## Motivation and context
The TVMScript parser already tracks Python AST locations for
diagnostics, but TIRx statements and expression results emitted through
`IRBuilder` did not retain those locations. After parsing, a direct
intrinsic call, an inlined helper body, or a `TilePrimitiveCall`
therefore could not be traced back to the source range that produced it.
Inline expansion also needs more than a single flat location. The
generated IR should retain both the caller location and the
helper-definition location, while ordinary nested AST evaluation within
one source should not accumulate redundant enclosing spans.
## Changes
- Add an active source-span stack to `IRBuilder`, with scoped push/pop
support.
- Make the parser activate the current AST source range while visiting
statements and evaluating expressions.
- Attach the active span to emitted TIRx statements and to expression
results that do not already carry an explicit span.
- Normalize nested spans from the same source to the innermost relevant
range.
- Preserve cross-source inline expansion history as a `SequentialSpan`,
ordered from the call site to the expanded definition.
- Reuse the same source-coordinate calculation for diagnostics and IR
spans so their line and column conventions remain consistent.
Source spans remain diagnostic metadata: functions parsed from different
source locations keep the same structural hash and remain structurally
equal.
## Testing
- Verify exact parser source coordinates against diagnostic coordinates.
- Verify spans on direct intrinsic calls and `TilePrimitiveCall` nodes.
- Verify that inline expansion produces a `SequentialSpan` containing
caller and callee ranges.
- Verify direct `IRBuilder.with_source_span` behavior.
- Verify that source spans do not affect structural identity.
- Run changed-files pre-commit checks, including clang-format.
Focused result: 9 tests passed.
## 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 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
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.
Hongyi Jin