| # 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 |
| |
| import collections |
| import typing |
| import numpy as np |
| |
| from tensorflow.python.eager import context |
| from tensorflow.python.framework import constant_op |
| from tensorflow.python.framework import dtypes |
| from tensorflow.python.framework import extension_type |
| 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 bitwise_ops |
| from tensorflow.python.ops import gen_math_ops |
| from tensorflow.python.ops import math_ops |
| from tensorflow.python.ops import variables |
| 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.types import core as core_tf_types |
| 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.atan2._tf_fallback_dispatchers[:] |
| |
| # Override the behavior of gen_math_ops.atan2 and make it look like add. |
| @dispatch.dispatch_for_types(gen_math_ops.atan2, CustomTensor) |
| def custom_atan2(y, x, name=None): # pylint: disable=unused-variable |
| return CustomTensor( |
| gen_math_ops.add(y.tensor, x.tensor, name), (x.score + y.score) / 2.0) |
| |
| self.assertEqual( |
| len(math_ops.atan2._tf_fallback_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.atan2(y, x) |
| 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 = [7., 8., 2.] |
| a_plus_b = gen_math_ops.atan2(a, b) |
| self.assertAllClose(a_plus_b, [0.14189707, 0.24497867, 0.98279375]) |
| |
| # 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.atan2(a, None) |
| |
| # Clean up |
| gen_math_ops.atan2._tf_fallback_dispatchers = original_handlers |
| |
| def testAddDispatchForTypes_With_PythonOp(self): |
| original_handlers = test_op._tf_fallback_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_fallback_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(math.abs(x), 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(math.abs(convert_to_tensor(x)), 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 |
| |
| |
| class MaskedTensor(extension_type.ExtensionType): |
| """Simple ExtensionType for testing v2 dispatch.""" |
| values: ops.Tensor |
| mask: ops.Tensor |
| |
| |
| class SillyTensor(extension_type.ExtensionType): |
| """Simple ExtensionType for testing v2 dispatch.""" |
| value: ops.Tensor |
| how_silly: float |
| |
| |
| @test_util.run_all_in_graph_and_eager_modes |
| class DispatchV2Test(test_util.TensorFlowTestCase): |
| |
| def testDispatchForOneSignature(self): |
| |
| @dispatch.dispatch_for_api(math_ops.add, { |
| "x": MaskedTensor, |
| "y": MaskedTensor |
| }) |
| def masked_add(x, y, name=None): |
| with ops.name_scope(name): |
| return MaskedTensor(x.values + y.values, x.mask & y.mask) |
| |
| try: |
| x = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1]) |
| y = MaskedTensor([1, 1, 1, 1, 1], [1, 1, 0, 1, 0]) |
| z = math_ops.add(x, y) |
| self.assertAllEqual(z.values, x.values + y.values) |
| self.assertAllEqual(z.mask, x.mask & y.mask) |
| |
| finally: |
| # Clean up dispatch table. |
| dispatch.unregister_dispatch_target(math_ops.add, masked_add) |
| |
| def testDispatchSignatureWithUnspecifiedParameter(self): |
| |
| @dispatch.dispatch_for_api(math_ops.add, {"x": MaskedTensor}) |
| def masked_add(x, y): |
| if y is None: |
| return x |
| y_values = y.values if isinstance(y, MaskedTensor) else y |
| y_mask = y.mask if isinstance(y, MaskedTensor) else True |
| return MaskedTensor(x.values + y_values, x.mask & y_mask) |
| |
| try: |
| a = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1]) |
| b = constant_op.constant([10, 20, 30, 40, 50]) |
