test_reduce_op.py

#   Copyright (c) 2018 PaddlePaddle 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.

from __future__ import print_function

import unittest
import numpy as np
from op_test import OpTest, skip_check_grad_ci, convert_float_to_uint16
import paddle
import paddle.fluid.core as core
import paddle.fluid as fluid
from paddle.fluid import compiler, Program, program_guard
from paddle.fluid.framework import convert_np_dtype_to_dtype_


class TestSumOp(OpTest):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
        self.outputs = {'Out': self.inputs['X'].sum(axis=0)}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')


class TestSumOp_fp16(OpTest):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.inputs = {
            'X': np.random.uniform(0, 0.1, (5, 6, 10)).astype("float16")
        }
        self.attrs = {'dim': [0, 1, 2]}
        self.outputs = {
            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
        }
        self.gradient = self.calc_gradient()

    def test_check_output(self):
        self.check_output()

    def calc_gradient(self):
        x = self.inputs["X"]
        grad = np.ones(x.shape, dtype=x.dtype)
        return grad,

    def test_check_grad(self):
        self.check_grad(['X'], 'Out', user_defined_grads=self.gradient)


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestSumOp_bf16(OpTest):
    def setUp(self):
        np.random.seed(100)
        self.op_type = "reduce_sum"
        self.dtype = np.uint16
        self.x = np.random.uniform(0, 0.1, (2, 5, 10)).astype(np.float32)
        self.attrs = {'dim': [0, 1, 2]}
        self.out = self.x.sum(axis=tuple(self.attrs['dim']))
        self.gradient = self.calc_gradient()

        self.inputs = {'X': convert_float_to_uint16(self.x)}
        self.outputs = {'Out': convert_float_to_uint16(self.out)}
        self.gradient = self.calc_gradient()

    def test_check_output(self):
        place = core.CUDAPlace(0)
        self.check_output_with_place(place)

    def test_check_grad(self):
        place = core.CUDAPlace(0)
        self.check_grad_with_place(
            place, ['X'], 'Out', user_defined_grads=self.gradient)

    def calc_gradient(self):
        x = self.x
        grad = np.ones(x.shape, dtype=x.dtype)
        return [grad]


class TestSumOp_fp16_withInt(OpTest):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.inputs = {
            # ref to https://en.wikipedia.org/wiki/Half-precision_floating-point_format
            # Precision limitations on integer values between 0 and 2048 can be exactly represented
            'X': np.random.randint(0, 30, (10, 10)).astype("float16")
        }
        self.attrs = {'dim': [0, 1]}
        self.outputs = {
            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
        }
        self.gradient = self.calc_gradient()

    def test_check_output(self):
        self.check_output()

    def calc_gradient(self):
        x = self.inputs["X"]
        grad = np.ones(x.shape, dtype=x.dtype)
        return grad,

    def test_check_grad(self):
        self.check_grad(['X'], 'Out', user_defined_grads=self.gradient)


class TestSumOp5D(OpTest):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.inputs = {
            'X': np.random.random((1, 2, 5, 6, 10)).astype("float64")
        }
        self.outputs = {'Out': self.inputs['X'].sum(axis=0)}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')


class TestSumOp6D(OpTest):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.inputs = {
            'X': np.random.random((1, 1, 2, 5, 6, 10)).astype("float64")
        }
        self.outputs = {'Out': self.inputs['X'].sum(axis=0)}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')


class TestSumOp8D(OpTest):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.inputs = {
            'X': np.random.random((1, 3, 1, 2, 1, 4, 3, 10)).astype("float64")
        }
        self.attrs = {'dim': (0, 3)}
        self.outputs = {'Out': self.inputs['X'].sum(axis=(0, 3))}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')


@skip_check_grad_ci(
    reason="reduce_max is discontinuous non-derivable function,"
    " its gradient check is not supported by unittest framework.")
class TestMaxOp(OpTest):
    """Remove Max with subgradient from gradient check to confirm the success of CI."""

