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<!doctype html><html lang=en-us><head><meta charset=utf-8><meta http-equiv=x-ua-compatible content="IE=edge"><meta name=viewport content="width=device-width,initial-scale=1,maximum-scale=1,user-scalable=no"><title>MLIR Reduce - MLIR</title><meta name=description content="Multi-Level IR Compiler Framework"><meta name=generator content="Hugo 0.119.0"><link href=https://mlir.llvm.org/index.xml rel=alternate type=application/rss+xml><link rel=canonical href=https://mlir.llvm.org/docs/Tools/mlir-reduce/><link rel=stylesheet href=https://mlir.llvm.org/css/theme.css><script src=https://use.fontawesome.com/releases/v5.0.6/js/all.js></script> <link rel=stylesheet href=https://mlir.llvm.org/css/chroma.min.css><script src=https://cdn.jsdelivr.net/npm/jquery@3.3.1/dist/jquery.min.js></script> <script src=https://cdn.jsdelivr.net/npm/jquery.easing@1.4.1/jquery.easing.min.js></script> <script src=https://mlir.llvm.org/js/bundle.js></script> <script type=text/javascript src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> <script type=text/x-mathjax-config> MathJax.Hub.Config({ tex2jax: { inlineMath: [['$', '$'] ], displayMath: [ ['$$','$$'], ["\\[","\\]"] ] } }); </script><link rel=apple-touch-icon sizes=180x180 href="/apple-touch-icon.png?v=1"><link rel=icon type=image/png sizes=32x32 href="/favicon-32x32.png?v=1"><link rel=icon type=image/png sizes=16x16 href="/favicon-16x16.png?v=1"><link rel=manifest href="/site.webmanifest?v=1"><link rel=mask-icon href="/safari-pinned-tab.svg?v=1" color=#3775e0><link rel="shortcut icon" href="/favicon.ico?v=1"><meta name=msapplication-TileColor content="#2d89ef"><meta name=theme-color content="#ffffff"><link rel=icon href=/favicon.svg type=image/svg+xml sizes=any><style>:root{}</style></head><body><div class=container><header><h1><div><img src=https://mlir.llvm.org//mlir-logo.png width=40px align=absmiddle> MLIR</div></h1>Multi-Level IR Compiler Framework</header><div class=global-menu><nav><ul><li class=parent><a href>Community</a><ul class=sub-menu><li class=child><a href=https://llvm.discourse.group/c/mlir/31>Forums</a></li><li class=child><a href=https://discord.gg/xS7Z362>Chat</a></li></ul></li><li><a href=/getting_started/Debugging/>Debugging Tips</a></li><li><a href=/getting_started/Faq/>FAQ</a></li><li class=parent><a href=https://github.com/llvm/llvm-project/tree/main/mlir>Source</a><ul class=sub-menu><li class=child><a href=/doxygen/>Doxygen</a></li><li class=child><a href=https://github.com/llvm/llvm-project/tree/main/mlir>GitHub</a></li></ul></li><li><a href="https://bugs.llvm.org/buglist.cgi?bug_status=__open__&list_id=177877&order=changeddate%20DESC%2Cpriority%2Cbug_severity&product=MLIR&query_format=specific">Bugs</a></li><li><a href=https://github.com/llvm/mlir-www/tree/main/website/static/LogoAssets>Logo Assets</a></li><li><a href=https://www.youtube.com/MLIRCompiler>Youtube Channel</a></li></ul></nav></div><div class=content-container><main><h1>MLIR Reduce</h1><nav id=TableOfContents><ul><li><a href=#how-to-use-it>How to Use it</a><ul><li><a href=#write-the-script-for-testing-interestingness>Write the script for testing interestingness</a></li></ul></li><li><a href=#available-reduction-strategies>Available reduction strategies</a><ul><li><a href=#operation-elimination>Operation elimination</a></li><li><a href=#rewrite-patterns-into-simpler-forms>Rewrite patterns into simpler forms</a></li><li><a href=#reduce-with-built-in-optimization-passes>Reduce with built-in optimization passes</a></li></ul></li><li><a href=#build-a-custom-mlir-reduce>Build a custom mlir-reduce</a></li><li><a href=#future-works>Future works</a></li></ul></nav>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 <code>mlir-reduce</code>, which is similar to <a href=https://llvm.org/docs/CommandGuide/bugpoint.html>bugpoint</a>, a tool that can reduce the size of the input needed to trigger the error.<code>mlir-reduce</code> 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 <code>mlir-opt</code> invocation fails after it runs on the certain MLIR input. Afterwards, select your reduction strategy then <code>mlir-reduce</code> will do the remaining works for you.