| c = [10, 20, 30, 40, 50] |
| d = 50 |
| e = None |
| # As long as `x` is a MaskedTensor, the dispatcher will be called |
| # (regardless of the type for `y`): |
| self.assertAllEqual(math_ops.add(a, b).values, [11, 22, 33, 44, 55]) |
| self.assertAllEqual(math_ops.add(a, c).values, [11, 22, 33, 44, 55]) |
| self.assertAllEqual(math_ops.add(a, d).values, [51, 52, 53, 54, 55]) |
| self.assertAllEqual(math_ops.add(a, e).values, [1, 2, 3, 4, 5]) |
| |
| finally: |
| # Clean up dispatch table. |
| dispatch.unregister_dispatch_target(math_ops.add, masked_add) |
| |
| def testDispatchForMultipleSignatures(self): |
| |
| @dispatch.dispatch_for_api(math_ops.add, {"x": MaskedTensor}, |
| {"y": MaskedTensor}) |
| def masked_add(x, y, name=None): |
| with ops.name_scope(name): |
| x_values = x.values if isinstance(x, MaskedTensor) else x |
| x_mask = x.mask if isinstance(x, MaskedTensor) else True |
| y_values = y.values if isinstance(y, MaskedTensor) else y |
| y_mask = y.mask if isinstance(y, MaskedTensor) else True |
| return MaskedTensor(x_values + y_values, x_mask & y_mask) |
| |
| try: |
| x = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1]) |
| y = constant_op.constant([10, 20, 30, 40, 50]) |
| z = math_ops.add(x, y) |
| self.assertAllEqual(z.values, x.values + y) |
| self.assertAllEqual(z.mask, x.mask) |
| |
| finally: |
| # Clean up dispatch table. |
| dispatch.unregister_dispatch_target(math_ops.add, masked_add) |
| |
| def testDispatchForList(self): |
| |
| @dispatch.dispatch_for_api(array_ops.concat, |
| {"values": typing.List[MaskedTensor]}) |
| def masked_concat(values, axis, name=None): |
| with ops.name_scope(name): |
| return MaskedTensor( |
| array_ops.concat([v.values for v in values], axis), |
| array_ops.concat([v.mask for v in values], axis)) |
| |
| try: |
| x = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1]) |
| y = MaskedTensor([1, 1, 1], [1, 1, 0]) |
| z = array_ops.concat([x, y], axis=0) |
| self.assertAllEqual(z.values, array_ops.concat([x.values, y.values], 0)) |
| self.assertAllEqual(z.mask, array_ops.concat([x.mask, y.mask], 0)) |
| |
| finally: |
| # Clean up dispatch table. |
| dispatch.unregister_dispatch_target(array_ops.concat, masked_concat) |
| |
| def testDispatchForUnion(self): |
| MaybeMasked = typing.Union[MaskedTensor, ops.Tensor] |
| |
| @dispatch.dispatch_for_api(math_ops.add, { |
| "x": MaybeMasked, |
| "y": MaybeMasked |
| }) |
| def masked_add(x, y, name=None): |
| with ops.name_scope(name): |
| x_values = x.values if isinstance(x, MaskedTensor) else x |
| x_mask = x.mask if isinstance(x, MaskedTensor) else True |
| y_values = y.values if isinstance(y, MaskedTensor) else y |
| y_mask = y.mask if isinstance(y, MaskedTensor) else True |
| return MaskedTensor(x_values + y_values, x_mask & y_mask) |
| |
| try: |
| x = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1]) |
| y = constant_op.constant([10, 20, 30, 40, 50]) |
| z = math_ops.add(x, y) |
| self.assertAllEqual(z.values, x.values + y) |
| self.assertAllEqual(z.mask, x.mask) |
| |
| finally: |
| # Clean up dispatch table. |
| dispatch.unregister_dispatch_target(math_ops.add, masked_add) |
| |
| def testDispatchForTensorLike(self): |
| MaskedOrTensorLike = typing.Union[MaskedTensor, core_tf_types.TensorLike] |
| |
| @dispatch.dispatch_for_api(math_ops.add) |
| def masked_add(x: MaskedOrTensorLike, y: MaskedOrTensorLike, name=None): |
| with ops.name_scope(name): |
| x_values = x.values if isinstance(x, MaskedTensor) else x |
