STT-tensorflow/tensorflow/python/util/dispatch_test.py

# Copyright 2018 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.
# ==============================================================================
"""Tests for operator dispatch."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_math_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops.linalg import linear_operator_diag
from tensorflow.python.ops.proto_ops import decode_proto
from tensorflow.python.platform import googletest
from tensorflow.python.platform import test
from tensorflow.python.platform import tf_logging
from tensorflow.python.util import deprecation
from tensorflow.python.util import dispatch
from tensorflow.python.util import nest
from tensorflow.python.util.tf_export import get_canonical_name_for_symbol
from tensorflow.python.util.tf_export import tf_export


class CustomTensor(object):
  """A fake composite tensor class, for testing type-based dispatching."""

  def __init__(self, tensor, score):
    self.tensor = ops.convert_to_tensor(tensor)
    self.score = score


@tf_export("test_op")
@dispatch.add_dispatch_support
def test_op(x, y, z):
  """A fake op for testing dispatch of Python ops."""
  return x + (2 * y) + (3 * z)


class TensorTracer(object):
  """An object used to trace TensorFlow graphs.

  This is an example class that is used to test global op dispatchers.  The
  global op dispatcher for TensorTracers is defined below.
  """

  def __init__(self, name, args=None, kwargs=None):
    self.name = name
    self.args = args
    self.kwargs = kwargs
    self.shape = array_ops.ones(shape=(4, 4)).shape
    self.dtype = dtypes.float32

  def __repr__(self):
    if self.args is None and self.kwargs is None:
      return self.name
    else:
      args = [str(x) for x in self.args]
      args += sorted(
          ["{}={}".format(name, x) for (name, x) in self.kwargs.items()])
      return "{}({})".format(self.name, ", ".join(args))

  @property
  def is_tensor_like(self):
    return True

  @classmethod
  def _overload_all_operators(cls):  # pylint: disable=invalid-name
    """Register overloads for all operators."""
    for operator in ops.Tensor.OVERLOADABLE_OPERATORS:
      cls._overload_operator(operator)

  @classmethod
  def _overload_operator(cls, operator):  # pylint: disable=invalid-name
    """Overload an operator with the same overloading as `ops.Tensor`."""
    tensor_oper = getattr(ops.Tensor, operator)

    # Compatibility with Python 2:
    # Python 2 unbound methods have type checks for the first arg,
    # so we need to extract the underlying function
    tensor_oper = getattr(tensor_oper, "__func__", tensor_oper)
    setattr(cls, operator, tensor_oper)

TensorTracer._overload_all_operators()  # pylint: disable=protected-access


class TensorTracerOpDispatcher(dispatch.GlobalOpDispatcher):
  """Global op dispatcher for TensorTracer."""

  def _flatten_with_slice_flattening(self, x):
    flat = []
    for val in nest.flatten(x):
      if isinstance(val, slice):
        flat.extend((val.start, val.stop, val.step))
      else:
        flat.append(val)
    return flat

  def handle(self, op, args, kwargs):
    # Dispatcher only applies if at least one arg is a TensorTracer.
    if not (any(self.is_tensor_tracer_arg(x) for x in args) or
            any(self.is_tensor_tracer_arg(x) for x in kwargs.values())):
      return self.NOT_SUPPORTED

    symbol_name = get_canonical_name_for_symbol(op)
    return TensorTracer(symbol_name, args, kwargs)

  def is_tensor_tracer_arg(self, value):
    return any(isinstance(x, TensorTracer) for x in
               self._flatten_with_slice_flattening(value))


@test_util.run_all_in_graph_and_eager_modes
class DispatchTest(test_util.TensorFlowTestCase):

  def testAddDispatchForTypes_With_CppOp(self):
    original_handlers = gen_math_ops.add._tf_dispatchers[:]

