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2024/11/15 20:55:49 来源：https://blog.csdn.net/asterfusion/article/details/143121718  浏览:  次   关键词：深圳建工是国企还是私企_网站注册平台_深圳关键词排名推广_站长之家seo工具包
深圳建工是国企还是私企_网站注册平台_深圳关键词排名推广_站长之家seo工具包

【温馨提示：本篇文章涉及较多代码，请移步至wx公号星融元Asterfusion，发送关键词“分布式”获取完整版及Q&A】

随着AI、大模型的快速发展，传统的集中式计算已无法应对激增的数据处理需求，而分布式计算是指将一个计算任务分解成多个子任务，由多个计算节点并行地进行计算，并将结果汇总得到最终结果的计算方式，能够更高效、更稳定、更灵活地处理大规模数据和复杂计算任务，在各行各业中得到了广泛的应用。

那如何从零到一搭建分布式计算的环境呢？本文将从硬件选型，到服务器侧的基础配置、GPU驱动安装和集合通讯库配置，以及无损以太网的启用，直至大模型导入和训练测试，带您跑通搭建分布式计算环境的全流程。

硬件准备

GPU服务器选型

GPU拥有大量的计算核心，可以同时处理多个数据任务，是构成智算中心的关键硬件。

从智算中心方案的整体设计层面来看：GPU服务器集群和存储服务器集群分别通过计算网络（Scale-out网络）和存储网络连接。另外两张管理网中，业务管理网用于GPU服务器互联，进行AIOS管理面通信，带外管理则连接整个智算中心的所有设备，用于运维接入管理。

图 1 ：智算中心方案的概要设计拓扑

明确了智算中心的整体设计后，我们将对比通用计算服务器与GPU服务器的内部硬件连接拓扑图，来具体了解GPU服务器的选型逻辑：

图 2 ：通用计算服务器内部的硬件连接拓扑
图 3 ： GPU 服务器内部的硬件连接拓扑

图2是一台通用计算服务器内部的硬件连接拓扑，这台服务器的核心是两块AMD的EPYC CPU，根据IO Chiplet扩展出了若干接口，辅助CPU充分释放通用计算能力。

图3是一台GPU服务器内部的硬件连接拓扑，这台服务器配备了8块A100 GPU，8张用于计算通信的RDMA网卡，以及2张用于存储通信的RDMA网卡，所有的IO组件设计，都是为了让这8块GPU充分释放算力。

通过上面两张硬件连接拓扑图可以看到，通用服务器和GPU服务器从基本的硬件构造上就有着非常大的差异，一个是围绕通用CPU来构建，另一个是围绕着GPU来构建的。因此，在硬件选型阶段，就需要注意差别，通常来讲通用服务器是没有办法复用改造成一台高性能的GPU服务器，PCIe接口数量、服务器空间、散热设计、电源等方面都不能满足要求。

当通过计算任务确定算力需求，进而确定了所需要的GPU型号和数量之后，我们也就可以再继续规划整个GPU集群的组网了。

由于资源限制，本次实验验证中，使用三台通用服务器稍加改造进行后续的并行训练和推理测试。

计算节点的硬件配置如下：

CPU：Intel(R) Xeon(R) CPU E5-2678 v3 @ 2.50GHz * 2

GPU：NVIDIA GeForce RTX 4060 Ti 16G * 1

内存：128G

存储：10T HDD * 2

网卡：MGMT、CX5

其他部分：

散热：GPU为全高尺寸，但服务器只有2U，所以只能拆掉上盖板;

电源：通用服务器通常没有预留足够的供电接口，因此需要使用外置电源对GPU进行额外供电；

电源选择的是Great Wall 额定650W X6，功率上可以同时满足3块GPU（RTX4060Ti需要外接150W的供电）的供电要求，并且支持3个8pin接口，用来分别连接三块GPU。

图 4 ：电源选型示意图
图 5 ： GPU 和 RDMA 网卡上机安装后的实拍图

高性能计算网选型

智算中心的管理网相较于传统的通用计算数据中心来说，没有太大差异。比较特殊的就是Scale-out计算网络和存储网络，这两张网络承载的业务流量决定了交换机设备的选型需求：支持RDMA、低时延、高吞吐。

如下图所示，在组网连接方面也有所不同，这里会通过将GPU分组（图中#L0～7一组，#L8～15一组），组成只有一跳的高带宽互联域（HB域），并通过针对智算场景优化的Rail交换机连接，实现了高效的数据传输和计算协同。

图 6 ：组网连接示意

这次实验验证中，计算网的交换机选用星融元Asterfusion®️ CX-N系列超低时延交换机，具体型号为CX308P-48Y-N。

型号

业务接口

交换容量

CX864E-N

64 x 800GE OSFP，2 x 10GE SFP+

102.4Tbps

CX732Q-N

32 x 400GE QSFP-DD, 2 x 10GE SFP+

25.6Tbps

CX664D-N

64 x 200GE QSFP56, 2 x 10GE SFP+

25.6Tbps

CX564P-N

64 x 100GE QSFP28, 2 x 10GE SFP+

12.8Tbps

CX532P-N

32 x 100GE QSFP28, 2 x 10GE SFP+

6.4Tbps

CX308P-48Y-N

48 x 25GE SFP28, 8 x 100GE QSFP28

4.0Tbps

表1：具体型号规格示意

提升大模型训练效率

CX-N数据中心交换机的单机转发时延（400ns）低至业界平均水平的1/4~1/5，将网络时延在AI/ML应用端到端时延中的占比降至最低，同时多维度的高可靠设计确保网络在任何时候都不中断，帮助大模型的训练大幅度降低训练时间、提升整体效率。

全系列标配RoCEv2能力

区别于传统厂家多等级License权限管理方式，CX-N数据中心交换机所有应用场景License权限一致，全系列标配RoCEv2能力，提供PFC、ECN、Easy RoCE等一系列面向生产环境的增强网络特性，用户无须为此类高级特性额外付出网络建设成本，帮助用户获得更高的ROI。

开放、中立的AI/ML网络

星融元AI/ML网络解决方案的开放性确保用户能够重用已有的系统（K8s、Prometheus等）对网络进行管理，无需重复投入；星融元以“中立的网络供应商参与AI生态”的理念为用户提供专业的网络方案，帮助用户规避“全栈方案锁定”的风险。

最终，实验环节的组网拓扑和基础配置如下所示。

图 7 ：实验拓扑和基础配置示意

软件准备

以上，我们已经完成了硬件选型，接下来我们将进行软件层面的配置：部署 RoCEv2 交换机、配置GPU 服务器、安装 GPU 驱动和集合通讯库。

RoCEv2交换机

图 8 ： CX308P-48Y-N 设备图

本次并行训练的环境中设备数量较少，组网相对简单：

1. 将CX5网卡的25GE业务接口连接到CX308P；

2. 在交换机上一键启用全局RoCE的无损配置；

3. 将三个25G业务口划分到一个VLAN下组成一个二层网络；

如前文提到，CX-N数据中心交换机全系列标配RoCEv2能力，配合星融元AsterNOS网络操作系统，只需要两行命令行便可配置所有必要的QoS规则和参数，具体命令行如下：

noone@MacBook-Air ~ % ssh admin@10.230.1.17
Linux AsterNOS 5.10.0-8-2-amd64 #1 SMP Debian 5.10.46-4 (2021-08-03) x86_64_          _                _   _   ___   ____  / \    ___ | |_   ___  _ __ | \ | | / _ \ / ___| / _ \  / __|| __| / _ \| '__||  \| || | | |\___ \ / ___ \ \__ \| |_ |  __/| |   | |\  || |_| | ___) |
/_/   \_\|___/ \__| \___||_|   |_| \_| \___/ |____/ ------- Asterfusion Network Operating System -------Help:    http://www.asterfusion.com/Last login: Sun Sep 29 17:10:46 2024 from 172.16.20.241AsterNOS# configure terminal 
AsterNOS(config)# qos roce lossless   
AsterNOS(config)# qos service-policy roce_lossless 
AsterNOS(config)# end
AsterNOS# show qos roceoperational    description
------------------  -------------  ---------------------------------------------------
status              bind           qos roce binding status
mode                lossless       Roce Mode
cable-length        40m            Cable Length(in meters) for Roce Lossless Config
congestion-control
- congestion-mode   ECN            congestion-control
- enabled-tc        3,4            Congestion config enabled Traffic Class
- max-threshold     750000         Congestion config max-threshold
- min-threshold     15360          Congestion config max-threshold
pfc
- pfc-priority      3,4            switch-prio on which PFC is enabled
- rx-enabled        enable         PFC Rx Enabled status
- tx-enabled        enable         PFC Tx Enabled status
trust
- trust-mode        dscp           Trust Setting on the port for packet classificationRoCE DSCP->SP mapping configurations
==========================================
dscp                       switch-prio
-----------------------  -------------
0,1,2,3,4,5,6,7                      0
10,11,12,13,14,15,8,9                1
16,17,18,19,20,21,22,23              2
24,25,26,27,28,29,30,31              3
32,33,34,35,36,37,38,39              4
40,41,42,43,44,45,46,47              5
48,49,50,51,52,53,54,55              6
56,57,58,59,60,61,62,63              7RoCE SP->TC mapping and ETS configurations
================================================switch-prio  mode    weight
-------------  ------  --------6  SP7  SPRoCE pool config
======================
name                     switch-prio
-----------------------  -------------
egress_lossy_profile     0 1 2 5 6
ingress_lossy_profile    0 1 2 5 6
egress_lossless_profile  3 4
roce_lossless_profile    3 4

GPU服务器基础配置

以下所有操作，在三台服务器上都需要执行，本文档中的配置步骤以server3为例。

关闭防火墙和SELinux

[root@server3 ~]# systemctl stop firewalld
[root@server3 ~]# systemctl disable firewalld
[root@server3 ~]# setenforce 0
[root@server3 ~]# sed -i 's/SELINUX=enforcing/SELINUX=disabled/g' /etc/sysconfig/selinux

配置服务器间免密登陆

[root@server3 ~]# ssh-keygen
[root@server3 ~]# ssh-copy-id root@server1
[root@server3 ~]# ssh-copy-id root@server2

