46 KiB
Pass Infrastructure
[TOC]
Passes represent the basic infrastructure for transformation and optimization. This document provides an overview of the pass infrastructure in MLIR and how to use it.
See MLIR specification for more information about MLIR and its core aspects, such as the IR structure and operations.
See MLIR Rewrites for a quick start on graph rewriting in MLIR. If a transformation involves pattern matching operation DAGs, this is a great place to start.
Operation Pass
In MLIR, the main unit of abstraction and transformation is an
operation. As such, the pass manager is designed to
work on instances of operations at different levels of nesting. The structure of
the pass manager, and the concept of nesting, is detailed
further below. All passes in MLIR derive from OperationPass
and adhere to the
following restrictions; any noncompliance will lead to problematic behavior in
multithreaded and other advanced scenarios:
- Modify any state referenced or relied upon outside the current being operated on. This includes adding or removing operations from the parent block, changing the attributes(depending on the contract of the current operation)/operands/results/successors of the current operation.
- Modify the state of another operation not nested within the current
operation being operated on.
- Other threads may be operating on these operations simultaneously.
- Inspect the state of sibling operations.
- Other threads may be modifying these operations in parallel.
- Inspecting the state of ancestor/parent operations is permitted.
- Maintain mutable pass state across invocations of
runOnOperation
. A pass may be run on many different operations with no guarantee of execution order.- When multithreading, a specific pass instance may not even execute on all operations within the IR. As such, a pass should not rely on running on all operations.
- Maintain any global mutable state, e.g. static variables within the source file. All mutable state should be maintained by an instance of the pass.
- Must be copy-constructible
- Multiple instances of the pass may be created by the pass manager to process operations in parallel.
When creating an operation pass, there are two different types to choose from depending on the usage scenario:
OperationPass : Op-Specific
An op-specific
operation pass operates explicitly on a given operation type.
This operation type must adhere to the restrictions set by the pass manager for
pass execution.
To define an op-specific operation pass, a derived class must adhere to the following:
- Inherit from the CRTP class
OperationPass
and provide the operation type as an additional template parameter. - Override the virtual
void runOnOperation()
method.
A simple pass may look like:
namespace {
/// Here we utilize the CRTP `PassWrapper` utility class to provide some
/// necessary utility hooks. This is only necessary for passes defined directly
/// in C++. Passes defined declaratively use a cleaner mechanism for providing
/// these utilities.
struct MyFunctionPass : public PassWrapper<MyFunctionPass,
OperationPass<FuncOp>> {
void runOnOperation() override {
// Get the current FuncOp operation being operated on.
FuncOp f = getOperation();
// Walk the operations within the function.
f.walk([](Operation *inst) {
....
});
}
};
} // end anonymous namespace
/// Register this pass so that it can be built via from a textual pass pipeline.
/// (Pass registration is discussed more below)
void registerMyPass() {
PassRegistration<MyFunctionPass>();
}
OperationPass : Op-Agnostic
An op-agnostic
pass operates on the operation type of the pass manager that it
is added to. This means that passes of this type may operate on several
different operation types. Passes of this type are generally written generically
using operation interfaces and traits. Examples of
this type of pass are
Common Sub-Expression Elimination
and Inlining.
To create an operation pass, a derived class must adhere to the following:
- Inherit from the CRTP class
OperationPass
. - Override the virtual
void runOnOperation()
method.
A simple pass may look like:
/// Here we utilize the CRTP `PassWrapper` utility class to provide some
/// necessary utility hooks. This is only necessary for passes defined directly
/// in C++. Passes defined declaratively use a cleaner mechanism for providing
/// these utilities.
struct MyOperationPass : public PassWrapper<MyOperationPass, OperationPass<>> {
void runOnOperation() override {
// Get the current operation being operated on.
Operation *op = getOperation();
...
}
};
Dependent Dialects
Dialects must be loaded in the MLIRContext before entities from these dialects
(operations, types, attributes, ...) can be created. Dialects must also be
loaded before starting the execution of a multi-threaded pass pipeline. To this
end, a pass that may create an entity from a dialect that isn't guaranteed to
already ne loaded must express this by overriding the getDependentDialects()
method and declare this list of Dialects explicitly.
Initialization
In certain situations, a Pass may contain state that is constructed dynamically,
but is potentially expensive to recompute in successive runs of the Pass. One
such example is when using PDL
-based
patterns, which are compiled into a bytecode during
runtime. In these situations, a pass may override the following hook to
initialize this heavy state:
LogicalResult initialize(MLIRContext *context)
This hook is executed once per run of a full pass pipeline, meaning that it does
not have access to the state available during a runOnOperation
call. More
concretely, all necessary accesses to an MLIRContext
should be driven via the
provided context
parameter, and methods that utilize "per-run" state such as
getContext
/getOperation
/getAnalysis
/etc. must not be used.
In case of an error during initialization, the pass is expected to emit an error
diagnostic and return a failure()
which will abort the pass pipeline execution.