    # Override the behavior of gen_math_ops.add.
    @dispatch.dispatch_for_types(gen_math_ops.add, CustomTensor)
    def custom_add(x, y, name=None):  # pylint: disable=unused-variable
      return CustomTensor(gen_math_ops.add(x.tensor, y.tensor, name),
                          (x.score+y.score) / 2.0)
    self.assertEqual(len(math_ops.add._tf_dispatchers),
                     len(original_handlers) + 1)

    # Test that we see the overridden behavior when using CustomTensors.
    x = CustomTensor([1, 2, 3], 2.0)
    y = CustomTensor([7, 8, 2], 0.0)
    x_plus_y = gen_math_ops.add(x, y)
    self.assertAllEqual(self.evaluate(x_plus_y.tensor), [8, 10, 5])
    self.assertNear(x_plus_y.score, 1.0, 0.001)

    # Test that we still get the right behavior when using normal Tensors.
    a = [1, 2, 3]
    b = [4, 5, 6]
    a_plus_b = gen_math_ops.add(a, b)
    self.assertAllEqual(a_plus_b, [5, 7, 9])

    # Test that we still get a TypeError or ValueError if we pass some
    # type that's not supported by any dispatcher.
    with self.assertRaises((TypeError, ValueError)):
      gen_math_ops.add(a, None)

    # Clean up
    gen_math_ops.add._tf_dispatchers = original_handlers

  def testAddDispatchForTypes_With_PythonOp(self):
    original_handlers = test_op._tf_dispatchers[:]

    @dispatch.dispatch_for_types(test_op, CustomTensor)
    def override_for_test_op(x, y, z):  # pylint: disable=unused-variable
      return CustomTensor(test_op(x.tensor, y.tensor, z.tensor),
                          (x.score + y.score + z.score) / 3.0)

    x = CustomTensor([1, 2, 3], 0.2)
    y = CustomTensor([7, 8, 2], 0.4)
    z = CustomTensor([0, 1, 2], 0.6)

    result = test_op(x, y, z)
    self.assertAllEqual(self.evaluate(result.tensor), [15, 21, 13])
    self.assertNear(result.score, 0.4, 0.001)

    # Clean up
    test_op._tf_dispatchers = original_handlers

  def testDispatchForTypes_SignatureMismatch(self):
    with self.assertRaisesRegex(
        AssertionError, "The decorated function's "
        "signature must exactly match.*"):

      @dispatch.dispatch_for_types(test_op, CustomTensor)
      def override_for_test_op(a, b, c):  # pylint: disable=unused-variable
        return CustomTensor(test_op(a.tensor, b.tensor, c.tensor),
                            (a.score + b.score + c.score) / 3.0)

  def testDispatchForTypes_OpDoesNotSupportDispatch(self):
    def some_op(x, y):
      return x + y

    with self.assertRaisesRegex(AssertionError, "Dispatching not enabled for"):

      @dispatch.dispatch_for_types(some_op, CustomTensor)
      def override_for_some_op(x, y):  # pylint: disable=unused-variable
        return x if x.score > 0 else y

  @test.mock.patch.object(tf_logging, "warning", autospec=True)
  def testInteractionWithDeprecationWarning(self, mock_warning):
    @deprecation.deprecated(date=None, instructions="Instructions")
    @dispatch.add_dispatch_support
    def some_op(x):
      return x

    some_op(5)

    message = mock_warning.call_args[0][0] % mock_warning.call_args[0][1:]
    self.assertRegex(
        message, r".*some_op \(from __main__\) is deprecated and will be "
        "removed in a future version.*")

  def testGlobalDispatcher(self):
    original_global_dispatchers = dispatch._GLOBAL_DISPATCHERS
    try:
      TensorTracerOpDispatcher().register()

      x = TensorTracer("x")
      y = TensorTracer("y")
      trace = math_ops.reduce_sum(math_ops.add(math_ops.abs(x), y), axis=3)
      self.assertEqual(
          str(trace),
          "math.reduce_sum(math.add(name=None, x=math.abs(x), y=y), axis=3)")

