test_strided_slice_op.py

# Copyright (c) 2019 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.

import unittest

import numpy as np
from op_test import OpTest

import paddle
import paddle.fluid as fluid

paddle.enable_static()


def strided_slice_native_forward(input, axes, starts, ends, strides):
    dim = input.ndim
    start = []
    end = []
    stride = []
    for i in range(dim):
        start.append(0)
        end.append(input.shape[i])
        stride.append(1)

    for i in range(len(axes)):
        start[axes[i]] = starts[i]
        end[axes[i]] = ends[i]
        stride[axes[i]] = strides[i]

    result = {
        1: lambda input, start, end, stride: input[
            start[0] : end[0] : stride[0]
        ],
        2: lambda input, start, end, stride: input[
            start[0] : end[0] : stride[0], start[1] : end[1] : stride[1]
        ],
        3: lambda input, start, end, stride: input[
            start[0] : end[0] : stride[0],
            start[1] : end[1] : stride[1],
            start[2] : end[2] : stride[2],
        ],
        4: lambda input, start, end, stride: input[
            start[0] : end[0] : stride[0],
            start[1] : end[1] : stride[1],
            start[2] : end[2] : stride[2],
            start[3] : end[3] : stride[3],
        ],
        5: lambda input, start, end, stride: input[
            start[0] : end[0] : stride[0],
            start[1] : end[1] : stride[1],
            start[2] : end[2] : stride[2],
            start[3] : end[3] : stride[3],
            start[4] : end[4] : stride[4],
        ],
        6: lambda input, start, end, stride: input[
            start[0] : end[0] : stride[0],
            start[1] : end[1] : stride[1],
            start[2] : end[2] : stride[2],
            start[3] : end[3] : stride[3],
            start[4] : end[4] : stride[4],
            start[5] : end[5] : stride[5],
        ],
    }[dim](input, start, end, stride)

    return result


class TestStrideSliceOp(OpTest):
    def setUp(self):
        self.initTestCase()
        self.op_type = 'strided_slice'
        self.python_api = paddle.strided_slice
        self.output = strided_slice_native_forward(
            self.input, self.axes, self.starts, self.ends, self.strides
        )

        self.inputs = {'Input': self.input}
        self.outputs = {'Out': self.output}
        self.attrs = {
            'axes': self.axes,
            'starts': self.starts,
            'ends': self.ends,
            'strides': self.strides,
            'infer_flags': self.infer_flags,
        }

    def test_check_output(self):
        self.check_output(check_eager=True)

    def test_check_grad(self):
        self.check_grad(set(['Input']), 'Out', check_eager=True)

    def initTestCase(self):
        self.input = np.random.rand(100)
        self.axes = [0]
        self.starts = [-4]
        self.ends = [-3]
        self.strides = [1]
        self.infer_flags = [1]


class TestStrideSliceOp1(TestStrideSliceOp):
    def initTestCase(self):
        self.input = np.random.rand(100)
        self.axes = [0]
        self.starts = [3]
        self.ends = [8]
        self.strides = [1]
        self.infer_flags = [1]


class TestStrideSliceOp2(TestStrideSliceOp):
    def initTestCase(self):
        self.input = np.random.rand(100)
        self.axes = [0]
        self.starts = [5]
        self.ends = [0]
        self.strides = [-1]
        self.infer_flags = [1]


class TestStrideSliceOp3(TestStrideSliceOp):
    def initTestCase(self):
        self.input = np.random.rand(100)
        self.axes = [0]
        self.starts = [-1]
        self.ends = [-3]
        self.strides = [-1]
        self.infer_flags = [1]


class TestStrideSliceOp4(TestStrideSliceOp):
    def initTestCase(self):
        self.input = np.random.rand(3, 4, 10)
        self.axes = [0, 1, 2]
        self.starts = [0, -1, 0]
        self.ends = [2, -3, 5]
        self.strides = [1, -1, 1]
        self.infer_flags = [1, 1, 1]


class TestStrideSliceOp5(TestStrideSliceOp):
    def initTestCase(self):
        self.input = np.random.rand(5, 5, 5)
        self.axes = [0, 1, 2]
        self.starts = [1, 0, 0]
        self.ends = [2, 1, 3]
        self.strides = [1, 1, 1]
        self.infer_flags = [1, 1, 1]


