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      全流程演示：如何从0到1构建分布式GPU计算环境

      星融元 • 来源：jf_55437772 • 作者：jf_55437772 • 2024-11-15 11:30 • 次阅读

      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。

      wKgaoWc2v4aAY_w2AAgWySfI2xA522.png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：

      wKgZoWc2v56AXyPYAADqc9gRfvU646.png图2：通用计算服务器内部的硬件连接拓扑wKgZoWc2v7KATB5kAARyuXoP2h8166.png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。

      wKgaomc2v86AQXSTAAyhSSqXz_8435.png图4：电源选型示意图wKgaomc2v9yACmNzAAH92guvC0k485.png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。

      wKgZomc2v-6Ae2DUAAaVDUCE_QM268.png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。

      wKgaomc2v_-AXJrXAADIGx729E8590.png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

      wKgaomc2wA2AGmhbAAB0x2Vd0uI115.png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 172.16.20.241

AsterNOS# configure terminal 
AsterNOS(config)# qos roce lossless   
AsterNOS(config)# qos service-policy roce_lossless 
AsterNOS(config)# end
AsterNOS# show qos roce
                    operational    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 classification

 RoCE 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              7

 RoCE SP->TC mapping and ETS configurations
================================================
  switch-prio  mode    weight
-------------  ------  --------
            6  SP
            7  SP

 RoCE 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
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      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
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      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
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      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 ~]#
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      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]#
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      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
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      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...................
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      [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
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      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]#
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      • 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/lib64

Open 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: Internal
 
Transports
-----------------------
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: yes
 
Resource Managers
-----------------------
Cray Alps: no
Grid Engine: no
LSF: no
Moab: no
Slurm: yes
ssh/rsh: yes
Torque: no
 
OMPIO 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]#
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      • 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/
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      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 limitation

server1:18697:18697 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to ens11f1

server1:18697:18697 [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.so

server1:18697:18697 [0] NCCL INFO NET/Plugin: Using internal network plugin.

server2:20893:20893 [0] NCCL INFO cudaDriverVersion 12040

server2:20893:20893 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to ens11f1

server2:20893:20893 [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.so

server2:20893:20893 [0] NCCL INFO NET/Plugin: Using internal network plugin.

server1:18697:18697 [0] NCCL INFO cudaDriverVersion 12040

NCCL version 2.21.5+cuda12.4

server3:2458:2458 [0] NCCL INFO cudaDriverVersion 12040

server3:2458:2458 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to ens11f1

server3:2458:2458 [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.so

server3:2458:2458 [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 ens11f1

server2:20893:20907 [0] NCCL INFO NCCL_IB_HCA set to mlx5_1:1

server2:20893:20907 [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 0

server2:20893:20907 [0] NCCL INFO Using network IB

server3:2458:2473 [0] NCCL INFO NCCL_IB_DISABLE set by environment to 0.

server3:2458:2473 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to ens11f1

server3:2458:2473 [0] NCCL INFO NCCL_IB_HCA set to mlx5_1:1

server1:18697:18712 [0] NCCL INFO NCCL_IB_DISABLE set by environment to 0.

server1:18697:18712 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to ens11f1

server3:2458:2473 [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_1:1

server3:2458:2473 [0] NCCL INFO Using non-device net plugin version 0

server3:2458:2473 [0] NCCL INFO Using network IB

server1:18697:18712 [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 0

server1:18697:18712 [0] NCCL INFO Using network IB

server1:18697:18712 [0] NCCL INFO ncclCommInitRank comm 0x23622c0 rank 0 nranks 3 cudaDev 0 nvmlDev 0 busId 2000 commId 0x35491327c8228dd0 - Init START

server3:2458:2473 [0] NCCL INFO ncclCommInitRank comm 0x346ffc0 rank 2 nranks 3 cudaDev 0 nvmlDev 0 busId 2000 commId 0x35491327c8228dd0 - Init START

server2:20893:20907 [0] NCCL INFO ncclCommInitRank comm 0x2a1af20 rank 1 nranks 3 cudaDev 0 nvmlDev 0 busId 2000 commId 0x35491327c8228dd0 - Init START

server3:2458:2473 [0] NCCL INFO Setting affinity for GPU 0 to 0f,ff000fff

server2:20893:20907 [0] NCCL INFO Setting affinity for GPU 0 to 0f,ff000fff

server1:18697:18712 [0] NCCL INFO Setting affinity for GPU 0 to 0f,ff000fff

server1:18697:18712 [0] NCCL INFO comm 0x23622c0 rank 0 nRanks 3 nNodes 3 localRanks 1 localRank 0 MNNVL 0

server1:18697:18712 [0] NCCL INFO Channel 00/02 :  0  1  2

server1:18697:18712 [0] NCCL INFO Channel 01/02 :  0  1  2

server1:18697:18712 [0] NCCL INFO Trees [0] 2/-1/-1->0->-1 [1] 2/-1/-1->0->1

server1:18697:18712 [0] NCCL INFO P2P Chunksize set to 131072

server3:2458:2473 [0] NCCL INFO comm 0x346ffc0 rank 2 nRanks 3 nNodes 3 localRanks 1 localRank 0 MNNVL 0

