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mindspore/tests/ut/python/pynative_mode/test_cont_cases.py

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support if by if not inline add testcase of net of if by if
2020-08-13 14:47:30 +08:00
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
""" test control ops """
import numpy as np
from mindspore import dtype as ms
from mindspore import Tensor
from mindspore import context
from mindspore import nn
from mindspore import ms_function
from mindspore.common.parameter import Parameter, ParameterTuple
from mindspore.ops import composite as C
from mindspore.ops import operations as P
# from tests.vm_impl.math_ops_vm_impl import *
# from tests.vm_impl.vm_interface import *
# from tests.vm_impl import *
remove name arg from gradoperation
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grad_by_list = C.GradOperation(get_by_list=True)
grad_all = C.GradOperation(get_all=True)
fix test cases
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support if by if not inline add testcase of net of if by if
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def setup_module():
context.set_context(mode=context.PYNATIVE_MODE, enable_sparse=False)
def test_while_with_param_forward_with_const_branch():
class MyWhileNet(nn.Cell):
def __init__(self):
super().__init__()
self.max = P.ReduceMax()
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
self.reduce = P.ReduceSum()
@ms_function
def construct(self, idx, end, x):
out = self.zero
while idx < end:
if 2 > 1:
out = out + self.param
else:
out = out + idx + self.param
idx = idx + 1
return out
while_net = MyWhileNet()
net = while_net
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
net(idx, end, x)
def test_while_opt_endless():
"""endless during optimization case"""
class MyWhileNet(nn.Cell):
def __init__(self):
super().__init__()
self.max = P.ReduceMax()
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
self.reduce = P.ReduceSum()
self.addn = P.AddN()
def construct(self, idx, end, x):
addn1 = self.addn((x, x, x))
out = addn1
while idx < end:
out = self.addn((out, addn1))
idx = idx + 1
out = self.addn((out, x))
return out
class GradNet(nn.Cell):
def __init__(self, net):
super(GradNet, self).__init__()
self.net = net
@ms_function
def construct(self, *inputs):
fix test cases
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return grad_all(self.net)(*inputs)
support if by if not inline add testcase of net of if by if
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while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.ones([2, 2, 2]).astype(np.float32) * 3, dtype=ms.float32)
net(idx, end, x)
def test_no_while_call():
class MyWhileNet(nn.Cell):
def __init__(self):
super().__init__()
self.max = P.ReduceMax()
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
self.reduce = P.ReduceSum()
@ms_function
def construct(self, idx, end, x):
out = self.zero
if 2 > 1:
out = out + self.param
else:
out = out + idx + self.param
return out
while_net = MyWhileNet()
net = while_net
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
net(idx, end, x)
def test_while_with_param_grad_with_const_branch():
class MyWhileNet(nn.Cell):
def __init__(self):
super().__init__()
self.max = P.ReduceMax()
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
self.reduce = P.ReduceSum()
def construct(self, idx, end, x):
out = self.zero
while idx < end:
if 2 > 1:
out = out + self.param
else:
out = out + idx + self.param
idx = idx + 1
return out
class GradNet(nn.Cell):
def __init__(self, net):
super(GradNet, self).__init__()
self.net = net
self.weights = ParameterTuple(net.trainable_params())
@ms_function
def construct(self, a, b, c):
fix test cases
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return grad_by_list(self.net, self.weights)(a, b, c)
support if by if not inline add testcase of net of if by if
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while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
net(idx, end, x)
def test_for_while_with_param_grad_with_const_branch():
class MyWhileNet(nn.Cell):
def __init__(self):
super().__init__()
self.max = P.ReduceMax()
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
self.reduce = P.ReduceSum()
self.start = Tensor(np.array(0), dtype=ms.int32)
def construct(self, idx, end, x):
out = self.zero
for _ in range(0, 2):
idx = self.start
while idx < end:
if 2 > 1:
out = out + self.param
else:
out = out + idx + self.param
idx = idx + 1
return out
class GradNet(nn.Cell):
def __init__(self, net):