| x_mask = x.mask if isinstance(x, MaskedTensor) else True |
| y_values = y.values if isinstance(y, MaskedTensor) else y |
| y_mask = y.mask if isinstance(y, MaskedTensor) else True |
| return MaskedTensor(x_values + y_values, x_mask & y_mask) |
| |
| try: |
| x = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1]) |
| y1 = [10, 20, 30, 40, 50] |
| y2 = np.array([10, 20, 30, 40, 50]) |
| y3 = constant_op.constant([10, 20, 30, 40, 50]) |
| y4 = variables.Variable([5, 4, 3, 2, 1]) |
| if not context.executing_eagerly(): |
| self.evaluate(variables.global_variables_initializer()) |
| for y in [y1, y2, y3, y4]: |
| z = math_ops.add(x, y) |
| self.assertAllEqual(z.values, x.values + y) |
| self.assertAllEqual(z.mask, x.mask) |
| |
| finally: |
| # Clean up dispatch table. |
| dispatch.unregister_dispatch_target(math_ops.add, masked_add) |
| |
| def testDispatchForOptional(self): |
| # Note: typing.Optional[X] == typing.Union[X, NoneType]. |
| |
| @dispatch.dispatch_for_api( |
| array_ops.where_v2, { |
| "condition": MaskedTensor, |
| "x": typing.Optional[MaskedTensor], |
| "y": typing.Optional[MaskedTensor] |
| }) |
| def masked_where(condition, x=None, y=None, name=None): |
| del condition, x, y, name |
| return "stub" |
| |
| try: |
| x = MaskedTensor([True, False, True, True, True], [1, 0, 1, 1, 1]) |
| self.assertEqual(array_ops.where_v2(x), "stub") |
| self.assertEqual(array_ops.where_v2(x, x, x), "stub") |
| |
| finally: |
| # Clean up dispatch table. |
| dispatch.unregister_dispatch_target(array_ops.where_v2, masked_where) |
| |
| def testDispatchForSignatureFromAnnotations(self): |
| |
| @dispatch.dispatch_for_api(math_ops.add) |
| def masked_add(x: MaskedTensor, y: MaskedTensor, name=None): |
| with ops.name_scope(name): |
| return MaskedTensor(x.values + y.values, x.mask & y.mask) |
| |
| try: |
| x = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1]) |
| y = MaskedTensor([1, 1, 1, 1, 1], [1, 1, 0, 1, 0]) |
| z = math_ops.add(x, y) |
| self.assertAllEqual(z.values, x.values + y.values) |
| self.assertAllEqual(z.mask, x.mask & y.mask) |
| |
| finally: |
| # Clean up dispatch table. |
| dispatch.unregister_dispatch_target(math_ops.add, masked_add) |
| |
| def testDispatchForPositionalSignature(self): |
| |
| @dispatch.dispatch_for_api(math_ops.add, {0: MaskedTensor, 1: MaskedTensor}) |
| def masked_add(x, y, name=None): |
| with ops.name_scope(name): |
| return MaskedTensor(x.values + y.values, x.mask & y.mask) |
| |
| try: |
| x = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1]) |
| y = MaskedTensor([1, 1, 1, 1, 1], [1, 1, 0, 1, 0]) |
| z = math_ops.add(x, y) |
| self.assertAllEqual(z.values, x.values + y.values) |
| self.assertAllEqual(z.mask, x.mask & y.mask) |
| |
| finally: |
| # Clean up dispatch table. |
| dispatch.unregister_dispatch_target(math_ops.add, masked_add) |
| |
| def testDispatchWithVarargs(self): |
| |
| @dispatch.dispatch_for_api(math_ops.add, { |
| "x": MaskedTensor, |
| "y": MaskedTensor |
| }) |
| def masked_add(*args, **kwargs): |
| self.assertAllEqual(args[0].values, x.values) |
| self.assertAllEqual(args[1].values, y.values) |
| self.assertEmpty(kwargs) |
| return "stub" |
| |
| try: |
| x = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1]) |
| y = MaskedTensor([1, 1, 1, 1, 1], [1, 1, 0, 1, 0]) |
| self.assertEqual(math_ops.add(x, y), "stub") |
| |
| finally: |
| # Clean up dispatch table. |
| dispatch.unregister_dispatch_target(math_ops.add, masked_add) |
| |
| def testDispatchWithKwargs(self): |
| |
