This is allowing to build an OpPassManager from a StringRef instead of an
Identifier, which enables building pipelines without an MLIRContext.
An identifier is still cached on-demand on the OpPassManager for efficiency
during the IR traversal.
This patch adds basic support for vectorization of uniform values to SuperVectorizer.
For now, only invariant values to the target vector loops are considered uniform. This
enables the vectorization of loops that use function arguments and external definitions
to the vector loops. We could extend uniform support in the future if we implement some
kind of divergence analysis algorithm.
Reviewed By: nicolasvasilache, aartbik
Differential Revision: https://reviews.llvm.org/D86756
This allows to defers the check for traits to the execution instead of forcing it on the pipeline creation.
In particular, this is making our pipeline creation tolerant to dialects not being loaded in the context yet.
Reviewed By: rriddle, GMNGeoffrey
Differential Revision: https://reviews.llvm.org/D86915
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
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 94f5d24877 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
Unsigned and Signless attributes use uintN_t and signed attributes use intN_t, where N is the fixed width. The 1-bit variants use bool.
Differential Revision: https://reviews.llvm.org/D86739
This patch add the missing operands to the acc.loop operation. Only the device_type
information is not part of the operation for now.
Reviewed By: rriddle, kiranchandramohan
Differential Revision: https://reviews.llvm.org/D86753
This adds some initial support for regions and does not support formatting the specific arguments of a region. For now this can be achieved by using a custom directive that formats the arguments and then parses the region.
Differential Revision: https://reviews.llvm.org/D86760
Symbol names are a special form of StringAttr that get treated specially in certain areas, such as formatting. This revision adds a special derived attr for them in ODS and adds support in the assemblyFormat for formatting them properly.
Differential Revision: https://reviews.llvm.org/D86759
This revision adds support for custom directives to the declarative assembly format. This allows for users to use C++ for printing and parsing subsections of an otherwise declaratively specified format. The custom directive is structured as follows:
```
custom-directive ::= `custom` `<` UserDirective `>` `(` Params `)`
```
`user-directive` is used as a suffix when this directive is used during printing and parsing. When parsing, `parseUserDirective` will be invoked. When printing, `printUserDirective` will be invoked. The first parameter to these methods must be a reference to either the OpAsmParser, or OpAsmPrinter. The type of rest of the parameters is dependent on the `Params` specified in the assembly format.
Differential Revision: https://reviews.llvm.org/D84719
* This is just enough to create regions/blocks and iterate over them.
* Does not yet implement the preferred iteration strategy (python pseudo containers).
* Refinements need to come after doing basic mappings of operations and values so that the whole hierarchy can be used.
Differential Revision: https://reviews.llvm.org/D86683
This is intended to ease the transition for client with a lot of
dependencies. It'll be removed in the coming weeks.
Differential Revision: https://reviews.llvm.org/D86755
Adding a conversion pattern for the parallel Operation. This will
help the conversion of parallel operation with standard dialect to
parallel operation with llvm dialect. The type conversion of the block
arguments in a parallel region are controlled by the pattern for the
parallel Operation. Without this pattern, a parallel Operation with
block arguments cannot be converted from standard to LLVM dialect.
Other OpenMP operations without regions are marked as legal. When
translation of OpenMP operations with regions are added then patterns
for these operations can also be added.
Also uses all the standard to llvm patterns. Patterns of other dialects
can be added later if needed.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D86273
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 patch introduces a hook to encode descriptor set
and binding number into `spv.globalVariable`'s symbolic name. This
allows to preserve this information, and at the same time legalize
the global variable for the conversion to LLVM dialect.
This is required for `mlir-spirv-cpu-runner` to convert kernel
arguments into LLVM.
Also, a couple of some nits added:
- removed unused comment
- changed to a capital letter in the comment
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D86515
This makes OpPassManager more of a "container" of passes and not responsible to drive the execution.
As such we also make it constructible publicly, which will allow to build arbitrary pipeline decoupled from the execution. We'll make use of this facility to expose "dynamic pipeline" in the future.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D86391
This patch adds an optional name to SPIR-V module.
This will help with lowering from GPU dialect (so that we
can pass the kernel module name) and will be more naturally
aligned with `GPUModuleOp`/`ModuleOp`.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D86386
The PDL Interpreter dialect provides a lower level abstraction compared to the PDL dialect, and is targeted towards low level optimization and interpreter code generation. The dialect operations encapsulates low-level pattern match and rewrite "primitives", such as navigating the IR (Operation::getOperand), creating new operations (OpBuilder::create), etc. Many of the operations within this dialect also fuse branching control flow with some form of a predicate comparison operation. This type of fusion reduces the amount of work that an interpreter must do when executing.
An example of this representation is shown below:
```mlir
// The following high level PDL pattern:
pdl.pattern : benefit(1) {
%resultType = pdl.type
%inputOperand = pdl.input
%root, %results = pdl.operation "foo.op"(%inputOperand) -> %resultType
pdl.rewrite %root {
pdl.replace %root with (%inputOperand)
}
}
// May be represented in the interpreter dialect as follows:
module {
func @matcher(%arg0: !pdl.operation) {
pdl_interp.check_operation_name of %arg0 is "foo.op" -> ^bb2, ^bb1
^bb1:
pdl_interp.return
^bb2:
pdl_interp.check_operand_count of %arg0 is 1 -> ^bb3, ^bb1
^bb3:
pdl_interp.check_result_count of %arg0 is 1 -> ^bb4, ^bb1
^bb4:
%0 = pdl_interp.get_operand 0 of %arg0
pdl_interp.is_not_null %0 : !pdl.value -> ^bb5, ^bb1
^bb5:
%1 = pdl_interp.get_result 0 of %arg0
pdl_interp.is_not_null %1 : !pdl.value -> ^bb6, ^bb1
^bb6:
pdl_interp.record_match @rewriters::@rewriter(%0, %arg0 : !pdl.value, !pdl.operation) : benefit(1), loc([%arg0]), root("foo.op") -> ^bb1
}
module @rewriters {
func @rewriter(%arg0: !pdl.value, %arg1: !pdl.operation) {
pdl_interp.replace %arg1 with(%arg0)
pdl_interp.return
}
}
}
```
Differential Revision: https://reviews.llvm.org/D84579
This assertion does not achieve what it meant to do originally, as it
would fire only when applied to an unregistered operation, which is a
fairly rare circumstance (it needs a dialect or context allowing
unregistered operation in the input in the first place).
Instead we relax it to only fire when it should have matched but didn't
because of the misconfiguration.
Differential Revision: https://reviews.llvm.org/D86588
Provides fast, generic way of setting a mask up to a certain
point. Potential use cases that may benefit are create_mask
and transfer_read/write operations in the vector dialect.
Reviewed By: bkramer
Differential Revision: https://reviews.llvm.org/D86501
* Generic mlir.ir.Attribute class.
* First standard attribute (mlir.ir.StringAttr), following the same pattern as generic vs standard types.
* NamedAttribute class.
Differential Revision: https://reviews.llvm.org/D86250
This will allow out-of-tree translation to register the dialects they expect
to see in their input, on the model of getDependentDialects() for passes.
Differential Revision: https://reviews.llvm.org/D86409
Refactor the way the reduction tree pass works in the MLIR Reduce tool by introducing a set of utilities that facilitate the implementation of new Reducer classes to be used in the passes.
This will allow for the fast implementation of general transformations to operate on all mlir modules as well as custom transformations for different dialects.
These utilities allow for the implementation of Reducer classes by simply defining a method that indexes the operations/blocks/regions to be transformed and a method to perform the deletion or transfomration based on the indexes.
Create the transformSpace class member in the ReductionNode class to keep track of the indexes that have already been transformed or deleted at a current level.
Delete the FunctionReducer class and replace it with the OpReducer class to reflect this new API while performing the same transformation and allowing the instantiation of a reduction pass for different types of operations at the module's highest hierarchichal level.
Modify the SinglePath Traversal method to reflect the use of the new API.
Reviewed: jpienaar
Differential Revision: https://reviews.llvm.org/D85591
Add a folder to the affine.parallel op so that loop bounds expressions are canonicalized.
Additionally, a new AffineParallelNormalizePass is added to adjust affine.parallel ops so that the lower bound is always 0 and the upper bound always represents a range with a step size of 1.
Differential Revision: https://reviews.llvm.org/D84998
This patch adds the capability to perform constraint redundancy checks for `FlatAffineConstraints` using `Simplex`, via a new member function `FlatAffineConstraints::removeRedundantConstraints`. The pre-existing redundancy detection algorithm runs a full rational emptiness check for each inequality separately for checking redundancy. Leveraging the existing `Simplex` infrastructure, in this patch we have an algorithm for redundancy checks that can check each constraint by performing pivots on the tableau, which provides an alternative to running Fourier-Motzkin elimination for each constraint separately.
Differential Revision: https://reviews.llvm.org/D84935
- This utility to merge a block anywhere into another one can help inline single
block regions into other blocks.
- Modified patterns test to use the new function.
Differential Revision: https://reviews.llvm.org/D86251
Add the unsigned complements to the existing FPToSI and SIToFP operations in the
standard dialect, with one-to-one lowerings to the corresponding LLVM operations.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D85557
PDL presents a high level abstraction for the rewrite pattern infrastructure available in MLIR. This abstraction allows for representing patterns transforming MLIR, as MLIR. This allows for applying all of the benefits that the general MLIR infrastructure provides, to the infrastructure itself. This means that pattern matching can be more easily verified for correctness, targeted by frontends, and optimized.
PDL abstracts over various different aspects of patterns and core MLIR data structures. Patterns are specified via a `pdl.pattern` operation. These operations contain a region body for the "matcher" code, and terminate with a `pdl.rewrite` that either dispatches to an external rewriter or contains a region for the rewrite specified via `pdl`. The types of values in `pdl` are handle types to MLIR C++ types, with `!pdl.attribute`, `!pdl.operation`, and `!pdl.type` directly mapping to `mlir::Attribute`, `mlir::Operation*`, and `mlir::Value` respectively.
An example pattern is shown below:
```mlir
// pdl.pattern contains metadata similarly to a `RewritePattern`.
pdl.pattern : benefit(1) {
// External input operand values are specified via `pdl.input` operations.
// Result types are constrainted via `pdl.type` operations.
%resultType = pdl.type
%inputOperand = pdl.input
%root, %results = pdl.operation "foo.op"(%inputOperand) -> %resultType
pdl.rewrite(%root) {
pdl.replace %root with (%inputOperand)
}
}
```
This is a culmination of the work originally discussed here: https://groups.google.com/a/tensorflow.org/g/mlir/c/j_bn74ByxlQ
Differential Revision: https://reviews.llvm.org/D84578
Provide C API for MLIR standard attributes. Since standard attributes live
under lib/IR in core MLIR, place the C APIs in the IR library as well (standard
ops will go in a separate library).
Affine map and integer set attributes are only exposed as placeholder types
with IsA support due to the lack of C APIs for the corresponding types.
Integer and floating point attribute APIs expecting APInt and APFloat are not
exposed pending decision on how to support APInt and APFloat.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D86143
* The binding for Type is trivial and should be non-controversial.
* The way that I define the IntegerType should serve as a pattern for what I want to do next.
* I propose defining the rest of the standard types in this fashion and then generalizing for dialect types as necessary.
* Essentially, creating/accessing a concrete Type (vs interacting with the string form) is done by "casting" to the concrete type (i.e. IntegerType can be constructed with a Type and will throw if the cast is illegal).
* This deviates from some of our previous discussions about global objects but I think produces a usable API and we should go this way.
