Enable querying shape function library ops from the module. Currently
supports singular or array of them (as long as array has all unique ops
in mappings). The preferred canonical form would have one library, but
given the invariant on the mapping, this can easily be achieved by a
simple merging pass.
Preferred the attribute approach vs naming convention as these could be
added in multiple different ways.
* Works in tandem with prototype packaging scripts here: https://github.com/stellaraccident/mlir-py-release
* The `mlir` top-level now differentiates between in-tree builds where all packages are co-located and distribution mode where all native components are under a top-level `_mlir_libs` package.
* Also fixes the generated dialect python installation again. Hopefully the last tweak.
* With this, I am able to install and generate archives with the above setup script on Linux. Archive size=31M with just host codegen and headers/shared-libraries. Will need more linker tweaks when wiring up the next dependent project.
Differential Revision: https://reviews.llvm.org/D93936
Add command line option to read the configuration dumped by the MLIR crash
reproducer and adds those to the other command line options parsed by mlir-opt.
Simple convenience that enables `mlir-opt --run-reproducer /tmp/repro.mlir`
instead of needing to copy&paste the configuration.
Differential Revision: https://reviews.llvm.org/D93924
- Add `PyAffineMap` to wrap around `MlirAffineMap`.
- Add `mlirPythonAffineMapToCapsule` and `mlirPythonCapsuleToAffineMap` to interoperate with python capsule.
- Add and test some simple bindings of `PyAffineMap`.
Differential Revision: https://reviews.llvm.org/D93200
The asmprinter would crash when dumping IR objects that had their
operands dropped. With this change, we now get this output, which
makes op->dump() style debugging more useful.
%5 = "firrtl.eq"(<<NULL>>, <<NULL>>) : (<<NULL TYPE>>, <<NULL TYPE>>) -> !firrtl.uint<1>
Previously the asmprinter would crash getting the types of the null operands.
Differential Revision: https://reviews.llvm.org/D93869
Implement Bug 46698, making ODS synthesize a getType() method that returns a
specific C++ class for OneResult methods where we know that class. This eliminates
a common source of casts in things like:
myOp.getType().cast<FIRRTLType>().getPassive()
because we know that myOp always returns a FIRRTLType. This also encourages
op authors to type their results more tightly (which is also good for
verification).
I chose to implement this by splitting the OneResult trait into itself plus a
OneTypedResult trait, given that many things are using `hasTrait<OneResult>`
to conditionalize various logic.
While this changes makes many many ops get more specific getType() results, it
is generally drop-in compatible with the previous behavior because 'x.cast<T>()'
is allowed when x is already known to be a T. The one exception to this is that
we need declarations of the types used by ops, which is why a couple headers
needed additional #includes.
I updated a few things in tree to remove the now-redundant `.cast<>`'s, but there
are probably many more than can be removed.
Differential Revision: https://reviews.llvm.org/D93790
1. Add new methods to Async runtime API to support yielding async values
2. Add lowering from `async.yield` with value payload to the new runtime API calls
`async.value` lowering requires that payload type is convertible to LLVM and supported by `llvm.mlir.cast` (DialectCast) operation.
Reviewed By: csigg
Differential Revision: https://reviews.llvm.org/D93592
Previously for each op we generate a separate serialization
method for it. Those serialization methods duplicate the logic
of parsing operands/results/attributes and such.
This commit creates a generic method and let suitable op-specific
serialization method to call into it.
wc -l SPIRVSerialization.inc: before 8304; after: 5597 (So -2707)
Reviewed By: hanchung, ThomasRaoux
Differential Revision: https://reviews.llvm.org/D93535
Previously for each op we generate a separate deserialization
method for it. Those deserialization methods duplicate the logic
of parsing operands/results/attributes and such.
This commit creates a generic method and let suitable op-specific
deserialization method to call into it.
wc -l SPIRVSerialization.inc: before 13290; after: 8304 (So -4986)
Reviewed By: hanchung, ThomasRaoux
Differential Revision: https://reviews.llvm.org/D93504
This commit renames various SPIR-V related conversion files for
consistency. It drops the "Convert" prefix to various files and
fixes various comment headers.
