llvm-project/mlir/test/lib/Rewrite/TestPDLByteCode.cpp

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[mlir][PDL] Add support for PDL bytecode and expose PDL support to OwningRewritePatternList PDL patterns are now supported via a new `PDLPatternModule` class. This class contains a ModuleOp with the pdl::PatternOp operations representing the patterns, as well as a collection of registered C++ functions for native constraints/creations/rewrites/etc. that may be invoked via the pdl patterns. Instances of this class are added to an OwningRewritePatternList in the same fashion as C++ RewritePatterns, i.e. via the `insert` method. The PDL bytecode is an in-memory representation of the PDL interpreter dialect that can be efficiently interpreted/executed. The representation of the bytecode boils down to a code array(for opcodes/memory locations/etc) and a memory buffer(for storing attributes/operations/values/any other data necessary). The bytecode operations are effectively a 1-1 mapping to the PDLInterp dialect operations, with a few exceptions in cases where the in-memory representation of the bytecode can be more efficient than the MLIR representation. For example, a generic `AreEqual` bytecode op can be used to represent AreEqualOp, CheckAttributeOp, and CheckTypeOp. The execution of the bytecode is split into two phases: matching and rewriting. When matching, all of the matched patterns are collected to avoid the overhead of re-running parts of the matcher. These matched patterns are then considered alongside the native C++ patterns, which rewrite immediately in-place via `RewritePattern::matchAndRewrite`, for the given root operation. When a PDL pattern is matched and has the highest benefit, it is passed back to the bytecode to execute its rewriter. Differential Revision: https://reviews.llvm.org/D89107
2020-12-02 06:30:18 +08:00
//===- TestPDLByteCode.cpp - Test rewriter bytecode functionality ---------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
#include "mlir/Pass/Pass.h"
#include "mlir/Pass/PassManager.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
using namespace mlir;
/// Custom constraint invoked from PDL.
static LogicalResult customSingleEntityConstraint(PDLValue value,
ArrayAttr constantParams,
PatternRewriter &rewriter) {
Operation *rootOp = value.cast<Operation *>();
return success(rootOp->getName().getStringRef() == "test.op");
}
static LogicalResult customMultiEntityConstraint(ArrayRef<PDLValue> values,
ArrayAttr constantParams,
PatternRewriter &rewriter) {
return customSingleEntityConstraint(values[1], constantParams, rewriter);
}
static LogicalResult
customMultiEntityVariadicConstraint(ArrayRef<PDLValue> values,
ArrayAttr constantParams,
PatternRewriter &rewriter) {
if (llvm::any_of(values, [](const PDLValue &value) { return !value; }))
return failure();
ValueRange operandValues = values[0].cast<ValueRange>();
TypeRange typeValues = values[1].cast<TypeRange>();
if (operandValues.size() != 2 || typeValues.size() != 2)
return failure();
return success();
}
[mlir][PDL] Add support for PDL bytecode and expose PDL support to OwningRewritePatternList PDL patterns are now supported via a new `PDLPatternModule` class. This class contains a ModuleOp with the pdl::PatternOp operations representing the patterns, as well as a collection of registered C++ functions for native constraints/creations/rewrites/etc. that may be invoked via the pdl patterns. Instances of this class are added to an OwningRewritePatternList in the same fashion as C++ RewritePatterns, i.e. via the `insert` method. The PDL bytecode is an in-memory representation of the PDL interpreter dialect that can be efficiently interpreted/executed. The representation of the bytecode boils down to a code array(for opcodes/memory locations/etc) and a memory buffer(for storing attributes/operations/values/any other data necessary). The bytecode operations are effectively a 1-1 mapping to the PDLInterp dialect operations, with a few exceptions in cases where the in-memory representation of the bytecode can be more efficient than the MLIR representation. For example, a generic `AreEqual` bytecode op can be used to represent AreEqualOp, CheckAttributeOp, and CheckTypeOp. The execution of the bytecode is split into two phases: matching and rewriting. When matching, all of the matched patterns are collected to avoid the overhead of re-running parts of the matcher. These matched patterns are then considered alongside the native C++ patterns, which rewrite immediately in-place via `RewritePattern::matchAndRewrite`, for the given root operation. When a PDL pattern is matched and has the highest benefit, it is passed back to the bytecode to execute its rewriter. Differential Revision: https://reviews.llvm.org/D89107
2020-12-02 06:30:18 +08:00
// Custom creator invoked from PDL.