Analysis Management
An important concept, along with transformation passes, are analyses. These are conceptually similar to transformation passes, except that they compute information on a specific operation without modifying it. In MLIR, analyses are not passes but free-standing classes that are computed lazily on-demand and cached to avoid unnecessary recomputation. An analysis in MLIR must adhere to the following:
- Provide a valid constructor taking either an
Operation*
orOperation*
andAnalysisManager &
.- The provided
AnalysisManager &
should be used to query any necessary analysis dependencies.
- The provided
- Must not modify the given operation.
An analysis may provide additional hooks to control various behavior:
bool isInvalidated(const AnalysisManager::PreservedAnalyses &)
Given a preserved analysis set, the analysis returns true if it should truly be invalidated. This allows for more fine-tuned invalidation in cases where an analysis wasn't explicitly marked preserved, but may be preserved (or invalidated) based upon other properties such as analyses sets. If the analysis uses any other analysis as a dependency, it must also check if the dependency was invalidated.
Querying Analyses
The base OperationPass
class provides utilities for querying and preserving
analyses for the current operation being processed.
- OperationPass automatically provides the following utilities for querying
analyses:
getAnalysis<>
- Get an analysis for the current operation, constructing it if necessary.
getCachedAnalysis<>
- Get an analysis for the current operation, if it already exists.
getCachedParentAnalysis<>
- Get an analysis for a given parent operation, if it exists.
getCachedChildAnalysis<>
- Get an analysis for a given child operation, if it exists.
getChildAnalysis<>
- Get an analysis for a given child operation, constructing it if necessary.
Using the example passes defined above, let's see some examples:
/// An interesting analysis.
struct MyOperationAnalysis {
// Compute this analysis with the provided operation.
MyOperationAnalysis(Operation *op);
};
struct MyOperationAnalysisWithDependency {
MyOperationAnalysisWithDependency(Operation *op, AnalysisManager &am) {
// Request other analysis as dependency
MyOperationAnalysis &otherAnalysis = am.getAnalysis<MyOperationAnalysis>();
...
}
bool isInvalidated(const AnalysisManager::PreservedAnalyses &pa) {
// Check if analysis or its dependency were invalidated
return !pa.isPreserved<MyOperationAnalysisWithDependency>() ||
!pa.isPreserved<MyOperationAnalysis>();
}
};
void MyOperationPass::runOnOperation() {
// Query MyOperationAnalysis for the current operation.
MyOperationAnalysis &myAnalysis = getAnalysis<MyOperationAnalysis>();
// Query a cached instance of MyOperationAnalysis for the current operation.
// It will not be computed if it doesn't exist.
auto optionalAnalysis = getCachedAnalysis<MyOperationAnalysis>();
if (optionalAnalysis)
...
// Query a cached instance of MyOperationAnalysis for the parent operation of
// the current operation. It will not be computed if it doesn't exist.
auto optionalAnalysis = getCachedParentAnalysis<MyOperationAnalysis>();
if (optionalAnalysis)
...
}
Preserving Analyses
Analyses that are constructed after being queried by a pass are cached to avoid unnecessary computation if they are requested again later. To avoid stale analyses, all analyses are assumed to be invalidated by a pass. To avoid invalidation, a pass must specifically mark analyses that are known to be preserved.
- All Pass classes automatically provide the following utilities for
preserving analyses:
markAllAnalysesPreserved
markAnalysesPreserved<>
void MyOperationPass::runOnOperation() {
// Mark all analyses as preserved. This is useful if a pass can guarantee
// that no transformation was performed.
markAllAnalysesPreserved();
// Mark specific analyses as preserved. This is used if some transformation
// was performed, but some analyses were either unaffected or explicitly
// preserved.
markAnalysesPreserved<MyAnalysis, MyAnalyses...>();
}
Pass Failure
Passes in MLIR are allowed to gracefully fail. This may happen if some invariant
of the pass was broken, potentially leaving the IR in some invalid state. If
such a situation occurs, the pass can directly signal a failure to the pass
manager via the signalPassFailure
method. If a pass signaled a failure when
executing, no other passes in the pipeline will execute and the top-level call
to PassManager::run
will return failure
.
void MyOperationPass::runOnOperation() {
// Signal failure on a broken invariant.
if (some_broken_invariant)
return signalPassFailure();
}
Pass Manager
The above sections introduced the different types of passes and their
invariants. This section introduces the concept of a PassManager, and how it can
be used to configure and schedule a pass pipeline. There are two main classes
related to pass management, the PassManager
and the OpPassManager
. The
PassManager
class acts as the top-level entry point, and contains various
configurations used for the entire pass pipeline. The OpPassManager
class is
used to schedule passes to run at a specific level of nesting. The top-level
PassManager
also functions as an OpPassManager
.
OpPassManager
An OpPassManager
is essentially a collection of passes to execute on an
operation of a specific type. This operation type must adhere to the following
requirement:
-
Must be registered and marked
IsolatedFromAbove
.- Passes are expected to not modify operations at or above the current operation being processed. If the operation is not isolated, it may inadvertently modify or traverse the SSA use-list of an operation it is not supposed to.
