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Commit 64de7843 authored by qiyuxinlin's avatar qiyuxinlin
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format kvc2, delete quant_configs, move model_configs to ~/.ktransformers

parent 9dd24ecd
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Showing with 744 additions and 625 deletions
csrc/balance_serve/kvc2/src/async_store.cpp
View file @ 64de7843
......@@ -35,23 +35,23 @@ struct ArrayStore {
if (to <= size) {
return;
}
//TODO: extend file
// TODO: extend file
size = to;
//LOG_INFO("Extend file to `, size `", to, size_in_bytes());
// LOG_INFO("Extend file to `, size `", to, size_in_bytes());
}
ArrayStore(size_t element_size, size_t size, std::filesystem::path data_path)
: element_size(element_size),
element_size_aligned((element_size + DeviceBlockSize - 1) / DeviceBlockSize),
data_path(data_path) {
//TODO: prefix cache
// TODO: prefix cache
}
void read(size_t index, void* buffer) {
//TODO: read from file
// TODO: read from file
}
void write(size_t index, void* buffer) {
//TODO: write to file
// TODO: write to file
}
};
......@@ -98,15 +98,15 @@ struct IODealerImpl {
IODealerImpl(bool use_io_uring, int IO_DEPTH) : use_io_uring(use_io_uring), IO_DEPTH(IO_DEPTH) {}
void queue_consumer() {
//TODO:
// TODO:
}
void io_perf() {
//TODO:
// TODO:
}
void io_dealer() {
//TODO:
// TODO:
}
};
......@@ -130,7 +130,7 @@ void IODealer::stop() {
if (io_impl->stop) {
return;
}
//LOG_INFO("Stopping IO Dealer");
// LOG_INFO("Stopping IO Dealer");
io_impl->stop = true;
}
......
csrc/balance_serve/kvc2/src/gpu_cache.cpp
View file @ 64de7843
......@@ -77,7 +77,6 @@ GPUPageCache::GPUPageCache(GPUPageCacheConfig& config) : config(config) {
gpu_only_occupations.resize(config.total_kvcache_pages, false);
}
num_free_pages = config.total_kvcache_pages;
for (size_t i = 0; i < config.layer_count; i++) {
if (config.k_cache_on)
......@@ -248,18 +247,19 @@ void GPUPageCache::append_col_to_request(std::vector<std::shared_ptr<CudaStreamM
auto gpu_block_idx = k_handles[0][at]->gpu_block_idx.value();
for (size_t layer = 0; layer < config.layer_count; layer++) {
for (size_t which_gpu = 0; which_gpu < config.gpu_devices_id.size(); which_gpu++) {
if (config.k_cache_on) {
assert(k_handles[layer][at]->data != nullptr);
reqs[which_gpu]->sizes.push_back(tp_size[which_gpu]);
reqs[which_gpu]->host_mem_addresses.push_back(offset_by_bytes(k_handles[layer][at]->data, tp_offset[which_gpu]));
reqs[which_gpu]->host_mem_addresses.push_back(
offset_by_bytes(k_handles[layer][at]->data, tp_offset[which_gpu]));
reqs[which_gpu]->device_mem_addresses.push_back(k_cache[which_gpu][layer][gpu_block_idx].data_ptr());
}
if (config.v_cache_on) {
assert(v_handles[layer][at]->data != nullptr);
reqs[which_gpu]->sizes.push_back(tp_size[which_gpu]);
reqs[which_gpu]->host_mem_addresses.push_back(offset_by_bytes(v_handles[layer][at]->data, tp_offset[which_gpu]));
reqs[which_gpu]->host_mem_addresses.push_back(
offset_by_bytes(v_handles[layer][at]->data, tp_offset[which_gpu]));
reqs[which_gpu]->device_mem_addresses.push_back(v_cache[which_gpu][layer][gpu_block_idx].data_ptr());
}
}
......
csrc/balance_serve/kvc2/src/metrics.h
View file @ 64de7843
#pragma once
#include "prometheus/counter.h"
#include "prometheus/exposer.h"
#include "prometheus/gauge.h"
#include "prometheus/histogram.h"
#include "prometheus/registry.h"
#include <atomic>
#include <chrono>
#include <memory>
#include <string>
#include <thread>
#include <vector>
#include "prometheus/counter.h"
#include "prometheus/exposer.h"
#include "prometheus/gauge.h"
#include "prometheus/histogram.h"
#include "prometheus/registry.h"
#include "utils/timer.hpp"
......
csrc/balance_serve/kvc2/src/model_config.h
View file @ 64de7843
#ifndef __MODEL_CONFIG_HPP_
#define __MODEL_CONFIG_HPP_
#include <iostream>
#include "nlohmann/json.hpp"
#include <iostream>
#include <filesystem>
#include <fstream>
......@@ -13,7 +13,7 @@ using ModelName = std::string;
// We must assure this can be load by config.json
class ModelConfig {
public:
public:
DimSize hidden_size;
DimSize intermediate_size;
size_t max_position_embeddings;
......@@ -23,10 +23,13 @@ class ModelConfig {
size_t num_key_value_heads;
size_t vocab_size;
NLOHMANN_DEFINE_TYPE_INTRUSIVE(ModelConfig, hidden_size, intermediate_size, max_position_embeddings, model_type,
num_attention_heads, num_hidden_layers, num_key_value_heads, vocab_size);
NLOHMANN_DEFINE_TYPE_INTRUSIVE(ModelConfig, hidden_size, intermediate_size,
max_position_embeddings, model_type,
num_attention_heads, num_hidden_layers,
num_key_value_heads, vocab_size);
void load_from(std::filesystem::path path) {
std::cout << "Load from " << path << std::endl;
std::ifstream i(path);
nlohmann::json j;
i >> j;
......@@ -38,12 +41,14 @@ using QuantType = std::string;
static const QuantType NoQuantType = "";
class QuantConfig {
public:
public:
QuantType name;
// For GEMV
QuantType type_of_dot_vector = NoQuantType;
inline bool can_be_used_as_matrix() { return type_of_dot_vector != NoQuantType; }
inline bool can_be_used_as_matrix() {
return type_of_dot_vector != NoQuantType;
}
bool can_be_used_as_vector;
......@@ -56,8 +61,11 @@ class QuantConfig {
URL reference = "";
NLOHMANN_DEFINE_TYPE_INTRUSIVE_WITH_DEFAULT(QuantConfig, name, type_of_dot_vector, can_be_used_as_vector,
bytes_per_element, has_scale, has_min, block_element_count,
NLOHMANN_DEFINE_TYPE_INTRUSIVE_WITH_DEFAULT(QuantConfig, name,
type_of_dot_vector,
can_be_used_as_vector,
bytes_per_element, has_scale,
has_min, block_element_count,
block_element_size, reference);
};
......@@ -65,14 +73,18 @@ inline std::map<QuantType, QuantConfig> quant_configs;
inline std::map<ModelName, ModelConfig> model_configs;
inline void load_quant_configs(std::filesystem::path path) {
std::cout << __FUNCTION__ << " from " << path << std::endl;
std::ifstream i(path);
nlohmann::json j;
i >> j;
quant_configs = j.get<std::map<QuantType, QuantConfig>>();
std::cout << "Loaded Quant Configs" << std::endl;
for (auto& [k, v] : quant_configs) {
std::cout << " - " << k << std::endl;
if (std::filesystem::exists(path)) {
std::cout << __FUNCTION__ << " from " << path << std::endl;
std::ifstream i(path);
i >> j;
quant_configs = j.get<std::map<QuantType, QuantConfig>>();
std::cout << "Loaded Quant Configs" << std::endl;
for (auto &[k, v] : quant_configs) {
std::cout << " - " << k << std::endl;
}
} else {
std::cout << __FUNCTION__ << " no file at " << path << std::endl;
}
}
......@@ -83,14 +95,18 @@ inline void dump_quant_configs(std::filesystem::path path) {
}
inline void load_model_configs(std::filesystem::path path) {
std::cout << __FUNCTION__ << " from " << path << std::endl;
std::ifstream i(path);
nlohmann::json j;
i >> j;
model_configs = j.get<std::map<ModelName, ModelConfig>>();
std::cout << "Loaded Model Configs" << std::endl;
for (auto& [k, v] : model_configs) {
std::cout << " - " << k << std::endl;
if (std::filesystem::exists(path)) {
std::cout << __FUNCTION__ << " from " << path << std::endl;
std::ifstream i(path);
i >> j;
model_configs = j.get<std::map<ModelName, ModelConfig>>();
std::cout << "Loaded Model Configs" << std::endl;
for (auto &[k, v] : model_configs) {
std::cout << " - " << k << std::endl;
}
} else {
std::cout << __FUNCTION__ << " no file at " << path << std::endl;
}
}
......
csrc/balance_serve/kvc2/src/page_aligned_memory_pool.cpp
View file @ 64de7843
......@@ -17,13 +17,14 @@ PageAlignedMemoryPool::PageAlignedMemoryPool(size_t size_in_bytes) {
assert(total_pages >= Blocks);
page_per_block = total_pages / Blocks;
for (size_t block_index = 0; block_index < Blocks; block_index ++) {
first_page[block_index] = reinterpret_cast<void*>(reinterpret_cast<intptr_t>(data) + static_cast<intptr_t>(block_index) * page_per_block * PageSize);
for (size_t block_index = 0; block_index < Blocks; block_index++) {
first_page[block_index] = reinterpret_cast<void*>(reinterpret_cast<intptr_t>(data) +
static_cast<intptr_t>(block_index) * page_per_block * PageSize);
count_page[block_index] =
block_index == Blocks - 1 ? (total_pages - page_per_block * (Blocks - 1)) : page_per_block;
SPDLOG_DEBUG("first_page[{}] = {}, count_page[{}] = {}",
block_index, reinterpret_cast<intptr_t>(first_page[block_index]) - reinterpret_cast<intptr_t>(data),
block_index, count_page[block_index]);
block_index == Blocks - 1 ? (total_pages - page_per_block * (Blocks - 1)) : page_per_block;
SPDLOG_DEBUG("first_page[{}] = {}, count_page[{}] = {}", block_index,
reinterpret_cast<intptr_t>(first_page[block_index]) - reinterpret_cast<intptr_t>(data), block_index,
count_page[block_index]);
bitmap[block_index].resize(count_page[block_index], 0);
}
SPDLOG_INFO("PageAlignedMemoryPool with size {} Mbytes, {} pages", total_size / (1 << 20), page_count());
......@@ -53,7 +54,7 @@ void* PageAlignedMemoryPool::alloc_in_block(size_t block_index, size_t alloc_siz
size_t free_pages = 0;
for (size_t i = 0; i < count_page[block_index]; i++) {
if (bitmap[block_index][i] == 0) {
free_pages ++;
free_pages++;
if (free_pages == alloc_size) {
size_t page_index = i + 1 - free_pages;
for (size_t page = page_index; page < page_index + alloc_size; page++) {
......@@ -73,7 +74,7 @@ void* PageAlignedMemoryPool::alloc_in_block(size_t block_index, size_t alloc_siz
void* PageAlignedMemoryPool::alloc(size_t size) {
size_t alloc_size = div_up(size, PageSize);
auto cnt = now_block.fetch_add(1, std::memory_order_relaxed);
for (size_t i = 0; i < Blocks; i ++) {
for (size_t i = 0; i < Blocks; i++) {
auto result = alloc_in_block((i + cnt) % Blocks, alloc_size);
if (result != nullptr) {
allocated.fetch_add(alloc_size * PageSize, std::memory_order_relaxed);
......@@ -119,5 +120,6 @@ void PageAlignedMemoryPool::defragment() {}
/// 调试打印
std::string PageAlignedMemoryPool::debug() {
return fmt::format("PageAlignedMemoryPool: total_size: {}MB, allocated: {}, alloc/free count: {}/{}\n",
readable_number(total_size), readable_number(size_t(allocated)), size_t(alloc_count), size_t(free_count));
readable_number(total_size), readable_number(size_t(allocated)), size_t(alloc_count),
size_t(free_count));
}
csrc/balance_serve/kvc2/src/page_aligned_memory_pool.h
View file @ 64de7843
#pragma once
#include <algorithm> // std::sort
#include <cstddef> // size_t
#include <mutex> // std::mutex
#include <vector>
#include <assert.h>
#include <bitset>
#include <algorithm> // std::sort
#include <atomic>
#include <bitset>
#include <cstddef> // size_t
#include <mutex> // std::mutex
#include <vector>
constexpr size_t PageSize = 4096;
......@@ -18,7 +18,7 @@ struct PageAlignedMemoryPool {
void* data = nullptr;
size_t total_size = 0, total_pages = 0;
std::atomic_size_t now_block = 0;
std::atomic_size_t allocated = 0; // allocated_size
std::atomic_size_t alloc_count = 0;
......@@ -26,10 +26,11 @@ struct PageAlignedMemoryPool {
std::mutex lock[Blocks];
size_t page_per_block = 0;
void *first_page[Blocks];
void* first_page[Blocks];
size_t count_page[Blocks];
std::vector<int8_t> bitmap[Blocks];
void* alloc_in_block(size_t block_index, size_t alloc_size);
public:
/// 构造函数和析构函数
explicit PageAlignedMemoryPool(size_t size_in_bytes);
......
