers_draft = std::stoi(argv[i]);
if (!llama_supports_gpu_offload()) {
fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers-draft option will be ignored\n");
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
}
return true;
}
if (arg == "--main-gpu" || arg == "-mg") {
CHECK_ARG
params.main_gpu = std::stoi(argv[i]);
#ifndef GGML_USE_CUDA_SYCL_VULKAN
fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the main GPU has no effect.\n");
#endif // GGML_USE_CUDA_SYCL_VULKAN
return true;
}
if (arg == "--split-mode" || arg == "-sm") {
CHECK_ARG
std::string arg_next = argv[i];
if (arg_next == "none") {
params.split_mode = LLAMA_SPLIT_MODE_NONE;
}
else if (arg_next == "layer") {
params.split_mode = LLAMA_SPLIT_MODE_LAYER;
}
else if (arg_next == "row") {
#ifdef GGML_USE_SYCL
fprintf(stderr, "warning: The split mode value:[row] is not supported by llama.cpp with SYCL. It's developing.\nExit!\n");
exit(1);
#endif // GGML_USE_SYCL
params.split_mode = LLAMA_SPLIT_MODE_ROW;
}
else {
invalid_param = true;
return true;
}
#ifndef GGML_USE_CUDA_SYCL_VULKAN
fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the split mode has no effect.\n");
#endif // GGML_USE_CUDA_SYCL_VULKAN
return true;
}
if (arg == "--tensor-split" || arg == "-ts") {
CHECK_ARG
std::string arg_next = argv[i];
// split string by , and /
const std::regex regex{ R"([,/]+)" };
std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 };
std::vector<std::string> split_arg{ it, {} };
if (split_arg.size() >= llama_max_devices()) {
invalid_param = true;
return true;
}
for (size_t i = 0; i < llama_max_devices(); ++i) {
if (i < split_arg.size()) {
params.tensor_split[i] = std::stof(split_arg[i]);
}
else {
params.tensor_split[i] = 0.0f;
}
}
#ifndef GGML_USE_CUDA_SYCL_VULKAN
fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting a tensor split has no effect.\n");
#endif // GGML_USE_CUDA_SYCL_VULKAN
return true;
}
if (arg == "--rpc") {
CHECK_ARG
params.rpc_servers = argv[i];
return true;
}
if (arg == "--no-mmap") {
params.use_mmap = false;
return true;
}
if (arg == "--numa") {
CHECK_ARG
std::string value(argv[i]);
/**/ if (value == "distribute" || value == "") { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; }
else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
else { invalid_param = true; }
return true;
}
if (arg == "-v" || arg == "--verbose") {
params.verbosity = 1;
return true;
}
if (arg == "--verbosity") {
CHECK_ARG
params.verbosity = std::stoi(argv[i]);
return true;
}
if (arg == "--verbose-prompt") {
params.verbose_prompt = true;
return true;
}
if (arg == "--no-display-prompt") {
params.display_prompt = false;
return true;
}
if (arg == "-r" || arg == "--reverse-prompt") {
CHECK_ARG
params.antiprompt.emplace_back(argv[i]);
return true;
}
if (arg == "-ld" || arg == "--logdir") {
CHECK_ARG
params.logdir = argv[i];
if (params.logdir.back() != DIRECTORY_SEPARATOR) {
params.logdir += DIRECTORY_SEPARATOR;
}
return true;
}
if (arg == "-lcs" || arg == "--lookup-cache-static") {
CHECK_ARG
params.lookup_cache_static = argv[i];
return true;
}
if (arg == "-lcd" || arg == "--lookup-cache-dynamic") {
CHECK_ARG
params.lookup_cache_dynamic = argv[i];
return true;
}
if (arg == "--save-all-logits" || arg == "--kl-divergence-base") {
CHECK_ARG
params.logits_file = argv[i];
return true;
}
if (arg == "--perplexity" || arg == "--all-logits") {
params.logits_all = true;
return true;
}
if (arg == "--ppl-stride") {
CHECK_ARG
params.ppl_stride = std::stoi(argv[i]);
return true;
}
if (arg == "--ppl-output-type") {
CHECK_ARG
params.ppl_output_type = std::stoi(argv[i]);
return true;
}
if (arg == "-ptc" || arg == "--print-token-count") {
CHECK_ARG
params.n_print = std::stoi(argv[i]);
return true;
}
if (arg == "--check-tensors") {
params.check_tensors = true;
return true;
}
if (arg == "--hellaswag") {
params.hellaswag = true;
return true;
}
if (arg == "--hellaswag-tasks") {
CHECK_ARG
params.hellaswag_tasks = std::stoi(argv[i]);
return true;
}
if (arg == "--winogrande") {
params.winogrande = true;
return true;
}
if (arg == "--winogrande-tasks") {
CHECK_ARG
params.winogrande_tasks = std::stoi(argv[i]);
return true;
}
if (arg == "--multiple-choice") {
params.multiple_choice = true;
return true;
}
if (arg == "--multiple-choice-tasks") {
CHECK_ARG
params.multiple_choice_tasks = std::stoi(argv[i]);
return true;
}
if (arg == "--kl-divergence") {
params.kl_divergence = true;
return true;
}
if (arg == "--ignore-eos") {
params.ignore_eos = true;
return true;
}
if (arg == "--penalize-nl") {
sparams.penalize_nl = true;
return true;
}
if (arg == "-l" || arg == "--logit-bias") {
CHECK_ARG
std::stringstream ss(argv[i]);
llama_token key;
char sign;
std::string value_str;
try {
if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
sparams.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
}
else {
throw std::exception();
}
}
catch (const std::exception&) {
invalid_param = true;
return true;
}
return true;
}
if (arg == "-h" || arg == "--help" || arg == "--usage" ) {
params.usage = true;
return true;
}
if (arg == "--version") {
fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
exit(0);
}
if (arg == "--in-prefix-bos") {
params.input_prefix_bos = true;
params.enable_chat_template = false;
return true;
}
if (arg == "--in-prefix") {
CHECK_ARG
params.input_prefix = argv[i];
params.enable_chat_template = false;
return true;
}
if (arg == "--in-suffix") {
CHECK_ARG
params.input_suffix = argv[i];
params.enable_chat_template = false;
return true;
}
if (arg == "--spm-infill") {
params.spm_infill = true;
return true;
}
if (arg == "--grammar") {
CHECK_ARG
sparams.grammar = argv[i];
return true;
}
if (arg == "--grammar-file") {
CHECK_ARG
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
std::copy(
std::istreambuf_iterator<char>(file),
std::istreambuf_iterator<char>(),
std::back_inserter(sparams.grammar)
);
return true;
}
if (arg == "-j" || arg == "--json-schema") {
CHECK_ARG
sparams.grammar = json_schema_to_grammar(json::parse(argv[i]));
return true;
}
if (arg == "--override-kv") {
CHECK_ARG
if (!string_parse_kv_override(argv[i], params.kv_overrides)) {
fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
invalid_param = true;
return true;
}
return true;
}
if (arg == "--host") {
CHECK_ARG
params.hostname = argv[i];
return true;
}
if (arg == "--port") {
CHECK_ARG
params.port = std::stoi(argv[i]);
return true;
}
if (arg == "--path") {
CHECK_ARG
params.public_path = argv[i];
return true;
}
if (arg == "--api-key") {
CHECK_ARG
params.api_keys.push_back(argv[i]);
return true;
}
if (arg == "--api-key-file") {
CHECK_ARG
std::ifstream key_file(argv[i]);
if (!key_file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
std::string key;
while (std::getline(key_file, key)) {
if (!key.empty()) {
params.api_keys.push_back(key);
}
}
key_file.close();
return true;
}
if (arg == "--ssl-key-file") {
CHECK_ARG
params.ssl_file_key = argv[i];
return true;
}
if (arg == "--ssl-cert-file") {
CHECK_ARG
params.ssl_file_cert = argv[i];
return true;
}
if (arg == "--timeout" || arg == "-to") {
CHECK_ARG
params.timeout_read = std::stoi(argv[i]);
params.timeout_write = std::stoi(argv[i]);
return true;
}
if (arg == "--threads-http") {
CHECK_ARG
params.n_threads_http = std::stoi(argv[i]);
return true;
}
if (arg == "-spf" || arg == "--system-prompt-file") {
CHECK_ARG
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
std::string system_prompt;
std::copy(
std::istreambuf_iterator<char>(file),
std::istreambuf_iterator<char>(),
std::back_inserter(system_prompt)
);
params.system_prompt = system_prompt;
return true;
}
if (arg == "--log-format") {
CHECK_ARG
if (std::strcmp(argv[i], "json") == 0) {
params.log_json = true;
} else if (std::strcmp(argv[i], "text") == 0) {
params.log_json = false;
} else {
invalid_param = true;
return true;
}
return true;
}
if (arg == "--no-slots") {
params.endpoint_slots = false;
return true;
}
if (arg == "--metrics") {
params.endpoint_metrics = true;
return true;
}
if (arg == "--slot-save-path") {
CHECK_ARG
params.slot_save_path = argv[i];
// if doesn't end with DIRECTORY_SEPARATOR, add it
if (!params.slot_save_path.empty() && params.slot_save_path[params.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) {
params.slot_save_path += DIRECTORY_SEPARATOR;
}
return true;
}
if (arg == "--chat-template") {
CHECK_ARG
if (!llama_chat_verify_template(argv[i])) {
fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]);
fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n");
invalid_param = true;
return true;
}
params.chat_template = argv[i];
return true;
}
if (arg == "--slot-prompt-similarity" || arg == "-sps") {
CHECK_ARG
params.slot_prompt_similarity = std::stof(argv[i]);
return true;
}
if (arg == "-pps") {
params.is_pp_shared = true;
return true;
}
if (arg == "-npp") {
CHECK_ARG
auto p = string_split<int>(argv[i], split_delim);
params.n_pp.insert(params.n_pp.end(), p.begin(), p.end());
return true;
}
if (arg == "-ntg") {
CHECK_ARG
auto p = string_split<int>(argv[i], split_delim);
params.n_tg.insert(params.n_tg.end(), p.begin(), p.end());
return true;
}
if (arg == "-npl") {
CHECK_ARG
auto p = string_split<int>(argv[i], split_delim);
params.n_pl.insert(params.n_pl.end(), p.begin(), p.end());
return true;
}
if (arg == "--context-file") {
CHECK_ARG
std::ifstream file(argv[i], std::ios::binary);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
params.context_files.push_back(argv[i]);
return true;
}
if (arg == "--chunk-size") {
CHECK_ARG
params.chunk_size = std::stoi(argv[i]);
return true;
}
if (arg == "--chunk-separator") {
CHECK_ARG
params.chunk_separator = argv[i];
return true;
}
if (arg == "--junk") {
CHECK_ARG
params.n_junk = std::stoi(argv[i]);
return true;
}
if (arg == "--pos") {
CHECK_ARG
params.i_pos = std::stoi(argv[i]);
return true;
}
if (arg == "-o" || arg == "--output" || arg == "--output-file") {
CHECK_ARG
params.out_file = argv[i];
params.cvector_outfile = argv[i];
params.lora_outfile = argv[i];
return true;
}
if (arg == "-ofreq" || arg == "--output-frequency") {
CHECK_ARG
params.n_out_freq = std::stoi(argv[i]);
return true;
}
if (arg == "--save-frequency") {
CHECK_ARG
params.n_save_freq = std::stoi(argv[i]);
return true;
}
if (arg == "--process-output") {
params.process_output = true;
return true;
}
if (arg == "--no-ppl") {
params.compute_ppl = false;
return true;
}
if (arg == "--chunk" || arg == "--from-chunk") {
CHECK_ARG
params.i_chunk = std::stoi(argv[i]);
return true;
}
// cvector params
if (arg == "--positive-file") {
CHECK_ARG
params.cvector_positive_file = argv[i];
return true;
}
if (arg == "--negative-file") {
CHECK_ARG
params.cvector_negative_file = argv[i];
return true;
}
if (arg == "--pca-batch") {
CHECK_ARG
params.n_pca_batch = std::stoi(argv[i]);
return true;
}
if (arg == "--pca-iter") {
CHECK_ARG
params.n_pca_iterations = std::stoi(argv[i]);
return true;
}
if (arg == "--method") {
CHECK_ARG
std::string value(argv[i]);
/**/ if (value == "pca") { params.cvector_dimre_method = DIMRE_METHOD_PCA; }
else if (value == "mean") { params.cvector_dimre_method = DIMRE_METHOD_MEAN; }
else { invalid_param = true; }
return true;
}
if (arg == "--no-warmup") {
params.warmup = false;
return true;
}
#ifndef LOG_DISABLE_LOGS
// Parse args for logging parameters
if (log_param_single_parse(argv[i])) {
// Do nothing, log_param_single_parse automatically does it's thing
// and returns if a match was found and parsed.
