ROCm Kernels: How Do You Build and Share Them?

⚙️ ROCm kernels allow developers to build GPU-accelerated code optimized for AMD hardware using open-source tools.
🧠 Custom ROCm kernels can dramatically reduce AI inference time, improving throughput for deep learning workloads.
🚀 PyTorch ROCm integration makes it easy for developers to switch from NVIDIA to AMD GPUs.
🧰 Hugging Face ROCm tags simplify discoverability and deployment of AMD-compatible machine learning models.
🖥️ The AMD MI300X GPU features up to 192GB of HBM3, redefining compute capacity for large-scale AI on a single GPU.

ROCm Kernels 101: What They Are and Why They Matter

ROCm kernels are the base for GPU-accelerated computing on AMD hardware. They let developers write custom, high-performance code that runs right on Radeon and Instinct GPUs. These kernels use HIP C++. This language is like CUDA, so it makes cross-platform development easier. NVIDIA's CUDA has been the main choice for AI and GPU programming for a long time. But ROCm is a good choice because it is open-source, works with many vendors, and is built for AMD GPUs. So, ROCm kernels are important for anyone building AI systems that need to grow, especially with AMD’s new server hardware like the MI300X.

AMD ROCm Ecosystem Overview: Enabling Productive GPU Development

The ROCm ecosystem is AMD’s answer to CUDA. It is a full set of tools, libraries, and APIs made to get the most out of AMD GPUs. Its goal is to make high-performance computing available to developers everywhere by removing exclusive limits.

Core Components of ROCm

HIP (Heterogeneous-Compute Interface for Portability): This is the C++ runtime and API layer. It lets developers write code that works on both AMD and NVIDIA hardware. You can run code written in HIP on either system with little to no changes.
ROCm Runtime: Responsible for managing GPU resources, scheduling kernels, and handling memory transfers between CPU and GPU.
Libraries Designed for Deep Learning:
- MIOpen: This provides well-tuned versions of common neural network operations like convolutions, activations, batch norm, pooling, and other things. It is like cuDNN in the CUDA world.
- rocBLAS / hipBLAS: High-performance BLAS (Basic Linear Algebra Subprograms) libraries for operations like matrix multiplication, essential for deep learning and scientific computing.
- RCCL (Radeon Collective Communication Library): Supports fast inter-GPU communication for multi-GPU training setups, similar to NCCL.

Compatibility and Cross-framework Integration

One of ROCm’s strong points is that it works with popular ML systems. Teams can run models right on AMD GPUs using:

PyTorch ROCm: Full support for training and inference tasks using ROCm backend.
TensorFlow ROCm: This offers more and more support, especially for training the current best models.
Hugging Face ROCm Tags: If you need models that work with AMD hardware, Hugging Face’s hub now has official rocm tags. These tags make it easy to find pre-trained language and vision models that work right away on AMD computers.

By using open tools and letting the community help with development, the ROCm system puts AMD at the front of AI hardware for both research and production.

Setting Up Your Environment for ROCm Kernel Development

Before you develop and use ROCm kernels, you need a setup that works well. You must set things up carefully, from what hardware works to what software versions you need. This makes sure you get the most from your GPU resources.

Hardware Requirements

To run ROCm kernels, your system should have:

AMD GPUs Supported by ROCm: Particularly MI100, MI200, and the powerful MI300X series.
PCIe Gen 4.0 or Higher: Ensures fast communication between CPU and GPU.
64-bit Linux System: Typically Ubuntu 20.04 LTS or newer is recommended.

Want a lot of power? The MI300X is AMD’s main product for AI. It offers a very large 192 GB of unified HBM3 memory. This lets huge AI models run and use all memory efficiently.

Recommended Software Stack

A strong ROCm development setup includes:

ROCm SDK: You can get this for free from AMD’s GitHub or developer portal. Check if it works with your Ubuntu version.
HIP SDK and hipcc Compiler: For writing and compiling HIP C++ kernels.
Docker (Optional, Very Helpful): AMD offers Docker containers that are already built and made to be easy to use. These images come with the ROCm runtime, libraries like MIOpen, and often include systems like PyTorch ROCm.
Python 3.8+ & PyTorch ROCm: You need to connect HIP kernels to Python. This is important for making them work with ML systems.