csrc/balance_serve/kvc2/src/prefix.cpp
View file @ 64de7843
......@@ -339,7 +339,7 @@ struct Prefix {
void update_location(CacheInfo info, Location location) { locations.location_map[info] = location; }
Prefix* to_first_prefix_without_disk_locations(CacheInfo k_info/*, CacheInfo v_info*/) { // just k_info
Prefix* to_first_prefix_without_disk_locations(CacheInfo k_info /*, CacheInfo v_info*/) { // just k_info
auto now_prefix = this;
while (now_prefix->prev != nullptr) {
auto& prev = now_prefix->prev;
......@@ -561,7 +561,7 @@ struct PrefixTree {
if (need_lock) {
sl = std::shared_lock<std::shared_mutex>(rw_lock);
}
//TODO: prefix cache
// TODO: prefix cache
}
PrefixMatch look_up_or_insert(Token* data, TokenLength length) {
......@@ -579,7 +579,6 @@ struct PrefixTree {
return re;
}
std::shared_ptr<Prefix> new_prefix_node(Prefix* prev, TokenLength prev_match_length, Token* data, TokenLength length,
bool need_lock = true) {
std::unique_lock<std::shared_mutex> ul;
......@@ -700,9 +699,7 @@ struct DoubleCacheHandle : public DoubleCacheHandleInterface {
}
}
}
std::vector<MatchStatus> matched_status() override {
assert(false);
}
std::vector<MatchStatus> matched_status() override { assert(false); }
bool any_match() {
if (enable_alt) {
......@@ -1066,7 +1063,6 @@ struct DoubleCacheHandle : public DoubleCacheHandleInterface {
};
struct KVC2 : KVC2Interface {
KVC2Config config;
std::shared_ptr<Metrics> met;
......@@ -1194,7 +1190,7 @@ struct KVC2 : KVC2Interface {
auto v_loc = disk_cache->allocate(h->v_info(), div_up(new_length, NumTokenPerBlock));
h->k_seg_locs.add_location(now_prefix->start_length / NumTokenPerBlock, k_loc);
h->v_seg_locs.add_location(now_prefix->start_length / NumTokenPerBlock, v_loc);
// split it to prefix trees
for (auto tail = h->match.prefix; tail != now_prefix->prev; tail = tail->prev) {
TokenLength local_ids_length = tail->local_length();
......@@ -1207,7 +1203,7 @@ struct KVC2 : KVC2Interface {
// allocate a big space on disk
auto k_loc = disk_cache->allocate(h->k_info(), div_up(new_length, NumTokenPerBlock));
h->k_seg_locs.add_location(now_prefix->start_length / NumTokenPerBlock, k_loc);
// split it to prefix trees
for (auto tail = h->match.prefix; tail != now_prefix->prev; tail = tail->prev) {
TokenLength local_ids_length = tail->local_length();
......@@ -1231,7 +1227,7 @@ struct KVC2 : KVC2Interface {
h->kvc2_top = this;
h->set_cache_info(model_name, quant_type, config.k_cache_on, config.v_cache_on);
h->ids = Tokens(id, id + length);
if (config.k_cache_on)
h->set_raw_handles(true, k_cache);
if (config.v_cache_on)
......@@ -1261,7 +1257,7 @@ struct KVC2 : KVC2Interface {
re->kvc2_top = this;
SPDLOG_DEBUG("Lookup TokenLength {}", length);
if (config.gpu_only == false) {
//TODO:
// TODO:
}
return re;
};
......@@ -1694,9 +1690,11 @@ void GPUPageCache::gpu_background_flush() {
if (col_uls.empty())
continue;
for (size_t l = 0; l < config.layer_count; l++) {
if (config.k_cache_on && (occupations[l][i]->gpu_cc.dirty.load() == false || occupations[l][i]->cpu_cc.dirty.load()))
if (config.k_cache_on &&
(occupations[l][i]->gpu_cc.dirty.load() == false || occupations[l][i]->cpu_cc.dirty.load()))
goto next_gpu_page;
if (config.v_cache_on && (v_occupations[l][i]->gpu_cc.dirty.load() == false || v_occupations[l][i]->cpu_cc.dirty.load()))
if (config.v_cache_on &&
(v_occupations[l][i]->gpu_cc.dirty.load() == false || v_occupations[l][i]->cpu_cc.dirty.load()))
goto next_gpu_page;
}
......
csrc/balance_serve/kvc2/test/kvc2test/common.hpp
View file @ 64de7843
......@@ -139,18 +139,18 @@ std::vector<Token> random_ids(size_t length, std::mt19937& gen) {
return re;
}
std::vector<layer_data> slice(std::vector<layer_data>& h1,size_t start,size_t end){
std::vector<layer_data> slice(std::vector<layer_data>& h1, size_t start, size_t end) {
std::vector<layer_data> re;
for(auto&l:h1){
for (auto& l : h1) {
layer_data new_layer;
new_layer.insert(new_layer.end(),l.begin()+start,l.begin()+end);
new_layer.insert(new_layer.end(), l.begin() + start, l.begin() + end);
re.push_back(new_layer);
}
return re;
}
void cmp_handle_data(std::vector<layer_data> h1, std::vector<layer_data> h2,
std::optional<size_t> blocks = std::nullopt) {
std::optional<size_t> blocks = std::nullopt) {
assert(h1.size() == h2.size());
for (size_t i = 0; i < h1.size(); i++) {
......
csrc/balance_serve/kvc2/test/kvc2test/flush-back.cpp
View file @ 64de7843
......@@ -7,9 +7,9 @@ int main(int argc, char* argv[]) {
config.gpu_cache_config->total_kvcache_pages = 12;
auto kvc2 = kvc2::create_kvc2(config);
// #pragma omp parallel for
// #pragma omp parallel for
for (size_t ti = 0; ti < 2; ti++) {
SPDLOG_WARN("Test {}",ti);
SPDLOG_WARN("Test {}", ti);
auto [kcache, vcache] = kvc2->get_kvcache();
std::mt19937 gen(ti + 123);
size_t total_page = 10;
......
csrc/balance_serve/kvc2/test/kvc2test/lookup-gpu-mt-without-vcache.cpp
View file @ 64de7843
......@@ -14,7 +14,7 @@ int main(int argc, char* argv[]) {
qw25_7B_gpu_config.v_cache_on = false;
config.gpu_cache_config = qw25_7B_gpu_config;
config.v_cache_on = false;
init(argc, argv);
spdlog::set_level(spdlog::level::debug);
auto kvc2 = kvc2::create_kvc2(config);
......
csrc/balance_serve/kvc2/test/kvc2test/lookup-without-vcache.cpp
View file @ 64de7843
......@@ -11,11 +11,10 @@
#include "common.hpp"
int main(int argc, char* argv[]) {
qw25_7B_gpu_config.v_cache_on = false;
config.gpu_cache_config = qw25_7B_gpu_config;
config.v_cache_on = false;
init(argc, argv);
spdlog::set_level(spdlog::level::debug);
auto kvc2 = kvc2::create_kvc2(config);
......
csrc/balance_serve/kvc2/test/page_pool_test.cpp
View file @ 64de7843
#include <unistd.h>
#include <iostream>
#include <random>
#include <thread>
#include <vector>
#include <random>
#include <unistd.h>
#include "page_aligned_memory_pool.cpp"
#define SPDLOG_ACTIVE_LEVEL SPDLOG_LEVEL_DEBUG
#define FMT_HEADER_ONLY
#include "spdlog/spdlog.h"
// 每个线程执行的任务
void thread_task(PageAlignedMemoryPool& pool) {
std::mt19937 gen(123);
......@@ -22,8 +21,8 @@ void thread_task(PageAlignedMemoryPool& pool) {
void* ptr = pool.alloc(size);
// SPDLOG_DEBUG(pool.debug());
if (ptr) {
pool.free(ptr, size);
// allocated.push_back({ptr, size});
pool.free(ptr, size);
// allocated.push_back({ptr, size});
}
// sleep((int)(gen() % 1000) / 1000.0);
}
......@@ -35,21 +34,20 @@ void thread_task(PageAlignedMemoryPool& pool) {
int main(int argc, char* argv[]) {
spdlog::set_level(spdlog::level::debug);
// 创建一个内存池
PageAlignedMemoryPool pool(40ll * 1024 * 1024 * 1024); // 40 G
PageAlignedMemoryPool pool(40ll * 1024 * 1024 * 1024); // 40 G
// 创建线程
const int num_threads = 32;
std::vector<std::thread> threads;
for (int i = 0; i < num_threads; ++i) {
threads.emplace_back(thread_task, std::ref(pool));
threads.emplace_back(thread_task, std::ref(pool));
}
// 等待所有线程完成
for (auto& t : threads) {
t.join();
t.join();
}
// 输出调试信息
......
csrc/balance_serve/kvc2/test/test_periodic_task.cpp
View file @ 64de7843
#include "utils/periodic_task.hpp"
#include <atomic>
#include <cassert>
#include <chrono>
#include <cstdio>
#include <future>
#include <iostream>
#include <thread>
#include <future>
#include <atomic>
#include <cassert>
#include "utils/periodic_task.hpp"
// 1. 任务是否按预期执行
void testPeriodicTaskExecution() {
std::atomic<int> execution_count{0};
auto task = [&execution_count]() {
execution_count++;
};
std::atomic<int> execution_count{0};
auto task = [&execution_count]() { execution_count++; };
periodic::PeriodicTask periodic_task(task, std::chrono::milliseconds(50));
periodic::PeriodicTask periodic_task(task, std::chrono::milliseconds(50));
std::this_thread::sleep_for(std::chrono::seconds(2));
std::this_thread::sleep_for(std::chrono::seconds(2));
assert(execution_count >= 20); // 确保任务执行了至少 20 次
std::cout << "Test 1 passed: Task executed periodically." << std::endl;
std::cout << "Task executed " << execution_count.load() << " times." << std::endl;
assert(execution_count >= 20); // 确保任务执行了至少 20 次
std::cout << "Test 1 passed: Task executed periodically." << std::endl;
std::cout << "Task executed " << execution_count.load() << " times." << std::endl;
}
// 2. 提前唤醒任务的功能
void testWakeUpImmediately() {
std::atomic<int> execution_count{0};
auto task = [&execution_count]() {
execution_count++;
};
std::atomic<int> execution_count{0};
auto task = [&execution_count]() { execution_count++; };
periodic::PeriodicTask periodic_task(task, std::chrono::milliseconds(200));
periodic::PeriodicTask periodic_task(task, std::chrono::milliseconds(200));
// 提前唤醒任务
periodic_task.wakeUp();
std::this_thread::sleep_for(std::chrono::milliseconds(50)); // 等待任务执行
// 提前唤醒任务
periodic_task.wakeUp();
std::this_thread::sleep_for(std::chrono::milliseconds(50)); // 等待任务执行
std::cout << "Execution count after wakeUp: " << execution_count.load() << std::endl;
assert(execution_count == 1); // 确保任务立即执行
std::cout << "Test 2 passed: Task woke up immediately." << std::endl;
std::cout << "Execution count after wakeUp: " << execution_count.load() << std::endl;
assert(execution_count == 1); // 确保任务立即执行
std::cout << "Test 2 passed: Task woke up immediately." << std::endl;
}
// 3. wakeUpWait() 的等待功能
void testWakeUpWait() {
std::promise<void> promise;
std::future<void> future = promise.get_future();
auto task = [&promise]() {
std::this_thread::sleep_for(std::chrono::milliseconds(100)); // 模拟任务执行
promise.set_value(); // 任务完成时设置 promise
};
periodic::PeriodicTask periodic_task(task, std::chrono::milliseconds(200));
// 调用 wakeUpWait 并等待任务完成
std::future<void> wakeup_future = periodic_task.wakeUpWait();
wakeup_future.wait(); // 等待任务完成
assert(wakeup_future.valid()); // 确保 future 是有效的
std::cout << "Test 3 passed: wakeUpWait() works correctly." << std::endl;
std::cout << "wakeUpWait() future is valid." << std::endl;
std::promise<void> promise;
std::future<void> future = promise.get_future();
auto task = [&promise]() {
std::this_thread::sleep_for(std::chrono::milliseconds(100)); // 模拟任务执行
promise.set_value(); // 任务完成时设置 promise
};
periodic::PeriodicTask periodic_task(task, std::chrono::milliseconds(200));
// 调用 wakeUpWait 并等待任务完成
std::future<void> wakeup_future = periodic_task.wakeUpWait();
wakeup_future.wait(); // 等待任务完成
assert(wakeup_future.valid()); // 确保 future 是有效的
std::cout << "Test 3 passed: wakeUpWait() works correctly." << std::endl;
std::cout << "wakeUpWait() future is valid." << std::endl;
}
// 4. 任务抛出异常的处理
void testTaskExceptionHandling() {
auto task = []() {
throw std::runtime_error("Test exception");
};
auto task = []() { throw std::runtime_error("Test exception"); };
periodic::PeriodicTask periodic_task(task, std::chrono::milliseconds(200));
periodic::PeriodicTask periodic_task(task, std::chrono::milliseconds(200));
std::this_thread::sleep_for(std::chrono::milliseconds(300)); // 等待一段时间
