AMD Kernels

This repo contains customized kernels for AMD Instinct series GPUs. Please make sure your Triton compiler is v2.1 or later, and is from the OpenAI Triton repository here. To install Triton, please see these instructions. You can also install in your python venv using latest wheels: pip install --pre pytorch-triton-rocm torch --index-url https://download.pytorch.org/whl/nightly/rocm6.2

How to add your own benchmark

This repo is configured with a custom Github runner donated by AMD. You can queue jobs to this runner by either merging your code or by opening a pull request. We don't need to merge your code for you to run benchmarks.

The main things you need to run your own benchmark

1. In kernels/ create a new file that must start with the name test_. This is because we use pytest to discover your kernel
2. If you want your benchmark results to persist in a Github Artifact, we recommend using the builtin Triton benchmark.run(save_path="./perf-artifacts/your_kernel", show_plots=True, print_data=True)
3. In your PR, if you don't want to run testing on all the kernels, you can specify a specific kernel you want to test by adding a line like the following to your PR description: ci-exactly: <test-file-name.py> as seen in this PR: Example

Have fun! We intend for this to be a social repo, if you have any other requests for things we could do better please let us know!

Existing benchmarks

test_flash-attention.py

This script contains the Flash Attention kernel with the following support

• Arbitrary Q and KV sequence lengths, and arbitrary head sizes
• Autoregressive or "causal" masking
• Flash Attention v2 with variable sequence lengths
• Multi and Grouped Query attention
• ALiBi bias
• Matrix bias

These are currently supported for the forward kernel only.

test_matmul.py

This script contains the GEMM kernel that supports int8, int32, fp16, fp32, bf16 datatypes.

test_softmax.py

Kernel that implements Softmax over a row of tensor.

test_rmsnorm.py

Kernel that implements RMS Norm over a row of tensor.

test_layernorm.py

Kernel that implements Layer Normalization over a row on tensor

test_dotproduct.py

Kernel that implements the dot product of two vectors

Dev roadmap

CI changes

• Doesn't make sense to run the full benchmark suite on each PR, instead only run changed files
• Considering we have a node, running the tests sequentially seems like a miss, instead should allocate a test to a free gpu. Investigate tech like pytest-xdist
• Setting up triton env takes a few min, we should cache this since it almost never changes

UX changes Instead of submitting jobs via Github we could do it via Discord. UX would be a

• user submits a kernel.py in #rocm channel on discord.gg/gpumode and that gets picked up a Discord bot
• Given a script, use the bot to automatically open a PR for benchmarking. This can be done thanks to tools like https://github.com/PyGithub/PyGithub
• Once the triggered Github action is complete the bot can reply to the original user message with a link to the generated Github artifact. If the job fails then the bot should link to the failed Github Action
• Nice to have would be to give users a sense of their position on the queue