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# Copyright 2017 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.
# ==============================================================================
"""Functional tests for XLA Gather Op."""
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.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import flags
from tensorflow.python.platform import test
FLAGS = flags.FLAGS
class GatherTest(xla_test.XLATestCase):
def _buildParams(self, data, dtype):
data = data.astype(dtype.as_numpy_dtype)
# For complex types, adds an index-dependent imaginary component so we can
# tell we got the right value.
if dtype.is_complex:
return data + 10j * data
return data
def testScalar1D(self):
with self.test_session() as session, self.test_scope():
data = np.array([0, 1, 2, 3, 7, 5])
for dtype in self.all_tf_types:
for indices in 4, [4], [1, 2, 2, 4, 5]:
params_np = self._buildParams(data, dtype)
params = array_ops.placeholder(dtype=dtype)
indices_tf = constant_op.constant(indices)
gather_t = array_ops.gather(params, indices_tf)
gather_val = session.run(gather_t, feed_dict={params: params_np})
np_val = params_np[indices]
self.assertAllEqual(np_val, gather_val)
def testScalar2D(self):
with self.test_session() as session, self.test_scope():
data = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11],
[12, 13, 14]])
for dtype in self.all_tf_types:
for axis in 0, 1, -1:
params_np = self._buildParams(data, dtype)
params = array_ops.placeholder(dtype=dtype)
indices = constant_op.constant(2)
gather_t = array_ops.gather(params, indices, axis=axis)
gather_val = session.run(gather_t, feed_dict={params: params_np})
expected = np.take(params_np, 2, axis=axis)
self.assertAllEqual(expected, gather_val)
def testSimpleTwoD32(self):
with self.test_session() as session, self.test_scope():
data = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11],
[12, 13, 14]])
for dtype in self.all_tf_types:
for axis in 0, 1, -1:
params_np = self._buildParams(data, dtype)
params = array_ops.placeholder(dtype=dtype)
# The indices must be in bounds for any axis.
indices = constant_op.constant([0, 1, 0, 2])
gather_t = array_ops.gather(params, indices, axis=axis)
gather_val = session.run(gather_t, feed_dict={params: params_np})
expected = np.take(params_np, [0, 1, 0, 2], axis=axis)
self.assertAllEqual(expected, gather_val)
def testSimpleTwoD32_Int64Indices(self):
if np.int64 not in self.int_types:
return
with self.test_session() as session, self.test_scope():
data = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11],
[12, 13, 14]])
# The indices must be in bounds for any axis.
indices_np = np.array([0, 1, 0, 2])
for dtype in self.all_tf_types:
for axis in 0, 1, -1:
params_np = self._buildParams(data, dtype)
params = array_ops.placeholder(dtype=dtype)
indices = array_ops.placeholder(dtype=dtypes.int64)
gather_t = array_ops.gather(params, indices, axis=axis)
gather_val = session.run(
gather_t, feed_dict={
params: params_np,
indices: indices_np
})
expected = np.take(params_np, [0, 1, 0, 2], axis=axis)
self.assertAllEqual(expected, gather_val)
def testHigherRank(self):
"""Check that scalar and empty indices shapes work as well."""
