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tensorflow/configure.py
TensorFlower Gardener 698e70e1c6 Merge pull request #74090 from apraga:no-distutil 
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2024-10-02 15:16:02 -07:00

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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""configure script to get build parameters from user."""
import argparse
import errno
import json
import os
import platform
import re
import shutil
import subprocess
import sys
_DEFAULT_CUDA_COMPUTE_CAPABILITIES = '3.5,7.0'
_SUPPORTED_ANDROID_NDK_VERSIONS = [19, 20, 21, 25]
_DEFAULT_PROMPT_ASK_ATTEMPTS = 10
_TF_BAZELRC_FILENAME = '.tf_configure.bazelrc'
_TF_WORKSPACE_ROOT = ''
_TF_BAZELRC = ''
_TF_CURRENT_BAZEL_VERSION = None
NCCL_LIB_PATHS = [
'lib64/', 'lib/powerpc64le-linux-gnu/', 'lib/x86_64-linux-gnu/', ''
]
# List of files to configure when building Bazel on Apple platforms.
APPLE_BAZEL_FILES = [
'tensorflow/lite/ios/BUILD', 'tensorflow/lite/objc/BUILD',
'tensorflow/lite/swift/BUILD',
'tensorflow/lite/tools/benchmark/experimental/ios/BUILD'
]
# List of files to move when building for iOS.
IOS_FILES = [
'tensorflow/lite/objc/TensorFlowLiteObjC.podspec',
'tensorflow/lite/swift/TensorFlowLiteSwift.podspec',
]
class UserInputError(Exception):
pass
def is_windows():
return platform.system() == 'Windows'
def is_linux():
return platform.system() == 'Linux'
def is_macos():
return platform.system() == 'Darwin'
def is_ppc64le():
return platform.machine() == 'ppc64le'
def is_s390x():
return platform.machine() == 's390x'
def is_cygwin():
return platform.system().startswith('CYGWIN_NT')
def get_input(question):
try:
try:
answer = raw_input(question)
except NameError:
answer = input(question) # pylint: disable=bad-builtin
except EOFError:
answer = ''
return answer
def symlink_force(target, link_name):
"""Force symlink, equivalent of 'ln -sf'.
Args:
target: items to link to.
link_name: name of the link.
"""
try:
os.symlink(target, link_name)
except OSError as e:
if e.errno == errno.EEXIST:
os.remove(link_name)
os.symlink(target, link_name)
else:
raise e
def write_to_bazelrc(line):
with open(_TF_BAZELRC, 'a') as f:
f.write(line + '\n')
def write_action_env_to_bazelrc(var_name, var):
write_to_bazelrc('build --action_env {}="{}"'.format(var_name, str(var)))
def write_repo_env_to_bazelrc(config_name, var_name, var):
write_to_bazelrc(
'build:{} --repo_env {}="{}"'.format(config_name, var_name, str(var))
)
def run_shell(cmd, allow_non_zero=False, stderr=None):
if stderr is None:
stderr = sys.stdout
if allow_non_zero:
try:
output = subprocess.check_output(cmd, stderr=stderr)
except subprocess.CalledProcessError as e:
output = e.output
else:
output = subprocess.check_output(cmd, stderr=stderr)
return output.decode('UTF-8').strip()
def cygpath(path):
"""Convert path from posix to windows."""
return os.path.abspath(path).replace('\\', '/')
def get_python_path(environ_cp, python_bin_path):
"""Get the python site package paths."""
python_paths = []
if environ_cp.get('PYTHONPATH'):
python_paths = environ_cp.get('PYTHONPATH').split(':')
try:
stderr = open(os.devnull, 'wb')
library_paths = run_shell([
python_bin_path, '-c',
'import site; print("\\n".join(site.getsitepackages()))'
],
stderr=stderr).split('\n')
except subprocess.CalledProcessError:
library_paths = [
run_shell([
python_bin_path,
'-c',
'import sysconfig;print(sysconfig.get_path("purelib")',
])
]
all_paths = set(python_paths + library_paths)
# Sort set so order is deterministic
all_paths = sorted(all_paths)
paths = []
for path in all_paths:
if os.path.isdir(path):
paths.append(path)
return paths
def get_python_major_version(python_bin_path):
"""Get the python major version."""
return run_shell([python_bin_path, '-c', 'import sys; print(sys.version[0])'])
def setup_python(environ_cp):
"""Setup python related env variables."""
# Get PYTHON_BIN_PATH, default is the current running python.
default_python_bin_path = sys.executable
ask_python_bin_path = ('Please specify the location of python. [Default is '
'{}]: ').format(default_python_bin_path)
while True:
python_bin_path = get_from_env_or_user_or_default(environ_cp,
'PYTHON_BIN_PATH',
ask_python_bin_path,
default_python_bin_path)
# Check if the path is valid
if os.path.isfile(python_bin_path) and os.access(python_bin_path, os.X_OK):
break
elif not os.path.exists(python_bin_path):
print('Invalid python path: {} cannot be found.'.format(python_bin_path))
else:
print('{} is not executable. Is it the python binary?'.format(
python_bin_path))
environ_cp['PYTHON_BIN_PATH'] = ''
# Convert python path to Windows style before checking lib and version
if is_windows() or is_cygwin():
python_bin_path = cygpath(python_bin_path)
# Get PYTHON_LIB_PATH
python_lib_path = environ_cp.get('PYTHON_LIB_PATH')
if not python_lib_path:
python_lib_paths = get_python_path(environ_cp, python_bin_path)
if environ_cp.get('USE_DEFAULT_PYTHON_LIB_PATH') == '1':
python_lib_path = python_lib_paths[0]
else:
print('Found possible Python library paths:\n %s' %
'\n '.join(python_lib_paths))
default_python_lib_path = python_lib_paths[0]
python_lib_path = get_input(
'Please input the desired Python library path to use. '
'Default is [{}]\n'.format(python_lib_paths[0]))
if not python_lib_path:
python_lib_path = default_python_lib_path
environ_cp['PYTHON_LIB_PATH'] = python_lib_path
python_major_version = get_python_major_version(python_bin_path)
if python_major_version == '2':
write_to_bazelrc('build --host_force_python=PY2')
# Convert python path to Windows style before writing into bazel.rc
if is_windows() or is_cygwin():
python_lib_path = cygpath(python_lib_path)
# Set-up env variables used by python_configure.bzl
write_action_env_to_bazelrc('PYTHON_BIN_PATH', python_bin_path)
write_action_env_to_bazelrc('PYTHON_LIB_PATH', python_lib_path)
write_to_bazelrc('build --python_path=\"{}"'.format(python_bin_path))
environ_cp['PYTHON_BIN_PATH'] = python_bin_path
# If chosen python_lib_path is from a path specified in the PYTHONPATH
# variable, need to tell bazel to include PYTHONPATH
if environ_cp.get('PYTHONPATH'):
python_paths = environ_cp.get('PYTHONPATH').split(':')
if python_lib_path in python_paths:
write_action_env_to_bazelrc('PYTHONPATH', environ_cp.get('PYTHONPATH'))
# Write tools/python_bin_path.sh
with open(
os.path.join(_TF_WORKSPACE_ROOT, 'tools', 'python_bin_path.sh'),
'w') as f:
f.write('export PYTHON_BIN_PATH="{}"'.format(python_bin_path))
def reset_tf_configure_bazelrc():
"""Reset file that contains customized config settings."""
