mirror of
https://github.com/kvcache-ai/ktransformers.git
synced 2026-03-15 02:47:22 +00:00
* 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
141 lines
6.6 KiB
C++
141 lines
6.6 KiB
C++
#include "metrics.h"
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namespace kvc2 {
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Metrics::Metrics(const MetricsConfig& config)
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: registry_(std::make_shared<prometheus::Registry>()), exposer_(config.endpoint) {
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// 注册 prefix_nodes Counter
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auto& prefix_nodes_family = prometheus::BuildCounter()
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.Name(std::string(METRIC_PREFIX) + "_prefix_nodes")
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.Help("Number of prefix nodes")
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.Register(*registry_);
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prefix_nodes = &prefix_nodes_family.Add({});
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// 注册 prefix_block_count Counter
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auto& prefix_block_count_family = prometheus::BuildCounter()
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.Name(std::string(METRIC_PREFIX) + "_prefix_block_count")
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.Help("Number of prefix blocks")
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.Register(*registry_);
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prefix_block_count = &prefix_block_count_family.Add({});
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// 定义统一的桶大小,最大为 10000 ms (10 s)
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std::vector<double> common_buckets = {1.0, 5.0, 10.0, 50.0, 100.0, 500.0, 1000.0, 5000.0, 10000.0};
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// 注册 raw_insert_time_ms Histogram
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auto& raw_insert_time_ms_family = prometheus::BuildHistogram()
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.Name(std::string(METRIC_PREFIX) + "_raw_insert_time_ms")
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.Help("function raw insert's time in milliseconds")
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.Register(*registry_);
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raw_insert_time_ms = &raw_insert_time_ms_family.Add({}, common_buckets);
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// 注册 lookup_time_ms Histogram
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auto& lookup_time_ms_family = prometheus::BuildHistogram()
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.Name(std::string(METRIC_PREFIX) + "_lookup_time_ms")
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.Help("function lookup's time in milliseconds")
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.Register(*registry_);
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lookup_time_ms = &lookup_time_ms_family.Add({}, common_buckets);
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// 注册 lookup_prefixmatch_length Histogram
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auto& lookup_prefixmatch_length_family = prometheus::BuildHistogram()
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.Name(std::string(METRIC_PREFIX) + "_lookup_prefixmatch_length")
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.Help("function lookup's prefix match length")
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.Register(*registry_);
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lookup_prefixmatch_length = &lookup_prefixmatch_length_family.Add({}, common_buckets);
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// 注册 matched_length_percentage Histogram
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auto& matched_length_percentage_family = prometheus::BuildHistogram()
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.Name(std::string(METRIC_PREFIX) + "_matched_length_percentage")
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.Help("function matched length percentage")
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.Register(*registry_);
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matched_length_percentage = &matched_length_percentage_family.Add({}, common_buckets);
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// 注册 disk_usage Gauge
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auto& disk_usage_family =
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prometheus::BuildGauge().Name(std::string(METRIC_PREFIX) + "_disk_usage").Help("disk usage").Register(*registry_);
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disk_usage = &disk_usage_family.Add({});
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// 注册 memory_pool_size Gauge
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memory_pool_size_family_ = &prometheus::BuildGauge()
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.Name(std::string(METRIC_PREFIX) + "_memory_pool_size")
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.Help("memory pool size")
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.Register(*registry_);
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// 注册 memory_pool_node_count Gauge
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memory_pool_node_count_family_ = &prometheus::BuildGauge()
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.Name(std::string(METRIC_PREFIX) + "_memory_pool_node_count")
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.Help("memory pool node count")
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.Register(*registry_);
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// 注册 lru_entry_count Gauge
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lru_entry_count_family_ = &prometheus::BuildGauge()
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.Name(std::string(METRIC_PREFIX) + "_lru_entry_count")
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.Help("lru entry count")
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.Register(*registry_);
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// 注册 gpu_page_count Gauge
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gpu_page_count_family_ = &prometheus::BuildGauge()
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.Name(std::string(METRIC_PREFIX) + "_gpu_page_count")
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.Help("gpu page count")
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.Register(*registry_);
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// 注册 append_tokens_time_ms Histogram
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auto& append_tokens_time_ms_family = prometheus::BuildHistogram()
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.Name(std::string(METRIC_PREFIX) + "_append_tokens_time_ms")
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.Help("append tokens time in milliseconds")
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.Register(*registry_);
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append_tokens_time_ms = &append_tokens_time_ms_family.Add({}, common_buckets);
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// 注册 gpu_flush_back_time_ms Histogram
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auto& gpu_flush_back_time_ms_family = prometheus::BuildHistogram()
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.Name(std::string(METRIC_PREFIX) + "_gpu_flush_back_time_ms")
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.Help("gpu flush back time in milliseconds")
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.Register(*registry_);
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gpu_flush_back_time_ms = &gpu_flush_back_time_ms_family.Add({}, common_buckets);
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// 注册 cpu_flush_back_time_ms Histogram
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auto& cpu_flush_back_time_ms_family = prometheus::BuildHistogram()
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.Name(std::string(METRIC_PREFIX) + "_cpu_flush_back_time_ms")
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.Help("cpu flush back time in milliseconds")
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.Register(*registry_);
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cpu_flush_back_time_ms = &cpu_flush_back_time_ms_family.Add({}, common_buckets);
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exposer_.RegisterCollectable(registry_);
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}
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// 析构函数
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Metrics::~Metrics() {
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// 停止指标暴露
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// exposer_.Stop();
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}
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// 获取 memory_pool_size 指标
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prometheus::Gauge* Metrics::memory_pool_size(const std::string& type) {
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return &memory_pool_size_family_->Add({{"type", type}});
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}
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// 获取 memory_pool_node_count 指标
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prometheus::Gauge* Metrics::memory_pool_node_count(const std::string& type) {
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return &memory_pool_node_count_family_->Add({{"type", type}});
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}
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// 获取 lru_entry_count 指标
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prometheus::Gauge* Metrics::lru_entry_count(const std::string& type) {
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return &lru_entry_count_family_->Add({{"type", type}});
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}
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// 获取 gpu_page_count 指标
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prometheus::Gauge* Metrics::gpu_page_count(std::string type) {
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return &gpu_page_count_family_->Add({{"type", type}});
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}
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TimeObserver::TimeObserver(prometheus::Histogram* h) {
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histogram_ = h;
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timer_.start();
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}
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TimeObserver::~TimeObserver() {
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timer_.stop();
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histogram_->Observe(timer_.elapsedNs() / 1e6); // ns -> ms
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}
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} // namespace kvc2
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