菜单
Convert LLK lib/API tile-size args to ckernel::TensorShape and maintain TRISC TensorShape coverage. Use when adding TensorShape parameters, replacing face_r_dim/num_faces, editing LLK_VALIDATE_TENSOR_SHAPE_*, regenerating tensor_shape_coverage_*.h, or reviewing TensorShape PRs.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/tenstorrent/tt-metal --skill tensor-shape命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Generate, refresh, and validate an LLK performance report by running a perf sweep end to end and checking the resulting perf_data CSV. Use when asked to produce or refresh a perf report for an op, run a perf sweep, or when a report is missing, partial, stale, or implausible.
Generate, refresh, and validate an LLK performance report by running a perf sweep end to end and checking the resulting perf_data CSV. Use when asked to produce or refresh a perf report for an op, run a perf sweep, or when a report is missing, partial, stale, or implausible.
Create, extend, debug, and validate Quasar LLK performance tests, their PerfRunType kernel paths, and tile/dimension sweep coverage. Use when adding perf_[op]_quasar.py, wiring PerfConfig, implementing UNPACK_ISOLATE / MATH_ISOLATE / PACK_ISOLATE / L1_CONGESTION, choosing PERF_TILE_SIZES or dest-fill matrices, or investigating implausible Quasar perf metrics and dvalid handshakes.
基于 SOC 职业分类
正在显示 SKILL.md
| name | tensor-shape |
| description | Convert LLK lib/API tile-size args to ckernel::TensorShape and maintain TRISC TensorShape coverage. Use when adding TensorShape parameters, replacing face_r_dim/num_faces, editing LLK_VALIDATE_TENSOR_SHAPE_*, regenerating tensor_shape_coverage_*.h, or reviewing TensorShape PRs. |
| argument-hint | [convert|coverage|review] [op-or-path] |
| user_invocable | true |
/tensor-shape convert unpack_A
/tensor-shape coverage harvest unpack_A
/tensor-shape review
Read the canonical reference and follow it:
Read(".claude/references/tensor-shape.md")
For metal-layer propagation after signature changes, also read .claude/references/metal-integration.md.
Plumb TensorShape through an LLK op (lib → metal API → tests/fuser):
face_r_dim / num_faces (and similar) with ckernel::TensorShape.tensor_shape.h + the matching tensor_shape_coverage_{unpack|math|pack}.h.LLK_VALIDATE_TENSOR_SHAPE_{UNPACK|MATH|PACK}("_llk_foo_...", tensor_shape) — string literal name; no API registry edit in coverage.h.num_faces_r_dim / num_faces_c_dim for new logic; keep flat total_num_faces() only when intentionally preserving old mop behavior.get_operand_tensor_shape(operand_id).make_tensor_shape / cpp_tensor_shape; never legacy helper for 32×16.Discover or regenerate TRISC allowlists:
TT_LLK_DISABLE_ASSERTS=1 + DPRINT, then:
helpers/tensor_shape_coverage_parser.py harvest|emit|summaryTENSOR_SHAPE_FR*_NF* in tensor_shape_coverage.h if needed + TRISC predicate update via emit.fn_name → add to MATH_FUNCTIONS / UNPACK_FUNCTIONS in the parser only.Apply the Review Checklist in .claude/references/tensor-shape.md. Especially correct the misconception that every new API must be listed in coverage.h — coverage is TRISC-scoped; call sites only need the validate macro + string literal.
| Role | Path |
|---|---|
| Struct / helpers | common/tensor_shape.h |
| Macros / named shapes | common/tensor_shape_coverage.h |
| TRISC tables | common/tensor_shape_coverage_{math,unpack,pack}.h |
| Parser | tests/python_tests/helpers/tensor_shape_coverage_parser.py |
| Python TileShape | tests/python_tests/helpers/tile_shape.py |
| Metal operand helper | tt_metal/hw/ckernels/{arch}/metal/llk_io/llk_operands.h |