Normalizing memrefs failed when a caller of symbolic use in a function
can not be casted to `CallOp`. This patch avoids the failure by checking
the result of the casting. If the caller can not be casted to `CallOp`,
it is skipped.
Differential Revision: https://reviews.llvm.org/D87746
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
The current BufferPlacement transformation cannot handle loops properly. Buffers
passed via backedges will not be freed automatically introducing memory leaks.
This CL adds support for loops to overcome these limitations.
Differential Revision: https://reviews.llvm.org/D85513
Currently, there is no option to allow for unrolling a loop up to a specific factor (specified by the user).
The code for doing that is there and there are benefits when unrolling is done to smaller loops (smaller than the factor specified).
Reviewed By: bondhugula
Differential Revision: https://reviews.llvm.org/D87111
In this PR, the users of BufferPlacement can configure
BufferAssginmentTypeConverter. These new configurations would give the user more
freedom in the process of converting function signature, and return and call
operation conversions.
These are the new features:
- Accepting callback functions for decomposing types (i.e. 1 to N type
conversion such as unpacking tuple types).
- Defining ResultConversionKind for specifying whether a function result
with a certain type should be appended to the function arguments list or
should be kept as function result. (Usage:
converter.setResultConversionKind<MemRefType>(AppendToArgumentList))
- Accepting callback functions for composing or decomposing values (i.e. N
to 1 and 1 to N value conversion).
Differential Revision: https://reviews.llvm.org/D85133
This reverts commit 94f5d248772ba0f1f9c8b0746fe75a5d246c5540 because
of failing the following tests:
MLIR :: Dialect/Linalg/tensors-to-buffers.mlir
MLIR :: Transforms/buffer-placement-preparation-allowed-memref-results.mlir
MLIR :: Transforms/buffer-placement-preparation.mlir
In this PR, the users of BufferPlacement can configure
BufferAssginmentTypeConverter. These new configurations would give the user more
freedom in the process of converting function signature, and return and call
operation conversions.
These are the new features:
- Accepting callback functions for decomposing types (i.e. 1 to N type
conversion such as unpacking tuple types).
- Defining ResultConversionKind for specifying whether a function result
with a certain type should be appended to the function arguments list or
should be kept as function result. (Usage:
converter.setResultConversionKind<MemRefType>(AppendToArgumentList))
- Accepting callback functions for composing or decomposing values (i.e. N
to 1 and 1 to N value conversion).
Differential Revision: https://reviews.llvm.org/D85133
When dealing with dialects that will results in function calls to
external libraries, it is important to be able to handle maps as some
dialects may require mapped data. Before this patch, the detection of
whether normalization can apply or not, operations are compared to an
explicit list of operations (`alloc`, `dealloc`, `return`) or to the
presence of specific operation interfaces (`AffineReadOpInterface`,
`AffineWriteOpInterface`, `AffineDMAStartOp`, or `AffineDMAWaitOp`).
This patch add a trait, `MemRefsNormalizable` to determine if an
operation can have its `memrefs` normalized.
This trait can be used in turn by dialects to assert that such
operations are compatible with normalization of `memrefs` with
nontrivial memory layout specification. An example is given in the
literal tests.
Differential Revision: https://reviews.llvm.org/D86236
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
registry.insert<mlir::standalone::StandaloneDialect>();
registry.insert<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
Differential Revision: https://reviews.llvm.org/D85622
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
registry.insert<mlir::standalone::StandaloneDialect>();
registry.insert<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
Differential Revision: https://reviews.llvm.org/D85622
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
mlir::registerDialect<mlir::standalone::StandaloneDialect>();
mlir::registerDialect<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
This changes the behavior of constructing MLIRContext to no longer load globally registered dialects on construction. Instead Dialects are only loaded explicitly on demand:
- the Parser is lazily loading Dialects in the context as it encounters them during parsing. This is the only purpose for registering dialects and not load them in the context.
- Passes are expected to declare the dialects they will create entity from (Operations, Attributes, or Types), and the PassManager is loading Dialects into the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only need to load the dialect for the IR it will emit, and the optimizer is self-contained and load the required Dialects. For example in the Toy tutorial, the compiler only needs to load the Toy dialect in the Context, all the others (linalg, affine, std, LLVM, ...) are automatically loaded depending on the optimization pipeline enabled.
