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README.md
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...@@ -23,14 +23,13 @@ Our vision for KTransformers is to serve as a flexible platform for experimentin ...@@ -23,14 +23,13 @@ Our vision for KTransformers is to serve as a flexible platform for experimentin
<h2 id="Updates">🔥 Updates</h2> <h2 id="Updates">🔥 Updates</h2>
* **Feb 10, 2025**: Support Deepseek-R1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~28x speedup. The detailed tutorial is [here](./doc/en/DeepseekR1_V3_tutorial.md). * **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**: 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).
* **Aug 28, 2024**: Decrease DeepseekV2's required VRAM from 21G to 11G. * **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 15, 2024**: Update detailed [tutorial](doc/en/injection_tutorial.md) for injection and multi-GPU.
* **Aug 14, 2024**: Support llamfile as linear backend. * **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 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. * **Aug 9, 2024**: Support windows native.
<!-- * **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). -->
<h2 id="show-cases">🌟 Show Cases</h2> <h2 id="show-cases">🌟 Show Cases</h2>
<div> <div>
...@@ -69,7 +68,7 @@ https://github.com/user-attachments/assets/4c6a8a38-05aa-497d-8eb1-3a5b3918429c ...@@ -69,7 +68,7 @@ https://github.com/user-attachments/assets/4c6a8a38-05aa-497d-8eb1-3a5b3918429c
</p> </p>
<h3>1M Context Local Inference on a Desktop with Only 24GB VRAM</h3> <!-- <h3>1M Context Local Inference on a Desktop with Only 24GB VRAM</h3>
<p align="center"> <p align="center">
https://github.com/user-attachments/assets/a865e5e4-bca3-401e-94b8-af3c080e6c12 https://github.com/user-attachments/assets/a865e5e4-bca3-401e-94b8-af3c080e6c12
...@@ -91,228 +90,20 @@ https://github.com/user-attachments/assets/a865e5e4-bca3-401e-94b8-af3c080e6c12 ...@@ -91,228 +90,20 @@ https://github.com/user-attachments/assets/a865e5e4-bca3-401e-94b8-af3c080e6c12
* **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. * **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). * **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> <strong>More advanced features will coming soon, so stay tuned!</strong>
<h2 id="quick-start">🚀 Quick Start</h2> <h2 id="quick-start">🚀 Quick Start</h2>
<h3>Preparation</h3>
Some preparation:
- CUDA 12.1 and above, if you didn't have it yet, you may install from [here](https://developer.nvidia.com/cuda-downloads).
```sh
# Adding CUDA to PATH
export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export CUDA_PATH=/usr/local/cuda
```
- Linux-x86_64 with gcc, g++ and cmake
```sh
sudo apt-get update
sudo apt-get install gcc g++ cmake ninja-build
```
- We recommend using [Conda](https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh) to create a virtual environment with Python=3.11 to run our program.
```sh
conda create --name ktransformers python=3.11
conda activate ktransformers # you may need to run ‘conda init’ and reopen shell first
```
- Make sure that PyTorch, packaging, ninja is installed
```
pip install torch packaging ninja cpufeature numpy
```
<h3>Installation</h3>
1. Use a Docker image, see [documentation for Docker](./doc/en/Docker.md)
2. You can install using Pypi (for linux):
```
pip install ktransformers --no-build-isolation
```
for windows we prepare a pre compiled whl package on [ktransformers-0.2.0+cu125torch24avx2-cp312-cp312-win_amd64.whl](https://github.com/kvcache-ai/ktransformers/releases/download/v0.2.0/ktransformers-0.2.0+cu125torch24avx2-cp312-cp312-win_amd64.whl), which require cuda-12.5, torch-2.4, python-3.11, more pre compiled package are being produced.
3. Or you can download source code and compile:
- init source code
```sh
git clone https://github.com/kvcache-ai/ktransformers.git
cd ktransformers
git submodule init
git submodule update
```
- [Optional] If you want to run with website, please [compile the website](./doc/en/api/server/website.md) before execute ```bash install.sh```
- Compile and install (for Linux)
```
bash install.sh
```
- Compile and install(for Windows)
```
install.bat
```
4. If you are developer, you can make use of the makefile to compile and format the code. <br> the detailed usage of makefile is [here](./doc/en/makefile_usage.md)
<h3>Local Chat</h3>
We provide a simple command-line local chat Python script that you can run for testing.
> Note that this is a very simple test tool only support one round chat without any memory about last input, if you want to try full ability of the model, you may go to [RESTful API and Web UI](#id_666). We use the DeepSeek-V2-Lite-Chat-GGUF model as an example here. But we also support other models, you can replace it with any other model that you want to test.