    def setUp(self):
        self.op_type = "reduce_max"
        self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
        self.attrs = {'dim': [-1]}
        self.outputs = {
            'Out': self.inputs['X'].max(axis=tuple(self.attrs['dim']))
        }

    def test_check_output(self):
        self.check_output()


@skip_check_grad_ci(
    reason="reduce_min is discontinuous non-derivable function,"
    " its gradient check is not supported by unittest framework.")
class TestMinOp(OpTest):
    """Remove Min with subgradient from gradient check to confirm the success of CI."""

    def setUp(self):
        self.op_type = "reduce_min"
        self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
        self.attrs = {'dim': [2]}
        self.outputs = {
            'Out': self.inputs['X'].min(axis=tuple(self.attrs['dim']))
        }

    def test_check_output(self):
        self.check_output()


class TestMin6DOp(OpTest):
    """Remove Min with subgradient from gradient check to confirm the success of CI."""

    def setUp(self):
        self.op_type = "reduce_min"
        self.inputs = {
            'X': np.random.random((2, 4, 3, 5, 6, 10)).astype("float64")
        }
        self.attrs = {'dim': [2, 4]}
        self.outputs = {
            'Out': self.inputs['X'].min(axis=tuple(self.attrs['dim']))
        }

    def test_check_output(self):
        self.check_output()


class TestMin8DOp(OpTest):
    """Remove Min with subgradient from gradient check to confirm the success of CI."""

    def setUp(self):
        self.op_type = "reduce_min"
        self.inputs = {
            'X': np.random.random((2, 4, 3, 5, 6, 3, 2, 4)).astype("float64")
        }
        self.attrs = {'dim': [2, 3, 4]}
        self.outputs = {
            'Out': self.inputs['X'].min(axis=tuple(self.attrs['dim']))
        }

    def test_check_output(self):
        self.check_output()


class TestProdOp(OpTest):
    def setUp(self):
        self.op_type = "reduce_prod"
        self.init_data_type()
        self.inputs = {'X': np.random.random((5, 6, 10)).astype(self.data_type)}
        self.outputs = {'Out': self.inputs['X'].prod(axis=0)}

    def init_data_type(self):
        self.data_type = "float32" if core.is_compiled_with_rocm(
        ) else "float64"

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')


class TestProd6DOp(OpTest):
    def setUp(self):
        self.op_type = "reduce_prod"
        self.init_data_type()
        self.inputs = {
            'X': np.random.random((5, 6, 2, 3, 4, 2)).astype(self.data_type)
        }
        self.attrs = {'dim': [2, 3, 4]}
        self.outputs = {
            'Out': self.inputs['X'].prod(axis=tuple(self.attrs['dim']))
        }

    def init_data_type(self):
        self.data_type = "float32" if core.is_compiled_with_rocm(
        ) else "float64"

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')


class TestProd8DOp(OpTest):
    def setUp(self):
        self.op_type = "reduce_prod"
        self.init_data_type()
        self.inputs = {
            'X': np.random.random(
                (2, 5, 3, 2, 2, 3, 4, 2)).astype(self.data_type)
        }
        self.attrs = {'dim': [2, 3, 4]}
        self.outputs = {
            'Out': self.inputs['X'].prod(axis=tuple(self.attrs['dim']))
        }

    def init_data_type(self):
        self.data_type = "float32" if core.is_compiled_with_rocm(
        ) else "float64"

