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prima.cpp/common/profiler.cpp
Lizonghang 45a1e55eec reduce kv cache from available memory
2024-11-28 20:21:21 +04:00

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#include "log.h"
#include "profiler.h"
#include "ggml.h"
#include "ggml-backend.h"
#include "llama.h"
#if defined(_WIN32) || defined(_WIN64)
#include <windows.h>
#elif defined(__linux__)
#include <unistd.h>
#include <sys/sysinfo.h>
#include <sys/stat.h>
#include <sys/types.h>
#include <fcntl.h>
#include <unistd.h>
#include <dirent.h>
#elif defined(__APPLE__) && defined(__MACH__)
#include <sys/sysctl.h>
#include <sys/param.h>
#include <sys/mount.h>
#include <mach/mach.h>
#include <unistd.h>
#endif
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#ifdef GGML_USE_CUDA
#include "ggml-cuda.h"
#include <cuda_runtime.h>
#endif
#include <cmath>
#include <chrono>
#include <fstream>
#include <string>
#include <cstring>
#include <sstream>
#include <sys/types.h>
#include <vector>
#include <inttypes.h>
#include <thread>
#include <random>
#include <regex>
const char * device_name() {
static char device_name[256];
#if defined(_WIN32) || defined(_WIN64)
DWORD size = sizeof(device_name);
if (GetComputerNameA(device_name, &size) == 0) {
strncpy(device_name, "Unknown Windows Device", sizeof(device_name));
}
#elif defined(__linux__)
if (gethostname(device_name, sizeof(device_name)) != 0) {
strncpy(device_name, "Unknown Linux Device", sizeof(device_name));
}
#elif defined(__APPLE__) && defined(__MACH__)
if (gethostname(device_name, sizeof(device_name)) != 0) {
strncpy(device_name, "Unknown Mac Device", sizeof(device_name));
}
#else
strncpy(device_name, "Unknown Device", sizeof(device_name));
#endif
return device_name;
}
uint32_t device_cpu_cores() {
unsigned int core_count = 1; // default to 1 in case of failure
#if defined(_WIN32) || defined(_WIN64)
SYSTEM_INFO sysinfo;
GetSystemInfo(&sysinfo);
core_count = sysinfo.dwNumberOfProcessors;
#elif defined(__linux__)
core_count = sysconf(_SC_NPROCESSORS_ONLN);
#elif defined(__APPLE__) && defined(__MACH__)
int mib[4];
size_t len = sizeof(core_count);
mib[0] = CTL_HW;
mib[1] = HW_AVAILCPU;
if (sysctl(mib, 2, &core_count, &len, NULL, 0) != 0 || core_count < 1) {
mib[1] = HW_NCPU; // total number of cpus
if (sysctl(mib, 2, &core_count, &len, NULL, 0) != 0 || core_count < 1) {
core_count = 1; // default to 1 if sysctl fails
}
}
#endif
return core_count;
}
static float device_flops(struct llama_model * model, enum ggml_type src0t, enum ggml_type src1t, profiler_backend_type btype, int n_threads) {
const int n_repeat = 1;
const int n_embd = llama_n_embd(model);
std::vector<float> matrix_A(n_embd * n_embd, 1.0f);
std::vector<float> matrix_B(n_embd * n_embd, 1.0f / n_embd);
ggml_backend_t backend = NULL;
switch (btype) {
case PROFILER_BACKEND_TYPE_CPU:
backend = ggml_backend_cpu_init();
break;
case PROFILER_BACKEND_TYPE_METAL:
#ifdef GGML_USE_METAL
backend = ggml_backend_metal_init();
#endif
break;
case PROFILER_BACKEND_TYPE_CUDA:
#ifdef GGML_USE_CUDA
backend = ggml_backend_cuda_init(0);
#endif
break;
}
if (!backend) {
LOG_INF("%s: ggml backend init failed\n", __func__);
return 0.0f;
}
struct ggml_init_params params = {
/*.mem_size =*/ 2 * ggml_tensor_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true, // the tensors will be allocated later by ggml_backend_alloc_ctx_tensors()
};
struct ggml_context * ctx = ggml_init(params);
struct ggml_tensor * tensor_a = ggml_new_tensor_2d(ctx, src0t, n_embd, n_embd);
struct ggml_tensor * tensor_b = ggml_new_tensor_2d(ctx, src1t, n_embd, n_embd);
ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend);
ggml_backend_tensor_set(tensor_a, matrix_A.data(), 0, ggml_nbytes(tensor_a));
ggml_backend_tensor_set(tensor_b, matrix_B.data(), 0, ggml_nbytes(tensor_b));
struct ggml_cgraph * gf = NULL;
struct ggml_context * ctx_cgraph = NULL;
struct ggml_tensor * cur = NULL;
{
struct ggml_init_params params0 = {
/*.mem_size =*/ ggml_tensor_overhead() * (n_repeat + 2) + ggml_graph_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true, // the tensors will be allocated later by ggml_gallocr_alloc_graph()
};
ctx_cgraph = ggml_init(params0);
gf = ggml_new_graph(ctx_cgraph);
cur = ggml_mul_mat(ctx_cgraph, tensor_a, tensor_b);
for (int i = 0; i < n_repeat - 1; i++) {
cur = ggml_mul_mat(ctx_cgraph, tensor_a, cur);
}
ggml_build_forward_expand(gf, cur);
}
ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
ggml_gallocr_alloc_graph(allocr, gf);
if (ggml_backend_is_cpu(backend)) {
ggml_backend_cpu_set_n_threads(backend, n_threads);
}
// warm-up
// ggml_backend_graph_compute(backend, gf);
const int64_t t_start = ggml_time_us();
ggml_backend_graph_compute(backend, gf);
const int64_t t_end = ggml_time_us();
double elapsed_seconds = ((double)t_end - (double)t_start) / 1e6; // convert to seconds
double flops = (2.0 * n_repeat * (double)n_embd * (double)n_embd * (double)n_embd) / elapsed_seconds / 1e9; // convert to GFLOPS
ggml_free(ctx_cgraph);
ggml_gallocr_free(allocr);
ggml_free(ctx);
ggml_backend_buffer_free(buffer);
ggml_backend_free(backend);
return (float)flops;
}
float device_cpu_flops(struct llama_model * model, enum ggml_type src0t, enum ggml_type src1t, int n_threads) {
return device_flops(model, src0t, src1t, PROFILER_BACKEND_TYPE_CPU, n_threads);
}
float device_metal_flops(struct llama_model * model, enum ggml_type src0t, enum ggml_type src1t) {