<h4>Run Example</h4>
```shell
# Begin from root of your cloned repo!
# Begin from root of your cloned repo!!
# Begin from root of your cloned repo!!!
# Download mzwing/DeepSeek-V2-Lite-Chat-GGUF from huggingface
mkdir DeepSeek-V2-Lite-Chat-GGUF
cd DeepSeek-V2-Lite-Chat-GGUF
wget https://huggingface.co/mzwing/DeepSeek-V2-Lite-Chat-GGUF/resolve/main/DeepSeek-V2-Lite-Chat.Q4_K_M.gguf -O DeepSeek-V2-Lite-Chat.Q4_K_M.gguf
cd .. # Move to repo's root dir
# Start local chat
python -m ktransformers.local_chat --model_path deepseek-ai/DeepSeek-V2-Lite-Chat --gguf_path ./DeepSeek-V2-Lite-Chat-GGUF
# If you see “OSError: We couldn't connect to 'https://huggingface.co' to load this file”, try：
# GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite
# python ktransformers.local_chat --model_path ./DeepSeek-V2-Lite --gguf_path ./DeepSeek-V2-Lite-Chat-GGUF
```
It features the following arguments:
- `--model_path` (required): Name of the model (such as "deepseek-ai/DeepSeek-V2-Lite-Chat" which will automatically download configs from [Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite)). Or if you already got local files you may directly use that path to initialize the model.
> Note: <strong>.safetensors</strong> files are not required in the directory. We only need config files to build model and tokenizer.
- `--gguf_path` (required): Path of a directory containing GGUF files which could that can be downloaded from [Hugging Face](https://huggingface.co/mzwing/DeepSeek-V2-Lite-Chat-GGUF/tree/main). Note that the directory should only contains GGUF of current model, which means you need one separate directory for each model.
- `--optimize_rule_path` (required except for Qwen2Moe and DeepSeek-V2): Path of YAML file containing optimize rules. There are two rule files pre-written in the [ktransformers/optimize/optimize_rules](ktransformers/optimize/optimize_rules) directory for optimizing DeepSeek-V2 and Qwen2-57B-A14, two SOTA MoE models.
- `--max_new_tokens`: Int (default=1000). Maximum number of new tokens to generate.
- `--cpu_infer`: Int (default=10). The number of CPUs used for inference. Should ideally be set to the (total number of cores - 2).
<h3 id="suggested-model"> Suggested Model</h3>
| Model Name | Model Size | VRAM | Minimum DRAM | Recommended DRAM |
| ------------------------------ | ---------- | ----- | --------------- | ----------------- |
| DeepSeek-R1-q4_k_m | 377G | 14G | 382G | 512G |
| DeepSeek-V3-q4_k_m | 377G | 14G | 382G | 512G |
| DeepSeek-V2-q4_k_m | 133G | 11G | 136G | 192G |
| DeepSeek-V2.5-q4_k_m | 133G | 11G | 136G | 192G |
| DeepSeek-V2.5-IQ4_XS | 117G | 10G | 107G | 128G |
| Qwen2-57B-A14B-Instruct-q4_k_m | 33G | 8G | 34G | 64G |
| DeepSeek-V2-Lite-q4_k_m | 9.7G | 3G | 13G | 16G |
| Mixtral-8x7B-q4_k_m | 25G | 1.6G | 51G | 64G |
| Mixtral-8x22B-q4_k_m | 80G | 4G | 86.1G | 96G |
| InternLM2.5-7B-Chat-1M | 15.5G | 15.5G | 8G(32K context) | 150G (1M context) |
More will come soon. Please let us know which models you are most interested in.
Be aware that you need to be subject to their corresponding model licenses when using [DeepSeek](https://huggingface.co/deepseek-ai/DeepSeek-V2/blob/main/LICENSE) and [QWen](https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE).
<details>
<summary>Click To Show how to run other examples</summary>
* Qwen2-57B
```sh
pip install flash_attn # For Qwen2
mkdir Qwen2-57B-GGUF && cd Qwen2-57B-GGUF
wget https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct-GGUF/resolve/main/qwen2-57b-a14b-instruct-q4_k_m.gguf?download=true -O qwen2-57b-a14b-instruct-q4_k_m.gguf
cd ..
