test_variable.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.

import unittest
from functools import reduce

import numpy as np

import paddle
from paddle import base
from paddle.base import core
from paddle.base.framework import (
    Program,
    convert_np_dtype_to_dtype_,
    default_main_program,
)

paddle.enable_static()


class TestVariable(unittest.TestCase):
    def setUp(self):
        np.random.seed(2022)

    def test_np_dtype_convert(self):
        DT = core.VarDesc.VarType
        convert = convert_np_dtype_to_dtype_
        self.assertEqual(DT.FP32, convert(np.float32))
        self.assertEqual(DT.FP16, convert("float16"))
        self.assertEqual(DT.FP64, convert("float64"))
        self.assertEqual(DT.INT32, convert("int32"))
        self.assertEqual(DT.INT16, convert("int16"))
        self.assertEqual(DT.INT64, convert("int64"))
        self.assertEqual(DT.BOOL, convert("bool"))
        self.assertEqual(DT.INT8, convert("int8"))
        self.assertEqual(DT.UINT8, convert("uint8"))

    def test_var(self):
        b = default_main_program().current_block()
        w = b.create_var(
            dtype="float64", shape=[784, 100], lod_level=0, name="fc.w"
        )
        self.assertNotEqual(str(w), "")
        self.assertEqual(core.VarDesc.VarType.FP64, w.dtype)
        self.assertEqual((784, 100), w.shape)
        self.assertEqual("fc.w", w.name)
        self.assertEqual("fc.w@GRAD", w.grad_name)
        self.assertEqual(0, w.lod_level)

        w = b.create_var(name='fc.w')
        self.assertEqual(core.VarDesc.VarType.FP64, w.dtype)
        self.assertEqual((784, 100), w.shape)
        self.assertEqual("fc.w", w.name)
        self.assertEqual("fc.w@GRAD", w.grad_name)
        self.assertEqual(0, w.lod_level)

        self.assertRaises(
            ValueError, lambda: b.create_var(name="fc.w", shape=(24, 100))
        )

        w = b.create_var(
            dtype=paddle.base.core.VarDesc.VarType.STRINGS,
            shape=[1],
            name="str_var",
        )
        self.assertEqual(None, w.lod_level)

    def test_element_size(self):
        with base.program_guard(Program(), Program()):
            x = paddle.static.data(name='x1', shape=[2], dtype='bool')
            self.assertEqual(x.element_size(), 1)

            x = paddle.static.data(name='x2', shape=[2], dtype='float16')
            self.assertEqual(x.element_size(), 2)

            x = paddle.static.data(name='x3', shape=[2], dtype='float32')
            self.assertEqual(x.element_size(), 4)

            x = paddle.static.data(name='x4', shape=[2], dtype='float64')
            self.assertEqual(x.element_size(), 8)

            x = paddle.static.data(name='x5', shape=[2], dtype='int8')
            self.assertEqual(x.element_size(), 1)

            x = paddle.static.data(name='x6', shape=[2], dtype='int16')
            self.assertEqual(x.element_size(), 2)

            x = paddle.static.data(name='x7', shape=[2], dtype='int32')
            self.assertEqual(x.element_size(), 4)

            x = paddle.static.data(name='x8', shape=[2], dtype='int64')
            self.assertEqual(x.element_size(), 8)

            x = paddle.static.data(name='x9', shape=[2], dtype='uint8')
            self.assertEqual(x.element_size(), 1)

    def test_step_scopes(self):
        prog = Program()
        b = prog.current_block()
        var = b.create_var(
            name='step_scopes', type=core.VarDesc.VarType.STEP_SCOPES
        )
        self.assertEqual(core.VarDesc.VarType.STEP_SCOPES, var.type)

    def _test_slice(self, place):
        b = default_main_program().current_block()
        w = b.create_var(dtype="float64", shape=[784, 100, 100], lod_level=0)

        for i in range(3):
            nw = w[i]
            self.assertEqual((100, 100), nw.shape)

        nw = w[:]
        self.assertEqual((784, 100, 100), nw.shape)

        nw = w[:, :]
        self.assertEqual((784, 100, 100), nw.shape)

        nw = w[:, :, -1]
        self.assertEqual((784, 100), nw.shape)

        nw = w[1, 1, 1]

        self.assertEqual(len(nw.shape), 0)

        nw = w[:, :, :-1]
        self.assertEqual((784, 100, 99), nw.shape)

        self.assertEqual(0, nw.lod_level)

        main = base.Program()
        with base.program_guard(main):
            exe = base.Executor(place)
            tensor_array = np.array(
                [
                    [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
                    [[10, 11, 12], [13, 14, 15], [16, 17, 18]],
                    [[19, 20, 21], [22, 23, 24], [25, 26, 27]],
                ]
            ).astype('float32')
            var = paddle.assign(tensor_array)
            var1 = var[0, 1, 1]
            var2 = var[1:]
            var3 = var[0:1]
            var4 = var[::-1]
            var5 = var[1, 1:, 1:]
            var_reshape = paddle.reshape(var, [3, -1, 3])
            var6 = var_reshape[:, :, -1]
            var7 = var[:, :, :-1]
            var8 = var[:1, :1, :1]
            var9 = var[:-1, :-1, :-1]
            var10 = var[::-1, :1, :-1]
            var11 = var[:-1, ::-1, -1:]
            var12 = var[1:2, 2:, ::-1]
            var13 = var[2:10, 2:, -2:-1]
            var14 = var[1:-1, 0:2, ::-1]
            var15 = var[::-1, ::-1, ::-1]

