Merge branch 'main' into feat-more-context

.github/workflows/docker-image.yml

@@ -0,0 +1,90 @@
+name: DockerHub CI
+
+on:
+  release:
+    types: [published]
+  # push:
+  #   branches:
+  #     - main
+env:
+  DOCKERHUB_REPO: ${{ secrets.DOCKERHUB_USERNAME }}/ktransformers
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - name: Run tests
+        run: |
+          if [ -f docker-compose.test.yml ]; then
+            docker-compose --file docker-compose.test.yml build
+            docker-compose --file docker-compose.test.yml run sut
+          else
+            docker build . --file Dockerfile
+          fi
+
+  docker_task:
+    needs: test
+    name: ${{ matrix.instruct}}
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+        # for amd64
+          - {instruct: "FANCY",   platform: "linux/amd64"}
+          - {instruct: "AVX512",  platform: "linux/amd64"}
+          - {instruct: "AVX2",    platform: "linux/amd64"}   
+          - {instruct: "NATIVE",  platform: "linux/amd64"}
+        # for arm64
+          - {instruct: "NATIVE",  platform: "linux/arm64"}
+
+    steps:
+        - name: Move Docker data directory
+          run: |
+            sudo systemctl stop docker
+            sudo mkdir -p /mnt/docker
+            sudo rsync -avz /var/lib/docker/ /mnt/docker
+            sudo rm -rf /var/lib/docker 
+            sudo ln -s /mnt/docker /var/lib/docker
+            sudo systemctl start docker
+
+        -
+          name: Set up QEMU
+          uses: docker/setup-qemu-action@v3
+
+        -
+          name: Set up Docker Buildx
+          uses: docker/setup-buildx-action@v3
+
+        -
+          name: Login to Docker Hub
+          uses: docker/login-action@v3
+          with:
+            username: ${{ secrets.DOCKERHUB_USERNAME }}
+            password: ${{ secrets.DOCKERHUB_TOKEN }}
+        -
+          name: Build and push for amd64
+          if: matrix.platform == 'linux/amd64'
+          uses: docker/build-push-action@v6
+          with:
+            push: true
+            platforms: |
+              linux/amd64
+            tags: |
+              ${{ env.DOCKERHUB_REPO }}:latest-${{ matrix.instruct }}
+              ${{ env.DOCKERHUB_REPO }}:${{ github.event.release.tag_name }}-${{ matrix.instruct }}
+            build-args: |
+              CPU_INSTRUCT=${{ matrix.instruct }}
+        -
+          name: Build and push for arm64
+          if: matrix.platform == 'linux/arm64'
+          uses: docker/build-push-action@v6
+          with:
+            push: true
+            platforms: |
+              linux/arm64
+            tags: |
+              ${{ env.DOCKERHUB_REPO }}:latest-${{ matrix.instruct }}
+              ${{ env.DOCKERHUB_REPO }}:${{ github.event.release.tag_name }}-${{ matrix.instruct }}
+            build-args: |
+              CPU_INSTRUCT=${{ matrix.instruct }}

.gitignore

@@ -28,3 +28,4 @@ ktransformers/tests/chat_txt.txt
 mmlu_result_q4km.json
 mmlu_result_q4km.log
 ktransformers/tests/mmlu_result_silicon.log
+ktransformers/ktransformers_ext/cuda_musa/

Dockerfile

@@ -11,6 +11,7 @@ EOF
 
 
 FROM pytorch/pytorch:2.3.1-cuda12.1-cudnn8-devel as compile_server
+ARG CPU_INSTRUCT=NATIVE
 WORKDIR /workspace
 ENV CUDA_HOME /usr/local/cuda
 COPY --from=web_compile /home/ktransformers /workspace/ktransformers
@@ -28,8 +29,9 @@ git submodule init &&
 git submodule update &&
 pip install ninja pyproject numpy cpufeature &&
 pip install flash-attn &&
-CPU_INSTRUCT=NATIVE  KTRANSFORMERS_FORCE_BUILD=TRUE TORCH_CUDA_ARCH_LIST="8.0;8.6;8.7;8.9;9.0+PTX" pip install . --no-build-isolation --verbose &&
+CPU_INSTRUCT=${CPU_INSTRUCT}  KTRANSFORMERS_FORCE_BUILD=TRUE TORCH_CUDA_ARCH_LIST="8.0;8.6;8.7;8.9;9.0+PTX" pip install . --no-build-isolation --verbose &&
-pip cache purge
+pip cache purge &&
+cp /usr/lib/x86_64-linux-gnu/libstdc++.so.6 /opt/conda/lib/
 EOF
 