Differential Revision: https://reviews.llvm.org/D86179
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 greatly simplifies a large portion of the underlying infrastructure, allows for lookups of singleton classes to be much more efficient and always thread-safe(no locking). As a result of this, the dialect symbol registry has been removed as it is no longer necessary.
For users broken by this change, an alert was sent out(https://llvm.discourse.group/t/removing-kinds-from-attributes-and-types) that helps prevent a majority of the breakage surface area. All that should be necessary, if the advice in that alert was followed, is removing the kind passed to the ::get methods.
Differential Revision: https://reviews.llvm.org/D86121
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>()
LinalgDistribution options to allow more general distributions.
Changing the signature of the callback to send in the ranges for all
the parallel loops and expect a vector with the Value to use for the
processor-id and number-of-processors for each of the parallel loops.
Differential Revision: https://reviews.llvm.org/D86095
There should be an equivalent std.floor op to std.ceil. This includes
matching lowerings for SPIRV, NVVM, ROCDL, and LLVM.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D85940
Create a reduction pass that accepts an optimization pass as argument
and only replaces the golden module in the pipeline if the output of the
optimization pass is smaller than the input and still exhibits the
interesting behavior.
Add a -test-pass option to test individual passes in the MLIR Reduce
tool.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D84783
Provide C API for MLIR standard types. Since standard types live under lib/IR
in core MLIR, place the C APIs in the IR library as well (standard ops will go
into a separate library). This also defines a placeholder for affine maps that
are necessary to construct a memref, but are not yet exposed to the C API.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D86094
This function is available on llvm::Type and has been used by some clients of
the LLVM dialect before the transition. Implement the MLIR counterpart.
Reviewed By: schweitz
Differential Revision: https://reviews.llvm.org/D85847
- Add variants of getAnalysis() and friends that operate on a specific derived
operation types.
- Add OpPassManager::getAnalysis() to always call the base getAnalysis() with OpT.
- With this, an OperationPass can call getAnalysis<> using an analysis type that
is generic (works on Operation *) or specific to the OpT for the pass. Anything
else will fail to compile.
- Extend AnalysisManager unit test to test this, and add a new PassManager unit
test to test this functionality in the context of an OperationPass.
Differential Revision: https://reviews.llvm.org/D84897
Legacy implementation of the LLVM dialect in MLIR contained an instance of
llvm::Module as it was required to parse LLVM IR types. The access to the data
layout of this module was exposed to the users for convenience, but in practice
this layout has always been the default one obtained by parsing an empty layout
description string. Current implementation of the dialect no longer relies on
wrapping LLVM IR types, but it kept an instance of DataLayout for
compatibility. This effectively forces a single data layout to be used across
all modules in a given MLIR context, which is not desirable. Remove DataLayout
from the LLVM dialect and attach it as a module attribute instead. Since MLIR
does not yet have support for data layouts, use the LLVM DataLayout in string
form with verification inside MLIR. Introduce the layout when converting a
module to the LLVM dialect and keep the default "" description for
compatibility.
This approach should be replaced with a proper MLIR-based data layout when it
becomes available, but provides an immediate solution to compiling modules with
different layouts, e.g. for GPUs.
This removes the need for LLVMDialectImpl, which is also removed.
Depends On D85650
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D85652
This will help refactoring some of the tools to prepare for the explicit registration of
Dialects.
Differential Revision: https://reviews.llvm.org/D86023
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
Explicitly declare ReductionTreeBase base class in ReductionTreePass copy constructor.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D85983
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.
Masked loading/storing in various forms can be optimized
into simpler memory operations when the mask is all true
or all false. Note that the backend does similar optimizations
but doing this early may expose more opportunities for further
optimizations. This further prepares progressively lowering
transfer read and write into 1-D memory operations.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D85769
This revision removes all of the lingering usages of Type::getKind. A consequence of this is that FloatType is now split into 4 derived types that represent each of the possible float types(BFloat16Type, Float16Type, Float32Type, and Float64Type). Other than this split, this revision is NFC.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D85566
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
- Add "using namespace mlir::tblgen" in several of the TableGen/*.cpp files and
eliminate the tblgen::prefix to reduce code clutter.
Differential Revision: https://reviews.llvm.org/D85800
Provide printing functions for most IR objects in C API (except Region that
does not have a `print` function, and Module that is expected to be printed as
Operation instead). The printing is based on a callback that is called with
chunks of the string representation and forwarded user-defined data.
Reviewed By: stellaraccident, Jing, mehdi_amini
Differential Revision: https://reviews.llvm.org/D85748
Using intptr_t is a consensus for MLIR C API, but the change was missing
from 75f239e975 (that was using unsigned initially) due to a
misrebase.
Reviewed By: stellaraccident, mehdi_amini
Differential Revision: https://reviews.llvm.org/D85751
This patch adds the translation of the proc_bind clause in a
parallel operation.
The values that can be specified for the proc_bind clause are
specified in the OMP.td tablegen file in the llvm/Frontend/OpenMP
directory. From this single source of truth enumeration for
proc_bind is generated in llvm and mlir (used in specification of
the parallel Operation in the OpenMP dialect). A function to return
the enum value from the string representation is also generated.
A new header file (DirectiveEmitter.h) containing definitions of
classes directive, clause, clauseval etc is created so that it can
be used in mlir as well.
Reviewers: clementval, jdoerfert, DavidTruby
Differential Revision: https://reviews.llvm.org/D84347
Now that LLVM dialect types are implemented directly in the dialect, we can use
MLIR hooks for verifying type construction invariants. Implement the verifiers
and use them in the parser.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D85663
Linalg to processors.
This changes adds infrastructure to distribute the loops generated in
Linalg to processors at the time of generation. This addresses use
case where the instantiation of loop is done just to distribute
them. The option to distribute is added to TilingOptions for now and
will allow specifying the distribution as a transformation option,
just like tiling and promotion are specified as options.
Differential Revision: https://reviews.llvm.org/D85147
- Fix ODS framework to suppress build methods that infer result types and are
ambiguous with collective variants. This applies to operations with a single variadic
inputs whose result types can be inferred.
- Extended OpBuildGenTest to test these kinds of ops.
Differential Revision: https://reviews.llvm.org/D85060
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
This reverts commit 9f24640b7e.
We hit some dead-locks on thread exit in some configurations: TLS exit handler is taking a lock.
Temporarily reverting this change as we're debugging what is going on.
Implement the Reduction Tree Pass framework as part of the MLIR Reduce tool. This is a parametarizable pass that allows for the implementation of custom reductions passes in the tool.
Implement the FunctionReducer class as an example of a Reducer class parameter for the instantiation of a Reduction Tree Pass.
Create a pass pipeline with a Reduction Tree Pass with the FunctionReducer class specified as parameter.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D83969
This also beefs up the test coverage:
- Make unranked memref testing consistent with ranked memrefs.
- Add testing for the invalid element type cases.
This is not quite NFC: index types are now allowed in unranked memrefs.
Differential Revision: https://reviews.llvm.org/D85541
This revision refactors the default definition of the attribute and type `classof` methods to use the TypeID of the concrete class instead of invoking the `kindof` method. The TypeID is already used as part of uniquing, and this allows for removing the need for users to define any of the type casting utilities themselves.
Differential Revision: https://reviews.llvm.org/D85356
Subclass data is useful when a certain amount of memory is allocated, but not all of it is used. In the case of Type, that hasn't been the case for a while and the subclass is just taking up a full `unsigned`. Removing this frees up ~8 bytes for almost every type instance.
Differential Revision: https://reviews.llvm.org/D85348
This class allows for defining thread local objects that have a set non-static lifetime. This internals of the cache use a static thread_local map between the various different non-static objects and the desired value type. When a non-static object destructs, it simply nulls out the entry in the static map. This will leave an entry in the map, but erase any of the data for the associated value. The current use cases for this are in the MLIRContext, meaning that the number of items in the static map is ~1-2 which aren't particularly costly enough to warrant the complexity of pruning. If a use case arises that requires pruning of the map, the functionality can be added.
This is especially useful in the context of MLIR for implementing thread-local caching of context level objects that would otherwise have very high lock contention. This revision adds a thread local cache in the MLIRContext for attributes, identifiers, and types to reduce some of the locking burden. This led to a speedup of several hundred miliseconds when compiling a conversion pass on a very large mlir module(>300K operations).
Differential Revision: https://reviews.llvm.org/D82597
This allows for bucketing the different possible storage types, with each bucket having its own allocator/mutex/instance map. This greatly reduces the amount of lock contention when multi-threading is enabled. On some non-trivial .mlir modules (>300K operations), this led to a compile time decrease of a single conversion pass by around half a second(>25%).
Differential Revision: https://reviews.llvm.org/D82596
This change adds initial support needed to generate OpenCL compliant SPIRV.
If Kernel capability is declared then memory model becomes OpenCL.
If Addresses capability is declared then addressing model becomes Physical64.
Additionally for Kernel capability interface variable ABI attributes are not
generated as entry point function is expected to have normal arguments.
Differential Revision: https://reviews.llvm.org/D85196
This revision adds a folding pattern to replace affine.min ops by the actual min value, when it can be determined statically from the strides and bounds of enclosing scf loop .
This matches the type of expressions that Linalg produces during tiling and simplifies boundary checks. For now Linalg depends both on Affine and SCF but they do not depend on each other, so the pattern is added there.
In the future this will move to a more appropriate place when it is determined.
The canonicalization of AffineMinOp operations in the context of enclosing scf.for and scf.parallel proceeds by:
1. building an affine map where uses of the induction variable of a loop
are replaced by `%lb + %step * floordiv(%iv - %lb, %step)` expressions.
2. checking if any of the results of this affine map divides all the other
results (in which case it is also guaranteed to be the min).
3. replacing the AffineMinOp by the result of (2).
The algorithm is functional in simple parametric tiling cases by using semi-affine maps. However simplifications of such semi-affine maps are not yet available and the canonicalization does not succeed yet.
Differential Revision: https://reviews.llvm.org/D82009
This patch moves the registration to a method in the MLIRContext: getOrCreateDialect<ConcreteDialect>()
This method requires dialect to provide a static getDialectNamespace()
and store a TypeID on the Dialect itself, which allows to lazyily
create a dialect when not yet loaded in the context.
As a side effect, it means that duplicated registration of the same
dialect is not an issue anymore.
To limit the boilerplate, TableGen dialect generation is modified to
emit the constructor entirely and invoke separately a "init()" method
that the user implements.
Differential Revision: https://reviews.llvm.org/D85495
Original modeling of LLVM IR types in the MLIR LLVM dialect had been wrapping
LLVM IR types and therefore required the LLVMContext in which they were created
to outlive them, which was solved by placing the LLVMContext inside the dialect
and thus having the lifetime of MLIRContext. This has led to numerous issues
caused by the lack of thread-safety of LLVMContext and the need to re-create
LLVM IR modules, obtained by translating from MLIR, in different LLVM contexts
to enable parallel compilation. Similarly, llvm::Module had been introduced to
keep track of identified structure types that could not be modeled properly.
A recent series of commits changed the modeling of LLVM IR types in the MLIR
LLVM dialect so that it no longer wraps LLVM IR types and has no dependence on
LLVMContext and changed the ownership model of the translated LLVM IR modules.
Remove LLVMContext and LLVM modules from the implementation of MLIR LLVM
dialect and clean up the remaining uses.