      proto_val = TensorTracer("proto")
      trace = decode_proto(proto_val, "message_type", ["field"], ["float32"])
      self.assertIn("io.decode_proto(bytes=proto,", str(trace))

    finally:
      # Clean up.
      dispatch._GLOBAL_DISPATCHERS = original_global_dispatchers

  def testGlobalDispatcherConvertToTensor(self):
    original_global_dispatchers = dispatch._GLOBAL_DISPATCHERS
    try:
      TensorTracerOpDispatcher().register()

      x = TensorTracer("x")
      y = TensorTracer("y")
      trace = math_ops.add(math_ops.abs(
          ops.convert_to_tensor_v2_with_dispatch(x)), y)
      self.assertEqual(
          str(trace),
          "math.add(name=None, x=math.abs(convert_to_tensor(x)), y=y)")

    finally:
      # Clean up.
      dispatch._GLOBAL_DISPATCHERS = original_global_dispatchers

  def testGlobalDispatcherGetItem(self):
    original_global_dispatchers = dispatch._GLOBAL_DISPATCHERS
    try:
      TensorTracerOpDispatcher().register()

      x = TensorTracer("x")
      trace = x[0]
      self.assertEqual(
          str(trace),
          "__operators__.getitem(x, 0)")

      x = TensorTracer("x")
      y = TensorTracer("y")
      trace = x[y]
      self.assertEqual(
          str(trace),
          "__operators__.getitem(x, y)")

      x = TensorTracer("x")
      y = TensorTracer("y")
      trace = x[:y]  # pylint: disable=invalid-slice-index
      self.assertEqual(
          str(trace),
          "__operators__.getitem(x, slice(None, y, None))")

      x = array_ops.ones(shape=(3, 3))
      y = TensorTracer("y")
      trace = x[y]
      self.assertEqual(
          str(trace),
          "__operators__.getitem(%s, y)" % x)

      trace = x[:y]  # pylint: disable=invalid-slice-index
      self.assertEqual(
          str(trace),
          "__operators__.getitem(%s, slice(None, y, None))" % x)

    finally:
      # Clean up.
      dispatch._GLOBAL_DISPATCHERS = original_global_dispatchers

  def testGlobalDispatcherLinearOperators(self):
    original_global_dispatchers = dispatch._GLOBAL_DISPATCHERS
    try:
      TensorTracerOpDispatcher().register()

      x = TensorTracer("x")

      # To grab the eigenvalues the diag operator just calls convert_to_tensor
      # (twice) in this case.
      trace = linear_operator_diag.LinearOperatorDiag(x).eigvals()
      self.assertEqual(
          str(trace),
          "convert_to_tensor(convert_to_tensor(x, dtype=None, dtype_hint=None, "
          "name=diag))")

      # The diagonal tensor addition gets traced even though the linear_operator
      # API only uses dispatchable ops instead of directly exposing dispatching.
      trace = linear_operator_diag.LinearOperatorDiag(x).add_to_tensor(x)
      self.assertIn(
          "linalg.set_diag(convert_to_tensor(x, name=x), __operators__.add("
          "convert_to_tensor(x, dtype=None, dtype_hint=None, name=diag), "
          "linalg.diag_part(convert_to_tensor(x, name=x)), "
          "name=",
          str(trace))

      # The dispatch-supporting ops the non-singular check calls out to
      # get traced.
      trace = linear_operator_diag.LinearOperatorDiag(x).assert_non_singular()
      self.assertIn("debugging.assert_less", str(trace))
      self.assertIn(
          "message=Singular operator:  Diagonal contained zero values.",
          str(trace))

    finally:
      # Clean up.
      dispatch._GLOBAL_DISPATCHERS = original_global_dispatchers

if __name__ == "__main__":
  googletest.main()