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Refactor: restructure repository to focus on kt-kernel and KT-SFT modulesq recon (#1581)

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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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<div align="center">
<!-- <h1>KTransformers</h1> -->
<p align="center">
<picture>
<img alt="KTransformers" src="https://github.com/user-attachments/assets/d5a2492f-a415-4456-af99-4ab102f13f8b" width=50%>
</picture>
</p>
<h3>A Flexible Framework for Experiencing Cutting-edge LLM Inference Optimizations</h3>
<strong><a href="#show-cases">🌟 Show Cases</a> | <a href="#quick-start">🚀 Quick Start</a> | <a href="#tutorial">📃 Tutorial</a> | <a href="#Citation">🔥 Citation </a> | <a href="https://github.com/kvcache-ai/ktransformers/discussions">💬 Discussion </a>|<a href="#FAQ"> 🙋 FAQ</a> </strong>
<picture>
<img alt="KTransformers" src="https://github.com/user-attachments/assets/d5a2492f-a415-4456-af99-4ab102f13f8b" width=50%>
</picture>
</p>
<h3>High-Performance CPU-GPU Hybrid Inference for Large Language Models</h3>
</div>
<h2 id="intro">🎉 Introduction</h2>
KTransformers, pronounced as Quick Transformers, is designed to enhance your 🤗 <a href="https://github.com/huggingface/transformers">Transformers</a> experience with advanced kernel optimizations and placement/parallelism strategies.
<br/><br/>
KTransformers is a flexible, Python-centric framework designed with extensibility at its core.
By implementing and injecting an optimized module with a single line of code, users gain access to a Transformers-compatible
interface, RESTful APIs compliant with OpenAI and Ollama, and even a simplified ChatGPT-like web UI.
<br/><br/>
Our vision for KTransformers is to serve as a flexible platform for experimenting with innovative LLM inference optimizations. Please let us know if you need any other features.
## 🎯 Overview
<h2 id="Updates">🔥 Updates</h2>
KTransformers is a research project focused on efficient inference and fine-tuning of large language models through CPU-GPU heterogeneous computing. The project has evolved into **two core modules**: [kt-kernel](./kt-kernel/) and [KT-SFT](./KT-SFT/).
* **Nov 6, 2025**: Support Kimi-K2-Thinking inference ([Tutorial](./doc/en/Kimi-K2-Thinking.md)) and fine-tune ([Tutorial](./doc/en/SFT_Installation_Guide_KimiK2.md))
* **Nov 4, 2025**: KTransformers Fine-Tuning × LLaMA-Factory Integration. ([Tutorial](./doc/en/KTransformers-Fine-Tuning_User-Guide.md))
* **Oct 27, 2025**: Support Ascend NPU. ([Tutorial](./doc/zh/DeepseekR1_V3_tutorial_zh_for_Ascend_NPU.md))
* **Oct 10, 2025**: Integrating into SGLang. ([Roadmap](https://github.com/sgl-project/sglang/issues/11425))
* **Sept 11, 2025**: Support Qwen3-Next. ([Tutorial](./doc/en/Qwen3-Next.md))
* **Sept 05, 2025**: Support Kimi-K2-0905. ([Tutorial](./doc/en/Kimi-K2.md))
* **July 26, 2025**: Support SmallThinker and GLM4-MoE. ([Tutorial](./doc/en/SmallThinker_and_Glm4moe.md))
* **July 11, 2025**: Support Kimi-K2. ([Tutorial](./doc/en/Kimi-K2.md))
* **June 30, 2025**: Support 3-layer (GPU-CPU-Disk) [prefix cache](./doc/en/prefix_cache.md) reuse.
* **May 14, 2025**: Support Intel Arc GPU ([Tutorial](./doc/en/xpu.md)).