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')


class TestAllOp(OpTest):
    def setUp(self):
        self.op_type = "reduce_all"
        self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
        self.outputs = {'Out': self.inputs['X'].all()}
        self.attrs = {'reduce_all': True}

    def test_check_output(self):
        self.check_output()


class TestAll8DOp(OpTest):
    def setUp(self):
        self.op_type = "reduce_all"
        self.inputs = {
            'X': np.random.randint(0, 2,
                                   (2, 5, 3, 2, 2, 3, 4, 2)).astype("bool")
        }
        self.attrs = {'reduce_all': True, 'dim': (2, 3, 4)}
        self.outputs = {'Out': self.inputs['X'].all(axis=self.attrs['dim'])}

    def test_check_output(self):
        self.check_output()


class TestAllOpWithDim(OpTest):
    def setUp(self):
        self.op_type = "reduce_all"
        self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
        self.attrs = {'dim': (1, )}
        self.outputs = {'Out': self.inputs['X'].all(axis=self.attrs['dim'])}

    def test_check_output(self):
        self.check_output()


class TestAll8DOpWithDim(OpTest):
    def setUp(self):
        self.op_type = "reduce_all"
        self.inputs = {
            'X': np.random.randint(0, 2,
                                   (2, 5, 3, 2, 2, 3, 4, 2)).astype("bool")
        }
        self.attrs = {'dim': (1, 3, 4)}
        self.outputs = {'Out': self.inputs['X'].all(axis=self.attrs['dim'])}

    def test_check_output(self):
        self.check_output()


class TestAllOpWithKeepDim(OpTest):
    def setUp(self):
        self.op_type = "reduce_all"
        self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
        self.attrs = {'dim': [1], 'keep_dim': True}
        self.outputs = {
            'Out': np.expand_dims(
                self.inputs['X'].all(axis=1), axis=1)
        }

    def test_check_output(self):
        self.check_output()


class TestAll8DOpWithKeepDim(OpTest):
    def setUp(self):
        self.op_type = "reduce_all"
        self.inputs = {
            'X': np.random.randint(0, 2,
                                   (2, 5, 3, 2, 2, 3, 4, 2)).astype("bool")
        }
        self.attrs = {'dim': (5, ), 'keep_dim': True}
        self.outputs = {
            'Out': np.expand_dims(
                self.inputs['X'].all(axis=self.attrs['dim']), axis=5)
        }

    def test_check_output(self):
        self.check_output()


class TestAllOpError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):
            # The input type of reduce_all_op must be Variable.
            input1 = 12
            self.assertRaises(TypeError, fluid.layers.reduce_all, input1)
            # The input dtype of reduce_all_op must be bool.
            input2 = fluid.layers.data(
                name='input2', shape=[12, 10], dtype="int32")
            self.assertRaises(TypeError, fluid.layers.reduce_all, input2)


class TestAnyOp(OpTest):
    def setUp(self):
        self.op_type = "reduce_any"
        self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
        self.outputs = {'Out': self.inputs['X'].any()}
        self.attrs = {'reduce_all': True}

    def test_check_output(self):
        self.check_output()


class TestAny8DOp(OpTest):
    def setUp(self):
        self.op_type = "reduce_any"
        self.inputs = {
            'X': np.random.randint(0, 2,
                                   (2, 5, 3, 2, 2, 3, 4, 2)).astype("bool")
        }
        self.attrs = {'reduce_all': True, 'dim': (3, 5, 4)}
        self.outputs = {'Out': self.inputs['X'].any(axis=self.attrs['dim'])}

    def test_check_output(self):
        self.check_output()


class TestAnyOpWithDim(OpTest):
    def setUp(self):
        self.op_type = "reduce_any"
        self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
        self.attrs = {'dim': [1]}
        self.outputs = {'Out': self.inputs['X'].any(axis=1)}

    def test_check_output(self):
        self.check_output()


class TestAny8DOpWithDim(OpTest):
    def setUp(self):
        self.op_type = "reduce_any"
        self.inputs = {
            'X': np.random.randint(0, 2,
                                   (2, 5, 3, 2, 2, 3, 4, 2)).astype("bool")
        }
        self.attrs = {'dim': (3, 6)}
        self.outputs = {'Out': self.inputs['X'].any(axis=self.attrs['dim'])}

