format kvc2, delete quant_configs, move model_configs to ~/.ktransformers

csrc/balance_serve/kvc2/src/async_store.cpp

@@ -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

@@ -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

 #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

 #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

@@ -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

 #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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

 
+#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

-#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

+#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

@@ -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

 #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

 #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.h

 #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
+}; // namespace scheduler
\ No newline at end of file

csrc/balance_serve/sched/utils/arithmetic.hpp

 #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;