return true;
}
if (log_param_pair_parse( /*check_but_dont_parse*/ true, argv[i])) {
// We have a matching known parameter requiring an argument,
// now we need to check if there is anything after this argv
// and flag invalid_param or parse it.
CHECK_ARG
if (!log_param_pair_parse( /*check_but_dont_parse*/ false, argv[i - 1], argv[i])) {
invalid_param = true;
return true;
}
return true;
}
// End of Parse args for logging parameters
#endif // LOG_DISABLE_LOGS
return false;
}
#ifdef __GNUC__
#ifdef __MINGW32__
#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
#else
#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
#endif
#else
#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...)
#endif
void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
const llama_sampling_params & sparams = params.sparams;
std::string sampler_type_chars;
std::string sampler_type_names;
for (const auto sampler_type : sparams.samplers_sequence) {
sampler_type_chars += static_cast<char>(sampler_type);
sampler_type_names += llama_sampling_type_to_str(sampler_type) + ";";
}
sampler_type_names.pop_back();
struct option_info {
LLAMA_COMMON_ATTRIBUTE_FORMAT(4, 5)
option_info(const std::string & tags, const char * args, const char * desc, ...) : tags(tags), args(args), desc(desc) {
va_list args_list;
va_start(args_list, desc);
char buffer[1024];
vsnprintf(buffer, sizeof(buffer), desc, args_list);
va_end(args_list);
this->desc = buffer;
}
option_info(const std::string & grp) : grp(grp) {}
std::string tags;
std::string args;
std::string desc;
std::string grp;
};
std::vector<option_info> options;
// TODO: filter by tags
options.push_back({ "general" });
options.push_back({ "*", "-h, --help, --usage", "print usage and exit" });
options.push_back({ "*", " --version", "show version and build info" });
options.push_back({ "*", "-v, --verbose", "print verbose information" });
options.push_back({ "*", " --verbosity N", "set specific verbosity level (default: %d)", params.verbosity });
options.push_back({ "*", " --verbose-prompt", "print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false" });
options.push_back({ "*", " --no-display-prompt", "don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false" });
options.push_back({ "*", "-co, --color", "colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false" });
options.push_back({ "*", "-s, --seed SEED", "RNG seed (default: %d, use random seed for < 0)", params.seed });
options.push_back({ "*", "-t, --threads N", "number of threads to use during generation (default: %d)", params.cpuparams.n_threads });
options.push_back({ "*", "-tb, --threads-batch N", "number of threads to use during batch and prompt processing (default: same as --threads)" });
options.push_back({ "speculative", "-td, --threads-draft N", "number of threads to use during generation (default: same as --threads)" });
options.push_back({ "speculative", "-tbd, --threads-batch-draft N","number of threads to use during batch and prompt processing (default: same as --threads-draft)" });
#ifndef GGML_USE_OPENMP
// these options are available only with the internal threadpool
options.push_back({ "*", "-C, --cpu-mask M", "CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: \"\")"});
options.push_back({ "*", "-Cr, --cpu-range lo-hi", "range of CPUs for affinity. Complements --cpu-mask"});
options.push_back({ "*", " --cpu-strict <0|1>", "use strict CPU placement (default: %u)\n", (unsigned) params.cpuparams.strict_cpu});
options.push_back({ "*", " --priority N", "set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams.priority});
options.push_back({ "*", " --poll <0...100>", "use polling level to wait for work (0 - no polling, default: %u)\n", (unsigned) params.cpuparams.poll});
options.push_back({ "*", "-Cb, --cpu-mask-batch M", "CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask)"});
options.push_back({ "*", "-Crb, --cpu-range-batch lo-hi", "ranges of CPUs for affinity. Complements --cpu-mask-batch"});
options.push_back({ "*", " --cpu-strict-batch <0|1>","use strict CPU placement (default: same as --cpu-strict)"});
options.push_back({ "*", " --priority-batch N", "set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: --priority)"});
options.push_back({ "*", " --poll-batch <0|1>", "use polling to wait for work (default: same as --poll"});
options.push_back({ "speculative", "-Cd, --cpu-mask-draft M", "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)"});
options.push_back({ "speculative", "-Crd, --cpu-range-draft lo-hi", "Ranges of CPUs for affinity. Complements --cpu-mask-draft"});
options.push_back({ "speculative", " --cpu-strict-draft <0|1>","Use strict CPU placement for draft model (default: same as --cpu-strict)"});
options.push_back({ "speculative", " --priority-draft N", "Set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: same as --priority)"});
options.push_back({ "speculative", " --poll-draft <0|1>", "Use polling to wait for draft model work (default: same as --poll])"});
options.push_back({ "speculative", "-Cbd, --cpu-mask-batch-draft M","Draft model CPU affinity mask. Complements cpu-range-draft-batch (default: same as --cpu-mask-draft)"});
options.push_back({ "speculative", "-Crbd, --cpu-range-batch-draft lo-hi",
"Ranges of CPUs for affinity. Complements --cpu-mask-draft-batch)"});
options.push_back({ "speculative", " --cpu-strict-batch-draft <0|1>",
"Use strict CPU placement for draft model (default: --cpu-strict-draft)"});
options.push_back({ "speculative", " --priority-batch-draft N","Set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: --priority-draft)"});
options.push_back({ "speculative", " --poll-batch-draft <0|1>","Use polling to wait for draft model work (default: --poll-draft)"});
#endif // GGML_USE_OPENMP
options.push_back({ "speculative", " --draft N", "number of tokens to draft for speculative decoding (default: %d)", params.n_draft });
options.push_back({ "speculative", "-ps, --p-split N", "speculative decoding split probability (default: %.1f)", (double)params.p_split });
options.push_back({ "*", "-lcs, --lookup-cache-static FNAME",
"path to static lookup cache to use for lookup decoding (not updated by generation)" });
options.push_back({ "*", "-lcd, --lookup-cache-dynamic FNAME",
"path to dynamic lookup cache to use for lookup decoding (updated by generation)" });
options.push_back({ "*", "-c, --ctx-size N", "size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx });
options.push_back({ "*", "-n, --predict N", "number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)", params.n_predict });
options.push_back({ "*", "-b, --batch-size N", "logical maximum batch size (default: %d)", params.n_batch });
options.push_back({ "*", "-ub, --ubatch-size N", "physical maximum batch size (default: %d)", params.n_ubatch });
options.push_back({ "*", " --keep N", "number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep });
options.push_back({ "*", " --chunks N", "max number of chunks to process (default: %d, -1 = all)", params.n_chunks });
options.push_back({ "*", "-fa, --flash-attn", "enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled" });
options.push_back({ "*", "-p, --prompt PROMPT", "prompt to start generation with\n"
"in conversation mode, this will be used as system prompt\n"
"(default: '%s')", params.prompt.c_str() });
options.push_back({ "*", "-f, --file FNAME", "a file containing the prompt (default: none)" });
options.push_back({ "*", " --in-file FNAME", "an input file (repeat to specify multiple files)" });
options.push_back({ "*", "-bf, --binary-file FNAME", "binary file containing the prompt (default: none)" });
options.push_back({ "*", "-e, --escape", "process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false" });
options.push_back({ "*", " --no-escape", "do not process escape sequences" });
options.push_back({ "main", "-ptc, --print-token-count N", "print token count every N tokens (default: %d)", params.n_print });
options.push_back({ "main", " --prompt-cache FNAME", "file to cache prompt state for faster startup (default: none)" });
options.push_back({ "main", " --prompt-cache-all", "if specified, saves user input and generations to cache as well\n"
"not supported with --interactive or other interactive options" });
options.push_back({ "main", " --prompt-cache-ro", "if specified, uses the prompt cache but does not update it" });
options.push_back({ "main", "-r, --reverse-prompt PROMPT",
"halt generation at PROMPT, return control in interactive mode\n"
"can be specified more than once for multiple prompts" });
options.push_back({ "main", "-sp, --special", "special tokens output enabled (default: %s)", params.special ? "true" : "false" });
options.push_back({ "main", "-cnv, --conversation", "run in conversation mode, does not print special tokens and suffix/prefix\n"
"if suffix/prefix are not specified, default chat template will be used\n"
"(default: %s)", params.conversation ? "true" : "false" });
options.push_back({ "main infill", "-i, --interactive", "run in interactive mode (default: %s)", params.interactive ? "true" : "false" });
options.push_back({ "main infill", "-if, --interactive-first", "run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false" });
options.push_back({ "main infill", "-mli, --multiline-input", "allows you to write or paste multiple lines without ending each in '\\'" });
options.push_back({ "main infill", " --in-prefix-bos", "prefix BOS to user inputs, preceding the `--in-prefix` string" });
options.push_back({ "main infill", " --in-prefix STRING", "string to prefix user inputs with (default: empty)" });
options.push_back({ "main infill", " --in-suffix STRING", "string to suffix after user inputs with (default: empty)" });
options.push_back({ "main", " --no-warmup", "skip warming up the model with an empty run" });
options.push_back({ "server infill",
" --spm-infill", "use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: %s)", params.spm_infill ? "enabled" : "disabled" });
options.push_back({ "sampling" });
options.push_back({ "*", " --samplers SAMPLERS", "samplers that will be used for generation in the order, separated by \';\'\n"
"(default: %s)", sampler_type_names.c_str() });
options.push_back({ "*", " --sampling-seq SEQUENCE",
"simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str() });
options.push_back({ "*", " --ignore-eos", "ignore end of stream token and continue generating (implies --logit-bias EOS-inf)" });
options.push_back({ "*", " --penalize-nl", "penalize newline tokens (default: %s)", sparams.penalize_nl ? "true" : "false" });
options.push_back({ "*", " --temp N", "temperature (default: %.1f)", (double)sparams.temp });
options.push_back({ "*", " --top-k N", "top-k sampling (default: %d, 0 = disabled)", sparams.top_k });
options.push_back({ "*", " --top-p N", "top-p sampling (default: %.1f, 1.0 = disabled)", (double)sparams.top_p });
options.push_back({ "*", " --min-p N", "min-p sampling (default: %.1f, 0.0 = disabled)", (double)sparams.min_p });
options.push_back({ "*", " --tfs N", "tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)sparams.tfs_z });