* **Apr 29, 2025**: Support AMX-Int8、 AMX-BF16 and Qwen3MoE ([Tutorial](./doc/en/AMX.md))
## 🔥 Updates
https://github.com/user-attachments/assets/fafe8aec-4e22-49a8-8553-59fb5c6b00a2
* **Nov 6, 2025**: Support Kimi-K2-Thinking inference and fine-tune
* **Nov 4, 2025**: KTransformers Fine-Tuning × LLaMA-Factory Integration
* **Oct 27, 2025**: Support Ascend NPU
* **Oct 10, 2025**: Integrating into SGLang ([Roadmap](https://github.com/sgl-project/sglang/issues/11425), [Blog](https://lmsys.org/blog/2025-10-22-KTransformers/))
* **Sept 11, 2025**: Support Qwen3-Next
* **Sept 05, 2025**: Support Kimi-K2-0905
* **July 26, 2025**: Support SmallThinker and GLM4-MoE
* **June 30, 2025**: Support 3-layer (GPU-CPU-Disk) prefix cache reuse
* **May 14, 2025**: Support Intel Arc GPU
* **Apr 29, 2025**: Support AMX-Int8、AMX-BF16 and Qwen3MoE
* **Apr 9, 2025**: Experimental support for LLaMA 4 models
* **Apr 2, 2025**: Support Multi-concurrency
* **Mar 15, 2025**: Support ROCm on AMD GPU
* **Mar 5, 2025**: Support unsloth 1.58/2.51 bits weights and IQ1_S/FP8 hybrid weights; 139K longer context for DeepSeek-V3/R1
* **Feb 25, 2025**: Support FP8 GPU kernel for DeepSeek-V3 and R1
* **Feb 10, 2025**: Support Deepseek-R1 and V3, up to 3~28x speedup
* **Apr 9, 2025**: Experimental support for LLaMA 4 models ([Tutorial](./doc/en/llama4.md)).
* **Apr 2, 2025**: Support Multi-concurrency. ([Tutorial](./doc/en/balance-serve.md)).
---
https://github.com/user-attachments/assets/faa3bda2-928b-45a7-b44f-21e12ec84b8a
## 📦 Core Modules
* **Mar 15, 2025**: Support ROCm on AMD GPU ([Tutorial](./doc/en/ROCm.md)).
* **Mar 5, 2025**: Support unsloth 1.58/2.51 bits weights and [IQ1_S/FP8 hybrid](./doc/en/fp8_kernel.md) weights. Support 139K [Longer Context](./doc/en/DeepseekR1_V3_tutorial.md#v022--v023-longer-context--fp8-kernel) for DeepSeek-V3 and R1 in 24GB VRAM.
* **Feb 25, 2025**: Support [FP8 GPU kernel](./doc/en/fp8_kernel.md) for DeepSeek-V3 and R1; [Longer Context](./doc/en/DeepseekR1_V3_tutorial.md#v022-longer-context).
* **Feb 15, 2025**: Longer Context (from 4K to 8K for 24GB VRAM) & Slightly Faster Speed （+15%, up to 16 Tokens/s), update [docs](./doc/en/DeepseekR1_V3_tutorial.md) and [online books](https://kvcache-ai.github.io/ktransformers/).
* **Feb 10, 2025**: Support Deepseek-R1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~28x speedup. For detailed show case and reproduction tutorial, see [here](./doc/en/DeepseekR1_V3_tutorial.md).
* **Aug 28, 2024**: Decrease DeepseekV2's required VRAM from 21G to 11G.
* **Aug 15, 2024**: Update detailed [tutorial](doc/en/injection_tutorial.md) for injection and multi-GPU.
* **Aug 14, 2024**: Support llamfile as linear backend.
* **Aug 12, 2024**: Support multiple GPU; Support new model: mixtral 8\*7B and 8\*22B; Support q2k, q3k, q5k dequant on gpu.
* **Aug 9, 2024**: Support windows native.
### 🚀 [kt-kernel](./kt-kernel/) - High-Performance Inference Kernels
<!-- * **Aug 28, 2024**: Support 1M context under the InternLM2.5-7B-Chat-1M model, utilizing 24GB of VRAM and 150GB of DRAM. The detailed tutorial is [here](./doc/en/long_context_tutorial.md). -->
CPU-optimized kernel operations for heterogeneous LLM inference.
<h2 id="show-cases">🌟 Show Cases</h2>
![image-20251011010558909](./doc/assets/heterogeneous_computing.png)
<div>
<h3>GPT-4/o1-level Local VSCode Copilot on a Desktop with only 24GB VRAM</h3>
</div>
**Key Features:**
- **AMX/AVX Acceleration**: Intel AMX and AVX512/AVX2 optimized kernels for INT4/INT8 quantized inference
- **MoE Optimization**: Efficient Mixture-of-Experts inference with NUMA-aware memory management
- **Quantization Support**: CPU-side INT4/INT8 quantized weights, GPU-side GPTQ support
- **Easy Integration**: Clean Python API for SGLang and other frameworks
https://github.com/user-attachments/assets/ebd70bfa-b2c1-4abb-ae3b-296ed38aa285
</p>
- **[NEW!!!] Local 671B DeepSeek-Coder-V3/R1:** Running its Q4_K_M version using only 14GB VRAM and 382GB DRAM([Tutorial](./doc/en/DeepseekR1_V3_tutorial.md)).
