forked from mindspore-Ecosystem/mindspore
445 lines
12 KiB
Python
445 lines
12 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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""" test_stop_gradient """
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import numpy as np
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import pytest
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import mindspore as ms
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import mindspore.common.dtype as mstype
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import mindspore.nn as nn
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from mindspore import Parameter, ParameterTuple
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from mindspore import Tensor
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from mindspore import context
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from mindspore.common.api import ms_function
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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 mindspore.ops.functional import stop_gradient
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from mindspore.ops.primitive import prim_attr_register, PrimitiveWithInfer
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from ..ut_filter import non_graph_engine
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from ....mindspore_test_framework.utils.bprop_util import bprop
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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(module):
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context.set_context(mode=context.PYNATIVE_MODE)
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def stop_func(x, y):
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""" stop_func"""
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c = x * y
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c_s = x + y
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return c_s, c
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def stop_test1(x, y):
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""" stop_test1 """
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c = x * y
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c_s = stop_gradient(c)
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return c_s
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def stop_test2(x, y):
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""" stop_test2 """
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c = x * y
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c_s = stop_gradient(c)
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d = c_s + x * y
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return d * y
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def stop_test3(x, y):
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""" stop_test3 """
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x = x * y
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z = stop_test1(x, y)
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k = z * y
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return k
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def stop_test5(x, y):
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""" stop_test3 """
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x = x + y
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o1, o2 = stop_func(x, y)
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c = stop_gradient(o1)
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c = o2 + c
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return c
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def stop_test4(x, y):
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""" stop_test4 """
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c = x + y
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c_s = stop_gradient(c)
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e = c + c_s
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return e
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@ms_function
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def grad_stop_test(x, y):
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""" grad_stop_test """
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return grad_all(stop_test2)(x, y)
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@ms_function
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def grad_stop_test1(x, y):
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""" grad_stop_test1 """
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return grad_all(stop_test3)(x, y)
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@ms_function
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def grad_stop_test5(x, y):
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""" grad_stop_test5 """
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return grad_all(stop_test5)(x, y)
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def test_stop():
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""" test_stop """
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print("test_stop:", grad_stop_test(1, 1))
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def test_stop1():
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""" test_stop1 """
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print("test_stop1:", grad_stop_test1(2, 3))
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def test_stop5():
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""" test_stop1 """
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print("test_stop5:", grad_stop_test5(2, 3))
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class GradWrap(nn.Cell):
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""" GradWrap definition """
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def __init__(self, network):
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super(GradWrap, self).__init__()
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self.network = network
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self.weights = ParameterTuple(network.get_parameters())
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@ms_function
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def construct(self, x, label):
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weights = self.weights
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return grad_by_list(self.network, weights)(x, label)
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@non_graph_engine
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def test_softmaxloss_grad():
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""" test_softmaxloss_grad """
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class NetWithLossClass(nn.Cell):
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""" NetWithLossClass definition """
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def __init__(self, network):
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super(NetWithLossClass, self).__init__()
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self.loss = nn.SoftmaxCrossEntropyWithLogits()
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self.network = network
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@ms_function
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def construct(self, x, label):
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predict = self.network(x)
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return self.loss(predict, label)
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class Net(nn.Cell):
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""" Net definition """
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def __init__(self):
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super(Net, self).__init__()
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self.weight = Parameter(Tensor(np.ones([64, 10])), name="weight")
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self.bias = Parameter(Tensor(np.ones([10]).astype(np.float32)), name="bias")