options.push_back({ "*", " --typical N", "locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)sparams.typical_p });
options.push_back({ "*", " --repeat-last-n N", "last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", sparams.penalty_last_n });
options.push_back({ "*", " --repeat-penalty N", "penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)sparams.penalty_repeat });
options.push_back({ "*", " --presence-penalty N", "repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)sparams.penalty_present });
options.push_back({ "*", " --frequency-penalty N", "repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)sparams.penalty_freq });
options.push_back({ "*", " --dynatemp-range N", "dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)sparams.dynatemp_range });
options.push_back({ "*", " --dynatemp-exp N", "dynamic temperature exponent (default: %.1f)", (double)sparams.dynatemp_exponent });
options.push_back({ "*", " --mirostat N", "use Mirostat sampling.\n"
"Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"
"(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", sparams.mirostat });
options.push_back({ "*", " --mirostat-lr N", "Mirostat learning rate, parameter eta (default: %.1f)", (double)sparams.mirostat_eta });
options.push_back({ "*", " --mirostat-ent N", "Mirostat target entropy, parameter tau (default: %.1f)", (double)sparams.mirostat_tau });
options.push_back({ "*", " -l TOKEN_ID(+/-)BIAS", "modifies the likelihood of token appearing in the completion,\n"
"i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"
"or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'" });
options.push_back({ "main", " --cfg-negative-prompt PROMPT",
"negative prompt to use for guidance (default: '%s')", sparams.cfg_negative_prompt.c_str() });
options.push_back({ "main", " --cfg-negative-prompt-file FNAME",
"negative prompt file to use for guidance" });
options.push_back({ "main", " --cfg-scale N", "strength of guidance (default: %.1f, 1.0 = disable)", (double)sparams.cfg_scale });
options.push_back({ "main", " --chat-template JINJA_TEMPLATE",
"set custom jinja chat template (default: template taken from model's metadata)\n"
"if suffix/prefix are specified, template will be disabled\n"
"only commonly used templates are accepted:\n"
"https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template" });
options.push_back({ "grammar" });
options.push_back({ "*", " --grammar GRAMMAR", "BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", sparams.grammar.c_str() });
options.push_back({ "*", " --grammar-file FNAME", "file to read grammar from" });
options.push_back({ "*", "-j, --json-schema SCHEMA",
"JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\n"
"For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead" });
options.push_back({ "embedding" });
options.push_back({ "embedding", " --pooling {none,mean,cls,last}",
"pooling type for embeddings, use model default if unspecified" });
options.push_back({ "embedding", " --attention {causal,non-causal}",
"attention type for embeddings, use model default if unspecified" });
options.push_back({ "context hacking" });
options.push_back({ "*", " --rope-scaling {none,linear,yarn}",
"RoPE frequency scaling method, defaults to linear unless specified by the model" });
options.push_back({ "*", " --rope-scale N", "RoPE context scaling factor, expands context by a factor of N" });
options.push_back({ "*", " --rope-freq-base N", "RoPE base frequency, used by NTK-aware scaling (default: loaded from model)" });
options.push_back({ "*", " --rope-freq-scale N", "RoPE frequency scaling factor, expands context by a factor of 1/N" });
options.push_back({ "*", " --yarn-orig-ctx N", "YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx });
options.push_back({ "*", " --yarn-ext-factor N", "YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor });
options.push_back({ "*", " --yarn-attn-factor N", "YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor });
options.push_back({ "*", " --yarn-beta-slow N", "YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow });
options.push_back({ "*", " --yarn-beta-fast N", "YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast });
options.push_back({ "*", "-gan, --grp-attn-n N", "group-attention factor (default: %d)", params.grp_attn_n });
options.push_back({ "*", "-gaw, --grp-attn-w N", "group-attention width (default: %.1f)", (double)params.grp_attn_w });
options.push_back({ "*", "-dkvc, --dump-kv-cache", "verbose print of the KV cache" });
options.push_back({ "*", "-nkvo, --no-kv-offload", "disable KV offload" });
options.push_back({ "*", "-ctk, --cache-type-k TYPE", "KV cache data type for K (default: %s)", params.cache_type_k.c_str() });
options.push_back({ "*", "-ctv, --cache-type-v TYPE", "KV cache data type for V (default: %s)", params.cache_type_v.c_str() });
options.push_back({ "perplexity" });
options.push_back({ "perplexity", " --all-logits", "return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false" });
options.push_back({ "perplexity", " --hellaswag", "compute HellaSwag score over random tasks from datafile supplied with -f" });
options.push_back({ "perplexity", " --hellaswag-tasks N", "number of tasks to use when computing the HellaSwag score (default: %zu)", params.hellaswag_tasks });
options.push_back({ "perplexity", " --winogrande", "compute Winogrande score over random tasks from datafile supplied with -f" });
options.push_back({ "perplexity", " --winogrande-tasks N", "number of tasks to use when computing the Winogrande score (default: %zu)", params.winogrande_tasks });
options.push_back({ "perplexity", " --multiple-choice", "compute multiple choice score over random tasks from datafile supplied with -f" });
options.push_back({ "perplexity", " --multiple-choice-tasks N",
"number of tasks to use when computing the multiple choice score (default: %zu)", params.multiple_choice_tasks });
options.push_back({ "perplexity", " --kl-divergence", "computes KL-divergence to logits provided via --kl-divergence-base" });
options.push_back({ "perplexity", " --ppl-stride N", "stride for perplexity calculation (default: %d)", params.ppl_stride });
options.push_back({ "perplexity", " --ppl-output-type {0,1}",
"output type for perplexity calculation (default: %d)", params.ppl_output_type });
options.push_back({ "parallel" });
options.push_back({ "*", "-dt, --defrag-thold N", "KV cache defragmentation threshold (default: %.1f, < 0 - disabled)", (double)params.defrag_thold });
options.push_back({ "*", "-np, --parallel N", "number of parallel sequences to decode (default: %d)", params.n_parallel });
options.push_back({ "*", "-ns, --sequences N", "number of sequences to decode (default: %d)", params.n_sequences });
options.push_back({ "*", "-cb, --cont-batching", "enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled" });
options.push_back({ "*", "-nocb, --no-cont-batching", "disable continuous batching" });
options.push_back({ "multi-modality" });
options.push_back({ "*", " --mmproj FILE", "path to a multimodal projector file for LLaVA. see examples/llava/README.md" });
options.push_back({ "*", " --image FILE", "path to an image file. use with multimodal models. Specify multiple times for batching" });
options.push_back({ "backend" });
options.push_back({ "*", " --rpc SERVERS", "comma separated list of RPC servers" });
if (llama_supports_mlock()) {
options.push_back({ "*", " --mlock", "force system to keep model in RAM rather than swapping or compressing" });
}
if (llama_supports_mmap()) {
options.push_back({ "*", " --no-mmap", "do not memory-map model (slower load but may reduce pageouts if not using mlock)" });
}
options.push_back({ "*", " --numa TYPE", "attempt optimizations that help on some NUMA systems\n"
" - distribute: spread execution evenly over all nodes\n"
" - isolate: only spawn threads on CPUs on the node that execution started on\n"
" - numactl: use the CPU map provided by numactl\n"
"if run without this previously, it is recommended to drop the system page cache before using this\n"
"see https://github.com/ggerganov/llama.cpp/issues/1437" });
if (llama_supports_gpu_offload()) {
options.push_back({ "*", "-ngl, --gpu-layers N",
"number of layers to store in VRAM" });
options.push_back({ "*", "-ngld, --gpu-layers-draft N",
"number of layers to store in VRAM for the draft model" });
options.push_back({ "*", "-sm, --split-mode SPLIT_MODE",
"how to split the model across multiple GPUs, one of:\n"
" - none: use one GPU only\n"
" - layer (default): split layers and KV across GPUs\n"
" - row: split rows across GPUs" });
options.push_back({ "*", "-ts, --tensor-split SPLIT",
"fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1" });
options.push_back({ "*", "-mg, --main-gpu i", "the GPU to use for the model (with split-mode = none),\n"
"or for intermediate results and KV (with split-mode = row) (default: %d)", params.main_gpu });
}
options.push_back({ "model" });
options.push_back({ "*", " --check-tensors", "check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false" });
options.push_back({ "*", " --override-kv KEY=TYPE:VALUE",
"advanced option to override model metadata by key. may be specified multiple times.\n"
"types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false" });
options.push_back({ "*", " --lora FNAME", "apply LoRA adapter (can be repeated to use multiple adapters)" });
options.push_back({ "*", " --lora-scaled FNAME S", "apply LoRA adapter with user defined scaling S (can be repeated to use multiple adapters)" });
options.push_back({ "*", " --control-vector FNAME", "add a control vector\n"
"note: this argument can be repeated to add multiple control vectors" });
options.push_back({ "*", " --control-vector-scaled FNAME SCALE",
"add a control vector with user defined scaling SCALE\n"
"note: this argument can be repeated to add multiple scaled control vectors" });
options.push_back({ "*", " --control-vector-layer-range START END",
"layer range to apply the control vector(s) to, start and end inclusive" });
options.push_back({ "*", "-m, --model FNAME", "model path (default: models/$filename with filename from --hf-file\n"
"or --model-url if set, otherwise %s)", DEFAULT_MODEL_PATH });
options.push_back({ "*", "-md, --model-draft FNAME", "draft model for speculative decoding (default: unused)" });
options.push_back({ "*", "-mu, --model-url MODEL_URL", "model download url (default: unused)" });
options.push_back({ "*", "-hfr, --hf-repo REPO", "Hugging Face model repository (default: unused)" });
options.push_back({ "*", "-hff, --hf-file FILE", "Hugging Face model file (default: unused)" });
options.push_back({ "*", "-hft, --hf-token TOKEN", "Hugging Face access token (default: value from HF_TOKEN environment variable)" });
options.push_back({ "retrieval" });
options.push_back({ "retrieval", " --context-file FNAME", "file to load context from (repeat to specify multiple files)" });
options.push_back({ "retrieval", " --chunk-size N", "minimum length of embedded text chunks (default: %d)", params.chunk_size });
