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103 lines
3.5 KiB
103 lines
3.5 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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from mindspore.ops import functional as F
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from mindspore.common.parameter import Parameter
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class TestOptAssignNet_1(Cell):
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def __init__(self):
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super(TestOptAssignNet_1, self).__init__()
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self.add = P.Add()
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self.reduce_max = P.ReduceMax()
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self.param = Parameter(
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Tensor(np.zeros([2, 2, 2]).astype(np.float32)), name='param')
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def construct(self, x, y):
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add_res = self.add(x, y)
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F.depend(add_res, F.assign(self.param, add_res))
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return self.reduce_max(add_res)
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class TestOptAssignNet_2(Cell):
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def __init__(self):
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super(TestOptAssignNet_2, self).__init__()
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self.add = P.Add()
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self.param = Parameter(
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Tensor(np.zeros([2, 2, 2]).astype(np.float32)), name='param')
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def construct(self, x, y):
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add_res = self.add(x, y)
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F.depend(add_res, F.assign(self.param, add_res))
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return add_res
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def test_opt_assign_output_1():
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np.random.seed(0)
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input_x = np.random.normal(0, 1, [2, 2, 2]).astype(np.float32)
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input_y = np.random.normal(0, 1, [2, 2, 2]).astype(np.float32)
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context.set_context(mode=context.GRAPH_MODE,
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enable_graph_kernel=True, device_target="GPU")
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net = TestOptAssignNet_1()
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result_open_gk = net(Tensor(input_x), Tensor(input_y))
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context.set_context(mode=context.GRAPH_MODE,
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enable_graph_kernel=False, device_target="GPU")
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net_beta = TestOptAssignNet_1()
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result_close_gk = net_beta(Tensor(input_x), Tensor(input_y))
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res = np.allclose(result_open_gk.asnumpy(), result_close_gk.asnumpy(), rtol=1.e-4, atol=1.e-7, equal_nan=True)
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assert res
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def test_opt_assign_output_2():
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np.random.seed(0)
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input_x = np.random.normal(0, 1, [2, 2, 2]).astype(np.float32)
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input_y = np.random.normal(0, 1, [2, 2, 2]).astype(np.float32)
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context.set_context(mode=context.GRAPH_MODE,
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enable_graph_kernel=True, device_target="GPU")
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net = TestOptAssignNet_2()
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result_open_gk = net(Tensor(input_x), Tensor(input_y))
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context.set_context(mode=context.GRAPH_MODE,
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enable_graph_kernel=False, device_target="GPU")
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net_beta = TestOptAssignNet_2()
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result_close_gk = net_beta(Tensor(input_x), Tensor(input_y))
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res = np.allclose(result_open_gk.asnumpy(), result_close_gk.asnumpy(), rtol=1.e-4, atol=1.e-7, equal_nan=True)
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assert res
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_opt_assign_gpu_1():
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test_opt_assign_output_1()
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_opt_assign_gpu_2():
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test_opt_assign_output_2()
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