# 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. # ============================================================================== """Tests for reduction operators.""" import functools import itertools from absl.testing import parameterized import numpy as np from tensorflow.compiler.tests import xla_test from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.platform import googletest @parameterized.named_parameters(('32_bit_index', dtypes.int32), ('64_bit_index', dtypes.int64)) class ReduceOpsTest(xla_test.XLATestCase, parameterized.TestCase): def _testReduction(self, tf_reduce_fn, np_reduce_fn, dtype, test_inputs, index_dtype, rtol=1e-4, atol=1e-4): """Tests that the output of 'tf_reduce_fn' matches numpy's output.""" for test_input in test_inputs: with self.session() as sess: with self.test_scope(): a = array_ops.placeholder(dtype) index = array_ops.placeholder(index_dtype) out = tf_reduce_fn(a, index) result = sess.run(out, {a: test_input, index: [0]}) self.assertAllClose( result, np_reduce_fn(test_input, axis=0), rtol=rtol, atol=atol) result = sess.run(out, {a: test_input, index: [1]}) self.assertAllClose( result, np_reduce_fn(test_input, axis=1), rtol=rtol, atol=atol) result = sess.run(out, {a: test_input, index: [-1]}) self.assertAllClose( result, np_reduce_fn(test_input, axis=1), rtol=rtol, atol=atol) # MLIR bridge doesn't return the same error so it can't be matched # directly. if not test_util.is_mlir_bridge_enabled(): with self.assertRaisesWithPredicateMatch( errors_impl.InvalidArgumentError, 'Invalid reduction dim'): sess.run(out, {a: test_input, index: [-33]}) with self.assertRaisesWithPredicateMatch( errors_impl.InvalidArgumentError, 'Invalid reduction dim'): sess.run(out, {a: test_input, index: [2]}) REAL_DATA = [ np.zeros(shape=(2, 0)), np.zeros(shape=(0, 30)), np.arange(1, 7).reshape(2, 3), np.arange(-10, -4).reshape(2, 3), np.arange(-4, 2).reshape(2, 3), ] COMPLEX_DATA = [ np.zeros(shape=(2, 0)).astype(np.complex64), np.zeros(shape=(0, 30)).astype(np.complex64), np.arange(1, 13, dtype=np.float32).view(np.complex64).reshape(2, 3), np.arange(-14, -2, dtype=np.float32).view(np.complex64).reshape(2, 3), np.arange(-4, 8, dtype=np.float32).view(np.complex64).reshape(2, 3), ] NONEMPTY_REAL_DATA = [x for x in REAL_DATA if np.size(x) > 0] NONEMPTY_COMPLEX_DATA = [x for x in COMPLEX_DATA if np.size(x) > 0] BOOL_DATA = [ np.array([], dtype=np.bool_).reshape(2, 0), np.array([], dtype=np.bool_).reshape(0, 3), np.array([[False, True, False], [True, True, False]]), ] ONES = [np.ones([34000, 2])] def testReduceSumF32(self, index_dtype): self._testReduction(math_ops.reduce_sum, np.sum, np.float32, self.REAL_DATA, index_dtype) def testReduceSumC64(self, index_dtype): self._testReduction(math_ops.reduce_sum, np.sum, np.complex64, self.COMPLEX_DATA, index_dtype) def testReduceProdF32(self, index_dtype): self._testReduction(math_ops.reduce_prod, np.prod, np.float32, self.REAL_DATA, index_dtype) def testReduceProdC64(self, index_dtype): self._testReduction(math_ops.reduce_prod, np.prod, np.complex64, self.COMPLEX_DATA, index_dtype) def testReduceMin(self, index_dtype): def reference_min(dtype, inp, axis): """Wrapper around np.amin that returns +infinity for an empty input.""" if inp.shape[axis] == 0: if np.issubdtype(dtype, np.floating): return np.full(inp.shape[0:axis] + inp.shape[axis + 1:], float('inf')) return np.full(inp.shape[0:axis] + inp.shape[axis + 1:], np.iinfo(dtype).max) return np.amin(inp, axis) for dtype in set(self.all_types).intersection( [np.float32, np.int32, np.int64]): self._testReduction(math_ops.reduce_min, functools.partial(reference_min, dtype), dtype, self.REAL_DATA, index_dtype) def