cuTile Python → cuTile.jl (Julia) Conversion

tilegym-converting-cutile-to-julia

Converts cuTile Python GPU kernels (@ct.kernel) to cuTile.jl Julia equivalents. Handles kernel syntax translation, 0-indexed to 1-indexed conversion, broadcasting differences, memory layout (row-major to column-major), type system mapping, and launch API differences. Use when converting, porting, or translating cuTile Python kernels to Julia cuTile.jl, or debugging/optimizing existing Julia cuTile translations.

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SKILL.md

Full skill instructions

cuTile 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>.jl with cuTile.jl kernel + bridge function(s)
Convert kernel signature (see translations/workflow.md Phase 2)
Convert kernel body (apply references/api-mapping.md + references/critical-rules.md)
Write Julia test — julia/test/test_<op>.jl using Test stdlib + NNlib.jl for reference
Register test — add include(...) in julia/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.toml under [compat] julia)
CUDA 13.1+ driver
Blackwell GPU (compute capability 10+)
Dependencies managed via julia/Project.toml: CUDA.jl, cuTile.jl, NNlib.jl, Test

Install skill

Copy and run in your terminal

npx -y skills add https://github.com/nvidia/skills --skill tilegym-converting-cutile-to-julia

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