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Author SHA1 Message Date
Jeffrey Morgan
bd933c24bc testing new cmake script 2024-03-03 00:51:07 -08:00
27 changed files with 126 additions and 1574 deletions

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#include "dyn_ext_server.h"
#include <stdio.h>
#include <string.h>
#ifdef __linux__
#include <dlfcn.h>
#define LOAD_LIBRARY(lib, flags) dlopen(lib, flags)
#define LOAD_SYMBOL(handle, sym) dlsym(handle, sym)
#define LOAD_ERR() strdup(dlerror())
#define UNLOAD_LIBRARY(handle) dlclose(handle)
#elif _WIN32
#include <windows.h>
#define LOAD_LIBRARY(lib, flags) LoadLibrary(lib)
#define LOAD_SYMBOL(handle, sym) GetProcAddress(handle, sym)
#define UNLOAD_LIBRARY(handle) FreeLibrary(handle)
inline char *LOAD_ERR() {
LPSTR messageBuffer = NULL;
size_t size = FormatMessageA(
FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM |
FORMAT_MESSAGE_IGNORE_INSERTS,
NULL, GetLastError(), MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT),
(LPSTR)&messageBuffer, 0, NULL);
char *resp = strdup(messageBuffer);
LocalFree(messageBuffer);
return resp;
}
#else
#include <dlfcn.h>
#define LOAD_LIBRARY(lib, flags) dlopen(lib, flags)
#define LOAD_SYMBOL(handle, sym) dlsym(handle, sym)
#define LOAD_ERR() strdup(dlerror())
#define UNLOAD_LIBRARY(handle) dlclose(handle)
#endif
void dyn_init(const char *libPath, struct dynamic_llama_server *s,
ext_server_resp_t *err) {
int i = 0;
struct lookup {
char *s;
void **p;
} l[] = {
{"llama_server_init", (void *)&s->llama_server_init},
{"llama_server_start", (void *)&s->llama_server_start},
{"llama_server_stop", (void *)&s->llama_server_stop},
{"llama_server_completion", (void *)&s->llama_server_completion},
{"llama_server_completion_next_result",
(void *)&s->llama_server_completion_next_result},
{"llama_server_completion_cancel",
(void *)&s->llama_server_completion_cancel},
{"llama_server_release_task_result",
(void *)&s->llama_server_release_task_result},
{"llama_server_tokenize", (void *)&s->llama_server_tokenize},
{"llama_server_detokenize", (void *)&s->llama_server_detokenize},
{"llama_server_embedding", (void *)&s->llama_server_embedding},
{"llama_server_release_json_resp",
(void *)&s->llama_server_release_json_resp},
{"", NULL},
};
printf("loading library %s\n", libPath);
s->handle = LOAD_LIBRARY(libPath, RTLD_LOCAL|RTLD_NOW);
if (!s->handle) {
err->id = -1;
char *msg = LOAD_ERR();
snprintf(err->msg, err->msg_len,
"Unable to load dynamic server library: %s", msg);
free(msg);
return;
}
for (i = 0; l[i].p != NULL; i++) {
*l[i].p = LOAD_SYMBOL(s->handle, l[i].s);
if (!l[i].p) {
UNLOAD_LIBRARY(s->handle);
err->id = -1;
char *msg = LOAD_ERR();
snprintf(err->msg, err->msg_len, "symbol lookup for %s failed: %s",
l[i].s, msg);
free(msg);
return;
}
}
}
inline void dyn_llama_server_init(struct dynamic_llama_server s,
ext_server_params_t *sparams,
ext_server_resp_t *err) {
s.llama_server_init(sparams, err);
}
inline void dyn_llama_server_start(struct dynamic_llama_server s) {
s.llama_server_start();
}
inline void dyn_llama_server_stop(struct dynamic_llama_server s) {
s.llama_server_stop();
}
inline void dyn_llama_server_completion(struct dynamic_llama_server s,
const char *json_req,
ext_server_resp_t *resp) {
s.llama_server_completion(json_req, resp);
}
inline void dyn_llama_server_completion_next_result(
struct dynamic_llama_server s, const int task_id,
ext_server_task_result_t *result) {
s.llama_server_completion_next_result(task_id, result);
}
inline void dyn_llama_server_completion_cancel(
struct dynamic_llama_server s, const int task_id, ext_server_resp_t *err) {
s.llama_server_completion_cancel(task_id, err);
}
inline void dyn_llama_server_release_task_result(
struct dynamic_llama_server s, ext_server_task_result_t *result) {
s.llama_server_release_task_result(result);
}
inline void dyn_llama_server_tokenize(struct dynamic_llama_server s,
const char *json_req,
char **json_resp,
ext_server_resp_t *err) {
s.llama_server_tokenize(json_req, json_resp, err);
}
inline void dyn_llama_server_detokenize(struct dynamic_llama_server s,
const char *json_req,
char **json_resp,
ext_server_resp_t *err) {
s.llama_server_detokenize(json_req, json_resp, err);
}
inline void dyn_llama_server_embedding(struct dynamic_llama_server s,
const char *json_req,
char **json_resp,
ext_server_resp_t *err) {
s.llama_server_embedding(json_req, json_resp, err);
}
inline void dyn_llama_server_release_json_resp(
struct dynamic_llama_server s, char **json_resp) {
s.llama_server_release_json_resp(json_resp);
}

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#include <stdlib.h>
#include "ext_server.h"
#ifdef __cplusplus
extern "C" {
#endif
struct dynamic_llama_server {
void *handle;
void (*llama_server_init)(ext_server_params_t *sparams,
ext_server_resp_t *err);
void (*llama_server_start)();
void (*llama_server_stop)();
void (*llama_server_completion)(const char *json_req,
ext_server_resp_t *resp);
void (*llama_server_completion_next_result)(const int task_id,
ext_server_task_result_t *result);
void (*llama_server_completion_cancel)(const int task_id,
ext_server_resp_t *err);
void (*llama_server_release_task_result)(ext_server_task_result_t *result);
void (*llama_server_tokenize)(const char *json_req, char **json_resp,
ext_server_resp_t *err);
void (*llama_server_detokenize)(const char *json_req, char **json_resp,
ext_server_resp_t *err);
void (*llama_server_embedding)(const char *json_req, char **json_resp,
ext_server_resp_t *err);
void (*llama_server_release_json_resp)(char **json_resp);
};
void dyn_init(const char *libPath, struct dynamic_llama_server *s,
ext_server_resp_t *err);
// No good way to call C function pointers from Go so inline the indirection
void dyn_llama_server_init(struct dynamic_llama_server s,
ext_server_params_t *sparams,
ext_server_resp_t *err);
void dyn_llama_server_start(struct dynamic_llama_server s);
void dyn_llama_server_stop(struct dynamic_llama_server s);
void dyn_llama_server_completion(struct dynamic_llama_server s,
const char *json_req,
ext_server_resp_t *resp);
void dyn_llama_server_completion_next_result(
struct dynamic_llama_server s, const int task_id,
ext_server_task_result_t *result);
void dyn_llama_server_completion_cancel(struct dynamic_llama_server s,
const int task_id,
ext_server_resp_t *err);
void dyn_llama_server_release_task_result(
struct dynamic_llama_server s, ext_server_task_result_t *result);
void dyn_llama_server_tokenize(struct dynamic_llama_server s,
const char *json_req, char **json_resp,
ext_server_resp_t *err);
void dyn_llama_server_detokenize(struct dynamic_llama_server s,
const char *json_req,
char **json_resp,
ext_server_resp_t *err);
void dyn_llama_server_embedding(struct dynamic_llama_server s,
const char *json_req, char **json_resp,
ext_server_resp_t *err);
void dyn_llama_server_release_json_resp(struct dynamic_llama_server s,
char **json_resp);
#ifdef __cplusplus
}
#endif

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# Ollama specific CMakefile to include in llama.cpp/examples/server
set(TARGET ext_server)
option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
if (WIN32)
add_library(${TARGET} SHARED ../../../ext_server/ext_server.cpp ../../llama.cpp)
else()
add_library(${TARGET} STATIC ../../../ext_server/ext_server.cpp ../../llama.cpp)
endif()
target_include_directories(${TARGET} PRIVATE ../../common)
target_include_directories(${TARGET} PRIVATE ../..)
target_include_directories(${TARGET} PRIVATE ../../..)
target_compile_features(${TARGET} PRIVATE cxx_std_11)
target_compile_definitions(${TARGET} PUBLIC LLAMA_SERVER_LIBRARY=1)
target_link_libraries(${TARGET} PRIVATE ggml llava common )
set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
target_compile_definitions(${TARGET} PRIVATE SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>)
install(TARGETS ext_server LIBRARY)
if (CUDAToolkit_FOUND)
target_include_directories(${TARGET} PRIVATE ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES})
if (WIN32)
target_link_libraries(${TARGET} PRIVATE nvml)
endif()
endif()