class TestStrideSliceOp6(TestStrideSliceOp):
    def initTestCase(self):
        self.input = np.random.rand(5, 5, 5)
        self.axes = [0, 1, 2]
        self.starts = [1, -1, 0]
        self.ends = [2, -3, 3]
        self.strides = [1, -1, 1]
        self.infer_flags = [1, 1, 1]


class TestStrideSliceOp7(TestStrideSliceOp):
    def initTestCase(self):
        self.input = np.random.rand(5, 5, 5)
        self.axes = [0, 1, 2]
        self.starts = [1, 0, 0]
        self.ends = [2, 2, 3]
        self.strides = [1, 1, 1]
        self.infer_flags = [1, 1, 1]


class TestStrideSliceOp8(TestStrideSliceOp):
    def initTestCase(self):
        self.input = np.random.rand(1, 100, 1)
        self.axes = [1]
        self.starts = [1]
        self.ends = [2]
        self.strides = [1]
        self.infer_flags = [1]


class TestStrideSliceOp9(TestStrideSliceOp):
    def initTestCase(self):
        self.input = np.random.rand(1, 100, 1)
        self.axes = [1]
        self.starts = [-1]
        self.ends = [-2]
        self.strides = [-1]
        self.infer_flags = [1]


class TestStrideSliceOp10(TestStrideSliceOp):
    def initTestCase(self):
        self.input = np.random.rand(10, 10)
        self.axes = [0, 1]
        self.starts = [1, 0]
        self.ends = [2, 2]
        self.strides = [1, 1]
        self.infer_flags = [1, 1]


class TestStrideSliceOp11(TestStrideSliceOp):
    def initTestCase(self):
        self.input = np.random.rand(3, 3, 3, 4)
        self.axes = [0, 1, 2, 3]
        self.starts = [1, 0, 0, 0]
        self.ends = [2, 2, 3, 4]
        self.strides = [1, 1, 1, 2]
        self.infer_flags = [1, 1, 1, 1]


class TestStrideSliceOp12(TestStrideSliceOp):
    def initTestCase(self):
        self.input = np.random.rand(3, 3, 3, 4, 5)
        self.axes = [0, 1, 2, 3, 4]
        self.starts = [1, 0, 0, 0, 0]
        self.ends = [2, 2, 3, 4, 4]
        self.strides = [1, 1, 1, 1, 1]
        self.infer_flags = [1, 1, 1, 1]


class TestStrideSliceOp13(TestStrideSliceOp):
    def initTestCase(self):
        self.input = np.random.rand(3, 3, 3, 6, 7, 8)
        self.axes = [0, 1, 2, 3, 4, 5]
        self.starts = [1, 0, 0, 0, 1, 2]
        self.ends = [2, 2, 3, 1, 2, 8]
        self.strides = [1, 1, 1, 1, 1, 2]
        self.infer_flags = [1, 1, 1, 1, 1]


class TestStrideSliceOp14(TestStrideSliceOp):
    def initTestCase(self):
        self.input = np.random.rand(4, 4, 4, 4)
        self.axes = [1, 2, 3]
        self.starts = [-5, 0, -7]
        self.ends = [-1, 2, 4]
        self.strides = [1, 1, 1]
        self.infer_flags = [1, 1, 1]


class TestStrideSliceOpBool(TestStrideSliceOp):
    def test_check_grad(self):
        pass


class TestStrideSliceOpBool1D(TestStrideSliceOpBool):
    def initTestCase(self):
        self.input = np.random.rand(100).astype("bool")
        self.axes = [0]
        self.starts = [3]
        self.ends = [8]
        self.strides = [1]
        self.infer_flags = [1]


class TestStrideSliceOpBool2D(TestStrideSliceOpBool):
    def initTestCase(self):
        self.input = np.random.rand(10, 10).astype("bool")
        self.axes = [0, 1]
        self.starts = [1, 0]
        self.ends = [2, 2]
        self.strides = [1, 1]
        self.infer_flags = [1, 1]


class TestStrideSliceOpBool3D(TestStrideSliceOpBool):
    def initTestCase(self):
        self.input = np.random.rand(3, 4, 10).astype("bool")
        self.axes = [0, 1, 2]
        self.starts = [0, -1, 0]
        self.ends = [2, -3, 5]
        self.strides = [1, -1, 1]
        self.infer_flags = [1, 1, 1]


