llvm/mlir/lib/Pass/PassDetail.h

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//===- PassDetail.h - MLIR Pass details -------------------------*- C++ -*-===//
//
// 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
//
//===----------------------------------------------------------------------===//
#ifndef MLIR_PASS_PASSDETAIL_H_
#define MLIR_PASS_PASSDETAIL_H_
#include "mlir/Pass/Pass.h"
#include "mlir/Pass/PassManager.h"
namespace mlir {
namespace detail {
//===----------------------------------------------------------------------===//
Refactor the pass manager to support operations other than FuncOp/ModuleOp. This change generalizes the structure of the pass manager to allow arbitrary nesting pass managers for other operations, at any level. The only user visible change to existing code is the fact that a PassManager must now provide an MLIRContext on construction. A new class `OpPassManager` has been added that represents a pass manager on a specific operation type. `PassManager` will remain the top-level entry point into the pipeline, with OpPassManagers being nested underneath. OpPassManagers will still be implicitly nested if the operation type on the pass differs from the pass manager. To explicitly build a pipeline, the 'nest' methods on OpPassManager may be used: // Pass manager for the top-level module. PassManager pm(ctx); // Nest a pipeline operating on FuncOp. OpPassManager &fpm = pm.nest<FuncOp>(); fpm.addPass(...); // Nest a pipeline under the FuncOp pipeline that operates on spirv::ModuleOp OpPassManager &spvModulePM = pm.nest<spirv::ModuleOp>(); // Nest a pipeline on FuncOps inside of the spirv::ModuleOp. OpPassManager &spvFuncPM = spvModulePM.nest<FuncOp>(); To help accomplish this a new general OperationPass is added that operates on opaque Operations. This pass can be inserted in a pass manager of any type to operate on any operation opaquely. An example of this opaque OperationPass is a VerifierPass, that simply runs the verifier opaquely on the current operation. /// Pass to verify an operation and signal failure if necessary. class VerifierPass : public OperationPass<VerifierPass> { void runOnOperation() override { Operation *op = getOperation(); if (failed(verify(op))) signalPassFailure(); markAllAnalysesPreserved(); } }; PiperOrigin-RevId: 266840344
2019-09-03 04:24:47 +02:00
// OpToOpPassAdaptor
//===----------------------------------------------------------------------===//
/// An adaptor pass used to run operation passes over nested operations.
class OpToOpPassAdaptor
: public PassWrapper<OpToOpPassAdaptor, OperationPass<>> {
public:
OpToOpPassAdaptor(OpPassManager &&mgr);
OpToOpPassAdaptor(const OpToOpPassAdaptor &rhs) = default;
Refactor the pass manager to support operations other than FuncOp/ModuleOp. This change generalizes the structure of the pass manager to allow arbitrary nesting pass managers for other operations, at any level. The only user visible change to existing code is the fact that a PassManager must now provide an MLIRContext on construction. A new class `OpPassManager` has been added that represents a pass manager on a specific operation type. `PassManager` will remain the top-level entry point into the pipeline, with OpPassManagers being nested underneath. OpPassManagers will still be implicitly nested if the operation type on the pass differs from the pass manager. To explicitly build a pipeline, the 'nest' methods on OpPassManager may be used: // Pass manager for the top-level module. PassManager pm(ctx); // Nest a pipeline operating on FuncOp. OpPassManager &fpm = pm.nest<FuncOp>(); fpm.addPass(...); // Nest a pipeline under the FuncOp pipeline that operates on spirv::ModuleOp OpPassManager &spvModulePM = pm.nest<spirv::ModuleOp>(); // Nest a pipeline on FuncOps inside of the spirv::ModuleOp. OpPassManager &spvFuncPM = spvModulePM.nest<FuncOp>(); To help accomplish this a new general OperationPass is added that operates on opaque Operations. This pass can be inserted in a pass manager of any type to operate on any operation opaquely. An example of this opaque OperationPass is a VerifierPass, that simply runs the verifier opaquely on the current operation. /// Pass to verify an operation and signal failure if necessary. class VerifierPass : public OperationPass<VerifierPass> { void runOnOperation() override { Operation *op = getOperation(); if (failed(verify(op))) signalPassFailure(); markAllAnalysesPreserved(); } }; PiperOrigin-RevId: 266840344
2019-09-03 04:24:47 +02:00
/// Run the held pipeline over all operations.
void runOnOperation(bool verifyPasses);
Refactor the pass manager to support operations other than FuncOp/ModuleOp. This change generalizes the structure of the pass manager to allow arbitrary nesting pass managers for other operations, at any level. The only user visible change to existing code is the fact that a PassManager must now provide an MLIRContext on construction. A new class `OpPassManager` has been added that represents a pass manager on a specific operation type. `PassManager` will remain the top-level entry point into the pipeline, with OpPassManagers being nested underneath. OpPassManagers will still be implicitly nested if the operation type on the pass differs from the pass manager. To explicitly build a pipeline, the 'nest' methods on OpPassManager may be used: // Pass manager for the top-level module. PassManager pm(ctx); // Nest a pipeline operating on FuncOp. OpPassManager &fpm = pm.nest<FuncOp>(); fpm.addPass(...); // Nest a pipeline under the FuncOp pipeline that operates on spirv::ModuleOp OpPassManager &spvModulePM = pm.nest<spirv::ModuleOp>(); // Nest a pipeline on FuncOps inside of the spirv::ModuleOp. OpPassManager &spvFuncPM = spvModulePM.nest<FuncOp>(); To help accomplish this a new general OperationPass is added that operates on opaque Operations. This pass can be inserted in a pass manager of any type to operate on any operation opaquely. An example of this opaque OperationPass is a VerifierPass, that simply runs the verifier opaquely on the current operation. /// Pass to verify an operation and signal failure if necessary. class VerifierPass : public OperationPass<VerifierPass> { void runOnOperation() override { Operation *op = getOperation(); if (failed(verify(op))) signalPassFailure(); markAllAnalysesPreserved(); } }; PiperOrigin-RevId: 266840344
2019-09-03 04:24:47 +02:00
void runOnOperation() override;
/// Merge the current pass adaptor into given 'rhs'.
void mergeInto(OpToOpPassAdaptor &rhs);
/// Returns the pass managers held by this adaptor.