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# Extern C Server
This directory contains a thin facade we layer on top of the Llama.cpp server to
expose `extern C` interfaces to access the functionality through direct API
calls in-process. The llama.cpp code uses compile time macros to configure GPU
type along with other settings. During the `go generate ./...` execution, the
build will generate one or more copies of the llama.cpp `extern C` server based
on what GPU libraries are detected to support multiple GPU types as well as CPU
only support. The Ollama go build then embeds these different servers to support
different GPUs and settings at runtime.
If you are making changes to the code in this directory, make sure to disable
caching during your go build to ensure you pick up your changes. A typical
iteration cycle from the top of the source tree looks like:
```
go generate ./... && go build -a .
```

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#include "ext_server.h"
#include <atomic>
// Necessary evil since the server types are not defined in a header
#include "server.cpp"
// Low level API access to verify GPU access
#if defined(GGML_USE_CUBLAS)
#if defined(GGML_USE_HIPBLAS)
#include <hip/hip_runtime.h>
#include <hipblas/hipblas.h>
#include <hip/hip_fp16.h>
#ifdef __HIP_PLATFORM_AMD__
// for rocblas_initialize()
#include "rocblas/rocblas.h"
#endif // __HIP_PLATFORM_AMD__
#define cudaGetDevice hipGetDevice
#define cudaError_t hipError_t
#define cudaSuccess hipSuccess
#define cudaGetErrorString hipGetErrorString
#else
#include <cuda_runtime.h>
#include <cublas_v2.h>
#include <cuda_fp16.h>
#endif // defined(GGML_USE_HIPBLAS)
#endif // GGML_USE_CUBLAS
// Expose the llama server as a callable extern "C" API
llama_server_context *llama = NULL;
std::thread ext_server_thread;
bool shutting_down = false;
std::atomic_int recv_counter;
// RAII wrapper for tracking in-flight recv calls
class atomicRecv {
public:
atomicRecv(std::atomic<int> &atomic) : atomic(atomic) {
++this->atomic;
}
~atomicRecv() {
--this->atomic;
}
private:
std::atomic<int> &atomic;
};
void llama_server_init(ext_server_params *sparams, ext_server_resp_t *err) {
recv_counter = 0;
assert(err != NULL && sparams != NULL);
log_set_target(stderr);
if (!sparams->verbose_logging) {
server_verbose = true;
log_disable();
}
LOG_TEE("system info: %s\n", llama_print_system_info());
err->id = 0;
err->msg[0] = '\0';
try {
llama = new llama_server_context;
gpt_params params;
params.n_ctx = sparams->n_ctx;
params.n_batch = sparams->n_batch;
if (sparams->n_threads > 0) {
params.n_threads = sparams->n_threads;
}
params.n_parallel = sparams->n_parallel;
params.rope_freq_base = sparams->rope_freq_base;
params.rope_freq_scale = sparams->rope_freq_scale;
if (sparams->memory_f16) {
params.cache_type_k = "f16";
params.cache_type_v = "f16";
} else {
params.cache_type_k = "f32";
params.cache_type_v = "f32";
}
params.n_gpu_layers = sparams->n_gpu_layers;
params.main_gpu = sparams->main_gpu;
params.use_mlock = sparams->use_mlock;
params.use_mmap = sparams->use_mmap;
params.numa = (ggml_numa_strategy)sparams->numa;
params.embedding = sparams->embedding;
if (sparams->model != NULL) {
params.model = sparams->model;
}
if (sparams->lora_adapters != NULL) {
for (ext_server_lora_adapter *la = sparams->lora_adapters; la != NULL;
la = la->next) {
params.lora_adapter.push_back(std::make_tuple(la->adapter, la->scale));
}
params.use_mmap = false;
}
if (sparams->mmproj != NULL) {
params.mmproj = std::string(sparams->mmproj);
}
#if defined(GGML_USE_CUBLAS)
// Before attempting to init the backend which will assert on error, verify the CUDA/ROCM GPU is accessible
LOG_TEE("Performing pre-initialization of GPU\n");
int id;
cudaError_t cudaErr = cudaGetDevice(&id);
if (cudaErr != cudaSuccess) {
err->id = -1;
snprintf(err->msg, err->msg_len, "Unable to init GPU: %s", cudaGetErrorString(cudaErr));
return;
}
#endif
llama_backend_init();
llama_numa_init(params.numa);
// load the model
if (!llama->load_model(params)) {
// TODO - consider modifying the logging logic or patching load_model so
// we can capture more detailed error messages and pass them back to the
// caller for better UX
err->id = -1;
snprintf(err->msg, err->msg_len, "error loading model %s",
params.model.c_str());
return;
}
llama->initialize();
} catch (std::exception &e) {
err->id = -1;
snprintf(err->msg, err->msg_len, "exception %s", e.what());
} catch (...) {
err->id = -1;
snprintf(err->msg, err->msg_len,
"Unknown exception initializing llama server");
}
}
void llama_server_start() {
assert(llama != NULL);
// TODO mutex to protect thread creation
ext_server_thread = std::thread([&]() {
try {
LOG_TEE("llama server main loop starting\n");
ggml_time_init();
llama->queue_tasks.on_new_task(std::bind(
&llama_server_context::process_single_task, llama, std::placeholders::_1));
llama->queue_tasks.on_finish_multitask(std::bind(
&llama_server_context::on_finish_multitask, llama, std::placeholders::_1));
llama->queue_tasks.on_run_slots(std::bind(
&llama_server_context::update_slots, llama));
llama->queue_results.on_multitask_update(std::bind(
&llama_server_queue::update_multitask,
&llama->queue_tasks,
std::placeholders::_1,
std::placeholders::_2,
std::placeholders::_3
));
llama->queue_tasks.start_loop();
} catch (std::exception &e) {
LOG_TEE("caught exception in llama server main loop: %s\n", e.what());
} catch (...) {
LOG_TEE("caught unknown exception in llama server main loop\n");
}
LOG_TEE("\nllama server shutting down\n");
llama_backend_free();
});
}
void llama_server_stop() {
assert(llama != NULL);
// Shutdown any in-flight requests and block incoming requests.
LOG_TEE("\ninitiating shutdown - draining remaining tasks...\n");
shutting_down = true;
while (recv_counter.load() > 0) {
std::this_thread::sleep_for(std::chrono::milliseconds(50));
}
// This may take a while for any pending tasks to drain
// TODO - consider a timeout to cancel tasks if it's taking too long
llama->queue_tasks.terminate();
ext_server_thread.join();
delete llama;
llama = NULL;
LOG_TEE("llama server shutdown complete\n");
shutting_down = false;
}
void llama_server_completion(const char *json_req, ext_server_resp_t *resp) {
assert(llama != NULL && json_req != NULL && resp != NULL);
resp->id = -1;
resp->msg[0] = '\0';
try {
if (shutting_down) {
throw std::runtime_error("server shutting down");
}
json data = json::parse(json_req);
resp->id = llama->queue_tasks.get_new_id();
llama->queue_results.add_waiting_task_id(resp->id);
llama->request_completion(resp->id, data, false, false, -1);
} catch (std::exception &e) {
snprintf(resp->msg, resp->msg_len, "exception %s", e.what());
} catch (...) {
snprintf(resp->msg, resp->msg_len, "Unknown exception during completion");
}
}
void llama_server_completion_next_result(const int task_id,
ext_server_task_result_t *resp) {
assert(llama != NULL && resp != NULL);
resp->id = -1;
resp->stop = false;
resp->error = false;
resp->json_resp = NULL;
std::string result_json;
try {
atomicRecv ar(recv_counter);
task_result result = llama->queue_results.recv(task_id);
result_json =
result.result_json.dump(-1, ' ', false, json::error_handler_t::replace);
resp->id = result.id;
resp->stop = result.stop;
resp->error = result.error;
if (result.error) {
LOG_TEE("next result cancel on error\n");
llama->request_cancel(task_id);
LOG_TEE("next result removing waiting tak ID: %d\n", task_id);
llama->queue_results.remove_waiting_task_id(task_id);
} else if (result.stop) {
LOG_TEE("next result cancel on stop\n");
llama->request_cancel(task_id);
LOG_TEE("next result removing waiting task ID: %d\n", task_id);
llama->queue_results.remove_waiting_task_id(task_id);
} else if (shutting_down) {
LOG_TEE("aborting completion due to shutdown %d\n", task_id);
llama->request_cancel(task_id);
llama->queue_results.remove_waiting_task_id(task_id);
resp->stop = true;
}
} catch (std::exception &e) {
resp->error = true;
resp->id = -1;
result_json = "{\"error\":\"exception " + std::string(e.what()) + "\"}";
LOG_TEE("llama server completion exception %s\n", e.what());
} catch (...) {
resp->error = true;
resp->id = -1;
result_json = "{\"error\":\"Unknown exception during completion\"}";
LOG_TEE("llama server completion unknown exception\n");
}
const std::string::size_type size = result_json.size() + 1;
resp->json_resp = new char[size];
snprintf(resp->json_resp, size, "%s", result_json.c_str());
}
void llama_server_release_task_result(ext_server_task_result_t *result) {
if (result == NULL || result->json_resp == NULL) {
return;
}
delete[] result->json_resp;
}
void llama_server_completion_cancel(const int task_id, ext_server_resp_t *err) {
assert(llama != NULL && err != NULL);
err->id = 0;
err->msg[0] = '\0';
try {
llama->request_cancel(task_id);
llama->queue_results.remove_waiting_task_id(task_id);
} catch (std::exception &e) {
err->id = -1;
snprintf(err->msg, err->msg_len, "exception %s", e.what());
} catch (...) {
err->id = -1;
snprintf(err->msg, err->msg_len,
"Unknown exception completion cancel in llama server");
}
}
void llama_server_tokenize(const char *json_req, char **json_resp,
ext_server_resp_t *err) {
assert(llama != NULL && json_req != NULL && json_resp != NULL && err != NULL);
*json_resp = NULL;
err->id = 0;
err->msg[0] = '\0';
try {
if (shutting_down) {
throw std::runtime_error("server shutting down");
}
const json body = json::parse(json_req);
std::vector<llama_token> tokens;
if (body.count("content") != 0) {
tokens = llama->tokenize(body["content"], false);
}
const json data = format_tokenizer_response(tokens);
std::string result_json = data.dump();
const std::string::size_type size = result_json.size() + 1;
*json_resp = new char[size];
snprintf(*json_resp, size, "%s", result_json.c_str());
} catch (std::exception &e) {
err->id = -1;
snprintf(err->msg, err->msg_len, "exception %s", e.what());
} catch (...) {
err->id = -1;
snprintf(err->msg, err->msg_len, "Unknown exception during tokenize");
}
}
void llama_server_release_json_resp(char **json_resp) {
if (json_resp == NULL || *json_resp == NULL) {
return;
}
delete[] *json_resp;
}
void llama_server_detokenize(const char *json_req, char **json_resp,
ext_server_resp_t *err) {
assert(llama != NULL && json_req != NULL && json_resp != NULL && err != NULL);
*json_resp = NULL;
err->id = 0;
err->msg[0] = '\0';
try {
if (shutting_down) {
throw std::runtime_error("server shutting down");
}
const json body = json::parse(json_req);
std::string content;
if (body.count("tokens") != 0) {
const std::vector<llama_token> tokens = body["tokens"];
content = tokens_to_str(llama->ctx, tokens.cbegin(), tokens.cend());
}
const json data = format_detokenized_response(content);
std::string result_json = data.dump();
const std::string::size_type size = result_json.size() + 1;
*json_resp = new char[size];
snprintf(*json_resp, size, "%s", result_json.c_str());
} catch (std::exception &e) {
err->id = -1;
snprintf(err->msg, err->msg_len, "exception %s", e.what());
} catch (...) {
err->id = -1;
snprintf(err->msg, err->msg_len, "Unknown exception during detokenize");
}
}
void llama_server_embedding(const char *json_req, char **json_resp,
ext_server_resp_t *err) {
assert(llama != NULL && json_req != NULL && json_resp != NULL && err != NULL);
*json_resp = NULL;
err->id = 0;
err->msg[0] = '\0';
try {
if (shutting_down) {
throw std::runtime_error("server shutting down");
}
const json body = json::parse(json_req);
json prompt;
if (body.count("content") != 0) {
prompt = body["content"];
} else {
prompt = "";
}
const int task_id = llama->queue_tasks.get_new_id();
llama->queue_results.add_waiting_task_id(task_id);
llama->request_completion(task_id, {{"prompt", prompt}, {"n_predict", 0}}, false, true, -1);
atomicRecv ar(recv_counter);
task_result result = llama->queue_results.recv(task_id);
std::string result_json = result.result_json.dump();
const std::string::size_type size = result_json.size() + 1;
*json_resp = new char[size];
snprintf(*json_resp, size, "%s", result_json.c_str());
llama->queue_results.remove_waiting_task_id(task_id);
} catch (std::exception &e) {
err->id = -1;
snprintf(err->msg, err->msg_len, "exception %s", e.what());
} catch (...) {
err->id = -1;
snprintf(err->msg, err->msg_len, "Unknown exception during embedding");
}
}