server2:20893:20907 [0] NCCL INFO comm 0x2a1af20 rank 1 nRanks 3 nNodes 3 localRanks 1 localRank 0 MNNVL 0

server3:2458:2473 [0] NCCL INFO Trees [0] 1/-1/-1->2->0 [1] -1/-1/-1->2->0

server3:2458:2473 [0] NCCL INFO P2P Chunksize set to 131072

server2:20893:20907 [0] NCCL INFO Trees [0] -1/-1/-1->1->2 [1] 0/-1/-1->1->-1

server2:20893:20907 [0] NCCL INFO P2P Chunksize set to 131072

server3:2458:2473 [0] NCCL INFO Channel 00/0 : 1[0] -> 2[0] [receive] via NET/IB/0

server3:2458:2473 [0] NCCL INFO Channel 01/0 : 1[0] -> 2[0] [receive] via NET/IB/0

server3:2458:2473 [0] NCCL INFO Channel 00/0 : 2[0] -> 0[0] [send] via NET/IB/0

server3:2458:2473 [0] NCCL INFO Channel 01/0 : 2[0] -> 0[0] [send] via NET/IB/0

server2:20893:20907 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[0] [receive] via NET/IB/0

server2:20893:20907 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[0] [receive] via NET/IB/0

server2:20893:20907 [0] NCCL INFO Channel 00/0 : 1[0] -> 2[0] [send] via NET/IB/0

server2:20893:20907 [0] NCCL INFO Channel 01/0 : 1[0] -> 2[0] [send] via NET/IB/0

server1:18697:18712 [0] NCCL INFO Channel 00/0 : 2[0] -> 0[0] [receive] via NET/IB/0

server1:18697:18712 [0] NCCL INFO Channel 01/0 : 2[0] -> 0[0] [receive] via NET/IB/0

server1:18697:18712 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[0] [send] via NET/IB/0

server1:18697:18712 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[0] [send] via NET/IB/0

server3:2458:2475 [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 rings

server1:18697:18712 [0] NCCL INFO Channel 01/0 : 1[0] -> 0[0] [receive] via NET/IB/0

server3:2458:2473 [0] NCCL INFO Connected all rings

server2:20893:20907 [0] NCCL INFO Connected all rings

server1:18697:18712 [0] NCCL INFO Channel 00/0 : 0[0] -> 2[0] [send] via NET/IB/0

server2:20893:20907 [0] NCCL INFO Channel 00/0 : 2[0] -> 1[0] [receive] via NET/IB/0

server1:18697:18712 [0] NCCL INFO Channel 01/0 : 0[0] -> 2[0] [send] via NET/IB/0

server3:2458:2473 [0] NCCL INFO Channel 00/0 : 0[0] -> 2[0] [receive] via NET/IB/0

server2:20893:20907 [0] NCCL INFO Channel 01/0 : 1[0] -> 0[0] [send] via NET/IB/0

server3:2458:2473 [0] NCCL INFO Channel 01/0 : 0[0] -> 2[0] [receive] via NET/IB/0

server3:2458:2473 [0] NCCL INFO Channel 00/0 : 2[0] -> 1[0] [send] via NET/IB/0

server3:2458:2473 [0] NCCL INFO Connected all trees

server1:18697:18712 [0] NCCL INFO Connected all trees

server1:18697:18712 [0] NCCL INFO NCCL_ALGO set by environment to ring

server3:2458:2473 [0] NCCL INFO NCCL_ALGO set by environment to ring

server3:2458:2473 [0] NCCL INFO threadThresholds 8/8/64 | 24/8/64 | 512 | 512

server3:2458:2473 [0] NCCL INFO 2 coll channels, 2 collnet channels, 0 nvls channels, 2 p2p channels, 2 p2p channels per peer

server2:20893:20907 [0] NCCL INFO Connected all trees

server2:20893:20907 [0] NCCL INFO NCCL_ALGO set by environment to ring

server2:20893:20907 [0] NCCL INFO threadThresholds 8/8/64 | 24/8/64 | 512 | 512

server2:20893:20907 [0] NCCL INFO 2 coll channels, 2 collnet channels, 0 nvls channels, 2 p2p channels, 2 p2p channels per peer

server1:18697:18712 [0] NCCL INFO threadThresholds 8/8/64 | 24/8/64 | 512 | 512

server1:18697:18712 [0] NCCL INFO 2 coll channels, 2 collnet channels, 0 nvls channels, 2 p2p channels, 2 p2p channels per peer

server2:20893:20907 [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.so

server2:20893:20907 [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 COMPLETE

server3:2458:2473 [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.so

server3:2458:2473 [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 COMPLETE

