* feat(tools): add official no-GPU compile-only entry (#2913)
TileLang has no first-class compile-only path for CPU-only CI.
This adds the official entry so later CE work can emit inspectable
kernel source without a GPU.
Signed-off-by: Oliver Zhang <zhiyuanzhang0904@163.com>
* feat(tools): strengthen optional compile-only CUDA target (#2913)
`--target cuda` should emit real CUDA C when the wheel has codegen FFI,
and soft-fail clearly on Metal-style wheels instead of an AttributeError.
Signed-off-by: Oliver Zhang <zhiyuanzhang0904@163.com>
* fix(tools): honor module order and CUDA target options (#2913)
discover_prim_func skipped an earlier PrimFunc when a later @jit existed,
and option-bearing CUDA strings skipped the FFI soft-fail.
Signed-off-by: Oliver Zhang <zhiyuanzhang0904@163.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(tools): drop stale --output_file when compile-only fails (#2913)
CE reuses the same output path; a failed run must not leave yesterday's
assembly. Unlink before compile, matching the CuTe wrapper.
Signed-off-by: Oliver Zhang <zhiyuanzhang0904@163.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(tools): reject JSON auto and pin bare CUDA JSON (#2913)
CodeRabbit: '{"kind":"auto"}' bypassed the auto ban, and
'{"kind":"cuda"}' skipped the sm_80 pin. Apply the same rules as
string targets.
Signed-off-by: Oliver Zhang <zhiyuanzhang0904@163.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(tools): refuse --output_file when it is the input (#2913)
Unlink-before-compile deleted example.py if someone passed the same
path twice. Compare resolved/samefile paths first.
Signed-off-by: Oliver Zhang <zhiyuanzhang0904@163.com>
* fix(tools): emit #line from compile-only when the pass exists (#2913)
Pass TL_EMIT_LINE_DIRECTIVES so CE can map generated C back to the
example. Older wheels do not register the key; skip it there.
Signed-off-by: Oliver Zhang <zhiyuanzhang0904@163.com>
* test(tools): require #line to name the input file and store line (#2913)
CodeRabbit: a bare "#line" token can pass on an unrelated directive.
Parse `#line N "file"` and match the example path plus the marked store.
Signed-off-by: Oliver Zhang <zhiyuanzhang0904@163.com>
* test(tools): give in-process #line lookup its own marker (#2913)
_marker_line scans the test file, so a shared line_marker_store hit the
example string (line 37) instead of _add (line 50). ROCm CI asserted that.
Signed-off-by: Oliver Zhang <zhiyuanzhang0904@163.com>
* docs(tools): document the compile-only CLI (#2913)
Co-authored-by: Oliver Zhang <LibertychaserUS@users.noreply.github.com>
* docs(tools): document remaining compile-only helpers (#2913)
Co-authored-by: Oliver Zhang <LibertychaserUS@users.noreply.github.com>
---------
Signed-off-by: Oliver Zhang <zhiyuanzhang0904@163.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Oliver Zhang <LibertychaserUS@users.noreply.github.com>
A large number of tests import CUDA-only internals directly (for example
`tilelang.cuda.pipeline.CUDAPassPipelineBodyPrologue`) or exercise
NVIDIA-specific features such as TMA, LDGSTG, mbarrier, warp specialization,
tcgen05 and tfloat32. `src/cuda/CMakeLists.txt` only compiles a small
"ALWAYS" subset when `USE_CUDA=OFF`, so those FFI symbols are never
registered and the tests fail with:
AttributeError: module 'tilelang.cuda._ffi_api' has no attribute
'AnnotateDeviceBoundTmaCopies'
This is not ROCm-specific -- the same tests fail on any build without the
CUDA backend, including Metal and CPU-only.
Add `@tilelang.testing.requires_cuda` to those tests, plus three
portability fixes that keep coverage on non-CUDA backends instead of
skipping it:
* `test_tilelang_transform_layout_inference.py`: CUDA emits
`__launch_bounds__(N, 1)` while HIP emits `__launch_bounds__(N)`.
Match both.
* `test_tilelang_transform_pipeline_planning.py`: PipelinePlanning
deliberately strips pipeline annotations on ROCm targets other than
gfx950 (see the guard in src/transform/pipeline_planning.cc), so assert
the documented sequential fallback there.
* `test_tilelang_kernel_gemm.py`: the two f32f32f32 cases use `T.tfloat32`,
which is NVIDIA-only, so narrow `requires_cuda_or_cdna` to
`requires_cuda`.
Also refresh three stale RDNA tests: gfx12 is now a supported generation
(`get_arch` accepts 11 and 12), so they can no longer assert that gfx1200
is rejected. Force an unsupported generation instead, which is the only
way to reach the guard now that `target_is_rdna` reports only gfx11/gfx12.
Verified on 8x MI300X (gfx942, ROCm 7.2 container, torch nightly rocm7.2):
the ROCm suite goes from 105 failures to 0 with no loss of passing tests.
* Add experimental IKET CUDA backend hooks
* Keep IKET APIs under namespace
* Use real Perfetto screenshot for IKET example
* Show IKET events in Perfetto timeline screenshot
* Add IKET validation overhead example
* Remove IKET validation overhead example
* Fix IKET lint issues
* Move IKET integration to CUDA tools
* Add tools documentation and fix workflow regressions
---------
Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>