static void customCreate(ArrayRef<PDLValue> args, ArrayAttr constantParams,
PatternRewriter &rewriter, PDLResultList &results) {
results.push_back(rewriter.createOperation(
OperationState(args[0].cast<Operation *>()->getLoc(), "test.success")));
[mlir][PDL] Add support for PDL bytecode and expose PDL support to OwningRewritePatternList PDL patterns are now supported via a new `PDLPatternModule` class. This class contains a ModuleOp with the pdl::PatternOp operations representing the patterns, as well as a collection of registered C++ functions for native constraints/creations/rewrites/etc. that may be invoked via the pdl patterns. Instances of this class are added to an OwningRewritePatternList in the same fashion as C++ RewritePatterns, i.e. via the `insert` method. The PDL bytecode is an in-memory representation of the PDL interpreter dialect that can be efficiently interpreted/executed. The representation of the bytecode boils down to a code array(for opcodes/memory locations/etc) and a memory buffer(for storing attributes/operations/values/any other data necessary). The bytecode operations are effectively a 1-1 mapping to the PDLInterp dialect operations, with a few exceptions in cases where the in-memory representation of the bytecode can be more efficient than the MLIR representation. For example, a generic `AreEqual` bytecode op can be used to represent AreEqualOp, CheckAttributeOp, and CheckTypeOp. The execution of the bytecode is split into two phases: matching and rewriting. When matching, all of the matched patterns are collected to avoid the overhead of re-running parts of the matcher. These matched patterns are then considered alongside the native C++ patterns, which rewrite immediately in-place via `RewritePattern::matchAndRewrite`, for the given root operation. When a PDL pattern is matched and has the highest benefit, it is passed back to the bytecode to execute its rewriter. Differential Revision: https://reviews.llvm.org/D89107
2020-12-02 06:30:18 +08:00
}
static void customVariadicResultCreate(ArrayRef<PDLValue> args,
ArrayAttr constantParams,
PatternRewriter &rewriter,
PDLResultList &results) {
Operation *root = args[0].cast<Operation *>();
results.push_back(root->getOperands());
results.push_back(root->getOperands().getTypes());
}
static void customCreateType(ArrayRef<PDLValue> args, ArrayAttr constantParams,
PatternRewriter &rewriter,
PDLResultList &results) {
results.push_back(rewriter.getF32Type());
}
[mlir][PDL] Add support for PDL bytecode and expose PDL support to OwningRewritePatternList PDL patterns are now supported via a new `PDLPatternModule` class. This class contains a ModuleOp with the pdl::PatternOp operations representing the patterns, as well as a collection of registered C++ functions for native constraints/creations/rewrites/etc. that may be invoked via the pdl patterns. Instances of this class are added to an OwningRewritePatternList in the same fashion as C++ RewritePatterns, i.e. via the `insert` method. The PDL bytecode is an in-memory representation of the PDL interpreter dialect that can be efficiently interpreted/executed. The representation of the bytecode boils down to a code array(for opcodes/memory locations/etc) and a memory buffer(for storing attributes/operations/values/any other data necessary). The bytecode operations are effectively a 1-1 mapping to the PDLInterp dialect operations, with a few exceptions in cases where the in-memory representation of the bytecode can be more efficient than the MLIR representation. For example, a generic `AreEqual` bytecode op can be used to represent AreEqualOp, CheckAttributeOp, and CheckTypeOp. The execution of the bytecode is split into two phases: matching and rewriting. When matching, all of the matched patterns are collected to avoid the overhead of re-running parts of the matcher. These matched patterns are then considered alongside the native C++ patterns, which rewrite immediately in-place via `RewritePattern::matchAndRewrite`, for the given root operation. When a PDL pattern is matched and has the highest benefit, it is passed back to the bytecode to execute its rewriter. Differential Revision: https://reviews.llvm.org/D89107
2020-12-02 06:30:18 +08:00
/// Custom rewriter invoked from PDL.
