nvidia-tilegym-converting-cutile-to-julia

name	nvidia-tilegym-converting-cutile-to-julia
description	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.
license	CC-BY-4.0 AND Apache-2.0
metadata	{"author":"TileGym Team <[e-mail]>","tags":["cutile","julia","conversion","gpu","kernel"]}

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

1. Analyze the Python kernel: identify patterns, shapes, dtypes, operations
2. Write Julia kernel — julia/kernels/<op>.jl with cuTile.jl kernel + bridge function(s)
3. Convert kernel signature (see translations/workflow.md Phase 2)
4. Convert kernel body (apply references/api-mapping.md + references/critical-rules.md)
5. Write Julia test — julia/test/test_<op>.jl using Test stdlib + NNlib.jl for reference
6. Register test — add include(...) in julia/test/runtests.jl
7. Validate — run the bundled validator: python <skill-dir>/scripts/validate_cutile_jl.py <file.jl>
8. 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