 ENTRYPOINT ["tail", "-f", "/dev/null"]

doc/en/DeepseekR1_V3_tutorial.md

@@ -226,6 +226,7 @@ Intel is currently the only CPU vendor that supports AMX-like instructions, whic
 ### Easier
 * Official Docker images to simplify installation
 * Fix the server integration for web API access
+* Fix the local chat only accepting a single line prompt (currently \n begins generating prompt)
 * Support for more quantization types, including the highly requested dynamic quantization from unsloth
 
 Stay tuned for more updates! 

doc/en/api/server/website.md

@@ -8,6 +8,20 @@ This document provides the necessary steps to set up and run the web service for
 
 Before you can compile the web code, make sure you have installed [Node.js](https://nodejs.org) version 18.3 or higher
 
+Note: The version of Node.js in the Ubuntu or Debian GNU/Linux software repository is too low, causing compilation errors. Users can also install Node.js through the Nodesource repository, provided they uninstall the outdated version first.
+
+```bash
+
+  # sudo apt-get remove nodejs npm -y && sudo apt-get autoremove -y
+  sudo apt-get update -y && sudo apt-get install -y apt-transport-https ca-certificates curl gnupg
+  curl -fsSL https://deb.nodesource.com/gpgkey/nodesource-repo.gpg.key | sudo gpg --dearmor -o /usr/share/keyrings/nodesource.gpg
+  sudo chmod 644 /usr/share/keyrings/nodesource.gpg
+  echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/nodesource.gpg] https://deb.nodesource.com/node_23.x nodistro main" | sudo tee /etc/apt/sources.list.d/nodesource.list
+  sudo apt-get update -y
+  sudo apt-get install nodejs -y
+
+```
+
 Once npm is installed, navigate to the `ktransformers/website` directory:
 
 ```bash

doc/en/install.md

@@ -27,11 +27,11 @@ Some preparation:
   fi
   ```
 
-- Linux-x86_64 with gcc, g++ and cmake
+- Linux-x86_64 with gcc, g++ and cmake (using Ubuntu as an example)
   
   ```sh
   sudo apt-get update
-  sudo apt-get install gcc g++ cmake ninja-build
+  sudo apt-get install build-essential cmake ninja-build
   ```
 
 - We recommend using [Miniconda3](https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh) or [Anaconda3](https://repo.anaconda.com/archive/Anaconda3-2024.10-1-Linux-x86_64.sh) to create a virtual environment with Python=3.11 to run our program. Assuming your Anaconda installation directory is `~/anaconda3`, you should ensure that the version identifier of the GNU C++standard library used by Anaconda includes `GLIBCXX-3.4.32`

doc/zh/DeepseekR1_V3_tutorial_zh.md

@@ -160,9 +160,14 @@ DeepSeek 的 MLA 操作符计算密集。虽然全部在 CPU 上运行是可行
 
 5. 为什么选择英特尔 CPU？
 英特尔目前是唯一支持 AMX 类似指令的 CPU 供应商，与仅支持 AVX 的替代方案相比，性能显著更好。
+
 ## 常见问题解答
 ### R1 不返回思考过程
 注意！如果测试 R1 可能会跳过思考。因此，可以添加参数：`--force_think true`。详细信息在 [常见问题解答](./FAQ.md) 部分中。 <br>
 