| @dispatch.dispatch_for_api(math_ops.add, { |
| "x": MaskedTensor, |
| "y": MaskedTensor |
| }) |
| def masked_add(*args, **kwargs): |
| self.assertAllEqual(kwargs["x"].values, x.values) |
| self.assertAllEqual(kwargs["y"].values, y.values) |
| self.assertEmpty(args) |
| return "stub" |
| |
| try: |
| x = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1]) |
| y = MaskedTensor([1, 1, 1, 1, 1], [1, 1, 0, 1, 0]) |
| self.assertEqual(math_ops.add(x=x, y=y), "stub") |
| |
| finally: |
| # Clean up dispatch table. |
| dispatch.unregister_dispatch_target(math_ops.add, masked_add) |
| |
| def testDispatchErrorForBadAPI(self): |
| |
| def api_without_dispatch_support(x): |
| return x + 1 |
| |
| with self.assertRaisesRegex(ValueError, ".* does not support dispatch."): |
| |
| @dispatch.dispatch_for_api(api_without_dispatch_support, |
| {"x": MaskedTensor}) |
| def my_version(x): # pylint: disable=unused-variable |
| del x |
| |
| def testDispatchErrorForNoSignature(self): |
| with self.assertRaisesRegex(ValueError, |
| "must be called with at least one signature"): |
| |
| @dispatch.dispatch_for_api(math_ops.add) |
| def my_add(x, y, name=None): # pylint: disable=unused-variable |
| del x, y, name |
| |
| def testDispatchErrorSignatureMismatchParamName(self): |
| with self.assertRaisesRegex( |
| ValueError, r"Dispatch function's signature \(x, why, name=None\) does " |
| r"not match API's signature \(x, y, name=None\)."): |
| |
| @dispatch.dispatch_for_api(math_ops.add, {"x": MaskedTensor}) |
| def my_add(x, why, name=None): # pylint: disable=unused-variable |
| del x, why, name |
| |
| def testDispatchErrorSignatureMismatchExtraParam(self): |
| with self.assertRaisesRegex( |
| ValueError, r"Dispatch function's signature \(x, y, name=None, extra_" |
| r"arg=None\) does not match API's signature \(x, y, name=None\)."): |
| |
| @dispatch.dispatch_for_api(math_ops.add, {"x": MaskedTensor}) |
| def my_add(x, y, name=None, extra_arg=None): # pylint: disable=unused-variable |
| del x, y, name, extra_arg |
| |
| def testDispatchErrorForUnsupportedTypeAnnotation(self): |
| with self.assertRaisesRegex( |
| ValueError, |
| "Type annotation .* is not currently supported by dispatch."): |
| |
| @dispatch.dispatch_for_api(math_ops.add, |
| {"x": typing.Tuple[MaskedTensor]}) |
| def my_add(x, y, name=None): # pylint: disable=unused-variable |
| del x, y, name |
| |
| def testDispatchErrorForUnknownParameter(self): |
| with self.assertRaisesRegex( |
| ValueError, "signature includes annotation for unknown parameter 'z'."): |
| |
| @dispatch.dispatch_for_api(math_ops.add, {"z": MaskedTensor}) |
| def my_add(x, y, name=None): # pylint: disable=unused-variable |
| del x, y, name |
| |
| def testDispatchErrorUnsupportedKeywordOnlyAnnotation(self): |
| |
| @dispatch.add_dispatch_support |
| def foo(x, *, y): |
| return x + y |
| |
| with self.assertRaisesRegex( |
| ValueError, "Dispatch currently only supports type " |
| "annotations for positional parameters"): |
| |
| @dispatch.dispatch_for_api(foo, {"y": MaskedTensor}) |
| def masked_foo(x, *, y): # pylint: disable=unused-variable |
| del x, y |
| |
| def testDispatchErrorBadSignatureType(self): |
| with self.assertRaisesRegex( |
| TypeError, "signatures must be dictionaries mapping parameter " |
| "names to type annotations"): |
| |
| @dispatch.dispatch_for_api(math_ops.add, [MaskedTensor]) |
| def my_add(x, y, name=None): # pylint: disable=unused-variable |
| del x, y, name |
| |
| with self.assertRaisesRegex( |