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#if defined(LLAMA_SERVER_LIBRARY)
#ifndef LLAMA_SERVER_H
#define LLAMA_SERVER_H
#include <stdbool.h>
#include <stddef.h>
#include <stdint.h>
#include <stdio.h>
int __main(int argc, char **argv);
// This exposes extern C entrypoints into the llama_server
// To enable the server compile with LLAMA_SERVER_LIBRARY
#ifdef __cplusplus
extern "C" {
#endif
typedef struct ext_server_resp {
int id; // < 0 on error
size_t msg_len; // caller must allocate msg and set msg_len
char *msg;
} ext_server_resp_t;
// Allocated and freed by caller
typedef struct ext_server_lora_adapter {
char *adapter;
float scale;
struct ext_server_lora_adapter *next;
} ext_server_lora_adapter_t;
// Allocated and freed by caller
typedef struct ext_server_params {
char *model;
uint32_t n_ctx; // token context window, 0 = from model
uint32_t n_batch; // prompt processing maximum batch size
uint32_t n_threads; // number of threads to use for generation
int32_t n_parallel; // number of parallel sequences to decodewra
float rope_freq_base; // RoPE base frequency, 0 = from model
float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model
bool memory_f16; // use f16 instead of f32 for memory kv
int32_t n_gpu_layers; // number of layers to store in VRAM (-1 - use default)
int32_t main_gpu; // the GPU that is used for scratch and small tensors
bool use_mlock; // force system to keep model in RAM
bool use_mmap; // use mmap if possible
int numa; // attempt optimizations that help on some NUMA systems
bool embedding; // get only sentence embedding
ext_server_lora_adapter_t *lora_adapters;
char *mmproj;
bool verbose_logging; // Enable verbose logging of the server
} ext_server_params_t;
typedef struct ext_server_task_result {
int id;
bool stop;
bool error;
char *json_resp; // null terminated, memory managed by ext_server
} ext_server_task_result_t;
// Initialize the server once per process
// err->id = 0 for success and err->msg[0] = NULL
// err->id != 0 for failure, and err->msg contains error message
void llama_server_init(ext_server_params_t *sparams, ext_server_resp_t *err);
// Run the main loop, called once per init
void llama_server_start();
// Stop the main loop and free up resources allocated in init and start. Init
// must be called again to reuse
void llama_server_stop();
// json_req null terminated string, memory managed by caller
// resp->id >= 0 on success (task ID)
// resp->id < 0 on error, and resp->msg contains error message
void llama_server_completion(const char *json_req, ext_server_resp_t *resp);
// Caller must call llama_server_release_task_result to free resp->json_resp
void llama_server_completion_next_result(const int task_id,
ext_server_task_result_t *result);
void llama_server_completion_cancel(const int task_id, ext_server_resp_t *err);
void llama_server_release_task_result(ext_server_task_result_t *result);
// Caller must call llama_server_releaes_json_resp to free json_resp if err.id <
// 0
void llama_server_tokenize(const char *json_req, char **json_resp,
ext_server_resp_t *err);
void llama_server_detokenize(const char *json_req, char **json_resp,
ext_server_resp_t *err);
void llama_server_embedding(const char *json_req, char **json_resp,
ext_server_resp_t *err);
void llama_server_release_json_resp(char **json_resp);
#ifdef __cplusplus
}
#endif
#endif
#endif // LLAMA_SERVER_LIBRARY