配置服务器软件源

[root@server3 ~]# ll /etc/yum.repos.d/
总用量 80
-rw-r--r-- 1 root root 2278 9月  19 08:00 CentOS-Base.repo
-rw-r--r-- 1 root root  232 9月  19 08:00 cuda-rhel7.repo
-rw-r--r-- 1 root root  210 9月  19 08:00 cudnn-local-rhel7-8.9.7.29.repo
drwxr-xr-x 2 root root 4096 9月  19 07:58 disable.d
-rw-r--r-- 1 root root  664 9月  19 08:00 epel.repo
-rw-r--r-- 1 root root  381 9月  19 08:00 hashicorp.repo
-rw-r--r-- 1 root root  218 9月  19 08:00 kubernetes.repo
-rw-r--r-- 1 root root  152 9月  19 08:00 MariaDB.repo
-rw-r--r-- 1 root root  855 9月  19 08:00 remi-modular.repo
-rw-r--r-- 1 root root  456 9月  19 08:00 remi-php54.repo
-rw-r--r-- 1 root root 1314 9月  19 08:00 remi-php70.repo
-rw-r--r-- 1 root root 1314 9月  19 08:00 remi-php71.repo
-rw-r--r-- 1 root root 1314 9月  19 08:00 remi-php72.repo
-rw-r--r-- 1 root root 1314 9月  19 08:00 remi-php73.repo
-rw-r--r-- 1 root root 1314 9月  19 08:00 remi-php74.repo
-rw-r--r-- 1 root root 1314 9月  19 08:00 remi-php80.repo
-rw-r--r-- 1 root root 1314 9月  19 08:00 remi-php81.repo
-rw-r--r-- 1 root root 1314 9月  19 08:00 remi-php82.repo
-rw-r--r-- 1 root root 2605 9月  19 08:00 remi.repo
-rw-r--r-- 1 root root  750 9月  19 08:00 remi-safe.repo
[root@server3 ~]# more /etc/yum.repos.d/*.repo
::::::::::::::
/etc/yum.repos.d/CentOS-Base.repo
::::::::::::::
# CentOS-Base.repo
#
# The mirror system uses the connecting IP address of the client and the
# update status of each mirror to pick mirrors that are updated to and
# geographically close to the client.  You should use this for CentOS updates
# unless you are manually picking other mirrors.
#
# If the mirrorlist= does not work for you, as a fall back you can try the 
# remarked out baseurl= line instead.
#
#[base]
name=CentOS-7 - Base - mirrors.aliyun.com
failovermethod=priority
baseurl=http://mirrors.aliyun.com/centos/7/os/x86_64/http://mirrors.aliyuncs.com/centos/7/os/x86_64/http://mirrors.cloud.aliyuncs.com/centos/7/os/x86_64/
gpgcheck=1
gpgkey=http://mirrors.aliyun.com/centos/RPM-GPG-KEY-CentOS-7#released updates 
[updates]
name=CentOS-7 - Updates - mirrors.aliyun.com
failovermethod=priority
baseurl=http://mirrors.aliyun.com/centos/7/updates/x86_64/http://mirrors.aliyuncs.com/centos/7/updates/x86_64/http://mirrors.cloud.aliyuncs.com/centos/7/updates/x86_64/
gpgcheck=1
gpgkey=http://mirrors.aliyun.com/centos/RPM-GPG-KEY-CentOS-7#additional packages that may be useful
[extras]
name=CentOS-7 - Extras - mirrors.aliyun.com
failovermethod=priority
baseurl=http://mirrors.aliyun.com/centos/7/extras/x86_64/http://mirrors.aliyuncs.com/centos/7/extras/x86_64/http://mirrors.cloud.aliyuncs.com/centos/7/extras/x86_64/
gpgcheck=1
gpgkey=http://mirrors.aliyun.com/centos/RPM-GPG-KEY-CentOS-7#additional packages that extend functionality of existing packages
[centosplus]
name=CentOS-7 - Plus - mirrors.aliyun.com
failovermethod=priority
baseurl=http://mirrors.aliyun.com/centos/7/centosplus/x86_64/http://mirrors.aliyuncs.com/centos/7/centosplus/x86_64/http://mirrors.cloud.aliyuncs.com/centos/7/centosplus/x86_64/
gpgcheck=1
enabled=0
gpgkey=http://mirrors.aliyun.com/centos/RPM-GPG-KEY-CentOS-7#contrib - packages by Centos Users
[contrib]
name=CentOS-7 - Contrib - mirrors.aliyun.com
failovermethod=priority
baseurl=http://mirrors.aliyun.com/centos/7/contrib/x86_64/http://mirrors.aliyuncs.com/centos/7/contrib/x86_64/http://mirrors.cloud.aliyuncs.com/centos/7/contrib/x86_64/
gpgcheck=1
enabled=0
gpgkey=http://mirrors.aliyun.com/centos/RPM-GPG-KEY-CentOS-7
::::::::::::::
/etc/yum.repos.d/cuda-rhel7.repo
::::::::::::::
[cuda-rhel7-x86_64]
name=cuda-rhel7-x86_64
baseurl=https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64
enabled=1
gpgcheck=1
gpgkey=https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/D42D0685.pub
::::::::::::::
/etc/yum.repos.d/cudnn-local-rhel7-8.9.7.29.repo
::::::::::::::
[cudnn-local-rhel7-8.9.7.29]
name=cudnn-local-rhel7-8.9.7.29
baseurl=file:///var/cudnn-local-repo-rhel7-8.9.7.29
enabled=1
gpgcheck=1
gpgkey=file:///var/cudnn-local-repo-rhel7-8.9.7.29/90F10142.pub
obsoletes=0
::::::::::::::
/etc/yum.repos.d/epel.repo
::::::::::::::
[epel]
name=Extra Packages for Enterprise Linux 7 - $basearch
baseurl=http://mirrors.aliyun.com/epel/7/$basearch
failovermethod=priority
enabled=1
gpgcheck=0
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-EPEL-7[epel-debuginfo]
name=Extra Packages for Enterprise Linux 7 - $basearch - Debug
baseurl=http://mirrors.aliyun.com/epel/7/$basearch/debug
failovermethod=priority
enabled=0
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-EPEL-7
gpgcheck=0[epel-source]
name=Extra Packages for Enterprise Linux 7 - $basearch - Source
baseurl=http://mirrors.aliyun.com/epel/7/SRPMS
failovermethod=priority
enabled=0
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-EPEL-7
gpgcheck=0
::::::::::::::
/etc/yum.repos.d/hashicorp.repo
::::::::::::::
[hashicorp]
name=Hashicorp Stable - $basearch
baseurl=https://rpm.releases.hashicorp.com/RHEL/$releasever/$basearch/stable
enabled=0
gpgcheck=1
gpgkey=https://rpm.releases.hashicorp.com/gpg[hashicorp-test]
name=Hashicorp Test - $basearch
baseurl=https://rpm.releases.hashicorp.com/RHEL/$releasever/$basearch/test
enabled=0
gpgcheck=1
gpgkey=https://rpm.releases.hashicorp.com/gpg
::::::::::::::
/etc/yum.repos.d/kubernetes.repo
::::::::::::::
[kubernetes]
name=Kubernetes
baseurl=https://mirrors.aliyun.com/kubernetes-new/core/stable/v1.28/rpm/
enabled=1
gpgcheck=1
gpgkey=https://mirrors.aliyun.com/kubernetes-new/core/stable/v1.28/rpm/repodata/repomd.xml.key
::::::::::::::
/etc/yum.repos.d/MariaDB.repo
::::::::::::::
[mariadb]
name = MariaDB
baseurl = https://mirror.mariadb.org/yum/11.2/centos74-amd64
gpgkey = https://yum.mariadb.org/RPM-GPG-KEY-MariaDB
gpgcheck = 0
::::::::::::::
/etc/yum.repos.d/remi-modular.repo
::::::::::::::
# Repository: https://rpms.remirepo.net/
# Blog:       https://blog.remirepo.net/
# Forum:      https://forum.remirepo.net/[remi-modular]
name=Remi's Modular repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/modular/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/modular/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/modular/mirror
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-modular-test]
name=Remi's Modular testing repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/modular-test/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/modular-test/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/modular-test/mirror
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi::::::::::::::
/etc/yum.repos.d/remi-php54.repo
::::::::::::::
# This repository only provides PHP 5.4 and its extensions
# NOTICE: common dependencies are in "remi-safe"[remi-php54]
name=Remi's PHP 5.4 RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/php54/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/php54/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/php54/mirror
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi::::::::::::::
/etc/yum.repos.d/remi-php70.repo
::::::::::::::
# This repository only provides PHP 7.0 and its extensions
# NOTICE: common dependencies are in "remi-safe"[remi-php70]
name=Remi's PHP 7.0 RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/php70/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/php70/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/php70/mirror
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php70-debuginfo]
name=Remi's PHP 7.0 RPM repository for Enterprise Linux 7 - $basearch - debuginfo
baseurl=http://rpms.remirepo.net/enterprise/7/debug-php70/$basearch/
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php70-test]
name=Remi's PHP 7.0 test RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/test70/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/test70/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/test70/mirror
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php70-test-debuginfo]
name=Remi's PHP 7.0 test RPM repository for Enterprise Linux 7 - $basearch - debuginfo
baseurl=http://rpms.remirepo.net/enterprise/7/debug-test70/$basearch/
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi
::::::::::::::
/etc/yum.repos.d/remi-php71.repo
::::::::::::::
# This repository only provides PHP 7.1 and its extensions
# NOTICE: common dependencies are in "remi-safe"[remi-php71]
name=Remi's PHP 7.1 RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/php71/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/php71/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/php71/mirror
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php71-debuginfo]
name=Remi's PHP 7.1 RPM repository for Enterprise Linux 7 - $basearch - debuginfo
baseurl=http://rpms.remirepo.net/enterprise/7/debug-php71/$basearch/
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php71-test]
name=Remi's PHP 7.1 test RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/test71/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/test71/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/test71/mirror
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php71-test-debuginfo]
name=Remi's PHP 7.1 test RPM repository for Enterprise Linux 7 - $basearch - debuginfo
baseurl=http://rpms.remirepo.net/enterprise/7/debug-test71/$basearch/
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi
::::::::::::::
/etc/yum.repos.d/remi-php72.repo
::::::::::::::
# This repository only provides PHP 7.2 and its extensions
# NOTICE: common dependencies are in "remi-safe"[remi-php72]
name=Remi's PHP 7.2 RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/php72/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/php72/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/php72/mirror
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php72-debuginfo]
name=Remi's PHP 7.2 RPM repository for Enterprise Linux 7 - $basearch - debuginfo
baseurl=http://rpms.remirepo.net/enterprise/7/debug-php72/$basearch/
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php72-test]
name=Remi's PHP 7.2 test RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/test72/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/test72/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/test72/mirror
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php72-test-debuginfo]
name=Remi's PHP 7.2 test RPM repository for Enterprise Linux 7 - $basearch - debuginfo
baseurl=http://rpms.remirepo.net/enterprise/7/debug-test72/$basearch/
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi
::::::::::::::
/etc/yum.repos.d/remi-php73.repo
::::::::::::::
# This repository only provides PHP 7.3 and its extensions
# NOTICE: common dependencies are in "remi-safe"[remi-php73]
name=Remi's PHP 7.3 RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/php73/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/php73/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/php73/mirror
enabled=1
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php73-debuginfo]
name=Remi's PHP 7.3 RPM repository for Enterprise Linux 7 - $basearch - debuginfo
baseurl=http://rpms.remirepo.net/enterprise/7/debug-php73/$basearch/
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php73-test]
name=Remi's PHP 7.3 test RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/test73/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/test73/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/test73/mirror
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php73-test-debuginfo]
name=Remi's PHP 7.3 test RPM repository for Enterprise Linux 7 - $basearch - debuginfo
baseurl=http://rpms.remirepo.net/enterprise/7/debug-test73/$basearch/
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi
::::::::::::::
/etc/yum.repos.d/remi-php74.repo
::::::::::::::
# This repository only provides PHP 7.4 and its extensions
# NOTICE: common dependencies are in "remi-safe"[remi-php74]
name=Remi's PHP 7.4 RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/php74/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/php74/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/php74/mirror
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php74-debuginfo]
name=Remi's PHP 7.4 RPM repository for Enterprise Linux 7 - $basearch - debuginfo
baseurl=http://rpms.remirepo.net/enterprise/7/debug-php74/$basearch/
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php74-test]
name=Remi's PHP 7.4 test RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/test74/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/test74/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/test74/mirror
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php74-test-debuginfo]
name=Remi's PHP 7.4 test RPM repository for Enterprise Linux 7 - $basearch - debuginfo
baseurl=http://rpms.remirepo.net/enterprise/7/debug-test74/$basearch/
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi
::::::::::::::
/etc/yum.repos.d/remi-php80.repo
::::::::::::::
# This repository only provides PHP 8.0 and its extensions
# NOTICE: common dependencies are in "remi-safe"[remi-php80]
name=Remi's PHP 8.0 RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/php80/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/php80/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/php80/mirror
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php80-debuginfo]
name=Remi's PHP 8.0 RPM repository for Enterprise Linux 7 - $basearch - debuginfo
baseurl=http://rpms.remirepo.net/enterprise/7/debug-php80/$basearch/
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php80-test]
name=Remi's PHP 8.0 test RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/test80/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/test80/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/test80/mirror
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php80-test-debuginfo]
name=Remi's PHP 8.0 test RPM repository for Enterprise Linux 7 - $basearch - debuginfo
baseurl=http://rpms.remirepo.net/enterprise/7/debug-test80/$basearch/
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi
::::::::::::::
/etc/yum.repos.d/remi-php81.repo
::::::::::::::
# This repository only provides PHP 8.1 and its extensions
# NOTICE: common dependencies are in "remi-safe"[remi-php81]
name=Remi's PHP 8.1 RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/php81/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/php81/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/php81/mirror
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php81-debuginfo]
name=Remi's PHP 8.1 RPM repository for Enterprise Linux 7 - $basearch - debuginfo
baseurl=http://rpms.remirepo.net/enterprise/7/debug-php81/$basearch/
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php81-test]
name=Remi's PHP 8.1 test RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/test81/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/test81/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/test81/mirror
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php81-test-debuginfo]
name=Remi's PHP 8.1 test RPM repository for Enterprise Linux 7 - $basearch - debuginfo
baseurl=http://rpms.remirepo.net/enterprise/7/debug-test81/$basearch/
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi
::::::::::::::
/etc/yum.repos.d/remi-php82.repo
::::::::::::::
# This repository only provides PHP 8.2 and its extensions
# NOTICE: common dependencies are in "remi-safe"[remi-php82]
name=Remi's PHP 8.2 RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/php82/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/php82/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/php82/mirror
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php82-debuginfo]
name=Remi's PHP 8.2 RPM repository for Enterprise Linux 7 - $basearch - debuginfo
baseurl=http://rpms.remirepo.net/enterprise/7/debug-php82/$basearch/
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php82-test]
name=Remi's PHP 8.2 test RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/test82/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/test82/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/test82/mirror
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php82-test-debuginfo]
name=Remi's PHP 8.2 test RPM repository for Enterprise Linux 7 - $basearch - debuginfo
baseurl=http://rpms.remirepo.net/enterprise/7/debug-test82/$basearch/
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi
::::::::::::::
/etc/yum.repos.d/remi.repo
::::::::::::::
# Repository: http://rpms.remirepo.net/
# Blog:       http://blog.remirepo.net/
# Forum:      http://forum.remirepo.net/[remi]
name=Remi's RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/remi/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/remi/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/remi/mirror
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php55]
name=Remi's PHP 5.5 RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/php55/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/php55/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/php55/mirror
# NOTICE: common dependencies are in "remi-safe"
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php56]
name=Remi's PHP 5.6 RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/php56/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/php56/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/php56/mirror
# NOTICE: common dependencies are in "remi-safe"
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-test]
name=Remi's test RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/test/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/test/mirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/test/mirror
# WARNING: If you enable this repository, you must also enable "remi"
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-debuginfo]
name=Remi's RPM repository for Enterprise Linux 7 - $basearch - debuginfo
baseurl=http://rpms.remirepo.net/enterprise/7/debug-remi/$basearch/
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php55-debuginfo]
name=Remi's PHP 5.5 RPM repository for Enterprise Linux 7 - $basearch - debuginfo
baseurl=http://rpms.remirepo.net/enterprise/7/debug-php55/$basearch/
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-php56-debuginfo]
name=Remi's PHP 5.6 RPM repository for Enterprise Linux 7 - $basearch - debuginfo
baseurl=http://rpms.remirepo.net/enterprise/7/debug-php56/$basearch/
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-test-debuginfo]
name=Remi's test RPM repository for Enterprise Linux 7 - $basearch - debuginfo
baseurl=http://rpms.remirepo.net/enterprise/7/debug-test/$basearch/
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi::::::::::::::
/etc/yum.repos.d/remi-safe.repo
::::::::::::::
# This repository is safe to use with RHEL/CentOS base repository
# it only provides additional packages for the PHP stack
# all dependencies are in base repository or in EPEL[remi-safe]
name=Safe Remi's RPM repository for Enterprise Linux 7 - $basearch
#baseurl=http://rpms.remirepo.net/enterprise/7/safe/$basearch/
#mirrorlist=https://rpms.remirepo.net/enterprise/7/safe/httpsmirror
mirrorlist=http://cdn.remirepo.net/enterprise/7/safe/mirror
enabled=1
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi[remi-safe-debuginfo]
name=Remi's RPM repository for Enterprise Linux 7 - $basearch - debuginfo
baseurl=http://rpms.remirepo.net/enterprise/7/debug-remi/$basearch/
enabled=0
gpgcheck=1
gpgkey=file:///etc/pki/rpm-gpg/RPM-GPG-KEY-remi
[root@server3 ~]#

