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[add] support RMSProp CPU Kernel

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pull/9432/head
xuguoyang 4 years ago
parent d6f6269ff1
commit 8fc85139c1
3 changed files with 348 additions and 0 deletions
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77
mindspore/ccsrc/backend/kernel_compiler/cpu/rmsprop_cpu_kernel.cc
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/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "backend/kernel_compiler/cpu/rmsprop_cpu_kernel.h"
namespace mindspore {
namespace kernel {
void RMSPropCPUKernel::InitKernel(const CNodePtr &kernel_node) {
auto node_name = AnfAlgo::GetCNodeName(kernel_node);
if (node_name == "ApplyCenteredRMSProp") {
use_center_ = true;
}
if (node_name == "ApplyRMSProp") {
decay_ = AnfAlgo::GetNodeAttr<float>(kernel_node, "rho");
momentum_ = AnfAlgo::GetNodeAttr<float>(kernel_node, "momentum");
epsilon_ = AnfAlgo::GetNodeAttr<float>(kernel_node, "epsilon");
}
auto input_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
for (auto &dim : input_shape) {
size_ *= dim;
}
}
bool RMSPropCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
const std::vector<kernel::AddressPtr> & /*workspace*/,
const std::vector<kernel::AddressPtr> &outputs) {
if (!use_center_) {
float *variable = reinterpret_cast<float *>(inputs[0]->addr);
float *mean_square = reinterpret_cast<float *>(inputs[1]->addr);
float *moment = reinterpret_cast<float *>(inputs[2]->addr);
float *learning_rate = reinterpret_cast<float *>(inputs[3]->addr);
float *gradients = reinterpret_cast<float *>(inputs[4]->addr);
for (size_t i = 0; i < size_; i++) {
mean_square[i] += (gradients[i] * gradients[i] - mean_square[i]) * (1.0 - decay_);
moment[i] = moment[i] * momentum_ + (gradients[i] * learning_rate[0]) / sqrt(mean_square[i] + epsilon_);
variable[i] -= moment[i];
}
} else {
float *variable = reinterpret_cast<float *>(inputs[0]->addr);
float *mean_gradients = reinterpret_cast<float *>(inputs[1]->addr);
float *mean_square = reinterpret_cast<float *>(inputs[2]->addr);
float *moment = reinterpret_cast<float *>(inputs[3]->addr);
float *gradients = reinterpret_cast<float *>(inputs[4]->addr);
float *learning_rate = reinterpret_cast<float *>(inputs[5]->addr);
float *decay = reinterpret_cast<float *>(inputs[6]->addr);
float *momentum = reinterpret_cast<float *>(inputs[7]->addr);
float *epsilon = reinterpret_cast<float *>(inputs[8]->addr);
for (size_t i = 0; i < size_; i++) {
mean_square[i] += (gradients[i] * gradients[i] - mean_square[i]) * (1.0 - decay[0]);
mean_gradients[i] += (gradients[i] - mean_gradients[i]) * (1.0 - decay[0]);
auto denom = (mean_square[i] - mean_gradients[i] * mean_gradients[i]) + epsilon[0];
if (denom > 0) {
moment[i] = moment[i] * momentum[0] + (gradients[i] * learning_rate[0]) / sqrt(denom);
variable[i] -= moment[i];
}
}
}
return true;
}
} // namespace kernel
} // namespace mindspore

69
mindspore/ccsrc/backend/kernel_compiler/cpu/rmsprop_cpu_kernel.h
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/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_RMSPROP_CPU_KERNEL_H_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_RMSPROP_CPU_KERNEL_H_
#include <vector>
#include "backend/kernel_compiler/cpu/cpu_kernel.h"
#include "backend/kernel_compiler/cpu/cpu_kernel_factory.h"
namespace mindspore {
namespace kernel {
class RMSPropCPUKernel : public CPUKernel {
public:
RMSPropCPUKernel() : size_(1), use_center_(false), decay_(0.0), momentum_(0.9), epsilon_(1e-12) {}
~RMSPropCPUKernel() override = default;
void InitKernel(const CNodePtr &kernel_node) override;
bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
