forked from mindspore-Ecosystem/mindspore
1010 lines
32 KiB
Python
1010 lines
32 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 control ops """
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import numpy as np
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from mindspore import dtype as ms
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from mindspore import Tensor
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from mindspore import context
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from mindspore import nn
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from mindspore import ms_function
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from mindspore.common.parameter import Parameter, ParameterTuple
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from mindspore.ops import composite as C
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from mindspore.ops import operations as P
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# from tests.vm_impl.math_ops_vm_impl import *
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# from tests.vm_impl.vm_interface import *
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# from tests.vm_impl import *
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grad_by_list = C.GradOperation(get_by_list=True)
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grad_all = C.GradOperation(get_all=True)
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def setup_module():
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context.set_context(mode=context.PYNATIVE_MODE, enable_sparse=False)
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def test_while_with_param_forward_with_const_branch():
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
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self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
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self.reduce = P.ReduceSum()
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@ms_function
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def construct(self, idx, end, x):
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out = self.zero
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while idx < end:
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if 2 > 1:
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out = out + self.param
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else:
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out = out + idx + self.param
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idx = idx + 1
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return out
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while_net = MyWhileNet()
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net = while_net
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idx = Tensor(np.array(0), dtype=ms.int32)
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end = Tensor(np.array(4), dtype=ms.int32)
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x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
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net(idx, end, x)
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def test_while_opt_endless():
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"""endless during optimization case"""
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
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self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
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self.reduce = P.ReduceSum()
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self.addn = P.AddN()
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def construct(self, idx, end, x):
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addn1 = self.addn((x, x, x))
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out = addn1
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while idx < end:
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out = self.addn((out, addn1))
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idx = idx + 1
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out = self.addn((out, x))
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return out
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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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@ms_function
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def construct(self, *inputs):
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return grad_all(self.net)(*inputs)
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while_net = MyWhileNet()
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net = GradNet(while_net)
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idx = Tensor(np.array(0), dtype=ms.int32)
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end = Tensor(np.array(4), dtype=ms.int32)
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x = Tensor(np.ones([2, 2, 2]).astype(np.float32) * 3, dtype=ms.float32)
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net(idx, end, x)
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def test_no_while_call():
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
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self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
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self.reduce = P.ReduceSum()
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@ms_function
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def construct(self, idx, end, x):
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out = self.zero
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if 2 > 1:
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out = out + self.param
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else:
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out = out + idx + self.param
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return out
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while_net = MyWhileNet()
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net = while_net
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idx = Tensor(np.array(0), dtype=ms.int32)
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end = Tensor(np.array(4), dtype=ms.int32)
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x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
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net(idx, end, x)
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def test_while_with_param_grad_with_const_branch():
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
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self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
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self.reduce = P.ReduceSum()
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def construct(self, idx, end, x):
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out = self.zero
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while idx < end:
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if 2 > 1:
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out = out + self.param
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else:
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out = out + idx + self.param
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idx = idx + 1
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return out
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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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self.weights = ParameterTuple(net.trainable_params())
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@ms_function
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def construct(self, a, b, c):
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return grad_by_list(self.net, self.weights)(a, b, c)
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while_net = MyWhileNet()
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net = GradNet(while_net)
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idx = Tensor(np.array(0), dtype=ms.int32)
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end = Tensor(np.array(4), dtype=ms.int32)
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x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
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net(idx, end, x)
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def test_for_while_with_param_grad_with_const_branch():
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
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self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
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self.reduce = P.ReduceSum()
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self.start = Tensor(np.array(0), dtype=ms.int32)
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def construct(self, idx, end, x):
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out = self.zero
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for _ in range(0, 2):
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idx = self.start
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while idx < end:
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if 2 > 1:
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out = out + self.param
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else:
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out = out + idx + self.param
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idx = idx + 1
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return out
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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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self.weights = ParameterTuple(net.trainable_params())
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@ms_function
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def construct(self, a, b, c):
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return grad_by_list(self.net, self.weights)(a, b, c)
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while_net = MyWhileNet()
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net = GradNet(while_net)
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idx = Tensor(np.array(0), dtype=ms.int32)
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end = Tensor(np.array(4), dtype=ms.int32)
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x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
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net(idx, end, x)
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def test_for_while_with_param_grad_basic():
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
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self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
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self.reduce = P.ReduceSum()
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self.start = Tensor(np.array(0), dtype=ms.int32)
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def construct(self, idx, end, x):
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out = self.zero
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for _ in range(0, 2):
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idx = self.start
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while idx < end:
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out = out + self.param
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idx = idx + 1
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return out
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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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self.weights = ParameterTuple(net.trainable_params())
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@ms_function
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def construct(self, a, b, c):
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return grad_by_list(self.net, self.weights)(a, b, c)
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while_net = MyWhileNet()
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net = GradNet(while_net)
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idx = Tensor(np.array(0), dtype=ms.int32)
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end = Tensor(np.array(4), dtype=ms.int32)
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x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
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net(idx, end, x)
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def test_for_while_with_param_grad_normal():
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
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self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
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self.reduce = P.ReduceSum()
