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168 lines
6.5 KiB
168 lines
6.5 KiB
# 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 pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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class NetCenteredRMSProp(nn.Cell):
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def __init__(self, lr, decay, momentum, epsilon):
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super(NetCenteredRMSProp, self).__init__()
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self.rms_opt = P.ApplyCenteredRMSProp()
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self.lr = lr
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self.decay = decay
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self.momentum = momentum
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self.epsilon = epsilon
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def construct(self, var, g, mg, rms, mom):
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return self.rms_opt(var, mg, rms, mom, g, self.lr, self.decay, self.momentum, self.epsilon)
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class NetRMSProp(nn.Cell):
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def __init__(self, lr, decay, momentum, epsilon):
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super(NetRMSProp, self).__init__()
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self.lr = lr
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self.decay = decay
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self.momentum = momentum
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self.epsilon = epsilon
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self.rms_opt = P.ApplyRMSProp()
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def construct(self, var, g, mg, rms, mom):
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return self.rms_opt(var, rms, mom, self.lr, g, self.decay, self.momentum, self.epsilon)
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def rmsprop_numpy(variable, gradients, mean_square, moment,
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learning_rate, decay, momentum, epsilon):
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mean_square = mean_square * decay + (1.0 - decay) * gradients * gradients
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moment = momentum * moment + learning_rate / np.sqrt(mean_square + epsilon) * gradients
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variable = variable - moment
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def rmspropcented_numpy(variable, gradients, mean_gradients, mean_square, moment,
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learning_rate, decay, momentum, epsilon):
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mean_gradients = mean_gradients * decay + (1.0 - decay) * gradients
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mean_square = mean_square * decay + (1.0 - decay) * gradients * gradients
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moment = momentum * moment + learning_rate / np.sqrt(
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mean_square - mean_gradients * mean_gradients + epsilon) * gradients
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variable = variable - moment
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_rmsprop():
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learning_rate, decay, momentum, epsilon, centered = [0.5, 0.8, 0.9, 1e-3, True]
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variable_np = np.array([1.0, 2.0], dtype=np.float32)
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gradients_np = np.array([0.1, 0.2], dtype=np.float32)
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mean_gradients_np = np.array([0.0, 0.0], dtype=np.float32)
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mean_square_np = np.array([epsilon, epsilon], dtype=np.float32)
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moment_np = np.array([0.0, 0.0], dtype=np.float32)
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variable_ms = Tensor(variable_np)
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gradients_ms = Tensor(gradients_np)
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mean_gradients_ms = Tensor(mean_gradients_np)
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mean_square_ms = Tensor(mean_square_np)
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moment_ms = Tensor(moment_np)
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if centered:
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rmspropcented_numpy(variable_np, gradients_np, mean_gradients_np, mean_square_np, moment_np,
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learning_rate, decay, momentum, epsilon)
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net = NetCenteredRMSProp(learning_rate, decay, momentum, epsilon)
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_ = net(variable_ms, gradients_ms, mean_gradients_ms, mean_square_ms, moment_ms)
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else:
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rmsprop_numpy(variable_np, gradients_np, mean_square_np, moment_np,
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learning_rate, decay, momentum, epsilon)
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net = NetRMSProp(learning_rate, decay, momentum, epsilon)
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_ = net(variable_ms, gradients_ms, mean_gradients_ms, mean_square_ms, moment_ms)
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error = np.ones(shape=variable_np.shape) * 10e-6
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diff = variable_ms.asnumpy() - variable_np
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assert np.all(diff < error)
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error = np.ones(shape=gradients_np.shape) * 10e-6
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diff = gradients_ms.asnumpy() - gradients_np
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assert np.all(diff < error)
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error = np.ones(shape=mean_gradients_np.shape) * 10e-6
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diff = mean_gradients_ms.asnumpy() - mean_gradients_np
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assert np.all(diff < error)
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error = np.ones(shape=mean_square_np.shape) * 10e-6
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diff = mean_square_ms.asnumpy() - mean_square_np
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assert np.all(diff < error)
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error = np.ones(shape=moment_np.shape) * 10e-6
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diff = moment_ms.asnumpy() - moment_np
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assert np.all(diff < error)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_rmspropcenter():
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learning_rate, decay, momentum, epsilon, centered = [0.1, 0.3, 0.9, 1.0, False]
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variable_np = np.array([1.0, 2.0], dtype=np.float32)
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gradients_np = np.array([0.1, 0.2], dtype=np.float32)
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mean_gradients_np = np.array([0.0, 0.0], dtype=np.float32)
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mean_square_np = np.array([epsilon, epsilon], dtype=np.float32)
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moment_np = np.array([0.0, 0.0], dtype=np.float32)
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variable_ms = Tensor(variable_np)
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gradients_ms = Tensor(gradients_np)
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mean_gradients_ms = Tensor(mean_gradients_np)
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mean_square_ms = Tensor(mean_square_np)
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moment_ms = Tensor(moment_np)
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if centered:
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rmspropcented_numpy(variable_np, gradients_np, mean_gradients_np, mean_square_np, moment_np,
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learning_rate, decay, momentum, epsilon)
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net = NetCenteredRMSProp(learning_rate, decay, momentum, epsilon)
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_ = net(variable_ms, gradients_ms, mean_gradients_ms, mean_square_ms, moment_ms)
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else:
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rmsprop_numpy(variable_np, gradients_np, mean_square_np, moment_np,
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learning_rate, decay, momentum, epsilon)
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net = NetRMSProp(learning_rate, decay, momentum, epsilon)
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_ = net(variable_ms, gradients_ms, mean_gradients_ms, mean_square_ms, moment_ms)
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error = np.ones(shape=variable_np.shape) * 10e-6
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diff = variable_ms.asnumpy() - variable_np
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assert np.all(diff < error)
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error = np.ones(shape=gradients_np.shape) * 10e-6
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diff = gradients_ms.asnumpy() - gradients_np
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assert np.all(diff < error)
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error = np.ones(shape=mean_gradients_np.shape) * 10e-6
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diff = mean_gradients_ms.asnumpy() - mean_gradients_np
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assert np.all(diff < error)
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error = np.ones(shape=mean_square_np.shape) * 10e-6
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diff = mean_square_ms.asnumpy() - mean_square_np
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assert np.all(diff < error)
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error = np.ones(shape=moment_np.shape) * 10e-6
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diff = moment_ms.asnumpy() - moment_np
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assert np.all(diff < error)
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