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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>'func' Dialect - 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/Dialects/Func/><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>'func' Dialect</h1>This dialect provides documentation for operations within the Func dialect.This dialect contains operations surrounding high order function abstractions, such as calls.Please post an RFC on the <a href=https://llvm.discourse.group/c/mlir/31>forum</a> before adding or changing any operation in this dialect.<nav id=TableOfContents><ul><li><a href=#operations>Operations</a><ul><li><a href=#funccall_indirect-funccallindirectop><code>func.call_indirect</code> (func::CallIndirectOp)</a></li><li><a href=#funccall-funccallop><code>func.call</code> (func::CallOp)</a></li><li><a href=#funcconstant-funcconstantop><code>func.constant</code> (func::ConstantOp)</a></li><li><a href=#funcfunc-funcfuncop><code>func.func</code> (func::FuncOp)</a></li><li><a href=#funcreturn-funcreturnop><code>func.return</code> (func::ReturnOp)</a></li></ul></li></ul></nav><h2 id=operations>Operations <a class=headline-hash href=#operations>¶</a></h2><a href=https://github.com/llvm/llvm-project/blob/main/mlir/include/mlir/Dialect/Func/IR/FuncOps.td>source</a><h3 id=funccall_indirect-funccallindirectop><code>func.call_indirect</code> (func::CallIndirectOp) <a class=headline-hash href=#funccall_indirect-funccallindirectop>¶</a></h3>Indirect call operationSyntax:<pre tabindex=0><code>operation ::= `func.call_indirect` $callee `(` $callee_operands `)` attr-dict `:` type($callee) </code></pre>The <code>func.call_indirect</code> operation represents an indirect call to a value of function type. The operands and result types of the call must match the specified function type.Function values can be created with the <a href=#funcconstant-constantop><code>func.constant</code> operation</a>.Example:<div class=highlight><pre tabindex=0 class=chroma><code class=language-mlir data-lang=mlir>%func = func.constant @my_func : (tensor<16xf32>, tensor<16xf32>) -> tensor<16xf32> %result = func.call_indirect %func(%0, %1) : (tensor<16xf32>, tensor<16xf32>) -> tensor<16xf32> </code></pre></div>Interfaces: <code>CallOpInterface</code><h4 id=operands>Operands: <a class=headline-hash href=#operands>¶</a></h4><table><thead><tr><th style=text-align:center>Operand</th><th>Description</th></tr></thead><tbody><tr><td style=text-align:center><code>callee</code></td><td>function type</td></tr><tr><td style=text-align:center><code>callee_operands</code></td><td>variadic of any type</td></tr></tbody></table><h4 id=results>Results: <a class=headline-hash href=#results>¶</a></h4><table><thead><tr><th style=text-align:center>Result</th><th>Description</th></tr></thead><tbody><tr><td style=text-align:center><code>results</code></td><td>variadic of any type</td></tr></tbody></table><h3 id=funccall-funccallop><code>func.call</code> (func::CallOp) <a class=headline-hash href=#funccall-funccallop>¶</a></h3>Call operationSyntax:<pre tabindex=0><code>operation ::= `func.call` $callee `(` $operands `)` attr-dict `:` functional-type($operands, results) </code></pre>The <code>func.call</code> operation represents a direct call to a function that is within the same symbol scope as the call. The operands and result types of the call must match the specified function type. The callee is encoded as a symbol reference attribute named “callee”.Example:<div class=highlight><pre tabindex=0 class=chroma><code class=language-mlir data-lang=mlir>%2 = func.call @my_add(%0, %1) : (f32, f32) -> f32 </code></pre></div>Traits: <code>MemRefsNormalizable</code>Interfaces: <code>CallOpInterface</code>, <code>SymbolUserOpInterface</code><h4 id=attributes>Attributes: <a class=headline-hash href=#attributes>¶</a></h4><table><tr><th>Attribute</th><th>MLIR Type</th><th>Description</th></tr><tr><td><code>callee</code></td><td>::mlir::FlatSymbolRefAttr</td><td>flat symbol reference attribute</td></tr></table><h4 id=operands-1>Operands: <a class=headline-hash href=#operands-1>¶</a></h4><table><thead><tr><th style=text-align:center>Operand</th><th>Description</th></tr></thead><tbody><tr><td style=text-align:center><code>operands</code></td><td>variadic of any type</td></tr></tbody></table><h4 id=results-1>Results: <a class=headline-hash href=#results-1>¶</a></h4><table><thead><tr><th style=text-align:center>Result</th><th>Description</th></tr></thead><tbody><tr><td style=text-align:center>«unnamed»</td><td>variadic of any type</td></tr></tbody></table><h3 