Update CustomOp doc with changes for GPU support

example/extensions/lib_custom_op/README.md

@@ -15,8 +15,8 @@
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
-CustomOp Example and Tutorial
-=============================
+C++ Custom Operator Example and Tutorial
+========================================
 
 ## Introduction
 
@@ -28,26 +28,39 @@ Custom operators (CustomOp) enable users to write new operators without compilin
 
 ### 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 interact with the execution of other native MXNet operators.
+Custom Operator support was recently merged (#15921, #17270) and is not available in a released version of MXNet yet. It will be part of the forthcoming 1.7 release, until then please install MXNet by compiling from source or downloading one of the nightly builds. For running the example below, it doesn’t matter if it is a CUDA, MKLDNN or Vanila build. The custom operator doesn’t interact with the execution of other native MXNet operators. Note that if you want to run GPU examples and write your custom operators running on GPU, you still need MXNet CUDA build.
 
-### Run An Example:
+### Run An Example
 
-You can start getting familiar with custom operators by running some examples provided in the **example/extensions/lib_custom_op** directory. Start with a common linear algebra operator like `gemm` (Generalized Matrix Multiplication). Go to `lib_custom_op` directory and follow these steps:
+You can start getting familiar with custom operators by running some examples provided in the `example/extensions/lib_custom_op` directory. Start with a common linear algebra operator like `gemm` (Generalized Matrix Multiplication). Go to `lib_custom_op` directory and follow these 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 for the custom gemm operator.
-2. Run `python test_gemm.py`. It’ll first load the above .so library, find the operators, register them in the MXNet backend, print "Found x operators", then invoke the operator like a regular MXNet operator and output the result.
+2. Run `python test_gemm.py`. It’ll first load the library compiled from step 1, find the operators, register them in the MXNet backend, then invoke the operator like a regular MXNet operator and output the result.
+```
+[19:22:02] ../src/c_api/c_api.cc:286: Found 2 operators in library
+[19:22:02] ../src/c_api/c_api.cc:350: 	Op[0] my_gemm
+[19:22:02] ../src/c_api/c_api.cc:350: 	Op[1] state_gemm
+[19:22:02] ../src/c_api/c_api.cc:785: Found 0 partitioners in library
+--------start ndarray compute---------
+[[ 50.]
+ [122.]]
+<NDArray 2x1 @cpu(0)>
+...
+```
+
+Note that you can safely ignore the `Found 0 partitioners` info as it is not related to the custom operator.
 
-### Basic Files For Gemm Library:
+### Basic Files For Gemm Library
 
 * **lib_custom_op/gemm_lib.cc**: This file has a 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 the library containing the custom operator, invokes the operator using both NDArray and Symbol APIs, and prints outputs of the forward and backward passes. The outputs should be the same as the regular MXNet `gemm` operator.
 
