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namespace arm_compute
{
/**
@page add_operator Adding new operators

@tableofcontents

@section S4_1_introduction Introduction
In Compute Library there are two main parts or modules:
- The core library consists of a low-level collection of algorithms implemented in C++ and optimized for Arm CPUs and GPUs. The core module is designed to be embedded in other projects and it doesn't perform any memory management or scheduling.
- The runtime library is a wrapper of the core library and provides other additional features like memory management, multithreaded execution of workloads and allocation of the intermediate tensors.

The library can be integrated in an existing external library or application that provides its own scheduler or a specific memory manager. In that case, the right solution is to use only the core library which means that the user must also manage all the memory allocation not only for the input/output tensor but also for the intermediate tensors/variables necessary. On the other hand, if the user doesn't want to care about allocation and multithreading then the right choice is to use the functions from the runtime library.

Apart from these components that get linked into the application, the sources also include the validation test suite and the C++ reference implementations against which all the operators are validated.


@section S4_1_supporting_new_operators Supporting new operators

Following are the steps involved in adding support for a new operator in Compute Library
- Add new data types (if required)
- Add the kernel to the core library.
- Add the function to the runtime library.
- Add validation tests.
    - Add the reference implementation.
    - Add the fixture
    - register the tests.

@subsection S4_1_1_add_datatypes Adding new data types

Compute Library declares a few new datatypes related to its domain, kernels, and functions in the library process Tensors and Images (Computer Vision functions). Tensors are multi-dimensional arrays with a maximum of Coordinates::num_max_dimensions dimensions; depending on the number of dimensions tensors can be interpreted as various objects. A scalar can be represented as a zero-dimensional tensor and a vector of numbers can be represented as a one-dimensional tensor. Furthermore, an image is just a 2D tensor, a 3D tensor can be seen as an array of images and a 4D tensor as a 2D array of images, etc.
All the datatype classes or structures are grouped in the core library folder arm_compute/core  like the @ref ITensor, @ref ITensorInfo (all the information of a tensor), TensorShape and simpler types are in arm_compute/core/Types.h.

If an operator handles a new datatype, it must be added to the library. While adding a new data type to the library, it's necessary to implement the function to enable printing, the to_string() method and the output stream insertion (<<) operator. Every datatype implements these two functions in utils/TypePrinter.h

A quick example, in <a href="https://github.com/ARM-software/ComputeLibrary/blob/master/arm_compute/core/Types.h">Types.h</a> we add:

@snippet arm_compute/core/Types.h DataLayout enum definition

And for printing:

@snippet utils/TypePrinter.h Print DataLayout type

In Compute Library, we use namespaces to group all the operators, functions, classes and interfaces. The main namespace to use is arm_compute. In the test suite, the test framework and the individual tests use nested namespaces like @ref test::validation or @ref test::benchmark to group the different purposes of various parts of the suite.
Utility functions like conversion or type cast operators, that are shared by multiple operators are in arm_compute/core/Utils.h. Non-inlined function definitions go in the corresponding .cpp files in the src folder.
Similarly, all common functions that process shapes, like calculating output shapes of an operator or shape conversions etc are in arm_compute/core/utils/misc/ShapeCalculator.h.


@subsection S4_1_2_add_kernel Add a kernel
As we mentioned at the beginning, the kernel is the implementation of the operator or algorithm partially using a specific programming language related to the backend we want to use. Adding a kernel in the library means implementing the algorithm in a SIMD technology like NEON or OpenCL. All kernels in Compute Library must implement a common interface IKernel or one of the specific subinterfaces.
IKernel is the common interface for all the kernels in the core library, it contains the main methods for configure and run the kernel itself, such as window()  that return the maximum window the kernel can be executed on or is_parallelisable() for indicate whether or not the kernel is parallelizable. If the kernel is parallelizable then the window returned by the window() method can be split into sub-windows which can then be run in parallel, in the other case, only the window returned by window() can be passed to the run method.
There are specific interfaces for OpenCL and Neon: @ref ICLKernel, INEKernel (using INEKernel = @ref ICPPKernel).