- Prefill Speed (tokens/s):
- KTransformers: 54.21 (32 cores) → 74.362 (dual-socket, 2×32 cores) → 255.26 (optimized AMX-based MoE kernel, V0.3 only) → 286.55 (selectively using 6 experts, V0.3 only)
- Compared to 10.31 tokens/s in llama.cpp with 2×32 cores, achieving up to **27.79× speedup**.
- Decode Speed (tokens/s):
- KTransformers: 8.73 (32 cores) → 11.26 (dual-socket, 2×32 cores) → 13.69 (selectively using 6 experts, V0.3 only)
- Compared to 4.51 tokens/s in llama.cpp with 2×32 cores, achieving up to **3.03× speedup**.
- Upcoming Open Source Release:
- AMX optimizations and selective expert activation will be open-sourced in V0.3.
- Currently available only in preview binary distribution, which can be downloaded [here](./doc/en/DeepseekR1_V3_tutorial.md).
- **Local 236B DeepSeek-Coder-V2:** Running its Q4_K_M version using only 21GB VRAM and 136GB DRAM, attainable on a local desktop machine, which scores even better than GPT4-0613 in [BigCodeBench](https://huggingface.co/blog/leaderboard-bigcodebench).
<p align="center">
<picture>
<img alt="DeepSeek-Coder-V2 Score" src="https://github.com/user-attachments/assets/d052924e-8631-44de-aad2-97c54b965693" width=100%>
</picture>
</p>
- **Faster Speed:** Achieving 126 tokens/s for 2K prompt prefill and 13.6 tokens/s for generation through MoE offloading and injecting advanced kernels from [Llamafile](https://github.com/Mozilla-Ocho/llamafile/tree/main) and [Marlin](https://github.com/IST-DASLab/marlin).
- **VSCode Integration:** Wrapped into an OpenAI and Ollama compatible API for seamless integration as a backend for [Tabby](https://github.com/TabbyML/tabby) and various other frontends.
<p align="center">
https://github.com/user-attachments/assets/4c6a8a38-05aa-497d-8eb1-3a5b3918429c
</p>
<!-- <h3>1M Context Local Inference on a Desktop with Only 24GB VRAM</h3>
<p align="center">
https://github.com/user-attachments/assets/a865e5e4-bca3-401e-94b8-af3c080e6c12
* **1M Context InternLM 2.5 7B**: Operates at full bf16 precision, utilizing 24GB VRAM and 150GB DRAM, which is feasible on a local desktop setup. It achieves a 92.88% success rate on the 1M "Needle In a Haystack" test and 100% on the 128K NIAH test.
<p align="center">
<picture>
<img alt="Single Needle Retrieval 128K" src="./doc/assets/needle_128K.png" width=100%>
</picture>
</p>
<p align="center">
<picture>
<img alt="Single Needle Retrieval 1000K" src="./doc/assets/needle_1M.png" width=100%>
</picture>
</p>
* **Enhanced Speed**: Reaches 16.91 tokens/s for generation with a 1M context using sparse attention, powered by llamafile kernels. This method is over 10 times faster than full attention approach of llama.cpp.
* **Flexible Sparse Attention Framework**: Offers a flexible block sparse attention framework for CPU offloaded decoding. Compatible with SnapKV, Quest, and InfLLm. Further information is available [here](./doc/en/long_context_introduction.md).
-->
<strong>More advanced features will coming soon, so stay tuned!</strong>
<h2 id="quick-start">🚀 Quick Start</h2>
Getting started with KTransformers is simple! Follow the steps below to set up and start using it.
we have already supported vendors:
- Metax
- Sanechips (ZhuFeng V1.0)
- Intel
- Ascend
- Kunpeng
- AMD
### 📥 Installation
To install KTransformers, follow the official [Installation Guide](https://kvcache-ai.github.io/ktransformers/en/install.html).
<h2 id="tutorial">📃 Brief Injection Tutorial</h2>
At the heart of KTransformers is a user-friendly, template-based injection framework.