Differential Revision: https://reviews.llvm.org/D85622
This changes the behavior of constructing MLIRContext to no longer load globally registered dialects on construction. Instead Dialects are only loaded explicitly on demand:
- the Parser is lazily loading Dialects in the context as it encounters them during parsing. This is the only purpose for registering dialects and not load them in the context.
- Passes are expected to declare the dialects they will create entity from (Operations, Attributes, or Types), and the PassManager is loading Dialects into the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only need to load the dialect for the IR it will emit, and the optimizer is self-contained and load the required Dialects. For example in the Toy tutorial, the compiler only needs to load the Toy dialect in the Context, all the others (linalg, affine, std, LLVM, ...) are automatically loaded depending on the optimization pipeline enabled.
-- 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
This diff attempts to resolve the TODO in `getOpIndexSet` (formerly
known as `getInstIndexSet`), which states "Add support to handle IfInsts
surronding `op`".
Major changes in this diff:
1. Overload `getIndexSet`. The overloaded version considers both
`AffineForOp` and `AffineIfOp`.
2. The `getInstIndexSet` is updated accordingly: its name is changed to
`getOpIndexSet` and its implementation is based on a new API `getIVs`
instead of `getLoopIVs`.
3. Add `addAffineIfOpDomain` to `FlatAffineConstraints`, which extracts
new constraints from the integer set of `AffineIfOp` and merges it to
the current constraint system.
4. Update how a `Value` is determined as dim or symbol for
`ValuePositionMap` in `buildDimAndSymbolPositionMaps`.
Differential Revision: https://reviews.llvm.org/D84698
Always define a remapping for the memref replacement (`indexRemap`)
with the proper number of inputs, including all the `outerIVs`, so that
the number of inputs and the operands provided for the map don't mismatch.
Reviewed By: bondhugula, andydavis1
Differential Revision: https://reviews.llvm.org/D85177
Remove use of iterator::difference_type to know where to insert a
moved or erased block during undo actions.
Differential Revision: https://reviews.llvm.org/D85066
-- 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
- Add getArgumentTypes() to Region (missed from before)
- Adopt Region argument API in `hasMultiplyAddBody`
- Fix 2 typos in comments
Differential Revision: https://reviews.llvm.org/D84807
The MemRefDataFlow pass does store to load forwarding
only for affine store/loads. This patch updates the pass
to use affine read/write interface which enables vector
forwarding.
Reviewed By: dcaballe, bondhugula, ftynse
Differential Revision: https://reviews.llvm.org/D84302
This revision adds support for much deeper type conversion integration into the conversion process, and enables auto-generating cast operations when necessary. Type conversions are now largely automatically managed by the conversion infra when using a ConversionPattern with a provided TypeConverter. This removes the need for patterns to do type cast wrapping themselves and moves the burden to the infra. This makes it much easier to perform partial lowerings when type conversions are involved, as any lingering type conversions will be automatically resolved/legalized by the conversion infra.
To support this new integration, a few changes have been made to the type materialization API on TypeConverter. Materialization has been split into three separate categories:
* Argument Materialization: This type of materialization is used when converting the type of block arguments when calling `convertRegionTypes`. This is useful for contextually inserting additional conversion operations when converting a block argument type, such as when converting the types of a function signature.
* Source Materialization: This type of materialization is used to convert a legal type of the converter into a non-legal type, generally a source type. This may be called when uses of a non-legal type persist after the conversion process has finished.
* Target Materialization: This type of materialization is used to convert a non-legal, or source, type into a legal, or target, type. This type of materialization is used when applying a pattern on an operation, but the types of the operands have not yet been converted.
Differential Revision: https://reviews.llvm.org/D82831
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
Some dialects have semantics which is not well represented by common
SSA structures with dominance constraints. This patch allows
operations to declare the 'kind' of their contained regions.
Currently, two kinds are allowed: "SSACFG" and "Graph". The only
difference between them at the moment is that SSACFG regions are
required to have dominance, while Graph regions are not required to
have dominance. The intention is that this Interface would be
generated by ODS for existing operations, although this has not yet
been implemented. Presumably, if someone were interested in code
generation, we might also have a "CFG" dialect, which defines control
flow, but does not require SSA.