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self.fc = P.MatMul()
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self.fc2 = nn.Dense(10, 10)
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self.biasAdd = P.BiasAdd()
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self.relu = nn.ReLU()
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self.cast = P.Cast()
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@ms_function
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def construct(self, x):
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x = self.fc(x, self.weight)
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x = self.cast(x, mstype.float32)
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x = self.relu(self.fc2(x))
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x = self.fc2(x)
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x = stop_gradient(x)
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x = self.biasAdd(x, self.bias)
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return x
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net = GradWrap(NetWithLossClass(Net()))
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predict = Tensor(np.ones([1, 64]))
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label = Tensor(np.zeros([1, 10]).astype(np.float32))
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print("pynative run")
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out = net(predict, label)
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print("out:", out)
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def test_stop_gradient_1():
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class Mul(nn.Cell):
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def __init__(self):
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super(Mul, self).__init__()
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@ms_function
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def construct(self, x, y):
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ret = x * y
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ret = stop_gradient(ret)
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return ret
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dx, dy = bprop(Mul(), Tensor(np.ones([2, 2]).astype(np.float32)),
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Tensor(np.ones([2, 2]).astype(np.float32)), wrt=['inputs'])
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expect = np.zeros([2, 2])
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assert (dx.asnumpy() == expect).all()
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assert (dy.asnumpy() == expect).all()
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def test_stop_gradient_2():
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class Mul(nn.Cell):
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def __init__(self):
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super(Mul, self).__init__()
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@ms_function
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def construct(self, x, y):
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c = x * y
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z = x * y
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return c, z
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class MulAdd(nn.Cell):
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def __init__(self):
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super(MulAdd, self).__init__()
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self.mul = Mul()
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@ms_function
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def construct(self, x, y):
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u = x + y
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v = x - y
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c, z = self.mul(u, v)
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c = stop_gradient(c)
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ret1 = c + x + y
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ret2 = z + y + y
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return ret1, ret2
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dx = bprop(MulAdd(), Tensor(np.ones([2, 2]).astype(np.float32)),
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Tensor(np.ones([2, 2]).astype(np.float32)))
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expect = np.array([[3.0, 3.0], [3.0, 3.0]])
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assert (dx.asnumpy() == expect).all()
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def test_stop_gradient_3():
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class TupleGetItem(nn.Cell):
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def __init__(self):
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super(TupleGetItem, self).__init__()
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@ms_function
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def construct(self, x1, x2, x3, x4, x5):
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z1 = x1 + x1
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z2 = x1 * x2
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t = (z1, z2, x3, x4, x5)
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z2 = t[1]
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z2 = stop_gradient(z2)
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return z1, z2, x3, x4, x5
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dx = bprop(TupleGetItem(),
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Tensor(np.ones([2]).astype(np.float32)),
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Tensor(np.ones([2]).astype(np.float32)),
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Tensor(np.ones([2]).astype(np.float32)),
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Tensor(np.ones([2]).astype(np.float32)),
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Tensor(np.ones([2]).astype(np.float32)))
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expect = np.array([[2.0, 2.0], [2.0, 2.0]])
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assert (dx.asnumpy() == expect).all()
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def test_stop_gradient_4():
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def stop_test(x):
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return stop_gradient(x)
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assert grad_all(stop_test)(Tensor(1, dtype=ms.int32)) == (1,)
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def test_stop_gradient_5():
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def stop_test(x):
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y = x + x
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y = stop_gradient(y)
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ret = x + y
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return ret
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assert grad_all(stop_test)(Tensor(1, dtype=ms.int32)) == (1,)
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def test_stop_gradient_6():
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def stop_test(x, y):
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ret = x * y
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ret = stop_gradient(ret)
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return ret
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assert grad_all(stop_test)(Tensor(1, dtype=ms.int32), Tensor(3, dtype=ms.int32)) == (0, 0)
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class PrimWithMultiOutputs(PrimitiveWithInfer):
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@prim_attr_register
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def __init__(self):
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"""init"""
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def __call__(self, x, y):
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"""Implement by vm mode."""
