ML Compiler Stack in 5,000 Lines of Python (2026): Hackable, Open-Source, and CUDA-Optimized

ML Compiler Stack in 5,000 Lines of Python (2026): Hackable, Open-Source, and CUDA-Optimized

A groundbreaking open-source project has redefined accessibility in machine learning compilation by delivering a fully functional ML compiler stack in just 5,000 lines of pure Python. Named Deplodock, this lightweight system lowers LLMs to CUDA via six intermediate representations (IRs), offering unmatched transparency compared to bloated frameworks like PyTorch, TVM, or MLIR. Designed not for raw speed, but for education and research, Deplodock enables developers to trace every transformation — from PyTorch graphs to optimized CUDA kernels — without a GPU.

Step 1: PyTorch to High-Level IR

Deplodock begins by converting PyTorch FX graphs into a Torch IR, preserving the original computational structure. This stage ensures compatibility with any PyTorch model, including attention mechanisms and RMSNorm layers. Unlike monolithic compilers, Deplodock keeps this IR human-readable, making it ideal for debugging and curriculum use.

Step 2: Lowering to Six Intermediate Representations

The compiler sequentially transforms the Torch IR through six distinct IRs, each narrowing the abstraction gap to hardware. This modular design supports future frontends from ONNX or JAX and aligns with modern model compression techniques like low-rank distillation (Sy et al., 2024). Each IR is independently testable, enabling targeted optimization research.

Step 3: Tensor IR — Unified Operation Decomposition

Tensor IR breaks down all operations into three elemental forms: Elementwise, Reduction, and IndexMap. This unified surface simplifies frontend integration and enables tensor optimization across diverse architectures. The design mirrors insights from SlimLlama’s low-rank approximation methods, enhancing adaptability without sacrificing accuracy.

Step 4: Loop IR and Kernel Fusion

Loop IR fuses adjacent operations into single loop nests, eliminating intermediate buffers and reducing memory overhead — a critical improvement highlighted in Lillama’s activation distillation work. This fusion strategy mirrors structured pruning, removing redundant computations while preserving performance. The result: efficient memory access patterns even on edge devices like those running TinyLlama.

Step 5: Tile IR and GPU-Aware Scheduling

Tile IR introduces GPU-specific optimizations: mapping loop axes to CUDA threads and blocks, applying 2×2 register tiling, and embedding double-buffered shared memory. These techniques maximize data reuse and minimize latency — key for real-time inference on low-power hardware. The IR remains framework-agnostic, enabling future ports to Metal or HIP.

Step 6: CUDA Kernel Generation

At the Kernel IR stage, hardware primitives like cp.async and __syncthreads are explicitly annotated. The final CUDA IR performs a tree walk to generate nvcc-ready code, fully optimized for NVIDIA GPUs. Crucially, the entire pipeline runs offline — no GPU required — making it perfect for academic research and embedded AI development.

Why This Matters: Democratizing ML Compilation in 2026

As tiny LLMs like TinyLlama gain traction in edge deployments, understanding the compilation pipeline is no longer optional — it’s essential. Deplodock empowers students, researchers, and embedded engineers to modify, extend, and debug ML compilers without C++ expertise. Its 5,000-line Python codebase is a living textbook for IR lowering, kernel optimization, and tensor transformation.

Performance and Real-World Impact

Benchmarked against eager PyTorch and torch.compile on Qwen2.5-7B blocks, Deplodock delivers competitive throughput with dramatically improved interpretability. It handles complex attention patterns and avoids memory blowup — a known pain point in traditional frameworks. With its minimal footprint, Deplodock is ideal for teaching, prototyping, and deploying optimized models on resource-constrained devices.

Deplodock doesn’t aim to replace industry tools — it empowers the next generation to build them. For developers seeking to master PyTorch optimization, understand MLIR’s IR design, or dive into CUDA kernel development, this open-source stack is your starting point.

Ready to explore how TinyLlama was optimized for edge inference? Read our guide on model compression techniques.