#ifdef GGML_USE_METAL
return device_flops(model, src0t, src1t, PROFILER_BACKEND_TYPE_METAL, 4);
#endif
(void)model;
(void)src0t;
(void)src1t;
return 0.0f;
}
float device_cuda_flops(struct llama_model * model, enum ggml_type src0t, enum ggml_type src1t) {
#ifdef GGML_USE_CUDA
return device_flops(model, src0t, src1t, PROFILER_BACKEND_TYPE_CUDA, 4);
#endif
(void)model;
(void)src0t;
(void)src1t;
return 0.0f;
}
float device_inp_embd_delay(struct llama_model * model, enum ggml_type src0t, int n_tokens, int n_threads) {
const int n_vocab = llama_n_vocab(model);
const int n_embd = llama_n_embd(model);
ggml_backend_t backend = ggml_backend_cpu_init();
if (!backend) {
LOG_INF("%s: ggml backend init failed\n", __func__);
return 0.0f;
}
size_t ctx_size = 0;
ctx_size += 2 * ggml_tensor_overhead(); // tensors
struct ggml_init_params params = {
/*.mem_size =*/ ctx_size,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true, // the tensors will be allocated later by ggml_backend_alloc_ctx_tensors()
};
struct ggml_context * ctx = ggml_init(params);
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_tokens);
struct ggml_tensor * tok_embd = ggml_new_tensor_2d(ctx, src0t, n_embd, n_vocab);
ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend);
std::vector<int32_t> matrix_A(n_tokens);
for (int i = 0; i < n_tokens; ++i) {
matrix_A[i] = i % n_vocab;
}
const size_t embd_size = n_vocab * n_embd;
void * matrix_B = nullptr;
// quantization and dequantization functions
ggml_type_traits_t qfns = ggml_internal_get_type_traits(src0t);
if (!qfns.from_float || !qfns.to_float) {
LOG_INF("Unsupported or uninitialized quantization type: %d\n", src0t);
ggml_free(ctx);
ggml_backend_buffer_free(buffer);
ggml_backend_free(backend);
return 0.0f;
}
size_t QK_K = 0;
switch (src0t) {
case GGML_TYPE_F32: {
matrix_B = malloc(embd_size * sizeof(float));
float * matrix_B_f32 = static_cast<float *>(matrix_B);
for (size_t i = 0; i < embd_size; ++i) {
matrix_B_f32[i] = static_cast<float>(rand()) / RAND_MAX;
}
break;
}
case GGML_TYPE_F16: {
matrix_B = malloc(embd_size * sizeof(ggml_fp16_t));
std::vector<float> temp_f32(embd_size);
for (size_t i = 0; i < embd_size; ++i) {
temp_f32[i] = static_cast<float>(rand()) / RAND_MAX;
}
ggml_fp32_to_fp16_row(temp_f32.data(), static_cast<ggml_fp16_t *>(matrix_B), embd_size);
break;
}
case GGML_TYPE_Q4_K:
case GGML_TYPE_Q6_K:
case GGML_TYPE_Q8_K:
QK_K = 256;
matrix_B = malloc((embd_size / QK_K) * ggml_type_size(src0t));
break;
default:
LOG_INF("Unsupported type: %d\n", src0t);
ggml_free(ctx);
ggml_backend_buffer_free(buffer);
ggml_backend_free(backend);
return 0.0f;
}
ggml_backend_tensor_set(inp_tokens, matrix_A.data(), 0, ggml_nbytes(inp_tokens));
ggml_backend_tensor_set(tok_embd, matrix_B, 0, ggml_nbytes(tok_embd));
struct ggml_cgraph * gf = NULL;
struct ggml_context * ctx_cgraph = NULL;
{
struct ggml_init_params params0 = {
/*.mem_size =*/ ggml_tensor_overhead() * 3 + ggml_graph_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true, // the tensors will be allocated later by ggml_gallocr_alloc_graph()
};
ctx_cgraph = ggml_init(params0);
gf = ggml_new_graph(ctx_cgraph);
struct ggml_tensor * cur = ggml_get_rows(ctx_cgraph, tok_embd, inp_tokens);
ggml_build_forward_expand(gf, cur);
}
ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
ggml_gallocr_alloc_graph(allocr, gf);
if (ggml_backend_is_cpu(backend)) {
ggml_backend_cpu_set_n_threads(backend, n_threads);
}
// warm-up
// ggml_backend_graph_compute(backend, gf);
const int64_t t_start = ggml_time_us();
ggml_backend_graph_compute(backend, gf);
const int64_t t_end = ggml_time_us();
double elapsed_ms = ((double)t_end - (double)t_start) / 1e3; // convert to ms
ggml_free(ctx_cgraph);
ggml_gallocr_free(allocr);
ggml_free(ctx);
ggml_backend_buffer_free(buffer);
ggml_backend_free(backend);
return (float)elapsed_ms;
}
uint64_t device_physical_memory(bool available) {
uint64_t memory = 0;
#if defined(_WIN32) || defined(_WIN64)
MEMORYSTATUSEX status;
status.dwLength = sizeof(status);
GlobalMemoryStatusEx(&status);
if (available) {
memory = status.ullAvailPhys;
} else {
memory = status.ullTotalPhys;
}
#elif defined(__linux__)
if (available) {
// read available memory from /proc/meminfo
std::ifstream meminfo("/proc/meminfo");
std::string line;
if (meminfo.is_open()) {
while (std::getline(meminfo, line)) {
if (line.find("MemAvailable:") == 0) {
std::istringstream iss(line);
std::string key;
uint64_t kb;
iss >> key >> kb;
memory = kb * 1024;
break;
}
}
meminfo.close();
}
} else {
// get total memory using sysinfo
struct sysinfo info;
if (sysinfo(&info) == 0) {
memory = info.totalram * info.mem_unit;
}
}
#elif defined(__APPLE__) && defined(__MACH__)
if (available) {
mach_port_t host = mach_host_self();
vm_statistics64_data_t vm_stats;
mach_msg_type_number_t count = HOST_VM_INFO64_COUNT;
if (host_statistics64(host, HOST_VM_INFO64, (host_info64_t)&vm_stats, &count) == KERN_SUCCESS) {
memory = (vm_stats.free_count + vm_stats.inactive_count) * sysconf(_SC_PAGESIZE);
}
} else {
int mib[2];
size_t len = sizeof(memory);
mib[0] = CTL_HW;
mib[1] = HW_MEMSIZE;
sysctl(mib, 2, &memory, &len, NULL, 0);
}
#endif
return memory;
}
uint64_t device_swap_memory(bool available) {
uint64_t swap_memory = 0;
#if defined(_WIN32) || defined(_WIN64)
PERFORMANCE_INFORMATION performance_info;
performance_info.cb = sizeof(performance_info);
if (GetPerformanceInfo(&performance_info, sizeof(performance_info))) {
