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# 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 sorting operators."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.compiler.tests import xla_test
from tensorflow.compiler.tf2xla.python import xla
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.platform import test
class XlaSortOpTest(xla_test.XLATestCase):
def _assertOpOutputMatchesExpected(self, op, args, expected):
with self.cached_session() as session:
with self.test_scope():
placeholders = [
array_ops.placeholder(dtypes.as_dtype(arg.dtype), arg.shape)
for arg in args
]
feeds = {placeholders[i]: args[i] for i in range(0, len(args))}
output = op(*placeholders)
if isinstance(output, ops.Tensor):
output = [output]
results = session.run(output, feeds)
for result, v in zip(results, expected):
self.assertAllClose(v, result, rtol=1e-3)
def testSort(self):
supported_types = set(
[dtypes.bfloat16.as_numpy_dtype, np.float32, np.int32, np.uint32])
for dtype in supported_types.intersection(self.numeric_types):
x = np.arange(101, dtype=dtype)
np.random.shuffle(x)
self._assertOpOutputMatchesExpected(
xla.sort, [x], expected=[np.arange(101, dtype=dtype)])
def testKeyValueSort(self):
supported_types = set(
[dtypes.bfloat16.as_numpy_dtype, np.float32, np.int32, np.uint32])
for key_type in supported_types.intersection(self.numeric_types):
for value_type in supported_types.intersection(self.numeric_types):
x = np.arange(101, dtype=key_type)
np.random.shuffle(x)
y = (-x).astype(value_type)
self._assertOpOutputMatchesExpected(
xla.key_value_sort, [x, y],
expected=[
np.arange(101, dtype=key_type),
-np.arange(101, dtype=value_type)
])
def testTopK(self):
supported_types = set(
[dtypes.bfloat16.as_numpy_dtype, np.float32, np.int32, np.uint32])
for dtype in supported_types.intersection(self.numeric_types):
# Use small input size for bfloat16. Otherwise, we'll get duplicate values
# after conversion to bfloat16, so the possible resulting index array is
# no longer unique.
if dtype == dtypes.bfloat16.as_numpy_dtype:
array_size = 20
k_options = [0, 1, 2, 10, 20]
else:
array_size = 200 * 1000
k_options = [0, 1, 2, 10, 20, 100, 1000, 200 * 1000]
for x in [np.arange(array_size)]:
np.random.shuffle(x)
for k in k_options:
indices = x.argsort()[::-1][:k]
def topk(v, k=k):
return nn_ops.top_k(v, k=k, sorted=True)
self._assertOpOutputMatchesExpected(
topk, [x.astype(dtype)],
expected=[x[indices].astype(dtype), indices])
def testTopK2D(self):
supported_types = set(
[dtypes.bfloat16.as_numpy_dtype, np.float32, np.int32, np.uint32])
for dtype in supported_types.intersection(self.numeric_types):
# Use small input size for bfloat16. Otherwise, we'll get duplicate values
# after conversion to bfloat16, so the possible resulting index array is
# no longer unique.
if dtype == dtypes.bfloat16.as_numpy_dtype:
array_size = 10
k_options = [0, 1, 2, 10]
else:
array_size = 200 * 1000
k_options = [0, 1, 2, 10, 20, 100, 1000, 200 * 1000]
batch = 16
for x in [np.arange(batch * array_size)]:
np.random.shuffle(x)
x = np.reshape(x, [batch, array_size])
for k in k_options:
indices = x.argsort(axis=1)[::, -1:-k - 1:-1]
expected = np.sort(x, axis=1)[::, -1:-k - 1:-1]
def topk(v, k=k):
return nn_ops.top_k(v, k=k, sorted=True)
self._assertOpOutputMatchesExpected(
topk, [x.astype(dtype)],
expected=[expected.astype(dtype), indices])
def testTopKZeros(self):
"""Tests that positive and negative zeros sort correctly."""
# Only bfloat16 is implemented.
bfloat16 = dtypes.bfloat16.as_numpy_dtype
if bfloat16 not in self.numeric_types:
return
with self.cached_session() as sess:
p = array_ops.placeholder(dtypes.bfloat16)
with self.test_scope():
topk = nn_ops.top_k(p, k=4)
results = sess.run(
topk,
{p: np.array([0., -0., 0., 3., -0., -4., 0., -0.], dtype=bfloat16)})
self.assertAllEqual(
np.array([3., 0., 0., 0.], dtype=bfloat16), results[0])
self.assertEqual(list([3, 0, 2, 6]), list(results[1]))
def testTopKInfinities(self):
"""Tests that positive and negative infinity sort correctly."""
# Only bfloat16 is implemented.
bfloat16 = dtypes.bfloat16.as_numpy_dtype
if bfloat16 not in self.numeric_types:
return
with self.cached_session() as sess:
p = array_ops.placeholder(dtypes.bfloat16)
with self.test_scope():
topk = nn_ops.top_k(p, k=6)
results = sess.run(topk, {
p: np.array(
[1, 2, float("inf"), -float("inf"), -1, -2], dtype=bfloat16)
})
self.assertAllEqual(
np.array(
[float("inf"), 2.0, 1.0, -1.0, -2.0, -float("inf")],
dtype=bfloat16), results[0])
self.assertEqual(list([2, 1, 0, 4, 5, 3]), list(results[1]))
if __name__ == "__main__":
test.main()
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