This is useful for several reasons:
* In some situations the user can guarantee that thread-safety isn't necessary and don't want to pay the cost of synchronization, e.g., when parsing a very large module.
* For things like logging threading is not desirable as the output is not guaranteed to be in stable order.
This flag also subsumes the pass manager flag for multi-threading.
Differential Revision: https://reviews.llvm.org/D79266
This revision adds a mode to the crash reproducer generator to attempt to generate a more local reproducer. This will attempt to generate a reproducer right before the offending pass that fails. This is useful for the majority of failures that are specific to a single pass, and situations where some passes in the pipeline are not registered with a specific tool.
Differential Revision: https://reviews.llvm.org/D78314
This change refactors pass options to be more similar to how statistics are modeled. More specifically, the options are specified directly on the pass instead of in a separate options class. (Note that the behavior and specification for pass pipelines remains the same.) This brings about several benefits:
* The specification of options is much simpler
* The round-trip format of a pass can be generated automatically
* This gives a somewhat deeper integration with "configuring" a pass, which we could potentially expose to users in the future.
PiperOrigin-RevId: 286953824
This adds an additional filtering mode for printing after a pass that checks to see if the pass actually changed the IR before printing it. This "change" detection is implemented using a SHA1 hash of the current operation and its children.
PiperOrigin-RevId: 284291089
It would be nice if we could detect if stats were enabled or not and use 'Requires', but this isn't possible to do at configure time.
Fixestensorflow/mlir#296
PiperOrigin-RevId: 284200271
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
This allows for them to be used on other non-function, or even other function-like, operations. The algorithms are already generic, so this is simply changing the derived pass type. The majority of this change is just ensuring that the nesting of these passes remains the same, as the pass manager won't auto-nest them anymore.
PiperOrigin-RevId: 276573038
This cl adds support for generating a .mlir file containing a reproducer for crashes and failures that happen during pass execution. The reproducer contains a comment detailing the configuration of the pass manager(e.g. the textual description of the pass pipeline that the pass manager was executing), along with the original input module.
Example Output:
// configuration: -pass-pipeline='func(cse, canonicalize), inline'
// note: verifyPasses=false
module {
...
}
PiperOrigin-RevId: 274088134
Allow printing out pipelines in a format that is as close as possible to the
textual pass pipeline format. Individual passes can override the print function
in order to format any options that may have been used to construct that pass.
PiperOrigin-RevId: 273813627
This allows individual passes to define options structs and for these options to be parsed per instance of the pass while building the pass pipeline from the command line provided textual specification.
The user can specify these per-instance pipeline options like so:
```
struct MyPassOptions : public PassOptions<MyPassOptions> {
Option<int> exampleOption{*this, "flag-name", llvm:🆑:desc("...")};
List<int> exampleListOption{*this, "list-flag-name", llvm:🆑:desc("...")};
};
static PassRegistration<MyPass, MyPassOptions> pass("my-pass", "description");
```
PiperOrigin-RevId: 273650140
This allows for users other than those on the command line to apply a textual description of a pipeline to a given pass manager.
PiperOrigin-RevId: 269017028
This allows for explicitly specifying the pipeline to add to the pass manager. This includes the nesting structure, as well as the passes/pipelines to run. A textual pipeline string is defined as a series of names, each of which may in itself recursively contain a nested pipeline description. A name is either the name of a registered pass, or pass pipeline, (e.g. "cse") or the name of an operation type (e.g. "func").
For example, the following pipeline:
$ mlir-opt foo.mlir -cse -canonicalize -lower-to-llvm
Could now be specified as:
$ mlir-opt foo.mlir -pass-pipeline='func(cse, canonicalize), lower-to-llvm'
This will allow for running pipelines on nested operations, like say spirv modules. This does not remove any of the current functionality, and in fact can be used in unison. The new option is available via 'pass-pipeline'.
PiperOrigin-RevId: 268954279
This allows for parallelizing across pipelines of multiple operation types. AdaptorPasses can now hold pass managers for multiple operation types and will dispatch based upon the operation being operated on.
PiperOrigin-RevId: 268017344
This is done via a new set of instrumentation hooks runBeforePipeline/runAfterPipeline, that signal the lifetime of a pass pipeline on a specific operation type. These hooks also provide the parent thread of the pipeline, allowing for accurate merging of timers running on different threads.