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@ -1,125 +0,0 @@
# common logic accross linux and darwin
init_vars() {
case "${GOARCH}" in
"amd64")
ARCH="x86_64"
;;
"arm64")
ARCH="arm64"
;;
*)
ARCH=$(uname -m | sed -e "s/aarch64/arm64/g")
esac
LLAMACPP_DIR=../llama.cpp
CMAKE_DEFS=""
CMAKE_TARGETS="--target ext_server"
if echo "${CGO_CFLAGS}" | grep -- '-g' >/dev/null; then
CMAKE_DEFS="-DCMAKE_BUILD_TYPE=RelWithDebInfo -DCMAKE_VERBOSE_MAKEFILE=on -DLLAMA_GPROF=on -DLLAMA_SERVER_VERBOSE=on ${CMAKE_DEFS}"
else
# TODO - add additional optimization flags...
CMAKE_DEFS="-DCMAKE_BUILD_TYPE=Release -DLLAMA_SERVER_VERBOSE=off ${CMAKE_DEFS}"
fi
case $(uname -s) in
"Darwin")
LIB_EXT="dylib"
WHOLE_ARCHIVE="-Wl,-force_load"
NO_WHOLE_ARCHIVE=""
GCC_ARCH="-arch ${ARCH}"
;;
"Linux")
LIB_EXT="so"
WHOLE_ARCHIVE="-Wl,--whole-archive"
NO_WHOLE_ARCHIVE="-Wl,--no-whole-archive"
# Cross compiling not supported on linux - Use docker
GCC_ARCH=""
;;
*)
;;
esac
if [ -z "${CMAKE_CUDA_ARCHITECTURES}" ] ; then
CMAKE_CUDA_ARCHITECTURES="50;52;61;70;75;80"
fi
}
git_module_setup() {
if [ -n "${OLLAMA_SKIP_PATCHING}" ]; then
echo "Skipping submodule initialization"
return
fi
# Make sure the tree is clean after the directory moves
if [ -d "${LLAMACPP_DIR}/gguf" ]; then
echo "Cleaning up old submodule"
rm -rf ${LLAMACPP_DIR}
fi
git submodule init
git submodule update --force ${LLAMACPP_DIR}
}
apply_patches() {
# Wire up our CMakefile
if ! grep ollama ${LLAMACPP_DIR}/examples/server/CMakeLists.txt; then
echo 'include (../../../ext_server/CMakeLists.txt) # ollama' >>${LLAMACPP_DIR}/examples/server/CMakeLists.txt
fi
if [ -n "$(ls -A ../patches/*.diff)" ]; then
# apply temporary patches until fix is upstream
for patch in ../patches/*.diff; do
for file in $(grep "^+++ " ${patch} | cut -f2 -d' ' | cut -f2- -d/); do
(cd ${LLAMACPP_DIR}; git checkout ${file})
done
done
for patch in ../patches/*.diff; do
(cd ${LLAMACPP_DIR} && git apply ${patch})
done
fi
# Avoid duplicate main symbols when we link into the cgo binary
sed -e 's/int main(/int __main(/g' <${LLAMACPP_DIR}/examples/server/server.cpp >${LLAMACPP_DIR}/examples/server/server.cpp.tmp &&
mv ${LLAMACPP_DIR}/examples/server/server.cpp.tmp ${LLAMACPP_DIR}/examples/server/server.cpp
}
build() {
cmake -S ${LLAMACPP_DIR} -B ${BUILD_DIR} ${CMAKE_DEFS}
cmake --build ${BUILD_DIR} ${CMAKE_TARGETS} -j8
mkdir -p ${BUILD_DIR}/lib/
g++ -fPIC -g -shared -o ${BUILD_DIR}/lib/libext_server.${LIB_EXT} \
${GCC_ARCH} \
${WHOLE_ARCHIVE} ${BUILD_DIR}/examples/server/libext_server.a ${NO_WHOLE_ARCHIVE} \
${BUILD_DIR}/common/libcommon.a \
${BUILD_DIR}/libllama.a \
-Wl,-rpath,\$ORIGIN \
-lpthread -ldl -lm \
${EXTRA_LIBS}
}
compress_libs() {
echo "Compressing payloads to reduce overall binary size..."
pids=""
rm -rf ${BUILD_DIR}/lib/*.${LIB_EXT}*.gz
for lib in ${BUILD_DIR}/lib/*.${LIB_EXT}* ; do
gzip -n --best -f ${lib} &
pids+=" $!"
done
echo
for pid in ${pids}; do
wait $pid
done
echo "Finished compression"
}
# Keep the local tree clean after we're done with the build
cleanup() {
(cd ${LLAMACPP_DIR}/examples/server/ && git checkout CMakeLists.txt server.cpp)
if [ -n "$(ls -A ../patches/*.diff)" ]; then
for patch in ../patches/*.diff; do
for file in $(grep "^+++ " ${patch} | cut -f2 -d' ' | cut -f2- -d/); do
(cd ${LLAMACPP_DIR}; git checkout ${file})
done
done
fi
}

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@ -1,77 +0,0 @@
#!/bin/bash
# This script is intended to run inside the go generate
# working directory must be ./llm/generate/
# TODO - add hardening to detect missing tools (cmake, etc.)
set -ex
set -o pipefail
echo "Starting darwin generate script"
source $(dirname $0)/gen_common.sh
init_vars
git_module_setup
apply_patches
sign() {
if [ -n "$APPLE_IDENTITY" ]; then
codesign -f --timestamp --deep --options=runtime --sign "$APPLE_IDENTITY" --identifier ai.ollama.ollama $1
fi
}
COMMON_DARWIN_DEFS="-DCMAKE_OSX_DEPLOYMENT_TARGET=11.0 -DCMAKE_SYSTEM_NAME=Darwin"
case "${GOARCH}" in
"amd64")
COMMON_CPU_DEFS="${COMMON_DARWIN_DEFS} -DCMAKE_SYSTEM_PROCESSOR=${ARCH} -DCMAKE_OSX_ARCHITECTURES=${ARCH} -DLLAMA_METAL=off -DLLAMA_NATIVE=off"
#
# CPU first for the default library, set up as lowest common denominator for maximum compatibility (including Rosetta)
#
CMAKE_DEFS="${COMMON_CPU_DEFS} -DLLAMA_ACCELERATE=off -DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
BUILD_DIR="${LLAMACPP_DIR}/build/darwin/${ARCH}/cpu"
echo "Building LCD CPU"
build
sign ${LLAMACPP_DIR}/build/darwin/${ARCH}/cpu/lib/libext_server.dylib
compress_libs
#
# ~2011 CPU Dynamic library with more capabilities turned on to optimize performance
# Approximately 400% faster than LCD on same CPU
#
init_vars
CMAKE_DEFS="${COMMON_CPU_DEFS} -DLLAMA_ACCELERATE=off -DLLAMA_AVX=on -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
BUILD_DIR="${LLAMACPP_DIR}/build/darwin/${ARCH}/cpu_avx"
echo "Building AVX CPU"
build
sign ${LLAMACPP_DIR}/build/darwin/${ARCH}/cpu_avx/lib/libext_server.dylib
compress_libs
#
# ~2013 CPU Dynamic library
# Approximately 10% faster than AVX on same CPU
#
init_vars
CMAKE_DEFS="${COMMON_CPU_DEFS} -DLLAMA_ACCELERATE=on -DLLAMA_AVX=on -DLLAMA_AVX2=on -DLLAMA_AVX512=off -DLLAMA_FMA=on -DLLAMA_F16C=on ${CMAKE_DEFS}"
BUILD_DIR="${LLAMACPP_DIR}/build/darwin/${ARCH}/cpu_avx2"
echo "Building AVX2 CPU"
EXTRA_LIBS="${EXTRA_LIBS} -framework Accelerate -framework Foundation"
build
sign ${LLAMACPP_DIR}/build/darwin/${ARCH}/cpu_avx2/lib/libext_server.dylib
compress_libs
;;
"arm64")
CMAKE_DEFS="${COMMON_DARWIN_DEFS} -DLLAMA_ACCELERATE=on -DCMAKE_SYSTEM_PROCESSOR=${ARCH} -DCMAKE_OSX_ARCHITECTURES=${ARCH} -DLLAMA_METAL=on ${CMAKE_DEFS}"
BUILD_DIR="${LLAMACPP_DIR}/build/darwin/${ARCH}/metal"
EXTRA_LIBS="${EXTRA_LIBS} -framework Accelerate -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders"
build
sign ${LLAMACPP_DIR}/build/darwin/${ARCH}/metal/lib/libext_server.dylib
compress_libs
;;
*)
echo "GOARCH must be set"
echo "this script is meant to be run from within go generate"
exit 1
;;
esac
cleanup

