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Fix issue with gradients of functions which return multiple values.

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PiperOrigin-RevId: 173730922
This commit is contained in:
Alexandre Passos 2017-10-27 16:10:54 -07:00 committed by TensorFlower Gardener
parent 80374a7b47
commit e1d7615ebc
2 changed files with 35 additions and 6 deletions
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34
tensorflow/python/eager/backprop.py
View File

@ -35,6 +35,7 @@ from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.util import nest
@ -322,7 +323,10 @@ def implicit_val_and_grad(f):
```
Args:
f: The function to be differentiated.
f: function to be differentiated. If `f` returns a scalar, this scalar will
be differentiated. If `f` returns a tensor or list of tensors, by default
a scalar will be computed by adding all their values to produce a single
scalar.
Returns:
A function which, when called, returns a tuple pair.
@ -384,7 +388,10 @@ def implicit_grad(f):
```
Args:
f: The function to be differentiated.
f: function to be differentiated. If `f` returns a scalar, this scalar will
be differentiated. If `f` returns a tensor or list of tensors, by default
a scalar will be computed by adding all their values to produce a single
scalar.
Returns:
A function which, when called, returns a list of (gradient, variable) pairs.
@ -467,7 +474,12 @@ def gradients_function(f, params=None):
```
Args:
f: function to be differentiated.
f: function to be differentiated. If `f` returns a scalar, this scalar will
be differentiated. If `f` returns a tensor or list of tensors, by default
a scalar will be computed by adding all their values to produce a single
scalar. If desired, the tensors can be elementwise multiplied by the
tensors passed as the `dy` keyword argument to the returned gradient
function.
params: list of parameter names of f or list of integers indexing the
parameters with respect to which we'll differentiate. Passing None
differentiates with respect to all parameters.
@ -559,7 +571,12 @@ def val_and_grad_function(f, params=None):
```
Args:
f: function to be differentiated.
f: function to be differentiated. If `f` returns a scalar, this scalar will
be differentiated. If `f` returns a tensor or list of tensors, by default
a scalar will be computed by adding all their values to produce a single
scalar. If desired, the tensors can be elementwise multiplied by the
tensors passed as the `dy` keyword argument to the returned gradient
function.
params: list of parameter names of f or list of integers indexing the
parameters with respect to which we'll differentiate. Passing `None`
differentiates with respect to all parameters.
@ -632,12 +649,17 @@ def make_vjp(f, params=None):
sources.append(args[i])
tape.watch(args[i])
result = f(*args)
flat_result = nest.flatten(result)
flat_result = [gen_array_ops.identity(x) for x in flat_result]
result = nest.pack_sequence_as(result, flat_result)
finally:
t = tape.pop_tape()
def vjp(dy=None):
if dy is not None:
dy = [ops.convert_to_tensor(x) for x in nest.flatten(dy)]
return imperative_grad.imperative_grad(
_default_vspace, t, nest.flatten(result), sources,
output_gradients=nest.flatten(dy) if dy is not None else None)
output_gradients=dy)
return result, vjp
return decorated
@ -697,7 +719,7 @@ _default_vspace = imperative_grad.VSpace(
aggregate_fn=_aggregate_grads,
tensor_id=ops.tensor_id,
zeros=array_ops.zeros,
ones_like=array_ops.ones_like)
ones_like=lambda x: ops.convert_to_tensor(array_ops.ones_like(x)))
class GradientTape(object):

7
tensorflow/python/eager/backprop_test.py
View File

@ -401,6 +401,13 @@ class BackpropTest(test.TestCase):
backprop.gradients_function(part)(constant_op.constant(1.0))[0],
2.0)
def testReturnSameThing(self):
def f(x):
return x, 2 * x
self.assertAllEqual(backprop.gradients_function(f)(1.0)[0], 3.0)
def testExceptionSafety(self):
def f(unused_x):