forked from mindspore-Ecosystem/mindspore
389 lines
12 KiB
Python
389 lines
12 KiB
Python
# Copyright 2020 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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""" test_ascend_control_sink """
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import pytest
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import numpy as np
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore as ms
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from mindspore.ops import functional as F
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from mindspore.ops import operations as op
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from mindspore.common import dtype as mstype
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from mindspore.common.tensor import Tensor
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from mindspore.common.parameter import Parameter
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from mindspore.common.initializer import initializer
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class ControlSimpleIf(nn.Cell):
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def __init__(self):
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super().__init__()
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self.addn = op.AddN()
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def construct(self, x, y, z, input1, input2):
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addn1 = self.addn([input1, input1, input1])
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addn2 = self.addn([input2, input2, input2])
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addn11 = self.addn([addn1, addn1, addn1])
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addn22 = self.addn([addn2, addn2, addn2])
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cond1 = x > y
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cond2 = y > z
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# dodge pylint
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if cond1 and cond2:
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out = self.addn([addn11, addn11])
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else:
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out = self.addn([addn22, addn22])
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out_me = self.addn([out, input1])
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return out_me
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class ControlSimpleIfWithAssign(nn.Cell):
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def __init__(self, input_shape):
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super().__init__()
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self.addn = op.AddN()
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self.assign = op.Assign()
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self.input_data = Parameter(initializer(1, input_shape, mstype.float32), name="var")
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def construct(self, x, y, input_data):
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if x > y:
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out = self.addn([input_data, input_data, input_data])
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else:
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out = self.assign(self.input_data, input_data)
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return out
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class ControlIfinIf(nn.Cell):
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"""pass"""
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def construct(self, x, y):
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if x > y:
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x = x + 1
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if y < 0:
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y = y + 1
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else:
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y = y + 2
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else:
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x = x + 2
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x = x + y
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return x
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class ControlIfbyIfbyIf(nn.Cell):
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def __init__(self):
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super().__init__()
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self.addn = op.AddN()
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def construct(self, x, y, cond1, cond2, input_data):
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tri_in = self.addn([input_data, input_data, input_data])
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if x > y:
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addn_1 = self.addn([tri_in, tri_in])
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else:
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addn_1 = self.addn([tri_in, tri_in, tri_in])
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if cond1:
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addn_2 = self.addn([addn_1, addn_1])
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else:
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addn_2 = self.addn([addn_1, addn_1, addn_1])
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if cond2:
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out = self.addn([addn_2, addn_2, addn_2])
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else:
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out = self.addn([addn_2, addn_2])
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return out
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class ControlSimpleWhile(nn.Cell):
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def __init__(self):
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super().__init__()
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self.addn = op.AddN()
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def construct(self, x, y, input_data):
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out = input_data
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while x:
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out = self.addn([input_data, input_data, input_data])
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x = y
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return out
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class ControlMixedWhileIf(nn.Cell):
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def __init__(self):
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super().__init__()
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self.assign = op.Assign()
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self.var = Parameter(initializer(1, (1), mstype.float32), name="var")
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def construct(self, x, y, z, c2, c4):
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out = c4
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self.assign(self.var, c4)
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while x < c2:
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y = c4
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self.assign(self.var, c4)
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while y < c2 and x < c2:
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if 2 * y < c2:
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y = y + 2
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else:
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y = y + 1
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out = out + y
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z = c4
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self.assign(self.var, c4)
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while z < c2:
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z = z + 1
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out = out + z
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x = x + 1
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out = out + x
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while x < 2 * c2:
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y = c4
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self.assign(self.var, c4)
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x = x + 1
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while y < c2:
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z = c4
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self.assign(self.var, c4)
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while z < c2:
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z = z + 1
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if x < c2:
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y = y - 1
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else:
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y = y + 1
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out = out + z
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out = out + y
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out = out + x
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return out
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class AndOperation(nn.Cell):
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def __init__(self):
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super().__init__()
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self.reduce_sum = op.ReduceSum()
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def construct(self, x, y):
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x_sum = self.reduce_sum(x)
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y_sum = self.reduce_sum(y)
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out = x_sum and y_sum
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return out
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class OrOperation(nn.Cell):
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def __init__(self):
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super().__init__()
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self.reduce_sum = op.ReduceSum()
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def construct(self, x, y):
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x_sum = self.reduce_sum(x)
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y_sum = self.reduce_sum(y)
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out = x_sum or y_sum
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return out
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class NotOperation(nn.Cell):
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def __init__(self):
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super().__init__()
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self.reduce_sum = op.ReduceSum()
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def construct(self, x):
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x_sum = self.reduce_sum(x)
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return not x_sum
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class SimpleCell(nn.Cell):
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def __init__(self, i):
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super().__init__()
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self.i = i
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def construct(self, x):
