forked from mindspore-Ecosystem/mindspore
114 lines
5.2 KiB
Python
114 lines
5.2 KiB
Python
# Copyright 2022 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 copy
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor, Parameter, context
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from mindspore.ops import operations as P
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from mindspore.nn.optim.adam import _update_run_op
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class OriNet(nn.Cell):
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"""Origin net uses _update_run_op"""
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def __init__(self, decay_flag):
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super(OriNet, self).__init__()
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self.decay_flag = decay_flag
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self.optim_filter = True
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def construct(self, param, m, v, lr, beta1, beta2, eps, weight_decay, gradient):
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next_param = _update_run_op(beta1, beta2, eps, lr, weight_decay, param, m, v, gradient,
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self.decay_flag, self.optim_filter)
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return next_param
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class FissionNet(nn.Cell):
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"""Fission net uses P.AdamWeightDecay()"""
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def __init__(self):
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super(FissionNet, self).__init__()
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self.optim_filter = True
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def construct(self, param, m, v, lr, beta1, beta2, eps, weight_decay, gradient):
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if self.optim_filter:
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adam = P.AdamWeightDecay()
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next_param = adam(param, m, v, lr, beta1, beta2, eps, weight_decay, gradient)
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return next_param
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return gradient
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def test_adam_weight_decay_fission_1_decay_flag_is_true():
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"""
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Feature: AdamWeightDecay op
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Description: test the rightness of AdamWeightDecay kernel, decay_flag is true
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Expectation: the output is wrong
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"""
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decay_flag = True # equivalent to weight_decay is not zero
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weight_decay = Parameter(Tensor(np.array([0.9]).astype(np.float32)), name="weight_decay")
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beta1 = Parameter(Tensor(np.array([0.9]).astype(np.float32)), name="beta1")
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beta2 = Parameter(Tensor(np.array([0.999]).astype(np.float32)), name="beta2")
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eps = Parameter(Tensor(np.array([1e-8]).astype(np.float32)), name="eps")
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lr = Parameter(Tensor(np.array([0.001]).astype(np.float32)), name="lr")
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gradient = Parameter(Tensor(np.array([[2, 3], [1, 5]]).astype(np.float32)), name="gradient")
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# The inputs: param, m and v will be modified in-place by P.AdamWeightDecay() or _update_run_op(),
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# so here defines two copied of them: (param1, m1, v1) and (param2, m2, v2)
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param1 = Parameter(Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32)), name="param1")
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m1 = Parameter(Tensor(np.array([[5, 6], [7, 8]]).astype(np.float32)), name="m1")
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v1 = Parameter(Tensor(np.array([[3, 1], [7, 4]]).astype(np.float32)), name="v1")
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param2 = copy.deepcopy(param1)
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m2 = copy.deepcopy(m1)
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v2 = copy.deepcopy(v1)
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context.set_context(mode=context.PYNATIVE_MODE, device_target='Ascend')
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origin_net = OriNet(decay_flag)
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output1 = origin_net(param1, m1, v1, lr, beta1, beta2, eps, weight_decay, gradient)
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fission_net = FissionNet()
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output2 = fission_net(param2, m2, v2, lr, beta1, beta2, eps, weight_decay, gradient)
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assert (output1.asnumpy() == output2[0].asnumpy()).all()
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def test_adam_weight_decay_fission_2_decay_flag_is_false():
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"""
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Feature: AdamWeightDecay op
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Description: test the rightness of ScaleGrad kernel, decay_flag is false
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Expectation: the output is wrong
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"""
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decay_flag = False # equivalent to weight_decay is zero
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weight_decay = Parameter(Tensor(np.array([0]).astype(np.float32)), name="weight_decay")
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beta1 = Parameter(Tensor(np.array([0.9]).astype(np.float32)), name="beta1")
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beta2 = Parameter(Tensor(np.array([0.999]).astype(np.float32)), name="beta2")
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eps = Parameter(Tensor(np.array([1e-8]).astype(np.float32)), name="eps")
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lr = Parameter(Tensor(np.array([0.001]).astype(np.float32)), name="lr")
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gradient = Parameter(Tensor(np.array([[2, 3], [1, 5]]).astype(np.float32)), name="gradient")
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# The inputs: param, m and v will be modified in-place by P.AdamWeightDecay() or _update_run_op(),
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# so here defines two copied of them: (param1, m1, v1) and (param2, m2, v2)
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param1 = Parameter(Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32)), name="param1")
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m1 = Parameter(Tensor(np.array([[5, 6], [7, 8]]).astype(np.float32)), name="m1")
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v1 = Parameter(Tensor(np.array([[3, 1], [7, 4]]).astype(np.float32)), name="v1")
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param2 = copy.deepcopy(param1)
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m2 = copy.deepcopy(m1)
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v2 = copy.deepcopy(v1)
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context.set_context(mode=context.PYNATIVE_MODE, device_target='Ascend')
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origin_net = OriNet(decay_flag)
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output1 = origin_net(param1, m1, v1, lr, beta1, beta2, eps, weight_decay, gradient)
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fission_net = FissionNet()
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output2 = fission_net(param2, m2, v2, lr, beta1, beta2, eps, weight_decay, gradient)
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assert (output1.asnumpy() == output2[0].asnumpy()).all()
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