The new behavior is mostly identical to the previous behavior, since
registered operations without a RegionKindInterface are assumed to
contain SSACFG regions. However, the behavior has changed for
unregistered operations. Previously, these were checked for
dominance, however the new behavior allows dominance violations, in
order to allow the processing of unregistered dialects with Graph
regions. One implication of this is that regions in unregistered
operations with more than one op are no longer CSE'd (since it
requires dominance info).
I've also reorganized the LangRef documentation to remove assertions
about "sequential execution", "SSA Values", and "Dominance". Instead,
the core IR is simply "ordered" (i.e. totally ordered) and consists of
"Values". I've also clarified some things about how control flow
passes between blocks in an SSACFG region. Control Flow must enter a
region at the entry block and follow terminator operation successors
or be returned to the containing op. Graph regions do not define a
notion of control flow.
see discussion here:
https://llvm.discourse.group/t/rfc-allowing-dialects-to-relax-the-ssa-dominance-condition/833/53
Differential Revision: https://reviews.llvm.org/D80358
Up until now, there has been an implicit agreement that when an operation is marked as
"erased" all uses of that operation's results are guaranteed to be removed during conversion. How this works in practice is that there is either an assert/crash/asan failure/etc. This revision adds support for properly detecting when an erased operation has dangling users, emits and error and fails the conversion.
Differential Revision: https://reviews.llvm.org/D82830
- Arguments of the first block of a region are considered region arguments.
- Add API on Region class to deal with these arguments directly instead of
using the front() block.
- Changed several instances of existing code that can use this API
- Fixes https://bugs.llvm.org/show_bug.cgi?id=46535
Differential Revision: https://reviews.llvm.org/D83599
Summary:
Almost all uses of these iterators, including implicit ones, really
only need the const variant (as it should be). The only exception is
in NewGVN, which changes the order of dominator tree child nodes.
Change-Id: I4b5bd71e32d71b0c67b03d4927d93fe9413726d4
Reviewers: arsenm, RKSimon, mehdi_amini, courbet, rriddle, aartbik
Subscribers: wdng, Prazek, hiraditya, kuhar, rogfer01, rriddle, jpienaar, shauheen, antiagainst, nicolasvasilache, arpith-jacob, mgester, lucyrfox, aartbik, liufengdb, stephenneuendorffer, Joonsoo, grosul1, vkmr, Kayjukh, jurahul, msifontes, cfe-commits, llvm-commits
Tags: #clang, #mlir, #llvm
Differential Revision: https://reviews.llvm.org/D83087
ViewLikeOpInterfaces introduce new aliases that need to be added to the alias
list. This is necessary to place deallocs in the right positions.
Differential Revision: https://reviews.llvm.org/D83044
This pass removes redundant dialect-independent Copy operations in different
situations like the following:
%from = ...
%to = ...
... (no user/alias for %to)
copy(%from, %to)
... (no user/alias for %from)
dealloc %from
use(%to)
Differential Revision: https://reviews.llvm.org/D82757
Summary: The current BufferPlacement implementation does not support
nested region control flow. This CL adds support for nested regions via
the RegionBranchOpInterface and the detection of branch-like
(ReturnLike) terminators inside nested regions.
Differential Revision: https://reviews.llvm.org/D81926
Summary: The patch fixes an off by one error in the method collapseParallelLoops. It ensures the same normalized bound is used for the computation of the division and the remainder.
Reviewers: herhut
Reviewed By: herhut
Subscribers: mehdi_amini, rriddle, jpienaar, shauheen, antiagainst, nicolasvasilache, arpith-jacob, mgester, lucyrfox, aartbik, liufengdb, stephenneuendorffer, Joonsoo, grosul1, Kayjukh, jurahul, msifontes
Tags: #mlir
Differential Revision: https://reviews.llvm.org/D82634
When there is a mix of affine load/store and non-affine operations (e.g. std.load, std.store),
affine-loop-fusion ignores the present of non-affine ops, thus changing the program semantics.
E.g. we have a program of three affine loops operating on the same memref in which one of them uses std.load and std.store, as follows.
```
affine.for
affine.store %1
affine.for
std.load %1
std.store %1
affine.for
affine.load %1
affine.store %1
```
affine-loop-fusion will produce the following result which changed the program semantics:
```
affine.for
std.load %1
std.store %1
affine.for
affine.store %1
affine.load %1
affine.store %1
```
This patch is to fix the above problem by checking non-affine users of the memref that are between the source and destination nodes of interest.
Differential Revision: https://reviews.llvm.org/D82158