options.push_back({ "retrieval", " --chunk-separator STRING",
"separator between chunks (default: '%s')", params.chunk_separator.c_str() });
options.push_back({ "passkey" });
options.push_back({ "passkey", " --junk N", "number of times to repeat the junk text (default: %d)", params.n_junk });
options.push_back({ "passkey", " --pos N", "position of the passkey in the junk text (default: %d)", params.i_pos });
options.push_back({ "imatrix" });
options.push_back({ "imatrix", "-o, --output FNAME", "output file (default: '%s')", params.out_file.c_str() });
options.push_back({ "imatrix", " --output-frequency N", "output the imatrix every N iterations (default: %d)", params.n_out_freq });
options.push_back({ "imatrix", " --save-frequency N", "save an imatrix copy every N iterations (default: %d)", params.n_save_freq });
options.push_back({ "imatrix", " --process-output", "collect data for the output tensor (default: %s)", params.process_output ? "true" : "false" });
options.push_back({ "imatrix", " --no-ppl", "do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false" });
options.push_back({ "imatrix", " --chunk N", "start processing the input from chunk N (default: %d)", params.i_chunk });
options.push_back({ "bench" });
options.push_back({ "bench", "-pps", "is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false" });
options.push_back({ "bench", "-npp n0,n1,...", "number of prompt tokens" });
options.push_back({ "bench", "-ntg n0,n1,...", "number of text generation tokens" });
options.push_back({ "bench", "-npl n0,n1,...", "number of parallel prompts" });
options.push_back({ "embedding" });
options.push_back({ "embedding", " --embd-normalize", "normalisation for embendings (default: %d) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)", params.embd_normalize });
options.push_back({ "embedding", " --embd-output-format", "empty = default, \"array\" = [[],[]...], \"json\" = openai style, \"json+\" = same \"json\" + cosine similarity matrix" });
options.push_back({ "embedding", " --embd-separator", "separator of embendings (default \\n) for example \"<#sep#>\"" });
options.push_back({ "server" });
options.push_back({ "server", " --host HOST", "ip address to listen (default: %s)", params.hostname.c_str() });
options.push_back({ "server", " --port PORT", "port to listen (default: %d)", params.port });
options.push_back({ "server", " --path PATH", "path to serve static files from (default: %s)", params.public_path.c_str() });
options.push_back({ "server", " --embedding(s)", "restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled" });
options.push_back({ "server", " --api-key KEY", "API key to use for authentication (default: none)" });
options.push_back({ "server", " --api-key-file FNAME", "path to file containing API keys (default: none)" });
options.push_back({ "server", " --ssl-key-file FNAME", "path to file a PEM-encoded SSL private key" });
options.push_back({ "server", " --ssl-cert-file FNAME", "path to file a PEM-encoded SSL certificate" });
options.push_back({ "server", " --timeout N", "server read/write timeout in seconds (default: %d)", params.timeout_read });
options.push_back({ "server", " --threads-http N", "number of threads used to process HTTP requests (default: %d)", params.n_threads_http });
options.push_back({ "server", " --system-prompt-file FNAME",
"set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications" });
options.push_back({ "server", " --log-format {text,json}",
"log output format: json or text (default: json)" });
options.push_back({ "server", " --metrics", "enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled" });
options.push_back({ "server", " --no-slots", "disables slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled" });
options.push_back({ "server", " --slot-save-path PATH", "path to save slot kv cache (default: disabled)" });
options.push_back({ "server", " --chat-template JINJA_TEMPLATE",
"set custom jinja chat template (default: template taken from model's metadata)\n"
"only commonly used templates are accepted:\n"
"https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template" });
options.push_back({ "server", "-sps, --slot-prompt-similarity SIMILARITY",
"how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity });
options.push_back({ "server", " --lora-init-without-apply", "load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: %s)", params.lora_init_without_apply ? "enabled" : "disabled"});
#ifndef LOG_DISABLE_LOGS
options.push_back({ "logging" });
options.push_back({ "*", " --simple-io", "use basic IO for better compatibility in subprocesses and limited consoles" });
options.push_back({ "*", "-ld, --logdir LOGDIR", "path under which to save YAML logs (no logging if unset)" });
options.push_back({ "logging", " --log-test", "Run simple logging test" });
options.push_back({ "logging", " --log-disable", "Disable trace logs" });
options.push_back({ "logging", " --log-enable", "Enable trace logs" });
options.push_back({ "logging", " --log-file FNAME", "Specify a log filename (without extension)" });
options.push_back({ "logging", " --log-new", "Create a separate new log file on start. "
"Each log file will have unique name: \"<name>.<ID>.log\"" });
options.push_back({ "logging", " --log-append", "Don't truncate the old log file." });
#endif // LOG_DISABLE_LOGS
options.push_back({ "cvector" });
options.push_back({ "cvector", "-o, --output FNAME", "output file (default: '%s')", params.cvector_outfile.c_str() });
options.push_back({ "cvector", " --positive-file FNAME", "positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str() });
options.push_back({ "cvector", " --negative-file FNAME", "negative prompts file, one prompt per line (default: '%s')", params.cvector_negative_file.c_str() });
options.push_back({ "cvector", " --pca-batch N", "batch size used for PCA. Larger batch runs faster, but uses more memory (default: %d)", params.n_pca_batch });
options.push_back({ "cvector", " --pca-iter N", "number of iterations used for PCA (default: %d)", params.n_pca_iterations });
options.push_back({ "cvector", " --method {pca,mean}", "dimensionality reduction method to be used (default: pca)" });
options.push_back({ "export-lora" });
options.push_back({ "export-lora", "-m, --model", "model path from which to load base model (default '%s')", params.model.c_str() });
options.push_back({ "export-lora", " --lora FNAME", "path to LoRA adapter (can be repeated to use multiple adapters)" });
options.push_back({ "export-lora", " --lora-scaled FNAME S", "path to LoRA adapter with user defined scaling S (can be repeated to use multiple adapters)" });
options.push_back({ "export-lora", "-o, --output FNAME", "output file (default: '%s')", params.lora_outfile.c_str() });
printf("usage: %s [options]\n", argv[0]);
for (const auto & o : options) {
if (!o.grp.empty()) {
printf("\n%s:\n\n", o.grp.c_str());
continue;
}
printf(" %-32s", o.args.c_str());
if (o.args.length() > 30) {
printf("\n%34s", "");
}
const auto desc = o.desc;
size_t start = 0;
size_t end = desc.find('\n');
while (end != std::string::npos) {
printf("%s\n%34s", desc.substr(start, end - start).c_str(), "");
start = end + 1;
end = desc.find('\n', start);
}
printf("%s\n", desc.substr(start).c_str());
}
printf("\n");
}
std::string gpt_params_get_system_info(const gpt_params & params) {
std::ostringstream os;
os << "system_info: n_threads = " << params.cpuparams.n_threads;
if (params.cpuparams_batch.n_threads != -1) {
os << " (n_threads_batch = " << params.cpuparams_batch.n_threads << ")";
}
#if defined(_WIN32) && (_WIN32_WINNT >= 0x0601) && !defined(__MINGW64__) // windows 7 and later
// TODO: windows + arm64 + mingw64
DWORD logicalProcessorCount = GetActiveProcessorCount(ALL_PROCESSOR_GROUPS);
os << " / " << logicalProcessorCount << " | " << llama_print_system_info();
#else
os << " / " << std::thread::hardware_concurrency() << " | " << llama_print_system_info();
#endif
return os.str();
}
//
// String utils
//
std::vector<std::string> string_split(std::string input, char separator) {
std::vector<std::string> parts;
size_t separator_pos = input.find(separator);
while (separator_pos != std::string::npos) {
std::string part = input.substr(0, separator_pos);
parts.emplace_back(part);
input = input.substr(separator_pos + 1);
separator_pos = input.find(separator);
}
parts.emplace_back(input);
return parts;
}
std::string string_strip(const std::string & str) {
size_t start = 0;
size_t end = str.size();
while (start < end && std::isspace(str[start])) {
start++;
}
while (end > start && std::isspace(str[end - 1])) {
end--;
}
return str.substr(start, end - start);
}
std::string string_get_sortable_timestamp() {
using clock = std::chrono::system_clock;
const clock::time_point current_time = clock::now();
const time_t as_time_t = clock::to_time_t(current_time);
char timestamp_no_ns[100];
std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t));
const int64_t ns = std::chrono::duration_cast<std::chrono::nanoseconds>(
current_time.time_since_epoch() % 1000000000).count();
char timestamp_ns[11];
snprintf(timestamp_ns, 11, "%09" PRId64, ns);
return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns);
}
void string_replace_all(std::string & s, const std::string & search, const std::string & replace) {
if (search.empty()) {
return;
}
std::string builder;
builder.reserve(s.length());
size_t pos = 0;
size_t last_pos = 0;
while ((pos = s.find(search, last_pos)) != std::string::npos) {
builder.append(s, last_pos, pos - last_pos);
builder.append(replace);
last_pos = pos + search.length();
}
builder.append(s, last_pos, std::string::npos);
s = std::move(builder);
}
void string_process_escapes(std::string & input) {
std::size_t input_len = input.length();
std::size_t output_idx = 0;
for (std::size_t input_idx = 0; input_idx < input_len; ++input_idx) {
if (input[input_idx] == '\\' && input_idx + 1 < input_len) {
switch (input[++input_idx]) {
case 'n': input[output_idx++] = '\n'; break;
case 'r': input[output_idx++] = '\r'; break;
case 't': input[output_idx++] = '\t'; break;
case '\'': input[output_idx++] = '\''; break;
case '\"': input[output_idx++] = '\"'; break;
case '\\': input[output_idx++] = '\\'; break;
case 'x':
// Handle \x12, etc
if (input_idx + 2 < input_len) {
const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 };
char *err_p = nullptr;
const long val = std::strtol(x, &err_p, 16);
if (err_p == x + 2) {
input_idx += 2;
input[output_idx++] = char(val);
break;
}
}
// fall through
default: input[output_idx++] = '\\';
input[output_idx++] = input[input_idx]; break;
}
} else {
input[output_idx++] = input[input_idx];
}
}
input.resize(output_idx);
}
bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
const char * sep = strchr(data, '=');
if (sep == nullptr || sep - data >= 128) {
fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
return false;
}
llama_model_kv_override kvo;
std::strncpy(kvo.key, data, sep - data);
kvo.key[sep - data] = 0;
sep++;
if (strncmp(sep, "int:", 4) == 0) {