安装Python3

准备工作目录
[root@server3 lichao]# mkdir AIGC
[root@server3 lichao]# cd AIGC/安装Python3安装编译环境和依赖包
[root@server3 AIGC]# yum install wget gcc openssl-devel bzip2-devel libffi-devel
[root@server3 AIGC]# yum install openssl11 openssl11-devel openssl-devel
解压源码包
[root@server3 AIGC]# tar xvf Python-3.11.9.tar.xz 
[root@server3 AIGC]# cd Python-3.11.9
[root@server3 Python-3.11.9]# 
设置环境变量
[root@server3 Python-3.11.9]# export CFLAGS=$(pkg-config --cflags openssl11)
[root@server3 Python-3.11.9]# export LDFLAGS=$(pkg-config --libs openssl11)
进行编译安装
[root@server3 Python-3.11.9]# mkdir -p /home/lichao/opt/python3.11.9
[root@server3 Python-3.11.9]# ./configure --prefix=/home/lichao/opt/python3.11.9
[root@server3 Python-3.11.9]# make && make install
创建软链接，用于全局访问
[root@server3 Python-3.11.9]# cd /home/lichao/opt/python3.11.9/
[root@server3 python3.11.9]# ln -s /home/lichao/opt/python3.11.9/bin/python3 /usr/bin/python3
[root@server3 python3.11.9]# ln -s /home/lichao/opt/python3.11.9/bin/pip3 /usr/bin/pip3
[root@server3 python3.11.9]# ll /usr/bin/python3 
lrwxrwxrwx 1 root root 41 5月  16 08:32 /usr/bin/python3 -> /home/lichao/opt/python3.11.9/bin/python3
[root@server3 python3.11.9]# ll /usr/bin/pip3
lrwxrwxrwx 1 root root 38 5月  16 08:32 /usr/bin/pip3 -> /home/lichao/opt/python3.11.9/bin/pip3
验证测试
[root@server3 python3.11.9]# python3
Python 3.11.9 (main, May 16 2024, 08:23:00) [GCC 4.8.5 20150623 (Red Hat 4.8.5-44)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> exit()
[root@server3 python3.11.9]#

安装MLNX网卡驱动

下文以CentOS7为例，详细介绍了Mellanox网卡MLNX_OFED的驱动安装和固件升级方法。

本次下载的驱动版本为：MLNX_OFED_LINUX-4.7-3.2.9.0-rhel7.6-x86_64.tgz。

[root@server3 ~]# tar –zxvf MLNX_OFED_LINUX-4.7-3.2.9.0-rhel7.6-x86_64.tgz
[root@server3 ~]# cd MLNX_OFED_LINUX-4.7-3.2.9.0-rhel7.6-x86_64
查看当前系统的内核版本
[root@server3 MLNX_OFED_LINUX-4.7-3.2.9.0-rhel7.6-x86_64]# uname -r
3.10.0-957.el7.x86_64
查看当前驱动所支持的内核版本
[root@server3 MLNX_OFED_LINUX-4.7-3.2.9.0-rhel7.6-x86_64]# cat .supported_kernels 
3.10.0-957.el7.x86_64 
注：由以上可知下载的默认驱动支持当前的内核版本
如果当前内核与支持内核不匹配，手动编译适合内核的驱动，在编译之前首先安装gcc编译环境和kernel开发包
[root@server3 MLNX_OFED_LINUX-4.7-3.2.9.0-rhel7.6-x86_64]#yum  install gcc gcc-c++
libstdc++-devel kernel-default-devel 
添加针对当前内核版本的驱动
[root@server3 MLNX_OFED_LINUX-4.7-3.2.9.0-rhel7.6-x86_64]#./mlnx_add_kernel_support.sh -m /root/MLNX_OFED_LINUX-4.7-3.2.9.0-rhel7.6-x86_64  -v
注：完成后生成的驱动文件在/tmp目录下
[root@server3 MLNX_OFED_LINUX-4.7-3.2.9.0-rhel7.6-x86_64]# ls -l /tmp/MLNX_OFED_LINUX-4.7-3.2.9.0-rhel7.6-x86_64-ext.tgz
-rw-r--r-- 1 root root 282193833 Dec 23 09:49 /tmp/MLNX_OFED_LINUX-4.7-3.2.9.0-rhel7.6-x86_64-ext.tgz
安装驱动
[root@server3 tmp]# tar xzvf MLNX_OFED_LINUX-4.7-3.2.9.0-rhel7.6-x86_64-ext.tgz
[root@server3 tmp]# cd MLNX_OFED_LINUX-4.7-3.2.9.0-rhel7.6-x86_64-ext
[root@server3 tmp]# ./mlnxofedinstall
最后启动openibd服务
[root@server3 ~]#/etc/init.d/openibd start
[root@server3 ~]#chkconfig openibd on

安装GPU驱动和集合通讯库安装配置

安装配置

  • 安装GPU驱动和CUDA、CUDNN

安装开始前，请根据自己的GPU型号、操作系统版本去英伟达官网下载相对应的软件包。

[root@server3 AIGC]# ll
总用量 1733448
-rw-r--r--  1 root root 1430373861 5月  16 08:55 cudnn-local-repo-rhel7-8.9.7.29-1.0-1.x86_64.rpm
drwxr-xr-x  7 root root        141 5月  17 13:45 nccl-tests
-rwxr-xr-x  1 root root  306736632 5月  16 08:43 NVIDIA-Linux-x86_64-550.67.run
drwxrwxr-x 10 1000 1000       4096 5月  17 13:21 openmpi-4.1.6
-rw-r--r--  1 root root   17751702 9月  30 2023 openmpi-4.1.6.tar.gz
drwxr-xr-x 17 root root       4096 5月  16 08:23 Python-3.11.9
-rw-r--r--  1 root root   20175816 4月   2 13:11 Python-3.11.9.tar.xz
[root@server3 AIGC]# ./NVIDIA-Linux-x86_64-550.67.run
Verifying archive integrity... OK
Uncompressing NVIDIA Accelerated Graphics Driver for Linux-x86_64 550.67...................


