Support returning resource handles from function in XLA

There are a couple of reasons to do this:
 - resource handle are regular tensors part of a public API that
 can potentially be returned from a function.
 - When tfe.defun is executed under GradientTape, it generates a
 function returning resource handles in certain cases.

This CL adds support for returning resource handles from an XLA
compiled function. These resource handles must have been passed as
arguments to the function. In other words, we don't yet support
returning resources created inside the function. tfe.defun never
makes functions that create resources.

PiperOrigin-RevId: 210442856

1 files changed, 98 insertions, 0 deletions

diff --git a/tensorflow/compiler/tests/eager_test.py b/tensorflow/compiler/tests/eager_test.py
index e32f3d4b7f..63cee550fd 100644
--- a/tensorflow/compiler/tests/eager_test.py
+++ b/tensorflow/compiler/tests/eager_test.py
@@ -351,6 +351,38 @@ class EagerFunctionTest(xla_test.XLATestCase):
       var = f(v)
       self.assertEqual(2.0, var.numpy())
 
+  def testReturnResourceHandle(self):
+    with self.test_scope():
+      v = resource_variable_ops.ResourceVariable([[1.0, 2.0], [3.0, 4.0]])
+
+      def f(v):
+        return v.handle
+
+      f = function.defun(f)
+      handle = f(v)
+      self.assertAllEqual(v.numpy(),
+                          resource_variable_ops.read_variable_op(
+                              handle, dtypes.float32).numpy())
+
+  def testReturnMultipleResourceHandles(self):
+    with self.test_scope():
+      v1 = resource_variable_ops.ResourceVariable(1.25)
+      v2 = resource_variable_ops.ResourceVariable(2.0)
+
+      def f(v):
+        return v.handle, 3.0 * v, v2.handle, v + v2
+
+      f = function.defun(f)
+      v1_handle, v1_times_3, v2_handle, variable_sum = f(v1)
+      self.assertAllEqual(v1.numpy(),
+                          resource_variable_ops.read_variable_op(
+                              v1_handle, dtypes.float32).numpy())
+      self.assertEqual(3.75, v1_times_3.numpy())
+      self.assertAllEqual(v2.numpy(),
+                          resource_variable_ops.read_variable_op(
+                              v2_handle, dtypes.float32).numpy())
+      self.assertEqual(3.25, variable_sum.numpy())
+
   def testAllArgumentKinds(self):
     """Test a complex function that takes different argument kinds.
 
@@ -457,6 +489,72 @@ class EagerFunctionTest(xla_test.XLATestCase):
       y = two_x_plus_1(x)
       self.assertAllEqual([5, 7, 9], y.numpy())
 
+  def testNestedDefunWithVariable(self):
+    with self.test_scope():
+      v0 = resource_variable_ops.ResourceVariable(5.0)
+
+      @function.defun
+      def g(x):
+        x = v0 * x
+        return x
+
+      @function.defun
+      def f(x):
+        x = g(v0 * x)
+        return x
+
+      x = constant_op.constant(3.0)
+      y = f(x)
+
+    self.assertEqual(75, y.numpy())
+
+  def testNestedDefunInGradientTape(self):
+    with self.test_scope():
+      v0 = resource_variable_ops.ResourceVariable(5.0)
+
+      @function.defun
+      def g(x):
+        x = v0 * x
+        return x
+
+      @function.defun
+      def f(x):
+        x = g(v0 * x)
+        return x
+
+      x = constant_op.constant(3.0)
+      with backprop.GradientTape() as tape:
+        y = f(x)
+      dy = tape.gradient(y, v0)
+
+    self.assertEqual(75, y.numpy())
+    self.assertEqual(30, dy.numpy())
+
+  def testNestedDefunInGradientTapeDifferentVars(self):
+    with self.test_scope():
+      v0 = resource_variable_ops.ResourceVariable(5.0)
+      v1 = resource_variable_ops.ResourceVariable(3.0)
+
+      @function.defun
+      def g(x):
+        x = v1 * x
+        return x
+
+      @function.defun
+      def f(x):
+        x = g(v0 * x)
+        return x
+
+      x = constant_op.constant(3.0)
+      with backprop.GradientTape(persistent=True) as tape:
+        y = f(x)
+      dy_v0 = tape.gradient(y, v0)
+      dy_v1 = tape.gradient(y, v1)
+
+    self.assertEqual(45, y.numpy())
+    self.assertEqual(9, dy_v0.numpy())
+    self.assertEqual(15, dy_v1.numpy())
+
 
 class ExcessivePaddingTest(xla_test.XLATestCase):
   """Test that eager execution works with TPU flattened tensors.