open(_TF_BAZELRC, 'w').close()
def cleanup_makefile():
"""Delete any leftover BUILD files from the Makefile build.
These files could interfere with Bazel parsing.
"""
makefile_download_dir = os.path.join(_TF_WORKSPACE_ROOT, 'tensorflow',
'contrib', 'makefile', 'downloads')
if os.path.isdir(makefile_download_dir):
for root, _, filenames in os.walk(makefile_download_dir):
for f in filenames:
if f.endswith('BUILD'):
os.remove(os.path.join(root, f))
def get_var(environ_cp,
var_name,
query_item,
enabled_by_default,
question=None,
yes_reply=None,
no_reply=None):
"""Get boolean input from user.
If var_name is not set in env, ask user to enable query_item or not. If the
response is empty, use the default.
Args:
environ_cp: copy of the os.environ.
var_name: string for name of environment variable, e.g. "TF_NEED_CUDA".
query_item: string for feature related to the variable, e.g. "CUDA for
Nvidia GPUs".
enabled_by_default: boolean for default behavior.
question: optional string for how to ask for user input.
yes_reply: optional string for reply when feature is enabled.
no_reply: optional string for reply when feature is disabled.
Returns:
boolean value of the variable.
Raises:
UserInputError: if an environment variable is set, but it cannot be
interpreted as a boolean indicator, assume that the user has made a
scripting error, and will continue to provide invalid input.
Raise the error to avoid infinitely looping.
"""
if not question:
question = 'Do you wish to build TensorFlow with {} support?'.format(
query_item)
if not yes_reply:
yes_reply = '{} support will be enabled for TensorFlow.'.format(query_item)
if not no_reply:
no_reply = 'No {}'.format(yes_reply)
yes_reply += '\n'
no_reply += '\n'
if enabled_by_default:
question += ' [Y/n]: '
else:
question += ' [y/N]: '
var = environ_cp.get(var_name)
if var is not None:
var_content = var.strip().lower()
true_strings = ('1', 't', 'true', 'y', 'yes')
false_strings = ('0', 'f', 'false', 'n', 'no')
if var_content in true_strings:
var = True
elif var_content in false_strings:
var = False
else:
raise UserInputError(
'Environment variable %s must be set as a boolean indicator.\n'
'The following are accepted as TRUE : %s.\n'
'The following are accepted as FALSE: %s.\n'
'Current value is %s.' %
(var_name, ', '.join(true_strings), ', '.join(false_strings), var))
while var is None:
user_input_origin = get_input(question)
user_input = user_input_origin.strip().lower()
if user_input == 'y':
print(yes_reply)
var = True
elif user_input == 'n':
print(no_reply)
var = False
elif not user_input:
if enabled_by_default:
print(yes_reply)
var = True
else:
print(no_reply)
var = False
else:
print('Invalid selection: {}'.format(user_input_origin))
return var
def set_action_env_var(environ_cp,
var_name,
query_item,
enabled_by_default,
question=None,
yes_reply=None,
no_reply=None,
bazel_config_name=None):
"""Set boolean action_env variable.
Ask user if query_item will be enabled. Default is used if no input is given.
Set environment variable and write to .bazelrc.
Args:
environ_cp: copy of the os.environ.
var_name: string for name of environment variable, e.g. "TF_NEED_CUDA".
query_item: string for feature related to the variable, e.g. "CUDA for
Nvidia GPUs".
enabled_by_default: boolean for default behavior.
question: optional string for how to ask for user input.
yes_reply: optional string for reply when feature is enabled.
no_reply: optional string for reply when feature is disabled.
bazel_config_name: adding config to .bazelrc instead of action_env.
"""
var = int(
get_var(environ_cp, var_name, query_item, enabled_by_default, question,
yes_reply, no_reply))
if not bazel_config_name:
write_action_env_to_bazelrc(var_name, var)
elif var:
write_to_bazelrc('build --config=%s' % bazel_config_name)
environ_cp[var_name] = str(var)
def convert_version_to_int(version):
"""Convert a version number to a integer that can be used to compare.
Version strings of the form X.YZ and X.Y.Z-xxxxx are supported. The
'xxxxx' part, for instance 'homebrew' on OS/X, is ignored.
Args:
version: a version to be converted
Returns:
An integer if converted successfully, otherwise return None.
"""
version = version.split('-')[0]
version_segments = version.split('.')
# Treat "0.24" as "0.24.0"
if len(version_segments) == 2:
version_segments.append('0')
for seg in version_segments:
if not seg.isdigit():
return None
version_str = ''.join(['%03d' % int(seg) for seg in version_segments])
return int(version_str)
def retrieve_bazel_version():
"""Retrieve installed bazel version (or bazelisk).
Returns:
The bazel version detected.