std::this_thread::sleep_for(std::chrono::milliseconds(300)); // 等待一段时间
std::cout << "Test 4 passed: Task exception is handled correctly." << std::endl;
std::cout << "Exception handled and task did not crash." << std::endl;
std::cout << "Test 4 passed: Task exception is handled correctly." << std::endl;
std::cout << "Exception handled and task did not crash." << std::endl;
}
// 5. 线程是否能正确停止
void testTaskStop() {
std::atomic<bool> stopped{false};
auto task = [&stopped]() {
while (!stopped) {
std::this_thread::sleep_for(std::chrono::milliseconds(50));
}
};
std::atomic<bool> stopped{false};
auto task = [&stopped]() {
while (!stopped) {
std::this_thread::sleep_for(std::chrono::milliseconds(50));
}
};
periodic::PeriodicTask periodic_task(task, std::chrono::milliseconds(100));
periodic::PeriodicTask periodic_task(task, std::chrono::milliseconds(100));
std::this_thread::sleep_for(std::chrono::seconds(1)); // 运行一段时间
std::this_thread::sleep_for(std::chrono::seconds(1)); // 运行一段时间
stopped = true; // 请求停止
std::this_thread::sleep_for(std::chrono::milliseconds(50)); // 等待线程停止
stopped = true; // 请求停止
std::this_thread::sleep_for(std::chrono::milliseconds(50)); // 等待线程停止
std::cout << "Test 5 passed: Task thread stops correctly." << std::endl;
std::cout << "Task has been stopped successfully." << std::endl;
std::cout << "Test 5 passed: Task thread stops correctly." << std::endl;
std::cout << "Task has been stopped successfully." << std::endl;
}
// 6. 高频唤醒的情况下任务执行是否正常
void testHighFrequencyWakeUp() {
std::atomic<int> execution_count{0};
auto task = [&execution_count]() {
execution_count++;
};
std::atomic<int> execution_count{0};
auto task = [&execution_count]() { execution_count++; };
periodic::PeriodicTask periodic_task(task, std::chrono::milliseconds(200));
periodic::PeriodicTask periodic_task(task, std::chrono::milliseconds(200));
for (int i = 0; i < 100; ++i) {
periodic_task.wakeUp();
std::this_thread::sleep_for(std::chrono::milliseconds(10)); // 每 10 毫秒唤醒一次
}
for (int i = 0; i < 100; ++i) {
periodic_task.wakeUp();
std::this_thread::sleep_for(std::chrono::milliseconds(10)); // 每 10 毫秒唤醒一次
}
std::this_thread::sleep_for(std::chrono::seconds(1)); // 等待任务执行完成
std::this_thread::sleep_for(std::chrono::seconds(1)); // 等待任务执行完成
assert(execution_count > 50); // 确保任务至少执行了 50 次
std::cout << "Test 6 passed: Task handles frequent wake ups correctly." << std::endl;
std::cout << "Task executed " << execution_count.load() << " times." << std::endl;
assert(execution_count > 50); // 确保任务至少执行了 50 次
std::cout << "Test 6 passed: Task handles frequent wake ups correctly." << std::endl;
std::cout << "Task executed " << execution_count.load() << " times." << std::endl;
}
// 7. 多个 wakeUpWait() 调用的处理
void testMultipleWakeUpWait() {
std::atomic<int> execution_count{0};
auto task = [&execution_count]() {
std::this_thread::sleep_for(std::chrono::milliseconds(100)); // 模拟任务执行
execution_count++;
};
std::atomic<int> execution_count{0};
auto task = [&execution_count]() {
std::this_thread::sleep_for(std::chrono::milliseconds(100)); // 模拟任务执行
execution_count++;
};
periodic::PeriodicTask periodic_task(task, std::chrono::milliseconds(200));
periodic::PeriodicTask periodic_task(task, std::chrono::milliseconds(200));
// 同时调用两个 wakeUpWait
std::future<void> future1 = periodic_task.wakeUpWait();
std::future<void> future2 = periodic_task.wakeUpWait();
// 同时调用两个 wakeUpWait
std::future<void> future1 = periodic_task.wakeUpWait();
std::future<void> future2 = periodic_task.wakeUpWait();
future1.wait();
future2.wait();
future1.wait();
future2.wait();
assert(execution_count == 1); // 确保任务只执行了一次
std::cout << "Test 7 passed: Multiple wakeUpWait() calls are handled correctly." << std::endl;
std::cout << "Task executed " << execution_count.load() << " times." << std::endl;
assert(execution_count == 1); // 确保任务只执行了一次
std::cout << "Test 7 passed: Multiple wakeUpWait() calls are handled correctly." << std::endl;
std::cout << "Task executed " << execution_count.load() << " times." << std::endl;
}
// 8. 任务函数为空的边界情况
void testEmptyTaskFunction() {
auto task = []() {
// 空任务函数
};
auto task = []() {
// 空任务函数
};
periodic::PeriodicTask periodic_task(task, std::chrono::milliseconds(100));
periodic::PeriodicTask periodic_task(task, std::chrono::milliseconds(100));
std::this_thread::sleep_for(std::chrono::seconds(1)); // 等待一段时间
std::this_thread::sleep_for(std::chrono::seconds(1)); // 等待一段时间
std::cout << "Test 8 passed: Empty task function works correctly." << std::endl;
std::cout << "Empty task function executed without issues." << std::endl;
std::cout << "Test 8 passed: Empty task function works correctly." << std::endl;
std::cout << "Empty task function executed without issues." << std::endl;
}
int main() {
std::cout << "Starting tests..." << std::endl;
std::cout << "Starting tests..." << std::endl;
// testWakeUpImmediately();
testPeriodicTaskExecution();
testWakeUpImmediately();
testWakeUpWait();
testTaskExceptionHandling();
testTaskStop();
testHighFrequencyWakeUp();
testMultipleWakeUpWait();
testEmptyTaskFunction();
// testWakeUpImmediately();
testPeriodicTaskExecution();
testWakeUpImmediately();
testWakeUpWait();
testTaskExceptionHandling();
testTaskStop();
testHighFrequencyWakeUp();
testMultipleWakeUpWait();
testEmptyTaskFunction();
std::cout << "All tests passed!" << std::endl;
std::cout << "All tests passed!" << std::endl;
return 0;
return 0;
}
csrc/balance_serve/sched/bind.cpp
View file @ 64de7843
#include "scheduler.h"
#include <memory>
#include <pybind11/numpy.h>
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include <memory>
#include "scheduler.h"
#include <torch/extension.h>
......@@ -16,19 +16,25 @@ PYBIND11_MODULE(sched_ext, m) {
.def_readwrite("layer_count", &scheduler::ModelSettings::layer_count)
.def_readwrite("num_k_heads", &scheduler::ModelSettings::num_k_heads)
.def_readwrite("k_head_dim", &scheduler::ModelSettings::k_head_dim)
.def_readwrite("bytes_per_params", &scheduler::ModelSettings::bytes_per_params)
.def_readwrite("bytes_per_kv_cache_element", &scheduler::ModelSettings::bytes_per_kv_cache_element)
.def_readwrite("bytes_per_params",
&scheduler::ModelSettings::bytes_per_params)
.def_readwrite("bytes_per_kv_cache_element",
&scheduler::ModelSettings::bytes_per_kv_cache_element)
.def("params_size", &scheduler::ModelSettings::params_nbytes)
.def("bytes_per_token_kv_cache", &scheduler::ModelSettings::bytes_per_token_kv_cache)
.def("bytes_per_token_kv_cache",
&scheduler::ModelSettings::bytes_per_token_kv_cache)
// 添加 pickle 支持
.def(py::pickle(
[](const scheduler::ModelSettings& self) { // __getstate__
return py::make_tuple(self.params_count, self.layer_count, self.num_k_heads, self.k_head_dim,
self.bytes_per_params, self.bytes_per_kv_cache_element);
[](const scheduler::ModelSettings &self) { // __getstate__
return py::make_tuple(self.params_count, self.layer_count,
self.num_k_heads, self.k_head_dim,
self.bytes_per_params,
self.bytes_per_kv_cache_element);
},
[](py::tuple t) { // __setstate__
[](py::tuple t) { // __setstate__
if (t.size() != 6)
throw std::runtime_error("Invalid state! t.size() = " + std::to_string(t.size()));
throw std::runtime_error("Invalid state! t.size() = " +
std::to_string(t.size()));
scheduler::ModelSettings ms;
ms.params_count = t[0].cast<size_t>();
ms.layer_count = t[1].cast<size_t>();
......@@ -40,22 +46,24 @@ PYBIND11_MODULE(sched_ext, m) {
}));
py::class_<scheduler::SampleOptions>(m, "SampleOptions")
.def(py::init<>())
.def_readwrite("temperature", &scheduler::SampleOptions::temperature)
.def_readwrite("top_p", &scheduler::SampleOptions::top_p) // 确保 top_p 也能被访问
.def(py::pickle(
[](const scheduler::SampleOptions& self) {
return py::make_tuple(self.temperature, self.top_p); // 序列化 temperature 和 top_p
},
[](py::tuple t) {
if (t.size() != 2) // 确保解包时参数数量匹配
throw std::runtime_error("Invalid state! t.size() = " + std::to_string(t.size()));
.def(py::init<>())
.def_readwrite("temperature", &scheduler::SampleOptions::temperature)
.def_readwrite("top_p",
&scheduler::SampleOptions::top_p) // 确保 top_p 也能被访问
.def(py::pickle(
[](const scheduler::SampleOptions &self) {
return py::make_tuple(self.temperature,
self.top_p); // 序列化 temperature 和 top_p
},
[](py::tuple t) {
if (t.size() != 2) // 确保解包时参数数量匹配
throw std::runtime_error("Invalid state! t.size() = " +
std::to_string(t.size()));
scheduler::SampleOptions so;
so.temperature = t[0].cast<double>();
so.top_p = t[1].cast<double>(); // 反序列化 top_p
so.top_p = t[1].cast<double>(); // 反序列化 top_p
return so;
}
));
}));
py::class_<scheduler::Settings>(m, "Settings")
.def(py::init<>())
......@@ -65,33 +73,43 @@ PYBIND11_MODULE(sched_ext, m) {
.def_readwrite("page_size", &scheduler::Settings::page_size)
.def_readwrite("gpu_device_id", &scheduler::Settings::gpu_device_id)
.def_readwrite("gpu_memory_size", &scheduler::Settings::gpu_memory_size)
.def_readwrite("memory_utilization_percentage", &scheduler::Settings::memory_utilization_percentage)
.def_readwrite("memory_utilization_percentage",
&scheduler::Settings::memory_utilization_percentage)
.def_readwrite("max_batch_size", &scheduler::Settings::max_batch_size)
.def_readwrite("recommended_chunk_prefill_token_count",
&scheduler::Settings::recommended_chunk_prefill_token_count)
.def_readwrite(
"recommended_chunk_prefill_token_count",
&scheduler::Settings::recommended_chunk_prefill_token_count)
.def_readwrite("sample_options", &scheduler::Settings::sample_options)
.def_readwrite("sched_metrics_port", &scheduler::Settings::sched_metrics_port)
.def_readwrite("sched_metrics_port",
&scheduler::Settings::sched_metrics_port)
.def_readwrite("gpu_only", &scheduler::Settings::gpu_only)
.def_readwrite("use_self_defined_head_dim", &scheduler::Settings::use_self_defined_head_dim)
.def_readwrite("self_defined_head_dim", &scheduler::Settings::self_defined_head_dim)
.def_readwrite("full_kv_cache_on_each_gpu", &scheduler::Settings::full_kv_cache_on_each_gpu)
.def_readwrite("use_self_defined_head_dim",
&scheduler::Settings::use_self_defined_head_dim)
.def_readwrite("self_defined_head_dim",
&scheduler::Settings::self_defined_head_dim)
.def_readwrite("full_kv_cache_on_each_gpu",
&scheduler::Settings::full_kv_cache_on_each_gpu)
.def_readwrite("k_cache_on", &scheduler::Settings::k_cache_on)
.def_readwrite("v_cache_on", &scheduler::Settings::v_cache_on)
.def_readwrite("kvc2_config_path", &scheduler::Settings::kvc2_config_path)
.def_readwrite("kvc2_root_path", &scheduler::Settings::kvc2_root_path)
.def_readwrite("memory_pool_size_GB", &scheduler::Settings::memory_pool_size_GB)
.def_readwrite("memory_pool_size_GB",
&scheduler::Settings::memory_pool_size_GB)
.def_readwrite("evict_count", &scheduler::Settings::evict_count)
