rondogency commented on a change in pull request #19016: URL: https://github.com/apache/incubator-mxnet/pull/19016#discussion_r480819866
########## 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: are you sure about this? since gemm_lib is still 10700 ########## File path: example/extensions/lib_custom_op/test_transposecsr.py ########## @@ -54,25 +54,25 @@ print("indices:", c.indices.asnumpy()) print("indptr:", c.indptr.asnumpy()) -print("--------start symbolic compute--------") +print("--------start Gluon compute--------") d = mx.sym.Variable('d') Review comment: should this sym also get deprecated then? ########## File path: python/mxnet/library.py ########## @@ -24,6 +24,17 @@ from .ndarray.register import _make_ndarray_function from .symbol.register import _make_symbol_function +class MXlib: + """Holds a pointed to a loaded shared library and closes it on destruction""" + def __init__(self, handle): + self.handle = handle + def __del__(self): + libdl = ctypes.CDLL("libdl.so") + libdl.dlclose(self.handle) Review comment: so it is to elaborate libdl.so lives longer than libmxnet.so? ########## File path: src/lib_api.cc ########## @@ -0,0 +1,1593 @@ +/* + * 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) 2019 by Contributors + * \file lib_api.cc + * \brief APIs to interact with libraries Review comment: can we say "extension API to load dynamic loaded custom libraries", and also say it will depend on lib_api.h (2-stop file instead of 1-stop header file) ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected]