"""
bazel_executable = shutil.which('bazel')
if bazel_executable is None:
bazel_executable = shutil.which('bazelisk')
if bazel_executable is None:
print('Cannot find bazel. Please install bazel/bazelisk.')
sys.exit(1)
stderr = open(os.devnull, 'wb')
curr_version = run_shell([bazel_executable, '--version'],
allow_non_zero=True,
stderr=stderr)
if curr_version.startswith('bazel '):
curr_version = curr_version.split('bazel ')[1]
curr_version_int = convert_version_to_int(curr_version)
# Check if current bazel version can be detected properly.
if not curr_version_int:
print('WARNING: current bazel installation is not a release version.')
return curr_version
print('You have bazel %s installed.' % curr_version)
return curr_version
def set_cc_opt_flags(environ_cp):
"""Set up architecture-dependent optimization flags.
Also append CC optimization flags to bazel.rc..
Args:
environ_cp: copy of the os.environ.
"""
if is_ppc64le():
# gcc on ppc64le does not support -march, use mcpu instead
default_cc_opt_flags = '-mcpu=native'
elif is_windows():
default_cc_opt_flags = '/arch:AVX'
else:
# On all other platforms, no longer use `-march=native` as this can result
# in instructions that are too modern being generated. Users that want
# maximum performance should compile TF in their environment and can pass
# `-march=native` there.
# See https://github.com/tensorflow/tensorflow/issues/45744 and duplicates
default_cc_opt_flags = '-Wno-sign-compare'
question = ('Please specify optimization flags to use during compilation when'
' bazel option "--config=opt" is specified [Default is %s]: '
) % default_cc_opt_flags
cc_opt_flags = get_from_env_or_user_or_default(environ_cp, 'CC_OPT_FLAGS',
question, default_cc_opt_flags)
for opt in cc_opt_flags.split():
write_to_bazelrc('build:opt --copt=%s' % opt)
write_to_bazelrc('build:opt --host_copt=%s' % opt)
def set_tf_cuda_clang(environ_cp):
"""set TF_CUDA_CLANG action_env.
Args:
environ_cp: copy of the os.environ.
"""
question = 'Do you want to use clang as CUDA compiler?'
yes_reply = 'Clang will be used as CUDA compiler.'
no_reply = 'nvcc will be used as CUDA compiler.'
set_action_env_var(
environ_cp,
'TF_CUDA_CLANG',
None,
True,
question=question,
yes_reply=yes_reply,
no_reply=no_reply,
bazel_config_name='cuda_clang',
)
def set_tf_download_clang(environ_cp):
"""Set TF_DOWNLOAD_CLANG action_env."""
question = 'Do you wish to download a fresh release of clang? (Experimental)'
yes_reply = 'Clang will be downloaded and used to compile tensorflow.'
no_reply = 'Clang will not be downloaded.'
set_action_env_var(
environ_cp,
'TF_DOWNLOAD_CLANG',
None,
False,
question=question,
yes_reply=yes_reply,
no_reply=no_reply,
bazel_config_name='download_clang')
def get_from_env_or_user_or_default(environ_cp, var_name, ask_for_var,
var_default):
"""Get var_name either from env, or user or default.
If var_name has been set as environment variable, use the preset value, else
ask for user input. If no input is provided, the default is used.
Args:
environ_cp: copy of the os.environ.
var_name: string for name of environment variable, e.g. "TF_NEED_CUDA".
ask_for_var: string for how to ask for user input.
var_default: default value string.
Returns:
string value for var_name
"""
var = environ_cp.get(var_name)
if not var:
var = get_input(ask_for_var)
print('\n')
if not var:
var = var_default
return var
def prompt_loop_or_load_from_env(environ_cp,
var_name,
var_default,
ask_for_var,
check_success,
error_msg,
suppress_default_error=False,
resolve_symlinks=False,
n_ask_attempts=_DEFAULT_PROMPT_ASK_ATTEMPTS):
"""Loop over user prompts for an ENV param until receiving a valid response.
For the env param var_name, read from the environment or verify user input
until receiving valid input. When done, set var_name in the environ_cp to its
new value.
Args:
environ_cp: (Dict) copy of the os.environ.
var_name: (String) string for name of environment variable, e.g. "TF_MYVAR".
var_default: (String) default value string.
ask_for_var: (String) string for how to ask for user input.
check_success: (Function) function that takes one argument and returns a
boolean. Should return True if the value provided is considered valid. May
contain a complex error message if error_msg does not provide enough
information. In that case, set suppress_default_error to True.
error_msg: (String) String with one and only one '%s'. Formatted with each
invalid response upon check_success(input) failure.
suppress_default_error: (Bool) Suppress the above error message in favor of
one from the check_success function.
resolve_symlinks: (Bool) Translate symbolic links into the real filepath.
n_ask_attempts: (Integer) Number of times to query for valid input before
raising an error and quitting.
Returns:
[String] The value of var_name after querying for input.
Raises:
UserInputError: if a query has been attempted n_ask_attempts times without
success, assume that the user has made a scripting error, and will
continue to provide invalid input. Raise the error to avoid infinitely
looping.
"""
default = environ_cp.get(var_name) or var_default
full_query = '%s [Default is %s]: ' % (
ask_for_var,
default,
)
for _ in range(n_ask_attempts):
val = get_from_env_or_user_or_default(environ_cp, var_name, full_query,
default)
if check_success(val):
break
if not suppress_default_error:
print(error_msg % val)
environ_cp[var_name] = ''
else:
raise UserInputError('Invalid %s setting was provided %d times in a row. '
'Assuming to be a scripting mistake.' %
(var_name, n_ask_attempts))
if resolve_symlinks:
val = os.path.realpath(val)
environ_cp[var_name] = val
return val
def set_clang_cuda_compiler_path(environ_cp):
"""Set CLANG_CUDA_COMPILER_PATH."""