const std::vector<AddressPtr> &outputs) override;
private:
size_t size_;
bool use_center_;
float decay_;
float momentum_;
float epsilon_;
};
MS_REG_CPU_KERNEL(ApplyRMSProp,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
RMSPropCPUKernel);
MS_REG_CPU_KERNEL(ApplyCenteredRMSProp,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
RMSPropCPUKernel);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_RMSPROP_CPU_KERNEL_H_

202
tests/st/ops/cpu/test_rmsprop.py
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import numpy as np
import pytest
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.parameter import Parameter
from mindspore.common.initializer import initializer
from mindspore.ops import operations as P
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
class NetCenteredRMSProp(nn.Cell):
def __init__(self, lr, decay, momentum, epsilon, var, g, mg, rms, mom):
super(NetCenteredRMSProp, self).__init__()
self.rms_opt = P.ApplyCenteredRMSProp()
self.lr = lr
self.decay = decay
self.momentum = momentum
self.epsilon = epsilon
self.var = var
self.g = g
self.mg = mg
self.rms = rms
self.mom = mom
def construct(self):
return self.rms_opt(self.var, self.mg, self.rms, self.mom, self.g, self.lr, self.decay, self.momentum,
self.epsilon)
class NetRMSProp(nn.Cell):
def __init__(self, lr, decay, momentum, epsilon, var, g, mg, rms, mom):
super(NetRMSProp, self).__init__()
self.lr = lr
self.decay = decay
self.momentum = momentum
self.epsilon = epsilon
self.var = var
self.g = g
self.mg = mg
self.rms = rms
self.mom = mom
self.rms_opt = P.ApplyRMSProp()
def construct(self):
return self.rms_opt(self.var, self.rms, self.mom, self.lr, self.g, self.decay, self.momentum, self.epsilon)
def rmsprop_numpy(variable, gradients, mean_square, moment,
learning_rate, decay, momentum, epsilon):
mean_square = mean_square * decay + (1.0 - decay) * gradients * gradients
moment = momentum * moment + learning_rate / np.sqrt(mean_square + epsilon) * gradients
variable = variable - moment
return variable, gradients, mean_square, moment
def rmspropcented_numpy(variable, gradients, mean_gradients, mean_square, moment,
learning_rate, decay, momentum, epsilon):
mean_gradients = mean_gradients * decay + (1.0 - decay) * gradients
mean_square = mean_square * decay + (1.0 - decay) * gradients * gradients
moment = momentum * moment + learning_rate / np.sqrt(
mean_square - mean_gradients * mean_gradients + epsilon) * gradients
variable = variable - moment
return variable, gradients, mean_gradients, mean_square, moment
@pytest.mark.level0
@pytest.mark.platform_cpu_training
@pytest.mark.env_onecard
def test_rmsprop():
learning_rate, decay, momentum, epsilon, centered = [0.5, 0.8, 0.9, 1e-3, True]
variable_np = np.array([1.0, 2.0], dtype=np.float32)
gradients_np = np.array([0.1, 0.2], dtype=np.float32)
mean_gradients_np = np.array([0.0, 0.0], dtype=np.float32)
mean_square_np = np.array([epsilon, epsilon], dtype=np.float32)
moment_np = np.array([0.0, 0.0], dtype=np.float32)
variable = Tensor(variable_np)
gradients = Tensor(gradients_np)
mean_gradients = Tensor(mean_gradients_np)
mean_square = Tensor(mean_square_np)
moment = Tensor(moment_np)
variable_ms = Parameter(initializer(variable, variable.shape), name='var')
gradients_ms = Parameter(initializer(gradients, gradients.shape), name='grad')
mean_gradients_ms = Parameter(initializer(mean_gradients, mean_gradients.shape), name='mg')
mean_square_ms = Parameter(initializer(mean_square, mean_square.shape), name='msr')
moment_ms = Parameter(initializer(moment, moment.shape), name='mom')
if centered:
variable_np, gradients_np, mean_gradients_np, mean_square_np, moment_np = \
