rondogency commented on a change in pull request #19016: URL: https://github.com/apache/incubator-mxnet/pull/19016#discussion_r481297345
########## File path: example/extensions/lib_custom_op/relu_lib.cc ########## @@ -0,0 +1,171 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you 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. + */ + +/*! + * Copyright (c) 2020 by Contributors + * \file relu_lib.cu + * \brief simple custom relu and noisy relu operator implemented using CUDA function + */ + +#include <iostream> +#include "relu_lib.h" + +using namespace mxnet::ext; + +MXReturnValue parseAttrs(const std::unordered_map<std::string, std::string>& attrs, + int* num_in, int* num_out) { + *num_in = 1; + *num_out = 1; + return MX_SUCCESS; +} + +MXReturnValue inferType(const std::unordered_map<std::string, std::string>& attrs, + std::vector<int>* intypes, + std::vector<int>* outtypes) { + outtypes->at(0) = intypes->at(0); + return MX_SUCCESS; +} + +MXReturnValue inferShape(const std::unordered_map<std::string, std::string>& attrs, + std::vector<std::vector<unsigned int>>* inshapes, + std::vector<std::vector<unsigned int>>* outshapes) { + outshapes->at(0) = inshapes->at(0); + return MX_SUCCESS; +} + +MXReturnValue forwardCPU(const std::unordered_map<std::string, std::string>& attrs, + std::vector<MXTensor>* inputs, + std::vector<MXTensor>* outputs, + const OpResource& res) { + float* in_data = inputs->at(0).data<float>(); + float* out_data = outputs->at(0).data<float>(); + for (int i=0; i<inputs->at(0).size(); i++) { + out_data[i] = in_data[i] > 0 ? in_data[i] : 0; + } + return MX_SUCCESS; +} + +MXReturnValue backwardCPU(const std::unordered_map<std::string, std::string>& attrs, + std::vector<MXTensor>* inputs, + std::vector<MXTensor>* outputs, + const OpResource& res) { + float* out_grad = inputs->at(0).data<float>(); + float* in_data = inputs->at(1).data<float>(); + float* in_grad = outputs->at(0).data<float>(); + for (int i=0; i<inputs->at(1).size(); i++) { + in_grad[i] = in_data[i] > 0 ? 1 * out_grad[i] : 0; + } + return MX_SUCCESS; +} + +REGISTER_OP(my_relu) +.setParseAttrs(parseAttrs) +.setInferType(inferType) +.setInferShape(inferShape) +.setForward(forwardCPU, "cpu") +.setForward(forwardGPU, "gpu") +.setBackward(backwardCPU, "cpu") +.setBackward(backwardGPU, "gpu"); + + +MyStatefulReluCPU::MyStatefulReluCPU(const std::unordered_map<std::string, std::string>& attrs) + : attrs_(attrs) {} + +MXReturnValue MyStatefulReluCPU::Forward(std::vector<MXTensor>* inputs, + std::vector<MXTensor>* outputs, + const OpResource& op_res) { + return forwardCPU(attrs_, inputs, outputs, op_res); +} + +MXReturnValue MyStatefulReluCPU::Backward(std::vector<MXTensor>* inputs, + std::vector<MXTensor>* outputs, + const OpResource& op_res) { + return backwardCPU(attrs_, inputs, outputs, op_res); +} + +MyStatefulReluGPU::MyStatefulReluGPU(const std::unordered_map<std::string, std::string>& attrs) + : attrs_(attrs) {} + +MXReturnValue MyStatefulReluGPU::Forward(std::vector<MXTensor>* inputs, + std::vector<MXTensor>* outputs, + const OpResource& op_res) { + return forwardGPU(attrs_, inputs, outputs, op_res); +} + +MXReturnValue MyStatefulReluGPU::Backward(std::vector<MXTensor>* inputs, + std::vector<MXTensor>* outputs, + const OpResource& op_res) { + return backwardGPU(attrs_, inputs, outputs, op_res); +} + + +MXReturnValue createOpStateCPU(const std::unordered_map<std::string, std::string>& attrs, + CustomStatefulOp** op_inst) { + *op_inst = new MyStatefulReluCPU(attrs); + return MX_SUCCESS; +} + +MXReturnValue createOpStateGPU(const std::unordered_map<std::string, std::string>& attrs, + CustomStatefulOp** op_inst) { + *op_inst = new MyStatefulReluGPU(attrs); + return MX_SUCCESS; +} + +REGISTER_OP(my_state_relu) +.setParseAttrs(parseAttrs) +.setInferType(inferType) +.setInferShape(inferShape) +.setCreateOpState(createOpStateCPU, "cpu") +.setCreateOpState(createOpStateGPU, "gpu"); + +MXReturnValue noisyForwardCPU(const std::unordered_map<std::string, std::string>& attrs, + std::vector<MXTensor>* inputs, + std::vector<MXTensor>* outputs, + const OpResource& res) { + float* in_data = inputs->at(0).data<float>(); + float* out_data = outputs->at(0).data<float>(); + + mx_cpu_rand_t* states = res.get_cpu_rand_states(); + std::normal_distribution<float> dist_normal; + + for (int i=0; i<inputs->at(0).size(); ++i) { + float noise = dist_normal(*states); + out_data[i] = in_data[i] + noise > 0 ? in_data[i] + noise : 0; + } + return MX_SUCCESS; +} + +REGISTER_OP(my_noisy_relu) +.setParseAttrs(parseAttrs) +.setInferType(inferType) +.setInferShape(inferShape) +.setForward(noisyForwardCPU, "cpu") +.setForward(noisyForwardGPU, "gpu") +.setBackward(backwardCPU, "cpu") +.setBackward(backwardGPU, "gpu"); + +MXReturnValue initialize(int version) { + if (version >= 20000) { Review comment: discussed offline and we will change example corresponding to master ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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