Changes

Summary

[Transforms] Use make_early_inc_range (NFC) (details)
[mlir][sparse] add affine subscripts to sparse compilation pass (details)

Commit 24c8eaec9467b2aaf70b0db33a4e4dd415139a50 by kazu [Transforms] Use make_early_inc_range (NFC)
	llvm/lib/Transforms/InstCombine/InstructionCombining.cpp
	llvm/lib/Transforms/Instrumentation/InstrProfiling.cpp
	llvm/lib/Transforms/Vectorize/VPlanTransforms.cpp
	llvm/lib/Transforms/Coroutines/CoroCleanup.cpp
	llvm/lib/Transforms/Coroutines/CoroSplit.cpp
	llvm/lib/Transforms/Utils/LowerSwitch.cpp
	llvm/lib/Transforms/IPO/ThinLTOBitcodeWriter.cpp
	llvm/lib/Transforms/Coroutines/CoroEarly.cpp
	llvm/lib/Transforms/Vectorize/LoopVectorize.cpp
	llvm/lib/Transforms/ObjCARC/ObjCARCAPElim.cpp
	llvm/lib/Transforms/ObjCARC/ObjCARCOpts.cpp
	llvm/lib/Transforms/Utils/SimplifyCFG.cpp
Commit b1d44e59020a2a7adfc81bffb07577fd091d0778 by ajcbik [mlir][sparse] add affine subscripts to sparse compilation pass This enables the sparsification of more kernels, such as convolutions where there is a x(i+j) subscript. It also enables more tensor invariants such as x(1) or other affine subscripts such as x(i+1). Currently, we reject sparsity altogether for such tensors. Despite this restriction, however, we can already handle a lot more kernels with compound subscripts for dense access (viz. convolution with dense input and sparse filter). Some unit tests and an integration test demonstrate new capability. Reviewed By: bixia Differential Revision: https://reviews.llvm.org/D109783
	mlir/test/Dialect/SparseTensor/sparse_affine.mlir
	mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp
	mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_filter_conv2d.mlir