Migrating all private STL code to the standard STL case but keeping it under the CPP namespace to avoid confusion. Starting with the type_traits header.
Differential Revision: https://reviews.llvm.org/D130727
* https://discourse.llvm.org/t/rfc-removing-the-quant-dialect/3643/8
* Removes most ops. Leaves casts given final comment (can remove more in a followup).
* There are a few uses in Tosa keeping some of the utilities alive. In a followup, I will probably elect to just move simplified versions of them into Tosa itself vs having this quasi-library dependency.
Differential Revision: https://reviews.llvm.org/D120204
Change `sinf` range reduction to mod pi/16 to be shared with `cosf`.
Previously, `sinf` used range reduction `mod pi`, but this cannot be used to implement `cosf` since the minimax algorithm for `cosf` does not converge due to critical points at `pi/2`. In order to be able to share the same range reduction functions for both `sinf` and `cosf`, we change the range reduction to `mod pi/16` for the following reasons:
- The table size is sufficiently small: 32 entries for `sin(k * pi/16)` with `k = 0..31`. It could be reduced to 16 entries if we treat the final sign separately, with an extra multiplication at the end.
- The polynomials' degrees are reduced to 7/8 from 15, with extra computations to combine `sin` and `cos` with trig sum equality.
- The number of exceptional cases reduced to 2 (with FMA) and 3 (without FMA).
- The latency is reduced while maintaining similar throughput as before.
Reviewed By: zimmermann6
Differential Revision: https://reviews.llvm.org/D130629
In the Transform dialect extensions, provide the separate mechanism to
declare dependent dialects (the dialects the transform IR depends on)
and the generated dialects (the dialects the payload IR may be
transformed into). This allows the Transform dialect clients that are
only constructing the transform IR to avoid loading the dialects
relevant for the payload IR along with the Transform dialect itself,
thus decreasing the build/link time.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D130289
This operation is a NavigationOp that simplifies the writing of transform IR.
Since there is no way of refering to an interface by name, the current implementation uses
an EnumAttr and depends on the interfaces it supports.
In the future, it would be worthwhile to remove this dependence and generalize.
Differential Revision: https://reviews.llvm.org/D130267
- add zstd to `llvm::compression` namespace
- add a CMake option `LLVM_ENABLE_ZSTD` with behavior mirroring that of `LLVM_ENABLE_ZLIB`
- add tests for zstd to `llvm/unittests/Support/CompressionTest.cpp`
- debian users should install libzstd when using `LLVM_ENABLE_ZSTD=FORCE_ON` from source due to this bug https://bugs.launchpad.net/ubuntu/+source/libzstd/+bug/1941956
Reviewed By: leonardchan, MaskRay
Differential Revision: https://reviews.llvm.org/D128465
The rules in the linalg file were very specific to sparse tensors so will
find a better home under sparse tensor dialect than linalg dialect. Also
moved some rewriting from sparsification into this new "pre-rewriting" file.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D129910
Since the very first commits, the Python and C MLIR APIs have had mis-placed registration/load functionality for dialects, extensions, etc. This was done pragmatically in order to get bootstrapped and then just grew in. Downstreams largely bypass and do their own thing by providing various APIs to register things they need. Meanwhile, the C++ APIs have stabilized around this and it would make sense to follow suit.
The thing we have observed in canonical usage by downstreams is that each downstream tends to have native entry points that configure its installation to its preferences with one-stop APIs. This patch leans in to this approach with `RegisterEverything.h` and `mlir._mlir_libs._mlirRegisterEverything` being the one-stop entry points for the "upstream packages". The `_mlir_libs.__init__.py` now allows customization of the environment and Context by adding "initialization modules" to the `_mlir_libs` package. If present, `_mlirRegisterEverything` is treated as such a module. Others can be added by downstreams by adding a `_site_initialize_{i}.py` module, where '{i}' is a number starting with zero. The number will be incremented and corresponding module loaded until one is not found. Initialization modules can:
* Perform load time customization to the global environment (i.e. registering passes, hooks, etc).
* Define a `register_dialects(registry: DialectRegistry)` function that can extend the `DialectRegistry` that will be used to bootstrap the `Context`.
* Define a `context_init_hook(context: Context)` function that will be added to a list of callbacks which will be invoked after dialect registration during `Context` initialization.
Note that the `MLIRPythonExtension.RegisterEverything` is not included by default when building a downstream (its corresponding behavior was prior). For downstreams which need the default MLIR initialization to take place, they must add this back in to their Python CMake build just like they add their own components (i.e. to `add_mlir_python_common_capi_library` and `add_mlir_python_modules`). It is perfectly valid to not do this, in which case, only the things explicitly depended on and initialized by downstreams will be built/packaged. If the downstream has not been set up for this, it is recommended to simply add this back for the time being and pay the build time/package size cost.
CMake changes:
* `MLIRCAPIRegistration` -> `MLIRCAPIRegisterEverything` (renamed to signify what it does and force an evaluation: a number of places were incidentally linking this very expensive target)
* `MLIRPythonSoure.Passes` removed (without replacement: just drop)
* `MLIRPythonExtension.AllPassesRegistration` removed (without replacement: just drop)
* `MLIRPythonExtension.Conversions` removed (without replacement: just drop)
* `MLIRPythonExtension.Transforms` removed (without replacement: just drop)
Header changes:
* `mlir-c/Registration.h` is deleted. Dialect registration functionality is now in `IR.h`. Registration of upstream features are in `mlir-c/RegisterEverything.h`. When updating MLIR and a couple of downstreams, I found that proper usage was commingled so required making a choice vs just blind S&R.