    def test_check_output(self):
        self.check_output()


class TestAnyOpWithKeepDim(OpTest):
    def setUp(self):
        self.op_type = "reduce_any"
        self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
        self.attrs = {'dim': (1, ), 'keep_dim': True}
        self.outputs = {
            'Out': np.expand_dims(
                self.inputs['X'].any(axis=self.attrs['dim']), axis=1)
        }

    def test_check_output(self):
        self.check_output()


class TestAny8DOpWithKeepDim(OpTest):
    def setUp(self):
        self.op_type = "reduce_any"
        self.inputs = {
            'X': np.random.randint(0, 2,
                                   (2, 5, 3, 2, 2, 3, 4, 2)).astype("bool")
        }
        self.attrs = {'dim': (1, ), 'keep_dim': True}
        self.outputs = {
            'Out': np.expand_dims(
                self.inputs['X'].any(axis=self.attrs['dim']), axis=1)
        }

    def test_check_output(self):
        self.check_output()


class TestAnyOpError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):
            # The input type of reduce_any_op must be Variable.
            input1 = 12
            self.assertRaises(TypeError, fluid.layers.reduce_any, input1)
            # The input dtype of reduce_any_op must be bool.
            input2 = fluid.layers.data(
                name='input2', shape=[12, 10], dtype="int32")
            self.assertRaises(TypeError, fluid.layers.reduce_any, input2)


class Test1DReduce(OpTest):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.inputs = {'X': np.random.random(120).astype("float64")}
        self.outputs = {'Out': self.inputs['X'].sum(axis=0)}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')


class Test2DReduce0(Test1DReduce):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.attrs = {'dim': [0]}
        self.inputs = {'X': np.random.random((20, 10)).astype("float64")}
        self.outputs = {'Out': self.inputs['X'].sum(axis=0)}


class Test2DReduce1(Test1DReduce):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.attrs = {'dim': [1]}
        self.inputs = {'X': np.random.random((20, 10)).astype("float64")}
        self.outputs = {
            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
        }


class Test3DReduce0(Test1DReduce):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.attrs = {'dim': [1]}
        self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
        self.outputs = {
            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
        }


class Test3DReduce1(Test1DReduce):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.attrs = {'dim': [2]}
        self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
        self.outputs = {
            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
        }


class Test3DReduce2(Test1DReduce):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.attrs = {'dim': [-2]}
        self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
        self.outputs = {
            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
        }


class Test3DReduce3(Test1DReduce):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.attrs = {'dim': [1, 2]}
        self.inputs = {'X': np.random.random((5, 6, 7)).astype("float64")}
        self.outputs = {
            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
        }


class Test8DReduce0(Test1DReduce):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.attrs = {'dim': (4, 2, 3)}
        self.inputs = {
            'X': np.random.random((2, 5, 3, 2, 2, 3, 4, 2)).astype("float64")
        }
        self.outputs = {
            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
        }


class TestKeepDimReduce(Test1DReduce):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
        self.attrs = {'dim': [1], 'keep_dim': True}
        self.outputs = {
            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']),
                                        keepdims=self.attrs['keep_dim'])
        }


class TestKeepDim8DReduce(Test1DReduce):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.inputs = {
            'X': np.random.random((2, 5, 3, 2, 2, 3, 4, 2)).astype("float64")
        }
        self.attrs = {'dim': (3, 4, 5), 'keep_dim': True}
        self.outputs = {
            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']),
                                        keepdims=self.attrs['keep_dim'])
        }


@skip_check_grad_ci(
    reason="reduce_max is discontinuous non-derivable function,"
    " its gradient check is not supported by unittest framework.")
class TestReduceMaxOpMultiAxises(OpTest):
    """Remove Max with subgradient from gradient check to confirm the success of CI."""

