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2025-03-23Sub-channel quantized type implementation (#120172)Sandeep Dasgupta
This is an implementation for [RFC: Supporting Sub-Channel Quantization in MLIR](https://discourse.llvm.org/t/rfc-supporting-sub-channel-quantization-in-mlir/82694). In order to make the review process easier, the PR has been divided into the following commit labels: 1. **Add implementation for sub-channel type:** Includes the class design for `UniformQuantizedSubChannelType`, printer/parser and bytecode read/write support. The existing types (per-tensor and per-axis) are unaltered. 2. **Add implementation for sub-channel type:** Lowering of `quant.qcast` and `quant.dcast` operations to Linalg operations. 3. **Adding C/Python Apis:** We first define he C-APIs and build the Python-APIs on top of those. 4. **Add pass to normalize generic ....:** This pass normalizes sub-channel quantized types to per-tensor per-axis types, if possible. A design note: - **Explicitly storing the `quantized_dimensions`, even when they can be derived for ranked tensor.** While it's possible to infer quantized dimensions from the static shape of the scales (or zero-points) tensor for ranked data tensors ([ref](https://discourse.llvm.org/t/rfc-supporting-sub-channel-quantization-in-mlir/82694/3) for background), there are cases where this can lead to ambiguity and issues with round-tripping. ``` Consider the example: tensor<2x4x!quant.uniform<i8:f32:{0:2, 0:2}, {{s00:z00, s01:z01}}>> ``` The shape of the scales tensor is [1, 2], which might suggest that only axis 1 is quantized. While this inference is technically correct, as the block size for axis 0 is a degenerate case (equal to the dimension size), it can cause problems with round-tripping. Therefore, even for ranked tensors, we are explicitly storing the quantized dimensions. Suggestions welcome! PS: I understand that the upcoming holidays may impact your schedule, so please take your time with the review. There's no rush.
2024-09-26[mlir] Improvements to the 'quant' dialect (#100667)Rafael Ubal
Full revamp of the 'quant' dialect. This is an implementation for the RFC at https://discourse.llvm.org/t/rfc-improvements-in-the-quant-dialect/79942
2023-05-12[mlir] Move casting calls from methods to function callsTres Popp
The MLIR classes Type/Attribute/Operation/Op/Value support cast/dyn_cast/isa/dyn_cast_or_null functionality through llvm's doCast functionality in addition to defining methods with the same name. This change begins the migration of uses of the method to the corresponding function call as has been decided as more consistent. Note that there still exist classes that only define methods directly, such as AffineExpr, and this does not include work currently to support a functional cast/isa call. Caveats include: - This clang-tidy script probably has more problems. - This only touches C++ code, so nothing that is being generated. Context: - https://mlir.llvm.org/deprecation/ at "Use the free function variants for dyn_cast/cast/isa/…" - Original discussion at https://discourse.llvm.org/t/preferred-casting-style-going-forward/68443 Implementation: This first patch was created with the following steps. The intention is to only do automated changes at first, so I waste less time if it's reverted, and so the first mass change is more clear as an example to other teams that will need to follow similar steps. Steps are described per line, as comments are removed by git: 0. Retrieve the change from the following to build clang-tidy with an additional check: https://github.com/llvm/llvm-project/compare/main...tpopp:llvm-project:tidy-cast-check 1. Build clang-tidy 2. Run clang-tidy over your entire codebase while disabling all checks and enabling the one relevant one. Run on all header files also. 3. Delete .inc files that were also modified, so the next build rebuilds them to a pure state. 4. Some changes have been deleted for the following reasons: - Some files had a variable also named cast - Some files had not included a header file that defines the cast functions - Some files are definitions of the classes that have the casting methods, so the code still refers to the method instead of the function without adding a prefix or removing the method declaration at the same time. ``` ninja -C $BUILD_DIR clang-tidy run-clang-tidy -clang-tidy-binary=$BUILD_DIR/bin/clang-tidy -checks='-*,misc-cast-functions'\ -header-filter=mlir/ mlir/* -fix rm -rf $BUILD_DIR/tools/mlir/**/*.inc git restore mlir/lib/IR mlir/lib/Dialect/DLTI/DLTI.cpp\ mlir/lib/Dialect/Complex/IR/ComplexDialect.cpp\ mlir/lib/**/IR/\ mlir/lib/Dialect/SparseTensor/Transforms/SparseVectorization.cpp\ mlir/lib/Dialect/Vector/Transforms/LowerVectorMultiReduction.cpp\ mlir/test/lib/Dialect/Test/TestTypes.cpp\ mlir/test/lib/Dialect/Transform/TestTransformDialectExtension.cpp\ mlir/test/lib/Dialect/Test/TestAttributes.cpp\ mlir/unittests/TableGen/EnumsGenTest.cpp\ mlir/test/python/lib/PythonTestCAPI.cpp\ mlir/include/mlir/IR/ ``` Differential Revision: https://reviews.llvm.org/D150123
2023-01-10Move from llvm::makeArrayRef to ArrayRef deduction guides - last partserge-sans-paille
This is a follow-up to https://reviews.llvm.org/D140896, split into several parts as it touches a lot of files. Differential Revision: https://reviews.llvm.org/D141298
2022-01-13[mlir] Introduce C API for PDL dialect typesDenys Shabalin
This change introduces C API helper functions to work with PDL types. Modification closely follow the format of the https://reviews.llvm.org/D116546. Reviewed By: ftynse Differential Revision: https://reviews.llvm.org/D117221
2022-01-05[mlir] Introduce C API for the Quantization dialect typesAlex Zinenko
Reviewed By: nicolasvasilache Differential Revision: https://reviews.llvm.org/D116546