hello-mlir/lib/Dialect.cpp

//
// Created by ricardo on 29/05/25.
//

#include "Dialect.h"
#include "hello/Dialect.cpp.inc"

#include <mlir/Interfaces/FunctionImplementation.h>
#include <mlir/Transforms/InliningUtils.h>
#include <oneapi/tbb/detail/_template_helpers.h>

struct HelloDialectInlinerInterface : mlir::DialectInlinerInterface
{
    using DialectInlinerInterface::DialectInlinerInterface;

    bool isLegalToInline(mlir::Operation* call, mlir::Operation* callable, bool wouldBeCloned) const override
    {
        return true;
    }

    bool isLegalToInline(mlir::Region* dest, mlir::Region* src, bool wouldBeCloned,
                         mlir::IRMapping& valueMapping) const override
    {
        return true;
    }

    bool isLegalToInline(mlir::Operation* op, mlir::Region* dest, bool wouldBeCloned,
                         mlir::IRMapping& valueMapping) const override
    {
        return true;
    }

    void handleTerminator(mlir::Operation* op, mlir::ValueRange returnValues) const override
    {
        // Only the `hello.returnOp` is the function terminator
        auto returnOp = llvm::cast<mlir::hello::ReturnOp>(op);

        assert(returnOp.getNumOperands() == returnValues.size());

        for (const auto& it : llvm::enumerate(returnOp.getOperands()))
        {
            returnValues[it.index()].replaceAllUsesWith(it.value());
        }
    }

    mlir::Operation* materializeCallConversion(mlir::OpBuilder& builder, mlir::Value input, mlir::Type resultType,
                                               mlir::Location conversionLoc) const override
    {
        return builder.create<mlir::hello::CastOp>(conversionLoc, resultType, input);
    }
};

void mlir::hello::HelloDialect::initialize()
{
    addOperations<
#define GET_OP_LIST
#include "hello/Ops.cpp.inc"
    >();
    addInterfaces<HelloDialectInlinerInterface>();
}

using namespace mlir;
using namespace mlir::hello;


/// A generalized parser for binary operations. This parses the different forms
/// of 'printBinaryOp' below.
static ParseResult parseBinaryOp(OpAsmParser& parser,
                                 OperationState& result)
{
    SmallVector<OpAsmParser::UnresolvedOperand, 2> operands;
    SMLoc operandsLoc = parser.getCurrentLocation();
    Type type;
    if (parser.parseOperandList(operands, /*requiredOperandCount=*/2) ||
        parser.parseOptionalAttrDict(result.attributes) ||
        parser.parseColonType(type))
        return failure();

    // If the type is a function type, it contains the input and result types of
    // this operation.
    if (FunctionType funcType = llvm::dyn_cast<FunctionType>(type))
    {
        if (parser.resolveOperands(operands, funcType.getInputs(), operandsLoc,
                                   result.operands))
            return failure();
        result.addTypes(funcType.getResults());
        return success();
    }

    // Otherwise, the parsed type is the type of both operands and results.
    if (parser.resolveOperands(operands, type, result.operands))
        return failure();
    result.addTypes(type);
    return success();
}

/// A generalized printer for binary operations. It prints in two different
/// forms depending on if all of the types match.
static void printBinaryOp(OpAsmPrinter& printer, Operation* op)
{
    printer << " " << op->getOperands();
    printer.printOptionalAttrDict(op->getAttrs());
    printer << " : ";

    // If all of the types are the same, print the type directly.
    Type resultType = *op->result_type_begin();
    if (llvm::all_of(op->getOperandTypes(),
                     [=](Type type) { return type == resultType; }))
    {
        printer << resultType;
        return;
    }

    // Otherwise, print a functional type.
    printer.printFunctionalType(op->getOperandTypes(), op->getResultTypes());
}

//===----------------------------------------------------------------------===//
// ConstantOp
//===----------------------------------------------------------------------===//

/// Build a constant operation.
/// The builder is passed as an argument, so is the state that this method is
/// expected to fill in order to build the operation.
void ConstantOp::build(OpBuilder& builder, OperationState& state,
                       double value)
{
    auto dataType = RankedTensorType::get({}, builder.getF64Type());
    auto dataAttribute = DenseElementsAttr::get(dataType, value);
    build(builder, state, dataType, dataAttribute);
}

