## 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.
This PR simplifies Jenkins pytest execution around standard pytest-xdist
behavior.
- Runs each already-filtered CPU/GPU suite once with `-n auto`; the
broad suite keeps load-group scheduling because its order-sensitive
cases require it.
- Removes external sharding, wrapper/profile code, JUnit XML generation
and publication, the skipped-test XML consumer, obsolete suite naming,
and orphaned helpers.
- Retains one inert `task_clear_pytest.sh` entry point only because PR
jobs evaluate their Jenkinsfile from the trusted base branch before
checking out the PR; it performs no cleanup or reporting and can be
removed after this pipeline lands.
- Corrects stale broad-suite paths and explicit target guards, and
migrates a scalar stride test to the current `T.handle` pointer
semantics while preserving its negative lowering check.
- Prevents nested MetaSchedule/XGBoost unit tests from multiplying CPU
fanout without serializing the full suite.
- Builds only the `tvm_runtime` target for the secondary GPU
configuration and removes its unconsumed `gpu2` artifact upload.
The result reduces parallelism to one layer managed by pytest-xdist
while preserving GPU filtering and native failure visibility.
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.
Drop accumulated dead code in the test-support package: helpers with
zero call sites, unused capability probes, dead FFI re-exports, and
orphaned pytest plumbing. Verified by repo-wide grep that nothing
references any of these.
tvm.testing's test-gating layer had a number of one-line helper
functions that add a name but no behavior. Inline the thin ones so call
sites name the underlying flag/feature/condition directly.
Pytest plugin (plugin.py): _target_to_requirement built its skip / gpu
marks through two one-line wrappers (_gpu_mark_and_skip / _skip_only)
plus a per-kind if ladder. Replace them with two frozensets (GPU- vs
CPU-family kinds) and resolve the skip probe by name:
marks.append(pytest.mark.skipif(not getattr(env, f"has_{kind}")(),
reason=f"need {kind}"))
The cuda+cudnn / cuda+cublas accelerator-library cases are remapped
inline (cudnn before cublas). Adds two direct unit tests for the
cudnn/cublas special-case and the unknown-kind ([]) fallback.
tvm.testing.env (env.py): inline the pure probe wrappers that just
forwarded to a primitive --
* build-flag (5): has_cutlass/rpc/nnapi/openclml/mrvl ->
env.build_flag_enabled ("USE_X"). The private _build_flag_enabled is
promoted to the public build_flag_enabled; the composed probes
(has_cudnn/cublas/nccl/hipblas) and the hexagon/adreno probes call it
too.
* cpu-feature (5 pure):
has_arm_dot/arm_fp16/aarch64_sve/aarch64_sme/x86_amx ->
env.has_cpu_feature("..."). The composed has_x86_vnni (avx512vnni OR
avxvnni) and has_x86_avx512 (a five-feature set) are kept -- not thin
wrappers.
Also drops the obsolete test_build_flag_probe_matches_libinfo self-test
and the matching _BOOL_PROBES entries.
The runtime device probes (has_cuda/has_rocm/...) are intentionally left
as-is: the pytest plugin resolves env.has_<kind>() from each target
kind, so those names are load-bearing rather than thin wrappers.
test_env.py asserted "derived probe implies parent" for several probes,
but each derived probe is defined as `parent() and <flag>`, so the
implication holds by construction and the assertion can never fail.
Remove these no-op guards:
- test_tensorcore_implies_cuda (has_tensorcore = has_cuda() and ...)
- test_cudagraph_implies_cuda (has_cudagraph = has_cuda() and ...)
- test_library_probe_implies_parent_device (cudnn/cublas/nccl =
has_cuda(); hipblas = has_rocm())
- test_runtime_hexagon_run_implies_toolchain (has_hexagon =
has_hexagon_toolchain() and ...)
All in-tree uses of tvm.testing.parameters() were migrated to native
pytest.mark.parametrize in #19803, so remove the helper itself along
with the plugin machinery that only served it:
- python/tvm/testing/utils.py: delete the parameters() function and the
_parametrize_group counter.
- python/tvm/testing/plugin.py: delete
_parametrize_correlated_parameters and its call in
pytest_generate_tests.
- tests/python/testing/test_tvm_testing_features.py: drop the
joint-parameter tests that exercised parameters() (the parameter() and
fixture() tests stay).
This removes the public tvm.testing.parameters symbol;
tvm.testing.parameter (singular) and tvm.testing.fixture are unchanged.
