Remove graph_callable.py and all references to it.

Our APIs for creating functions are tfe.defun() and tfe.make_template();
graph_callable is no longer needed.

Additionally, change GraphModeFunction to accept a FuncGraph instead of
a CapturingGraph --- graph_callable was preventing us from doing this.

This is part of ongoing hygiene work to help us think more clearly about the function APIs we'll want to provide. GraphModeFunction (which is renamed to GraphCallable here) previously had way too many attributes.

PiperOrigin-RevId: 209502276

2 files changed, 0 insertions, 52 deletions

diff --git a/tensorflow/contrib/eager/python/BUILD b/tensorflow/contrib/eager/python/BUILD
index f7933639a0..fa3f1bb7ad 100644
--- a/tensorflow/contrib/eager/python/BUILD
+++ b/tensorflow/contrib/eager/python/BUILD
@@ -104,7 +104,6 @@ cuda_py_test(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python/eager:graph_callable",
         "//tensorflow/python/eager:test",
         "//tensorflow/python:variables",
     ],
diff --git a/tensorflow/contrib/eager/python/saver_test.py b/tensorflow/contrib/eager/python/saver_test.py
index 90a3711475..91bc75213c 100644
--- a/tensorflow/contrib/eager/python/saver_test.py
+++ b/tensorflow/contrib/eager/python/saver_test.py
@@ -21,15 +21,11 @@ import os
 
 from tensorflow.contrib.eager.python import saver as _saver
 from tensorflow.python.eager import context
-from tensorflow.python.eager import graph_callable
 from tensorflow.python.eager import test
-from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import resource_variable_ops
-from tensorflow.python.ops import variable_scope
 from tensorflow.python.training import adam
 from tensorflow.python.training import gradient_descent
 from tensorflow.python.training import momentum
@@ -142,53 +138,6 @@ class SaverTest(test.TestCase):
         with _saver.restore_variables_on_create(ckpt_prefix):
           _ = model(resource_variable_ops.ResourceVariable(1.0, name='v2'))
 
-  def testSaveRestoreGraphCallable(self):
-    with ops.device(self._dev()):
-      @graph_callable.graph_callable(
-          [graph_callable.ShapeAndDtype(shape=(), dtype=dtypes.float32)])
-      def model(x):
-        v = variable_scope.get_variable(
-            'v', initializer=init_ops.zeros_initializer(), shape=())
-        return v + x
-
-      # Default 2 + 0 = 2
-      self.assertEqual(
-          2, model(array_ops.constant(2, dtype=dtypes.float32)).numpy())
-
-      # Save the variable value 0.
-      ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
-      _saver.Saver(model.variables).save(ckpt_prefix)
-
-      # update variable to 1, so that 2 + 1 = 3
-      model.variables[0].assign(1.)
-      self.assertEqual(
-          3, model(array_ops.constant(2, dtype=dtypes.float32)).numpy())
-
-      # load the variable value 0, so that 2 + 0 = 2
-      _saver.Saver(model.variables).restore(ckpt_prefix)
-      self.assertEqual(
-          2, model(array_ops.constant(2, dtype=dtypes.float32)).numpy())
-
-      # update checkpoint variable to 1 and memory value to 2.
-      model.variables[0].assign(1.)
-      _saver.Saver(model.variables).save(ckpt_prefix)
-      model.variables[0].assign(2.)
-      self.assertEqual(
-          4, model(array_ops.constant(2, dtype=dtypes.float32)).numpy())
-
-      # reset the graph and reload on create, so that 1 + 2 = 3
-      ops.reset_default_graph()
-      with _saver.restore_variables_on_create(ckpt_prefix):
-        @graph_callable.graph_callable(
-            [graph_callable.ShapeAndDtype(shape=(), dtype=dtypes.float32)])
-        def model2(x):
-          v = variable_scope.get_variable(
-              'v', initializer=init_ops.zeros_initializer(), shape=())
-          return v + x
-
-        self.assertEqual(
-            3, model2(array_ops.constant(2, dtype=dtypes.float32)).numpy())
-
 
 class GetOptimizerTests(test.TestCase):