The only part of LLVM IR that remains necessary for the LLVM dialect is the
data layout. It should be moved from the dialect level to the module level and
replaced with an MLIR-based representation to remove the dependency of the
LLVMDialect on LLVM IR library.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D85445
Due to the original type system implementation, LLVMDialect in MLIR contains an
LLVMContext in which the relevant objects (types, metadata) are created. When
an MLIR module using the LLVM dialect (and related intrinsic-based dialects
NVVM, ROCDL, AVX512) is converted to LLVM IR, it could only live in the
LLVMContext owned by the dialect. The type system no longer relies on the
LLVMContext, so this limitation can be removed. Instead, translation functions
now take a reference to an LLVMContext in which the LLVM IR module should be
constructed. The caller of the translation functions is responsible for
ensuring the same LLVMContext is not used concurrently as the translation no
longer uses a dialect-wide context lock.
As an additional bonus, this change removes the need to recreate the LLVM IR
module in a different LLVMContext through printing and parsing back, decreasing
the compilation overhead in JIT and GPU-kernel-to-blob passes.
Reviewed By: rriddle, mehdi_amini
Differential Revision: https://reviews.llvm.org/D85443
This new pattern mixes vector.transpose and direct lowering to vector.reduce.
This allows more progressive lowering than immediately going to insert/extract and
composes more nicely with other canonicalizations.
This has 2 use cases:
1. for very wide vectors the generated IR may be much smaller
2. when we have a custom lowering for transpose ops we can target it directly
rather than rely LLVM
Differential Revision: https://reviews.llvm.org/D85428
When any of the memrefs in a structured linalg op has a zero dimension, it becomes dead.
This is consistent with the fact that linalg ops deduce their loop bounds from their operands.
Note however that this is not the case for the `tensor<0xelt_type>` which is a special convention
that must be lowered away into either `memref<elt_type>` or just `elt_type` before this
canonicalization can kick in.
Differential Revision: https://reviews.llvm.org/D85413
The RewritePattern will become one of several, and will be part of the LLVM conversion pass (instead of a separate pass following LLVM conversion).
Reviewed By: herhut
Differential Revision: https://reviews.llvm.org/D84946
Historical modeling of the LLVM dialect types had been wrapping LLVM IR types
and therefore needed access to the instance of LLVMContext stored in the
LLVMDialect. The new modeling does not rely on that and only needs the
MLIRContext that is used for uniquing, similarly to other MLIR types. Change
LLVMType::get<Kind>Ty functions to take `MLIRContext *` instead of
`LLVMDialect *` as first argument. This brings the code base closer to
completely removing the dependence on LLVMContext from the LLVMDialect,
together with additional support for thread-safety of its use.
Depends On D85371
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D85372
This prepares for the removal of llvm::Module and LLVMContext from the
mlir::LLVMDialect.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D85371
The intrinsics were already supported and vector.transfer_read/write lowered
direclty into these operations. By providing them as individual ops, however,
clients can used them directly, and it opens up progressively lowering transfer
operations at higher levels (rather than direct lowering to LLVM IR as done now).
Reviewed By: bkramer
Differential Revision: https://reviews.llvm.org/D85357
Previous type model in the LLVM dialect did not support identified structure
types properly and therefore could use stateless translations implemented as
free functions. The new model supports identified structs and must keep track
of the identified structure types present in the target context (LLVMContext or
MLIRContext) to avoid creating duplicate structs due to LLVM's type
auto-renaming. Expose the stateful type translation classes and use them during
translation, storing the state as part of ModuleTranslation.
Drop the test type translation mechanism that is no longer necessary and update
the tests to exercise type translation as part of the main translation flow.
Update the code in vector-to-LLVM dialect conversion that relied on stateless
translation to use the new class in a stateless manner.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D85297
`promoteMemRefDescriptors` also converts types of every operand, not only
memref-typed ones. I think `promoteMemRefDescriptors` name does not imply that.
Differential Revision: https://reviews.llvm.org/D85325
Previously, `LinalgOperand` is defined with `Type<Or<..,>>`, which produces
not very readable error messages when it is not matched, e.g.,
```
'linalg.generic' op operand #0 must be anonymous_326, but got ....
```
It is simply because the `description` property is not properly set.
This diff switches to use `AnyTypeOf` for `LinalgOperand`, which automatically
generates a description based on the allowed types provided.
As a result, the error message now becomes:
```
'linalg.generic' op operand #0 must be ranked tensor of any type values or strided memref of any type values, but got ...
```
Which is clearer and more informative.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D84428
Introduce an initial version of C API for MLIR core IR components: Value, Type,
Attribute, Operation, Region, Block, Location. These APIs allow for both
inspection and creation of the IR in the generic form and intended for wrapping
in high-level library- and language-specific constructs. At this point, there
is no stability guarantee provided for the API.
Reviewed By: stellaraccident, lattner
Differential Revision: https://reviews.llvm.org/D83310
The extent tensor type is a `tensor<?xindex>` that is used in the shape dialect.
To facilitate the use of this type when working with the shape dialect, we
expose the helper function for its construction.
Differential Revision: https://reviews.llvm.org/D85121
- Moved TypeRange into its own header/cpp file, and add hashing support.
- Change FunctionType::get() and TupleType::get() to use TypeRange
Differential Revision: https://reviews.llvm.org/D85075
Introduces the expand and compress operations to the Vector dialect
(important memory operations for sparse computations), together
with a first reference implementation that lowers to the LLVM IR
dialect to enable running on CPU (and other targets that support
the corresponding LLVM IR intrinsics).
Reviewed By: reidtatge
Differential Revision: https://reviews.llvm.org/D84888
This revision adds a transformation and a pattern that rewrites a "maybe masked" `vector.transfer_read %view[...], %pad `into a pattern resembling:
```
%1:3 = scf.if (%inBounds) {
scf.yield %view : memref<A...>, index, index
} else {
%2 = linalg.fill(%extra_alloc, %pad)
%3 = subview %view [...][...][...]
linalg.copy(%3, %alloc)
memref_cast %extra_alloc: memref<B...> to memref<A...>
scf.yield %4 : memref<A...>, index, index
}
%res= vector.transfer_read %1#0[%1#1, %1#2] {masked = [false ... false]}
```
where `extra_alloc` is a top of the function alloca'ed buffer of one vector.
This rewrite makes it possible to realize the "always full tile" abstraction where vector.transfer_read operations are guaranteed to read from a padded full buffer.
The extra work only occurs on the boundary tiles.
A new first-party modeling for LLVM IR types in the LLVM dialect has been
developed in parallel to the existing modeling based on wrapping LLVM `Type *`
instances. It resolves the long-standing problem of modeling identified
structure types, including recursive structures, and enables future removal of
LLVMContext and related locking mechanisms from LLVMDialect.
This commit only switches the modeling by (a) renaming LLVMTypeNew to LLVMType,
(b) removing the old implementaiton of LLVMType, and (c) updating the tests. It
is intentionally minimal. Separate commits will remove the infrastructure built
for the transition and update API uses where appropriate.
Depends On D85020
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D85021
These are intended to smoothen the transition and may be removed in the future
in favor of more MLIR-compatible APIs. They intentionally have the same
semantics as the existing functions, which must remain stable until the
transition is complete.
Depends On D85019
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D85020
With new LLVM dialect type modeling, the dialect types no longer wrap LLVM IR
types. Therefore, they need to be translated to and from LLVM IR during export
and import. Introduce the relevant functionality for translating types. It is
currently exercised by an ad-hoc type translation roundtripping test that will
be subsumed by the actual translation test when the type system transition is
complete.
Depends On D84339
Reviewed By: herhut
Differential Revision: https://reviews.llvm.org/D85019
This revision adds a transformation and a pattern that rewrites a "maybe masked" `vector.transfer_read %view[...], %pad `into a pattern resembling:
```
%1:3 = scf.if (%inBounds) {
scf.yield %view : memref<A...>, index, index
} else {
%2 = vector.transfer_read %view[...], %pad : memref<A...>, vector<...>
%3 = vector.type_cast %extra_alloc : memref<...> to
memref<vector<...>> store %2, %3[] : memref<vector<...>> %4 =
memref_cast %extra_alloc: memref<B...> to memref<A...> scf.yield %4 :
memref<A...>, index, index
}
%res= vector.transfer_read %1#0[%1#1, %1#2] {masked = [false ... false]}
```
where `extra_alloc` is a top of the function alloca'ed buffer of one vector.
This rewrite makes it possible to realize the "always full tile" abstraction where vector.transfer_read operations are guaranteed to read from a padded full buffer.
The extra work only occurs on the boundary tiles.
Differential Revision: https://reviews.llvm.org/D84631
This reverts commit 35b65be041.
Build is broken with -DBUILD_SHARED_LIBS=ON with some undefined
references like:
VectorTransforms.cpp:(.text._ZN4llvm12function_refIFvllEE11callback_fnIZL24createScopedInBoundsCondN4mlir25VectorTransferOpInterfaceEE3$_8EEvlll+0xa5): undefined reference to `mlir::edsc::op::operator+(mlir::Value, mlir::Value)'
The current modeling of LLVM IR types in MLIR is based on the LLVMType class
that wraps a raw `llvm::Type *` and delegates uniquing, printing and parsing to
LLVM itself. This model makes thread-safe type manipulation hard and is being
progressively replaced with a cleaner MLIR model that replicates the type
system. Introduce a set of classes reflecting the LLVM IR type system in MLIR
instead of wrapping the existing types. These are currently introduced as
separate classes without affecting the dialect flow, and are exercised through
a test dialect. Once feature parity is reached, the old implementation will be
gradually substituted with the new one.
Depends On D84171
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D84339
This revision adds a transformation and a pattern that rewrites a "maybe masked" `vector.transfer_read %view[...], %pad `into a pattern resembling:
```
%1:3 = scf.if (%inBounds) {
scf.yield %view : memref<A...>, index, index
} else {
%2 = vector.transfer_read %view[...], %pad : memref<A...>, vector<...>
%3 = vector.type_cast %extra_alloc : memref<...> to
memref<vector<...>> store %2, %3[] : memref<vector<...>> %4 =
memref_cast %extra_alloc: memref<B...> to memref<A...> scf.yield %4 :
memref<A...>, index, index
}
%res= vector.transfer_read %1#0[%1#1, %1#2] {masked = [false ... false]}
```
where `extra_alloc` is a top of the function alloca'ed buffer of one vector.
This rewrite makes it possible to realize the "always full tile" abstraction where vector.transfer_read operations are guaranteed to read from a padded full buffer.
The extra work only occurs on the boundary tiles.
Differential Revision: https://reviews.llvm.org/D84631
This is an operation that can returns a new ValueShape with a different shape. Useful for composing shape function calls and reusing existing shape transfer functions.
Just adding the op in this change.
Differential Revision: https://reviews.llvm.org/D84217
The current output is a bit clunky and requires including files+macros everywhere, or manually wrapping the file inclusion in a registration function. This revision refactors the pass backend to automatically generate `registerFooPass`/`registerFooPasses` functions that wrap the pass registration. `gen-pass-decls` now takes a `-name` input that specifies a tag name for the group of passes that are being generated. For each pass, the generator now produces a `registerFooPass` where `Foo` is the name of the definition specified in tablegen. It also generates a `registerGroupPasses`, where `Group` is the tag provided via the `-name` input parameter, that registers all of the passes present.
Differential Revision: https://reviews.llvm.org/D84983
This change allow CooperativeMatrix Load/Store operations to use pointer type
that may not match the matrix element type. This allow us to declare buffer
with a larger type size than the matrix element type. This follows SPIR-V spec
and this is needed to be able to use cooperative matrix in combination with
shared local memory efficiently.
Differential Revision: https://reviews.llvm.org/D84993
In a context in which `shape.broadcast` is known not to produce an error value,
we want it to operate solely on extent tensors. The operation's behavior is
then undefined in the error case as the result type cannot hold this value.
