AITER is AMD’s centralized repository that support various of high performance AI operators for AI workloads acceleration, where a good unified place for all the customer operator-level requests, which can match different customers' needs. Developers can focus on operators, and let the customers integrate this op collection into their own private/public/whatever framework.
Some summary of the features:
- C++ level API
- Python level API
- The underneath kernel could come from triton/ck/asm
- Not just inference kernels, but also training kernels and GEMM+communication kernels—allowing for workarounds in any kernel-framework combination for any architecture limitation.
git clone --recursive https://github.com/ROCm/aiter.git
cd aiter
python3 setup.py develop
If you happen to forget the --recursive during clone, you can use the following command after cd aiter
git submodule sync && git submodule update --init --recursive
There are number of op test, you can run them with: python3 op_tests/test_layernorm2d.py
| Ops | Description |
|---|---|
| ELEMENT WISE | ops: + - * / |
| SIGMOID | (x) = 1 / (1 + e^-x) |
| AllREDUCE | Reduce + Broadcast |
| KVCACHE | W_K W_V |
| MHA | Multi-Head Attention |
| MLA | Multi-head Latent Attention with KV-Cache layout |
| PA | Paged Attention |
| FusedMoe | Mixture of Experts |
| QUANT | BF16/FP16 -> FP8/INT4 |
| RMSNORM | root mean square |
| LAYERNORM | x = (x - u) / (σ2 + ϵ) e*0.5 |
| ROPE | Rotary Position Embedding |
| GEMM | D=αAβB+C |
