Individual skill
tilegym-converting-cutile-to-triton
Converts cuTile GPU kernels (@ct.kernel) to Triton (@triton.jit). Handles standard in-repo conversion, debugging (cudaErrorIllegalAddress, shape mismatch, numerical mismatch), and mapping cuTile idioms (ct.load/ct.store, ct.Constant, ct.launch) to Triton equivalents. Covers dual-kernel layout flags (e.g. transpose=True/False + autotune grid via META) per translations/advanced-patterns.md. Use when converting, porting, or translating cuTile kernels to Triton, or debugging existing Triton translations.
skills/tilegym-converting-cutile-to-triton/SKILL.md
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- Nov 6, 2026
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- agent-skills Repository topic
- agentic-ai Repository topic
- ai-agents Repository topic
- claude-code Repository topic
- codex Repository topic
- coding-agents Repository topic
- cuda Repository topic
- developer-tools Repository topic
- llm Repository topic
- omniverse Repository topic
- cutile Declared skill tag
- triton Declared skill tag
- conversion Declared skill tag
- gpu Declared skill tag
- kernel Declared skill tag
- testing Derived category
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BENCHMARK.mddoc · 8,261 bytesSKILL.mdcore · 17,064 bytesSKILL.mdcore · 17,064 bytesSKILL.mdcore · 17,064 bytesSKILL.mdcore · 17,064 bytesSKILL.mdcore · 17,064 bytesSKILL.mdcore · 17,064 bytesSKILL.mdcore · 17,064 bytesevals/evals.jsonasset · 5,399 bytesexamples/01_vector_add/cutile_kernel.pyruns · 3,862 bytesexamples/01_vector_add/triton_kernel.pyruns · 3,395 bytesexamples/02_softmax/cutile_kernel.pyruns · 5,004 bytesexamples/02_softmax/triton_kernel.pyruns · 8,872 bytesexamples/03_layernorm/cutile_kernel.pyruns · 10,818 bytesexamples/03_layernorm/triton_kernel.pyruns · 12,036 bytesexamples/04_matmul/cutile_kernel.pyruns · 6,957 bytesexamples/04_matmul/triton_kernel.pyruns · 13,640 bytesexamples/05_attention/cutile_kernel.pyruns · 8,011 bytesexamples/05_attention/triton_kernel.pyruns · 17,945 bytesreferences/api-mapping.mddoc · 17,281 bytesreferences/debugging.mddoc · 11,686 bytesreferences/gotchas.mddoc · 4,180 bytesreferences/harness-integration.mddoc · 6,476 bytesreferences/optimization-strategy.mddoc · 7,999 bytesreferences/optimizing-reference.mddoc · 24,007 bytesreferences/performance-gotchas.mddoc · 2,375 bytesskill-card.mddoc · 5,795 bytesskill.oms.sigasset · 9,577 bytestranslations/advanced-patterns.mddoc · 8,117 bytestranslations/file-structure.mddoc · 3,119 bytestranslations/workflow.mddoc · 26,722 bytes
Read excerpt
# cuTile → Triton Conversion
Convert `@ct.kernel` kernels to `@triton.jit`. API mapping: [references/api-mapping.md](./references/api-mapping.md) (cuTile → Triton).
*In this skill’s Markdown, Triton launch syntax `kernel[grid](…)` uses Unicode brackets so link checkers do not parse `[grid](…)` as a hyperlink; use normal ASCII brackets in real Triton code.*
## Instructions
Follow the phase-gated workflow in [translations/workflow.md](./translations/workflow.md). Every conversion should go through **analyze → convert → validate → test → benchmark**, with explicit gates before moving on. Use the documents in [Workflow Selection](#workflow-selection) when the task matches a special case (errors, layout flags, perf).
0. **Optimization strategy (perf-sensitive / attention)** — If the op is **attention, FMHA, sliding window, soft cap, or GQA** (e.g. Gemma `gemma_attention`), read **[references/optimization-strategy.md](./references/optimization-strategy.md)** **before** converting the inner loop, then apply **[§4 Gemma FMHA checklist](./references/optimization-strategy.md#4-gemma-fmha--gemma_attention-conversion-checklist-mandatory)**. For other GEMM/BMM/attention-adjacent kernels, still skim **§2–§3** of that file after TMA is done.
1. **Select path** — Existing TileGym op: standard mode in `translations/workflow.md`. If the cuTile source uses `transpose` / `transpose_v`, dual layouts, or MLA-style paths, read [translations/advanced-patterns.md](./translations/advanced-patterns.md) **before** writing Triton (two kernels + `META` grid, not one kernel + `tl.trans`).