eager: Add tests for function.py

PiperOrigin-RevId: 165821200

tensorflow/python/eager/BUILD

@@ -133,6 +133,21 @@ cuda_py_test(
     ],
 )
 
+cuda_py_test(
+    name = "function_test",
+    srcs = ["function_test.py"],
+    additional_deps = [
+        ":backprop",
+        ":context",
+        ":function",
+        ":tape",
+        ":tensor",
+        ":test",
+        "//tensorflow/python:clip_ops",
+        "//tensorflow/python:math_ops",
+    ],
+)
+
 py_library(
     name = "test",
     srcs = ["test.py"],

tensorflow/python/eager/function_test.py

@@ -0,0 +1,230 @@
+# 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.
+# ==============================================================================
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.eager import backprop
+from tensorflow.python.eager import context
+from tensorflow.python.eager import function
+from tensorflow.python.eager import tape
+from tensorflow.python.eager import tensor
+from tensorflow.python.eager import test
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors
+from tensorflow.python.framework import function as tf_function
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import clip_ops
+from tensorflow.python.ops import math_ops
+
+
+class FunctionTest(test.TestCase):
+
+  def testBasic(self):
+    matmul = function.defun(math_ops.matmul)
+    t = tensor.Tensor([[1.0, 2.0], [3.0, 4.0]])
+    sq = matmul(t, t, transpose_a=True)
+    sq2 = matmul(sq, t, transpose_a=True)
+    self.assertAllEqual(sq.numpy().reshape(-1), [10, 14, 14, 20])
+    self.assertAllEqual(sq2.numpy().reshape(-1), [52, 76, 74, 108])
+
+  def testBasicGraphMode(self):
+    matmul = function.defun(math_ops.matmul)
+
+    @function.defun
+    def sq(a):
+      return matmul(a, a)
+
+    t = tensor.Tensor([[1.0, 2.0], [3.0, 4.0]])
+    out = sq(t)
+    self.assertAllEqual(out.numpy(), math_ops.matmul(t, t).numpy())
+
+  def testTensorConversionWithDefun(self):
+
+    @function.defun
+    def f(x):
+      return math_ops.add(x, tensor.Tensor(3))
+
+    self.assertAllEqual(5, f(tensor.Tensor(2)).numpy())
+
+  def testTensorConversionCall(self):
+
+    @function.defun
+    def f(x):
+      return math_ops.add(x, tensor.Tensor(3))
+
+    @function.defun
+    def g(x):
+      return f(f(x))
+
+    self.assertAllEqual(8, g(tensor.Tensor(2)).numpy())
+
+  def testDefunCallBackprop(self):
+
+    @function.defun
+    def f(x):
+      return math_ops.add(x, x)
+
+    @function.defun
+    def g(x):
+      return backprop.gradients_function(f, [0])(x)[0]
+
+    self.assertAllEqual(2, g(tensor.Tensor(2)).numpy())
+
+  def testCallShape(self):
+
+    @function.defun
+    def f(x):
+      return x + 1
+
+    @function.defun
+    def g(x):
+      x = f(x)
+      self.assertEqual(x.shape.as_list(), [])
+      return None
+
+    g(tensor.Tensor(1.0))
+
+  def testGradientTensorConversionWithDefun(self):
+    three = tensor.Tensor(3.0)
+
+    @function.defun
+    def f(x):
+      return math_ops.add(x, three)
+
+    def g(x):
+      tape.watch(three)
+      return f(x)
+
+    g = backprop.implicit_grad(g)(tensor.Tensor(1.0))[0][1]
+    self.assertEqual(g.numpy(), 1.0)
+
+  def testGradient(self):
+    matmul = function.defun(math_ops.matmul)
+
+    def sq(x):
+      return matmul(x, x, transpose_a=True)
+
+    t = tensor.Tensor([[1.0, 2.0], [3.0, 4.0]])
+    grad_t, = backprop.gradients_function(sq, [0])(t)
+    self.assertAllEqual(grad_t.numpy(), [[6, 6], [14, 14]])