This allows researchers to easily replace original torch modules with optimized variants. It also simplifies the process of combining multiple optimizations, allowing the exploration of their synergistic effects.
</br>
<p align="center">
<picture>
<img alt="Inject-Struction" src="https://github.com/user-attachments/assets/6b4c1e54-9f6d-45c5-a3fc-8fa45e7d257e" width=65%>
</picture>
</p>
Given that vLLM already serves as a great framework for large-scale deployment optimizations, KTransformers is particularly focused on local deployments that are constrained by limited resources. We pay special attention to heterogeneous computing opportunities, such as GPU/CPU offloading of quantized models. For example, we support the efficient <a herf="https://github.com/Mozilla-Ocho/llamafile/tree/main">Llamafile</a> and <a herf="https://github.com/IST-DASLab/marlin">Marlin</a> kernels for CPU and GPU, respectively. More details can be found <a herf="doc/en/operators/llamafile.md">here</a>.
<h3>Example Usage</h3>
To utilize the provided kernels, users only need to create a YAML-based injection template and add the call to `optimize_and_load_gguf` before using the Transformers model.
```python
with torch.device("meta"):
model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
optimize_and_load_gguf(model, optimize_config_path, gguf_path, config)
...
generated = prefill_and_generate(model, tokenizer, input_tensor.cuda(), max_new_tokens=1000)
**Quick Start:**
```bash
cd kt-kernel
pip install .
```
In this example, the AutoModel is first initialized on the meta device to avoid occupying any memory resources. Then, `optimize_and_load_gguf` iterates through all sub-modules of the model, matches rules specified in your YAML rule file, and replaces them with advanced modules as specified.
**Use Cases:**
After injection, the original `generate` interface is available, but we also provide a compatible `prefill_and_generate` method, which enables further optimizations like CUDAGraph to improve generation speed.
- CPU-GPU hybrid inference for large MoE models
- Integration with SGLang for production serving
- Heterogeneous expert placement (hot experts on GPU, cold experts on CPU)
<h3>How to custom your model</h3>
**Performance Examples:**
| Model | Hardware Configuration | Total Throughput | Output Throughput |
|-------|------------------------|------------------|-------------------|
| DeepSeek-R1-0528 (FP8) | 8×L20 GPU + Xeon Gold 6454S | 227.85 tokens/s | 87.58 tokens/s (8-way concurrency) |
A detailed tutorial of the injection and multi-GPU using DeepSeek-V2 as an example is given [here](doc/en/injection_tutorial.md).
👉 **[Full Documentation →](./kt-kernel/README.md)**
Below is an example of a YAML template for replacing all original Linear modules with Marlin, an advanced 4-bit quantization kernel.
---
```yaml
- match:
name: "^model\\.layers\\..*$" # regular expression
class: torch.nn.Linear # only match modules matching name and class simultaneously
replace:
class: ktransformers.operators.linear.KTransformerLinear # optimized Kernel on quantized data types
device: "cpu" # which devices to load this module when initializing
kwargs:
generate_device: "cuda"
generate_linear_type: "QuantizedLinearMarlin"
### 🎓 [KT-SFT](./KT-SFT/) - Fine-Tuning Framework
KTransformers × LLaMA-Factory integration for ultra-large MoE model fine-tuning.
![image-20251011010558909](./doc/assets/image-20251011010558909.png)
**Key Features:**
- **Resource Efficient**: Fine-tune 671B DeepSeek-V3 with just **70GB GPU memory** + 1.3TB RAM
- **LoRA Support**: Full LoRA fine-tuning with heterogeneous acceleration
- **LLaMA-Factory Integration**: Seamless integration with popular fine-tuning framework
- **Production Ready**: Chat, batch inference, and metrics evaluation
**Performance Examples:**
| Model | Configuration | Throughput | GPU Memory |
|-------|--------------|------------|------------|
| DeepSeek-V3 (671B) | LoRA + AMX | ~40 tokens/s | 70GB (multi-GPU) |
| DeepSeek-V2-Lite (14B) | LoRA + AMX | ~530 tokens/s | 6GB |
**Quick Start:**
```bash
cd KT-SFT
# Install environment following KT-SFT/README.md
USE_KT=1 llamafactory-cli train examples/train_lora/deepseek3_lora_sft_kt.yaml
```
Each rule in the YAML file has two parts: `match` and `replace`. The `match` part specifies which module should be replaced, and the `replace` part specifies the module to be injected into the model along with the initialization keywords.
