| # 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.cached_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 = constant_op.constant(params_np[indices]) |
| self.assertAllEqual(np_val, gather_val) |
| |
| def testScalar2D(self): |
| with self.cached_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 = constant_op.constant( |
| np.take(params_np, 2, axis=axis), dtype) |
| self.assertAllEqual(expected, gather_val) |
| |
| def testSimpleTwoD32(self): |
| with self.cached_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 = constant_op.constant( |
| np.take(params_np, [0, 1, 0, 2], axis=axis), dtype) |
| self.assertAllEqual(expected, gather_val) |
| |
| def testSimpleTwoD32_Int64Indices(self): |
| if np.int64 not in self.int_types: |
| return |
| |
| with self.cached_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 = constant_op.constant( |
| np.take(params_np, [0, 1, 0, 2], axis=axis), dtype) |
| 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.cached_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 = constant_op.constant( |
| np.take(params, indices, axis=axis), dtype) |
| self.assertAllEqual(gather_np, gather_value) |
| |
| def testIndicesWithDifferentDimensions(self): |
| with self.cached_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.cached_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() |
