/* * ****************************************************************************** * * * * * * 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. * * * * See the NOTICE file distributed with this work for additional * * information regarding copyright ownership. * * 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) // @author Abdelrauf(rauf@konduit.ai) #include #include #include #include #include namespace sd { namespace ops { CONFIGURABLE_OP_IMPL(adabelief_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, "ADABELIEF 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, "ADABELIEF UPDATER OP: input state M must have the same shape as gradient," " expected shape %s, but got %s!", ShapeUtils::shapeAsString(gradient->shapeInfo()).c_str(), ShapeUtils::shapeAsString(initStateM->shapeInfo()).c_str()); bool bParamsSupply = 7 == block.width() || 4 == block.getTArguments()->size(); auto iteration = block.getIArguments()->size() > 0 ? INT_ARG(0) : 0; REQUIRE_TRUE(bParamsSupply, 0, "ADABELIEF 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, "ADABELIEF UPDATER OP: Learning rate has to be a scalar, but instead got rank %i!", lr->rankOf()); REQUIRE_TRUE(beta1->isScalar(), 0, "ADABELIEF UPDATER OP: beta 1 has to be a scalar, but instead got rank %i!", beta1->rankOf()); REQUIRE_TRUE(beta2->isScalar(), 0, "ADABELIEF UPDATER OP: beta 2 has to be a scalar, but instead got rank %i!", beta2->rankOf()); REQUIRE_TRUE(epsilon->isScalar(), 0, "ADABELIEF UPDATER OP: Epsilon has to be a scalar, but instead got rank %i!", epsilon->rankOf()); dLr = lr->e(0); dBeta1 = beta1->e(0); dBeta2 = beta2->e(0); dEpsilon = epsilon->e(0); } else { dLr = T_ARG(0); dBeta1 = T_ARG(1); dBeta2 = T_ARG(2); dEpsilon = T_ARG(3); } helpers::updaterAdaBelief(block.launchContext(), *gradient, *initStateU, *initStateM, *update, *stateU, *stateM, dLr, dBeta1, dBeta2, dEpsilon, iteration); return Status::OK(); } DECLARE_TYPES(adabelief_updater) { getOpDescriptor()->setAllowedInputTypes({ ALL_FLOATS }) ->setSameMode(true); } } }