            x = paddle.static.data(name='x', shape=[-1, 13], dtype='float32')
            y = paddle.static.nn.fc(x, size=1, activation=None)
            y_1 = y[:, 0]
            feeder = base.DataFeeder(place=place, feed_list=[x])
            data = []
            data.append(np.random.randint(10, size=[13]).astype('float32'))
            exe.run(base.default_startup_program())

            local_out = exe.run(
                main,
                feed=feeder.feed([data]),
                fetch_list=[
                    var,
                    var1,
                    var2,
                    var3,
                    var4,
                    var5,
                    var6,
                    var7,
                    var8,
                    var9,
                    var10,
                    var11,
                    var12,
                    var13,
                    var14,
                    var15,
                ],
            )

            np.testing.assert_array_equal(local_out[1], tensor_array[0, 1, 1:2])
            np.testing.assert_array_equal(local_out[2], tensor_array[1:])
            np.testing.assert_array_equal(local_out[3], tensor_array[0:1])
            np.testing.assert_array_equal(local_out[4], tensor_array[::-1])
            np.testing.assert_array_equal(local_out[5], tensor_array[1, 1:, 1:])
            np.testing.assert_array_equal(
                local_out[6], tensor_array.reshape((3, -1, 3))[:, :, -1]
            )
            np.testing.assert_array_equal(local_out[7], tensor_array[:, :, :-1])
            np.testing.assert_array_equal(
                local_out[8], tensor_array[:1, :1, :1]
            )
            np.testing.assert_array_equal(
                local_out[9], tensor_array[:-1, :-1, :-1]
            )
            np.testing.assert_array_equal(
                local_out[10], tensor_array[::-1, :1, :-1]
            )
            np.testing.assert_array_equal(
                local_out[11], tensor_array[:-1, ::-1, -1:]
            )
            np.testing.assert_array_equal(
                local_out[12], tensor_array[1:2, 2:, ::-1]
            )
            np.testing.assert_array_equal(
                local_out[13], tensor_array[2:10, 2:, -2:-1]
            )
            np.testing.assert_array_equal(
                local_out[14], tensor_array[1:-1, 0:2, ::-1]
            )
            np.testing.assert_array_equal(
                local_out[15], tensor_array[::-1, ::-1, ::-1]
            )

    def _test_slice_index_tensor(self, place):
        data = np.random.rand(2, 3).astype("float32")
        prog = paddle.static.Program()
        with paddle.static.program_guard(prog):
            x = paddle.assign(data)
            idx0 = [1, 0]
            idx1 = [0, 1]
            idx2 = [0, 0]
            idx3 = [1, 1]

            out0 = x[paddle.assign(np.array(idx0))]
            out1 = x[paddle.assign(np.array(idx1))]
            out2 = x[paddle.assign(np.array(idx2))]
            out3 = x[paddle.assign(np.array(idx3))]

        exe = paddle.static.Executor(place)
        result = exe.run(prog, fetch_list=[out0, out1, out2, out3])

        expected = [data[idx0], data[idx1], data[idx2], data[idx3]]

        self.assertTrue((result[0] == expected[0]).all())
        self.assertTrue((result[1] == expected[1]).all())
        self.assertTrue((result[2] == expected[2]).all())
        self.assertTrue((result[3] == expected[3]).all())

    def _test_slice_index_list(self, place):
        data = np.random.rand(2, 3).astype("float32")
        prog = paddle.static.Program()
        with paddle.static.program_guard(prog):
            x = paddle.assign(data)
            idx0 = [1, 0]
            idx1 = [0, 1]
            idx2 = [0, 0]
            idx3 = [1, 1]

            out0 = x[idx0]
            out1 = x[idx1]
            out2 = x[idx2]
            out3 = x[idx3]

        exe = paddle.static.Executor(place)
        result = exe.run(prog, fetch_list=[out0, out1, out2, out3])

        expected = [data[idx0], data[idx1], data[idx2], data[idx3]]

        self.assertTrue((result[0] == expected[0]).all())
        self.assertTrue((result[1] == expected[1]).all())
        self.assertTrue((result[2] == expected[2]).all())
        self.assertTrue((result[3] == expected[3]).all())

    def _test_slice_index_ellipsis(self, place):
        data = np.random.rand(2, 3, 4).astype("float32")
        prog = paddle.static.Program()
        with paddle.static.program_guard(prog):
            x = paddle.assign(data)
            y = paddle.assign([1, 2, 3, 4])
            out1 = x[0:, ..., 1:]
            out2 = x[0:, ...]
            out3 = x[..., 1:]
            out4 = x[...]
            out5 = x[[1, 0], [0, 0]]
            out6 = x[([1, 0], [0, 0])]
            out7 = y[..., 0]