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@ -1,196 +0,0 @@
#!/bin/bash
# This script is intended to run inside the go generate
# working directory must be llm/generate/
# First we build one or more CPU based LLM libraries
#
# Then if we detect CUDA, we build a CUDA dynamic library, and carry the required
# library dependencies
#
# Then if we detect ROCm, we build a dynamically loaded ROCm lib. The ROCM
# libraries are quite large, and also dynamically load data files at runtime
# which in turn are large, so we don't attempt to cary them as payload
set -ex
set -o pipefail
# See https://llvm.org/docs/AMDGPUUsage.html#processors for reference
amdGPUs() {
if [ -n "${AMDGPU_TARGETS}" ]; then
echo "${AMDGPU_TARGETS}"
return
fi
GPU_LIST=(
"gfx900"
"gfx906:xnack-"
"gfx908:xnack-"
"gfx90a:xnack+"
"gfx90a:xnack-"
"gfx1010"
"gfx1012"
"gfx1030"
"gfx1100"
"gfx1101"
"gfx1102"
)
(
IFS=$';'
echo "'${GPU_LIST[*]}'"
)
}
echo "Starting linux generate script"
if [ -z "${CUDACXX}" ]; then
if [ -x /usr/local/cuda/bin/nvcc ]; then
export CUDACXX=/usr/local/cuda/bin/nvcc
else
# Try the default location in case it exists
export CUDACXX=$(command -v nvcc)
fi
fi
COMMON_CMAKE_DEFS="-DCMAKE_POSITION_INDEPENDENT_CODE=on -DLLAMA_NATIVE=off -DLLAMA_AVX=on -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off"
source $(dirname $0)/gen_common.sh
init_vars
git_module_setup
apply_patches
if [ -z "${OLLAMA_SKIP_CPU_GENERATE}" ]; then
# Users building from source can tune the exact flags we pass to cmake for configuring
# llama.cpp, and we'll build only 1 CPU variant in that case as the default.
if [ -n "${OLLAMA_CUSTOM_CPU_DEFS}" ]; then
echo "OLLAMA_CUSTOM_CPU_DEFS=\"${OLLAMA_CUSTOM_CPU_DEFS}\""
CMAKE_DEFS="${OLLAMA_CUSTOM_CPU_DEFS} -DCMAKE_POSITION_INDEPENDENT_CODE=on ${CMAKE_DEFS}"
BUILD_DIR="${LLAMACPP_DIR}/build/linux/${ARCH}/cpu"
echo "Building custom CPU"
build
compress_libs
else
# Darwin Rosetta x86 emulation does NOT support AVX, AVX2, AVX512
# -DLLAMA_AVX -- 2011 Intel Sandy Bridge & AMD Bulldozer
# -DLLAMA_F16C -- 2012 Intel Ivy Bridge & AMD 2011 Bulldozer (No significant improvement over just AVX)
# -DLLAMA_AVX2 -- 2013 Intel Haswell & 2015 AMD Excavator / 2017 AMD Zen
# -DLLAMA_FMA (FMA3) -- 2013 Intel Haswell & 2012 AMD Piledriver
# Note: the following seem to yield slower results than AVX2 - ymmv
# -DLLAMA_AVX512 -- 2017 Intel Skylake and High End DeskTop (HEDT)
# -DLLAMA_AVX512_VBMI -- 2018 Intel Cannon Lake
# -DLLAMA_AVX512_VNNI -- 2021 Intel Alder Lake
COMMON_CPU_DEFS="-DCMAKE_POSITION_INDEPENDENT_CODE=on -DLLAMA_NATIVE=off"
if [ -z "${OLLAMA_CPU_TARGET}" -o "${OLLAMA_CPU_TARGET}" = "cpu" ]; then
#
# CPU first for the default library, set up as lowest common denominator for maximum compatibility (including Rosetta)
#
CMAKE_DEFS="${COMMON_CPU_DEFS} -DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
BUILD_DIR="${LLAMACPP_DIR}/build/linux/${ARCH}/cpu"
echo "Building LCD CPU"
build
compress_libs
fi
if [ -z "${OLLAMA_CPU_TARGET}" -o "${OLLAMA_CPU_TARGET}" = "cpu_avx" ]; then
#
# ~2011 CPU Dynamic library with more capabilities turned on to optimize performance
# Approximately 400% faster than LCD on same CPU
#
init_vars
CMAKE_DEFS="${COMMON_CPU_DEFS} -DLLAMA_AVX=on -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
BUILD_DIR="${LLAMACPP_DIR}/build/linux/${ARCH}/cpu_avx"
echo "Building AVX CPU"
build
compress_libs
fi
if [ -z "${OLLAMA_CPU_TARGET}" -o "${OLLAMA_CPU_TARGET}" = "cpu_avx2" ]; then
#
# ~2013 CPU Dynamic library
# Approximately 10% faster than AVX on same CPU
#
init_vars
CMAKE_DEFS="${COMMON_CPU_DEFS} -DLLAMA_AVX=on -DLLAMA_AVX2=on -DLLAMA_AVX512=off -DLLAMA_FMA=on -DLLAMA_F16C=on ${CMAKE_DEFS}"
BUILD_DIR="${LLAMACPP_DIR}/build/linux/${ARCH}/cpu_avx2"
echo "Building AVX2 CPU"
build
compress_libs
fi
fi
else
echo "Skipping CPU generation step as requested"
fi
# If needed, look for the default CUDA toolkit location
if [ -z "${CUDA_LIB_DIR}" ] && [ -d /usr/local/cuda/lib64 ]; then
CUDA_LIB_DIR=/usr/local/cuda/lib64
fi
# If needed, look for CUDA on Arch Linux
if [ -z "${CUDA_LIB_DIR}" ] && [ -d /opt/cuda/targets/x86_64-linux/lib ]; then
CUDA_LIB_DIR=/opt/cuda/targets/x86_64-linux/lib
fi
# Allow override in case libcudart is in the wrong place
if [ -z "${CUDART_LIB_DIR}" ]; then
CUDART_LIB_DIR="${CUDA_LIB_DIR}"
fi
if [ -d "${CUDA_LIB_DIR}" ]; then
echo "CUDA libraries detected - building dynamic CUDA library"
init_vars
CUDA_MAJOR=$(ls "${CUDA_LIB_DIR}"/libcudart.so.* | head -1 | cut -f3 -d. || true)
if [ -n "${CUDA_MAJOR}" ]; then
CUDA_VARIANT=_v${CUDA_MAJOR}
fi
CMAKE_DEFS="-DLLAMA_CUBLAS=on -DLLAMA_CUDA_FORCE_MMQ=on -DCMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES} ${COMMON_CMAKE_DEFS} ${CMAKE_DEFS}"
BUILD_DIR="${LLAMACPP_DIR}/build/linux/${ARCH}/cuda${CUDA_VARIANT}"
EXTRA_LIBS="-L${CUDA_LIB_DIR} -lcudart -lcublas -lcublasLt -lcuda"
build
# Cary the CUDA libs as payloads to help reduce dependency burden on users
#
# TODO - in the future we may shift to packaging these separately and conditionally
# downloading them in the install script.
DEPS="$(ldd ${BUILD_DIR}/lib/libext_server.so )"
for lib in libcudart.so libcublas.so libcublasLt.so ; do
DEP=$(echo "${DEPS}" | grep ${lib} | cut -f1 -d' ' | xargs || true)
if [ -n "${DEP}" -a -e "${CUDA_LIB_DIR}/${DEP}" ]; then
cp "${CUDA_LIB_DIR}/${DEP}" "${BUILD_DIR}/lib/"
elif [ -e "${CUDA_LIB_DIR}/${lib}.${CUDA_MAJOR}" ]; then
cp "${CUDA_LIB_DIR}/${lib}.${CUDA_MAJOR}" "${BUILD_DIR}/lib/"
elif [ -e "${CUDART_LIB_DIR}/${lib}" ]; then
cp -d ${CUDART_LIB_DIR}/${lib}* "${BUILD_DIR}/lib/"
else
cp -d "${CUDA_LIB_DIR}/${lib}*" "${BUILD_DIR}/lib/"
fi
done
compress_libs
fi
if [ -z "${ROCM_PATH}" ]; then
# Try the default location in case it exists
ROCM_PATH=/opt/rocm
fi
if [ -z "${CLBlast_DIR}" ]; then
# Try the default location in case it exists
if [ -d /usr/lib/cmake/CLBlast ]; then
export CLBlast_DIR=/usr/lib/cmake/CLBlast
fi
fi
if [ -d "${ROCM_PATH}" ]; then
echo "ROCm libraries detected - building dynamic ROCm library"
if [ -f ${ROCM_PATH}/lib/librocm_smi64.so.? ]; then
ROCM_VARIANT=_v$(ls ${ROCM_PATH}/lib/librocm_smi64.so.? | cut -f3 -d. || true)
fi
init_vars
CMAKE_DEFS="${COMMON_CMAKE_DEFS} ${CMAKE_DEFS} -DLLAMA_HIPBLAS=on -DCMAKE_C_COMPILER=$ROCM_PATH/llvm/bin/clang -DCMAKE_CXX_COMPILER=$ROCM_PATH/llvm/bin/clang++ -DAMDGPU_TARGETS=$(amdGPUs) -DGPU_TARGETS=$(amdGPUs)"
BUILD_DIR="${LLAMACPP_DIR}/build/linux/${ARCH}/rocm${ROCM_VARIANT}"
EXTRA_LIBS="-L${ROCM_PATH}/lib -L/opt/amdgpu/lib/x86_64-linux-gnu/ -Wl,-rpath,${ROCM_PATH}/lib,-rpath,/opt/amdgpu/lib/x86_64-linux-gnu/ -lhipblas -lrocblas -lamdhip64 -lrocsolver -lamd_comgr -lhsa-runtime64 -lrocsparse -ldrm -ldrm_amdgpu"
build
# Note: the ROCM libs and runtime library files are too large to embed, so we depend on
# them being present at runtime on the host
compress_libs
fi
cleanup

