This allows for the conversion to match `A(B()) -> C()` with a pattern matching
`A` and marking `B` for deletion.
Also add better assertions when an operation is erased while still having uses.
Differential Revision: https://reviews.llvm.org/D99442
This is an assumption that is made in numerous places in the code. In
particular, in the code generated by mlir-tblgen for operand/result accessors
in ops with attr-sized operand or result lists. Make sure to verify this
assumption.
Note that the operation traits are verified before running the custom op
verifier, which can expect the trait verifier to have passed, but some traits
may be verified before the AttrSizedOperand/ResultTrait and should not make
such assumptions.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D99183
verifyCompatibleShapes is not transitive. Create an n-ary version and
update SameOperandShapes and SameOperandAndResultShapes traits to use
it.
Differential Revision: https://reviews.llvm.org/D98331
The current implementation of Value involves a pointer int pair with several different kinds of owners, i.e. BlockArgumentImpl*, Operation *, TrailingOpResult*. This design arose from the desire to save memory overhead for operations that have a very small number of results (generally 0-2). There are, unfortunately, many problematic aspects of the current implementation that make Values difficult to work with or just inefficient.
Operation result types are stored as a separate array on the Operation. This is very inefficient for many reasons: we use TupleType for multiple results, which can lead to huge amounts of memory usage if multi-result operations change types frequently(they do). It also means that simple methods like Value::getType/Value::setType now require complex logic to get to the desired type.
Value only has one pointer bit free, severely limiting the ability to use it in things like PointerUnion/PointerIntPair. Given that we store the kind of a Value along with the "owner" pointer, we only leave one bit free for users of Value. This creates situations where we end up nesting PointerUnions to be able to use Value in one.
As noted above, most of the methods in Value need to branch on at least 3 different cases which is both inefficient, possibly error prone, and verbose. The current storage of results also creates problems for utilities like ValueRange/TypeRange, which want to efficiently store base pointers to ranges (of which Operation* isn't really useful as one).
This revision greatly simplifies the implementation of Value by the introduction of a new ValueImpl class. This class contains all of the state shared between all of the various derived value classes; i.e. the use list, the type, and the kind. This shared implementation class provides several large benefits:
* Most of the methods on value are now branchless, and often one-liners.
* The "kind" of the value is now stored in ValueImpl instead of Value
This frees up all of Value's pointer bits, allowing for users to take full advantage of PointerUnion/PointerIntPair/etc. It also allows for storing more operation results as "inline", 6 now instead of 2, freeing up 1 word per new inline result.
* Operation result types are now stored in the result, instead of a side array
This drops the size of zero-result operations by 1 word. It also removes the memory crushing use of TupleType for operations results (which could lead up to hundreds of megabytes of "dead" TupleTypes in the context). This also allowed restructured ValueRange, making it simpler and one word smaller.
This revision does come with two conceptual downsides:
* Operation::getResultTypes no longer returns an ArrayRef<Type>
This conceptually makes some usages slower, as the iterator increment is slightly more complex.
* OpResult::getOwner is slightly more expensive, as it now requires a little bit of arithmetic
From profiling, neither of the conceptual downsides have resulted in any perceivable hit to performance. Given the advantages of the new design, most compiles are slightly faster.
Differential Revision: https://reviews.llvm.org/D97804
Some elementwise operations are not scalarizable, vectorizable, or tensorizable.
Split `ElementwiseMappable` trait into the following, more precise traits.
- `Elementwise`
- `Scalarizable`
- `Vectorizable`
- `Tensorizable`
This allows for reuse of `Elementwise` in dialects like HLO.
Differential Revision: https://reviews.llvm.org/D97674
Move the results in line with the op instead. This results in each
operation having its own types recorded vs single tuple type, but comes
at benefit that every mutation doesn't incurs uniquing. Ran into cases
where updating result type of operation led to very large memory usage.
Differential Revision: https://reviews.llvm.org/D97652
This also exposed a bug in Dialect loading where it was not correctly identifying identifiers that had the dialect namespace as a prefix.
Differential Revision: https://reviews.llvm.org/D97431
A majority of operations have a very small number of interfaces, which means that the cost of using a hash map is generally larger for interface lookups than just a binary search. In the future when there are a number of operations with large amounts of interfaces, we can switch to a hybrid approach that optimizes lookups based on the number of interfaces. For now, however, a binary search is the best approach.
This dropped compile time on a largish TF MLIR module by 20%(half a second).
Differential Revision: https://reviews.llvm.org/D96085
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 reverts commit 511dd4f438 along with
a couple fixes.
