* Extract "value" functionality of `FlatAffineConstraints` into a new derived `FlatAffineValueConstraints` class. Current users of `FlatAffineConstraints` can use `FlatAffineValueConstraints` without additional code changes, thus NFC.
* `FlatAffineConstraints` no longer associates dimensions with SSA Values. All functionality that requires this, is moved to `FlatAffineValueConstraints`.
* `FlatAffineConstraints` no longer makes assumptions about where Values associated with dimensions are coming from.
Differential Revision: https://reviews.llvm.org/D107725
When we vectorize a scalar constant, the vector constant is inserted before its
first user if the scalar constant is defined outside the loops to be vectorized.
It is possible that the vector constant does not dominate all its users. To fix
the problem, we find the innermost vectorized loop that encloses that first user
and insert the vector constant at the top of the loop body.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D106609
Introduce a new rewrite driver (MultiOpPatternRewriteDriver) to rewrite
a supplied list of ops and other ops. Provide a knob to restrict
rewrites strictly to those ops or also to affected ops (but still not to
completely related ops).
This rewrite driver is commonly needed to run any simplification and
cleanup at the end of a transforms pass or transforms utility in a way
that only simplifies relevant IR. This makes it easy to write test cases
while not performing unrelated whole IR simplification that may
invalidate other state at the caller.
The introduced utility provides more freedom to developers of transforms
and transform utilities to perform focussed and local simplification. In
several cases, it provides greater efficiency as well as more
simplification when compared to repeatedly calling
`applyOpPatternsAndFold`; in other cases, it avoids the need to
undesirably call `applyPatternsAndFoldGreedily` to do unrelated
simplification in a FuncOp.
Update a few transformations that were earlier using
applyOpPatternsAndFold (SimplifyAffineStructures,
affineDataCopyGenerate, a linalg transform).
TODO:
- OpPatternRewriteDriver can be removed as it's a special case of
MultiOpPatternRewriteDriver, i.e., both can be merged.
Differential Revision: https://reviews.llvm.org/D106232
The normalizeAffineForOp and normalizedAffineParallel methods were
misplaced in the AffineLoopNormalize pass file while their declarations
were in affine utils. Move these to affine Utils.cpp. NFC.
Differential Revision: https://reviews.llvm.org/D105468
Deduce circumstances where an affine load could not possibly be read by an operation (such as an affine load), and if so, eliminate the load
Differential Revision: https://reviews.llvm.org/D105041
MemRefDataFlow performs mem2reg style operations for affine load/stores. Unfortunately, it is not presently correct in the presence of external operations such as memref.cast, or function calls. This diff extends the functionality of the pass to remain correct in the presence of such ops.
Differential Revision: https://reviews.llvm.org/D104053
* Remove dependency: Standard --> MemRef
* Add dependencies: GPUToNVVMTransforms --> MemRef, Linalg --> MemRef, MemRef --> Tensor
* Note: The `subtensor_insert_propagate_dest_cast` test case in MemRef/canonicalize.mlir will be moved to Tensor/canonicalize.mlir in a subsequent commit, which moves over the remaining Tensor ops from the Standard dialect to the Tensor dialect.
Differential Revision: https://reviews.llvm.org/D104506
Make store to load fwd condition for -memref-dataflow-opt less
conservative. Post dominance info is not really needed. Add additional
check for common cases.
Differential Revision: https://reviews.llvm.org/D104174
To control the number of outer parallel loops, we need to process the
outer loops first and hence pre-order walk fixes the issue.
Reviewed By: bondhugula
Differential Revision: https://reviews.llvm.org/D104361
Prevent users of `iter_args` of an affine for loop from being hoisted
out of it. Otherwise, LICM leads to a violation of the SSA dominance
(as demonstrated in the added test case).
Fixes: https://bugs.llvm.org/show_bug.cgi?id=50103
Reviewed By: bondhugula, ayzhuang
Differential Revision: https://reviews.llvm.org/D102984
Splitting the memref dialect lead to an introduction of several dependencies
to avoid compilation issues. The canonicalize pass also depends on the
memref dialect, but it shouldn't. This patch resolves the dependencies
and the unintuitive includes are removed. However, the dependency moves
to the constructor of the std dialect.
Differential Revision: https://reviews.llvm.org/D102060
This covers the extremely common case of replacing all uses of a Value
with a new op that is itself a user of the original Value.
This should also be a little bit more efficient than the
`SmallPtrSet<Operation *, 1>{op}` idiom that was being used before.
Differential Revision: https://reviews.llvm.org/D102373
This patch adds support for vectorizing loops with 'iter_args'
implementing known reductions along the vector dimension. Comparing to
the non-vector-dimension case, two additional things are done during
vectorization of such loops:
- The resulting vector returned from the loop is reduced to a scalar
using `vector.reduce`.
