windows系统下配置Mamba环境_windows mamba

最近在学习AI知识，试图在安装最近爆火的mamba模型时遇到困难。参考网上各位达人，经过努力，终于在win11系统anaconda中成功编译安装了mamba包。

我用的是visual studio 2019

1.在windows下构建Mamba使用环境：

conda create -n mamba python=3.10

conda activate mamba

注：Mamba需要triton，然而triton没有windows版，有人编译了triton2.0.0的windows版本，但python是3.10的。所以需要创建python-3.10的环境

2.安装torch：

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

这里torch很大，下载很慢，我是用迅雷下载后安装的

3.安装cmake

pip install cmake

4.安装triton

下载triton2.0.0的windows二进制文件，地址：https://hf-mirror.com/r4ziel/xformers_pre_built/blob/main/triton-2.0.0-cp310-cp310-win_amd64.whl，安装：

pip install D:\Downloads\triton-2.0.0-cp310-cp310-win_amd64.whl

5.下载mamba的源码并安装依赖包

git clone https://github.com/state-spaces/mamba，

切换到mamba目录：

cd mamba

修改setup.py，添加编译参数：-DM_LOG2E=1.44269504

注：selective_scan_fwd_kernel.cuh和selective_scan_bwd_kernel.cuh使用了次预定义常量，但文件里没定义。

将csrc\selective_scan\selective_scan_fwd_kernel.cuh中selective_scan_fwd_launch和csrc\selective_scan\selective_scan_bwd_kernel.cuh中selective_scan_bwd_launch里最内层的匿名函数内容抽取为独立方法。

selective_scan_fwd_kernel.cuh：

template<int kNThreads, int kNItems, int kNRows, bool kIsEvenLen, bool kIsVariableB, bool kIsVariableC, bool kHasZ, typename input_t, typename weight_t>
void selective_scan_fwd_call(SSMParamsBase &params, cudaStream_t stream) {
                    using Ktraits = Selective_Scan_fwd_kernel_traits<kNThreads, kNItems, kNRows, kIsEvenLen, kIsVariableB, kIsVariableC, kHasZ, input_t, weight_t>;
                    // constexpr int kSmemSize = Ktraits::kSmemSize;
                    constexpr int kSmemSize = Ktraits::kSmemSize + kNRows * MAX_DSTATE * sizeof(typename Ktraits::scan_t);
                    // printf("smem_size = %d\n", kSmemSize);
                    dim3 grid(params.batch, params.dim / kNRows);
                    auto kernel = &selective_scan_fwd_kernel<Ktraits>;
                    if (kSmemSize >= 48 * 1024) {
                        C10_CUDA_CHECK(cudaFuncSetAttribute(
                            kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
                    }
                    kernel<<<grid, Ktraits::kNThreads, kSmemSize, stream>>>(params);
                    C10_CUDA_KERNEL_LAUNCH_CHECK();
}
 
template<int kNThreads, int kNItems, typename input_t, typename weight_t>
void selective_scan_fwd_launch(SSMParamsBase &params, cudaStream_t stream) {
    // Only kNRows == 1 is tested for now, which ofc doesn't differ from previously when we had each block
    // processing 1 row.
    BOOL_SWITCH(params.seqlen % (kNThreads * kNItems) == 0, kIsEvenLen, [&] {
        BOOL_SWITCH(params.is_variable_B, kIsVariableB, [&] {
            BOOL_SWITCH(params.is_variable_C, kIsVariableC, [&] {
                BOOL_SWITCH(params.z_ptr != nullptr , kHasZ, [&] {
                    selective_scan_fwd_call<kNThreads, kNItems, 1, kIsEvenLen, kIsVariableB, kIsVariableC, kHasZ, input_t, weight_t>(params, stream);
                });
            });
        });
    });
}

selective_scan_bwd_kernel.cuh：

template<int kNThreads, int kNItems, bool kIsEvenLen, bool kIsVariableB, bool kIsVariableC, bool kDeltaSoftplus, bool kHasZ, typename input_t, typename weight_t>
void selective_scan_bwd_call(SSMParamsBwd &params, cudaStream_t stream) {
                        using Ktraits = Selective_Scan_bwd_kernel_traits<kNThreads, kNItems, kIsEvenLen, kIsVariableB, kIsVariableC, kDeltaSoftplus, kHasZ, input_t, weight_t>;
                        // using Ktraits = Selective_Scan_bwd_kernel_traits<kNThreads, kNItems, true, kIsVariableB, kIsVariableC, kDeltaSoftplus, kHasZ, input_t, weight_t>;
                        // TODO: check this
                        constexpr int kSmemSize = Ktraits::kSmemSize + MAX_DSTATE * sizeof(typename Ktraits::scan_t) + (kNThreads + 4 * MAX_DSTATE) * sizeof(typename Ktraits::weight_t);
                        // printf("smem_size = %d\n", kSmemSize);
                        dim3 grid(params.batch, params.dim);
                        auto kernel = &selective_scan_bwd_kernel<Ktraits>;
                        if (kSmemSize >= 48 * 1024) {
                            C10_CUDA_CHECK(cudaFuncSetAttribute(
                                kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
                        }
                        kernel<<<grid, Ktraits::kNThreads, kSmemSize, stream>>>(params);
                        C10_CUDA_KERNEL_LAUNCH_CHECK();
 
}
 
template<int kNThreads, int kNItems, typename input_t, typename weight_t>
void selective_scan_bwd_launch(SSMParamsBwd &params, cudaStream_t stream) {
    BOOL_SWITCH(params.seqlen % (kNThreads * kNItems) == 0, kIsEvenLen, [&] {
        BOOL_SWITCH(params.is_variable_B, kIsVariableB, [&] {
            BOOL_SWITCH(params.is_variable_C, kIsVariableC, [&] {
                BOOL_SWITCH(params.delta_softplus, kDeltaSoftplus, [&] {
                    BOOL_SWITCH(params.z_ptr != nullptr , kHasZ, [&] {
                        selective_scan_bwd_call<kNThreads, kNItems, kIsEvenLen, kIsVariableB, kIsVariableC, kDeltaSoftplus, kHasZ, input_t, weight_t>(params, stream);
                    });
                });
            });
        });
    });
 
}

注：估计是宏嵌套过多，展开后代码超过编译器允许长度了。

设置环境变量：

set MAMBA_FORCE_BUILD=TRUE

pip install .

编译安装成功，在虚拟环境的Lib\site-packages目录下，多了422MB文件：selective_scan_cuda.cp310-win_amd64.pyd。

6.安装causal-conv1d（可选）

git clone https://github.com/Dao-AILab/causal-conv1d.git

cd causal-conv1d

set CAUSAL_CONV1D_FORCE_BUILD=TRUE

打开csrc\causal_conv1d.cpp文件：

159、277、278行：将判断条件中的and改为&&

pip install .

编译安装成功，在虚拟环境的Lib\site-packages目录下，多了157MB文件：causal_conv1d_cuda.cp310-win_amd64.pyd。

说明：

代码构建时出现错误：

subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1.

其实是ninja编译过程中发生了错误。

代码构建失败后，build目录会被删除，重新构建很花时间。

我注释了D:\anaconda3\envs\mamba\Lib\site-packages\pip\_internal\wheel_builder.py的285行的call_subprocess调用，使build目录保留下来，

然后cd build\temp.win-amd64-cpython-310\Release到build.ninja所在目录，

再执行ninja，重现错误就快很多了。