sep += 4;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
kvo.val_i64 = std::atol(sep);
} else if (strncmp(sep, "float:", 6) == 0) {
sep += 6;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
kvo.val_f64 = std::atof(sep);
} else if (strncmp(sep, "bool:", 5) == 0) {
sep += 5;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
if (std::strcmp(sep, "true") == 0) {
kvo.val_bool = true;
} else if (std::strcmp(sep, "false") == 0) {
kvo.val_bool = false;
} else {
fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
return false;
}
} else if (strncmp(sep, "str:", 4) == 0) {
sep += 4;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
if (strlen(sep) > 127) {
fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data);
return false;
}
strncpy(kvo.val_str, sep, 127);
kvo.val_str[127] = '\0';
} else {
fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
return false;
}
overrides.emplace_back(std::move(kvo));
return true;
}
//
// Filesystem utils
//
// Validate if a filename is safe to use
// To validate a full path, split the path by the OS-specific path separator, and validate each part with this function
bool fs_validate_filename(const std::string & filename) {
if (!filename.length()) {
// Empty filename invalid
return false;
}
if (filename.length() > 255) {
// Limit at common largest possible filename on Linux filesystems
// to avoid unnecessary further validation
// (On systems with smaller limits it will be caught by the OS)
return false;
}
std::u32string filename_utf32;
try {
std::wstring_convert<std::codecvt_utf8<char32_t>, char32_t> converter;
filename_utf32 = converter.from_bytes(filename);
// If the reverse conversion mismatches, it means overlong UTF-8 sequences were used,
// or invalid encodings were encountered. Reject such attempts
std::string filename_reencoded = converter.to_bytes(filename_utf32);
if (filename_reencoded != filename) {
return false;
}
} catch (const std::exception &) {
return false;
}
// Check for forbidden codepoints:
// - Control characters
// - Unicode equivalents of illegal characters
// - UTF-16 surrogate pairs
// - UTF-8 replacement character
// - Byte order mark (BOM)
// - Illegal characters: / \ : * ? " < > |
for (char32_t c : filename_utf32) {
if (c <= 0x1F // Control characters (C0)
|| c == 0x7F // Control characters (DEL)
|| (c >= 0x80 && c <= 0x9F) // Control characters (C1)
|| c == 0xFF0E // Fullwidth Full Stop (period equivalent)
|| c == 0x2215 // Division Slash (forward slash equivalent)
|| c == 0x2216 // Set Minus (backslash equivalent)
|| (c >= 0xD800 && c <= 0xDFFF) // UTF-16 surrogate pairs
|| c == 0xFFFD // Replacement Character (UTF-8)
|| c == 0xFEFF // Byte Order Mark (BOM)
|| c == '/' || c == '\\' || c == ':' || c == '*' // Illegal characters
|| c == '?' || c == '"' || c == '<' || c == '>' || c == '|') {
return false;
}
}
// Reject any leading or trailing ' ', or any trailing '.', these are stripped on Windows and will cause a different filename
// Unicode and other whitespace is not affected, only 0x20 space
if (filename.front() == ' ' || filename.back() == ' ' || filename.back() == '.') {
return false;
}
// Reject any ".." (currently stricter than necessary, it should be fine to just check for == ".." instead)
if (filename.find("..") != std::string::npos) {
return false;
}
// Reject "."
if (filename == ".") {
return false;
}
return true;
}
// returns true if successful, false otherwise
bool fs_create_directory_with_parents(const std::string & path) {
#ifdef _WIN32
std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
std::wstring wpath = converter.from_bytes(path);
// if the path already exists, check whether it's a directory
const DWORD attributes = GetFileAttributesW(wpath.c_str());
if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) {
return true;
}
size_t pos_slash = 0;
// process path from front to back, procedurally creating directories
while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) {
const std::wstring subpath = wpath.substr(0, pos_slash);
const wchar_t * test = subpath.c_str();
const bool success = CreateDirectoryW(test, NULL);
if (!success) {
const DWORD error = GetLastError();
// if the path already exists, ensure that it's a directory
if (error == ERROR_ALREADY_EXISTS) {
const DWORD attributes = GetFileAttributesW(subpath.c_str());
if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) {
return false;
}
} else {
return false;
}
}
pos_slash += 1;
}
return true;
#else
// if the path already exists, check whether it's a directory
struct stat info;
if (stat(path.c_str(), &info) == 0) {
return S_ISDIR(info.st_mode);
}
size_t pos_slash = 1; // skip leading slashes for directory creation
// process path from front to back, procedurally creating directories
while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) {
const std::string subpath = path.substr(0, pos_slash);
struct stat info;
// if the path already exists, ensure that it's a directory
if (stat(subpath.c_str(), &info) == 0) {
if (!S_ISDIR(info.st_mode)) {
return false;
}
} else {
// create parent directories
const int ret = mkdir(subpath.c_str(), 0755);
if (ret != 0) {
return false;
}
}
pos_slash += 1;
}
return true;
#endif // _WIN32
}
std::string fs_get_cache_directory() {
std::string cache_directory = "";
auto ensure_trailing_slash = [](std::string p) {
// Make sure to add trailing slash
if (p.back() != DIRECTORY_SEPARATOR) {
p += DIRECTORY_SEPARATOR;
}
return p;
};
if (getenv("LLAMA_CACHE")) {
cache_directory = std::getenv("LLAMA_CACHE");
} else {
#ifdef __linux__
if (std::getenv("XDG_CACHE_HOME")) {
cache_directory = std::getenv("XDG_CACHE_HOME");
} else {
cache_directory = std::getenv("HOME") + std::string("/.cache/");
}
#elif defined(__APPLE__)
cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
#elif defined(_WIN32)
cache_directory = std::getenv("LOCALAPPDATA");
#endif // __linux__
cache_directory = ensure_trailing_slash(cache_directory);
cache_directory += "llama.cpp";
}
return ensure_trailing_slash(cache_directory);
}
std::string fs_get_cache_file(const std::string & filename) {
GGML_ASSERT(filename.find(DIRECTORY_SEPARATOR) == std::string::npos);
std::string cache_directory = fs_get_cache_directory();
const bool success = fs_create_directory_with_parents(cache_directory);
if (!success) {
throw std::runtime_error("failed to create cache directory: " + cache_directory);
}
return cache_directory + filename;
}
//
// Model utils
//
struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
llama_init_result iparams;
auto mparams = llama_model_params_from_gpt_params(params);
llama_model * model = nullptr;
if (!params.hf_repo.empty() && !params.hf_file.empty()) {
model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
} else if (!params.model_url.empty()) {
model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
} else {
model = llama_load_model_from_file(params.model.c_str(), mparams);
}
if (model == NULL) {
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
return iparams;
}
auto cparams = llama_context_params_from_gpt_params(params);
llama_context * lctx = llama_new_context_with_model(model, cparams);
if (lctx == NULL) {
fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
llama_free_model(model);
return iparams;
}
if (!params.control_vectors.empty()) {
if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model);
const auto cvec = llama_control_vector_load(params.control_vectors);
if (cvec.n_embd == -1) {
llama_free(lctx);
llama_free_model(model);
return iparams;
}
int err = llama_control_vector_apply(lctx,
cvec.data.data(),
cvec.data.size(),
cvec.n_embd,
params.control_vector_layer_start,
params.control_vector_layer_end);
if (err) {
llama_free(lctx);
llama_free_model(model);
return iparams;
}
}
// load and optionally apply lora adapters
for (auto & la : params.lora_adapters) {
llama_lora_adapter_container loaded_la;
loaded_la.path = la.path;
loaded_la.scale = la.scale;
loaded_la.adapter = llama_lora_adapter_init(model, la.path.c_str());
if (loaded_la.adapter == nullptr) {
fprintf(stderr, "%s: error: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
llama_free(lctx);
llama_free_model(model);
return iparams;
}
iparams.lora_adapters.push_back(loaded_la); // copy to list of loaded adapters
}
if (!params.lora_init_without_apply) {
llama_lora_adapters_apply(lctx, iparams.lora_adapters);
}
if (params.ignore_eos) {
params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
}
if (params.warmup) {
LOG("warming up the model with an empty run\n");
std::vector<llama_token> tmp;
llama_token bos = llama_token_bos(model);
llama_token eos = llama_token_eos(model);
// some models (e.g. T5) don't have a BOS token
if (bos != -1) {
tmp.push_back(bos);
}
tmp.push_back(eos);
if (llama_model_has_encoder(model)) {
llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size(), 0, 0));
llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
if (decoder_start_token_id == -1) {
decoder_start_token_id = bos;
}
tmp.clear();
tmp.push_back(decoder_start_token_id);
}
if (llama_model_has_decoder(model)) {
llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
}
llama_kv_cache_clear(lctx);
llama_synchronize(lctx);
llama_reset_timings(lctx);
}
iparams.model = model;
iparams.context = lctx;
return iparams;
}
void llama_lora_adapters_apply(struct llama_context * ctx, std::vector<llama_lora_adapter_container> & lora_adapters) {
llama_lora_adapter_clear(ctx);
for (auto & la : lora_adapters) {
if (la.scale != 0.0f) {
llama_lora_adapter_set(ctx, la.adapter, la.scale);
}
}
}
struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & params) {
auto mparams = llama_model_default_params();
if (params.n_gpu_layers != -1) {
mparams.n_gpu_layers = params.n_gpu_layers;
}
mparams.rpc_servers = params.rpc_servers.c_str();
mparams.main_gpu = params.main_gpu;
mparams.split_mode = params.split_mode;
mparams.tensor_split = params.tensor_split;
mparams.use_mmap = params.use_mmap;
mparams.use_mlock = params.use_mlock;
mparams.check_tensors = params.check_tensors;
if (params.kv_overrides.empty()) {
mparams.kv_overrides = NULL;
} else {
GGML_ASSERT(params.kv_overrides.back().key[0] == 0 && "KV overrides not terminated with empty key");
mparams.kv_overrides = params.kv_overrides.data();
}
return mparams;
}
static ggml_type kv_cache_type_from_str(const std::string & s) {
if (s == "f32") {
return GGML_TYPE_F32;
}
if (s == "f16") {
return GGML_TYPE_F16;
}
if (s == "q8_0") {
return GGML_TYPE_Q8_0;
}
if (s == "q4_0") {
return GGML_TYPE_Q4_0;
}
if (s == "q4_1") {
return GGML_TYPE_Q4_1;
}
if (s == "iq4_nl") {
return GGML_TYPE_IQ4_NL;
}
if (s == "q5_0") {
return GGML_TYPE_Q5_0;
}
if (s == "q5_1") {
return GGML_TYPE_Q5_1;
}
throw std::runtime_error("Invalid cache type: " + s);
}
struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) {
auto cparams = llama_context_default_params();
cparams.n_ctx = params.n_ctx;
cparams.n_seq_max = params.n_parallel;
cparams.n_batch = params.n_batch;
cparams.n_ubatch = params.n_ubatch;
cparams.n_threads = params.cpuparams.n_threads;
cparams.n_threads_batch = params.cpuparams_batch.n_threads == -1 ?
