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# Copyright 2015 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.
# ==============================================================================
"""Script Language Operators. See the @{$python/script_ops} guide.
@@py_func
"""
# pylint: disable=g-bad-name
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import threading
import numpy as np
import six
from tensorflow.python import pywrap_tensorflow
from tensorflow.python.framework import function
from tensorflow.python.framework import ops
from tensorflow.python.ops import gen_script_ops
class FuncRegistry(object):
"""A helper class to keep track of registered py functions.
FuncRegistry keeps a map from unique tokens (string) to python
functions, which takes numpy arrays and outputs numpy arrays.
"""
def __init__(self):
self._lock = threading.Lock()
self._unique_id = 0 # GUARDED_BY(self._lock)
self._funcs = {}
def insert(self, func):
"""Registers `func` and returns a unique token for this entry."""
token = self._next_unique_token()
self._funcs[token] = func
return token
def remove(self, token):
"""Removes the registered function corresponding to `token`."""
self._funcs.pop(token, None)
@staticmethod
def _convert(value):
"""Converts an arg to numpy, avoiding dangerous string and unicode dtypes.
Numpy pads with zeros when using string and unicode dtypes if different
components of a tensor have different lengths. This is bad: ignoring the
padding is wrong for text data, and removing the padding is wrong for binary
data. To avoid this bug, we redo the conversion using an object dtype.
Args:
value: Value to convert to a numpy array.
Returns:
A numpy array.
"""
result = np.asarray(value, order="C")
if result.dtype.char in "SU" and result is not value:
return np.asarray(value, order="C", dtype=object)
return result
def __call__(self, token, args):
"""Calls the registered function for `token` with args."""
func = self._funcs[token]
if func is None:
raise ValueError("callback %s is not found" % token)
ret = func(*args)
# Strings seem to lead to a memory leak here if they're not wrapped in a
# list.
if isinstance(ret, six.binary_type):
ret = [ret]
# Ensures that we return either a single numpy array or a list of numpy
# arrays.
if isinstance(ret, (tuple, list)):
return [self._convert(x) for x in ret]
else:
return self._convert(ret)
def size(self):
"""Returns how many functions are currently registered."""
return len(self._funcs)
def _next_unique_token(self):
"""Returns a unique token."""
with self._lock:
uid = self._unique_id
self._unique_id += 1
return "pyfunc_%d" % uid
# Global registry for py functions.
_py_funcs = FuncRegistry()
pywrap_tensorflow.InitializePyTrampoline(_py_funcs)
class CleanupFunc(object):
"""A helper class to remove a registered function from _py_funcs."""
def __init__(self, token):
self._token = token
def __del__(self):
_py_funcs.remove(self._token)
def py_func(func, inp, Tout, stateful=True, name=None):
"""Wraps a python function and uses it as a TensorFlow op.
Given a python function `func`, which takes numpy arrays as its
inputs and returns numpy arrays as its outputs, wrap this function as an
operation in a TensorFlow graph. The following snippet constructs a simple
TensorFlow graph that invokes the `np.sinh()` NumPy function as a operation
in the graph:
```python
def my_func(x):
# x will be a numpy array with the contents of the placeholder below
return np.sinh(x)
inp = tf.placeholder(tf.float32)
y = tf.py_func(my_func, [inp], tf.float32)
```
**N.B.** The `tf.py_func()` operation has the following known limitations:
* The body of the function (i.e. `func`) will not be serialized in a
`GraphDef`. Therefore, you should not use this function if you need to
serialize your model and restore it in a different environment.
* The operation must run in the same address space as the Python program
that calls `tf.py_func()`. If you are using distributed TensorFlow, you
must run a `tf.train.Server` in the same process as the program that calls
`tf.py_func()` and you must pin the created operation to a device in that
server (e.g. using `with tf.device():`).
Args:
func: A Python function, which accepts a list of NumPy `ndarray` objects
having element types that match the corresponding `tf.Tensor` objects
in `inp`, and returns a list of `ndarray` objects (or a single `ndarray`)
having element types that match the corresponding values in `Tout`.
inp: A list of `Tensor` objects.
Tout: A list or tuple of tensorflow data types or a single tensorflow data
type if there is only one, indicating what `func` returns.
stateful: (Boolean.) If True, the function should be considered stateful.
If a function is stateless, when given the same input it will return the
same output and have no observable side effects. Optimizations such as
common subexpression elimination are only performed on stateless
operations.
name: A name for the operation (optional).
Returns:
A list of `Tensor` or a single `Tensor` which `func` computes.
"""
token = _py_funcs.insert(func)
# We tie the registered function's life-time with the current
# default graph. I.e., when the current graph is destroyed, we
# should remove its py funcs.
g = ops.get_default_graph()
# pylint: disable=protected-access
while isinstance(g, function._FuncGraph):
# If the py_func was declared inside a _FuncGraph, its lifetime should be
# bound to that of the outer graph instead.
g = g._outer_graph
cleanup = CleanupFunc(token)
# TODO(zhifengc): Consider adding a Graph method to collect
# `cleanup` objects in one of its member.
if not hasattr(g, "_cleanup_py_funcs_used_in_graph"):
g._cleanup_py_funcs_used_in_graph = []
# When g is destroyed, elements in _cleanup_py_funcs_used_in_graph
# will be destroyed and their __del__ will remove the 'token' from
# the funcs registry.
g._cleanup_py_funcs_used_in_graph.append(cleanup)
# pylint: enable=protected-access
if isinstance(Tout, (list, tuple)):
is_list_or_tuple = True
else:
Tout = [Tout]
is_list_or_tuple = False
# pylint: disable=protected-access
if stateful:
result = gen_script_ops._py_func(
input=inp, token=token, Tout=Tout, name=name)
else:
result = gen_script_ops._py_func_stateless(
input=inp, token=token, Tout=Tout, name=name)
# pylint: enable=protected-access
return result if is_list_or_tuple else result[0]
ops.NotDifferentiable("PyFunc")
ops.NotDifferentiable("PyFuncStateless")
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