//===- Dialect.cpp - Toy IR Dialect registration in MLIR ------------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
//
// This file implements the dialect for the Toy IR: custom type parsing and
// operation verification.
//
//===----------------------------------------------------------------------===//

#include "toy/Dialect.h"

#include "mlir/IR/Builders.h"
#include "mlir/IR/BuiltinTypes.h"
#include "mlir/IR/FunctionImplementation.h"
#include "mlir/IR/OpImplementation.h"

using namespace mlir;
using namespace mlir::toy;

#include "toy/Dialect.cpp.inc"

//===----------------------------------------------------------------------===//
// ToyDialect
//===----------------------------------------------------------------------===//

/// Dialect initialization, the instance will be owned by the context. This is
/// the point of registration of types and operations for the dialect.
void ToyDialect::initialize() {
  addOperations<
#define GET_OP_LIST
#include "toy/Ops.cpp.inc"
      >();
}

//===----------------------------------------------------------------------===//
// Toy Operations
//===----------------------------------------------------------------------===//

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

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

/// A generalized printer for binary operations. It prints in two different
/// forms depending on if all of the types match.
static void printBinaryOp(mlir::OpAsmPrinter &printer, mlir::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(mlir::OpBuilder &builder, mlir::OperationState &state,
                       double value) {
  auto dataType = RankedTensorType::get({}, builder.getF64Type());
  auto dataAttribute = DenseElementsAttr::get(dataType, value);
  ConstantOp::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.
mlir::ParseResult ConstantOp::parse(mlir::OpAsmParser &parser,
                                    mlir::OperationState &result) {
  mlir::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(mlir::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.
mlir::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<mlir::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<mlir::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 mlir::success();
}

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

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

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

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

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

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

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

void FuncOp::build(mlir::OpBuilder &builder, mlir::OperationState &state,
                   llvm::StringRef name, mlir::FunctionType type,
                   llvm::ArrayRef<mlir::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());
}

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

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

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

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

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

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

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

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

mlir::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 mlir::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<mlir::UnrankedTensorType>(inputType) ||
      llvm::isa<mlir::UnrankedTensorType>(resultType))
    return mlir::success();

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

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

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

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

//===----------------------------------------------------------------------===//
// TableGen'd op method definitions
//===----------------------------------------------------------------------===//

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