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return self.i * x
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class CellListInWhileByWhile(nn.Cell):
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def __init__(self):
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super().__init__()
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self.cell_list = nn.CellList()
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self.cell_list.append(SimpleCell(4))
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self.cell_list.append(SimpleCell(5))
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self.cell_list.append(SimpleCell(6))
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def construct(self, t, x):
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out = t
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while x < 3:
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out += 4
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x += 1
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x = 0
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while x < 3:
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add = self.cell_list[x](t)
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out = out + add
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x += 1
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return out
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def cell_list_in_while_by_while():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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net = CellListInWhileByWhile()
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t = Tensor(10, mstype.int32)
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x = Tensor(0, mstype.int32)
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out = net(t, x)
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return out
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@pytest.mark.level1
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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_simple_if():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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x = np.array(3).astype(np.float32)
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y = np.array(2).astype(np.float32)
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z = np.array(3).astype(np.float32)
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input_shape = (127, 7, 53, 31)
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input1 = np.random.randn(*input_shape).astype(np.float32)
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input2 = np.random.randn(*input_shape).astype(np.float32)
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net = ControlSimpleIf()
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output = net(Tensor(x), Tensor(y), Tensor(z), Tensor(input1), Tensor(input2))
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expect = input2 * 3 * 3 * 2 + input1
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assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
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@pytest.mark.level1
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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_simple_if_with_assign():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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x = np.array(0).astype(np.float32)
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y = np.array(1).astype(np.float32)
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input_shape = (127, 7, 53, 31)
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input_data = np.random.randn(*input_shape).astype(np.float32)
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net = ControlSimpleIfWithAssign(input_shape)
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output = net(Tensor(x), Tensor(y), Tensor(input_data))
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expect = input_data
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assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
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@pytest.mark.level1
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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_if_in_if():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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x = np.array(2.345678).astype(np.float32)
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y = np.array(1.234567).astype(np.float32)
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net = ControlIfinIf()
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output = net(Tensor(x), Tensor(y))
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expect = x + y + 3
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assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
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@pytest.mark.level1
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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_if_by_if_by_if():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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x = np.array(2.345678).astype(np.float32)
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y = np.array(1.234567).astype(np.float32)
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cond1 = np.array(True).astype(np.bool)
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cond2 = np.array(False).astype(np.bool)
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input_shape = (127, 7, 53, 31)
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input_data = np.random.randn(*input_shape).astype(np.float32)
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net = ControlIfbyIfbyIf()
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output = net(Tensor(x), Tensor(y), Tensor(cond1), Tensor(cond2), Tensor(input_data))
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expect = input_data * 3 * 2 * 2 * 2
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assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
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@pytest.mark.level1
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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_simple_while():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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x = np.array(True).astype(np.bool)
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y = np.array(False).astype(np.bool)
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input_shape = (127, 7, 53, 31)
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input_data = np.random.randn(*input_shape).astype(np.float32)
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net = ControlSimpleWhile()
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output = net(Tensor(x), Tensor(y), Tensor(input_data))
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expect = input_data * 3
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assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
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@pytest.mark.level1
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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_mixed_while_if():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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x = np.array(2).astype(np.int32)
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y = np.array(14).astype(np.int32)
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z = np.array(1).astype(np.int32)
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c2 = Tensor([14], mstype.int32)
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c4 = Tensor([0], mstype.int32)
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net = ControlMixedWhileIf()
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output = net(Tensor(x), Tensor(y), Tensor(z), c2, c4)
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expect = np.array(3318).astype(np.int32)
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assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
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@pytest.mark.level1
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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_and_or_operation():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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x = np.array([0, 1]).astype(np.float32)
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y = np.array([0, 0]).astype(np.float32)
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net = AndOperation()
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output = net(Tensor(x), Tensor(y))
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expect = np.sum(x) and np.sum(y)
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assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
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net = OrOperation()
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output = net(Tensor(x), Tensor(y))
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expect = np.sum(x) or np.sum(y)
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assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
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net = NotOperation()
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output = net(Tensor(x))
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expect = not np.sum(x)
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assert np.allclose(expect, output.asnumpy(), 0.0001, 0.0001)
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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_control_flow_ref():
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"""
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Feature: Control flow graph sinking scenarios.
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Description: If the return value of subgraph is Ref, should run graph mode with kernelbykernel.
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Expectation: No exception.
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"""
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class IFFuncNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.param_a = Parameter(Tensor(1, ms.float32), name="a")
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self.param_b = Parameter(Tensor(2, ms.float32), name="b")
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self.one = Tensor(1, ms.float32)
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def subfunc(self, x):
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if x > 4:
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return self.param_a
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return self.param_b
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def construct(self, x):
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out = self.one
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F.assign(self.param_a, 3)
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out += self.subfunc(x)
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F.assign(self.param_b, 2)
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return out
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net = IFFuncNet()
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input_x = Tensor(6, ms.float32)
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out = net(input_x)
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assert out == 4
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