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return x, y
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def infer_shape(self, x_shape, y_shape):
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return x_shape, y_shape
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def infer_dtype(self, x_type, y_type):
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return x_type, y_type
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def get_bprop(self):
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def bprop(x, y, out, dout):
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return (dout[0], dout[1])
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return bprop
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def test_stop_gradient_7():
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class PrimWithMultiOutputs_(nn.Cell):
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def __init__(self):
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super(PrimWithMultiOutputs_, self).__init__()
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self.prim_with_multi_outputs = PrimWithMultiOutputs()
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@ms_function
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def construct(self, x1, x2):
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x1, x2 = self.prim_with_multi_outputs(x1, x2)
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x1 = stop_gradient(x1)
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return x1, x2
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dx, dy = bprop(PrimWithMultiOutputs_(), Tensor(np.ones([2]).astype(np.float32)),
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Tensor(np.ones([2]).astype(np.float32)), wrt=['inputs'])
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expect_dx = np.zeros([2])
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expect_dy = np.ones([2])
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assert (dx.asnumpy() == expect_dx).all()
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assert (dy.asnumpy() == expect_dy).all()
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def test_stop_gradient_8():
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class PrimWithMultiOutputs_(nn.Cell):
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def __init__(self):
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super(PrimWithMultiOutputs_, self).__init__()
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self.prim_with_multi_output = PrimWithMultiOutputs()
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@ms_function
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def construct(self, x1, x2):
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x1, x2 = stop_gradient(self.prim_with_multi_output(x1, x2))
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return x1, x2
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dx, dy = bprop(PrimWithMultiOutputs_(), Tensor(np.ones([2]).astype(np.float32)),
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Tensor(np.ones([2]).astype(np.float32)), wrt=['inputs'])
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expect_dx = np.zeros([2])
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expect_dy = np.zeros([2])
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assert (dx.asnumpy() == expect_dx).all()
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assert (dy.asnumpy() == expect_dy).all()
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def test_stop_gradient_9():
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class Mul(nn.Cell):
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def __init__(self):
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super(Mul, self).__init__()
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@ms_function
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def construct(self, x, y):
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c = x * y
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z = x * y
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return c, z
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class MulAdd(nn.Cell):
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def __init__(self):
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super(MulAdd, self).__init__()
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self.mul = Mul()
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@ms_function
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def construct(self, x, y):
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u = x + y
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v = x - y
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c, z = self.mul(u, v)
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c1 = stop_gradient(c)
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c2 = c
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ret1 = c1 + x + y + c2
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ret2 = z + y + y
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return ret1, ret2
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dx = bprop(MulAdd(), Tensor(np.ones([2, 2]).astype(np.float32)),
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Tensor(np.ones([2, 2]).astype(np.float32)))
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expect = np.array([[5.0, 5.0], [5.0, 5.0]])
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assert (dx.asnumpy() == expect).all()
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class PrimWithNoBprop(PrimitiveWithInfer):
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@prim_attr_register
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def __init__(self):
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"""init"""
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def __call__(self, x, y):
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"""Implement by vm mode."""
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return x, y
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def infer_shape(self, x_shape, y_shape):
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return x_shape, y_shape
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def infer_dtype(self, x_type, y_type):
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return x_type, y_type
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def test_stop_gradient_10():
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class PrimWithNoBprop_(nn.Cell):
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def __init__(self):
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super(PrimWithNoBprop_, self).__init__()
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self.prim_with_no_bprop = PrimWithNoBprop()
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@ms_function
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def construct(self, x, y):
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x = x * y
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x, y = self.prim_with_no_bprop(x, y)
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x = stop_gradient(x)
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y = stop_gradient(y)
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return x, y
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dx = bprop(PrimWithNoBprop_(), Tensor(np.ones([2]).astype(np.float32)),
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Tensor(np.ones([2]).astype(np.float32)))
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expect_dx = np.zeros([2])
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assert (dx.asnumpy() == expect_dx).all()
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def test_stop_gradient_11():
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class PrimWithNoBprop_(nn.Cell):
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def __init__(self):
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super(PrimWithNoBprop_, self).__init__()
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self.prim_with_no_bprop = PrimWithNoBprop()
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@ms_function
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def construct(self, x, y):
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x, y = self.prim_with_no_bprop(x, y)
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x = stop_gradient(x)
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return x, y
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with pytest.raises(RuntimeError):
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bprop(PrimWithNoBprop_(), Tensor(np.ones([2]).astype(np.float32)),
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Tensor(np.ones([2]).astype(np.float32)))
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def test_stop_print():
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class StopPrint(nn.Cell):
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def __init__(self):
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super(StopPrint, self).__init__()
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self.printm = P.Print()
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def construct(self, x, y):
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self.printm("StopPrint", x)
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self.printm(y)
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return x, y
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grad_all(StopPrint())(Tensor(np.ones([2]).astype(np.float32)),
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Tensor(np.ones([2]).astype(np.float32)))
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