## Summary
Compiler Targets can carry device-type semantics that runtime
device-name parsing does not preserve.
- add `tvm.device_from_target` for canonical Target-to-Device
translation
- use explicit runtime constructors where the device kind is fixed
- update target-derived utilities, tests, and documentation to use the
explicit boundary
## 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.
## Summary
- Make `PrimExpr` a typed C++ view over `Expr` values whose
`ExprNode::ty` is `PrimType`, instead of using a separate runtime node
class as the proof of primitive-ness.
- Use the shared `ir::Call` node for Relax, TIRX, and primitive-valued
calls, while keeping primitive-only APIs explicit at their semantic
boundaries.
- Keep Python on the general `Expr` surface for primitive-typed values
so `isinstance` behavior does not imply a nominal primitive-expression
subclass.
## Design Rationale
The main advantage of this change is that common expression nodes such
as `Call` can be unified without specializing each one to `PrimType`. A
single `ir::Call` can represent a Relax tensor call, a Relax scalar
call, or a primitive-valued intrinsic call; the result type stored in
`ExprNode::ty` determines whether that particular value can be viewed as
`PrimExpr`.
This keeps the IR node hierarchy focused on expression structure rather
than result-type categories. Nodes that are intrinsically primitive,
such as integer and floating-point literals or TIRX primitive operators,
still have strongly typed C++ APIs and data structures. General nodes
whose result type may vary, such as `Call`, remain general `Expr` nodes
and are narrowed to `PrimExpr` only where primitive-only semantics are
required.
The PR also keeps the compatibility surface practical: C++
primitive-only APIs continue to accept `PrimExpr`, Python exposes a
compatibility predicate for checking the primitive typed category, and
visitors/printers use one natural `Call` path rather than duplicating
Relax and primitive call handling. Missing expression types are
represented explicitly with `Type::Missing()` so constructors can leave
type inference to later analysis without relying on nullable `Type`
values.
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.
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.
CompareBeforeAfter, skip_parameterizations, and xfail_parameterizations
have no remaining users anywhere in the repo. CompareBeforeAfter (a base
class for TIR before/after transform tests) has been superseded by the
inline assert_structural_equal(transform(Before), Expected) pattern, and
the {skip,xfail}_parameterizations helpers (which marked specific
parametrizations at runtime) are unused -- native pytest.param(...,
marks=...) covers that need.
Also drop the private _mark_parameterizations helper they relied on and
the now-unused 'import textwrap'.
This pr updates the contributor guide and tvm.testing
docstrings/comments to describe the current gating API
---------
Co-authored-by: Tianqi Chen <tqchen@users.noreply.github.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
This pr is the Follow-up to #19777. This pr removes the last
`requires_*` decorators so test gating is plain pytest everywhere, with
no custom indirection left.
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`
The tvm_ffi Object metaclass now gives every subclass `__slots__ = ()`,
so the Disco Python wrappers can no longer store instance attributes and
every session construction fails with AttributeError. Declare the
attributes each
wrapper actually stores as named slots, fix the NVSHMEM `dist_gemm.cu`
so TVM builds with `USE_NVSHMEM = ON`, and gate the disco tests on the
disco runtime being present so they skip cleanly on builds (e.g. the pip
wheel) that report `USE_NCCL` / `USE_NVSHMEM = ON` without shipping it.
### Session attribute storage
- `DPackedFunc` / `DModule`: `__slots__ = ("session",)`.
- `Session`: `__slots__ = ("_cache", "_import_python_module")`
This PR adds an autoload mechanism for out-of-tree backends, simplifies
TVM's Python library loading, and removes `TVMError` in favor of native
Python errors.
## Autoload out-of-tree backends
Out-of-tree packages can register an autoload callable under the
`tvm.backends` entry-point group (mirroring torch's device-backend
autoload). At `import tvm` startup each entry point is discovered and
its callable invoked once, after the core runtime and the `tvm`
namespace are fully initialized, so an extension can register
ops/targets/funcs or load extra libraries.
```toml
[project.entry-points."tvm.backends"]
tvm_foo = "tvm_foo:_autoload"
```
A failing extension is caught and surfaced via `warnings.warn` so it
cannot break `import tvm`. Autoload can be disabled with
`TVM_DEVICE_BACKEND_AUTOLOAD=0`.
## Simplify library loading
The library-loading path in `base.py` is consolidated around a single
`_LOADED_LIBS` dict (basename to ctypes handle) so downstream and
autoloaded extensions can skip already-loaded libraries; the per-backend
runtime DSO list is folded into `load_backend_libs`. Accumulated cruft
is removed: the Python-3.9 check, the readline shim, the `_FFI_MODE`
ctypes check, the `base.__version__` re-export, and `py_str` (call sites
inline `.decode("utf-8")`).
