Add support for inlining toy call operations.

The GenericCallOp needed to have the CallOpInterface to be picked up by the inliner. This also adds a CastOp to perform shape casts that are generated during inlining. The casts generated by the inliner will be folded away after shape inference.

PiperOrigin-RevId: 275150438

mlir/examples/toy/Ch4/CMakeLists.txt

@@ -21,6 +21,7 @@ add_toy_chapter(toyc-ch4
 add_dependencies(toyc-ch4 ToyCh4OpsIncGen)
 add_dependencies(toyc-ch4 ToyCh4ShapeInferenceInterfaceIncGen)
 add_dependencies(toyc-ch4 ToyCh4CombineIncGen)
+add_dependencies(toyc-ch4 MLIRCallOpInterfacesIncGen)
 include_directories(include/)
 include_directories(${CMAKE_CURRENT_BINARY_DIR})
 include_directories(${CMAKE_CURRENT_BINARY_DIR}/include/)

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

@@ -23,6 +23,11 @@
 #else
 #define TOY_OPS
 
+#ifdef MLIR_CALLINTERFACES
+#else
+include "mlir/Analysis/CallInterfaces.td"
+#endif // MLIR_CALLINTERFACES
+
 #ifdef SHAPE_INFERENCE_INTERFACE
 #else
 include "toy/ShapeInferenceInterface.td"
@@ -111,7 +116,27 @@ def AddOp : Toy_Op<"add",
   >];
 }
 
-def GenericCallOp : Toy_Op<"generic_call"> {
+def CastOp : Toy_Op<"cast",
+    [DeclareOpInterfaceMethods<ShapeInferenceOpInterface>, NoSideEffect,
+     SameOperandsAndResultShape]> {
+  let summary = "shape cast operation";
+  let description = [{
+    The "cast" operation converts a tensor from one type to an equivalent type
+    without changing any data elements. The source and destination types
+    must both be tensor types with the same element type. If both are ranked
+    then the rank should be the same and static dimensions should match. The
+    operation is invalid if converting to a mismatching constant dimension.
+  }];
+
+  let arguments = (ins F64Tensor:$input);
+  let results = (outs F64Tensor:$output);
+
+  // Set the folder bit so that we can fold redundant cast operations.
+  let hasFolder = 1;
+}
+
+def GenericCallOp : Toy_Op<"generic_call",
+    [DeclareOpInterfaceMethods<CallOpInterface>]> {
   let summary = "generic call operation";
   let description = [{
     Generic calls represent calls to a user defined function that needs to

mlir/examples/toy/Ch4/mlir/Dialect.cpp

@@ -64,6 +64,17 @@ struct ToyInlinerInterface : public DialectInlinerInterface {
     for (const auto &it : llvm::enumerate(returnOp.getOperands()))
       valuesToRepl[it.index()]->replaceAllUsesWith(it.value());
   }
+
+  /// Attempts to materialize a conversion for a type mismatch between a call
+  /// from this dialect, and a callable region. This method should generate an
+  /// operation that takes 'input' as the only operand, and produces a single
+  /// result of 'resultType'. If a conversion can not be generated, nullptr
+  /// should be returned.
+  Operation *materializeCallConversion(OpBuilder &builder, Value *input,
+                                       Type resultType,
+                                       Location conversionLoc) const final {
+    return builder.create<CastOp>(conversionLoc, resultType, input);
+  }
 };
 
 //===----------------------------------------------------------------------===//
@@ -94,7 +105,12 @@ static void buildConstantOp(mlir::Builder *builder, mlir::OperationState &state,
   ConstantOp::build(builder, state, dataType, dataAttribute);
 }
 
-/// Verifier for constant operation.
+/// Infer the output shape of the CastOp, this is required by the shape
+/// inference interface.
+void CastOp::inferShapes() { getResult()->setType(getOperand()->getType()); }
+
+/// Verifier for the constant operation. This corresponds to the `::verify(...)`
+/// in the op definition.
 static mlir::LogicalResult verify(ConstantOp op) {
   // If the return type of the constant is not an unranked tensor, the shape
   // must match the shape of the attribute holding the data.
@@ -139,6 +155,16 @@ static void buildGenericCallOp(mlir::Builder *builder,
   state.addAttribute("callee", builder->getSymbolRefAttr(callee));
 }
 
+/// Return the callee of the generic call operation, this is required by the
+/// call interface.
+CallInterfaceCallable GenericCallOp::getCallableForCallee() {
+  return getAttrOfType<SymbolRefAttr>("callee");
+}
+
+/// Get the argument operands to the called function, this is required by the
+/// call interface.
+Operation::operand_range GenericCallOp::getArgOperands() { return inputs(); }
+
 static void buildMulOp(mlir::Builder *builder, mlir::OperationState &state,
                        mlir::Value *lhs, mlir::Value *rhs) {
   state.addTypes(builder->getTensorType(builder->getF64Type()));

mlir/examples/toy/Ch4/mlir/ShapeInferencePass.cpp

@@ -80,8 +80,13 @@ public:
 
