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+# MLIR Reduce
+
+[TOC]
+
+An MLIR input may trigger bugs after series of transformations. To root cause
+the problem or help verification after fixes, We 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
+
+So far we provide two modes, the first one is reduction-tree, it will generate
+several reduced outputs and try to do further reduction in between them. It will
+form a tree structure which tells how each intermediate output is reduced.
+According to different tree traversal mode, it may lead to different result. You
+can run with `-reduction-tree='traversal-mode=0'` for example.
+
+The second mode is opt-reduction-pass, it applies the given passes to reduce the
+input. Run it with arg `-opt-reduction-pass='opt-pass=symbol-dce'`.
+
+### Write the script for testing interesting
+
+As mentioned, you need to provide a command to specify `mlir-reduce` which case
+you're intersting. 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 tranformation passes and some of them are useful for
+reducing the input size, e.g., Symbol-DCE. You can just use them to reduce your
+case.
+
+## 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);
+  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.