# Upon clang 19 drop the check for 16
default_clang_path = '/usr/lib/llvm-18/bin/clang'
if not os.path.exists(default_clang_path):
default_clang_path = '/usr/lib/llvm-17/bin/clang'
if not os.path.exists(default_clang_path):
default_clang_path = '/usr/lib/llvm-16/bin/clang'
if not os.path.exists(default_clang_path):
default_clang_path = shutil.which('clang') or ''
clang_cuda_compiler_path = prompt_loop_or_load_from_env(
environ_cp,
var_name='CLANG_CUDA_COMPILER_PATH',
var_default=default_clang_path,
ask_for_var='Please specify clang path that to be used as host compiler.',
check_success=os.path.exists,
resolve_symlinks=True,
error_msg='Invalid clang path. %s cannot be found.',
)
# Set CLANG_CUDA_COMPILER_PATH
environ_cp['CLANG_CUDA_COMPILER_PATH'] = clang_cuda_compiler_path
write_action_env_to_bazelrc('CLANG_CUDA_COMPILER_PATH',
clang_cuda_compiler_path)
return clang_cuda_compiler_path
def create_android_ndk_rule(environ_cp):
"""Set ANDROID_NDK_HOME and write Android NDK WORKSPACE rule."""
if is_windows() or is_cygwin():
default_ndk_path = cygpath('%s/Android/Sdk/ndk-bundle' %
environ_cp['APPDATA'])
elif is_macos():
default_ndk_path = '%s/library/Android/Sdk/ndk-bundle' % environ_cp['HOME']
else:
default_ndk_path = '%s/Android/Sdk/ndk-bundle' % environ_cp['HOME']
def valid_ndk_path(path):
return (os.path.exists(path) and
os.path.exists(os.path.join(path, 'source.properties')))
android_ndk_home_path = prompt_loop_or_load_from_env(
environ_cp,
var_name='ANDROID_NDK_HOME',
var_default=default_ndk_path,
ask_for_var='Please specify the home path of the Android NDK to use.',
check_success=valid_ndk_path,
error_msg=('The path %s or its child file "source.properties" '
'does not exist.'))
write_action_env_to_bazelrc('ANDROID_NDK_HOME', android_ndk_home_path)
write_action_env_to_bazelrc(
'ANDROID_NDK_API_LEVEL',
get_ndk_api_level(environ_cp, android_ndk_home_path))
def create_android_sdk_rule(environ_cp):
"""Set Android variables and write Android SDK WORKSPACE rule."""
if is_windows() or is_cygwin():
default_sdk_path = cygpath('%s/Android/Sdk' % environ_cp['APPDATA'])
elif is_macos():
default_sdk_path = '%s/library/Android/Sdk' % environ_cp['HOME']
else:
default_sdk_path = '%s/Android/Sdk' % environ_cp['HOME']
def valid_sdk_path(path):
return (os.path.exists(path) and
os.path.exists(os.path.join(path, 'platforms')) and
os.path.exists(os.path.join(path, 'build-tools')))
android_sdk_home_path = prompt_loop_or_load_from_env(
environ_cp,
var_name='ANDROID_SDK_HOME',
var_default=default_sdk_path,
ask_for_var='Please specify the home path of the Android SDK to use.',
check_success=valid_sdk_path,
error_msg=('Either %s does not exist, or it does not contain the '
'subdirectories "platforms" and "build-tools".'))
platforms = os.path.join(android_sdk_home_path, 'platforms')
api_levels = sorted(os.listdir(platforms))
api_levels = [x.replace('android-', '') for x in api_levels]
def valid_api_level(api_level):
return os.path.exists(
os.path.join(android_sdk_home_path, 'platforms',
'android-' + api_level))
android_api_level = prompt_loop_or_load_from_env(
environ_cp,
var_name='ANDROID_API_LEVEL',
var_default=api_levels[-1],
ask_for_var=('Please specify the Android SDK API level to use. '
'[Available levels: %s]') % api_levels,
check_success=valid_api_level,
error_msg='Android-%s is not present in the SDK path.')
build_tools = os.path.join(android_sdk_home_path, 'build-tools')
versions = sorted(os.listdir(build_tools))
def valid_build_tools(version):
return os.path.exists(
os.path.join(android_sdk_home_path, 'build-tools', version))
android_build_tools_version = prompt_loop_or_load_from_env(
environ_cp,
var_name='ANDROID_BUILD_TOOLS_VERSION',
var_default=versions[-1],
ask_for_var=('Please specify an Android build tools version to use. '
'[Available versions: %s]') % versions,
check_success=valid_build_tools,
error_msg=('The selected SDK does not have build-tools version %s '
'available.'))
write_action_env_to_bazelrc('ANDROID_BUILD_TOOLS_VERSION',
android_build_tools_version)
write_action_env_to_bazelrc('ANDROID_SDK_API_LEVEL', android_api_level)
write_action_env_to_bazelrc('ANDROID_SDK_HOME', android_sdk_home_path)
def get_ndk_api_level(environ_cp, android_ndk_home_path):
"""Gets the appropriate NDK API level to use for the provided Android NDK path.
"""
# First check to see if we're using a blessed version of the NDK.
properties_path = '%s/source.properties' % android_ndk_home_path
if is_windows() or is_cygwin():
properties_path = cygpath(properties_path)
with open(properties_path, 'r') as f:
filedata = f.read()
revision = re.search(r'Pkg.Revision = (\d+)', filedata)
if revision:
ndk_version = revision.group(1)
else:
raise Exception('Unable to parse NDK revision.')
if int(ndk_version) not in _SUPPORTED_ANDROID_NDK_VERSIONS:
print('WARNING: The NDK version in %s is %s, which is not '
'supported by Bazel (officially supported versions: %s). Please use '
'another version. Compiling Android targets may result in confusing '
'errors.\n' %
(android_ndk_home_path, ndk_version, _SUPPORTED_ANDROID_NDK_VERSIONS))
write_action_env_to_bazelrc('ANDROID_NDK_VERSION', ndk_version)
# Now grab the NDK API level to use. Note that this is different from the
# SDK API level, as the NDK API level is effectively the *min* target SDK
# version.
meta = open(os.path.join(android_ndk_home_path, 'meta/platforms.json'))
platforms = json.load(meta)
meta.close()
aliases = platforms['aliases']
api_levels = sorted(list(set([aliases[i] for i in aliases])))
android_ndk_api_level = prompt_loop_or_load_from_env(
environ_cp,
var_name='ANDROID_NDK_API_LEVEL',
var_default='21', # 21 is required for ARM64 support.
