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 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
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
registry.insert<mlir::standalone::StandaloneDialect>();
registry.insert<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
Differential Revision: https://reviews.llvm.org/D85622
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
registry.insert<mlir::standalone::StandaloneDialect>();
registry.insert<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
Differential Revision: https://reviews.llvm.org/D85622
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
mlir::registerDialect<mlir::standalone::StandaloneDialect>();
mlir::registerDialect<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
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 changes the behavior of constructing MLIRContext to no longer load globally registered dialects on construction. Instead Dialects are only loaded explicitly on demand:
- the Parser is lazily loading Dialects in the context as it encounters them during parsing. This is the only purpose for registering dialects and not load them in the context.
- Passes are expected to declare the dialects they will create entity from (Operations, Attributes, or Types), and the PassManager is loading Dialects into the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only need to load the dialect for the IR it will emit, and the optimizer is self-contained and load the required Dialects. For example in the Toy tutorial, the compiler only needs to load the Toy dialect in the Context, all the others (linalg, affine, std, LLVM, ...) are automatically loaded depending on the optimization pipeline enabled.
Differential Revision: https://reviews.llvm.org/D85622
This changes the behavior of constructing MLIRContext to no longer load globally registered dialects on construction. Instead Dialects are only loaded explicitly on demand:
- the Parser is lazily loading Dialects in the context as it encounters them during parsing. This is the only purpose for registering dialects and not load them in the context.
- Passes are expected to declare the dialects they will create entity from (Operations, Attributes, or Types), and the PassManager is loading Dialects into the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only need to load the dialect for the IR it will emit, and the optimizer is self-contained and load the required Dialects. For example in the Toy tutorial, the compiler only needs to load the Toy dialect in the Context, all the others (linalg, affine, std, LLVM, ...) are automatically loaded depending on the optimization pipeline enabled.
This 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
This simple patch translates the num_threads and if clauses of the parallel
operation. Also includes test cases.
A minor change was made to parsing of the if clause to parse AnyType and
return the parsed type. Updates to test cases also.
Reviewed by: SouraVX
Differential Revision: https://reviews.llvm.org/D84798
Historically, LLVMDialect has been required in the conversion from LLVM IR in
order to be able to construct types. This is no longer necessary with the new
type model and the dialect can be replaced with a local LLVM context.
Reviewed By: rriddle, mehdi_amini
Differential Revision: https://reviews.llvm.org/D85444
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
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
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
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
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 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
Linkage support is already present in the LLVM dialect, and is being translated
for globals other than functions. Translation support has been missing for
functions because their conversion goes through a different code path than
other globals.
Differential Revision: https://reviews.llvm.org/D84149
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
- Provide default value for `ArrayRef<NamedAttribute> attributes` parameter of
the collective params build method.
- Change the `genSeparateArgParamBuilder` function to not generate build methods
that may be ambiguous with the new collective params build method.
- This change should help eliminate passing empty NamedAttribue ArrayRef when the
collective params build method is used
- Extend op-decl.td unit test to make sure the ambiguous build methods are not
generated.
Differential Revision: https://reviews.llvm.org/D83517
`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
Summary:
With this change, a function argument attribute of the form
"llvm.align" = <int> will be translated to the corresponding align
attribute in LLVM by the ModuleConversion.
Differential Revision: https://reviews.llvm.org/D82161
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
Summary:
This patch adds support for flush operation in OpenMP dialect and translation of this construct to LLVM IR.
The OpenMP IRBuilder is used for this translation.
The patch includes code changes and testcase modifications.
Reviewed By: ftynse, kiranchandramohan
Differential Revision: https://reviews.llvm.org/D79937
For IR generated by a compiler, this is really simple: you just take the
datalayout from the beginning of the file, and apply it to all the IR
later in the file. For optimization testcases that don't care about the
datalayout, this is also really simple: we just use the default
datalayout.
The complexity here comes from the fact that some LLVM tools allow
overriding the datalayout: some tools have an explicit flag for this,
some tools will infer a datalayout based on the code generation target.