- @ref ICLKernel is the common interface for all the OpenCL kernels. It implements the inherited methods and adds all the methods necessary to configure the CL kernel, such as set/return the Local-Workgroup-Size hint, add single, array or tensor argument, set the targeted GPU architecture according to the CL device. All these methods are used during the configuration and the run of the operator.
- INEKernel inherits from @ref IKernel as well and it's the common interface for all kernels implemented in NEON, it adds just the run and the name methods.

There are two others implementation of @ref IKernel called @ref ICLSimpleKernel and INESimpleKernel, they are the interface for simple kernels that have just one input tensor and one output tensor.
Creating a new kernel implies adding new files:
- src/core/CL/kernels/CLReshapeLayerKernel.h
- src/core/CL/cl_kernels/reshape_layer.cl
- src/core/CL/kernels/CLReshapeLayerKernel.cpp
- src/core/CL/CLKernelLibrary.cpp

Neon kernel
- arm_compute/core/NEON/kernels/NEReshapeLayerKernel.h
- src/core/NEON/kernels/NEReshapeLayerKernel.cpp

We must register the new layer in the respective libraries:
- src/core/CL/CLKernels.h
- arm_compute/core/NEON/NEKernels.h

These files contain the list of all kernels available in the corresponding Compute Library's backend, for example CLKernels:
@code{.cpp}
... 
#include "src/core/CL/kernels/CLMinMaxLayerKernel.h"
#include "src/core/CL/kernels/CLMinMaxLocationKernel.h"
... 
#include "src/core/CL/kernels/CLReshapeLayerKernel.h"
... 

@endcode

For OpenCL we need to update the CLKernelLibrary.cpp, adding the appropriate code to embed the .cl kernel in the library. The OpenCL code can be compiled offline and embed in the library as binary.
The essential operation we want to do with a kernel will be
- create the kernel object
- initialize the kernel with the input/output and any other parameters that may be required
- retrieve the execution window of the kernel and run the whole kernel window in the current thread or use the multithreading.

Each kernel will have to implement the method:
- %validate: is a static function that checks if the given info will lead to a valid configuration of the kernel.
- configure: configure the kernel, its window, accessor, valid region, etc for the given set of tensors and other parameters.
- run: execute the kernel in the window

The structure of the kernel .cpp file should be similar to the next ones.
For OpenCL:
@snippet src/core/CL/kernels/CLReshapeLayerKernel.cpp CLReshapeLayerKernel Kernel
The run will call the function defined in the .cl file.

For the NEON backend case:
@snippet src/core/NEON/kernels/NEReshapeLayerKernel.cpp NEReshapeLayerKernel Kernel

In the NEON case, there is no need to add an extra file and we implement the kernel in the same NEReshapeLayerKernel.cpp file.
If the tests are already in place, the new kernel can be tested using the existing tests by adding the configure and run of the kernel to the compute_target() in the fixture.


@subsection S4_1_3_add_function Add a function

%Memory management and scheduling the underlying kernel(s) must be handled by the function implementation. A kernel class must support window() API which return the execute window for the configuration that the kernel is configured for. A window specifies the dimensions of a workload. It has a start and end on each of the dimension. A maximum of Coordinates::num_max_dimensions is supported. The run time layer is expected to query the kernel for the window size and schedule the window as it sees fit. It could choose to split the window into sub windows so that it could be run in parallel. The split must adhere to the following rules

- max[n].start() <= sub[n].start() < max[n].end()
- sub[n].start() < sub[n].end() <= max[n].end()
- max[n].step() == sub[n].step()
- (sub[n].start() - max[n].start()) % max[n].step() == 0
- (sub[n].end() - sub[n].start()) % max[n].step() == 0

@ref CPPScheduler::schedule provides a sample implementation that is used for NEON kernels.
%Memory management is the other aspect that the runtime layer is supposed to handle. %Memory management of the tensors is abstracted using TensorAllocator. Each tensor holds a pointer to a TensorAllocator object, which is used to allocate and free the memory at runtime. The implementation that is currently supported in Compute Library allows memory blocks, required to be fulfilled for a given operator, to be grouped together under a @ref MemoryGroup. Each group can be acquired and released. The underlying implementation of memory groups vary depending on whether NEON or CL is used. The memory group class uses memory pool to provide the required memory. It also uses the memory manager to manage the lifetime and a IPoolManager to manage the memory pools registered with the memory manager.