<h2 id=how-to-use-it>How to Use it <a class=headline-hash href=#how-to-use-it>¶</a></h2><code>mlir-reduce</code> adopts the reduction-tree algorithm to reduce the input. It generates several reduced outputs and further reduces in between them according to the tree traversal strategy. The different strategies may lead to different results and different time complexity. You can run as <code>-reduction-tree='traversal-mode=0'</code> to select the mode for example.<h3 id=write-the-script-for-testing-interestingness>Write the script for testing interestingness <a class=headline-hash href=#write-the-script-for-testing-interestingness>¶</a></h3>As mentioned, you need to provide a command to <code>mlir-reduce</code> which identifies cases you’re interested in. For each intermediate output generated during reduction, <code>mlir-reduce</code> will run the command over the it, the script should returns 1 for interesting case, 0 otherwise. The sample script,<div class=highlight><pre tabindex=0 class=chroma><code class=language-shell data-lang=shell>mlir-opt -convert-vector-to-spirv $1 | grep "failed to materialize" if [[ $? -eq 1 ]]; then exit 1 else exit 0 fi </code></pre></div>The sample usage will be like, note that the <code>test</code> argument is part of the mode argument.<div class=highlight><pre tabindex=0 class=chroma><code class=language-shell data-lang=shell>mlir-reduce $INPUT -reduction-tree='traversal-mode=0 test=$TEST_SCRIPT' </code></pre></div><h2 id=available-reduction-strategies>Available reduction strategies <a class=headline-hash href=#available-reduction-strategies>¶</a></h2><h3 id=operation-elimination>Operation elimination <a class=headline-hash href=#operation-elimination>¶</a></h3><code>mlir-reduce</code> 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, <code>mlir-reduce</code> always checks the validity and it expects the user will provide a valid input as well.<h3 id=rewrite-patterns-into-simpler-forms>Rewrite patterns into simpler forms <a class=headline-hash href=#rewrite-patterns-into-simpler-forms>¶</a></h3>In some cases, rewrite an operation into a simpler or smaller form can still retain the interestingness. For example, <code>mlir-reduce</code> will try to rewrite a <code>tensor<?xindex></code> with unknown rank into a constant rank one like <code>tensor<1xi32></code>. Not only produce a simpler operation, it may introduce further reduction chances because of precise type information.MLIR supports dialects and <code>mlir-reduce</code> 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 <code>DialectReductionPatternInterface</code>. For example,<div class=highlight><pre tabindex=0 class=chroma><code class=language-c++ data-lang=c++>#include "mlir/Reducer/ReductionPatternInterface.h" struct MyReductionPatternInterface : public DialectReductionPatternInterface { virtual void populateReductionPatterns(RewritePatternSet &patterns) const final { populateMyReductionPatterns(patterns); } } </code></pre></div><code>mlir-reduce</code> will call <code>populateReductionPatterns</code> to collect the reduction rewrite patterns provided by each dialect. Here’s a hint, if you use <a href=/docs/DeclarativeRewrites/>DRR</a> to write the reduction patterns, you can leverage the method <code>populateWithGenerated</code> generated by <code>mlir-tblgen</code>.<h3 id=reduce-with-built-in-optimization-passes>Reduce with built-in optimization passes <a class=headline-hash href=#reduce-with-built-in-optimization-passes>¶</a></h3>MLIR provides amount of transformation passes and some of them are useful for reducing the input size, e.g., Symbol-DCE. <code>mlir-reduce</code> will schedule them along with above two strategies.