params.cpuparams.n_threads : params.cpuparams_batch.n_threads;
cparams.seed = params.seed;
cparams.logits_all = params.logits_all;
cparams.embeddings = params.embedding;
cparams.rope_scaling_type = params.rope_scaling_type;
cparams.rope_freq_base = params.rope_freq_base;
cparams.rope_freq_scale = params.rope_freq_scale;
cparams.yarn_ext_factor = params.yarn_ext_factor;
cparams.yarn_attn_factor = params.yarn_attn_factor;
cparams.yarn_beta_fast = params.yarn_beta_fast;
cparams.yarn_beta_slow = params.yarn_beta_slow;
cparams.yarn_orig_ctx = params.yarn_orig_ctx;
cparams.pooling_type = params.pooling_type;
cparams.attention_type = params.attention_type;
cparams.defrag_thold = params.defrag_thold;
cparams.cb_eval = params.cb_eval;
cparams.cb_eval_user_data = params.cb_eval_user_data;
cparams.offload_kqv = !params.no_kv_offload;
cparams.flash_attn = params.flash_attn;
cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
cparams.type_v = kv_cache_type_from_str(params.cache_type_v);
return cparams;
}
struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params) {
struct ggml_threadpool_params tpp;
ggml_threadpool_params_init(&tpp, params.n_threads); // setup the defaults
if (params.mask_valid) {
std::memcpy(&tpp.cpumask, &params.cpumask, GGML_MAX_N_THREADS);
}
tpp.prio = params.priority;
tpp.poll = params.poll;
tpp.strict_cpu = params.strict_cpu;
return tpp;
}
#ifdef LLAMA_USE_CURL
static bool starts_with(const std::string & str, const std::string & prefix) {
// While we wait for C++20's std::string::starts_with...
return str.rfind(prefix, 0) == 0;
}
static bool llama_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {
// Initialize libcurl
std::unique_ptr<CURL, decltype(&curl_easy_cleanup)> curl(curl_easy_init(), &curl_easy_cleanup);
if (!curl) {
fprintf(stderr, "%s: error initializing libcurl\n", __func__);
return false;
}
bool force_download = false;
// Set the URL, allow to follow http redirection
curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);
// Check if hf-token or bearer-token was specified
if (!hf_token.empty()) {
std::string auth_header = "Authorization: Bearer ";
auth_header += hf_token.c_str();
struct curl_slist *http_headers = NULL;
http_headers = curl_slist_append(http_headers, auth_header.c_str());
curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers);
}
#if defined(_WIN32)
// CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of
// operating system. Currently implemented under MS-Windows.
curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
#endif
// Check if the file already exists locally
struct stat model_file_info;
auto file_exists = (stat(path.c_str(), &model_file_info) == 0);
// If the file exists, check its JSON metadata companion file.
std::string metadata_path = path + ".json";
nlohmann::json metadata;
std::string etag;
std::string last_modified;
if (file_exists) {
// Try and read the JSON metadata file (note: stream autoclosed upon exiting this block).
std::ifstream metadata_in(metadata_path);
if (metadata_in.good()) {
try {
metadata_in >> metadata;
fprintf(stderr, "%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
if (metadata.contains("url") && metadata.at("url").is_string()) {
auto previous_url = metadata.at("url").get<std::string>();
if (previous_url != url) {
fprintf(stderr, "%s: Model URL mismatch: %s != %s\n", __func__, url.c_str(), previous_url.c_str());
return false;
}
}
if (metadata.contains("etag") && metadata.at("etag").is_string()) {
etag = metadata.at("etag");
}
if (metadata.contains("lastModified") && metadata.at("lastModified").is_string()) {
last_modified = metadata.at("lastModified");
}
} catch (const nlohmann::json::exception & e) {
fprintf(stderr, "%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
return false;
}
}
} else {
fprintf(stderr, "%s: no previous model file found %s\n", __func__, path.c_str());
}
// Send a HEAD request to retrieve the etag and last-modified headers
struct llama_load_model_from_url_headers {
std::string etag;
std::string last_modified;
};
llama_load_model_from_url_headers headers;
{
typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
llama_load_model_from_url_headers *headers = (llama_load_model_from_url_headers *) userdata;
static std::regex header_regex("([^:]+): (.*)\r\n");
static std::regex etag_regex("ETag", std::regex_constants::icase);
static std::regex last_modified_regex("Last-Modified", std::regex_constants::icase);
std::string header(buffer, n_items);
std::smatch match;
if (std::regex_match(header, match, header_regex)) {
const std::string & key = match[1];
const std::string & value = match[2];
if (std::regex_match(key, match, etag_regex)) {
headers->etag = value;
} else if (std::regex_match(key, match, last_modified_regex)) {
headers->last_modified = value;
}
}
return n_items;
};
curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 1L); // will trigger the HEAD verb
curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); // hide head request progress
curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
CURLcode res = curl_easy_perform(curl.get());
if (res != CURLE_OK) {
fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
return false;
}
long http_code = 0;
curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
if (http_code != 200) {
// HEAD not supported, we don't know if the file has changed
// force trigger downloading
force_download = true;
fprintf(stderr, "%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
}
}
bool should_download = !file_exists || force_download;
if (!should_download) {
if (!etag.empty() && etag != headers.etag) {
fprintf(stderr, "%s: ETag header is different (%s != %s): triggering a new download\n", __func__, etag.c_str(), headers.etag.c_str());
should_download = true;
} else if (!last_modified.empty() && last_modified != headers.last_modified) {
fprintf(stderr, "%s: Last-Modified header is different (%s != %s): triggering a new download\n", __func__, last_modified.c_str(), headers.last_modified.c_str());
should_download = true;
}
}
if (should_download) {
std::string path_temporary = path + ".downloadInProgress";
if (file_exists) {
fprintf(stderr, "%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
if (remove(path.c_str()) != 0) {
fprintf(stderr, "%s: unable to delete file: %s\n", __func__, path.c_str());
return false;
}
}
// Set the output file
struct FILE_deleter {
void operator()(FILE * f) const {
fclose(f);
}
};
std::unique_ptr<FILE, FILE_deleter> outfile(fopen(path_temporary.c_str(), "wb"));
if (!outfile) {
fprintf(stderr, "%s: error opening local file for writing: %s\n", __func__, path.c_str());
return false;
}
typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * data, size_t size, size_t nmemb, void * fd);
auto write_callback = [](void * data, size_t size, size_t nmemb, void * fd) -> size_t {
return fwrite(data, size, nmemb, (FILE *)fd);
};
curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 0L);
curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, outfile.get());
// display download progress
curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 0L);
// helper function to hide password in URL
auto llama_download_hide_password_in_url = [](const std::string & url) -> std::string {
std::size_t protocol_pos = url.find("://");
if (protocol_pos == std::string::npos) {
return url; // Malformed URL
}
std::size_t at_pos = url.find('@', protocol_pos + 3);
if (at_pos == std::string::npos) {
return url; // No password in URL
}
return url.substr(0, protocol_pos + 3) + "********" + url.substr(at_pos);
};
// start the download
fprintf(stderr, "%s: downloading from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
auto res = curl_easy_perform(curl.get());
if (res != CURLE_OK) {
fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
return false;
}
long http_code = 0;
curl_easy_getinfo (curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
if (http_code < 200 || http_code >= 400) {
fprintf(stderr, "%s: invalid http status code received: %ld\n", __func__, http_code);
return false;
}
// Causes file to be closed explicitly here before we rename it.
outfile.reset();
// Write the updated JSON metadata file.
