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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.test_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):
# TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU.
if self.device in ["XLA_CPU", "XLA_GPU"]:
return
supported_types = set([dtypes.bfloat16.as_numpy_dtype, np.float32])
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 testTopK(self):
# TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU.
if self.device in ["XLA_CPU", "XLA_GPU"]:
return
# Only bfloat16 is implemented.
bfloat16 = dtypes.bfloat16.as_numpy_dtype
if bfloat16 in self.numeric_types:
for x in [np.arange(20)]:
np.random.shuffle(x)
for k in [0, 1, 2, 10, 20]:
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(bfloat16)],
expected=[x[indices].astype(bfloat16), indices])
def testTopKZeros(self):
"""Tests that positive and negative zeros sort correctly."""
# TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU.
if self.device in ["XLA_CPU", "XLA_GPU"]:
return
# Only bfloat16 is implemented.
bfloat16 = dtypes.bfloat16.as_numpy_dtype
if bfloat16 not in self.numeric_types:
return
with self.test_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, 1, 2]), list(results[1]))
def testTopKInfinities(self):
"""Tests that positive and negative infinity sort correctly."""
# TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU.
if self.device in ["XLA_CPU", "XLA_GPU"]:
return
# Only bfloat16 is implemented.
bfloat16 = dtypes.bfloat16.as_numpy_dtype
if bfloat16 not in self.numeric_types:
return
with self.test_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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