class TestStrideSliceOpBool4D(TestStrideSliceOpBool):
    def initTestCase(self):
        self.input = np.random.rand(3, 3, 3, 4).astype("bool")
        self.axes = [0, 1, 2, 3]
        self.starts = [1, 0, 0, 0]
        self.ends = [2, 2, 3, 4]
        self.strides = [1, 1, 1, 2]
        self.infer_flags = [1, 1, 1, 1]


class TestStrideSliceOpBool5D(TestStrideSliceOpBool):
    def initTestCase(self):
        self.input = np.random.rand(3, 3, 3, 4, 5).astype("bool")
        self.axes = [0, 1, 2, 3, 4]
        self.starts = [1, 0, 0, 0, 0]
        self.ends = [2, 2, 3, 4, 4]
        self.strides = [1, 1, 1, 1, 1]
        self.infer_flags = [1, 1, 1, 1]


class TestStrideSliceOpBool6D(TestStrideSliceOpBool):
    def initTestCase(self):
        self.input = np.random.rand(3, 3, 3, 6, 7, 8).astype("bool")
        self.axes = [0, 1, 2, 3, 4, 5]
        self.starts = [1, 0, 0, 0, 1, 2]
        self.ends = [2, 2, 3, 1, 2, 8]
        self.strides = [1, 1, 1, 1, 1, 2]
        self.infer_flags = [1, 1, 1, 1, 1]


class TestStridedSliceOp_starts_ListTensor(OpTest):
    def setUp(self):
        self.op_type = "strided_slice"
        self.config()

        starts_tensor = []
        for index, ele in enumerate(self.starts):
            starts_tensor.append(
                ("x" + str(index), np.ones((1)).astype('int32') * ele)
            )

        self.inputs = {'Input': self.input, 'StartsTensorList': starts_tensor}
        self.outputs = {'Out': self.output}
        self.attrs = {
            'axes': self.axes,
            'starts': self.starts_infer,
            'ends': self.ends,
            'strides': self.strides,
            'infer_flags': self.infer_flags,
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 2]
        self.ends = [3, 3, 4]
        self.axes = [0, 1, 2]
        self.strides = [1, 1, 1]
        self.infer_flags = [1, -1, 1]
        self.output = strided_slice_native_forward(
            self.input, self.axes, self.starts, self.ends, self.strides
        )

        self.starts_infer = [1, 10, 2]

    def test_check_output(self):
        self.check_output()

    def test_check_grad_normal(self):
        self.check_grad(['Input'], 'Out', max_relative_error=0.006)


class TestStridedSliceOp_ends_ListTensor(OpTest):
    def setUp(self):
        self.op_type = "strided_slice"
        self.config()

        ends_tensor = []
        for index, ele in enumerate(self.ends):
            ends_tensor.append(
                ("x" + str(index), np.ones((1)).astype('int32') * ele)
            )

        self.inputs = {'Input': self.input, 'EndsTensorList': ends_tensor}
        self.outputs = {'Out': self.output}
        self.attrs = {
            'axes': self.axes,
            'starts': self.starts,
            'ends': self.ends_infer,
            'strides': self.strides,
            'infer_flags': self.infer_flags,
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 0]
        self.ends = [3, 3, 4]
        self.axes = [0, 1, 2]
        self.strides = [1, 1, 2]
        self.infer_flags = [1, -1, 1]
        self.output = strided_slice_native_forward(
            self.input, self.axes, self.starts, self.ends, self.strides
        )

        self.ends_infer = [3, 1, 4]

    def test_check_output(self):
        self.check_output()

    def test_check_grad_normal(self):
        self.check_grad(['Input'], 'Out', max_relative_error=0.006)


class TestStridedSliceOp_starts_Tensor(OpTest):
    def setUp(self):
        self.op_type = "strided_slice"
        self.config()
        self.inputs = {
            'Input': self.input,
            "StartsTensor": np.array(self.starts, dtype="int32"),
        }
        self.outputs = {'Out': self.output}
        self.attrs = {
            'axes': self.axes,
            # 'starts': self.starts,
            'ends': self.ends,
            'strides': self.strides,
            'infer_flags': self.infer_flags,
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 2]
        self.ends = [2, 3, 4]
        self.axes = [0, 1, 2]
        self.strides = [1, 1, 1]
        self.infer_flags = [-1, -1, -1]
        self.output = strided_slice_native_forward(
            self.input, self.axes, self.starts, self.ends, self.strides
        )