Passes can be added to a pass manager via addPass
. The pass must either be an
op-specific
pass operating on the same operation type as OpPassManager
, or
an op-agnostic
pass.
An OpPassManager
is generally created by explicitly nesting a pipeline within
another existing OpPassManager
via the nest<>
method. This method takes the
operation type that the nested pass manager will operate on. At the top-level, a
PassManager
acts as an OpPassManager
. Nesting in this sense, corresponds to
the structural nesting within
Regions of the IR.
For example, the following .mlir
:
module {
spv.module "Logical" "GLSL450" {
func @foo() {
...
}
}
}
Has the nesting structure of:
`module`
`spv.module`
`function`
Below is an example of constructing a pipeline that operates on the above structure:
// Create a top-level `PassManager` class. If an operation type is not
// explicitly specific, the default is the builtin `module` operation.
PassManager pm(ctx);
// Note: We could also create the above `PassManager` this way.
PassManager pm(ctx, /*operationName=*/"module");
// Add a pass on the top-level module operation.
pm.addPass(std::make_unique<MyModulePass>());
// Nest a pass manager that operates on `spirv.module` operations nested
// directly under the top-level module.
OpPassManager &nestedModulePM = pm.nest<spirv::ModuleOp>();
nestedModulePM.addPass(std::make_unique<MySPIRVModulePass>());
// Nest a pass manager that operates on functions within the nested SPIRV
// module.
OpPassManager &nestedFunctionPM = nestedModulePM.nest<FuncOp>();
nestedFunctionPM.addPass(std::make_unique<MyFunctionPass>());
// Run the pass manager on the top-level module.
ModuleOp m = ...;
if (failed(pm.run(m)))
... // One of the passes signaled a failure.
The above pass manager contains the following pipeline structure:
OpPassManager<ModuleOp>
MyModulePass
OpPassManager<spirv::ModuleOp>
MySPIRVModulePass
OpPassManager<FuncOp>
MyFunctionPass
These pipelines are then run over a single operation at a time. This means that, for example, given a series of consecutive passes on FuncOp, it will execute all on the first function, then all on the second function, etc. until the entire program has been run through the passes. This provides several benefits:
- This improves the cache behavior of the compiler, because it is only touching a single function at a time, instead of traversing the entire program.
- This improves multi-threading performance by reducing the number of jobs that need to be scheduled, as well as increasing the efficiency of each job. An entire function pipeline can be run on each function asynchronously.
Dynamic Pass Pipelines
In some situations it may be useful to run a pass pipeline within another pass,
to allow configuring or filtering based on some invariants of the current
operation being operated on. For example, the
Inliner Pass may want to run
intraprocedural simplification passes while it is inlining to produce a better
cost model, and provide more optimal inlining. To enable this, passes may run an
arbitrary OpPassManager
on the current operation being operated on or any
operation nested within the current operation via the LogicalResult Pass::runPipeline(OpPassManager &, Operation *)
method. This method returns
whether the dynamic pipeline succeeded or failed, similarly to the result of the
top-level PassManager::run
method. A simple example is shown below:
void MyModulePass::runOnOperation() {
ModuleOp module = getOperation();
if (hasSomeSpecificProperty(module)) {
OpPassManager dynamicPM("module");
...; // Build the dynamic pipeline.
if (failed(runPipeline(dynamicPM, module)))
return signalPassFailure();
}
}
Note: though above the dynamic pipeline was constructed within the
runOnOperation
method, this is not necessary and pipelines should be cached
when possible as the OpPassManager
class can be safely copy constructed.
The mechanism described in this section should be used whenever a pass pipeline
should run in a nested fashion, i.e. when the nested pipeline cannot be
scheduled statically along with the rest of the main pass pipeline. More
specifically, a PassManager
should generally never need to be constructed
within a Pass
. Using runPipeline
also ensures that all analyses,
instrumentations, and other pass manager related
components are integrated with the dynamic pipeline being executed.
Instance Specific Pass Options
MLIR provides a builtin mechanism for passes to specify options that configure
its behavior. These options are parsed at pass construction time independently
for each instance of the pass. Options are defined using the Option<>
and
ListOption<>
classes, and follow the
LLVM command line flag definition
rules. See below for a few examples:
struct MyPass ... {
/// Make sure that we have a valid default constructor and copy constructor to
/// ensure that the options are initialized properly.
MyPass() = default;
MyPass(const MyPass& pass) {}
/// Any parameters after the description are forwarded to llvm::cl::list and
/// llvm::cl::opt respectively.
Option<int> exampleOption{*this, "flag-name", llvm::cl::desc("...")};
ListOption<int> exampleListOption{*this, "list-flag-name",
llvm::cl::desc("...")};
};
For pass pipelines, the PassPipelineRegistration
templates take an additional
template parameter for an optional Option
struct definition. This struct
should inherit from mlir::PassPipelineOptions
and contain the desired pipeline
options. When using PassPipelineRegistration
, the constructor now takes a
function with the signature void (OpPassManager &pm, const MyPipelineOptions&)
which should construct the passes from the options and pass them to the pm:
struct MyPipelineOptions : public PassPipelineOptions {
// The structure of these options is the same as those for pass options.