        exe = paddle.static.Executor(place)
        result = exe.run(
            prog, fetch_list=[out1, out2, out3, out4, out5, out6, out7]
        )

        expected = [
            data[0:, ..., 1:],
            data[0:, ...],
            data[..., 1:],
            data[...],
            data[[1, 0], [0, 0]],
            data[([1, 0], [0, 0])],
            np.array([1]),
        ]

        self.assertTrue((result[0] == expected[0]).all())
        self.assertTrue((result[1] == expected[1]).all())
        self.assertTrue((result[2] == expected[2]).all())
        self.assertTrue((result[3] == expected[3]).all())
        self.assertTrue((result[4] == expected[4]).all())
        self.assertTrue((result[5] == expected[5]).all())
        self.assertTrue((result[6] == expected[6]).all())

    def _test_slice_index_list_bool(self, place):
        data = np.random.rand(2, 3, 4).astype("float32")
        np_idx = np.array([[True, False, False], [True, False, True]])
        prog = paddle.static.Program()
        with paddle.static.program_guard(prog):
            x = paddle.assign(data)
            idx0 = [True, False]
            idx1 = [False, True]
            idx2 = [True, True]
            idx3 = [False, False, 1]
            idx4 = [True, False, 0]
            idx5 = paddle.assign(np_idx)

            out0 = x[idx0]
            out1 = x[idx1]
            out2 = x[idx2]
            out3 = x[idx3]
            out4 = x[idx4]
            out5 = x[idx5]
            out6 = x[x < 0.36]
            out7 = x[x > 0.6]

        exe = paddle.static.Executor(place)
        result = exe.run(
            prog, fetch_list=[out0, out1, out2, out3, out4, out5, out6, out7]
        )

        expected = [
            data[idx0],
            data[idx1],
            data[idx2],
            data[idx3],
            data[idx4],
            data[np_idx],
            data[data < 0.36],
            data[data > 0.6],
        ]

        self.assertTrue((result[0] == expected[0]).all())
        self.assertTrue((result[1] == expected[1]).all())
        self.assertTrue((result[2] == expected[2]).all())
        self.assertTrue((result[3] == expected[3]).all())
        self.assertTrue((result[4] == expected[4]).all())
        self.assertTrue((result[5] == expected[5]).all())
        self.assertTrue((result[6] == expected[6]).all())
        self.assertTrue((result[7] == expected[7]).all())

        with self.assertRaises(IndexError):
            res = x[[True, False, False]]

    def _test_slice_index_scalar_bool(self, place):
        data = np.random.rand(1, 3, 4).astype("float32")
        np_idx = np.array([True])
        prog = paddle.static.Program()
        with paddle.static.program_guard(prog):
            x = paddle.assign(data)
            idx = paddle.assign(np_idx)

            out = x[idx]

        exe = paddle.static.Executor(place)
        result = exe.run(prog, fetch_list=[out])

        expected = [data[np_idx]]

        self.assertTrue((result[0] == expected[0]).all())

    def test_slice(self):
        places = [base.CPUPlace()]
        if core.is_compiled_with_cuda():
            places.append(core.CUDAPlace(0))

        for place in places:
            self._test_slice(place)
            self._test_slice_index_tensor(place)
            self._test_slice_index_list(place)
            self._test_slice_index_ellipsis(place)
            self._test_slice_index_list_bool(place)
            self._test_slice_index_scalar_bool(place)

    def _tostring(self):
        b = default_main_program().current_block()
        w = b.create_var(dtype="float64", lod_level=0)
        self.assertTrue(isinstance(str(w), str))

        if core.is_compiled_with_cuda():
            wc = b.create_var(dtype="int", lod_level=0)
            self.assertTrue(isinstance(str(wc), str))

    def test_tostring(self):
        with base.dygraph.guard():
            self._tostring()

        with base.program_guard(default_main_program()):
            self._tostring()

    def test_fake_interface_only_api(self):
        b = default_main_program().current_block()
        var = b.create_var(dtype="float64", lod_level=0)
        with base.dygraph.guard():
            self.assertRaises(AssertionError, var.numpy)
            self.assertRaises(AssertionError, var.backward)
            self.assertRaises(AssertionError, var.gradient)
            self.assertRaises(AssertionError, var.clear_gradient)

    def test_variable_in_dygraph_mode(self):
        b = default_main_program().current_block()
        var = b.create_var(dtype="float64", shape=[1, 1])
        with base.dygraph.guard():
            self.assertTrue(var.to_string(True).startswith('name:'))

            self.assertFalse(var.persistable)
            var.persistable = True
            self.assertTrue(var.persistable)

            self.assertFalse(var.stop_gradient)
            var.stop_gradient = True
            self.assertTrue(var.stop_gradient)

            self.assertTrue(var.name.startswith('_generated_var_'))
            self.assertEqual(var.shape, (1, 1))
            self.assertEqual(var.dtype, base.core.VarDesc.VarType.FP64)
            self.assertEqual(var.type, base.core.VarDesc.VarType.LOD_TENSOR)

