bazel 工具函数
Bazel 官方文档
不管是写WORKSPACE，BUILD、.bzl(主要是一些函数)或者其他文件，都要遵循一些Bazel的规则，有些规则是bazel内置的函数使用说明，有的规则是一些语法分析用到的。
例如：
cc_library(
name = "mkl_dnn",
srcs = glob([
"src/common/*.cpp",
"src/common/*.hpp",
"src/cpu/*.cpp",
"src/cpu/*.hpp",
"src/cpu/**/*.cpp",
"src/cpu/**/*.hpp",
"src/cpu/xbyak/*.h",
]),
hdrs = glob(["include/*"]),
copts = [
"-fexceptions",
"-DUSE_MKL",
"-DUSE_CBLAS",
],
includes = [
"include",
"src",
"src/common",
"src/cpu",
"src/cpu/gemm",
"src/cpu/xbyak",
],
nocopts = "-fno-exceptions",
visibility = ["//visibility:public"],
deps = select({
"@org_tensorflow//tensorflow:linux_x86_64": [
"@mkl_linux//:mkl_headers",
"@mkl_linux//:mkl_libs_linux",
],
"//conditions:default": [],
}),
文章目录
- visibility
- 可配置的属性 config_setting
- glob
- select
- workspace
- repository_rule
visibility
visibility = ["//visibility:public"],
有5种形式的label
(1)["//visibility:public"]: Anyone can use this rule
(2) ["//visibility:private"]: Only rules in this package can use this rule.
(3) ["//some/package:__pkg__", "//other/package:__pkg__"]: Only rules in some/package and other/package (defined in some/package/BUILDand other/package/BUILD) have access to this rule. 必须是规定的package下面的BUILD文件中的rule才有权限使用
(4)["//project:__subpackages__", "//other:__subpackages__"]: Only rules in packages project or other or in one of their sub-packages have access to this rule. 指定的package下面的包括子目录都可以使用
(5) ["//some/package:my_package_group"]: A package group is a named set of package names.
可配置的属性 config_setting
cc_library(
name = "multiplatform_lib",
srcs = select({
":x86_mode": ["x86_impl.cc"],
":arm_mode": ["arm_impl.cc"]
})
)
config_setting(
name = "x86_mode",
values = { "cpu": "x86" }
)
config_setting(
name = "arm_mode",
values = { "cpu": "arm" }
)
在属性值的设置中使用select()可以根据输入的config_setting的值来进行匹配。比如
bazel build :multiplatform_lib --cpu=arm
sets multiplatform_lib’s srcs to [“arm_impl.cc”], while bazel build :multiplatform_lib --cpu=x86sets srcs to [“x86_impl.cc”]
glob
glob(include, exclude=[], exclude_directories=1)
Glob是一个辅助函数，用于在任何位置获得想要的文件列表。可以使用*通配符以及目录分隔符/，另外**表示递归通配符只能在目录分隔符/之间使用，比如"x/**/*.java" is valid, but “test**/testdata.xml” and “**.java” are both invalid. No other wildcards are supported.
select
select(
{conditionA: valuesA, conditionB: valuesB, ...},
no_match_error = "custom message"
)
这也是一个辅助函数，可以使得rule的属性被配置，选取的方式通过读Bazel的命令行flag。
里面的conditionA是在config_setting中定义出来的。比如：
config_setting(
name = "simple",
values = {"compilation_mode": "opt"}
)
那么在命令行中如果有 --compilation_mode=opt那么就是使用了这个simple
workspace
workspace(name = "com_example_project")
在WORKSPACE文件中使用，每个仓库的WORKSPACE文件都应该有这个函数，来设置这个仓库的全局名称。这个名称被用来存放输出目录。
repository_rule
repository_rule 只能在 WORKSPACE 文件里面使用,在加载 .bzl 文件时候使用非密封的操作。
例如：
WORKSPACE 文件
workspace(name = "org_tensorflow")
# Load tf_repositories() before loading dependencies for other repository so
# that dependencies like com_google_protobuf won't be overridden.
load("//tensorflow:workspace.bzl", "tf_repositories")
# Please add all new TensorFlow dependencies in workspace.bzl.
tf_repositories()
//tensorflow:workspace.bzl 文件：
load("//third_party/mkl:build_defs.bzl", "mkl_repository")
load("//third_party:repo.bzl", "tf_http_archive")
def tf_repositories(path_prefix = "", tf_repo_name = ""):
"""All external dependencies for TF builds."""
