This patch moves all functionality from IntegerPolyhedron to IntegerRelation.
IntegerPolyhedron is now implemented as a relation with no domain. All existing
functionality is extended to work on relations.
This patch does not affect external users like FlatAffineConstraints as they
can still continue to use IntegerPolyhedron abstraction.
This patch is part of a series of patches to support relations in Presburger
library.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D120652
Add support for translating data layout specifications for integer and float
types between MLIR and LLVM IR. This is a first step towards removing the
string-based LLVM dialect data layout attribute on modules. The latter is still
available and will remain so until the first-class MLIR modeling can fully
replace it.
Depends On D120739
Reviewed By: wsmoses
Differential Revision: https://reviews.llvm.org/D120740
Add support for integer and float types into the data layout subsystem with
default logic similar to LLVM IR. Given the flexibility of the sybsystem, the
logic can be easily overwritten by operations if necessary. This provides the
connection necessary, e.g., for the GPU target where alignment requirements for
integers and floats differ from those provided by default (although still
compatible with the LLVM IR model). Previously, it was impossible to use
non-default alignment requirements for integer and float types, which could
lead to incorrect address and size calculations when targeting GPUs.
Depends On D120737
Reviewed By: wsmoses
Differential Revision: https://reviews.llvm.org/D120739
This patch adds assemblyFormat for `omp.critical.declare`, `omp.atomic.read`,
`omp.atomic.write`, `omp.atomic.update` and `omp.atomic.capture`.
Also removing those clauses from `parseClauses` that aren't needed
anymore, thanks to the new assemblyFormats.
Reviewed By: NimishMishra, rriddle
Differential Revision: https://reviews.llvm.org/D120248
sparsity values.
Previously, we can't properly handle input tensors with a dimension
ordering that is different from the natural ordering or with a mixed of
compressed and dense dimensions. This change fixes the problems by
passing the dimension ordering and sparsity values to the runtime
routine.
Modify an existing test to test the situation.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D120777
This adds an option to configure the CMake python search priming
behaviour that was introduced in D118148. In some environments the
priming would cause the "real" search to fail. The default behaviour is
unchanged, i.e. the search will be primed.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D120765
The remanants of Standard was renamed to Func, but the test directory
remained named as Standard. In adidition to fixing the name, this commit
also moves the tests for operations not in the Func dialect to the proper
parent dialect test directory.
The Func has a large number of legacy dependencies carried over from the old
Standard dialect, which was pervasive and contained a large number of varied
operations. With the split of the standard dialect and its demise, a lot of lingering
dead dependencies have survived to the Func dialect. This commit removes a
large majority of then, greatly reducing the dependence surface area of the
Func dialect.
The last remaining operations in the standard dialect all revolve around
FuncOp/function related constructs. This patch simply handles the initial
renaming (which by itself is already huge), but there are a large number
of cleanups unlocked/necessary afterwards:
* Removing a bunch of unnecessary dependencies on Func
* Cleaning up the From/ToStandard conversion passes
* Preparing for the move of FuncOp to the Func dialect
See the discussion at https://discourse.llvm.org/t/standard-dialect-the-final-chapter/6061
Differential Revision: https://reviews.llvm.org/D120624
Previously, convertToMLIRSparseTensor assumes identity storage ordering and all
compressed dimensions. This change extends the function with two parameters for
users to specify the storage ordering and the sparsity of each dimension.
Modify PyTACO to reflect this change.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D120643
As discussed in https://reviews.llvm.org/D119743 scf.parallel would continuously stack allocate since the alloca op was placd in the wsloop rather than the omp.parallel. This PR is the second stage of the fix for that problem. Specifically, we now introduce an alloca scope around the inlined body of the scf.parallel and enable a canonicalization to hoist the allocations to the surrounding allocation scope (e.g. omp.parallel).
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D120423
The llvm.mlir.global operation accepts a region as initializer. This region
corresponds to an LLVM IR constant expression and therefore should not accept
operations with side effects. Add a corresponding verifier.
Reviewed By: wsmoses, bondhugula
Differential Revision: https://reviews.llvm.org/D120632
The revision renames the following OpDSL functions:
```
TypeFn.cast -> TypeFn.cast_signed
BinaryFn.min -> BinaryFn.min_signed
BinaryFn.max -> BinaryFn.max_signed
```
The corresponding enum values on the C++ side are renamed accordingly:
```
#linalg.type_fn<cast> -> #linalg.type_fn<cast_signed>
#linalg.binary_fn<min> -> #linalg.binary_fn<min_signed>
#linalg.binary_fn<max> -> #linalg.binary_fn<max_signed>
```
Depends On D120110
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D120562
The revision extends OpDSL with unary and binary function attributes. A function attribute, makes the operations used in the body of a structured operation configurable. For example, a pooling operation may take an aggregation function attribute that specifies if the op shall implement a min or a max pooling. The goal of this revision is to define less and more flexible operations.
