forked from tensorflow/tensorflow
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathfunction.py
More file actions
418 lines (355 loc) · 14.9 KB
/
function.py
File metadata and controls
418 lines (355 loc) · 14.9 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
# Copyright 2015 Google Inc. 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.
# =============================================================================
"""Python front-end supports for functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import inspect
import re
from tensorflow.core.framework import attr_value_pb2
from tensorflow.core.framework import function_pb2
from tensorflow.core.framework import op_def_pb2
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import op_def_registry
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
def _make_argname_from_tensor_name(name):
return re.sub(":0$", "", name).replace(":", "_")
def _tensor_to_argdef(t):
arg = op_def_pb2.OpDef.ArgDef()
arg.name = _make_argname_from_tensor_name(t.name)
arg.type = t.dtype.as_datatype_enum
return arg
def _get_node_def_attr(op):
# pylint: disable=protected-access
return op._node_def.attr
# pylint: enable=protected-access
def _add_input_array(op, start, limit, dtype, func):
"""Adds a _ListToArray node in the func for op.inputs[start:limit]."""
node = function_pb2.FunctionDef.Node()
node.op = "_ListToArray"
ret_name = op.name + "_L2A_" + str(start)
node.ret.extend([ret_name])
node.arg.extend([_make_argname_from_tensor_name(x.name)
for x in op.inputs[start:limit]])
num = limit - start
node.attr["Tin"].CopyFrom(attr_value_pb2.AttrValue(
list=attr_value_pb2.AttrValue.ListValue(type=[dtype] * num)))
node.attr["T"].CopyFrom(attr_value_pb2.AttrValue(type=dtype))
node.attr["N"].CopyFrom(attr_value_pb2.AttrValue(i=num))
func.node.extend([node])
return ret_name
def _add_output_array(op, start, limit, dtype, func):
"""Adds a _ArrayToList node in the func for op.outputs[start:limit]."""
dtype_proto = attr_value_pb2.AttrValue(type=dtype)
# A node converting N*T to list(T)
node = function_pb2.FunctionDef.Node()
node.op = "_ArrayToList"
arg_name = op.name + "_A2L_" + str(start)
ret_name = arg_name + "_out"
node.ret.append(ret_name)
node.arg.append(arg_name)
node.attr["T"].CopyFrom(dtype_proto)
num = limit - start
node.attr["N"].CopyFrom(attr_value_pb2.AttrValue(i=num))
node.attr["out_types"].CopyFrom(attr_value_pb2.AttrValue(
list=attr_value_pb2.AttrValue.ListValue(type=[dtype] * num)))
func.node.extend([node])
num = limit - start
# Adds an identity node for each element in the array N*T so that
# uses of each element can be added easily later. These Identity
# will be eliminated before graph execution.
for i in xrange(num):
node = function_pb2.FunctionDef.Node()
node.op = "Identity"
node.arg.append(ret_name + ":" + str(i))
node.ret.append(_make_argname_from_tensor_name(op.outputs[i].name))
node.attr["T"].CopyFrom(dtype_proto)
func.node.extend([node])
return arg_name
def _add_output_list(op, start, limit, dtype_lst, func):
"""Adds a _ArrayToList node in the func for op.outputs[start:limit]."""
ret_name = op.name + "_Lst_" + str(start) + "_" + str(limit)
num = limit - start
assert len(dtype_lst) == num
# Adds an identity node for each element in the array N*T so that
# uses of each element can be added easily later. These Identity
# will be eliminated before graph execution.
for i in xrange(num):
node = function_pb2.FunctionDef.Node()
node.op = "Identity"
node.arg.append(ret_name + ":" + str(i))
node.ret.append(_make_argname_from_tensor_name(op.outputs[i].name))
node.attr["T"].CopyFrom(attr_value_pb2.AttrValue(type=dtype_lst[i]))
func.node.extend([node])
return ret_name
def _add_op_node(graph, op, func):
"""Converts an op to a function def node and add it to `func`."""