Option<int> exampleOption{*this, "flag-name", llvm::cl::desc("...")};
ListOption<int> exampleListOption{*this, "list-flag-name",
llvm::cl::desc("...")};
};
void registerMyPasses() {
PassPipelineRegistration<MyPipelineOptions>(
"example-pipeline", "Run an example pipeline.",
[](OpPassManager &pm, const MyPipelineOptions &pipelineOptions) {
// Initialize the pass manager.
});
}
Pass Statistics
Statistics are a way to keep track of what the compiler is doing and how effective various transformations are. It is often useful to see what effect specific transformations have on a particular input, and how often they trigger. Pass statistics are specific to each pass instance, which allow for seeing the effect of placing a particular transformation at specific places within the pass pipeline. For example, they help answer questions like "What happens if I run CSE again here?".
Statistics can be added to a pass by using the 'Pass::Statistic' class. This
class takes as a constructor arguments: the parent pass, a name, and a
description. This class acts like an atomic unsigned integer, and may be
incremented and updated accordingly. These statistics rely on the same
infrastructure as
llvm::Statistic
and thus have similar usage constraints. Collected statistics can be dumped by
the pass manager programmatically via
PassManager::enableStatistics
; or via -pass-statistics
and
-pass-statistics-display
on the command line.
An example is shown below:
struct MyPass ... {
/// Make sure that we have a valid default constructor and copy constructor to
/// ensure that the options are initialized properly.
MyPass() = default;
MyPass(const MyPass& pass) {}
StringRef getArgument() const final {
// This is the argument used to refer to the pass in
// the textual format (on the commandline for example).
return "argument";
}
StringRef getDescription() const final {
// This is a brief description of the pass.
return "description";
}
/// Define the statistic to track during the execution of MyPass.
Statistic exampleStat{this, "exampleStat", "An example statistic"};
void runOnOperation() {
...
// Update the statistic after some invariant was hit.
++exampleStat;
...
}
};
The collected statistics may be aggregated in two types of views:
A pipeline view that models the structure of the pass manager, this is the default view:
$ mlir-opt -pass-pipeline='func(my-pass,my-pass)' foo.mlir -pass-statistics
===-------------------------------------------------------------------------===
... Pass statistics report ...
===-------------------------------------------------------------------------===
'func' Pipeline
MyPass
(S) 15 exampleStat - An example statistic
VerifierPass
MyPass
(S) 6 exampleStat - An example statistic
VerifierPass
VerifierPass
A list view that aggregates the statistics of all instances of a specific pass together:
$ mlir-opt -pass-pipeline='func(my-pass, my-pass)' foo.mlir -pass-statistics -pass-statistics-display=list
===-------------------------------------------------------------------------===
... Pass statistics report ...
===-------------------------------------------------------------------------===
MyPass
(S) 21 exampleStat - An example statistic
Pass Registration
Briefly shown in the example definitions of the various pass types is the
PassRegistration
class. This mechanism allows for registering pass classes so
that they may be created within a
textual pass pipeline description. An
example registration is shown below:
void registerMyPass() {
PassRegistration<MyPass>();
}
MyPass
is the name of the derived pass class.- The pass
getArgument()
method is used to get the identifier that will be used to refer to the pass. - The pass
getDescription()
method provides a short summary describing the pass.
For passes that cannot be default-constructed, PassRegistration
accepts an
optional argument that takes a callback to create the pass:
void registerMyPass() {
PassRegistration<MyParametricPass>(
[]() -> std::unique_ptr<Pass> {
std::unique_ptr<Pass> p = std::make_unique<MyParametricPass>(/*options*/);
/*... non-trivial-logic to configure the pass ...*/;
return p;
});
}
This variant of registration can be used, for example, to accept the configuration of a pass from command-line arguments and pass it to the pass constructor.
Note: Make sure that the pass is copy-constructible in a way that does not share data as the pass manager may create copies of the pass to run in parallel.
Pass Pipeline Registration
Described above is the mechanism used for registering a specific derived pass
class. On top of that, MLIR allows for registering custom pass pipelines in a
similar fashion. This allows for custom pipelines to be available to tools like
mlir-opt in the same way that passes are, which is useful for encapsulating
common pipelines like the "-O1" series of passes. Pipelines are registered via a
similar mechanism to passes in the form of PassPipelineRegistration
. Compared
to PassRegistration
, this class takes an additional parameter in the form of a
pipeline builder that modifies a provided OpPassManager
.
void pipelineBuilder(OpPassManager &pm) {
pm.addPass(std::make_unique<MyPass>());
pm.addPass(std::make_unique<MyOtherPass>());
}
void registerMyPasses() {
// Register an existing pipeline builder function.