<h2 id=build-a-custom-mlir-reduce>Build a custom mlir-reduce <a class=headline-hash href=#build-a-custom-mlir-reduce>¶</a></h2>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 <code>mlir-reduce</code>. Link it with <code>MLIRReduceLib</code> and implement it like,<div class=highlight><pre tabindex=0 class=chroma><code class=language-c++ data-lang=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)); } </code></pre></div><h2 id=future-works>Future works <a class=headline-hash href=#future-works>¶</a></h2><code>mlir-reduce</code> is missing several features,<ul><li><code>-reduction-tree</code> now only supports <code>Single-Path</code> traversal mode, extends it with different traversal strategies may reduce the input better.</li><li>Produce the optimal result when interrupted. The reduction process may take a quite long time, it’ll be better to get an optimal result so far while an interrupt is triggered.</li></ul><div class=edit-meta> </div><nav class=pagination><a class="nav nav-prev" href=https://mlir.llvm.org/docs/Tools/MLIRLSP/ title="MLIR : Language Server Protocol"> Prev - MLIR : Language Server Protocol</a> <a class="nav nav-next" href=https://mlir.llvm.org/docs/Tools/mlir-rewrite/ title=mlir-rewrite>Next - mlir-rewrite </a></nav><footer>Powered by <a href=https://gohugo.io>Hugo</a>. Theme by <a href=https://themes.gohugo.io/hugo-theme-techdoc/>TechDoc</a>. 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href=https://mlir.llvm.org/docs/SymbolsAndSymbolTables/>Symbols and Symbol Tables</a></li><li><a href=https://mlir.llvm.org/docs/DeclarativeRewrites/>Table-driven Declarative Rewrite Rule (DRR)</a></li><li class=has-sub-menu><a href=https://mlir.llvm.org/docs/Traits/>Traits+</a><ul class=sub-menu><li><a href=https://mlir.llvm.org/docs/Traits/Broadcastable/>The `Broadcastable` Trait</a></li></ul></li><li class=has-sub-menu><a href=https://mlir.llvm.org/docs/Tutorials/>Tutorials+</a><ul class=sub-menu><li><a href=https://mlir.llvm.org/docs/Tutorials/CreatingADialect/>Creating a Dialect</a></li><li><a href=https://mlir.llvm.org/docs/Tutorials/QuickstartRewrites/>Quickstart tutorial to adding MLIR graph rewrite</a></li><li class=has-sub-menu><a href=https://mlir.llvm.org/docs/Tutorials/Toy/>Toy Tutorial+</a><ul class=sub-menu><li><a href=https://mlir.llvm.org/docs/Tutorials/Toy/Ch-1/>Chapter 1: Toy Language and AST</a></li><li><a href=https://mlir.llvm.org/docs/Tutorials/Toy/Ch-2/>Chapter 2: Emitting Basic MLIR</a></li><li><a href=https://mlir.llvm.org/docs/Tutorials/Toy/Ch-3/>Chapter 3: High-level Language-Specific Analysis and Transformation</a></li><li><a href=https://mlir.llvm.org/docs/Tutorials/Toy/Ch-4/>Chapter 4: Enabling Generic Transformation with Interfaces</a></li><li><a href=https://mlir.llvm.org/docs/Tutorials/Toy/Ch-5/>Chapter 5: Partial Lowering to Lower-Level Dialects for Optimization</a></li><li><a href=https://mlir.llvm.org/docs/Tutorials/Toy/Ch-6/>Chapter 6: Lowering to LLVM and CodeGeneration</a></li><li><a href=https://mlir.llvm.org/docs/Tutorials/Toy/Ch-7/>Chapter 7: Adding a Composite Type to Toy</a></li></ul></li><li class=has-sub-menu><a href=https://mlir.llvm.org/docs/Tutorials/transform/>Transform Dialect Tutorial+</a><ul class=sub-menu><li><a href=https://mlir.llvm.org/docs/Tutorials/transform/Ch0/>Chapter 0: A Primer on “Structured” Linalg Operations</a></li><li><a href=https://mlir.llvm.org/docs/Tutorials/transform/Ch1/>Chapter 1: Combining Existing Transformations</a></li><li><a href=https://mlir.llvm.org/docs/Tutorials/transform/Ch2/>Chapter 2: Adding a Simple New Transformation Operation</a></li><li><a href=https://mlir.llvm.org/docs/Tutorials/transform/Ch3/>Chapter 3: More than Simple Transform Operations</a></li><li><a href=https://mlir.llvm.org/docs/Tutorials/transform/Ch4/>Chapter 4: Matching Payload with Transform Operations</a></li><li><a href=https://mlir.llvm.org/docs/Tutorials/transform/ChH/>Chapter H: Reproducing Halide Schedule</a></li></ul></li><li><a href=https://mlir.llvm.org/docs/Tutorials/UnderstandingTheIRStructure/>Understanding the IR Structure</a></li><li><a href=https://mlir.llvm.org/docs/Tutorials/MlirOpt/>Using `mlir-opt`</a></li><li><a href=https://mlir.llvm.org/docs/Tutorials/DataFlowAnalysis/>Writing DataFlow Analyses in MLIR</a></li></ul></li></ul></li></ul></nav><div class=sidebar-footer></div></div></div><a href=# id=backtothetop-fixed class=backtothetop data-backtothetop-duration=600 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