Add option in tf.gradients() to return zero tensors for unconnected gradients.

tf.gradients currently returns [NONE] when the gradient of unconnected variables
is required. This backwards compatable change adds in the option to have zero
tensors returned that match the dimensions of the input tensor.

PiperOrigin-RevId: 215725488

tensorflow/python/BUILD

@@ -2152,6 +2152,7 @@ py_library(
         ":array_grad",
         ":array_ops",
         ":bitwise_ops",
+        ":check_ops",
         ":cond_v2_impl",
         ":control_flow_grad",
         ":control_flow_ops",
@@ -2172,8 +2173,11 @@ py_library(
         ":random_grad",
         ":resource_variable_ops",
         ":spectral_grad",
+        ":tensor_array_ops",
+        ":tensor_util",
         ":util",
         ":variable_scope",
+        "//tensorflow/core:protos_all_py",
         "//tensorflow/python/eager:backprop",
         "//tensorflow/python/eager:context",
         "//tensorflow/python/eager:tape",

tensorflow/python/ops/gradients.py

@@ -25,4 +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
 # pylint: enable=unused-import

tensorflow/python/ops/gradients_impl.py

@@ -20,6 +20,7 @@ from __future__ import print_function
 
 import collections
 import contextlib
+import enum  # pylint: disable=g-bad-import-order
 import sys
 import warnings
 
@@ -537,6 +538,26 @@ 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,
@@ -545,7 +566,8 @@ def gradients(ys,
               colocate_gradients_with_ops=False,
               gate_gradients=False,
               aggregation_method=None,
-              stop_gradients=None):
+              stop_gradients=None,
+              unconnected_gradients=UnconnectedGradients.NONE):
   """Constructs symbolic derivatives of sum of `ys` w.r.t. x in `xs`.
 
   `ys` and `xs` are each a `Tensor` or a list of tensors.  `grad_ys`
@@ -596,6 +618,23 @@ def gradients(ys,
   All integer tensors are considered constant with respect to all `xs`, as if
   they were included in `stop_gradients`.
 
+  `unconnected_gradients` determines the value returned for each x in xs if it
+  is unconnected in the graph to ys. By default this is None to safeguard
+  against errors. MAthematically these gradients are zero which can be requested
+  using the `'zero'` option. `tf.UnconnectedGradients` provides the
+  following options and behaviors:
+
+  ```python
+  a = tf.ones([1, 2])
+  b = tf.ones([3, 1])
+  g1 = tf.gradients([b], [a], unnconnected_gradients='none')
+  sess.run(g1)  # [None]
+
+  g2 = tf.gradients([b], [a], unconnected_gradients='zero')
+  sess.run(g2)  # [array([[0., 0.]], dtype=float32)]
+  ```
+
+
   Args:
     ys: A `Tensor` or list of tensors to be differentiated.
     xs: A `Tensor` or list of tensors to be used for differentiation.
@@ -611,6 +650,10 @@ def gradients(ys,
       Accepted values are constants defined in the class `AggregationMethod`.
     stop_gradients: Optional. A `Tensor` or list of tensors not to differentiate
       through.
+    unconnected_gradients: Optional. Specifies the gradient value returned when
+      the given input tensors are unconnected. Accepted values are constants
+      defined in the class `tf.UnconnectedGradients` and the default value is
+      `none`.
 
   Returns:
     A list of `sum(dy/dx)` for each x in `xs`.
@@ -627,7 +670,8 @@ def gradients(ys,
   # mutating new ops.
   with ops.get_default_graph()._mutation_lock():  # pylint: disable=protected-access
     return _GradientsHelper(ys, xs, grad_ys, name, colocate_gradients_with_ops,
-                            gate_gradients, aggregation_method, stop_gradients)
+                            gate_gradients, aggregation_method, stop_gradients,
+                            unconnected_gradients)
 
 
 def _GradientsHelper(ys,
@@ -638,6 +682,7 @@ def _GradientsHelper(ys,
                      gate_gradients=False,
                      aggregation_method=None,
                      stop_gradients=None,
+                     unconnected_gradients=UnconnectedGradients.NONE,
                      src_graph=None):
   """Implementation of gradients()."""
   if context.executing_eagerly():
@@ -645,6 +690,11 @@ def _GradientsHelper(ys,
                        "is enabled. Use tf.GradientTape instead.")
   if src_graph is None:
     src_graph = ops.get_default_graph()
+  try:
+    unconnected_gradients = UnconnectedGradients(unconnected_gradients)
+  except ValueError:
+    raise ValueError(
+        "Unknown value for unconnected_gradients: %r" % unconnected_gradients)
 
   # If src_graph is a _FuncGraph (i.e. a function body), gather it and all
   # ancestor graphs. This is necessary for correctly handling captured values.
@@ -856,7 +906,7 @@ def _GradientsHelper(ys,
 
   if loop_state:
     loop_state.PostProcessing()
-  return [_GetGrad(grads, x) for x in xs]
+  return [_GetGrad(grads, x, unconnected_gradients) for x in xs]
 
 
 def _HasAnyNotNoneGrads(grads, op):
@@ -924,12 +974,19 @@ def _SetGrad(grads, t, grad):
     op_grads[t.value_index] = grad
 
 
-def _GetGrad(grads, t):
+def _GetGrad(grads, t, unconnected_gradients):
   """Gets gradient for tensor "t"."""
   op = t.op
   op_grads = grads.get(op)
   if not op_grads:
+    if unconnected_gradients == UnconnectedGradients.ZERO:
+      return array_ops.zeros_like(t)
+    elif unconnected_gradients == UnconnectedGradients.NONE:
       return None
+    else:
+      raise ValueError(
+          "Unknown value for unconnected_gradients: %r" % unconnected_gradients)
+
   t_grad = op_grads[t.value_index]
   assert not isinstance(
       t_grad, list), ("gradients list should have been aggregated by now.")

