/******************************************************************************* * Copyright (c) 2015-2018 Skymind, Inc. * * 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 raver119@gmail.com // #include #if NOT_EXCLUDED(OP_axpy) #include namespace nd4j { namespace ops { CONFIGURABLE_OP_IMPL(axpy, 2, 1, false, -2, 0) { auto x = INPUT_VARIABLE(0); auto y = INPUT_VARIABLE(1); auto z = OUTPUT_VARIABLE(0); REQUIRE_TRUE(x->isSameShape(y),0, "Axpy: both arguments should have the same shape") double a = (double) 1.0f; if (block.width() > 2) { auto alpha = INPUT_VARIABLE(2); REQUIRE_TRUE(alpha->isScalar(), 0, "Axpy: alpha argument should be scalar or TArg"); } else if (block.getTArguments()->size() > 0) { a = T_ARG(0); } /* auto lambda = LAMBDA_TT(_y, _x, a) { return a * _x + _y; }; y->applyPairwiseLambda(x, lambda, z); */ // FIXME: set proper extras here ExtraArguments arguments({a}); y->applyPairwiseTransform(pairwise::Axpy, x, z, &arguments); return ND4J_STATUS_OK; } DECLARE_TYPES(axpy) { getOpDescriptor() ->setAllowedInputTypes(0, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF}) ->setAllowedInputTypes(1, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF}) ->setAllowedOutputTypes(0, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF}); } } } #endif