-## Writing Custom Operator Library:
+## Writing Custom CPU Operator Library
 
-For building a library containing your own custom operator, compose a C++ source file like `myop_lib.cc`, include `lib_api.h` header file, and write your custom operator implementation with these essential functions:
+For building your own library containing custom CPU operator, compose a C++ source file like `myop_lib.cc`, include `lib_api.h` header file, and write your custom operator implementation with these essential functions:
 - `initialize` - Library Initialization Function
 - `REGISTER_OP` - Operator Registration Marco
 - `parseAttrs` - Attribute Parser
@@ -67,76 +80,85 @@ mx.library.load(‘libmyop_lib.so’)
 mx.nd.my_op(...)
 ```
 
-### Writing Regular Custom Operator:
+### Writing 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.
+```c++
+    REGISTER_OP(my_op_name)
+    .setForward(forward, "cpu")
+    .setBackward(backward, "cpu")
+    .setParseAttrs(parseAttrs)
+    .setInferType(inferType)
+    .setInferShape(inferShape);
+```
 
 Also there are some optional functions you can specify:
 
 * [backward](./gemm_lib.cc#L90) - Backward gradient function:
     * This function specifies the computation of the backward pass of the operator.
-
-            MXReturnValue backward(
-                std::map<std::string, std::string> attrs,
-                std::vector<MXTensor> inputs,
-                std::vector<MXTensor> outputs,
-                OpResource res)
+```c++
+    MXReturnValue backward(
+        std::map<std::string, std::string> attrs,
+        std::vector<MXTensor> inputs,
+        std::vector<MXTensor> outputs,
+        OpResource res)
+```
 
 * [mutateInputs](./gemm_lib.cc#L214) - Specify mutable input:
     * This function allows you to mark some inputs to be mutable inputs. It is useful when using aux parameters for BatchNorm-like operators.
-
-            MXReturnValue mutateInputs(
-                std::map<std::string, std::string> attrs,
-                std::vector<int> &input_indices)
+```c++
+    MXReturnValue mutateInputs(
+        std::map<std::string, std::string> attrs,
+        std::vector<int> &input_indices)
+```
 
 Let’s take a closer look at those registry functions:
 
@@ -146,21 +168,154 @@ Let’s take a closer look at those registry functions:
 
 * **inferShape**: This function is similar to the `inferType` function, except it is used for populating the output data shapes. You need to figure out the shapes of each output tensors for this computation. For example, if the inputs are images with shape (224,224,3) and you write a padding operator to make 10px borders for the images, then your output shape will be (234,234,3).
 
-* **forward**: This function executes the main forward computation. It takes four arguments. The 1st argument is the attributes. The 2nd argument is the input `MXTensors` which stores all data and info of input ndarrays. The 3rd argument is the output `MXTensors`. The 4th argument is the `OpResource` object for memory allocation and other utilities. Additionally, you can use a `dltensor` tensor structure stored in the `MXTensor` as a more standardized data structure for computing.
+* **forward**: This function executes the main forward computation. It takes four arguments. The 1st argument is the attributes. The 2nd argument is the input `MXTensors` which stores all data and info of input ndarrays. The 3rd argument is the output `MXTensors`. The 4th argument is the `OpResource` object for memory allocation and other utilities. The details of `OpResource` are covered in the below section.
+Additionally, you can use a `dltensor` tensor structure stored in the `MXTensor` as a more standardized data structure for computing.
 
 * **backward**: This function is doing the backward gradient computation. It will be similar to the forward function. And you need to figure out the formula of the backward gradient computation.
 
 * **mutateInputs**: This function is for marking mutable inputs. It takes two arguments. The 1st argument is the attributes. The 2nd argument is a list of input indices that are mutable among all input tensors. It is useful when some inputs are auxiliary model parameters and might be altered during forward/backward computation. Remember, the index number of `input_indices` should not exceed the number of inputs.
 
-### Writing Stateful Custom Operator:
+### Writing Stateful Custom Operator
 
 A stateful custom operator is useful when a forward/backward call needs some data or ‘state’ from previous forward/backward calls. Normally we create a class, and make instance variables store the states used for computing or caching.
 
 Most of the building blocks for making a stateful custom operator is the same as regular custom operator, except it’ll register `createOpState` instead of a `forward` function for the computation.
 
 * [createOpState](./gemm_lib.cc#L204) - Create stateful operator instance:
     * This function takes two arguments. The 1st argument is attributes. The 2nd argument is a placeholder for `CustomStatefulOp` object. You must [define a class that inherits CustomStatefulOp](./gemm_lib.cc#L178) and override the forward function (optionally the backward function). Then you need to create an instance of your class and assign it to the placeholder. In this way, all of the forward/backward calls will use the same methods in that instance, and the instance is able to keep the state of the operator.
+```c++
+    MXReturnValue createOpState(
+        std::map<std::string, std::string> attrs,
+        CustomStatefulOp** op_inst)
+```
+
+* The operator registering function will look like this:
+```c++
+    REGISTER_OP(my_state_op)
+    ...
+    .setCreateOpState(createOpState, "cpu");
+```
+
+## Writing Custom GPU Operator Library
+
+Most of the building blocks for registering GPU custom operators are the exactly same as CPU ones, except you need to specify the `"gpu"` context name when registering `forward`, `backward` or `createOpState` function.
+
+### Run A GPU Example
+
+For illustration purposes, we provided a `ReLU` (Rectified Linear Unit) activation operator that can run on GPU. Make sure you have installed a CUDA compatible MXNet build. Go to `lib_custom_op` directory and follow these steps: 
+
+1. Run `make relu_lib`. The Makefile will invoke `NVCC` compiler to compile the CUDA kernel along with regular custom operator functions from `relu_lib.cu` to generate `librelu_lib.so` library.
