Files
ktransformers/archive/csrc/balance_serve/kvc2/test/kvc2test/lookup-gpu.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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C++

/**
* @Description :
* @Author : Xie Weiyu
* @Date : 2024-11-22 09:52:48
* @Version : 1.0.0
* @LastEditors : Xie Weiyu
* @LastEditTime : 2024-11-25 08:38:33
* @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
**/
#include "common.hpp"
int main(int argc, char* argv[]) {
init(argc, argv);
spdlog::set_level(spdlog::level::debug);
auto kvc2 = kvc2::create_kvc2(config);
std::mt19937 gen(123);
auto ids1 = random_ids(10 * config.num_token_per_page, gen);
auto k1 = random_kvcache(10, gen);
auto v1 = random_kvcache(10, gen);
kvc2->raw_insert(test_model_name, test_quant_type, ids1.data(), ids1.size(), k1, v1);
// complete same
{
auto h = kvc2->lookup_to_gpu(test_model_name, test_quant_type, ids1.data(), ids1.size(),
ids1.size() + 5 * config.num_token_per_page);
auto k = h->handle_data(true);
auto v = h->handle_data(false);
cmp_handle_data(k1, k, 10);
cmp_handle_data(v1, v, 10);
auto block_idx = h->get_gpu_block_idx();
auto [kcache, vcache] = kvc2->get_kvcache();
auto k_from_gpu = empty_kvcache(15);
auto v_from_gpu = empty_kvcache(15);
size_t gpu_count = config.gpu_cache_config->gpu_devices_id.size();
size_t element_size_per_gpu = test_cache_info.element_size(config.num_token_per_page) / gpu_count;
for (size_t i = 0; i < k_from_gpu.size(); i++) {
for (size_t j = 0; j < block_idx.size(); j++) {
size_t b_idx = block_idx[j];
for (size_t gpu_idx = 0; gpu_idx < gpu_count; gpu_idx++) {
{
auto kt = kcache[gpu_idx][i][b_idx].to(torch::kCPU);
void* src = kt.data_ptr();
void* dst = offset_by_bytes(k_from_gpu[i][j], gpu_idx * element_size_per_gpu);
memcpy(dst, src, element_size_per_gpu);
}
{
auto vt = vcache[gpu_idx][i][b_idx].to(torch::kCPU);
void* src = vt.data_ptr();
void* dst = offset_by_bytes(v_from_gpu[i][j], gpu_idx * element_size_per_gpu);
memcpy(dst, src, element_size_per_gpu);
}
}
}
}
cmp_handle_data(k1, k_from_gpu, 10);
cmp_handle_data(v1, v_from_gpu, 10);
}
// prefix and evict
{
auto h = kvc2->lookup_to_gpu(test_model_name, test_quant_type, ids1.data(), config.num_token_per_page * 3,
config.gpu_cache_config->total_kvcache_pages * config.num_token_per_page);
auto k = h->handle_data(true);
auto v = h->handle_data(false);
cmp_handle_data(k1, k, 3);
cmp_handle_data(v1, v, 3);
auto block_idx = h->get_gpu_block_idx();
auto [kcache, vcache] = kvc2->get_kvcache();
auto k_from_gpu = empty_kvcache(3);
auto v_from_gpu = empty_kvcache(3);
size_t gpu_count = config.gpu_cache_config->gpu_devices_id.size();
size_t element_size_per_gpu = test_cache_info.element_size(config.num_token_per_page) / gpu_count;
for (size_t i = 0; i < k_from_gpu.size(); i++) {
for (size_t j = 0; j < 3; j++) {
size_t b_idx = block_idx[j];
for (size_t gpu_idx = 0; gpu_idx < gpu_count; gpu_idx++) {
{
auto kt = kcache[gpu_idx][i][b_idx].to(torch::kCPU);
void* src = kt.data_ptr();
void* dst = offset_by_bytes(k_from_gpu[i][j], gpu_idx * element_size_per_gpu);
memcpy(dst, src, element_size_per_gpu);
}
{
auto vt = vcache[gpu_idx][i][b_idx].to(torch::kCPU);
void* src = vt.data_ptr();
void* dst = offset_by_bytes(v_from_gpu[i][j], gpu_idx * element_size_per_gpu);
memcpy(dst, src, element_size_per_gpu);
}
}
}
}
cmp_handle_data(k1, k_from_gpu, 3);
cmp_handle_data(v1, v_from_gpu, 3);
}
// // complete prefix
// {
// std::vector<Token> ids2(ids1.begin(), ids1.begin() + 3 * config.num_token_per_page);
// auto h = kvc2->lookup(test_model_name, test_quant_type, ids2.data(), ids2.size(),
// ids2.size() + 3 * config.num_token_per_page);
// auto k = h->handle_data(true);
// auto v = h->handle_data(false);
// cmp_handle_data(k1, k, 3);
// cmp_handle_data(v1, v, 3);
// }
// // common prefix
// {
// std::vector<Token> ids2(ids1.begin(), ids1.begin() + 3 * config.num_token_per_page);
// auto rids = random_ids(config.num_token_per_page * 2 + config.num_token_per_page / 2, gen);
// ids2.insert(ids2.end(), rids.begin(), rids.end());
// auto h = kvc2->lookup(test_model_name, test_quant_type, ids2.data(), ids2.size(), ids2.size());
// auto k = h->handle_data(true);
// auto v = h->handle_data(false);
// cmp_handle_data(k1, k, 3);
// cmp_handle_data(v1, v, 3);
// }
// // no prefix
// {
// std::vector<Token> ids2 = random_ids(config.num_token_per_page, gen);
// auto h = kvc2->lookup(test_model_name, test_quant_type, ids2.data(), ids2.size(), ids2.size());
// assert(h->matched_length() == 0);
// }
// // insert partly new
// auto k2 = random_kvcache(10, gen);
// auto v2 = random_kvcache(10, gen);
// copy_kvcache(k1, k2, 0, 5);
// copy_kvcache(v1, v2, 0, 5);
// auto ids2 = random_ids(10 * config.num_token_per_page, gen);
// for (size_t i = 0; i < 5 * config.num_token_per_page; i++) {
// ids2[i] = ids1[i];
// }
// kvc2->raw_insert(test_model_name, test_quant_type, ids2.data(), ids2.size(), k2, v2);
// // read new part
// {
// std::vector<Token> ids(ids2.begin(), ids2.begin() + 7 * config.num_token_per_page);
// auto h = kvc2->lookup(test_model_name, test_quant_type, ids.data(), ids.size(),
// ids.size() + 7 * config.num_token_per_page);
// auto k = h->handle_data(true);
// auto v = h->handle_data(false);
// cmp_handle_data(k, k2, 7);
// cmp_handle_data(v, v2, 7);
// }
SPDLOG_CRITICAL("All Test Passed: {}", argv[0]);
return 0;
}