text: # 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."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  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.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)
  
          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)
  
  
  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()
  