Vector to SCF conversion still had issues due to the interaction with the natural alignment derived by the LLVM data layout. One traditional workaround is to allocate aligned. However, this does not always work for vector sizes that are non-powers of 2.
This revision implements a more portable mechanism where the intermediate allocation is always a memref of elemental vector type. AllocOp is extended to use the natural LLVM DataLayout alignment for non-scalar types, when the alignment is not specified in the first place.
An integration test is added that exercises the transfer to scf.for + scalar lowering with a 5x5 transposition.
Differential Revision: https://reviews.llvm.org/D87150
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
registry.insert<mlir::standalone::StandaloneDialect>();
registry.insert<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
Differential Revision: https://reviews.llvm.org/D85622
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
registry.insert<mlir::standalone::StandaloneDialect>();
registry.insert<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
Differential Revision: https://reviews.llvm.org/D85622
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
mlir::registerDialect<mlir::standalone::StandaloneDialect>();
mlir::registerDialect<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
This changes the behavior of constructing MLIRContext to no longer load globally registered dialects on construction. Instead Dialects are only loaded explicitly on demand:
- the Parser is lazily loading Dialects in the context as it encounters them during parsing. This is the only purpose for registering dialects and not load them in the context.
- Passes are expected to declare the dialects they will create entity from (Operations, Attributes, or Types), and the PassManager is loading Dialects into the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only need to load the dialect for the IR it will emit, and the optimizer is self-contained and load the required Dialects. For example in the Toy tutorial, the compiler only needs to load the Toy dialect in the Context, all the others (linalg, affine, std, LLVM, ...) are automatically loaded depending on the optimization pipeline enabled.
Differential Revision: https://reviews.llvm.org/D85622
This changes the behavior of constructing MLIRContext to no longer load globally registered dialects on construction. Instead Dialects are only loaded explicitly on demand:
- the Parser is lazily loading Dialects in the context as it encounters them during parsing. This is the only purpose for registering dialects and not load them in the context.
- Passes are expected to declare the dialects they will create entity from (Operations, Attributes, or Types), and the PassManager is loading Dialects into the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only need to load the dialect for the IR it will emit, and the optimizer is self-contained and load the required Dialects. For example in the Toy tutorial, the compiler only needs to load the Toy dialect in the Context, all the others (linalg, affine, std, LLVM, ...) are automatically loaded depending on the optimization pipeline enabled.
This 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.
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
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
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
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
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
Having the input dumped on failure seems like a better
default: I debugged FileCheck tests for a while without knowing
about this option, which really helps to understand failures.
Remove `-dump-input-on-failure` and the environment variable
FILECHECK_DUMP_INPUT_ON_FAILURE which are now obsolete.
Differential Revision: https://reviews.llvm.org/D81422
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
Thanks to a recent change that made `::build` functions take an instance of
`OpBuilder`, it is now possible to build operations within a region attached to
the operation about to be created. Exercise this on `scf::ForOp` by taking a
callback that populates the loop body while the loop is being created.
Additionally, provide helper functions to build perfect nests of `ForOp`s,
with support for iteration arguments. These functions provide the same
functionality as EDSC LoopNestBuilder with simpler implementation, without
relying on edsc::ScopedContext, and using `OpBuilder` in an unambiguous way.
Compatibility functions for EDSC are provided, but may be removed in the
future.
Differential Revision: https://reviews.llvm.org/D79688
All ops of the SCF dialect now use the `scf.` prefix instead of `loop.`. This
is a part of dialect renaming.
Differential Revision: https://reviews.llvm.org/D79844
This dialect contains various structured control flow operaitons, not only
loops, reflect this in the name. Drop the Ops suffix for consistency with other
dialects.
Note that this only moves the files and changes the C++ namespace from 'loop'
to 'scf'. The visible IR prefix remains the same and will be updated
separately. The conversions will also be updated separately.
Differential Revision: https://reviews.llvm.org/D79578
Summary:
In the particular case of an insertion in a block without a terminator, the BlockBuilder insertion point should be block->end().
Adding a unit test to exercise this.
Differential Revision: https://reviews.llvm.org/D79363
This revision allows masked vector transfers with m-D buffers and n-D vectors to
progressively lower to m-D buffer and 1-D vector transfers.
For a vector.transfer_read, assuming a `memref<(leading_dims) x (major_dims) x (minor_dims) x type>` and a `vector<(minor_dims) x type>` are involved in the transfer, this generates pseudo-IR resembling:
```
if (any_of(%ivs_major + %offsets, <, major_dims)) {
%v = vector_transfer_read(
{%offsets_leading, %ivs_major + %offsets_major, %offsets_minor},
%ivs_minor):
memref<(leading_dims) x (major_dims) x (minor_dims) x type>,
vector<(minor_dims) x type>;
} else {
%v = splat(vector<(minor_dims) x type>, %fill)
}
```
Differential Revision: https://reviews.llvm.org/D79062
OperationHandle mostly existed to mirror the behavior of ValueHandle.
This has become unnecessary and can be retired.
Differential Revision: https://reviews.llvm.org/D78692
367229e100 retired ValueHandle but
mistakenly removed the implementation for `negate` which was not
tested and would result in linking errors.
This revision adds the implementation back and provides a test.
Summary: Functional.h contains many different methods that have a direct, and more efficient, equivalent in LLVM. This revision replaces all usages with the LLVM equivalent, and removes the header. This is part of larger cleanup, pr45513, merging MLIR support facilities into LLVM.
