win11环境下成功安装mamba

1. Mamba环境搭建

参考：https://blog.csdn.net/yyywxk/article/details/136071016

conda clean --all
conda create -n mamba_env python=3.10.13
conda activate mamba_env
conda install cudatoolkit==11.8 -c nvidia
pip install torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 --index-url https://download.pytorch.org/whl/cu118
conda install -c "nvidia/label/cuda-11.8.0" cuda-nvcc
conda install packaging

2. triton安装

下载triton文件：https://github.com/PrashantSaikia/Triton-for-Windows/tree/main

# 激活刚才创建的mamba环境
conda activate mamba_env# 安装triton
pip install 【文件路径】\triton-2.0.0-cp310-cp310-win_amd64.whl
# 如：pip install D:\mamba\triton-2.0.0-cp310-cp310-win_amd64.whl

3. causal_conv1d安装

3.1 下载causal_conv1d工程文件源码

下载causal_conv1d工程文件源码：https://github.com/Dao-AILab/causal-conv1d/releases
这里有各个版本的causal_conv1d，找到v1.1.1

进入v1.1.1资源界面：

拉到最后，点击Source code(zip)，直接下载

3.2 修改setup.py文件

解压causal-conv1d-1.1.1.zip文件
将里面的源码setup.py进行以下改动：
参考：https://blog.csdn.net/yyywxk/article/details/136071016和https://blog.csdn.net/m0_59115667/article/details/137794459

将下面的代码

FORCE_BUILD = os.getenv("CAUSAL_CONV1D_FORCE_BUILD", "FALSE") == "TRUE"
SKIP_CUDA_BUILD = os.getenv("CAUSAL_CONV1D_SKIP_CUDA_BUILD", "FALSE") == "TRUE"
# For CI, we want the option to build with C++11 ABI since the nvcr images use C++11 ABI
FORCE_CXX11_ABI = os.getenv("CAUSAL_CONV1D_FORCE_CXX11_ABI", "FALSE") == "TRUE"

修改为

FORCE_BUILD = os.getenv("CAUSAL_CONV1D_FORCE_BUILD", "FALSE") == "FALSE"
SKIP_CUDA_BUILD = os.getenv("CAUSAL_CONV1D_SKIP_CUDA_BUILD", "FALSE") == "FALSE"
# For CI, we want the option to build with C++11 ABI since the nvcr images use C++11 ABI
FORCE_CXX11_ABI = os.getenv("CAUSAL_CONV1D_FORCE_CXX11_ABI", "FALSE") == "FALSE"

保存

3.3 安装 causal_conv1d

# 激活刚才创建的mamba环境
conda activate mamba_env# 打开causal_conv1d所在文件夹
cd/d D:\Anaconda\Mamba\causal-conv1d-1.1.1      # 改成你自己的causal-conv1d-1.1.1文件路径# 安装
pip install . 或者  python setup.py install

4. Mamba安装

4.1 下载mamba工程文件源码

下载mamba工程文件源码：https://github.com/state-spaces/mamba/releases?page=2
步骤跟causal_conv1d一样，这里有各个版本的mamba，找到v1.1.1

4.2 修改setup.py文件

解压mamba-1.1.1文件
mamba工程文件的源码setup.py中我们要进行以下改动：
将下面的代码

FORCE_BUILD = os.getenv("MAMBA_FORCE_BUILD", "FALSE") == "TRUE"
SKIP_CUDA_BUILD = os.getenv("MAMBA_SKIP_CUDA_BUILD", "FALSE") == "TRUE"

修改为

FORCE_BUILD = os.getenv("MAMBA_FORCE_BUILD", "FALSE") == "FALSE"
SKIP_CUDA_BUILD = os.getenv("MAMBA_SKIP_CUDA_BUILD", "FALSE") == "FALSE"

4.3 安装 mamba

# 激活mamba环境
conda activate mamba_env# 打开文件夹
cd/d D:\Anaconda\Mamba\mamba-1.1.1    # 改成你自己的mamba-1.1.1文件路径# 安装
pip install . 或者  python setup.py install

