| # 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() |
