forked from mindspore-Ecosystem/mindspore
130 lines
3.7 KiB
Python
130 lines
3.7 KiB
Python
# Copyright 2021-2022 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 pytest
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from mindspore import context
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from mindspore import Tensor, nn
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from mindspore.ops import composite as C
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from mindspore.common import dtype as mstype
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grad_all = C.GradOperation(get_all=True)
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class SingleIfNet(nn.Cell):
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def construct(self, x, y):
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x += 1
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if x < y:
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y += x
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else:
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y -= x
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y += 5
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return y
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class SingleIfNet1(nn.Cell):
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def construct(self, x, y):
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x += 1
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out = self.func(x, y)
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out *= 2
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return out
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def func(self, x, y):
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if x < y:
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y += x
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else:
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y -= x
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y += 5
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return y
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class GradNet(nn.Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.net = net
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def construct(self, *inputs):
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return grad_all(self.net)(*inputs)
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def control_flow_single_if(input_net, x, y, expect1, expect2):
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# graph mode
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context.set_context(mode=context.GRAPH_MODE)
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net = input_net()
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grad_net = GradNet(net)
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forward_net = input_net()
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graph_forward_res = forward_net(x, y)
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assert graph_forward_res == expect1
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graph_backward_res = grad_net(x, y)
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assert graph_backward_res == expect2
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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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_single_if():
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x = Tensor(2, mstype.int32)
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y = Tensor(5, mstype.int32)
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expect1 = Tensor(13, mstype.int32)
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expect2 = (Tensor(1, mstype.int32), Tensor(1, mstype.int32))
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control_flow_single_if(SingleIfNet, x, y, expect1, expect2)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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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_single_if_01():
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x = Tensor(2, mstype.int32)
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y = Tensor(5, mstype.int32)
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expect1 = Tensor(26, mstype.int32)
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expect2 = (Tensor(2, mstype.int32), Tensor(2, mstype.int32))
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control_flow_single_if(SingleIfNet1, x, y, expect1, expect2)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_single_if_any():
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"""
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Feature: compile and run control flow with if statement
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Description: true-branch func graph refer a CNode in construct as free variable.
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That CNode and the inputs will be specialized before ProcessCNode
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of true-branch func graph, so it's no need to specialize the inputs
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of that CNode again if it's a specialized func graph.
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Expectation: success
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"""
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x = Tensor([True, True, False])
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y = Tensor([False])
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class Net(nn.Cell):
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def __init__(self, input1, input2):
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super().__init__()
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self.input1 = input1
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self.input2 = input2
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def construct(self):
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if self.input1.all() == self.input2:
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return self.input1.any()
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return self.input2
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context.set_context(mode=context.GRAPH_MODE)
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net = Net(x, y)
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output = net()
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assert output
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