llvm-project/mlir/docs/Tools/mlir-reduce.md

# MLIR Reduce

[TOC]

An MLIR input may trigger bugs after series of transformations. To root cause
the problem or help verification after fixes, developers want to be able to
reduce the size of a reproducer for a bug. This document describes
`mlir-reduce`, which is similar to
[bugpoint](https://llvm.org/docs/CommandGuide/bugpoint.html), a tool that can
reduce the size of the input needed to trigger the error.

`mlir-reduce` supports reducing the input in several ways, including simply
deleting code not required to reproduce an error, applying the reducer
patterns heuristically or run with optimization passes to reduce the input. To
use it, the first thing you need to do is, provide a command which tells if an
input is interesting, e.g., exhibits the characteristics that you would like to
focus on. For example, you may want to see if `mlir-opt` invocation fails after
it runs on the certain MLIR input. Afterwards, select your reduction strategy
then `mlir-reduce` will do the remining works for you.

## How to Use it

`mlir-reduce` adopts reduction-tree algorithm to reduce the input. it generates
several reduced outputs and do the further reduction in between them according
to the tree traversal strategy. The different strategies may lead to different
result and different time complexity. You can run as
`-reduction-tree='traversal-mode=0'` to select the mode for example.

### Write the script for testing interesting

As mentioned, you need to provide a command to specify `mlir-reduce` which case
you're interesting. For each intermediate output generated during reduction,
`mlir-reduce` will run the command over the it, the script should returns 1 for
interesting case, 0 otherwise. The sample script,

```shell
mlir-opt -convert-vector-to-spirv $1 | grep "failed to materialize"
if [[ $? -eq 1 ]]; then
  exit 1
else
  exit 0
fi
```

The sample usage will be like, note that the `test` argument is part of the mode
argument.

```shell
mlir-reduce $INPUT -reduction-tree='traversal-mode=0 test=$TEST_SCRIPT'
```

## Available reduction strategies

### Operation elimination

`mlir-reduce` will try to remove the operations directly. This is the most
aggressive reduction as it may result in an invalid output as long as it ends up
retaining the error message that the test script is interesting. To avoid that,
`mlir-reduce` always checks the validity and it expects the user will provide a
valid input as well.

### Rewrite patterns into simpler forms

In some cases, rewrite an operation into a simpler or smaller form can still
retain the interestingness. For example, `mlir-reduce` will try to rewrite a
`tensor<?xindex>` with unknown rank into a constant rank one like
`tensor<1xi32>`. Not only produce a simpler operation, it may introduce further
reduction chances because of precise type information.

MLIR supports dialects and `mlir-reduce` supports rewrite patterns for every
dialect as well. Which means you can have the dialect specific rewrite patterns.
To do that, you need to implement the `DialectReductionPatternInterface`. For
example,

```c++
#include "mlir/Reducer/ReductionPatternInterface.h"

struct MyReductionPatternInterface : public DialectReductionPatternInterface {
  virtual void
  populateReductionPatterns(RewritePatternSet &patterns) const final {
    populateMyReductionPatterns(patterns);
  }
}
```

`mlir-reduce` will call `populateReductionPatterns` to collect the reduction
rewrite patterns provided by each dialect. Here's a hint, if you use
[DRR](../DeclarativeRewrites.md) to write the reduction patterns, you can
leverage the method `populateWithGenerated` generated by `mlir-tblgen`.

### Reduce with built-in optimization passes

MLIR provides amount of transformation passes and some of them are useful for
reducing the input size, e.g., Symbol-DCE. `mlir-reduce` will schedule them
along with above two strategies.

## Build a custom mlir-reduce

In the cases of, 1. have defined a custom syntax, 2. the failure is specific to
certain dialects or 3. there's a dialect specific reducer patterns, you need to
build your own `mlir-reduce`. Link it with `MLIRReduceLib` and implement it
like,

```c++
#include "mlir/Tools/mlir-reduce/MlirReduceMain.h"
using namespace mlir;

int main(int argc, char **argv) {
  DialectRegistry registry;
  registerMyDialects(registry);
  // Register the DialectReductionPatternInterface if any.
  MLIRContext context(registry);
  return failed(mlirReduceMain(argc, argv, context));
}

```

## Future works

`mlir-reduce` is missing several features,

*   `-reduction-tree` now only supports `Single-Path` traversal mode, extends it
with different traveral strategies may reduce the input better.
*   Produce the optimial result when interruped. The reduction process may take
a quite long time, it'll be better to get an optimal result so far while an
interrup is triggered.