[root@server3 AIGC]# yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-rhel7.repo
已加载插件：fastestmirror, nvidia
adding repo from: https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-rhel7.repo
grabbing file https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-rhel7.repo to /etc/yum.repos.d/cuda-rhel7.repo
repo saved to /etc/yum.repos.d/cuda-rhel7.repo
[root@server3 AIGC]# yum install libnccl-2.21.5-1+cuda12.4 libnccl-devel-2.21.5-1+cuda12.4 libnccl-static-2.21.5-1+cuda12.4
[root@server3 AIGC]# yum install cudnn-local-repo-rhel7-8.9.7.29-1.0-1.x86_64.rpm

 安装完成后，可以通过nvidia-smi查看驱动和CUDA版本。如果版本不匹配，则执行此命令行会报错。

[root@server3 AIGC]# nvidia-smi 
Mon Jun  3 11:59:36 2024       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.67                 Driver Version: 550.67         CUDA Version: 12.4     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA GeForce RTX 4060 Ti     Off |   00000000:02:00.0 Off |                  N/A |
|  0%   34C    P0             27W /  165W |       1MiB /  16380MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------++-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+
[root@server3 AIGC]#

 

  • 编译安装OpenMPI
[root@server3 AIGC]# tar xvf openmpi-4.1.6.tar.gz 
[root@server3 openmpi-4.1.6]# 
[root@server3 openmpi-4.1.6]# mkdir -p /home/lichao/lib/openmpi
[root@server3 openmpi-4.1.6]# ./configure --prefix=/home/lichao/lib/openmpi -with-cuda=/usr/local/cuda-12.4 -with-nccl=/usr/lib64Open MPI configuration:
-----------------------
Version: 4.1.6
Build MPI C bindings: yes
Build MPI C++ bindings (deprecated): no
Build MPI Fortran bindings: mpif.h, use mpi
MPI Build Java bindings (experimental): no
Build Open SHMEM support: yes
Debug build: no
Platform file: (none)Miscellaneous
-----------------------
CUDA support: yes
HWLOC support: internal
Libevent support: internal
Open UCC: no
PMIx support: InternalTransports
-----------------------
Cisco usNIC: no
Cray uGNI (Gemini/Aries): no
Intel Omnipath (PSM2): no
Intel TrueScale (PSM): no
Mellanox MXM: no
Open UCX: yes
OpenFabrics OFI Libfabric: no
OpenFabrics Verbs: yes
Portals4: no
Shared memory/copy in+copy out: yes
Shared memory/Linux CMA: yes
Shared memory/Linux KNEM: no
Shared memory/XPMEM: no
TCP: yesResource Managers
-----------------------
Cray Alps: no
Grid Engine: no
LSF: no
Moab: no
Slurm: yes
ssh/rsh: yes
Torque: noOMPIO File Systems
-----------------------
DDN Infinite Memory Engine: no
Generic Unix FS: yes
IBM Spectrum Scale/GPFS: no
Lustre: no
PVFS2/OrangeFS: no[root@server3 openmpi-4.1.6]#

  • 编译安装NCCL-Test
[root@server3 lichao]# cd AIGC/
[root@server3 AIGC]# git clone https://github.com/NVIDIA/nccl-tests.git
[root@server3 AIGC]# cd nccl-tests/
[root@server3 nccl-tests]# make clean
[root@server3 nccl-tests]# make MPI=1 MPI_HOME=/home/lichao/opt/openmpi/ CUDA_HOME=/usr/local/cuda-12.4/ NCCL_HOME=/usr/lib64/