We have seen the various interfaces for a kernel in the core library, the same structure the same file structure design exists in the runtime module. IFunction is the base class for all the functions, it has two child interfaces: ICLSimpleFunction and INESimpleFunction that are used as base class for functions which call a single kernel.

The new operator has to implement %validate(), configure() and run(), these methods will call the respective function in the kernel considering that the multi-threading is used for the kernels which are parallelizable, by default std::thread::hardware_concurrency() threads are used. For NEON function can be used CPPScheduler::set_num_threads() to manually set the number of threads, whereas for OpenCL kernels all the kernels are enqueued on the queue associated with CLScheduler and the queue is then flushed.
For the runtime functions, there is an extra method implemented: prepare(), this method prepares the function for the run, it does all the heavy operations that are done only once (reshape the weight, release the memory not necessary after the reshape, etc). The prepare method can be called standalone or in the first run, if not called before, after then the function will be marked as prepared.
The files we add are:

OpenCL function
- arm_compute/runtime/CL/functions/CLReshapeLayer.h
- src/runtime/CL/functions/CLReshapeLayer.cpp

Neon function
- arm_compute/runtime/NEON/functions/NEReshapeLayer.h
- src/runtime/NEON/functions/NEReshapeLayer.cpp

As we did in the kernel we have to edit the runtime libraries to register the new operator modifying the relative library file:
- arm_compute/runtime/CL/CLFunctions.h
- arm_compute/runtime/NEON/NEFunctions.h

For the special case where the new function calls only one kernel, we could use as base class ICLSimpleFunction or INESimpleFunction. The configure and the validate methods will simply call the corresponding functions. The structure will be:
@snippet src/runtime/CL/functions/CLReshapeLayer.cpp CLReshapeLayer snippet


If the function is more complicated and calls more than one kernel we have to use the memory manager to manage the intermediate tensors; in the configure() method we call the manage() function passing the tensor to keep track, in the run method we will have to acquire all the buffer managed and released at the end.
For OpenCL if we want to add two tensor input and reshape the result:

@code{.cpp}
using namespace arm_compute;

CLAddReshapeLayer:: CLAddReshapeLayer(std::shared_ptr<IMemoryManager> memory_manager)
    : _memory_group(std::move(memory_manager))
{
}

void CLAddReshapeLayer::configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output)
{
    // Allocate memory
    TensorInfo info();
    add_output.allocator()->init(info);

    // Manage intermediate buffers
    memory_group.manage(&_addOutput);

    // Initialise kernel
    _add_kernel.configure(input1, input2, &add_output);
    _reshape_kernel.configure(&add_output, output);

    // Allocate intermediate tensors
    add_output.allocator()->allocate();
}

Status CLAddReshapeLayer::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
{
    TensorInfo add_output();
    ARM_COMPUTE_RETURN_ERROR_ON(CLAddLayerKernel::validate(input1, input2, add_output));
    ARM_COMPUTE_RETURN_ERROR_ON(CLReshapeLayerKernel::validate(add_output, output));
    return Status{};
}

void CLAddReshapeLayer::run()
{
    memory_group.acquire();

    // Run Add
    add_kernel.run();

    // Run Reshape
    CLScheduler::get().enqueue(reshape_kernel);

    memory_group.release();
}

@endcode

For NEON:

@code{.cpp}
using namespace arm_compute;

NEAddReshapeLayer:: NEAddReshapeLayer (std::shared_ptr<IMemoryManager> memory_manager)
    : _memory_group(std::move(memory_manager))
{
}

void NEAddReshapeLayer::configure(const ITensor *input1, const ITensor *input2, ITensor *output)
{
    // Allocate memory
    TensorInfo info();
    add_output.allocator()->init(info);

    // Manage intermediate buffers
    memory_group.manage(&_addOutput);

    // Initialise kernel
    add_kernel.configure(input1, input2, &addOutput);
    reshape_kernel.configure(&addOutput, output);

    // Allocate intermediate tensors
    add_output.allocator()->allocate();
}

void NEAddReshapeLayer::run()
{
    memory_group.acquire();

    // Run Add
    add_kernel.run();

    // Run Reshape
    NEScheduler::get().schedule(_reshape_kernel.get(), Window::DimY);

    memory_group.release();
}
@endcode


At this point, everything is in place at the library level. If you are following an tests driven implementation and all the tests are already in place, we can call the function configuration in the fixture and remove any redundant code like the allocation of the intermediate tensors since it's done in the function. Run the final tests to check the results match with the expected results from the reference implementation.

