examples/toy/Ch2/mlir/MLIRGen.cpp - platform/external/tensorflow - Git at Google

 //===- MLIRGen.cpp - MLIR Generation from a Toy AST -----------------------===//
 //
 // Copyright 2019 The MLIR Authors.
 //
 // Licensed under the Apache License, Version 2.0 (the "License");
 // you may not use this file except in compliance with the License.
 // You may obtain a copy of the License at
 //
 //   http://www.apache.org/licenses/LICENSE-2.0
 //
 // Unless required by applicable law or agreed to in writing, software
 // distributed under the License is distributed on an "AS IS" BASIS,
 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 // See the License for the specific language governing permissions and
 // limitations under the License.
 // =============================================================================
 //
 // This file implements a simple IR generation targeting MLIR from a Module AST
 // for the Toy language.
 //
 //===----------------------------------------------------------------------===//

 #include "toy/MLIRGen.h"
 #include "toy/AST.h"

 #include "mlir/Analysis/Verifier.h"
 #include "mlir/Dialect/StandardOps/Ops.h"
 #include "mlir/IR/Attributes.h"
 #include "mlir/IR/Builders.h"
 #include "mlir/IR/Function.h"
 #include "mlir/IR/Location.h"
 #include "mlir/IR/MLIRContext.h"
 #include "mlir/IR/Module.h"
 #include "mlir/IR/StandardTypes.h"
 #include "mlir/IR/Types.h"

 #include "llvm/ADT/STLExtras.h"
 #include "llvm/ADT/ScopedHashTable.h"
 #include "llvm/Support/raw_ostream.h"
 #include <numeric>

 using namespace toy;
 using llvm::cast;
 using llvm::dyn_cast;
 using llvm::isa;
 using llvm::ScopedHashTableScope;
 using llvm::SmallVector;
 using llvm::StringRef;
 using llvm::Twine;

 namespace {

 /// Implementation of a simple MLIR emission from the Toy AST.
 ///
 /// This will emit operations that are specific to the Toy language, preserving
 /// the semantics of the language and (hopefully) allow to perform accurate
 /// analysis and transformation based on these high level semantics.
 ///
 /// At this point we take advantage of the "raw" MLIR APIs to create operations
 /// that haven't been registered in any way with MLIR. These operations are
 /// unknown to MLIR, custom passes could operate by string-matching the name of
 /// these operations, but no other type checking or semantics are associated
 /// with them natively by MLIR.
 class MLIRGenImpl {
 public:
   MLIRGenImpl(mlir::MLIRContext &context)
       : context(context), builder(&context) {}

   /// Public API: convert the AST for a Toy module (source file) to an MLIR
   /// Module operation.
   mlir::ModuleOp mlirGen(ModuleAST &moduleAST) {
     // We create an empty MLIR module and codegen functions one at a time and
     // add them to the module.
     theModule = mlir::ModuleOp::create(mlir::UnknownLoc::get(&context));

     for (FunctionAST &F : moduleAST) {
       auto func = mlirGen(F);
       if (!func)
         return nullptr;
       theModule.push_back(func);
     }

     // FIXME: (in the next chapter...) without registering a dialect in MLIR,
     // this won't do much, but it should at least check some structural
     // properties of the generated MLIR module.
     if (failed(mlir::verify(theModule))) {
       theModule.emitError("module verification error");
       return nullptr;
     }

     return theModule;
   }

 private:
   /// In MLIR (like in LLVM) a "context" object holds the memory allocation and
   /// ownership of many internal structures of the IR and provides a level of
   /// "uniquing" across multiple modules (types for instance).
   mlir::MLIRContext &context;

   /// A "module" matches a Toy source file: containing a list of functions.
   mlir::ModuleOp theModule;

   /// The builder is a helper class to create IR inside a function. The builder
   /// is stateful, in particular it keeeps an "insertion point": this is where
   /// the next operations will be introduced.
   mlir::OpBuilder builder;

   /// The symbol table maps a variable name to a value in the current scope.
   /// Entering a function creates a new scope, and the function arguments are
   /// added to the mapping. When the processing of a function is terminated, the
   /// scope is destroyed and the mappings created in this scope are dropped.
   llvm::ScopedHashTable<StringRef, mlir::Value *> symbolTable;

   /// Helper conversion for a Toy AST location to an MLIR location.
   mlir::Location loc(Location loc) {
     return builder.getFileLineColLoc(builder.getIdentifier(*loc.file), loc.line,
                                      loc.col);
   }

   /// Declare a variable in the current scope, return success if the variable
   /// wasn't declared yet.
   mlir::LogicalResult declare(llvm::StringRef var, mlir::Value *value) {
     if (symbolTable.count(var))
       return mlir::failure();
     symbolTable.insert(var, value);
     return mlir::success();
   }

   /// Create the prototype for an MLIR function with as many arguments as the
   /// provided Toy AST prototype.
   mlir::FuncOp mlirGen(PrototypeAST &proto) {
     // This is a generic function, the return type will be inferred later.
     llvm::SmallVector<mlir::Type, 4> ret_types;
     // Arguments type is uniformly a generic array.
     llvm::SmallVector<mlir::Type, 4> arg_types(proto.getArgs().size(),
                                                getType(VarType{}));
     auto func_type = builder.getFunctionType(arg_types, ret_types);
     auto function = mlir::FuncOp::create(loc(proto.loc()), proto.getName(),
                                          func_type, /* attrs = */ {});