Differential Revision: https://reviews.llvm.org/D84933
Replaced definition of named ND ConvOps with tensor comprehension
syntax which reduces boilerplate code significantly. Furthermore,
new ops to support TF convolutions added (without strides and dilations).
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D84628
-- 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
This patch introduces new intrinsics in LLVM dialect:
- `llvm.intr.floor`
- `llvm.intr.maxnum`
- `llvm.intr.minnum`
- `llvm.intr.smax`
- `llvm.intr.smin`
These intrinsics correspond to SPIR-V ops from GLSL
extended instruction set (`spv.GLSL.Floor`, `spv.GLSL.FMax`,
`spv.GLSL.FMin`, `spv.GLSL.SMax` and `spv.GLSL.SMin`
respectively). Also conversion patterns for them were added.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D84661
This commit is part of a greater project which aims to add
full end-to-end support for convolutions inside mlir. The
reason behind having conv ops for each rank rather than
having one generic ConvOp is to enable better optimizations
for every N-D case which reflects memory layout of input/kernel
buffers better and simplifies code as well. We expect plain linalg.conv
to be progressively retired.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D83879
The current modeling of LLVM IR types in MLIR is based on the LLVMType class
that wraps a raw `llvm::Type *` and delegates uniquing, printing and parsing to
LLVM itself. This is model makes thread-safe type manipulation hard and is
being progressively replaced with a cleaner MLIR model that replicates the type
system. In the new model, LLVMType will no longer have an underlying LLVM IR
type. Restrict access to this type in the current model in preparation for the
change.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D84389
This adds conversions for const_size and to_extent_tensor. Also, cast-like operations are now folded away if the source and target types are the same.
Differential Revision: https://reviews.llvm.org/D84745
- 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
For the purpose of vector transforms, the Tablegen-based infra is subsumed by simple C++ pattern application. Deprecate declarative transforms whose complexity does not pay for itself.
Differential Revision: https://reviews.llvm.org/D84753
- replace DotOp, now that DRR rules have been dropped.
- Capture arguments mismatch in the parser. The number of parsed arguments must
equal the number of expected arguments.
Reviewed By: ftynse, nicolasvasilache
Differential Revision: https://reviews.llvm.org/D82952
The LowerAffine psas was a FunctionPass only for legacy
reasons. Making this Op-agnostic allows it to be used from command
line when affine expressions are within operations other than
`std.func`.
Differential Revision: https://reviews.llvm.org/D84590
Introduce support for mutable storage in the StorageUniquer infrastructure.
This makes MLIR have key-value storage instead of just uniqued key storage. A
storage instance now contains a unique immutable key and a mutable value, both
stored in the arena allocator that belongs to the context. This is a
preconditio for supporting recursive types that require delayed initialization,
in particular LLVM structure types. The functionality is exercised in the test
pass with trivial self-recursive type. So far, recursive types can only be
printed in parsed in a closed type system. Removing this restriction is left
for future work.
Differential Revision: https://reviews.llvm.org/D84171
This patch introduces 2 new optional attributes to `llvm.load`
and `llvm.store` ops: `volatile` and `nontemporal`. These attributes
are translated into proper LLVM as a `volatile` marker and a metadata node
respectively. They are also helpful with SPIR-V to LLVM dialect conversion
since they are the mappings for `Volatile` and `NonTemporal` Memory Operands.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D84396
This member is already publicly declared on the base class. The
redundant declaration is mangled differently though and in some
unoptimized build it requires a definition to also exist. However we
have a definition for the base ShapedType class, removing the
declaration here will redirect every use to the base class member
instead.
Differential Revision: https://reviews.llvm.org/D84615
Previous changes generalized some of the operands and results. Complete
a larger group of those to simplify progressive lowering. Also update
some of the declarative asm form due to generalization. Tried to keep it
mostly mechanical.
Based on https://reviews.llvm.org/D84439 but less restrictive, else we
don't allow shape_of to be able to produce a ranked output and doesn't
allow for iterative refinement here. We can consider making it more
restrictive later.
The operation `shape.shape_of` now returns an extent tensor `tensor<?xindex>` in
cases when no error are possible. All consuming operation will eventually accept
both, shapes and extent tensors.
Differential Revision: https://reviews.llvm.org/D84160
The operation `shape.const_shape` was used for constants of type shape only.
We can now also use it to create constant extent tensors.
Differential Revision: https://reviews.llvm.org/D84157
This patch introduces branch weights metadata to `llvm.cond_br` op in
LLVM Dialect. It is modelled as optional `ElementsAttr`, for example:
```
llvm.cond_br %cond weights(dense<[1, 3]> : vector<2xi32>), ^bb1, ^bb2
```
When exporting to proper LLVM, this attribute is transformed into metadata
node. The test for metadata creation is added to `../Target/llvmir.mlir`.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D83658
This is an update of the documentation for `spv.Variable`.
Removed `bind` and `built_in` that are now used with `spv.globalVariable`
instead.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D84196
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
Loop bound inference is right now very limited as it supports only permutation maps and thus
it is impossible to implement convolution with linalg.generic as it requires more advanced
loop bound inference. This commits solves it for the convolution case.
Depends On D83158
Differential Revision: https://reviews.llvm.org/D83191
This patch refactors a small part of the Super Vectorizer code to
a utility so that it can be used independently from the pass. This
aligns vectorization with other utilities that we already have for loop
transformations, such as fusion, interchange, tiling, etc.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D84289
Introduces the scatter/gather operations to the Vector dialect
(important memory operations for sparse computations), together
with a first reference implementation that lowers to the LLVM IR
dialect to enable running on CPU (and other targets that support
the corresponding LLVM IR intrinsics).
The operations can be used directly where applicable, or can be used
during progressively lowering to bring other memory operations closer to
hardware ISA support for a gather/scatter. The semantics of the operation
closely correspond to those of the corresponding llvm intrinsics.
Note that the operation allows for a dynamic index vector (which is
important for sparse computations). However, this first reference
lowering implementation "serializes" the address computation when
base + index_vector is converted to a vector of pointers. Exploring
how to use SIMD properly during these step is TBD. More general
memrefs and idiomatic versions of striding are also TBD.
Reviewed By: arpith-jacob
Differential Revision: https://reviews.llvm.org/D84039
The utility function getViewSizes in Linalg has been recently updated to
support a different form of Linalg operations. In doing so, the code looking
like `smallvector.push_back(smallvector[i])` was introduced. Unlike std
vectors, this can lead to undefined behavior if the vector must grow upon
insertion: `smallvector[i]` returns a reference to the element, `push_back`
takes a const reference to the element, and then grows the vector storage
before accessing the referenced value. After the resize, the reference may
become dangling, which leads to undefined behavior detected by ASAN as
use-after-free. Work around the issue by forcing the value to be copied by
putting it into a temporary variable.
This commit adds functionality needed for implementation of convolutions with
linalg.generic op. Since linalg.generic right now expects indexing maps to be
just permutations, offset indexing needed in convolutions is not possible.
Therefore in this commit we address the issue by adding support for symbols inside
indexing maps which enables more advanced indexing. The upcoming commit will
solve the problem of computing loop bounds from such maps.
Differential Revision: https://reviews.llvm.org/D83158
Summary: Vector contract patterns were only parameterized by a `vectorTransformsOptions`. As a result, even if an mlir file was containing several occurrences of `vector.contract`, all of them would be lowered in the same way. More granularity might be required . This Diff adds a `constraint` argument to each of these patterns which allows the user to specify with more precision on which `vector.contract` should each of the lowering apply.
Differential Revision: https://reviews.llvm.org/D83960
When the IfOp returns values, it can easily be obtained from one of the Values.
However, when no values are returned, the information is lost.
This revision lets the caller specify a capture IfOp* to return the produced
IfOp.
Differential Revision: https://reviews.llvm.org/D84025
- This will enable tweaking IR printing options when enabling printing (for ex,
tweak elideLargeElementsAttrs to create smaller IR logs)
Differential Revision: https://reviews.llvm.org/D83930
This also fixes the outdated use of `n_views` in the documentation.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D83795
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
- Add function `verifyTypes` that Op's can call to do type checking verification
along the control flow edges described the Op's RegionBranchOpInterface.
- We cannot rely on the verify methods on the OpInterface because the interface
functions assume valid Ops, so they may crash if invoked on unverified Ops.
(For example, scf.for getSuccessorRegions() calls getRegionIterArgs(), which
dereferences getBody() block. If the scf.for is invalid with no body, this
can lead to a segfault). `verifyTypes` can be called post op-verification to
avoid this.
Differential Revision: https://reviews.llvm.org/D82829
This folds shape.broadcast where at least one operand is a scalar to the
other operand.
Also add an assemblyFormat for shape.broadcast and shape.concat.
Differential Revision: https://reviews.llvm.org/D83854
Summary:
This makes sure that their constant arguments are sorted to the back
and hence eases the specification of rewrite patterns.
Differential Revision: https://reviews.llvm.org/D83856
Add `shape.shape_eq` operation to the shape dialect.
The operation allows to test shapes and extent tensors for equality.
Differential Revision: https://reviews.llvm.org/D82528
This adds a `parseOptionalAttribute` method to the OpAsmParser that allows for parsing optional attributes, in a similar fashion to how optional types are parsed. This also enables the use of attribute values as the first element of an assembly format optional group.
Differential Revision: https://reviews.llvm.org/D83712
- 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
This patch introduces lowering of the OpenMP parallel operation to LLVM
IR using the OpenMPIRBuilder.
Functions topologicalSort and connectPhiNodes are generalised so that
they work with operations also. connectPhiNodes is also made static.
Lowering works for a parallel region with multiple blocks. Clauses and
arguments of the OpenMP operation are not handled.
Reviewed By: rriddle, anchu-rajendran
Differential Revision: https://reviews.llvm.org/D81660
Summary: The native alignment may generally not be used when lowering a vector.transfer to the underlying load/store operation. This revision fixes the unmasked load/store alignment to match that of the masked path.
Differential Revision: https://reviews.llvm.org/D83684
Per the Vulkan's SPIR-V environment spec, "for the OpSRem and OpSMod
instructions, if either operand is negative the result is undefined."
So we cannot directly use spv.SRem/spv.SMod if either operand can be
negative. Emulate it via spv.UMod.
Because the emulation uses spv.SNegate, this commit also defines
spv.SNegate.
Differential Revision: https://reviews.llvm.org/D83679
The namespace can be specified using the `cppNamespace` field. This matches the functionality already present on dialects, enums, etc. This fixes problems with using interfaces on operations in a different namespace than the interface was defined in.
Differential Revision: https://reviews.llvm.org/D83604
Introduce pass to convert parallel affine.for op into 1-D affine.parallel op.
Run using --affine-parallelize. Removes test-detect-parallel: pass for checking
parallel affine.for ops.
Signed-off-by: Yash Jain <yash.jain@polymagelabs.com>
Differential Revision: https://reviews.llvm.org/D83193
This revision folds vector.transfer operations by updating the `masked` bool array attribute when more unmasked dimensions can be discovered.
Differential revision: https://reviews.llvm.org/D83586
We temporarily had separate OUTER lowering (for matmat flavors) and
AXPY lowering (for matvec flavors). With the new generalized
"vector.outerproduct" semantics, these cases can be merged into
a single lowering method. This refactoring will simplify future
decisions on cost models and lowering heuristics.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D83585
This specialization allows sharing more code where an AXPY follows naturally
in cases where an OUTERPRODUCT on a scalar would be generated.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D83453
TransposeOp are often followed by ExtractOp.
