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1# Copyright 2022 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ============================================================================
15import pytest
16import mindspore.context as context
17import mindspore.nn as nn
18import mindspore as ms
19import mindspore.ops.operations.sparse_ops as P
20from mindspore import Tensor
21
22
23class Net(nn.Cell):
24
25    def __init__(self):
26        super(Net, self).__init__()
27        self.op = P.SparseMatrixAdd()
28
29    def construct(self, a_shape, a_batch_pointer, a_indptr, a_indices,
30                  a_values, b_shape, b_batch_pointer, b_indptr, b_indices,
31                  b_values, alpha, beta):
32        return self.op(a_shape, a_batch_pointer, a_indptr, a_indices, a_values,
33                       b_shape, b_batch_pointer, b_indptr, b_indices, b_values,
34                       alpha, beta)
35
36
37def dyn_case():
38    net = Net()
39
40    a_indptr_dyn = Tensor(shape=[None], dtype=ms.int32)
41    a_indices_dyn = Tensor(shape=[None], dtype=ms.int32)
42    a_values_dyn = Tensor(shape=[None], dtype=ms.float32)
43    a_pointers_dyn = Tensor(shape=[None], dtype=ms.int32)
44    shape_dyn = Tensor(shape=[None], dtype=ms.int32)
45    b_indptr_dyn = Tensor(shape=[None], dtype=ms.int32)
46    b_indices_dyn = Tensor(shape=[None], dtype=ms.int32)
47    b_values_dyn = Tensor(shape=[None], dtype=ms.float32)
48    b_pointers_dyn = Tensor(shape=[None], dtype=ms.int32)
49    alpha = Tensor(1, ms.float32)
50    beta = Tensor(1, ms.float32)
51    net.set_inputs(shape_dyn, a_pointers_dyn, a_indptr_dyn, a_indices_dyn,
52                   a_values_dyn, shape_dyn, b_pointers_dyn, b_indptr_dyn,
53                   b_indices_dyn, b_values_dyn, alpha, beta)
54
55    a_indptr = Tensor([0, 1, 2], dtype=ms.int32)
56    a_indices = Tensor([0, 1], dtype=ms.int32)
57    a_values = Tensor([1, 2], dtype=ms.float32)
58    a_pointers = Tensor([0, a_values.shape[0]], dtype=ms.int32)
59    shape = Tensor([2, 6], dtype=ms.int32)
60    b_indptr = Tensor([0, 1, 2], dtype=ms.int32)
61    b_indices = Tensor([0, 1], dtype=ms.int32)
62    b_values = Tensor([1, 2], dtype=ms.float32)
63    b_pointers = Tensor([0, b_values.shape[0]], dtype=ms.int32)
64    out = net(shape, a_pointers, a_indptr, a_indices, a_values, shape,
65              b_pointers, b_indptr, b_indices, b_values, alpha, beta)
66
67    exepct_shapes = [(2,), (2,), (3,), (2,), (2,)]
68    for i in range(5):
69        assert out[i].asnumpy().shape == exepct_shapes[i]
70
71
72@pytest.mark.level0
73@pytest.mark.platform_x86_gpu
74@pytest.mark.env_onecard
75def test_sparse_matrix_add_dyn():
76    """
77    Feature: test SparseMatrixAdd in gpu.
78    Description: test the ops in dynamic case.
79    Expectation: success.
80    """
81    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
82    dyn_case()
83    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
84    dyn_case()
85