python -m ktransformers.local_chat --model_name Qwen/Qwen2-57B-A14B-Instruct --gguf_path ./Qwen2-57B-GGUF
# If you see “OSError: We couldn't connect to 'https://huggingface.co' to load this file”, try：
# GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct
# python ktransformers/local_chat.py --model_path ./Qwen2-57B-A14B-Instruct --gguf_path ./DeepSeek-V2-Lite-Chat-GGUF
```
* DeepseekV2
```sh
mkdir DeepSeek-V2-Chat-0628-GGUF && cd DeepSeek-V2-Chat-0628-GGUF
# Download weights
wget https://huggingface.co/bartowski/DeepSeek-V2-Chat-0628-GGUF/resolve/main/DeepSeek-V2-Chat-0628-Q4_K_M/DeepSeek-V2-Chat-0628-Q4_K_M-00001-of-00004.gguf -o DeepSeek-V2-Chat-0628-Q4_K_M-00001-of-00004.gguf
wget https://huggingface.co/bartowski/DeepSeek-V2-Chat-0628-GGUF/resolve/main/DeepSeek-V2-Chat-0628-Q4_K_M/DeepSeek-V2-Chat-0628-Q4_K_M-00002-of-00004.gguf -o DeepSeek-V2-Chat-0628-Q4_K_M-00002-of-00004.gguf
wget https://huggingface.co/bartowski/DeepSeek-V2-Chat-0628-GGUF/resolve/main/DeepSeek-V2-Chat-0628-Q4_K_M/DeepSeek-V2-Chat-0628-Q4_K_M-00003-of-00004.gguf -o DeepSeek-V2-Chat-0628-Q4_K_M-00003-of-00004.gguf
wget https://huggingface.co/bartowski/DeepSeek-V2-Chat-0628-GGUF/resolve/main/DeepSeek-V2-Chat-0628-Q4_K_M/DeepSeek-V2-Chat-0628-Q4_K_M-00004-of-00004.gguf -o DeepSeek-V2-Chat-0628-Q4_K_M-00004-of-00004.gguf
cd .. Getting started with KTransformers is simple! Follow the steps below to set up and start using it.
python -m ktransformers.local_chat --model_name deepseek-ai/DeepSeek-V2-Chat-0628 --gguf_path ./DeepSeek-V2-Chat-0628-GGUF ### 📥 Installation
# If you see “OSError: We couldn't connect to 'https://huggingface.co' to load this file”, try： To install KTransformers, follow the official [Installation Guide](https://kvcache-ai.github.io/ktransformers/).
# GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat-0628
# python -m ktransformers.local_chat --model_path ./DeepSeek-V2-Chat-0628 --gguf_path ./DeepSeek-V2-Chat-0628-GGUF
```
| model name | weights download link |
|----------|----------|
| Qwen2-57B | [Qwen2-57B-A14B-gguf-Q4K-M](https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct-GGUF/tree/main) |
| DeepseekV2-coder |[DeepSeek-Coder-V2-Instruct-gguf-Q4K-M](https://huggingface.co/LoneStriker/DeepSeek-Coder-V2-Instruct-GGUF/tree/main) |
| DeepseekV2-chat |[DeepSeek-V2-Chat-gguf-Q4K-M](https://huggingface.co/bullerwins/DeepSeek-V2-Chat-0628-GGUF/tree/main) |
| DeepseekV2-lite | [DeepSeek-V2-Lite-Chat-GGUF-Q4K-M](https://huggingface.co/mzwing/DeepSeek-V2-Lite-Chat-GGUF/tree/main) |
</details>
<!-- pin block for jump -->
<span id='id_666'>
<h3>RESTful API and Web UI</h3>
Start without website:
```sh
ktransformers --model_path deepseek-ai/DeepSeek-V2-Lite-Chat --gguf_path /path/to/DeepSeek-V2-Lite-Chat-GGUF --port 10002
```
Start with website:
```sh
ktransformers --model_path deepseek-ai/DeepSeek-V2-Lite-Chat --gguf_path /path/to/DeepSeek-V2-Lite-Chat-GGUF --port 10002 --web True
```
Or you want to start server with transformers, the model_path should include safetensors
```bash
ktransformers --type transformers --model_path /mnt/data/model/Qwen2-0.5B-Instruct --port 10002 --web True
```
Access website with url [http://localhost:10002/web/index.html#/chat](http://localhost:10002/web/index.html#/chat) :
<p align="center">
<picture>
<img alt="Web UI" src="https://github.com/user-attachments/assets/615dca9b-a08c-4183-bbd3-ad1362680faf" width=90%>
</picture>
</p>
More information about the RESTful API server can be found [here](doc/en/api/server/server.md). You can also find an example of integrating with Tabby [here](doc/en/api/server/tabby.md).
<h2 id="tutorial">📃 Brief Injection Tutorial</h2> <h2 id="tutorial">📃 Brief Injection Tutorial</h2>
At the heart of KTransformers is a user-friendly, template-based injection framework. At the heart of KTransformers is a user-friendly, template-based injection framework.