In certain cases however, it is unnecessary (and even detrimental) to lower a TransposeOp to either a flat transpose (llvm.matrix intrinsics) or to unrolled scalar insert / extract chains.
Providing foldings of ExtractOp mitigates some of the unnecessary complexity.
Differential revision: https://reviews.llvm.org/D83487
This revision adds support for vectorizing named and generic contraction ops to vector.contract. Cases in which the memref is 0-D are special cased to emit std.load/std.store instead of vector.transfer. Relevant tests are added.
Differential revision: https://reviews.llvm.org/D83307
This commit augments spv.CopyMemory's implementation to support 2 memory
access operands. Hence, more closely following the spec. The following
changes are introduces:
- Customize logic for spv.CopyMemory serialization and deserialization.
- Add 2 additional attributes for source memory access operand.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D83241
This patch adds type conversion for 4 SPIR-V types: array, runtime array, pointer
and struct. This conversion is integrated using a separate function
`populateSPIRVToLLVMTypeConversion()` that adds new type conversions. At the moment,
this is a basic skeleton that allows to perfom conversion from SPIR-V array,
runtime array and pointer types to LLVM typesystem. There is no support of array
strides or storage classes. These will be supported on the case by case basis.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D83399
Added `getSizeInBytes()` function as a class member to several SPIR-V types:
`ScalarType`, `ArrayType` and `VectorType`. This function aims at exposing
the functionality of `getTypeNumBytes()` from `SPIRVLowering.cpp`. Support
of more types will be added on demand.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D83285
Summary:
Added canonicalization and folding was:
- Folding when either input is an attribute indicating a scalar input
which can always be broadcasted.
- Canonicalization where it can be determined that either input shape is
a scalar.
- Canonicalization where the partially specified input shapes can be
proven to be broadcastable always.
Differential Revision: https://reviews.llvm.org/D83194
The ConvertVectorToLLVM pass defines options that can be passed
on the command line (currently only reassociation of FP reductions
through -convert-vector-to-llvm='reassociate-fp-reductions). This
CL enables setting these options programmatically (forward looking
to more options than just reassociation, as well as setting the
values from code rather than command line).
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D83420
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
- This will eliminate the need to pass an empty `ArrayRef<NamedAttribute>{}` when
no named attributes are required on the function.
Differential Revision: https://reviews.llvm.org/D83356
Create the framework and testing environment for MLIR Reduce - a tool
with the objective to reduce large test cases into smaller ones while
preserving their interesting behavior.
Implement the functionality to parse command line arguments, parse the
MLIR test cases into modules and run the interestingness tests on
the modules.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D82803
This revision adds foldings for ExtractOp operations that come from previous InsertOp.
InsertOp have cumulative semantic where multiple chained inserts are necessary to produce the final value from which the extracts are obtained.
Additionally, TransposeOp may be interleaved and need to be tracked in order to follow the producer consumer relationships and properly compute positions.
Differential revision: https://reviews.llvm.org/D83150
Similar to OwningModuleRef, OwningSPIRVModuleRef signals ownership
transfer clearly. This is useful for APIs like spirv::deserialize,
where a spirv::ModuleOp is returned by deserializing SPIR-V binary
module.
This addresses the ASAN error as reported in
https://bugs.llvm.org/show_bug.cgi?id=46272
Differential Revision: https://reviews.llvm.org/D81652
with the objective to reduce large test cases into smaller ones while
preserving their interesting behavior.
Implement the framework to parse the command line arguments, parse the
input MLIR test case into a module and call reduction passes on the MLIR module.
Implement the Tester class which allows the different reduction passes to test the
interesting behavior of the generated reduced variants of the test case and keep track
of the most reduced generated variant.
The UnrollVectorPattern is can be used in a programmable fashion by:
```
OwningRewritePatternList patterns;
patterns.insert<UnrollVectorPattern<AddFOp>>(ArrayRef<int64_t>{2, 2}, ctx);
patterns.insert<UnrollVectorPattern<vector::ContractionOp>>(
ArrayRef<int64_t>{2, 2, 2}, ctx);
...
applyPatternsAndFoldGreedily(getFunction(), patterns);
```
Differential revision: https://reviews.llvm.org/D83064
Introduce pass to convert parallel affine.for op into 1-D
affine.parallel op. Run using --affine-parallelize. Removes
test-detect-parallel: pass for checking parallel affine.for ops.
Differential Revision: https://reviews.llvm.org/D82672
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
Default vector.contract lowering essentially yields a series of sdot/ddot
operations. However, for some layouts a series of saxpy/daxpy operations,
chained through fma are more efficient. This CL introduces a choice between
the two lowering paths. A default heuristic is to follow.
Some preliminary avx2 performance numbers for matrix-times-vector.
Here, dot performs best for 64x64 A x b and saxpy for 64x64 A^T x b.
```
------------------------------------------------------------
A x b A^T x b
------------------------------------------------------------
GFLOPS sdot (reassoc) saxpy sdot (reassoc) saxpy
------------------------------------------------------------
1x1 0.6 0.9 0.6 0.9
2x2 2.5 3.2 2.4 3.5
4x4 6.4 8.4 4.9 11.8
8x8 11.7 6.1 5.0 29.6
16x16 20.7 10.8 7.3 43.3
32x32 29.3 7.9 6.4 51.8
64x64 38.9 79.3
128x128 32.4 40.7
------------------------------------------------------------
```
Reviewed By: nicolasvasilache, ftynse
Differential Revision: https://reviews.llvm.org/D83012
This commit augments spv.CopyMemory's implementation to support 2 memory
access operands. Hence, more closely following the spec. The following
changes are introduces:
- Customize logic for spv.CopyMemory serialization and deserialization.
- Add 2 additional attributes for source memory access operand.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D82710
This patch adds the capability to perform exact integer emptiness checks for FlatAffineConstraints using the General Basis Reduction algorithm (GBR). Previously, only a heuristic was available for emptiness checks, which was not guaranteed to always give a conclusive result.
This patch adds a `Simplex` class, which can be constructed using a `FlatAffineConstraints`, and can find an integer sample point (if one exists) using the GBR algorithm. Additionally, it adds two classes `Matrix` and `Fraction`, which are used by `Simplex`.
The integer emptiness check functionality can be accessed through the new `FlatAffineConstraints::isIntegerEmpty()` function, which runs the existing heuristic first and, if that proves to be inconclusive, runs the GBR algorithm to produce a conclusive result.
Differential Revision: https://reviews.llvm.org/D80860
This allow lowering to support scf.for and scf.if with results. As right now
spv region operations don't have return value the results are demoted to
Function memory. We create one allocation per result right before the region
and store the yield values in it. Then we can load back the value from
allocation to be able to use the results.
Differential Revision: https://reviews.llvm.org/D82246
MSVC 2017 doesn't support the case where a trailing variadic template list comes after template types with default parameters. Until we upgrade to VS 2019, we can't use the simplified definitions.
Moving forward dialects should only be registered in a thread safe context. This matches the existing usage we have today, but it allows for removing quite a bit of expensive locking from the context.
This led to ~.5 a second compile time improvement when running one conversion pass on a very large .mlir file(hundreds of thousands of operations).
Differential Revision: https://reviews.llvm.org/D82595
This revision adds support to ODS for generating interfaces for attributes and types, in addition to operations. These interfaces can be specified using `AttrInterface` and `TypeInterface` in place of `OpInterface`. All of the features of `OpInterface` are supported except for the `verify` method, which does not have a matching representation in the Attribute/Type world. Generating these interface can be done using `gen-(attr|type)-interface-(defs|decls|docs)`.
Differential Revision: https://reviews.llvm.org/D81884
This revisions add mechanisms to Attribute/Type for attaching traits and interfaces. The mechanisms are modeled 1-1 after those for operations to keep the system consistent. AttrBase and TypeBase now accepts a trailing list of `Trait` types that will be attached to the object. These traits should inherit from AttributeTrait::TraitBase and TypeTrait::TraitBase respectively as necessary. A followup commit will refactor the interface gen mechanisms in ODS to support Attribute/Type interface generation and add tests for the mechanisms.
Differential Revision: https://reviews.llvm.org/D81883
Also fixed bug in type inferface generator to address bug where operands and
attributes are interleaved.
Differential Revision: https://reviews.llvm.org/D82819
Current Affine comparison builders, which use operator overload, default to signed comparison. This creates the possibility of misuse of these builders and potential correctness issues when dealing with unsigned integers. This change makes the distinction between signed and unsigned comparison builders and forces the caller to make a choice between the two.
Differential Revision: https://reviews.llvm.org/D82323
Summary:
The patch makes the index type lowering of the GPU to NVVM/ROCDL conversion configurable. It introduces a pass option that controls the bitwidth used when lowering index computations and uses the LowerToLLVMOptions structure to control the Standard to LLVM lowering.
This commit fixes a use-after-free bug introduced by the reverted commit d10b1a3. It implements the following changes:
- Added a getDefaultOptions method to the LowerToLLVMOptions struct that returns a reference to statically allocated default options.
- Use the getDefaultOptions method to provide default LowerToLLVMOptions (instead of an initializer list).
- Added comments to clarify the required lifetime of the LowerToLLVMOptions
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D82475
`llvm.mlir.constant` was originally introduced as an LLVM dialect counterpart
to `std.constant`. As such, it was supporting "function pointer" constants
derived from the symbol name. This is different from `std.constant` that allows
for creation of a "function" constant since MLIR, unlike LLVM IR, supports
this. Later, `llvm.mlir.addressof` was introduced as an Op that obtains a
constant pointer to a global in the LLVM dialect. It naturally extends to
functions (in LLVM IR, functions are globals) and should be used for defining
"function pointer" values instead.
Fixes PR46344.
Differential Revision: https://reviews.llvm.org/D82667
Rationale:
In general, passing "fastmath" from MLIR to LLVM backend is not supported, and even just providing such a feature for experimentation is under debate. However, passing fine-grained fastmath related attributes on individual operations is generally accepted. This CL introduces an option to instruct the vector-to-llvm lowering phase to annotate floating-point reductions with the "reassociate" fastmath attribute, which allows the LLVM backend to use SIMD implementations for such constructs. Oher lowering passes can start using this mechanism right away in cases where reassociation is allowed.
Benefit:
For some microbenchmarks on x86-avx2, speedups over 20 were observed for longer vector (due to cleaner, spill-free and SIMD exploiting code).
Usage:
mlir-opt --convert-vector-to-llvm="reassociate-fp-reductions"
Reviewed By: ftynse, mehdi_amini
Differential Revision: https://reviews.llvm.org/D82624
Add a pass to rewrite sequential chains of `spirv::CompositeInsert`
operations into `spirv::CompositeConstruct` operations.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D82198
This patch add support for 'spv.CopyMemory'. The following changes are
introduced:
- 'CopyMemory' op is added to SPIRVOps.td.
- Custom parse and print methods are introduced.
- A few Roundtripping tests are added.
Differential Revision: https://reviews.llvm.org/D82384
Initially, unranked memref descriptors in the LLVM dialect were designed only
to be passed into functions. An assertion was guarding against returning
unranked memrefs from functions in the standard-to-LLVM conversion. This is
insufficient for functions that wish to return an unranked memref such that the
caller does not know the rank in advance, and hence cannot allocate the
descriptor and pass it in as an argument.
Introduce a calling convention for returning unranked memref descriptors as
follows. An unranked memref descriptor always points to a ranked memref
descriptor stored on stack of the current function. When an unranked memref
descriptor is returned from a function, the ranked memref descriptor it points
to is copied to dynamically allocated memory, the ownership of which is
transferred to the caller. The caller is responsible for deallocating the
dynamically allocated memory and for copying the pointed-to ranked memref
descriptor onto its stack.