mkl_repository(
name = "mkl_linux",
build_file = clean_dep("//third_party/mkl:mkl.BUILD"),
sha256 = "a936d6b277a33d2a027a024ea8e65df62bd2e162c7ca52c48486ed9d5dc27160",
strip_prefix = "mklml_lnx_2019.0.5.20190502",
urls = [
"https://storage.googleapis.com/mirror.tensorflow.org/github.com/intel/mkl-dnn/releases/download/v0.20-rc/mklml_lnx_2019.0.5.20190502.tgz",
"https://github.com/intel/mkl-dnn/releases/download/v0.20-rc/mklml_lnx_2019.0.5.20190502.tgz",
],
)
mkl_repository(
name = "mkl_windows",
build_file = clean_dep("//third_party/mkl:mkl.BUILD"),
sha256 = "535857b17643d7f7546b58fc621244e7cfcc4fff2aa2ebd3fc5b4e126bfc36cf",
strip_prefix = "mklml_win_2019.0.5.20190502",
urls = [
"https://storage.googleapis.com/mirror.tensorflow.org/github.com/intel/mkl-dnn/releases/download/v0.20-rc/mklml_win_2019.0.5.20190502.zip",
"https://github.com/intel/mkl-dnn/releases/download/v0.20-rc/mklml_win_2019.0.5.20190502.zip",
],
)
mkl_repository(
name = "mkl_darwin",
build_file = clean_dep("//third_party/mkl:mkl.BUILD"),
sha256 = "2fbb71a0365d42a39ea7906568d69b1db3bfc9914fee75eedb06c5f32bf5fa68",
strip_prefix = "mklml_mac_2019.0.5.20190502",
urls = [
"https://storage.googleapis.com/mirror.tensorflow.org/github.com/intel/mkl-dnn/releases/download/v0.20-rc/mklml_mac_2019.0.5.20190502.tgz",
"https://github.com/intel/mkl-dnn/releases/download/v0.20-rc/mklml_mac_2019.0.5.20190502.tgz",
],
)
# Important: If you are upgrading MKL-DNN, then update the version numbers
# in third_party/mkl_dnn/mkldnn.BUILD. In addition, the new version of
# MKL-DNN might require upgrading MKL ML libraries also. If they need to be
# upgraded then update the version numbers on all three versions above
# (Linux, Mac, Windows).
tf_http_archive(
name = "mkl_dnn",
build_file = clean_dep("//third_party/mkl_dnn:mkldnn.BUILD"),
sha256 = "a198a9bd3c584607e6a467f780beca92c8411cd656fcc8ec6fa5abe73d4af823",
strip_prefix = "mkl-dnn-0.20.3",
urls = [
"https://storage.googleapis.com/mirror.tensorflow.org/github.com/intel/mkl-dnn/archive/v0.20.3.tar.gz",
"https://github.com/intel/mkl-dnn/archive/v0.20.3.tar.gz",
],
)
tf_http_archive(
name = "mkl_dnn_v1",
build_file = clean_dep("//third_party/mkl_dnn:mkldnn.BUILD"),
sha256 = "fcc2d951f7170eade0cfdd0d8d1d58e3e7785bd326bca6555f3722f8cba71811",
strip_prefix = "mkl-dnn-1.0-pc2",
urls = [
"https://storage.googleapis.com/mirror.tensorflow.org/github.com/intel/mkl-dnn/archive/v1.0-pc2.tar.gz",
"https://github.com/intel/mkl-dnn/archive/v1.0-pc2.tar.gz",
],
)
mkl_repository 在文件//third_party/mkl:build_defs.bzl 中定义：
_TF_MKL_ROOT = "TF_MKL_ROOT"
def _enable_local_mkl(repository_ctx):
return _TF_MKL_ROOT in repository_ctx.os.environ
def _mkl_autoconf_impl(repository_ctx):
"""Implementation of the local_mkl_autoconf repository rule."""