PassPipelineRegistration<>(
"argument", "description", pipelineBuilder);
// Register an inline pipeline builder.
PassPipelineRegistration<>(
"argument", "description", [](OpPassManager &pm) {
pm.addPass(std::make_unique<MyPass>());
pm.addPass(std::make_unique<MyOtherPass>());
});
}
Textual Pass Pipeline Specification
The previous sections detailed how to register passes and pass pipelines with a
specific argument and description. Once registered, these can be used to
configure a pass manager from a string description. This is especially useful
for tools like mlir-opt
, that configure pass managers from the command line,
or as options to passes that utilize
dynamic pass pipelines.
To support the ability to describe the full structure of pass pipelines, MLIR supports a custom textual description of pass pipelines. The textual description includes the nesting structure, the arguments of the passes and pass pipelines to run, and any options for those passes and pipelines. A textual pipeline is defined as a series of names, each of which may in itself recursively contain a nested pipeline description. The syntax for this specification is as follows:
pipeline ::= op-name `(` pipeline-element (`,` pipeline-element)* `)`
pipeline-element ::= pipeline | (pass-name | pass-pipeline-name) options?
options ::= '{' (key ('=' value)?)+ '}'
op-name
- This corresponds to the mnemonic name of an operation to run passes on,
e.g.
func
ormodule
.
- This corresponds to the mnemonic name of an operation to run passes on,
e.g.
pass-name
|pass-pipeline-name
- This corresponds to the argument of a registered pass or pass pipeline,
e.g.
cse
orcanonicalize
.
- This corresponds to the argument of a registered pass or pass pipeline,
e.g.
options
- Options are specific key value pairs representing options defined by a pass or pass pipeline, as described in the "Instance Specific Pass Options" section. See this section for an example usage in a textual pipeline.
For example, the following pipeline:
$ mlir-opt foo.mlir -cse -canonicalize -convert-std-to-llvm='use-bare-ptr-memref-call-conv=1'
Can also be specified as (via the -pass-pipeline
flag):
$ mlir-opt foo.mlir -pass-pipeline='func(cse,canonicalize),convert-std-to-llvm{use-bare-ptr-memref-call-conv=1}'
In order to support round-tripping a pass to the textual representation using
OpPassManager::printAsTextualPipeline(raw_ostream&)
, override StringRef Pass::getArgument()
to specify the argument used when registering a pass.
Declarative Pass Specification
Some aspects of a Pass may be specified declaratively, in a form similar to operations. This specification simplifies several mechanisms used when defining passes. It can be used for generating pass registration calls, defining boilerplate pass utilities, and generating pass documentation.
Consider the following pass specified in C++:
struct MyPass : PassWrapper<MyPass, OperationPass<ModuleOp>> {
MyPass() = default;
MyPass(const MyPass &) {}
...
// Specify any options.
Option<bool> option{
*this, "example-option",
llvm::cl::desc("An example option"), llvm::cl::init(true)};
ListOption<int64_t> listOption{
*this, "example-list",
llvm::cl::desc("An example list option"), llvm::cl::ZeroOrMore,
llvm::cl::MiscFlags::CommaSeparated};
// Specify any statistics.
Statistic statistic{this, "example-statistic", "An example statistic"};
};
/// Expose this pass to the outside world.
std::unique_ptr<Pass> foo::createMyPass() {
return std::make_unique<MyPass>();
}
/// Register this pass.
void foo::registerMyPass() {
PassRegistration<MyPass>();
}
This pass may be specified declaratively as so:
def MyPass : Pass<"my-pass", "ModuleOp"> {
let summary = "My Pass Summary";
let description = [{
Here we can now give a much larger description of `MyPass`, including all of
its various constraints and behavior.
}];
// A constructor must be provided to specify how to create a default instance
// of MyPass.
let constructor = "foo::createMyPass()";
// Specify any options.
let options = [
Option<"option", "example-option", "bool", /*default=*/"true",
"An example option">,
ListOption<"listOption", "example-list", "int64_t",
"An example list option",
"llvm::cl::ZeroOrMore, llvm::cl::MiscFlags::CommaSeparated">
];
// Specify any statistics.
let statistics = [
Statistic<"statistic", "example-statistic", "An example statistic">
];
}
Using the gen-pass-decls
generator, we can generate most of the boilerplate
above automatically. This generator takes as an input a -name
parameter, that
provides a tag for the group of passes that are being generated. This generator
produces two chunks of output:
The first is a code block for registering the declarative passes with the global
registry. For each pass, the generator produces a registerFooPass
where Foo
is the name of the definition specified in tablegen. It also generates a
registerGroupPasses
, where Group
is the tag provided via the -name
input
parameter, that registers all of the passes present.
// gen-pass-decls -name="Example"
#define GEN_PASS_REGISTRATION
#include "Passes.h.inc"
void registerMyPasses() {
// Register all of the passes.
registerExamplePasses();
// Register `MyPass` specifically.
registerMyPassPass();
}
The second is a base class for each of the passes, containing most of the boiler
plate related to pass definitions. These classes are named in the form of
MyPassBase
, where MyPass
is the name of the pass definition in tablegen. We
can update the original C++ pass definition as so:
/// Include the generated base pass class definitions.