View File

@ -1,209 +0,0 @@
#!powershell
$ErrorActionPreference = "Stop"
function init_vars {
$script:SRC_DIR = $(resolve-path "..\..\")
$script:llamacppDir = "../llama.cpp"
$script:cmakeDefs = @("-DBUILD_SHARED_LIBS=on", "-DLLAMA_NATIVE=off", "-A", "x64")
$script:cmakeTargets = @("ext_server")
$script:ARCH = "amd64" # arm not yet supported.
if ($env:CGO_CFLAGS -contains "-g") {
$script:cmakeDefs += @("-DCMAKE_VERBOSE_MAKEFILE=on", "-DLLAMA_SERVER_VERBOSE=on")
$script:config = "RelWithDebInfo"
} else {
$script:cmakeDefs += @("-DLLAMA_SERVER_VERBOSE=off")
$script:config = "Release"
}
# Try to find the CUDA dir
if ($env:CUDA_LIB_DIR -eq $null) {
$d=(get-command -ea 'silentlycontinue' nvcc).path
if ($d -ne $null) {
$script:CUDA_LIB_DIR=($d| split-path -parent)
$script:CUDA_INCLUDE_DIR=($script:CUDA_LIB_DIR|split-path -parent)+"\include"
}
} else {
$script:CUDA_LIB_DIR=$env:CUDA_LIB_DIR
}
$script:GZIP=(get-command -ea 'silentlycontinue' gzip).path
$script:DUMPBIN=(get-command -ea 'silentlycontinue' dumpbin).path
if ($null -eq $env:CMAKE_CUDA_ARCHITECTURES) {
$script:CMAKE_CUDA_ARCHITECTURES="50;52;61;70;75;80"
} else {
$script:CMAKE_CUDA_ARCHITECTURES=$env:CMAKE_CUDA_ARCHITECTURES
}
# Note: 10 Windows Kit signtool crashes with GCP's plugin
${script:SignTool}="C:\Program Files (x86)\Windows Kits\8.1\bin\x64\signtool.exe"
if ("${env:KEY_CONTAINER}") {
${script:OLLAMA_CERT}=$(resolve-path "${script:SRC_DIR}\ollama_inc.crt")
}
}
function git_module_setup {
# TODO add flags to skip the init/patch logic to make it easier to mod llama.cpp code in-repo
& git submodule init
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
& git submodule update --force "${script:llamacppDir}"
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
}
function apply_patches {
# Wire up our CMakefile
if (!(Select-String -Path "${script:llamacppDir}/examples/server/CMakeLists.txt" -Pattern 'ollama')) {
Add-Content -Path "${script:llamacppDir}/examples/server/CMakeLists.txt" -Value 'include (../../../ext_server/CMakeLists.txt) # ollama'
}
# Apply temporary patches until fix is upstream
$patches = Get-ChildItem "../patches/*.diff"
foreach ($patch in $patches) {
# Extract file paths from the patch file
$filePaths = Get-Content $patch.FullName | Where-Object { $_ -match '^\+\+\+ ' } | ForEach-Object {
$parts = $_ -split ' '
($parts[1] -split '/', 2)[1]
}
# Checkout each file
Set-Location -Path ${script:llamacppDir}
foreach ($file in $filePaths) {
git checkout $file
}
}
# Apply each patch
foreach ($patch in $patches) {
Set-Location -Path ${script:llamacppDir}
git apply $patch.FullName
}
# Avoid duplicate main symbols when we link into the cgo binary
$content = Get-Content -Path "${script:llamacppDir}/examples/server/server.cpp"
$content = $content -replace 'int main\(', 'int __main('
Set-Content -Path "${script:llamacppDir}/examples/server/server.cpp" -Value $content
}
function build {
write-host "generating config with: cmake -S ${script:llamacppDir} -B $script:buildDir $script:cmakeDefs"
& cmake --version
& cmake -S "${script:llamacppDir}" -B $script:buildDir $script:cmakeDefs
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
write-host "building with: cmake --build $script:buildDir --config $script:config ($script:cmakeTargets | ForEach-Object { "--target", $_ })"
& cmake --build $script:buildDir --config $script:config ($script:cmakeTargets | ForEach-Object { "--target", $_ })
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
}
function install {
rm -ea 0 -recurse -force -path "${script:buildDir}/lib"
md "${script:buildDir}/lib" -ea 0 > $null
cp "${script:buildDir}/bin/${script:config}/ext_server.dll" "${script:buildDir}/lib"
cp "${script:buildDir}/bin/${script:config}/llama.dll" "${script:buildDir}/lib"
# Display the dll dependencies in the build log
if ($script:DUMPBIN -ne $null) {
& "$script:DUMPBIN" /dependents "${script:buildDir}/bin/${script:config}/ext_server.dll" | select-string ".dll"
}
}
function sign {
if ("${env:KEY_CONTAINER}") {
write-host "Signing ${script:buildDir}/lib/*.dll"
foreach ($file in (get-childitem "${script:buildDir}/lib/*.dll")){
& "${script:SignTool}" sign /v /fd sha256 /t http://timestamp.digicert.com /f "${script:OLLAMA_CERT}" `
/csp "Google Cloud KMS Provider" /kc "${env:KEY_CONTAINER}" $file
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
}
}
}
function compress_libs {
if ($script:GZIP -eq $null) {
write-host "gzip not installed, not compressing files"
return
}
write-host "Compressing dlls..."
$libs = dir "${script:buildDir}/lib/*.dll"
foreach ($file in $libs) {
& "$script:GZIP" --best -f $file
}
}
function cleanup {
$patches = Get-ChildItem "../patches/*.diff"
foreach ($patch in $patches) {
# Extract file paths from the patch file
$filePaths = Get-Content $patch.FullName | Where-Object { $_ -match '^\+\+\+ ' } | ForEach-Object {
$parts = $_ -split ' '
($parts[1] -split '/', 2)[1]
}
# Checkout each file
Set-Location -Path ${script:llamacppDir}
foreach ($file in $filePaths) {
git checkout $file
}
}
Set-Location "${script:llamacppDir}/examples/server"
git checkout CMakeLists.txt server.cpp
}
init_vars
git_module_setup
apply_patches
# -DLLAMA_AVX -- 2011 Intel Sandy Bridge & AMD Bulldozer
# -DLLAMA_F16C -- 2012 Intel Ivy Bridge & AMD 2011 Bulldozer (No significant improvement over just AVX)
# -DLLAMA_AVX2 -- 2013 Intel Haswell & 2015 AMD Excavator / 2017 AMD Zen
# -DLLAMA_FMA (FMA3) -- 2013 Intel Haswell & 2012 AMD Piledriver
$script:commonCpuDefs = @("-DCMAKE_POSITION_INDEPENDENT_CODE=on")
init_vars
$script:cmakeDefs = $script:commonCpuDefs + @("-DLLAMA_AVX=off", "-DLLAMA_AVX2=off", "-DLLAMA_AVX512=off", "-DLLAMA_FMA=off", "-DLLAMA_F16C=off") + $script:cmakeDefs
$script:buildDir="${script:llamacppDir}/build/windows/${script:ARCH}/cpu"
write-host "Building LCD CPU"
build
install
sign
compress_libs
init_vars
$script:cmakeDefs = $script:commonCpuDefs + @("-DLLAMA_AVX=on", "-DLLAMA_AVX2=off", "-DLLAMA_AVX512=off", "-DLLAMA_FMA=off", "-DLLAMA_F16C=off") + $script:cmakeDefs
$script:buildDir="${script:llamacppDir}/build/windows/${script:ARCH}/cpu_avx"
write-host "Building AVX CPU"
build
install
sign
compress_libs
init_vars
$script:cmakeDefs = $script:commonCpuDefs + @("-DLLAMA_AVX=on", "-DLLAMA_AVX2=on", "-DLLAMA_AVX512=off", "-DLLAMA_FMA=on", "-DLLAMA_F16C=on") + $script:cmakeDefs
$script:buildDir="${script:llamacppDir}/build/windows/${script:ARCH}/cpu_avx2"
write-host "Building AVX2 CPU"
build
install
sign
compress_libs
if ($null -ne $script:CUDA_LIB_DIR) {
# Then build cuda as a dynamically loaded library
$nvcc = "$script:CUDA_LIB_DIR\nvcc.exe"
$script:CUDA_VERSION=(get-item ($nvcc | split-path | split-path)).Basename
if ($null -ne $script:CUDA_VERSION) {
$script:CUDA_VARIANT="_"+$script:CUDA_VERSION
}
init_vars
$script:buildDir="${script:llamacppDir}/build/windows/${script:ARCH}/cuda$script:CUDA_VARIANT"
$script:cmakeDefs += @("-DLLAMA_CUBLAS=ON", "-DLLAMA_AVX=on", "-DLLAMA_AVX2=off", "-DCUDAToolkit_INCLUDE_DIR=$script:CUDA_INCLUDE_DIR", "-DCMAKE_CUDA_ARCHITECTURES=${script:CMAKE_CUDA_ARCHITECTURES}")
build
install
sign
compress_libs
}
# TODO - actually implement ROCm support on windows
$script:buildDir="${script:llamacppDir}/build/windows/${script:ARCH}/rocm"
rm -ea 0 -recurse -force -path "${script:buildDir}/lib"
md "${script:buildDir}/lib" -ea 0 > $null
echo $null >> "${script:buildDir}/lib/.generated"
cleanup
write-host "`ngo generate completed"