    def setUp(self):
        self.op_type = "reduce_max"
        self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
        self.attrs = {'dim': [-2, -1]}
        self.outputs = {
            'Out': self.inputs['X'].max(axis=tuple(self.attrs['dim']))
        }

    def test_check_output(self):
        self.check_output()


@skip_check_grad_ci(
    reason="reduce_min is discontinuous non-derivable function,"
    " its gradient check is not supported by unittest framework.")
class TestReduceMinOpMultiAxises(OpTest):
    """Remove Min with subgradient from gradient check to confirm the success of CI."""

    def setUp(self):
        self.op_type = "reduce_min"
        self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
        self.attrs = {'dim': [1, 2]}
        self.outputs = {
            'Out': self.inputs['X'].min(axis=tuple(self.attrs['dim']))
        }

    def test_check_output(self):
        self.check_output()


class TestKeepDimReduceSumMultiAxises(OpTest):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
        self.attrs = {'dim': [-2, -1], 'keep_dim': True}
        self.outputs = {
            'Out':
            self.inputs['X'].sum(axis=tuple(self.attrs['dim']), keepdims=True)
        }

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')


class TestReduceSumWithDimOne(OpTest):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.inputs = {'X': np.random.random((100, 1, 1)).astype("float64")}
        self.attrs = {'dim': [1, 2], 'keep_dim': True}
        self.outputs = {
            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']),
                                        keepdims=True)
        }

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')


class TestReduceSumWithNumelOne(OpTest):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.inputs = {'X': np.random.random((100, 1)).astype("float64")}
        self.attrs = {'dim': [1], 'keep_dim': False}
        self.outputs = {
            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']),
                                        keepdims=False)
        }

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')


class TestReduceAll(OpTest):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.inputs = {'X': np.random.random((100, 1, 1)).astype("float64")}
        self.attrs = {'reduce_all': True, 'keep_dim': False}
        self.outputs = {'Out': self.inputs['X'].sum()}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')


class Test1DReduceWithAxes1(OpTest):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.inputs = {'X': np.random.random(100).astype("float64")}
        self.attrs = {'dim': [0], 'keep_dim': False}
        self.outputs = {'Out': self.inputs['X'].sum(axis=0)}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')


class TestReduceWithDtype(OpTest):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.inputs = {'X': np.random.random((6, 2, 10)).astype("float64")}
        self.outputs = {'Out': self.inputs['X'].sum().astype('float64')}
        self.attrs = {'reduce_all': True}
        self.attrs.update({
            'in_dtype': int(convert_np_dtype_to_dtype_(np.float32)),
            'out_dtype': int(convert_np_dtype_to_dtype_(np.float64))
        })

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')


class TestReduceWithDtype1(TestReduceWithDtype):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.inputs = {'X': np.random.random((6, 2, 10)).astype("float64")}
        self.outputs = {'Out': self.inputs['X'].sum(axis=1)}
        self.attrs = {'dim': [1]}
        self.attrs.update({
            'in_dtype': int(convert_np_dtype_to_dtype_(np.float32)),
            'out_dtype': int(convert_np_dtype_to_dtype_(np.float64))
        })


class TestReduceWithDtype2(TestReduceWithDtype):
    def setUp(self):
        self.op_type = "reduce_sum"
        self.inputs = {'X': np.random.random((6, 2, 10)).astype("float64")}
        self.outputs = {'Out': self.inputs['X'].sum(axis=1, keepdims=True)}
        self.attrs = {'dim': [1], 'keep_dim': True}
        self.attrs.update({
            'in_dtype': int(convert_np_dtype_to_dtype_(np.float32)),
            'out_dtype': int(convert_np_dtype_to_dtype_(np.float64))
        })


class TestReduceSumOpError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):
            # The input type of reduce_sum_op must be Variable.
            x1 = fluid.create_lod_tensor(
                np.array([[-1]]), [[1]], fluid.CPUPlace())
            self.assertRaises(TypeError, fluid.layers.reduce_sum, x1)
            # The input dtype of reduce_sum_op  must be float32 or float64 or int32 or int64.
            x2 = fluid.layers.data(name='x2', shape=[4], dtype="uint8")
            self.assertRaises(TypeError, fluid.layers.reduce_sum, x2)