集合通信性能测试方法（all_reduce）

[root@server1 lichao]# cat run_nccl-test.sh/home/lichao/opt/openmpi/bin/mpirun --allow-run-as-root \-np 3 \-host "server1,server2,server3" \-mca btl ^openib \-x NCCL_DEBUG=INFO \-x NCCL_ALGO=ring \-x NCCL_IB_DISABLE=0 \-x NCCL_IB_GID_INDEX=3 \-x NCCL_SOCKET_IFNAME=ens11f1 \-x NCCL_IB_HCA=mlx5_1:1 \/home/lichao/AIGC/nccl-tests/build/all_reduce_perf -b 128 -e 8G -f 2 -g 1[root@server1 lichao]# ./run_nccl-test.sh# nThread 1 nGpus 1 minBytes 128 maxBytes 8589934592 step: 2(factor) warmup iters: 5 iters: 20 agg iters: 1 validation: 1 graph: 0## Using devices#  Rank  0 Group  0 Pid  18697 on    server1 device  0 [0x02] NVIDIA GeForce RTX 4060 Ti#  Rank  1 Group  0 Pid  20893 on    server2 device  0 [0x02] NVIDIA GeForce RTX 4060 Ti#  Rank  2 Group  0 Pid   2458 on    server3 device  0 [0x02] NVIDIA GeForce RTX 4060 Ti## Reducing maxBytes to 5261099008 due to memory limitationserver1:18697:18697 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to ens11f1server1:18697:18697 [0] NCCL INFO Bootstrap : Using ens11f1:172.16.0.11<0>server1:18697:18697 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so)server1:18697:18697 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.soserver1:18697:18697 [0] NCCL INFO NET/Plugin: Using internal network plugin.server2:20893:20893 [0] NCCL INFO cudaDriverVersion 12040server2:20893:20893 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to ens11f1server2:20893:20893 [0] NCCL INFO Bootstrap : Using ens11f1:172.16.0.12<0>server2:20893:20893 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so)server2:20893:20893 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.soserver2:20893:20893 [0] NCCL INFO NET/Plugin: Using internal network plugin.server1:18697:18697 [0] NCCL INFO cudaDriverVersion 12040NCCL version 2.21.5+cuda12.4server3:2458:2458 [0] NCCL INFO cudaDriverVersion 12040server3:2458:2458 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to ens11f1server3:2458:2458 [0] NCCL INFO Bootstrap : Using ens11f1:172.16.0.13<0>server3:2458:2458 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so)server3:2458:2458 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.soserver3:2458:2458 [0] NCCL INFO NET/Plugin: Using internal network plugin.server2:20893:20907 [0] NCCL INFO NCCL_IB_DISABLE set by environment to 0.server2:20893:20907 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to ens11f1server2:20893:20907 [0] NCCL INFO NCCL_IB_HCA set to mlx5_1:1server2:20893:20907 [0] NCCL INFO NET/IB : Using [0]mlx5_1:1/RoCE [RO]; OOB ens11f1:172.16.0.12<0>server2:20893:20907 [0] NCCL INFO Using non-device net plugin version 0server2:20893:20907 [0] NCCL INFO Using network IBserver3:2458:2473 [0] NCCL INFO NCCL_IB_DISABLE set by environment to 0.server3:2458:2473 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to ens11f1server3:2458:2473 [0] NCCL INFO NCCL_IB_HCA set to mlx5_1:1server1:18697:18712 [0] NCCL INFO NCCL_IB_DISABLE set by environment to 0.server1:18697:18712 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to ens11f1server3:2458:2473 [0] NCCL INFO NET/IB : Using [0]mlx5_1:1/RoCE [RO]; OOB ens11f1:172.16.0.13<0>server1:18697:18712 [0] NCCL INFO NCCL_IB_HCA set to mlx5_1:1server3:2458:2473 [0] NCCL INFO Using non-device net plugin version 0server3:2458:2473 [0] NCCL INFO Using network IBserver1:18697:18712 [0] NCCL INFO NET/IB : Using [0]mlx5_1:1/RoCE [RO]; OOB ens11f1:172.16.0.11<0>server1:18697:18712 [0] NCCL INFO Using non-device net plugin version 0server1:18697:18712 [0] NCCL INFO Using network IBserver1:18697:18712 [0] NCCL INFO ncclCommInitRank comm 0x23622c0 rank 0 nranks 3 cudaDev 0 nvmlDev 0 busId 2000 commId 0x35491327c8228dd0 - Init STARTserver3:2458:2473 [0] NCCL INFO ncclCommInitRank comm 0x346ffc0 rank 2 nranks 3 cudaDev 0 nvmlDev 0 busId 2000 commId 0x35491327c8228dd0 - Init STARTserver2:20893:20907 [0] NCCL INFO ncclCommInitRank comm 0x2a1af20 rank 1 nranks 3 cudaDev 0 nvmlDev 0 busId 2000 commId 0x35491327c8228dd0 - Init STARTserver3:2458:2473 [0] NCCL INFO Setting affinity for GPU 0 to 0f,ff000fffserver2:20893:20907 [0] NCCL INFO Setting affinity for GPU 0 to 0f,ff000fffserver1:18697:18712 [0] NCCL INFO Setting affinity for GPU 0 to 0f,ff000fffserver1:18697:18712 [0] NCCL INFO comm 0x23622c0 rank 0 nRanks 3 nNodes 3 localRanks 1 localRank 0 MNNVL 0server1:18697:18712 [0] NCCL INFO Channel 00/02 :    0   1   2server1:18697:18712 [0] NCCL INFO Channel 01/02 :    0   1   2server1:18697:18712 [0] NCCL INFO Trees [0] 2/-1/-1->0->-1 [1] 2/-1/-1->0->1server1:18697:18712 [0] NCCL INFO P2P Chunksize set to 131072server3:2458:2473 [0] NCCL INFO comm 0x346ffc0 rank 2 nRanks 3 nNodes 3 localRanks 1 localRank 0 MNNVL 0server2:20893:20907 [0] NCCL INFO comm 0x2a1af20 rank 1 nRanks 3 nNodes 3 localRanks 1 localRank 0 MNNVL 0server3:2458:2473 [0] NCCL INFO Trees [0] 1/-1/-1->2->0 [1] -1/-1/-1->2->0server3:2458:2473 [0] NCCL INFO P2P Chunksize set to 131072server2:20893:20907 [0] NCCL INFO Trees [0] -1/-1/-1->1->2 [1] 0/-1/-1->1->-1server2:20893:20907 [0] NCCL INFO P2P Chunksize set to 131072server3:2458:2473 [0] NCCL INFO Channel 00/0 : 1[0] -> 2[0] [receive] via NET/IB/0server3:2458:2473 [0] NCCL INFO Channel 01/0 : 1[0] -> 2[0] [receive] via NET/IB/0server3:2458:2473 [0] NCCL INFO Channel 00/0 : 2[0] -> 0[0] [send] via NET/IB/0server3:2458:2473 [0] NCCL INFO Channel 01/0 : 2[0] -> 0[0] [send] via NET/IB/0server2:20893:20907 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[0] [receive] via NET/IB/0server2:20893:20907 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[0] [receive] via NET/IB/0server2:20893:20907 [0] NCCL INFO Channel 00/0 : 1[0] -> 2[0] [send] via NET/IB/0server2:20893:20907 [0] NCCL INFO Channel 01/0 : 1[0] -> 2[0] [send] via NET/IB/0server1:18697:18712 [0] NCCL INFO Channel 00/0 : 2[0] -> 0[0] [receive] via NET/IB/0server1:18697:18712 [0] NCCL INFO Channel 01/0 : 2[0] -> 0[0] [receive] via NET/IB/0server1:18697:18712 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[0] [send] via NET/IB/0server1:18697:18712 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[0] [send] via NET/IB/0server3:2458:2475 [0] NCCL INFO NCCL_IB_GID_INDEX set by environment to 3.server1:18697:18714 [0] NCCL INFO NCCL_IB_GID_INDEX set by environment to 3.server2:20893:20909 [0] NCCL INFO NCCL_IB_GID_INDEX set by environment to 3.server1:18697:18712 [0] NCCL INFO Connected all ringsserver1:18697:18712 [0] NCCL INFO Channel 01/0 : 1[0] -> 0[0] [receive] via NET/IB/0server3:2458:2473 [0] NCCL INFO Connected all ringsserver2:20893:20907 [0] NCCL INFO Connected all ringsserver1:18697:18712 [0] NCCL INFO Channel 00/0 : 0[0] -> 2[0] [send] via NET/IB/0server2:20893:20907 [0] NCCL INFO Channel 00/0 : 2[0] -> 1[0] [receive] via NET/IB/0server1:18697:18712 [0] NCCL INFO Channel 01/0 : 0[0] -> 2[0] [send] via NET/IB/0server3:2458:2473 [0] NCCL INFO Channel 00/0 : 0[0] -> 2[0] [receive] via NET/IB/0server2:20893:20907 [0] NCCL INFO Channel 01/0 : 1[0] -> 0[0] [send] via NET/IB/0server3:2458:2473 [0] NCCL INFO Channel 01/0 : 0[0] -> 2[0] [receive] via NET/IB/0server3:2458:2473 [0] NCCL INFO Channel 00/0 : 2[0] -> 1[0] [send] via NET/IB/0server3:2458:2473 [0] NCCL INFO Connected all treesserver1:18697:18712 [0] NCCL INFO Connected all treesserver1:18697:18712 [0] NCCL INFO NCCL_ALGO set by environment to ringserver3:2458:2473 [0] NCCL INFO NCCL_ALGO set by environment to ringserver3:2458:2473 [0] NCCL INFO threadThresholds 8/8/64 | 24/8/64 | 512 | 512server3:2458:2473 [0] NCCL INFO 2 coll channels, 2 collnet channels, 0 nvls channels, 2 p2p channels, 2 p2p channels per peerserver2:20893:20907 [0] NCCL INFO Connected all treesserver2:20893:20907 [0] NCCL INFO NCCL_ALGO set by environment to ringserver2:20893:20907 [0] NCCL INFO threadThresholds 8/8/64 | 24/8/64 | 512 | 512server2:20893:20907 [0] NCCL INFO 2 coll channels, 2 collnet channels, 0 nvls channels, 2 p2p channels, 2 p2p channels per peerserver1:18697:18712 [0] NCCL INFO threadThresholds 8/8/64 | 24/8/64 | 512 | 512server1:18697:18712 [0] NCCL INFO 2 coll channels, 2 collnet channels, 0 nvls channels, 2 p2p channels, 2 p2p channels per peerserver2:20893:20907 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.soserver2:20893:20907 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin.server2:20893:20907 [0] NCCL INFO ncclCommInitRank comm 0x2a1af20 rank 1 nranks 3 cudaDev 0 nvmlDev 0 busId 2000 commId 0x35491327c8228dd0 - Init COMPLETEserver3:2458:2473 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.soserver3:2458:2473 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin.server3:2458:2473 [0] NCCL INFO ncclCommInitRank comm 0x346ffc0 rank 2 nranks 3 cudaDev 0 nvmlDev 0 busId 2000 commId 0x35491327c8228dd0 - Init COMPLETEserver1:18697:18712 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.soserver1:18697:18712 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin.server1:18697:18712 [0] NCCL INFO ncclCommInitRank comm 0x23622c0 rank 0 nranks 3 cudaDev 0 nvmlDev 0 busId 2000 commId 0x35491327c8228dd0 - Init COMPLETE##                                                              out-of-place                       in-place          #       size         count      type   redop    root     time   algbw   busbw #wrong     time   algbw   busbw #wrong#        (B)    (elements)                               (us)  (GB/s)  (GB/s)            (us)  (GB/s)  (GB/s)      128            32     float     sum      -1    28.39    0.00    0.01      0    27.35    0.00    0.01      0256            64     float     sum      -1    29.44    0.01    0.01      0    28.54    0.01    0.01      0512           128     float     sum      -1    29.99    0.02    0.02      0    29.66    0.02    0.02      01024           256     float     sum      -1    32.89    0.03    0.04      0    30.64    0.03    0.04      02048           512     float     sum      -1    34.81    0.06    0.08      0    31.87    0.06    0.09      04096          1024     float     sum      -1    37.32    0.11    0.15      0    36.09    0.11    0.15      08192          2048     float     sum      -1    45.11    0.18    0.24      0    43.12    0.19    0.25      016384          4096     float     sum      -1    57.92    0.28    0.38      0    56.98    0.29    0.38      032768          8192     float     sum      -1    72.68    0.45    0.60      0    70.79    0.46    0.62      065536         16384     float     sum      -1    95.77    0.68    0.91      0    93.73    0.70    0.93      0131072         32768     float     sum      -1    162.7    0.81    1.07      0    161.5    0.81    1.08      0262144         65536     float     sum      -1    177.3    1.48    1.97      0    177.4    1.48    1.97      0524288        131072     float     sum      -1    301.4    1.74    2.32      0    302.0    1.74    2.31      01048576        262144     float     sum      -1    557.9    1.88    2.51      0    559.2    1.88    2.50      02097152        524288     float     sum      -1   1089.8    1.92    2.57      0   1092.2    1.92    2.56      04194304       1048576     float     sum      -1   2165.7    1.94    2.58      0   2166.6    1.94    2.58      08388608       2097152     float     sum      -1   4315.7    1.94    2.59      0   4316.1    1.94    2.59      016777216       4194304     float     sum      -1   8528.8    1.97    2.62      0   8529.3    1.97    2.62      033554432       8388608     float     sum      -1    16622    2.02    2.69      0    16610    2.02    2.69      067108864      16777216     float     sum      -1    32602    2.06    2.74      0    32542    2.06    2.75      0134217728      33554432     float     sum      -1    63946    2.10    2.80      0    63831    2.10    2.80      0268435456      67108864     float     sum      -1   126529    2.12    2.83      0   126412    2.12    2.83      0536870912     134217728     float     sum      -1   251599    2.13    2.85      0   251327    2.14    2.85      01073741824     268435456     float     sum      -1   500664    2.14    2.86      0   501911    2.14    2.85      02147483648     536870912     float     sum      -1  1001415    2.14    2.86      0  1000178    2.15    2.86      04294967296    1073741824     float     sum      -1  1999361    2.15    2.86      0  1997380    2.15    2.87      0server1:18697:18697 [0] NCCL INFO comm 0x23622c0 rank 0 nranks 3 cudaDev 0 busId 2000 - Destroy COMPLETEserver2:20893:20893 [0] NCCL INFO comm 0x2a1af20 rank 1 nranks 3 cudaDev 0 busId 2000 - Destroy COMPLETEserver3:2458:2458 [0] NCCL INFO comm 0x346ffc0 rank 2 nranks 3 cudaDev 0 busId 2000 - Destroy COMPLETE# Out of bounds values : 0 OK# Avg bus bandwidth    : 1.66163#[root@server1 lichao]#

结果详解

- size (B)：操作处理的数据的大小，以字节为单位；

- count (elements)：操作处理的元素的数量；

- type：元素的数据类型；

- redop：使用的归约操作；

- root：对于某些操作（如 reduce 和 broadcast），这列指定了根节点的编号，值是 -1 表示这个操作没有根节点（all-reduce 操作涉及到所有的节点）；

- time (us)：操作的执行时间，以微秒为单位；

- algbw (GB/s)：算法带宽，以每秒吉字节（GB/s）为单位；

- busbw (GB/s)：总线带宽，以每秒吉字节（GB/s）为单位；

- wrong：错误的数量，如果这个值不是 0，那可能表示有一些错误发生。

在这个例子中，你可以看到，当处理的数据量增大时，算法带宽和总线带宽都有所提高，这可能表示 NCCL 能够有效地利用大量的数据。

查看结果时，需要关注如下几点：

1. 数据量增加时，带宽是否会下降（下降明显不符合预期）；

2. 更关注带宽的峰值，每次算到的带宽峰值，可以只关注 in 或者 out；

3. 平均值，在数据量递增的情况下，可能无法体现最终的结果；

4. 请确保数据量足够大，可以压到带宽上限（通过调整 b、e 或者 n 选项）。

常用参数及解释

- GPU 数量

  - -t,--nthreads <num threads> 每个进程的线程数量配置， 默认 1；

  -  -g,--ngpus <GPUs per thread> 每个线程的 GPU 数量，默认 1；

- 数据大小配置

  - -b,--minbytes <min size in bytes> 开始的最小数据量，默认 32M；

  - -e,--maxbytes <max size in bytes> 结束的最大数据量，默认 32M；

-  数据步长设置

  -  -i,--stepbytes <increment size> 每次增加的数据量，默认: 1M；

  -  -f,--stepfactor <increment factor> 每次增加的倍数，默认禁用；

- NCCL 操作相关配置

  - -o,--op <sum/prod/min/max/avg/all>指定那种操作为reduce，仅适用于Allreduce、Reduce或ReduceScatter等缩减操作。默认值为：求和（Sum）；

  - -d,--datatype <nccltype/all>指定使用哪种数据类型，默认 : Float；

- 性能相关配置

  - -n,--iters <iteration count> 每次操作（一次发送）循环多少次，默认 : 20；

  - -w,--warmup_iters <warmup iteration count> 预热迭代次数（不计时），默认：5；

  - -m,--agg_iters <aggregation count> 每次迭代中要聚合在一起的操作数，默认：1；

  - -a,--average <0/1/2/3> 在所有 ranks 计算均值作为最终结果 (MPI=1 only). <0=Rank0,1=Avg,2=Min,3=Max>，默认：1；