Reviewed By: hanchung, ThomasRaoux
Differential Revision: https://reviews.llvm.org/D93489
Previously all SCF to SPIR-V conversion patterns were tested as
the -convert-gpu-to-spirv pass. That obscured the structure we
want. This commit fixed it.
Reviewed By: ThomasRaoux, hanchung
Differential Revision: https://reviews.llvm.org/D93488
Adds rewrite patterns to convert select+cmp instructions into clamp
instructions whenever possible. Support is added to convert:
- FOrdLessThan, FOrdLessThanEqual to GLSLFClampOp.
- SLessThan, SLessThanEqual to GLSLSClampOp.
- ULessThan, ULessThanEqual to GLSLUClampOp.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D93618
LLVMType contains numerous static constructors that were initially introduced
for API compatibility with LLVM. Most of these merely forward to arguments to
`SpecificType::get` (MLIR defines classes for all types, unlike LLVM IR), while
some introduce subtle semantics differences due to different modeling of MLIR
types (e.g., structs are not auto-renamed in case of conflicts). Furthermore,
these constructors don't match MLIR idioms and actively prevent us from making
the LLVM dialect type system more open. Remove them and use `SpecificType::get`
instead.
Depends On D93680
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D93681
Introduce a translation of OpenMP workshare loop construct to LLVM IR. This is
a minimalist version to enable the pipeline and currently only supports static
loop schedule (default in the specification) on non-collapsed loops. Other
features will be added on per-need basis.
Reviewed By: kiranchandramohan
Differential Revision: https://reviews.llvm.org/D92055
One common situation is to create a lot of IR at a well known location,
e.g. when doing a big rewrite from one dialect to another where you're expanding
ops out into lots of other ops.
For these sorts of situations, it is annoying to pass the location into
every create call. As we discused in a few threads on the forum, a way to help
with this is to produce a new sort of builder that holds a location and provides
it to each of the create<> calls automatically.
This patch implements an ImplicitLocOpBuilder class that does this. We've had
good experience with this in the CIRCT project, and it makes sense to upstream to
MLIR.
I picked a random pass to adopt it to show the impact, but I don't think there is
any particular need to force adopt it in the codebase.
Differential Revision: https://reviews.llvm.org/D93717
LLVMType contains multiple instance methods that were introduced initially for
compatibility with LLVM API. These methods boil down to `cast` followed by
type-specific call. Arguably, they are mostly used in an LLVM cast-follows-isa
anti-pattern. This doesn't connect nicely to the rest of the MLIR
infrastructure and actively prevents it from making the LLVM dialect type
system more open, e.g., reusing built-in types when appropriate. Remove such
instance methods and replaces their uses with apporpriate casts and methods on
derived classes. In some cases, the result may look slightly more verbose, but
most cases should actually use a stricter subtype of LLVMType anyway and avoid
the isa/cast.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D93680
This commit addresses the issue of lowering affine.for and
affine.parallel having return values. Relevant test cases are also
added.
Signed-off-by: Prateek Gupta <prateek@polymagelabs.com>
Differential Revision: https://reviews.llvm.org/D93090
This is a temporary fix until figuring out how to correct the forward
declare in mlir/include/mlir/Support/LLVM.h
Differential Revision: https://reviews.llvm.org/D93666
Extend unroll to support all element-wise ops and allow unrolling for ops with
vector operands of with the same shape as the destination but different element
type (like Cmp or Select).
Differential Revision: https://reviews.llvm.org/D93121
This revision drops init_tensor arguments from Linalg on tensors and instead uniformizes the output buffers and output tensors to be consistent.
This significantly simplifies the usage of Linalg on tensors and is a stepping stone for
its evolution towards a mixed tensor and shape abstraction discussed in https://llvm.discourse.group/t/linalg-and-shapes/2421/19.
Differential Revision: https://reviews.llvm.org/D93469
Transfer_ops can now work on both buffers and tensor. Right now, lowering of
the tensor case is not supported yet.
Differential Revision: https://reviews.llvm.org/D93500
This class used to serve a few useful purposes:
* Allowed containing a null DictionaryAttr
* Provided some simple mutable API around a DictionaryAttr
The first of which is no longer an issue now that there is much better caching support for attributes in general, and a cache in the context for empty dictionaries. The second results in more trouble than it's worth because it mutates the internal dictionary on every action, leading to a potentially large number of dictionary copies. NamedAttrList is a much better alternative for the second use case, and should be modified as needed to better fit it's usage as a DictionaryAttrBuilder.