PiperOrigin-RevId: 267909193
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
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.
Example:
$ mlir-opt foo.mlir -experimental-mt-pm -cse -canonicalize -convert-to-llvmir -pass-timing
===-------------------------------------------------------------------------===
... Pass execution timing report ...
===-------------------------------------------------------------------------===
Total Execution Time: 0.0078 seconds
---User Time--- ---Wall Time--- --- Name ---
0.0175 ( 88.3%) 0.0055 ( 70.4%) Function Pipeline
0.0018 ( 9.3%) 0.0006 ( 8.1%) CSE
0.0013 ( 6.3%) 0.0004 ( 5.8%) (A) DominanceInfo
0.0017 ( 8.7%) 0.0006 ( 7.1%) FunctionVerifier
0.0128 ( 64.6%) 0.0039 ( 50.5%) Canonicalizer
0.0011 ( 5.7%) 0.0004 ( 4.7%) FunctionVerifier
0.0004 ( 2.1%) 0.0004 ( 5.2%) ModuleVerifier
0.0010 ( 5.3%) 0.0010 ( 13.4%) LLVMLowering
0.0009 ( 4.3%) 0.0009 ( 11.0%) ModuleVerifier
0.0198 (100.0%) 0.0078 (100.0%) Total
PiperOrigin-RevId: 240636269
* 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.
* 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.
* print-ir-module-scope
- Always print the Module IR, even for non module passes.
PiperOrigin-RevId: 238523649
Below shows the output for an example mlir-opt command line.
mlir-opt foo.mlir -verify-each=false -cse -canonicalize -cse -cse -pass-timing
list view (-pass-timing-display=list):
* 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 mode is similar to the output of 'time-passes' in llvm-opt.
===-------------------------------------------------------------------------===
... Pass execution timing report ...
===-------------------------------------------------------------------------===
Total Execution Time: 0.0097 seconds (0.0096 wall clock)
---User Time--- --System Time-- --User+System-- ---Wall Time--- --- Name ---
0.0051 ( 58.3%) 0.0001 ( 12.2%) 0.0052 ( 53.8%) 0.0052 ( 53.8%) Canonicalizer
0.0025 ( 29.1%) 0.0005 ( 58.2%) 0.0031 ( 31.9%) 0.0031 ( 32.0%) CSE
0.0011 ( 12.6%) 0.0003 ( 29.7%) 0.0014 ( 14.3%) 0.0014 ( 14.2%) DominanceInfo
0.0087 (100.0%) 0.0009 (100.0%) 0.0097 (100.0%) 0.0096 (100.0%) Total
pipeline view (-pass-timing-display=pipeline):
* 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.
===-------------------------------------------------------------------------===
... Pass execution timing report ...
===-------------------------------------------------------------------------===
Total Execution Time: 0.0082 seconds (0.0081 wall clock)
---User Time--- --System Time-- --User+System-- ---Wall Time--- --- Name ---
0.0042 (100.0%) 0.0039 (100.0%) 0.0082 (100.0%) 0.0081 (100.0%) Function Pipeline
0.0005 ( 11.6%) 0.0008 ( 21.1%) 0.0013 ( 16.1%) 0.0013 ( 16.2%) CSE
0.0002 ( 5.0%) 0.0004 ( 9.3%) 0.0006 ( 7.0%) 0.0006 ( 7.0%) (A) DominanceInfo
0.0026 ( 61.8%) 0.0018 ( 45.6%) 0.0044 ( 54.0%) 0.0044 ( 54.1%) Canonicalizer
0.0005 ( 11.7%) 0.0005 ( 13.0%) 0.0010 ( 12.3%) 0.0010 ( 12.4%) CSE
0.0003 ( 6.1%) 0.0003 ( 8.3%) 0.0006 ( 7.2%) 0.0006 ( 7.1%) (A) DominanceInfo
0.0002 ( 3.8%) 0.0001 ( 2.8%) 0.0003 ( 3.3%) 0.0003 ( 3.3%) CSE
0.0042 (100.0%) 0.0039 (100.0%) 0.0082 (100.0%) 0.0081 (100.0%) Total
PiperOrigin-RevId: 237825367
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