tilegym-converting-cutile-to-julia

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