6762ca15c4
The session returned by cached_session uses soft placement, something we don't want for XLA_* devices. With soft placement ops lacking XLA kernels silently fall back and run on the CPU, misleading us into thinking we have more test coverage than we actually do. With this test some tests (rightly) start failing because they were testing ops with dtypes the XLA kernels do not support. I've removed these dtypes from the tests. This CL partially addresses b/132430685. It stubs out "cached_session" and "test_session" to raise errors, so we have more confidence that the compiler is being exercised. However, we still use XLA_* devices to exercise XLA, which has a different code path than xla.compile and tpu.rewrite. This needs to be incrementally fixed. PiperOrigin-RevId: 248437673
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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