👉 **[Full Documentation →](./KT-SFT/README.md)**
You can find example rule templates for optimizing DeepSeek-V2 and Qwen2-57B-A14, two SOTA MoE models, in the [ktransformers/optimize/optimize_rules](ktransformers/optimize/optimize_rules) directory. These templates are used to power the `local_chat.py` demo.
---
If you are interested in our design principles and the implementation of the injection framework, please refer to the [design document](doc/en/deepseek-v2-injection.md).
## 🔥 Citation
<h2 id="Citation">🔥 Citation</h2>
If you use KTransformers in your research, please cite our paper:
If you use KTransformers for your research, please cite our [paper](https://madsys.cs.tsinghua.edu.cn/publication/ktransformers-unleashing-the-full-potential-of-cpu/gpu-hybrid-inference-for-moe-models/):
```
```bibtex
@inproceedings{10.1145/3731569.3764843,
title = {KTransformers: Unleashing the Full Potential of CPU/GPU Hybrid Inference for MoE Models},
author = {Chen, Hongtao and Xie, Weiyu and Zhang, Boxin and Tang, Jingqi and Wang, Jiahao and Dong, Jianwei and Chen, Shaoyuan and Yuan, Ziwei and Lin, Chen and Qiu, Chengyu and Zhu, Yuening and Ou, Qingliang and Liao, Jiaqi and Chen, Xianglin and Ai, Zhiyuan and Wu, Yongwei and Zhang, Mingxing},
booktitle = {Proceedings of the ACM SIGOPS 31st Symposium on Operating Systems Principles},
year = {2025}
title = {KTransformers: Unleashing the Full Potential of CPU/GPU Hybrid Inference for MoE Models},
author = {Chen, Hongtao and Xie, Weiyu and Zhang, Boxin and Tang, Jingqi and Wang, Jiahao and Dong, Jianwei and Chen, Shaoyuan and Yuan, Ziwei and Lin, Chen and Qiu, Chengyu and Zhu, Yuening and Ou, Qingliang and Liao, Jiaqi and Chen, Xianglin and Ai, Zhiyuan and Wu, Yongwei and Zhang, Mingxing},
booktitle = {Proceedings of the ACM SIGOPS 31st Symposium on Operating Systems Principles},
year = {2025}
}
```
<h2 id="ack">Acknowledgment and Contributors</h2>
## 👥 Contributors & Team
The development of KTransformers is based on the flexible and versatile framework provided by Transformers. We also benefit from advanced kernels such as GGUF/GGML, Llamafile, Marlin, sglang and flashinfer. We are planning to contribute back to the community by upstreaming our modifications.
Developed and maintained by:
- [MADSys Lab](https://madsys.cs.tsinghua.edu.cn/) @ Tsinghua University
- [Approaching.AI](http://approaching.ai/)
- Community contributors
KTransformers is actively maintained and developed by contributors from the <a href="https://madsys.cs.tsinghua.edu.cn/">MADSys group</a> at Tsinghua University and members from <a href="http://approaching.ai/">Approaching.AI</a>. We welcome new contributors to join us in making KTransformers faster and easier to use.
We welcome contributions! Please feel free to submit issues and pull requests.
<h2 id="ack">Discussion</h2>
## 💬 Community & Support
If you have any questions, feel free to open an issue. Alternatively, you can join our WeChat group for further discussion. QR Code: [WeChat Group](WeChatGroup.png)
- **GitHub Issues**: [Report bugs or request features](https://github.com/kvcache-ai/ktransformers/issues)
- **GitHub Discussions**: [Ask questions and share ideas](https://github.com/kvcache-ai/ktransformers/discussions)
- **WeChat Group**: See [archive/WeChatGroup.png](./archive/WeChatGroup.png)
<h2 id="FAQ">🙋 FAQ</h2>
## 📦 Legacy Code
Some common questions are answered in the [FAQ](doc/en/FAQ.md).
The original integrated KTransformers framework has been archived to the [`archive/`](./archive/) directory for reference. The project now focuses on the two core modules above for better modularity and maintainability.
For the original documentation with full quick-start guides and examples, see:
- [archive/README_LEGACY.md](./archive/README_LEGACY.md) (English)
- [archive/README_ZH_LEGACY.md](./archive/README_ZH_LEGACY.md) (中文)