Extract UnconnectedGradients from gradients_impl to simplify dependencies

PiperOrigin-RevId: 218661408

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"],

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",
     ],
 )

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:

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())
 

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)

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

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

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"