samskalicky commented on a change in pull request #17241: Add CustomOp tutorial doc URL: https://github.com/apache/incubator-mxnet/pull/17241#discussion_r363930093
########## File path: example/extensions/lib_custom_op/README.md ########## @@ -0,0 +1,69 @@ +CustomOp Example and Tutorial +==== + +## Getting Started + +## Have MXNet Ready: + +First you should install MXNet either from compiling from source code or download from nightly build. It doesn’t matter if the build comes with CUDA or MKLDNN. The custom operator doesn’t intervene with the execution of other native MXNet operators. + +## Run An Example: + +You can start getting familiar with custom operator by running some examples we provide in the *example/extensions/lib_custom_op* directory. There are 2 examples: a simple 2D gemm operator, a subgraph operator, and a Makefile. + +Let’s start with gemm operator. Go to that directory and follow the steps: + +1. run *make gemm_lib*, the Makefile will generate a dynamic library libgemm_lib.so compiled from gemm_lib.cc. This is the library you are going to load that contains everything of the custom gemm operator. +2. run *python test_gemm.py*, and it’ll first load the above .so library, find operators, register them in the MXNet backend, and print "Found x operators"; then invoke the operator like a regular MXNet operator and print the result. + +## Basic Files For GEMM Library: + +* lib_custom_op/gemm_lib.cc: This file has source code implementation of all required components of a custom operator, as well as the registration of the custom operator. + +* lib_custom_op/Makefile: Compile source code to a dynamic shared library, with a header file include/mxnet/lib_api.h from MXNet source code. Currently the custom operator is compatible with C++11 onwards. + +* lib_custom_op/test_gemm.py: This file calls mx.library.load(‘libgemm_lib.so’) to load custom operator, invoke the operator using both ndarray and symbol API, and print outputs of forward and backward pass. The outputs should be the same as the regular MXNet gemm operator. + +## Writing Custom Operators: + +## Regular Custom Operator: + +There are several basic building blocks for making a (stateless) custom operator: + +* parseAttrs - Attributes Parser: This function specifies number of input and output tensors for the custom operator. + +* inferType - Type Inference: This function specifies how custom operator infers output data types using input data types + +* inferShape - Shape Inference: This function specifies how custom operator infers output tensor shape using input shape + +* forward - Forward function: This function specifies the computation of forward pass of the operator + +* REGISTER_OP(my_op_name) Macro: This macro registers custom operator to all MXNet APIs by its name, and you need to call setters to bind the above functions to the registered operator. + +Also there are some operational functions you can specify: Review comment: operational ==> optional ---------------------------------------------------------------- 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: us...@infra.apache.org With regards, Apache Git Services