ask_for_var=(
'Please specify the (min) Android NDK API level to use. '
'[Available levels: %s]'
)
% api_levels,
check_success=(lambda *_: True),
error_msg='Android-%s is not present in the NDK path.',
)
return android_ndk_api_level
def set_gcc_host_compiler_path(environ_cp):
"""Set GCC_HOST_COMPILER_PATH."""
default_gcc_host_compiler_path = shutil.which('gcc') or ''
gcc_host_compiler_path = prompt_loop_or_load_from_env(
environ_cp,
var_name='GCC_HOST_COMPILER_PATH',
var_default=default_gcc_host_compiler_path,
ask_for_var='Please specify which gcc should be used by nvcc as the host '
'compiler.',
check_success=os.path.exists,
resolve_symlinks=True,
error_msg='Invalid gcc path. %s cannot be found.',
)
write_action_env_to_bazelrc('GCC_HOST_COMPILER_PATH', gcc_host_compiler_path)
def choose_compiler(environ_cp):
question = 'Do you want to use Clang to build TensorFlow?'
yes_reply = 'Clang will be used to compile TensorFlow.'
no_reply = 'GCC will be used to compile TensorFlow.'
var = int(
get_var(
environ_cp, 'TF_NEED_CLANG', None, True, question, yes_reply, no_reply
)
)
return var
def choose_compiler_Win(environ_cp):
question = 'Do you want to use Clang to build TensorFlow?'
yes_reply = 'Add "--config=win_clang" to compile TensorFlow with CLANG.'
no_reply = 'MSVC will be used to compile TensorFlow.'
var = int(
get_var(
environ_cp, 'TF_NEED_CLANG', None, True, question, yes_reply, no_reply
)
)
return var
def set_clang_compiler_path(environ_cp):
"""Set CLANG_COMPILER_PATH and environment variables.
Loop over user prompts for clang path until receiving a valid response.
Default is used if no input is given. Set CLANG_COMPILER_PATH and write
environment variables CC and BAZEL_COMPILER to .bazelrc.
Args:
environ_cp: (Dict) copy of the os.environ.
Returns:
string value for clang_compiler_path.
"""
# Default path if clang-18 is installed by using apt-get install
# remove 16 logic upon release of 19
default_clang_path = '/usr/lib/llvm-18/bin/clang'
if not os.path.exists(default_clang_path):
default_clang_path = '/usr/lib/llvm-17/bin/clang'
if not os.path.exists(default_clang_path):
default_clang_path = '/usr/lib/llvm-16/bin/clang'
if not os.path.exists(default_clang_path):
default_clang_path = shutil.which('clang') or ''
clang_compiler_path = prompt_loop_or_load_from_env(
environ_cp,
var_name='CLANG_COMPILER_PATH',
var_default=default_clang_path,
ask_for_var='Please specify the path to clang executable.',
check_success=os.path.exists,
resolve_symlinks=True,
error_msg=(
'Invalid clang path. %s cannot be found. Note that TensorFlow now'
' requires clang to compile. You may override this behavior by'
' setting TF_NEED_CLANG=0'
),
)
write_action_env_to_bazelrc('CLANG_COMPILER_PATH', clang_compiler_path)
write_to_bazelrc('build --repo_env=CC=%s' % clang_compiler_path)
write_to_bazelrc('build --repo_env=BAZEL_COMPILER=%s' % clang_compiler_path)
return clang_compiler_path
def set_clang_compiler_path_win(environ_cp):
"""Set CLANG_COMPILER_PATH and environment variables.
Loop over user prompts for clang path until receiving a valid response.
Default is used if no input is given. Set CLANG_COMPILER_PATH and write
environment variables CC and BAZEL_COMPILER to .bazelrc.
Args:
environ_cp: (Dict) copy of the os.environ.
Returns:
string value for clang_compiler_path.
"""
# Default path if clang-16 is installed by using apt-get install
default_clang_path = 'C:/Program Files/LLVM/bin/clang.exe'
if not os.path.exists(default_clang_path):
default_clang_path = shutil.which('clang') or ''
clang_compiler_path = prompt_loop_or_load_from_env(
environ_cp,
var_name='CLANG_COMPILER_PATH',
var_default=default_clang_path,
ask_for_var='Please specify the path to clang executable.',
check_success=os.path.exists,
resolve_symlinks=True,
error_msg=(
'Invalid clang path. %s cannot be found. Note that Clang is now'
'preferred compiler. You may use MSVC by removing --config=win_clang'
),
)
write_action_env_to_bazelrc('CLANG_COMPILER_PATH', clang_compiler_path)
write_to_bazelrc(f'build --repo_env=CC="{clang_compiler_path}"')
write_to_bazelrc(f'build --repo_env=BAZEL_COMPILER="{clang_compiler_path}"')
return clang_compiler_path
def retrieve_clang_version(clang_executable):
"""Retrieve installed clang version.
Args:
clang_executable: (String) path to clang executable
Returns:
The clang version detected.
"""
stderr = open(os.devnull, 'wb')
curr_version = run_shell([clang_executable, '--version'],
allow_non_zero=True,
stderr=stderr)
curr_version_split = curr_version.lower().split('clang version ')
if len(curr_version_split) > 1:
curr_version = curr_version_split[1].split()[0].split('git')
if len(curr_version) > 1:
print('WARNING: current clang installation is not a release version.\n')
curr_version = curr_version[0]
curr_version_int = convert_version_to_int(curr_version)
# Check if current clang version can be detected properly.
if not curr_version_int:
print('WARNING: current clang installation version unknown.\n')
return None
print('You have Clang %s installed.\n' % curr_version)
return curr_version
# Disable clang extension that rejects type definitions within offsetof.
# This was added in clang-16 by https://reviews.llvm.org/D133574.
# Still required for clang-17.
# Can be removed once upb is updated, since a type definition is used within
# offset of in the current version of ubp. See
# https://github.com/protocolbuffers/upb/blob/9effcbcb27f0a665f9f345030188c0b291e32482/upb/upb.c#L183.
def disable_clang_offsetof_extension(clang_version):
if int(clang_version.split('.')[0]) in (16, 17):
write_to_bazelrc('build --copt=-Wno-gnu-offsetof-extensions')
def set_hermetic_cuda_version(environ_cp):
"""Set HERMETIC_CUDA_VERSION."""