if _enable_local_mkl(repository_ctx):
# Symlink lib and include local folders.
mkl_root = repository_ctx.os.environ[_TF_MKL_ROOT]
mkl_lib_path = "%s/lib" % mkl_root
repository_ctx.symlink(mkl_lib_path, "lib")
mkl_include_path = "%s/include" % mkl_root
repository_ctx.symlink(mkl_include_path, "include")
mkl_license_path = "%s/license.txt" % mkl_root
repository_ctx.symlink(mkl_license_path, "license.txt")
else:
# setup remote mkl repository.
repository_ctx.download_and_extract(
repository_ctx.attr.urls,
sha256 = repository_ctx.attr.sha256,
stripPrefix = repository_ctx.attr.strip_prefix,
)
# Also setup BUILD file.
repository_ctx.symlink(repository_ctx.attr.build_file, "BUILD")
mkl_repository = repository_rule(
implementation = _mkl_autoconf_impl,
environ = [
_TF_MKL_ROOT,
],
attrs = {
"build_file": attr.label(),
"urls": attr.string_list(default = []),
"sha256": attr.string(default = ""),
"strip_prefix": attr.string(default = ""),
},
)
可以看出在这里定义了repository_rule， attrs 里面定义了build_file （这里这个文件主要是 mkl.BUILD 文件，在mkl.BUILD文件内定义了相关的操作library）。 定义attrs ，就可以在implementation 的函数内部使用repository_ctx.attr.
Implementation 函数：
只有一个输入参数，repository_ctx， 函数返回为None 表示给定指定参数后该规则是可复制的或者返回具有该规则的一组参数的字典，该字典会将该规则转换为可重复的规则，从而生成相同的存储库。 例如，对于跟踪git存储库的规则，这意味着将返回特定的提交标识符，而不是最初指定的浮动分支。
输入参数repository_ctx可用于访问属性值和非密封功能（查找二进制文件，执行二进制文件，在存储库中创建文件或从Internet下载文件）。更多的函数参考链接
例如：
licenses(["notice"]) # 3-Clause BSD
exports_files(["license.txt"])
filegroup(
name = "LICENSE",
srcs = [
"license.txt",
],
visibility = ["//visibility:public"],
)
cc_library(
name = "mkl_headers",
srcs = glob(["include/*(.cc|.cpp|.cxx|.c++|.C|.c|.h|.hh|.hpp|.ipp|.hxx|.inc|.S|.s|.asm|.a|.lib|.pic.a|.lo|.lo.lib|.pic.lo|.so|.dylib|.dll|.o|.obj|.pic.o)"]),
includes = ["include"],
visibility = ["//visibility:public"],
)
在其他的BUILD文件，就可以通过 @mkl_linux//:mkl_headers进行访问，例如：
cc_library(
name = "intel_binary_blob",
visibility = ["//visibility:public"],
deps = select({
"@org_tensorflow//tensorflow:linux_x86_64": [
"@mkl_linux//:mkl_headers",
"@mkl_linux//:mkl_libs_linux",
],
"@org_tensorflow//tensorflow:macos": [
"@mkl_darwin//:mkl_headers",
"@mkl_darwin//:mkl_libs_darwin",
],
"@org_tensorflow//tensorflow:windows": [
"@mkl_windows//:mkl_headers",
"@mkl_windows//:mkl_libs_windows",
],
"//conditions:default": [],
}),
)