class API_TestSumOp(unittest.TestCase):
    def run_static(self,
                   shape,
                   x_dtype,
                   attr_axis,
                   attr_dtype=None,
                   np_axis=None):
        if np_axis is None:
            np_axis = attr_axis

        places = [fluid.CPUPlace()]
        if core.is_compiled_with_cuda():
            places.append(fluid.CUDAPlace(0))
        for place in places:
            with fluid.program_guard(fluid.Program(), fluid.Program()):
                data = fluid.data("data", shape=shape, dtype=x_dtype)
                result_sum = paddle.sum(x=data,
                                        axis=attr_axis,
                                        dtype=attr_dtype)

                exe = fluid.Executor(place)
                input_data = np.random.rand(*shape).astype(x_dtype)
                res, = exe.run(feed={"data": input_data},
                               fetch_list=[result_sum])

            self.assertTrue(
                np.allclose(
                    res, np.sum(input_data.astype(attr_dtype), axis=np_axis)))

    def test_static(self):
        shape = [10, 10]
        axis = 1

        self.run_static(shape, "bool", axis, attr_dtype=None)
        self.run_static(shape, "bool", axis, attr_dtype="int32")
        self.run_static(shape, "bool", axis, attr_dtype="int64")
        self.run_static(shape, "bool", axis, attr_dtype="float16")

        self.run_static(shape, "int32", axis, attr_dtype=None)
        self.run_static(shape, "int32", axis, attr_dtype="int32")
        self.run_static(shape, "int32", axis, attr_dtype="int64")
        self.run_static(shape, "int32", axis, attr_dtype="float64")

        self.run_static(shape, "int64", axis, attr_dtype=None)
        self.run_static(shape, "int64", axis, attr_dtype="int64")
        self.run_static(shape, "int64", axis, attr_dtype="int32")

        self.run_static(shape, "float32", axis, attr_dtype=None)
        self.run_static(shape, "float32", axis, attr_dtype="float32")
        self.run_static(shape, "float32", axis, attr_dtype="float64")
        self.run_static(shape, "float32", axis, attr_dtype="int64")

        self.run_static(shape, "float64", axis, attr_dtype=None)
        self.run_static(shape, "float64", axis, attr_dtype="float32")
        self.run_static(shape, "float64", axis, attr_dtype="float64")

        shape = [5, 5, 5]
        self.run_static(shape, "int32", (0, 1), attr_dtype="int32")
        self.run_static(
            shape, "int32", (), attr_dtype="int32", np_axis=(0, 1, 2))

    def test_dygraph(self):
        np_x = np.random.random([2, 3, 4]).astype('int32')
        with fluid.dygraph.guard():
            x = fluid.dygraph.to_variable(np_x)
            out0 = paddle.sum(x).numpy()
            out1 = paddle.sum(x, axis=0).numpy()
            out2 = paddle.sum(x, axis=(0, 1)).numpy()
            out3 = paddle.sum(x, axis=(0, 1, 2)).numpy()

        self.assertTrue((out0 == np.sum(np_x, axis=(0, 1, 2))).all())
        self.assertTrue((out1 == np.sum(np_x, axis=0)).all())
        self.assertTrue((out2 == np.sum(np_x, axis=(0, 1))).all())
        self.assertTrue((out3 == np.sum(np_x, axis=(0, 1, 2))).all())


class TestAllAPI(unittest.TestCase):
    def setUp(self):
        np.random.seed(123)
        paddle.enable_static()
        self.places = [fluid.CPUPlace()]
        if core.is_compiled_with_cuda():
            self.places.append(fluid.CUDAPlace(0))