| TypeError, "signatures must be dictionaries mapping parameter " |
| "names to type annotations"): |
| |
| @dispatch.dispatch_for_api(math_ops.multiply, {None: MaskedTensor}) |
| def my_multiply(x, y, name=None): # pylint: disable=unused-variable |
| del x, y, name |
| |
| def testDispatchErrorNotCallable(self): |
| with self.assertRaisesRegex(TypeError, |
| "Expected dispatch_target to be callable"): |
| dispatch.dispatch_for_api(math_ops.abs, {0: MaskedTensor})("not_callable") |
| |
| def testRegisterDispatchableType(self): |
| Car = collections.namedtuple("Car", ["size", "speed"]) |
| dispatch.register_dispatchable_type(Car) |
| |
| @dispatch.dispatch_for_api(math_ops.add, {"x": Car, "y": Car}) |
| def add_car(x, y, name=None): |
| with ops.name_scope(name): |
| return Car(x.size + y.size, x.speed + y.speed) |
| |
| try: |
| x = Car(constant_op.constant(1), constant_op.constant(3)) |
| y = Car(constant_op.constant(10), constant_op.constant(20)) |
| z = math_ops.add(x, y) |
| self.assertAllEqual(z.size, 11) |
| self.assertAllEqual(z.speed, 23) |
| |
| finally: |
| # Clean up dispatch table. |
| dispatch.unregister_dispatch_target(math_ops.add, add_car) |
| |
| def testTypeCheckersAreCached(self): |
| checker1 = dispatch.make_type_checker(int) |
| checker2 = dispatch.make_type_checker(int) |
| self.assertIs(checker1, checker2) |
| |
| def testDispatchTargetWithNoNameArgument(self): |
| |
| @dispatch.dispatch_for_api(math_ops.add, { |
| "x": MaskedTensor, |
| "y": MaskedTensor |
| }) |
| def masked_add(x, y): |
| return MaskedTensor(x.values + y.values, x.mask & y.mask) |
| |
| try: |
| x = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1]) |
| y = MaskedTensor([1, 1, 1, 1, 1], [1, 1, 0, 1, 0]) |
| |
| # pass name w/ keyword arg |
| a = math_ops.add(x, y, name="MyAdd") |
| if not context.executing_eagerly(): # names not defined in eager mode. |
| self.assertRegex(a.values.name, r"^MyAdd/add.*") |
| self.assertRegex(a.mask.name, r"^MyAdd/and.*") |
| |
| # pass name w/ positional arg |
| b = math_ops.add(x, y, "B") |
| if not context.executing_eagerly(): # names not defined in eager mode. |
| self.assertRegex(b.values.name, r"^B/add.*") |
| self.assertRegex(b.mask.name, r"^B/and.*") |
| |
| # default name value |
| c = math_ops.add(x, y) |
| if not context.executing_eagerly(): # names not defined in eager mode. |
| self.assertRegex(c.values.name, r"^add.*") |
| self.assertRegex(c.mask.name, r"^and.*") |
| |
| finally: |
| # Clean up dispatch table. |
| dispatch.unregister_dispatch_target(math_ops.add, masked_add) |
| |
| def testDispatchApiWithNoNameArg(self): |
| # Note: The "tensor_equals" API has no "name" argument. |
| signature = {"self": MaskedTensor, "other": MaskedTensor} |
| |
| @dispatch.dispatch_for_api(math_ops.tensor_equals, signature) |
| def masked_tensor_equals(self, other): |
| del self, other |
| |
| dispatch.unregister_dispatch_target(math_ops.tensor_equals, |
| masked_tensor_equals) # clean up. |
| |
| with self.assertRaisesRegexp( |
| ValueError, r"Dispatch function's signature \(self, other, name=None\) " |
| r"does not match API's signature \(self, other\)\."): |
| |
| @dispatch.dispatch_for_api(math_ops.tensor_equals, signature) |
| def masked_tensor_equals_2(self, other, name=None): |
| del self, other, name |
| |
| del masked_tensor_equals_2 # avoid pylint unused variable warning. |
| |
| def testDispatchWithIterableParams(self): |
| # The add_n API supports having `inputs` be an iterable (and not just |
| # a sequence). |