+## 问题
+* 修复服务器集成功能以实现网络API访问支持
+* 修复本地聊天功能仅支持单行提示输入的问题（目前输入换行符(\n)即开始生成提示）
+
 ### 更多常见问题解答
 [详见](./FAQ.md)

ktransformers/ktransformers_ext/CMakeLists.txt

@@ -30,6 +30,8 @@ if (NOT MSVC)
     option(LLAMA_F16C                        "llama: enable F16C"                               OFF)
 endif()
 option(LLAMA_AVX512_FANCY_SIMD               "llama: enable AVX512-VL, AVX512-BW, AVX512-DQ, AVX512-VNNI"                        OFF)
+option(KTRANSFORMERS_USE_CUDA                "ktransformers: use CUDA"                          OFF)
+option(KTRANSFORMERS_USE_MUSA                "ktransformers: use MUSA"                          OFF)
 
 # Architecture specific
 # TODO: probably these flags need to be tweaked on some architectures
@@ -208,8 +210,31 @@ include_directories(${CMAKE_CURRENT_SOURCE_DIR}/../../third_party)
 if (WIN32)
     include_directories("$ENV{CUDA_PATH}/include")
 elseif (UNIX)
-    find_package(CUDA REQUIRED)
+    if (KTRANSFORMERS_USE_CUDA)
-    include_directories("${CUDA_INCLUDE_DIRS}")
+        find_package(CUDA REQUIRED)
+        include_directories("${CUDA_INCLUDE_DIRS}")
+        add_compile_definitions(KTRANSFORMERS_USE_CUDA=1)
+    endif()
+
+    if (KTRANSFORMERS_USE_MUSA)
+        if (NOT EXISTS $ENV{MUSA_PATH})
+            if (NOT EXISTS /opt/musa)
+                set(MUSA_PATH /usr/local/musa)
+            else()
+                set(MUSA_PATH /opt/musa)
+            endif()
+        else()
+            set(MUSA_PATH $ENV{MUSA_PATH})
+        endif()
+
+        list(APPEND CMAKE_MODULE_PATH "${MUSA_PATH}/cmake")
+
+        find_package(MUSAToolkit)
+        if (MUSAToolkit_FOUND)
+            message(STATUS "MUSA Toolkit found")
+            add_compile_definitions(KTRANSFORMERS_USE_MUSA=1)
+        endif()
+    endif()
 endif()
 
 aux_source_directory(${CMAKE_CURRENT_SOURCE_DIR} SOURCE_DIR1)
@@ -225,10 +250,15 @@ target_link_libraries(${PROJECT_NAME} PRIVATE llama)
 if(WIN32)
     target_link_libraries(${PROJECT_NAME} PRIVATE "$ENV{CUDA_PATH}/lib/x64/cudart.lib")#CUDA::cudart
 elseif(UNIX)
-    if(NOT DEFINED ENV{CUDA_HOME} OR "$ENV{CUDA_HOME}" STREQUAL "")
+    if(KTRANSFORMERS_USE_CUDA)
-        set(ENV{CUDA_HOME} "/usr/local/cuda")
+        if(NOT DEFINED ENV{CUDA_HOME} OR "$ENV{CUDA_HOME}" STREQUAL "")
+            set(ENV{CUDA_HOME} "/usr/local/cuda")
+        endif()
+        target_link_libraries(${PROJECT_NAME} PRIVATE "$ENV{CUDA_HOME}/lib64/libcudart.so")
+    endif()
+    if(KTRANSFORMERS_USE_MUSA)
+        target_link_libraries(${PROJECT_NAME} PRIVATE MUSA::musart)
     endif()
-    target_link_libraries(${PROJECT_NAME} PRIVATE "$ENV{CUDA_HOME}/lib64/libcudart.so")
 endif()
 
 # Define the USE_NUMA option

ktransformers/ktransformers_ext/cpu_backend/cpuinfer.h

@@ -17,7 +17,11 @@
 #include <queue>
 #include <thread>
 #include <vector>
-#include "cuda_runtime.h"
+#ifdef KTRANSFORMERS_USE_CUDA
+#include "vendors/cuda.h"
+#elif KTRANSFORMERS_USE_MUSA
+#include "vendors/musa.h"
+#endif
 
 #include "backend.h"
 #include "task_queue.h"

ktransformers/ktransformers_ext/cpu_backend/vendors/README.md

@@ -0,0 +1,3 @@
+## TODO
+
+This directory can be removed after updating the version of `llama.cpp`.

