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https://github.com/kvcache-ai/ktransformers.git
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57d14d22bc
* 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
62 lines
1.9 KiB
C++
62 lines
1.9 KiB
C++
/**
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* @Description :
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* @Author : Xie Weiyu
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* @Date : 2024-11-22 09:52:48
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* @Version : 1.0.0
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* @LastEditors : Xie Weiyu
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* @LastEditTime : 2024-11-25 07:51:09
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* @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
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**/
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#include "common.hpp"
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int main(int argc, char* argv[]) {
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qw25_7B_gpu_config.v_cache_on = false;
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config.gpu_cache_config = qw25_7B_gpu_config;
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config.v_cache_on = false;
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init(argc, argv);
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spdlog::set_level(spdlog::level::debug);
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auto kvc2 = kvc2::create_kvc2(config);
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std::mt19937 gen(123);
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auto ids1 = random_ids(10 * config.num_token_per_page, gen);
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auto k1 = random_kvcache(10, gen);
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kvc2->raw_insert(test_model_name, test_quant_type, ids1.data(), ids1.size(), k1, {});
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// complete same
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#pragma omp parallel for
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for (size_t ti = 0; ti < 3; ti++) {
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auto h = kvc2->lookup_to_gpu(test_model_name, test_quant_type, ids1.data(), ids1.size(),
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ids1.size() + 2 * config.num_token_per_page);
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auto k = h->handle_data(true);
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cmp_handle_data(k1, k, 10);
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auto block_idx = h->get_gpu_block_idx();
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auto [kcache, vcache] = kvc2->get_kvcache();
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auto k_from_gpu = empty_kvcache(15);
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size_t gpu_count = config.gpu_cache_config->gpu_devices_id.size();
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size_t element_size_per_gpu = test_cache_info.element_size(config.num_token_per_page) / gpu_count;
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for (size_t i = 0; i < k_from_gpu.size(); i++) {
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for (size_t j = 0; j < block_idx.size(); j++) {
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size_t b_idx = block_idx[j];
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for (size_t gpu_idx = 0; gpu_idx < gpu_count; gpu_idx++) {
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{
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auto kt = kcache[gpu_idx][i][b_idx].to(torch::kCPU);
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void* src = kt.data_ptr();
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void* dst = offset_by_bytes(k_from_gpu[i][j], gpu_idx * element_size_per_gpu);
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memcpy(dst, src, element_size_per_gpu);
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}
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}
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}
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}
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cmp_handle_data(k1, k_from_gpu, 10);
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}
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SPDLOG_CRITICAL("All Test Passed: {}", argv[0]);
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return 0;
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}
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