- In some cases a mask is applied to the vector yielded at the end of
the loop to prevent garbage values from being written to the
accumulator.
Vectorization of reduction loops is disabled by default. To enable it, a
map from loops to array of reduction descriptors should be explicitly passed to
`vectorizeAffineLoops`, or `vectorize-reductions=true` should be passed
to the SuperVectorize pass.
Current limitations:
- Loops with a non-unit step size are not supported.
- n-D vectorization with n > 1 is not supported.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D100694
This enables to express more complex parallel loops in the affine framework,
for example, in cases of tiling by sizes not dividing loop trip counts perfectly
or inner wavefront parallelism, among others. One can't use affine.max/min
and supply values to the nested loop bounds since the results of such
affine.max/min operations aren't valid symbols. Making them valid symbols
isn't an option since they would introduce selection trees into memref
subscript arithmetic as an unintended and undesired consequence. Also
add support for converting such loops to SCF. Drop some API that isn't used in
the core repo from AffineParallelOp since its semantics becomes ambiguous in
presence of max/min bounds. Loop normalization is currently unavailable for
such loops.
Depends On D101171
Reviewed By: bondhugula
Differential Revision: https://reviews.llvm.org/D101172
Introduce a basic support for parallelizing affine loops with reductions
expressed using iteration arguments. Affine parallelism detector now has a flag
to assume such reductions are parallel. The transformation handles a subset of
parallel reductions that are can be expressed using affine.parallel:
integer/float addition and multiplication. This requires to detect the
reduction operation since affine.parallel only supports a fixed set of
reduction operators.
Reviewed By: chelini, kumasento, bondhugula
Differential Revision: https://reviews.llvm.org/D101171
This patch collects operations that have users in a for loop and uses
them when loop invariant operations are detected and hoisted.
Reviewed By: bondhugula, vinayaka-polymage
Differential Revision: https://reviews.llvm.org/D99761
This nicely aligns the naming with RewritePatternSet. This type isn't
as widely used, but we keep a using declaration in to help with
downstream consumption of this change.
Differential Revision: https://reviews.llvm.org/D99131
This doesn't change APIs, this just cleans up the many in-tree uses of these
names to use the new preferred names. We'll keep the old names around for a
couple weeks to help transitions.
Differential Revision: https://reviews.llvm.org/D99127
This updates the codebase to pass the context when creating an instance of
OwningRewritePatternList, and starts removing extraneous MLIRContext
parameters. There are many many more to be removed.
Differential Revision: https://reviews.llvm.org/D99028
This patch adds support for vectorizing loops with 'iter_args' when those loops
are not a vector dimension. This allows vectorizing outer loops with an inner
'iter_args' loop (e.g., reductions). Vectorizing scenarios where 'iter_args'
loops are vector dimensions would require more work (e.g., analysis,
generating horizontal reduction, etc.) not included in this patch.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D97892
This patch replaces the root-terminal vectorization approach implemented in the
Affine vectorizer with a topological order approach that vectorizes all the
operations within the target loop nest. These are the most important changes
introduced by the new algorithm:
* Removed tracking of root and terminal ops. Existing vectorization
functionality is preserved and extended so that loop nests without
root-terminal chains can be vectorized.
* Vectorizing a loop nest now only requires a single topological traversal.
* A new vector loop nest is incrementally built along the vectorization
process. The original scalar loop is kept intact. No cloning guard is needed
to recover the scalar loop if vectorization fails. This approach also
simplifies the challenging task of replacing a loop operation amid the
vectorization process without invalidating the analysis information that
depends on the original loop.
* Vectorization of specific operations has been implemented as independent,
preparing them to be moved to a potential vectorization interface.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D97442
This patch fixes a heap-use-after-free introduced by the recent changes
in the vectorizer: https://reviews.llvm.org/rG95db7b4aeaad590f37720898e339a6d54313422f
The problem is due to the way candidate loops are visited. All candidate loops
are pattern-matched beforehand using the 'NestedMatch' utility. These matches may
intersect with each other so it may happen that we try to vectorize a loop that
was previously vectorized. The new vectorization algorithm replaces the original
loops that are vectorized with new loops and, therefore, any reference to the
original loops in the pre-computed matches becomes invalid.
This patch fixes the problem by classifying the candidate matches into buckets
before vectorization. Each bucket contains all the matches that intersect. The
vectorizer uses these buckets to make sure that we only vectorize *one* match from
each bucket, at most.
Differential Revision: https://reviews.llvm.org/D98382
This patch adds support for vectorizing loops with 'iter_args' when those loops
are not a vector dimension. This allows vectorizing outer loops with an inner
'iter_args' loop (e.g., reductions). Vectorizing scenarios where 'iter_args'
loops are vector dimensions would require more work (e.g., analysis,
generating horizontal reduction, etc.) not included in this patch.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D97892
This patch replaces the root-terminal vectorization approach implemented in the
Affine vectorizer with a topological order approach that vectorizes all the
operations within the target loop nest. These are the most important changes
introduced by the new algorithm:
* Removed tracking of root and terminal ops. Existing vectorization
functionality is preserved and extended so that loop nests without
root-terminal chains can be vectorized.