    def test_create_selected_rows(self):
        b = default_main_program().current_block()

        var = b.create_var(
            name="var",
            shape=[1, 1],
            dtype="float32",
            type=base.core.VarDesc.VarType.SELECTED_ROWS,
            persistable=True,
        )

        def _test():
            var.lod_level()

        self.assertRaises(Exception, _test)

    def test_size(self):
        prog = paddle.static.Program()
        with paddle.static.program_guard(prog):
            x = paddle.assign(np.random.rand(2, 3, 4).astype("float32"))
            exe = paddle.static.Executor(base.CPUPlace())
            exe.run(paddle.static.default_startup_program())

            output = exe.run(prog, fetch_list=[x.size()])
            self.assertEqual(output[0], [24])

    def test_detach(self):
        b = default_main_program().current_block()
        x = b.create_var(shape=[2, 3, 5], dtype="float64", lod_level=0)
        detach_x = x.detach()
        self.assertEqual(x.persistable, detach_x.persistable)
        self.assertEqual(x.shape, detach_x.shape)
        self.assertEqual(x.dtype, detach_x.dtype)
        self.assertEqual(x.type, detach_x.type)
        self.assertTrue(detach_x.stop_gradient)

        xx = b.create_var(name='xx', type=core.VarDesc.VarType.STEP_SCOPES)
        self.assertRaises(AssertionError, xx.detach)

        startup = paddle.static.Program()
        main = paddle.static.Program()
        scope = base.core.Scope()
        with paddle.static.scope_guard(scope):
            with paddle.static.program_guard(main, startup):
                x = paddle.static.data(
                    name='x', shape=[3, 2, 1], dtype='float32'
                )
                x.persistable = True
                feed_data = np.ones(shape=[3, 2, 1], dtype=np.float32)
                detach_x = x.detach()
                exe = paddle.static.Executor(paddle.CPUPlace())
                exe.run(startup)
                result = exe.run(
                    main, feed={'x': feed_data}, fetch_list=[x, detach_x]
                )
                self.assertTrue((result[1] == feed_data).all())
                self.assertTrue((result[0] == result[1]).all())

                modified_value = np.zeros(shape=[3, 2, 1], dtype=np.float32)
                detach_x.set_value(modified_value, scope)
                result = exe.run(main, fetch_list=[x, detach_x])
                self.assertTrue((result[1] == modified_value).all())
                self.assertTrue((result[0] == result[1]).all())

                modified_value = np.random.uniform(
                    -1, 1, size=[3, 2, 1]
                ).astype('float32')
                x.set_value(modified_value, scope)
                result = exe.run(main, fetch_list=[x, detach_x])
                self.assertTrue((result[1] == modified_value).all())
                self.assertTrue((result[0] == result[1]).all())


class TestVariableSlice(unittest.TestCase):
    def setUp(self):
        np.random.seed(2022)

    def _test_item_none(self, place):
        data = np.random.rand(2, 3, 4).astype("float32")
        prog = paddle.static.Program()
        with paddle.static.program_guard(prog):
            x = paddle.assign(data)
            out0 = x[0:, None, 1:]
            out1 = x[0:, None]
            out2 = x[None, 1:]
            out3 = x[None]
            out4 = x[..., None, :, None]

        outs = [out0, out1, out2, out3, out4]
        exe = paddle.static.Executor(place)
        result = exe.run(prog, fetch_list=outs)

        expected = [
            data[0:, None, 1:],
            data[0:, None],
            data[None, 1:],
            data[None],
            data[..., None, :, None],
        ]
        for i in range(len(outs)):
            self.assertEqual(outs[i].shape, expected[i].shape)
            self.assertTrue((result[i] == expected[i]).all())

    def _test_item_none_and_decrease(self, place):
        data = np.random.rand(2, 3, 4).astype("float32")
        prog = paddle.static.Program()
        with paddle.static.program_guard(prog):
            x = paddle.assign(data)
            out0 = x[0, 1:, None]
            out1 = x[0, None]
            out2 = x[None, 1]
            out3 = x[None]
            out4 = x[0, 0, 0, None]
            out5 = x[None, 0, 0, 0, None]

        outs = [out0, out1, out2, out3, out4, out5]
        exe = paddle.static.Executor(place)
        result = exe.run(prog, fetch_list=outs)
        expected = [
            data[0, 1:, None],
            data[0, None],
            data[None, 1],
            data[None],
            data[0, 0, 0, None],
            data[None, 0, 0, 0, None],
        ]

        for i in range(len(outs)):
            self.assertEqual(outs[i].shape, expected[i].shape)
            self.assertTrue((result[i] == expected[i]).all())

    def test_slice(self):
        places = [base.CPUPlace()]
        if core.is_compiled_with_cuda():
            places.append(core.CUDAPlace(0))

        for place in places:
            self._test_item_none(place)
            self._test_item_none_and_decrease(place)


class TestListIndex(unittest.TestCase):
    # note(chenjianye):
    # Non-tuple sequence for multidimensional indexing is supported in numpy < 1.23.
    # For List case, the outermost `[]` will be treated as tuple `()` in version less than 1.23,
    # which is used to wrap index elements for multiple axes.
    # And from 1.23, this will be treat as a whole and only works on one axis.
    #
    # e.g. x[[[0],[1]]] == x[([0],[1])] == x[[0],[1]] (in version < 1.23)
    #      x[[[0],[1]]] == x[array([[0],[1]])] (in version >= 1.23)
    #
    # Here, we just modify the code to remove the impact of numpy version changes,
    # changing x[[[0],[1]]] to x[tuple([[0],[1]])] == x[([0],[1])] == x[[0],[1]].
    # Whether the paddle behavior in this case will change is still up for debate.

