forked from mindspore-Ecosystem/mindspore
143 lines
4.2 KiB
Python
143 lines
4.2 KiB
Python
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.common.parameter import Parameter
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from mindspore.common import dtype
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from mindspore.nn import Cell
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import mindspore.ops.operations as P
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import mindspore.ops.functional as F
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context.set_context(mode=context.GRAPH_MODE)
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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_basic():
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class SubNet(Cell):
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def __init__(self):
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super().__init__()
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self.mul = P.Mul()
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self.add = P.Add()
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a = np.full((1,), 5, dtype=np.float32)
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self.a = Parameter(Tensor(a), name='a')
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b = np.full((1,), 4, dtype=np.float32)
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self.b = Parameter(Tensor(b), name='b')
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def construct(self, x):
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if self.a > self.b:
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x = self.mul(x, 1)
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while self.b < 6:
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x = self.add(x, x)
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self.b += 1
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return x
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class Net(Cell):
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def __init__(self):
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super().__init__()
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self.subnet = SubNet()
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self.relu = P.ReLU()
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self.add = P.Add()
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a = np.full((1,), 5, dtype=np.float32)
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self.a = Parameter(Tensor(a), name='a')
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b = np.full((1,), 4, dtype=np.float32)
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self.b = Parameter(Tensor(b), name='b')
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c = np.full((1,), 7, dtype=np.float32)
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self.c = Parameter(Tensor(c), name='c')
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def func(self, x):
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for _ in range(0, 2):
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x = self.add(x, 0)
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return x
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def construct(self, x):
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if self.a > self.b:
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x = self.subnet(x)
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else:
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x = self.relu(x)
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if self.a < self.c:
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x = self.func(x)
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else:
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x = self.add(x, 2)
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return x
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input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
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net = Net()
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out_ms = net(Tensor(input_np))
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out_np = input_np * 4
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assert np.allclose(out_ms.asnumpy(), out_np)
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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_branch_same_shape():
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"""
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Feature: control flow function.
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Description: Two branch must return the same shape.
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Expectation: Null.
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"""
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class Net(Cell):
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def __init__(self):
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super().__init__()
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self.a = 1
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def construct(self, x, y):
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for k in range(1):
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if x != 1:
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for _ in range(1):
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y = k * x
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y = self.a + y
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if x > 5:
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break
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if x == 5:
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for _ in range(1):
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y = self.a - y
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if x == y:
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continue
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return x + y
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x = np.array([-1], np.float32)
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y = np.array([2], np.float32)
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net = Net()
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grad_net = F.grad(net, grad_position=(1, 1))
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context.set_context(mode=context.GRAPH_MODE)
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fgrad = grad_net(Tensor(x), Tensor(y))
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print(fgrad)
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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_parallel_if_add_by_zero():
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"""
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Feature: AddByZero optimization in parallel if.
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Description: AddByZero optimization should not be performed when one node is a Load CNode.
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Expectation: out is a value before second assignment.
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"""
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class Net(Cell):
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def __init__(self):
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super().__init__()
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self.param_a = Parameter(Tensor(3, dtype.float32), name="a")
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self.zero = Tensor(0, dtype.float32)
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def construct(self, x):
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out = self.zero
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if x > 0:
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F.assign(self.param_a, 1)
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out = out + self.param_a
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F.assign(self.param_a, 6)
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return out
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context.set_context(mode=context.GRAPH_MODE)
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x = Tensor(3, dtype.float32)
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net = Net()
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out = net(x)
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assert np.allclose(out.asnumpy(), np.array(1, np.float32))
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