/*******************************************************************************
* Copyright (c) 2019-2020 Konduit K.K.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://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.
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author Oleh Semeniv (oleg.semeniv@gmail.com)
#include <ops/declarable/headers/updaters.h>
#include <ops/declarable/CustomOperations.h>
#include <helpers/ConstantTadHelper.h>
#include <execution/Threads.h>
#include <array/NDArray.h>
namespace sd {
namespace ops {
CONFIGURABLE_OP_IMPL(ada_max_updater, 3, 3, true, 0, 0) {
const auto gradient = INPUT_VARIABLE(0);
const auto initStateU = INPUT_VARIABLE(1);
const auto initStateM = INPUT_VARIABLE(2);
auto update = OUTPUT_VARIABLE(0);
auto stateU = OUTPUT_VARIABLE(1);
auto stateM = OUTPUT_VARIABLE(2);
// todo maybe we need an error like on Java side
if (gradient->isEmpty() || initStateU->isEmpty() || initStateM->isEmpty())
return Status::OK();
REQUIRE_TRUE(gradient->isSameShape(initStateU), 0, "ADA_MAX UPDATER OP: input state V must have the same shape as gradient,"
" expected shape %s, but got %s!", ShapeUtils::shapeAsString(gradient->shapeInfo()).c_str(),
ShapeUtils::shapeAsString(initStateU->shapeInfo()).c_str());
REQUIRE_TRUE(gradient->isSameShape(initStateM), 0, "ADA_MAX UPDATER OP: input state M must have the same shape as gradient,"
ShapeUtils::shapeAsString(initStateM->shapeInfo()).c_str());
bool bParamsSupply = 7 == block.width() || 4 == block.getTArguments()->size();
int iteration = block.getIArguments()->size() > 0 ? INT_ARG(0) : 0;
REQUIRE_TRUE(bParamsSupply, 0, "ADA_MAX UPDATER OP: learning rate, beta 1, beta 2 and epsilon were not provided!");
double dLr, dBeta1, dBeta2, dEpsilon;
if (block.width() > 3) {
const auto lr = INPUT_VARIABLE(3);
const auto beta1 = INPUT_VARIABLE(4);
const auto beta2 = INPUT_VARIABLE(5);
const auto epsilon = INPUT_VARIABLE(6);
REQUIRE_TRUE(lr->isScalar(), 0, "ADA_MAX UPDATER OP: Learning rate has to be a scalar, but instead got rank %i!", lr->rankOf());
REQUIRE_TRUE(beta1->isScalar(), 0, "ADA_MAX UPDATER OP: beta 1 has to be a scalar, but instead got rank %i!", beta1->rankOf());
REQUIRE_TRUE(beta2->isScalar(), 0, "ADA_MAX UPDATER OP: beta 2 has to be a scalar, but instead got rank %i!", beta2->rankOf());
REQUIRE_TRUE(epsilon->isScalar(), 0, "ADA_MAX UPDATER OP: Epsilon has to be a scalar, but instead got rank %i!", epsilon->rankOf());
dLr = lr->e<double>(0);
dBeta1 = beta1->e<double>(0);
dBeta2 = beta2->e<double>(0);
dEpsilon = epsilon->e<double>(0);
}
else {
dLr = T_ARG(0);
dBeta1 = T_ARG(1);
dBeta2 = T_ARG(2);
dEpsilon = T_ARG(3);
helpers::updaterAdaMax(block.launchContext(), *gradient, *initStateU, *initStateM, *update, *stateU, *stateM, dLr, dBeta1, dBeta2, dEpsilon, iteration);
DECLARE_TYPES(ada_max_updater) {
getOpDescriptor()->setAllowedInputTypes({ ALL_FLOATS })
->setSameMode(true);