       // Ask the operation to infer its output shapes.
       LLVM_DEBUG(llvm::dbgs() << "Inferring shape for: " << *op << "\n");
-      auto shapeOp = dyn_cast<ShapeInference>(op);
-      shapeOp.inferShapes();
+      if (auto shapeOp = dyn_cast<ShapeInference>(op)) {
+        shapeOp.inferShapes();
+      } else {
+        op->emitError("unable to infer shape of operation without shape "
+                      "inference interface");
+        return signalPassFailure();
+      }
     }
 
     // If the operation worklist isn't empty, this indicates a failure.

mlir/examples/toy/Ch4/mlir/ToyCombine.cpp

@@ -32,6 +32,11 @@ namespace {
 #include "ToyCombine.inc"
 } // end anonymous namespace
 
+/// Fold simple cast operations that return the same type as the input.
+OpFoldResult CastOp::fold(ArrayRef<Attribute> operands) {
+  return mlir::impl::foldCastOp(*this);
+}
+
 /// This is an example of a c++ rewrite pattern for the TransposeOp. It
 /// optimizes the following scenario: transpose(transpose(x)) -> transpose(x)
 struct SimplifyRedundantTranspose : public mlir::OpRewritePattern<TransposeOp> {

mlir/examples/toy/Ch4/toyc.cpp

@@ -122,9 +122,6 @@ int dumpMLIR() {
     // Apply any generic pass manager command line options and run the pipeline.
     applyPassManagerCLOptions(pm);
 
-    // Add a run of the canonicalizer to optimize the mlir module.
-    pm.addPass(mlir::createCanonicalizerPass());
-
     // Inline all functions into main and then delete them.
     pm.addPass(mlir::createInlinerPass());
     pm.addPass(mlir::toy::createDeadFunctionEliminationPass());
@@ -132,6 +129,7 @@ int dumpMLIR() {
     // Now that there is only one function, we can infer the shapes of each of
     // the operations.
     pm.addPass(mlir::toy::createShapeInferencePass());
+    pm.addPass(mlir::createCanonicalizerPass());
 
     if (mlir::failed(pm.run(*module)))
       return 4;

mlir/test/Examples/Toy/Ch4/shape_inference.mlir

@@ -0,0 +1,30 @@
+// RUN: toyc-ch4 %s -emit=mlir -opt 2>&1 | FileCheck %s
+
+// Check the result of inlining+shape inference on an input module.
+
+func @multiply_transpose(%arg0: tensor<*xf64>, %arg1: tensor<*xf64>) -> tensor<*xf64> {
+  %0 = "toy.transpose"(%arg1) : (tensor<*xf64>) -> tensor<*xf64>
+  %1 = "toy.mul"(%arg0, %0) : (tensor<*xf64>, tensor<*xf64>) -> tensor<*xf64>
+  "toy.return"(%1) : (tensor<*xf64>) -> ()
+}
+func @main() {
+  %0 = "toy.constant"() {value = dense<[[1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>} : () -> tensor<2x3xf64>
+  %1 = "toy.reshape"(%0) : (tensor<2x3xf64>) -> tensor<2x3xf64>
+  %2 = "toy.constant"() {value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00]> : tensor<6xf64>} : () -> tensor<6xf64>
+  %3 = "toy.reshape"(%2) : (tensor<6xf64>) -> tensor<2x3xf64>
+  %4 = "toy.generic_call"(%1, %3) {callee = @multiply_transpose} : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64>
+  %5 = "toy.generic_call"(%3, %1) {callee = @multiply_transpose} : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64>
+  "toy.print"(%5) : (tensor<*xf64>) -> ()
+  "toy.return"() : () -> ()
+}
+
+// CHECK-NOT: func @multiply_transpose
+// CHECK-NOT: tensor<*xf64>
+
+// CHECK-LABEL: func @main() {
+// CHECK:         [[VAL_0:%.*]] = "toy.constant"() {value = dense<{{\[\[}}1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>} : () -> tensor<2x3xf64>
+// CHECK:         [[VAL_1:%.*]] = "toy.constant"() {value = dense<{{\[\[}}1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>} : () -> tensor<2x3xf64>
+// CHECK:         [[VAL_2:%.*]] = "toy.transpose"([[VAL_0]]) : (tensor<2x3xf64>) -> tensor<3x2xf64>
+// CHECK:         [[VAL_3:%.*]] = "toy.mul"([[VAL_1]], [[VAL_2]]) : (tensor<2x3xf64>, tensor<3x2xf64>) -> tensor<2x2xf64>
+// CHECK:         "toy.print"([[VAL_3]]) : (tensor<2x2xf64>) -> ()
+// CHECK:         "toy.return"() : () -> ()