## 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.
In the past we have been using `DataType` in PrimExpr.dtype field to
check type information for PrimExpr while still having BaseExpr.ty for
richer type information. DataType is also used both in runtime and
compiler. This PR streamlines the boundary:
- PrimExpr.ty now carries PrimType that replaces original use of
`DataType`
- Runtime use will now favor DLPack DLDataType, removing one layer of
indirection.
- Constants attributes where values are usually runtime values, will use
`DLDataType`
- DataType will be phased out after this PR
We also brings up helper functions in PrimType, but also limits them to
a more concise set so the functions do not grow with the data type codes
in DLPack.
This is a major refactor that changes the IR primitive. It helps to
bring possible future benefits:
- Unified type mechanism through Expr.ty
- Possibility of carry future Type nodes
Migration Guide:
- Use `PrimType` when code reasons about compiler expression types,
tensor element compiler types, or constructs a `PrimExpr`/compiler type.
- Use existing source types such as `expr.ty()`, `ExprOp.expr_ty()`, or
TE tensor element `dtype` where possible instead of rebuilding a type
from dtype text.
- Use raw `DLDataType` for runtime constants, ABI paths, dtype-valued
attrs, and storage/runtime helper logic.
- Prefer direct `PrimType` equality, `MatchesCode(...)`,
`MatchesElementType(...)`, and `WithCode(...)` over local wrappers or
string dtype checks.
Performance:
Using Object type instead of DLDataType would indeed bring some
performance impact to the IR. We have done the following performance
optimizations:
- Make sure most of the outputs reuse one of the PrimType from inputs
- Cache a thread local PrimType based on input so we don't repeatly
realloc
We did benchmarks show that rewrite simplify operation stays within
+-10% overhead of original one. Which merits the refactor given the
benefit the unfication brings
## Background
`class Integer : public IntImm` and `class Bool : public IntImm` were
thin
wrappers sharing `IntImmNode` with no separate node class and no FFI
registration. They existed to provide implicit int→Integer constructors
and
a `.IntValue()` / `operator bool()` accessor, but the same functionality
is
available directly through `IntImm`.
## What this PR does
Migrates all call sites away from `Integer` / `Bool` and then deletes
the
class definitions. The changes are split into four commits, each
independently buildable:
**Commit 1 – [REFACTOR][TIR]** Replace IR-position `Integer(N)` /
`Bool(b)`
constructors with `IntImm(DataType::Int(32), N)` /
`IntImm(DataType::Bool(), b)`
across ~62 source files (arith, relax analysis, s_tir schedule state,
transform
passes, codegen).
**Commit 2 – [REFACTOR][SCHEDULE]** Migrate `Schedule` and
`MetaSchedule`
trace-boxing code: `Integer(N)` attrs in `TracedSchedule` →
`IntImm(DataType::Int(32), N)`;
`ffi::Array<Integer>` schedule-rule parameters → `int64_t`; `Bool(b)`
attrs →
`IntImm(DataType::Bool(), b)`.
**Commit 3 – [REFACTOR][TOPI]** Migrate topi container signatures
(`ffi::Array<Integer>` → `ffi::Array<int64_t>`) and update all internal
usages (`.IntValue()` → plain int64_t, `.defined()` → removed,
`->value` → direct indexing). Also handles stray `Integer` / `Bool`
variables in clml codegen, make_packed_api, infer_layout_utils, and
relax distributed code.
**Commit 4 – [REFACTOR][IR]** Delete `class Bool`, `class Integer`,
`TypeTraits<Bool>`, and `TypeTraits<Integer>` from
`include/tvm/ir/expr.h`.
## Canonical replacements
| Old | New |
|-----|-----|
| `Integer(N)` | `IntImm(DataType::Int(32), N)` |
| `Bool(b)` | `IntImm(DataType::Bool(), b)` |
| `x.IntValue()` | `x->value` |
| `x` as bool | `x->value != 0` |
| `ffi::Array<Integer>` | `ffi::Array<int64_t>` |
## Testing
- All 118 C++ unit tests pass (`./cpptest`)
- `tests/python/s_tir/` — 1251 passed (14 pre-existing failures
unrelated to this change, all in TIR transform tests with
annotation-mismatch errors)
- `tests/python/relax/` — passes (excluding pre-existing
torch/torchvision import failures in frontend tests)
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
## Summary
Support dynamic `repeats` for ONNX Tile in the Relax frontend.
