laywerrobot/lib/python3.6/site-packages/tensorflow/python/ops/script_ops.py
2020-08-27 21:55:39 +02:00

460 lines
18 KiB
Python

# 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."""
# pylint: disable=g-bad-name
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import threading
# Used by py_util.cc to get tracebacks.
import traceback # pylint: disable=unused-import
import weakref
import numpy as np
import six
from tensorflow.python import pywrap_tensorflow
from tensorflow.python.eager import backprop
from tensorflow.python.eager import context
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import function
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_script_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.util import compat
from tensorflow.python.util import nest
from tensorflow.python.util.tf_export import tf_export
# Map from EagerPyFunc token to tuple (tape, eager args, eager outputs);
# used for differentiation.
tape_cache = {}
class EagerFunc(object):
"""A wrapper for a function owned by an EagerPyFunc."""
def __init__(self, func, Tout, is_grad_func):
"""Constructs an EagerFunc.
Args:
func: The function to wrap.
Tout: A list of datatypes for the output; an empty list if the output is
None.
is_grad_func: Whether this EagerFunc is the gradient of another
EagerPyFunc.
"""
self._func = func
self._out_dtypes = Tout
self._is_grad_func = is_grad_func
def _convert(self, value, dtype):
"""Converts `value` to a tensor of type `dtype`, with error checking.
Args:
value: The tensor to convert.
dtype: The desired dtype.
Returns:
A tensor of type `dtype`, or a zeros tensor if value is None and
this function is in fact a grdient function.
Raises:
RuntimeError: if `value` is a variable.
"""
if isinstance(value, resource_variable_ops.ResourceVariable):
raise RuntimeError(
"Attempting to return a variable from an eagerly executed py_func. "
"Only numeric data structures like Tensors or NumPy arrays should "
"be returned; to return the value of a variable, make sure to obtain "
"the Tensor backing it by calling `.read_value()` on the variable in "
"question: %s" % value)
if value is None and self._is_grad_func:
# Gradient functions may legitimately return a list that contains
# both Tensors and Python Nones. Unfortuantely this breaks the
# OpKernel, so for now we replace None objects with zeros, which is
# mathematically correct but will prevent short-circuiting gradient
# computations.
#
# TODO(akshayka): Make it possible to return a list of both Tensors and
# Nones from an EagerPyFunc.
return constant_op.constant(0.0, dtype=dtype)
return ops.convert_to_tensor(value, dtype=dtype)
def __call__(self, device, token, args):
"""Passes `args` to `self._func`, which is executed eagerly."""
with context.eager_mode(), backprop.GradientTape() as tape:
for tensor in args:
tape.watch(tensor)
ret = self._func(*args)
# Use tf.identity to copy the returned tensors to device if neccesary.
with ops.device(device):
if isinstance(ret, (tuple, list)):
outputs = [
array_ops.identity(self._convert(x, dtype=dtype))
for (x, dtype) in zip(ret, self._out_dtypes)
]
elif ret is None:
outputs = None
else:
outputs = array_ops.identity(
self._convert(ret, dtype=self._out_dtypes[0]))
tape_cache[compat.as_bytes(token)] = (tape, args, outputs)
return outputs
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)
# Only store weakrefs to the functions. The strong reference is stored in
# the graph.
self._funcs = weakref.WeakValueDictionary()
def insert(self, func):
"""Registers `func` and returns a unique token for this entry."""
token = self._next_unique_token()
# Store a weakref to the function
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, dtype=None):
"""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.
Additionally, we convert unicode strings to (byte-)strings for
compatibility.
Args:
value: Value to convert to a numpy array.
dtype: (Optional.) Desired NumPy type for the returned value.
Returns:
A numpy array.
"""
result = np.asarray(value, dtype=dtype, order="C")
if result.dtype.char == "S" and result is not value:
return np.asarray(value, order="C", dtype=object)
elif result.dtype.char == "U" and result is not value:
value = np.vectorize(lambda x: x.encode("utf8"))(value)
return np.asarray(value, order="C", dtype=object)
elif result.dtype.char == "U":
return result.astype(np.bytes_)
else:
return result
def __call__(self, token, device, args):
"""Calls the registered function for `token` with args.
Args:
token: A key into this `FuncRegistry` identifying which function to call.
device: Name of the device on which outputs of `token`'s corresponding
operation should be placed. Used iff the function registered for `token`
is an EagerPyFunc.
args: The arguments to pass to the function registered for `token`.
Returns:
The output of the function registered for `token`.
Raises:
ValueError: if no function is registered for `token`.
"""
func = self._funcs.get(token, None)
if func is None:
raise ValueError("callback %s is not found" % token)
if isinstance(func, EagerFunc):
# NB: Different invocations of the same py_func will share the same
# token, and the entries they stash in the tape_cache will collide.
# In practice, when executing a graph, this should only happen if
# the py_func is in a while_loop whose iterations are run in parallel
# or if the graph is being driven by concurrent session.run() calls.
#
# TODO(akshayka): Key the tape cache in a thread-safe way.
return func(device, token, args)
else:
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)
def _internal_py_func(func,
inp,
Tout,
stateful=None,
eager=False,
is_grad_func=False,
name=None):
"""See documentation for py_func and eager_py_func."""