if (available) {
swap_memory = (performance_info.PageFileTotal - performance_info.PageFileUsage) * performance_info.PageSize;
} else {
swap_memory = performance_info.PageFileTotal * performance_info.PageSize;
}
}
#elif defined(__linux__)
std::ifstream meminfo("/proc/meminfo");
std::string line;
uint64_t total_swap = 0;
uint64_t free_swap = 0;
if (meminfo.is_open()) {
while (std::getline(meminfo, line)) {
if (line.find("SwapTotal:") == 0) {
std::istringstream iss(line);
std::string key;
uint64_t kb;
iss >> key >> kb;
total_swap = kb * 1024;
} else if (line.find("SwapFree:") == 0) {
std::istringstream iss(line);
std::string key;
uint64_t kb;
iss >> key >> kb;
free_swap = kb * 1024;
}
}
meminfo.close();
}
if (available) {
swap_memory = free_swap;
} else {
swap_memory = total_swap;
}
#elif defined(__APPLE__) && defined(__MACH__)
int mib[2] = {CTL_VM, VM_SWAPUSAGE};
struct xsw_usage swap;
size_t len = sizeof(swap);
if (sysctl(mib, 2, &swap, &len, NULL, 0) == 0) {
if (available) {
swap_memory = swap.xsu_avail;
} else {
swap_memory = swap.xsu_total;
}
}
#endif
return swap_memory;
}
static size_t get_page_size() {
size_t page_size = 0;
#ifdef _WIN32
SYSTEM_INFO si;
GetSystemInfo(&si);
page_size = si.dwPageSize;
#elif defined(__APPLE__) || defined(__linux__)
page_size = sysconf(_SC_PAGESIZE);
#endif
return page_size;
}
static std::string get_default_device_path() {
#ifdef __linux__
// find the first block device under /sys/block
const std::string block_path = "/sys/block/";
DIR * dir = opendir(block_path.c_str());
if (!dir) {
LOG_INF("Unable to open %s\n", block_path.c_str());
return "";
}
struct dirent * entry;
while ((entry = readdir(dir)) != nullptr) {
if (entry->d_name[0] != '.') { // ignore hidden files/directories
std::string device = entry->d_name;
closedir(dir);
return "/dev/" + device;
}
}
closedir(dir);
LOG_INF("No block devices found in %s\n", block_path.c_str());
return "";
#elif __APPLE__
// use the root device as a default
return "/";
#elif _WIN32
// use the default drive (usually C:)
char volume_name[MAX_PATH];
if (GetVolumeInformation("C:\\", volume_name, sizeof(volume_name), NULL, NULL, NULL, NULL, 0)) {
return "C:\\";
} else {
LOG_INF("Failed to determine default volume\n");
return "";
}
#else
LOG_INF("Unsupported platform\n");
return "";
#endif
}
static size_t get_default_readahead_size() {
const std::string device_path = get_default_device_path();
#ifdef __linux__
std::string device = device_path.empty() ? get_default_device_path() : device_path;
if (device.empty()) return 0;
// read from sysfs
std::string sysfs_path = "/sys/block/" + device.substr(device.find_last_of("/") + 1) + "/queue/read_ahead_kb";
std::ifstream file(sysfs_path);
if (file.is_open()) {
size_t read_ahead_kb;
file >> read_ahead_kb;
file.close();
return read_ahead_kb * 1024; // convert to bytes
} else {
std::cerr << "Unable to open: " << sysfs_path << "\n";
return 0;
}
#elif __APPLE__
// use statfs to determine default block size
struct statfs stats;
std::string path = device_path.empty() ? "/" : device_path;
if (statfs(path.c_str(), &stats) == 0) {
return stats.f_iosize; // return in bytes
} else {
LOG_INF("statfs failed\n");
return 0;
}
#elif _WIN32
// use GetDiskFreeSpace to get default cluster size
std::string drive = device_path.empty() ? "C:\\" : device_path;
DWORD sectorsPerCluster, bytesPerSector, numberOfFreeClusters, totalNumberOfClusters;
if (GetDiskFreeSpace(drive.c_str(), &sectorsPerCluster, &bytesPerSector, &numberOfFreeClusters, &totalNumberOfClusters)) {
return sectorsPerCluster * bytesPerSector; // return in bytes
} else {
LOG_INF("GetDiskFreeSpace failed\n");
return 0;
}
#else
LOG_INF("Unsupported platform\n");
return 0;
#endif
}
static void external_fio_impl(float * read_bw, float * write_bw, bool op_rand, int n_threads) {
const char * test_file = "fio_test";
const char * fio_conf_template = R"(
[global]
ioengine=posixaio
direct=1
time_based=1
runtime=2
size=500M
group_reporting=1
[read-job]
rw=%s
bs=%s
filename=%s
numjobs=%d
[write-job]
rw=%s
bs=%s
filename=%s
numjobs=%d
)";
size_t page_size = get_page_size();
if (page_size == 0) {
LOG_INF("Error: Unable to get system page size\n");
return;
}
// format the page size as a readable string (e.g., "16k" or "4k")
char page_size_str[8];
if (page_size >= 1024) {
snprintf(page_size_str, sizeof(page_size_str), "%zuk", page_size / 1024);
} else {
snprintf(page_size_str, sizeof(page_size_str), "%zu", page_size);
}
size_t readahead_size = get_default_readahead_size();
if (readahead_size == 0) {
LOG_INF("Error: Unable to get system readahead size\n");
return;
}
// format the readahead size as a readable string (e.g., "128k" or "1m")
char readahead_str[8];
if (readahead_size >= 1024 * 1024) {
snprintf(readahead_str, sizeof(readahead_str), "%zuM", readahead_size / 1024 / 1024);
} else if (readahead_size >= 1024) {
snprintf(readahead_str, sizeof(readahead_str), "%zuk", readahead_size / 1024);
} else {
snprintf(readahead_str, sizeof(readahead_str), "%zu", readahead_size);
}
const char * read_type = op_rand ? "randread" : "read";
const char * write_type = op_rand ? "randwrite" : "write";
const char * block_size = op_rand ? page_size_str : readahead_str;
// write config to a file
char fio_conf[1024];
snprintf(fio_conf, sizeof(fio_conf), fio_conf_template,
read_type, block_size, test_file, n_threads,
write_type, block_size, test_file, n_threads);
const char * conf_file = "config.fio";