    def test_check_output(self):
        self.check_output()

    def test_check_grad_normal(self):
        self.check_grad(['Input'], 'Out', max_relative_error=0.006)


class TestStridedSliceOp_ends_Tensor(OpTest):
    def setUp(self):
        self.op_type = "strided_slice"
        self.config()
        self.inputs = {
            'Input': self.input,
            "EndsTensor": np.array(self.ends, dtype="int32"),
        }
        self.outputs = {'Out': self.output}
        self.attrs = {
            'axes': self.axes,
            'starts': self.starts,
            # 'ends': self.ends,
            'strides': self.strides,
            'infer_flags': self.infer_flags,
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 2]
        self.ends = [2, 3, 4]
        self.axes = [0, 1, 2]
        self.strides = [1, 1, 1]
        self.infer_flags = [-1, -1, -1]
        self.output = strided_slice_native_forward(
            self.input, self.axes, self.starts, self.ends, self.strides
        )

    def test_check_output(self):
        self.check_output()

    def test_check_grad_normal(self):
        self.check_grad(['Input'], 'Out', max_relative_error=0.006)


class TestStridedSliceOp_listTensor_Tensor(OpTest):
    def setUp(self):
        self.config()
        ends_tensor = []
        for index, ele in enumerate(self.ends):
            ends_tensor.append(
                ("x" + str(index), np.ones((1)).astype('int32') * ele)
            )
        self.op_type = "strided_slice"

        self.inputs = {
            'Input': self.input,
            "StartsTensor": np.array(self.starts, dtype="int32"),
            "EndsTensorList": ends_tensor,
        }
        self.outputs = {'Out': self.output}
        self.attrs = {
            'axes': self.axes,
            # 'starts': self.starts,
            # 'ends': self.ends,
            'strides': self.strides,
            'infer_flags': self.infer_flags,
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, 0, 2]
        self.ends = [2, 3, 4]
        self.axes = [0, 1, 2]
        self.strides = [1, 1, 1]
        self.infer_flags = [-1, -1, -1]
        self.output = strided_slice_native_forward(
            self.input, self.axes, self.starts, self.ends, self.strides
        )

    def test_check_output(self):
        self.check_output()

    def test_check_grad_normal(self):
        self.check_grad(['Input'], 'Out', max_relative_error=0.006)


class TestStridedSliceOp_strides_Tensor(OpTest):
    def setUp(self):
        self.op_type = "strided_slice"
        self.config()
        self.inputs = {
            'Input': self.input,
            "StridesTensor": np.array(self.strides, dtype="int32"),
        }
        self.outputs = {'Out': self.output}
        self.attrs = {
            'axes': self.axes,
            'starts': self.starts,
            'ends': self.ends,
            # 'strides': self.strides,
            'infer_flags': self.infer_flags,
        }

    def config(self):
        self.input = np.random.random([3, 4, 5, 6]).astype("float64")
        self.starts = [1, -1, 2]
        self.ends = [2, 0, 4]
        self.axes = [0, 1, 2]
        self.strides = [1, -1, 1]
        self.infer_flags = [-1, -1, -1]
        self.output = strided_slice_native_forward(
            self.input, self.axes, self.starts, self.ends, self.strides
        )

    def test_check_output(self):
        self.check_output()

    def test_check_grad_normal(self):
        self.check_grad(['Input'], 'Out', max_relative_error=0.006)


