kvcache-ai-ktransformers/archive/csrc/ktransformers_ext/cpu_backend/backend.cpp
Jiaqi Liao 57d14d22bc
Refactor: restructure repository to focus on kt-kernel and KT-SFT modulesq recon (#1581)
* refactor: move legacy code to archive/ directory

  - Moved ktransformers, csrc, third_party, merge_tensors to archive/
  - Moved build scripts and configurations to archive/
  - Kept kt-kernel, KT-SFT, doc, and README files in root
  - Preserved complete git history for all moved files

* refactor: restructure repository to focus on kt-kernel and KT-SFT modules

* fix README

* fix README

* fix README

* fix README

* docs: add performance benchmarks to kt-kernel section

Add comprehensive performance data for kt-kernel to match KT-SFT's presentation:
- AMX kernel optimization: 21.3 TFLOPS (3.9× faster than PyTorch)
- Prefill phase: up to 20× speedup vs baseline
- Decode phase: up to 4× speedup
- NUMA optimization: up to 63% throughput improvement
- Multi-GPU (8×L20): 227.85 tokens/s total throughput with DeepSeek-R1 FP8

Source: https://lmsys.org/blog/2025-10-22-KTransformers/

This provides users with concrete performance metrics for both core modules,
making it easier to understand the capabilities of each component.

* refactor: improve kt-kernel performance data with specific hardware and models

Replace generic performance descriptions with concrete benchmarks:
- Specify exact hardware: 8×L20 GPU + Xeon Gold 6454S, Single/Dual-socket Xeon + AMX
- Include specific models: DeepSeek-R1-0528 (FP8), DeepSeek-V3 (671B)
- Show detailed metrics: total throughput, output throughput, concurrency details
- Match KT-SFT presentation style for consistency

This provides users with actionable performance data they can use to evaluate
hardware requirements and expected performance for their use cases.

* fix README

* docs: clean up performance table and improve formatting

* add pic for README

* refactor: simplify .gitmodules and backup legacy submodules

- Remove 7 legacy submodules from root .gitmodules (archive/third_party/*)
- Keep only 2 active submodules for kt-kernel (llama.cpp, pybind11)
- Backup complete .gitmodules to archive/.gitmodules
- Add documentation in archive/README.md for researchers who need legacy submodules

This reduces initial clone size by ~500MB and avoids downloading unused dependencies.

* refactor: move doc/ back to root directory

Keep documentation in root for easier access and maintenance.

* refactor: consolidate all images to doc/assets/

- Move kt-kernel/assets/heterogeneous_computing.png to doc/assets/
- Remove KT-SFT/assets/ (images already in doc/assets/)
- Update KT-SFT/README.md image references to ../doc/assets/
- Eliminates ~7.9MB image duplication
- Centralizes all documentation assets in one location