| @dispatch.dispatch_for_api(math_ops.add_n, |
| {"inputs": typing.List[MaskedTensor]}) |
| def masked_add_n(inputs): |
| masks = array_ops.stack([x.mask for x in inputs]) |
| return MaskedTensor( |
| math_ops.add_n([x.values for x in inputs]), |
| math_ops.reduce_all(masks, axis=0)) |
| |
| try: |
| generator = (MaskedTensor([i], [True]) for i in range(5)) |
| y = math_ops.add_n(generator) |
| self.assertAllEqual(y.values, [0 + 1 + 2 + 3 + 4]) |
| self.assertAllEqual(y.mask, [True]) |
| |
| finally: |
| # Clean up dispatch table. |
| dispatch.unregister_dispatch_target(math_ops.add_n, masked_add_n) |
| |
| def testBadIterableParametersError(self): |
| fn = lambda x: [t + 1 for t in x] |
| with self.assertRaisesRegex( |
| TypeError, "iterable_parameters should be a list or tuple of string"): |
| dispatch.add_dispatch_support(iterable_parameters="x")(fn) |
| |
| def testUnregisterDispatchTargetBadTargetError(self): |
| fn = lambda x: x + 1 |
| with self.assertRaisesRegex(ValueError, ".* does not support dispatch"): |
| dispatch.unregister_dispatch_target(fn, fn) |
| |
| def testUnregisterDispatchTargetBadDispatchTargetError(self): |
| fn = lambda x: x + 1 |
| with self.assertRaisesRegex(ValueError, ".* was not registered for .*"): |
| dispatch.unregister_dispatch_target(math_ops.add, fn) |
| |
| def testAddDuplicateApiDisptacherError(self): |
| some_op = lambda x: x |
| some_op = dispatch.add_type_based_api_dispatcher(some_op) |
| with self.assertRaisesRegex( |
| ValueError, ".* already has a type-based API dispatcher."): |
| some_op = dispatch.add_type_based_api_dispatcher(some_op) |
| |
| def testGetApisWithTypeBasedDispatch(self): |
| dispatch_apis = dispatch.apis_with_type_based_dispatch() |
| self.assertIn(math_ops.add, dispatch_apis) |
| self.assertIn(array_ops.concat, dispatch_apis) |
| |
| def testTypeBasedDispatchTargetsFor(self): |
| MaskedTensorList = typing.List[typing.Union[MaskedTensor, ops.Tensor]] |
| try: |
| |
| @dispatch.dispatch_for_api(math_ops.add) |
| def masked_add(x: MaskedTensor, y: MaskedTensor): |
| del x, y |
| |
| @dispatch.dispatch_for_api(array_ops.concat) |
| def masked_concat(values: MaskedTensorList, axis): |
| del values, axis |
| |
| @dispatch.dispatch_for_api(math_ops.add) |
| def silly_add(x: SillyTensor, y: SillyTensor): |
| del x, y |
| |
| @dispatch.dispatch_for_api(math_ops.abs) |
| def silly_abs(x: SillyTensor): |
| del x |
| |
| # Note: `expeced` does not contain keys or values from SillyTensor. |
| targets = dispatch.type_based_dispatch_signatures_for(MaskedTensor) |
| expected = {math_ops.add: [{"x": MaskedTensor, "y": MaskedTensor}], |
| array_ops.concat: [{"values": MaskedTensorList}]} |
| self.assertEqual(targets, expected) |
| |
| finally: |
| # Clean up dispatch table. |
| dispatch.unregister_dispatch_target(math_ops.add, masked_add) |
| dispatch.unregister_dispatch_target(array_ops.concat, masked_concat) |
| dispatch.unregister_dispatch_target(math_ops.add, silly_add) |
| dispatch.unregister_dispatch_target(math_ops.abs, silly_abs) |
| |
| def testDispatchForUnaryElementwiseAPIs(self): |
| |
| @dispatch.dispatch_for_unary_elementwise_apis(MaskedTensor) |
| def unary_elementwise_api_handler(api_func, x): |
| return MaskedTensor(api_func(x.values), x.mask) |
| |
| try: |
| x = MaskedTensor([1, -2, -3], [True, True, False]) |
| # Test calls with positional & keyword argument (& combinations) |
| abs_x = math_ops.abs(x) |
| sign_x = math_ops.sign(x=x) |
| neg_x = math_ops.negative(x, "neg_x") |
