triton-operator-dev

Original：🇨🇳 Chinese

Translated

Task Orchestration for Full-Process Development of Ascend Triton Operators. Used when users need to develop Triton Operators, covering the complete workflow of environment configuration → requirement design → code generation → static inspection → precision verification → performance evaluation → document generation → performance optimization.

2installs

Sourceascend/agent-skills

Added on2026-04-15

NPX Install

npx skill4agent add ascend/agent-skills triton-operator-dev

SKILL.md Content (Chinese)

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Full-Process Development of Triton Operators

Task Orchestration

Stage	Skill	Output
1	triton-operator-env-config	Available development environment
2	triton-operator-design	Operator requirement document
3	triton-operator-code-gen	Executable code
4	triton-operator-code-review	Code inspection report
5	triton-operator-precision-eval	Precision verification report
6	triton-operator-performance-eval	Performance evaluation report
7	triton-operator-doc-gen	Interface document
8	triton-operator-performance-optim	Optimized code

Sub-Skill Overview

1. triton-operator-env-config

Trigger: First-time development or environment exceptions
Core: Check CANN → Python → torch → triton-ascend in sequence
Verification: Run
```
01-vector-add.py
```

2. triton-operator-design

Trigger: Need to design a new operator
Core: Requirement analysis → prototype design → specification constraints → feature implementation
Key: Must include specific calculation method of Tiling strategy

3. triton-operator-code-gen

Trigger: Requirement document exists, need to generate code
Process: Confirm calculation logic → design Tiling → generate Kernel → generate test cases

Dependency: Must read

references/hardware-architecture.md

and

references/templates.md

first

4. triton-operator-code-review

Trigger: After code generation, before precision verification
Core: Host-side inspection → Device-side inspection → performance risk inspection
Key: Static analysis of Ascend API constraint compliance, Mask integrity, and precision processing

5. triton-operator-precision-eval

Trigger: After code inspection passes, before performance evaluation
Core: Compare with PyTorch reference implementation → calculate error metrics → generate precision report
Key: Reduction operations must use FP32 to ensure rtol/atol meet thresholds

6. triton-operator-performance-eval

Trigger: After precision verification passes, before performance optimization
Core: msprof performance collection → bottleneck diagnosis → hardware utilization analysis → performance report
Key: Must use msprof/msprof op, other timing methods are not accepted

7. triton-operator-doc-gen

Trigger: Need to generate interface documents
Output: Standardized Ascend NPU interface documents (product support table, parameter description, call examples)

8. triton-operator-performance-optim

Trigger: Performance does not meet standards
Process: Performance diagnosis → basic tuning → hardware specialization → advanced optimization
Key: Must use msprof to locate bottlenecks first, re-verify precision after optimization

Quick Decision

Scenario	Skip Stages
Environment already configured	1
Design document already exists	2
Only need documents	1,2,3,4,5,6,8
Only need code	1,2,4,5,6,7,8
Only need optimization	1,2,3,4,5,6,7
Skip inspection and verification	4,5,6

Common Anti-Patterns

❌ Ignore UB size (192KB)
❌ Do not use FP32 for reduction operations
❌ BLOCK is not a multiple of 16 (Cube unit)
❌ Forget Mask (Ascend has zero fault tolerance)
❌ Confuse the purposes of Vector Core and Cube Core