ktransformers/ktransformers_ext/cpu_backend/vendors/cuda.h

@@ -0,0 +1,3 @@
+#pragma once
+
+#include <cuda_runtime.h>

ktransformers/ktransformers_ext/cpu_backend/vendors/musa.h

@@ -0,0 +1,7 @@
+#pragma once
+
+#include <musa_runtime.h>
+
+#define cudaLaunchHostFunc musaLaunchHostFunc
+#define cudaStream_t musaStream_t
+#define cudaHostFn_t musaHostFn_t

ktransformers/ktransformers_ext/cuda/binding.cpp

@@ -7,7 +7,9 @@
 **/
 
 #include "custom_gguf/ops.h"
+#ifdef KTRANSFORMERS_USE_CUDA
 #include "gptq_marlin/ops.h"
+#endif
 // Python bindings
 #include <pybind11/pybind11.h>
 #include <pybind11/stl.h>
@@ -52,7 +54,8 @@ PYBIND11_MODULE(KTransformersOps, m) {
         return dequantize_iq4_xs((int8_t*)data, num_bytes, blk_size, ele_per_blk, device, target_dtype);
         }, "Function to dequantize iq4_xs data.",
         py::arg("data"), py::arg("num_bytes"), py::arg("blk_size"), py::arg("ele_per_blk"), py::arg("device"), py::arg("target_dtype"));
-    
+
+#ifdef KTRANSFORMERS_USE_CUDA
     m.def("gptq_marlin_gemm", &gptq_marlin_gemm, "Function to perform GEMM using Marlin quantization.",
         py::arg("a"), py::arg("b_q_weight"), py::arg("b_scales"), py::arg("g_idx"),
         py::arg("perm"), py::arg("workspace"), py::arg("num_bits"), py::arg("size_m"),

ktransformers/operators/attention.py

@@ -58,18 +58,10 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
     def get_absorbed(self) -> Tuple[torch.Tensor, torch.Tensor]:
         if not (hasattr(self, 'q_absorb') and hasattr(self, 'out_absorb')):
             kv_b_proj = self.kv_b_proj.weight.view(self.num_heads, -1, self.kv_lora_rank)
-            q_absorb = kv_b_proj[:, :self.qk_nope_head_dim, :].reshape(-1, self.kv_lora_rank)
+            self.q_absorb = kv_b_proj[:, :self.qk_nope_head_dim, :].view(self.num_heads, self.qk_nope_head_dim, self.kv_lora_rank)
-            out_absorb = kv_b_proj[:, self.qk_nope_head_dim:, :].reshape(-1, self.kv_lora_rank)
+            self.out_absorb = kv_b_proj[:, self.qk_nope_head_dim:, :].view(self.num_heads, self.v_head_dim, self.kv_lora_rank)
-            self.q_absorb = nn.Linear(self.kv_lora_rank, self.num_heads * self.qk_nope_head_dim, 
+            
-                                      bias=False, dtype=q_absorb.dtype, device=q_absorb.device)
+        return self.q_absorb, self.out_absorb
-            self.q_absorb.weight.data = q_absorb
-            self.out_absorb = nn.Linear(self.kv_lora_rank, self.num_heads * self.v_head_dim, 
-                                        bias=False, dtype=out_absorb.dtype, device=out_absorb.device)
-            self.out_absorb.weight.data = out_absorb
-            #del self.orig_module.kv_b_proj
-        q_absorb = self.q_absorb.weight.view(self.num_heads, self.qk_nope_head_dim, self.kv_lora_rank)
-        out_absorb = self.out_absorb.weight.view(self.num_heads, self.v_head_dim, self.kv_lora_rank)
-        return q_absorb, out_absorb
 
     def forward_chunck(
         self,
@@ -105,7 +97,7 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
         if past_key_value is not None:
             if self.layer_idx is None:
                 raise ValueError(
-                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    f"The cache structure has changed since transformer version v4.36. If you are using {self.__class__.__name__} "
                     "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
                     "with a layer index."
                 )
@@ -129,8 +121,6 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
             # compressed_kv [pages, page_size, 1, self.kv_lora_rank]
             
         q_absorb, out_absorb = self.get_absorbed()
-        # if hasattr(self.orig_module, 'kv_b_proj'):
-        #     del self.orig_module.kv_b_proj
 
