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 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`
Validating the apache-tvm wheel in a minimal environment (no torch,
scipy, cloudpickle, or tornado installed) produced 33 pytest collection
errors from module-level imports of optional packages. Add
pytest.importorskip guards so these modules are reported as skipped
instead of erroring during collection.
Indirect import chains guarded:
- tvm.topi.testing imports scipy
- tvm.s_tir.meta_schedule.testing.local_rpc (tvm.rpc.tracker) requires
tornado
- tvm.s_tir.dlight.benchmark imports cloudpickle
Also remove a stray pre-license-header `import pytest` in
test_runtime_builtin_paged_attention_kv_cache_flashinfer.py.
## Summary
Lifts 10 host-toolchain / CLI / process / utility modules from
`python/tvm/contrib/` to a new `python/tvm/support/` package, and
deletes two dead contrib shims.
`tvm.support` is the home for Python helpers that integrate TVM with
external CLIs and host-side tools — compilers, archivers, subprocess
pools, and build-info queries. These are load-bearing internal pieces
that TVM's compile/link/run paths depend on. `tvm.contrib` is reserved
for optional vendor SDK integrations and experimental features. The
distinction is documented in the `tvm.support` package docstring.
Moved (one commit each):
- `tvm.contrib.cc` → `tvm.support.cc`
- `tvm.contrib.nvcc` → `tvm.support.nvcc`
- `tvm.contrib.rocm` → `tvm.support.rocm`
- `tvm.contrib.ndk` → `tvm.support.ndk`
- `tvm.contrib.xcode` → `tvm.support.xcode`
- `tvm.contrib.clang` → `tvm.support.clang`
- `tvm.contrib.emcc` → `tvm.support.emcc`
- `tvm.contrib.popen_pool` → `tvm.support.popen_pool`
- `tvm.contrib.utils` → `tvm.support.utils`
- `tvm.contrib.tar` → `tvm.support.tar`
Deleted:
- `tvm.contrib.spirv` — single `optimize()` wrapping `spirv-opt`; zero
importers.
- `tvm.contrib.rpc` — self-deprecation shim with "removed in 0.5"
banner; honoring it.
Package conversion:
- `python/tvm/support.py` → `python/tvm/support/__init__.py` with
inclusion-rule docstring.
- `libinfo()` extracted into `python/tvm/support/libinfo.py`.
- `FrontendTestModule` dropped (audit confirmed zero callers outside its
own definition).
## Compatibility
Hard break — no `tvm.contrib.<mod>` re-export shims. All callers updated
in this PR.
C++-side FFI registry keys (`tvm.contrib.nvcc.*`, etc.) are unchanged —
only the Python module path moves. Renaming the FFI keys is a separate
follow-up.
## Summary
This PR adds the initial TIRx support needed for low-level programming
of Blackwell-class GPU architectures. As part of the ongoing TIRx
refactor, it introduces TVMScript support for directly scripting
advanced hardware features without relying on scheduling as the primary
programming interface.
The change keeps existing `s_tir` script support intact while making
direct scripting a first-class path for TIRx programs.
## Main Changes
- Add TIRx operator dispatch and layout infrastructure.
- Add TVMScript support for new low-level TIRx operations.
- Add analysis, transform, and lowering support for TIRx IR nodes.
- Add CUDA/Blackwell-oriented codegen and intrinsic coverage.
- Add Python and C++ integration points for TIRx scripting and runtime
support.
## Validation
- `pre-commit run --all-files`
- `ninja -C build -j32`
- `CUDA_VISIBLE_DEVICES=2 pytest tests/python/tirx/ -n 16`
- `1723 passed, 47 skipped, 32 warnings`
- `CUDA_VISIBLE_DEVICES=2 python -m pytest -v
tests/python/all-platform-minimal-test`
- `37 passed, 105 skipped`
- `TVM_TEST_TARGETS=llvm python -m pytest -v tests/python/tirx-analysis
tests/python/tirx-base tests/python/tirx-transform -n 16`
- `664 passed, 25 skipped, 9 xfailed, 1 xpassed`
## Local CI Notes
Some full CI-equivalent jobs were not locally reproducible because this
machine is missing parts of the Apache TVM CI environment, including
`llvm-config-15/17`, Vulkan, ROCm, Maven, Sphinx, Doxygen, Emscripten,
and ARM/QEMU cross-toolchain components. Metal-specific tests were
skipped locally because no Metal runtime is available.
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.
This PR starts the step 0 to phase out relay from the current
development main branch. This PR focuses on the python
components of relay, autotvm, auto_scheduler. To make the change
manageable, we will also do followup steps on te.Schedule and
c++ components in followup PRs.
To continue support community members who depends on
legacy flows, the [v0.19.0](https://github.com/apache/tvm/tree/v0.19.0)
branch will continue contain these components.
As noted in [discussion on phasing out legacy components](https://discuss.tvm.apache.org/t/phasing-out-legacy-components/17703/30),
this would help us to do two purposes:
- By removing outdated or redundant elements, we can significantly
reduce complexity and improve maintainability.
- Unify our focus: Concentrating our efforts on the new unity flow
will allow for more efficient development and innovation.
It is also a good opportunity for us to revisit and reduce CI time.
The past relay legacy flow contains a lot of end to end tests that
requires hardware resources to run and causing long CI time.
Moving onwards, we can focus more on unit-tests that focuses
on structural equality and runs within seconds, while be mindful
about tests that requires hardware resources (by restricting them
to specific folders and CI nightly in some cases).
---
Co-authored-by: Siyuan Feng <hzfengsy@sjtu.edu.cn>
The current unittest folder is too large and contains too many files and
too many components. This PR refactors the unittest folder by moving the
files to the corresponding folders.
Tianqi Chen