Original message:
Now the context is the first, rather than the last input.
This better matches the rest of the infrastructure and makes
it easier to move these types to being declaratively specified.
Phabricator: https://reviews.llvm.org/D96111
Now the context is the first, rather than the last input.
This better matches the rest of the infrastructure and makes
it easier to move these types to being declaratively specified.
Differential Revision: https://reviews.llvm.org/D96111
A cast-like operation is one that converts from a set of input types to a set of output types. The arity of the inputs may be from 0-N, whereas the arity of the outputs may be anything from 1-N. Cast-like operations are removable in cases where they produce a "no-op", i.e when the input types and output types match 1-1.
Differential Revision: https://reviews.llvm.org/D94831
The functions will be removed by January 20th.
All call sites within MLIR have been converted in previous changes.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D94191
This class used to serve a few useful purposes:
* Allowed containing a null DictionaryAttr
* Provided some simple mutable API around a DictionaryAttr
The first of which is no longer an issue now that there is much better caching support for attributes in general, and a cache in the context for empty dictionaries. The second results in more trouble than it's worth because it mutates the internal dictionary on every action, leading to a potentially large number of dictionary copies. NamedAttrList is a much better alternative for the second use case, and should be modified as needed to better fit it's usage as a DictionaryAttrBuilder.
Differential Revision: https://reviews.llvm.org/D93442
This better matches the rest of the infrastructure, is much simpler, and makes it easier to move these types to being declaratively specified.
Differential Revision: https://reviews.llvm.org/D93432
Trailing objects are really nice for storing additional data inline with the main class, and is something that we heavily take advantage of for Operation(and many other classes). To get the address of the inline data you need to compute the address by doing some pointer arithmetic taking into account any objects stored before the object you want to access. Most classes keep the count of the number of objects, so this is relatively cheap to compute. This is not the case for results though, which have two different types(inline and trailing) that are not necessarily as cheap to compute as the count for other objects. This revision moves the storage for results to before the operation and stores them in reverse order. This allows for getting results to still be very fast given that they are never iterated directly in order, and also greatly improves the speed when accessing the other trailing objects of an operation(operands/regions/blocks/etc).
This reduced compile time when compiling a decently sized mlir module by about ~400ms, or 2.17s -> 1.76s.
Differential Revision: https://reviews.llvm.org/D92687
This is part of a larger refactoring the better congregates the builtin structures under the BuiltinDialect. This also removes the problematic "standard" naming that clashes with the "standard" dialect, which is not defined within IR/. A temporary forward is placed in StandardTypes.h to allow time for downstream users to replaced references.
Differential Revision: https://reviews.llvm.org/D92435
PDL patterns are now supported via a new `PDLPatternModule` class. This class contains a ModuleOp with the pdl::PatternOp operations representing the patterns, as well as a collection of registered C++ functions for native constraints/creations/rewrites/etc. that may be invoked via the pdl patterns. Instances of this class are added to an OwningRewritePatternList in the same fashion as C++ RewritePatterns, i.e. via the `insert` method.
The PDL bytecode is an in-memory representation of the PDL interpreter dialect that can be efficiently interpreted/executed. The representation of the bytecode boils down to a code array(for opcodes/memory locations/etc) and a memory buffer(for storing attributes/operations/values/any other data necessary). The bytecode operations are effectively a 1-1 mapping to the PDLInterp dialect operations, with a few exceptions in cases where the in-memory representation of the bytecode can be more efficient than the MLIR representation. For example, a generic `AreEqual` bytecode op can be used to represent AreEqualOp, CheckAttributeOp, and CheckTypeOp.
The execution of the bytecode is split into two phases: matching and rewriting. When matching, all of the matched patterns are collected to avoid the overhead of re-running parts of the matcher. These matched patterns are then considered alongside the native C++ patterns, which rewrite immediately in-place via `RewritePattern::matchAndRewrite`, for the given root operation. When a PDL pattern is matched and has the highest benefit, it is passed back to the bytecode to execute its rewriter.
Differential Revision: https://reviews.llvm.org/D89107
Null types are commonly used as an error marker. Catch them in the constructor
of Operation if they are present in the result type list, as otherwise this
could lead to further surprising behavior when querying op result types.
Fix AsyncToLLVM and StandardToLLVM that were using null types when constructing
operations.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D91770
This patch adds an `ElementwiseMappable` trait as discussed in the RFC
here:
https://llvm.discourse.group/t/rfc-std-elementwise-ops-on-tensors/2113/23
This trait can power a number of transformations and analyses.