         # q_nope [bsz, self.num_heads, q_len, self.qk_nope_head_dim]
         # q_pe [bsz, self.num_heads, q_len, self.qk_rope_head_dim]
@@ -227,7 +217,7 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
         if past_key_value is not None:
             if self.layer_idx is None:
                 raise ValueError(
-                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    f"The cache structure has changed since transformer version v4.36. If you are using {self.__class__.__name__} "
                     "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
                     "with a layer index."
                 )
@@ -379,7 +369,7 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
         if past_key_value is not None:
             if self.layer_idx is None:
                 raise ValueError(
-                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    f"The cache structure has changed since version transformer verision v4.36. If you are using {self.__class__.__name__} "
                     "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
                     "with a layer index."
                 )

ktransformers/operators/flashinfer_wrapper.py

@@ -9,7 +9,7 @@ flashinfer_enabled = False
 
 try:
     import flashinfer
-    flashinfer_enabled = False
+    flashinfer_enabled = False # disabled now, TODO:use new version of flashinfer and enable
     print("found flashinfer")
     
 except ImportError:

ktransformers/server/backend/interfaces/transformers.py

@@ -381,13 +381,13 @@ class TransformersInterface(BackendInterfaceBase):
 
         self.profiler.create_and_start_timer("prefill")
 
-               
+        if Config().user_force_think:
+            think = '<think>\n'
+            print(think, end="",flush=True)
+            yield think
+        
         for t in self.prefill(input_ids, self.check_is_new(thread_id)):
             # output think token after prefill done
-            if Config().user_force_think:
-                think = '<think>\n'
-                print(think, end="",flush=True)
-                yield think
             if t is not None:
                 print(t, end="",flush=True)
                 yield t

ktransformers/tests/mmlu_pro_test.py

@@ -176,7 +176,7 @@ if __name__ == "__main__":
     parser.add_argument("--result", type=str, default="./mmlu_pro.json", help="Path to save the result JSON file")
     parser.add_argument("--log", type=str, default="./mmlu_pro.log", help="Path to save the log file")
     parser.add_argument("--model", type=str, default="Pro/deepseek-ai/DeepSeek-V3", help="Model name or path")
-    parser.add_argument("--api_url", type=str, default="http://localhost:10002/v1/chat/completions", help="API URL")
+    parser.add_argument("--api_url", type=str, default="http://localhost:15488/v1/chat/completions", help="API URL")
     # parser.add_argument("--api_url", type=str, default="https://api.siliconflow.cn/v1/chat/completions", help="API URL")
 
     args = parser.parse_args()

setup.py

@@ -1,16 +1,16 @@
 #!/usr/bin/env python
 # coding=utf-8
 '''
-Description  :  
+Description  :
 Author       : chenxl
 Date         : 2024-07-27 16:15:27
 Version      : 1.0.0
-LastEditors  : chenxl 
+LastEditors  : chenxl
 LastEditTime : 2024-08-14 16:36:19
 Adapted from:
 https://github.com/Dao-AILab/flash-attention/blob/v2.6.3/setup.py
 Copyright (c) 2023, Tri Dao.
-Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
+Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
 '''
 
 import os
@@ -30,6 +30,11 @@ from wheel.bdist_wheel import bdist_wheel as _bdist_wheel
 from setuptools import setup, Extension
 from cpufeature.extension import CPUFeature
 from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CUDA_HOME
+try:
+    from torch_musa.utils.simple_porting import SimplePorting
+    from torch_musa.utils.musa_extension import BuildExtension, MUSAExtension, MUSA_HOME
+except ImportError:
+    MUSA_HOME=None
 