node = function_pb2.FunctionDef.Node()
node.op = op.type
# pylint: disable=protected-access
if graph._is_function(op.type):
op_def = graph._get_function(op.type).signature
else:
op_def = op_def_registry.get_registered_ops()[op.type]
# pylint: enable=protected-access
attrs = _get_node_def_attr(op)
out_index = 0
for arg_def in op_def.output_arg:
if arg_def.number_attr:
dtype = arg_def.type or attrs[arg_def.type_attr].type
num = attrs[arg_def.number_attr].i
node.ret.append(_add_output_array(op, out_index, out_index + num, dtype,
func))
out_index += num
elif arg_def.type_list_attr:
dtype_lst = attrs[arg_def.type_list_attr].list.type
num = len(dtype_lst)
node.ret.append(_add_output_list(op, out_index, out_index + num,
dtype_lst, func))
out_index += num
else:
node.ret.append(_make_argname_from_tensor_name(op.outputs[
out_index].name))
out_index += 1
inp_index = 0
for arg_def in op_def.input_arg:
if arg_def.number_attr:
dtype = arg_def.type or attrs[arg_def.type_attr].type
num = attrs[arg_def.number_attr].i
node.arg.append(_add_input_array(op, inp_index, inp_index + num, dtype,
func))
inp_index += num
elif arg_def.type_list_attr:
num = len(attrs[arg_def.type_list_attr].list.type)
node.arg.extend([_make_argname_from_tensor_name(op.inputs[i].name)
for i in range(inp_index, inp_index + num)])
inp_index += num
else:
node.arg.append(_make_argname_from_tensor_name(op.inputs[inp_index].name))
inp_index += 1
node.dep.extend([_make_argname_from_tensor_name(x.name)
for x in op.control_inputs])
for k, v in _get_node_def_attr(op).iteritems():
node.attr[k].CopyFrom(v)
func.node.extend([node])
# pylint: disable=line-too-long
def graph_to_function_def(graph, name, inputs, outputs):
"""Returns `graph` as a `FunctionDef` protocol buffer.
This method creates a [`FunctionDef`](
https://www.tensorflow.org/code/tensorflow/core/framework/function.proto)
protocol buffer that contains all the ops present in the graph. The
graph effectively becomes the body of the function.
The arguments `inputs` and `outputs` will be listed as the inputs
and outputs tensors of the function. They must be lists of
tensors present in the graph. The lists can optionally be empty.
The returned protocol buffer can be passed to the
[`Graph.add_function()`](#Graph.add_function) method of a
different graph to make it available there.
Args:
graph: GraphDef proto.
name: string. The name to use for the function.
inputs: List of tensors. Inputs to the function.
outputs: List of tensors. Outputs of the function.
Returns:
A FunctionDef protocol buffer.
"""
# pylint: enable=line-too-long
func = function_pb2.FunctionDef()
func.signature.name = name
func.signature.input_arg.extend([_tensor_to_argdef(graph.get_tensor_by_name(
i.name)) for i in inputs])
func.signature.output_arg.extend([_tensor_to_argdef(graph.get_tensor_by_name(
o.name)) for o in outputs])
func_arg_placeholders = set([i.name for i in inputs])
g = ops.get_default_graph()
for op in graph.get_operations():
tensor_name = op.values()[0].name
if tensor_name not in func_arg_placeholders:
_add_op_node(g, op, func)
return func
def call_function(func_def, *inputs, **kwargs):
"""Calls the function described by `func_def`.
This adds a `call` op to the default graph that calls the function described
by `func_def` with the tensors listed in `inputs` as arguments. It returns
the outputs of the call, which are one or more tensors.
`func_def` is a
[`FunctionDef`](
https://www.tensorflow.org/code/tensorflow/core/framework/function.proto)
protcol buffer describing a
TensorFlow function. See [`define_function()`](#define_function) for an
easy way to create one from a Python function.
You can pass an optional keyword parameters `name=string` to name the
added operation.
`func_def` is automatically added to the function library of the graph if
needed.
Args:
func_def: A `FunctionDef` protocol buffer.
*inputs: A list of tensors
**kwargs: Optional keyword arguments. Can only contain 'name'.
Returns:
A list of tensors representing the outputs of the call to `func_def`.
Raises:
ValueError: if the arguments are invalid.
"""
name = kwargs.pop("name", None)
if kwargs:
raise ValueError("Unknown keyword arguments: %s" % kwargs.keys())
func_name = func_def.signature.name
with ops.op_scope(inputs, name, func_name) as name:
if len(inputs) != len(func_def.signature.input_arg):
raise ValueError("Expected number of arguments: %d" %
len(func_def.signature.input_arg))
output_types = [dtypes.DType(x.type) for x in func_def.signature.output_arg]
# TODO(touts): Pass compute_shapes as "try if function exists"
g = ops.get_default_graph()
op = g.create_op(func_name,
list(inputs),
output_types,
name=name,
compute_shapes=False)
if op.outputs:
if len(op.outputs) == 1:
return op.outputs[0]
else:
return tuple(op.outputs)
else:
return op
def define_function(func, input_types):
"""Creates a `FunctionDef` for a python function.