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self.start = Tensor(np.array(0), dtype=ms.int32)
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def construct(self, idx, end, x):
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out = x
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for _ in range(0, 2):
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idx = self.start
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while idx < end:
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out = out + self.param
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idx = idx + 1
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return out
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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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self.weights = ParameterTuple(net.trainable_params())
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@ms_function
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def construct(self, a, b, c):
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return grad_by_list(self.net, self.weights)(a, b, c)
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while_net = MyWhileNet()
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net = GradNet(while_net)
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idx = Tensor(np.array(0), dtype=ms.int32)
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end = Tensor(np.array(4), dtype=ms.int32)
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x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
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net(idx, end, x)
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def test_while_with_param_basic_grad():
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
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self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
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self.t2 = Tensor(np.array(2), dtype=ms.float32)
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def construct(self, idx, end, x):
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out = self.zero
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while idx < end:
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out = out + self.param
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idx = idx + 1
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return out + self.param
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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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self.weights = ParameterTuple(net.trainable_params())
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@ms_function
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def construct(self, a, b, c):
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return grad_by_list(self.net, self.weights)(a, b, c)
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while_net = MyWhileNet()
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net = GradNet(while_net)
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idx = Tensor(np.array(0), dtype=ms.int32)
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end = Tensor(np.array(3), dtype=ms.int32)
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x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
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net(idx, end, x)
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def test_while_with_param_basic_grad_mul():
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
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self.zero = Tensor(np.ones(([2, 2, 2])), ms.float32)
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self.t2 = Tensor(np.array(2), dtype=ms.float32)
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def construct(self, idx, end, x):
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out = self.zero
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while idx < end:
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out = out * self.param
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idx = idx + 1
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return out + self.param
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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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self.weights = ParameterTuple(net.trainable_params())
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@ms_function
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def construct(self, a, b, c):
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return grad_by_list(self.net, self.weights)(a, b, c)
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while_net = MyWhileNet()
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net = GradNet(while_net)
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idx = Tensor(np.array(0), dtype=ms.int32)
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end = Tensor(np.array(3), dtype=ms.int32)
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x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
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net(idx, end, x)
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def test_while_with_param_basic_grad_two():
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
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self.weight = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="loss")
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self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
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self.t2 = Tensor(np.array(2), dtype=ms.float32)
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def construct(self, idx, end, x):
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out = self.zero
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while idx < end:
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out = out + self.param + self.weight
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idx = idx + 1
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return out + self.param
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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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self.weights = ParameterTuple(net.trainable_params())
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@ms_function
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def construct(self, a, b, c):
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return grad_by_list(self.net, self.weights)(a, b, c)
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while_net = MyWhileNet()
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net = GradNet(while_net)
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idx = Tensor(np.array(0), dtype=ms.int32)
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end = Tensor(np.array(3), dtype=ms.int32)
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x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
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net(idx, end, x)
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def test_while_with_param_basic_grad_three():
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
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self.weight = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="loss")
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self.key = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="key")
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self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
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self.t2 = Tensor(np.array(2), dtype=ms.float32)
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def construct(self, idx, end, x):
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out = self.zero
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while idx < end:
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out = out + self.param + self.weight + self.key
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idx = idx + 1
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return out + self.param
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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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self.weights = ParameterTuple(net.trainable_params())
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@ms_function
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def construct(self, a, b, c):
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return grad_by_list(self.net, self.weights)(a, b, c)
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while_net = MyWhileNet()
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net = GradNet(while_net)
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idx = Tensor(np.array(0), dtype=ms.int32)
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end = Tensor(np.array(3), dtype=ms.int32)
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x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
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net(idx, end, x)
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def test_while_if_with_param_grad():
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
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self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
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self.t2 = Tensor(np.array(2), dtype=ms.float32)
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def construct(self, idx, end, x):
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out = self.zero
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while idx < end:
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if self.max(out) < self.max(x):
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out = out + self.param * 2
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else:
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out = out + self.param
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idx = idx + 1
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return out + self.param
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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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self.weights = ParameterTuple(net.trainable_params())
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@ms_function
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def construct(self, a, b, c):
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return grad_by_list(self.net, self.weights)(a, b, c)
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while_net = MyWhileNet()
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net = GradNet(while_net)
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idx = Tensor(np.array(0), dtype=ms.int32)
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end = Tensor(np.array(3), dtype=ms.int32)
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x = Tensor(np.ones([2, 2, 2]).astype(np.float32), dtype=ms.float32)
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net(idx, end, x)
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def test_while_with_param_grad_not_enter_while():
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class MyWhileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.max = P.ReduceMax()
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self.param = Parameter(Tensor(np.arange(2 * 2 * 2).reshape((2, 2, 2)), ms.float32), name="weight")
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self.zero = Tensor(np.zeros(([2, 2, 2])), ms.float32)
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def construct(self, idx, end, x):
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out = self.zero
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while idx < end:
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out = out + self.param * 3
|
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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):
|
|
return grad_by_list(self.net, self.weights)(a, b, c)
|
|
|
|
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):
|
|
return grad_all(self.net)(*inputs)
|
|
|
|
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):
|
|
return grad_by_list(self.net, self.weights)(*inputs)
|
|
|
|
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):
|
|
return grad_by_list(self.net, self.weights)(*inputs)
|
|
|
|
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):
|
|
return grad_by_list(self.net, self.weights)(*inputs)
|
|
|
|
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()
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sub = P.Sub()
|
|
mul = P.Mul()
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|
div = P.RealDiv()
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|
if 2 < 12:
|
|
a = add(a, b)
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|
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)
|