Forge Agent

Product Introduction

1. Definition: Forge Agent is an automated GPU kernel optimization system that transforms PyTorch models into highly optimized CUDA and Triton kernels. It operates as a parallel AI-driven compiler, generating hardware-specific code for NVIDIA GPUs.
2. Core Value Proposition: It eliminates months of manual kernel tuning by deploying 32 parallel AI agents to explore optimization strategies (e.g., tensor cores, kernel fusion, memory coalescing), validated for correctness and benchmarked against industry standards like torch.compile.

Main Features

1. Parallel AI Swarm Optimization:
   • How it works: 32 AI agents concurrently test optimization techniques (tensor core usage, shared memory allocation, warp scheduling). A "judge" agent validates kernel correctness via numerical equivalence checks before benchmarking.
   • Technologies: PyTorch FX graph capture, Triton IR, Nsight Compute for metrics.
2. Hardware-Aware Emulation:
   • Emulates 86+ NVIDIA GPU architectures (e.g., A100/H100/L40S) with <2% performance error. Tests kernels on unreleased architectures without physical hardware.
   • Metrics tracked: Memory bandwidth utilization, cache hit rates, bank conflicts, warp stall cycles.
3. Automated Correctness Validation:
   • Every generated kernel undergoes bit-level numerical validation against a reference PyTorch model output. Guarantees 97.6% correctness before deployment.
4. Multi-DSL Support:
   • Generates optimized kernels in CUDA, Triton, CUTLASS (CUTE), and TileLang. Integrates with PyTorch via custom operators.
5. Real-Time Profiling Terminal:
   • Inline Nsight Compute metrics displayed during code editing. Identifies bottlenecks (e.g., low occupancy, uncoalesced memory access) and suggests fixes.

Problems Solved

1. Pain Point: Manual CUDA/Triton kernel optimization requires months of expert effort and deep hardware knowledge.
2. Target Audience:
   • ML Engineers scaling LLMs (e.g., Llama 3.1, Qwen 2.5).
   • Hardware Developers at NVIDIA, AMD, or cloud providers optimizing for new GPUs.
   • Research Teams publishing SOTA models needing deployment-ready kernels.
3. Use Cases:
   • Accelerating transformer inference (5x faster than torch.compile on Llama 3.1 8B).
   • Auto-generating Triton kernels for custom PyTorch ops.
   • Validating kernel performance across GPU generations (e.g., A100 → H100).

Unique Advantages

1. Differentiation vs. torch.compile:
   • Forge Agent outperforms torch.compile by 4-5x on 7B-8B LLMs by leveraging low-level hardware intrinsics (e.g., tensor cores) and kernel fusion.
   • Provides architecture-specific optimizations, whereas torch.compile uses generalized graphs.
2. Key Innovation:
   • Massively parallel AI search space exploration – Tests 10,000+ kernel configurations/hour.
   • Cross-architecture emulation with near-native accuracy, reducing dependency on cloud GPU access.

Frequently Asked Questions (FAQ)

1. Does Forge Agent replace CUDA programmers?
   No – it automates repetitive optimization tasks, freeing experts to focus on algorithmic innovation. Manual kernel tuning is still needed for highly specialized workloads.
2. How does Forge guarantee kernel correctness?
   Every kernel is validated via numerical equivalence checks against PyTorch’s CPU output, with a 97.6% correctness rate. Incorrect kernels are discarded pre-benchmarking.
3. What PyTorch versions are supported?
   Forge supports PyTorch 2.0+, including dynamic graph models and custom autograd functions.
4. Can I test kernels for unreleased GPUs?
   Yes – Forge’s emulator supports unreleased architectures (e.g., Blackwell) with <2% performance prediction error.
5. Is local data processed on my machine?
   Yes – when using Ollama or vLLM integration, code and model data never leave your local environment. Cloud-based runs require explicit opt-in.