* Vectorizing a loop nest now only requires a single topological traversal.
* A new vector loop nest is incrementally built along the vectorization
process. The original scalar loop is kept intact. No cloning guard is needed
to recover the scalar loop if vectorization fails. This approach also
simplifies the challenging task of replacing a loop operation amid the
vectorization process without invalidating the analysis information that
depends on the original loop.
* Vectorization of specific operations has been implemented as independent,
preparing them to be moved to a potential vectorization interface.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D97442
Separating the AffineMapAccessInterface from AffineRead/WriteOp interface so that dialects which extend Affine capabilities (e.g. PlaidML PXA = parallel extensions for Affine) can utilize relevant passes (e.g. MemRef normalization).
Reviewed By: bondhugula
Differential Revision: https://reviews.llvm.org/D96284
These properties were useful for a few things before traits had a better integration story, but don't really carry their weight well these days. Most of these properties are already checked via traits in most of the code. It is better to align the system around traits, and improve the performance/cost of traits in general.
Differential Revision: https://reviews.llvm.org/D96088
This makes ignoring a result explicit by the user, and helps to prevent accidental errors with dropped results. Marking LogicalResult as no discard was always the intention from the beginning, but got lost along the way.
Differential Revision: https://reviews.llvm.org/D95841
Add a pass option to control the number of nested parallel loops produced by
the parallelization passes. This is useful to build end-to-end passes targeting
systems that don't need multiple parallel dimensions (e.g., CPUs typically need
only one).
Reviewed By: wsmoses, chelini
Differential Revision: https://reviews.llvm.org/D92765
Add support to normalize affine.for ops i.e., convert the lower bound to zero
and loop step to one. The Upper bound is set to the trip count of the loop.
The exact value of loopIV is calculated just inside the body of affine.for.
Currently loops with lower bounds having single result are supported. No such
restriction exists on upper bounds.
Differential Revision: https://reviews.llvm.org/D92233
Given that OpState already implicit converts to Operator*, this seems reasonable.
The alternative would be to add more functions to OpState which forward to Operation.
Reviewed By: rriddle, ftynse
Differential Revision: https://reviews.llvm.org/D92266
This patch renames AffineParallelNormalize to AffineLoopNormalize to make it
more generic and be able to hold more loop normalization transformations in
the future for affine.for and affine.parallel ops. Eventually, it could also be
extended to support scf.for and scf.parallel. As a starting point for affine.for,
the patch also adds support for removing single iteration affine.for ops to the
the pass.
Differential Revision: https://reviews.llvm.org/D90267
This class represents a rewrite pattern list that has been frozen, and thus immutable. This replaces the uses of OwningRewritePatternList in pattern driver related API, such as dialect conversion. When PDL becomes more prevalent, this API will allow for optimizing a set of patterns once without the need to do this per run of a pass.
Differential Revision: https://reviews.llvm.org/D89104
There are several pieces of pattern rewriting infra in IR/ that really shouldn't be there. This revision moves those pieces to a better location such that they are easier to evolve in the future(e.g. with PDL). More concretely this revision does the following:
* Create a Transforms/GreedyPatternRewriteDriver.h and move the apply*andFold methods there.
The definitions for these methods are already in Transforms/ so it doesn't make sense for the declarations to be in IR.
* Create a new lib/Rewrite library and move PatternApplicator there.
This new library will be focused on applying rewrites, and will also include compiling rewrites with PDL.
Differential Revision: https://reviews.llvm.org/D89103
Adding missing code that should have been part of "D85869: Utility to
vectorize loop nest using strategy."
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D88346
This patch adds a utility based on SuperVectorizer to vectorize an
affine loop nest using a given vectorization strategy. This strategy allows
targeting specific loops for vectorization instead of relying of the
SuperVectorizer analysis to choose the right loops to vectorize.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D85869
Add support to tile affine.for ops with parametric sizes (i.e., SSA
values). Currently supports hyper-rectangular loop nests with constant
lower bounds only. Move methods
- moveLoopBody(*)
- getTileableBands(*)
- checkTilingLegality(*)
- tilePerfectlyNested(*)
- constructTiledIndexSetHyperRect(*)
to allow reuse with constant tile size API. Add a test pass -test-affine
-parametric-tile to test parametric tiling.
Differential Revision: https://reviews.llvm.org/D87353
Rename 'setInsertionPointAfter(Value)' API to avoid ambiguity with
'setInsertionPointAfter(Operation *)' for SingleResult operations which
implicitly convert to Value (see D86756).
Differential Revision: https://reviews.llvm.org/D87155
Diego Caballero