 class CpuInstructInfo:
     CPU_INSTRUCT = os.getenv("CPU_INSTRUCT", "NATIVE")
@@ -40,7 +45,7 @@ class CpuInstructInfo:
     CMAKE_FANCY = "-DLLAMA_NATIVE=OFF -DLLAMA_FMA=ON -DLLAMA_F16C=ON -DLLAMA_AVX=ON -DLLAMA_AVX2=ON -DLLAMA_AVX512=ON -DLLAMA_AVX512_FANCY_SIMD=ON"
     CMAKE_AVX512 = "-DLLAMA_NATIVE=OFF -DLLAMA_FMA=ON -DLLAMA_F16C=ON -DLLAMA_AVX=ON -DLLAMA_AVX2=ON -DLLAMA_AVX512=ON"
     CMAKE_AVX2 = "-DLLAMA_NATIVE=OFF -DLLAMA_FMA=ON -DLLAMA_F16C=ON -DLLAMA_AVX=ON -DLLAMA_AVX2=ON"
-    
+
 class VersionInfo:
     THIS_DIR = os.path.dirname(os.path.abspath(__file__))
     PACKAGE_NAME = "ktransformers"
@@ -49,6 +54,16 @@ class VersionInfo:
     )
     FORCE_BUILD = os.getenv("KTRANSFORMERS_FORCE_BUILD", "FALSE") == "TRUE"
 
+    def get_musa_bare_metal_version(self, musa_dir):
+        raw_output = subprocess.run(
+            [musa_dir + "/bin/mcc", "-v"], check=True,
+            stdout=subprocess.PIPE, stderr=subprocess.STDOUT).stdout.decode("utf-8")
+        output = raw_output.split()
+        release_idx = output.index("version") + 1
+        bare_metal_version = parse(output[release_idx].split(",")[0])
+        musa_version = f"{bare_metal_version.major}{bare_metal_version.minor}"
+        return musa_version
+
     def get_cuda_bare_metal_version(self, cuda_dir):
         raw_output = subprocess.check_output(
             [cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True)
@@ -58,7 +73,7 @@ class VersionInfo:
         cuda_version = f"{bare_metal_version.major}{bare_metal_version.minor}"
         return cuda_version
 
-    def get_cuda_version_of_torch(self,):
+    def get_cuda_version_of_torch(self):
         torch_cuda_version = parse(torch.version.cuda)
         cuda_version = f"{torch_cuda_version.major}{torch_cuda_version.minor}"
         return cuda_version
@@ -117,7 +132,7 @@ class VersionInfo:
         torch_version_raw = parse(torch.__version__)
         torch_version = f"{torch_version_raw.major}{torch_version_raw.minor}"
         return torch_version
-    
+
     def get_flash_version(self,):
         version_file = os.path.join(
             Path(VersionInfo.THIS_DIR), VersionInfo.PACKAGE_NAME, "__init__.py")
@@ -128,12 +143,21 @@ class VersionInfo:
         return flash_version
 
     def get_package_version(self, full_version=False):
-        flash_version = self.get_flash_version()
+        flash_version = str(self.get_flash_version())
-        package_version = f"{str(flash_version)}+cu{self.get_cuda_bare_metal_version(CUDA_HOME)}torch{self.get_torch_version()}{self.get_cpu_instruct()}"
+        torch_version = self.get_torch_version()
+        cpu_instruct = self.get_cpu_instruct()
+        backend_version = ""
+        if CUDA_HOME is not None:
+            backend_version = f"cu{self.get_cuda_bare_metal_version(CUDA_HOME)}"
+        elif MUSA_HOME is not None:
+            backend_version = f"mu{self.get_musa_bare_metal_version(MUSA_HOME)}"
+        else:
+            raise ValueError("Unsupported backend: CUDA_HOME and MUSA_HOME are not set.")
+        package_version = f"{flash_version}+{backend_version}torch{torch_version}{cpu_instruct}"
         if full_version:
             return package_version
         if not VersionInfo.FORCE_BUILD:
-            return str(flash_version)
+            return flash_version
         return package_version
 
