Windows Development

DirectML Custom Operators Samples

This section provides sample code demonstrating how to implement and utilize custom operators within DirectML. Custom operators allow you to extend the functionality of DirectML by defining new operations not natively supported, enabling greater flexibility for advanced machine learning models.

Introduction to Custom Operators

DirectML offers a rich set of built-in operators for common deep learning tasks. However, some research or specialized models may require operations that are not standard. Custom operators in DirectML allow developers to define these operations, compile them, and integrate them seamlessly into their DirectML workflows.

Key Concepts

  • Operator Definition: Defining the structure and behavior of your custom operator, including its inputs, outputs, and the underlying computation.
  • Kernel Implementation: Writing the actual code that performs the computation for your operator. This can leverage various low-level APIs or existing libraries.
  • Operator Registration: Registering your custom operator with DirectML so that it can be discovered and used.
  • Integration with Models: Incorporating custom operators into existing or new DirectML model graphs.

Sample 1: Simple Element-wise Custom Operator

This sample demonstrates a basic custom operator that performs an element-wise operation, such as a custom activation function or a simple mathematical transformation.

Code Snippet (Conceptual C++):


#include <dxml/dxml.h>

// Assume dx::OperatorHelper is available for defining custom operators
class MyCustomAddOperator : public dx::OperatorHelper {
public:
    MyCustomAddOperator(dx::IResourceBindingContainer* pBindingContainer, dx::OperatorOptions options)
        : OperatorHelper(pBindingContainer, options) {}

    virtual void CreateOperator() override;
    virtual void CompileOperator(
        dx::IDMLDevice* dmlDevice,
        dx::ICommandRecorder* commandRecorder,
        dx::OperatorCreationOptions creationOptions,
        dx::OperatorInfo& operatorInfo) override;
    virtual dx::Status Execute(
        dx::ICommandRecorder* commandRecorder,
        dx::ResourceBindings& bindings) override;

    // ... other methods ...
};

void MyCustomAddOperator::CreateOperator() {
    // Define input and output tensors
    auto inputA = Input(0);
    auto inputB = Input(1);
    auto output = Output(0);

    // Define the operation: output = inputA + inputB
    // This is a simplified representation; actual kernel implementation is complex.
    // For real implementation, you'd use compute shaders or similar.
}

void MyCustomAddOperator::CompileOperator(...) {
    // Logic to compile the operator's kernel
    // This might involve generating or linking shader code.
}

dx::Status MyCustomAddOperator::Execute(...) {
    // Logic to execute the operator on the GPU
    // Dispatch compute shaders, manage resources.
    return dx::Status::OK;
}

Sample 2: Custom Convolution Layer

This sample shows a more complex example of implementing a custom convolution layer. This is useful for research purposes or when a specific convolution variant is needed.

Code Snippet (Conceptual C++/HLSL):

Implementing custom kernels for complex operations like convolution often involves writing High-Level Shading Language (HLSL) code that runs on the GPU.


// Simplified HLSL for a custom convolution kernel
struct PixelData {
    float4 value;
};

RWTexture2D<float4> outputTexture : register(u0);
Texture2D<float4> inputTexture : register(t0);
Texture2D<float4> kernelTexture : register(t1);

// ... other shader parameters like strides, padding, etc. ...

[numthreads(8, 8, 1)]
void CSMain( uint3 dispatchThreadId : SV_DispatchThreadID )
{
    // Calculate output pixel coordinates
    uint outputX = dispatchThreadId.x;
    uint outputY = dispatchThreadId.y;

    // Simplified convolution logic:
    // Iterate over kernel, sample input, accumulate result
    float sum = 0.0f;
    // ... loop through kernel weights and corresponding input pixels ...

    outputTexture[dispatchThreadId.xy] = float4(sum, sum, sum, 1.0f);
}

Getting Started

To implement your own custom operators:

  1. Download the DirectML samples: Clone or download the official DirectML samples repository from GitHub.
  2. Explore the existing custom operator samples: Study the provided code to understand the structure and patterns.
  3. Define your operator: Use the DirectML API to define the inputs, outputs, and metadata of your new operator.
  4. Implement the kernel: Write the compute shader or other code that performs the actual computation.
  5. Register and use: Register your operator and integrate it into your model graph.

Further Resources

For detailed API documentation and advanced usage, refer to the official DirectML documentation and the comprehensive set of samples available in the DirectML GitHub repository.