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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
18
19import mindspore.context as context
20import mindspore.nn as nn
21from mindspore import Tensor
22from mindspore.ops import operations as P
23from mindspore.ops import composite as C
24from mindspore.ops.operations import _inner_ops as inner
25
26class MatMulNet(nn.Cell):
27    def __init__(self):
28        super(MatMulNet, self).__init__()
29        self.matmul = P.MatMul()
30
31    def construct(self, x, y):
32        return self.matmul(x, y)
33
34
35class MatMul_d(nn.Cell):
36    def __init__(self):
37        super(MatMul_d, self).__init__()
38        self.test_dynamic = inner.GpuConvertToDynamicShape()
39        self.matmul = P.MatMul()
40
41    def construct(self, x, y):
42        x = self.test_dynamic(x)
43        y = self.test_dynamic(y)
44        return self.matmul(x, y)
45
46
47class MatMulComposite(nn.Cell):
48    def __init__(self):
49        super(MatMulComposite, self).__init__()
50        self.matmul = C.matmul
51
52    def construct(self, x, y):
53        return self.matmul(x, y)
54
55
56@pytest.mark.level0
57@pytest.mark.platform_x86_gpu_training
58@pytest.mark.env_onecard
59def test_MatMul_dynamic():
60
61    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
62    net = MatMul_d()
63
64    x1 = np.arange(2).reshape(1, 2).astype(np.float32)
65    y1 = np.arange(4).reshape(2, 2).astype(np.float32)
66    output1 = net(Tensor(x1), Tensor(y1))
67    expect1 = np.matmul(x1, y1)
68    np.testing.assert_array_almost_equal(output1.asnumpy(), expect1)
69
70    x2 = np.arange(102).reshape(34, 3).astype(np.float32)
71    y2 = np.arange(18).reshape(3, 6).astype(np.float32)
72    output2 = net(Tensor(x2), Tensor(y2))
73    expect2 = np.matmul(x2, y2)
74    np.testing.assert_array_almost_equal(output2.asnumpy(), expect2)
75
76
77@pytest.mark.level0
78@pytest.mark.platform_x86_gpu_training
79@pytest.mark.env_onecard
80def test_matmul_float64():
81
82    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
83    net = MatMulNet()
84
85    x = np.arange(102).reshape(34, 3).astype(np.float64)
86    y = np.arange(18).reshape(3, 6).astype(np.float64)
87    output = net(Tensor(x), Tensor(y))
88    expect = np.matmul(x, y)
89    np.testing.assert_array_almost_equal(output.asnumpy(), expect)
90
91@pytest.mark.level0
92@pytest.mark.platform_x86_gpu_training
93@pytest.mark.env_onecard
94def test_matmul_composite():
95
96    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
97    net = MatMulComposite()
98
99    scalars = [np.random.randn(1).astype(np.float32), np.random.randn(1).astype(np.float32),
100               np.random.randn(1, 1).astype(np.float32),
101               np.random.randn(1, 1, 1).astype(np.float32)]
102    for x in scalars:
103        for y in scalars:
104            output = net(Tensor(x), Tensor(y))
105            expect = np.matmul(x, y)
106            np.testing.assert_array_almost_equal(output.asnumpy(), expect, decimal=4)
107
108    broadcastables = [
109        np.random.randn(3).astype(np.float32), np.random.randn(3).astype(np.float32),
110        np.random.randn(6).astype(np.float32), np.random.randn(6, 4).astype(np.float32),
111        np.random.randn(5, 2).astype(np.float32), np.random.randn(2).astype(np.float32),
112        np.random.randn(2, 9).astype(np.float32), np.random.randn(9, 8).astype(np.float32),
113        np.random.randn(6).astype(np.float32), np.random.randn(2, 6, 5).astype(np.float32),
114        np.random.randn(9, 2, 7).astype(np.float32), np.random.randn(7).astype(np.float32),
115        np.random.randn(5, 2, 4).astype(np.float32), np.random.randn(6, 1, 4, 9).astype(np.float32),
116        np.random.randn(7, 1, 5, 3, 2).astype(np.float32), np.random.randn(8, 1, 6, 1, 2, 9).astype(np.float32)
117    ]
118    for i in range(8):
119        x = broadcastables[2*i]
120        y = broadcastables[2*i + 1]
121        output = net(Tensor(x), Tensor(y))
122        expect = np.matmul(x, y)
123        np.testing.assert_array_almost_equal(output.asnumpy(), expect, decimal=4)
124