super(GradNet, self).__init__()
self.net = net
self.weights = ParameterTuple(net.trainable_params())
@ms_function
def construct(self, a, b, c):
fix test cases
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return grad_by_list(self.net, self.weights)(a, b, c)
support if by if not inline add testcase of net of if by if
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while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
net(idx, end, x)
def test_for_while_with_param_grad_basic():
class MyWhileNet(nn.Cell):
def __init__(self):
super().__init__()
self.max = P.ReduceMax()
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
self.reduce = P.ReduceSum()
self.start = Tensor(np.array(0), dtype=ms.int32)
def construct(self, idx, end, x):
out = self.zero
for _ in range(0, 2):
idx = self.start
while idx < end:
out = out + self.param
idx = idx + 1
return out
class GradNet(nn.Cell):
def __init__(self, net):
super(GradNet, self).__init__()
self.net = net
self.weights = ParameterTuple(net.trainable_params())
@ms_function
def construct(self, a, b, c):
fix test cases
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return grad_by_list(self.net, self.weights)(a, b, c)
support if by if not inline add testcase of net of if by if
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while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
net(idx, end, x)
def test_for_while_with_param_grad_normal():
class MyWhileNet(nn.Cell):
def __init__(self):
super().__init__()
self.max = P.ReduceMax()
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
self.reduce = P.ReduceSum()
self.start = Tensor(np.array(0), dtype=ms.int32)
def construct(self, idx, end, x):
out = x
for _ in range(0, 2):
idx = self.start
while idx < end:
out = out + self.param
idx = idx + 1
return out
class GradNet(nn.Cell):
def __init__(self, net):
super(GradNet, self).__init__()
self.net = net
self.weights = ParameterTuple(net.trainable_params())
@ms_function
def construct(self, a, b, c):
fix test cases
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return grad_by_list(self.net, self.weights)(a, b, c)
support if by if not inline add testcase of net of if by if
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while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
net(idx, end, x)
def test_while_with_param_basic_grad():
class MyWhileNet(nn.Cell):
def __init__(self):
super().__init__()
self.max = P.ReduceMax()
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
self.t2 = Tensor(np.array(2), dtype=ms.float32)
def construct(self, idx, end, x):
out = self.zero
while idx < end:
out = out + self.param
idx = idx + 1
return out + self.param
class GradNet(nn.Cell):
def __init__(self, net):
super(GradNet, self).__init__()
self.net = net
self.weights = ParameterTuple(net.trainable_params())
@ms_function
def construct(self, a, b, c):
fix test cases
2020-08-25 14:38:08 +08:00
return grad_by_list(self.net, self.weights)(a, b, c)
support if by if not inline add testcase of net of if by if
2020-08-13 14:47:30 +08:00
while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(3), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
net(idx, end, x)
def test_while_with_param_basic_grad_mul():
class MyWhileNet(nn.Cell):
def __init__(self):
super().__init__()
self.max = P.ReduceMax()
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
self.zero = Tensor(np.ones(([2, 2, 2])), ms.float32)
self.t2 = Tensor(np.array(2), dtype=ms.float32)
def construct(self, idx, end, x):
out = self.zero
while idx < end:
out = out * self.param
idx = idx + 1
return out + self.param
class GradNet(nn.Cell):
def __init__(self, net):
super(GradNet, self).__init__()
self.net = net
self.weights = ParameterTuple(net.trainable_params())
@ms_function
def construct(self, a, b, c):
fix test cases
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return grad_by_list(self.net, self.weights)(a, b, c)
support if by if not inline add testcase of net of if by if
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while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(3), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
net(idx, end, x)
def test_while_with_param_basic_grad_two():
class MyWhileNet(nn.Cell):
def __init__(self):
super().__init__()
self.max = P.ReduceMax()
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
self.weight = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="loss")
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
self.t2 = Tensor(np.array(2), dtype=ms.float32)