# Test python API
class TestStridedSliceAPI(unittest.TestCase):
    def test_1(self):
        input = np.random.random([3, 4, 5, 6]).astype("float64")
        minus_1 = fluid.layers.fill_constant([1], "int32", -1)
        minus_3 = fluid.layers.fill_constant([1], "int32", -3)
        starts = fluid.layers.data(
            name='starts', shape=[3], dtype='int32', append_batch_size=False
        )
        ends = fluid.layers.data(
            name='ends', shape=[3], dtype='int32', append_batch_size=False
        )
        strides = fluid.layers.data(
            name='strides', shape=[3], dtype='int32', append_batch_size=False
        )

        x = fluid.layers.data(
            name="x",
            shape=[3, 4, 5, 6],
            append_batch_size=False,
            dtype="float64",
        )
        out_1 = paddle.strided_slice(
            x,
            axes=[0, 1, 2],
            starts=[-3, 0, 2],
            ends=[3, 100, -1],
            strides=[1, 1, 1],
        )
        out_2 = paddle.strided_slice(
            x,
            axes=[0, 1, 3],
            starts=[minus_3, 0, 2],
            ends=[3, 100, -1],
            strides=[1, 1, 1],
        )
        out_3 = paddle.strided_slice(
            x,
            axes=[0, 1, 3],
            starts=[minus_3, 0, 2],
            ends=[3, 100, minus_1],
            strides=[1, 1, 1],
        )
        out_4 = paddle.strided_slice(
            x, axes=[0, 1, 2], starts=starts, ends=ends, strides=strides
        )

        out_5 = x[-3:3, 0:100:2, -1:2:-1]
        out_6 = x[minus_3:3:1, 0:100:2, :, minus_1:2:minus_1]
        out_7 = x[minus_1, 0:100:2, :, -1:2:-1]

        exe = fluid.Executor(place=fluid.CPUPlace())
        res_1, res_2, res_3, res_4, res_5, res_6, res_7 = exe.run(
            fluid.default_main_program(),
            feed={
                "x": input,
                'starts': np.array([-3, 0, 2]).astype("int32"),
                'ends': np.array([3, 2147483648, -1]).astype("int64"),
                'strides': np.array([1, 1, 1]).astype("int32"),
            },
            fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6, out_7],
        )
        assert np.array_equal(res_1, input[-3:3, 0:100, 2:-1, :])
        assert np.array_equal(res_2, input[-3:3, 0:100, :, 2:-1])
        assert np.array_equal(res_3, input[-3:3, 0:100, :, 2:-1])
        assert np.array_equal(res_4, input[-3:3, 0:100, 2:-1, :])
        assert np.array_equal(res_5, input[-3:3, 0:100:2, -1:2:-1, :])
        assert np.array_equal(res_6, input[-3:3, 0:100:2, :, -1:2:-1])
        assert np.array_equal(res_7, input[-1, 0:100:2, :, -1:2:-1])

    def test_dygraph_op(self):
        x = paddle.zeros(shape=[3, 4, 5, 6], dtype="float32")
        axes = [1, 2, 3]
        starts = [-3, 0, 2]
        ends = [3, 2, 4]
        strides_1 = [1, 1, 1]
        sliced_1 = paddle.strided_slice(
            x, axes=axes, starts=starts, ends=ends, strides=strides_1
        )
        assert sliced_1.shape == (3, 2, 2, 2)

    @unittest.skipIf(
        not paddle.is_compiled_with_cuda(),
        "Cannot use CUDAPinnedPlace in CPU only version",
    )
    def test_cuda_pinned_place(self):
        with paddle.fluid.dygraph.guard():
            x = paddle.to_tensor(
                np.random.randn(2, 10), place=paddle.CUDAPinnedPlace()
            )
            self.assertTrue(x.place.is_cuda_pinned_place())
            y = x[:, ::2]
            self.assertFalse(x.place.is_cuda_pinned_place())
            self.assertFalse(y.place.is_cuda_pinned_place())


class ArrayLayer(paddle.nn.Layer):
    def __init__(self, input_size=224, output_size=10, array_size=1):
        super().__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.array_size = array_size
        for i in range(self.array_size):
            setattr(
                self,
                self.create_name(i),
                paddle.nn.Linear(input_size, output_size),
            )

    def create_name(self, index):
        return 'linear_' + str(index)

    def forward(self, inps):
        array = []
        for i in range(self.array_size):
            linear = getattr(self, self.create_name(i))
            array.append(linear(inps))

        tensor_array = self.create_tensor_array(array)

        tensor_array = self.array_slice(tensor_array)

        array1 = paddle.concat(tensor_array)
        array2 = paddle.concat(tensor_array[::-1])
        return array1 + array2 * array2