| invert_x = bitwise_ops.invert(x, name="invert_x") |
| ones_like_x = array_ops.ones_like(x, name="ones_like_x") |
| ones_like_x_float = array_ops.ones_like( |
| x, dtypes.float32, name="ones_like_x_float") |
| self.assertAllEqual(abs_x.values, [1, 2, 3]) |
| self.assertAllEqual(sign_x.values, [1, -1, -1]) |
| self.assertAllEqual(neg_x.values, [-1, 2, 3]) |
| self.assertAllEqual(invert_x.values, [-2, 1, 2]) |
| self.assertAllEqual(ones_like_x.values, [1, 1, 1]) |
| self.assertAllEqual(ones_like_x_float.values, [1., 1., 1.]) |
| for result in [ |
| abs_x, sign_x, neg_x, invert_x, ones_like_x, ones_like_x_float |
| ]: |
| self.assertAllEqual(result.mask, [True, True, False]) |
| if not context.executing_eagerly(): # names not defined in eager mode. |
| self.assertRegex(neg_x.values.name, r"^neg_x/Neg:.*") |
| self.assertRegex(invert_x.values.name, r"^invert_x/.*") |
| self.assertRegex(ones_like_x.values.name, r"^ones_like_x/.*") |
| self.assertRegex(ones_like_x_float.values.name, |
| r"^ones_like_x_float/.*") |
| |
| finally: |
| dispatch.unregister_elementwise_api_handler(unary_elementwise_api_handler) |
| |
| def testDispatchForBinaryElementwiseAPIs(self): |
| |
| @dispatch.dispatch_for_binary_elementwise_apis(MaskedTensor, MaskedTensor) |
| def binary_elementwise_api_handler(api_func, x, y): |
| return MaskedTensor(api_func(x.values, y.values), x.mask & y.mask) |
| |
| try: |
| x = MaskedTensor([1, -2, -3], [True, True, False]) |
| y = MaskedTensor([10, 20, 30], [True, False, True]) |
| # Test calls with positional & keyword arguments (& combinations) |
| x_times_y = math_ops.multiply(x, y) |
| x_plus_y = math_ops.add(x, y=y) |
| x_minus_y = math_ops.subtract(x=x, y=y) |
| min_x_y = math_ops.minimum(x, y, "min_x_y") |
| y_times_x = math_ops.multiply(y, x, name="y_times_x") |
| y_plus_x = math_ops.add(y, y=x, name="y_plus_x") |
| y_minus_x = math_ops.subtract(x=y, y=x, name="y_minus_x") |
| self.assertAllEqual(x_times_y.values, [10, -40, -90]) |
| self.assertAllEqual(x_plus_y.values, [11, 18, 27]) |
| self.assertAllEqual(x_minus_y.values, [-9, -22, -33]) |
| self.assertAllEqual(min_x_y.values, [1, -2, -3]) |
| self.assertAllEqual(y_times_x.values, [10, -40, -90]) |
| self.assertAllEqual(y_plus_x.values, [11, 18, 27]) |
| self.assertAllEqual(y_minus_x.values, [9, 22, 33]) |
| for result in [ |
| x_times_y, x_plus_y, x_minus_y, min_x_y, y_times_x, y_plus_x, |
| y_minus_x |
| ]: |
| self.assertAllEqual(result.mask, [True, False, False]) |
| if not context.executing_eagerly(): # names not defined in eager mode. |
| self.assertRegex(min_x_y.values.name, r"^min_x_y/Minimum:.*") |
| self.assertRegex(min_x_y.mask.name, r"^min_x_y/and:.*") |
| self.assertRegex(y_times_x.values.name, r"^y_times_x/.*") |
| self.assertRegex(y_plus_x.values.name, r"^y_plus_x/.*") |
| self.assertRegex(y_minus_x.values.name, r"^y_minus_x/.*") |
| |
| finally: |
| dispatch.unregister_elementwise_api_handler( |
| binary_elementwise_api_handler) |
| |
| def testDuplicateDispatchForUnaryElementwiseAPIsError(self): |
| |
| @dispatch.dispatch_for_unary_elementwise_apis(MaskedTensor) |
| def handler(api_func, x): |
| return MaskedTensor(api_func(x.values), x.mask) |
| |
| try: |
| with self.assertRaisesRegex( |
| ValueError, r"A unary elementwise dispatch handler \(.*\) has " |
| "already been registered for .*"): |
| |
| @dispatch.dispatch_for_unary_elementwise_apis(MaskedTensor) |
| def another_handler(api_func, x): |
| return MaskedTensor(api_func(x.values), ~x.mask) |
| |
| del another_handler |
