forked from mindspore-Ecosystem/mindspore
348 lines
11 KiB
Python
348 lines
11 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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import numpy as np
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import mindspore.ops.composite as C
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from mindspore import Tensor, Parameter
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from mindspore import context
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from mindspore.common import dtype as mstype
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from mindspore.common.parameter import ParameterTuple
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from mindspore.nn import Cell
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
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def test_net_vargs_expand():
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class AddNet(Cell):
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def __init__(self):
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super(AddNet, self).__init__()
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self.w = Parameter(
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Tensor(np.ones((3, 4, 5), np.float32)), "w2", requires_grad=True)
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def construct(self, x, y):
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return x + y
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x = Tensor(np.random.normal(0, 1, [3, 4, 5]).astype(np.float32))
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y = Tensor(np.random.normal(0, 1, [3, 4, 5]).astype(np.float32))
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sens = Tensor(np.random.normal(0, 1, [3, 4, 5]).astype(np.float32))
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net = AddNet()
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_ = C.grad_all_with_sens(net, net.trainable_params())(x, y, sens)
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class VarNet(Cell):
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def __init__(self, net):
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super(VarNet, self).__init__()
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self.b = Parameter(
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Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b", requires_grad=True)
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self.w = Parameter(
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Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "w", requires_grad=True)
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self.net = net
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def construct(self, *args):
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return self.net(*args) * self.w + self.b
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class SecondNet(Cell):
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def __init__(self):
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super(SecondNet, self).__init__()
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self.b2 = Parameter(
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Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b2", requires_grad=True)
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def construct(self, *args):
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res = args[0] + args[1]
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return res + self.b2
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class Bprop(Cell):
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def __init__(self, func, wrt_params, params, grad_op, sens=None):
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super(Bprop, self).__init__(auto_prefix=False)
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self.func = func
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self.wrt_params = wrt_params
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self.params = None
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if self.wrt_params and params:
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self.params = ParameterTuple(params)
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self.grad = grad_op
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self.with_sens = False
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self.sens = sens
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if sens:
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self.sens = Tensor(sens, dtype=mstype.float32)
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self.with_sens = True
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def construct(self, *inputs):
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# pylint: disable=no-else-return
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if self.wrt_params:
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if self.with_sens:
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return self.grad(self.func, self.params)(*inputs, self.sens)
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else:
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return self.grad(self.func, self.params)(*inputs)
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elif self.with_sens:
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return self.grad(self.func)(*inputs, self.sens)
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else:
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return self.grad(self.func)(*inputs)
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def test_all_var_args_grad_with_sens():
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""""test grad_by_list_with_sens with all var args input"""
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class GradNet(Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.weights = ParameterTuple(net.trainable_params())
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self.net = net
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def construct(self, *inputs):
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return C.grad_by_list_with_sens(self.net, self.weights)(*inputs)
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x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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sens = Tensor(1.0, dtype=mstype.float32)
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net = VarNet(SecondNet())
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grad_net = GradNet(net)
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_ = grad_net(x, y, sens)
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def test_grad_list_var_args():
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class GradNet(Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.weights = ParameterTuple(net.trainable_params())
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self.net = net
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def construct(self, *inputs):
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return C.grad_by_list(self.net, self.weights)(*inputs)
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x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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net = VarNet(SecondNet())
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grad_net = GradNet(net)
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_ = grad_net(x, y)
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def test_grad_all_var_args():
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class GradNet(Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.weights = ParameterTuple(net.trainable_params())
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self.net = net
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def construct(self, *inputs):
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return C.grad_all(self.net)(*inputs)
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x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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net = VarNet(SecondNet())
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grad_net = GradNet(net)
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_ = grad_net(x, y)
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def test_grad_all_var_args_with_sens():
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class GradNet(Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.weights = ParameterTuple(net.trainable_params())
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self.net = net
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def construct(self, *inputs):
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return C.grad_all_with_sens(self.net)(*inputs)
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x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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sens = Tensor(1.0, dtype=mstype.float32)
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net = VarNet(SecondNet())
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grad_net = GradNet(net)
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_ = grad_net(x, y, sens)
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def test_grad_var_args_with_sens():
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class GradNet(Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.weights = ParameterTuple(net.trainable_params())
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self.net = net
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def construct(self, *inputs):
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return C.grad_with_sens(self.net)(*inputs)