- 测试相关配置

  - -p,--parallel_init <0/1> 使用线程并行初始化 NCCL，默认: 0；

  - -c,--check <0/1> 检查结果的正确性。在大量GPU上可能会非常慢，默认：1；

  - -z,--blocking <0/1> 使NCCL集合阻塞，即在每个集合之后让CPU等待和同步，默认：0；

  - -G,--cudagraph <num graph launches>  将迭代作为CUDA图形捕获，然后重复指定的次数，默认：0；

实验测试

完成硬件、软件的选型和配置后，下一步将进行实践测试。

获取LLaMA-Factory源码包

因为网络问题很难直接通过git clone命令行拉取，建议通过打包下载后自己上传的方式进行：

noone@MacBook-Air Downloads % scp LLaMA-Factory-0.8.3.zip root@10.230.1.13:/tmp[root@server3 AIGC]# pwd
/home/lichao/AIGC
[root@server3 AIGC]# cp /tmp/LLaMA-Factory-0.8.3.zip ./
[root@server3 AIGC]# unzip LLaMA-Factory-0.8.3.zip
[root@server3 AIGC]# cd LLaMA-Factory-0.8.3
[root@server3 LLaMA-Factory-0.8.3]# ll
总用量 128
drwxr-xr-x  2 root root    83 9月  13 05:04 assets
drwxr-xr-x  2 root root   122 9月   6 08:26 cache
-rw-r--r--  1 root root  1378 7月  18 19:36 CITATION.cff
drwxr-xr-x  6 root root  4096 9月  13 05:03 data
drwxr-xr-x  4 root root    43 7月  18 19:36 docker
drwxr-xr-x  5 root root    44 7月  18 19:36 evaluation
drwxr-xr-x 10 root root   182 7月  18 19:36 examples
-rw-r--r--  1 root root 11324 7月  18 19:36 LICENSE
-rw-r--r--  1 root root   242 7月  18 19:36 Makefile
-rw-r--r--  1 root root    33 7月  18 19:36 MANIFEST.in
-rw-r--r--  1 root root   645 7月  18 19:36 pyproject.toml
-rw-r--r--  1 root root 44424 7月  18 19:36 README.md
-rw-r--r--  1 root root 44093 7月  18 19:36 README_zh.md
-rw-r--r--  1 root root   245 7月  18 19:36 requirements.txt
drwxr-xr-x  3 root root    16 9月   6 18:48 saves
drwxr-xr-x  2 root root   219 7月  18 19:36 scripts
-rw-r--r--  1 root root  3361 7月  18 19:36 setup.py
drwxr-xr-x  4 root root   101 9月   6 08:22 src
drwxr-xr-x  5 root root    43 7月  18 19:36 tests
[root@server3 LLaMA-Factory-0.8.3]#

安装LLaMA-Factory，并进行验证

[root@server3 LLaMA-Factory-0.8.3]# pip install -e ".[torch,metrics]"
[root@server3 LLaMA-Factory-0.8.3]# llamafactory-cli version
[2024-09-23 08:51:28,722] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
----------------------------------------------------------
| Welcome to LLaMA Factory, version 0.8.3                |
|                                                        |
| Project page: https://github.com/hiyouga/LLaMA-Factory |
----------------------------------------------------------
[root@server3 LLaMA-Factory-0.8.3]#

下载训练时所需的预训练模型和数据集

根据当前GPU服务器所配置的GPU硬件规格，选择适合的训练方法、模型和数据集。

GPU型号：NVIDIA GeForce RTX 4060 Ti 16GB

预训练模型：Qwen/Qwen1.5-0.5B-Chat

数据集：identity、alpaca_zh_demo

# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install
git clone https://hf-mirror.com/Qwen/Qwen1.5-0.5B-Chat
# If you want to clone without large files - just their pointers
GIT_LFS_SKIP_SMUDGE=1 git clone https://hf-mirror.com/Qwen/Qwen1.5-0.5B-Chat

因为网络问题通过命令行很难直接下载，这里使用huggingface的国内镜像站拉取预训练模型数据，并使用“GIT_LFS_SKIP_SMUDGE=1”变量跳过大文件，随后手工下载后再上传。

如果觉得麻烦，也可以安装使用huggingface的命令行工具，下载预训练模型和数据集。同样地，安装完成后，需要配置一些环境变量（使用镜像站hf-mirror.com）来解决网络问题。

1. 安装依赖
[root@server3 LLaMA-Factory-0.8.3]# pip3 install -U huggingface_hub
2. 设置环境变量
[root@server3 LLaMA-Factory-0.8.3]# export HF_ENDPOINT=https://hf-mirror.com
可以写入 ~/.bashrc 永久生效。
3. 确认环境变量生效
[root@server3 LLaMA-Factory-0.8.3]# huggingface-cli envCopy-and-paste the text below in your GitHub issue.- huggingface_hub version: 0.24.5
- Platform: Linux-3.10.0-1160.118.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.11.9
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Token path ?: /root/.cache/huggingface/token
- Has saved token ?: True
- Who am I ?: richard-open-source
- Configured git credential helpers: 
- FastAI: N/A
- Tensorflow: N/A
- Torch: 2.4.0
- Jinja2: 3.1.4
- Graphviz: N/A
- keras: N/A
- Pydot: N/A
- Pillow: 10.4.0
- hf_transfer: N/A
- gradio: 4.43.0
- tensorboard: N/A
- numpy: 1.26.4
- pydantic: 2.9.0
- aiohttp: 3.10.3
- ENDPOINT: https://hf-mirror.com
- HF_HUB_CACHE: /root/.cache/huggingface/hub
- HF_ASSETS_CACHE: /root/.cache/huggingface/assets
- HF_TOKEN_PATH: /root/.cache/huggingface/token
- HF_HUB_OFFLINE: False
- HF_HUB_DISABLE_TELEMETRY: False
- HF_HUB_DISABLE_PROGRESS_BARS: None
- HF_HUB_DISABLE_SYMLINKS_WARNING: False
- HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
- HF_HUB_ENABLE_HF_TRANSFER: False
- HF_HUB_ETAG_TIMEOUT: 10
- HF_HUB_DOWNLOAD_TIMEOUT: 10[root@server3 LLaMA-Factory-0.8.3]# 
4.1 下载模型
[root@server3 LLaMA-Factory-0.8.3]# huggingface-cli download --resume-download Qwen/Qwen1.5-0.5B-Chat --local-dir ./models/Qwen1.5-0.5B-Chat
4.2 下载数据集
[root@server3 LLaMA-Factory-0.8.3]# huggingface-cli download --repo-type dataset --resume-download alpaca_zh_demo --local-dir ./datasets/alpaca_zh_demo

 下载预训练模型

[root@server3 AIGC]# mkdir models
[root@server3 AIGC]# cd models/
[root@server3 models]# GIT_LFS_SKIP_SMUDGE=1 git clone https://hf-mirror.com/Qwen/Qwen1.5-0.5B-Chat
[root@server3 models]# tree -h Qwen1.5-0.5B-Chat/
Qwen1.5-0.5B-Chat/
├── [ 656]  config.json
├── [ 661]  config.json.raw
├── [ 206]  generation_config.json
├── [7.1K]  LICENSE
├── [1.6M]  merges.txt
├── [1.2G]  model.safetensors
├── [4.2K]  README.md
├── [1.3K]  tokenizer_config.json
├── [6.7M]  tokenizer.json
└── [2.6M]  vocab.json0 directories, 10 files
[root@server3 models]#

下载数据集

默认情况下，LLaMA-Factory项目文件下的data目录，自带了一些本地数据集可直接使用。

[root@server3 LLaMA-Factory-0.8.3]# tree -h data/
data/
├── [841K]  alpaca_en_demo.json
├── [621K]  alpaca_zh_demo.json
├── [  32]  belle_multiturn
│   └── [2.7K]  belle_multiturn.py
├── [733K]  c4_demo.json
├── [ 13K]  dataset_info.json
├── [1.5M]  dpo_en_demo.json
├── [833K]  dpo_zh_demo.json
├── [722K]  glaive_toolcall_en_demo.json
├── [665K]  glaive_toolcall_zh_demo.json
├── [  27]  hh_rlhf_en
│   └── [3.3K]  hh_rlhf_en.py
├── [ 20K]  identity.json
├── [892K]  kto_en_demo.json
├── [  45]  mllm_demo_data
│   ├── [ 12K]  1.jpg
│   ├── [ 22K]  2.jpg
│   └── [ 16K]  3.jpg
├── [3.1K]  mllm_demo.json
├── [9.8K]  README.md
├── [9.2K]  README_zh.md
├── [  27]  ultra_chat
│   └── [2.3K]  ultra_chat.py
└── [1004K]  wiki_demo.txt4 directories, 20 files
[root@server3 LLaMA-Factory-0.8.3]#

 

使用准备好的模型与数据集，在单机上进行训练测试

LLaMA-Factory支持通过WebUI微调大语言模型。在完成安装后，我们可以使用WebUI进行快速调测验证，没问题后可使用命令行工具进行多机分布式训练。

[root@server3 LLaMA-Factory-0.8.3]# llamafactory-cli webui
[2024-09-23 17:54:45,786] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
Running on local URL:  http://0.0.0.0:7861To create a public link, set `share=True` in `launch()`.

 使用命令行运行多机分布式训练任务

1. 准备目录
[root@server3 LLaMA-Factory-0.8.3]# mkdir asterun
[root@server3 LLaMA-Factory-0.8.3]# mkdir -p asterun/saves/qwen/full/sft
2. 根据集群环境和训练任务，准备分布式训练的配置文件
[root@server3 LLaMA-Factory-0.8.3]# cat asterun/qwen_full_sft_ds2.yaml 
### model
model_name_or_path: /home/lichao/AIGC/models/Qwen1.5-0.5B-Chat### method
stage: sft
do_train: true
finetuning_type: full
deepspeed: examples/deepspeed/ds_z2_config.json### dataset
dataset: identity,alpaca_zh_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16### output
output_dir: asterun/saves/qwen/full/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: truereport_to: tensorboard
logging_dir: asterun/saves/qwen/full/sft/runs### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 2
learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: true
ddp_timeout: 180000000### eval
val_size: 0.1
per_device_eval_batch_size: 1
eval_strategy: steps
eval_steps: 500
[root@server3 LLaMA-Factory-0.8.3]# 
3. 用同样的方式，在Server1和Server2上准备运行环境
步骤略。
4. 依次在集群中的3个GPU节点上启动分布式训练任务
主节点rank0：
[root@server3 LLaMA-Factory-0.8.3]# FORCE_TORCHRUN=1 NNODES=3 RANK=0 MASTER_ADDR=172.16.0.13 MASTER_PORT=29500 llamafactory-cli train asterun/qwen_full_sft_ds2.yaml
从节点rank1：
[root@server2 LLaMA-Factory-0.8.3]# FORCE_TORCHRUN=1 NNODES=3 RANK=1 MASTER_ADDR=172.16.0.13 MASTER_PORT=29500 llamafactory-cli train asterun/qwen_full_sft_ds2.yaml
从节点rank2：
[root@server1 LLaMA-Factory-0.8.3]# FORCE_TORCHRUN=1 NNODES=3 RANK=2 MASTER_ADDR=172.16.0.13 MASTER_PORT=29500 llamafactory-cli train asterun/qwen_full_sft_ds2.yaml