Differential Revision: https://reviews.llvm.org/D93442
Reductions in innermost loops become harder for the backend to disambiguate
after bufferization into memrefs, resulting in less efficient load-update-store
cycles. By scalarizing innermost reductions, the backend is more likely to assign
a register to perform the reduction (also prepares vectorization). Even though
we could scalarize reductions for more outer loops and while-loops as well,
currently scalarization is only done for chains of innermost for-loops, where
it matters most, to avoid complicating codegen unnecessary (viz. adding lots
of yield instructions).
This CL also refactors condition simplification into the merger class,
where it belongs, so that conditions are simplified only once per loop
nest and not repeatedly as was currently done. This CL also fixes a few
minor bugs, some layout issues, and comments.
Reviewed By: penpornk
Differential Revision: https://reviews.llvm.org/D93143
This operation is used to materialize a tensor of a particular
shape. The shape could be specified as a mix of static and dynamic
values.
The use of this operation is to be an `init` tensor for Linalg
structured operation on tensors where the bounds of the computation
depends on the shape of the output of the linalg operation. The result
of this operation will be used as the `init` tensor of such Linalg
operations. To note,
1) The values in the tensor materialized is not used. Any operation to
which this is an init tensor is expected to overwrite the entire
tensor.
2) The tensor is materialized only for the shape of the output and to
make the loop bounds depend only on operands of the structured
operation.
Based on (1) and (2) it is assumed that these operations eventually go
away since they are only used in `dim` operations that can be
canonicalized to make this operation dead. Such canonicalization are
added here too.
Differential Revision: https://reviews.llvm.org/D93374
This better matches the rest of the infrastructure, is much simpler, and makes it easier to move these types to being declaratively specified.
Differential Revision: https://reviews.llvm.org/D93432
The LLVM IR 'switch' instruction allows control flow to be transferred
to one of any number of branches depending on an integer control value,
or a default value if the control does not match any branch values. This patch
adds `llvm.switch` to the MLIR LLVMIR dialect, as well as translation routines
for lowering it to LLVM IR.
To store a variable number of operands for a variable number of branch
destinations, the new op makes use of the `AttrSizedOperandSegments`
trait. It stores its default branch operands as one segment, and all
remaining case branches' operands as another. It also stores pairs of
begin and end offset values to delineate the sub-range of each case branch's
operands. There's probably a better way to implement this, since the
offset computation complicates several parts of the op definition. This is the
approach I settled on because in doing so I was able to delegate to the default
op builder member functions. However, it may be preferable to instead specify
`skipDefaultBuilders` in the op's ODS, or use a completely separate
approach; feedback is welcome!
Another contentious part of this patch may be the custom printer and
parser functions for the op. Ideally I would have liked the MLIR to be
printed in this way:
```
llvm.switch %0, ^bb1(%1 : !llvm.i32) [
1: ^bb2,
2: ^bb3(%2, %3 : !llvm.i32, !llvm.i32)
]
```
The above would resemble how LLVM IR is formatted for the 'switch'
instruction. But I found it difficult to print and parse something like
this, whether I used the declarative assembly format or custom functions.
I also was not sure a multi-line format would be welcome -- it seems
like most MLIR ops do not use newlines. Again, I'd be happy to hear any
feedback here as well, or on any other aspect of the patch.
Differential Revision: https://reviews.llvm.org/D93005
This commit shuffles SPIR-V code around to better follow MLIR
convention. Specifically,
* Created IR/, Transforms/, Linking/, and Utils/ subdirectories and
moved suitable code inside.
* Created SPIRVEnums.{h|cpp} for SPIR-V C/C++ enums generated from
SPIR-V spec. Previously they are cluttered inside SPIRVTypes.{h|cpp}.
* Fixed include guards in various header files (both .h and .td).
* Moved serialization tests under test/Target/SPIRV.
* Renamed TableGen backend -gen-spirv-op-utils into -gen-spirv-attr-utils
as it is only generating utility functions for attributes.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D93407
Chengji Yao