......
doc/SUMMARY.md
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# Ktransformer # Ktransformer
[Introduction](./README.md) [Introduction](./README.md)
# DeepSeek # Install
- [Deepseek-R1/V3 Tutorial](en/DeepseekR1_V3_tutorial.md) - [Installation Guide](en/install.md)
- [Deepseek-V2 Injection](en/deepseek-v2-injection.md)
- [Injection Tutorial](en/injection_tutorial.md)
# Server # Tutorial
- [Deepseek-R1/V3 Show Case](en/DeepseekR1_V3_tutorial.md)
- [Why KTransformers So Fast](en/deepseek-v2-injection.md)
- [Injection Tutorial](en/injection_tutorial.md)
- [Multi-GPU Tutorial](en/multi-gpu-tutorial.md)
# Server(Temperary Deprected)
- [Server](en/api/server/server.md) - [Server](en/api/server/server.md)
- [Website](en/api/server/website.md) - [Website](en/api/server/website.md)
- [Tabby](en/api/server/tabby.md) - [Tabby](en/api/server/tabby.md)
......
doc/en/DeepseekR1_V3_tutorial.md
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<!-- omit in toc --> <!-- omit in toc -->
# GPT-4/o1-level Local VSCode Copilot on a Desktop with only 24GB VRAM # GPT-4/o1-level Local VSCode Copilot on a Desktop with only 24GB VRAM
- [SUMMARY](#summary) - [SUMMARY](#summary)
- [Prerequisites](#prerequisites) - [Show Case Environment](#show-case-environment)
- [Bench Result](#bench-result) - [Bench Result](#bench-result)
- [V0.2](#v02) - [V0.2](#v02)
- [Settings](#settings) - [Settings](#settings)
...@@ -50,7 +50,7 @@ We also give our upcoming optimizations previews, including an Intel AMX-acceler ...@@ -50,7 +50,7 @@ We also give our upcoming optimizations previews, including an Intel AMX-acceler
The binary distribution is available now and the source code will come ASAP! Check out the wheel package [here](https://github.com/kvcache-ai/ktransformers/releases/download/v0.1.4/ktransformers-0.3.0rc0+cu126torch26fancy-cp311-cp311-linux_x86_64.whl) The binary distribution is available now and the source code will come ASAP! Check out the wheel package [here](https://github.com/kvcache-ai/ktransformers/releases/download/v0.1.4/ktransformers-0.3.0rc0+cu126torch26fancy-cp311-cp311-linux_x86_64.whl)
## Prerequisites ## Show Case Environment
We run our best performance tests (V0.2) on <br> We run our best performance tests (V0.2) on <br>
CPU: Intel (R) Xeon (R) Gold 6454S 1T DRAM (2 NUMA nodes) <br> CPU: Intel (R) Xeon (R) Gold 6454S 1T DRAM (2 NUMA nodes) <br>
GPU: 4090D 24G VRAM <br> GPU: 4090D 24G VRAM <br>
...@@ -110,10 +110,6 @@ is speed up which is inspiring. So our showcase makes use of this finding* ...@@ -110,10 +110,6 @@ is speed up which is inspiring. So our showcase makes use of this finding*
#### Single socket version (32 cores) #### Single socket version (32 cores)
Our local_chat test command is: Our local_chat test command is:
``` shell ``` shell
git clone https://github.com/kvcache-ai/ktransformers.git
cd ktransformers
git submodule init
git submodule update
numactl -N 1 -m 1 python ./ktransformers/local_chat.py --model_path <your model path> --gguf_path <your gguf path> --prompt_file <your prompt txt file> --cpu_infer 33 --max_new_tokens 1000 numactl -N 1 -m 1 python ./ktransformers/local_chat.py --model_path <your model path> --gguf_path <your gguf path> --prompt_file <your prompt txt file> --cpu_infer 33 --max_new_tokens 1000
<when you see chat, then press enter to load the text prompt_file> <when you see chat, then press enter to load the text prompt_file>
``` ```
...@@ -121,24 +117,28 @@ numactl -N 1 -m 1 python ./ktransformers/local_chat.py --model_path <your model ...@@ -121,24 +117,28 @@ numactl -N 1 -m 1 python ./ktransformers/local_chat.py --model_path <your model
`<your gguf path>` can also be online, but as its large we recommend you download it and quantize the model to what you want (notice it's the dir path) <br> `<your gguf path>` can also be online, but as its large we recommend you download it and quantize the model to what you want (notice it's the dir path) <br>
`--max_new_tokens 1000` is the max output token length. If you find the answer is truncated, you `--max_new_tokens 1000` is the max output token length. If you find the answer is truncated, you
can increase the number for longer answer (But be aware of OOM, and increase it will slow down the generation rate.). can increase the number for longer answer (But be aware of OOM, and increase it will slow down the generation rate.).
