tilegym-converting-cutile-to-julia
Convierte kernels GPU de cuTile Python (@ct.kernel) a sus equivalentes en Julia de cuTile.jl. Maneja la traducción de sintaxis de kernels, conversión de indexación basada en 0 a basada en 1,…
npx skills add https://github.com/nvidia/skills --skill tilegym-converting-cutile-to-juliacuTile Python → cuTile.jl (Julia) Conversion
Convert @ct.kernel Python kernels to Julia function ... end cuTile.jl kernels.
Workflow Selection
- Standard conversion → Full workflow:
translations/workflow.md - Errors (
MethodError,IRError, numerical mismatch) →references/debugging.md - Quick reference →
references/api-mapping.md+references/critical-rules.md - Test patterns →
references/testing.md
Architecture
Julia kernels are standalone — no Python bridge, no pytest integration. The Julia sub-project
lives in julia/ at the repo root with its own Project.toml for dependency management.
julia/ # Self-contained Julia sub-project
├── Project.toml # Dependencies: CUDA.jl, cuTile.jl, NNlib.jl, Test
├── kernels/ # cuTile.jl kernel implementations
│ ├── add.jl # ← Ground-truth: 1D element-wise with alpha scaling (tensor+tensor, tensor+scalar)
│ ├── matmul.jl # ← Ground-truth: 2D tiled MMA, standard Julia layout (M,K)×(K,N)→(M,N)
│ └── softmax.jl # ← Ground-truth: 3 strategies (TMA, online, chunked) using ct.load/ct.store
└── test/ # Julia-native tests (using Test stdlib)
├── runtests.jl # Test runner entry point
├── test_add.jl
├── test_matmul.jl
└── test_softmax.jl
Ground-truth reference: Always consult julia/kernels/*.jl and julia/test/*.jl for patterns that compile and pass tests. These are the canonical examples of working cuTile.jl code.
Instructions
- Analyze the Python kernel: identify patterns, shapes, dtypes, operations
- Write Julia kernel —
julia/kernels/<op>.jlwith cuTile.jl kernel + bridge function(s) - Convert kernel signature (see
translations/workflow.mdPhase 2) - Convert kernel body (apply
references/api-mapping.md+references/critical-rules.md) - Write Julia test —
julia/test/test_<op>.jlusingTeststdlib +NNlib.jlfor reference - Register test — add
include(...)injulia/test/runtests.jl - Validate — run the bundled validator:
python <skill-dir>/scripts/validate_cutile_jl.py <file.jl> - Test — run
julia --project=julia/ julia/test/runtests.jl
Full conversion checklist with post-conversion verification → translations/workflow.md
⚠️ Top Pitfalls
The most dangerous translation errors. Full rules (17 total) in references/critical-rules.md.
| # | Pitfall | One-line fix |
|---|---|---|
| 1 | ct.full() doesn't exist in Julia | Use fill(val, shape), zeros(T, dims...), or ones(T, dims...) |
| 2 | max(a, b) on tiles → IRError | Use max.(a, b) (broadcast dot) |
| 3 | IRError / MethodError mentioning IRStructurizer | Compiler bug — file upstream with minimal reproducer |
| 4 | ct.launch arg order silently wrong | Args are positional — match kernel signature exactly |
| 5 | ct.load with order — index positions wrong | order remaps BOTH shape AND index (Critical Rule 16) |
Worked Examples
Side-by-side Python → Julia conversions matching the released Julia kernels in julia/kernels/. Each directory contains cutile_python.py (before) and cutile_julia.jl (after).
| # | Example | Key Patterns | When to Reference |
|---|---|---|---|
| 01 | add | 1D ct.load/ct.store, alpha scaling, scalar broadcast, fill/zeros, keyword load/store | Starting point; basic TMA + element-wise patterns |
| 02 | matmul | muladd, TF32 conversion, K-loop with for, 2D swizzle, standard Julia layout, ct.@compiler_options | MMA / tensor core operations |
| 03 | softmax | Persistent scheduling, for loops, gather/scatter, padding_mode, multi-pass | Large-tensor reduction patterns |
These match the released kernels in julia/kernels/ (add.jl, matmul.jl, softmax.jl). The examples are simplified teaching versions — always consult julia/kernels/*.jl for the canonical, tested implementations.
Reference Documents
| Category | Document | Content |
|---|---|---|
| Workflows | translations/workflow.md | Full conversion workflow with todo list, validation loop, checklist |
| Rules | references/critical-rules.md | 17 Critical Rules for cuTile Python → Julia conversion |
| API | references/api-mapping.md | Python↔Julia bidirectional API mapping + kernel patterns |
| Testing | references/testing.md | Julia-native test patterns, tolerances, failure diagnosis |
| Debugging | references/debugging.md | Julia-specific error diagnosis + IR debug commands |
| Scripts | scripts/validate_cutile_jl.py | Static validation for Julia anti-patterns (run it) |
| Ground Truth | julia/kernels/*.jl + julia/test/*.jl | Actual working implementations in the codebase |
Environment Setup
Prerequisite — Julia: this skill requires the Julia version declared in julia/Project.toml under [compat] julia. If julia --version is missing or older than that, install from the official Julia site at https://julialang.org/install/ following the verified installer instructions for your OS. Resume below once julia --version is compatible.
Then, from the repo root:
# Install Julia dependencies declared in julia/Project.toml
julia --project=julia/ -e 'using Pkg; Pkg.instantiate()'
# Run tests
julia --project=julia/ julia/test/runtests.jl
Requirements:
- Julia (minimum version declared in
julia/Project.tomlunder[compat] julia) - CUDA 13.1+ driver
- Blackwell GPU (compute capability 10+)
- Dependencies managed via
julia/Project.toml: CUDA.jl, cuTile.jl, NNlib.jl, Test