    def setUp(self):
        np.random.seed(2022)

    def numel(self, shape):
        return reduce(lambda x, y: x * y, shape, 1)

    def test_static_graph_list_index(self):
        paddle.enable_static()

        inps_shape = [3, 4, 5, 2]
        array = np.arange(self.numel(inps_shape), dtype='float32').reshape(
            inps_shape
        )

        index_shape = [3, 3, 2, 1]
        index = np.arange(self.numel(index_shape)).reshape(index_shape)

        for _ in range(3):
            program = paddle.static.Program()

            index_mod = (index % (array.shape[0])).tolist()

            with paddle.static.program_guard(program):
                x = paddle.static.data(
                    name='x', shape=array.shape, dtype='float32'
                )

                y = x[index_mod]

                place = (
                    paddle.base.CPUPlace()
                    if not paddle.base.core.is_compiled_with_cuda()
                    else paddle.base.CUDAPlace(0)
                )

                prog = paddle.static.default_main_program()
                exe = paddle.static.Executor(place)

                exe.run(paddle.static.default_startup_program())
                fetch_list = [y.name]

                getitem_np = array[tuple(index_mod)]
                getitem_pp = exe.run(
                    prog, feed={x.name: array}, fetch_list=fetch_list
                )
                np.testing.assert_array_equal(getitem_np, getitem_pp[0])

            array = array[0]
            index = index[0]

    def test_dygraph_list_index(self):
        paddle.disable_static()

        inps_shape = [3, 4, 5, 3]
        array = np.arange(self.numel(inps_shape)).reshape(inps_shape)

        index_shape = [2, 3, 4, 5, 6]
        index = np.arange(self.numel(index_shape)).reshape(index_shape)
        for _ in range(len(inps_shape) - 1):
            pt = paddle.to_tensor(array)
            index_mod = (index % (array.shape[-1])).tolist()
            try:
                getitem_np = array[tuple(index_mod)]

            except:
                with self.assertRaises(ValueError):
                    getitem_pp = pt[index_mod]
                array = array[0]
                index = index[0]
                continue
            getitem_pp = pt[index_mod]
            np.testing.assert_array_equal(getitem_np, getitem_pp.numpy())

            array = array[0]
            index = index[0]

    def test_static_graph_list_index_muti_dim(self):
        paddle.enable_static()
        inps_shape = [3, 4, 5]
        array = np.arange(self.numel(inps_shape), dtype='float32').reshape(
            inps_shape
        )

        index_shape = [2, 2]
        index1 = np.arange(self.numel(index_shape)).reshape(index_shape)
        index2 = np.arange(self.numel(index_shape)).reshape(index_shape) + 2

        value_shape = [3, 2, 2, 3]
        value_np = (
            np.arange(self.numel(value_shape), dtype='float32').reshape(
                value_shape
            )
            + 100
        )

        index_mod1 = (index1 % (min(array.shape))).tolist()
        index_mod2 = (index2 % (min(array.shape))).tolist()

        program = paddle.static.Program()
        with paddle.static.program_guard(program):
            x = paddle.static.data(name='x', shape=array.shape, dtype='float32')

            value = paddle.static.data(
                name='value', shape=value_np.shape, dtype='float32'
            )
            index1 = paddle.static.data(
                name='index1', shape=index1.shape, dtype='int32'
            )
            index2 = paddle.static.data(
                name='index2', shape=index2.shape, dtype='int32'
            )

            y = x[index1, index2]

            place = (
                paddle.base.CPUPlace()
                if not paddle.base.core.is_compiled_with_cuda()
                else paddle.base.CUDAPlace(0)
            )

            prog = paddle.static.default_main_program()
            exe = paddle.static.Executor(place)

            exe.run(paddle.static.default_startup_program())
            fetch_list = [y.name]
            array2 = array.copy()

            y2 = array2[index_mod1, index_mod2]

            getitem_pp = exe.run(
                prog,
                feed={
                    x.name: array,
                    index1.name: index_mod1,
                    index2.name: index_mod2,
                },
                fetch_list=fetch_list,
            )

            np.testing.assert_array_equal(
                y2,
                getitem_pp[0],
                err_msg=f'\n numpy:{y2},\n paddle:{getitem_pp[0]}',
            )

    def test_dygraph_list_index_muti_dim(self):
        paddle.disable_static()
        inps_shape = [3, 4, 5]
        array = np.arange(self.numel(inps_shape), dtype='float32').reshape(
            inps_shape
        )