Use pytest.mark.parametrize instead.
## Summary
The old Hexagon app and test wrappers depend on RPC helper artifacts
that are no longer part of the supported app flow. This PR removes those
wrappers and related helper references while keeping the core Hexagon
target, codegen, and runtime implementation in place.
## Changes
- Remove the obsolete Hexagon app wrapper directories.
- Remove the Hexagon contrib test directory and its dedicated pytest/RPC
launcher helpers.
- Drop stale CI/docs references to the removed app and test helper
paths.
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`
## 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 enables ruff pyupgrade (UP) rules with py310 target, auto-fixing
~5600 annotation modernizations (PEP 585 generics, PEP 604 unions,
deprecated typing imports).
Also removes from __future__ import annotations from ir/module.py and
rmsnorm.py, bumps requires-python to >=3.10, and removes absolute_import
aliases from topi/contrib files.
This PR removes unused `from tvm import te` imports from 25 test files
across the codebase, continuing the ongoing TE → TVMScript migration
cleanup.
Changes:
- Remove unused `from tvm import te` from 24 test files in
tir-transform/, s_tir/transform/, codegen/, arith/,
all-platform-minimal-test/, and testing/
- Replace `te.var("x")` with `tvm.tir.Var("x", "int32")` in
`test_s_tir_transform_decorate_device_scope.py` (the only file where te
was actually used)
- Clean up stale `tvm.tir.ir_builder` comment references in
`test_tir_transform_convert_ssa.py`
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 refactors test infrastructure by removing the CompareBeforeAfter
base class from tvm.testing and converting all dependent tests to use a
simpler, more explicit pattern.
We need this change as latest pytest do not allow calling fixture as
inner patterns which the previous CompareBeforeAfter depend on.
This PR fixes the type annotation checker in
`tvm.tir.schedule._type_checker` to correctly handle subscripted
generics (e.g., `Union[str, int]`, `List[str]`, `Tuple[str, int]`) in
Python 3.14+.
## Background
In Python 3.14, the internal representation of generic types has
changed:
- `Union[str, int]` is now of type `typing.Union` instead of
`typing._GenericAlias` or `typing._SpecialGenericAlias`
- These types now have `__origin__` attribute directly on the type
object
- The existing type checker failed to recognize these new
representations, causing the dispatcher to fall through to "atomic"
instead of correctly identifying them as "union", "list", etc.
## Changes
Added a check for `__origin__` attribute at the beginning of the method
to handle Python 3.14's new generic type representations. This is fully
backward compatible since the new `__origin__` check is only applied
when the attribute exists.
## Tests
Added parametrized tests to verify the dispatcher correctly handles
subscripted generics:
- `Union[str, int]` → identified as "union"
- `List[str]` → identified as "list"
- `Dict[str, int]` → identified as "dict"
- `Tuple[str, int]` → identified as "tuple"
- `Union[List[str], Dict[str, int]]` → identified as "union" with nested
generics
This PR cleans up the python API to make things more consistent
with existing python array api and torch.
Device update
- device_id => index, to be consistent with torch
- device_type => dlpack_device_type() returns int
- added type property same as torch.device
API updates:
- Move the convenient method like cpu() out into tvm runtime to keep device minimal
- tvm_ffi._init_api => tvm_ffi.init_ffi_api
- tvm_ffi.register_func => tvm_ffi.register_global_func
This PR cleans up the container redirections and headers
so the files directly points to new ones in ffi folder
- runtime/shape_tuple.h => ffi/container/shape.h
- for IntTuple alias, introduce runtime/int_tuple.h for now
- runtime/container/array.h => ffi/container/array.h
- runtime/container/map.h => ffi/container/map.h
- runtime/container/optional.h => ffi/optional.h
- runtime/container/string.h => ffi/string.h
- runtime/container/variant.h => ffi/container/variant.h
- runtime/container/tuple.h => ffi/container/tuple.h
We also introduce limited number of tvm::ffi classes into tvm namespace,
when they are commonly used and their is no ambiguity.
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>
Prior to this commit, the `tvm.testing.parameter` utility defined a
fixture with the default `scope="function"`. However, this prevents
use of these parameters as arguments for other fixtures that are
themselves cached using pytest. Since these are parameters, not large
values that would be expensive to compute, there is no downside to
caching them at the pytest level.
This commit updates the scope of fixtures generated using
`tvm.testing.parameter` to use `scope="session"` instead of the
default `scope="function"`.
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