metadata.update({
{"url", url},
{"etag", headers.etag},
{"lastModified", headers.last_modified}
});
std::ofstream(metadata_path) << metadata.dump(4);
fprintf(stderr, "%s: file metadata saved: %s\n", __func__, metadata_path.c_str());
if (rename(path_temporary.c_str(), path.c_str()) != 0) {
fprintf(stderr, "%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
return false;
}
}
return true;
}
struct llama_model * llama_load_model_from_url(
const char * model_url,
const char * path_model,
const char * hf_token,
const struct llama_model_params & params) {
// Basic validation of the model_url
if (!model_url || strlen(model_url) == 0) {
fprintf(stderr, "%s: invalid model_url\n", __func__);
return NULL;
}
if (!llama_download_file(model_url, path_model, hf_token)) {
return NULL;
}
// check for additional GGUFs split to download
int n_split = 0;
{
struct gguf_init_params gguf_params = {
/*.no_alloc = */ true,
/*.ctx = */ NULL,
};
auto * ctx_gguf = gguf_init_from_file(path_model, gguf_params);
if (!ctx_gguf) {
fprintf(stderr, "\n%s: failed to load input GGUF from %s\n", __func__, path_model);
return NULL;
}
auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_SPLIT_COUNT);
if (key_n_split >= 0) {
n_split = gguf_get_val_u16(ctx_gguf, key_n_split);
}
gguf_free(ctx_gguf);
}
if (n_split > 1) {
char split_prefix[PATH_MAX] = {0};
char split_url_prefix[LLAMA_CURL_MAX_URL_LENGTH] = {0};
// Verify the first split file format
// and extract split URL and PATH prefixes
{
if (!llama_split_prefix(split_prefix, sizeof(split_prefix), path_model, 0, n_split)) {
fprintf(stderr, "\n%s: unexpected model file name: %s"
" n_split=%d\n", __func__, path_model, n_split);
return NULL;
}
if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model_url, 0, n_split)) {
fprintf(stderr, "\n%s: unexpected model url: %s"
" n_split=%d\n", __func__, model_url, n_split);
return NULL;
}
}
// Prepare download in parallel
std::vector<std::future<bool>> futures_download;
for (int idx = 1; idx < n_split; idx++) {
futures_download.push_back(std::async(std::launch::async, [&split_prefix, &split_url_prefix, &n_split, hf_token](int download_idx) -> bool {
char split_path[PATH_MAX] = {0};
llama_split_path(split_path, sizeof(split_path), split_prefix, download_idx, n_split);
char split_url[LLAMA_CURL_MAX_URL_LENGTH] = {0};
llama_split_path(split_url, sizeof(split_url), split_url_prefix, download_idx, n_split);
return llama_download_file(split_url, split_path, hf_token);
}, idx));
}
// Wait for all downloads to complete
for (auto & f : futures_download) {
if (!f.get()) {
return NULL;
}
}
}
return llama_load_model_from_file(path_model, params);
}
struct llama_model * llama_load_model_from_hf(
const char * repo,
const char * model,
const char * path_model,
const char * hf_token,
const struct llama_model_params & params) {
// construct hugging face model url:
//
// --repo ggml-org/models --file tinyllama-1.1b/ggml-model-f16.gguf
// https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf
//
// --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf
// https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf
//
std::string model_url = "https://huggingface.co/";
model_url += repo;
model_url += "/resolve/main/";
model_url += model;
return llama_load_model_from_url(model_url.c_str(), path_model, hf_token, params);
}
#else
struct llama_model * llama_load_model_from_url(
const char * /*model_url*/,
const char * /*path_model*/,
const char * /*hf_token*/,
const struct llama_model_params & /*params*/) {
fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__);
return nullptr;
}
struct llama_model * llama_load_model_from_hf(
const char * /*repo*/,
const char * /*model*/,
const char * /*path_model*/,
const char * /*hf_token*/,
const struct llama_model_params & /*params*/) {
fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__);
return nullptr;
}
#endif // LLAMA_USE_CURL
//
// Batch utils
//
void llama_batch_clear(struct llama_batch & batch) {
batch.n_tokens = 0;
}
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++;
}
//
// Vocab utils
//
std::vector<llama_token> llama_tokenize(
const struct llama_context * ctx,
const std::string & text,
bool add_special,
bool parse_special) {
return llama_tokenize(llama_get_model(ctx), text, add_special, parse_special);
}
std::vector<llama_token> llama_tokenize(
const struct llama_model * model,
const std::string & text,
bool add_special,
bool parse_special) {
// upper limit for the number of tokens
int n_tokens = text.length() + 2 * add_special;
std::vector<llama_token> result(n_tokens);
n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
if (n_tokens < 0) {
result.resize(-n_tokens);
int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
GGML_ASSERT(check == -n_tokens);
} else {
result.resize(n_tokens);
}
return result;
}
std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
std::string piece;
piece.resize(piece.capacity()); // using string internal cache, 15 bytes + '\n'
const int n_chars = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
if (n_chars < 0) {
piece.resize(-n_chars);
int check = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
GGML_ASSERT(check == -n_chars);
}
else {
piece.resize(n_chars);
}
return piece;
}
std::string llama_detokenize(llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
std::string text;
text.resize(std::max(text.capacity(), tokens.size()));
int32_t n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
if (n_chars < 0) {
text.resize(-n_chars);
n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
GGML_ASSERT(n_chars <= (int32_t)text.size()); // whitespace trimming is performed after per-token detokenization
}
text.resize(n_chars);
// NOTE: the original tokenizer decodes bytes after collecting the pieces.
return text;
}
//
// Chat template utils
//
bool llama_chat_verify_template(const std::string & tmpl) {
llama_chat_message chat[] = {{"user", "test"}};
int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
return res >= 0;
}
std::string llama_chat_apply_template(const struct llama_model * model,
const std::string & tmpl,
const std::vector<llama_chat_msg> & msgs,
bool add_ass) {
int alloc_size = 0;
bool fallback = false; // indicate if we must fallback to default chatml
std::vector<llama_chat_message> chat;
for (auto & msg : msgs) {
chat.push_back({msg.role.c_str(), msg.content.c_str()});
alloc_size += (msg.role.size() + msg.content.size()) * 1.25;
}
const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str();
std::vector<char> buf(alloc_size);
// run the first time to get the total output length
int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), add_ass, buf.data(), buf.size());
// error: chat template is not supported
if (res < 0) {
if (ptr_tmpl != nullptr) {
// if the custom "tmpl" is not supported, we throw an error
// this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template()
throw std::runtime_error("this custom template is not supported");
} else {
// If the built-in template is not supported, we default to chatml
res = llama_chat_apply_template(nullptr, "chatml", chat.data(), chat.size(), add_ass, buf.data(), buf.size());
fallback = true;
}
}
// if it turns out that our buffer is too small, we resize it
if ((size_t) res > buf.size()) {
buf.resize(res);
res = llama_chat_apply_template(
fallback ? nullptr : model,
fallback ? "chatml" : ptr_tmpl,
chat.data(), chat.size(), add_ass, buf.data(), buf.size());
}
std::string formatted_chat(buf.data(), res);
return formatted_chat;
}
std::string llama_chat_format_single(const struct llama_model * model,
const std::string & tmpl,
const std::vector<llama_chat_msg> & past_msg,
const llama_chat_msg & new_msg,
bool add_ass) {
std::ostringstream ss;
auto fmt_past_msg = past_msg.empty() ? "" : llama_chat_apply_template(model, tmpl, past_msg, false);
std::vector<llama_chat_msg> chat_new(past_msg);
// if the past_msg ends with a newline, we must preserve it in the formatted version
if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') {
ss << "\n";
};
// format chat with new_msg
chat_new.push_back(new_msg);
auto fmt_new_msg = llama_chat_apply_template(model, tmpl, chat_new, add_ass);
// get the diff part
ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size());
return ss.str();
}
std::string llama_chat_format_example(const struct llama_model * model,
const std::string & tmpl) {
std::vector<llama_chat_msg> msgs = {
{"system", "You are a helpful assistant"},
{"user", "Hello"},
{"assistant", "Hi there"},
{"user", "How are you?"},
};
return llama_chat_apply_template(model, tmpl, msgs, true);
}
//
// KV cache utils
//
void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) {
static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+";
printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d",
view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
llama_kv_cache_view_cell * c_curr = view.cells;
llama_seq_id * cs_curr = view.cells_sequences;
for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
if (i % row_size == 0) {
printf("\n%5d: ", i);
}
int seq_count = 0;
for (int j = 0; j < view.n_seq_max; j++) {
if (cs_curr[j] >= 0) { seq_count++; }
}
putchar(slot_chars[std::min(sizeof(slot_chars) - 2, size_t(seq_count))]);
}
printf("\n=== Done dumping\n");
}
void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size) {
static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n",
view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
std::unordered_map<llama_seq_id, size_t> seqs;
llama_kv_cache_view_cell * c_curr = view.cells;
llama_seq_id * cs_curr = view.cells_sequences;
for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
for (int j = 0; j < view.n_seq_max; j++) {
if (cs_curr[j] < 0) { continue; }
if (seqs.find(cs_curr[j]) == seqs.end()) {
if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
const size_t sz = seqs.size();
seqs[cs_curr[j]] = sz;
}
}
if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
}
printf("=== Sequence legend: ");
for (const auto & it : seqs) {
printf("%zu=%d, ", it.second, it.first);
}
printf("'+'=other sequence ids");
c_curr = view.cells;
cs_curr = view.cells_sequences;
for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
if (i % row_size == 0) {
printf("\n%5d: ", i);
}
for (int j = 0; j < view.n_seq_max; j++) {
if (cs_curr[j] >= 0) {
const auto & it = seqs.find(cs_curr[j]);
putchar(it != seqs.end() ? int(slot_chars[it->second]) : '+');
} else {
putchar('.');
}
}
putchar(' ');
}
printf("\n=== Done dumping\n");
}
//
// Embedding utils
//
void llama_embd_normalize(const float * inp, float * out, int n, int embd_norm) {
double sum = 0.0;
switch (embd_norm) {
case -1: // no normalisation
sum = 1.0;
break;
case 0: // max absolute
for (int i = 0; i < n; i++) {
if (sum < std::abs(inp[i])) sum = std::abs(inp[i]);
}
sum /= 32760.0; // make an int16 range
break;
case 2: // euclidean
for (int i = 0; i < n; i++) {
sum += inp[i] * inp[i];
}
sum = std::sqrt(sum);
break;
default: // p-norm (euclidean is p-norm p=2)
for (int i = 0; i < n; i++) {
sum += std::pow(std::abs(inp[i]), embd_norm);
}
sum = std::pow(sum, 1.0 / embd_norm);
break;
}
const float norm = sum > 0.0 ? 1.0 / sum : 0.0f;
for (int i = 0; i < n; i++) {
out[i] = inp[i] * norm;
}
}
float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n){
double sum = 0.0;
double sum1 = 0.0;
double sum2 = 0.0;
for (int i = 0; i < n; i++) {
sum += embd1[i] * embd2[i];
sum1 += embd1[i] * embd1[i];
sum2 += embd2[i] * embd2[i];
}
// Handle the case where one or both vectors are zero vectors
if (sum1 == 0.0 || sum2 == 0.0) {
if (sum1 == 0.0 && sum2 == 0.0) {
return 1.0f; // two zero vectors are similar
}
return 0.0f;
}
return sum / (sqrt(sum1) * sqrt(sum2));
}
//
// Control vector utils
//
static llama_control_vector_data llama_control_vector_load_one(const llama_control_vector_load_info & load_info) {
llama_control_vector_data result = { -1, {} };
ggml_context * ctx = nullptr;
struct gguf_init_params meta_gguf_params = {
/* .no_alloc = */ false,
/* .ctx = */ &ctx,
};
struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params);
if (!ctx_gguf) {
fprintf(stderr, "%s: failed to load control vector file from %s\n", __func__, load_info.fname.c_str());
return result;
}
int32_t n_tensors = gguf_get_n_tensors(ctx_gguf);
if (n_tensors == 0) {
fprintf(stderr, "%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str());
}
for (int i = 0; i < n_tensors; i++) {
std::string name = gguf_get_tensor_name(ctx_gguf, i);
int layer_idx = -1;
// split on '.'