A subsequent patch adds a convert-elementwise-to-linalg pass exhibits
how this trait allows writing generic transformations.
See https://reviews.llvm.org/D90354 for that patch.
This trait slightly changes some verifier messages, but the diagnostics
are usually about as good. I fiddled with the ordering of the trait in
the .td file trait lists to minimize the changes here.
Differential Revision: https://reviews.llvm.org/D90731
This revision refactors the base Op/AbstractOperation classes to reduce the amount of generated code size when defining a new operation. The current scheme involves taking the address of functions defined directly on Op and Trait classes. This is problematic because even when these functions are empty/unused we still result in these functions being defined in the main executable. In this revision, we switch to using SFINAE and template type filtering to remove remove functions that are not needed/used. For example, if an operation does not define a custom `print` method we shouldn't define a templated `printAssembly` method for it. The same applies to parsing/folding/verification/etc. This dropped MLIR code size for a large downstream library by ~10%(~1 mb in an opt build).
Differential Revision: https://reviews.llvm.org/D90196
This trait simply adds a fold of f(f(x)) = f(x) when an operation is labelled as idempotent
Reviewed By: rriddle, andyly
Differential Revision: https://reviews.llvm.org/D89421
This is the same diff as https://reviews.llvm.org/D88809/ except side effect
free check is removed for involution and a FIXME is added until the dependency
is resolved for shared builds. The old diff has more details on possible fixes.
Reviewed By: rriddle, andyly
Differential Revision: https://reviews.llvm.org/D89333
When attempting to compute a differential orderIndex we were calculating the
bailout condition correctly, but then an errant "+ 1" meant the orderIndex we
created was invalid.
Added test.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D89115
This reverts commit 1ceaffd95a.
The build is broken with -DBUILD_SHARED_LIBS=ON ; seems like a possible
layering issue to investigate:
tools/mlir/lib/IR/CMakeFiles/obj.MLIRIR.dir/Operation.cpp.o: In function `mlir::MemoryEffectOpInterface::hasNoEffect(mlir::Operation*)':
Operation.cpp:(.text._ZN4mlir23MemoryEffectOpInterface11hasNoEffectEPNS_9OperationE[_ZN4mlir23MemoryEffectOpInterface11hasNoEffectEPNS_9OperationE]+0x9c): undefined reference to `mlir::MemoryEffectOpInterface::getEffects(llvm::SmallVectorImpl<mlir::SideEffects::EffectInstance<mlir::MemoryEffects::Effect> >&)'
This change allows folds to be done on a newly introduced involution trait rather than having to manually rewrite this optimization for every instance of an involution
Reviewed By: rriddle, andyly, stephenneuendorffer
Differential Revision: https://reviews.llvm.org/D88809
- Introduce a new BlockRange class to represent range of blocks (constructible from
an ArrayRef<Block *> or a SuccessorRange);
- Change Operation::create() methods to use TypeRange for result types, ValueRange for
operands and BlockRange for successors.
Differential Revision: https://reviews.llvm.org/D86985
Instead of storing a StringRef, we keep an Identifier which otherwise requires a lock on the context to retrieve.
This will allow to get an Identifier for any registered Operation for "free".
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D86994
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.
These hooks were introduced before the Interfaces mechanism was available.
DialectExtractElementHook is unused and entirely removed. The
DialectConstantFoldHook is used a fallback in the
operation fold() method, and is replaced by a DialectInterface.
The DialectConstantDecodeHook is used for interpreting OpaqueAttribute
and should be revamped, but is replaced with an interface in 1:1 fashion
for now.
Differential Revision: https://reviews.llvm.org/D85595
- 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
The SingleBlockImplicitTerminator op trait provides a function
`ensureRegionTerminator` that injects an appropriate terminator into the block
if necessary, which is used during operation constructing and parsing.
Currently, this function directly modifies the IR using low-level APIs on
Operation and Block. If this function is called from a conversion pattern,
these manipulations are not reflected in the ConversionPatternRewriter and thus
cannot be undone or, worse, lead to tricky memory errors and malformed IR.
Change `ensureRegionTerminator` to take an instance of `OpBuilder` instead of
`Builder`, and use it to construct the block and the terminator when required.
Maintain overloads taking an instance of `Builder` and creating a simple
`OpBuilder` to use in parsers, which don't have an `OpBuilder` and cannot
interact with the dialect conversion mechanism. This change was one of the
reasons to make `<OpTy>::build` accept an `OpBuilder`.
Differential Revision: https://reviews.llvm.org/D80138
Mehdi Amini