Tilegym Converting Cutile To Triton

# Count kernels (only main kernel gets @triton.jit, helpers stay plain def)
grep "@ct\.kernel" source.py | wc -l

# Check for patterns needing special handling
grep "ct\.transpose\|ct\.permute" source.py   # → use tl.trans/tl.permute
grep "ct\.astype" source.py                    # → use .to(dtype)
grep "ct\.load\|ct\.store" source.py          # → TMA for 2D+ (tl.make_tensor_descriptor), NOT raw tl.load(ptr+offs)
grep "ct\.launch" source.py                    # → bracket launch: kernel then [grid] then (args)
grep "ct\.Constant\|ct\.ConstInt" source.py    # → tl.constexpr
grep "ct\.cdiv" source.py                      # → triton.cdiv (host) or Python (a+b-1)//b
grep "ct\.bid\|ct\.num_blocks" source.py       # → tl.program_id/tl.num_programs
grep "1 << .*\.bit_length" source.py           # → triton.next_power_of_2 if needed
grep "transpose\|transpose_v" source.py       # → if hit, read translations/advanced-patterns.md (dual kernels + META grid)

Conversion Progress:
 [ ] Step 0 (attention / Gemma FMHA / GQA / soft cap / sliding window): Read [references/optimization-strategy.md](./references/optimization-strategy.md) and apply §4 checklist before inner-loop Triton
 [ ] Step 1: Pre-flight — run grep commands above, note special patterns and 2D+ loads (→ TMA)
 [ ] Step 2: Analyze source cuTile kernel (identify patterns, shapes, dtypes)
 [ ] Step 3: Create Triton file with correct structure (see translations/file-structure.md)
 [ ] Step 4: Convert kernel signature (tensor args → pointer args, Constant → constexpr)
 [ ] Step 4b: TMA (MANDATORY for 2D+ loads) — use tl.make_tensor_descriptor for every 2D+ tile load/store; do NOT ship raw tl.load(ptr+offs,mask) for block-shaped access (see workflow.md § TMA OPTIMIZATION)
 [ ] Step 5: Convert kernel body (apply gotchas table below + API mapping)
 [ ] Step 6: Convert host wrapper (grid tuple/lambda, bracket-style launch: kernel, grid, then arguments; no ct.launch); call triton.set_allocator(alloc_fn) if using TMA
 [ ] Step 7: Validate — run pytest or syntax check on Triton file
 [ ] Step 8: Test — run pytest, verify X passed 0 failed
 [ ] Step 9: If test fails → fix → re-validate → re-test (loop until green)
 [ ] Step 10: Benchmark — run perf test, compare vs cuTile (see workflow.md § PERFORMANCE ANALYSIS)
 [ ] Step 10b: If GEMM/BMM/attention and Triton >20% slower → walk [references/optimization-strategy.md](./references/optimization-strategy.md) §2–§3 then [references/optimizing-reference.md](./references/optimizing-reference.md) (EVEN_K, transpose, grid, autotune, epilogue subtile), then re-benchmark
 [ ] Step 10c: If op has `transpose` / layout flag → read [translations/advanced-patterns.md](./translations/advanced-patterns.md); verify **separate kernels** per layout (not transpose-kernel + `tl.trans`); **autotuned** launches use `lambda META: (triton.cdiv(..., META["BLOCK_H"]), ...)` — no fixed `BLOCK_H`/`BLOCK_N` through `apply()` unless autotune is disabled

Post-conversion Verification (TMA is mandatory for 2D+ loads):
 [ ] TMA: All 2D+ tile loads use tl.make_tensor_descriptor(...).load([...]); no raw ptr+mask for block-shaped 2D+ access (else 5x-20x regression)
 [ ] Grid uses tuple or lambda (not 3-tuple required like cuTile)
 [ ] Triton autotune added if cuTile op used kernel_configs/autotune (see workflow § PERFORMANCE ANALYSIS)
 [ ] Host grid uses triton.cdiv where appropriate (not (a+b-1)//b only)
 [ ] Pointer/offset indexing: Triton uses element offsets (ptr + offs), not block index in tl.load (or use TMA descriptor)
 [ ] ct.astype(x, dtype) → x.to(dtype) in Triton
 [ ] ct.mma(a, b, acc=acc) → tl.dot(a, b, acc) (no keyword in Triton)
 [ ] Optional/None args: Triton allows None in kernel args if desired (cuTile required dummy+flag)
 [ ] Masking applied when BLOCK_SIZE > actual dimension (same as cuTile); with TMA, masks can often be removed for full tiles
 [ ] Reduction divisor uses actual_size, NOT BLOCK_SIZE
 [ ] fp32/tf32: Triton defaults allow_tf32=True; match cuTile behavior if you had explicit tf32 cast
 [ ] If any 2D+ load uses raw ptr+mask (exception only): document WHY TMA was not used
 [ ] tl.assume() alignment hints added for strides and pointers

MANDATORY COMPLETION GATES:
 [ ] 1. CORRECTNESS: pytest passes with 0 failures
     Command: python -m pytest {test_path} -k "test_op and triton" -vs --tb=short
     Gate: "X passed, 0 failed"

 [ ] 2. TMA OPTIMIZATION: All 2D+ tile loads use tl.make_tensor_descriptor
     Verify: grep -n "tl.load.*mask" triton_file.py | wc -l  # Should be 0 for 2D+ ops
     Skip = 5-20x performance regression

 [ ] 3. PERFORMANCE TEST: Triton within 20% of cuTile baseline
     Command: python -m pytest {test_path} -k "test_perf" --print-record -v
     OR: Run benchmark script: cd tests/benchmark && python bench_{op}.py
     Gate: Triton TFLOPS >= 0.8 * CuTile TFLOPS

 [ ] 4. PERFORMANCE COMPARISON RECORDED:
     Document results:
     | Config | Triton (TFLOPS) | CuTile (TFLOPS) | Ratio |
     |--------|-----------------|-----------------|-------|
     | [fill] | [fill]          | [fill]          | [fill]|

CONVERSION COMPLETE: All 4 gates passed? → YES / NO