 推理测试

安装GGUF库

下载llama.cpp源码包到服务器，解压到工作目录
[root@server3 AIGC]# unzip llama.cpp-master.zip
[root@server3 AIGC]# cd llama.cpp-master
[root@server3 llama.cpp-master]# ll
总用量 576
-rw-r--r--  1 root root  33717 9月  26 11:38 AUTHORS
drwxr-xr-x  2 root root     37 9月  26 11:38 ci
drwxr-xr-x  2 root root    164 9月  26 11:38 cmake
-rw-r--r--  1 root root   6591 9月  26 11:38 CMakeLists.txt
-rw-r--r--  1 root root   3164 9月  26 11:38 CMakePresets.json
drwxr-xr-x  3 root root   4096 9月  26 11:38 common
-rw-r--r--  1 root root   2256 9月  26 11:38 CONTRIBUTING.md
-rwxr-xr-x  1 root root 199470 9月  26 11:38 convert_hf_to_gguf.py
-rwxr-xr-x  1 root root  15993 9月  26 11:38 convert_hf_to_gguf_update.py
-rwxr-xr-x  1 root root  19106 9月  26 11:38 convert_llama_ggml_to_gguf.py
-rwxr-xr-x  1 root root  14901 9月  26 11:38 convert_lora_to_gguf.py
drwxr-xr-x  4 root root    109 9月  26 11:38 docs
drwxr-xr-x 43 root root   4096 9月  26 11:38 examples
-rw-r--r--  1 root root   1556 9月  26 11:38 flake.lock
-rw-r--r--  1 root root   7469 9月  26 11:38 flake.nix
drwxr-xr-x  5 root root     85 9月  26 11:38 ggml
drwxr-xr-x  6 root root    116 9月  26 11:38 gguf-py
drwxr-xr-x  2 root root    154 9月  26 11:38 grammars
drwxr-xr-x  2 root root     21 9月  26 11:38 include
-rw-r--r--  1 root root   1078 9月  26 11:38 LICENSE
-rw-r--r--  1 root root  50865 9月  26 11:38 Makefile
drwxr-xr-x  2 root root    163 9月  26 11:38 media
drwxr-xr-x  2 root root   4096 9月  26 11:38 models
-rw-r--r--  1 root root    163 9月  26 11:38 mypy.ini
-rw-r--r--  1 root root   2044 9月  26 11:38 Package.swift
drwxr-xr-x  3 root root     40 9月  26 11:38 pocs
-rw-r--r--  1 root root 124786 9月  26 11:38 poetry.lock
drwxr-xr-x  2 root root   4096 9月  26 11:38 prompts
-rw-r--r--  1 root root   1280 9月  26 11:38 pyproject.toml
-rw-r--r--  1 root root    528 9月  26 11:38 pyrightconfig.json
-rw-r--r--  1 root root  28481 9月  26 11:38 README.md
drwxr-xr-x  2 root root   4096 9月  26 11:38 requirements
-rw-r--r--  1 root root    505 9月  26 11:38 requirements.txt
drwxr-xr-x  2 root root   4096 9月  26 11:38 scripts
-rw-r--r--  1 root root   5090 9月  26 11:38 SECURITY.md
drwxr-xr-x  2 root root     97 9月  26 11:38 spm-headers
drwxr-xr-x  2 root root    289 9月  26 11:38 src
drwxr-xr-x  2 root root   4096 9月  26 11:38 tests
[root@server3 llama.cpp-master]# 进入gguf-py子目录，安装GGUF库
[root@server3 llama.cpp-master]# cd gguf-py
[root@server3 gguf-py]# ll
总用量 12
drwxr-xr-x 2 root root   40 9月  26 11:38 examples
drwxr-xr-x 2 root root  230 9月  26 11:38 gguf
-rw-r--r-- 1 root root 1072 9月  26 11:38 LICENSE
-rw-r--r-- 1 root root 1049 9月  26 11:38 pyproject.toml
-rw-r--r-- 1 root root 2719 9月  26 11:38 README.md
drwxr-xr-x 2 root root  151 9月  26 11:38 scripts
drwxr-xr-x 2 root root   71 9月  26 11:38 tests
[root@server3 gguf-py]# pip install --editable .
Looking in indexes: https://mirrors.aliyun.com/pypi/simple/
Obtaining file:///home/lichao/AIGC/llama.cpp-master/gguf-pyInstalling build dependencies ... doneChecking if build backend supports build_editable ... doneGetting requirements to build editable ... donePreparing editable metadata (pyproject.toml) ... done
Requirement already satisfied: numpy>=1.17 in /home/lichao/opt/python3.11.9/lib/python3.11/site-packages (from gguf==0.10.0) (1.26.4)
Requirement already satisfied: pyyaml>=5.1 in /home/lichao/opt/python3.11.9/lib/python3.11/site-packages (from gguf==0.10.0) (6.0.2)
Requirement already satisfied: sentencepiece<=0.2.0,>=0.1.98 in /home/lichao/opt/python3.11.9/lib/python3.11/site-packages (from gguf==0.10.0) (0.2.0)
Requirement already satisfied: tqdm>=4.27 in /home/lichao/opt/python3.11.9/lib/python3.11/site-packages (from gguf==0.10.0) (4.66.5)
Building wheels for collected packages: ggufBuilding editable for gguf (pyproject.toml) ... doneCreated wheel for gguf: filename=gguf-0.10.0-py3-none-any.whl size=3403 sha256=4a0851426e263076c64c9854be9dfe95493844062484d001fddb08c1be5fa2caStored in directory: /tmp/pip-ephem-wheel-cache-iiq8ofh3/wheels/80/80/9b/c6c23d750f4bd20fc0c2c75e51253d89c61a2369247fb694db
Successfully built gguf
Installing collected packages: gguf
Successfully installed gguf-0.10.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.
[root@server3 gguf-py]#

