TensorFlow 源码编译构建

大型工程编译坑还是太多，这波先把坑给大家踩一踩。

Reference

主要参考tensorflow官方网站和自己之前的一些经验吧。

https://www.tensorflow.org/install/source

这次主要是在物理机上搞的，既然公司有容器化的战略，那么下一步全部上容器，云化。

依赖安装

像tensorflow这种工程的安装，主要是版本依赖的问题居多，那么我先把我在版本依赖的坑写一下。

• Python 版本 3.6.9

• Pip list

1. 1. Numpy 1.19.1

1. 1. Wheel 0.34.2

不支持在 Doc 外粘贴 block参考一下我的配置

• Bazel 版本

• 我刚开始用了 0.24.1, 有些坑，后来改成0.26.1搞定了。

• Tensorflow 用 bazel来做构造工具，其性质跟 blade是一样的。

1. • 我们可以通过 cat 一下 configure.py 这个文件来查看一下，你需要哪个版本，然后对应安装即可。

cd tensorflow
cat configure.py | grep BAZEL_VERSION
_TF_MIN_BAZEL_VERSION = '0.24.1'
_TF_MAX_BAZEL_VERSION = '0.26.1'

1. • 直接到release网页下载下来，然后进行安装，（服务器可能会预装 bazel，但是版本可能会有点高）

https://github.com/bazelbuild/bazel/releases
https://github.com/bazelbuild/bazel/releases/tag/0.26.1
wget https://github.com/bazelbuild/bazel/releases/download/0.26.1/bazel-0.26.1-installer-linux-x86_64.sh

1. • 然后把bazel 安装在自己的目录，使能一下PATH

chmod a+x bazel-0.24.1-installer-linux-x86_64.sh
./bazel-0.24.1-installer-linux-x86_64.sh --prefix=/path/to/you
export PATH=/data01/zangchuanqi/bin:$PATH

• GCC 版本

• • GCC 版本也有点曲折，之前用的是4.9.2也就是系统默认版本

• • 但是中间eigen（CPU线性代数加速库）需要5.x以上的版本来编译，所以换成5.3，然后又有bug。最终我用了7.3.0编译成功的，再试一下8.3.0吧。