<br>
The command numactl -N 1 -m 1 aims to advoid data transfer between numa nodes<br> The command `numactl -N 1 -m 1` aims to advoid data transfer between numa nodes<br>
Attention! If you are testing R1 and it may skip thinking. So you can add arg: `--force_think true`. This is explained in [FAQ](#faq) part Attention! If you are testing R1 and it may skip thinking. So you can add arg: `--force_think true`. This is explained in [FAQ](#faq) part
#### Dual socket version (64 cores) #### Dual socket version (64 cores)
Make suer before you install (use install.sh or `make dev_install`), setting the env var `USE_NUMA=1` by `export USE_NUMA=1` (if already installed, reinstall it with this env var set) <br>
Our local_chat test command is: Make suer before you install (use install.sh or `make dev_install`), setting the env var `USE_NUMA=1` by `export USE_NUMA=1` (if already installed, reinstall it with this env var set). You may check the doc [here](./install.md) for install details. <br>
Test Command:
``` shell ``` shell
git clone https://github.com/kvcache-ai/ktransformers.git # ---For those who have not installed ktransformers---
cd ktransformers # git clone https://github.com/kvcache-ai/ktransformers.git
git submodule init # cd ktransformers
git submodule update # git submodule init
export USE_NUMA=1 # git submodule update
make dev_install # or sh ./install.sh # export USE_NUMA=1
# make dev_install # or sh ./install.sh
# ----------------------------------------------------
python ./ktransformers/local_chat.py --model_path <your model path> --gguf_path <your gguf path> --prompt_file <your prompt txt file> --cpu_infer 65 --max_new_tokens 1000 python ./ktransformers/local_chat.py --model_path <your model path> --gguf_path <your gguf path> --prompt_file <your prompt txt file> --cpu_infer 65 --max_new_tokens 1000
<when you see chat, then press enter to load the text prompt_file> <when you see chat, then press enter to load the text prompt_file>
``` ```
The parameters' meaning is the same. But As we use dual socket, we set cpu_infer to 65 The parameters' meaning is the same. But As we use dual socket, we set cpu_infer to 65
### V0.3 Showcase ### V0.3 Showcase
#### Dual socket version (64 cores) #### Dual socket version (64 cores)
......
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# Tutorial: Heterogeneous and Local DeepSeek-V2 Inference # Tutorial: Heterogeneous and Local MoE Inference
DeepSeek-(Code)-V2 is a series of strong mixture-of-experts (MoE) models, featuring a total of 236 billion parameters, with 21 billion parameters activated per token. This model has demonstrated remarkable reasoning capabilities across various benchmarks, positioning it as one of the SOTA open models and nearly comparable in performance to GPT-4. DeepSeek-(Code)-V2 is a series of strong mixture-of-experts (MoE) models, featuring a total of 236 billion parameters, with 21 billion parameters activated per token. This model has demonstrated remarkable reasoning capabilities across various benchmarks, positioning it as one of the SOTA open models and nearly comparable in performance to GPT-4. DeepSeek-R1 uses a similar architecture to DeepSeek-V2, but with a bigger number of parameters.
<p align="center"> <p align="center">
<picture> <picture>
......
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# How to Run DeepSeek-R1
In this document, we will show you how to install and run KTransformers on your local machine. There are two versions:
* V0.2 is the current main branch.
* V0.3 is a preview version only provides binary distribution for now.
* To reproduce our DeepSeek-R1/V3 results, please refer to [Deepseek-R1/V3 Tutorial](./DeepseekR1_V3_tutorial.md) for more detail settings after installation.
## Preparation
Some preparation:
- CUDA 12.1 and above, if you didn't have it yet, you may install from [here](https://developer.nvidia.com/cuda-downloads).
```sh
# Adding CUDA to PATH
export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export CUDA_PATH=/usr/local/cuda
```
- Linux-x86_64 with gcc, g++ and cmake
```sh
sudo apt-get update
sudo apt-get install gcc g++ cmake ninja-build
```
- We recommend using [Conda](https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh) to create a virtual environment with Python=3.11 to run our program.
```sh
conda create --name ktransformers python=3.11
conda activate ktransformers # you may need to run ‘conda init’ and reopen shell first
```
- Make sure that PyTorch, packaging, ninja is installed
```
pip install torch packaging ninja cpufeature numpy
```
## Installation
<!-- 1. ~~Use a Docker image, see [documentation for Docker](./doc/en/Docker.md)~~
>We are working on the latest docker image, please wait for a while.
2. ~~You can install using Pypi (for linux):~~
> We are working on the latest pypi package, please wait for a while.
```
pip install ktransformers --no-build-isolation
```
for windows we prepare a pre compiled whl package on [ktransformers-0.2.0+cu125torch24avx2-cp312-cp312-win_amd64.whl](https://github.com/kvcache-ai/ktransformers/releases/download/v0.2.0/ktransformers-0.2.0+cu125torch24avx2-cp312-cp312-win_amd64.whl), which require cuda-12.5, torch-2.4, python-3.11, more pre compiled package are being produced. -->
* Download source code and compile:
- init source code
```sh
git clone https://github.com/kvcache-ai/ktransformers.git
cd ktransformers
git submodule init
git submodule update
```
- [Optional] If you want to run with website, please [compile the website](./doc/en/api/server/website.md) before execute ```bash install.sh```
- For Linux
- For simple install:
```shell
bash install.sh
```
- For those who have two cpu and 1T RAM:
```shell
# Make sure your system has dual sockets and double size RAM than the model's size (e.g. 1T RAM for 512G model)
export USE_NUMA=1
bash install.sh # or `make dev_install`
```
- For Windows
```shell
install.bat
```
* If you are developer, you can make use of the makefile to compile and format the code. <br> the detailed usage of makefile is [here](./doc/en/makefile_usage.md)
<h3>Local Chat</h3>
We provide a simple command-line local chat Python script that you can run for testing.
