def load_file_system_library(library_filename):
"""Loads a TensorFlow plugin, containing file system implementation.
Pass `library_filename` to a platform-specific mechanism for dynamically
loading a library. The rules for determining the exact location of the
library are platform-specific and are not documented here.
Args:
library_filename: Path to the plugin.
Relative or absolute filesystem path to a dynamic library file.
Returns:
None.
Raises:
RuntimeError: when unable to load the library.
"""
status = py_tf.TF_NewStatus()
lib_handle = py_tf.TF_LoadLibrary(library_filename, status)
try:
error_code = py_tf.TF_GetCode(status)
if error_code != 0:
error_msg = compat.as_text(py_tf.TF_Message(status))
# pylint: disable=protected-access
raise errors._make_specific_exception(None, None, error_msg,
error_code)
# pylint: enable=protected-access
finally:
py_tf.TF_DeleteStatus(status)
def load_op_library(library_filename):
"""Loads a TensorFlow plugin, containing custom ops and kernels.
Pass "library_filename" to a platform-specific mechanism for dynamically
loading a library. The rules for determining the exact location of the
library are platform-specific and are not documented here. When the
library is loaded, ops and kernels registered in the library via the
REGISTER_* macros are made available in the TensorFlow process. Note
that ops with the same name as an existing op are rejected and not
registered with the process.
Args:
library_filename: Path to the plugin.
Relative or absolute filesystem path to a dynamic library file.
Returns:
A python module containing the Python wrappers for Ops defined in
the plugin.
Raises:
RuntimeError: when unable to load the library or get the python wrappers.
"""
status = py_tf.TF_NewStatus()
lib_handle = py_tf.TF_LoadLibrary(library_filename, status)
try:
error_code = py_tf.TF_GetCode(status)
if error_code != 0:
error_msg = compat.as_text(py_tf.TF_Message(status))
with _OP_LIBRARY_MAP_LOCK:
if (error_code == error_codes_pb2.ALREADY_EXISTS
and 'has already been loaded' in error_msg
and library_filename in _OP_LIBRARY_MAP):
return _OP_LIBRARY_MAP[library_filename]
# pylint: disable=protected-access
raise errors._make_specific_exception(None, None, error_msg,
error_code)
# pylint: enable=protected-access
finally:
py_tf.TF_DeleteStatus(status)
op_list_str = py_tf.TF_GetOpList(lib_handle)
op_list = op_def_pb2.OpList()
op_list.ParseFromString(compat.as_bytes(op_list_str))
wrappers = py_tf.GetPythonWrappers(op_list_str)
# Get a unique name for the module.
module_name = hashlib.md5(wrappers).hexdigest()
module = imp.new_module(module_name)
# pylint: disable=exec-used
exec(wrappers, module.__dict__)
# Stash away the library handle for making calls into the dynamic library.
module.LIB_HANDLE = lib_handle
# OpDefs of the list of ops defined in the library.
module.OP_LIST = op_list
sys.modules[module_name] = module
# Memoize the filename to module mapping.
with _OP_LIBRARY_MAP_LOCK:
_OP_LIBRARY_MAP[library_filename] = module
return module
def load_op_library(library_filename):
"""Loads a TensorFlow plugin, containing custom ops and kernels.
Pass "library_filename" to a platform-specific mechanism for dynamically
loading a library. The rules for determining the exact location of the
library are platform-specific and are not documented here. When the
library is loaded, ops and kernels registered in the library via the
`REGISTER_*` macros are made available in the TensorFlow process. Note
that ops with the same name as an existing op are rejected and not
registered with the process.
Args:
library_filename: Path to the plugin.
Relative or absolute filesystem path to a dynamic library file.
Returns:
A python module containing the Python wrappers for Ops defined in
the plugin.
Raises:
RuntimeError: when unable to load the library or get the python wrappers.
