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