Provide default lowerings for std.return, std.call and std.indirect_call that
maintain the conversion defined above.
This convention is additionally exercised by a runtime test to guard against
memory errors.
Differential Revision: https://reviews.llvm.org/D82647
Using fully qualified names wherever possible avoids ambiguous class and function names. This is a follow-up to D82371.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D82471
Replace any `rank(shape_of(tensor))` that relies on a ranked tensor with the
corresponding constant `const_size`.
Differential Revision: https://reviews.llvm.org/D82077
This patch introduces conversion patterns for `spv.module` and `spv._module_end`.
SPIR-V module is converted into `ModuleOp`. This will play a role of enclosing
scope to LLVM ops. At the moment, SPIR-V module attributes (such as memory model,
etc) are ignored.
Differential Revision: https://reviews.llvm.org/D82468
This revision adds a new support header, InterfaceSupport, to contain various generic bits of functionality for implementing "Interfaces". Interfaces embody a mechanism for attaching concept-based polymorphism to a type system. With this refactoring a new InterfaceMap type is added to allow for efficient interface lookups without going through an indirect call. This should provide a decent performance speedup without changing the size of AbstractOperation.
In a future revision, this functionality will also be used to bring Interface like functionality to Attributes and Types.
Differential Revision: https://reviews.llvm.org/D81882
Introduced `llvm.intr.bitreverse` and `llvm.intr.ctpop` LLVM bit
intrinsics to LLVM dialect. These intrinsics help with SPIR-V to
LLVM conversion, allowing a direct mapping from `spv.BitReverse`
and `spv.BitCount` respectively. Tests are added to `roundtrip.mlir`
and `llvm-intrinsics.mlir`.
Differential Revision: https://reviews.llvm.org/D82285
This patch provides an implementation for `spv.func` conversion. The pattern
is populated in a separate method added to the pass. At the moment, the type
signature conversion only includes the supported types. The conversion pattern
also matches SPIR-V function control attributes to LLVM function attributes.
Those are modelled as `passthrough` attributes in LLVM dialect. The following
mapping are used:
- None: no attributes passed
- Inline: `alwaysinline` seems to be the right equivalent (`inlinehint` is
semantically weaker in my opinion)
- DontInline: `noinline`
- Pure and Const: I think those can be modelled as `readonly` and `readnone`
attributes respectively.
Also, 2 patterns added for return ops conversion (`spv.Return` for void return
and `spv.ReturnValue` for a single value return).
Differential Revision: https://reviews.llvm.org/D81931
Example of Matmul implementation in linalg.generic operation contained few mistakes that can puzzle new startes when trying to run the example.
Differential Revision: https://reviews.llvm.org/D82289
The patch makes the index type lowering of the GPU to NVVM/ROCDL
conversion configurable. It introduces a pass option that controls the
bitwidth used when lowering index computations.
Differential Revision: https://reviews.llvm.org/D80285
Summary:
We already had a parallel loop specialization pass that is used to
enable unrolling and consecutive vectorization by rewriting loops
whose bound is defined as a min of a constant and a dynamic value
into a loop with static bound (the constant) and the minimum as
bound, wrapped into a conditional to dispatch between the two.
This adds the same rewriting for for loops.
Differential Revision: https://reviews.llvm.org/D82189
Avoid using max on unsigned constants, in case the caller is using 0 we
end up with:
warning: taking the max of unsigned zero and a value is always equal to the other value [-Wmax-unsigned-zero]
Instead we can just use native TableGen to fold the comparison here.
Allow lhs and rhs to have different type than accumulator/destination. Some
hardware like GPUs support natively operations like uint8xuint8xuint32.
Differential Revision: https://reviews.llvm.org/D82069
Use direct vector constants for the 1-D case. This approach
scales much better than generating elaborate insertion operations
that are eventually folded into a constant. We could of course
generalize the 1-D case to higher ranks, but this simplification
already helps in scaling some microbenchmarks that would formerly
crash on the intermediate IR length.
Reviewed By: reidtatge
Differential Revision: https://reviews.llvm.org/D82144
All class derived from `edsc::NestedBuilder` in core MLIR have been replaced
with alternatives based on OpBuilder+callbacks. The *Builder EDSC
infrastructure has been deprecated. Remove edsc::NestedBuilder.
This completes the "structured builders" refactoring.
Differential Revision: https://reviews.llvm.org/D82128
Callback-based constructions of blocks where the body is populated in the same
function as the block creation is a natural extension of callback-based loop
construction. They provide more concise and simple APIs than EDSC BlockBuilder
at less than 20% infrastructural code cost, and are compatible with
ScopedContext. BlockBuilder, Blockhandle and related functionality has been
deprecated, remove them.
Differential Revision: https://reviews.llvm.org/D82015
Callback-based loop construction, with loop bodies being constructed during the
construction of the parent op using a function, is now fully supported by the
core infrastructure. This provides almost the same level of brevity as EDSC
LoopBuilder at less than 30% infrastructural code cost. Functional equivalents
compatible with EDSC ScopedContext are implemented on top of the main builders.
LoopBuilder and related functionality has been deprecated, remove it.
Differential Revision: https://reviews.llvm.org/D81874
This revision removes the TypeConverter parameter passed to the apply* methods, and instead moves the responsibility of region type conversion to patterns. The types of a region can be converted using the 'convertRegionTypes' method, which acts similarly to the existing 'applySignatureConversion'. This method ensures that all blocks within, and including those moved into, a region will have the block argument types converted using the provided converter.
This has the benefit of making more of the legalization logic controlled by patterns, instead of being handled explicitly by the driver. It also opens up the possibility to support multiple type conversions at some point in the future.
This revision also adds a new utility class `FailureOr<T>` that provides a LogicalResult friendly facility for returning a failure or a valid result value.
Differential Revision: https://reviews.llvm.org/D81681
Existing implementation of affine loop nest builders relies on EDSC
ScopedContext, which is not used pervasively. Provide a common OpBuilder-based
helper function to construct a perfect nest of affine loops with the body of
the innermost loop populated by a callback. Use this function to implement the
EDSC version.
Affine "for" loops differ from SCF "for" loops by (1) not allowing to yield
values and (2) supporting short-hand form for constant bounds, which justifies
a separate implementation of the loop nest builder for the same of simplicity.
Differential Revision: https://reviews.llvm.org/D81955
Traditionally patterns have always had the root operation kind hardcoded to a specific operation name. This has worked well for quite some time, but it has certain limitations that make it undesirable. For example, some lowering have the same implementation for many different operations types with a few lowering entire dialects using the same pattern implementation. This problem has led to several "solutions":
a) Provide a template implementation to the user so that they can instantiate it for each operation combination, generally requiring the inclusion of the auto-generated operation definition file.
b) Use a non-templated pattern that allows for providing the name of the operation to match
- No one ever does this, because enumerating operation names can be cumbersome and so this quickly devolves into solution a.
This revision removes the restriction that patterns have a hardcoded root type, and allows for a class patterns that could match "any" operation type. The major downside of root-agnostic patterns is that they make certain pattern analyses more difficult, so it is still very highly encouraged that an operation specific pattern be used whenever possible.
Differential Revision: https://reviews.llvm.org/D82066
This class enables for abstracting more of the details for the rewrite process, and will allow for clients to apply specific cost models to the pattern list. This allows for DialectConversion and the GreedyPatternRewriter to share the same underlying matcher implementation. This also simplifies the plumbing necessary to support dynamic patterns.
Differential Revision: https://reviews.llvm.org/D81985
Summary:
The "i1" (viz. bool) type does not have a proper equivalent on the "C"
size. So, to avoid any ABIs issues, we simply use print_i32 on an i32
value of one or zero for true and false. This has the added advantage
that one less function needs to be implemented when porting the runtime
support library.
Reviewers: ftynse, bkramer, nicolasvasilache
Reviewed By: ftynse
Subscribers: mehdi_amini, rriddle, jpienaar, shauheen, antiagainst, nicolasvasilache, arpith-jacob, mgester, lucyrfox, liufengdb, stephenneuendorffer, Joonsoo, grosul1, frgossen, Kayjukh, jurahul, msifontes
Tags: #mlir
Differential Revision: https://reviews.llvm.org/D82048
Summary:
Fixed build of D81618
Add a pattern for expanding tanh op into exp form.
A `tanh` is expanded into:
1) 1-exp^{-2x} / 1+exp^{-2x}, if x => 0
2) exp^{2x}-1 / exp^{2x}+1 , if x < 0.
Differential Revision: https://reviews.llvm.org/D82040
Summary:
This is to provide a utility to remove unsupported constraints or for
pipelines that happen to receive these but cannot lower them due to not
supporting assertions.
Differential Revision: https://reviews.llvm.org/D81560
The ScopedBuilder class in EDSC is being gradually phased out in favor of core
OpBuilder-based helpers with callbacks. Provide helper functions that are
compatible with `edsc::ScopedContext` and can be used to create and populate
blocks using callbacks that take block arguments as callback arguments. This
removes the need for `edsc::BlockHandle`, forward-declaration of `Value`s used
for block arguments and the tag `edsc::Append` class, leading to noticable
reduction in the verbosity of the code using helper functions.
Remove "eager mode" construction tests that are only relevant to the
`BlockBuilder`-based approach.
`edsc::BlockHandle` and `edsc::BlockBuilder` are now deprecated and will be
removed soon.
Differential Revision: https://reviews.llvm.org/D82008
This patch adjust the load/store matrix intrinsics, formerly known as
llvm.matrix.columnwise.load/store, to improve the naming and allow
passing of extra information (volatile).
The patch performs the following changes:
* Rename columnwise.load/store to column.major.load/store. This is more
expressive and also more in line with the naming in Clang.
* Changes the stride arguments from i32 to i64. The stride can be
larger than i32 and this makes things more uniform with the way
things are handled in Clang.
* A new boolean argument is added to indicate whether the load/store
is volatile. The lowering respects that when emitting vector
load/store instructions
* MatrixBuilder is updated to require both Alignment and IsVolatile
arguments, which are passed through to the generated intrinsic. The
alignment is set using the `align` attribute.
The changes are grouped together in a single patch, to have a single
commit that breaks the compatibility. We probably should be fine with
updating the intrinsics, as we did not yet officially support them in
the last stable release. If there are any concerns, we can add
auto-upgrade rules for the columnwise intrinsics though.
Reviewers: anemet, Gerolf, hfinkel, andrew.w.kaylor, LuoYuanke, nicolasvasilache, rjmccall, ftynse
Reviewed By: anemet, nicolasvasilache
Differential Revision: https://reviews.llvm.org/D81472
- This will allow calling these functions from Op's that support this interface (like FuncOp) directly:
```
FuncOp func = ...
func.isPrivate()
```
Differential Revision: https://reviews.llvm.org/D82060
Summary:
- Define the MatrixTimesScalar operation and add roundtrip tests.
- Added a new base class for matrix-specific operations to avoid invalid operands type mismatch check.
- Created a separate Matrix arithmetic operations td file to add more operations in the future.
- Augmented the automatically generated verify method to print more fine-grained error messages.
- Made minor Updates to the matrix type tests.
Reviewers: antiagainst, rriddle, mravishankar
Reviewed By: antiagainst
Subscribers: mehdi_amini, jpienaar, shauheen, nicolasvasilache, arpith-jacob, mgester, lucyrfox, aartbik, liufengdb, stephenneuendorffer, Joonsoo, bader, grosul1, frgossen, Kayjukh, jurahul, msifontes
Tags: #mlir
Differential Revision: https://reviews.llvm.org/D81677
Summary:
Two integration tests focused on i1 vectors, which exposed omissions
in the llvm backend which have since then been fixed. Note that this also
exposed an inaccuracy for print_i1 which has been fixed in this CL:
for a pure C ABI, int should be used rather than bool.