shape = (2, 1, 3, 2)
for indices_shape in (), (0,), (2, 0), (2, 3):
for dtype in self.all_tf_types:
for axis in 0, 1, 2, 3, -1, -2:
params = self._buildParams(np.random.randn(*shape), dtype)
indices = np.random.randint(shape[axis], size=indices_shape)
with self.test_session() as sess, self.test_scope():
tf_params = array_ops.placeholder(dtype=dtype)
tf_indices = constant_op.constant(indices, dtype=dtypes.int32)
gather = array_ops.gather(tf_params, tf_indices, axis=axis)
gather_value = sess.run(gather, feed_dict={tf_params: params})
gather_np = np.take(params, indices, axis=axis)
self.assertAllEqual(gather_np, gather_value)
def testIndicesWithDifferentDimensions(self):
with self.test_session():
for dtype in self.numeric_tf_types:
params = array_ops.placeholder(dtype=dtype)
indices = array_ops.placeholder(dtype=np.int32)
with self.test_scope():
gather = array_ops.gather(params, indices)
self.assertAllEqual(
7, gather.eval(feed_dict={params: [4, 7, 2], indices: 1}))
self.assertAllEqual(
[7], gather.eval(feed_dict={params: [4, 7, 2], indices: [1]}))
self.assertAllEqual(
[[7]], gather.eval(feed_dict={params: [4, 7, 2], indices: [[1]]}))
def testGatherPrecision(self):
with self.test_session() as session, self.test_scope():
data = np.array([[0, 0, 0, 0], [0, 2 * (1 + np.exp2(-8)), 0, 0],
[0, 0, 0, 0], [0.015789, 0.0985, 0.55789, 0.3842]])
indices = np.array([1, 2, 3, 1])
dtype = dtypes.float32
params_np = self._buildParams(data, dtype)
params = array_ops.placeholder(dtype=dtype)
indices_tf = constant_op.constant(indices)
gather_t = array_ops.gather(params, indices_tf)
gather_val = session.run(gather_t, feed_dict={params: params_np})
np_val = params_np[indices]
self.assertAllEqual(np_val, gather_val)
class GatherBenchmark(test.Benchmark):
"""Microbenchmarks for the gather op."""
def _benchmarkGather(self, name, axis, gather_indices, use_xla_jit):
def BuilderFn():
inputs = variables.Variable(
array_ops.zeros([100, 100, 10, 100, 50], dtype=dtypes.float32),
dtype=dtypes.float32,
name='input')
indices = variables.Variable(
gather_indices, dtype=dtypes.int32, name='indices')
gather_t = array_ops.gather(inputs, indices, axis=axis)
return '%s.axis%d' % (name, axis), [gather_t]
xla_test.Benchmark(self, BuilderFn, use_xla_jit=use_xla_jit, device='cpu')
def _benchmarkSliceGather(self, axis, use_xla_jit):
"""Benchmarks a gather op that's really a dynamic slice."""
self._benchmarkGather('slice_gather', axis, [1], use_xla_jit)
def _benchmarkNontrivialGather(self, axis, use_xla_jit):
self._benchmarkGather('nontrivial_gather', axis, [9, 1, 0, 2] * 4,
use_xla_jit)
def benchmarkSliceGatherAxis0(self):
self._benchmarkSliceGather(axis=0, use_xla_jit=False)
def benchmarkSliceGatherAxis0XLA(self):
self._benchmarkSliceGather(axis=0, use_xla_jit=True)
def benchmarkSliceGatherAxis1(self):
self._benchmarkSliceGather(axis=1, use_xla_jit=False)
def benchmarkSliceGatherAxis1XLA(self):
self._benchmarkSliceGather(axis=1, use_xla_jit=True)
def benchmarkSliceGatherAxis4(self):
self._benchmarkSliceGather(axis=4, use_xla_jit=False)
def benchmarkSliceGatherAxis4XLA(self):
self._benchmarkSliceGather(axis=4, use_xla_jit=True)
def benchmarkNontrivialGatherAxis0(self):
self._benchmarkNontrivialGather(axis=0, use_xla_jit=False)
def benchmarkNontrivialGatherAxis0XLA(self):
self._benchmarkNontrivialGather(axis=0, use_xla_jit=True)
def benchmarkNontrivialGatherAxis1(self):
self._benchmarkNontrivialGather(axis=1, use_xla_jit=False)
def benchmarkNontrivialGatherAxis1XLA(self):
self._benchmarkNontrivialGather(axis=1, use_xla_jit=True)
def benchmarkNontrivialGatherAxis4(self):
self._benchmarkNontrivialGather(axis=4, use_xla_jit=False)
def benchmarkNontrivialGatherAxis4XLA(self):
self._benchmarkNontrivialGather(axis=4, use_xla_jit=True)
if __name__ == '__main__':
test.main()
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