• • 以上版本都是gcc官方的release

• • 有一些坑，我记录在这儿了。 源码编译GCC（我的博客）

基本需要的依赖都安装完了的话，下边在安装，就十分顺滑了。

安装TensorFlow

• 配置

cd tensorflow
./configure

• 说明一下，常规操作一般只需要CPU的话，就无需选择 XLA 选项和CUDA开关，但是一般有GPU加速的需求，就需要按照我的这个配置来。

• 这是一个交互式的脚本文件，所以呢，就是需要填Y或者N，然后也需要填一下 CUDA路径，这根据你所需要的版本来定。

• 仔细看一下我的选择

• • Python 环境，选择了自己的virtual环境

• • XLA 选择Y

• • CUDA的选的10

• • CUDNN选的7

• • CUDA PATH 手动选了/data01/zangchuanqi/workspace/cuda

• • Clang 不选了

• • GCC 选了自定义的7.3.0 /data01/zangchuanqi/gcc-7.3.0/build/install/bin/gcc

• • 其他的就default了

(tvm.venv) zangchuanqi@myserver:~/workspace/tensorflow$ ./configure
WARNING: --batch mode is deprecated. Please instead explicitly shut down your Bazel server using the command "bazel shutdown".
You have bazel 0.24.1 installed.
Please specify the location of python. [Default is /data01/zangchuanqi/tvm.venv/bin/python]:
Found possible Python library paths:
 /data01/zangchuanqi/tvm.venv/lib/python3.6/site-packages
 /data01/zangchuanqi/workspace/tvm/topi/python
 /data01/zangchuanqi/workspace/tvm/python
Please input the desired Python library path to use.  Default is [/data01/zangchuanqi/tvm.venv/lib/python3.6/site-packages]
Do you wish to build TensorFlow with XLA JIT support? [Y/n]: y
XLA JIT support will be enabled for TensorFlow.
Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: n
No OpenCL SYCL support will be enabled for TensorFlow.
Do you wish to build TensorFlow with ROCm support? [y/N]: n
No ROCm support will be enabled for TensorFlow.
Do you wish to build TensorFlow with CUDA support? [y/N]: Y
CUDA support will be enabled for TensorFlow.
Do you wish to build TensorFlow with TensorRT support? [y/N]: n
No TensorRT support will be enabled for TensorFlow.
Could not find any cudnn.h matching version '' in any subdirectory:
    ''
    'include'
    'include/cuda'
    'include/*-linux-gnu'
    'extras/CUPTI/include'
    'include/cuda/CUPTI'
of:
    '/lib'
    '/lib/x86_64-linux-gnu'
    '/lib32'
    '/libx32'
    '/opt/tiger/ss_lib/so'
    '/usr'
    '/usr/lib'
    '/usr/lib/x86_64-linux-gnu'
    '/usr/lib/x86_64-linux-gnu/libfakeroot'
    '/usr/local/cuda'
    '/usr/local/cuda/lib64'
    '/usr/local/cudnn/lib64'
Asking for detailed CUDA configuration...
Please specify the CUDA SDK version you want to use. [Leave empty to default to CUDA 10]: 10
Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7]: 7
Please specify the locally installed NCCL version you want to use. [Leave empty to use http://github.com/nvidia/nccl]:
Please specify the comma-separated list of base paths to look for CUDA libraries and headers. [Leave empty to use the default]: /data01/zangchuanqi/workspace/cuda
Found CUDA 10.0 in:
  /data01/zangchuanqi/workspace/cuda/lib64
  /data01/zangchuanqi/workspace/cuda/include
Found cuDNN 7 in:
  /data01/zangchuanqi/workspace/cuda/lib64
  /data01/zangchuanqi/workspace/cuda/include
Please specify a list of comma-separated CUDA compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your build time and binary size, and that TensorFlow only supports compute capabilities >= 3.5 [Default is: 7.0,7.0,7.0,7.0,7.0,7.0,7.0,7.0]:
Do you want to use clang as CUDA compiler? [y/N]:
nvcc will be used as CUDA compiler.
Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:/data01/zangchuanqi/gcc-7.3.0/build/install/bin/gcc
Do you wish to build TensorFlow with MPI support? [y/N]: n
No MPI support will be enabled for TensorFlow.
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native -Wno-sign-compare]:
Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n
Not configuring the WORKSPACE for Android builds.
Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See .bazelrc for more details.
    --config=mkl         # Build with MKL support.
    --config=monolithic      # Config for mostly static monolithic build.
    --config=gdr         # Build with GDR support.
    --config=verbs        # Build with libverbs support.
    --config=ngraph        # Build with Intel nGraph support.
    --config=numa         # Build with NUMA support.
    --config=dynamic_kernels     # (Experimental) Build kernels into separate shared objects.
    --config=v2          # Build TensorFlow 2.x instead of 1.x.
Preconfigured Bazel build configs to DISABLE default on features:
    --config=noaws        # Disable AWS S3 filesystem support.
    --config=nogcp        # Disable GCP support.
    --config=nohdfs        # Disable HDFS support.
    --config=noignite       # Disable Apache Ignite support.
    --config=nokafka       # Disable Apache Kafka support.
    --config=nonccl        # Disable NVIDIA NCCL support.
Configuration finished

• 编译安装

1. • Bazel 构建

1. • 生成wheel包到../work/tensorflow_pkg

1. • Pip 安装 到当前环境

1. 1. • 参数说明

1. 1. 1. • Opt 使能优化

1. 1. 1. • Cuda 使能GPU

bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
./bazel-bin/tensorflow/tools/pip_package/build_pip_package ../work/tensorflow_pkg
pip install ../work/tensorflow_pkg/tensorflow-version-tags.whl

• Have a Test

(test.venv) zangchuanqi@myserver:~$ python
Python 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31)
[GCC 7.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> print tf.__version__
 File "<stdin>", line 1
  print tf.__version__
      ^
SyntaxError: Missing parentheses in call to 'print'. Did you mean print(tf.__version__)?
>>> print (tf.__version__)
1.15.3

大功告成！

使能XLA

• https://tensorflow.juejin.im/performance/xla/jit.html

A. 环境变量设置

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# XLA FLAGS
export XLA_FLAGS="--xla_hlo_profile --xla_dump_to=/tmp/foo --xla_dump_hlo_as_text --xla_dump_hlo_as_dot --xla_dump_hlo_as_html --xla_dump_hlo_as_proto"
# 输出所有的hlo pass
export XLA_FLAGS="--xla_hlo_profile --xla_dump_hlo_pass_re=.* --xla_dump_to=./test_dump --xla_dump_hlo_as_dot"
# log 选项
export TF_CPP_MIN_VLOG_LEVEL=4
export TF_CPP_MIN_LOG_LEVEL=0

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B. 代码case

• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/mnist/mnist_softmax_xla.py

C. 结果查看

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• 生成的图如下

• 优化前的

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• 优化后

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