## Remove TVMError in favor of native Python errors
`TVMError` added a layer atop `RuntimeError` that downstream code had to
import and learn. It is removed; the registered FFI error kinds
(`InternalError`, `RPCError`, `OpError`, `DiagnosticError`,
`ScheduleError`) now subclass `RuntimeError` directly while staying
registered, so the FFI keeps throwing the right kinds. All `TVMError`
imports, `except`/`raise`/`isinstance` uses, and
`pytest.raises(tvm.TVMError)` sites move to the `RuntimeError` builtin.
## 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 cleans up technical debt in the TIR simplification machinery via
two commits:
**Commit 1: Phase out ControlFlowGraph and NarrowPredicateExpression**
- Remove `ControlFlowGraph` (~2360 lines) from `src/tirx/analysis/` —
used only in
non-default config paths that are no longer maintained
- Remove `NarrowPredicateExpression` from `src/arith/` — sole non-test
caller was `ControlFlowGraph`
- Remove gated config fields `propagate_knowns_to_prove_conditional` and
`propagate_knowns_to_simplify_expressions` from `SimplifyConfig`
- Remove `use_dataflow_analysis` from `RemoveNoOpConfig`
- Delete the associated test files and test cases that tested the
now-removed paths
- ~3800 lines deleted
**Commit 2: Rename Simplify → StmtSimplify**
- Rename `src/tirx/transform/simplify.{h,cc}` → `stmt_simplify.{h,cc}`
- Rename C++ identifiers: `Simplify` → `StmtSimplify`, `SimplifyConfig`
→ `StmtSimplifyConfig`
- Rename FFI keys: `"tirx.Simplify"` → `"tirx.StmtSimplify"`,
`"tirx.transform.Simplify"` → `"tirx.transform.StmtSimplify"`
- Update Python wrappers and all call sites (~40 files)
- Clarifies that this pass operates on statements (distinct from
expression-level `arith::Analyzer::Simplify()`)
## Test plan
- [x] `tests/python/tirx-transform/test_tir_transform_simplify.py` — 52
tests pass
- [x] `tests/python/tirx-transform/test_tir_transform_remove_no_op.py` —
18 pass, 5 xfail
- [x] `tests/python/arith/` — full arith test suite passes
- [x] `tests/python/tirx-transform/` — full suite: 315 passed, 8
xfailed, 1 xpassed (pre-existing vectorize failure unrelated to this
change)
- [x] `pre-commit run --all-files` — all hooks pass
## 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.
## Summary
`support.GetLibInfo` exposed ~30 build-time `TVM_INFO_*` strings (git
hash, LLVM/MLIR versions, every `USE_*` flag). Real callers reduce to
"is this feature enabled?" — better answered at runtime. `USE_CUDA=ON`
does not mean a CUDA device is loadable; runtime discovery is the actual
signal. Git versioning is already tracked via `tvm.__version__`.
Changes:
- Delete `src/support/libinfo.cc`, `cmake/modules/LibInfo.cmake`,
`tests/lint/check_cmake_options.py`, and the `check-cmake-options`
pre-commit hook.
- Delete `tvm.support.libinfo()` (no shim — callers migrate to runtime
discovery).
- Add `tvm.support.detect_active_modules()` which queries the FFI global
function registry for `ffi.Module.create.<kind>` registrations (cuda,
vulkan, opencl). `describe()` now prints active runtimes instead of
CMake build flags.
- Migrate 5 in-tree callers: `_get_targets()` uses `cudnn.exists()` /
`tvm.runtime.enabled()` for CUDNN and Hexagon; `_cmake_flag_enabled()`
is rewritten as a static map from cmake flag names to
`tvm.runtime.enabled()` or FFI-registry probes; `clml_sdk_version()`
uses the existing `relax.get_openclml_version` FFI global;
`test_clml_ops.py` uses the new helper.
After this PR: `src/support/` is header-only.
Replace `str(target.kind)` with `target.kind.name` for `Target` objects
since `target.kind` is a `TargetKind` object while `target.kind.name`
yields a string describing the target
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 legacy runtime contrib backends that have no existing
compiler backend,
no active development. They can always be brought back in future in case
we find there is a need
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 renames tir.Block to SBlock. This clearly indicate the
scheduable property of the block and is a prereq for followup stir
passes refactor.