tensorflow/python/ops/gradients_test.py

@@ -350,6 +350,40 @@ class GradientsTest(test_util.TensorFlowTestCase):
       for a, b in zip(npgrad1, npgrad2):
         np.testing.assert_allclose(a, b)
 
+  def testUnconnectedGradientsNoneUnconnectedGradients(self):
+    with ops.Graph().as_default():
+      x = constant(1.0, shape=[2, 2])
+      y = constant(3.0, shape=[3, 1])
+      grad = gradients.gradients(
+          [y], [x], unconnected_gradients="none")
+    self.assertIsNone(grad[0])
+
+  def testUnconnectedGradientsZerosUnconnectedGradients(self):
+    with ops.Graph().as_default():
+      x = constant(1.0, shape=[2, 2])
+      y = constant(3.0, shape=[3, 1])
+      grads = gradients.gradients(
+          [y], [x], unconnected_gradients="zero")
+      with self.cached_session() as sess:
+        self.assertAllEqual([[0.0, 0.0], [0.0, 0.0]], sess.run(grads)[0])
+
+  def testUnconnectedGradientsZeroConnectedGradients(self):
+    with ops.Graph().as_default():
+      x = constant(1.0)
+      y = x * 3.0
+      grad = gradients.gradients(
+          [y], [x], unconnected_gradients="zero")
+      with self.cached_session() as sess:
+        self.assertEquals(3.0, sess.run(grad)[0])
+
+  def testUnknownUnconnectedGradientsValueGiven(self):
+    with ops.Graph().as_default():
+      x = constant(1.0)
+      y = constant(1.0)
+      with self.assertRaisesRegexp(
+          ValueError, "Unknown value for unconnected_gradients: 'nonsense'"):
+        gradients.gradients([y], [x], unconnected_gradients="nonsense")
+
 
 class FunctionGradientsTest(test_util.TensorFlowTestCase):
 

tensorflow/tools/api/golden/v1/tensorflow.-unconnected-gradients.pbtxt

@@ -0,0 +1,12 @@
+path: "tensorflow.UnconnectedGradients"
+tf_class {
+  is_instance: "<enum \'UnconnectedGradients\'>"
+  member {
+    name: "NONE"
+    mtype: "<enum \'UnconnectedGradients\'>"
+  }
+  member {
+    name: "ZERO"
+    mtype: "<enum \'UnconnectedGradients\'>"
+  }
+}

tensorflow/tools/api/golden/v1/tensorflow.pbtxt

@@ -248,6 +248,10 @@ tf_module {
     name: "TextLineReader"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "UnconnectedGradients"
+    mtype: "<class \'enum.EnumMeta\'>"
+  }
   member {
     name: "VERSION"
     mtype: "<type \'str\'>"
@@ -1234,7 +1238,7 @@ tf_module {
   }
   member_method {
     name: "gradients"
-    argspec: "args=[\'ys\', \'xs\', \'grad_ys\', \'name\', \'colocate_gradients_with_ops\', \'gate_gradients\', \'aggregation_method\', \'stop_gradients\'], varargs=None, keywords=None, defaults=[\'None\', \'gradients\', \'False\', \'False\', \'None\', \'None\'], "
+    argspec: "args=[\'ys\', \'xs\', \'grad_ys\', \'name\', \'colocate_gradients_with_ops\', \'gate_gradients\', \'aggregation_method\', \'stop_gradients\', \'unconnected_gradients\'], varargs=None, keywords=None, defaults=[\'None\', \'gradients\', \'False\', \'False\', \'None\', \'None\', \'UnconnectedGradients.NONE\'], "
   }
   member_method {
     name: "greater"

tensorflow/tools/api/golden/v2/tensorflow.-unconnected-gradients.pbtxt

@@ -0,0 +1,12 @@
+path: "tensorflow.UnconnectedGradients"
+tf_class {
+  is_instance: "<enum \'UnconnectedGradients\'>"
+  member {
+    name: "NONE"
+    mtype: "<enum \'UnconnectedGradients\'>"
+  }
+  member {
+    name: "ZERO"
+    mtype: "<enum \'UnconnectedGradients\'>"
+  }
+}

tensorflow/tools/api/golden/v2/tensorflow.pbtxt

@@ -220,6 +220,10 @@ tf_module {
     name: "TensorShape"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "UnconnectedGradients"
+    mtype: "<class \'enum.EnumMeta\'>"
+  }
   member {
     name: "VERSION"
     mtype: "<type \'str\'>"
@@ -1134,7 +1138,7 @@ tf_module {
   }
   member_method {
     name: "gradients"
-    argspec: "args=[\'ys\', \'xs\', \'grad_ys\', \'name\', \'colocate_gradients_with_ops\', \'gate_gradients\', \'aggregation_method\', \'stop_gradients\'], varargs=None, keywords=None, defaults=[\'None\', \'gradients\', \'False\', \'False\', \'None\', \'None\'], "
+    argspec: "args=[\'ys\', \'xs\', \'grad_ys\', \'name\', \'colocate_gradients_with_ops\', \'gate_gradients\', \'aggregation_method\', \'stop_gradients\', \'unconnected_gradients\'], varargs=None, keywords=None, defaults=[\'None\', \'gradients\', \'False\', \'False\', \'None\', \'None\', \'UnconnectedGradients.NONE\'], "
   }
   member_method {
     name: "greater"