is_list_or_tuple = False
if isinstance(Tout, (list, tuple)):
is_list_or_tuple = True
else:
Tout = [Tout]
if eager:
func = EagerFunc(func, Tout, is_grad_func)
token = _py_funcs.insert(func)
# We tie the registered function's lifetime with the current default graph,
# i.e., when the current graph is destroyed, we remove its py funcs.
graph = ops.get_default_graph()
# pylint: disable=protected-access
while isinstance(graph, function._FuncGraph):
# If the py_func was declared inside a _FuncGraph, its lifetime should be
# bound to that of the outer graph instead.
graph = graph._outer_graph
# TODO(zhifengc): Consider adding a Graph method to collect
# `cleanup` objects in one of its member.
if not hasattr(graph, "_py_funcs_used_in_graph"):
graph._py_funcs_used_in_graph = []
# Store a reference to the function in the graph to ensure it stays alive
# as long as the graph lives. When the graph is destroyed, the function
# is left to the garbage collector for destruction as well.
graph._py_funcs_used_in_graph.append(func)
# pylint: enable=protected-access
if eager:
result = gen_script_ops.eager_py_func(
input=inp, token=token, Tout=Tout, name=name)
else:
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)
return result if is_list_or_tuple else result[0]
# TODO(akshayka): Implement higher-order derivatives.
@ops.RegisterGradient("EagerPyFunc")
def _EagerPyFuncGrad(op, dy):
"""Computes the gradient of an EagerPyFunc."""
token = op.get_attr("token")
def eagerly_executed_grad(dy):
tape, eager_inputs, eager_outputs = tape_cache.pop(compat.as_bytes(token))
return tape.gradient(eager_outputs, eager_inputs, output_gradients=dy)
with ops.control_dependencies(op.outputs):
return _internal_py_func(
func=eagerly_executed_grad,
inp=[dy] if isinstance(dy, ops.Tensor) else dy,
Tout=[tensor.dtype for tensor in op.inputs],
eager=True,
is_grad_func=True)
def eager_py_func(func, inp, Tout, name=None):
"""Wraps a python function into a TensorFlow op that executes it eagerly.
This function allows expressing computations in a TensorFlow graph as
Python functions. In particular, it wraps a Python function `func`
in a once-differentiable TensorFlow operation that executes it with eager
exeuction enabled. As a consequence, `tf.contrib.eager.py_func` makes it
possible to express control flow using Python constructs (`if`, `while`,
`for`, etc.), instead of TensorFlow control flow constructs (@{tf.cond},
@{tf.while_loop}). For example, you might use `tf.contrib.eager.py_func` to
implement the log huber function:
```python
def log_huber(x, m):
if tf.abs(x) <= m:
return x**2
else:
return m**2 * (1 - 2 * tf.log(m) + tf.log(x**2))
x = tf.placeholder(tf.float32)
m = tf.placeholder(tf.float32)
y = tf.contrib.eager.py_func(func=log_huber, inp=[x, m], Tout=tf.float32)
dy_dx = tf.gradients(y, x)[0]
with tf.Session() as sess:
# The session executes `log_huber` eagerly. Given the feed values below,
# it will take the first branch, so `y` evaluates to 1.0 and
# `dy_dx` evaluates to 2.0.
y, dy_dx = sess.run([y, dy_dx], feed_dict={x: 1.0, m: 2.0})
```
You can also use `tf.contrib.eager.py_func` to debug your models at runtime
using Python tools, i.e., you can isolate portions of your code that
you want to debug, wrap them in Python functions and insert `pdb` tracepoints
or print statements as desired, and wrap those functions in
`tf.contrib.eager.py_func`.
For more information on eager execution, see @{$guide/eager}.
`tf.contrib.eager.py_func` is similar in spirit to @{tf.py_func}, but unlike
the latter, the former lets you use TensorFlow operations in the wrapped
Python function. In particular, while @{tf.py_func} only runs on CPUs and
wraps functions that take NumPy arrays as inputs and return NumPy arrays as
outputs, `tf.contrib.eager.py_func` can be placed on GPUs and wraps functions
that take Tensors as inputs, execute TensorFlow operations in their bodies,
and return Tensors as outputs.
Like @{tf.py_func}, `tf.contrib.eager.py_func` has the following limitations
with respect to serialization and distribution:
* 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.contrib.eager.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.contrib.eager.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 `Tensor` objects
having element types that match the corresponding `tf.Tensor` objects
in `inp` and returns a list of `Tensor` objects (or a single
`Tensor`, or `None`) 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; an empty list
if no value is returned (i.e., if the return value is `None`).
name: A name for the operation (optional).
Returns:
A list of `Tensor` or a single `Tensor` which `func` computes; an empty list
if `func` returns None.
"""
return _internal_py_func(func=func, inp=inp, Tout=Tout, eager=True, name=name)
@tf_export("py_func")
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
arguments 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)
input = tf.placeholder(tf.float32)
y = tf.py_func(my_func, [input], 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 `ndarray` objects as arguments and
returns a list of `ndarray` objects (or a single `ndarray`). This function
must accept as many arguments as there are tensors in `inp`, and these
argument types will match the corresponding `tf.Tensor` objects
in `inp`. The returns `ndarray`s must match the number and types defined
`Tout`.
Important Note: Input and output numpy `ndarray`s of `func` are not
guaranteed to be copies. In some cases their underlying memory will be
shared with the corresponding TensorFlow tensors.
In-place modification or storing `func` input or return values in
python datastructures without explicit (np.)copy
can have non-deterministic consequences.
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.
"""
if context.executing_eagerly():
result = func(*[x.numpy() for x in inp])
result = nest.flatten(result)
result = [x if x is None else ops.convert_to_tensor(x) for x in result]
if len(result) == 1:
# Mimic the automatic unwrapping in graph-mode py_func
result, = result
return result
return _internal_py_func(
func=func, inp=inp, Tout=Tout, stateful=stateful, eager=False, name=name)
ops.NotDifferentiable("PyFunc")
ops.NotDifferentiable("PyFuncStateless")