Main changes:
- Data structure change from Block to SBlock
- Syntax change from T.block to T.sblock
Introduces the below features over texture annotation
- Lowering, codegen and runtime for texture.
- image2d_array_t support - Added depth dimension allows more
allocations using texture instead of falling back to buffer when the
texture limits exceeds.
- A comprehensive set of schedules for Adreno textures.
- Texture packing of arbitrary types up to 128 bit (FP16-NCHW8c,
INT8-NCHW16c ...etc.).
- A clBufferDescriptor debug dump controlled by cmake options.
- Pipeline definition for adreno target.
While covering these features the below interfaces or passes or enhanced
which need a review.
- alloc_tensor: VDevice information is passed across these API's. The
way of texture allocation is ```alloc_storage``` allocates buffer/image
objects as requested followed by alloc_tensor being a view of any scope.
This takes care of optimum utilization backing memory across different
image objects or scopes.
- Constants Saving: Handled by adding memory scope section in
executable. This introduces a new header magic to retain the backward
compatibility.
- Static Memory Planing: Mostly port from Relay static memory planner
with mixed mode allocator.
---------
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Sanjay <sanjs@qti.qualcomm.com>
This PR fixes a few places where the python import of runtime tensor
is incorrect. The error wasn't revealed in the previous
NDArray->Tensor rename PR since these imports are not at the top
level.
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 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.
* [FFI][REFACTOR] Establish tvm_ffi as a standalone python module
This PR establishes tvm_ffi as a standalone python module.
The ffi is structured as a minimal pip module that can be
directly install by path or url.
examples/get_started provided a minimal example.
This is a major change as we are decoupling tvm_ffi as a
separate package, users need to install tvm_ffi separately.
Thanks to its minimal dependency, tvm_ffi can be easily installed
even just from the source by pip install ./ffi
This change would enable future improvement for library plugins
to have lightweight dependencies by just working on top of
the tvm_ffi, while the main compiler toolchain and runtime
can be layered on top.
* [FFI] Improve traceback setups
This PR improves traceback related setups
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.
[REFACTOR] Phase out getattr based attribute handling
This PR phases out getattar based attribute handling as they are slower
and introduces extra code path.
This does mean that if an Object is not explicitly registered
in python side, we will no longer be able to access the field by name.
Likely this is also desirable as we would like to enable faster use that
updates the python end and do not rely on these behavior.
This PR phases out tvm._ffi redirections in favor of new FFI
new functions are now called via tvm.ffi.
We also enabled limited API support for python 3.12+
so the compiled binary can be forward compatible to future
python versions.
This PR modernizes the FFI foundation of the project and introduce
a new minimal and lightweight module [tvm ffi](https://github.com/apache/tvm/tree/refactor-s3/ffi)
based on our lessons in the past few years. It implements a modern
version of the [Unified Packed and Object RFC](https://github.com/apache/tvm-rfcs/blob/main/rfcs/0097-unify-packed-and-object.md)
that unifies the packed function call and object systems.
Summary of the change:
- A dedicated clean Any/AnyView that can store strong and weak
references of items
- Function(previously PackedFunc) system built on top of the Any/AnyView
- A minimal C API that backs the overall calls. We are stabilizing the
API with a goal to bring clean, stable FFI conventions for both compiled
and registered code
- A rewrite of core python binding and generated code based on the module
- Update existing code and test cases to the new module
- Latest dlpack support
The new module brings many benefits thanks to the cleaner design,
to name a few:
- Any can support both POD types(int) and object types.
- Containers (e.g. Array) can now also contain Any value, e.g. now
`Array<int>` is supported, no need for boxed types
- Error handling now upgrades to object-based, allowing cleaner
traceback across languages
- Map now preserves insertion orders
- Path toward isolated stabilize minimum core ABI/API foundation module
- Type traits based design that cleanly defines how values interact
with Any system
- Automatic conversion of different types based on traits if needed
Because FFI upgrade is at heart of the project, the change touches every
component of the system. Importantly, this is an upgrade of the ABI so the
change is not backward compatible. The code compiled under the old
FFI won't work under the new one. We did provide example ABI translation
(e.g. LegacyTVMArgValueToFFIAny) functions for compatibility.
The PR tries to leave files in their old places while creating redirections.
The goal is to have the first milestone landed and infrastructure in place,
so we can do further refactors to complete features and cleanup legacy code
as trackable PRs. As of now, python binding and compiled code are under the
new convention while RPC and some other bindings still relies on legacy ABI
translation. We will work on upgrades in the coming PRs, including areas such
as reflection, phasing out legacy redirections etc.
Tianqi Chen