static void customRewriter(ArrayRef<PDLValue> args, ArrayAttr constantParams,
PatternRewriter &rewriter, PDLResultList &results) {
Operation *root = args[0].cast<Operation *>();
[mlir][PDL] Add support for PDL bytecode and expose PDL support to OwningRewritePatternList PDL patterns are now supported via a new `PDLPatternModule` class. This class contains a ModuleOp with the pdl::PatternOp operations representing the patterns, as well as a collection of registered C++ functions for native constraints/creations/rewrites/etc. that may be invoked via the pdl patterns. Instances of this class are added to an OwningRewritePatternList in the same fashion as C++ RewritePatterns, i.e. via the `insert` method. The PDL bytecode is an in-memory representation of the PDL interpreter dialect that can be efficiently interpreted/executed. The representation of the bytecode boils down to a code array(for opcodes/memory locations/etc) and a memory buffer(for storing attributes/operations/values/any other data necessary). The bytecode operations are effectively a 1-1 mapping to the PDLInterp dialect operations, with a few exceptions in cases where the in-memory representation of the bytecode can be more efficient than the MLIR representation. For example, a generic `AreEqual` bytecode op can be used to represent AreEqualOp, CheckAttributeOp, and CheckTypeOp. The execution of the bytecode is split into two phases: matching and rewriting. When matching, all of the matched patterns are collected to avoid the overhead of re-running parts of the matcher. These matched patterns are then considered alongside the native C++ patterns, which rewrite immediately in-place via `RewritePattern::matchAndRewrite`, for the given root operation. When a PDL pattern is matched and has the highest benefit, it is passed back to the bytecode to execute its rewriter. Differential Revision: https://reviews.llvm.org/D89107
2020-12-02 06:30:18 +08:00
OperationState successOpState(root->getLoc(), "test.success");
successOpState.addOperands(args[1].cast<Value>());
[mlir][PDL] Add support for PDL bytecode and expose PDL support to OwningRewritePatternList PDL patterns are now supported via a new `PDLPatternModule` class. This class contains a ModuleOp with the pdl::PatternOp operations representing the patterns, as well as a collection of registered C++ functions for native constraints/creations/rewrites/etc. that may be invoked via the pdl patterns. Instances of this class are added to an OwningRewritePatternList in the same fashion as C++ RewritePatterns, i.e. via the `insert` method. The PDL bytecode is an in-memory representation of the PDL interpreter dialect that can be efficiently interpreted/executed. The representation of the bytecode boils down to a code array(for opcodes/memory locations/etc) and a memory buffer(for storing attributes/operations/values/any other data necessary). The bytecode operations are effectively a 1-1 mapping to the PDLInterp dialect operations, with a few exceptions in cases where the in-memory representation of the bytecode can be more efficient than the MLIR representation. For example, a generic `AreEqual` bytecode op can be used to represent AreEqualOp, CheckAttributeOp, and CheckTypeOp. The execution of the bytecode is split into two phases: matching and rewriting. When matching, all of the matched patterns are collected to avoid the overhead of re-running parts of the matcher. These matched patterns are then considered alongside the native C++ patterns, which rewrite immediately in-place via `RewritePattern::matchAndRewrite`, for the given root operation. When a PDL pattern is matched and has the highest benefit, it is passed back to the bytecode to execute its rewriter. Differential Revision: https://reviews.llvm.org/D89107
2020-12-02 06:30:18 +08:00
successOpState.addAttribute("constantParams", constantParams);
rewriter.createOperation(successOpState);
rewriter.eraseOp(root);
}
namespace {
struct TestPDLByteCodePass
: public PassWrapper<TestPDLByteCodePass, OperationPass<ModuleOp>> {
StringRef getArgument() const final { return "test-pdl-bytecode-pass"; }
StringRef getDescription() const final {
return "Test PDL ByteCode functionality";
}
[mlir][PDL] Add support for PDL bytecode and expose PDL support to OwningRewritePatternList PDL patterns are now supported via a new `PDLPatternModule` class. This class contains a ModuleOp with the pdl::PatternOp operations representing the patterns, as well as a collection of registered C++ functions for native constraints/creations/rewrites/etc. that may be invoked via the pdl patterns. Instances of this class are added to an OwningRewritePatternList in the same fashion as C++ RewritePatterns, i.e. via the `insert` method. The PDL bytecode is an in-memory representation of the PDL interpreter dialect that can be efficiently interpreted/executed. The representation of the bytecode boils down to a code array(for opcodes/memory locations/etc) and a memory buffer(for storing attributes/operations/values/any other data necessary). The bytecode operations are effectively a 1-1 mapping to the PDLInterp dialect operations, with a few exceptions in cases where the in-memory representation of the bytecode can be more efficient than the MLIR representation. For example, a generic `AreEqual` bytecode op can be used to represent AreEqualOp, CheckAttributeOp, and CheckTypeOp. The execution of the bytecode is split into two phases: matching and rewriting. When matching, all of the matched patterns are collected to avoid the overhead of re-running parts of the matcher. These matched patterns are then considered alongside the native C++ patterns, which rewrite immediately in-place via `RewritePattern::matchAndRewrite`, for the given root operation. When a PDL pattern is matched and has the highest benefit, it is passed back to the bytecode to execute its rewriter. Differential Revision: https://reviews.llvm.org/D89107
2020-12-02 06:30:18 +08:00
void runOnOperation() final {
ModuleOp module = getOperation();
// The test cases are encompassed via two modules, one containing the
// patterns and one containing the operations to rewrite.
