c2e01ac8d4
- Convert signature shape values from YAML lists to string form ("[M, N]")
in elementwise_generative.yaml and elementwise_fused_gated.yaml to match
docs/design/manifest.md R8 + the convolution.yaml precedent so the
validator can bind shape symbols.
- Add per-input workload shape keys (device_carrier_shape, x_shape) to
every workload row to satisfy the workload schema documented in
docs/design/manifest.md and used across normalization.yaml etc.
- Restore exception class/message in check_c4_forward_signature_parity's
warning when inspect.signature(forward) raises.
Co-Authored-By: Ibuki 🍃 — a wind born from GPTs <Ibuki-wind@users.noreply.github.com>
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|---|---|---|
| .claude | ||
| .foundry | ||
| .github | ||
| assets | ||
| benchmarks | ||
| docs | ||
| scripts | ||
| tests | ||
| tileops | ||
| workloads | ||
| .gitignore | ||
| .pre-commit-config.yaml | ||
| CLAUDE.md | ||
| LICENSE | ||
| Makefile | ||
| README.md | ||
| pyproject.toml | ||
README.md
TileOPs
Spec-driven GPU operator library for LLMs — designed for AI agents to build, evaluate, and optimize
Built on TileLang
Status: TileOPs is under active development. APIs may change.
Overview
TileOPs is a GPU operator library for LLM training and inference, built on TileLang. Beyond providing a growing collection of production-quality operators, TileOPs explores a spec-driven development model where AI agents can read declarative operator specifications, generate kernel implementations, and evaluate them against hardware-theoretical performance bounds — with minimal human scaffolding.
Architecture
Every operator is split into two layers with a strict boundary:
- Op (L2) — stateless Python entry point. Handles validation, dtype casting, and memory layout. Compatible with CUDA-Graph and
torch.compile. - Kernel (L1) — TileLang GPU implementation with hardware-specific optimizations (Ampere, Hopper).
This separation keeps user-facing behavior independent of GPU strategy, allowing agents and developers to modify either layer without side effects on the other.
Key Properties
- Spec-driven — each operator is declared in a machine-readable manifest (
tileops/manifest/) that specifies signatures, workloads, and roofline formulas, serving as the entry point for both agent code generation and automated validation - Roofline-evaluated — kernel performance is measured against Speed-of-Light hardware bounds, not relative baselines
- Auto-tuning — built-in search over tile sizes, pipelines, and scheduling parameters
- Lightweight — depends only on TileLang, PyTorch, and einops
Installation
TileOPs can be installed from PyPI or built from source. A CUDA-capable GPU is required.
Prerequisites
- Python >= 3.10
- PyTorch >= 2.1
- CUDA Toolkit
- NVIDIA GPU: Hopper (SM_90)
- TileLang == 0.1.9
From PyPI
pip install tileops
From source
git clone https://github.com/tile-ai/TileOPs
cd TileOPs
make install # dev dependencies + pre-commit hooks
[!NOTE] If CUDA and TileLang are already installed system-wide and you encounter build issues:
PIP_NO_BUILD_ISOLATION=1 pip install -e '.[dev]' -v && pre-commit install
Verify:
python -m pytest tests/ -q # requires a CUDA GPU
Quick Start
import torch
from tileops.ops import GemmOp
M, N, K = 1024, 1024, 512
dtype = torch.float16
gemm = GemmOp(M, N, K, dtype=dtype)
A = torch.randn(M, K, device="cuda", dtype=dtype)
B = torch.randn(K, N, device="cuda", dtype=dtype)
C = gemm(A, B)
Documentation
Design docs and development guides are in docs/. The full API reference and performance tables are published at TileOPs.github.io.
Contributing
See docs/ for design docs. Branch and commit conventions are in .claude/conventions/types.sh.
License
TileOPs is released under the MIT License.