def construct(self, idx, end, x):
out = self.zero
while idx < end:
out = out + self.param + self.weight
idx = idx + 1
return out + self.param
class GradNet(nn.Cell):
def __init__(self, net):
super(GradNet, self).__init__()
self.net = net
self.weights = ParameterTuple(net.trainable_params())
@ms_function
def construct(self, a, b, c):
fix test cases
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return grad_by_list(self.net, self.weights)(a, b, c)
support if by if not inline add testcase of net of if by if
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while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(3), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
net(idx, end, x)
def test_while_with_param_basic_grad_three():
class MyWhileNet(nn.Cell):
def __init__(self):
super().__init__()
self.max = P.ReduceMax()
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
self.weight = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="loss")
self.key = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="key")
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
self.t2 = Tensor(np.array(2), dtype=ms.float32)
def construct(self, idx, end, x):
out = self.zero
while idx < end:
out = out + self.param + self.weight + self.key
idx = idx + 1
return out + self.param
class GradNet(nn.Cell):
def __init__(self, net):
super(GradNet, self).__init__()
self.net = net
self.weights = ParameterTuple(net.trainable_params())
@ms_function
def construct(self, a, b, c):
fix test cases
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return grad_by_list(self.net, self.weights)(a, b, c)
support if by if not inline add testcase of net of if by if
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while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(3), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
net(idx, end, x)
def test_while_if_with_param_grad():
class MyWhileNet(nn.Cell):
def __init__(self):
super().__init__()
self.max = P.ReduceMax()
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
self.t2 = Tensor(np.array(2), dtype=ms.float32)
def construct(self, idx, end, x):
out = self.zero
while idx < end:
if self.max(out) < self.max(x):
out = out + self.param * 2
else:
out = out + self.param
idx = idx + 1
return out + self.param
class GradNet(nn.Cell):
def __init__(self, net):
super(GradNet, self).__init__()
self.net = net
self.weights = ParameterTuple(net.trainable_params())
@ms_function
def construct(self, a, b, c):
fix test cases
2020-08-25 14:38:08 +08:00
return grad_by_list(self.net, self.weights)(a, b, c)
support if by if not inline add testcase of net of if by if
2020-08-13 14:47:30 +08:00
while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(3), dtype=ms.int32)
x = Tensor(np.ones([2, 2, 2]).astype(np.float32), dtype=ms.float32)
net(idx, end, x)
def test_while_with_param_grad_not_enter_while():
class MyWhileNet(nn.Cell):
def __init__(self):
super().__init__()
self.max = P.ReduceMax()
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
def construct(self, idx, end, x):
out = self.zero
while idx < end:
out = out + self.param * 3
idx = idx + 1
return out + self.param
class GradNet(nn.Cell):
def __init__(self, net):
super(GradNet, self).__init__()
self.net = net
self.weights = ParameterTuple(net.trainable_params())
@ms_function
def construct(self, a, b, c):
fix test cases
2020-08-25 14:38:08 +08:00
return grad_by_list(self.net, self.weights)(a, b, c)
support if by if not inline add testcase of net of if by if
2020-08-13 14:47:30 +08:00
while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(3), dtype=ms.int32)
end = Tensor(np.array(0), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
net(idx, end, x)
def test_with_param_if_by_if_forward():
class MyIfByIfNet(nn.Cell):
def __init__(self):
super().__init__()
self.max = P.ReduceMax()
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
@ms_function
def construct(self, a, b, x):
out = self.zero
if a < b:
out = out + x + self.param
else:
out = out + x
if a == b:
out = out + x*3 + self.param
else:
out = out + x*2
return out
if_net = MyIfByIfNet()
net = if_net
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.ones([2, 2, 2]).astype(np.float32), dtype=ms.float32)
net(idx, end, x)
def test_with_param_if_by_if_grad_inputs():
class MyIfByIfNet(nn.Cell):
def __init__(self):
super().__init__()
self.max = P.ReduceMax()
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
def construct(self, a, b, x):
out = self.zero
if a < b:
out = out + x + self.param * 4
if a == b:
