-- This commit handles the returnOp in memref map layout normalization.
-- An initial filter is applied on FuncOps which helps us know which functions can be
a suitable candidate for memref normalization which doesn't lead to invalid IR.
-- Handles memref map normalization for external function assuming the external function
is normalizable.
Differential Revision: https://reviews.llvm.org/D85226
-- Introduces a pass that normalizes the affine layout maps to the identity layout map both within and across functions by rewriting function arguments and call operands where necessary.
-- Memref normalization is now implemented entirely in the module pass '-normalize-memrefs' and the limited intra-procedural version has been removed from '-simplify-affine-structures'.
-- Run using -normalize-memrefs.
-- Return ops are not handled and would be handled in the subsequent revisions.
Signed-off-by: Abhishek Varma <abhishek.varma@polymagelabs.com>
Differential Revision: https://reviews.llvm.org/D84490
AllocOp is updated in normalizeMemref(AllocOp allocOp), but, when the
AllocOp has `alignment` attribute, it was ignored and updated AllocOp
does not have `alignment` attribute. This patch fixes it.
Differential Revision: https://reviews.llvm.org/D83656
This patch enables affine loop fusion for loops with affine vector loads
and stores. For that, we only had to use affine memory op interfaces in
LoopFusionUtils.cpp and Utils.cpp so that vector loads and stores are
also taken into account.
Reviewed By: andydavis1, ftynse
Differential Revision: https://reviews.llvm.org/D80971
These libraries are distinct from other things in Analysis in that they
operate only on core IR concepts. This also simplifies dependencies
so that Dialect -> Analysis -> Parser -> IR. Previously, the parser depended
on portions of the the Analysis directory as well, which sometimes
caused issues with the way the cmake makefile generator discovers
dependencies on generated files during compilation.
Differential Revision: https://reviews.llvm.org/D79240
This revision refactors the structure of the operand storage such that there is no additional memory cost for resizable operand lists until it is required. This is done by using two different internal representations for the operand storage:
* One using trailing operands
* One using a dynamically allocated std::vector<OpOperand>
This allows for removing the resizable operand list bit, and will free up APIs from needing to workaround non-resizable operand lists.
Differential Revision: https://reviews.llvm.org/D78875
This class implements a switch-like dispatch statement for a value of 'T' using dyn_cast functionality. Each `Case<T>` takes a callable to be invoked if the root value isa<T>, the callable is invoked with the result of dyn_cast<T>() as a parameter.
Differential Revision: https://reviews.llvm.org/D78070
Summary:
Change AffineOps Dialect structure to better group both IR and Tranforms. This included extracting transforms directly related to AffineOps. Also move AffineOps to Affine.
Differential Revision: https://reviews.llvm.org/D76161
Summary: A number of transform import StandardOps despite not being dependent on it. Cleaned it up to better understand what dialects each of these transforms depend on.
Differential Revision: https://reviews.llvm.org/D76112
Summary:
NFC - Moved StandardOps/Ops.h to a StandardOps/IR dir to better match surrounding
directories. This is to match other dialects, and prepare for moving StandardOps
related transforms in out for Transforms and into StandardOps/Transforms.
Differential Revision: https://reviews.llvm.org/D74940
The refactored MemRefType::get() calls all intend to clone from another
memref type, with some modifications. In fact, some calls dropped memory space
during the cloning. Migrate them to the cloning API so that nothing gets
dropped if they are not explicitly listed.
It's close to NFC but not quite, as it helps with propagating memory spaces in
some places.
Differential Revision: https://reviews.llvm.org/D73296
This is an initial step to refactoring the representation of OpResult as proposed in: https://groups.google.com/a/tensorflow.org/g/mlir/c/XXzzKhqqF_0/m/v6bKb08WCgAJ
This change will make it much simpler to incrementally transition all of the existing code to use value-typed semantics.
PiperOrigin-RevId: 286844725
Introduce affine.prefetch: op to prefetch using a multi-dimensional
subscript on a memref; similar to affine.load but has no effect on
semantics, but only on performance.
Provide lowering through std.prefetch, llvm.prefetch and map to llvm's
prefetch instrinsic. All attributes reflected through the lowering -
locality hint, rw, and instr/data cache.
affine.prefetch %0[%i, %j + 5], false, 3, true : memref<400x400xi32>
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#225
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/225 from bondhugula:prefetch 4c3b4e93bc64d9a5719504e6d6e1657818a2ead0
PiperOrigin-RevId: 286212997
- allow symbols in index remapping provided for memref replacement
- fix memref normalize crash on cases with layout maps with symbols
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Reported by: Alex Zinenko
Closestensorflow/mlir#139
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/139 from bondhugula:memref-rep-symbols 2f48c1fdb5d4c58915bbddbd9f07b18541819233
PiperOrigin-RevId: 269851182
- take care of symbolic operands with alloc
- add missing check for compose map failure and a test case
- add test cases on strides
- drop incorrect check for one-to-one'ness
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#132
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/132 from bondhugula:normalize-memrefs 8aebf285fb0d7c19269d85255aed644657e327b7
PiperOrigin-RevId: 269105947
- address remaining comments from PR tensorflow/mlir#87 for better test coverage for
pipeline-data-transfer/replaceAllMemRefUsesWith
- remove dead tag allocs the same way they are removed for the replaced buffers
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#106
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/106 from bondhugula:followup 9e868666d047e8d43e5f82f43e4093b838c710fa
PiperOrigin-RevId: 267144774
avarmapml
Haruki Imai
River Riddle
Rahul Joshi
Diego Caballero
Stephen Neuendorffer
Rob Suderman
Tim Shen
Mehdi Amini
River Riddle
Uday Bondhugula
Kazuaki Ishizaki
Uday Bondhugula