By making TypeInterfaces and AttrInterfaces, Types and Attrs respectively it'd then be possible to use them anywhere where a Type or Attr may go. That is within the arguments and results of an Op definition, in a RewritePattern etc.
Prior to this change users had to separately define a Type or Attr, with a predicate to check whether a type or attribute implements a given interface. Such code will be redundant now.
Removing such occurrences in upstream dialects will be part of a separate patch.
As part of implementing this patch, slight refactoring had to be done. In particular, Interfaces cppClassName field was renamed to cppInterfaceName as it "clashed" with TypeConstraints cppClassName. In particular Interfaces cppClassName expected just the class name, without any namespaces, while TypeConstraints cppClassName expected a fully qualified class name.
Differential Revision: https://reviews.llvm.org/D129209
The current translation uses the old "ugly"/"raw" form which used PDLValue for the arguments
and results. This commit updates the C++ generation to use the recently added sugar that
allows for directly using the desired types for the arguments and result of PDL functions.
In addition, this commit also properly imports the C++ class for ODS operations, constraints,
and interfaces. This allows for a much more convienent C++ API than previously granted
with the raw/low-level types.
Differential Revision: https://reviews.llvm.org/D124817
This allows for the results of operations to be inferred in certain contexts,
and matches the support in PDL for result type inference. The main two
initial circumstances are when used as a replacement of another operation,
or when the operation being created implements InferTypeOpInterface.
Differential Revision: https://reviews.llvm.org/D124782
If we don't specify the result index while matching operand with the
result of certain operation, it's supposed to match all the results of
the operation with the operand. For registered op, it's easy to do that
by either indexing with number or name. For unregistered op, this commit
enables the numeric result indexing for this use case.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D126330
This commit refactors the expected form of native constraint and rewrite
functions, and greatly reduces the necessary user complexity required when
defining a native function. Namely, this commit adds in automatic processing
of the necessary PDLValue glue code, and allows for users to define
constraint/rewrite functions using the C++ types that they actually want to
use.
As an example, lets see a simple example rewrite defined today:
```
static void rewriteFn(PatternRewriter &rewriter, PDLResultList &results,
ArrayRef<PDLValue> args) {
ValueRange operandValues = args[0].cast<ValueRange>();
TypeRange typeValues = args[1].cast<TypeRange>();
...
// Create an operation at some point and pass it back to PDL.
Operation *op = rewriter.create<SomeOp>(...);
results.push_back(op);
}
```
After this commit, that same rewrite could be defined as:
```
static Operation *rewriteFn(PatternRewriter &rewriter ValueRange operandValues,
TypeRange typeValues) {
...
// Create an operation at some point and pass it back to PDL.
return rewriter.create<SomeOp>(...);
}
```
Differential Revision: https://reviews.llvm.org/D122086
This support has never really worked well, and is incredibly clunky to
use (it effectively creates two argument APIs), and clunky to generate (it isn't
clear how we should actually expose this from PDL frontends). Treating these
as just attribute arguments is much much cleaner in every aspect of the stack.
If we need to optimize lots of constant parameters, it would be better to
investigate internal representation optimizations (e.g. batch attribute creation),
that do not affect the user (we want a clean external API).
Differential Revision: https://reviews.llvm.org/D121569
This commit adds detailed documentation for PDLL, its language design, and
captures a bit of the rationale. This document captures everything in-tree at present,
and is intended to be an all encompassing manual for interacting with and understanding
PDLL.
Differential Revision: https://reviews.llvm.org/D119903