size_t dotpos = name.find('.');
if (dotpos != std::string::npos && name.substr(0, dotpos) == "direction") {
try {
layer_idx = std::stoi(name.substr(dotpos + 1));
} catch (...) {
layer_idx = -1;
}
}
if (layer_idx < 0) {
fprintf(stderr, "%s: invalid/unparsable direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
result.n_embd = -1;
break;
} else if (layer_idx == 0) {
fprintf(stderr, "%s: invalid (zero) direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
result.n_embd = -1;
break;
}
struct ggml_tensor * tensor = ggml_get_tensor(ctx, name.c_str());
if (tensor->type != GGML_TYPE_F32) {
fprintf(stderr, "%s: invalid (non-F32) direction tensor type in %s\n", __func__, load_info.fname.c_str());
result.n_embd = -1;
break;
}
if (ggml_n_dims(tensor) != 1) {
fprintf(stderr, "%s: invalid (non-1D) direction tensor shape in %s\n", __func__, load_info.fname.c_str());
result.n_embd = -1;
break;
}
if (result.n_embd == -1) {
result.n_embd = ggml_nelements(tensor);
} else if (ggml_nelements(tensor) != result.n_embd) {
fprintf(stderr, "%s: direction tensor in %s does not match previous dimensions\n", __func__, load_info.fname.c_str());
result.n_embd = -1;
break;
}
// extend if necessary - do not store data for layer 0 (it's not used)
result.data.resize(std::max(result.data.size(), static_cast<size_t>(result.n_embd * layer_idx)), 0.0f);
const float * src = (const float *) tensor->data;
float * dst = result.data.data() + result.n_embd * (layer_idx - 1); // layer 1 at [0]
for (int j = 0; j < result.n_embd; j++) {
dst[j] += src[j] * load_info.strength; // allows multiple directions for same layer in same file
}
}
if (result.n_embd == -1) {
fprintf(stderr, "%s: skipping %s due to invalid direction tensors\n", __func__, load_info.fname.c_str());
result.data.clear();
}
gguf_free(ctx_gguf);
ggml_free(ctx);
return result;
}
llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos) {
llama_control_vector_data result = { -1, {} };
for (const auto & info : load_infos) {
auto cur = llama_control_vector_load_one(info);
if (cur.n_embd == -1) {
result.n_embd = -1;
break;
}
if (result.n_embd != -1 && result.n_embd != cur.n_embd) {
fprintf(stderr, "%s: control vectors in %s does not match previous dimensions\n", __func__, info.fname.c_str());
result.n_embd = -1;
break;
}
if (result.n_embd == -1) {
result = std::move(cur);
} else {
result.data.resize(std::max(result.data.size(), cur.data.size()), 0.0f); // extend if necessary
for (size_t i = 0; i < cur.data.size(); i++) {
result.data[i] += cur.data[i];
}
}
}
if (result.n_embd == -1) {
fprintf(stderr, "%s: no valid control vector files passed\n", __func__);
result.data.clear();
}
return result;
}
//
// YAML utils
//
void yaml_dump_vector_float(FILE * stream, const char * prop_name, const std::vector<float> & data) {
if (data.empty()) {
fprintf(stream, "%s:\n", prop_name);
return;
}
fprintf(stream, "%s: [", prop_name);
for (size_t i = 0; i < data.size() - 1; ++i) {
fprintf(stream, "%e, ", data[i]);
}
fprintf(stream, "%e]\n", data.back());
}
void yaml_dump_vector_int(FILE * stream, const char * prop_name, const std::vector<int> & data) {
if (data.empty()) {
fprintf(stream, "%s:\n", prop_name);
return;
}
fprintf(stream, "%s: [", prop_name);
for (size_t i = 0; i < data.size() - 1; ++i) {
fprintf(stream, "%d, ", data[i]);
}
fprintf(stream, "%d]\n", data.back());
}
void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data) {
std::string data_str(data == NULL ? "" : data);
if (data_str.empty()) {
fprintf(stream, "%s:\n", prop_name);
return;
}
size_t pos_start = 0;
size_t pos_found = 0;
if (std::isspace(data_str[0]) || std::isspace(data_str.back())) {
data_str = std::regex_replace(data_str, std::regex("\n"), "\\n");
data_str = std::regex_replace(data_str, std::regex("\""), "\\\"");
data_str = std::regex_replace(data_str, std::regex(R"(\\[^n"])"), R"(\$&)");
data_str = "\"" + data_str + "\"";
fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
return;
}
if (data_str.find('\n') == std::string::npos) {
fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
return;
}
fprintf(stream, "%s: |\n", prop_name);
while ((pos_found = data_str.find('\n', pos_start)) != std::string::npos) {
fprintf(stream, " %s\n", data_str.substr(pos_start, pos_found-pos_start).c_str());
pos_start = pos_found + 1;
}
}
void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const llama_context * lctx,
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc) {
const llama_sampling_params & sparams = params.sparams;
fprintf(stream, "build_commit: %s\n", LLAMA_COMMIT);
fprintf(stream, "build_number: %d\n", LLAMA_BUILD_NUMBER);
fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false");
fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false");
fprintf(stream, "cpu_has_avx_vnni: %s\n", ggml_cpu_has_avx_vnni() ? "true" : "false");
fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false");
fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false");
fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false");
fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false");
fprintf(stream, "cpu_has_cuda: %s\n", ggml_cpu_has_cuda() ? "true" : "false");
fprintf(stream, "cpu_has_vulkan: %s\n", ggml_cpu_has_vulkan() ? "true" : "false");
fprintf(stream, "cpu_has_kompute: %s\n", ggml_cpu_has_kompute() ? "true" : "false");
fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false");
fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false");
fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false");
fprintf(stream, "cpu_has_sve: %s\n", ggml_cpu_has_sve() ? "true" : "false");
fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false");
fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false");
fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false");
fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false");
fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false");
fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false");
fprintf(stream, "cpu_has_matmul_int8: %s\n", ggml_cpu_has_matmul_int8() ? "true" : "false");
#ifdef NDEBUG
fprintf(stream, "debug: false\n");
#else
fprintf(stream, "debug: true\n");
#endif // NDEBUG
fprintf(stream, "model_desc: %s\n", model_desc);
fprintf(stream, "n_vocab: %d # output size of the final layer, 32001 for some models\n", llama_n_vocab(llama_get_model(lctx)));
#ifdef __OPTIMIZE__
fprintf(stream, "optimize: true\n");
#else
fprintf(stream, "optimize: false\n");
#endif // __OPTIMIZE__
fprintf(stream, "time: %s\n", timestamp.c_str());
fprintf(stream, "\n");
fprintf(stream, "###############\n");
fprintf(stream, "# User Inputs #\n");
fprintf(stream, "###############\n");
fprintf(stream, "\n");
fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str());
fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch);
yaml_dump_string_multiline(stream, "cfg_negative_prompt", sparams.cfg_negative_prompt.c_str());
fprintf(stream, "cfg_scale: %f # default: 1.0\n", sparams.cfg_scale);
fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks);
fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false");
fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx);
fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false");
fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n");
fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq);
yaml_dump_string_multiline(stream, "grammar", sparams.grammar.c_str());
fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n");
fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false");
fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks);
const auto logit_bias_eos = sparams.logit_bias.find(llama_token_eos(llama_get_model(lctx)));
const bool ignore_eos = logit_bias_eos != sparams.logit_bias.end() && logit_bias_eos->second == -INFINITY;
fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false");
yaml_dump_string_multiline(stream, "in_prefix", params.input_prefix.c_str());
fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false");
yaml_dump_string_multiline(stream, "in_suffix", params.input_prefix.c_str());
fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false");
fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false");
fprintf(stream, "keep: %d # default: 0\n", params.n_keep);
fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str());
fprintf(stream, "logit_bias:\n");
for (std::pair<llama_token, float> lb : sparams.logit_bias) {
if (ignore_eos && lb.first == logit_bias_eos->first) {
continue;
}
fprintf(stream, " %d: %f", lb.first, lb.second);
}
fprintf(stream, "lora:\n");
for (auto & la : params.lora_adapters) {
if (la.scale == 1.0f) {
fprintf(stream, " - %s\n", la.path.c_str());
}
}
fprintf(stream, "lora_scaled:\n");
for (auto & la : params.lora_adapters) {
if (la.scale != 1.0f) {
fprintf(stream, " - %s: %f\n", la.path.c_str(), la.scale);
}
}
fprintf(stream, "lora_init_without_apply: %s # default: false\n", params.lora_init_without_apply ? "true" : "false");
fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu);
fprintf(stream, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep);
fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat);
fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau);
fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta);
fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false");
fprintf(stream, "model: %s # default: %s\n", params.model.c_str(), DEFAULT_MODEL_PATH);
fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str());
fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers);
fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs);
fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false");
fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present);
yaml_dump_string_multiline(stream, "prompt", params.prompt.c_str());
fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str());
fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false");
fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false");
yaml_dump_vector_int(stream, "prompt_tokens", prompt_tokens);
fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.penalty_repeat);
fprintf(stream, "reverse_prompt:\n");
for (std::string ap : params.antiprompt) {
size_t pos = 0;
while ((pos = ap.find('\n', pos)) != std::string::npos) {
ap.replace(pos, 1, "\\n");
pos += 1;
}
fprintf(stream, " - %s\n", ap.c_str());
}
fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base);
fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale);
fprintf(stream, "seed: %u # default: -1 (random seed)\n", params.seed);
fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false");
fprintf(stream, "flash_attn: %s # default: false\n", params.flash_attn ? "true" : "false");
fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp);
const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices());
yaml_dump_vector_float(stream, "tensor_split", tensor_split_vector);
fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z);
fprintf(stream, "threads: %d # default: %u\n", params.cpuparams.n_threads, std::thread::hardware_concurrency());
fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k);
fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p);
fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p);
fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p);
fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false");
}
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