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/**
* Copyright (c) 2016-present, Facebook, Inc.
*
* 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 "caffe2/sgd/weight_scale_op.h"
namespace caffe2 {
REGISTER_CPU_OPERATOR(WeightScale, WeightScaleOp<CPUContext>);
OPERATOR_SCHEMA(WeightScale)
.NumInputs(2)
.NumOutputs(1)
.AllowInplace({{0, 0}, {1, 1}})
.DeviceInferenceFunction([](const OperatorDef& def) {
auto op_device =
def.has_device_option() ? def.device_option() : DeviceOption();
vector<DeviceOption> in_dev(def.input_size(), op_device);
vector<DeviceOption> out_dev(def.output_size(), op_device);
// ITER input lives on CPU
in_dev[1] = DeviceOption();
return std::make_pair(in_dev, out_dev);
})
.SetDoc(R"DOC(
Every `stepsize` iterations, multiply the weights by a constant `scale`:
nw = w * scale
)DOC")
.Input(0, "w", "Current weights")
.Input(1, "iter", "Training Iteration")
.Output(0, "nw", "Updated weights")
.Arg("stepsize", "Every iteration number to do weight scaling")
.Arg(
"upper_bound_iter",
"After iter passes this bound, do not perform the weight rescaling")
.Arg("scale", "The multiplicative factor applied to weights.");
SHOULD_NOT_DO_GRADIENT(WeightScale);
} // namespace caffe2
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