a3d2b5a910
Also, add verifier for Cumsum op to reject illegal axis value. GetScalarConstOfType doesn't support non int and float element types so reject ops with other element types. PiperOrigin-RevId: 326787027 Change-Id: I54afe4e494d711fa873b6329391603fbd8958c88
233 lines
7.7 KiB
Python
233 lines
7.7 KiB
Python
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Functional tests for scan ops."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from tensorflow.compiler.tests import xla_test
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import errors_impl
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.platform import test
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def numpy_reverse(x, axis):
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length = len(x.shape)
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if axis < 0:
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axis = length + axis
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ix = [
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slice(None, None, -1) if i == axis else slice(None) for i in range(length)
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]
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return x[ix]
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def handle_options(func, x, axis, exclusive, reverse):
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"""Adds tf options to numpy scan ops."""
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length = len(x.shape)
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if axis < 0:
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axis = length + axis
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if reverse:
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x = numpy_reverse(x, axis)
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if exclusive:
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ix_head = [slice(0, 1) if i == axis else slice(None) for i in range(length)]
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ix_init = [
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slice(0, -1) if i == axis else slice(None) for i in range(length)
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]
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if func == np.cumsum:
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init = np.zeros_like(x[ix_head])
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elif func == np.cumprod:
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init = np.ones_like(x[ix_head])
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else:
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raise ValueError("Unknown scan function.")
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x = np.concatenate([init, func(x[ix_init], axis)], axis=axis)
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else:
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x = func(x, axis=axis)
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if reverse:
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x = numpy_reverse(x, axis)
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return x
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class CumsumTest(xla_test.XLATestCase):
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valid_dtypes = [np.float32, np.int32]
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def axis_dtypes(self):
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return set(self.int_types).intersection([np.int32, np.int64])
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def _compare(self, x, axis, exclusive, reverse):
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np_out = handle_options(np.cumsum, x, axis, exclusive, reverse)
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with self.session(), self.test_scope():
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p = array_ops.placeholder(x.dtype)
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tf_out = math_ops.cumsum(p, axis, exclusive, reverse).eval(
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feed_dict={p: x})
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self.assertAllClose(np_out, tf_out)
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def _compareAll(self, x, axis):
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for exclusive in [True, False]:
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for reverse in [True, False]:
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self._compare(x, axis, exclusive, reverse)
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def testEmpty(self):
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for dtype in self.valid_dtypes:
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x = np.zeros([0]).astype(dtype)
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for axis in (-1, 0):
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self._compareAll(x, axis)
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def testAxisType(self):
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for dtype in self.valid_dtypes:
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x = np.arange(1, 6).reshape([5]).astype(dtype)
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for axis_dtype in self.axis_dtypes():
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with self.session(), self.test_scope():
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p = array_ops.placeholder(x.dtype)
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axis = constant_op.constant(0, axis_dtype)
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math_ops.cumsum(p, axis).eval(feed_dict={p: x})
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def test1D(self):
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for dtype in self.valid_dtypes:
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x = np.arange(1, 6).reshape([5]).astype(dtype)
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for axis in (-1, 0):
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self._compareAll(x, axis)
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def test2D(self):
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for dtype in self.valid_dtypes:
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x = np.arange(0, 10).reshape([2, 5]).astype(dtype)
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for axis in (-2, -1, 0, 1):
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self._compareAll(x, axis)
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def test3D(self):
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for dtype in self.valid_dtypes:
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x = np.arange(0, 20).reshape([2, 2, 5]).astype(dtype)
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for axis in (-3, -2, -1, 0, 1, 2):
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self._compareAll(x, axis)
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def test6D(self):
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for dtype in self.valid_dtypes:
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x = np.arange(1, 145).reshape([2, 2, 3, 3, 2, 2]).astype(dtype)
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for axis in range(-6, 6, 3):
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self._compareAll(x, axis)
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@test_util.disable_mlir_bridge("Error handling")
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def testInvalidAxis(self):
