diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 2ae6fae338d..5ba33aa3869 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -2203,6 +2203,7 @@ py_library(
srcs_version = "PY2AND3",
deps = [
":gradients_impl",
+ ":unconnected_gradients",
"//tensorflow/python/eager:function",
"//tensorflow/python/eager:tape",
],
@@ -2240,6 +2241,7 @@ py_library(
":spectral_grad",
":tensor_array_ops",
":tensor_util",
+ ":unconnected_gradients",
":util",
":variable_scope",
"//tensorflow/core:protos_all_py",
@@ -2250,6 +2252,15 @@ py_library(
],
)
+py_library(
+ name = "unconnected_gradients",
+ srcs = ["ops/unconnected_gradients.py"],
+ srcs_version = "PY2AND3",
+ deps = [
+ ":util",
+ ],
+)
+
py_library(
name = "histogram_ops",
srcs = ["ops/histogram_ops.py"],
diff --git a/tensorflow/python/eager/BUILD b/tensorflow/python/eager/BUILD
index 7d6ea10c946..c898869dd0e 100644
--- a/tensorflow/python/eager/BUILD
+++ b/tensorflow/python/eager/BUILD
@@ -279,10 +279,10 @@ py_library(
"//tensorflow/python:dtypes",
"//tensorflow/python:errors",
"//tensorflow/python:framework_ops",
- "//tensorflow/python:gradients_impl",
"//tensorflow/python:math_ops",
"//tensorflow/python:pywrap_tensorflow",
"//tensorflow/python:tensor_shape",
+ "//tensorflow/python:unconnected_gradients",
"//tensorflow/python:util",
"//tensorflow/python/eager:context",
"//tensorflow/python/eager:execute",
@@ -375,8 +375,8 @@ py_library(
srcs = ["imperative_grad.py"],
srcs_version = "PY2AND3",
deps = [
- "//tensorflow/python:gradients_impl",
"//tensorflow/python:pywrap_tensorflow",
+ "//tensorflow/python:unconnected_gradients",
"//tensorflow/python:util",
],
)
diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py
index 89a41770162..71ede721edf 100644
--- a/tensorflow/python/eager/backprop.py
+++ b/tensorflow/python/eager/backprop.py
@@ -35,9 +35,9 @@ 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 gen_math_ops
-from tensorflow.python.ops import gradients_impl
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import resource_variable_ops
+from tensorflow.python.ops.unconnected_gradients import UnconnectedGradients
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util import nest
from tensorflow.python.util import tf_contextlib
@@ -855,7 +855,7 @@ class GradientTape(object):
target,
sources,
output_gradients=None,
- unconnected_gradients=gradients_impl.UnconnectedGradients.NONE):
+ unconnected_gradients=UnconnectedGradients.NONE):
"""Computes the gradient using operations recorded in context of this tape.
Args:
diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py
index a4e2fb411b7..d7a43407055 100644
--- a/tensorflow/python/eager/function.py
+++ b/tensorflow/python/eager/function.py
@@ -481,8 +481,7 @@ class Function(object):
outputs,
self._func_graph.inputs,
grad_ys=gradients_wrt_outputs,
- src_graph=self._func_graph,
- unconnected_gradients=gradients_impl.UnconnectedGradients.NONE)
+ src_graph=self._func_graph)
backwards_graph_captures = list(backwards_graph.captures.keys())
diff --git a/tensorflow/python/eager/imperative_grad.py b/tensorflow/python/eager/imperative_grad.py
index 6e8bde99531..b9b4355e663 100644
--- a/tensorflow/python/eager/imperative_grad.py
+++ b/tensorflow/python/eager/imperative_grad.py
@@ -21,7 +21,7 @@ from __future__ import print_function
import collections
from tensorflow.python import pywrap_tensorflow
-from tensorflow.python.ops import gradients_impl
+from tensorflow.python.ops.unconnected_gradients import UnconnectedGradients
from tensorflow.python.util import compat
VSpace = collections.namedtuple("VSpace", [
@@ -34,7 +34,7 @@ def imperative_grad(
target,
sources,
output_gradients=None,
- unconnected_gradients=gradients_impl.UnconnectedGradients.NONE):
+ unconnected_gradients=UnconnectedGradients.NONE):
"""Computes gradients from the imperatively defined tape on top of the stack.
Works by filtering the tape, computing how many downstream usages are of each
@@ -59,8 +59,7 @@ def imperative_grad(
RuntimeError: if something goes wrong.