#define GEN_PASS_CLASSES
#include "Passes.h.inc"
/// Define the main class as deriving from the generated base class.
struct MyPass : MyPassBase<MyPass> {
/// The explicit constructor is no longer explicitly necessary when defining
/// pass options and statistics, the base class takes care of that
/// automatically.
...
/// The definitions of the options and statistics are now generated within
/// the base class, but are accessible in the same way.
};
/// Expose this pass to the outside world.
std::unique_ptr<Pass> foo::createMyPass() {
return std::make_unique<MyPass>();
}
Using the gen-pass-doc
generator, markdown documentation for each of the
passes can be generated. See Passes.md for example output of real
MLIR passes.
Tablegen Specification
The Pass
class is used to begin a new pass definition. This class takes as an
argument the registry argument to attribute to the pass, as well as an optional
string corresponding to the operation type that the pass operates on. The class
contains the following fields:
summary
- A short one line summary of the pass, used as the description when registering the pass.
description
- A longer, more detailed description of the pass. This is used when generating pass documentation.
dependentDialects
- A list of strings representing the
Dialect
classes this pass may introduce entities, Attributes/Operations/Types/etc., of.
- A list of strings representing the
constructor
- A code block used to create a default instance of the pass.
options
- A list of pass options used by the pass.
statistics
- A list of pass statistics used by the pass.
Options
Options may be specified via the Option
and ListOption
classes. The Option
class takes the following template parameters:
- C++ variable name
- A name to use for the generated option variable.
- argument
- The argument name of the option.
- type
- The C++ type of the option.
- default value
- The default option value.
- description
- A one line description of the option.
- additional option flags
- A string containing any additional options necessary to construct the option.
def MyPass : Pass<"my-pass"> {
let options = [
Option<"option", "example-option", "bool", /*default=*/"true",
"An example option">,
];
}
The ListOption
class takes the following fields:
- C++ variable name
- A name to use for the generated option variable.
- argument
- The argument name of the option.
- element type
- The C++ type of the list element.
- description
- A one line description of the option.
- additional option flags
- A string containing any additional options necessary to construct the option.
def MyPass : Pass<"my-pass"> {
let options = [
ListOption<"listOption", "example-list", "int64_t",
"An example list option",
"llvm::cl::ZeroOrMore, llvm::cl::MiscFlags::CommaSeparated">
];
}
Statistic
Statistics may be specified via the Statistic
, which takes the following
template parameters:
- C++ variable name
- A name to use for the generated statistic variable.
- display name
- The name used when displaying the statistic.
- description
- A one line description of the statistic.
def MyPass : Pass<"my-pass"> {
let statistics = [
Statistic<"statistic", "example-statistic", "An example statistic">
];
}
Pass Instrumentation
MLIR provides a customizable framework to instrument pass execution and analysis
computation, via the PassInstrumentation
class. This class provides hooks into
the PassManager that observe various events:
runBeforePipeline
- This callback is run just before a pass pipeline, i.e. pass manager, is executed.
runAfterPipeline
- This callback is run right after a pass pipeline has been executed, successfully or not.
runBeforePass
- This callback is run just before a pass is executed.
runAfterPass
- This callback is run right after a pass has been successfully executed.
If this hook is executed,
runAfterPassFailed
will not be.
- This callback is run right after a pass has been successfully executed.
If this hook is executed,
runAfterPassFailed
- This callback is run right after a pass execution fails. If this hook is
executed,
runAfterPass
will not be.
- This callback is run right after a pass execution fails. If this hook is
executed,
runBeforeAnalysis
- This callback is run just before an analysis is computed.
- If the analysis requested another analysis as a dependency, the
runBeforeAnalysis
/runAfterAnalysis
pair for the dependency can be called from inside of the currentrunBeforeAnalysis
/runAfterAnalysis
pair.
runAfterAnalysis
- This callback is run right after an analysis is computed.
PassInstrumentation instances may be registered directly with a
PassManager instance via the addInstrumentation
method.
Instrumentations added to the PassManager are run in a stack like fashion, i.e.
the last instrumentation to execute a runBefore*
hook will be the first to
execute the respective runAfter*
hook. The hooks of a PassInstrumentation
class are guaranteed to be executed in a thread safe fashion, so additional
synchronization is not necessary. Below in an example instrumentation that
counts the number of times the DominanceInfo
analysis is computed:
struct DominanceCounterInstrumentation : public PassInstrumentation {
/// The cumulative count of how many times dominance has been calculated.
unsigned &count;
DominanceCounterInstrumentation(unsigned &count) : count(count) {}
void runAfterAnalysis(llvm::StringRef, TypeID id, Operation *) override {
if (id == TypeID::get<DominanceInfo>())
++count;
}
};
MLIRContext *ctx = ...;
PassManager pm(ctx);
// Add the instrumentation to the pass manager.
unsigned domInfoCount;
pm.addInstrumentation(
std::make_unique<DominanceCounterInstrumentation>(domInfoCount));
// Run the pass manager on a module operation.