模型格式转换

将之前微调训练生成的safetensors格式的模型，转换为gguf格式
[root@server3 gguf-py]# cd .. 
[root@server3 llama.cpp-master]# python3 convert_hf_to_gguf.py /home/lichao/AIGC/LLaMA-Factory-0.8.3/asterun/saves/qwen/full/sft
INFO:hf-to-gguf:Loading model: sft
INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only
INFO:hf-to-gguf:Exporting model...
INFO:hf-to-gguf:gguf: loading model part 'model.safetensors'
INFO:hf-to-gguf:output.weight,             torch.bfloat16 --> F16, shape = {1024, 151936}
INFO:hf-to-gguf:token_embd.weight,         torch.bfloat16 --> F16, shape = {1024, 151936}
INFO:hf-to-gguf:blk.0.attn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.0.ffn_down.weight,     torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.0.ffn_gate.weight,     torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.0.ffn_up.weight,       torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.0.ffn_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.0.attn_k.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.0.attn_k.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.0.attn_output.weight,  torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.0.attn_q.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.0.attn_q.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.0.attn_v.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.0.attn_v.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.1.attn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.1.ffn_down.weight,     torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.1.ffn_gate.weight,     torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.1.ffn_up.weight,       torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.1.ffn_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.1.attn_k.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.1.attn_k.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.1.attn_output.weight,  torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.1.attn_q.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.1.attn_q.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.1.attn_v.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.1.attn_v.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.10.attn_norm.weight,   torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.10.ffn_down.weight,    torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.10.ffn_gate.weight,    torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.10.ffn_up.weight,      torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.10.ffn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.10.attn_k.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.10.attn_k.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.10.attn_output.weight, torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.10.attn_q.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.10.attn_q.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.10.attn_v.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.10.attn_v.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.11.attn_norm.weight,   torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.11.ffn_down.weight,    torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.11.ffn_gate.weight,    torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.11.ffn_up.weight,      torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.11.ffn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.11.attn_k.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.11.attn_k.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.11.attn_output.weight, torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.11.attn_q.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.11.attn_q.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.11.attn_v.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.11.attn_v.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.12.attn_norm.weight,   torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.12.ffn_down.weight,    torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.12.ffn_gate.weight,    torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.12.ffn_up.weight,      torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.12.ffn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.12.attn_k.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.12.attn_k.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.12.attn_output.weight, torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.12.attn_q.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.12.attn_q.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.12.attn_v.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.12.attn_v.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.13.attn_norm.weight,   torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.13.ffn_down.weight,    torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.13.ffn_gate.weight,    torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.13.ffn_up.weight,      torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.13.ffn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.13.attn_k.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.13.attn_k.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.13.attn_output.weight, torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.13.attn_q.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.13.attn_q.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.13.attn_v.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.13.attn_v.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.14.attn_norm.weight,   torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.14.ffn_down.weight,    torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.14.ffn_gate.weight,    torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.14.ffn_up.weight,      torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.14.ffn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.14.attn_k.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.14.attn_k.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.14.attn_output.weight, torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.14.attn_q.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.14.attn_q.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.14.attn_v.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.14.attn_v.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.15.attn_norm.weight,   torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.15.ffn_down.weight,    torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.15.ffn_gate.weight,    torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.15.ffn_up.weight,      torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.15.ffn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.15.attn_k.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.15.attn_k.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.15.attn_output.weight, torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.15.attn_q.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.15.attn_q.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.15.attn_v.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.15.attn_v.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.16.attn_norm.weight,   torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.16.ffn_down.weight,    torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.16.ffn_gate.weight,    torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.16.ffn_up.weight,      torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.16.ffn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.16.attn_k.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.16.attn_k.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.16.attn_output.weight, torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.16.attn_q.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.16.attn_q.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.16.attn_v.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.16.attn_v.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.17.attn_norm.weight,   torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.17.ffn_down.weight,    torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.17.ffn_gate.weight,    torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.17.ffn_up.weight,      torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.17.ffn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.17.attn_k.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.17.attn_k.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.17.attn_output.weight, torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.17.attn_q.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.17.attn_q.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.17.attn_v.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.17.attn_v.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.18.attn_norm.weight,   torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.18.ffn_down.weight,    torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.18.ffn_gate.weight,    torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.18.ffn_up.weight,      torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.18.ffn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.18.attn_k.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.18.attn_k.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.18.attn_output.weight, torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.18.attn_q.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.18.attn_q.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.18.attn_v.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.18.attn_v.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.19.attn_norm.weight,   torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.19.ffn_down.weight,    torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.19.ffn_gate.weight,    torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.19.ffn_up.weight,      torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.19.ffn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.19.attn_k.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.19.attn_k.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.19.attn_output.weight, torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.19.attn_q.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.19.attn_q.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.19.attn_v.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.19.attn_v.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.2.attn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.2.ffn_down.weight,     torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.2.ffn_gate.weight,     torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.2.ffn_up.weight,       torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.2.ffn_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.2.attn_k.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.2.attn_k.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.2.attn_output.weight,  torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.2.attn_q.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.2.attn_q.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.2.attn_v.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.2.attn_v.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.20.attn_norm.weight,   torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.20.ffn_down.weight,    torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.20.ffn_gate.weight,    torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.20.ffn_up.weight,      torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.20.ffn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.20.attn_k.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.20.attn_k.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.20.attn_output.weight, torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.20.attn_q.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.20.attn_q.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.20.attn_v.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.20.attn_v.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.21.attn_norm.weight,   torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.21.ffn_down.weight,    torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.21.ffn_gate.weight,    torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.21.ffn_up.weight,      torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.21.ffn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.21.attn_k.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.21.attn_k.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.21.attn_output.weight, torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.21.attn_q.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.21.attn_q.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.21.attn_v.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.21.attn_v.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.22.attn_norm.weight,   torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.22.ffn_down.weight,    torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.22.ffn_gate.weight,    torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.22.ffn_up.weight,      torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.22.ffn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.22.attn_k.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.22.attn_k.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.22.attn_output.weight, torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.22.attn_q.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.22.attn_q.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.22.attn_v.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.22.attn_v.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.23.attn_norm.weight,   torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.23.ffn_down.weight,    torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.23.ffn_gate.weight,    torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.23.ffn_up.weight,      torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.23.ffn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.23.attn_k.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.23.attn_k.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.23.attn_output.weight, torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.23.attn_q.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.23.attn_q.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.23.attn_v.bias,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.23.attn_v.weight,      torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.3.attn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.3.ffn_down.weight,     torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.3.ffn_gate.weight,     torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.3.ffn_up.weight,       torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.3.ffn_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.3.attn_k.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.3.attn_k.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.3.attn_output.weight,  torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.3.attn_q.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.3.attn_q.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.3.attn_v.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.3.attn_v.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.4.attn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.4.ffn_down.weight,     torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.4.ffn_gate.weight,     torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.4.ffn_up.weight,       torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.4.ffn_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.4.attn_k.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.4.attn_k.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.4.attn_output.weight,  torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.4.attn_q.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.4.attn_q.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.4.attn_v.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.4.attn_v.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.5.attn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.5.ffn_down.weight,     torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.5.ffn_gate.weight,     torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.5.ffn_up.weight,       torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.5.ffn_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.5.attn_k.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.5.attn_k.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.5.attn_output.weight,  torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.5.attn_q.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.5.attn_q.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.5.attn_v.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.5.attn_v.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.6.attn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.6.ffn_down.weight,     torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.6.ffn_gate.weight,     torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.6.ffn_up.weight,       torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.6.ffn_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.6.attn_k.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.6.attn_k.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.6.attn_output.weight,  torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.6.attn_q.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.6.attn_q.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.6.attn_v.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.6.attn_v.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.7.attn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.7.ffn_down.weight,     torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.7.ffn_gate.weight,     torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.7.ffn_up.weight,       torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.7.ffn_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.7.attn_k.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.7.attn_k.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.7.attn_output.weight,  torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.7.attn_q.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.7.attn_q.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.7.attn_v.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.7.attn_v.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.8.attn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.8.ffn_down.weight,     torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.8.ffn_gate.weight,     torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.8.ffn_up.weight,       torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.8.ffn_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.8.attn_k.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.8.attn_k.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.8.attn_output.weight,  torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.8.attn_q.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.8.attn_q.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.8.attn_v.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.8.attn_v.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.9.attn_norm.weight,    torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.9.ffn_down.weight,     torch.bfloat16 --> F16, shape = {2816, 1024}
INFO:hf-to-gguf:blk.9.ffn_gate.weight,     torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.9.ffn_up.weight,       torch.bfloat16 --> F16, shape = {1024, 2816}
INFO:hf-to-gguf:blk.9.ffn_norm.weight,     torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.9.attn_k.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.9.attn_k.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.9.attn_output.weight,  torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.9.attn_q.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.9.attn_q.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:blk.9.attn_v.bias,         torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:blk.9.attn_v.weight,       torch.bfloat16 --> F16, shape = {1024, 1024}
INFO:hf-to-gguf:output_norm.weight,        torch.bfloat16 --> F32, shape = {1024}
INFO:hf-to-gguf:Set meta model
INFO:hf-to-gguf:Set model parameters
INFO:hf-to-gguf:gguf: context length = 32768
INFO:hf-to-gguf:gguf: embedding length = 1024
INFO:hf-to-gguf:gguf: feed forward length = 2816
INFO:hf-to-gguf:gguf: head count = 16
INFO:hf-to-gguf:gguf: key-value head count = 16
INFO:hf-to-gguf:gguf: rope theta = 1000000.0
INFO:hf-to-gguf:gguf: rms norm epsilon = 1e-06
INFO:hf-to-gguf:gguf: file type = 1
INFO:hf-to-gguf:Set model tokenizer
INFO:gguf.vocab:Adding 151387 merge(s).
INFO:gguf.vocab:Setting special token type eos to 151646
INFO:gguf.vocab:Setting special token type pad to 151643
INFO:gguf.vocab:Setting special token type bos to 151643
INFO:gguf.vocab:Setting chat_template to {% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ '<|start_header_id|>system<|end_header_id|>' + system_message + '<|eot_id|>' }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|start_header_id|>user<|end_header_id|>' + content + '<|eot_id|><|start_header_id|>assistant<|end_header_id|>' }}{% elif message['role'] == 'assistant' %}{{ content + '<|eot_id|>' }}{% endif %}{% endfor %}
INFO:hf-to-gguf:Set model quantization version
INFO:gguf.gguf_writer:Writing the following files:
INFO:gguf.gguf_writer:/home/lichao/AIGC/LLaMA-Factory-0.8.3/asterun/saves/qwen/full/sft/Sft-620M-F16.gguf: n_tensors = 291, total_size = 1.2G
Writing: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.24G/1.24G [00:03<00:00, 338Mbyte/s]
INFO:hf-to-gguf:Model successfully exported to /home/lichao/AIGC/LLaMA-Factory-0.8.3/asterun/saves/qwen/full/sft/Sft-620M-F16.gguf
[root@server3 llama.cpp-master]# cd /home/lichao/AIGC/LLaMA-Factory-0.8.3/asterun/saves/qwen/full/sft
转换成功后，修改gguf格式的模型名称，方便后需使用辨认
[root@server3 sft]# ll
总用量 2883588
-rw-r--r-- 1 root root        104 9月  23 10:29 added_tokens.json
-rw-r--r-- 1 root root        358 9月  23 10:29 all_results.json
drwxr-xr-x 3 root root       4096 9月  19 09:59 checkpoint-1000
drwxr-xr-x 3 root root       4096 9月  19 10:05 checkpoint-1470
drwxr-xr-x 3 root root       4096 9月  13 11:02 checkpoint-489
drwxr-xr-x 3 root root       4096 9月  19 09:51 checkpoint-500
-rw-r--r-- 1 root root        731 9月  23 10:28 config.json
-rw-r--r-- 1 root root        175 9月  23 10:29 eval_results.json
-rw-r--r-- 1 root root        210 9月  23 10:28 generation_config.json
-rw-r--r-- 1 root root    1671853 9月  23 10:29 merges.txt
-rw-r--r-- 1 root root 1239173352 9月  23 10:28 model.safetensors
-rw-r--r-- 1 root root       1398 9月  23 10:29 README.md
drwxr-xr-x 2 root root        222 9月  23 10:29 runs
-rw-r--r-- 1 root root 1245334112 9月  26 11:58 Sft-620M-F16.gguf
-rw-r--r-- 1 root root        367 9月  23 10:29 special_tokens_map.json
-rw-r--r-- 1 root root       1720 9月  23 10:29 tokenizer_config.json
-rw-r--r-- 1 root root    7028230 9月  23 10:29 tokenizer.json
-rw-r--r-- 1 root root      11984 9月  23 10:28 trainer_log.jsonl
-rw-r--r-- 1 root root       9284 9月  23 10:29 trainer_state.json
-rw-r--r-- 1 root root       6584 9月  23 10:29 training_args.bin
-rw-r--r-- 1 root root      38333 9月  19 10:06 training_eval_loss.png
-rw-r--r-- 1 root root      37022 9月  23 10:29 training_loss.png
-rw-r--r-- 1 root root        218 9月  23 10:29 train_results.json
-rw-r--r-- 1 root root    2776833 9月  23 10:29 vocab.json
[root@server3 sft]# mv Sft-620M-F16.gguf qwen-sft-620M-F16.gguf

安装Ollama

OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://127.0.0.1:11434 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE:5m0s OLLAMA_LLM_LIBRARY: OLLAMA_LOAD_TIMEOUT:5m0s OLLAMA_MAX_LOADED_MODELS:0 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:/root/.ollama/models OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:0 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://*] OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR: ROCR_VISIBLE_DEVICES: http_proxy: https_proxy: no_proxy:]"
time=2024-09-26T12:04:20.753+02:00 level=INFO source=images.go:753 msg="total blobs: 0"
time=2024-09-26T12:04:20.754+02:00 level=INFO source=images.go:760 msg="total unused blobs removed: 0"
time=2024-09-26T12:04:20.754+02:00 level=INFO source=routes.go:1200 msg="Listening on 127.0.0.1:11434 (version 0.3.12)"
time=2024-09-26T12:04:20.755+02:00 level=INFO source=common.go:135 msg="extracting embedded files" dir=/tmp/ollama316805737/runners
time=2024-09-26T12:04:39.145+02:00 level=INFO source=common.go:49 msg="Dynamic LLM libraries" runners="[cpu cpu_avx cpu_avx2 cuda_v11 cuda_v12 rocm_v60102]"
time=2024-09-26T12:04:39.145+02:00 level=INFO source=gpu.go:199 msg="looking for compatible GPUs"
time=2024-09-26T12:04:39.283+02:00 level=INFO source=types.go:107 msg="inference compute" id=GPU-2d337ad0-020d-0464-2d00-715b0d00c7ba library=cuda variant=v12 compute=8.9 driver=12.4 name="NVIDIA GeForce RTX 4060 Ti" total="15.7 GiB" available="15.6 GiB"

注册模型

打开一个新的terminal

[root@server3 AIGC]# cd LLaMA-Factory-0.8.3/asterun/
[root@server3 asterun]# ll
总用量 4
-rw-r--r-- 1 root root 817 9月  19 09:33 qwen_full_sft_ds2.yaml
drwxr-xr-x 3 root root  18 9月  13 10:28 saves
创建模型的Modelfile文件
[root@server3 asterun]# touch qwen_full_sft_ds2.ollama.Modelfile
[root@server3 asterun]# vim qwen_full_sft_ds2.ollama.Modelfile 
[root@server3 asterun]# cat qwen_full_sft_ds2.ollama.Modelfile 
FROM /home/lichao/AIGC/LLaMA-Factory-0.8.3/asterun/saves/qwen/full/sft/qwen-sft-620M-F16.gguf
[root@server3 asterun]# cd ../..
使用Modelfile注册模型
[root@server3 AIGC]# ollama create qwen-full-sft -f ./LLaMA-Factory-0.8.3/asterun/qwen_full_sft_ds2.ollama.Modelfile 
transferring model data 100% 
using existing layer sha256:19d794be57081c1a5aa7e03c4045a0fdc5b8a40f080f0c550ab38033cf0d5d58 
creating new layer sha256:c33681b055686143e7d6e0bb0f1054c9910c05c3f4ab16932fbc567a8961929a 
writing manifest 
success 
[root@server3 AIGC]# 

使用注册好的模型运行推理服务
[root@server3 AIGC]# ollama run qwen-full-sft
>>> who are you?
<|im_end|>
我是 {{name}}，一个由 {{author}} 开发的人工智能助手，我可以帮助用户查询信息、安排日程、提供建议等。>>> can you speak english?I am an AI assistant developed by {{author}}.>>> 好吧，用中文交流吧。
没问题。>>> 你喜欢中国哪个城市？
每个城市都有其独特的魅力，各具特色，比如：
成都：美食之都，生活悠闲。
北京：历史悠久，文化丰富。
杭州：风景优美，以西湖闻名。
上海：现代化大都市，经济繁荣。>>> 感谢，再见
好的，我是个人工智能助手，很高兴见到您。>>> exit
[root@server3 AIGC]#

至此，已完成分布式计算环境的搭建与测试

【温馨提示：本篇文章涉及较多代码，请移步至wx公号星融元Asterfusion，发送关键词“分布式”获取完整版及Q&A】

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