std::ofstream conf(conf_file);
if (!conf) {
LOG_INF("Error: Unable to create configuration file\n");
return;
}
conf << fio_conf;
conf.close();
// run fio and redirect output to a file
const char * output_file = "fio_output.log";
std::string command = "fio " + std::string(conf_file) + " > " + std::string(output_file);
if (std::system(command.c_str()) != 0) {
LOG_INF("Error: Failed to run fio\n");
return;
}
// parse fio output
std::ifstream result(output_file);
if (!result) {
LOG_INF("Error: Failed to open fio output file\n");
return;
}
*read_bw = 0.0f;
*write_bw = 0.0f;
std::string line;
std::regex read_regex(R"(READ: bw=([0-9.]+)([a-zA-Z/]+))");
std::regex write_regex(R"(WRITE: bw=([0-9.]+)([a-zA-Z/]+))");
std::smatch match;
while (std::getline(result, line)) {
if (std::regex_search(line, match, read_regex)) {
float value = std::stof(match[1]);
std::string unit = match[2];
if (unit == "MiB/s") {
*read_bw = value * 1024.0f * 1024.0f / 1e9; // convert MiB/s to GB/s
} else if (unit == "MB/s") {
*read_bw = value / 1000.0f; // convert MB/s to GB/s
}
} else if (std::regex_search(line, match, write_regex)) {
float value = std::stof(match[1]);
std::string unit = match[2];
if (unit == "MiB/s") {
*write_bw = value * 1024.0f * 1024.0f / 1e9; // convert MiB/s to GB/s
} else if (unit == "MB/s") {
*write_bw = value / 1000.0f; // convert MB/s to GB/s
}
}
}
// clean up temporary files
std::remove(test_file);
std::remove(conf_file);
std::remove(output_file);
}
void device_disk_rnd_bw(float * read_rnd_bw, float * write_rnd_bw, int n_threads) {
external_fio_impl(read_rnd_bw, write_rnd_bw, true, n_threads);
}
void device_disk_seq_bw(float * read_seq_bw, float * write_seq_bw, int n_threads) {
external_fio_impl(read_seq_bw, write_seq_bw, false, n_threads);
}
float device_memory_bw(int n_thread) {
size_t buffer_size = 5L * 1024 * 1024; // 5 MiB
std::vector<std::thread> thread_pool;
std::vector<double> results(n_thread);
std::vector<char *> buffers(n_thread);
for (int i = 0; i < n_thread; ++i) {
buffers[i] = new char[buffer_size];
}
auto memory_bw_test = [](char * buffer, size_t size, double & result) {
// read test
volatile char temp = 0;
auto start = std::chrono::high_resolution_clock::now();
for (size_t i = 0; i < size; i += 64) {
temp += buffer[i];
}
auto end = std::chrono::high_resolution_clock::now();
std::chrono::duration<double> elapsed = end - start;
result = size / elapsed.count() / 1e9; // GB/s
};
for (int i = 0; i < n_thread; ++i) {
thread_pool.emplace_back(memory_bw_test, buffers[i], buffer_size, std::ref(results[i]));
}
for (auto & t : thread_pool) {
t.join();
}
double bandwidth = 0.0f;
for (double result : results) {
bandwidth += result;
}
for (int i = 0; i < n_thread; ++i) {
delete[] buffers[i];
}
return static_cast<float>(bandwidth);
}
float device_cuda_memory_bw(struct llama_model * model) {
#ifdef GGML_USE_CUDA
const int n_embd = llama_n_embd(model) * 2;
std::vector<float> matrix_A(n_embd * n_embd, 1.0f);
ggml_backend_t backend = ggml_backend_cuda_init(0);
if (!backend) {
LOG_INF("%s: ggml backend init failed\n", __func__);
return 0.0f;
}
struct ggml_init_params params = {
/*.mem_size =*/ ggml_tensor_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true, // the tensors will be allocated later by ggml_backend_alloc_ctx_tensors()
};
struct ggml_context * ctx = ggml_init(params);
struct ggml_tensor * tensor_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
tensor_a->op = GGML_OP_READ;
ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend);
ggml_backend_tensor_set(tensor_a, matrix_A.data(), 0, ggml_nbytes(tensor_a));
struct ggml_cgraph * gf = NULL;
struct ggml_context * ctx_cgraph = NULL;
{
struct ggml_init_params params0 = {
/*.mem_size =*/ ggml_tensor_overhead() + ggml_graph_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true, // the tensors will be allocated later by ggml_gallocr_alloc_graph()
};
ctx_cgraph = ggml_init(params0);
gf = ggml_new_graph(ctx_cgraph);
ggml_build_forward_expand(gf, tensor_a);
}
ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
ggml_gallocr_alloc_graph(allocr, gf);
const int64_t t_start = ggml_time_us();
ggml_backend_graph_compute(backend, gf);
const int64_t t_end = ggml_time_us();
double elapsed_s = ((double)t_end - (double)t_start) / 1e6;
size_t total_bytes = n_embd * n_embd * sizeof(float);
float bandwidth = (total_bytes / elapsed_s) / 1e9; // GB/s
ggml_free(ctx_cgraph);
ggml_gallocr_free(allocr);
ggml_free(ctx);
ggml_backend_buffer_free(buffer);
ggml_backend_free(backend);
return bandwidth;
#else
(void)model;
return 0.0f;
#endif
}
int device_has_metal(void) {
return ggml_cpu_has_metal();
}
int device_has_cuda(void) {
return ggml_cpu_has_cuda();
}
int device_has_vulkan(void) {
return ggml_cpu_has_vulkan();
}
int device_has_kompute(void) {
return ggml_cpu_has_kompute();
}
int device_has_gpublas(void) {
return ggml_cpu_has_gpublas();
}
int device_has_blas(void) {
return ggml_cpu_has_blas();
}
int device_has_sycl(void) {
return ggml_cpu_has_sycl();
}
void device_get_props(struct llama_model * model, int device, struct ggml_backend_dev_props * props) {
ggml_backend_buffer_type_t buft_type;
if (device == -1) { // type cpu
buft_type = ggml_backend_cpu_buffer_type();
} else { // type gpu
buft_type = llama_dev_buffer_type(model, device);
}
ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft_type);
ggml_backend_dev_get_props(dev, props);
}
static float device_compute_delay(struct device_info & dev_info, int n_layers) {