    def get_all_grads(self, param_name='weight'):
        grads = []
        for i in range(self.array_size):
            linear = getattr(self, self.create_name(i))
            param = getattr(linear, param_name)

            g = param.grad
            if g is not None:
                g = g.numpy()

            grads.append(g)

        return grads

    def clear_all_grad(self):
        param_names = ['weight', 'bias']
        for i in range(self.array_size):
            linear = getattr(self, self.create_name(i))
            for p in param_names:
                param = getattr(linear, p)
                param.clear_gradient()

    def array_slice(self, array):
        return array

    def create_tensor_array(self, tensors):
        tensor_array = None
        for i, tensor in enumerate(tensors):
            index = paddle.full(shape=[1], dtype='int64', fill_value=i)
            if tensor_array is None:
                tensor_array = paddle.tensor.array_write(tensor, i=index)
            else:
                paddle.tensor.array_write(tensor, i=index, array=tensor_array)
        return tensor_array


class TestStridedSliceTensorArray(unittest.TestCase):
    def setUp(self):
        paddle.disable_static()

    def grad_equal(self, g1, g2):
        if g1 is None:
            g1 = np.zeros_like(g2)
        if g2 is None:
            g2 = np.zeros_like(g1)
        return np.array_equal(g1, g2)

    def is_grads_equal(self, g1, g2):
        for i, g in enumerate(g1):

            self.assertTrue(
                self.grad_equal(g, g2[i]),
                msg="gradient_1:\n{} \ngradient_2:\n{}".format(g, g2),
            )

    def is_grads_equal_zeros(self, grads):
        for g in grads:
            self.assertTrue(
                self.grad_equal(np.zeros_like(g), g),
                msg="The gradient should be zeros, but received \n{}".format(g),
            )

    def create_case(self, net):
        inps1 = paddle.randn([1, net.input_size], dtype='float32')
        inps2 = inps1.detach().clone()
        l1 = net(inps1)
        s1 = l1.numpy()
        l1.sum().backward()
        grads_dy = net.get_all_grads()
        net.clear_all_grad()
        grads_zeros = net.get_all_grads()

        self.is_grads_equal_zeros(grads_zeros)

        func = paddle.jit.to_static(net.forward)
        l2 = func(inps2)
        s2 = l2.numpy()
        l2.sum().backward()
        grads_static = net.get_all_grads()
        net.clear_all_grad()
        # compare result of dygraph and static
        self.is_grads_equal(grads_static, grads_dy)
        np.testing.assert_array_equal(
            s1,
            s2,
            err_msg='dygraph graph result:\n{} \nstatic dygraph result:\n{}'.format(
                l1.numpy(), l2.numpy()
            ),
        )

    def test_strided_slice_tensor_array_cuda_pinned_place(self):
        if paddle.device.is_compiled_with_cuda():
            with paddle.fluid.dygraph.guard():

                class Simple(paddle.nn.Layer):
                    def __init__(self):
                        super().__init__()

                    def forward(self, inps):
                        tensor_array = None
                        for i, tensor in enumerate(inps):
                            index = paddle.full(
                                shape=[1], dtype='int64', fill_value=i
                            )
                            if tensor_array is None:
                                tensor_array = paddle.tensor.array_write(
                                    tensor, i=index
                                )
                            else:
                                paddle.tensor.array_write(
                                    tensor, i=index, array=tensor_array
                                )

                        array1 = paddle.concat(tensor_array)
                        array2 = paddle.concat(tensor_array[::-1])
                        return array1 + array2 * array2

                net = Simple()
                func = paddle.jit.to_static(net.forward)

                inps1 = paddle.to_tensor(
                    np.random.randn(2, 10),
                    place=paddle.CUDAPinnedPlace(),
                    stop_gradient=False,
                )
                inps2 = paddle.to_tensor(
                    np.random.randn(2, 10),
                    place=paddle.CUDAPinnedPlace(),
                    stop_gradient=False,
                )

                self.assertTrue(inps1.place.is_cuda_pinned_place())
                self.assertTrue(inps2.place.is_cuda_pinned_place())

                result = func([inps1, inps2])

                self.assertFalse(result.place.is_cuda_pinned_place())

    def test_strided_slice_tensor_array(self):
        class Net01(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[::-1]

        self.create_case(Net01(array_size=10))

        class Net02(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[::-2]

