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#include "caffe2/sgd/decay_adagrad_op.h"
namespace caffe2 {
REGISTER_CPU_OPERATOR(DecayAdagrad, DecayAdagradOp<float, CPUContext>);
OPERATOR_SCHEMA(DecayAdagrad)
.NumInputs(6)
.NumOutputs(3)
.AllowInplace({{0, 0}, {1, 1}, {2, 2}})
.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[5] = DeviceOption();
return std::make_pair(in_dev, out_dev);
})
.SetDoc(R"DOC(
Computes the DecayAdagrad update for an
input gradient and momentum parameters. Concretely, given inputs
(param, m1, m2, c, grad, lr, iters),
t = iters + 1
m1_o = (beta1 * m1) + (1 - beta1) * grad
m2_o = m2 + np.square(grad)
c = 1.0 or (1 - power(beta1, t))
grad_o = m1_o / c / (sqrt(m2_o) + epsilon)
param_o = param + lr * (grad_o + weight_decay * param)
and returns (param_o, m1_o, m2_o)
)DOC")
.Input(0, "param", "Parameters to be updated")
.Input(1, "moment_1", "First moment history")
.Input(2, "moment_2", "Second moment history")
.Input(3, "grad", "Gradient computed")
.Input(4, "lr", "learning rate")
.Input(5, "iter", "iteration number")
.Output(0, "output_param", "Updated parameters")
.Output(1, "output_moment_1", "Updated first moment")
.Output(2, "output_moment_2", "Updated second moment")
.Arg("beta1", "Default 0.9")
.Arg("beta2", "Default 0.999")
.Arg("epsilon", "Default 1e-5")
.Arg("weight_decay", "Default 0.0")
.Arg("bias_correction_first", "Default True");
SHOULD_NOT_DO_GRADIENT(DecayAdagrad);
} // namespace caffe2
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