Verifying Setup

Before developing:

Check GPU Availability:
```
rocminfo | grep -i name
```
OpenCL Check:
```
clinfo | grep -i amd
```
Compile and Run Sample Kernel:
AMD includes HIP sample projects. Compile one with:
```
hipcc -o sample sample.cpp
./sample
```

💡 Helpful Hint: Use Docker containers for development. Also, use them to make it easy to repeat your work. Mount volumes to test code easily and package your setup with little extra effort.

Writing Your First ROCm Kernel: A Walkthrough

Start with a simple example to see how a ROCm kernel works. A common task is vector addition. This task can run at the same time across thousands of threads.

Example HIP Kernel

__global__ void vector_add(const float* A, const float* B, float* C, int N) {
  int idx = blockIdx.x * blockDim.x + threadIdx.x;
  if (idx < N) {
    C[idx] = A[idx] + B[idx];
  }
}

Breaking Down the Parts

global: This marks the function as a kernel that runs on the GPU.
threadIdx/blockIdx: Allow parallel execution by giving each thread a unique index.
Memory Access Pattern: You should group global memory accesses when you can. This helps reduce delays.

Compile the kernel using:

hipcc -o vector_add vector_add.cpp

For bigger projects with many files and both host and device code, it is a good idea to use CMake with build flags just for HIP.

Testing and Benchmarking ROCm Kernels

Efficient GPU programming needs careful testing and profiling. You must make sure the kernel works right. But you also need to check that it runs faster than other versions.

Functional Testing

Use Python wrappers (ctypes, pybind11, or Cython) to call the HIP kernel from Python.
Compare outputs with NumPy or PyTorch versions to check it is correct.

Example:

assert np.allclose(output_from_rocm, A + B, atol=1e-5)

Performance Profiling

Launch the application using:

rocprof --hsa-trace --stats ./my_application

Alternatively, use the HIP_TRACE_API environment variable to trace API calls:
```
export HIP_TRACE_API=1
./binary
```

Benchmarking Strategy

Set starting points using CPU and standard GPU kernels.
Measure kernel execution time using torch.cuda.Event or Python’s time module.

Benchmark metrics to gather:

Throughput (elements/sec)
Latency per operation
Occupancy and register usage (via profiling tools)

This testing process tells you how to optimize it. And you might add shared memory, thread coarsening, or loop unrolling.

Integrating with PyTorch ROCm: Make It Useful

The real strength of ROCm is how it works with PyTorch ROCm. This lets developers add custom operations to deep learning processes. These operations are made better for AMD GPUs.

Steps to Create a Custom PyTorch ROCm Module

Make Kernel Available via Pybind11:
Write code to send tensor input to your C++ HIP kernel.

Define in PyTorch using Autograd Function:

class MyKernelFunction(torch.autograd.Function):
 @staticmethod
 def forward(ctx, input):
     return my_rocm_binding.vector_add(input)

Turn on TorchScript:
Add typing details or use the @torch.jit.script decorator. This makes operations ready to be used.
Memory Placement Awareness:
Always use .to("cuda") or .to(device) when creating tensors to avoid host-device copy errors.

MIOpen and rocBLAS can speed up many operations you would normally write yourself. They also offer other choices if a custom HIP kernel does not run faster.

If you have built a great ROCm kernel, it is time to share it. Hugging Face is a good place to make sure many people see it and work together.

Steps to Upload:

Organize Your Repo:
- README.md: Tell what the kernel does, its benchmark results, and what GPUs it works with.
- LICENSE: It is best to use MIT or Apache-2 for more widespread use.
- examples/: Show notebooks using your kernel in real tasks.
Upload with Hugging Face CLI:
Install and authenticate:
```
pip install huggingface_hub
huggingface-cli login
```

Create and push:

huggingface-cli repo create my-rocm-kernel
git add .
git commit -m "initial ROCm kernel"
git push

Tag It Right:
- Use tags like rocm, amd, mi300, and your kernel type (attention, conv2d) so people can find it.