MutableArrayRef<OpPassManager> getPassManagers() { return mgrs; }
Separate the Registration from Loading dialects in the Context 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 &registry) 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
2020-08-18 22:01:19 +02:00
/// Populate the set of dependent dialects for the passes in the current
/// adaptor.
void getDependentDialects(DialectRegistry &dialects) const override;
Add support for instance specific pass statistics. Statistics are a way to keep track of what the compiler is doing and how effective various optimizations are. It is useful to see what optimizations are contributing to making a particular program run faster. Pass-instance specific statistics take this even further as you can see the effect of placing a particular pass at specific places within the pass pipeline, e.g. they could help answer questions like "what happens if I run CSE again here". Statistics can be added to a pass by simply adding members of type 'Pass::Statistics'. This class takes as a constructor arguments: the parent pass pointer, a name, and a description. Statistics can be dumped by the pass manager in a similar manner to how pass timing information is dumped, i.e. via PassManager::enableStatistics programmatically; or -pass-statistics and -pass-statistics-display via the command line pass manager options. Below is an example: struct MyPass : public OperationPass<MyPass> { Statistic testStat{this, "testStat", "A test statistic"}; void runOnOperation() { ... ++testStat; ... } }; $ mlir-opt -pass-pipeline='func(my-pass,my-pass)' foo.mlir -pass-statistics Pipeline Display: ===-------------------------------------------------------------------------=== ... Pass statistics report ... ===-------------------------------------------------------------------------=== 'func' Pipeline MyPass (S) 15 testStat - A test statistic MyPass (S) 6 testStat - A test statistic List Display: ===-------------------------------------------------------------------------=== ... Pass statistics report ... ===-------------------------------------------------------------------------=== MyPass (S) 21 testStat - A test statistic PiperOrigin-RevId: 284022014
2019-12-05 20:52:58 +01:00
/// Return the async pass managers held by this parallel adaptor.
MutableArrayRef<SmallVector<OpPassManager, 1>> getParallelPassManagers() {
return asyncExecutors;
}
/// Returns the adaptor pass name.
std::string getAdaptorName();
private:
/// Run this pass adaptor synchronously.
void runOnOperationImpl(bool verifyPasses);
/// Run this pass adaptor asynchronously.
void runOnOperationAsyncImpl(bool verifyPasses);
/// Run the given operation and analysis manager on a single pass.
/// `parentInitGeneration` is the initialization generation of the parent pass
/// manager, and is used to initialize any dynamic pass pipelines run by the
/// given pass.
static LogicalResult run(Pass *pass, Operation *op, AnalysisManager am,
bool verifyPasses, unsigned parentInitGeneration);
/// Run the given operation and analysis manager on a provided op pass
/// manager. `parentInitGeneration` is the initialization generation of the
/// parent pass manager, and is used to initialize any dynamic pass pipelines
/// run by the given passes.
static LogicalResult runPipeline(
iterator_range<OpPassManager::pass_iterator> passes, Operation *op,
AnalysisManager am, bool verifyPasses, unsigned parentInitGeneration,
PassInstrumentor *instrumentor = nullptr,
const PassInstrumentation::PipelineParentInfo *parentInfo = nullptr);
/// A set of adaptors to run.
SmallVector<OpPassManager, 1> mgrs;
/// A set of executors, cloned from the main executor, that run asynchronously
/// on different threads. This is used when threading is enabled.
SmallVector<SmallVector<OpPassManager, 1>, 8> asyncExecutors;
// For accessing "runPipeline".
friend class mlir::PassManager;
};
//===----------------------------------------------------------------------===//
// PassCrashReproducerGenerator
//===----------------------------------------------------------------------===//
class PassCrashReproducerGenerator {
public:
PassCrashReproducerGenerator(
PassManager::ReproducerStreamFactory &streamFactory,
bool localReproducer);
~PassCrashReproducerGenerator();
/// Initialize the generator in preparation for reproducer generation. The
/// generator should be reinitialized before each run of the pass manager.
void initialize(iterator_range<PassManager::pass_iterator> passes,
Operation *op, bool pmFlagVerifyPasses);
/// Finalize the current run of the generator, generating any necessary
/// reproducers if the provided execution result is a failure.
void finalize(Operation *rootOp, LogicalResult executionResult);
/// Prepare a new reproducer for the given pass, operating on `op`.
void prepareReproducerFor(Pass *pass, Operation *op);
/// Prepare a new reproducer for the given passes, operating on `op`.
void prepareReproducerFor(iterator_range<PassManager::pass_iterator> passes,
Operation *op);
/// Remove the last recorded reproducer anchored at the given pass and
/// operation.
void removeLastReproducerFor(Pass *pass, Operation *op);
private:
struct Impl;
/// The internal implementation of the crash reproducer.
std::unique_ptr<Impl> impl;
};
} // end namespace detail
} // end namespace mlir
#endif // MLIR_PASS_PASSDETAIL_H_