+
+  def testGradientInFunction(self):
+
+    @function.defun
+    def f(x):
+      return backprop.gradients_function(lambda y: y * y, [0])(x)[0]
+
+    self.assertEqual(f(tensor.Tensor(1.0)).numpy(), 2.0)
+
+  def testFunctionOnDevice(self):
+    if not context.context().num_gpus():
+      self.skipTest('No GPUs found')
+
+    x = tensor.Tensor([1.]).as_gpu_tensor()
+    f = function.defun(math_ops.add)
+    y = f(x, x).as_cpu_tensor()
+    self.assertAllEqual(y.numpy(), [2.])
+
+  def testFunctionHandlesInputsOnDifferentDevices(self):
+    if not context.context().num_gpus():
+      self.skipTest('No GPUs found')
+
+    # The Reshape op requires the shape tensor to be placed in host memory.
+    reshape = function.defun(array_ops.reshape)
+    value = tensor.Tensor([1., 2.]).as_gpu_tensor()
+    shape = tensor.Tensor([2, 1])
+    reshaped = reshape(value, shape).as_cpu_tensor()
+    self.assertAllEqual(reshaped.numpy(), [[1], [2]])
+
+  def testFunctionHandlesInputsPlacedOnTheWrongDeviceGracefully(self):
+    if not context.context().num_gpus():
+      self.skipTest('No GPUs found')
+
+    # The Reshape op requires the shape tensor to be placed in host memory.
+    reshape = function.defun(array_ops.reshape)
+    value = tensor.Tensor([1., 2.]).as_gpu_tensor()
+    shape = tensor.Tensor([2, 1]).as_gpu_tensor()
+    with self.assertRaises(errors.InvalidArgumentError):
+      reshape(value, shape)
+
+  def testDifferentiableFunctionNoneOutputs(self):
+
+    @function.defun
+    def my_function(x):
+      return x, None
+
+    def wrapper(x):
+      return my_function(x)[0]
+
+    g = backprop.gradients_function(wrapper, [0])(tensor.Tensor(0.0))
+    self.assertAllEqual(g[0].numpy(), 1.)
+
+  def testNoneOutput(self):
+
+    @function.defun
+    def my_function(_):
+      return None
+
+    self.assertAllEqual(my_function(1), None)
+
+  def testNestedFunctions(self):
+    # TensorFlow function (which is what would be used in TensorFlow graph
+    # construction).
+    @tf_function.Defun(dtypes.int32, dtypes.int32)
+    def add(a, b):
+      return math_ops.add(a, b)
+
+    @function.defun
+    def add_one(x):
+      return add(x, 1)
+
+    self.assertAllEqual(3, add_one(tensor.Tensor(2)).numpy())
+
+  def testSequenceInputs(self):
+    clip_by_global_norm = function.defun(clip_ops.clip_by_global_norm)
+    t_list = [tensor.Tensor(1.0), tensor.Tensor(2.0)]
+    clipped_list, global_norm = clip_by_global_norm(t_list, tensor.Tensor(.2))
+    for t in clipped_list:
+      self.assertTrue(isinstance(t, tensor.Tensor))
+    self.assertTrue(isinstance(global_norm, tensor.Tensor))
+
+  def testNestedSequenceInputs(self):
+
+    def my_op(inputs):
+      a, b, c = inputs
+      e, f = b
+      g, h = e
+      return [a + a, [tuple([f + f, g + g]), h + h], c + c], a + f + g + h + c
+
+    my_eager_op = function.defun(my_op)
+    ret = my_eager_op([
+        tensor.Tensor(1), [(tensor.Tensor(2), tensor.Tensor(3)),
+                           tensor.Tensor(4)],
+        tensor.Tensor(5)
+    ])
+    self.assertEqual(len(ret), 2)
+    self.assertEqual(ret[0][0].numpy(), 2)
+    self.assertEqual(ret[0][1][0][0].numpy(), 8)
+    self.assertEqual(ret[0][1][0][1].numpy(), 4)
+    self.assertTrue(isinstance(ret[0][1][0], tuple))
+    self.assertEqual(ret[0][1][1].numpy(), 6)
+    self.assertEqual(ret[0][2].numpy(), 10)
+    self.assertEqual(ret[1].numpy(), 15)
+
+
+if __name__ == '__main__':
+  test.main()