 
@@ -218,11 +242,19 @@ class CMakeBuild(BuildExtension):
             f"-DPYTHON_EXECUTABLE={sys.executable}",
             f"-DCMAKE_BUILD_TYPE={cfg}",  # not used on MSVC, but no harm
         ]
+
+        if CUDA_HOME is not None:
+            cmake_args += ["-DKTRANSFORMERS_USE_CUDA=ON"]
+        elif MUSA_HOME is not None:
+            cmake_args += ["-DKTRANSFORMERS_USE_MUSA=ON"]
+        else:
+            raise ValueError("Unsupported backend: CUDA_HOME and MUSA_HOME are not set.")
+
         build_args = []
         if "CMAKE_ARGS" in os.environ:
             cmake_args += [
                 item for item in os.environ["CMAKE_ARGS"].split(" ") if item]
-            
+
         if CpuInstructInfo.CPU_INSTRUCT == CpuInstructInfo.FANCY:
             cpu_args = CpuInstructInfo.CMAKE_FANCY
         elif CpuInstructInfo.CPU_INSTRUCT == CpuInstructInfo.AVX512:
@@ -231,7 +263,7 @@ class CMakeBuild(BuildExtension):
             cpu_args = CpuInstructInfo.CMAKE_AVX2
         else:
             cpu_args = CpuInstructInfo.CMAKE_NATIVE
-        
+
         cmake_args += [
             item for item in cpu_args.split(" ") if item
         ]
@@ -288,28 +320,55 @@ class CMakeBuild(BuildExtension):
         print("Standard output:", result.stdout)
         print("Standard error:", result.stderr)
         subprocess.run(
-            ["cmake", "--build", ".", *build_args], cwd=build_temp, check=True
+            ["cmake", "--build", ".", "--verbose", *build_args], cwd=build_temp, check=True
         )
 
+if CUDA_HOME is not None:
+    ops_module = CUDAExtension('KTransformersOps', [
+        'ktransformers/ktransformers_ext/cuda/custom_gguf/dequant.cu',
+        'ktransformers/ktransformers_ext/cuda/binding.cpp',
+        'ktransformers/ktransformers_ext/cuda/gptq_marlin/gptq_marlin.cu'
+    ],
+    extra_compile_args={
+            'cxx': ['-O3', '-DKTRANSFORMERS_USE_CUDA'],
+            'nvcc': [
+                '-O3',
+                '--use_fast_math',
+                '-Xcompiler', '-fPIC',
+                '-DKTRANSFORMERS_USE_CUDA',
+            ]
+        }
+    )
+elif MUSA_HOME is not None:
+    SimplePorting(cuda_dir_path="ktransformers/ktransformers_ext/cuda", mapping_rule={
+        # Common rules
+        "at::cuda": "at::musa",
+        "#include <ATen/cuda/CUDAContext.h>": "#include \"torch_musa/csrc/aten/musa/MUSAContext.h\"",
+        "#include <c10/cuda/CUDAGuard.h>": "#include \"torch_musa/csrc/core/MUSAGuard.h\"",
+        }).run()
+    ops_module = MUSAExtension('KTransformersOps', [
+        'ktransformers/ktransformers_ext/cuda_musa/custom_gguf/dequant.mu',
+        'ktransformers/ktransformers_ext/cuda_musa/binding.cpp',
+        # TODO: Add Marlin support for MUSA.
+        # 'ktransformers/ktransformers_ext/cuda_musa/gptq_marlin/gptq_marlin.mu'
+    ],
+    extra_compile_args={
+            'cxx': ['force_mcc'],
+            'mcc': [
+                '-O3',
+                '-DKTRANSFORMERS_USE_MUSA',
+                '-DTHRUST_IGNORE_CUB_VERSION_CHECK',
+            ]
+        }
+    )
+else:
+    raise ValueError("Unsupported backend: CUDA_HOME and MUSA_HOME are not set.")
 
 setup(
     version=VersionInfo().get_package_version(),
     cmdclass={"bdist_wheel":BuildWheelsCommand ,"build_ext": CMakeBuild},
     ext_modules=[
         CMakeExtension("cpuinfer_ext"),
-        CUDAExtension('KTransformersOps', [
+        ops_module,
-            'ktransformers/ktransformers_ext/cuda/custom_gguf/dequant.cu',
-            'ktransformers/ktransformers_ext/cuda/binding.cpp',
-            'ktransformers/ktransformers_ext/cuda/gptq_marlin/gptq_marlin.cu'
-        ],
-        extra_compile_args={
-                'cxx': ['-O3'],
-                'nvcc': [
-                    '-O3',
-                    '--use_fast_math',
-                    '-Xcompiler', '-fPIC',
-                ]
-            }
-        )
     ]
 )