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x = np.arange(0, 10).reshape([2, 5]).astype(np.float32)
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with self.session(), self.test_scope():
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input_tensor = ops.convert_to_tensor(x)
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with self.assertRaisesWithPredicateMatch(
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errors_impl.InvalidArgumentError,
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lambda e: "Expected scan axis in the range [-2, 2)" in str(e)):
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math_ops.cumsum(input_tensor, -3).eval()
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with self.assertRaisesWithPredicateMatch(
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errors_impl.InvalidArgumentError,
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lambda e: "Expected scan axis in the range [-2, 2)" in str(e)):
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math_ops.cumsum(input_tensor, 2).eval()
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with self.assertRaisesWithPredicateMatch(
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errors_impl.InvalidArgumentError,
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lambda e: "axis must be a scalar" in str(e)):
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math_ops.cumsum(input_tensor, [0]).eval()
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class CumprodTest(xla_test.XLATestCase):
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valid_dtypes = [np.float32, np.int32]
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def axis_dtypes(self):
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return set(self.int_types).intersection([np.int32, np.int64])
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def _compare(self, x, axis, exclusive, reverse):
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np_out = handle_options(np.cumprod, x, axis, exclusive, reverse)
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with self.session(), self.test_scope():
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p = array_ops.placeholder(x.dtype)
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prod = math_ops.cumprod(p, axis, exclusive, reverse)
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tf_out = prod.eval(feed_dict={p: x})
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self.assertAllClose(np_out, tf_out)
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def _compareAll(self, x, axis):
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for exclusive in [True, False]:
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for reverse in [True, False]:
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self._compare(x, axis, exclusive, reverse)
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def testEmpty(self):
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for dtype in self.valid_dtypes:
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x = np.zeros([0]).astype(dtype)
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for axis in (-1, 0):
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self._compareAll(x, axis)
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def testAxisType(self):
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for dtype in self.valid_dtypes:
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x = np.arange(1, 6).reshape([5]).astype(dtype)
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for axis_dtype in self.axis_dtypes():
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with self.session(), self.test_scope():
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p = array_ops.placeholder(x.dtype)
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axis = constant_op.constant(0, axis_dtype)
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math_ops.cumprod(x, axis).eval(feed_dict={p: x})
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def test1D(self):
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for dtype in self.valid_dtypes:
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x = np.arange(1, 6).reshape([5]).astype(dtype)
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for axis in (-1, 0):
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self._compareAll(x, axis)
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def test2D(self):
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for dtype in self.valid_dtypes:
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x = np.arange(1, 11).reshape([2, 5]).astype(dtype)
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for axis in (-2, -1, 0, 1):
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self._compareAll(x, axis)
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def test3D(self):
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for dtype in self.valid_dtypes:
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x = np.arange(1, 21).reshape([2, 2, 5]).astype(dtype)
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for axis in (-3, -2, -1, 0, 1, 2):
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self._compareAll(x, axis)
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def test6D(self):
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for dtype in self.valid_dtypes:
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x = np.arange(1, 145).reshape([2, 2, 3, 3, 2, 2]).astype(dtype)
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for axis in range(-6, 6, 3):
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self._compareAll(x, axis)
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@test_util.disable_mlir_bridge("Error handling")
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def testInvalidAxis(self):
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x = np.arange(0, 10).reshape([2, 5]).astype(np.float32)
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with self.session(), self.test_scope():
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input_tensor = ops.convert_to_tensor(x)
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with self.assertRaisesWithPredicateMatch(
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errors_impl.InvalidArgumentError,
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lambda e: "Expected scan axis in the range [-2, 2)" in str(e)):
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math_ops.cumprod(input_tensor, -3).eval()
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with self.assertRaisesWithPredicateMatch(
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errors_impl.InvalidArgumentError,
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lambda e: "Expected scan axis in the range [-2, 2)" in str(e)):
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math_ops.cumprod(input_tensor, 2).eval()
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with self.assertRaisesWithPredicateMatch(
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errors_impl.InvalidArgumentError,
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lambda e: "axis must be a scalar" in str(e)):
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math_ops.cumprod(input_tensor, [0]).eval()
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if __name__ == "__main__":
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test.main()
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