Supporting this properly required plumbing through a bunch of new
machinery: we want to allow overriding the datalayout after the
datalayout is parsed from the file, but before we use any information
from it. Therefore, IR/bitcode parsing now has a callback to allow tools
to compute the datalayout at the appropriate time.
Not sure if I covered all the LLVM tools that want to use the callback.
(clang? lli? Misc IR manipulation tools like llvm-link?). But this is at
least enough for all the LLVM regression tests, and IR without a
datalayout is not something frontends should generate.
This change had some sort of weird effects for certain CodeGen
regression tests: if the datalayout is overridden with a datalayout with
a different program or stack address space, we now parse IR based on the
overridden datalayout, instead of the one written in the file (or the
default one, if none is specified). This broke a few AVR tests, and one
AMDGPU test.
Outside the CodeGen tests I mentioned, the test changes are all just
fixing CHECK lines and moving around datalayout lines in weird places.
Differential Revision: https://reviews.llvm.org/D78403
- Exports MLIR targets to be used out-of-tree.
- mimicks `add_clang_library` and `add_flang_library`.
- Fixes libMLIR.so
After https://reviews.llvm.org/D77515 libMLIR.so was no longer containing
any object files. We originally had a cludge there that made it work with
the static initalizers and when switchting away from that to the way the
clang shlib does it, I noticed that MLIR doesn't create a `obj.{name}` target,
and doesn't export it's targets to `lib/cmake/mlir`.
This is due to MLIR using `add_llvm_library` under the hood, which adds
the target to `llvmexports`.
Differential Revision: https://reviews.llvm.org/D78773
[MLIR] Fix libMLIR.so and LLVM_LINK_LLVM_DYLIB
Primarily, this patch moves all mlir references to LLVM libraries into
either LLVM_LINK_COMPONENTS or LINK_COMPONENTS. This enables magic in
the llvm cmake files to automatically replace reference to LLVM components
with references to libLLVM.so when necessary. Among other things, this
completes fixing libMLIR.so, which has been broken for some configurations
since D77515.
Unlike previously, the pattern is now that mlir libraries should almost
always use add_mlir_library. Previously, some libraries still used
add_llvm_library. However, this confuses the export of targets for use
out of tree because libraries specified with add_llvm_library are exported
by LLVM. Instead users which don't need/can't be linked into libMLIR.so
can specify EXCLUDE_FROM_LIBMLIR
A common error mode is linking with LLVM libraries outside of LINK_COMPONENTS.
This almost always results in symbol confusion or multiply defined options
in LLVM when the same object file is included as a static library and
as part of libLLVM.so. To catch these errors more directly, there's now
mlir_check_all_link_libraries.
To simplify usage of add_mlir_library, we assume that all mlir
libraries depend on LLVMSupport, so it's not necessary to separately specify
it.
tested with:
BUILD_SHARED_LIBS=on,
BUILD_SHARED_LIBS=off + LLVM_BUILD_LLVM_DYLIB,
BUILD_SHARED_LIBS=off + LLVM_BUILD_LLVM_DYLIB + LLVM_LINK_LLVM_DYLIB.
By: Stephen Neuendorffer <stephen.neuendorffer@xilinx.com>
Differential Revision: https://reviews.llvm.org/D79067
[MLIR] Move from using target_link_libraries to LINK_LIBS
This allows us to correctly generate dependencies for derived targets,
such as targets which are created for object libraries.
By: Stephen Neuendorffer <stephen.neuendorffer@xilinx.com>
Differential Revision: https://reviews.llvm.org/D79243
Three commits have been squashed to avoid intermediate build breakage.
This method has been commented as deprecated for a while. Remove
it and replace all uses with the equivalent getCalledOperand().
I also made a few cleanups in here. For example, to removes use
of getElementType on a pointer when we could just use getFunctionType
from the call.
Differential Revision: https://reviews.llvm.org/D78882
This change makes the ModuleTranslation threadsafe by locking on the
LLVMContext. Furthermore, we now clone the llvm module into a new
context when compiling to PTX similar to what the OrcJit does.
Differential Revision: https://reviews.llvm.org/D78207
River Riddle