> Note: this is a very simple test tool only support one round chat without any memory about last input, if you want to try full ability of the model, you may go to [RESTful API and Web UI](#id_666).
<h4>Run Example</h4>
```shell
# Begin from root of your cloned repo!
# Begin from root of your cloned repo!!
# Begin from root of your cloned repo!!!
# Download mzwing/DeepSeek-V2-Lite-Chat-GGUF from huggingface
mkdir DeepSeek-V2-Lite-Chat-GGUF
cd DeepSeek-V2-Lite-Chat-GGUF
wget https://huggingface.co/mzwing/DeepSeek-V2-Lite-Chat-GGUF/resolve/main/DeepSeek-V2-Lite-Chat.Q4_K_M.gguf -O DeepSeek-V2-Lite-Chat.Q4_K_M.gguf
cd .. # Move to repo's root dir
# Start local chat
python -m ktransformers.local_chat --model_path deepseek-ai/DeepSeek-V2-Lite-Chat --gguf_path ./DeepSeek-V2-Lite-Chat-GGUF
# If you see “OSError: We couldn't connect to 'https://huggingface.co' to load this file”, try：
# GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite
# python ktransformers.local_chat --model_path ./DeepSeek-V2-Lite --gguf_path ./DeepSeek-V2-Lite-Chat-GGUF
```
It features the following arguments:
- `--model_path` (required): Name of the model (such as "deepseek-ai/DeepSeek-V2-Lite-Chat" which will automatically download configs from [Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite)). Or if you already got local files you may directly use that path to initialize the model.
> Note: <strong>.safetensors</strong> files are not required in the directory. We only need config files to build model and tokenizer.
- `--gguf_path` (required): Path of a directory containing GGUF files which could that can be downloaded from [Hugging Face](https://huggingface.co/mzwing/DeepSeek-V2-Lite-Chat-GGUF/tree/main). Note that the directory should only contains GGUF of current model, which means you need one separate directory for each model.
- `--optimize_rule_path` (required except for Qwen2Moe and DeepSeek-V2): Path of YAML file containing optimize rules. There are two rule files pre-written in the [ktransformers/optimize/optimize_rules](ktransformers/optimize/optimize_rules) directory for optimizing DeepSeek-V2 and Qwen2-57B-A14, two SOTA MoE models.
- `--max_new_tokens`: Int (default=1000). Maximum number of new tokens to generate.
- `--cpu_infer`: Int (default=10). The number of CPUs used for inference. Should ideally be set to the (total number of cores - 2).
<details>
<summary>Supported Models/quantization</summary>
### Supported models include:
| ✅ **Supported Models** | ❌ **Deprecated Models** |
|------------------------|------------------------|
| DeepSeek-R1 | ~~InternLM2.5-7B-Chat-1M~~ |
| DeepSeek-V3 | |
| DeepSeek-V2 | |
| DeepSeek-V2.5 | |
| Qwen2-57B | |
| DeepSeek-V2-Lite | |
| Mixtral-8x7B | |
| Mixtral-8x22B | |
### Support quantize format:
| ✅ **Supported Formats** | ❌ **Deprecated Formats** |
|--------------------------|--------------------------|
| Q2_K_L | ~~IQ2_XXS~~ |
| Q2_K_XS | |
| Q3_K_M | |
| Q4_K_M | |
| Q5_K_M | |
| Q6_K | |
| Q8_0 | |
</details>
<details>
<summary>Suggested Model</summary>
| Model Name | Model Size | VRAM | Minimum DRAM | Recommended DRAM |
| ------------------------------ | ---------- | ----- | --------------- | ----------------- |
| DeepSeek-R1-q4_k_m | 377G | 14G | 382G | 512G |
| DeepSeek-V3-q4_k_m | 377G | 14G | 382G | 512G |
| DeepSeek-V2-q4_k_m | 133G | 11G | 136G | 192G |
| DeepSeek-V2.5-q4_k_m | 133G | 11G | 136G | 192G |
| DeepSeek-V2.5-IQ4_XS | 117G | 10G | 107G | 128G |
| Qwen2-57B-A14B-Instruct-q4_k_m | 33G | 8G | 34G | 64G |
| DeepSeek-V2-Lite-q4_k_m | 9.7G | 3G | 13G | 16G |
| Mixtral-8x7B-q4_k_m | 25G | 1.6G | 51G | 64G |
| Mixtral-8x22B-q4_k_m | 80G | 4G | 86.1G | 96G |
| InternLM2.5-7B-Chat-1M | 15.5G | 15.5G | 8G(32K context) | 150G (1M context) |
More will come soon. Please let us know which models you are most interested in.