ask_cuda_version = (
'Please specify the hermetic CUDA version you want to use '
'or leave empty to use the default version. '
)
hermetic_cuda_version = get_from_env_or_user_or_default(
environ_cp, 'HERMETIC_CUDA_VERSION', ask_cuda_version, None
)
if hermetic_cuda_version:
environ_cp['HERMETIC_CUDA_VERSION'] = hermetic_cuda_version
write_repo_env_to_bazelrc(
'cuda', 'HERMETIC_CUDA_VERSION', hermetic_cuda_version
)
def set_hermetic_cudnn_version(environ_cp):
"""Set HERMETIC_CUDNN_VERSION."""
ask_cudnn_version = (
'Please specify the hermetic cuDNN version you want to use '
'or leave empty to use the default version. '
)
hermetic_cudnn_version = get_from_env_or_user_or_default(
environ_cp, 'HERMETIC_CUDNN_VERSION', ask_cudnn_version, None
)
if hermetic_cudnn_version:
environ_cp['HERMETIC_CUDNN_VERSION'] = hermetic_cudnn_version
write_repo_env_to_bazelrc(
'cuda', 'HERMETIC_CUDNN_VERSION', hermetic_cudnn_version
)
def set_hermetic_cuda_compute_capabilities(environ_cp):
"""Set HERMETIC_CUDA_COMPUTE_CAPABILITIES."""
while True:
default_cuda_compute_capabilities = _DEFAULT_CUDA_COMPUTE_CAPABILITIES
ask_cuda_compute_capabilities = (
'Please specify a list of comma-separated CUDA compute capabilities '
'you want to build with.\nYou can find the compute capability of your '
'device at: https://developer.nvidia.com/cuda-gpus. Each capability '
'can be specified as "x.y" or "compute_xy" to include both virtual and'
' binary GPU code, or as "sm_xy" to only include the binary '
'code.\nPlease 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: '
'%s]: ' % default_cuda_compute_capabilities)
hermetic_cuda_compute_capabilities = get_from_env_or_user_or_default(
environ_cp,
'HERMETIC_CUDA_COMPUTE_CAPABILITIES',
ask_cuda_compute_capabilities,
default_cuda_compute_capabilities,
)
# Check whether all capabilities from the input is valid
all_valid = True
# Remove all whitespace characters before splitting the string
# that users may insert by accident, as this will result in error
hermetic_cuda_compute_capabilities = ''.join(
hermetic_cuda_compute_capabilities.split()
)
for compute_capability in hermetic_cuda_compute_capabilities.split(','):
m = re.match('[0-9]+.[0-9]+', compute_capability)
if not m:
# We now support sm_35,sm_50,sm_60,compute_70.
sm_compute_match = re.match('(sm|compute)_?([0-9]+[0-9]+)',
compute_capability)
if not sm_compute_match:
print('Invalid compute capability: %s' % compute_capability)
all_valid = False
else:
ver = int(sm_compute_match.group(2))
if ver < 30:
print(
'ERROR: TensorFlow only supports small CUDA compute'
' capabilities of sm_30 and higher. Please re-specify the list'
' of compute capabilities excluding version %s.' % ver)
all_valid = False
if ver < 35:
print('WARNING: XLA does not support CUDA compute capabilities '
'lower than sm_35. Disable XLA when running on older GPUs.')
else:
ver = float(m.group(0))
if ver < 3.0:
print('ERROR: TensorFlow only supports CUDA compute capabilities 3.0 '
'and higher. Please re-specify the list of compute '
'capabilities excluding version %s.' % ver)
all_valid = False
if ver < 3.5:
print('WARNING: XLA does not support CUDA compute capabilities '
'lower than 3.5. Disable XLA when running on older GPUs.')
if all_valid:
break
# Reset and Retry
environ_cp['HERMETIC_CUDA_COMPUTE_CAPABILITIES'] = ''
# Set HERMETIC_CUDA_COMPUTE_CAPABILITIES
environ_cp['HERMETIC_CUDA_COMPUTE_CAPABILITIES'] = (
hermetic_cuda_compute_capabilities
)
write_repo_env_to_bazelrc(
'cuda',
'HERMETIC_CUDA_COMPUTE_CAPABILITIES',
hermetic_cuda_compute_capabilities,
)
def set_cuda_local_path(environ_cp, dist_name, env_var):
ask_path = (
'Please specify the local {} path you want to use '
'or leave empty to use the default version. '
).format(dist_name)
local_path = get_from_env_or_user_or_default(
environ_cp, env_var, ask_path, None
)
if local_path:
environ_cp[env_var] = local_path
write_repo_env_to_bazelrc('cuda', env_var, local_path)
def set_other_cuda_vars(environ_cp):
"""Set other CUDA related variables."""
# If CUDA is enabled, always use GPU during build and test.
if environ_cp.get('TF_CUDA_CLANG') == '1':
write_to_bazelrc('build --config=cuda_clang')
else:
write_to_bazelrc('build --config=cuda')
def system_specific_test_config(environ_cp):
"""Add default build and test flags required for TF tests to bazelrc."""
