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cb.fabric-parity-gate

Name: cb.fabric-parity-gate
Availability: InStock
Author: Softmax

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Updated Aug 27, 2026

Search Tags:#testing #pytorch #cuda #fabric #backends #parity #gradients

Ready to run

npx skills add https://github.com/Metta-AI/cortical --skill cb-fabric-parity-gate

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cb.fabric-parity-gate
Source: https://github.com/Metta-AI/cortical/tree/main/skills/cb.fabric-parity-gate
Command: npx skills add https://github.com/Metta-AI/cortical --skill cb-fabric-parity-gate

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Fabric parity gate ensures that the active CUDA/backend Fabric path matches the PyTorch Fabric reference for outputs, states, and gradients before performance closure, preventing subtle mismatches from slipping into production.

Core Features & Use Cases

Verify parity across forward outputs, final state, input gradients, and parameter gradients on multiple backend paths.
Build and validate a minimal parity matrix covering active routes, reset behavior, and materialization modes.
Use during integration to certify readiness of fabrics/backend paths before rollout to production environments.

Quick Start

Run the Fabric parity gate at startup to verify outputs, states, and gradients across the active backend path before enabling performance measurements.

Frequently Asked Questions about cb.fabric-parity-gate

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

FAQPage Schema

How do I verify PyTorch Fabric CUDA backend parity across outputs and gradients?▼

Fabric parity gate validates CUDA backend implementations against the PyTorch Fabric reference by enforcing equality across forward outputs, materialized final state, input gradients, and full parameter gradients.

What is the best way to test Fabric backend implementation parity during development?▼

The best way to test Fabric backend parity is applying a gate across active routes, touched owners, transition buckets, chunking policies, resets, and materialization modes to validate outputs, states, and gradients.

How do I enforce gradient equality between custom CUDA backends and PyTorch Fabric?▼

To enforce gradient equality, the parity gate checks input gradients, provided initial carry gradients, and full parameter gradients against the PyTorch Fabric reference across multiple backend paths.

Can I validate Fabric backend readiness for production rollout using a parity matrix?▼

Yes, you can build and validate a minimal parity matrix covering active routes, reset behavior, and materialization modes to certify readiness of fabric backend paths before production rollout.

Does Fabric parity checking work across different materialization modes and chunking policies?▼

Fabric parity checking works across materialization modes, chunking policies, demotions, and surface keys to ensure the active CUDA backend path matches the reference implementation comprehensively.

When should I run a Fabric parity gate during the development lifecycle?▼

Run the Fabric parity gate at startup to verify outputs, states, and gradients across the active backend path before enabling performance measurements or closing performance validation.