@subsection S4_1_4_add_validation Add validation artifacts

@subsubsection S4_1_4_1_add_reference Add the reference implementation and the tests
As mentioned in the introduction, the reference implementation is a pure C++ implementation without any optimization or backend specific instruction.
The refence implementation consist of two files into the folder tests/validation/reference:
- tests/validation/reference/ReshapeLayer.h
- tests/validation/reference/ReshapeLayer.cpp

where we will put respectively the declaration and definition of the new operator.
All the utility functions that are used ONLY in the tests are in test/validation/helpers.h, for all the others, as mentioned before, there are helpers in the library.
Compute Library and the tests do use templates, the reference implementation is a generic implementation independent from the datatype and we use the templates to generalize the datatype concept.
Following the example, let's have a look at the ReshapeLayer operator:

- tests/validation/reference/ReshapeLayer.h

@snippet tests/validation/reference/ReshapeLayer.h ReshapeLayer

- tests/validation/reference/ReshapeLayer.cpp

@snippet tests/validation/reference/ReshapeLayer.cpp ReshapeLayer

An explicit instantiation of the template for the required datatypes must be added in the .cpp file.

@subsubsection S4_1_4_2_add_dataset Add dataset
One of the parameters of the tests is the dataset, it will be used to generate versions of the test case with different inputs.
To pass the dataset at the fixture data test case we have three cases
- the operator dataset is simple so it can be added directly in the test case data declaration
- we can create a class that return tuples at the test framework

@snippet tests/datasets/PoolingTypesDataset.h PoolingTypes datasets

- if we want to create dynamically the dataset combining different parameter, we can create the dataset using iterators.
For example, dataset for ReshapeLayer:

@snippet tests/datasets/ReshapeLayerDataset.h ReshapeLayer datasets

@subsubsection S4_1_4_3_add_fixture  Add a fixture and a data test case

Benchmark and validation tests are based on the same framework to setup and run the tests. In addition to running simple, self-contained test functions the framework supports fixtures and data test cases.
Fixtures can be used to share common setup, teardown or even run tasks among multiple test cases, for that purpose a fixture can define a "setup", "teardown" and "run" method.
Adding tests for the new operator in the runtime library we need to implement at least the setup method, that is used to call two methods for configure, run and return the output respectively of the target (CL or Neon) and the reference (C++ implementation).

For example let's have a look at Reshape Layer Fixture :

@snippet tests/validation/fixtures/ReshapeLayerFixture.h ReshapeLayer fixture

In the fixture class above we can see that the setup method computes the target and reference and store them in the two members _target and _reference which will be used later to check for correctness.
The compute_target method reflects the exact behavior expected when we call a function. The input and output tensor must be declared, function configured, tensors allocated, the input tensor filled with required data, and finally, the function must be run and the results returned.
This fixture is used in the test case, that is a parameterized test case that inherits from a fixture. The test case will have access to all public and protected members of the fixture. Only the setup and teardown methods of the fixture will be used. The setup method of the fixture needs to be a template and must accept inputs from the dataset as arguments.
The body of this function will be used as a test function.
For the fixture test case the first argument is the name of the test case (has to be unique within the enclosing test suite), the second argument is the class name of the fixture, the third argument is the dataset mode in which the test will be active (PRECOMMIT or NIGTHLY) and the fourth argument is the dataset.
For example:

@snippet tests/validation/CL/ActivationLayer.cpp CLActivationLayerFixture snippet

@code{.cpp}
TEST_SUITE(CL)
TEST_SUITE(ActivationLayer)
TEST_SUITE(Float)
TEST_SUITE(FP16)
@endcode
@snippet tests/validation/CL/ActivationLayer.cpp CLActivationLayer Test snippet
@code{.cpp}
TEST_SUITE_END()
TEST_SUITE_END()
TEST_SUITE_END()
TEST_SUITE_END()
@endcode

This will produce a set of tests that can be filtered with "CL/ReshapeLayer/Float/FP16/RunSmall". Each test produced from the cartesian product of the dataset is associated to a number and can be filtered specifying all the parameters.
*/
} // namespace arm_compute