        index_shape = [2, 2]
        index1 = np.arange(self.numel(index_shape)).reshape(index_shape)
        index2 = np.arange(self.numel(index_shape)).reshape(index_shape) + 2

        value_shape = [3, 2, 2, 3]
        value_np = (
            np.arange(self.numel(value_shape), dtype='float32').reshape(
                value_shape
            )
            + 100
        )

        index_mod1 = (index1 % (min(array.shape))).tolist()
        index_mod2 = (index2 % (min(array.shape))).tolist()

        x = paddle.to_tensor(array)
        index_t1 = paddle.to_tensor(index_mod1)
        index_t2 = paddle.to_tensor(index_mod2)

        y_np = array[index_t1, index_t2]
        y = x[index_t1, index_t2]
        np.testing.assert_array_equal(y.numpy(), y_np)

    def run_getitem_list_index(self, array, index):
        x = paddle.static.data(name='x', shape=array.shape, dtype='float32')

        y = x[index]
        place = paddle.base.CPUPlace()

        prog = paddle.static.default_main_program()
        exe = paddle.static.Executor(place)

        exe.run(paddle.static.default_startup_program())
        fetch_list = [y.name]
        array2 = array.copy()

        try:
            value_np = array2[index]
        except:
            with self.assertRaises(ValueError):
                getitem_pp = exe.run(
                    prog, feed={x.name: array}, fetch_list=fetch_list
                )
            return
        getitem_pp = exe.run(prog, feed={x.name: array}, fetch_list=fetch_list)

        np.testing.assert_allclose(
            value_np, getitem_pp[0], rtol=1e-5, atol=1e-8
        )

    def test_static_graph_getitem_bool_index(self):
        paddle.enable_static()

        # case 1:
        array = np.ones((4, 2, 3), dtype='float32')
        value_np = np.random.random((2, 3)).astype('float32')
        index = np.array([True, False, False, False])
        program = paddle.static.Program()
        with paddle.static.program_guard(program):
            self.run_getitem_list_index(array, index)

        # case 2:
        array = np.ones((4, 2, 3), dtype='float32')
        value_np = np.random.random((2, 3)).astype('float32')
        index = np.array([False, True, False, False])
        program = paddle.static.Program()
        with paddle.static.program_guard(program):
            self.run_getitem_list_index(array, index)

        # case 3:
        array = np.ones((4, 2, 3), dtype='float32')
        value_np = np.random.random((2, 3)).astype('float32')
        index = np.array([True, True, True, True])
        program = paddle.static.Program()
        with paddle.static.program_guard(program):
            self.run_getitem_list_index(array, index)

    def run_setitem_list_index(self, array, index, value_np):
        x = paddle.static.data(name='x', shape=array.shape, dtype='float32')

        value = paddle.static.data(
            name='value', shape=value_np.shape, dtype='float32'
        )

        y = paddle.static.setitem(x, index, value)
        place = paddle.base.CPUPlace()

        prog = paddle.static.default_main_program()
        exe = paddle.static.Executor(place)

        exe.run(paddle.static.default_startup_program())
        fetch_list = [y.name]
        array2 = array.copy()
        try:
            index = (
                tuple(index)
                if isinstance(index, list) and isinstance(index[0], list)
                else index
            )
            array2[index] = value_np
        except:
            with self.assertRaises(ValueError):
                setitem_pp = exe.run(
                    prog,
                    feed={x.name: array, value.name: value_np},
                    fetch_list=fetch_list,
                )
            return
        setitem_pp = exe.run(
            prog,
            feed={x.name: array, value.name: value_np},
            fetch_list=fetch_list,
        )

        np.testing.assert_allclose(array2, setitem_pp[0], rtol=1e-5, atol=1e-8)

    def test_static_graph_setitem_list_index(self):
        paddle.enable_static()
        # case 1:
        inps_shape = [3, 4, 5, 2, 3]
        array = np.arange(self.numel(inps_shape), dtype='float32').reshape(
            inps_shape
        )

        index_shape = [3, 3, 1, 2]
        index = np.arange(self.numel(index_shape)).reshape(index_shape)

        value_shape = inps_shape[3:]
        value_np = (
            np.arange(self.numel(value_shape), dtype='float32').reshape(
                value_shape
            )
            + 100
        )

        for _ in range(3):
            program = paddle.static.Program()

            index_mod = (index % (min(array.shape))).tolist()

            with paddle.static.program_guard(program):
                self.run_setitem_list_index(array, index_mod, value_np)

            array = array[0]
            index = index[0]

        # case 2:
        inps_shape = [3, 4, 5, 4, 3]
        array = np.arange(self.numel(inps_shape), dtype='float32').reshape(
            inps_shape
        )

        index_shape = [4, 3, 2, 2]
        index = np.arange(self.numel(index_shape)).reshape(index_shape)

        value_shape = [3]
        value_np = (
            np.arange(self.numel(value_shape), dtype='float32').reshape(
                value_shape
            )
            + 100
        )

        for _ in range(4):
            program = paddle.static.Program()
            index_mod = (index % (min(array.shape))).tolist()

            with paddle.static.program_guard(program):
                self.run_setitem_list_index(array, index_mod, value_np)

            array = array[0]
            index = index[0]

