221 lines
7.3 KiB
Python
221 lines
7.3 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Function for loading TensorFlow plugins."""
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import errno
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import hashlib
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import importlib
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import os
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import platform
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import sys
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from tensorflow.python.client import pywrap_tf_session as py_tf
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from tensorflow.python.eager import context
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from tensorflow.python.framework import _pywrap_python_op_gen
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from tensorflow.python.util import deprecation
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from tensorflow.python.util.tf_export import tf_export
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@tf_export('load_op_library')
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def load_op_library(library_filename):
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"""Loads a TensorFlow plugin, containing custom ops and kernels.
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Pass "library_filename" to a platform-specific mechanism for dynamically
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loading a library. The rules for determining the exact location of the
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library are platform-specific and are not documented here. When the
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library is loaded, ops and kernels registered in the library via the
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`REGISTER_*` macros are made available in the TensorFlow process. Note
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that ops with the same name as an existing op are rejected and not
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registered with the process.
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Args:
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library_filename: Path to the plugin.
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Relative or absolute filesystem path to a dynamic library file.
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Returns:
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A python module containing the Python wrappers for Ops defined in
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the plugin.
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Raises:
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RuntimeError: when unable to load the library or get the python wrappers.
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"""
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lib_handle = py_tf.TF_LoadLibrary(library_filename)
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try:
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wrappers = _pywrap_python_op_gen.GetPythonWrappers(
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py_tf.TF_GetOpList(lib_handle))
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finally:
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# Delete the library handle to release any memory held in C
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# that are no longer needed.
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py_tf.TF_DeleteLibraryHandle(lib_handle)
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# Get a unique name for the module.
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module_name = hashlib.sha1(wrappers).hexdigest()
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if module_name in sys.modules:
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return sys.modules[module_name]
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module_spec = importlib.machinery.ModuleSpec(module_name, None)
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module = importlib.util.module_from_spec(module_spec)
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# pylint: disable=exec-used
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exec(wrappers, module.__dict__)
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# Allow this to be recognized by AutoGraph.
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setattr(module, '_IS_TENSORFLOW_PLUGIN', True)
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sys.modules[module_name] = module
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return module
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@deprecation.deprecated(date=None,
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instructions='Use `tf.load_library` instead.')
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@tf_export(v1=['load_file_system_library'])
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def load_file_system_library(library_filename):
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"""Loads a TensorFlow plugin, containing file system implementation.
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Pass `library_filename` to a platform-specific mechanism for dynamically
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loading a library. The rules for determining the exact location of the
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library are platform-specific and are not documented here.
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Args:
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library_filename: Path to the plugin.
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Relative or absolute filesystem path to a dynamic library file.
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Returns:
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None.
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Raises:
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RuntimeError: when unable to load the library.
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"""
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py_tf.TF_LoadLibrary(library_filename)
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def _is_shared_object(filename):
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"""Check the file to see if it is a shared object, only using extension."""
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if platform.system() == 'Linux':
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if filename.endswith('.so'):
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return True
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else:
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index = filename.rfind('.so.')
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if index == -1:
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return False
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else:
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# A shared object with the API version in filename
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return filename[index + 4].isdecimal()
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elif platform.system() == 'Darwin':
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return filename.endswith('.dylib')
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elif platform.system() == 'Windows':
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return filename.endswith('.dll')
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else:
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return False
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@tf_export('load_library')
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def load_library(library_location):
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"""Loads a TensorFlow plugin.
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"library_location" can be a path to a specific shared object, or a folder.
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If it is a folder, all shared objects that are named "libtfkernel*" will be
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loaded. When the library is loaded, kernels registered in the library via the
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`REGISTER_*` macros are made available in the TensorFlow process.
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Args:
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library_location: Path to the plugin or the folder of plugins.
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Relative or absolute filesystem path to a dynamic library file or folder.
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Returns:
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None
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Raises:
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OSError: When the file to be loaded is not found.
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RuntimeError: when unable to load the library.
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"""
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if os.path.exists(library_location):
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if os.path.isdir(library_location):
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directory_contents = os.listdir(library_location)
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kernel_libraries = [
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os.path.join(library_location, f) for f in directory_contents
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if _is_shared_object(f)]
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else:
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kernel_libraries = [library_location]
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for lib in kernel_libraries:
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py_tf.TF_LoadLibrary(lib)
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else:
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raise OSError(
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errno.ENOENT,
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'The file or folder to load kernel libraries from does not exist.',
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library_location)
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def load_pluggable_device_library(library_location):
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"""Loads a TensorFlow PluggableDevice plugin.
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"library_location" can be a path to a specific shared object, or a folder.
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If it is a folder, all shared objects will be loaded. when the library is
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loaded, devices/kernels registered in the library via StreamExecutor C API
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and Kernel/Op Registration C API are made available in TensorFlow process.
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Args:
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library_location: Path to the plugin or folder of plugins. Relative or
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absolute filesystem path to a dynamic library file or folder.
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Raises:
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OSError: When the file to be loaded is not found.
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RuntimeError: when unable to load the library.
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"""
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if os.path.exists(library_location):
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if os.path.isdir(library_location):
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directory_contents = os.listdir(library_location)
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pluggable_device_libraries = [
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os.path.join(library_location, f)
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for f in directory_contents
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if _is_shared_object(f)
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]
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else:
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pluggable_device_libraries = [library_location]
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for lib in pluggable_device_libraries:
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py_tf.TF_LoadPluggableDeviceLibrary(lib)
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# Reinitialized physical devices list after plugin registration.
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context.context().reinitialize_physical_devices()
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else:
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raise OSError(
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errno.ENOENT,
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'The file or folder to load pluggable device libraries from does not '
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'exist.', library_location)
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@tf_export('experimental.register_filesystem_plugin')
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def register_filesystem_plugin(plugin_location):
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"""Loads a TensorFlow FileSystem plugin.
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Args:
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plugin_location: Path to the plugin. Relative or absolute filesystem plugin
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path to a dynamic library file.
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Returns:
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None
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Raises:
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OSError: When the file to be loaded is not found.
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RuntimeError: when unable to load the library.
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"""
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if os.path.exists(plugin_location):
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py_tf.TF_RegisterFilesystemPlugin(plugin_location)
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else:
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raise OSError(errno.ENOENT,
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'The file to load file system plugin from does not exist.',
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plugin_location)
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