testReduceMax(self, index_dtype): def reference_max(dtype, inp, axis): """Wrapper around np.amax that returns -infinity for an empty input.""" if inp.shape[axis] == 0: if np.issubdtype(dtype, np.floating): return np.full(inp.shape[0:axis] + inp.shape[axis + 1:], float('-inf')) return np.full(inp.shape[0:axis] + inp.shape[axis + 1:], np.iinfo(dtype).min) return np.amax(inp, axis) for dtype in set(self.all_types).intersection( [np.float32, np.int32, np.int64]): self._testReduction(math_ops.reduce_max, functools.partial(reference_max, dtype), dtype, self.REAL_DATA, index_dtype) def testReduceMeanF32(self, index_dtype): # TODO(phawkins): mean on XLA currently returns 0 instead of NaN when # reducing across zero inputs. self._testReduction(math_ops.reduce_mean, np.mean, np.float32, self.NONEMPTY_REAL_DATA, index_dtype) def testReduceMeanF16(self, index_dtype): if np.float16 in self.all_types: self._testReduction(math_ops.reduce_mean, np.mean, np.float16, self.ONES, index_dtype) def testReduceMeanC64(self, index_dtype): self._testReduction(math_ops.reduce_mean, np.mean, np.complex64, self.NONEMPTY_COMPLEX_DATA, index_dtype) def testReduceAll(self, index_dtype): self._testReduction(math_ops.reduce_all, np.all, np.bool_, self.BOOL_DATA, index_dtype) def testReduceAny(self, index_dtype): self._testReduction(math_ops.reduce_any, np.any, np.bool_, self.BOOL_DATA, index_dtype) @test_util.disable_mlir_bridge('Error messages differ') def testReduceSumWithDuplicateAxes(self, index_dtype): with self.session() as sess: with self.test_scope(): a = array_ops.placeholder(np.float32) index = array_ops.placeholder(np.int32) out = math_ops.reduce_sum(a, index) with self.assertRaisesWithPredicateMatch( errors_impl.InvalidArgumentError, 'Axes contains duplicate dimension'): sess.run(out, {a: [10, 20, 30], index: [0, 0]}) class ReduceOpPrecisionTest(xla_test.XLATestCase): def _testReduceSum(self, expected_result, dtype, test_inputs, rtol=1e-3, atol=1e-4): """Tests reduce sum on a list of input arrays. For each array in test_inputs, check that performing reduce sum on the array produces a value that is close to the expected result. Args: expected_result: the expected result. dtype: the data type of the reduce sum operation. test_inputs: a list of input arrays for the reduce sum operation. rtol: the relative error. atol: the absolute error. """ for test_input in test_inputs: with self.session() as sess: with self.test_scope(): a = array_ops.placeholder(dtype) index = array_ops.placeholder(dtypes.int32) out = math_ops.reduce_sum(a, index) result = sess.run(out, { a: np.array(test_input, dtype=dtype), index: [0] }) # Compare the results using float32 type. self.assertAllClose( np.float32(result), np.float32(expected_result), rtol=rtol, atol=atol) def testReduceSumF16(self): """Tests the reduce sum of float16 doesn't lose too much precision.""" if np.float16 not in self.all_types: return f16_max = np.finfo(np.float16).max self._testReduceSum( f16_max, np.float16, itertools.permutations([f16_max, f16_max, f16_max * (-1.0)], 3)) def testReduceSumBF16(self): """Tests the reduce sum of bfloat16 doesn't lose too much precision.""" if dtypes.bfloat16.as_numpy_dtype not in self.all_types: return bf16_max = np.float32(dtypes.bfloat16.max) f32_max = dtypes.float32.max value = min(bf16_max, f32_max - bf16_max) / 2 self._testReduceSum( dtypes.bfloat16.as_numpy_dtype(value), dtypes.bfloat16.as_numpy_dtype, itertools.permutations([bf16_max, value, bf16_max * (-1.0)], 3)) if __name__ == '__main__': googletest.main()