struct model_flops n_flops = dev_info.model_flops;
struct cpu_props cpu = dev_info.cpu_props;
double total_latency = 0.0f;
#ifdef GGML_USE_CUDA
struct gpu_props gpu = dev_info.gpu_props;
total_latency += (double)n_flops.layer_f32_f32 / (double)gpu.cuda_flops_f32_f32 / 1e9;
total_latency += (double)n_flops.layer_f16_f32 / (double)gpu.cuda_flops_f16_f32 / 1e9;
total_latency += (double)n_flops.layer_q4k_f32 / (double)gpu.cuda_flops_q4k_f32 / 1e9;
total_latency += (double)n_flops.layer_q6k_f32 / (double)gpu.cuda_flops_q6k_f32 / 1e9;
total_latency += (double)n_flops.layer_q80_f32 / (double)gpu.cuda_flops_q80_f32 / 1e9;
#else
total_latency += (double)n_flops.layer_f32_f32 / (double)cpu.flops_f32_f32 / 1e9;
total_latency += (double)n_flops.layer_f16_f32 / (double)cpu.flops_f16_f32 / 1e9;
total_latency += (double)n_flops.layer_q4k_f32 / (double)cpu.flops_q4k_f32 / 1e9;
total_latency += (double)n_flops.layer_q6k_f32 / (double)cpu.flops_q6k_f32 / 1e9;
total_latency += (double)n_flops.layer_q80_f32 / (double)cpu.flops_q80_f32 / 1e9;
#endif
total_latency *= n_layers;
total_latency += (double)n_flops.output_f32_f32 / (double)cpu.flops_f32_f32 / 1e9;
total_latency += (double)n_flops.output_f16_f32 / (double)cpu.flops_f16_f32 / 1e9;
total_latency += (double)n_flops.output_q4k_f32 / (double)cpu.flops_q4k_f32 / 1e9;
total_latency += (double)n_flops.output_q6k_f32 / (double)cpu.flops_q6k_f32 / 1e9;
total_latency += (double)n_flops.output_q80_f32 / (double)cpu.flops_q80_f32 / 1e9;
total_latency *= 1000; // convert to ms
total_latency += n_flops.inp_embd_ms;
return static_cast<float>(total_latency);
}
// estimate the memory access delay, except for the input embedding because it has been considered in n_flops.inp_embd_ms
static float device_memory_access_delay(struct device_info & dev_info, int n_layers) {
struct model_params n_params = dev_info.model_params;
int64_t total_bytes = 0;
total_bytes += n_params.layer_f32 * 4 +
n_params.layer_f16 * 2 +
n_params.layer_q4k / 2 +
n_params.layer_q6k * 3 / 8 +
n_params.layer_q80;
total_bytes *= n_layers;
total_bytes += n_params.output_f32 * 4 +
n_params.output_f16 * 2 +
n_params.output_q4k / 2 +
n_params.output_q6k * 3 / 8 +
n_params.output_q80;
#ifdef GGML_USE_CUDA
return (double)total_bytes / 1e6 / dev_info.gpu_props.read_bandwidth; // ms
#else
return (double)total_bytes / 1e6 / dev_info.memory.read_bandwidth; // ms
#endif
}
static float device_disk_access_delay(struct device_info & dev_info, struct llama_model * model, const struct llama_context_params cparams) {
auto n_params = dev_info.model_params;
int n_layers = llama_model_n_layers(model);
double kv_size_gb = static_cast<double>(llama_model_kvcache_size(model, cparams)) / 1e9; // convert to GB
int64_t total_bytes = 0;
total_bytes += n_params.layer_f32 * 4 +
n_params.layer_f16 * 2 +
n_params.layer_q4k / 2 +
n_params.layer_q6k * 3 / 8 +
n_params.layer_q80;
total_bytes *= n_layers;
total_bytes += n_params.output_f32 * 4 +
n_params.output_f16 * 2 +
n_params.output_q4k / 2 +
n_params.output_q6k * 3 / 8 +
n_params.output_q80;
float total_gbytes = (double)total_bytes / 1e9; // convert to GB
float mem_avail = dev_info.memory.available_physical * 1024.0f * 1024.0f * 1024.0f / 1e9; // convert to GB
mem_avail -= static_cast<float>(kv_size_gb);
// todo: consider activations which also consumes the available memory
#ifdef __linux__
float disk_read_bw = dev_info.disk.read_seq_bw; // GB/s
#else
float disk_read_bw = dev_info.disk.read_rnd_bw; // GB/s
#endif
return std::max(0.0, static_cast<double>(total_gbytes - mem_avail) / disk_read_bw * 1000); // convert to ms
}
void device_print_props(struct device_info * dev_info_set, int n, struct llama_model * model, const struct llama_context_params cparams) {
LOG_INF("\n-------------------------------------------------------------------------------------------\n");
LOG_INF("| Property ");
for (int i = 0; i < n; ++i) {
LOG_INF("| Rank %-8d", i);
GGML_ASSERT((int)dev_info_set[i].rank == i);
}
LOG_INF("\n-------------------------------------------------------------------------------------------\n");
LOG_INF("| Device Name ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.10s ", dev_info_set[i].device_name);
}
LOG_INF("\n");
LOG_INF("| CPU Name ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.10s ", dev_info_set[i].cpu_props.name);
}
LOG_INF("\n");
LOG_INF("| CPU Description ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.10s ", dev_info_set[i].cpu_props.description);
}
LOG_INF("\n");
LOG_INF("| Number of CPU cores ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10u ", dev_info_set[i].cpu_props.cores);
}
LOG_INF("\n");
LOG_INF("| CPU flops (F32xF32, GFLOPS) ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.1f ", dev_info_set[i].cpu_props.flops_f32_f32);
}
LOG_INF("\n");
LOG_INF("| CPU flops (F16xF32, GFLOPS) ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.1f ", dev_info_set[i].cpu_props.flops_f16_f32);
}
LOG_INF("\n");
LOG_INF("| CPU flops (Q4K x F32, GFLOPS)");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.1f ", dev_info_set[i].cpu_props.flops_q4k_f32);
}
LOG_INF("\n");
LOG_INF("| CPU flops (Q6K x F32, GFLOPS)");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.1f ", dev_info_set[i].cpu_props.flops_q6k_f32);
}
LOG_INF("\n");
LOG_INF("| CPU flops (Q80 x F32, GFLOPS)");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.1f ", dev_info_set[i].cpu_props.flops_q80_f32);