ModuleOp patternModule = module.lookupSymbol<ModuleOp>(
StringAttr::get(module->getContext(), "patterns"));
ModuleOp irModule = module.lookupSymbol<ModuleOp>(
StringAttr::get(module->getContext(), "ir"));
[mlir][PDL] Add support for PDL bytecode and expose PDL support to OwningRewritePatternList PDL patterns are now supported via a new `PDLPatternModule` class. This class contains a ModuleOp with the pdl::PatternOp operations representing the patterns, as well as a collection of registered C++ functions for native constraints/creations/rewrites/etc. that may be invoked via the pdl patterns. Instances of this class are added to an OwningRewritePatternList in the same fashion as C++ RewritePatterns, i.e. via the `insert` method. The PDL bytecode is an in-memory representation of the PDL interpreter dialect that can be efficiently interpreted/executed. The representation of the bytecode boils down to a code array(for opcodes/memory locations/etc) and a memory buffer(for storing attributes/operations/values/any other data necessary). The bytecode operations are effectively a 1-1 mapping to the PDLInterp dialect operations, with a few exceptions in cases where the in-memory representation of the bytecode can be more efficient than the MLIR representation. For example, a generic `AreEqual` bytecode op can be used to represent AreEqualOp, CheckAttributeOp, and CheckTypeOp. The execution of the bytecode is split into two phases: matching and rewriting. When matching, all of the matched patterns are collected to avoid the overhead of re-running parts of the matcher. These matched patterns are then considered alongside the native C++ patterns, which rewrite immediately in-place via `RewritePattern::matchAndRewrite`, for the given root operation. When a PDL pattern is matched and has the highest benefit, it is passed back to the bytecode to execute its rewriter. Differential Revision: https://reviews.llvm.org/D89107
2020-12-02 06:30:18 +08:00
if (!patternModule || !irModule)
return;
// Process the pattern module.
patternModule.getOperation()->remove();
PDLPatternModule pdlPattern(patternModule);
pdlPattern.registerConstraintFunction("multi_entity_constraint",
customMultiEntityConstraint);
pdlPattern.registerConstraintFunction("single_entity_constraint",
customSingleEntityConstraint);
pdlPattern.registerConstraintFunction("multi_entity_var_constraint",
customMultiEntityVariadicConstraint);
pdlPattern.registerRewriteFunction("creator", customCreate);
pdlPattern.registerRewriteFunction("var_creator",
customVariadicResultCreate);
pdlPattern.registerRewriteFunction("type_creator", customCreateType);
[mlir][PDL] Add support for PDL bytecode and expose PDL support to OwningRewritePatternList PDL patterns are now supported via a new `PDLPatternModule` class. This class contains a ModuleOp with the pdl::PatternOp operations representing the patterns, as well as a collection of registered C++ functions for native constraints/creations/rewrites/etc. that may be invoked via the pdl patterns. Instances of this class are added to an OwningRewritePatternList in the same fashion as C++ RewritePatterns, i.e. via the `insert` method. The PDL bytecode is an in-memory representation of the PDL interpreter dialect that can be efficiently interpreted/executed. The representation of the bytecode boils down to a code array(for opcodes/memory locations/etc) and a memory buffer(for storing attributes/operations/values/any other data necessary). The bytecode operations are effectively a 1-1 mapping to the PDLInterp dialect operations, with a few exceptions in cases where the in-memory representation of the bytecode can be more efficient than the MLIR representation. For example, a generic `AreEqual` bytecode op can be used to represent AreEqualOp, CheckAttributeOp, and CheckTypeOp. The execution of the bytecode is split into two phases: matching and rewriting. When matching, all of the matched patterns are collected to avoid the overhead of re-running parts of the matcher. These matched patterns are then considered alongside the native C++ patterns, which rewrite immediately in-place via `RewritePattern::matchAndRewrite`, for the given root operation. When a PDL pattern is matched and has the highest benefit, it is passed back to the bytecode to execute its rewriter. Differential Revision: https://reviews.llvm.org/D89107
