Compiler & Kernel Fusion (torch.compile) Integration
CommunitySpeed up training safely with torch.compile
Authorsovr610
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Torch-based compilation accelerates training/inference by reducing Python overhead and fusion improvements through TorchDynamo/AOT Autograd/TorchInductor, while maintaining safe fallbacks.
Core Features & Use Cases
- Safe wrapper for safe compilation: maybe_compile ensures non-breaking fallback on failure.
- Selective compilation via allowlist/blocklist and full-model compilation with various backends/modes.
- Shape management and bucketing to reduce recompilations, including dynamic shape hints and bucketing strategies for variable-length inputs.
- DDP/FSDP friendly workflows and smoketest health checks to verify correctness.
Quick Start
Wrap your model with maybe_compile(cfg, logger, sample_batch) to obtain a compiled model ready for training.
Dependency Matrix
Required Modules
torchyaml
Components
scriptsreferencesassets
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: Compiler & Kernel Fusion (torch.compile) Integration Download link: https://github.com/sovr610/refffiy/archive/main.zip#compiler-kernel-fusion-torch-compile-integration Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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