- Use TypeRange instead of ArrayRef<Type> where possible.
- Change some of the custom builders to also use TypeRange
Differential Revision: https://reviews.llvm.org/D87944
Binding MemRefs of f16 needs special handling as the type is not supported on
CPU. There was a bug in the type used.
Differential Revision: https://reviews.llvm.org/D86328
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
registry.insert<mlir::standalone::StandaloneDialect>();
registry.insert<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
Differential Revision: https://reviews.llvm.org/D85622
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
registry.insert<mlir::standalone::StandaloneDialect>();
registry.insert<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
Differential Revision: https://reviews.llvm.org/D85622
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
mlir::registerDialect<mlir::standalone::StandaloneDialect>();
mlir::registerDialect<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
This changes the behavior of constructing MLIRContext to no longer load globally registered dialects on construction. Instead Dialects are only loaded explicitly on demand:
- the Parser is lazily loading Dialects in the context as it encounters them during parsing. This is the only purpose for registering dialects and not load them in the context.
- Passes are expected to declare the dialects they will create entity from (Operations, Attributes, or Types), and the PassManager is loading Dialects into the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only need to load the dialect for the IR it will emit, and the optimizer is self-contained and load the required Dialects. For example in the Toy tutorial, the compiler only needs to load the Toy dialect in the Context, all the others (linalg, affine, std, LLVM, ...) are automatically loaded depending on the optimization pipeline enabled.
Differential Revision: https://reviews.llvm.org/D85622
This changes the behavior of constructing MLIRContext to no longer load globally registered dialects on construction. Instead Dialects are only loaded explicitly on demand:
- the Parser is lazily loading Dialects in the context as it encounters them during parsing. This is the only purpose for registering dialects and not load them in the context.
- Passes are expected to declare the dialects they will create entity from (Operations, Attributes, or Types), and the PassManager is loading Dialects into the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only need to load the dialect for the IR it will emit, and the optimizer is self-contained and load the required Dialects. For example in the Toy tutorial, the compiler only needs to load the Toy dialect in the Context, all the others (linalg, affine, std, LLVM, ...) are automatically loaded depending on the optimization pipeline enabled.
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
- 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
This changes the casing of MLIRGPUtoVulkanTransforms to be consistent
with other transform libraries.
Differential Revision: https://reviews.llvm.org/D82840
- 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.
Summary:
Use a nested symbol to identify the kernel to be invoked by a `LaunchFuncOp` in the GPU dialect.
This replaces the two attributes that were used to identify the kernel module and the kernel within seperately.
Differential Revision: https://reviews.llvm.org/D78551
Summary:
Workgroup size is written into the kernel. So to properly modelling
vulkan launch, we have to skip local workgroup size for vulkan launch
call op.
Differential Revision: https://reviews.llvm.org/D78307
Summary:
This is much cleaner, and fits the same structure as many other tablegen backends. This was not done originally as the CRTP in the pass classes made it overly verbose/complex.
Differential Revision: https://reviews.llvm.org/D77367
This revision removes all of the CRTP from the pass hierarchy in preparation for using the tablegen backend instead. This creates a much cleaner interface in the C++ code, and naturally fits with the rest of the infrastructure. A new utility class, PassWrapper, is added to replicate the existing behavior for passes not suitable for using the tablegen backend.
Differential Revision: https://reviews.llvm.org/D77350
ModulePass doesn't provide any special utilities and thus doesn't give enough benefit to warrant a special pass class. This revision replaces all usages with the more general OperationPass.
Differential Revision: https://reviews.llvm.org/D77339
This revision adds support for generating utilities for passes such as options/statistics/etc. that can be inferred from the tablegen definition. This removes additional boilerplate from the pass, and also makes it easier to remove the reliance on the pass registry to provide certain things(e.g. the pass argument).
Differential Revision: https://reviews.llvm.org/D76659
This removes the need to statically register conversion passes, and also puts all of the conversions within one centralized file.
Differential Revision: https://reviews.llvm.org/D76658
A memref argument is converted into a pointer-to-struct argument
of type `{T*, T*, i64, i64[N], i64[N]}*` in the wrapper function,
where T is the converted element type and N is the memref rank.
Differential Revision: https://reviews.llvm.org/D76059
* Adds GpuLaunchFuncToVulkanLaunchFunc conversion pass.
* Moves a serialization of the `spirv::Module` from LaunchFuncToVulkanCalls pass to newly created pass.
* Updates LaunchFuncToVulkanCalls instrumentation pass, adds `initVulkan` and `deinitVulkan` runtime calls.
* Adds `bindResource` call to bind specifc resource by the given descriptor set and descriptor binding.
* Eliminates static construction and desctruction of `VulkanRuntimeManager`.
Differential Revision: https://reviews.llvm.org/D75192
CMake allows calling target_link_libraries() without a keyword,
but this usage is not preferred when also called with a keyword,
and has surprising behavior. This patch explicitly specifies a
keyword when using target_link_libraries().
Differential Revision: https://reviews.llvm.org/D75725
Collect a list of conversion libraries in cmake, so we don't have to
list these explicitly in most binaries.
Differential Revision: https://reviews.llvm.org/D75222
Summary:
NFC - Moved StandardOps/Ops.h to a StandardOps/IR dir to better match surrounding
directories. This is to match other dialects, and prepare for moving StandardOps
related transforms in out for Transforms and into StandardOps/Transforms.
Differential Revision: https://reviews.llvm.org/D74940
Add an initial version of mlir-vulkan-runner execution driver.
A command line utility that executes a MLIR file on the Vulkan by
translating MLIR GPU module to SPIR-V and host part to LLVM IR before
JIT-compiling and executing the latter.
Differential Revision: https://reviews.llvm.org/D72696
Implement a pass to convert gpu.launch_func op into a sequence of
Vulkan runtime calls. The Vulkan runtime API surface is huge so currently we
don't expose separate external functions in IR for each of them, instead we
expose a few external functions to wrapper libraries which manages
Vulkan runtime.
Differential Revision: https://reviews.llvm.org/D74549