    def check_static_result(self, place):
        with fluid.program_guard(fluid.Program(), fluid.Program()):
            input = fluid.data(name="input", shape=[4, 4], dtype="bool")
            result = paddle.all(x=input)
            input_np = np.random.randint(0, 2, [4, 4]).astype("bool")

            exe = fluid.Executor(place)
            fetches = exe.run(fluid.default_main_program(),
                              feed={"input": input_np},
                              fetch_list=[result])
            self.assertTrue(np.allclose(fetches[0], np.all(input_np)))

    def test_static(self):
        for place in self.places:
            self.check_static_result(place=place)

    def test_dygraph(self):
        paddle.disable_static()
        for place in self.places:
            with fluid.dygraph.guard(place):
                np_x = np.random.randint(0, 2, (12, 10)).astype(np.bool)
                x = fluid.layers.assign(np_x)
                x = fluid.layers.cast(x, 'bool')

                out1 = paddle.all(x)
                np_out1 = out1.numpy()
                expect_res1 = np.all(np_x)
                self.assertTrue((np_out1 == expect_res1).all())

                out2 = paddle.all(x, axis=0)
                np_out2 = out2.numpy()
                expect_res2 = np.all(np_x, axis=0)
                self.assertTrue((np_out2 == expect_res2).all())

                out3 = paddle.all(x, axis=-1)
                np_out3 = out3.numpy()
                expect_res3 = np.all(np_x, axis=-1)
                self.assertTrue((np_out3 == expect_res3).all())

                out4 = paddle.all(x, axis=1, keepdim=True)
                np_out4 = out4.numpy()
                expect_res4 = np.all(np_x, axis=1, keepdims=True)
                self.assertTrue((np_out4 == expect_res4).all())

        paddle.enable_static()


class TestAnyAPI(unittest.TestCase):
    def setUp(self):
        np.random.seed(123)
        paddle.enable_static()
        self.places = [fluid.CPUPlace()]
        if core.is_compiled_with_cuda():
            self.places.append(fluid.CUDAPlace(0))

    def check_static_result(self, place):
        with fluid.program_guard(fluid.Program(), fluid.Program()):
            input = fluid.data(name="input", shape=[4, 4], dtype="bool")
            result = paddle.any(x=input)
            input_np = np.random.randint(0, 2, [4, 4]).astype("bool")

            exe = fluid.Executor(place)
            fetches = exe.run(fluid.default_main_program(),
                              feed={"input": input_np},
                              fetch_list=[result])
            self.assertTrue(np.allclose(fetches[0], np.any(input_np)))

    def test_static(self):
        for place in self.places:
            self.check_static_result(place=place)

    def test_dygraph(self):
        paddle.disable_static()
        for place in self.places:
            with fluid.dygraph.guard(place):
                np_x = np.random.randint(0, 2, (12, 10)).astype(np.bool)
                x = fluid.layers.assign(np_x)
                x = fluid.layers.cast(x, 'bool')

                out1 = paddle.any(x)
                np_out1 = out1.numpy()
                expect_res1 = np.any(np_x)
                self.assertTrue((np_out1 == expect_res1).all())

                out2 = paddle.any(x, axis=0)
                np_out2 = out2.numpy()
                expect_res2 = np.any(np_x, axis=0)
                self.assertTrue((np_out2 == expect_res2).all())

                out3 = paddle.any(x, axis=-1)
                np_out3 = out3.numpy()
                expect_res3 = np.any(np_x, axis=-1)
                self.assertTrue((np_out3 == expect_res3).all())

                out4 = paddle.any(x, axis=1, keepdim=True)
                np_out4 = out4.numpy()
                expect_res4 = np.any(np_x, axis=1, keepdims=True)
                self.assertTrue((np_out4 == expect_res4).all())

        paddle.enable_static()


if __name__ == '__main__':
    import paddle
    paddle.enable_static()
    unittest.main()