        # case 3:
        inps_shape = [3, 4, 5, 3, 3]
        array = np.arange(self.numel(inps_shape), dtype='float32').reshape(
            inps_shape
        )

        index_shape = [4, 3, 2, 2]
        index = np.arange(self.numel(index_shape)).reshape(index_shape)

        value_shape = [3, 2, 2, 3]
        value_np = (
            np.arange(self.numel(value_shape), dtype='float32').reshape(
                value_shape
            )
            + 100
        )
        index_mod = (index % (min(array.shape))).tolist()
        self.run_setitem_list_index(array, index_mod, value_np)

    def test_static_graph_setitem_bool_index(self):
        paddle.enable_static()

        # case 1:
        array = np.ones((4, 2, 3), dtype='float32')
        value_np = np.random.random((2, 3)).astype('float32')
        index = np.array([True, False, False, False])
        program = paddle.static.Program()
        with paddle.static.program_guard(program):
            self.run_setitem_list_index(array, index, value_np)

        # case 2:
        array = np.ones((4, 2, 3), dtype='float32')
        value_np = np.random.random((2, 3)).astype('float32')
        index = np.array([False, True, False, False])
        program = paddle.static.Program()
        with paddle.static.program_guard(program):
            self.run_setitem_list_index(array, index, value_np)

        # case 3:
        array = np.ones((4, 2, 3), dtype='float32')
        value_np = np.random.random((2, 3)).astype('float32')
        index = np.array([True, True, True, True])
        program = paddle.static.Program()
        with paddle.static.program_guard(program):
            self.run_setitem_list_index(array, index, value_np)

    def test_static_graph_setitem_bool_scalar_index(self):
        paddle.enable_static()
        array = np.ones((1, 2, 3), dtype='float32')
        value_np = np.random.random((2, 3)).astype('float32')
        index = np.array([True])
        program = paddle.static.Program()
        with paddle.static.program_guard(program):
            self.run_setitem_list_index(array, index, value_np)

    def test_static_graph_tensor_index_setitem_muti_dim(self):
        paddle.enable_static()
        inps_shape = [3, 4, 5, 4]
        array = np.arange(self.numel(inps_shape), dtype='float32').reshape(
            inps_shape
        )

        index_shape = [2, 3, 4]
        index1 = np.arange(self.numel(index_shape), dtype='int32').reshape(
            index_shape
        )
        index2 = (
            np.arange(self.numel(index_shape), dtype='int32').reshape(
                index_shape
            )
            + 2
        )

        value_shape = [4]
        value_np = (
            np.arange(self.numel(value_shape), dtype='float32').reshape(
                value_shape
            )
            + 100
        )
        for _ in range(3):
            index_mod1 = index1 % (min(array.shape))
            index_mod2 = index2 % (min(array.shape))

            array2 = array.copy()
            array2[index_mod1, index_mod2] = value_np
            array3 = array.copy()
            array3[index_mod1] = value_np

            program = paddle.static.Program()
            with paddle.static.program_guard(program):
                x1 = paddle.static.data(
                    name='x1', shape=array.shape, dtype='float32'
                )
                x2 = paddle.static.data(
                    name='x2', shape=array.shape, dtype='float32'
                )

                value = paddle.static.data(
                    name='value', shape=value_np.shape, dtype='float32'
                )
                index_1 = paddle.static.data(
                    name='index_1', shape=index1.shape, dtype='int32'
                )
                index_2 = paddle.static.data(
                    name='index_2', shape=index2.shape, dtype='int32'
                )

                x1_out = paddle.static.setitem(x1, (index_1, index_2), value)
                x2_out = paddle.static.setitem(x2, index_1, value)
                place = (
                    paddle.base.CPUPlace()
                    if not paddle.base.core.is_compiled_with_cuda()
                    else paddle.base.CUDAPlace(0)
                )

                prog = paddle.static.default_main_program()
                exe = paddle.static.Executor(place)

                exe.run(paddle.static.default_startup_program())
                fetch_list = [x1_out.name, x2_out.name]

                setitem_pp = exe.run(
                    prog,
                    feed={
                        x1.name: array,
                        x2.name: array,
                        value.name: value_np,
                        index_1.name: index_mod1,
                        index_2.name: index_mod2,
                    },
                    fetch_list=fetch_list,
                )
                np.testing.assert_array_equal(
                    array2,
                    setitem_pp[0],
                    err_msg='\n numpy:{},\n paddle:{}'.format(
                        array2, setitem_pp[0]
                    ),
                )
                np.testing.assert_array_equal(
                    array3,
                    setitem_pp[1],
                    err_msg='\n numpy:{},\n paddle:{}'.format(
                        array3, setitem_pp[1]
                    ),
                )
            array = array[0]
            index1 = index1[0]
            index2 = index2[0]

    def test_static_graph_array_index_muti_dim(self):
        paddle.enable_static()
        inps_shape = [3, 4, 5, 4]
        array = np.arange(self.numel(inps_shape), dtype='float32').reshape(
            inps_shape
        )