        self.create_case(Net02(input_size=112, array_size=11))

        class Net03(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[::-3]

        self.create_case(Net03(input_size=112, array_size=9))

        class Net04(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[1::-4]

        self.create_case(Net04(input_size=112, array_size=9))

        class Net05(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[:7:-4]

        self.create_case(Net05(input_size=112, array_size=9))

        class Net06(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[8:0:-4]

        self.create_case(Net06(input_size=112, array_size=9))

        class Net07(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[8:1:-4]

        self.create_case(Net07(input_size=112, array_size=9))

        class Net08(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[::2]

        self.create_case(Net08(input_size=112, array_size=11))

        class Net09(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[::3]

        self.create_case(Net09(input_size=112, array_size=9))

        class Net10(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[1::4]

        self.create_case(Net10(input_size=112, array_size=9))

        class Net11(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[:8:4]

        self.create_case(Net11(input_size=112, array_size=9))

        class Net12(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[1:8:4]

        self.create_case(Net12(input_size=112, array_size=9))

        class Net13(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[8:10:4]

        self.create_case(Net13(input_size=112, array_size=13))

        class Net14(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[3:10:4]

        self.create_case(Net14(input_size=112, array_size=13))

        class Net15(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[2:10:4]

        self.create_case(Net15(input_size=112, array_size=13))

        class Net16(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[3:10:3]

        self.create_case(Net16(input_size=112, array_size=13))

        class Net17(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[3:15:3]

        self.create_case(Net17(input_size=112, array_size=13))

        class Net18(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[0:15:3]

        self.create_case(Net18(input_size=112, array_size=13))

        class Net19(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[-1:-5:-3]

        self.create_case(Net19(input_size=112, array_size=13))

        class Net20(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[-1:-6:-3]

        self.create_case(Net20(input_size=112, array_size=13))

        class Net21(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[-3:-6:-3]

        self.create_case(Net21(input_size=112, array_size=13))

        class Net22(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[-5:-1:3]

        self.create_case(Net22(input_size=112, array_size=13))

        class Net23(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[-6:-1:3]

        self.create_case(Net23(input_size=112, array_size=13))

        class Net24(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[-6:-3:3]

        self.create_case(Net24(input_size=112, array_size=13))

        class Net25(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[0::3]

        self.create_case(Net25(input_size=112, array_size=13))

        class Net26(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[-60:20:3]

        self.create_case(Net26(input_size=112, array_size=13))

        class Net27(ArrayLayer):
            def array_slice(self, tensors):
                return tensors[-3:-60:-3]

        self.create_case(Net27(input_size=112, array_size=13))


@unittest.skipIf(
    not fluid.core.is_compiled_with_cuda(), "core is not compiled with CUDA"
)
class TestStridedSliceFloat16(unittest.TestCase):
    def init_test_case(self):
        self.op_type = 'strided_slice'
        self.input_shape = [3, 3, 3, 6, 7, 8]
        self.axes = [0, 1, 2, 3, 4, 5]
        self.starts = [1, 0, 0, 0, 1, 2]
        self.ends = [2, 2, 3, 1, 2, 8]
        self.strides = [1, 1, 1, 1, 1, 2]
        self.infer_flags = [1, 1, 1, 1, 1]

    def check_main(self, x_np, dtype):
        paddle.disable_static()
        x_np = x_np.astype(dtype)
        x = paddle.to_tensor(x_np)
        x.stop_gradient = False
        output = strided_slice_native_forward(
            x, self.axes, self.starts, self.ends, self.strides
        )
        x_grad = paddle.grad(output, x)
        output_np = output[0].numpy().astype('float32')
        x_grad_np = x_grad[0].numpy().astype('float32')
        paddle.enable_static()
        return output_np, x_grad_np

    def test_check(self):
        self.init_test_case()
        x_np = np.random.random(self.input_shape).astype("float16")

        output_np_fp16, x_grad_np_fp16 = self.check_main(x_np, 'float16')
        output_np_fp32, x_grad_np_fp32 = self.check_main(x_np, 'float32')

        np.testing.assert_allclose(output_np_fp16, output_np_fp32)

        np.testing.assert_allclose(x_grad_np_fp16, x_grad_np_fp32)


if __name__ == "__main__":
    unittest.main()