id=funcconstant-funcconstantop><code>func.constant</code> (func::ConstantOp) <a class=headline-hash href=#funcconstant-funcconstantop>¶</a></h3>ConstantSyntax:<pre tabindex=0><code>operation ::= `func.constant` attr-dict $value `:` type(results) </code></pre>The <code>func.constant</code> operation produces an SSA value from a symbol reference to a <code>func.func</code> operationExample:<div class=highlight><pre tabindex=0 class=chroma><code class=language-mlir data-lang=mlir>// Reference to function @myfn. %2 = func.constant @myfn : (tensor<16xf32>, f32) -> tensor<16xf32> // Equivalent generic forms %2 = "func.constant"() { value = @myfn } : () -> ((tensor<16xf32>, f32) -> tensor<16xf32>) </code></pre></div>MLIR does not allow direct references to functions in SSA operands because the compiler is multithreaded, and disallowing SSA values to directly reference a function simplifies this ( <a href=/docs/Rationale/Rationale/>rationale</a>).Traits: <code>AlwaysSpeculatableImplTrait</code>, <code>ConstantLike</code>Interfaces: <code>ConditionallySpeculatable</code>, <code>NoMemoryEffect (MemoryEffectOpInterface)</code>, <code>OpAsmOpInterface</code>, <code>SymbolUserOpInterface</code>Effects: <code>MemoryEffects::Effect{}</code><h4 id=attributes-1>Attributes: <a class=headline-hash href=#attributes-1>¶</a></h4><table><tr><th>Attribute</th><th>MLIR Type</th><th>Description</th></tr><tr><td><code>value</code></td><td>::mlir::FlatSymbolRefAttr</td><td>flat symbol reference attribute</td></tr></table><h4 id=results-2>Results: <a class=headline-hash href=#results-2>¶</a></h4><table><thead><tr><th style=text-align:center>Result</th><th>Description</th></tr></thead><tbody><tr><td style=text-align:center>«unnamed»</td><td>any type</td></tr></tbody></table><h3 id=funcfunc-funcfuncop><code>func.func</code> (func::FuncOp) <a class=headline-hash href=#funcfunc-funcfuncop>¶</a></h3>An operation with a name containing a single <code>SSACFG</code> regionOperations within the function cannot implicitly capture values defined outside of the function, i.e. Functions are <code>IsolatedFromAbove</code>. All external references must use function arguments or attributes that establish a symbolic connection (e.g. symbols referenced by name via a string attribute like SymbolRefAttr). An external function declaration (used when referring to a function declared in some other module) has no body. While the MLIR textual form provides a nice inline syntax for function arguments, they are internally represented as “block arguments” to the first block in the region.Only dialect attribute names may be specified in the attribute dictionaries for function arguments, results, or the function itself.Example:<div class=highlight><pre tabindex=0 class=chroma><code class=language-mlir data-lang=mlir>// External function definitions. func.func private @abort() func.func private @scribble(i32, i64, memref<? x 128 x f32, #layout_map0>) -> f64 // A function that returns its argument twice: func.func @count(%x: i64) -> (i64, i64) attributes {fruit = "banana"} { return %x, %x: i64, i64 } // A function with an argument attribute func.func private @example_fn_arg(%x: i32 {swift.self = unit}) // A function with a result attribute func.func private @example_fn_result() -> (f64 {dialectName.attrName = 0 : i64}) // A function with an attribute func.func private @example_fn_attr() attributes {dialectName.attrName = false} </code></pre></div>Traits: <code>AffineScope</code>, <code>AutomaticAllocationScope</code>, <code>IsolatedFromAbove</code>Interfaces: <code>CallableOpInterface</code>, <code>FunctionOpInterface</code>, <code>OpAsmOpInterface</code>, <code>Symbol</code><h4 id=attributes-2>Attributes: <a class=headline-hash href=#attributes-2>¶</a></h4><table><tr><th>Attribute</th><th>MLIR Type</th><th>Description</th></tr><tr><td><code>sym_name</code></td><td>::mlir::StringAttr</td><td>string attribute</td></tr><tr><td><code>function_type</code></td><td>::mlir::TypeAttr</td><td>type attribute of function type</td></tr><tr><td><code>sym_visibility</code></td><td>::mlir::StringAttr</td><td>string attribute</td></tr><tr><td><code>arg_attrs</code></td><td>::mlir::ArrayAttr</td><td>Array of dictionary attributes</td></tr><tr><td><code>res_attrs</code></td><td>::mlir::ArrayAttr</td><td>Array of dictionary attributes</td></tr></table><h3 id=funcreturn-funcreturnop><code>func.return</code> (func::ReturnOp) <a class=headline-hash href=#funcreturn-funcreturnop>¶</a></h3>Function return operationSyntax:<pre tabindex=0><code>operation ::= `func.return` attr-dict ($operands^ `:` type($operands))? </code></pre>The <code>func.return</code> operation represents a return operation within a function. The operation takes variable number of operands and produces no results. The operand number and types must match the signature of the function that contains the operation.Example:<div class=highlight><pre tabindex=0 class=chroma><code class=language-mlir data-lang=mlir>func.func @foo() : (i32, f8) { ... return %0, %1 : i32, f8 } </code></pre></div>Traits: <code>AlwaysSpeculatableImplTrait</code>, <code>HasParent<FuncOp></code>, <code>MemRefsNormalizable</code>, <code>ReturnLike</code>, <code>Terminator</code>Interfaces: <code>ConditionallySpeculatable</code>, <code>NoMemoryEffect (MemoryEffectOpInterface)</code>, <code>RegionBranchTerminatorOpInterface</code>Effects: <code>MemoryEffects::Effect{}</code><h4 id=operands-2>Operands: <a class=headline-hash href=#operands-2>¶</a></h4><table><thead><tr><th style=text-align:center>Operand</th><th>Description</th></tr></thead><tbody><tr><td style=text-align:center><code>operands</code></td><td>variadic of any type</td></tr></tbody></table><div class=edit-meta> </div><nav class=pagination><a class="nav nav-prev" href=https://mlir.llvm.org/docs/Dialects/EmitC/ title="'emitc' Dialect"> Prev - 'emitc' Dialect</a> <a class="nav nav-next" href=https://mlir.llvm.org/docs/Dialects/GPU/ title="'gpu' Dialect">Next - 'gpu' Dialect </a></nav><footer>Powered by <a href=https://gohugo.io>Hugo</a>. 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Dialect</a></li><li><a href=https://mlir.llvm.org/docs/Dialects/X86Vector/>'x86vector' Dialect</a></li><li><a href=https://mlir.llvm.org/docs/Dialects/XeGPU/>'xegpu' Dialect</a></li><li><a href=https://mlir.llvm.org/docs/Dialects/Builtin/>Builtin Dialect</a></li><li><a href=https://mlir.llvm.org/docs/Dialects/MatchOpInterfaces/>OpInterface definitions</a></li><li><a href=https://mlir.llvm.org/docs/Dialects/SPIR-V/>SPIR-V Dialect</a></li><li><a href=https://mlir.llvm.org/docs/Dialects/TOSA/>Tensor Operator Set Architecture (TOSA) Dialect</a></li><li><a href=https://mlir.llvm.org/docs/Dialects/Transform/>Transform Dialect</a></li></ul></li><li><a href=https://mlir.llvm.org/docs/Interfaces/>Interfaces</a></li><li><a href=https://mlir.llvm.org/docs/TargetLLVMIR/>LLVM IR Target</a></li><li><a href=https://mlir.llvm.org/docs/BytecodeFormat/>MLIR Bytecode Format</a></li><li><a href=https://mlir.llvm.org/docs/CAPI/>MLIR C API</a></li><li><a href=https://mlir.llvm.org/docs/LangRef/>MLIR Language Reference</a></li><li><a href=https://mlir.llvm.org/docs/ReleaseNotes/>MLIR Release Notes</a></li><li><a href=https://mlir.llvm.org/docs/Canonicalization/>Operation Canonicalization</a></li><li><a href=https://mlir.llvm.org/docs/OwnershipBasedBufferDeallocation/>Ownership-based Buffer Deallocation</a></li><li><a href=https://mlir.llvm.org/docs/PassManagement/>Pass Infrastructure</a></li><li><a href=https://mlir.llvm.org/docs/Passes/>Passes</a></li><li><a href=https://mlir.llvm.org/docs/PatternRewriter/>Pattern Rewriting : Generic DAG-to-DAG Rewriting</a></li><li><a href=https://mlir.llvm.org/docs/PDLL/>PDLL - PDL Language</a></li><li><a href=https://mlir.llvm.org/docs/Quantization/>Quantization</a></li><li class=has-sub-menu><a href=https://mlir.llvm.org/docs/Rationale/>Rationale+</a><ul class=sub-menu><li><a href=https://mlir.llvm.org/docs/Rationale/RationaleGenericDAGRewriter/>Generic DAG Rewriter Infrastructure Rationale</a></li><li><a href=https://mlir.llvm.org/docs/Rationale/RationaleLinalgDialect/>Linalg Dialect Rationale: The Case For Compiler-Friendly Custom Operations</a></li><li><a href=https://mlir.llvm.org/docs/Rationale/Rationale/>MLIR Rationale</a></li><li><a href=https://mlir.llvm.org/docs/Rationale/MLIRForGraphAlgorithms/>MLIR: Incremental Application to Graph Algorithms in ML Frameworks</a></li><li><a href=https://mlir.llvm.org/docs/Rationale/RationaleSimplifiedPolyhedralForm/>MLIR: The case for a simplified polyhedral form</a></li><li><a href=https://mlir.llvm.org/docs/Rationale/SideEffectsAndSpeculation/>Side Effects & Speculation</a></li><li><a href=https://mlir.llvm.org/docs/Rationale/UsageOfConst/>Usage of 'const' in MLIR, for core IR types</a></li></ul></li><li><a href=https://mlir.llvm.org/docs/ShapeInference/>Shape Inference</a></li><li><a href=https://mlir.llvm.org/docs/SPIRVToLLVMDialectConversion/>SPIR-V Dialect to LLVM Dialect conversion manual</a></li><li><a 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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