"""
try:
- unconnected_gradients = gradients_impl.UnconnectedGradients(
- unconnected_gradients)
+ unconnected_gradients = UnconnectedGradients(unconnected_gradients)
except ValueError:
raise ValueError(
"Unknown value for unconnected_gradients: %r" % unconnected_gradients)
diff --git a/tensorflow/python/ops/gradients.py b/tensorflow/python/ops/gradients.py
index 794465b10e3..cd11447e1f9 100644
--- a/tensorflow/python/ops/gradients.py
+++ b/tensorflow/python/ops/gradients.py
@@ -25,5 +25,5 @@ from tensorflow.python.ops.custom_gradient import custom_gradient
from tensorflow.python.ops.gradients_impl import AggregationMethod
from tensorflow.python.ops.gradients_impl import gradients
from tensorflow.python.ops.gradients_impl import hessians
-from tensorflow.python.ops.gradients_impl import UnconnectedGradients
+from tensorflow.python.ops.unconnected_gradients import UnconnectedGradients
# pylint: enable=unused-import
diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py
index aeecf843307..0cb6f80b2cc 100644
--- a/tensorflow/python/ops/gradients_impl.py
+++ b/tensorflow/python/ops/gradients_impl.py
@@ -20,7 +20,6 @@ from __future__ import print_function
import collections
import contextlib
-import enum # pylint: disable=g-bad-import-order
import warnings
import numpy as np
@@ -35,6 +34,7 @@ from tensorflow.python.framework import function as framework_function
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
+from tensorflow.python.framework.func_graph import FuncGraph
from tensorflow.python.ops import array_grad # pylint: disable=unused-import
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops # pylint: disable=unused-import
@@ -53,16 +53,11 @@ from tensorflow.python.ops import random_grad # pylint: disable=unused-import
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import spectral_grad # pylint: disable=unused-import
from tensorflow.python.ops import tensor_array_ops
+from tensorflow.python.ops.unconnected_gradients import UnconnectedGradients
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util import compat
-from tensorflow.python.util.lazy_loader import LazyLoader
from tensorflow.python.util.tf_export import tf_export
-# This is to avoid a circular dependency
-# backprop -> gradients_impl -> func_graph
-funct_graph = LazyLoader(
- "func_graph", globals(),
- "tensorflow.python.framework.func_graph")
# This is to avoid a circular dependency (eager.function depends on
# gradients_impl). This is set in eager/function.py.
@@ -455,12 +450,12 @@ def _RaiseNoGradWrtInitialLoopValError(op, from_ops, xs):
def _IsFunction(graph):
- return (isinstance(graph, funct_graph.FuncGraph) or
+ return (isinstance(graph, FuncGraph) or
isinstance(graph, framework_function._FuncGraph)) # pylint: disable=protected-access
def _Captures(func_graph):
- if isinstance(func_graph, funct_graph.FuncGraph):
+ if isinstance(func_graph, FuncGraph):
return func_graph.captures
else:
assert isinstance(func_graph, framework_function._FuncGraph) # pylint: disable=protected-access
@@ -539,26 +534,6 @@ def _Consumers(t, func_graphs):
return consumers
-@tf_export("UnconnectedGradients")
-class UnconnectedGradients(enum.Enum):
- """Controls how gradient computation behaves when y does not depend on x.
-
- The gradient of y with respect to x can be zero in two different ways: there
- could be no differentiable path in the graph connecting x to y (and so we can
- statically prove that the gradient is zero) or it could be that runtime values
- of tensors in a particular execution lead to a gradient of zero (say, if a
- relu unit happens to not be activated). To allow you to distinguish between
- these two cases you can choose what value gets returned for the gradient when
- there is no path in the graph from x to y:
-
- * `NONE`: Indicates that [None] will be returned if there is no path from x
- to y
- * `ZERO`: Indicates that a zero tensor will be returned in the shape of x.
- """
- NONE = "none"
- ZERO = "zero"
-
-
@tf_export("gradients")
def gradients(ys,
xs,
@@ -703,7 +678,7 @@ def _GradientsHelper(ys,
curr_graph = src_graph
while _IsFunction(curr_graph):
func_graphs.append(curr_graph)
- if isinstance(curr_graph, funct_graph.FuncGraph):
+ if isinstance(curr_graph, FuncGraph):
curr_graph = curr_graph.outer_graph
else:
assert isinstance(curr_graph, framework_function._FuncGraph) # pylint: disable=protected-access
diff --git a/tensorflow/python/ops/unconnected_gradients.py b/tensorflow/python/ops/unconnected_gradients.py
new file mode 100644
index 00000000000..03189a9f52d
--- /dev/null
+++ b/tensorflow/python/ops/unconnected_gradients.py
@@ -0,0 +1,43 @@
+# Copyright 2017 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.
+# ==============================================================================
+
+"""Utilities for calculating gradients."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import enum
+
+from tensorflow.python.util.tf_export import tf_export
+
+
+@tf_export("UnconnectedGradients")
+class UnconnectedGradients(enum.Enum):
+ """Controls how gradient computation behaves when y does not depend on x.
+
+ The gradient of y with respect to x can be zero in two different ways: there
+ could be no differentiable path in the graph connecting x to y (and so we can
+ statically prove that the gradient is zero) or it could be that runtime values
+ of tensors in a particular execution lead to a gradient of zero (say, if a
+ relu unit happens to not be activated). To allow you to distinguish between
+ these two cases you can choose what value gets returned for the gradient when
+ there is no path in the graph from x to y:
+
+ * `NONE`: Indicates that [None] will be returned if there is no path from x
+ to y
+ * `ZERO`: Indicates that a zero tensor will be returned in the shape of x.
+ """
+ NONE = "none"
+ ZERO = "zero"