+2. Run `python test_relu.py`. It’ll register the GPU `ReLU` operator in the MXNet backend, then invoke the operator by feeding an `NDArray` input with GPU context, and output the result tensor with GPU context.
+
+### Regular GPU Custom Operator
+
+Since most of the building blocks for registering GPU custom operators are the exactly same as CPU ones, the registering function for an operator supporting both GPU and CPU will look like this:
+```c++
+    REGISTER_OP(my_op_gpu)
+    ...
+    .setForward(forwardCPU, "cpu")
+    .setForward(forwardGPU, "gpu")
+    .setBackward(backwardCPU, "cpu")
+    .setBackward(backwardGPU, "gpu");
+```
+
+Note that you can write only the GPU funcitons, as operators don’t have to support both CPU and GPU functions (can be GPU only).
+
+After you register forward or backward functions with context name `“gpu”`, MXNet will dispatch forward or backward GPU functions you just registered when the operator is invoked with GPU context.
+
+In the registered `forwardGPU` function, specify the grid and block size and launch the CUDA kernel. In the GPU operators input and output tensors are pre-allocated on the GPU memory, just like in the CPU operator tensors are pre-allocated on the CPU memory. As a result, you don’t need to call `cudaMemcpy` to move the tensor data to GPU device.
+```c++
+    MXReturnValue forwardGPU(std::map<std::string, std::string> attrs,
+                             std::vector<MXTensor> inputs,
+                             std::vector<MXTensor> outputs,
+                             OpResource res) {
+        float* in_data = inputs[0].data<float>();
+        float* out_data = outputs[0].data<float>();
+        mx_stream_t cuda_stream = res.get_cuda_stream();
+        int64_t N = inputs[0].size();
+        int block = 256;
+        int grid = (N + (block - 1)) / block;
+        my_op_gpu_forward<<<grid,block,0,cuda_stream>>>(out_data, in_data, N);
+        return MX_SUCCESS;
+    }
+```
+
+Note that the `cuda_stream` object used for launching kernels is passed from MXNet backend via `OpResource` object. See below for details of `Operator Resource`.
+
+At this point all the attribute functions for each operator (`parseAttrs`, `inferShape`, etc.) run on the CPU, including the `forwardGPU` function. The only part that will actually run on the GPU is the CUDA kernel function that is launched from `forwardGPU` function (ie. my_op_gpu_forward).
+```c++
+    __global__ void my_op_gpu_forward(float *out, float *in, int64_t N) {
+        int tid = blockIdx.x * blockDim.x + threadIdx.x;
+        if (tid < N) {
+            // code your computational logic here
+        }
+    }
+```
+
+### Stateful GPU Custom Operator
+
+Recall that for stateful custom operators, you need to define a class that inherits `CustomStatefulOp` and overrides the `forward` and `backward` functions. Stateful operators are created context-aware, so you can create different classes for GPU and CPU stateful operators separately if desired. To do so, you register a createOpState function for each context separately like this
+```c++
+    REGISTER_OP(my_state_op_gpu)
+    ...
+    .setCreateOpState(createOpStateCPU, "cpu")
+    .setCreateOpState(createOpStateGPU, "gpu");
+```
+
+Then you can create different classes for CPU and GPU stateful operators. MXNet will create the stateful operator instance based on the running context when the operator is invoked, and call stateful `forward` or `backward` function from the instantiated stateful operator class.
+```c++
+    class MyStatefulOpCPU : public CustomStatefulOp {
+    public:
+        explicit MyStatefulOpCPU() {}
+        MXReturnValue Forward(...) {
+            // code your CPU forward computational logic here
+        }
+        MXReturnValue Backward(...) {
+            // code your CPU backward computational logic here
+        }
+        ~MyStatefulOpCPU() {}
+    };
+
+    class MyStatefulOpGPU : public CustomStatefulOp {
+    public:
+        explicit MyStatefulOpGPU() {}
+        MXReturnValue Forward(...) {
+            // code your GPU forward computational logic here
+        }
+        MXReturnValue Backward(...) {
+            // code your GPU backward computational logic here
+        }
+        ~MyStatefulOpGPU() {}
+    };
+
+    MXReturnValue createOpStateCPU(std::map<std::string,std::string> attrs,
+                                   CustomStatefulOp** op_inst) {
+        *op_inst = new MyStatefulOpCPU();
+        return MX_SUCCESS;
+    }
+
+    MXReturnValue createOpStateGPU(std::map<std::string,std::string> attrs,
+                                   CustomStatefulOp** op_inst) {
+        *op_inst = new MyStatefulOpGPU();
+        return MX_SUCCESS;
+    }
+```
+
+Optionally, you can use the same class for CPU and GPU, but you’ll need to check the `MXContext` type in the `MXTensors` to dispatch CPU or GPU `forward` or `backward` functions yourself to do the computation.
+
+## Operator Resource
+
+Most operators running in MXNet need some shared resources managed by MXNet. Custom operators also need `CPU memory allocation`, `GPU memory allocation`, and `CUDA stream` managed by MXNet backend to implement some functionalities. Those resources are provided in `OpResource` class in `forward` and `backward` functions.
+
+1. CPU memory allocation: MXNet manages memory very carefully to reduce the memory usage and risk of memory leak. Instead of using `malloc` to obtain a temporary workspace from heap memory, it is strongly recommended to use MXNet managed memory allocation function. The `alloc_cpu(int size)` function in `OpResource` class is an API to allocate a chunk of CPU memory through MXNet, and it is safe and easy to use.
+```c++
+    unsigned n = inputs[1].shape[0];
+    unsigned m = inputs[1].shape[1];
+    void *workspace = resource.alloc_cpu(n * m * sizeof(float));
+```
+
+2. GPU memory allocation: It is almost the same as CPU memory allocation, except the API name is `alloc_gpu(int size)` and the memory chunk is located in a GPU device.
+
+3. CUDA stream: The CUDA stream object, obtained from `get_cuda_stream()` API, helps custom operator reuse the existing MXNet CUDA stream in order to synchronize GPU running multiple kernels from multiple operators concurrently.
 
-            MXReturnValue createOpState(
-                std::map<std::string, std::string> attrs,
-                CustomStatefulOp** op_inst)
+When you write your own custom operators, you have the option to use some of the operator resources provided above.