Mamba 代码环境配置_mamba环境配置

Mamba介绍

Mamba是ICLR2024上出圈的建模时序数据的新架构，基于S4（Selective Structured State Space）架构。
但是Mamba官方源码https://github.com/state-spaces/mamba给出的环境配置方法，实际操作时经常出现相关bug，主要集中在causal-conv1d和mamba_ssm这两个包的安装上。

配置步骤

下面给出在3090上成功配置mamba源码环境的步骤：

1. 创建新的虚拟环境，conda安装cudatoolkit和pytorch等包

conda create -n mamba python=3.9
conda activate mamba
conda install cudatoolkit==11.7 -c nvidia
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
conda install -c "nvidia/label/cuda-11.7.0" cuda-nvcc
conda install packaging
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上述安装时如果出现找不到包的情况，可能是网络问题，也可能需要添加镜像源

2. 安装causal-conv1d

pip install causal-conv1d==1.0.0
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如果上述安装失败，则进行离线安装

wget https://github.com/Dao-AILab/causal-conv1d/releases/download/v1.0.0/causal_conv1d-1.0.0+cu118torch1.13cxx11abiFALSE-cp39-cp39-linux_x86_64.whl
pip install causal_conv1d-1.0.0+cu118torch1.13cxx11abiFALSE-cp39-cp39-linux_x86_64.whl
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3. 安装mamba_ssm

pip install mamba_ssm==1.0.1
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4. 测试是否安装成功，运行下面代码，不报错即可

import torch
from mamba_ssm import Mamba

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 parameters
    d_model=dim,  # Model dimension d_model
    d_state=16,  # SSM state expansion factor
    d_conv=4,  # Local convolution width
    expand=2,  # Block expansion factor
).to("cuda")
y = model(x)
assert y.shape == x.shape
print(y)
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