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@ -1,3 +0,0 @@
package generate
//go:generate sh ./gen_darwin.sh

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@ -1,3 +0,0 @@
package generate
//go:generate bash ./gen_linux.sh

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@ -1,3 +0,0 @@
package generate
//go:generate powershell -ExecutionPolicy Bypass -File ./gen_windows.ps1

@ -1 +0,0 @@
Subproject commit c29af7e2252d288f2ea58a7d437c1cb7c0abf160

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@ -6,6 +6,7 @@ import (
"log/slog"
"os"
"runtime"
"time"
"github.com/jmorganca/ollama/api"
"github.com/jmorganca/ollama/gpu"
@ -165,3 +166,12 @@ func newLlmServer(gpuInfo gpu.GpuInfo, workDir, model string, adapters, projecto
return nil, err2
}
func parseDurationMs(ms float64) time.Duration {
dur, err := time.ParseDuration(fmt.Sprintf("%fms", ms))
if err != nil {
panic(err)
}
return dur
}

14
llm/llm_darwin_amd64.go Normal file
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@ -0,0 +1,14 @@
//go:generate cmake -S server -B server/build/cpu -DCMAKE_OSX_DEPLOYMENT_TARGET=11.0 -DCMAKE_SYSTEM_NAME=Darwin -DCMAKE_SYSTEM_PROCESSOR=x86_64 -DCMAKE_OSX_ARCHITECTURES=x86_64 -DLLAMA_METAL=off -DLLAMA_NATIVE=off
//go:generate cmake -S server -B server/build/cpu_avx -DCMAKE_OSX_DEPLOYMENT_TARGET=11.0 -DCMAKE_SYSTEM_NAME=Darwin -DCMAKE_SYSTEM_PROCESSOR=x86_64 -DCMAKE_OSX_ARCHITECTURES=x86_64 -DLLAMA_METAL=off -DLLAMA_NATIVE=off -DLLAMA_AVX=on
//go:generate cmake -S server -B server/build/cpu_avx2 -DCMAKE_OSX_DEPLOYMENT_TARGET=11.0 -DCMAKE_SYSTEM_NAME=Darwin -DCMAKE_SYSTEM_PROCESSOR=x86_64 -DCMAKE_OSX_ARCHITECTURES=x86_64 -DLLAMA_METAL=off -DLLAMA_NATIVE=off -DLLAMA_AVX=on -DLLAMA_AVX2=on
//go:generate cmake --build server/build/cpu --target server -- -j4
//go:generate cmake --build server/build/cpu_avx --target server -- -j4
//go:generate cmake --build server/build/cpu_avx2 --target server -- -j4
package llm
import "embed"
//go:embed server/build/cpu/server
//go:embed server/build/cpu_avx/server
//go:embed server/build/cpu_avx2/server
var libEmbed embed.FS

8
llm/llm_darwin_arm64.go Normal file
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@ -0,0 +1,8 @@
//go:generate cmake -S server -B server/build/metal -DCMAKE_OSX_DEPLOYMENT_TARGET=11.0 -DCMAKE_SYSTEM_NAME=Darwin -DCMAKE_SYSTEM_PROCESSOR=arm64 -DCMAKE_OSX_ARCHITECTURES=arm64
//go:generate cmake --build server/build/metal --target server -- -j4
package llm
import "embed"
//go:embed server/build/metal/ggml-metal.metal server/build/metal/server
var libEmbed embed.FS

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@ -1,114 +0,0 @@
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 2b2f4a0f..25857bdd 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -31,6 +31,10 @@
#include <atomic>
#include <signal.h>
+#ifdef GGML_USE_CUBLAS
+extern "C" GGML_CALL void ggml_free_cublas(void);
+#endif
+
using json = nlohmann::json;
struct server_params {
@@ -363,6 +367,9 @@ struct llama_server_context
llama_free_model(model);
model = nullptr;
}
+#ifdef GGML_USE_CUBLAS
+ ggml_free_cublas();
+#endif
}
bool load_model(const gpt_params &params_)
@@ -3494,6 +3501,7 @@ int main(int argc, char **argv)
sigemptyset (&sigint_action.sa_mask);
sigint_action.sa_flags = 0;
sigaction(SIGINT, &sigint_action, NULL);
+ sigaction(SIGUSR1, &sigint_action, NULL);
#elif defined (_WIN32)
auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false;
diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 0c6501e9..75c12723 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -43,6 +43,7 @@
#define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
#define cublasComputeType_t hipblasDatatype_t //deprecated, new hipblasComputeType_t not in 5.6
#define cublasCreate hipblasCreate
+#define cublasDestroy hipblasDestroy
#define cublasGemmEx hipblasGemmEx
#define cublasGemmBatchedEx hipblasGemmBatchedEx
#define cublasGemmStridedBatchedEx hipblasGemmStridedBatchedEx
@@ -8694,10 +8695,10 @@ GGML_CALL bool ggml_cublas_loaded(void) {
return g_cublas_loaded;
}
-GGML_CALL void ggml_init_cublas() {
- static bool initialized = false;
+static bool g_cublas_initialized = false;
- if (!initialized) {
+GGML_CALL void ggml_init_cublas() {
+ if (!g_cublas_initialized) {
#ifdef __HIP_PLATFORM_AMD__
// Workaround for a rocBLAS bug when using multiple graphics cards:
@@ -8707,7 +8708,7 @@ GGML_CALL void ggml_init_cublas() {
#endif
if (cudaGetDeviceCount(&g_device_count) != cudaSuccess) {
- initialized = true;
+ g_cublas_initialized = true;
g_cublas_loaded = false;
fprintf(stderr, "%s: no " GGML_CUDA_NAME " devices found, " GGML_CUDA_NAME " will be disabled\n", __func__);
return;
@@ -8778,7 +8779,7 @@ GGML_CALL void ggml_init_cublas() {
// configure logging to stdout
// CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr));
- initialized = true;
+ g_cublas_initialized = true;
g_cublas_loaded = true;
}
}
@@ -12345,3 +12346,22 @@ GGML_CALL int ggml_backend_cuda_reg_devices() {
}
return device_count;
}
+
+extern "C" GGML_CALL void ggml_free_cublas(void);
+GGML_CALL void ggml_free_cublas(void) {
+ for (int id = 0; id < g_device_count; ++id) {
+#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+ if (g_device_caps[id].vmm) {
+ CU_CHECK(cuMemUnmap(g_cuda_pool_addr[id], g_cuda_pool_size[id]));
+ g_cuda_pool_size[id] = 0;
+ g_cuda_pool_addr[id] = 0;
+ }
+#endif
+ // TODO: free legacy non-vmm memory
+ // destroy cublas handle
+ CUBLAS_CHECK(cublasDestroy(g_cublas_handles[id]));
+ g_cublas_handles[id] = nullptr;
+ }
+
+ g_cublas_initialized = false;
+}
diff --git a/ggml-cuda.h b/ggml-cuda.h
index b1ebd61d..6dd58ddf 100644
--- a/ggml-cuda.h
+++ b/ggml-cuda.h
@@ -23,6 +23,9 @@ GGML_API GGML_CALL void ggml_init_cublas(void);
// Returns `true` if there are available CUDA devices and cublas loads successfully; otherwise, it returns `false`.
GGML_API GGML_CALL bool ggml_cublas_loaded(void);
+// Release CUDA resources
+GGML_API GGML_CALL void ggml_free_cublas(void);
+
GGML_API GGML_CALL void * ggml_cuda_host_malloc(size_t size);
GGML_API GGML_CALL void ggml_cuda_host_free(void * ptr);

