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.
## Rationale
`SizeVar` encodes nonnegativity in runtime subtype identity, which is
fragile under cloning and remapping. Symbolic integer values should use
one `Var` representation, with nonnegative facts recorded in the
analyzer at the use sites that establish them.
## Changes
- Remove `SizeVar` from the C++, Python, TE, TVMScript, FFI, visitor,
and serialization surfaces, and migrate callers to `Var`.
- Preserve the existing Relax constraint ownership model and use
`MarkGlobalNonNegValue` as the canonical path for global nonnegative
facts.
- Preserve `T.handle()` as the normal opaque-handle form. An optional
dtype constructs a typed pointer, with `T.handle("void")` reserved for
an explicit pointer-to-void.
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.
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 phases out legacy target string format in favor of the json
style format that is more well formed. It also simplfies our overall
code in handling multiple formats.
This PR initalizes the s_tir for scheduable TensorIR. The change mainly
starts from python side, the we will gradually move towards the c++ side
in followup PRs. The python main change:
tir.Schedule => s_tir.Schedule
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 formalizes original runtime::Module into ffi
as ffi.Module and cleans the APIs around it.
The goal is to stablize the Module API as extra API that can benefit the overall
ffi interactions. We also refactors the c++ code that depends on the Module.
* [FFI] Phase out ctypes fallback in favor of cython
In the past we maintained both ctypes fallback and cython path for our FFI.
One reason was cython was not that widely available then.
The situation has changed, so this PR removes the ctypes fallback
so we can focus on building cython based core for FFI.
Notably, the cython core still leverages ctypes, especially for
some of the features that do not need fast path, and primarily
uses cython to speedup core FFI features with fast path.
With cython, we can get most FFI costs down to level
of roughtly 1e-6 sec, which aligns with most of the costs like
overhead of a numpy operation.
* fix lint
* Fix the dso loading issue
* [REFACTOR] Phase out te.schedule python components
This PR phases out te.schedule python components.
te.compute is kept around for future usages.
tir.Schedule is a more modern version of the scheduling that we can use onwards.
Doing so also helps us to cleanup the testcases that relies on
explicit full build and execution. As we move future unit testcases
towards structural equality based unit tests.
* Simplify CI to focus on UT
The main rationale is that we should only have very few target
dependent UT in tests/python/codegen and possible
a new category in future for op-level integration if needed.
* Re-enable wasm
* fix lint
* remove hybrid,sparse autodoc and remove tests
---------
Co-authored-by: Siyuan Feng <hzfengsy@sjtu.edu.cn>
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