samskalicky commented on a change in pull request #17486: Update CustomOp doc
with changes for GPU support
URL: https://github.com/apache/incubator-mxnet/pull/17486#discussion_r375022066
##########
File path: example/extensions/lib_custom_op/README.md
##########
@@ -56,87 +72,111 @@ For building a library containing your own custom
operator, compose a C++ source
- `forward` - Forward Computation (can be replace with `createOpState`, see
below for details)
Then compile it to `libmyop_lib.so` dynamic library using the following
command:
+
```bash
g++ -shared -fPIC -std=c++11 myop_lib.cc -o libmyop_lib.so -I
../../../include/mxnet
```
+If you don't want to download MXNet source and choose only using `lib_api.h`
header, you can copy the header over to the same folder of `myop_lib.cc` and
run:
+
+```bash
+g++ -shared -fPIC -std=c++11 myop_lib.cc -o libmyop_lib.so
+```
+
Finally, you can write a Python script to load the library and run your custom
operator:
+
```python
import mxnet as mx
mx.library.load(‘libmyop_lib.so’)
mx.nd.my_op(...)
```
-### Writing Regular Custom Operator:
+### Writing A Regular Custom Operator
-There are several essential building blocks for making a (stateless) custom
operator:
+There are several essential building blocks for making a custom operator:
* [initialize](./gemm_lib.cc#L227):
* This function is the library initialization function necessary for any
dynamic libraries. It checks if you are using a compatible version of MXNet.
Note that this `version` parameter is passed from MXNet when library is loaded.
- MXReturnValue initialize(int version)
+```c++
+ MXReturnValue initialize(int version)
+```
* [parseAttrs](./gemm_lib.cc#L118):
* This function specifies number of input and output tensors for the
custom operator; also this is where a custom operator can validate the
attributes (ie. options) specified by the user.
- MXReturnValue parseAttrs(
- std::map<std::string,
- std::string> attrs,
- int* num_in,
- int* num_out)
+```c++
+ MXReturnValue parseAttrs(
+ std::map<std::string,
+ std::string> attrs,
+ int* num_in,
+ int* num_out)
+```
* [inferType](./gemm_lib.cc#L124):
* This function specifies how the custom operator infers output data types
using input data types.
- MXReturnValue inferType(
- std::map<std::string, std::string> attrs,
- std::vector<int> &intypes,
- std::vector<int> &outtypes)
+```c++
+ MXReturnValue inferType(
+ std::map<std::string, std::string> attrs,
+ std::vector<int> &intypes,
+ std::vector<int> &outtypes)
+```
* [inferShape](./gemm_lib.cc#L143):
* This function specifies how the custom operator infers output tensor
shape using input shape.
- MXReturnValue inferShape(
- std::map<std::string, std::string> attrs,
- std::vector<std::vector<unsigned int>> &inshapes,
- std::vector<std::vector<unsigned int>> &outshapes)
+```c++
+ MXReturnValue inferShape(
+ std::map<std::string, std::string> attrs,
+ std::vector<std::vector<unsigned int>> &inshapes,
+ std::vector<std::vector<unsigned int>> &outshapes)
+```
* [forward](./gemm_lib.cc#L56):
* This function specifies the computation of the forward pass of the
operator.
- MXReturnValue forward(
- std::map<std::string, std::string> attrs,
- std::vector<MXTensor> inputs,
- std::vector<MXTensor> outputs,
- OpResource res)
+```c++
+ MXReturnValue forward(
+ std::map<std::string, std::string> attrs,
+ std::vector<MXTensor> inputs,
+ std::vector<MXTensor> outputs,
+ OpResource res)
+```
* [REGISTER_OP(my_op_name)](./gemm_lib.cc#L169):
- * This macro registers the custom operator and its properties to MXNet
NDArray and Symbol APIs by its name.
-
- REGISTER_OP(my_op_name)
- .setForward(forward)
- .setParseAttrs(parseAttrs)
- .setInferType(inferType)
- .setInferShape(inferShape);
+ * This macro registers the custom operator and its properties to MXNet
NDArray and Symbol APIs by its name. Note that for operator running on CPU, you
need pass the name of the context `"cpu"` when registering forward or backward
function.
Review comment:
This macro registers the custom operator with MXNet. You can register
additional functions with the operator too. Note that you register functions
for each context for Forward and Backward, here we show an example for CPU.
These are the minimum required functions, but you can specify more as needed.
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