Python APIs removed:
* mlir.transforms and mlir.conversions (previously only had an __init__.py which indirectly triggered `mlirRegisterTransformsPasses()` and `mlirRegisterConversionPasses()` respectively). Downstream impact: Remove these imports if present (they now happen as part of default initialization).
* mlir._mlir_libs._all_passes_registration, mlir._mlir_libs._mlirTransforms, mlir._mlir_libs._mlirConversions. Downstream impact: None expected (these were internally used).
C-APIs changed:
* mlirRegisterAllDialects(MlirContext) now takes an MlirDialectRegistry instead. It also used to trigger loading of all dialects, which was already marked with a TODO to remove -- it no longer does, and for direct use, dialects must be explicitly loaded. Downstream impact: Direct C-API users must ensure that needed dialects are loaded or call `mlirContextLoadAllAvailableDialects(MlirContext)` to emulate the prior behavior. Also see the `ir.c` test case (e.g. ` mlirContextGetOrLoadDialect(ctx, mlirStringRefCreateFromCString("func"));`).
* mlirDialectHandle* APIs were moved from Registration.h (which now is restricted to just global/upstream registration) to IR.h, arguably where it should have been. Downstream impact: include correct header (likely already doing so).
C-APIs added:
* mlirContextLoadAllAvailableDialects(MlirContext): Corresponds to C++ API with the same purpose.
Python APIs added:
* mlir.ir.DialectRegistry: Mapping for an MlirDialectRegistry.
* mlir.ir.Context.append_dialect_registry(MlirDialectRegistry)
* mlir.ir.Context.load_all_available_dialects()
* mlir._mlir_libs._mlirAllRegistration: New native extension that exposes a `register_dialects(MlirDialectRegistry)` entry point and performs all upstream pass/conversion/transforms registration on init. In this first step, we eagerly load this as part of the __init__.py and use it to monkey patch the Context to emulate prior behavior.
* Type caster and capsule support for MlirDialectRegistry
This should make it possible to build downstream Python dialects that only depend on a subset of MLIR. See: https://github.com/llvm/llvm-project/issues/56037
Here is an example PR, minimally adapting IREE to these changes: https://github.com/iree-org/iree/pull/9638/files In this situation, IREE is opting to not link everything, since it is already configuring the Context to its liking. For projects that would just like to not think about it and pull in everything, add `MLIRPythonExtension.RegisterEverything` to the list of Python sources getting built, and the old behavior will continue.
Reviewed By: mehdi_amini, ftynse
Differential Revision: https://reviews.llvm.org/D128593
- add `FindZSTD.cmake`
- add zstd to `llvm::compression` namespace
- add a CMake option `LLVM_ENABLE_ZSTD` with behavior mirroring that of `LLVM_ENABLE_ZLIB`
- add tests for zstd to `llvm/unittests/Support/CompressionTest.cpp`
Reviewed By: leonardchan, MaskRay
Differential Revision: https://reviews.llvm.org/D128465
- add `FindZSTD.cmake`
- add zstd to `llvm::compression` namespace
- add a CMake option `LLVM_ENABLE_ZSTD` with behavior mirroring that of `LLVM_ENABLE_ZLIB`
- add tests for zstd to `llvm/unittests/Support/CompressionTest.cpp`
Reviewed By: leonardchan, MaskRay
Differential Revision: https://reviews.llvm.org/D128465
Between issues such as
https://github.com/llvm/llvm-project/issues/56323, the fact that this
lowering (unlike the code in amdgpu-to-rocdl) does not correctly set
up bounds checks (and thus will cause page faults on reads that might
need to be padded instead), and that fixing these problems would,
essentially, involve replicating amdgpu-to-rocdl, remove
--vector-to-rocdl for being broken. In addition, the lowering does not
support many aspects of transfer_{read,write}, like supervectors, and
may not work correctly in their presence.
We (the MLIR-based convolution generator at AMD) do not use this
conversion pass, nor are we aware of any other clients.
Migration strategies:
- Use VectorToLLVM
- If buffer ops are particularly needed in your application, use
amdgpu.raw_buffer_{load,store}
A VectorToAMDGPU pass may be introduced in the future.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D129308
Introduce a structured transform op that emits IR computing the multi-tile
sizes with requested parameters (target size and divisor) for the given
structured op. The sizes may fold to arithmetic constant operations when the
shape is constant. These operations may then be used to call the existing
tiling transformation with a single non-zero dynamic size (i.e. perform
strip-mining) for each of the dimensions separately, thus achieving multi-size
tiling with optional loop interchange. A separate test exercises the entire
script.
Depends On D129217
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D129287
Extend the definition of the Tile structured transform op to enable it
accepting handles to operations that produce tile sizes at runtime. This is
useful by itself and prepares for more advanced tiling strategies. Note that
the changes are relevant only to the transform dialect, the tiling
transformation itself already supports dynamic sizes.
Depends On D129216
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D129217
Infers block/grid dimensions/indices or ranges of such dimensions/indices.
Reviewed By: krzysz00
Differential Revision: https://reviews.llvm.org/D129036
This is moslty NFC and will allow tensor.parallel_insert_slice to gain
rank-reducing semantics by reusing the vast majority of the tensor.insert_slice impl.
Depends on D128857
Differential Revision: https://reviews.llvm.org/D128920
At the moment, two files are not installed by CMake.
- `lib/Headers/openmp_wrappers/time.h`
- `lib/Headers/ppc_wrappers/nmmintrin.h`
`builtin_headers_gen` is available as the source of rules_pkg.
The difference of the layout of installed headers makes cache hit harder.
NAKAMURA Takumi