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89 lines
3.0 KiB
89 lines
3.0 KiB
# 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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from mindspore import Tensor
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from mindspore.nn import Cell
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import mindspore.ops.operations as P
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class Net(Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.matmul = P.MatMul(transpose_a=True, transpose_b=True)
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def construct(self, x, y):
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return self.matmul(x, y)
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class Net1(Cell):
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def __init__(self):
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super(Net1, self).__init__()
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self.matmul = P.MatMul(transpose_a=True, transpose_b=True)
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self.add = P.BiasAdd()
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def construct(self, x, y, bias):
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res = self.matmul(x, y)
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return self.add(res, bias)
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def get_output(i0, i1, enable_graph_kernel=False):
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if enable_graph_kernel:
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context.set_context(enable_graph_kernel=True, save_graphs=False)
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net = Net()
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output = net(i0, i1)
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return output
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def get_output1(i0, i1, i2, enable_graph_kernel=False):
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if enable_graph_kernel:
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context.set_context(enable_graph_kernel=True, save_graphs=False)
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net = Net1()
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output = net(i0, i1, i2)
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return output
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def test_basic():
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i0 = Tensor(np.random.normal(1, 0.01, [800, 96]).astype(np.float16))
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i1 = Tensor(np.random.normal(1, 0.01, [128, 800]).astype(np.float16))
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expect = get_output(i0, i1, False)
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output = get_output(i0, i1, True)
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expect_np = expect.asnumpy().copy()
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output_np = output.asnumpy().copy()
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assert np.allclose(expect_np, output_np, 1.e-4, 1.e-7)
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def test_basic1():
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i0 = Tensor(np.random.normal(1, 0.01, [800, 96]).astype(np.float16))
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i1 = Tensor(np.random.normal(1, 0.01, [128, 800]).astype(np.float16))
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i2 = Tensor(np.random.normal(100, 0.01, [128,]).astype(np.float16))
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expect = get_output1(i0, i1, i2, False)
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output = get_output1(i0, i1, i2, True)
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expect_np = expect.asnumpy().copy()
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output_np = output.asnumpy().copy()
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assert np.allclose(expect_np, output_np, 6.e-4, 6.e-4)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_basic_ascend():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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test_basic()
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_basic_ascend1():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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test_basic1()
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