.def_readwrite("strategy_name", &scheduler::Settings::strategy_name)
.def_readwrite("kvc2_metrics_port", &scheduler::Settings::kvc2_metrics_port)
.def_readwrite("kvc2_metrics_port",
&scheduler::Settings::kvc2_metrics_port)
.def_readwrite("load_from_disk", &scheduler::Settings::load_from_disk)
.def_readwrite("save_to_disk", &scheduler::Settings::save_to_disk)
// derived
.def_readwrite("gpu_device_count", &scheduler::Settings::gpu_device_count)
.def_readwrite("total_kvcache_pages", &scheduler::Settings::total_kvcache_pages)
.def_readwrite("total_kvcache_pages",
&scheduler::Settings::total_kvcache_pages)
.def_readwrite("devices", &scheduler::Settings::devices)
.def("auto_derive", &scheduler::Settings::auto_derive);
py::class_<scheduler::BatchQueryTodo, std::shared_ptr<scheduler::BatchQueryTodo>>(m, "BatchQueryTodo")
py::class_<scheduler::BatchQueryTodo,
std::shared_ptr<scheduler::BatchQueryTodo>>(m, "BatchQueryTodo")
.def(py::init<>())
.def_readwrite("query_ids", &scheduler::BatchQueryTodo::query_ids)
.def_readwrite("query_tokens", &scheduler::BatchQueryTodo::query_tokens)
......@@ -99,31 +117,42 @@ PYBIND11_MODULE(sched_ext, m) {
.def_readwrite("block_indexes", &scheduler::BatchQueryTodo::block_indexes)
.def_readwrite("attn_masks", &scheduler::BatchQueryTodo::attn_masks)
.def_readwrite("rope_ranges", &scheduler::BatchQueryTodo::rope_ranges)
.def_readwrite("sample_options", &scheduler::BatchQueryTodo::sample_options)
.def_readwrite("prefill_mini_batches", &scheduler::BatchQueryTodo::prefill_mini_batches)
.def_readwrite("decode_mini_batches", &scheduler::BatchQueryTodo::decode_mini_batches)
.def_readwrite("sample_options",
&scheduler::BatchQueryTodo::sample_options)
.def_readwrite("prefill_mini_batches",
&scheduler::BatchQueryTodo::prefill_mini_batches)
.def_readwrite("decode_mini_batches",
&scheduler::BatchQueryTodo::decode_mini_batches)
.def_readwrite("stop_criteria", &scheduler::BatchQueryTodo::stop_criteria)
.def("debug", &scheduler::BatchQueryTodo::debug)
.def(py::pickle(
[](const scheduler::BatchQueryTodo& self) {
return py::make_tuple(self.query_ids, self.query_tokens, self.query_lengths, self.block_indexes,
self.attn_masks, self.rope_ranges, self.sample_options, self.prefill_mini_batches,
self.decode_mini_batches, self.stop_criteria);
[](const scheduler::BatchQueryTodo &self) {
return py::make_tuple(
self.query_ids, self.query_tokens, self.query_lengths,
self.block_indexes, self.attn_masks, self.rope_ranges,
self.sample_options, self.prefill_mini_batches,
self.decode_mini_batches, self.stop_criteria);
},
[](py::tuple t) {
if (t.size() != 10)
throw std::runtime_error("Invalid state! t.size() = " + std::to_string(t.size()));
throw std::runtime_error("Invalid state! t.size() = " +
std::to_string(t.size()));
scheduler::BatchQueryTodo bqt;
bqt.query_ids = t[0].cast<std::vector<scheduler::QueryID>>();
bqt.query_tokens = t[1].cast<std::vector<torch::Tensor>>();
bqt.query_lengths = t[2].cast<std::vector<scheduler::TokenLength>>();
bqt.query_lengths =
t[2].cast<std::vector<scheduler::TokenLength>>();
bqt.block_indexes = t[3].cast<std::vector<torch::Tensor>>();
bqt.attn_masks = t[4].cast<std::optional<torch::Tensor>>();
bqt.rope_ranges = t[5].cast<std::optional<torch::Tensor>>();
bqt.sample_options = t[6].cast<std::vector<scheduler::SampleOptions>>();
bqt.prefill_mini_batches = t[7].cast<std::vector<scheduler::PrefillTask>>();
bqt.decode_mini_batches = t[8].cast<std::vector<std::vector<scheduler::QueryID>>>();
bqt.stop_criteria = t[9].cast<std::vector<std::vector<std::vector<int>>>>();
bqt.sample_options =
t[6].cast<std::vector<scheduler::SampleOptions>>();
bqt.prefill_mini_batches =
t[7].cast<std::vector<scheduler::PrefillTask>>();
bqt.decode_mini_batches =
t[8].cast<std::vector<std::vector<scheduler::QueryID>>>();
bqt.stop_criteria =
t[9].cast<std::vector<std::vector<std::vector<int>>>>();
return bqt;
}));
......@@ -133,16 +162,20 @@ PYBIND11_MODULE(sched_ext, m) {
.def_readwrite("ok", &scheduler::QueryUpdate::ok)
.def_readwrite("is_prefill", &scheduler::QueryUpdate::is_prefill)
.def_readwrite("decode_done", &scheduler::QueryUpdate::decode_done)
.def_readwrite("active_position", &scheduler::QueryUpdate::active_position)
.def_readwrite("generated_token", &scheduler::QueryUpdate::generated_token)
.def_readwrite("active_position",
&scheduler::QueryUpdate::active_position)
.def_readwrite("generated_token",
&scheduler::QueryUpdate::generated_token)
.def(py::pickle(
[](const scheduler::QueryUpdate& self) {
return py::make_tuple(self.id, self.ok, self.is_prefill, self.decode_done, self.active_position,
[](const scheduler::QueryUpdate &self) {
return py::make_tuple(self.id, self.ok, self.is_prefill,
self.decode_done, self.active_position,
self.generated_token);
},
[](py::tuple t) {
if (t.size() != 6)
throw std::runtime_error("Invalid state! t.size() = " + std::to_string(t.size()));
throw std::runtime_error("Invalid state! t.size() = " +
std::to_string(t.size()));
scheduler::QueryUpdate qu;
qu.id = t[0].cast<scheduler::QueryID>();
qu.ok = t[1].cast<bool>();
......@@ -156,8 +189,7 @@ PYBIND11_MODULE(sched_ext, m) {
py::class_<scheduler::InferenceContext>(m, "InferenceContext")
.def(py::init<>())
.def_readwrite("k_cache", &scheduler::InferenceContext::k_cache)
.def_readwrite("v_cache", &scheduler::InferenceContext::v_cache)
;
.def_readwrite("v_cache", &scheduler::InferenceContext::v_cache);
py::class_<scheduler::QueryAdd>(m, "QueryAdd")
.def(py::init<>())
......@@ -173,15 +205,18 @@ PYBIND11_MODULE(sched_ext, m) {
.def("serialize", &scheduler::QueryAdd::serialize)
.def_static("deserialize", &scheduler::QueryAdd::deserialize)
.def(py::pickle(
[](const scheduler::QueryAdd& self) {
[](const scheduler::QueryAdd &self) {
return py::make_tuple(self.query_token,
// self.attn_mask,
self.query_length, self.estimated_length, self.sample_options, self.user_id,
self.SLO_TTFT_ms, self.SLO_TBT_ms, self.stop_criteria);
self.query_length, self.estimated_length,
self.sample_options, self.user_id,
self.SLO_TTFT_ms, self.SLO_TBT_ms,
self.stop_criteria);
},
[](py::tuple t) {
if (t.size() != 8)
throw std::runtime_error("Invalid state! t.size() = " + std::to_string(t.size()));
throw std::runtime_error("Invalid state! t.size() = " +
std::to_string(t.size()));
scheduler::QueryAdd qa;
qa.query_token = t[0].cast<std::vector<scheduler::Token>>();
// qa.attn_mask = t[1].cast<torch::Tensor>();
......@@ -195,14 +230,20 @@ PYBIND11_MODULE(sched_ext, m) {
return qa;
}));
py::class_<scheduler::Scheduler, std::shared_ptr<scheduler::Scheduler>>(m, "Scheduler")
py::class_<scheduler::Scheduler, std::shared_ptr<scheduler::Scheduler>>(
m, "Scheduler")
.def("init", &scheduler::Scheduler::init)
.def("run", &scheduler::Scheduler::run)
.def("stop", &scheduler::Scheduler::stop)
.def("add_query", &scheduler::Scheduler::add_query, py::call_guard<py::gil_scoped_release>())
.def("cancel_query", &scheduler::Scheduler::cancel_query, py::call_guard<py::gil_scoped_release>())
.def("update_last_batch", &scheduler::Scheduler::update_last_batch, py::call_guard<py::gil_scoped_release>())
.def("get_inference_context", &scheduler::Scheduler::get_inference_context);
.def("add_query", &scheduler::Scheduler::add_query,
py::call_guard<py::gil_scoped_release>())
.def("cancel_query", &scheduler::Scheduler::cancel_query,
py::call_guard<py::gil_scoped_release>())
.def("update_last_batch", &scheduler::Scheduler::update_last_batch,
py::call_guard<py::gil_scoped_release>())
.def("get_inference_context",
&scheduler::Scheduler::get_inference_context);
m.def("create_scheduler", &scheduler::create_scheduler, "Create a new Scheduler instance");
m.def("create_scheduler", &scheduler::create_scheduler,
"Create a new Scheduler instance");
}
csrc/balance_serve/sched/metrics.cpp
View file @ 64de7843
......@@ -2,89 +2,101 @@
#include <iostream>
// 构造函数
Metrics::Metrics(const MetricsConfig& config)
Metrics::Metrics(const MetricsConfig &config)
: registry_(std::make_shared<prometheus::Registry>()),
exposer_(config.endpoint),
stop_uptime_thread_(false),
exposer_(config.endpoint), stop_uptime_thread_(false),
start_time_(std::chrono::steady_clock::now()) {
// 定义统一的桶大小，最大为 10000 ms (10 s)
std::vector<double> common_buckets = {0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1.0, 5.0,
10.0, 50.0, 100.0, 500.0, 1000.0, 5000.0, 10000.0}; // 毫秒
std::vector<double> common_buckets = {
0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1.0, 5.0,
10.0, 50.0, 100.0, 500.0, 1000.0, 5000.0, 10000.0}; // 毫秒
// 注册 TTFT_ms Histogram
auto& TTFT_family = prometheus::BuildHistogram()
auto &TTFT_family = prometheus::BuildHistogram()
.Name(std::string(METRIC_PREFIX) + "_TTFT_ms")
.Help("Time to first token in milliseconds")
.Register(*registry_);
TTFT_ms = &TTFT_family.Add({{"model", config.model_name}}, common_buckets);
// 注册 TBT_ms Histogram
auto& TBT_family = prometheus::BuildHistogram()
auto &TBT_family = prometheus::BuildHistogram()
.Name(std::string(METRIC_PREFIX) + "_TBT_ms")
.Help("Time between tokens in milliseconds")
.Register(*registry_);
TBT_ms = &TBT_family.Add({{"model", config.model_name}}, common_buckets);
// 注册 schedule_time Histogram
auto& schedule_time_family = prometheus::BuildHistogram()
.Name(std::string(METRIC_PREFIX) + "_schedule_time_ms")
.Help("Time to generate schedule in milliseconds")
.Register(*registry_);
schedule_time = &schedule_time_family.Add({{"model", config.model_name}}, common_buckets);
auto &schedule_time_family =
prometheus::BuildHistogram()
.Name(std::string(METRIC_PREFIX) + "_schedule_time_ms")
.Help("Time to generate schedule in milliseconds")
.Register(*registry_);
schedule_time =
&schedule_time_family.Add({{"model", config.model_name}}, common_buckets);
// 注册 generated_tokens Counter
auto& generated_tokens_family = prometheus::BuildCounter()
.Name(std::string(METRIC_PREFIX) + "_generated_tokens_total")
.Help("Total generated tokens")
.Register(*registry_);
generated_tokens = &generated_tokens_family.Add({{"model", config.model_name}});
auto &generated_tokens_family =
prometheus::BuildCounter()
.Name(std::string(METRIC_PREFIX) + "_generated_tokens_total")
.Help("Total generated tokens")
.Register(*registry_);
generated_tokens =
&generated_tokens_family.Add({{"model", config.model_name}});
// 注册 throughput_query Gauge
auto& throughput_query_family = prometheus::BuildGauge()
.Name(std::string(METRIC_PREFIX) + "_throughput_query")
.Help("Throughput per second based on queries")
.Register(*registry_);
throughput_query = &throughput_query_family.Add({{"model", config.model_name}});
auto &throughput_query_family =
prometheus::BuildGauge()
.Name(std::string(METRIC_PREFIX) + "_throughput_query")
.Help("Throughput per second based on queries")
.Register(*registry_);
throughput_query =
