forked from mindspore-Ecosystem/mindspore
95 lines
3.0 KiB
Python
95 lines
3.0 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import operations as P
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np.random.seed(100)
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class MatMulNet(nn.Cell):
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def __init__(self, transpose_a=False, transpose_b=False):
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super(MatMulNet, self).__init__()
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self.matmul = P.MatMul(transpose_a, transpose_b)
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def construct(self, x, y):
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return self.matmul(x, y)
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def judge_result_correct(result, expect):
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assert result.dtype == expect.dtype
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assert result.shape == expect.shape
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assert np.allclose(result, expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64])
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def test_matmul_no_transpose_vec(dtype):
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"""
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Feature: matrix & vec
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Description: test cases for matmul between matrix and vector
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Expectation: the result match to scipy
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"""
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a = np.arange(1 * 3).reshape((1, 3)).astype(dtype)
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b = np.arange(3 * 5).reshape((3, 5)).astype(dtype)
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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net = MatMulNet()
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output = net(Tensor(a), Tensor(b)).asnumpy()
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expect = np.array([[25., 28., 31., 34., 37.]], dtype)
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judge_result_correct(output, expect)
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def np_matmul(a: np.ndarray, b: np.ndarray, trans_a: bool, trans_b: bool):
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if trans_a:
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a = a.T
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if trans_b:
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b = b.T
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return np.matmul(a, b)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('trans_a', [True, False])
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@pytest.mark.parametrize('trans_b', [True, False])
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@pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64])
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def test_matmul_matrix(trans_a, trans_b, dtype):
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"""
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Feature: ALL To ALL
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Description: test cases for matmul for all float types and transpose args combinations
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Expectation: the result match to scipy
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"""
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m, k, n = 5, 3, 4
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a = np.random.random((m, k)).astype(dtype)
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b = np.random.random((k, n)).astype(dtype)
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if trans_a:
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a = a.T
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if trans_b:
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b = b.T
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expect = np_matmul(a, b, trans_a, trans_b)
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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net = MatMulNet(transpose_a=trans_a, transpose_b=trans_b)
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output = net(Tensor(a), Tensor(b)).asnumpy()
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judge_result_correct(output, expect)
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