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1# Copyright 2021 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# ============================================================================
15
16import numpy as np
17import pytest
18import mindspore.context as context
19from mindspore import Tensor
20from mindspore.nn import Cell
21import mindspore.ops.operations as P
22
23class Net(Cell):
24    def __init__(self):
25        super(Net, self).__init__()
26        self.matmul = P.MatMul(transpose_a=False, transpose_b=False)
27
28    def construct(self, x, y):
29        return self.matmul(x, y)
30
31class Net1(Cell):
32    def __init__(self):
33        super(Net1, self).__init__()
34        self.bmm = P.BatchMatMul(transpose_a=False, transpose_b=False)
35
36    def construct(self, x, y):
37        return self.bmm(x, y)
38
39def get_output(i0, i1, net_cls, enable_graph_kernel=False):
40    context.set_context(enable_graph_kernel=enable_graph_kernel)
41    net = net_cls()
42    output = net(i0, i1)
43    return output
44
45def test_matmul():
46    i0 = Tensor(np.random.normal(1, 0.01, [96, 1]).astype(np.float32))
47    i1 = Tensor(np.random.normal(1, 0.01, [1, 128]).astype(np.float32))
48    expect = get_output(i0, i1, Net, False)
49    output = get_output(i0, i1, Net, True)
50    expect_np = expect.asnumpy().copy()
51    output_np = output.asnumpy().copy()
52    assert np.allclose(expect_np, output_np, 1.e-4, 1.e-7)
53
54def test_batchmatmul():
55    i0 = Tensor(np.random.normal(1, 0.01, [16, 96, 1]).astype(np.float32))
56    i1 = Tensor(np.random.normal(1, 0.01, [16, 1, 128]).astype(np.float32))
57    expect = get_output(i0, i1, Net1, False)
58    output = get_output(i0, i1, Net1, True)
59    expect_np = expect.asnumpy().copy()
60    output_np = output.asnumpy().copy()
61    assert np.allclose(expect_np, output_np, 6.e-4, 6.e-4)
62
63@pytest.mark.level1
64@pytest.mark.platform_arm_ascend_training
65@pytest.mark.platform_x86_ascend_training
66@pytest.mark.env_onecard
67def test_matmul_ascend():
68    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
69    test_matmul()
70
71@pytest.mark.level1
72@pytest.mark.platform_arm_ascend_training
73@pytest.mark.platform_x86_ascend_training
74@pytest.mark.env_onecard
75def test_batchmatmul_ascend():
76    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
77    test_batchmatmul()
78
79@pytest.mark.level1
80@pytest.mark.platform_x86_gpu_training
81@pytest.mark.env_onecard
82def test_matmul_gpu():
83    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
84    test_matmul()
85
86@pytest.mark.level1
87@pytest.mark.platform_x86_gpu_training
88@pytest.mark.env_onecard
89def test_batchmatmul_gpu():
90    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
91    test_batchmatmul()
92