ModuleOp m = ...;
if (failed(pm.run(m)))
...
llvm::errs() << "DominanceInfo was computed " << domInfoCount << " times!\n";
Standard Instrumentations
MLIR utilizes the pass instrumentation framework to provide a few useful developer tools and utilities. Each of these instrumentations are directly available to all users of the MLIR pass framework.
Pass Timing
The PassTiming instrumentation provides timing information about the execution
of passes and computation of analyses. This provides a quick glimpse into what
passes are taking the most time to execute, as well as how much of an effect a
pass has on the total execution time of the pipeline. Users can enable this
instrumentation directly on the PassManager via enableTiming
. This
instrumentation is also made available in mlir-opt via the -mlir-timing
flag.
The PassTiming instrumentation provides several different display modes for the
timing results, each of which is described below:
List Display Mode
In this mode, the results are displayed in a list sorted by total time with each
pass/analysis instance aggregated into one unique result. This view is useful
for getting an overview of what analyses/passes are taking the most time in a
pipeline. This display mode is available in mlir-opt via
-mlir-timing-display=list
.
$ mlir-opt foo.mlir -mlir-disable-threading -pass-pipeline='func(cse,canonicalize)' -convert-std-to-llvm -mlir-timing -mlir-timing-display=list
===-------------------------------------------------------------------------===
... Pass execution timing report ...
===-------------------------------------------------------------------------===
Total Execution Time: 0.0203 seconds
---Wall Time--- --- Name ---
0.0047 ( 55.9%) Canonicalizer
0.0019 ( 22.2%) VerifierPass
0.0016 ( 18.5%) LLVMLoweringPass
0.0003 ( 3.4%) CSE
0.0002 ( 1.9%) (A) DominanceInfo
0.0084 (100.0%) Total
Tree Display Mode
In this mode, the results are displayed in a nested pipeline view that mirrors the internal pass pipeline that is being executed in the pass manager. This view is useful for understanding specifically which parts of the pipeline are taking the most time, and can also be used to identify when analyses are being invalidated and recomputed. This is the default display mode.
$ mlir-opt foo.mlir -mlir-disable-threading -pass-pipeline='func(cse,canonicalize)' -convert-std-to-llvm -mlir-timing
===-------------------------------------------------------------------------===
... Pass execution timing report ...
===-------------------------------------------------------------------------===
Total Execution Time: 0.0249 seconds
---Wall Time--- --- Name ---
0.0058 ( 70.8%) 'func' Pipeline
0.0004 ( 4.3%) CSE
0.0002 ( 2.6%) (A) DominanceInfo
0.0004 ( 4.8%) VerifierPass
0.0046 ( 55.4%) Canonicalizer
0.0005 ( 6.2%) VerifierPass
0.0005 ( 5.8%) VerifierPass
0.0014 ( 17.2%) LLVMLoweringPass
0.0005 ( 6.2%) VerifierPass
0.0082 (100.0%) Total
Multi-threaded Pass Timing
When multi-threading is enabled in the pass manager the meaning of the display
slightly changes. First, a new timing column is added, User Time
, that
displays the total time spent across all threads. Secondly, the Wall Time
column displays the longest individual time spent amongst all of the threads.
This means that the Wall Time
column will continue to give an indicator on the
perceived time, or clock time, whereas the User Time
will display the total
cpu time.
$ mlir-opt foo.mlir -pass-pipeline='func(cse,canonicalize)' -convert-std-to-llvm -mlir-timing
===-------------------------------------------------------------------------===
... Pass execution timing report ...
===-------------------------------------------------------------------------===
Total Execution Time: 0.0078 seconds
---User Time--- ---Wall Time--- --- Name ---
0.0177 ( 88.5%) 0.0057 ( 71.3%) 'func' Pipeline
0.0044 ( 22.0%) 0.0015 ( 18.9%) CSE
0.0029 ( 14.5%) 0.0012 ( 15.2%) (A) DominanceInfo
0.0038 ( 18.9%) 0.0015 ( 18.7%) VerifierPass
0.0089 ( 44.6%) 0.0025 ( 31.1%) Canonicalizer
0.0006 ( 3.0%) 0.0002 ( 2.6%) VerifierPass
0.0004 ( 2.2%) 0.0004 ( 5.4%) VerifierPass
0.0013 ( 6.5%) 0.0013 ( 16.3%) LLVMLoweringPass
0.0006 ( 2.8%) 0.0006 ( 7.0%) VerifierPass
0.0200 (100.0%) 0.0081 (100.0%) Total
IR Printing
When debugging it is often useful to dump the IR at various stages of a pass
pipeline. This is where the IR printing instrumentation comes into play. This
instrumentation allows for conditionally printing the IR before and after pass
execution by optionally filtering on the pass being executed. This
instrumentation can be added directly to the PassManager via the
enableIRPrinting
method. mlir-opt
provides a few useful flags for utilizing
this instrumentation:
print-ir-before=(comma-separated-pass-list)
- Print the IR before each of the passes provided within the pass list.
print-ir-before-all
- Print the IR before every pass in the pipeline.
