Add lightllm kernel

.gitmodules

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+[submodule "third-party/cutlass"]
+	path = third-party/cutlass
+	url = https://github.com/NVIDIA/cutlass.git

docs/CN/source/getting_started/multimodal_model_quickstart.rst

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+..multimodal_model_quickstart.rst
+-------------------------
+
+下载多模态模型（如llava系列、internvl系列、qwen_vl系列等）的模型以后，在终端使用下面的代码部署API服务：
+
+.. code-block:: console
+
+    $ python -m lightllm.server.api_server --model_dir ~/models/llava-7b-chat --use_dynamic_prompt_cache --enable_multimodal
+
+.. note::
+    上面代码中的 ``--model_dir`` 参数需要修改为你本机实际的模型路径。

lightllm-kernel/CMakeLists.txt

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+cmake_minimum_required(VERSION 3.22)
+project(lightllm_kernel LANGUAGES CXX CUDA)
+
+# GPU 架构：缺省支持 A100(80)、Ampere(86)、Ada/L40s/4090(89)、Hopper(90)，
+if(NOT CMAKE_CUDA_ARCHITECTURES)
+  set(CMAKE_CUDA_ARCHITECTURES 80;86;89;90)
+endif()
+
+# 找 PyTorch & Python
+find_package(Torch REQUIRED)
+find_package(Python REQUIRED COMPONENTS Development)
+find_package(CUDAToolkit REQUIRED)
+
+# 收集 csrc 下的 .cpp/.cu
+file(GLOB_RECURSE SRC_CPP   CONFIGURE_DEPENDS "${PROJECT_SOURCE_DIR}/csrc/*.cpp")
+file(GLOB_RECURSE SRC_CUDA  CONFIGURE_DEPENDS "${PROJECT_SOURCE_DIR}/csrc/*.cu")
+
+# 编译生成 Python 扩展， _C.so
+if (NOT TARGET _C)
+  add_library(_C SHARED ${SRC_CPP} ${SRC_CUDA})
+
+  # C++17 更方便调度宏
+  target_compile_features(_C PRIVATE cxx_std_17)
+  target_include_directories(_C PRIVATE 
+    ${TORCH_INCLUDE_DIRS}
+    ${CUDAToolkit_INCLUDE_DIRS}
+    ${PROJECT_SOURCE_DIR}/include
+    ${PROJECT_SOURCE_DIR}/csrc
+    ${PROJECT_SOURCE_DIR}/../third-party/cutlass/include
+  )
+  target_link_libraries(_C
+      PRIVATE
+        ${TORCH_LIBRARIES}
+        Python::Python
+        CUDA::cudart
+        CUDA::cuda_driver)
+
+
+  # 输出文件名 _C.so，无前缀
+  set_target_properties(_C PROPERTIES
+      PREFIX ""
+      OUTPUT_NAME "_C"
+      BUILD_RPATH "\$ORIGIN;\$ORIGIN/../torch/lib"
+      INSTALL_RPATH "\$ORIGIN;\$ORIGIN/../torch/lib"
+  )
+endif()
+# 安装：把 _C.so、Python 包和 csrc 一起拷到 site-packages
+include(GNUInstallDirs)
+
+# 1) 计算 Python site-packages 路径
+
+message(STATUS "Installing to ARCH = ${Python_SITEARCH}")
+message(STATUS "Installing to PURE = ${Python_SITELIB}")
+
+# 2) 安装编译好的 _C.so 到 lightllm_kernel 目录
+install(TARGETS _C
+        LIBRARY DESTINATION ${Python_SITEARCH}/lightllm_kernel)
+
+# 3) 安装 Python 源码包
+install(DIRECTORY ${PROJECT_SOURCE_DIR}/lightllm_kernel
+        DESTINATION ${Python_SITELIB})
+
+# 4) 安装 csrc 源码以供 JIT fallback
+install(DIRECTORY ${PROJECT_SOURCE_DIR}/csrc
+        DESTINATION ${Python_SITELIB}/lightllm_kernel)

lightllm-kernel/LICENSE

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lightllm-kernel/Makefile

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+.PHONY: build clean submodule
+
+SUBMODULE_DIR = third-party/cutlass
+
+submodule:
+	git submodule update --init --recursive
+
+build: submodule
+	# 8.0-> A100, 8.6-> A10, 8.9-> L40s/4090, 9.0+PTX-> Hopper
+	TORCH_CUDA_ARCH_LIST="8.0;8.6;8.9;9.0+PTX" \
+	python -m pip install -v .
+
+clean:
+	rm -rf build dist *.egg-info