`func` is a Python function that receives zero or more tensors and returns at
least one tensor. It should add ops to the default graph the usual way by
calling TensorFlow functions such as `tf.constant()`, `tf.matmul()`, etc.
`input_types` is a dictionary of strings to `tf.Dtype` objects. Keys are
names arguments to `func`. The value indicate the type of tensor expected
by the function.
The returned `FunctionDef` protocol buffer is also added to the
default graph library. After it has been added you can add calls to
the function by passing it to `tf.call_function()`, together with a
list of tensors to use as inputs for the function.
Notes:
* `func` is called once, with `placeholder` tensors of the types specified in
`input_types` as arguments.
* Values returned by `func` must be tensors and they are recorded as being
the output of the function def.
* While `func` is a called, an empty graph is temporarily pushed as the
default graph. All ops added by `func` to that graph are part of the body
of the returned function def.
Example, but also see the [How To on functions](link_needed).
```python
# A function that receives two tensors x, y and returns their
# sum and difference.
def my_func(x, y):
return x + y, x - y
# Create a FunctionDef for 'my_func'. (This does not change the default
graph.)
my_func_def = tf.define_function(my_func, {'x': tf.float32, 'y': tf.float32})
# Build the graph, calling the function.
a = tf.constant([1.0])
b = tf.constant([2.0])
c, d = tf.call_function(my_func_def, a, b, name='mycall')
```
Args:
func: a Python function.
input_types: dict. Keys are the names of the arguments of `func`, values
are their expected `tf.DType`.
Returns:
A FunctionDef protocol buffer.
Raises:
ValueError: if the arguments are invalid.
"""
# TODO(touts): Lift the limitation that func can only receive Tensor args.
if inspect.isfunction(func):
func_name = func.__name__
elif inspect.ismethod(func):
func_name = func.im_self.__name__ + "." + func.__name__
else:
raise ValueError("Argument must be a function")
argspec = inspect.getargspec(func)
if argspec.varargs or argspec.keywords or argspec.defaults:
raise ValueError("Only functions with plain arglists are supported.")
if inspect.isfunction(func):
if len(argspec.args) != len(input_types):
raise ValueError("The function must have the same number of arguments "
"as the number of specified input types.")
args = argspec.args
elif inspect.ismethod(func):
if len(argspec.args) != 1 + len(input_types):
raise ValueError(
"The class function must have the same number of arguments "
"as the number of specified input types.")
args = argspec.args[1:] # 1st argument is the "class" type.
# Create the func_def object.
temp_graph = ops.Graph()
with temp_graph.as_default():
# List of placeholders for the function_def.
inputs = []
# Arglist to call 'func'
kwargs = {}
for argname in args:
if argname not in input_types:
raise ValueError("Missing type for argument: " + argname)
argholder = array_ops.placeholder(input_types[argname], name=argname)
inputs.append(argholder)
kwargs[argname] = argholder
# Call func and gather the output tensors.
outputs = func(**kwargs)
if not outputs:
raise ValueError("Function must return at least one tensor")
# Convenience: if func only returned one value, make it a tuple.
if not isinstance(outputs, (list, tuple)):
outputs = (outputs,)
# Build the FunctionDef
func_def = graph_to_function_def(temp_graph, func_name, inputs, outputs)
g = ops.get_default_graph()
g._add_function(func_def) # pylint: disable=protected-access
return func_def
class Defun(object):
"""Decorator used to define TensorFlow functions.
Use this decorator to make a Python function usable directly as a TensorFlow
function.
The decorated function must add ops to the default graph and return zero or
more `Tensor` objects. Call the decorator with named arguments, one for each
argument of the function to decorate, with the expected type of the argument
as value.
For example if the function to decorate accepts to `tf.float32` arguments
named `x` and `y`, call the decorator with:
@Defun(x=tf.float32, y=tf.float32)
def foo(x, y):
...
When you call the decorated function it will add `call` ops to the graph.
Example, but also see the [How To on functions](link_needed).
```python
# Defining the function.
@tf.Defun(x=tf.float32, y=tf.float32)
def MyFunc(x, y):
return x + y, x - y
# Building the graph.
a = tf.Constant([1.0])
b = tf.Constant([2.0])
c, d = MyFunc(a, b, name='mycall')
```
@@__init__
"""
def __init__(self, **input_types):
"""Create a `Defun` decorator.
Args:
**input_types: Dict mapping string with `tf.DType`
One key for each argument of the function to decorate.
"""
self._input_types = input_types
def __call__(self, f):
func_def = define_function(f, self._input_types)
return lambda *args, **kwargs: call_function(func_def, *args, **kwargs)