&throughput_query_family.Add({{"model", config.model_name}});
// 注册 throughput_generated_tokens Gauge
auto& throughput_generated_tokens_family = prometheus::BuildGauge()
.Name(std::string(METRIC_PREFIX) + "_throughput_generated_tokens")
.Help("Throughput per second based on generated tokens")
.Register(*registry_);
throughput_generated_tokens = &throughput_generated_tokens_family.Add({{"model", config.model_name}});
auto &throughput_generated_tokens_family =
prometheus::BuildGauge()
.Name(std::string(METRIC_PREFIX) + "_throughput_generated_tokens")
.Help("Throughput per second based on generated tokens")
.Register(*registry_);
throughput_generated_tokens =
&throughput_generated_tokens_family.Add({{"model", config.model_name}});
// 注册 event_count Counter family
event_count_family_ = &prometheus::BuildCounter()
.Name(std::string(METRIC_PREFIX) + "_event_count_total")
.Help("Count of various events")
.Register(*registry_);
batch_count_family_ = &prometheus::BuildCounter()
.Name(std::string(METRIC_PREFIX) + "_batch_count_total")
.Help("Count of various batch by status")
.Register(*registry_);
event_count_family_ =
&prometheus::BuildCounter()
.Name(std::string(METRIC_PREFIX) + "_event_count_total")
.Help("Count of various events")
.Register(*registry_);
batch_count_family_ =
&prometheus::BuildCounter()
.Name(std::string(METRIC_PREFIX) + "_batch_count_total")
.Help("Count of various batch by status")
.Register(*registry_);
// 注册 query_count Counter family
query_count_family_ = &prometheus::BuildCounter()
.Name(std::string(METRIC_PREFIX) + "_query_count_total")
.Help("Count of queries by status")
.Register(*registry_);
query_count_family_ =
&prometheus::BuildCounter()
.Name(std::string(METRIC_PREFIX) + "_query_count_total")
.Help("Count of queries by status")
.Register(*registry_);
// 注册 uptime_ms Gauge
auto& uptime_family = prometheus::BuildGauge()
auto &uptime_family = prometheus::BuildGauge()
.Name(std::string(METRIC_PREFIX) + "_uptime_ms")
.Help("Uptime of the scheduler in milliseconds")
.Register(*registry_);
uptime_ms = &uptime_family.Add({{"model", config.model_name}});
// 注册 GPU 利用率 Gauges
auto& gpu_util_family = prometheus::BuildGauge()
.Name(std::string(METRIC_PREFIX) + "_gpu_utilization_ratio")
.Help("Current GPU utilization ratio (0 to 1)")
.Register(*registry_);
auto &gpu_util_family =
prometheus::BuildGauge()
.Name(std::string(METRIC_PREFIX) + "_gpu_utilization_ratio")
.Help("Current GPU utilization ratio (0 to 1)")
.Register(*registry_);
for (size_t i = 0; i < config.gpu_count; ++i) {
gpu_utilization_gauges.push_back(
&gpu_util_family.Add({{"gpu_id", std::to_string(i)}, {"model", config.model_name}}));
gpu_utilization_gauges.push_back(&gpu_util_family.Add(
{{"gpu_id", std::to_string(i)}, {"model", config.model_name}}));
}
// 将 Registry 注册到 Exposer 中
......@@ -95,16 +107,15 @@ Metrics::Metrics(const MetricsConfig& config)
}
// 析构函数
Metrics::~Metrics() {
StopUptimeUpdater();
}
Metrics::~Metrics() { StopUptimeUpdater(); }
// 启动 uptime 更新线程
void Metrics::StartUptimeUpdater() {
uptime_thread_ = std::thread([this]() {
while (!stop_uptime_thread_) {
auto now = std::chrono::steady_clock::now();
std::chrono::duration<double, std::milli> uptime_duration = now - start_time_;
std::chrono::duration<double, std::milli> uptime_duration =
now - start_time_;
uptime_ms->Set(uptime_duration.count());
// fn_every_sec(this);
std::this_thread::sleep_for(std::chrono::seconds(1));
......@@ -121,15 +132,16 @@ void Metrics::StopUptimeUpdater() {
}
// 获取 event_count 指标
prometheus::Counter* Metrics::event_count(const std::string& type) {
return &event_count_family_->Add({{"type", type}}); // 可根据需要添加更多标签
prometheus::Counter *Metrics::event_count(const std::string &type) {
return &event_count_family_->Add({{"type", type}}); // 可根据需要添加更多标签
}
// 获取 query_count 指标
prometheus::Counter* Metrics::query_count(const std::string& status) {
return &query_count_family_->Add({{"status", status}}); // 可根据需要添加更多标签
prometheus::Counter *Metrics::query_count(const std::string &status) {
return &query_count_family_->Add(
{{"status", status}}); // 可根据需要添加更多标签
}
prometheus::Counter* Metrics::batch_count(const std::string& type) {
prometheus::Counter *Metrics::batch_count(const std::string &type) {
return &batch_count_family_->Add({{"type", type}});
}
csrc/balance_serve/sched/metrics.h
View file @ 64de7843
#ifndef Metrics_H
#define Metrics_H
#include <atomic>
#include <chrono>
#include <memory>
#include <prometheus/counter.h>
#include <prometheus/exposer.h>
#include <prometheus/gauge.h>
#include <prometheus/histogram.h>
#include <prometheus/registry.h>
#include <atomic>
#include <chrono>
#include <memory>
#include <string>
#include <thread>
#include <vector>
......@@ -21,46 +21,46 @@ class Metrics;
// 配置结构体
struct MetricsConfig {
std::string endpoint;
std::string model_name; // 模型名称，如 "gpt-4"
size_t gpu_count; // GPU数量
std::string model_name; // 模型名称，如 "gpt-4"
size_t gpu_count; // GPU数量
};
// Metrics 类，根据配置初始化 Prometheus 指标
class Metrics {
public:
public:
// 构造函数传入 MetricsConfig
Metrics(const MetricsConfig& config);
Metrics(const MetricsConfig &config);
~Metrics();
// 禁止拷贝和赋值
Metrics(const Metrics&) = delete;
Metrics& operator=(const Metrics&) = delete;
Metrics(const Metrics &) = delete;
Metrics &operator=(const Metrics &) = delete;
std::function<void(Metrics*)> fn_every_sec;
std::function<void(Metrics *)> fn_every_sec;
// 指标指针
prometheus::Gauge* uptime_ms;
prometheus::Histogram* TTFT_ms;
prometheus::Histogram* TBT_ms;
prometheus::Histogram* schedule_time;
prometheus::Gauge* throughput_query;
prometheus::Gauge* throughput_generated_tokens;
prometheus::Counter* generated_tokens;
std::vector<prometheus::Gauge*> gpu_utilization_gauges;
prometheus::Gauge *uptime_ms;
prometheus::Histogram *TTFT_ms;
prometheus::Histogram *TBT_ms;
prometheus::Histogram *schedule_time;
prometheus::Gauge *throughput_query;
prometheus::Gauge *throughput_generated_tokens;
prometheus::Counter *generated_tokens;
std::vector<prometheus::Gauge *> gpu_utilization_gauges;
// 计数器家族
prometheus::Counter* event_count(const std::string& type);
prometheus::Counter* query_count(const std::string& status);
prometheus::Counter* batch_count(const std::string& type);
prometheus::Counter *event_count(const std::string &type);
prometheus::Counter *query_count(const std::string &status);
prometheus::Counter *batch_count(const std::string &type);
private:
private:
std::shared_ptr<prometheus::Registry> registry_;
prometheus::Exposer exposer_;
// 计数器家族
prometheus::Family<prometheus::Counter>* event_count_family_;
prometheus::Family<prometheus::Counter>* batch_count_family_;
prometheus::Family<prometheus::Counter>* query_count_family_;
prometheus::Family<prometheus::Counter> *event_count_family_;
prometheus::Family<prometheus::Counter> *batch_count_family_;
prometheus::Family<prometheus::Counter> *query_count_family_;
// 线程和控制变量用于更新 uptime_ms
std::thread uptime_thread_;
......@@ -76,10 +76,13 @@ class Metrics {
};
struct HistogramTimerWrapper {
prometheus::Histogram* histogram;
prometheus::Histogram *histogram;
Timer timer;
inline HistogramTimerWrapper(prometheus::Histogram* histogram) : histogram(histogram), timer() { timer.start(); }
inline HistogramTimerWrapper(prometheus::Histogram *histogram)
: histogram(histogram), timer() {
timer.start();
}
inline ~HistogramTimerWrapper() { histogram->Observe(timer.elapsedMs()); }
};
#endif // Metrics_H
#endif // Metrics_H
csrc/balance_serve/sched/model_config.h
View file @ 64de7843
#ifndef __MODEL_CONFIG_HPP_
#define __MODEL_CONFIG_HPP_
#include <iostream>
#include "nlohmann/json.hpp"
#include <iostream>
#include <filesystem>
#include <fstream>
......@@ -13,7 +13,7 @@ using ModelName = std::string;
// We must assure this can be load by config.json
class ModelConfig {
public:
public:
DimSize hidden_size;
DimSize intermediate_size;
size_t max_position_embeddings;
......@@ -23,10 +23,13 @@ class ModelConfig {
size_t num_key_value_heads;
size_t vocab_size;
NLOHMANN_DEFINE_TYPE_INTRUSIVE(ModelConfig, hidden_size, intermediate_size, max_position_embeddings, model_type,
num_attention_heads, num_hidden_layers, num_key_value_heads, vocab_size);
NLOHMANN_DEFINE_TYPE_INTRUSIVE(ModelConfig, hidden_size, intermediate_size,
max_position_embeddings, model_type,
num_attention_heads, num_hidden_layers,
num_key_value_heads, vocab_size);
void load_from(std::filesystem::path path) {
std::cout << "Load from " << path << std::endl;
std::ifstream i(path);
nlohmann::json j;
i >> j;
......@@ -38,12 +41,14 @@ using QuantType = std::string;
static const QuantType NoQuantType = "";
class QuantConfig {
public:
public:
QuantType name;
// For GEMV
QuantType type_of_dot_vector = NoQuantType;
inline bool can_be_used_as_matrix() { return type_of_dot_vector != NoQuantType; }
inline bool can_be_used_as_matrix() {
return type_of_dot_vector != NoQuantType;
}
bool can_be_used_as_vector;
......@@ -56,8 +61,11 @@ class QuantConfig {
URL reference = "";
NLOHMANN_DEFINE_TYPE_INTRUSIVE_WITH_DEFAULT(QuantConfig, name, type_of_dot_vector, can_be_used_as_vector,
bytes_per_element, has_scale, has_min, block_element_count,
NLOHMANN_DEFINE_TYPE_INTRUSIVE_WITH_DEFAULT(QuantConfig, name,
type_of_dot_vector,
can_be_used_as_vector,
bytes_per_element, has_scale,
has_min, block_element_count,
block_element_size, reference);
};
......@@ -70,14 +78,13 @@ inline void load_quant_configs(std::filesystem::path path) {
std::cout << __FUNCTION__ << " from " << path << std::endl;
std::ifstream i(path);
i >> j;
quant_configs = j.get<std::map<QuantType, QuantConfig>>();
std::cout << "Loaded Quant Configs" << std::endl;
for (auto &[k, v] : quant_configs) {
std::cout << " - " << k << std::endl;
}
} else {
std::cout << __FUNCTION__ << " create new at " << path << std::endl;
}
quant_configs = j.get<std::map<QuantType, QuantConfig>>();
std::cout << "Loaded Quant Configs" << std::endl;
for (auto& [k, v] : quant_configs) {
std::cout << " - " << k << std::endl;
std::cout << __FUNCTION__ << " no file at " << path << std::endl;
}
}
......@@ -93,14 +100,13 @@ inline void load_model_configs(std::filesystem::path path) {
std::cout << __FUNCTION__ << " from " << path << std::endl;
std::ifstream i(path);
i >> j;
model_configs = j.get<std::map<ModelName, ModelConfig>>();
std::cout << "Loaded Model Configs" << std::endl;
for (auto &[k, v] : model_configs) {
std::cout << " - " << k << std::endl;
}
} else {
std::cout << __FUNCTION__ << " create new at " << path << std::endl;
}
model_configs = j.get<std::map<ModelName, ModelConfig>>();
std::cout << "Loaded Model Configs" << std::endl;
for (auto& [k, v] : model_configs) {
std::cout << " - " << k << std::endl;
std::cout << __FUNCTION__ << " no file at " << path << std::endl;
}
}
......