lightllm-kernel/README-CH.md

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+# LightLLM-Kernel
+
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+
+lightllm-kernel 是大模型推理系统 LightLLM 的 CUDA 算子库。它提供了在大型模型推理过程中所需的一系列自定义 GPU 运算算子，以加速关键步骤的计算。
+
+## 功能列表
+
+| Module       | Description                                                                                     |
+|--------------|-------------------------------------------------------------------------------------------------|
+| **Attention** | Optimized Multi-Head Attention kernels with fused QKV operations and efficient softmax         |
+| **MoE**       | Expert routing and computation kernels for Mixture-of-Experts architectures                    |
+| **Quant**     | Low-precision quantization support (INT8/INT4) for weights and activations                      |
+| **Extensions**| Continuous expansion of optimized operations for emerging model architectures                   |
+
+## 安装方法
+
+lightllm_kernel 提供了静态编译以及JIT（Just-In-Time）动态编译的安装方式。推荐使用静态编译安装以获得最佳性能，同时也支持开发者使用可编辑安装进行开发调试。
+
+### System Requirements
+- NVIDIA GPU with Compute Capability ≥ 7.0 (Volta+)
+- CUDA 11.8 or higher
+- Python 3.8+
+
+### Installation Methods
+
+#### Static Compilation (Recommended)
+```bash
+git clone https://github.com/YourUsername/lightllm_kernel.git
+cd lightllm_kernel
+make build
+# Alternative using pip
+pip install .
+```
+
+## 贡献指南
+欢迎社区开发者为 lightllm_kernel 做出贡献！如果您计划新增自定义算子或改进现有功能，请参考以下指南：
+- 新增算子实现：在 csrc/ 目录下添加您的 CUDA/C++ 源码文件，添加时建议参考现有算子的代码风格和结构。
+- 注册Python接口：在 csrc/ops_bindings.cpp中，将新增的算子通过 PyBind11 或 TORCH_LIBRARY 等机制注册到 Python 接口。
+- 导出算子到Python模块：在lightllm_kernel/ops/__init__.py只添加相应的导出代码，使新算子包含在 lightllm_kernel.ops 模块中。
+- 本地测试：开发完成后，请在本地对您的更改进行测试。您可以编译安装新的版本并编写简单的脚本调用新算子，检查其功能和性能是否符合预期。如果项目附带了测试用例，也请运行所有测试确保不引入回归。
+- 

lightllm-kernel/README.md

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+# LightLLM-Kernel
+
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+
+LightLLM-Kernel is a high-performance CUDA kernel library powering the LightLLM inference system. It provides optimized GPU implementations for critical operations in large language model (LLM) inference, delivering significant performance improvements through carefully crafted CUDA kernels.
+
+## Project Overview
+
+LightLLM-Kernel serves as the computational backbone for LightLLM framework, offering:
+- **Custom CUDA Kernels**: Highly optimized implementations for transformer-based model operations
+- **Memory Efficiency**: Reduced memory footprint through advanced quantization techniques
+- **Scalability**: Support for large model architectures including MoE (Mixture-of-Experts) models
+
+## Key Features
+
+### Core Modules
+| Module       | Description                                                                                     |
+|--------------|-------------------------------------------------------------------------------------------------|
+| **Attention** | Optimized Multi-Head Attention kernels with fused QKV operations and efficient softmax         |
+| **MoE**       | Expert routing and computation kernels for Mixture-of-Experts architectures                    |
+| **Quant**     | Low-precision quantization support (INT8/INT4) for weights and activations                      |
+| **Extensions**| Continuous expansion of optimized operations for emerging model architectures                   |
+
+## Installation
+
+### System Requirements
+- NVIDIA GPU with Compute Capability ≥ 7.0 (Volta+)
+- CUDA 11.8 or higher
+- Python 3.8+
+
+### Installation Methods
+
+#### Static Compilation (Recommended)
+```bash
+git clone https://github.com/YourUsername/lightllm_kernel.git
+cd lightllm_kernel
+make build
+# Alternative using pip
+pip install .