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triton-ascend-example-softmax

Implement a three-phase softmax reduction kernel with block tiling on Triton Ascend.

6|1|Updated Apr 19, 2026
Search Tags:#example#kernel#softmax#triton#ascend#three-stage#reduction
One-click install
npx skills add https://github.com/xchang1121/AutoResearch-CC-hook --skill triton-ascend-example-softmax
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: triton-ascend-example-softmax
Source: https://github.com/xchang1121/AutoResearch-CC-hook/tree/main/skills/triton-ascend/examples/triton-ascend-example-softmax
Command: npx skills add https://github.com/xchang1121/AutoResearch-CC-hook --skill triton-ascend-example-softmax

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a complete Triton Ascend softmax reduction implementation example, illustrating how to build a three-phase reduction (max → sum(exp) → normalize) with block tiling and precision strategies to achieve high performance on Ascend hardware.

Core Features & Use Cases

  • Three-stage reduction: max, sum(exp), normalize to compute softmax efficiently on Triton Ascend.
  • Block tiling and scalar accumulator techniques to improve numerical stability and throughput.
  • Use Case: design and benchmark reduce operators for neural networks on Ascend devices, with a ready-to-study kernel and reference PyTorch model.

Quick Start

Run the softmax example to study the Triton Ascend reduction kernel and integrate its patterns into your own reduce operators.

Frequently Asked Questions about triton-ascend-example-softmax

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I implement a high-performance softmax kernel on Ascend hardware using Triton?▼

Implement softmax on Triton Ascend by building a three-phase reduction pipeline computing max, sum(exp), and normalize, utilizing block tiling and scalar accumulators for optimized throughput and numerical stability.

What is the best way to handle numerical stability in a Triton softmax reduction operator?▼

Handle numerical stability in a Triton softmax operator by applying a three-stage reduction approach that subtracts the maximum value before computing the exponential sum and normalizing the results.

Can I use block tiling to optimize reduce operators for neural networks on Ascend devices?▼

Yes, you can use block tiling and scalar accumulator techniques to optimize reduce operators for neural networks on Ascend devices, improving both throughput and numerical stability.

Does the Triton Ascend softmax example include a reference model for benchmarking?▼

Yes, the Triton Ascend softmax example includes a ready-made reference PyTorch model alongside the structured kernel, allowing you to study, benchmark, and integrate the reduction patterns directly.

How does the three-stage reduction pipeline work for softmax computation?▼

The three-stage reduction pipeline computes softmax by first finding the block maximum, then calculating the sum of exponentials adjusted by that max, and finally normalizing the values to achieve stable results.

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