Example Use Case: Automating AI Models on AMD GPUs with Bot-Engine

Automation systems that use AI models for content, summaries, or images often need to process a lot of data quickly with little delay.

Here’s a Bot-Engine sample workflow:

Train a BERT-like transformer using custom ROCm attention kernels.
Use an automation tool (e.g., GoHighLevel) to start inference processes.
Give results using an API in real-time for different processes in many languages and time zones.

Real-World Impact:

✅ Using MI300X with better-performing kernels cut total inference time by 60% compared to regular CPU setups. This is very important for AI services that need to run all the time.

Supporting Advanced Architectures: AMD Instinct MI300 GPUs

The Instinct MI300 series is AMD’s strong move into supercomputing hardware made for AI.

Key Features:

192 GB Unified HBM3 Memory: This gets rid of memory limits for huge models like GPT-3 or LLaMA.
Shared CPU/GPU Architecture: This lets you do both inference and small data preparation on the same chip.
Better-designed L2 Cache and Memory Bandwidth: This cuts down on typical delays during operations that use a lot of matrices.

Use hipDeviceAttributeMaxSharedMemoryPerBlock to check and adjust kernels for the MI300 architecture, which has a lot of memory. These devices are made for using transformers, generative models, graph tasks, and big AI systems for businesses on a large scale.

To make your custom kernel used more and trusted:

✅ Good Documentation: List tensor shapes, what kind of data it expects, and what devices it works with.
🚀 Benchmark Results: Share comparison results with CPU and default PyTorch GPU operations.
🔄 Semantic Versioning: Use MAJOR.MINOR.PATCH format for clear understanding and updates.
🔎 Changelog & Issues: Be clear about fixes and updates. Ask users to report problems if something does not work.
🤝 Licensing: Use open-source licenses to get more people to work together and use it.

These good practices also help your kernel get used again in open tools for infrastructure like Hugging Face transformers or PyTorch Lightning systems.

Here are basic ROCm libraries and tools to find problems for more advanced development:

hip & hipcc: Main runtime and compiler for HIP kernels.
composable_kernel: This is for building kernels from parts for complex tensor operations.
MIOpen: Fast operations you can just add in for CNNs, RNNs, transformers.
rocBLAS & hipBLAS: Low-level linear algebra routines made specifically for GPUs.
ROCProfiler & rocminfo: Tools to understand performance. These are important for making things better.

Each of these tools helps with your custom kernel development. They make common tasks easier, watch how things run, or make it work better with big processes.

Contributing to the ROCm + Open Source Ecosystem

Your help guides the future of open AI:

🎯 Open Source Contributions: Submit kernels or framework patches to ROCm’s GitHub repositories.
💬 Join Discussions: Hugging Face moves ROCm support ahead with group discussions on its forums.
📣 Get Involved with the Community: Answer questions or write how-to guides on Reddit (r/Amd), Stack Overflow, or GitHub Discussions.

Even small kernels, benchmarks, or bug fixes help the system grow quickly and help everyone who uses it.

Final Thoughts: Why ROCm Belongs in Your ML or Automation Stack

ROCm kernels are a powerful tool for making AI available to everyone. You can build high-performance kernels using HIP C++. It works well with platforms like Hugging Face and PyTorch ROCm. And MI300X GPUs give very good performance. So, ROCm is quickly becoming the main choice for open AI development that can grow.

If you are working on base models, using models on devices, or automating processes, custom ROCm kernels give you speed, a clear view, and control. It is time to use this open platform that is helping change how AI infrastructure is built.

Citations

AMD. (2023). AMD Instinct MI300 Series Accelerators. Advanced Micro Devices. https://www.amd.com/en/data-center/instinct-mi300.html

Moore, S. (2023). ROCm 5.7: What’s New and What It Powers in AI. Serve The Home. https://www.servethehome.com/amd-rocm-5-7-whats-new-and-what-it-powers-in-ai/