csrc/balance_serve/sched/scheduler.cpp
View file @ 64de7843
......@@ -3,20 +3,20 @@
#include "nlohmann/json.hpp"
#include "spdlog/spdlog.h"
#include <optional>
#include "scheduler.h"
#include <optional>
#include <atomic>
#include <cassert>
#include <future>
#include <memory>
#include <queue>
#include "arithmetic.hpp"
#include "atomic_ptr_with_flags.hpp"
#include "easy_format.hpp"
#include "metrics.h"
#include "mpsc.hpp"
#include "timer.hpp"
#include <atomic>
#include <cassert>
#include <future>
#include <memory>
#include <queue>
#include "kvc2.h"
......@@ -28,7 +28,8 @@ void Settings::auto_derive() {
gpu_device_count = gpu_device_id.size();
if (torch::cuda::is_available()) {
size_t gpu_count = torch::cuda::device_count();
SPDLOG_INFO("Number of available GPUs: {}, want {}", gpu_count, gpu_device_count);
SPDLOG_INFO("Number of available GPUs: {}, want {}", gpu_count,
gpu_device_count);
if (gpu_count < gpu_device_count) {
SPDLOG_ERROR("Not enough GPUs available.");
exit(0);
......@@ -42,37 +43,49 @@ void Settings::auto_derive() {
}
if (model_settings.num_k_heads % gpu_device_count != 0) {
SPDLOG_ERROR("num_k_heads {} is not divisible by gpu_device_count {}", model_settings.num_k_heads,
gpu_device_count);
SPDLOG_ERROR("num_k_heads {} is not divisible by gpu_device_count {}",
model_settings.num_k_heads, gpu_device_count);
assert(false);
}
size_t gpu_memory_available = gpu_memory_size * memory_utilization_percentage;
if (gpu_memory_available * gpu_device_count < model_settings.params_nbytes()) {
SPDLOG_ERROR("GPU memory size {}G is smaller than {}G", gpu_memory_available * gpu_device_count / 1e9,
if (gpu_memory_available * gpu_device_count <
model_settings.params_nbytes()) {
SPDLOG_ERROR("GPU memory size {}G is smaller than {}G",
gpu_memory_available * gpu_device_count / 1e9,
model_settings.params_nbytes() / 1e9);
assert(false);
}
assert(model_settings.k_head_dim % model_settings.num_k_heads == 0);
size_t head_per_gpu = model_settings.num_k_heads / gpu_device_count;
size_t gpu_memory_for_kv_cache = gpu_memory_available /*- model_settings.params_nbytes() / gpu_device_count*/;
SPDLOG_INFO("Each GPU Total: {}MiB, Model Params: {}MiB, KVCache: {}MiB, Left: {}MiB", gpu_memory_size / (1 << 20),
model_settings.params_nbytes() / gpu_device_count / (1 << 20), gpu_memory_for_kv_cache / (1 << 20),
(gpu_memory_size - gpu_memory_available) / (1 << 20));
size_t gpu_memory_for_kv_cache =
gpu_memory_available /*- model_settings.params_nbytes() /
gpu_device_count*/
;
SPDLOG_INFO(
"Each GPU Total: {}MiB, Model Params: {}MiB, KVCache: {}MiB, Left: {}MiB",
gpu_memory_size / (1 << 20),
model_settings.params_nbytes() / gpu_device_count / (1 << 20),
gpu_memory_for_kv_cache / (1 << 20),
(gpu_memory_size - gpu_memory_available) / (1 << 20));
size_t kv_cache_on_cnt = (size_t)(k_cache_on) + (size_t)(v_cache_on);
size_t max_total_kvcache_pages =
gpu_memory_for_kv_cache / (kv_cache_on_cnt * head_per_gpu * model_settings.k_head_dim *
model_settings.bytes_per_kv_cache_element * page_size * model_settings.layer_count);
gpu_memory_for_kv_cache /
(kv_cache_on_cnt * head_per_gpu * model_settings.k_head_dim *
model_settings.bytes_per_kv_cache_element * page_size *
model_settings.layer_count);
if (total_kvcache_pages.has_value()) {
if (total_kvcache_pages.value() > max_total_kvcache_pages) {
SPDLOG_ERROR("total_kvcache_pages {} is larger than max_total_kvcache_pages {}", total_kvcache_pages.value(),
max_total_kvcache_pages);
SPDLOG_ERROR(
"total_kvcache_pages {} is larger than max_total_kvcache_pages {}",
total_kvcache_pages.value(), max_total_kvcache_pages);
assert(false);
}
} else {
total_kvcache_pages = max_total_kvcache_pages;
SPDLOG_INFO("total_kvcache_pages is auto derived as {}", max_total_kvcache_pages);
SPDLOG_INFO("total_kvcache_pages is auto derived as {}",
max_total_kvcache_pages);
}
if (page_size % 256 != 0) {
......@@ -88,7 +101,7 @@ void Settings::auto_derive() {
std::string BatchQueryTodo::debug() {
std::string re = "BatchQueryTodo: ";
re += "QueryIDs: ";
for (auto& id : query_ids) {
for (auto &id : query_ids) {
re += std::to_string(id) + " ";
}
return re;
......@@ -119,59 +132,61 @@ struct Query {
// Query status changed by this order
enum Status { Received, Preparing, Ready, Prefill, Decode, Done };
Status plan_status = Received;
TokenLength active_position; // the position where no kvcache now
TokenLength plan_position; // the position where no kvcache now, in plan
TokenLength active_position; // the position where no kvcache now
TokenLength plan_position; // the position where no kvcache now, in plan
size_t prepare_try_count = 0;
std::shared_ptr<kvc2::DoubleCacheHandleInterface> kvc2_handle = nullptr;
// derived from kvc2_handle
torch::Tensor block_index; // block indexes
torch::Tensor block_index; // block indexes
struct QueryContext {
ModelName model_name;
QuantType quant_type;
kvc2::KVC2Interface* kvc2_interface;
QueryMaintainer* query_maintainer;
Metrics* met;
kvc2::KVC2Interface *kvc2_interface;
QueryMaintainer *query_maintainer;
Metrics *met;
} ctx;
void after_load(bool ok);
void to_status(Status to);
void export_metrics() { ctx.met->query_count(status_to_string(plan_status))->Increment(1); }
void export_metrics() {
ctx.met->query_count(status_to_string(plan_status))->Increment(1);
}
Query(QueryID id, QueryAdd query_add, QueryContext context)
: id(id),
prompt_length(query_add.query_length),
no_kvcache_from(0),
: id(id), prompt_length(query_add.query_length), no_kvcache_from(0),
estimated_length(query_add.estimated_length),
sample_options(query_add.sample_options),
user_id(query_add.user_id),
SLO_TTFT_ms(query_add.SLO_TTFT_ms),
SLO_TBT_ms(query_add.SLO_TBT_ms),
stop_criteria(query_add.stop_criteria),
ctx(context) {
sample_options(query_add.sample_options), user_id(query_add.user_id),
SLO_TTFT_ms(query_add.SLO_TTFT_ms), SLO_TBT_ms(query_add.SLO_TBT_ms),
stop_criteria(query_add.stop_criteria), ctx(context) {
std::vector<int64_t> shape = {int64_t(query_add.estimated_length)};
query_token = torch::zeros(shape, torch::TensorOptions().dtype(torch::kInt32));
query_token =
torch::zeros(shape, torch::TensorOptions().dtype(torch::kInt32));
assert(query_token.is_contiguous());
if (query_token.is_contiguous() == false) {
SPDLOG_ERROR("Query Token must be contiguous!");
exit(1);
}
memcpy(query_token.data_ptr(), query_add.query_token.data(), query_add.query_length * sizeof(Token));
memcpy(query_token.data_ptr(), query_add.query_token.data(),
query_add.query_length * sizeof(Token));
no_kvcache_from = 0; // maybe match prefix later
no_kvcache_from = 0; // maybe match prefix later
export_metrics();
}
Token& token_at(size_t idx) { return reinterpret_cast<Token*>(query_token.data_ptr())[idx]; }
Token &token_at(size_t idx) {
return reinterpret_cast<Token *>(query_token.data_ptr())[idx];
}
void absorb_update(const QueryUpdate& update) {
void absorb_update(const QueryUpdate &update) {
SPDLOG_DEBUG("{}", update.debug());
active_position = update.active_position;
kvc2_handle->append_tokens(&token_at(0), active_position); // active_position is length -1
kvc2_handle->append_tokens(&token_at(0),
active_position); // active_position is length -1
if (update.is_prefill) {
if (active_position == prompt_length) {
token_at(active_position) = update.generated_token;
......@@ -187,15 +202,17 @@ struct Query {
}
}
void absorb_prefill_task(const PrefillTask& task) {
auto& [id, start, length] = task;
void absorb_prefill_task(const PrefillTask &task) {
auto &[id, start, length] = task;
this->plan_position = start + length;
if (this->plan_position == prompt_length) {
to_status(Decode);
}
}
void absorb_decode_task([[maybe_unused]] const QueryID& task) { this->plan_position += 1; }
void absorb_decode_task([[maybe_unused]] const QueryID &task) {
this->plan_position += 1;
}
PrefillTask get_prefill_task(size_t prefill_length) {
if (prefill_length + plan_position > prompt_length) {
......@@ -206,18 +223,18 @@ struct Query {
static std::string status_to_string(Status status) {
switch (status) {
case Received:
return "Received";
case Preparing:
return "Preparing";
case Ready:
return "Ready";
case Prefill:
return "Prefill";
case Decode:
return "Decode";
case Done:
return "Done";
case Received:
return "Received";
case Preparing:
return "Preparing";
case Ready:
return "Ready";
case Prefill:
return "Prefill";
case Decode:
return "Decode";
case Done:
return "Done";
}
assert(false);
}
......@@ -225,16 +242,19 @@ struct Query {
void debug() {
std::string status_string = status_to_string(plan_status);
SPDLOG_DEBUG(
"Query {}, prompt_length {}, estimated_length {}, plan status {}, plan position {} "
"active position {}",
id, prompt_length, estimated_length, status_string, plan_position, active_position);
SPDLOG_DEBUG("Query {}, prompt_length {}, estimated_length {}, plan status "
"{}, plan position {} "
"active position {}",
id, prompt_length, estimated_length, status_string,
plan_position, active_position);
}
};
std::string QueryUpdate::debug() const {
return fmt::format("Query {}, ok {}, is_prefill {}, done {}, active_position {}, gen token {}", id, ok, is_prefill,
decode_done, active_position, generated_token);
return fmt::format("Query {}, ok {}, is_prefill {}, done {}, active_position "
"{}, gen token {}",
id, ok, is_prefill, decode_done, active_position,
generated_token);
}
using Q = std::shared_ptr<Query>;
......@@ -258,18 +278,22 @@ struct KVC2_Maintainer {
.total_kvcache_pages = settings.total_kvcache_pages.value(),
.num_token_per_page = settings.page_size,
.num_k_heads = settings.model_settings.num_k_heads,
.k_head_dim =
settings.use_self_defined_head_dim ? settings.self_defined_head_dim : settings.model_settings.k_head_dim,
.k_head_dim = settings.use_self_defined_head_dim
? settings.self_defined_head_dim
: settings.model_settings.k_head_dim,
.full_kv_cache_on_each_gpu = settings.full_kv_cache_on_each_gpu,
.k_cache_on = settings.k_cache_on,
.v_cache_on = settings.v_cache_on,
.tensor_type = torch::kBFloat16,
};
auto model_configs_path = std::filesystem::path(settings.kvc2_config_path) / "model_configs.json";
auto model_configs_path =
std::filesystem::path(settings.kvc2_config_path) / "model_configs.json";
load_model_configs(model_configs_path);
auto my_model_config = ModelConfig();
my_model_config.load_from(std::filesystem::path(settings.model_settings.model_path) / "config.json");
my_model_config.load_from(
std::filesystem::path(settings.model_settings.model_path) /
"config.json");
model_configs[settings.model_name] = my_model_config;
dump_model_configs(model_configs_path);
......@@ -299,7 +323,7 @@ struct KVC2_Maintainer {
}
};
using EventAddQuery = std::pair<QueryAdd, std::promise<QueryID>*>;
using EventAddQuery = std::pair<QueryAdd, std::promise<QueryID> *>;
using EventUpdateQuery = BatchQueryUpdate;
using EventTakenBatch = std::shared_ptr<BatchQueryTodo>;
struct EventPrepare {
......@@ -311,55 +335,48 @@ struct EventPrepared {
bool ok;
};
struct EventQueryStatus{
struct EventQueryStatus {
QueryID query_id;
Query::Status now_status;
};
struct EventSchedule {};
using Event = std::variant<EventAddQuery, EventUpdateQuery, EventTakenBatch, EventPrepare, EventPrepared,
EventQueryStatus, EventSchedule>;
using Event =
std::variant<EventAddQuery, EventUpdateQuery, EventTakenBatch, EventPrepare,
EventPrepared, EventQueryStatus, EventSchedule>;
template <typename T>
std::string event_name(const T& event);
template <typename T> std::string event_name(const T &event);
template <>
std::string event_name(const EventAddQuery&) {
template <> std::string event_name(const EventAddQuery &) {
return "EventAddQuery";
}
template <>
std::string event_name(const EventUpdateQuery&) {
template <> std::string event_name(const EventUpdateQuery &) {
return "EventUpdateQuery";
}
template <>
std::string event_name(const EventTakenBatch&) {
template <> std::string event_name(const EventTakenBatch &) {
return "EventTakenBatch";
}
template <>
std::string event_name(const EventPrepare&) {
template <> std::string event_name(const EventPrepare &) {
return "EventPrepare";
}
template <>
std::string event_name(const EventPrepared&) {
template <> std::string event_name(const EventPrepared &) {