## Changes
- add a dynamic Tile conversion path for ONNX when `repeats` is a graph
input
- expose `topi.dyn_tile` to the Python/packed TOPI interface
- add frontend tests for dynamic `repeats`
## Validation
- `tests/python/relax/test_frontend_onnx.py -k test_tile_dynamic_repeats
-q`
- local end-to-end repro matches ONNX Runtime
## Issue
Fixes#18752
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 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.
[Relax][Transform] Add mode choice, NaN mode, and warning for take()
- Add a `mode` parameter to Relax’s `take()`
- Add `NaN` mode to `take()`
- Add unit tests covering all `take()` modes
- Add a warning log for `fast` mode
- Unify default modes in lower layers to `fast` for consistency with Relax
* Add support for hamming_window
* Add testcase for exportedProgram
* move key-value pair in exportedProgram to correct place
* Fix lint issue
* Changed default periodic value to True
* Fix lint issue
* cleanup relay c++
* [REFACTOR] Phase out relay c++ components
This PR phases out the relay C++ components and
simplifies the overall codegen runtime logic.
---------
Co-authored-by: Siyuan Feng <hzfengsy@sjtu.edu.cn>
* [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>
* updated topi.strided_slice to perform the same canonicalize index as
relax.strided_slice given a assume_inbound flag
* applied formatting
* removed debug statements
* set assume_inbound=True in leg_redistribute_replica_to_shard as this is assumed
in the associated unit tests
moved misplaced function description
fixed shape error and added a assume_inbound=True to test_strided_slice_symbolic_sliced_axis
added param description for assume_inbound
* added doc for param assume_inbound in dynamic_strided_slice
* added # fmt: off and # fmt: on
* Add windows operator
* remove TODO
* Convert ICHECKs to CHECKs
* Report errors using diagnostic context
* Use more readable CHECKs
* Remove example; move comments to test
* Revert "Remove example; move comments to test"
This is a partial revert.
This reverts commit c810c2db7637ce9537adc49d1016caddd5093d3a.
* Add newline to fix Sphinx error
* windows -> sliding_window
* whitespace
* fmt
* [Docs] Prevented docs/1 file from being generated.
Typo in tests/scripts/task_sphinx_precheck.sh caused $TVM_HOME/docs/1
file to be created with stderr output, rather than merged stderr and
stdout.
* [Docs] Corrected sphinx build warnings
- Previously, several warnings were generated by sphinx, but were
unintentionally suppressed. This PR resolves the sphinx warnings.
* [Docs] Corrected additional sphinx build warnings.
- Rebased on main and corrected warnings, now up to date as of commit
53e4c603.
* [Docs] Corrected additional sphinx build warnings
- Rebased on main and corrected warnings, now up to date as of commit
1f2ca068c.
* [Docs] Corrected additional sphinx build warnings
- Rebased on main and corrected warnings, now up to date as of commit
d0791d3db.
* [Docs] Ignore sphinx warnings from missing "git describe" and sckit-learn versions.
Co-authored-by: Eric Lunderberg <elunderberg@octoml.ai>
* [TensorFlow][Frontend] Adding InversePermutation Op
Computes the inverse permutation of a tensor. This Op is used by Mask R-CNN
or other object detection models.
* uncomment test_read_variable_op
* restore several tests
* fix lint error
* fix python linting error
* fix lint error
* restore mistakenly deleted codes
* [Relay/TOPI] Added 'offsets' and 'alignment' attributes to MATRIX_SET_DIAG.
* Added support for 'offsets' and 'alignment' attributes of MATRIX_SET_DIAG.
(Similar to MATRIX_SET_DIAG V3 of TF)
* Added tests for 'offsets' and 'alignment' attributes of MATRIX_SET_DIAG.
* Changes by black.
* * Added offset check in Relay.
* Minor changes.
* Added more tests and some minor documentation changes.
* Add Relay adv_index op
* Support single index tensor dynamic shape
* Support more dynamic index
* Fix lint
* Minor fix for comment
* Fix lint
* Fix lint
* Fix test
* Fix
Tianqi Chen