        index_shape = [2, 3, 4]
        index1 = np.arange(self.numel(index_shape), dtype='int32').reshape(
            index_shape
        )
        index2 = (
            np.arange(self.numel(index_shape), dtype='int32').reshape(
                index_shape
            )
            + 2
        )

        for _ in range(3):
            index_mod1 = index1 % (min(array.shape))
            index_mod2 = index2 % (min(array.shape))

            array2 = array.copy()
            array2[index_mod1, index_mod2] = 1
            y_np1 = array2[index_mod2, index_mod1]
            array3 = array.copy()
            array3[index_mod1] = 2.5
            y_np2 = array3[index_mod2]

            program = paddle.static.Program()
            with paddle.static.program_guard(program):
                x1 = paddle.static.data(
                    name='x1', shape=array.shape, dtype='float32'
                )
                x2 = paddle.static.data(
                    name='x2', shape=array.shape, dtype='float32'
                )

                x1_out = paddle.static.setitem(x1, (index_mod1, index_mod2), 1)
                x2_out = paddle.static.setitem(x2, index_mod1, 2.5)
                y1 = x1_out[index_mod2, index_mod1]
                y2 = x2_out[index_mod2]
                place = (
                    paddle.base.CPUPlace()
                    if not paddle.base.core.is_compiled_with_cuda()
                    else paddle.base.CUDAPlace(0)
                )

                prog = paddle.static.default_main_program()
                exe = paddle.static.Executor(place)
                exe.run(paddle.static.default_startup_program())
                fetch_list = [x1_out.name, x2_out.name, y1.name, y2.name]

                setitem_pp = exe.run(
                    prog,
                    feed={x1.name: array, x2.name: array},
                    fetch_list=fetch_list,
                )
                np.testing.assert_array_equal(
                    array2,
                    setitem_pp[0],
                    err_msg='\n numpy:{},\n paddle:{}'.format(
                        array2, setitem_pp[0]
                    ),
                )
                np.testing.assert_array_equal(
                    array3,
                    setitem_pp[1],
                    err_msg='\n numpy:{},\n paddle:{}'.format(
                        array3, setitem_pp[1]
                    ),
                )

                np.testing.assert_array_equal(
                    y_np1,
                    setitem_pp[2],
                    err_msg='\n numpy:{},\n paddle:{}'.format(
                        y_np1, setitem_pp[2]
                    ),
                )
                np.testing.assert_array_equal(
                    y_np2,
                    setitem_pp[3],
                    err_msg='\n numpy:{},\n paddle:{}'.format(
                        y_np2, setitem_pp[3]
                    ),
                )
            array = array[0]
            index1 = index1[0]
            index2 = index2[0]

    def test_dygraph_array_index_muti_dim(self):
        paddle.disable_static()
        inps_shape = [3, 4, 5, 4]
        array = np.arange(self.numel(inps_shape), dtype='float32').reshape(
            inps_shape
        )
        index_shape = [2, 3, 4]
        index1 = np.arange(self.numel(index_shape), dtype='int32').reshape(
            index_shape
        )
        index2 = (
            np.arange(self.numel(index_shape), dtype='int32').reshape(
                index_shape
            )
            + 2
        )

        for _ in range(3):
            index_mod1 = index1 % (min(array.shape))
            index_mod2 = index2 % (min(array.shape))
            index_mod_t1 = paddle.to_tensor(index_mod1)
            index_mod_t2 = paddle.to_tensor(index_mod2)
            # 2 dim getitem
            array1 = array.copy()
            y_np1 = array1[index_mod2, index_mod1]
            tensor1 = paddle.to_tensor(array)

            y_t1 = tensor1[index_mod_t2, index_mod_t1]

            np.testing.assert_array_equal(
                y_t1.numpy(),
                y_np1,
                err_msg=f'\n numpy:{y_np1},\n paddle:{y_t1.numpy()}',
            )
            # 1 dim getitem
            array2 = array.copy()
            y_np2 = array2[index_mod2]
            tensor2 = paddle.to_tensor(array)

            y_t2 = tensor2[index_mod_t2]
            np.testing.assert_array_equal(
                y_t2.numpy(),
                y_np2,
                err_msg=f'\n numpy:{y_np2},\n paddle:{y_t2.numpy()}',
            )

            # 2 dim setitem
            array1 = array.copy()
            array1[index_mod1, index_mod2] = 1
            tensor1[index_mod_t1, index_mod_t2] = 1
            np.testing.assert_array_equal(
                tensor1.numpy(),
                array1,
                err_msg='\n numpy:{},\n paddle:{}'.format(
                    array1, tensor1.numpy()
                ),
            )
            # 1 dim setitem
            array2 = array.copy()

            array2[index_mod1] = 2.5

            tensor2[index_mod_t1] = 2.5

            np.testing.assert_array_equal(
                tensor2.numpy(),
                array2,
                err_msg='\n numpy:{},\n paddle:{}'.format(
                    array2, tensor2.numpy()
                ),
            )

            array = array[0]
            index1 = index1[0]
            index2 = index2[0]


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