2020-12-02 06:30:18 +08:00
pdlPattern.registerRewriteFunction("rewriter", customRewriter);
RewritePatternSet patternList(std::move(pdlPattern));
[mlir][PDL] Add support for PDL bytecode and expose PDL support to OwningRewritePatternList PDL patterns are now supported via a new `PDLPatternModule` class. This class contains a ModuleOp with the pdl::PatternOp operations representing the patterns, as well as a collection of registered C++ functions for native constraints/creations/rewrites/etc. that may be invoked via the pdl patterns. Instances of this class are added to an OwningRewritePatternList in the same fashion as C++ RewritePatterns, i.e. via the `insert` method. The PDL bytecode is an in-memory representation of the PDL interpreter dialect that can be efficiently interpreted/executed. The representation of the bytecode boils down to a code array(for opcodes/memory locations/etc) and a memory buffer(for storing attributes/operations/values/any other data necessary). The bytecode operations are effectively a 1-1 mapping to the PDLInterp dialect operations, with a few exceptions in cases where the in-memory representation of the bytecode can be more efficient than the MLIR representation. For example, a generic `AreEqual` bytecode op can be used to represent AreEqualOp, CheckAttributeOp, and CheckTypeOp. The execution of the bytecode is split into two phases: matching and rewriting. When matching, all of the matched patterns are collected to avoid the overhead of re-running parts of the matcher. These matched patterns are then considered alongside the native C++ patterns, which rewrite immediately in-place via `RewritePattern::matchAndRewrite`, for the given root operation. When a PDL pattern is matched and has the highest benefit, it is passed back to the bytecode to execute its rewriter. Differential Revision: https://reviews.llvm.org/D89107
2020-12-02 06:30:18 +08:00
// Invoke the pattern driver with the provided patterns.
(void)applyPatternsAndFoldGreedily(irModule.getBodyRegion(),
std::move(patternList));
}
};
} // end anonymous namespace
namespace mlir {
namespace test {
void registerTestPDLByteCodePass() { PassRegistration<TestPDLByteCodePass>(); }
[mlir][PDL] Add support for PDL bytecode and expose PDL support to OwningRewritePatternList PDL patterns are now supported via a new `PDLPatternModule` class. This class contains a ModuleOp with the pdl::PatternOp operations representing the patterns, as well as a collection of registered C++ functions for native constraints/creations/rewrites/etc. that may be invoked via the pdl patterns. Instances of this class are added to an OwningRewritePatternList in the same fashion as C++ RewritePatterns, i.e. via the `insert` method. The PDL bytecode is an in-memory representation of the PDL interpreter dialect that can be efficiently interpreted/executed. The representation of the bytecode boils down to a code array(for opcodes/memory locations/etc) and a memory buffer(for storing attributes/operations/values/any other data necessary). The bytecode operations are effectively a 1-1 mapping to the PDLInterp dialect operations, with a few exceptions in cases where the in-memory representation of the bytecode can be more efficient than the MLIR representation. For example, a generic `AreEqual` bytecode op can be used to represent AreEqualOp, CheckAttributeOp, and CheckTypeOp. The execution of the bytecode is split into two phases: matching and rewriting. When matching, all of the matched patterns are collected to avoid the overhead of re-running parts of the matcher. These matched patterns are then considered alongside the native C++ patterns, which rewrite immediately in-place via `RewritePattern::matchAndRewrite`, for the given root operation. When a PDL pattern is matched and has the highest benefit, it is passed back to the bytecode to execute its rewriter. Differential Revision: https://reviews.llvm.org/D89107
2020-12-02 06:30:18 +08:00
} // namespace test
} // namespace mlir