Differential Revision: https://reviews.llvm.org/D78053
A certain number of EDSCs have a named form (e.g. `linalg.matmul`) and a generic form (e.g. `linalg.generic` with matmul traits).
Despite living in different namespaces, using the same name is confusiong in clients.
Rename them as `linalg_matmul` and `linalg_generic_matmul` respectively.
Summary:
Change AffineOps Dialect structure to better group both IR and Tranforms. This included extracting transforms directly related to AffineOps. Also move AffineOps to Affine.
Differential Revision: https://reviews.llvm.org/D76161
Summary:
1-bit integer is tricky in different dialects sometimes. E.g., there is no
arithmetic instructions on 1-bit integer in SPIR-V, i.e., `spv.IMul %0, %1 : i1`
is not valid. Instead, `spv.LogicalAnd %0, %1 : i1` is valid. Creating the op
directly makes lowering easier because we don't need to match a complicated
pattern like `!(!lhs && !rhs)`. Also, this matches the semantic better.
Also add assertions on inputs.
Differential Revision: https://reviews.llvm.org/D75764
Summary:
This revision allows model builder to create a linalg_matmul whose body
is a vector.contract. This shows the abstractions compose nicely.
Differential Revision: https://reviews.llvm.org/D74457
Summary: This was a missed case when ValueRange was originally added, and allows for constructing a ValueRange from the arguments of a block.
Differential Revision: https://reviews.llvm.org/D74363
In the previous state, we were relying on forcing the linker to include
all libraries in the final binary and the global initializer to self-register
every piece of the system. This change help moving away from this model, and
allow users to compose pieces more freely. The current change is only "fixing"
the dialect registration and avoiding relying on "whole link" for the passes.
The translation is still relying on the global registry, and some refactoring
is needed to make this all more convenient.
Differential Revision: https://reviews.llvm.org/D74461
Summary:
This revision adds EDSC support for VectorOps to enable the creation of a `vector_matmul` declaratively. The `vector_matmul` is a simple configuration
of the `vector.contract` op that follows the StructuredOps abstraction.
Differential Revision: https://reviews.llvm.org/D74284
This CL refactors EDSCs to layer them better and break unnecessary
dependencies. After this refactoring, the top-level EDSC target only
depends on IR but not on Dialects anymore and each dialect has its
own EDSC directory.
This simplifies the layering and breaks cyclic dependencies.
In particular, the declarative builder + folder are made explicit and
are now confined to Linalg.
As the refactoring occurred, certain classes and abstractions that were not
paying for themselves have been removed.
Differential Revision: https://reviews.llvm.org/D74302
This revision does the following post-commit cleanups:
1. don't use -1 magic constants,
2. drop commented out old test that does not belong here,
3. reformat and add a proper clang-format off on a CHECK directive.
Summary:
This diff extends the Linalg EDSC builders so we can easily create mixed
tensor/buffer linalg.generic ops. This is expected to be useful for
HLO -> Linalg lowering.
The StructuredIndexed struct is made to derive from ValueHandle and can
now capture a type + indexing expressions. This is used to represent return
tensors.
Pointwise unary and binary builders are extended to allow both output buffers
and return tensors. This has implications on the number of region arguments.
Reviewers: ftynse, hanchung, asaadaldien
Subscribers: mehdi_amini, rriddle, jpienaar, burmako, shauheen, antiagainst, arpith-jacob, mgester, lucyrfox, aartbik, liufengdb, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D73149
Summary:
This diff extends the Linalg EDSC builders so we can easily create mixed
tensor/buffer linalg.generic ops. This is expected to be useful for
HLO -> Linalg lowering.
The `StructuredIndexed` struct is made to derive from `ValueHandle` and can
now capture a type + indexing expressions. This is used to represent return
tensors.
Pointwise unary and binary builders are extended to allow both output buffers
and return tensors. This has implications on the number of region arguments.
Reviewers: ftynse, herhut, hanchung, asaadaldien, stellaraccident
Reviewed By: asaadaldien
Subscribers: merge_guards_bot, mehdi_amini, rriddle, jpienaar, burmako, shauheen, antiagainst, arpith-jacob, mgester, lucyrfox, aartbik, liufengdb, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D72863
mlir currently fails to build on Solaris:
/vol/llvm/src/llvm-project/dist/mlir/lib/Conversion/VectorToLoops/ConvertVectorToLoops.cpp:78:20: error: reference to 'index_t' is ambiguous
IndexHandle zero(index_t(0)), one(index_t(1));
^
/usr/include/sys/types.h:103:16: note: candidate found by name lookup is 'index_t'
typedef short index_t;
^
/vol/llvm/src/llvm-project/dist/mlir/include/mlir/EDSC/Builders.h:27:8: note: candidate found by name lookup is 'mlir::edsc::index_t'
struct index_t {
^
and many more.
Given that POSIX reserves all identifiers ending in `_t` 2.2.2 The Name Space <https://pubs.opengroup.org/onlinepubs/9699919799/functions/V2_chap02.html>, it seems
quite unwise to use such identifiers in user code, even more so without a distinguished
prefix.
The following patch fixes this by renaming `index_t` to `index_type`.
cases.
Tested on `amd64-pc-solaris2.11` and `sparcv9-sun-solaris2.11`.
Differential Revision: https://reviews.llvm.org/D72619
Nicolas Vasilache