mirror of
https://github.com/kvcache-ai/ktransformers.git
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* 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
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1.4 KiB
JavaScript
40 lines
No EOL
1.4 KiB
JavaScript
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module.exports = {
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// 配置 webpack-dev-server 行为。
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devServer: {
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open: false, // 编译后默认打开浏览器
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host: '0.0.0.0', // 域名
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port: 8082, // 端口
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https: false, // 是否https
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proxy: {
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'/api': {
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target: 'http://localhost:9016/v1', // 你的后端服务器地址
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changeOrigin: true, // 是否允许跨域
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pathRewrite: {
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'/api': '' // 将 '/api' 前缀替换为空,如果你的后端不需要这个前缀
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}
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}
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}
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},
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publicPath: '/web/', // 基本路径
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outputDir: 'dist', // 构建时的输出目录
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assetsDir: 'static', // 放置静态资源的目录
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indexPath: 'index.html', // html 的输出路径
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filenameHashing: true, // 文件名哈希值
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lintOnSave: false, // 是否在保存的时候使用 `eslint-loader` 进行检查。
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// 组件是如何被渲染到页面中的? (ast:抽象语法树;vDom:虚拟DOM)
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// template ---> ast ---> render ---> vDom ---> 真实的Dom ---> 页面
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// runtime-only:将template在打包的时候,就已经编译为render函数
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// runtime-compiler:在运行的时候才去编译template
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runtimeCompiler: false,
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transpileDependencies: [], // babel-loader 默认会跳过 node_modules 依赖。
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productionSourceMap: false, // 是否为生产环境构建生成 source map
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//调整内部的 webpack 配置
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configureWebpack: () => {},
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chainWebpack: () => {},
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} |