}
LOG_INF("\n");
LOG_INF("| Physical Mem Total (GB) ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.2f ", dev_info_set[i].memory.total_physical);
}
LOG_INF("\n");
LOG_INF("| Physical Mem Available (GB) ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.2f ", dev_info_set[i].memory.available_physical);
}
LOG_INF("\n");
LOG_INF("| Swap Mem Total (GB) ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.2f ", dev_info_set[i].memory.total_swap);
}
LOG_INF("\n");
LOG_INF("| Swap Mem Available (GB) ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.2f ", dev_info_set[i].memory.available_swap);
}
LOG_INF("\n");
LOG_INF("| Mem Read Bandwidth (GB/s) ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.2f ", dev_info_set[i].memory.read_bandwidth);
}
LOG_INF("\n");
LOG_INF("| Disk Read Seq Speed (GB/s) ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.2f ", dev_info_set[i].disk.read_seq_bw);
}
LOG_INF("\n");
LOG_INF("| Disk Write Seq Speed (GB/s) ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.2f ", dev_info_set[i].disk.write_seq_bw);
}
LOG_INF("\n");
LOG_INF("| Disk Read Rnd Speed (GB/s) ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.2f ", dev_info_set[i].disk.read_rnd_bw);
}
LOG_INF("\n");
LOG_INF("| Disk Write Rnd Speed (GB/s) ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.2f ", dev_info_set[i].disk.write_rnd_bw);
}
LOG_INF("\n");
LOG_INF("| GPU Metal ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10d ", dev_info_set[i].gpu_support.metal);
}
LOG_INF("\n");
LOG_INF("| GPU CUDA ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10d ", dev_info_set[i].gpu_support.cuda);
}
LOG_INF("\n");
LOG_INF("| GPU Vulkan ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10d ", dev_info_set[i].gpu_support.vulkan);
}
LOG_INF("\n");
LOG_INF("| GPU Kompute ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10d ", dev_info_set[i].gpu_support.kompute);
}
LOG_INF("\n");
LOG_INF("| GPU BLAS ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10d ", dev_info_set[i].gpu_support.gpublas);
}
LOG_INF("\n");
LOG_INF("| BLAS ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10d ", dev_info_set[i].gpu_support.blas);
}
LOG_INF("\n");
LOG_INF("| SYCL ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10d ", dev_info_set[i].gpu_support.sycl);
}
LOG_INF("\n");
LOG_INF("| GPU Name ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.10s ", dev_info_set[i].gpu_props.name);
}
LOG_INF("\n");
LOG_INF("| GPU Description ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.10s ", dev_info_set[i].gpu_props.description);
}
LOG_INF("\n");
LOG_INF("| GPU Mem Free (GB) ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.2f ", dev_info_set[i].gpu_props.memory_free);
}
LOG_INF("\n");
LOG_INF("| GPU Mem Total (GB) ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.2f ", dev_info_set[i].gpu_props.memory_total);
}
LOG_INF("\n");
LOG_INF("| VRAM Read Bandwidth (GB/s) ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.2f ", dev_info_set[i].gpu_props.read_bandwidth);
}
LOG_INF("\n");
LOG_INF("| Metal flops (F32xF32, GFLOPS)");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.1f ", dev_info_set[i].gpu_props.metal_flops_f32_f32);
}
LOG_INF("\n");
LOG_INF("| Metal flops (F16xF32, GFLOPS)");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.1f ", dev_info_set[i].gpu_props.metal_flops_f16_f32);
}
LOG_INF("\n");
LOG_INF("| Metal flops (Q4KxF32, GFLOPS)");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.1f ", dev_info_set[i].gpu_props.metal_flops_q4k_f32);
}
LOG_INF("\n");
LOG_INF("| Metal flops (Q6KxF32, GFLOPS)");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.1f ", dev_info_set[i].gpu_props.metal_flops_q6k_f32);
}
LOG_INF("\n");
LOG_INF("| Metal flops (Q80xF32, GFLOPS)");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.1f ", dev_info_set[i].gpu_props.metal_flops_q80_f32);
}
LOG_INF("\n");
LOG_INF("| CUDA flops (F32xF32, GFLOPS)");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.1f ", dev_info_set[i].gpu_props.cuda_flops_f32_f32);
}
LOG_INF("\n");
LOG_INF("| CUDA flops (F16xF32, GFLOPS)");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.1f ", dev_info_set[i].gpu_props.cuda_flops_f16_f32);
}
LOG_INF("\n");
LOG_INF("| CUDA flops (Q4KxF32, GFLOPS)");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.1f ", dev_info_set[i].gpu_props.cuda_flops_q4k_f32);
}
LOG_INF("\n");
LOG_INF("| CUDA flops (Q6KxF32, GFLOPS)");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.1f ", dev_info_set[i].gpu_props.cuda_flops_q6k_f32);
}
LOG_INF("\n");
LOG_INF("| CUDA flops (Q80xF32, GFLOPS)");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.1f ", dev_info_set[i].gpu_props.cuda_flops_q80_f32);
}
LOG_INF("\n");
LOG_INF("| Model flops (output F32xF32) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_flops.output_f32_f32);
LOG_INF("\n");
LOG_INF("| Model flops (output F16xF32) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_flops.output_f16_f32);
LOG_INF("\n");
LOG_INF("| Model flops (output Q4KxF32) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_flops.output_q4k_f32);
LOG_INF("\n");
LOG_INF("| Model flops (output Q6KxF32) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_flops.output_q6k_f32);
LOG_INF("\n");
LOG_INF("| Model flops (output Q80xF32) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_flops.output_q80_f32);
LOG_INF("\n");