Be aware that you need to be subject to their corresponding model licenses when using [DeepSeek](https://huggingface.co/deepseek-ai/DeepSeek-V2/blob/main/LICENSE) and [QWen](https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE).
</details>
<details>
<summary>Click To Show how to run other examples</summary>
* Qwen2-57B
```sh
pip install flash_attn # For Qwen2
mkdir Qwen2-57B-GGUF && cd Qwen2-57B-GGUF
wget https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct-GGUF/resolve/main/qwen2-57b-a14b-instruct-q4_k_m.gguf?download=true -O qwen2-57b-a14b-instruct-q4_k_m.gguf
cd ..
python -m ktransformers.local_chat --model_name Qwen/Qwen2-57B-A14B-Instruct --gguf_path ./Qwen2-57B-GGUF
# If you see “OSError: We couldn't connect to 'https://huggingface.co' to load this file”, try：
# GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct
# python ktransformers/local_chat.py --model_path ./Qwen2-57B-A14B-Instruct --gguf_path ./DeepSeek-V2-Lite-Chat-GGUF
```
* Deepseek-V2
```sh
mkdir DeepSeek-V2-Chat-0628-GGUF && cd DeepSeek-V2-Chat-0628-GGUF
# Download weights
wget https://huggingface.co/bartowski/DeepSeek-V2-Chat-0628-GGUF/resolve/main/DeepSeek-V2-Chat-0628-Q4_K_M/DeepSeek-V2-Chat-0628-Q4_K_M-00001-of-00004.gguf -o DeepSeek-V2-Chat-0628-Q4_K_M-00001-of-00004.gguf
wget https://huggingface.co/bartowski/DeepSeek-V2-Chat-0628-GGUF/resolve/main/DeepSeek-V2-Chat-0628-Q4_K_M/DeepSeek-V2-Chat-0628-Q4_K_M-00002-of-00004.gguf -o DeepSeek-V2-Chat-0628-Q4_K_M-00002-of-00004.gguf
wget https://huggingface.co/bartowski/DeepSeek-V2-Chat-0628-GGUF/resolve/main/DeepSeek-V2-Chat-0628-Q4_K_M/DeepSeek-V2-Chat-0628-Q4_K_M-00003-of-00004.gguf -o DeepSeek-V2-Chat-0628-Q4_K_M-00003-of-00004.gguf
wget https://huggingface.co/bartowski/DeepSeek-V2-Chat-0628-GGUF/resolve/main/DeepSeek-V2-Chat-0628-Q4_K_M/DeepSeek-V2-Chat-0628-Q4_K_M-00004-of-00004.gguf -o DeepSeek-V2-Chat-0628-Q4_K_M-00004-of-00004.gguf
cd ..
python -m ktransformers.local_chat --model_name deepseek-ai/DeepSeek-V2-Chat-0628 --gguf_path ./DeepSeek-V2-Chat-0628-GGUF
# If you see “OSError: We couldn't connect to 'https://huggingface.co' to load this file”, try：
# GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat-0628
# python -m ktransformers.local_chat --model_path ./DeepSeek-V2-Chat-0628 --gguf_path ./DeepSeek-V2-Chat-0628-GGUF
```
| model name | weights download link |
|----------|----------|
| Qwen2-57B | [Qwen2-57B-A14B-gguf-Q4K-M](https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct-GGUF/tree/main) |
| DeepseekV2-coder |[DeepSeek-Coder-V2-Instruct-gguf-Q4K-M](https://huggingface.co/LoneStriker/DeepSeek-Coder-V2-Instruct-GGUF/tree/main) |
| DeepseekV2-chat |[DeepSeek-V2-Chat-gguf-Q4K-M](https://huggingface.co/bullerwins/DeepSeek-V2-Chat-0628-GGUF/tree/main) |
| DeepseekV2-lite | [DeepSeek-V2-Lite-Chat-GGUF-Q4K-M](https://huggingface.co/mzwing/DeepSeek-V2-Lite-Chat-GGUF/tree/main) |
| DeepSeek-R1 | [DeepSeek-R1-gguf-Q4K-M](https://huggingface.co/unsloth/DeepSeek-R1-GGUF/tree/main/DeepSeek-R1-Q4_K_M) |
</details>
<!-- pin block for jump -->
<span id='id_666'>
<h3>RESTful API and Web UI (deprected) </h3>
Start without website:
```sh
ktransformers --model_path deepseek-ai/DeepSeek-V2-Lite-Chat --gguf_path /path/to/DeepSeek-V2-Lite-Chat-GGUF --port 10002
```
Start with website:
```sh
ktransformers --model_path deepseek-ai/DeepSeek-V2-Lite-Chat --gguf_path /path/to/DeepSeek-V2-Lite-Chat-GGUF --port 10002 --web True
```
Or you want to start server with transformers, the model_path should include safetensors
```bash
ktransformers --type transformers --model_path /mnt/data/model/Qwen2-0.5B-Instruct --port 10002 --web True
```
Access website with url [http://localhost:10002/web/index.html#/chat](http://localhost:10002/web/index.html#/chat) :
<p align="center">
<picture>
<img alt="Web UI" src="https://github.com/user-attachments/assets/615dca9b-a08c-4183-bbd3-ad1362680faf" width=90%>
</picture>
</p>
More information about the RESTful API server can be found [here](doc/en/api/server/server.md). You can also find an example of integrating with Tabby [here](doc/en/api/server/tabby.md).