write_to_bazelrc('test --test_size_filters=small,medium')
# Each instance of --test_tag_filters or --build_tag_filters overrides all
# previous instances, so we need to build up a complete list and write a
# single list of filters for the .bazelrc file.
# Filters to use with both --test_tag_filters and --build_tag_filters
test_and_build_filters = ['-benchmark-test', '-no_oss', '-oss_excluded']
# Additional filters for --test_tag_filters beyond those in
# test_and_build_filters
test_only_filters = ['-oss_serial']
if is_windows():
test_and_build_filters += ['-no_windows', '-windows_excluded']
if ((environ_cp.get('TF_NEED_CUDA', None) == '1') or
(environ_cp.get('TF_NEED_ROCM', None) == '1')):
test_and_build_filters += ['-no_windows_gpu', '-no_gpu']
else:
test_and_build_filters.append('-gpu')
elif is_macos():
test_and_build_filters += ['-gpu', '-nomac', '-no_mac', '-mac_excluded']
elif is_linux():
if ((environ_cp.get('TF_NEED_CUDA', None) == '1') or
(environ_cp.get('TF_NEED_ROCM', None) == '1')):
test_and_build_filters.append('-no_gpu')
write_to_bazelrc('test --test_env=LD_LIBRARY_PATH')
else:
test_and_build_filters.append('-gpu')
# Disable tests with "v1only" tag in "v2" Bazel config, but not in "v1" config
write_to_bazelrc('test:v1 --test_tag_filters=%s' %
','.join(test_and_build_filters + test_only_filters))
write_to_bazelrc('test:v1 --build_tag_filters=%s' %
','.join(test_and_build_filters))
write_to_bazelrc(
'test:v2 --test_tag_filters=%s' %
','.join(test_and_build_filters + test_only_filters + ['-v1only']))
write_to_bazelrc('test:v2 --build_tag_filters=%s' %
','.join(test_and_build_filters + ['-v1only']))
def set_system_libs_flag(environ_cp):
"""Set system libs flags."""
syslibs = environ_cp.get('TF_SYSTEM_LIBS', '')
if is_s390x() and 'boringssl' not in syslibs:
syslibs = 'boringssl' + (', ' + syslibs if syslibs else '')
if syslibs:
if ',' in syslibs:
syslibs = ','.join(sorted(syslibs.split(',')))
else:
syslibs = ','.join(sorted(syslibs.split()))
write_action_env_to_bazelrc('TF_SYSTEM_LIBS', syslibs)
for varname in ('PREFIX', 'LIBDIR', 'INCLUDEDIR', 'PROTOBUF_INCLUDE_PATH'):
if varname in environ_cp:
write_to_bazelrc('build --define=%s=%s' % (varname, environ_cp[varname]))
def set_windows_build_flags(environ_cp):
"""Set Windows specific build options."""
# First available in VS 16.4. Speeds up Windows compile times by a lot. See
# https://groups.google.com/a/tensorflow.org/d/topic/build/SsW98Eo7l3o/discussion
# pylint: disable=line-too-long
write_to_bazelrc(
'build --copt=/d2ReducedOptimizeHugeFunctions --host_copt=/d2ReducedOptimizeHugeFunctions'
)
if get_var(
environ_cp, 'TF_OVERRIDE_EIGEN_STRONG_INLINE', 'Eigen strong inline',
True, ('Would you like to override eigen strong inline for some C++ '
'compilation to reduce the compilation time?'),
'Eigen strong inline overridden.', 'Not overriding eigen strong inline, '
'some compilations could take more than 20 mins.'):
# Due to a known MSVC compiler issue
# https://github.com/tensorflow/tensorflow/issues/10521
# Overriding eigen strong inline speeds up the compiling of
# conv_grad_ops_3d.cc and conv_ops_3d.cc by 20 minutes,
# but this also hurts the performance. Let users decide what they want.
write_to_bazelrc('build --define=override_eigen_strong_inline=true')
def config_info_line(name, help_text):
"""Helper function to print formatted help text for Bazel config options."""
print('\t--config=%-12s\t# %s' % (name, help_text))
def configure_ios(environ_cp):
"""Configures TensorFlow for iOS builds."""
if not is_macos():
return
if not get_var(environ_cp, 'TF_CONFIGURE_IOS', 'iOS', False):
return
for filepath in APPLE_BAZEL_FILES:
existing_filepath = os.path.join(_TF_WORKSPACE_ROOT, filepath + '.apple')
renamed_filepath = os.path.join(_TF_WORKSPACE_ROOT, filepath)
symlink_force(existing_filepath, renamed_filepath)
for filepath in IOS_FILES:
filename = os.path.basename(filepath)
new_filepath = os.path.join(_TF_WORKSPACE_ROOT, filename)
symlink_force(filepath, new_filepath)
def get_gcc_compiler(environ_cp):
gcc_env = environ_cp.get('CXX') or environ_cp.get('CC') or shutil.which('gcc')
if gcc_env is not None:
gcc_version = run_shell([gcc_env, '--version']).split()
if gcc_version[0] in ('gcc', 'g++'):
return gcc_env
return None
def main():
global _TF_WORKSPACE_ROOT
global _TF_BAZELRC
global _TF_CURRENT_BAZEL_VERSION
parser = argparse.ArgumentParser()
parser.add_argument(
'--workspace',
type=str,
default=os.path.abspath(os.path.dirname(__file__)),
help='The absolute path to your active Bazel workspace.')
args = parser.parse_args()
_TF_WORKSPACE_ROOT = args.workspace
_TF_BAZELRC = os.path.join(_TF_WORKSPACE_ROOT, _TF_BAZELRC_FILENAME)
# Make a copy of os.environ to be clear when functions and getting and setting
# environment variables.
environ_cp = dict(os.environ)
try:
current_bazel_version = retrieve_bazel_version()
except subprocess.CalledProcessError as e:
print('Error retrieving bazel version: ', e.output.decode('UTF-8').strip())
raise e
_TF_CURRENT_BAZEL_VERSION = convert_version_to_int(current_bazel_version)
reset_tf_configure_bazelrc()
cleanup_makefile()
setup_python(environ_cp)
if is_windows():
environ_cp['TF_NEED_OPENCL'] = '0'
environ_cp['TF_CUDA_CLANG'] = '0'
