Merge branch 'mlir-call-attrs-iface' into mlir-call-attrs-llvm

Author: jeanPerier

mlir/examples/toy/Ch4/include/toy/Ops.td

@@ -215,7 +215,12 @@ def GenericCallOp : Toy_Op<"generic_call",
 
   // The generic call operation takes a symbol reference attribute as the
   // callee, and inputs for the call.
-  let arguments = (ins FlatSymbolRefAttr:$callee, Variadic<F64Tensor>:$inputs);
+  let arguments = (ins
+    FlatSymbolRefAttr:$callee,
+    Variadic<F64Tensor>:$inputs,
+    OptionalAttr<DictArrayAttr>:$arg_attrs,
+    OptionalAttr<DictArrayAttr>:$res_attrs
+  );
 
   // The generic call operation returns a single value of TensorType.
   let results = (outs F64Tensor);

mlir/examples/toy/Ch5/include/toy/Ops.td

@@ -214,7 +214,12 @@ def GenericCallOp : Toy_Op<"generic_call",
 
   // The generic call operation takes a symbol reference attribute as the
   // callee, and inputs for the call.
-  let arguments = (ins FlatSymbolRefAttr:$callee, Variadic<F64Tensor>:$inputs);
+  let arguments = (ins
+    FlatSymbolRefAttr:$callee,
+    Variadic<F64Tensor>:$inputs,
+    OptionalAttr<DictArrayAttr>:$arg_attrs,
+    OptionalAttr<DictArrayAttr>:$res_attrs
+  );
 
   // The generic call operation returns a single value of TensorType.
   let results = (outs F64Tensor);

mlir/examples/toy/Ch6/include/toy/Ops.td

@@ -214,7 +214,12 @@ def GenericCallOp : Toy_Op<"generic_call",
 
   // The generic call operation takes a symbol reference attribute as the
   // callee, and inputs for the call.
-  let arguments = (ins FlatSymbolRefAttr:$callee, Variadic<F64Tensor>:$inputs);
+  let arguments = (ins
+    FlatSymbolRefAttr:$callee,
+    Variadic<F64Tensor>:$inputs,
+    OptionalAttr<DictArrayAttr>:$arg_attrs,
+    OptionalAttr<DictArrayAttr>:$res_attrs
+  );
 
   // The generic call operation returns a single value of TensorType.
   let results = (outs F64Tensor);

mlir/examples/toy/Ch7/include/toy/Ops.td

@@ -237,7 +237,12 @@ def GenericCallOp : Toy_Op<"generic_call",
 
   // The generic call operation takes a symbol reference attribute as the
   // callee, and inputs for the call.
-  let arguments = (ins FlatSymbolRefAttr:$callee, Variadic<Toy_Type>:$inputs);
+  let arguments = (ins
+    FlatSymbolRefAttr:$callee,
+    Variadic<Toy_Type>:$inputs,
+    OptionalAttr<DictArrayAttr>:$arg_attrs,
+    OptionalAttr<DictArrayAttr>:$res_attrs
+  );
 
   // The generic call operation returns a single value of TensorType or
   // StructType.
@@ -250,7 +255,8 @@ def GenericCallOp : Toy_Op<"generic_call",
 
   // Add custom build methods for the generic call operation.
   let builders = [
-    OpBuilder<(ins "StringRef":$callee, "ArrayRef<Value>":$arguments)>
+    OpBuilder<(ins "Type":$result_type, "StringRef":$callee,
+              "ArrayRef<Value>":$arguments)>
   ];
 }
 

mlir/examples/toy/Ch7/mlir/Dialect.cpp

@@ -350,9 +350,10 @@ void FuncOp::print(mlir::OpAsmPrinter &p) {
 //===----------------------------------------------------------------------===//
 
 void GenericCallOp::build(mlir::OpBuilder &builder, mlir::OperationState &state,
-                          StringRef callee, ArrayRef<mlir::Value> arguments) {
+                          mlir::Type resultType, StringRef callee,
+                          ArrayRef<mlir::Value> arguments) {
   // Generic call always returns an unranked Tensor initially.
-  state.addTypes(UnrankedTensorType::get(builder.getF64Type()));
+  state.addTypes(resultType);
   state.addOperands(arguments);
   state.addAttribute("callee",
                      mlir::SymbolRefAttr::get(builder.getContext(), callee));

mlir/examples/toy/Ch7/mlir/MLIRGen.cpp

@@ -535,8 +535,7 @@ class MLIRGenImpl {
     }
     mlir::toy::FuncOp calledFunc = calledFuncIt->second;
     return builder.create<GenericCallOp>(
-        location, calledFunc.getFunctionType().getResult(0),
-        mlir::SymbolRefAttr::get(builder.getContext(), callee), operands);
+        location, calledFunc.getFunctionType().getResult(0), callee, operands);
   }
 
   /// Emit a print expression. It emits specific operations for two builtins: