[mlir][linalg] Extend elementwise (llvm#124661)

Implements Linalg elemwise named-op following the proposal and
discussions in RFC:
  https://discourse.llvm.org/t/rfc-extend-linalg-elemwise-named-ops-semantics/83927/1

Author: javedabsar1

mlir/include/mlir/Dialect/Linalg/IR/LinalgBase.td

@@ -61,6 +61,12 @@ def Linalg_Dialect : Dialect {
   }];
 }
 
+// Define the attribute enums matching elementwise op kind (e.g., add).
+def ElementwiseKindAttr : EnumAttr<Linalg_Dialect,
+                                   ElementwiseKind, "elementwise_kind"> {
+  let assemblyFormat = "`<` $value `>`";
+}
+
 // Define the function attribute enums matching the OpDSL functions.
 def UnaryFnAttr : EnumAttr<Linalg_Dialect, UnaryFn, "unary_fn"> {
   let assemblyFormat = "`<` $value `>`";

mlir/include/mlir/Dialect/Linalg/IR/LinalgEnums.td

@@ -55,6 +55,65 @@ def TernaryFn : I32EnumAttr<"TernaryFn", "", [
   let genSpecializedAttr = 0;
   let cppNamespace = "::mlir::linalg";
 }
+
+// Join two I32EnumAttrCase lists. This joining takes care that the
+// 'int enum values' in the combined list do not overlap. It does this
+// by adding to each element of second list the offset '!size(a)'.
+class JoinTwoI32EnumAttrCaseList< list<I32EnumAttrCase> a,
+                                  list<I32EnumAttrCase> b> {
+  int aSize = !size(a);
+  list<I32EnumAttrCase> result =
+             !foldl(a, b, acc, var,
+                    acc # [I32EnumAttrCase<var.symbol,
+                                           !add(var.value, aSize)
+                                           >]);
+}
+
+// Flatten 'list of list of I32EnumAttrCase' to 'list of I32EnumAttrCase'.
+// The flattening (via call to 'join') ensures no overlap in enum values.
+class ConcatI32EnumAtrCaseList< list<list<I32EnumAttrCase>> l> {
+  list<I32EnumAttrCase> result =
+             !foldl([]<I32EnumAttrCase>, l, acc, var,
+                    JoinTwoI32EnumAttrCaseList<acc, var>.result);
+}
+
+// Define a unified `enum class : i32` for all element-wise op functions.
+def ElementwiseKind :
+            I32EnumAttr<"ElementwiseKind",
+                        "",
+                        ConcatI32EnumAtrCaseList<[UnaryFn.enumerants,
+                                                  BinaryFn.enumerants,
+                                                  TernaryFn.enumerants]>.result
+                      > {
+  let genSpecializedAttr = 0;
+  let cppNamespace = "::mlir::linalg";
+}
+
+// Define an `enum class : i32` that marks where each individual enum class
+// e.g. UnaryFn, BinaryFn, etc. end in the unified enum class ElementwiseKind.
+def ElementwiseCaseLimits : I32EnumAttr<"ElementwiseCaseLimits", "", []> {
+  int last_unary = !size(UnaryFn.enumerants);
+  int last_binary = !add(last_unary, !size(BinaryFn.enumerants));
+  int last_ternary = !add(last_binary, !size(TernaryFn.enumerants));
+
+  let enumerants =  [
+         I32EnumAttrCase<"LastUnary", last_unary>,
+         I32EnumAttrCase<"LastBinary", last_binary>,
+         I32EnumAttrCase<"LastTernary", last_ternary>];
+  let genSpecializedAttr = 0;
+  let cppNamespace = "::mlir::linalg";
+}
+
+// Define an `enum class : i32` to categorise arity elementwise ops.
+def ElementwiseArityGroup : I32EnumAttr<"ElementwiseArityGroup", "", [
+  I32EnumAttrCase<"Unary", 1>,
+  I32EnumAttrCase<"Binary", 2>,
+  I32EnumAttrCase<"Ternary", 3>
+]> {
+  let genSpecializedAttr = 0;
+  let cppNamespace = "::mlir::linalg";
+}
+
 def TypeFn : I32EnumAttr<"TypeFn", "", [
   I32EnumAttrCase<"cast_signed", 0>,
   I32EnumAttrCase<"cast_unsigned", 1>

mlir/include/mlir/Dialect/Linalg/IR/LinalgStructuredOps.td

@@ -538,6 +538,126 @@ def BroadcastOp : LinalgStructuredBase_Op<"broadcast", [
   let hasCanonicalizer = 1;
 }
 