/// The 'OpAsmParser' class provides a collection of methods for parsing
/// various punctuation, as well as attributes, operands, types, etc. Each of
/// these methods returns a `ParseResult`. This class is a wrapper around
/// `LogicalResult` that can be converted to a boolean `true` value on failure,
/// or `false` on success. This allows for easily chaining together a set of
/// parser rules. These rules are used to populate an `mlir::OperationState`
/// similarly to the `build` methods described above.
ParseResult ConstantOp::parse(OpAsmParser& parser,
                              OperationState& result)
{
    DenseElementsAttr value;
    if (parser.parseOptionalAttrDict(result.attributes) ||
        parser.parseAttribute(value, "value", result.attributes))
        return failure();

    result.addTypes(value.getType());
    return success();
}

/// The 'OpAsmPrinter' class is a stream that allows for formatting
/// strings, attributes, operands, types, etc.
void ConstantOp::print(OpAsmPrinter& printer)
{
    printer << " ";
    printer.printOptionalAttrDict((*this)->getAttrs(), /*elidedAttrs=*/{"value"});
    printer << getValue();
}

/// Verifier for the constant operation. This corresponds to the
/// `let hasVerifier = 1` in the op definition.
LogicalResult ConstantOp::verify()
{
    // If the return type of the constant is not an unranked tensor, the shape
    // must match the shape of the attribute holding the data.
    auto resultType = llvm::dyn_cast<RankedTensorType>(getResult().getType());
    if (!resultType)
        return success();

    // Check that the rank of the attribute type matches the rank of the constant
    // result type.
    auto attrType = llvm::cast<RankedTensorType>(getValue().getType());
    if (attrType.getRank() != resultType.getRank())
    {
        return emitOpError("return type must match the one of the attached value "
                "attribute: ")
            << attrType.getRank() << " != " << resultType.getRank();
    }

    // Check that each of the dimensions match between the two types.
    for (int dim = 0, dimE = attrType.getRank(); dim < dimE; ++dim)
    {
        if (attrType.getShape()[dim] != resultType.getShape()[dim])
        {
            return emitOpError(
                    "return type shape mismatches its attribute at dimension ")
                << dim << ": " << attrType.getShape()[dim]
                << " != " << resultType.getShape()[dim];
        }
    }
    return success();
}

//===----------------------------------------------------------------------===//
// AddOp
//===----------------------------------------------------------------------===//

void AddOp::build(OpBuilder& builder, OperationState& state,
                  Value lhs, Value rhs)
{
    state.addTypes(UnrankedTensorType::get(builder.getF64Type()));
    state.addOperands({lhs, rhs});
}

ParseResult AddOp::parse(OpAsmParser& parser,
                         OperationState& result)
{
    return parseBinaryOp(parser, result);
}

void AddOp::print(OpAsmPrinter& p) { printBinaryOp(p, *this); }

void AddOp::inferShapes()
{
    getResult().setType(getLhs().getType());
}

//===----------------------------------------------------------------------===//
// GenericCallOp
//===----------------------------------------------------------------------===//

void GenericCallOp::build(OpBuilder& builder, OperationState& state,
                          StringRef callee, ArrayRef<Value> arguments)
{
    // Generic call always returns an unranked Tensor initially.
    state.addTypes(UnrankedTensorType::get(builder.getF64Type()));
    state.addOperands(arguments);
    state.addAttribute("callee",
                       SymbolRefAttr::get(builder.getContext(), callee));
}

CallInterfaceCallable GenericCallOp::getCallableForCallee()
{
    return (*this)->getAttrOfType<SymbolRefAttr>("callee");
}

void GenericCallOp::setCalleeFromCallable(CallInterfaceCallable callee)
{
    (*this)->setAttr("callee", mlir::cast<SymbolRefAttr>(callee));
}

Operation::operand_range GenericCallOp::getArgOperands()
{
    return getInputs();
}

MutableOperandRange GenericCallOp::getArgOperandsMutable()
{
    return getInputsMutable();
}

//===----------------------------------------------------------------------===//
// FuncOp
//===----------------------------------------------------------------------===//

void FuncOp::build(OpBuilder& builder, OperationState& state,
                   StringRef name, FunctionType type,
                   ArrayRef<NamedAttribute> attrs)
{
    // FunctionOpInterface provides a convenient `build` method that will populate
    // the state of our FuncOp, and create an entry block.
    buildWithEntryBlock(builder, state, name, type, attrs, type.getInputs());
}

ParseResult FuncOp::parse(OpAsmParser& parser,
                          OperationState& result)
{
    // Dispatch to the FunctionOpInterface provided utility method that parses the
    // function operation.
    auto buildFuncType =
        [](Builder& builder, ArrayRef<Type> argTypes,
           ArrayRef<Type> results,
           function_interface_impl::VariadicFlag,
           std::string&)
    {
        return builder.getFunctionType(argTypes, results);
    };

    return function_interface_impl::parseFunctionOp(
        parser, result, /*allowVariadic=*/false,
        getFunctionTypeAttrName(result.name), buildFuncType,
        getArgAttrsAttrName(result.name), getResAttrsAttrName(result.name));
}

void FuncOp::print(OpAsmPrinter& p)
{
    // Dispatch to the FunctionOpInterface provided utility method that prints the
    // function operation.
    function_interface_impl::printFunctionOp(
        p, *this, /*isVariadic=*/false, getFunctionTypeAttrName(),
        getArgAttrsAttrName(), getResAttrsAttrName());
}

//===----------------------------------------------------------------------===//
// MulOp
//===----------------------------------------------------------------------===//

void MulOp::build(OpBuilder& builder, OperationState& state,
                  Value lhs, Value rhs)
{
    state.addTypes(UnrankedTensorType::get(builder.getF64Type()));
    state.addOperands({lhs, rhs});
}

ParseResult MulOp::parse(OpAsmParser& parser,
                         OperationState& result)
{
    return parseBinaryOp(parser, result);
}

void MulOp::print(OpAsmPrinter& p) { printBinaryOp(p, *this); }

void MulOp::inferShapes()
{
    getResult().setType(getLhs().getType());
}

//===----------------------------------------------------------------------===//
// ReturnOp
//===----------------------------------------------------------------------===//

LogicalResult ReturnOp::verify()
{
    // We know that the parent operation is a function, because of the 'HasParent'
    // trait attached to the operation definition.
    auto function = cast<FuncOp>((*this)->getParentOp());

    /// ReturnOps can only have a single optional operand.
    if (getNumOperands() > 1)
        return emitOpError() << "expects at most 1 return operand";

    // The operand number and types must match the function signature.
    const auto& results = function.getFunctionType().getResults();
    if (getNumOperands() != results.size())
        return emitOpError() << "does not return the same number of values ("
            << getNumOperands() << ") as the enclosing function ("
            << results.size() << ")";

    // If the operation does not have an input, we are done.
    if (!hasOperand())
        return success();

    auto inputType = *operand_type_begin();
    auto resultType = results.front();

    // Check that the result type of the function matches the operand type.
    if (inputType == resultType || llvm::isa<UnrankedTensorType>(inputType) ||
        llvm::isa<UnrankedTensorType>(resultType))
        return success();

    return emitError() << "type of return operand (" << inputType
        << ") doesn't match function result type (" << resultType
        << ")";
}

//===----------------------------------------------------------------------===//
// TransposeOp
//===----------------------------------------------------------------------===//

void TransposeOp::build(OpBuilder& builder, OperationState& state,
                        Value value)
{
    state.addTypes(UnrankedTensorType::get(builder.getF64Type()));
    state.addOperands(value);
}

LogicalResult TransposeOp::verify()
{
    auto inputType = llvm::dyn_cast<RankedTensorType>(getOperand().getType());
    auto resultType = llvm::dyn_cast<RankedTensorType>(getType());
    if (!inputType || !resultType)
        return success();

    auto inputShape = inputType.getShape();
    if (!std::equal(inputShape.begin(), inputShape.end(),
                    resultType.getShape().rbegin()))
    {
        return emitError()
            << "expected result shape to be a transpose of the input";
    }
    return success();
}

void TransposeOp::inferShapes()
{
    // Transpose will reverse the shape of tensor.
    auto tensorType = llvm::cast<RankedTensorType>(getOperand().getType());
    // And assume that transpose only applies for matrix.
    SmallVector<int64_t, 2> dimensions(llvm::reverse(tensorType.getShape()));
    getResult().setType(RankedTensorType::get(dimensions, tensorType.getElementType()));
}

// CastOp

bool CastOp::areCastCompatible(TypeRange inputs, TypeRange outputs)
{
    if (inputs.size() != 1 || outputs.size() != 1)
    {
        return false;
    }

    const auto inputTensorType = mlir::dyn_cast<TensorType>(inputs.front());
    const auto outputTensorType = mlir::dyn_cast<TensorType>(outputs.front());

    if (!inputTensorType || !outputTensorType || inputTensorType.getElementType() != outputTensorType.getElementType())
    {
        return false;
    }

    // If both have rank, they must be to the size.
    // And the known size can be cast into unknown size.
    return !inputTensorType.hasRank() || !outputTensorType.hasRank() || inputTensorType == outputTensorType;
}

void CastOp::inferShapes()
{
    getResult().setType(getInput().getType());
}


#define GET_OP_CLASSES
#include "hello/Ops.cpp.inc"