LOG_INF("| Model flops (layer F32xF32) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_flops.layer_f32_f32);
LOG_INF("\n");
LOG_INF("| Model flops (layer F16xF32) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_flops.layer_f16_f32);
LOG_INF("\n");
LOG_INF("| Model flops (layer Q4KxF32) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_flops.layer_q4k_f32);
LOG_INF("\n");
LOG_INF("| Model flops (layer Q6KxF32) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_flops.layer_q6k_f32);
LOG_INF("\n");
LOG_INF("| Model flops (layer Q80xF32) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_flops.layer_q80_f32);
LOG_INF("\n");
LOG_INF("| Model params (input F32) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.input_f32);
LOG_INF("\n");
LOG_INF("| Model params (input F16) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.input_f16);
LOG_INF("\n");
LOG_INF("| Model params (input Q4K) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.input_q4k);
LOG_INF("\n");
LOG_INF("| Model params (input Q6K) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.input_q6k);
LOG_INF("\n");
LOG_INF("| Model params (input Q80) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.input_q80);
LOG_INF("\n");
LOG_INF("| Model params (layer F32) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.layer_f32);
LOG_INF("\n");
LOG_INF("| Model params (layer F16) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.layer_f16);
LOG_INF("\n");
LOG_INF("| Model params (layer Q4K) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.layer_q4k);
LOG_INF("\n");
LOG_INF("| Model params (layer Q6K) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.layer_q6k);
LOG_INF("\n");
LOG_INF("| Model params (layer Q80) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.layer_q80);
LOG_INF("\n");
LOG_INF("| Model params (output F32) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.output_f32);
LOG_INF("\n");
LOG_INF("| Model params (output F16) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.output_f16);
LOG_INF("\n");
LOG_INF("| Model params (output Q4K) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.output_q4k);
LOG_INF("\n");
LOG_INF("| Model params (output Q6K) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.output_q6k);
LOG_INF("\n");
LOG_INF("| Model params (output Q80) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.output_q80);
LOG_INF("\n");
// todo: calculate for each device, not only master
float latency = 0.0f;
int n_layers = llama_model_n_layers(model);
latency += device_compute_delay(dev_info_set[0], n_layers);
latency += device_memory_access_delay(dev_info_set[0], n_layers);
latency += device_disk_access_delay(dev_info_set[0], model, cparams); // if physical memory is not enough, some tensor weights will be released from memory and reloaded by mmap later
LOG_INF("| Token latency (ms) ");
LOG_INF("| %-10.2f ", latency);
LOG_INF("\n");
LOG_INF("-------------------------------------------------------------------------------------------\n\n");
}
size_t serialize(const struct device_info * dev_info, char ** buffer) {
// calculate total size for serialized buffer
size_t device_name_len = strlen(dev_info->device_name) + 1;
size_t cpu_name_len = strlen(dev_info->cpu_props.name) + 1;
size_t cpu_description_len = strlen(dev_info->cpu_props.description) + 1;
size_t gpu_name_len = strlen(dev_info->gpu_props.name) + 1;
size_t gpu_description_len = strlen(dev_info->gpu_props.description) + 1;
size_t total_size = sizeof(uint32_t)
+ sizeof(size_t) * 5 // for lengths of strings
+ device_name_len
+ cpu_name_len
+ cpu_description_len
+ gpu_name_len
+ gpu_description_len
+ sizeof(struct disk_props)
+ sizeof(uint32_t) // cpu_props.cores
+ sizeof(float) * 5 // cpu_props.flops_f32_f32, cpu_props.flops_f16_f32, cpu_props.flops_q4k_f32, cpu_props.flops_q6k_f32, cpu_props.flops_q80_f32
+ sizeof(struct memory_info)
+ sizeof(struct gpu_support)
+ sizeof(float) * 13; // gpu_props.memory_free, gpu_props.memory_total, gpu_props.read_bandwidth,
// gpu_props.metal_flops_f32_f32, gpu_props.metal_flops_f16_f32, gpu_props.metal_flops_q4k_f32, gpu_props.metal_flops_q6k_f32, gpu_props.metal_flops_q80_f32,
// gpu_props.cuda_flops_f32_f32, gpu_props.cuda_flops_f16_f32, gpu_props.cuda_flops_q4k_f32, gpu_props.cuda_flops_q6k_f32, gpu_props.cuda_flops_q80_f32
*buffer = (char *)malloc(total_size);
char * ptr = *buffer;
// rank
memcpy(ptr, &dev_info->rank, sizeof(uint32_t));
ptr += sizeof(uint32_t);
// copy string lengths and string data
memcpy(ptr, &device_name_len, sizeof(size_t));
ptr += sizeof(size_t);
memcpy(ptr, dev_info->device_name, device_name_len);
ptr += device_name_len;
memcpy(ptr, &cpu_name_len, sizeof(size_t));
ptr += sizeof(size_t);
memcpy(ptr, dev_info->cpu_props.name, cpu_name_len);
ptr += cpu_name_len;
memcpy(ptr, &cpu_description_len, sizeof(size_t));
ptr += sizeof(size_t);
memcpy(ptr, dev_info->cpu_props.description, cpu_description_len);
ptr += cpu_description_len;
memcpy(ptr, &gpu_name_len, sizeof(size_t));
ptr += sizeof(size_t);
memcpy(ptr, dev_info->gpu_props.name, gpu_name_len);
ptr += gpu_name_len;
memcpy(ptr, &gpu_description_len, sizeof(size_t));
ptr += sizeof(size_t);
memcpy(ptr, dev_info->gpu_props.description, gpu_description_len);
ptr += gpu_description_len;
// copy the non-string members
memcpy(ptr, &dev_info->disk, sizeof(struct disk_props));
ptr += sizeof(struct disk_props);
memcpy(ptr, &dev_info->cpu_props.cores, sizeof(uint32_t));