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x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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sens = Tensor(1.0, dtype=mstype.float32)
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net = VarNet(SecondNet())
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grad_net = GradNet(net)
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_ = grad_net(x, y, sens)
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def test_grad_with_param_sens():
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""""test grad_with_sens parameter"""
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class GradNet(Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.weights = ParameterTuple(net.trainable_params())
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self.net = net
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self.sens = Parameter(Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), name='sens', requires_grad=False)
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self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
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def construct(self, x, y):
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return self.grad(self.net, self.weights)(x, y, self.sens)
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x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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net = SecondNet()
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grad_net = GradNet(net)
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_ = grad_net(x, y)
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def test_var_args_grad():
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class VarNet(Cell):
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def __init__(self, net):
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super(VarNet, self).__init__()
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self.b = Parameter(
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Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b", requires_grad=True)
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self.net = net
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def construct(self, *args):
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return self.net(*args) + self.b
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class SecondNet(Cell):
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def __init__(self):
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super(SecondNet, self).__init__()
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self.b2 = Parameter(
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Tensor(np.ones([3, 4, 5]), dtype=mstype.float32), "b2", requires_grad=True)
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def construct(self, *args):
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res = args[0] + args[1]
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return res + self.b2
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class GradNet(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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def construct(self, x, y, sens):
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return C.grad_by_list_with_sens(self.net, self.weights)(x, y, sens)
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x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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sens = Tensor(1.0, dtype=mstype.float32)
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net = VarNet(SecondNet())
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grad_net = GradNet(net)
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_ = grad_net(x, y, sens)
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def test_var_args_positional():
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""""test grad_all with var args in inner graph"""
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class VarNet(Cell):
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def __init__(self, net):
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super(VarNet, self).__init__()
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self.net = net
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def construct(self, x, y):
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return self.net(x, y) * x
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class SecondNet(Cell):
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def __init__(self):
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super(SecondNet, self).__init__()
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def construct(self, *args):
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return args[0] + args[1]
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class GradNet(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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def construct(self, x, y):
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return C.grad_all(self.net)(x, y)
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x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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net = VarNet(SecondNet())
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grad_net = GradNet(net)
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_ = grad_net(x, y)
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def test_grad_within_if_else():
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class GradNet(Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.weights = ParameterTuple(net.trainable_params())
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self.net = net
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grad_op = C.GradOperation(
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name='grad', get_all=False, get_by_list=True, sens_param=True)
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self.grad = Bprop(self.net, True, self.weights, grad_op, 1.0)
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def construct(self, *inputs):
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return self.grad(*inputs)
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x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
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_ = Tensor(1.0, dtype=mstype.float32)
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net = VarNet(SecondNet())
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grad_net = GradNet(net)
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out = grad_net(x, y)
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print("test_grad_var_args_with_sens out=", out)
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def test_grad_for_concat():
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class GradNet(Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.weights = ParameterTuple(net.trainable_params())
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self.net = net
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grad_op = C.GradOperation(
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name='grad', get_all=True, get_by_list=False, sens_param=True)
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self.grad = Bprop(self.net, False, self.weights, grad_op)
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def construct(self, *inputs):
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return self.grad(*inputs)
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class Concat(Cell):
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def __init__(self, axis):
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super().__init__()
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self.concat = P.Concat(axis=axis)
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def construct(self, *input1):
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return self.concat(input1)
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class ConcatFactory:
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def __init__(self, input_shape, axis, dtype=np.float32):
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super(ConcatFactory, self).__init__()
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self.inputs_np = []
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for s in input_shape:
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self.inputs_np.append(np.random.randn(*s).astype(dtype))
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self.axis = axis
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self.out_numpy = np.concatenate(self.inputs_np, axis=self.axis)
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self.out_grad_np = self.out_numpy
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def grad_mindspore_impl(self):
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inputs = []
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for i in self.inputs_np:
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inputs.append(Tensor(i))
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net = Concat(axis=self.axis)
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grad_net = GradNet(net)
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grad_net.set_train()
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_ = grad_net(*inputs, Tensor(self.out_grad_np))
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def grad_cmp(self):
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self.grad_mindspore_impl()
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fact = ConcatFactory(input_shape=(
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(2, 184320, 1), (2, 46080, 1), (2, 11520, 1), (2, 2880, 1), (2, 720, 1)), axis=1)
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fact.grad_cmp()
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