return "EventPrepared";
}
template <>
std::string event_name(const EventQueryStatus&) {
template <> std::string event_name(const EventQueryStatus &) {
return "EventQueryStatus";
}
template <>
std::string event_name(const EventSchedule&) {
template <> std::string event_name(const EventSchedule &) {
return "EventSchedule";
}
// 用 std::visit 实现对 variant 的 event_name
std::string event_name(const Event& event) {
return std::visit([](const auto& e) { return event_name(e); }, event);
std::string event_name(const Event &event) {
return std::visit([](const auto &e) { return event_name(e); }, event);
}
static_assert(std::is_copy_constructible<Event>::value);
......@@ -383,13 +400,13 @@ struct QueryMaintainer : public Scheduler {
QueryMaintainer() = default;
void gen_batch_query_todo(BatchQueryTodo* re, const std::set<Q>& queries) {
void gen_batch_query_todo(BatchQueryTodo *re, const std::set<Q> &queries) {
std::vector<std::vector<QueryID>> d_batch(2);
size_t last_decode_batch = 0;
size_t prefill_num = 0;
size_t decode_num = 0;
size_t preill_length = 0;
for (auto& q : queries) {
for (auto &q : queries) {
if (q->plan_status == Query::Prefill) {
prefill_num += 1;
}
......@@ -397,13 +414,13 @@ struct QueryMaintainer : public Scheduler {
decode_num += 1;
}
}
if (prefill_num >= 2 || (prefill_num ==1 && settings.max_batch_size - 2 < decode_num)) {
preill_length = settings.recommended_chunk_prefill_token_count;
}
else {
if (prefill_num >= 2 ||
(prefill_num == 1 && settings.max_batch_size - 2 < decode_num)) {
preill_length = settings.recommended_chunk_prefill_token_count;
} else {
preill_length = settings.recommended_chunk_prefill_token_count * 2;
}
for (auto& q : queries) {
for (auto &q : queries) {
re->query_ids.push_back(q->id);
re->query_tokens.push_back(q->query_token);
re->query_lengths.push_back(q->prompt_length);
......@@ -427,7 +444,7 @@ struct QueryMaintainer : public Scheduler {
re->attn_masks = std::nullopt;
re->rope_ranges = std::nullopt;
for (auto& b : d_batch) {
for (auto &b : d_batch) {
if (b.empty())
continue;
re->decode_mini_batches.push_back(b);
......@@ -439,46 +456,54 @@ struct QueryMaintainer : public Scheduler {
// Interface
void init(Settings settings) override {
SPDLOG_INFO(
"\nScheduler Settings:\n"
" model_name: {}\n"
" quant_type: {}\n"
" model_path: {}\n"
" params_count: {}\n"
" layer_count: {}\n"
" num_k_heads: {}\n"
" k_head_dim: {}\n"
" bytes_per_params: {}\n"
" bytes_per_kv_cache_element: {}\n"
" page_size: {}\n"
" gpu_device_id: {}\n"
" gpu_memory_size: {}\n"
" memory_utilization_percentage: {}\n"
" max_batch_size: {}\n"
" recommended_chunk_prefill_token_count: {}\n"
" sched_metrics_port: {}\n"
" kvc2_config_path: {}\n"
" kvc2_root_path: {}\n"
" memory_pool_size_GB: {}\n"
" evict_count: {}\n"
" kvc2_metrics_port: {}\n"
" load_from_disk: {}\n"
" save_to_disk: {}\n"
" strategy_name: {}\n"
" gpu_device_count: {}\n",
settings.model_name, settings.quant_type, settings.model_settings.model_path,
settings.model_settings.params_count, settings.model_settings.layer_count, settings.model_settings.num_k_heads,
settings.model_settings.k_head_dim, settings.model_settings.bytes_per_params,
settings.model_settings.bytes_per_kv_cache_element,
settings.page_size, format_vector(settings.gpu_device_id), readable_number(settings.gpu_memory_size),
settings.memory_utilization_percentage, settings.max_batch_size, settings.recommended_chunk_prefill_token_count,
settings.sched_metrics_port, settings.kvc2_config_path, settings.kvc2_root_path, settings.memory_pool_size_GB,
settings.evict_count, settings.kvc2_metrics_port, settings.load_from_disk, settings.save_to_disk,
settings.strategy_name, settings.gpu_device_count);
SPDLOG_INFO("\nScheduler Settings:\n"
" model_name: {}\n"
" quant_type: {}\n"
" model_path: {}\n"
" params_count: {}\n"
" layer_count: {}\n"
" num_k_heads: {}\n"
" k_head_dim: {}\n"
" bytes_per_params: {}\n"
" bytes_per_kv_cache_element: {}\n"
" page_size: {}\n"
" gpu_device_id: {}\n"
" gpu_memory_size: {}\n"
" memory_utilization_percentage: {}\n"
" max_batch_size: {}\n"
" recommended_chunk_prefill_token_count: {}\n"
" sched_metrics_port: {}\n"
" kvc2_config_path: {}\n"
" kvc2_root_path: {}\n"
" memory_pool_size_GB: {}\n"
" evict_count: {}\n"
" kvc2_metrics_port: {}\n"
" load_from_disk: {}\n"
" save_to_disk: {}\n"
" strategy_name: {}\n"
" gpu_device_count: {}\n",
settings.model_name, settings.quant_type,
settings.model_settings.model_path,
settings.model_settings.params_count,
settings.model_settings.layer_count,
settings.model_settings.num_k_heads,
settings.model_settings.k_head_dim,
settings.model_settings.bytes_per_params,
settings.model_settings.bytes_per_kv_cache_element,
settings.page_size, format_vector(settings.gpu_device_id),
readable_number(settings.gpu_memory_size),
settings.memory_utilization_percentage, settings.max_batch_size,
settings.recommended_chunk_prefill_token_count,
settings.sched_metrics_port, settings.kvc2_config_path,
settings.kvc2_root_path, settings.memory_pool_size_GB,
settings.evict_count, settings.kvc2_metrics_port,
settings.load_from_disk, settings.save_to_disk,
settings.strategy_name, settings.gpu_device_count);
this->settings = settings;
kvc2_maintainer = std::shared_ptr<KVC2_Maintainer>(new KVC2_Maintainer(settings));
kvc2_maintainer =
std::shared_ptr<KVC2_Maintainer>(new KVC2_Maintainer(settings));
MetricsConfig met_conf = {
.endpoint = "0.0.0.0:" + std::to_string(settings.sched_metrics_port),
.model_name = settings.model_name,
......@@ -487,7 +512,7 @@ struct QueryMaintainer : public Scheduler {
SPDLOG_INFO("Creating scheduler metrics exporter on {}", met_conf.endpoint);
met = std::make_shared<Metrics>(met_conf);
met->fn_every_sec = [](Metrics* met) {
met->fn_every_sec = [](Metrics *met) {
auto generated_tokens = met->generated_tokens->Collect().counter.value;
SPDLOG_INFO("Last Sec Generated Tokens {}", generated_tokens);
};
......@@ -522,7 +547,8 @@ struct QueryMaintainer : public Scheduler {
// Here this function update last batch results and get the next batch
// in most cases, the batch is ready,
// if not, busy wait to get it
std::shared_ptr<BatchQueryTodo> update_last_batch(BatchQueryUpdate updates) override {
std::shared_ptr<BatchQueryTodo>
update_last_batch(BatchQueryUpdate updates) override {
event_loop_queue.enqueue(updates);
// Busy Wait
......@@ -547,20 +573,22 @@ struct QueryMaintainer : public Scheduler {
InferenceContext re;
re.k_cache = kvc2_maintainer->k_cache;
re.v_cache = kvc2_maintainer->v_cache;
// kvc2_maintainer->k_cache[0][0][0][0][0][0] = 42; // test whether we pass this to inference loop
// kvc2_maintainer->k_cache[0][0][0][0][0][0] = 42; // test whether we pass
// this to inference loop
return re;
}
virtual void strategy_add_query(Q new_query) = 0;
virtual void strategy_update_query(const EventUpdateQuery& update) = 0;
virtual void strategy_taken_batch(const EventTakenBatch& batch) = 0;
virtual void strategy_prepare(const EventPrepare& prepare) = 0;
virtual void strategy_prepared(const EventPrepared& prepared) = 0;
virtual void strategy_query_status(const EventQueryStatus& query_status) = 0;
virtual void strategy_schedule(const EventSchedule& event, BatchQueryTodo* new_batch) = 0;
void tackle_event(EventAddQuery& event) {
auto& query_add = event.first;
virtual void strategy_update_query(const EventUpdateQuery &update) = 0;
virtual void strategy_taken_batch(const EventTakenBatch &batch) = 0;
virtual void strategy_prepare(const EventPrepare &prepare) = 0;
virtual void strategy_prepared(const EventPrepared &prepared) = 0;
virtual void strategy_query_status(const EventQueryStatus &query_status) = 0;
virtual void strategy_schedule(const EventSchedule &event,
BatchQueryTodo *new_batch) = 0;
void tackle_event(EventAddQuery &event) {
auto &query_add = event.first;
QueryID id = query_id_counter;
event.second->set_value(id);
query_id_counter += 1;
......@@ -570,33 +598,36 @@ struct QueryMaintainer : public Scheduler {
strategy_add_query(new_query);
}
void tackle_event(const EventUpdateQuery& update) {
void tackle_event(const EventUpdateQuery &update) {
// SPDLOG_INFO("Tackle Update Query");
for (auto& u : update) {
for (auto &u : update) {
if (u.ok == false) {
SPDLOG_ERROR("Query {} is not exectued OK", u.id);
exit(1);
}
auto q = query_map[u.id];
if (q->plan_status == Query::Status::Prefill || q->plan_status == Query::Status::Decode) {
if (q->plan_status == Query::Status::Prefill ||
q->plan_status == Query::Status::Decode) {
q->absorb_update(u);
} else {
SPDLOG_DEBUG("Query {} is not in Prefill or Decode status, do not update it", u.id);
SPDLOG_DEBUG(
"Query {} is not in Prefill or Decode status, do not update it",
u.id);
}
}
strategy_update_query(update);
}
void tackle_event(const EventTakenBatch& batch) {
void tackle_event(const EventTakenBatch &batch) {
met->batch_count("Taken")->Increment(1);
for (auto& task : batch->prefill_mini_batches) {
for (auto &task : batch->prefill_mini_batches) {
auto [id, s, l] = task;
if (l == 0)
continue;
query_map.at(id)->absorb_prefill_task(task);
}
for (auto& mini_batch : batch->decode_mini_batches) {
for (auto& id : mini_batch) {
for (auto &mini_batch : batch->decode_mini_batches) {
for (auto &id : mini_batch) {
query_map.at(id)->absorb_decode_task(id);
}
}
......@@ -604,16 +635,18 @@ struct QueryMaintainer : public Scheduler {
strategy_taken_batch(batch);
}
void tackle_event(const EventPrepare& event) { strategy_prepare(event); }
void tackle_event(const EventPrepared& event) { strategy_prepared(event); }
void tackle_event(const EventQueryStatus& event) { strategy_query_status(event); }
void tackle_event(const EventPrepare &event) { strategy_prepare(event); }
void tackle_event(const EventPrepared &event) { strategy_prepared(event); }
void tackle_event(const EventQueryStatus &event) {
strategy_query_status(event);
}
void tackle_event(const EventSchedule& event) {
void tackle_event(const EventSchedule &event) {
// SPDLOG_INFO("Tackle Schedule Event");
HistogramTimerWrapper t(met->schedule_time);
BatchQueryTodo* new_batch = new BatchQueryTodo;
BatchQueryTodo *new_batch = new BatchQueryTodo;
strategy_schedule(event, new_batch);
// if (new_batch->query_ids.empty()) {
// SPDLOG_INFO("Nothing todo");
......@@ -660,7 +693,8 @@ struct QueryMaintainer : public Scheduler {
}
},
event);
if (event_loop_queue.size() == 0 && std::holds_alternative<EventSchedule>(event) == false) {
if (event_loop_queue.size() == 0 &&
std::holds_alternative<EventSchedule>(event) == false) {
// if this is not a schedule event, we need to schedule one
event_loop_queue.enqueue(EventSchedule());
}
......@@ -679,54 +713,58 @@ struct QueryMaintainer : public Scheduler {
void Query::to_status(Status to) {
SPDLOG_DEBUG("Calling to status query {}, to {}", id, status_to_string(to));
switch (to) {
case Received:
assert(false);
break;
case Preparing:
SPDLOG_INFO("Preparing Query {} {}", id,
prepare_try_count > 0 ? (std::to_string(prepare_try_count) + " Try") : "");
prepare_try_count += 1;
ctx.kvc2_interface->lookup_to_gpu_async(
ctx.model_name, ctx.quant_type, static_cast<kvc2::Token*>(query_token.data_ptr()), prompt_length,
estimated_length, [this](std::shared_ptr<kvc2::DoubleCacheHandleInterface> handle) {
if (handle == nullptr) {
SPDLOG_INFO("Get handle from kvc2 Failed.");
this->after_load(false);
} else {
SPDLOG_INFO("Get handle from kvc2 Success.");
this->kvc2_handle = handle;
this->to_status(Ready);
this->after_load(true);
}
});
break;
case Ready:
SPDLOG_INFO("Ready Query {}", id);
break;
case Prefill:
SPDLOG_INFO("Prefilling Query {}", id);
// assert(plan_status == Received);
plan_position = kvc2_handle->matched_length();
if (prompt_length - plan_position == 0) {
assert(prompt_length > 0);
plan_position -= 1;
}
break;
case Decode:
SPDLOG_INFO("Decoding Query {}", id);
// assert(plan_status == Prefill);
break;
case Done:
SPDLOG_INFO("Finish Query {}", id);
kvc2_handle = nullptr;
ctx.query_maintainer->event_loop_queue.enqueue(EventQueryStatus{
case Received:
assert(false);
break;
case Preparing:
SPDLOG_INFO("Preparing Query {} {}", id,
prepare_try_count > 0
? (std::to_string(prepare_try_count) + " Try")
: "");
prepare_try_count += 1;
