What is rocm-kernels?

Development guidance for writing and testing Triton code that runs optimized operations on AMD GPUs through ROCm, AMD's GPU software platform.

Implementing, injecting, benchmarking, and tuning RMSNorm, RoPE 3D, GEGLU, and AdaLN kernels in Hugging Face Diffusers and Transformers pipelines.

It provides project-specific patterns for checking and improving GPU kernels used by image, video, and language-model libraries.

rocm-kernels — Skill by huggingface

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods

npx agentmods add skills/huggingface/kernels/rocm-kernels

Any agent

npx skills add huggingface/kernels --skill rocm-kernels

Clone the repo

git clone --depth 1 https://github.com/huggingface/kernels

Made for: Claude Code, Codex.

Per session 93 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 6,420 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5	$0.00093	$0.06420
Opus 5	$0.00046	$0.03210
Sonnet 5	$0.00019	$0.01284
Haiku 4.5	$0.00009	$0.00642

Measured 2d ago against content hash 4abfad8e0ef0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

rocm-kernels scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/benchmark_e2e.py, scripts/benchmark_kernels.py, scripts/huggingface_kernels_example.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

kernel-builder/skills/rocm-kernels/SKILL.md · 509 lines

How it starts

The opening of the file, as written. The whole thing — 509 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ROCm Triton Kernels for Diffusers & Transformers

This skill provides patterns and guidance for developing optimized Triton kernels targeting AMD GPUs (MI355X, R9700) on ROCm, for use with HuggingFace diffusers (LTX-Video, SD3, FLUX) and transformers libraries.

Quick Start

Diffusers (LTX-Video)

Inject optimized kernels into LTX-Video pipeline:

import os
os.environ['TRITON_HIP_USE_BLOCK_PINGPONG'] = '1'
os.environ['TRITON_HIP_USE_ASYNC_COPY'] = '1'

from diffusers import LTXPipeline
pipe = LTXPipeline.from_pretrained("Lightricks/LTX-Video", torch_dtype=torch.bfloat16)
pipe.to("cuda")  # ROCm uses same API via HIP
inject_optimized_kernels(pipe)  # BEFORE CPU offloading
pipe.enable_model_cpu_offload()

For a minimal integration example (~150 lines):

python scripts/ltx_kernel_injection_example.py

Isolated Kernel Micro-benchmarks

# All 4 kernels: correctness + performance + bandwidth
python scripts/benchmark_kernels.py

# Single kernel
python scripts/benchmark_kernels.py --kernel rmsnorm
python scripts/benchmark_kernels.py --kernel rope
python scripts/benchmark_kernels.py --kernel geglu
python scripts/benchmark_kernels.py --kernel adaln

End-to-End Pipeline Benchmark

# Compare baseline vs Triton vs torch.compile
python scripts/benchmark_e2e.py --mode all

# Quick test
python scripts/benchmark_e2e.py --mode triton --num-frames 9 --steps 5

# Save results for comparison
python scripts/benchmark_e2e.py --mode all --output-json results.json

Target Model: LTX-Video

Architecture Overview

Component	Class	Has Weight	Count	Kernel
`transformer_blocks.*.norm1`	RMSNorm	No (elementwise_affine=False)	56	RMSNorm
`transformer_blocks.*.norm2`	RMSNorm	No	56	RMSNorm
`transformer_blocks.*.attn1.norm_q`	torch.nn.RMSNorm	Yes	28	RMSNorm
`transformer_blocks.*.attn1.norm_k`	torch.nn.RMSNorm	Yes	28	RMSNorm
`transformer_blocks.*.ff`	FeedForward	-	28	GELU (not GEGLU!)
Rotary position encoding	LTXVideoRotaryPosEmbed	-	1	RoPE 3D

Read the full file on GitHub · 509 lines

Files

What ships with it

18 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2d ago First seen · 509 lines · 93 tokens per session scan A 4abfad8e0ef0

Subscribe to this mod's changes

rocm-kernels is a skill published in the GitHub repository huggingface/kernels (729 stars, last pushed 5d ago), licensed Apache-2.0. It adds 93 tokens to every session and 6,420 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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