"""
lib_handle = py_tf.TF_LoadLibrary(library_filename)
op_list_str = py_tf.TF_GetOpList(lib_handle)
op_list = op_def_pb2.OpList()
op_list.ParseFromString(compat.as_bytes(op_list_str))
wrappers = py_tf.GetPythonWrappers(op_list_str)
# Delete the library handle to release any memory held in C
# that are no longer needed.
py_tf.TF_DeleteLibraryHandle(lib_handle)
# Get a unique name for the module.
module_name = hashlib.md5(wrappers).hexdigest()
if module_name in sys.modules:
return sys.modules[module_name]
module = imp.new_module(module_name)
# pylint: disable=exec-used
exec(wrappers, module.__dict__)
# Stash away the library handle for making calls into the dynamic library.
module.LIB_HANDLE = lib_handle
# OpDefs of the list of ops defined in the library.
module.OP_LIST = op_list
# Allow this to be recognized by AutoGraph.
setattr(module, '_IS_TENSORFLOW_PLUGIN', True)
sys.modules[module_name] = module
return module
def load_op_library(library_filename):
"""Loads a TensorFlow plugin, containing custom ops and kernels.
Pass "library_filename" to a platform-specific mechanism for dynamically
loading a library. The rules for determining the exact location of the
library are platform-specific and are not documented here.
Expects the symbols "RegisterOps", "RegisterKernels", and "GetOpList", to be
defined in the library.
Args:
library_filename: Path to the plugin.
Relative or absolute filesystem path to a dynamic library file.
Returns:
A python module containing the Python wrappers for Ops defined in
the plugin.
Raises:
RuntimeError: when unable to load the library or get the python wrappers.
"""
status = py_tf.TF_NewStatus()
lib_handle = py_tf.TF_LoadLibrary(library_filename, status)
try:
if py_tf.TF_GetCode(status) != 0:
raise RuntimeError(compat.as_text(py_tf.TF_Message(status)))
finally:
py_tf.TF_DeleteStatus(status)
op_list_str = py_tf.TF_GetOpList(lib_handle)
op_list = op_def_pb2.OpList()
op_list.ParseFromString(compat.as_bytes(op_list_str))
wrappers = py_tf.GetPythonWrappers(op_list_str, len(op_list_str))
# Get a unique name for the module.
module_name = hashlib.md5(wrappers).hexdigest()
module = imp.new_module(module_name)
# pylint: disable=exec-used
exec(wrappers, module.__dict__)
# Stash away the library handle for making calls into the dynamic library.
module.LIB_HANDLE = lib_handle
# OpDefs of the list of ops defined in the library.
module.OP_LIST = op_list
sys.modules[module_name] = module
return module
def load_file_system_library(library_filename):
"""Loads a TensorFlow plugin, containing file system implementation.
Pass `library_filename` to a platform-specific mechanism for dynamically
loading a library. The rules for determining the exact location of the
library are platform-specific and are not documented here.
Args:
library_filename: Path to the plugin.
Relative or absolute filesystem path to a dynamic library file.
Returns:
None.
Raises:
RuntimeError: when unable to load the library.
"""
py_tf.TF_LoadLibrary(library_filename)
def load_library(library_location):
"""Loads a TensorFlow plugin.
"library_location" can be a path to a specific shared object, or a folder.
If it is a folder, all sahred objects that are named "libtfkernel*" will be
loaded. When the library is loaded, kernels registered in the library via the
`REGISTER_*` macros are made available in the TensorFlow process.
Args:
library_location: Path to the plugin or the folder of plugins.
Relative or absolute filesystem path to a dynamic library file or folder.
Returns:
None
Raises:
OSError: When the file to be loaded is not found.
RuntimeError: when unable to load the library.
"""
if file_io.file_exists(library_location):
if file_io.is_directory(library_location):
directory_contents = file_io.list_directory(library_location)
kernel_libraries = [
os.path.join(library_location, f) for f in directory_contents
if _is_shared_object(f)
]
else:
kernel_libraries = [library_location]
for lib in kernel_libraries:
py_tf.TF_LoadLibrary(lib)
else:
raise OSError(
errno.ENOENT,
'The file or folder to load kernel libraries from does not exist.',
library_location)