out = out + x*3 + self.param * 3
return out
class GradNet(nn.Cell):
def __init__(self, net):
super(GradNet, self).__init__()
self.net = net
@ms_function
def construct(self, *inputs):
fix test cases
2020-08-25 14:38:08 +08:00
return grad_all(self.net)(*inputs)
support if by if not inline add testcase of net of if by if
2020-08-13 14:47:30 +08:00
if_net = MyIfByIfNet()
net = GradNet(if_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(0), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
net(idx, end, x)
def test_with_param_if_by_if_grad_parameter():
class MyIfByIfNet(nn.Cell):
def __init__(self):
super().__init__()
self.max = P.ReduceMax()
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
def construct(self, a, b, x):
out = self.zero
if a < b:
out = out + x + self.param * 2
if a == b:
out = out + x*3 + self.param
return out
class GradNet(nn.Cell):
def __init__(self, net):
super(GradNet, self).__init__()
self.net = net
self.weights = ParameterTuple(net.trainable_params())
@ms_function
def construct(self, *inputs):
fix test cases
2020-08-25 14:38:08 +08:00
return grad_by_list(self.net, self.weights)(*inputs)
support if by if not inline add testcase of net of if by if
2020-08-13 14:47:30 +08:00
if_net = MyIfByIfNet()
net = GradNet(if_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(2), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
net(idx, end, x)
def test_with_param_if_by_if_grad_param_excute_null():
class MyIfByIfNet(nn.Cell):
def __init__(self):
super().__init__()
self.max = P.ReduceMax()
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
def construct(self, a, b, x):
out = self.zero
if a < b:
out = out + x + self.param * 2
return out
class GradNet(nn.Cell):
def __init__(self, net):
super(GradNet, self).__init__()
self.net = net
self.weights = ParameterTuple(net.trainable_params())
@ms_function
def construct(self, *inputs):
fix test cases
2020-08-25 14:38:08 +08:00
return grad_by_list(self.net, self.weights)(*inputs)
support if by if not inline add testcase of net of if by if
2020-08-13 14:47:30 +08:00
if_net = MyIfByIfNet()
net = GradNet(if_net)
idx = Tensor(np.array(4), dtype=ms.int32)
end = Tensor(np.array(0), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
net(idx, end, x)
def test_if_by_if_return_inside_grad():
class MyIfByIfNet(nn.Cell):
def __init__(self):
super().__init__()
self.max = P.ReduceMax()
self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
def construct(self, a, b, x):
out = self.zero
if a < b:
return out + x + self.param
if a == b:
return out + self.param * 2
return out + self.param * 3
class GradNet(nn.Cell):
def __init__(self, net):
super(GradNet, self).__init__()
self.net = net
self.weights = ParameterTuple(net.trainable_params())
@ms_function
def construct(self, *inputs):
fix test cases
2020-08-25 14:38:08 +08:00
return grad_by_list(self.net, self.weights)(*inputs)
support if by if not inline add testcase of net of if by if
2020-08-13 14:47:30 +08:00
if_net = MyIfByIfNet()
net = GradNet(if_net)
idx = Tensor(np.array(1), dtype=ms.int32)
end = Tensor(np.array(0), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
net(idx, end, x)
def test_if_by_if_forward():
class MyIfByIfNet(nn.Cell):
def __init__(self):
super().__init__()
self.add = P.TensorAdd()
self.sub = P.Sub()
self.mul = P.Mul()
self.div = P.RealDiv()
@ms_function
def construct(self, a, b, x):
if a < b:
a = self.add(a, b)
else:
a = self.sub(a, b)
if a == x:
a = self.mul(a, b)
else:
a = self.div(a, b)
if b == x:
b = self.add(a, b)
else:
b = self.add(a, x)
a = a * b
out = a + b + x
return out
if_net = MyIfByIfNet()
net = if_net
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(4), dtype=ms.float32)
net(idx, end, x)
def test_if_by_if_forward_control_tuple_switch():
"""tuple_get from swtich op will generate new switch inside to eliminate tuple_get"""
class Branch3Net(nn.Cell):
def __init__(self):
super().__init__()
self.add = P.TensorAdd()
self.sub = P.Sub()
self.mul = P.Mul()
self.div = P.RealDiv()
def construct(self, a, b, x):
if b == x:
b = self.add(a, b)
else:
b = self.add(a, x)
return a, b, x
class Branch2Net(nn.Cell):
def __init__(self):
super().__init__()
self.add = P.TensorAdd()
self.sub = P.Sub()
self.mul = P.Mul()
self.div = P.RealDiv()
self.net = Branch3Net()
def construct(self, a, b, x):
if a == x:
a = self.mul(a, b)
else:
a = self.div(a, b)
return self.net(a, b, x)
class MyIfByIfNet(nn.Cell):
def __init__(self):
super().__init__()
self.add = P.TensorAdd()
self.sub = P.Sub()
self.mul = P.Mul()
self.div = P.RealDiv()