* fix pic path for README
2025-11-10 17:42:26 +08:00

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/**
* @Description :
* @Author : chenht2022
* @Date : 2024-07-22 02:03:05
* @Version : 1.0.0
* @LastEditors : chenht2022
* @LastEditTime : 2024-07-25 10:33:34
* @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
**/
#include "backend.h"
#ifdef USE_NUMA
#include <numa.h>
#include <numaif.h>
thread_local int Backend::numa_node = -1;
#endif
thread_local int Backend::thread_local_id = -1;
Backend::Backend(int max_thread_num) {
max_thread_num_ = max_thread_num;
thread_state_.resize(max_thread_num_);
for (int i = 0; i < max_thread_num_; i++) {
thread_state_[i].curr = std::make_unique<std::atomic<int>>();
thread_state_[i].status =
std::make_unique<std::atomic<ThreadStatus>>(ThreadStatus::WAITING);
}
workers_.resize(max_thread_num_);
for (int i = 1; i < max_thread_num_; i++) {
workers_[i] = std::thread(&Backend::worker_thread, this, i);
}
}
Backend::~Backend() {
for (int i = 0; i < max_thread_num_; i++) {
thread_state_[i].status->store(ThreadStatus::EXIT,
std::memory_order_release);
}
for (int i = 1; i < max_thread_num_; i++) {
if (workers_[i].joinable()) {
workers_[i].join();
}
}
}
int Backend::get_thread_num() { return max_thread_num_; }
void Backend::do_work_stealing_job(int task_num,
std::function<void(int)> init_func,
std::function<void(int)> compute_func,
std::function<void(int)> finalize_func) {
init_func_ = init_func;
compute_func_ = compute_func;
finalize_func_ = finalize_func;
#ifdef USE_NUMA
// numa node location will be calculated based on the number of threads
thread_num_ = max_thread_num_;
#else
thread_num_ = std::min(max_thread_num_, task_num);
#endif
int base = task_num / thread_num_;
int remain = task_num % thread_num_;
thread_state_[0].end = base + (0 < remain);
// 为主线程设置 thread_local_id
thread_local_id = 0;
for (int i = 1; i < thread_num_; i++) {
thread_state_[i].curr->store(thread_state_[i - 1].end,
std::memory_order_relaxed);
thread_state_[i].end = thread_state_[i - 1].end + base + (i < remain);
thread_state_[i].status->store(ThreadStatus::WORKING,
std::memory_order_release);
}
thread_state_[0].curr->store(0, std::memory_order_relaxed);
thread_state_[0].status->store(ThreadStatus::WORKING,
std::memory_order_release);
process_tasks(0);
for (int i = 1; i < thread_num_; i++) {
while (thread_state_[i].status->load(std::memory_order_acquire) ==
ThreadStatus::WORKING) {
}
}
}
void Backend::process_tasks(int thread_id) {
#ifdef USE_NUMA
if(numa_node == -1){
numa_node = thread_id * numa_num_configured_nodes() / thread_num_;
struct bitmask* mask = numa_bitmask_alloc(numa_num_configured_nodes());
numa_bitmask_setbit(mask, numa_node);
numa_bind(mask);
}
#endif
if (init_func_ != nullptr) {
init_func_(thread_id);
}
while (true) {
int task_id = thread_state_[thread_id].curr->fetch_add(
1, std::memory_order_acq_rel);
if (task_id >= thread_state_[thread_id].end) {
break;
}
compute_func_(task_id);
}
for (int t_offset = 1; t_offset < thread_num_; t_offset++) {
int t_i = (thread_id + t_offset) % thread_num_;
if (thread_state_[t_i].status->load(std::memory_order_acquire) !=
ThreadStatus::WORKING) {
continue;
}
while (true) {
int task_id = thread_state_[t_i].curr->fetch_add(
1, std::memory_order_acq_rel);
if (task_id >= thread_state_[t_i].end) {
break;
}
compute_func_(task_id);
}
}
if (finalize_func_ != nullptr) {
finalize_func_(thread_id);
}
thread_state_[thread_id].status->store(ThreadStatus::WAITING,
std::memory_order_release);
}
void Backend::worker_thread(int thread_id) {
auto start = std::chrono::steady_clock::now();
thread_local_id = thread_id; // è®¾ç½®çº¿ç¨æœ¬åœ°å<C2B0>˜é‡<C3A9>
while (true) {
ThreadStatus status =
thread_state_[thread_id].status->load(std::memory_order_acquire);
if (status == ThreadStatus::WORKING) {
process_tasks(thread_id);
start = std::chrono::steady_clock::now();
} else if (status == ThreadStatus::WAITING) {
auto now = std::chrono::steady_clock::now();
auto duration =
std::chrono::duration_cast<std::chrono::milliseconds>(now -
start)
.count();
if (duration > 50) {
std::this_thread::sleep_for(std::chrono::milliseconds(1));
}
} else if (status == ThreadStatus::EXIT) {
return;
}
}
}