ptr += sizeof(uint32_t);
memcpy(ptr, &dev_info->cpu_props.flops_f32_f32, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->cpu_props.flops_f16_f32, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->cpu_props.flops_q4k_f32, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->cpu_props.flops_q6k_f32, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->cpu_props.flops_q80_f32, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->memory, sizeof(struct memory_info));
ptr += sizeof(struct memory_info);
memcpy(ptr, &dev_info->gpu_support, sizeof(struct gpu_support));
ptr += sizeof(struct gpu_support);
memcpy(ptr, &dev_info->gpu_props.memory_free, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->gpu_props.memory_total, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->gpu_props.read_bandwidth, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->gpu_props.metal_flops_f32_f32, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->gpu_props.metal_flops_f16_f32, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->gpu_props.metal_flops_q4k_f32, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->gpu_props.metal_flops_q6k_f32, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->gpu_props.metal_flops_q80_f32, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->gpu_props.cuda_flops_f32_f32, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->gpu_props.cuda_flops_f16_f32, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->gpu_props.cuda_flops_q4k_f32, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->gpu_props.cuda_flops_q6k_f32, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->gpu_props.cuda_flops_q80_f32, sizeof(float));
// no need to synchronize model flops and model params
return total_size;
}
void deserialize(const char * buffer, struct device_info * dev_info) {
const char * ptr = buffer;
// rank
memcpy(&dev_info->rank, ptr, sizeof(uint32_t));
ptr += sizeof(uint32_t);
// device_name
size_t device_name_len;
memcpy(&device_name_len, ptr, sizeof(size_t));
ptr += sizeof(size_t);
dev_info->device_name = (char *)malloc(device_name_len);
memcpy(const_cast<void*>(static_cast<const void*>(dev_info->device_name)), ptr, device_name_len);
ptr += device_name_len;
// cpu_props.name
size_t cpu_name_len;
memcpy(&cpu_name_len, ptr, sizeof(size_t));
ptr += sizeof(size_t);
dev_info->cpu_props.name = (char *)malloc(cpu_name_len);
memcpy(const_cast<void*>(static_cast<const void*>(dev_info->cpu_props.name)), ptr, cpu_name_len);
ptr += cpu_name_len;
// cpu_props.description
size_t cpu_description_len;
memcpy(&cpu_description_len, ptr, sizeof(size_t));
ptr += sizeof(size_t);
dev_info->cpu_props.description = (char *)malloc(cpu_description_len);
memcpy(const_cast<void*>(static_cast<const void*>(dev_info->cpu_props.description)), ptr, cpu_description_len);
ptr += cpu_description_len;
// gpu_props.name
size_t gpu_name_len;
memcpy(&gpu_name_len, ptr, sizeof(size_t));
ptr += sizeof(size_t);
dev_info->gpu_props.name = (char *)malloc(gpu_name_len);
memcpy(const_cast<void*>(static_cast<const void*>(dev_info->gpu_props.name)), ptr, gpu_name_len);
ptr += gpu_name_len;
// gpu_props.description
size_t gpu_description_len;
memcpy(&gpu_description_len, ptr, sizeof(size_t));
ptr += sizeof(size_t);
dev_info->gpu_props.description = (char *)malloc(gpu_description_len);
memcpy(const_cast<void*>(static_cast<const void*>(dev_info->gpu_props.description)), ptr, gpu_description_len);
ptr += gpu_description_len;
// other non-string members
memcpy(&dev_info->disk, ptr, sizeof(struct disk_props));
ptr += sizeof(struct disk_props);
memcpy(&dev_info->cpu_props.cores, ptr, sizeof(uint32_t));
ptr += sizeof(uint32_t);
memcpy(&dev_info->cpu_props.flops_f32_f32, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->cpu_props.flops_f16_f32, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->cpu_props.flops_q4k_f32, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->cpu_props.flops_q6k_f32, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->cpu_props.flops_q80_f32, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->memory, ptr, sizeof(struct memory_info));
ptr += sizeof(struct memory_info);
memcpy(&dev_info->gpu_support, ptr, sizeof(struct gpu_support));
ptr += sizeof(struct gpu_support);
memcpy(&dev_info->gpu_props.memory_free, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->gpu_props.memory_total, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->gpu_props.read_bandwidth, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->gpu_props.metal_flops_f32_f32, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->gpu_props.metal_flops_f16_f32, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->gpu_props.metal_flops_q4k_f32, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->gpu_props.metal_flops_q6k_f32, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->gpu_props.metal_flops_q80_f32, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->gpu_props.cuda_flops_f32_f32, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->gpu_props.cuda_flops_f16_f32, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->gpu_props.cuda_flops_q4k_f32, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->gpu_props.cuda_flops_q6k_f32, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->gpu_props.cuda_flops_q80_f32, ptr, sizeof(float));
// no need to synchronize model flops and model params
}
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