forked from mindspore-Ecosystem/mindspore
70 lines
1.9 KiB
Python
70 lines
1.9 KiB
Python
import os
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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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context.set_context(mode=context.GRAPH_MODE)
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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_for_half_unroll_basic():
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"""
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Feature: Half unroll compile optimization for for statement.
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Description: Only test for statement.
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Expectation: Correct result and no exception.
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"""
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class ForLoopBasic(Cell):
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def __init__(self):
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super().__init__()
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self.array = (Tensor(np.array(10).astype(np.int32)), Tensor(np.array(5).astype(np.int32)))
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def construct(self, x):
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output = x
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for i in self.array:
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output += i
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return output
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net = ForLoopBasic()
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x = Tensor(np.array(10).astype(np.int32))
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os.environ['MS_DEV_FOR_HALF_UNROLL'] = '1'
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res = net(x)
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os.environ['MS_DEV_FOR_HALF_UNROLL'] = ''
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assert res == 25
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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_for_half_unroll_if():
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"""
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Feature: Half unroll compile optimization for for statement.
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Description: Test for-in statements.
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Expectation: Correct result and no exception.
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"""
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class ForLoopIf(Cell):
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def __init__(self):
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super().__init__()
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self.array = (Tensor(np.array(10).astype(np.int32)), Tensor(np.array(5).astype(np.int32)))
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def construct(self, x):
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output = x
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for i in self.array:
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if i < 10:
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output += i
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return output
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net = ForLoopIf()
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x = Tensor(np.array(10).astype(np.int32))
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os.environ['MS_DEV_FOR_HALF_UNROLL'] = '1'
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res = net(x)
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os.environ['MS_DEV_FOR_HALF_UNROLL'] = ''
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assert res == 15
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