server1:18697:18712 [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.so

server1:18697:18712 [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   0

    256      64   float   sum   -1  29.44  0.01  0.01   0  28.54  0.01  0.01   0

    512      128   float   sum   -1  29.99  0.02  0.02   0  29.66  0.02  0.02   0

    1024      256   float   sum   -1  32.89  0.03  0.04   0  30.64  0.03  0.04   0

    2048      512   float   sum   -1  34.81  0.06  0.08   0  31.87  0.06  0.09   0

    4096     1024   float   sum   -1  37.32  0.11  0.15   0  36.09  0.11  0.15   0

    8192     2048   float   sum   -1  45.11  0.18  0.24   0  43.12  0.19  0.25   0

   16384     4096   float   sum   -1  57.92  0.28  0.38   0  56.98  0.29  0.38   0

   32768     8192   float   sum   -1  72.68  0.45  0.60   0  70.79  0.46  0.62   0

   65536     16384   float   sum   -1  95.77  0.68  0.91   0  93.73  0.70  0.93   0

   131072     32768   float   sum   -1  162.7  0.81  1.07   0  161.5  0.81  1.08   0

   262144     65536   float   sum   -1  177.3  1.48  1.97   0  177.4  1.48  1.97   0

   524288    131072   float   sum   -1  301.4  1.74  2.32   0  302.0  1.74  2.31   0

  1048576    262144   float   sum   -1  557.9  1.88  2.51   0  559.2  1.88  2.50   0

  2097152    524288   float   sum   -1  1089.8  1.92  2.57   0  1092.2  1.92  2.56   0

  4194304    1048576   float   sum   -1  2165.7  1.94  2.58   0  2166.6  1.94  2.58   0

  8388608    2097152   float   sum   -1  4315.7  1.94  2.59   0  4316.1  1.94  2.59   0

  16777216    4194304   float   sum   -1  8528.8  1.97  2.62   0  8529.3  1.97  2.62   0

  33554432    8388608   float   sum   -1  16622  2.02  2.69   0  16610  2.02  2.69   0

  67108864   16777216   float   sum   -1  32602  2.06  2.74   0  32542  2.06  2.75   0

 134217728   33554432   float   sum   -1  63946  2.10  2.80   0  63831  2.10  2.80   0

 268435456   67108864   float   sum   -1  126529  2.12  2.83   0  126412  2.12  2.83   0

 536870912   134217728   float   sum   -1  251599  2.13  2.85   0  251327  2.14  2.85   0

 1073741824   268435456   float   sum   -1  500664  2.14  2.86   0  501911  2.14  2.85   0

 2147483648   536870912   float   sum   -1 1001415  2.14  2.86   0 1000178  2.15  2.86   0

 4294967296  1073741824   float   sum   -1 1999361  2.15  2.86   0 1997380  2.15  2.87   0

server1:18697:18697 [0] NCCL INFO comm 0x23622c0 rank 0 nranks 3 cudaDev 0 busId 2000 - Destroy COMPLETE

server2:20893:20893 [0] NCCL INFO comm 0x2a1af20 rank 1 nranks 3 cudaDev 0 busId 2000 - Destroy COMPLETE

server3:2458:2458 [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]#
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      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 每个进程的线程数量配置， 默认 1；

      - -g,--ngpus 每个线程的 GPU 数量，默认 1；

      - 数据大小配置

      - -b,--minbytes 开始的最小数据量，默认 32M；

      - -e,--maxbytes 结束的最大数据量，默认 32M；

      - 数据步长设置

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

      - -f,--stepfactor 每次增加的倍数，默认禁用；

      - NCCL 操作相关配置

      - -o,--op 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 指定使用哪种数据类型，默认 : Float；

      - 性能相关配置

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

      - -w,--warmup_iters 预热迭代次数（不计时），默认：5；

      - -m,--agg_iters 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 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]#
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      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]#
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      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
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      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 env

Copy-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
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      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.json

0 directories, 10 files
[root@server3 models]#
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      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.txt

4 directories, 20 files
[root@server3 LLaMA-Factory-0.8.3]#
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      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://0.0.0.0:7861

To create a public link, set `share=True` in `launch()`.
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      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: true

report_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
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      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-py
  Installing build dependencies ... done
  Checking if build backend supports build_editable ... done
  Getting requirements to build editable ... done
  Preparing 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: gguf
  Building editable for gguf (pyproject.toml) ... done
  Created wheel for gguf: filename=gguf-0.10.0-py3-none-any.whl size=3403 sha256=4a0851426e263076c64c9854be9dfe95493844062484d001fddb08c1be5fa2ca
  Stored 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]#
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      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
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      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"
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      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]#
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png
      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]#
      poYBAGDYdXCAWkKMAAAAK8RNs4s030.png

      至此，已完成分布式计算环境的搭建与测试

      审核编辑 黄宇​

      声明：本文内容及配图由入驻作者撰写或者入驻合作网站授权转载。文章观点仅代表作者本人，不代表德赢Vwin官网 网立场。文章及其配图仅供工程师学习之用，如有内容侵权或者其他违规问题，请联系本站处理。 举报投诉
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        的头像 发表于 11-10 15:33 •1517次阅读
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