rmspropcented_numpy(variable_np, gradients_np, mean_gradients_np, mean_square_np, moment_np,
learning_rate, decay, momentum, epsilon)
net = NetCenteredRMSProp(learning_rate, decay, momentum, epsilon, variable_ms, gradients_ms, mean_gradients_ms,
mean_square_ms, moment_ms)
_ = net()
else:
variable_np, gradients_np, mean_square_np, moment_np = \
rmsprop_numpy(variable_np, gradients_np, mean_square_np, moment_np,
learning_rate, decay, momentum, epsilon)
net = NetRMSProp(learning_rate, decay, momentum, epsilon, variable_ms, gradients_ms, mean_gradients_ms,
mean_square_ms, moment_ms)
_ = net()
error = np.ones(shape=variable_np.shape) * 10e-6
diff = variable_ms.asnumpy() - variable_np
assert np.all(diff < error)
error = np.ones(shape=gradients_np.shape) * 10e-6
diff = gradients_ms.asnumpy() - gradients_np
assert np.all(diff < error)
error = np.ones(shape=mean_gradients_np.shape) * 10e-6
diff = mean_gradients_ms.asnumpy() - mean_gradients_np
assert np.all(diff < error)
error = np.ones(shape=mean_square_np.shape) * 10e-6
diff = mean_square_ms.asnumpy() - mean_square_np
assert np.all(diff < error)
error = np.ones(shape=moment_np.shape) * 10e-6
diff = moment_ms.asnumpy() - moment_np
assert np.all(diff < error)
@pytest.mark.level0
@pytest.mark.platform_cpu_training
@pytest.mark.env_onecard
def test_rmspropcenter():
learning_rate, decay, momentum, epsilon, centered = [0.1, 0.3, 0.9, 1.0, False]
variable_np = np.array([1.0, 2.0], dtype=np.float32)
gradients_np = np.array([0.1, 0.2], dtype=np.float32)
mean_gradients_np = np.array([0.0, 0.0], dtype=np.float32)
mean_square_np = np.array([epsilon, epsilon], dtype=np.float32)
moment_np = np.array([0.0, 0.0], dtype=np.float32)
variable = Tensor(variable_np)
gradients = Tensor(gradients_np)
mean_gradients = Tensor(mean_gradients_np)
mean_square = Tensor(mean_square_np)
moment = Tensor(moment_np)
variable_ms = Parameter(initializer(variable, variable.shape), name='var')
gradients_ms = Parameter(initializer(gradients, gradients.shape), name='grad')
mean_gradients_ms = Parameter(initializer(mean_gradients, mean_gradients.shape), name='mg')
mean_square_ms = Parameter(initializer(mean_square, mean_square.shape), name='msr')
moment_ms = Parameter(initializer(moment, moment.shape), name='mom')
if centered:
variable_np, gradients_np, mean_gradients_np, mean_square_np, moment_np = \
rmspropcented_numpy(variable_np, gradients_np, mean_gradients_np, mean_square_np, moment_np,
learning_rate, decay, momentum, epsilon)
net = NetCenteredRMSProp(learning_rate, decay, momentum, epsilon, variable_ms, gradients_ms, mean_gradients_ms,
mean_square_ms, moment_ms)
_ = net()
else:
variable_np, gradients_np, mean_square_np, moment_np = \
rmsprop_numpy(variable_np, gradients_np, mean_square_np, moment_np,
learning_rate, decay, momentum, epsilon)
net = NetRMSProp(learning_rate, decay, momentum, epsilon, variable_ms, gradients_ms, mean_gradients_ms,
mean_square_ms, moment_ms)
_ = net()
error = np.ones(shape=variable_np.shape) * 10e-6
diff = variable_ms.asnumpy() - variable_np
assert np.all(diff < error)
error = np.ones(shape=gradients_np.shape) * 10e-6
diff = gradients_ms.asnumpy() - gradients_np
assert np.all(diff < error)
error = np.ones(shape=mean_gradients_np.shape) * 10e-6
diff = mean_gradients_ms.asnumpy() - mean_gradients_np
assert np.all(diff < error)
error = np.ones(shape=mean_square_np.shape) * 10e-6
diff = mean_square_ms.asnumpy() - mean_square_np
assert np.all(diff < error)
error = np.ones(shape=moment_np.shape) * 10e-6
diff = moment_ms.asnumpy() - moment_np
assert np.all(diff < error)
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