We may thus for example define an element wise op:
```
linalg.elem(lhs, rhs, outs=[out], op=BinaryFn.mul)
```
If the op argument is not set the default operation is used.
Depends On D120109
Reviewed By: nicolasvasilache, aartbik
Differential Revision: https://reviews.llvm.org/D120110
Add applyStaticChunkedWorkshareLoop method implementing static schedule when chunk-size is specified. Unlike a static schedule without chunk-size (where chunk-size is chosen by the runtime such that each thread receives one chunk), we need two nested loops: one for looping over the iterations of a chunk, and a second for looping over all chunks assigned to the threads.
This patch includes the following related changes:
* Adapt applyWorkshareLoop to triage between the schedule types, now possible since all schedules have been implemented. The default schedule is assumed to be non-chunked static, as without OpenMPIRBuilder.
* Remove the chunk parameter from applyStaticWorkshareLoop, it is ignored by the runtime. Change the value for the value passed to the init function to 0, as without OpenMPIRBuilder.
* Refactor CanonicalLoopInfo::setTripCount and CanonicalLoopInfo::mapIndVar as used by both, applyStaticWorkshareLoop and applyStaticChunkedWorkshareLoop.
* Enable Clang to use the OpenMPIRBuilder in the presence of the schedule clause.
Differential Revision: https://reviews.llvm.org/D114413
Add a pattern matcher for ExtractSliceOp when its source is a constant.
The matching heuristics can be governed by the control function since
generating a new constant is not always beneficial.
Differential Revision: https://reviews.llvm.org/D119605
If we have a chain of `tensor.insert_slice` ops inserting some
`tensor.pad` op into a `linalg.fill` and ranges do not overlap,
we can also elide the `tensor.pad` later.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D120446
Fold tensor.insert_slice(tensor.pad(<input>), linalg.fill) into
tensor.insert_slice(<input>, linalg.fill) if the padding value and
the filling value are the same.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D120410
Improve the LinalgOp verification to ensure the iterator types is known. Previously, unknown iterator types have been ignored without warning, which can lead to confusing bugs.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D120649
This patch removes the builders for `omp.wsloop` operation that aren't
specifically needed anywhere. We can add them later if the need arises.
Reviewed By: kiranchandramohan
Differential Revision: https://reviews.llvm.org/D120533
This patch moves IntegerPolyhedron::reset to FlatAffineConstraints::reset. This
function is not required in IntegerPolyhedron and creates ambiguity while
shifting implementations to IntegerRelation.
This patch is part of a series of patches to introduce relations in Presburger
library.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D120628
PDL currently doesn't support result values from constraints, meaning we need
to error out until this is actually supported to avoid crashes.
Differential Revision: https://reviews.llvm.org/D119782
This commits adds a C++ generator to PDLL that generates wrapper PDL patterns
directly usable in C++ code, and also generates the definitions of native constraints/rewrites
that have code bodies specified in PDLL. This generator is effectively the PDLL equivalent of
the current DRR generator, and will allow easy replacement of DRR patterns with PDLL patterns.
A followup will start to utilize this for end-to-end integration testing and show case how to
use this as a drop-in replacement for DRR tablegen usage.
Differential Revision: https://reviews.llvm.org/D119781
If the operand list or result list of an operation expression is not specified, we interpret
this as meaning that the operands/results are "unconstraint" (i.e. "could be anything").
We currently don't properly handle differentiating this case from the case of
"no operands/results". This commit adds the insertion of implicit value/type range
variables when these lists are unspecified. This allows for adding proper support
for when zero operands or results are expected.
Differential Revision: https://reviews.llvm.org/D119780
This commits starts to plumb PDLL down into MLIR and adds an initial
PDL generator. After this commit, we will have conceptually support
end-to-end execution of PDLL. Followups will add CPP generation to
match the current DRR setup, and begin to add various end-to-end
tests to test PDLL execution.
Differential Revision: https://reviews.llvm.org/D119779
This patch removes a redundant check in hasConsistentState which is always true
after introduction of PresburgerSpace.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D120615
This patch moves identifier kind specific insert/append functions like
`insertDimId`, `appendSymbolId`, etc. from IntegerPolyhedron to
FlatAffineConstraints.
This change allows for a smoother transition to IntegerRelation.
This change is part of a series of patches to introduce Relations in Presburger
library.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D120576
This patch factors out various checks for dimension compatibility to
PresburgerSpace::isEqual and PresburgerLocalSpace::isEqual (for local
identifiers).
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D120565
In the main-loop of the current coalesce implementation `i` was incremented
twice for some cases. This patch fixes this bug and adds a regression
testcase.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D120613