| |
| finally: |
| dispatch.unregister_elementwise_api_handler(handler) |
| |
| def testDuplicateDispatchForBinaryElementwiseAPIsError(self): |
| |
| @dispatch.dispatch_for_binary_elementwise_apis(MaskedTensor, MaskedTensor) |
| def handler(api_func, x, y): |
| return MaskedTensor(api_func(x.values, y.values), x.mask & y.mask) |
| |
| try: |
| with self.assertRaisesRegex( |
| ValueError, r"A binary elementwise dispatch handler \(.*\) has " |
| "already been registered for .*"): |
| |
| @dispatch.dispatch_for_binary_elementwise_apis(MaskedTensor, |
| MaskedTensor) |
| def another_handler(api_func, x, y): |
| return MaskedTensor(api_func(x.values, y.values), x.mask) |
| |
| del another_handler |
| |
| finally: |
| dispatch.unregister_elementwise_api_handler(handler) |
| |
| def testRegisterUnaryElementwiseApiAfterHandler(self): |
| # Test that it's ok to call register_unary_elementwise_api after |
| # dispatch_for_unary_elementwise_apis. |
| |
| @dispatch.dispatch_for_unary_elementwise_apis(MaskedTensor) |
| def handler(api_func, x): |
| return MaskedTensor(api_func(x.values), x.mask) |
| |
| try: |
| |
| @dispatch.register_unary_elementwise_api |
| @dispatch.add_dispatch_support |
| def some_op(x): |
| return x * 2 |
| |
| x = MaskedTensor([1, 2, 3], [True, False, True]) |
| y = some_op(x) |
| self.assertAllEqual(y.values, [2, 4, 6]) |
| self.assertAllEqual(y.mask, [True, False, True]) |
| |
| finally: |
| dispatch.unregister_elementwise_api_handler(handler) |
| |
| def testRegisterBinaryElementwiseApiAfterHandler(self): |
| # Test that it's ok to call register_binary_elementwise_api after |
| # dispatch_for_binary_elementwise_apis. |
| |
| @dispatch.dispatch_for_binary_elementwise_apis(MaskedTensor, MaskedTensor) |
| def handler(api_func, x, y): |
| return MaskedTensor(api_func(x.values, y.values), x.mask & y.mask) |
| |
| try: |
| |
| @dispatch.register_binary_elementwise_api |
| @dispatch.add_dispatch_support |
| def some_op(x, y): |
| return x * 2 + y |
| |
| x = MaskedTensor([1, 2, 3], [True, False, True]) |
| y = MaskedTensor([10, 20, 30], [True, True, False]) |
| z = some_op(x, y) |
| self.assertAllEqual(z.values, [12, 24, 36]) |
| self.assertAllEqual(z.mask, [True, False, False]) |
| |
| finally: |
| dispatch.unregister_elementwise_api_handler(handler) |
| |
| def testElementwiseApiLists(self): |
| self.assertIn(math_ops.abs, dispatch.unary_elementwise_apis()) |
| self.assertIn(math_ops.cos, dispatch.unary_elementwise_apis()) |
| self.assertIn(math_ops.add, dispatch.binary_elementwise_apis()) |
| self.assertIn(math_ops.multiply, dispatch.binary_elementwise_apis()) |
| |
| def testUpdateDocstringsWithAPILists(self): |
| dispatch.update_docstrings_with_api_lists() |
| self.assertRegex( |
| dispatch.dispatch_for_api.__doc__, |
| r"(?s) The TensorFlow APIs that may be overridden " |
| r"by `@dispatch_for_api` are:\n\n.*" |
| r" \* `tf\.concat\(values, axis, name='concat'\)`\n.*" |
| r" \* `tf\.math\.add\(x, y, name=None\)`\n.*") |
| self.assertRegex( |
| dispatch.dispatch_for_unary_elementwise_apis.__doc__, |
| r"(?s) The unary elementwise APIs are:\n\n.*" |
| r" \* `tf\.math\.abs\(x, name=None\)`\n.*" |
| r" \* `tf\.math\.cos\(x, name=None\)`\n.*") |
| self.assertRegex( |
| dispatch.dispatch_for_binary_elementwise_apis.__doc__, |
| r"(?s) The binary elementwise APIs are:\n\n.*" |
| r" \* `tf\.math\.add\(x, y, name=None\)`\n.*" |
| r" \* `tf\.math\.multiply\(x, y, name=None\)`\n.*") |
| |
| |
| if __name__ == "__main__": |
| googletest.main() |