+//===----------------------------------------------------------------------===//
+// Op definition for ElementwiseOp
+//===----------------------------------------------------------------------===//
+def ElementwiseOp : LinalgStructuredBase_Op<"elementwise", [
+                   AttrSizedOperandSegments]> {
+  let summary = [{ Performs element-wise operation }];
+  let description = [{
+    The attribute `kind` describes arithmetic operation to perform. The
+    operation kind can be unary (e.g. max), binary (e.g. add) or ternary
+    (e.g. select).
+
+    By default, all indexing maps are identities. In the case of default
+    indexing map, all input and output shapes must match. The number of dims in
+    each of the identity maps is equal to the rank of the output type.
+
+    Affine-maps for operands and result are required to be provided by the user
+    when a transpose and/or broadcast is needed on any operand. When a map is not
+    provided, default identity maps are inferred for each operand.
+
+    Iterator-types are always all `parallel`.
+    Iterator-types are needed for constructing the underlying structured op.
+
+    The number of dims of the iterator-types are inferred from the rank of
+    the result type.
+
+    Example:
+
+    Defining a unary linalg.elemwise with default indexing-map:
+      ```mlir
+      %exp = linalg.elemwise
+             kind=#linalg.elemwise_kind<exp>
+             ins(%x : tensor<4x16x8xf32>)
+             outs(%y: tensor<4x16x8xf32>) -> tensor<4x16x8xf32>
+      ```
+
+    Defining a binary linalg.elemwise with user-defined indexing-map:
+    ```mlir
+    %add = linalg.elemwise
+            kind=#linalg.elemwise_kind<add>
+            indexing_maps = [#transpose, #broadcast, #identity]
+            ins(%exp, %arg1 : tensor<4x16x8xf32>, tensor<4x16xf32>)
+            outs(%arg2: tensor<4x8x16xf32>) -> tensor<4x8x16xf32>
+    ```
+  }];
+
+  let arguments = (ins
+      Variadic<AnyType>:$inputs,
+      Variadic<AnyShaped>:$outputs,
+      ElementwiseKindAttr:$kind,
+      DefaultValuedOptionalAttr<AffineMapArrayAttr, "{}">:$indexing_maps
+    );
+
+  let results = (outs Variadic<AnyRankedTensor>:$result_tensors);
+  let regions = (region AnyRegion:$region);
+  let skipDefaultBuilders = 1;
+
+  let builders = [
+      OpBuilder<
+      (ins "ValueRange":$inputs, "ValueRange":$outputs,
+            CArg<"ArrayRef<NamedAttribute>", "{}">:$attributes),
+      [{
+        buildStructuredOp($_builder, $_state, std::nullopt, inputs, outputs,
+          attributes, ElementwiseOp::getRegionBuilder());
+      }]>
+    ];
+
+  let hasCustomAssemblyFormat = 1;
+  let hasFolder = 1;
+  let hasVerifier = 1;
+
+  let extraClassDeclaration = structuredOpsBaseDecls # [{
+      /// Get the arity enum corresponding to the kind of op, e.g. if arg is
+      /// `ElementwiseKind::add`, return `ElementwiseArityGroup::Binary`.
+      static ElementwiseArityGroup getArityGroup(ElementwiseKind n);
+
+      /// Both user-specified and default indexing map will always depend on
+      /// the current Op instance.
+      static bool hasDynamicIndexingMaps() { return true; }
+
+      /// Implements the block region builder for the elementwiseOp. This is
+      /// called by the 'fillStructuredOpRegion'.
+      static void regionBuilder(ImplicitLocOpBuilder &b,
+                                Block &block, ArrayRef<NamedAttribute> attrs);
+
+      static std::function<void(ImplicitLocOpBuilder &,
+                                Block &, ArrayRef<NamedAttribute>)>
+      getRegionBuilder() {
+        return regionBuilder;
+      }
+
+      /// Returns rank of the result tensor/memref. Useful for knowing
+      /// the dimensionality of the iteration space when others means
+      /// are not possible e.g. absence of user-provided indexing map.
+      unsigned getResultRank() {
+        Value output = getDpsInitOperand(0)->get();
+        ShapedType shapedType = llvm::cast<ShapedType>(output.getType());
+        return shapedType.getRank();
+      }
+
+      /// Returns N 'parallel' iterator types where N is rank of result.
+      SmallVector<utils::IteratorType> getIteratorTypesArray();
+
+      /// The default indexing maps are identities.
+      /// There will be N+1 such maps, where N is the arity of the Op.
+      static SmallVector<AffineMap>
+      getDefaultIndexingMaps(unsigned NumMaps, unsigned numDims,
+                             MLIRContext *context);
+
+      /// Destination passing style interface method.
+      ::mlir::MutableOperandRange getDpsInitsMutable() {
+        return getOutputsMutable();
+      }
+
+      // Generic methods.
+      std::string getLibraryCallName() {
+        return generateLibraryCallName(getOperation());
+      }
+    }];
+}
+
 //===----------------------------------------------------------------------===//
 // Op definition for MatmulOp
 //===----------------------------------------------------------------------===//