# 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.""" 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.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.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.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.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.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.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.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()