ctx.kvc2_interface->lookup_to_gpu_async(
ctx.model_name, ctx.quant_type,
static_cast<kvc2::Token *>(query_token.data_ptr()), prompt_length,
estimated_length,
[this](std::shared_ptr<kvc2::DoubleCacheHandleInterface> handle) {
if (handle == nullptr) {
SPDLOG_INFO("Get handle from kvc2 Failed.");
this->after_load(false);
} else {
SPDLOG_INFO("Get handle from kvc2 Success.");
this->kvc2_handle = handle;
this->to_status(Ready);
this->after_load(true);
}
});
break;
case Ready:
SPDLOG_INFO("Ready Query {}", id);
break;
case Prefill:
SPDLOG_INFO("Prefilling Query {}", id);
// assert(plan_status == Received);
plan_position = kvc2_handle->matched_length();
if (prompt_length - plan_position == 0) {
assert(prompt_length > 0);
plan_position -= 1;
}
break;
case Decode:
SPDLOG_INFO("Decoding Query {}", id);
// assert(plan_status == Prefill);
break;
case Done:
SPDLOG_INFO("Finish Query {}", id);
kvc2_handle = nullptr;
ctx.query_maintainer->event_loop_queue.enqueue(EventQueryStatus{
.query_id = id,
.now_status = to,
});
// assert(plan_status == Decode);
break;
});
// assert(plan_status == Decode);
break;
}
plan_status = to;
export_metrics();
......@@ -734,11 +772,14 @@ void Query::to_status(Status to) {
void Query::after_load(bool ok) {
if (ok) {
size_t page_count = div_up(estimated_length, ctx.query_maintainer->settings.page_size);
size_t page_count =
div_up(estimated_length, ctx.query_maintainer->settings.page_size);
std::vector<int64_t> shape;
shape.push_back(page_count);
block_index = torch::zeros(shape, torch::TensorOptions().dtype(torch::kInt32)).contiguous();
auto ptr = reinterpret_cast<int32_t*>(block_index.data_ptr());
block_index =
torch::zeros(shape, torch::TensorOptions().dtype(torch::kInt32))
.contiguous();
auto ptr = reinterpret_cast<int32_t *>(block_index.data_ptr());
auto vec_idx = kvc2_handle->get_gpu_block_idx();
for (size_t i = 0; i < vec_idx.size(); i++) {
ptr[i] = vec_idx[i];
......@@ -765,7 +806,7 @@ struct FCFS_single_prefill : public QueryMaintainer {
bool has_query_preparing = false;
std::optional<EventPrepare> wait_done_prepare = std::nullopt;
std::set<Q> active_query; // on going queries for LLMs
std::set<Q> active_query; // on going queries for LLMs
// interface all these are executed in a single thread
void strategy_add_query(Q new_query) override {
......@@ -774,71 +815,72 @@ struct FCFS_single_prefill : public QueryMaintainer {
has_query_preparing = true;
auto next_q = queue.front();
queue.pop();
event_loop_queue.enqueue(EventPrepare{next_q->id,true});
event_loop_queue.enqueue(EventPrepare{next_q->id, true});
}
}
void strategy_update_query(const EventUpdateQuery& update) override {
void strategy_update_query(const EventUpdateQuery &update) override {
for (auto u : update) {
auto& q = query_map[u.id];
auto &q = query_map[u.id];
if (q->plan_status == Query::Done) {
active_query.erase(q);
}
}
}
void strategy_taken_batch(const EventTakenBatch& batch) override {
for (auto& q : batch->query_ids) {
void strategy_taken_batch(const EventTakenBatch &batch) override {
for (auto &q : batch->query_ids) {
if (query_map[q]->plan_status != Query::Done) {
active_query.insert(query_map[q]);
}
}
}
void strategy_prepare(const EventPrepare& prepare) override {
if(prepare.first_try){
auto& q = query_map[prepare.query_id];
void strategy_prepare(const EventPrepare &prepare) override {
if (prepare.first_try) {
auto &q = query_map[prepare.query_id];
q->to_status(Query::Preparing);
}else{
assert(wait_done_prepare.has_value()==false);
} else {
assert(wait_done_prepare.has_value() == false);
wait_done_prepare = prepare;
wait_done_prepare->first_try = true;
}
}
void strategy_prepared(const EventPrepared& prepared) override {
void strategy_prepared(const EventPrepared &prepared) override {
assert(prepared.ok);
ready_queue.push(query_map[prepared.query_id]);
if (queue.empty() == false) {
auto next_q_prepare = queue.front();
queue.pop();
event_loop_queue.enqueue(EventPrepare{next_q_prepare->id,true});
event_loop_queue.enqueue(EventPrepare{next_q_prepare->id, true});
} else {
has_query_preparing = false;
}
}
void strategy_query_status(const EventQueryStatus& query_status) override{
if(query_status.now_status==Query::Done){
if(wait_done_prepare.has_value()){
void strategy_query_status(const EventQueryStatus &query_status) override {
if (query_status.now_status == Query::Done) {
if (wait_done_prepare.has_value()) {
event_loop_queue.enqueue(wait_done_prepare.value());
wait_done_prepare = std::nullopt;
}
}
}
void strategy_schedule([[maybe_unused]] const EventSchedule& event, BatchQueryTodo* new_batch) override {
void strategy_schedule([[maybe_unused]] const EventSchedule &event,
BatchQueryTodo *new_batch) override {
bool have_prefill = false;
for (auto& q : active_query) {
for (auto &q : active_query) {
if (q->plan_status == Query::Prefill) {
have_prefill = true;
}
}
if (have_prefill == false && ready_queue.empty() == false && active_query.size() < settings.max_batch_size) {
auto& next_q = ready_queue.front();
if (have_prefill == false && ready_queue.empty() == false &&
active_query.size() < settings.max_batch_size) {
auto &next_q = ready_queue.front();
ready_queue.pop();
SPDLOG_INFO("Active query {}", next_q->id);
......@@ -847,7 +889,7 @@ struct FCFS_single_prefill : public QueryMaintainer {
}
if (active_query.empty() == false)
SPDLOG_INFO("Active Query Size {}", active_query.size());
for (auto& q : active_query) {
for (auto &q : active_query) {
q->debug();
}
gen_batch_query_todo(new_batch, active_query);
......@@ -855,10 +897,11 @@ struct FCFS_single_prefill : public QueryMaintainer {
};
struct FCFS : public FCFS_single_prefill {
void strategy_schedule([[maybe_unused]] const EventSchedule& event, BatchQueryTodo* new_batch) override {
void strategy_schedule([[maybe_unused]] const EventSchedule &event,
BatchQueryTodo *new_batch) override {
int prefill_count = 0;
const int max_prefill_count = 2;
for (auto& q : active_query) {
for (auto &q : active_query) {
if (q->plan_status == Query::Prefill) {
prefill_count += 1;
}
......@@ -877,7 +920,7 @@ struct FCFS : public FCFS_single_prefill {
if (active_query.empty() == false) {
SPDLOG_DEBUG("Active Query Size {}", active_query.size());
}
for (auto& q : active_query) {
for (auto &q : active_query) {
q->debug();
}
gen_batch_query_todo(new_batch, active_query);
......@@ -900,7 +943,8 @@ std::shared_ptr<Scheduler> create_scheduler(Settings settings) {
}
NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(SampleOptions, temperature, top_p);
NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(QueryAdd, query_token, query_length, estimated_length, sample_options, user_id,
NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(QueryAdd, query_token, query_length,
estimated_length, sample_options, user_id,
SLO_TTFT_ms, SLO_TBT_ms);
std::string QueryAdd::serialize() {
......@@ -908,9 +952,9 @@ std::string QueryAdd::serialize() {
return j.dump();
}
QueryAdd QueryAdd::deserialize(const std::string& input) {
QueryAdd QueryAdd::deserialize(const std::string &input) {
json j = json::parse(input);
return j.get<QueryAdd>();
}
}; // namespace scheduler
}; // namespace scheduler
csrc/balance_serve/sched/scheduler.h
View file @ 64de7843
#pragma once
#include <torch/torch.h>
#include "model_config.h"
#include <cstdint>
#include <memory>
#include <optional>
#include <torch/torch.h>
#include <vector>
#include "model_config.h"
namespace scheduler {
......@@ -28,7 +28,9 @@ struct ModelSettings {
double bytes_per_kv_cache_element;
inline size_t params_nbytes() { return params_count * bytes_per_params; }
inline size_t bytes_per_token_kv_cache() { return bytes_per_kv_cache_element * num_k_heads * k_head_dim; }
inline size_t bytes_per_token_kv_cache() {
return bytes_per_kv_cache_element * num_k_heads * k_head_dim;
}
};
struct SampleOptions {
......@@ -37,15 +39,16 @@ struct SampleOptions {
};
struct Settings {
// something is aukward here, kvc2 only use model_name and quant_type to get model infos.
// something is aukward here, kvc2 only use model_name and quant_type to get
// model infos.
ModelName model_name;
QuantType quant_type;
// model_setting is ignore by kvc2
ModelSettings model_settings;
size_t page_size = 256; // how many token in a page
std::vector<size_t> gpu_device_id; //
size_t gpu_memory_size; // memory size in bytes of each GPU, each
size_t page_size = 256; // how many token in a page
std::vector<size_t> gpu_device_id; //
size_t gpu_memory_size; // memory size in bytes of each GPU, each
double memory_utilization_percentage;
size_t max_batch_size = 256;
......@@ -79,14 +82,16 @@ struct Settings {
void auto_derive();
};
using PrefillTask = std::tuple<QueryID, TokenLength, TokenLength>; // id, start, length
using PrefillTask =
std::tuple<QueryID, TokenLength, TokenLength>; // id, start, length
struct BatchQueryTodo {
// query
std::vector<QueryID> query_ids;
std::vector<torch::Tensor> query_tokens;
std::vector<TokenLength> query_lengths;
std::vector<torch::Tensor> block_indexes; // (max_num_blocks_per_seq), dtype torch.int32.
std::vector<torch::Tensor>
block_indexes; // (max_num_blocks_per_seq), dtype torch.int32.
std::optional<torch::Tensor> attn_masks;
std::optional<torch::Tensor> rope_ranges;
std::vector<SampleOptions> sample_options;
......@@ -94,8 +99,10 @@ struct BatchQueryTodo {
// mini batches, adjacent two mini batches are executed together
// tasks count must be <=2, because of flash infer attention
std::vector<PrefillTask> prefill_mini_batches; // prefill minibatch only has 1 prefill
std::vector<std::vector<QueryID>> decode_mini_batches; // decode minibatch has multiple decode
std::vector<PrefillTask>
prefill_mini_batches; // prefill minibatch only has 1 prefill
std::vector<std::vector<QueryID>>
decode_mini_batches; // decode minibatch has multiple decode
std::string debug();
bool empty();
......@@ -105,9 +112,9 @@ struct QueryUpdate {
QueryID id;
bool ok;
bool is_prefill;
bool decode_done; // no use for now
TokenLength active_position; // the position where no kvcache now,
// kvcache[active_position] == None
bool decode_done; // no use for now
TokenLength active_position; // the position where no kvcache now,
// kvcache[active_position] == None
Token generated_token;
......@@ -117,8 +124,8 @@ struct QueryUpdate {
using BatchQueryUpdate = std::vector<QueryUpdate>;
struct InferenceContext {
std::vector<torch::Tensor> k_cache; // [gpu num] (layer_count, num blocks,
// page size, kheadnum, head_dim)
std::vector<torch::Tensor> k_cache; // [gpu num] (layer_count, num blocks,
// page size, kheadnum, head_dim)
std::vector<torch::Tensor> v_cache;
};
......@@ -127,7 +134,7 @@ constexpr UserID NoUser = -1;
const int MAX_SLO_TIME = 1e9;
struct QueryAdd {
std::vector<Token> query_token; // int here
std::vector<Token> query_token; // int here
// torch::Tensor attn_mask;
TokenLength query_length;
TokenLength estimated_length;
......@@ -141,11 +148,11 @@ struct QueryAdd {
int SLO_TBT_ms = MAX_SLO_TIME;
std::string serialize();
static QueryAdd deserialize(const std::string& input);
static QueryAdd deserialize(const std::string &input);
};
class Scheduler {
public:
public:
virtual void init(Settings settings) = 0;
virtual void run() = 0;
......@@ -156,7 +163,8 @@ class Scheduler {
virtual void cancel_query(QueryID id) = 0;
// inference loop call this
virtual std::shared_ptr<BatchQueryTodo> update_last_batch(BatchQueryUpdate updates) = 0;
virtual std::shared_ptr<BatchQueryTodo>
update_last_batch(BatchQueryUpdate updates) = 0;
virtual InferenceContext get_inference_context() = 0;
virtual ~Scheduler() = default;
......@@ -164,4 +172,4 @@ class Scheduler {
std::shared_ptr<Scheduler> create_scheduler(Settings settings);
}; // namespace scheduler
\ No newline at end of file
}; // namespace scheduler
\ No newline at end of file
csrc/balance_serve/sched/utils/arithmetic.hpp
View file @ 64de7843
#include <type_traits>
template <typename T, typename U>
T div_up(T x, U by) {
template <typename T, typename U> T div_up(T x, U by) {
static_assert(std::is_integral_v<T>);
static_assert(std::is_integral_v<U>);
return (x + by - 1) / by;
......
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