## Summary
- Keep the Python test launcher close to plain `pytest -n auto`, move
nightly tests under `tests/nightly/python`, remove obsolete launchers
and collection bookkeeping, and partition CPU/GPU jobs with explicit
`gpu` marker expressions.
- Repair exact-pointer regressions at their owning boundaries: packed
raw-string ABI values, CUDA/Metal matrix intrinsic pointers, internal TE
extern offsets, MetaSchedule scalar annotations, localized
auto-tensorization scope matching, and typed DLTensor fixture fields.
- Preserve typed workspace calls in TIR and cast pointer-returning
external calls in CodeGenC, covered by a plain-TIRx 1024-byte global
workspace that is compiled as C++.
- Finish phasing out value-bearing Relax `R.Prim` annotations by
requiring an explicit dtype, removing obsolete value-based contracts,
and expressing the DISCO rank-dependent slices as explicit scalar
`call_tir` inputs.
- Gate the distributed callback on the optional DISCO runtime, NCCL, and
at least two GPUs so capability-limited jobs skip instead of failing.
- Remove the non-demonstrating pointer probe, use direct TVMScript
comparison for packed strings, and remove the four designated legacy
testing modules.
The seven repaired CPU categories cover packed raw strings (7 failures),
CUDA/Metal matrix access-pointer types (7), internal TE extern offsets
(1), a typed DLTensor fixture (1), MetaSchedule scalar annotations (1),
CodeGenC workspace return casts (12), and localized auto-tensorization
storage-scope matching (19).
## Validation
- Base: `ded6ad8dd212869c881efb5590f8a33fc972728e`
- Head: `a7277e86dbcfe0638c8c252d36760859c4ab4297`
- All 35 locally available original failing node IDs pass across the
focused runs.
- The full focused TE, TIR builtin-lowering, and CodeGenC files pass: 61
tests.
- The complete touched Relax/TVMScript set plus
PlanAndUpdateBufferAllocationLocation passes with 784 passed, 20
skipped, and 1 expected failure.
- The DISCO callback collects and skips when its runtime or two-GPU
environment is unavailable.
- Six direct mapping tests, twelve tensor-core sketches, and the dp4a
sketch pass unchanged.
- The compiler rebuild, branch-wide pre-commit hooks, and full-range
whitespace checks pass.
- The 13 broad CBLAS/TFLite nodes remain dependency-gated; their owning
TE and generated-C regressions compile.
No merge is included in this change.
Add tvm.testing.run_with_gpu_lock backed by the existing
tvm_ffi.utils.FileLock. Migrate live local GPU tests to acquire the
machine-local lock around device execution, synchronization, host
transfer, and checks while leaving target construction and compilation
outside the critical section.
Replace the custom xdist scheduler with standard xdist_group placement
for the order-dependent test family. RPC tests retain dynamic port
allocation and per-test process isolation rather than gaining a broad
category lock.
This PR introduces Relax AnyType as the primary top/base type spelling,
replacing the previous ObjectType naming for the type that represents
any Relax value.
Changes:
- Add AnyType/AnyTypeNode with relax.AnyType registration and keep
ObjectType/R.Object compatibility aliases.
- Update Relax type analysis, type visitors, opaque function defaults,
and script printer/parser handling to use AnyType/R.Any.
- Migrate affected Python/C++ call sites, docs, and focused tests to the
new spelling.
Validation:
- cmake --build build --parallel 16
- Focused Relax/TVMScript pytest: 704 passed, 1 xfailed
- pre_commit run --files <changed files>
This pr modernizes test gating. It replaces the heavy
`tvm.testing.Feature` machinery with a thin `tvm.testing.env` module of
`has_*()` capability probes, used via standard pytest.mark + skipif. And
markers move to `pyproject.toml`
This PR brings up the tirx namespace. We have been spliting out the
original tir namespace to include high-level component s_tir and this PR
updates the remaining low-level part as tirx namespace
This PR Updates the NDArray => Tensor.
Both tensor and ndarray are commonly used terms.
Because the term Tensor is getting more common in the context of ML,
we do the rename to stay more aligned with torch.Tensor and DLTensor.
In addition to the PackedFunc `"runtime.disco.worker_id"`, which
returns the worker ID wrapped in a `ShapeTuple`, this commit adds
`"runtime.disco.worker_rank"`, which returns the worker ID without
wrapping, and `"runtime.disco.device"`, which returns the device for
each worker.
The unit test added in this commit simulates loading of model weights
through a parameter transformation function.
Tianqi Chen