Reviewers: nicolasvasilache, ftynse, reidtatge, andydavis1, bkramer
Reviewed By: bkramer
Subscribers: mehdi_amini, rriddle, jpienaar, shauheen, antiagainst, nicolasvasilache, arpith-jacob, mgester, lucyrfox, liufengdb, stephenneuendorffer, Joonsoo, grosul1, frgossen, Kayjukh, jurahul, msifontes
Tags: #mlir
Differential Revision: https://reviews.llvm.org/D81957
Implement the missing lowering from `std.dim` to the LLVM dialect in case of a
dynamic dimension.
Differential Revision: https://reviews.llvm.org/D81834
Recent work has introduced support for constructing loops via `::build` with
callbacks that construct loop bodies using only the core OpBuilder. This is now
supported on all loop types that Linalg lowers to. Refactor LoopNestBuilder in
Linalg to rely on this functionality instead of using a custom EDSC-based
approach to creating loop nests.
The specialization targeting parallel loops is also simplified by factoring out
the recursive call into a separate static function and considering only two
alternatives: top-level loop is parallel or sequential.
This removes the last remaining in-tree use of edsc::LoopBuilder, which is now
deprecated and will be removed soon.
Differential Revision: https://reviews.llvm.org/D81873
Similarly to `scf::ForOp`, introduce additional `function_ref` arguments to
`::build` functions of SCF `ParallelOp` and `ReduceOp`. The provided functions
will be called to construct the body of the respective operations while
constructing the operation itself. Exercise them in LoopUtils.
Differential Revision: https://reviews.llvm.org/D81872
Summary:
This revision replaces MatmulOp, now that DRR rules have been dropped.
This revision also fixes minor parsing bugs and a plugs a few holes to get e2e paths working (e.g. library call emission).
During the replacement the i32 version had to be dropped because only the EDSC operators +, *, etc support type inference.
Deciding on a type-polymorphic behavior, and implementing it, is left for future work.
Reviewers: aartbik
Subscribers: mehdi_amini, rriddle, jpienaar, shauheen, antiagainst, arpith-jacob, mgester, lucyrfox, aartbik, liufengdb, stephenneuendorffer, Joonsoo, grosul1, frgossen, Kayjukh, jurahul, msifontes
Tags: #mlir
Differential Revision: https://reviews.llvm.org/D81935
- Modify HasParent trait to allow one of several op's as a parent -
- Expose this trait in the ODS framework using the ParentOneOf<> trait.
Differential Revision: https://reviews.llvm.org/D81880
This allows for passing a lambda to addDynamicallyLegalDialect without needing to explicit wrap with Optional<DynamicLegalityCallbackFn>.
Differential Revision: https://reviews.llvm.org/D81680
It is quite common for the same type to be converted many types throughout the conversion process, and there isn't any good reason why we aren't caching that result. Especially given that we currently use identity conversion to signify legality. This revision also adds a few additional helpers to TypeConverter.
Differential Revision: https://reviews.llvm.org/D81679
This revision replaces MatmulOp, now that DRR rules have been dropped.
This revision also fixes minor parsing bugs and a plugs a few holes to get e2e paths working (e.g. library call emission).
During the replacement the i32 version had to be dropped because only the EDSC operators +, *, etc support type inference.
Deciding on a type-polymorphic behavior, and implementing it, is left for future work.
Differential Revision: https://reviews.llvm.org/D79762
This is intended to avoid programming mistake where a temporary OpOperand is
created, for example:
for (OpOperand user : result.getUsers()) {
It can be confusing for the user, in particular since in MLIR most classes are intended to
be copied around by value while they have reference semantics.
Differential Revision: https://reviews.llvm.org/D81815
This reverts commit 32c757e4f8.
Broke the build bot:
******************** TEST 'MLIR :: Examples/standalone/test.toy' FAILED ********************
[...]
/tmp/ci-KIMiRFcVZt/lib/libMLIRLinalgToLLVM.a(LinalgToLLVM.cpp.o): In function `(anonymous namespace)::ConvertLinalgToLLVMPass::runOnOperation()':
LinalgToLLVM.cpp:(.text._ZN12_GLOBAL__N_123ConvertLinalgToLLVMPass14runOnOperationEv+0x100): undefined reference to `mlir::populateExpandTanhPattern(mlir::OwningRewritePatternList&, mlir::MLIRContext*)'
Summary:
Add a pattern for expanding tanh op into exp form.
A `tanh` is expanded into:
1) 1-exp^{-2x} / 1+exp^{-2x}, if x => 0
2) exp^{2x}-1 / exp^{2x}+1 , if x < 0.
Differential Revision: https://reviews.llvm.org/D81618
Similarly to `scf::ForOp`, introduce additional `function_ref` arguments to
`AffineForOp::build` that can be used to populate the body of the loop during
its construction. Provide compatibility functions for constructing affine loop
nests using `edsc::ScopedContext`.
`edsc::AffineLoopNestBuilder` and reletad functionality is now deprecated and
will be removed soon, users are expected to switch to `affineLoopNestBuilder`
that provides similar functionality with a simpler OpBuilder-based
implementation.
Differential Revision: https://reviews.llvm.org/D81754
Use ::Adaptor alias instead uniformly. Makes the naming more consistent as
adaptor can refer to attributes now too.
Differential Revision: https://reviews.llvm.org/D81789
Modify structure type in SPIR-V dialect to support:
1) Multiple decorations per structure member
2) Key-value based decorations (e.g., MatrixStride)
This commit kept the Offset decoration separate from members'
decorations container for easier implementation and logical clarity.
As such, all references to Structure layoutinfo are now offsetinfo,
and any member layout defining decoration (e.g., RowMajor for Matrix)
will be add to the members' decorations container along with its
value if any.
Differential Revision: https://reviews.llvm.org/D81426
Add `NoSideEffect` trait to `shape.to_extent_tensor` and
`shape.from_extent_tensor` and defined custom assembly format for the
operations.
Differential Revision: https://reviews.llvm.org/D81158
Modify structure type in SPIR-V dialect to support:
1) Multiple decorations per structure member
2) Key-value based decorations (e.g., MatrixStride)
This commit kept the Offset decoration separate from members'
decorations container for easier implementation and logical clarity.
As such, all references to Structure layoutinfo are now offsetinfo,
and any member layout defining decoration (e.g., RowMajor for Matrix)
will be add to the members' decorations container along with its
value if any.
Differential Revision: https://reviews.llvm.org/D81426
Modify structure type in SPIR-V dialect to support:
1) Multiple decorations per structure member
2) Key-value based decorations (e.g., MatrixStride)
This commit kept the Offset decoration separate from members'
decorations container for easier implementation and logical clarity.
As such, all references to Structure layoutinfo are now offsetinfo,
and any member layout defining decoration (e.g., RowMajor for Matrix)
will be add to the members' decorations container along with its
value if any.
Differential Revision: https://reviews.llvm.org/D81426
Following the previous revision `D81100`, this commit implements a templated class
that would provide conversion patterns for “straightforward” SPIR-V ops into
LLVM dialect. Templating allows to abstract away from concrete implementation
for each specific op. Those are mainly binary operations. Currently supported
and tested ops are:
- Arithmetic ops: `IAdd`, `ISub`, `IMul`, `FAdd`, `FSub`, `FMul`, `FDiv`, `FNegate`,
`SDiv`, `SRem` and `UDiv`
- Bitwise ops: `BitwiseAnd`, `BitwiseOr`, `BitwiseXor`
The implementation relies on `SPIRVToLLVMConversion` class that makes use of
`OpConversionPattern`.
Differential Revision: https://reviews.llvm.org/D81305
Allow for dynamic indices in the `dim` operation.
Rather than an attribute, the index is now an operand of type `index`.
This allows to apply the operation to dynamically ranked tensors.
The correct lowering of dynamic indices remains to be implemented.
Differential Revision: https://reviews.llvm.org/D81551
The operation `get_extent` now accepts the dimension as an operand and is no
longer limited to constant dimensions.
A helper function facilitates the common constant use case.
Differential Revision: https://reviews.llvm.org/D81248
This is useful for manipulating the standard dialect from transformations
outside of the standard dialect.
Differential Revision: https://reviews.llvm.org/D80609
These commits set up the skeleton for SPIR-V to LLVM dialect conversion.
I created SPIR-V to LLVM pass, registered it in Passes.td, InitAllPasses.h.
Added a pattern for `spv.BitwiseAndOp` and tests for it. Integer, float
and vector types are converted through LLVMTypeConverter.
Differential Revision: https://reviews.llvm.org/D81100
The SSA values created with `shape.const_size` are now named depending on the
value.
A constant size of 3, e.g., is now automatically named `%c3`.
Differential Revision: https://reviews.llvm.org/D81249
This parameter gives the developers the freedom to choose their desired function
signature conversion for preparing their functions for buffer placement. It is
introduced for BufferAssignmentFuncOpConverter, and also for
BufferAssignmentReturnOpConverter, and BufferAssignmentCallOpConverter to adapt
the return and call operations with the selected function signature conversion.
If the parameter is set, buffer placement won't also deallocate the returned
buffers.
Differential Revision: https://reviews.llvm.org/D81137
Summary:
This revision adds a common folding pattern that starts appearing on
vector_transfer ops.
Differential Revision: https://reviews.llvm.org/D81281
Summary:
`mlir-rocm-runner` is introduced in this commit to execute GPU modules on ROCm
platform. A small wrapper to encapsulate ROCm's HIP runtime API is also inside
the commit.
Due to behavior of ROCm, raw pointers inside memrefs passed to `gpu.launch`
must be modified on the host side to properly capture the pointer values
addressable on the GPU.
LLVM MC is used to assemble AMD GCN ISA coming out from
`ConvertGPUKernelToBlobPass` to binary form, and LLD is used to produce a shared
ELF object which could be loaded by ROCm HIP runtime.
gfx900 is the default target be used right now, although it could be altered via
an option in `mlir-rocm-runner`. Future revisions may consider using ROCm Agent
Enumerator to detect the right target on the system.
Notice AMDGPU Code Object V2 is used in this revision. Future enhancements may
upgrade to AMDGPU Code Object V3.
Bitcode libraries in ROCm-Device-Libs, which implements math routines exposed in
`rocdl` dialect are not yet linked, and is left as a TODO in the logic.
Reviewers: herhut
Subscribers: mgorny, tpr, dexonsmith, mehdi_amini, rriddle, jpienaar, shauheen, antiagainst, nicolasvasilache, csigg, arpith-jacob, mgester, lucyrfox, aartbik, liufengdb, stephenneuendorffer, Joonsoo, grosul1, frgossen, Kayjukh, jurahul, llvm-commits
Tags: #mlir, #llvm
Differential Revision: https://reviews.llvm.org/D80676
This revision adds a helper function to hoist vector.transfer_read /
vector.transfer_write pairs out of immediately enclosing scf::ForOp
iteratively, if the following conditions are true:
1. The 2 ops access the same memref with the same indices.
2. All operands are invariant under the enclosing scf::ForOp.
3. No uses of the memref either dominate the transfer_read or are
dominated by the transfer_write (i.e. no aliasing between the write and
the read across the loop)
To improve hoisting opportunities, call the `moveLoopInvariantCode` helper
function on the candidate loop above which to hoist. Hoisting the transfers
results in scf::ForOp yielding the value that originally transited through
memory.