self.net = Branch2Net()
@ms_function
def construct(self, a, b, x):
if a < b:
a = self.add(a, b)
else:
a = self.sub(a, b)
a, b, x = self.net(a, b, x)
a = a * b
out = a + b + x
return out
if_net = MyIfByIfNet()
net = if_net
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
net(idx, end, x)
def test_if_by_if_forward_control_inside_net():
class Branch3Net(nn.Cell):
def __init__(self):
super().__init__()
self.add = P.TensorAdd()
self.sub = P.Sub()
self.mul = P.Mul()
self.div = P.RealDiv()
def construct(self, a, b, x):
if b == x:
b = self.add(a, b)
else:
b = self.add(a, x)
a = a * b
out = a + b + x
return out
class Branch2Net(nn.Cell):
def __init__(self):
super().__init__()
self.add = P.TensorAdd()
self.sub = P.Sub()
self.mul = P.Mul()
self.div = P.RealDiv()
self.net = Branch3Net()
def construct(self, a, b, x):
if a == x:
a = self.mul(a, b)
else:
a = self.div(a, b)
return self.net(a, b, x)
class MyIfByIfNet(nn.Cell):
def __init__(self):
super().__init__()
self.add = P.TensorAdd()
self.sub = P.Sub()
self.mul = P.Mul()
self.div = P.RealDiv()
self.net = Branch2Net()
@ms_function
def construct(self, a, b, x):
if a < b:
a = self.add(a, b)
else:
a = self.sub(a, b)
out = self.net(a, b, x)
return out
if_net = MyIfByIfNet()
net = if_net
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
net(idx, end, x)
def test_if_by_if_forward_use_namespace():
class MyIfByIfNet(nn.Cell):
def __init__(self):
super().__init__()
self.add = P.TensorAdd()
self.sub = P.Sub()
self.mul = P.Mul()
self.div = P.RealDiv()
@ms_function
def construct(self, a, b, x):
if a < b:
a = P.TensorAdd()(a, b)
else:
a = P.Sub()(a, b)
if a == x:
a = P.Mul()(a, b)
else:
a = P.RealDiv()(a, b)
if b == x:
b = P.TensorAdd()(a, b)
else:
b = P.TensorAdd()(a, x)
a = a * b
out = a + b + x
return out
if_net = MyIfByIfNet()
net = if_net
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
net(idx, end, x)
def test_if_by_if_forward_use_global_op():
class MyIfByIfNet(nn.Cell):
def __init__(self):
super().__init__()
self.add = P.TensorAdd()
self.sub = P.Sub()
self.mul = P.Mul()
self.div = P.RealDiv()
@ms_function
def construct(self, a, b, x):
add = P.TensorAdd()
sub = P.Sub()
mul = P.Mul()
div = P.RealDiv()
if a < b:
a = add(a, b)
else:
a = sub(a, b)
if a == x:
a = mul(a, b)
else:
a = div(a, b)
if b == x:
b = add(a, b)
else:
b = add(a, x)
a = a * b
out = a + b + x
return out
if_net = MyIfByIfNet()
net = if_net
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
net(idx, end, x)
def test_for_with_if_by_if_forward():
class MyIfByIfNet(nn.Cell):
def __init__(self):
super().__init__()
self.add = P.TensorAdd()
self.sub = P.Sub()
@ms_function
def construct(self, a, b, x):
for _ in range(0, 4):
if a < b:
a = self.add(a, b)
else:
b = self.sub(b, x)
a = a * b
out = a + b + x
return out
if_net = MyIfByIfNet()
net = if_net
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
net(idx, end, x)
def test_for_with_if_by_if_forward_namespace():
class MyIfByIfNet(nn.Cell):
def __init__(self):
super().__init__()
self.add = P.TensorAdd()
self.sub = P.Sub()
self.mul = P.Mul()
self.div = P.RealDiv()
@ms_function
def construct(self, a, b, x):
for _ in range(0, 6):
if a < b:
a = P.TensorAdd()(a, b)
else:
b = P.Sub()(b, x)
a = a * b
out = a + b + x
return out
if_net = MyIfByIfNet()
net = if_net
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
net(idx, end, x)
def test_if_by_if_forward_const_branch_inner():
class MyIfByIfNet(nn.Cell):
def __init__(self):
super().__init__()
self.add = P.TensorAdd()
self.sub = P.Sub()
self.mul = P.Mul()
self.div = P.RealDiv()
@ms_function
def construct(self, a, b, x):
add = P.TensorAdd()
sub = P.Sub()
mul = P.Mul()
div = P.RealDiv()
if a < b:
a = add(a, b)
else:
a = sub(a, b)
if 2 > 1:
a = mul(a, b)
else:
a = div(a, b)
if b == x:
b = add(a, b)
else:
b = add(a, x)
a = a * b
out = a + b + x
return out
if_net = MyIfByIfNet()
net = if_net
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
net(idx, end, x)
def test_if_by_if_forward_all_const_branch():
class MyIfByIfNet(nn.Cell):
def __init__(self):
super().__init__()
self.add = P.TensorAdd()
self.sub = P.Sub()
self.mul = P.Mul()
self.div = P.RealDiv()
@ms_function
def construct(self, a, b, x):
add = P.TensorAdd()
sub = P.Sub()
mul = P.Mul()
div = P.RealDiv()
if 2 < 12:
a = add(a, b)
else:
a = sub(a, b)
if 2 > 1:
a = mul(a, b)
else:
a = div(a, b)
if 2 == 1:
b = add(a, b)
else:
b = add(a, x)
a = a * b
out = a + b + x
return out
if_net = MyIfByIfNet()
net = if_net
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
net(idx, end, x)