doc/en/multi-gpu-tutorial.md 0 → 100644
View file @ 19d4a50b
# Muti-GPU
Assume you have read the [Injection Tutorial](./injection_tutorial.md) and have a basic understanding of how to inject a model. In this tutorial, we will show you how to use KTransformers to run a model on multiple GPUs.
If you have multiple GPUs, you can set the device for each module to different GPUs.
DeepseekV2-Chat got 60 layers, if we got 2 GPUs, we can allocate 30 layers to each GPU. Complete multi GPU rule examples [here](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu.yaml).
<p align="center">
<picture>
<img alt="Inject-Struction" src="../assets/multi_gpu.png" width=60%>
</picture>
</p>
First of all, for multi-GPU, we have to inject an new operator `KDeepseekV2Model`. And set division of the layers to different GPUs. For our case, we have to set the `transfer_map` in the `KDeepseekV2Model` operatoras as follows:
```yaml
- match:
name: "^model$"
replace:
class: "ktransformers.operators.models.KDeepseekV2Model"
kwargs:
transfer_map:
30: "cuda:1"
```
And we have to set the device for each module in the model.
For example, for `routed experts`, the yaml for one GPU is:
```yaml
- match:
name: "^model\\.layers\\..*\\.mlp\\.experts$"
replace:
class: ktransformers.operators.experts.KTransformersExperts # Custom MoE kernel with expert parallelism
kwargs:
generate_device: "cuda:0"
generate_op: "MLPCUDAExperts"
out_device: "cuda:0"
recursive: False # Don't recursively inject submodules of this module
```
But for two GPUs, we need to set the device for each module in the model.
```yaml
# allcate 0-29 layers‘s out_device to cuda:0
- match:
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.mlp\\.experts$"
replace:
class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism
kwargs:
generate_device: "cpu"
generate_op: "KExpertsCPU"
out_device: "cuda:0"
recursive: False # don't recursively inject submodules of this module
# allocate 30-59 layers‘s out_device to cuda:1
- match:
name: "^model\\.layers\\.([345][0-9])\\.mlp\\.experts$"
replace:
class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism
kwargs:
generate_device: "cpu"
generate_op: "KExpertsCPU"
out_device: "cuda:1"
recursive: False # don't recursively inject submodules of this module
```
For other modules, we can set the device in the same way.
# How to fully utilize multi-GPU's VRAM
When you have multiple GPUs, you can fully utilize the VRAM of each GPU by moving more weights to the GPU.
For example, for DeepSeekV2-Chat, we can move the weights of the experts to the GPU.
For example, the yaml for two GPUs is:
```yaml
- match:
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.mlp\\.experts$"
replace:
class: ktransformers.operators.experts.KTransformersExperts
kwargs:
generate_device: "cpu"
generate_op: "KExpertsCPU"
out_device: "cuda:0"
recursive: False
```
But we got extra 60GB VRAM on cuda:0, we can move experts in layer 4~8 to cuda:0.
```yaml
# Add new rule before old rule.
- match:
name: "^model\\.layers\\.([4-8])\\.mlp\\.experts$" # inject experts in layer 4~8 as marlin expert
replace:
class: ktransformers.operators.experts.KTransformersExperts
kwargs:
generate_device: "cuda:0"
generate_op: "KExpertsMarlin"
recursive: False
- match:
name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.mlp\\.experts$"
replace:
class: ktransformers.operators.experts.KTransformersExperts
kwargs:
generate_device: "cpu"
generate_op: "KExpertsCPU"
out_device: "cuda:0"
recursive: False
```
Adjust the layer range as you want. Note that:
* The loading speed will be significantly slower for each expert moved to the GPU.
* You have to close the cuda graph if you want to move the experts to the GPU.
* For DeepSeek-R1/V3, each expert moved to the GPU will consume approximately 6GB of VRAM.
* The first matched rule in yaml will be applied. For example, if you have two rules that match the same layer, only the first rule's replacement will be valid.
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