This revision additionally exposes `moveLoopInvariantCode` as a helper in
LoopUtils.h and updates SliceAnalysis to support return scf::For values and
allow hoisting across multiple scf::ForOps.
Differential Revision: https://reviews.llvm.org/D81199
Forward declaring llvm::errs is not enough, as it is used as a default
parameter with a type that references the base class. So the class
hierarchy must be visible.
Summary:
This will inline the region to a shape.assuming in the case that the
input witness is found to be statically true.
Differential Revision: https://reviews.llvm.org/D80302
In the case of all inputs being constant and equal, cstr_eq will be
replaced with a true_witness.
Differential Revision: https://reviews.llvm.org/D80303
This allows replacing of this op with a true witness in the case of both
inputs being const_shapes and being found to be broadcastable.
Differential Revision: https://reviews.llvm.org/D80304
This allows assuming_all to be replaced when all inputs are known to be
statically passing witnesses.
Differential Revision: https://reviews.llvm.org/D80306
This will later be used during canonicalization and folding steps to replace
statically known passing constraints.
Differential Revision: https://reviews.llvm.org/D80307
Update linalg to affine lowering for convop to use affine load for input
whenever there is no padding. It had always been using std.loads because
max in index functions (needed for non-zero padding if not materializing
zeros) couldn't be represented in the non-zero padding cases.
In the future, the non-zero padding case could also be made to use
affine - either by materializing or using affine.execute_region. The
latter approach will not impact the scf/std output obtained after
lowering out affine.
Differential Revision: https://reviews.llvm.org/D81191
This simplifies a lot of handling of BoolAttr/IntegerAttr. For example, a lot of places currently have to handle both IntegerAttr and BoolAttr. In other places, a decision is made to pick one which can lead to surprising results for users. For example, DenseElementsAttr currently uses BoolAttr for i1 even if the user initialized it with an Array of i1 IntegerAttrs.
Differential Revision: https://reviews.llvm.org/D81047
This revision adds a helper function to hoist alloc/dealloc pairs and
alloca op out of immediately enclosing scf::ForOp if both conditions are true:
1. all operands are defined outside the loop.
2. all uses are ViewLikeOp or DeallocOp.
This is now considered Linalg-specific and will be generalized on a per-need basis.
Differential Revision: https://reviews.llvm.org/D81152
Add SubgroupId, SubgroupSize and NumSubgroups to GPU dialect ops and add the
lowering of those ops to SPIRV.
Differential Revision: https://reviews.llvm.org/D81042
Summary:
If the output filename was specified as "-", the ToolOutputFile class
would create a brand new raw_ostream object referring to the stdout.
This patch changes it to reuse the llvm::outs() singleton.
At the moment, this change should be "NFC", but it does enable other
enhancements, like the automatic stdout/stderr synchronization as
discussed on D80803.
I've checked the history, and I did not find any indication that this
class *has* to use a brand new stream object instead of outs() --
indeed, it is special-casing "-" in a number of places already, so this
change fits the pattern pretty well. I suspect the main reason for the
current state of affairs is that the class was originally introduced
(r111595, in 2010) as a raw_fd_ostream subclass, which made any other
solution impossible.
Another potential benefit of this patch is that it makes it possible to
move the raw_ostream class out of the business of special-casing "-" for
stdout handling. That state of affairs does not seem appropriate because
"-" is a valid filename (albeit hard to access with a lot of command
line tools) on most systems. Handling "-" in ToolOutputFile seems more
appropriate.
To make this possible, this patch changes the return type of
llvm::outs() and errs() to raw_fd_ostream&. Previously the functions
were constructing objects of that type, but returning a generic
raw_ostream reference. This makes it possible for new ToolOutputFile and
other code to use raw_fd_ostream methods like error() on the outs()
object. This does not seem like a bad thing (since stdout is a file
descriptor which can be redirected to anywhere, it makes sense to ask it
whether the writing was successful or if it supports seeking), and
indeed a lot of code was already depending on this fact via the
ToolOutputFile "back door".
Reviewers: dblaikie, JDevlieghere, MaskRay, jhenderson
Subscribers: hiraditya, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D81078
Summary:
Progressive lowering of vector.transpose into an operation that
is closer to an intrinsic, and thus the hardware ISA. Currently
under the common vector transform testing flag, as we prepare
deploying this transformation in the LLVM lowering pipeline.
Reviewers: nicolasvasilache, reidtatge, andydavis1, ftynse
Reviewed By: nicolasvasilache, ftynse
Subscribers: mehdi_amini, rriddle, jpienaar, shauheen, antiagainst, nicolasvasilache, arpith-jacob, mgester, lucyrfox, liufengdb, stephenneuendorffer, Joonsoo, grosul1, frgossen, Kayjukh, jurahul, llvm-commits
Tags: #llvm, #mlir
Differential Revision: https://reviews.llvm.org/D80772
Add a new pass to lower operations from the `shape` to the `std` dialect.
The conversion applies only to the `size_to_index` and `index_to_size`
operations and affected types.
Other patterns will be added as needed.
Differential Revision: https://reviews.llvm.org/D81091
The original design of TypeConverter expected specific converters to derive the
class and override virtual functions for conversions and materializations. This
did not scale well to multi-dialect conversions, so the design was changed to
register a list of converter and materializer functions, removing the need for
virtual functions. The only remaining virtual function, `convertSignatureArg`
is never overridden in-tree. Make it non-virtual, drop the virtual destructor
and thus remove vtable from TypeConverter.
If there exist TypeConverter users that need custom `convertSignatureArg`
behavior, it should be implemented using the callback registration mechanism
similar to that of conversions and materializations.
Differential Revision: https://reviews.llvm.org/D80993
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
This commit adds basic matrix type support to the SPIR-V dialect
including type definition, IR assembly, parsing, printing, and
(de)serialization.
Differential Revision: https://reviews.llvm.org/D80594
Dialect conversion infrastructure supports 1->N type conversions by requiring
individual conversions to provide facilities to generate operations
retrofitting N values into 1 of the original type when N > 1. This
functionality can also be used to materialize explicit "cast"-like operations,
but it did not support 1->1 type conversions until now. Modify TypeConverter to
support materialization of cast operations for 1-1 conversions.
This also makes materialization specification more extensible following the
same pattern as type conversions. Instead of overloading a virtual function,
users or subclasses of TypeConversion can now register type-specific
materialization callbacks that will be called in order for the given type.
Differential Revision: https://reviews.llvm.org/D79729
One header guard was overlooked when renaming LoopOps to SCF, rename it.
Also drop two unused macros, one of which referred to LoopOp (not "Ops",
hence the overlook).
Add BufferAssignmentCallOpConverter as a pattern rewriter for Buffer
Placement. It matches the signature of the caller operation with the callee
after rewriting the callee with FunctionAndBlockSignatureConverter.
Differential Revision: https://reviews.llvm.org/D80785
Keeping in the affine.for to gpu.launch conversions, which should
probably be the affine.parallel to gpu.launch conversion as well.
Differential Revision: https://reviews.llvm.org/D80747
Summary:
MSVC does not seem to like certain forward declarations.
https://reviews.llvm.org/D80728 introduces an error where
seemingly unrelated .cpp files that include the .h
(but do not otherwise use the class that depends on the forward declaration).
Instead of forward declaration, include the full vector ops definition.
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Tags: #llvm
Differential Revision: https://reviews.llvm.org/D80841
Summary:
Implemented the basic changes for defining master operation in OpenMP.
It uses the generic parser and printer.
Reviewed By: kiranchandramohan, ftynse
Differential Revision: https://reviews.llvm.org/D80689
This revision adds custom rewrites for patterns that arise during linalg structured
ops vectorization. These patterns allow the composition of linalg promotion,
vectorization and removal of redundant copies.
The patterns are voluntarily limited and restrictive atm.
More robust behavior will be implemented once more powerful side effect modeling and analyses are available on view/subview.
On the transfer_read side, the following pattern is rewritten:
```
%alloc = ...
[optional] %view = std.view %alloc ...
%subView = subview %allocOrView ...
[optional] linalg.fill(%allocOrView, %cst) ...
...
linalg.copy(%in, %subView) ...
vector.transfer_read %allocOrView[...], %cst ...
```
into
```
[unchanged] %alloc = ...
[unchanged] [optional] %view = std.view %alloc ...
[unchanged] [unchanged] %subView = subview %allocOrView ...
...
vector.transfer_read %in[...], %cst ...
```
On the transfer_write side, the following pattern is rewriten:
```
%alloc = ...
[optional] %view = std.view %alloc ...
%subView = subview %allocOrView...
...
vector.transfer_write %..., %allocOrView[...]
linalg.copy(%subView, %out)
```
Differential Revision: https://reviews.llvm.org/D80728
This utility factors out the machinery required to add iterArgs and yield values to an scf.ForOp.
Differential Revision: https://reviews.llvm.org/D80656
Buffer placement can now operates on functions that return buffers. These
buffers escape from the deallocation phase of buffer placement.
Differential Revision: https://reviews.llvm.org/D80696
https://reviews.llvm.org/D79246 introduces alignment propagation for vector transfer operations. Unfortunately, the alignment calculation is incorrect and can result in crashes.
This revision fixes the calculation by using the natural alignment of the memref elemental type, instead of the resulting vector type.
If more alignment is desired, it can be done in 2 ways:
1. use a proper vector.type_cast to transform a memref<axbxcxdxf32> into a memref<axbxvector<cxdxf32>> giving a natural alignment of vector<cxdxf32>
2. add an alignment attribute to vector transfer operations and propagate it.
With this change the alignment in the relevant tests goes down from 128 to 4.
Lastly, a few minor cleanups are performed and the custom `isMinorIdentityMap` is deprecated.
Differential Revision: https://reviews.llvm.org/D80734
operands of Generic ops.
Unit-extent dimensions are typically used for achieving broadcasting
behavior. The pattern added (along with canonicalization patterns
added previously) removes the use of unit-extent dimensions, and
instead uses a more canonical representation of the computation. This
new pattern is not added as a canonicalization for now since it
entails adding additional reshape operations. A pass is added to
exercise these patterns, along with an API entry to populate a
patterns list with these patterns.
Differential Revision: https://reviews.llvm.org/D79766
This allows constructing operand adaptor from existing op (useful for commonalizing verification as I want to do in a follow up).
I also add ability to use member initializers for the generated adaptor constructors for convenience.
Differential Revision: https://reviews.llvm.org/D80667
The operand of `from_extent_tensor` is now of the same index type as the result
type of the inverse operation `to_extent_tensor`.
Differential Revision: https://reviews.llvm.org/D80283
Make ConvertKernelFuncToCubin pass to be generic:
- Rename to ConvertKernelFuncToBlob.
- Allow specifying triple, target chip, target features.
- Initializing LLVM backend is supplied by a callback function.
- Lowering process from MLIR module to LLVM module is via another callback.
- Change mlir-cuda-runner to adopt the revised pass.
- Add new tests for lowering to ROCm HSA code object (HSACO).
- Tests for CUDA and ROCm are kept in separate directories.
Differential Revision: https://reviews.llvm.org/D80142
The operation `num_elements` determines the number of elements for a given
shape.
That is the product of its dimensions.
Differential Revision: https://reviews.llvm.org/D80281
Add the two conversion operations `index_to_size` and `size_to_index` to the
shape dialect.
This facilitates the conversion of index types between the shape and the
standard dialect.
Differential Revision: https://reviews.llvm.org/D80280
This changes will catch error where C++ op are used without being
registered, either through creation with the OpBuilder or when trying to
cast to the C++ op.
Differential Revision: https://reviews.llvm.org/D80651
Ling, Liyang