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@ -1,8 +0,0 @@
package llm
import (
"embed"
)
//go:embed llama.cpp/ggml-metal.metal llama.cpp/build/darwin/x86_64/*/lib/*.dylib*
var libEmbed embed.FS

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@ -1,8 +0,0 @@
package llm
import (
"embed"
)
//go:embed llama.cpp/ggml-metal.metal llama.cpp/build/darwin/arm64/*/lib/*.dylib*
var libEmbed embed.FS

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@ -1,8 +0,0 @@
package llm
import (
"embed"
)
//go:embed llama.cpp/build/linux/*/*/lib/*.so*
var libEmbed embed.FS

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@ -1,58 +0,0 @@
package llm
import (
"testing"
"github.com/jmorganca/ollama/gpu"
"github.com/stretchr/testify/assert"
)
func TestGetDynLibs(t *testing.T) {
availableDynLibs = map[string]string{
"cpu": "X_cpu",
}
assert.Equal(t, false, rocmDynLibPresent())
res := getDynLibs(gpu.GpuInfo{Library: "cpu"})
assert.Len(t, res, 1)
assert.Equal(t, availableDynLibs["cpu"], res[0])
variant := gpu.GetCPUVariant()
if variant != "" {
variant = "_" + variant
}
availableDynLibs = map[string]string{
"rocm_v5": "X_rocm_v5",
"rocm_v6": "X_rocm_v6",
"cpu" + variant: "X_cpu",
}
assert.Equal(t, true, rocmDynLibPresent())
res = getDynLibs(gpu.GpuInfo{Library: "rocm"})
assert.Len(t, res, 3)
assert.Equal(t, availableDynLibs["rocm_v5"], res[0])
assert.Equal(t, availableDynLibs["rocm_v6"], res[1])
assert.Equal(t, availableDynLibs["cpu"+variant], res[2])
res = getDynLibs(gpu.GpuInfo{Library: "rocm", Variant: "v6"})
assert.Len(t, res, 3)
assert.Equal(t, availableDynLibs["rocm_v6"], res[0])
assert.Equal(t, availableDynLibs["rocm_v5"], res[1])
assert.Equal(t, availableDynLibs["cpu"+variant], res[2])
res = getDynLibs(gpu.GpuInfo{Library: "cuda"})
assert.Len(t, res, 1)
assert.Equal(t, availableDynLibs["cpu"+variant], res[0])
res = getDynLibs(gpu.GpuInfo{Library: "default"})
assert.Len(t, res, 1)
assert.Equal(t, "default", res[0])
availableDynLibs = map[string]string{
"rocm": "X_rocm_v5",
"cpu" + variant: "X_cpu",
}
assert.Equal(t, true, rocmDynLibPresent())
res = getDynLibs(gpu.GpuInfo{Library: "rocm", Variant: "v6"})
assert.Len(t, res, 2)
assert.Equal(t, availableDynLibs["rocm"], res[0])
assert.Equal(t, availableDynLibs["cpu"+variant], res[1])
}

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@ -1,8 +0,0 @@
package llm
import (
"embed"
)
//go:embed llama.cpp/build/windows/*/*/lib/*.dll*
var libEmbed embed.FS

1
llm/server/.gitignore vendored Normal file
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@ -0,0 +1 @@
build

93
llm/server/CMakeLists.txt Normal file
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@ -0,0 +1,93 @@
cmake_minimum_required(VERSION 3.14)
project(llm)
include(FetchContent)
set(add_token_patch
git apply ${CMAKE_CURRENT_SOURCE_DIR}/patches/add_token.patch
)
set(FETCHCONTENT_BASE_DIR "${CMAKE_SOURCE_DIR}/build/llama.cpp")
FetchContent_Declare(
llama_cpp
GIT_REPOSITORY https://github.com/ggerganov/llama.cpp.git
GIT_TAG c29af7e2252d288f2ea58a7d437c1cb7c0abf160
# this could be risky if the patch doesn't apply
PATCH_COMMAND ${add_token_patch} || true
)
FetchContent_MakeAvailable(llama_cpp)
add_subdirectory(${llama_cpp_SOURCE_DIR}/examples/llava)
# code signing
function(sign target)
if(APPLE)
if(DEFINED ENV{APPLE_IDENTITY})
add_custom_command(TARGET ${target} POST_BUILD
COMMAND codesign
-f
--timestamp
--deep
--options=runtime
--sign "$ENV{APPLE_IDENTITY}"
--identifier ai.ollama.ollama
$<TARGET_FILE:${target}>
COMMENT "Signing macOS binary: ${target}"
)
endif()
elseif(WIN32)
find_program(SIGNTOOL_EXE NAMES signtool PATHS "C:\\Program Files (x86)\\Windows Kits\\8.1\\bin\\x64" NO_DEFAULT_PATH)
set(KEY_CONTAINER "$ENV{KEY_CONTAINER}")
set(OLLAMA_CERT "$ENV{OLLAMA_CERT}")
if(SIGNTOOL_EXE AND KEY_CONTAINER AND OLLAMA_CERT)
add_custom_command(TARGET ${target} POST_BUILD
COMMAND "${SIGNTOOL_EXE}"
"sign"
"/v"
"/fd" "sha256"
"/t" "http://timestamp.digicert.com"
"/f" "${OLLAMA_CERT}"
"/csp" "Google Cloud KMS Provider"
"/kc" "${KEY_CONTAINER}"
"$<TARGET_FILE:${target}>"
COMMENT "Signing Windows binary: ${target}"
)
endif()
endif()
endfunction()
set(CMAKE_CUDA_ARCHITECTURES "50;52;61;70;75;80")
function(gzip target)
set(gzip_target "gzip_${target}")
add_custom_target(${gzip_target} ALL
COMMAND gzip -k -f ${target}
COMMENT "Gzipping ${target}"
VERBATIM
)
add_dependencies(${gzip_target} ${target})
endfunction()
function(link_windows_libraries target)
if (WIN32)
target_link_libraries(${target} PRIVATE ws2_32)
endif()
endfunction()
add_executable(server ${llama_cpp_SOURCE_DIR}/examples/server/server.cpp ${llama_cpp_SOURCE_DIR})
target_compile_definitions(server PRIVATE)
target_link_libraries(server PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(server PRIVATE cxx_std_17)
link_windows_libraries(server)
sign(server)
gzip(server)
if(CMAKE_SYSTEM_PROCESSOR MATCHES "arm64")
configure_file(${llama_cpp_SOURCE_DIR}/ggml-metal.metal ${CMAKE_BINARY_DIR}/ggml-metal.metal COPYONLY)
endif()
# TODO: ROCm

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@ -1,15 +0,0 @@
package llm
import (
"fmt"
"time"
)
func parseDurationMs(ms float64) time.Duration {
dur, err := time.ParseDuration(fmt.Sprintf("%fms", ms))
if err != nil {
panic(err)
}
return dur
}