# TODO(ibiryukov): Investigate using clang as a cpu or cuda compiler on
# Windows.
environ_cp['TF_DOWNLOAD_CLANG'] = '0'
environ_cp['TF_NEED_MPI'] = '0'
if is_ppc64le():
# Enable MMA Dynamic Dispatch support if 'gcc' and if linker >= 2.35
gcc_env = get_gcc_compiler(environ_cp)
if gcc_env is not None:
# Use gold linker if 'gcc' and if 'ppc64le'
write_to_bazelrc('build --linkopt="-fuse-ld=gold"')
# Get the linker version
ld_version = run_shell([gcc_env, '-Wl,-version']).split()
ld_version_int = convert_version_to_int(ld_version[3])
if ld_version_int is None:
ld_version_int = convert_version_to_int(ld_version[4])
# Enable if 'ld' version >= 2.35
if ld_version_int >= 2035000:
write_to_bazelrc(
'build --copt="-DEIGEN_ALTIVEC_ENABLE_MMA_DYNAMIC_DISPATCH=1"')
with_xla_support = environ_cp.get('TF_ENABLE_XLA', None)
if with_xla_support is not None:
write_to_bazelrc('build --define=with_xla_support=%s' %
('true' if int(with_xla_support) else 'false'))
set_action_env_var(
environ_cp, 'TF_NEED_ROCM', 'ROCm', False, bazel_config_name='rocm')
if (environ_cp.get('TF_NEED_ROCM') == '1' and
'LD_LIBRARY_PATH' in environ_cp and
environ_cp.get('LD_LIBRARY_PATH') != '1'):
write_action_env_to_bazelrc('LD_LIBRARY_PATH',
environ_cp.get('LD_LIBRARY_PATH'))
if (environ_cp.get('TF_NEED_ROCM') == '1' and environ_cp.get('ROCM_PATH')):
write_action_env_to_bazelrc('ROCM_PATH', environ_cp.get('ROCM_PATH'))
if (environ_cp.get('TF_NEED_ROCM') == '1' and environ_cp.get('HIP_PLATFORM')):
write_action_env_to_bazelrc('HIP_PLATFORM', environ_cp.get('HIP_PLATFORM'))
if is_windows():
print('\nWARNING: Cannot build with CUDA support on Windows.\n'
'Starting in TF 2.11, CUDA build is not supported for Windows. '
'For using TensorFlow GPU on Windows, you will need to build/install '
'TensorFlow in WSL2.\n')
environ_cp['TF_NEED_CUDA'] = '0'
else:
environ_cp['TF_NEED_CUDA'] = str(
int(get_var(environ_cp, 'TF_NEED_CUDA', 'CUDA', False)))
if environ_cp.get('TF_NEED_CUDA') == '1':
set_hermetic_cuda_version(environ_cp)
set_hermetic_cudnn_version(environ_cp)
set_hermetic_cuda_compute_capabilities(environ_cp)
set_cuda_local_path(environ_cp, 'CUDA', 'LOCAL_CUDA_PATH')
set_cuda_local_path(environ_cp, 'CUDNN', 'LOCAL_CUDNN_PATH')
set_cuda_local_path(environ_cp, 'NCCL', 'LOCAL_NCCL_PATH')
if 'LD_LIBRARY_PATH' in environ_cp and environ_cp.get(
'LD_LIBRARY_PATH') != '1':
write_action_env_to_bazelrc('LD_LIBRARY_PATH',
environ_cp.get('LD_LIBRARY_PATH'))
set_tf_cuda_clang(environ_cp)
if environ_cp.get('TF_CUDA_CLANG') == '1':
# Set up which clang we should use as the cuda / host compiler.
clang_cuda_compiler_path = set_clang_cuda_compiler_path(environ_cp)
clang_version = retrieve_clang_version(clang_cuda_compiler_path)
disable_clang_offsetof_extension(clang_version)
else:
# Set up which gcc nvcc should use as the host compiler
# No need to set this on Windows
if not is_windows():
set_gcc_host_compiler_path(environ_cp)
set_other_cuda_vars(environ_cp)
else:
# CUDA not required. Ask whether we should use clang for the CPU build.
if is_linux():
environ_cp['TF_NEED_CLANG'] = str(choose_compiler(environ_cp))
if environ_cp.get('TF_NEED_CLANG') == '1':
clang_compiler_path = set_clang_compiler_path(environ_cp)
clang_version = retrieve_clang_version(clang_compiler_path)
disable_clang_offsetof_extension(clang_version)
if is_windows():
environ_cp['TF_NEED_CLANG'] = str(choose_compiler_Win(environ_cp))
if environ_cp.get('TF_NEED_CLANG') == '1':
clang_compiler_path = set_clang_compiler_path_win(environ_cp)
clang_version = retrieve_clang_version(clang_compiler_path)
disable_clang_offsetof_extension(clang_version)
# ROCm / CUDA are mutually exclusive.
# At most 1 GPU platform can be configured.
gpu_platform_count = 0
if environ_cp.get('TF_NEED_ROCM') == '1':
gpu_platform_count += 1
if environ_cp.get('TF_NEED_CUDA') == '1':
gpu_platform_count += 1
if gpu_platform_count >= 2:
raise UserInputError('CUDA / ROCm are mututally exclusive. '
'At most 1 GPU platform can be configured.')
set_cc_opt_flags(environ_cp)
set_system_libs_flag(environ_cp)
if is_windows():
set_windows_build_flags(environ_cp)
if get_var(environ_cp, 'TF_SET_ANDROID_WORKSPACE', 'android workspace', False,
('Would you like to interactively configure ./WORKSPACE for '
'Android builds?'), 'Searching for NDK and SDK installations.',
'Not configuring the WORKSPACE for Android builds.'):
create_android_ndk_rule(environ_cp)
create_android_sdk_rule(environ_cp)
system_specific_test_config(environ_cp)
configure_ios(environ_cp)
print('Preconfigured Bazel build configs. You can use any of the below by '
'adding "--config=<>" to your build command. See .bazelrc for more '
'details.')
config_info_line('mkl', 'Build with MKL support.')
config_info_line(
'mkl_aarch64',
'Build with oneDNN and Compute Library for the Arm Architecture (ACL).')
config_info_line('monolithic', 'Config for mostly static monolithic build.')
config_info_line('numa', 'Build with NUMA support.')
config_info_line(
'dynamic_kernels',
'(Experimental) Build kernels into separate shared objects.')
config_info_line('v1', 'Build with TensorFlow 1 API instead of TF 2 API.')
print('Preconfigured Bazel build configs to DISABLE default on features:')
config_info_line('nogcp', 'Disable GCP support.')
config_info_line('nonccl', 'Disable NVIDIA NCCL support.')
if __name__ == '__main__':
main()
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