$ mlir-opt foo.mlir -pass-pipeline='func(cse)' -print-ir-before=cse
*** IR Dump Before CSE ***
func @simple_constant() -> (i32, i32) {
%c1_i32 = constant 1 : i32
%c1_i32_0 = constant 1 : i32
return %c1_i32, %c1_i32_0 : i32, i32
}
print-ir-after=(comma-separated-pass-list)
- Print the IR after each of the passes provided within the pass list.
print-ir-after-all
- Print the IR after every pass in the pipeline.
$ mlir-opt foo.mlir -pass-pipeline='func(cse)' -print-ir-after=cse
*** IR Dump After CSE ***
func @simple_constant() -> (i32, i32) {
%c1_i32 = constant 1 : i32
return %c1_i32, %c1_i32 : i32, i32
}
print-ir-after-change
- Only print the IR after a pass if the pass mutated the IR. This helps to reduce the number of IR dumps for "uninteresting" passes.
- Note: Changes are detected by comparing a hash of the operation before and after the pass. This adds additional run-time to compute the hash of the IR, and in some rare cases may result in false-positives depending on the collision rate of the hash algorithm used.
- Note: This option should be used in unison with one of the other 'print-ir-after' options above, as this option alone does not enable printing.
$ mlir-opt foo.mlir -pass-pipeline='func(cse,cse)' -print-ir-after=cse -print-ir-after-change
*** IR Dump After CSE ***
func @simple_constant() -> (i32, i32) {
%c1_i32 = constant 1 : i32
return %c1_i32, %c1_i32 : i32, i32
}
print-ir-after-failure
- Only print IR after a pass failure.
- This option should not be used with the other
print-ir-after
flags above.
$ mlir-opt foo.mlir -pass-pipeline='func(cse,bad-pass)' -print-ir-failure
*** IR Dump After BadPass Failed ***
func @simple_constant() -> (i32, i32) {
%c1_i32 = constant 1 : i32
return %c1_i32, %c1_i32 : i32, i32
}
print-ir-module-scope
- Always print the top-level module operation, regardless of pass type or operation nesting level.
- Note: Printing at module scope should only be used when multi-threading
is disabled(
-mlir-disable-threading
)
$ mlir-opt foo.mlir -mlir-disable-threading -pass-pipeline='func(cse)' -print-ir-after=cse -print-ir-module-scope
*** IR Dump After CSE *** ('func' operation: @bar)
func @bar(%arg0: f32, %arg1: f32) -> f32 {
...
}
func @simple_constant() -> (i32, i32) {
%c1_i32 = constant 1 : i32
%c1_i32_0 = constant 1 : i32
return %c1_i32, %c1_i32_0 : i32, i32
}
*** IR Dump After CSE *** ('func' operation: @simple_constant)
func @bar(%arg0: f32, %arg1: f32) -> f32 {
...
}
func @simple_constant() -> (i32, i32) {
%c1_i32 = constant 1 : i32
return %c1_i32, %c1_i32 : i32, i32
}
Crash and Failure Reproduction
The pass manager in MLIR contains a builtin mechanism to
generate reproducibles in the event of a crash, or a
pass failure. This functionality can be enabled via
PassManager::enableCrashReproducerGeneration
or via the command line flag
pass-pipeline-crash-reproducer
. In either case, an argument is provided that
corresponds to the output .mlir
file name that the reproducible should be
written to. The reproducible contains the configuration of the pass manager that
was executing, as well as the initial IR before any passes were run. A potential
reproducible may have the form:
// configuration: -pass-pipeline='func(cse,canonicalize),inline' -verify-each
module {
func @foo() {
...
}
}
The configuration dumped can be passed to mlir-opt
by specifying
-run-reproducer
flag. This will result in parsing the first line configuration
of the reproducer and adding those to the command line options.
Beyond specifying a filename, one can also register a ReproducerStreamFactory
function that would be invoked in the case of a crash and the reproducer written
to its stream.
Local Reproducer Generation
An additional flag may be passed to
PassManager::enableCrashReproducerGeneration
, and specified via
pass-pipeline-local-reproducer
on the command line, that signals that the pass
manager should attempt to generate a "local" reproducer. This will attempt to
generate a reproducer containing IR right before the pass that fails. This is
useful for situations where the crash is known to be within a specific pass, or
when the original input relies on components (like dialects or passes) that may
not always be available.
Note: Local reproducer generation requires that multi-threading is
disabled(-mlir-disable-threading
)
For example, if the failure in the previous example came from canonicalize
,
the following reproducer will be generated:
// configuration: -pass-pipeline='func(canonicalize)' -verify-each -mlir-disable-threading
module {
func @foo() {
...
}
}