5. 查看所有成功安装的库

6. 测试mamba安装是否成功

# 激活mamba环境
conda activate mamba_env# 进入python编译
python# 加载库
import torch
import causal_conv1d
from mamba_ssm import Mamba# 代码函数
batch, length, dim = 2, 64, 16
x = torch.randn(batch, length, dim).to("cuda")
model = Mamba(# This module uses roughly 3 * expand * d_model^2 parametersd_model=dim, # Model dimension d_modeld_state=16,  # SSM state expansion factord_conv=4,    # Local convolution widthexpand=2,    # Block expansion factor
).to("cuda")
y = model(x)
assert y.shape == x.shape
print('success')

6.1 测试成功

6.2 测试失败：No module named ‘causal_conv1d_cuda’ 或 ‘selective_scan_cuda’

from mamba_ssm import Mamba不成功
报错：ModuleNotFoundError: No module named ‘causal_conv1d_cuda’ || ‘selective_scan_cuda’

6.3 解决方案

1. 根据报错文件路径，找到causal_conv1d_interface.py和selective_scan_interface.py文件
2. 用vscode或其他编辑软件打开
3. 注释或修改报错内容

a. causal_conv1d_interface.py和selective_scan_interface.py注释

文件：causal_conv1d_interface.py
# import causal_conv1d_cuda文件：selective_scan_interface.py
# import causal_conv1d_cuda
# import selective_scan_cuda

b. 修改causal_conv1d_interface.py中的causal_conv1d_fn函数

def causal_conv1d_fn(x, weight, bias=None, seq_idx=None, activation=None):"""x: (batch, dim, seqlen)weight: (dim, width)bias: (dim,)seq_idx: (batch, seqlen)activation: either None or "silu" or "swish"out: (batch, dim, seqlen)"""return CausalConv1dFn.apply(x, weight, bias, seq_idx, activation)

修改为

def causal_conv1d_fn(x, weight, bias=None, seq_idx=None, activation=None):"""x: (batch, dim, seqlen)weight: (dim, width)bias: (dim,)seq_idx: (batch, seqlen)activation: either None or "silu" or "swish"out: (batch, dim, seqlen)"""return causal_conv1d_ref(x, weight, bias, activation)

c. 修改selective_scan_interface.py中的selective_scan_fn和mamba_inner_fn函数

def selective_scan_fn(u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False,return_last_state=False):"""if return_last_state is True, returns (out, last_state)last_state has shape (batch, dim, dstate). Note that the gradient of the last state isnot considered in the backward pass."""return SelectiveScanFn.apply(u, delta, A, B, C, D, z, delta_bias, delta_softplus, return_last_state)def mamba_inner_fn(xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight,out_proj_weight, out_proj_bias,A, B=None, C=None, D=None, delta_bias=None, B_proj_bias=None,C_proj_bias=None, delta_softplus=True
):return MambaInnerFn.apply(xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight,out_proj_weight, out_proj_bias,A, B, C, D, delta_bias, B_proj_bias, C_proj_bias, delta_softplus)

修改为

def selective_scan_fn(u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False,return_last_state=False):"""if return_last_state is True, returns (out, last_state)last_state has shape (batch, dim, dstate). Note that the gradient of the last state isnot considered in the backward pass."""return selective_scan_ref(u, delta, A, B, C, D, z, delta_bias, delta_softplus, return_last_state)def mamba_inner_fn(xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight,out_proj_weight, out_proj_bias,A, B=None, C=None, D=None, delta_bias=None, B_proj_bias=None,C_proj_bias=None, delta_softplus=True
):return mamba_inner_ref(xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight,out_proj_weight, out_proj_bias,A, B, C, D, delta_bias, B_proj_bias, C_proj_bias, delta_softplus)

7. 卸载causal_conv1d和mamba-ssm

上述对你的电脑来说，可能也不会成功，……方便卸载：）再试试别的办法吧~~

pip uninstall causal_conv1d
pip uninstall mamba-ssm

8. 下载所需文件

不想去官网下载的，可以直接网盘下载我已经修改好setup.py文件所有文件。
链接：https://pan.baidu.com/s/1NKoqUPIGd_UexBdDFZxljQ
提取码：3asf