     // Mark the function as generic: it'll require type specialization for every
     // call site.
     if (function.getNumArguments())
       function.setAttr("toy.generic", builder.getUnitAttr());
     return function;
   }

   /// Emit a new function and add it to the MLIR module.
   mlir::FuncOp mlirGen(FunctionAST &funcAST) {
     // Create a scope in the symbol table to hold variable declarations.
     ScopedHashTableScope<llvm::StringRef, mlir::Value *> var_scope(symbolTable);

     // Create an MLIR function for the given prototype.
     mlir::FuncOp function(mlirGen(*funcAST.getProto()));
     if (!function)
       return nullptr;

     // Let's start the body of the function now!
     // In MLIR the entry block of the function is special: it must have the same
     // argument list as the function itself.
     auto &entryBlock = *function.addEntryBlock();
     auto &protoArgs = funcAST.getProto()->getArgs();

     // Declare all the function arguments in the symbol table.
     for (const auto &name_value :
          llvm::zip(protoArgs, entryBlock.getArguments())) {
       if (failed(declare(std::get<0>(name_value)->getName(),
                          std::get<1>(name_value))))
         return nullptr;
     }

     // Set the insertion point in the builder to the beginning of the function
     // body, it will be used throughout the codegen to create operations in this
     // function.
     builder.setInsertionPointToStart(&entryBlock);

     // Emit the body of the function.
     if (mlir::failed(mlirGen(*funcAST.getBody()))) {
       function.erase();
       return nullptr;
     }

     // Implicitly return void if no return statement was emitted.
     // FIXME: we may fix the parser instead to always return the last expression
     // (this would possibly help the REPL case later)
     if (function.getBody().back().back().getName().getStringRef() !=
         "toy.return") {
       ReturnExprAST fakeRet(funcAST.getProto()->loc(), llvm::None);
       mlirGen(fakeRet);
     }

     return function;
   }

   /// Emit a binary operation
   mlir::Value *mlirGen(BinaryExprAST &binop) {
     // First emit the operations for each side of the operation before emitting
     // the operation itself. For example if the expression is `a + foo(a)`
     // 1) First it will visiting the LHS, which will return a reference to the
     //    value holding `a`. This value should have been emitted at declaration
     //    time and registered in the symbol table, so nothing would be
     //    codegen'd. If the value is not in the symbol table, an error has been
     //    emitted and nullptr is returned.
     // 2) Then the RHS is visited (recursively) and a call to `foo` is emitted
     //    and the result value is returned. If an error occurs we get a nullptr
     //    and propagate.
     //
     mlir::Value *L = mlirGen(*binop.getLHS());
     if (!L)
       return nullptr;
     mlir::Value *R = mlirGen(*binop.getRHS());
     if (!R)
       return nullptr;
     auto location = loc(binop.loc());

     // Derive the operation name from the binary operator. At the moment we only
     // support '+' and '*'.
     const char *op_name = nullptr;
     switch (binop.getOp()) {
     case '+':
       op_name = "toy.add";
       break;
     case '*':
       op_name = "toy.mul";
       break;
     default:
       emitError(location, "error: invalid binary operator '")
           << binop.getOp() << "'";
       return nullptr;
     }

     // Build the MLIR operation from the name and the two operands. The return
     // type is always a generic array for binary operators.
     mlir::OperationState result(location, op_name);
     result.addTypes(getType(VarType{}));
     result.addOperands({L, R});
     return builder.createOperation(result)->getResult(0);
   }

   /// This is a reference to a variable in an expression. The variable is
   /// expected to have been declared and so should have a value in the symbol
   /// table, otherwise emit an error and return nullptr.
   mlir::Value *mlirGen(VariableExprAST &expr) {
     if (auto *variable = symbolTable.lookup(expr.getName()))
       return variable;

     emitError(loc(expr.loc()), "error: unknown variable '")
         << expr.getName() << "'";
     return nullptr;
   }

   /// Emit a return operation. This will return failure if any generation fails.
   mlir::LogicalResult mlirGen(ReturnExprAST &ret) {
     mlir::OperationState result(loc(ret.loc()), "toy.return");

     // `return` takes an optional expression, we need to account for it here.
     if (ret.getExpr().hasValue()) {
       auto *expr = mlirGen(*ret.getExpr().getValue());
       if (!expr)
         return mlir::failure();
       result.addOperands(expr);
     }

     builder.createOperation(result);
     return mlir::success();
   }

   /// Emit a literal/constant array. It will be emitted as a flattened array of
   /// data in an Attribute attached to a `toy.constant` operation.
   /// See documentation on [Attributes](LangRef.md#attributes) for more details.
   /// Here is an excerpt:
   ///
   ///   Attributes are the mechanism for specifying constant data in MLIR in
   ///   places where a variable is never allowed [...]. They consist of a name
   ///   and a concrete attribute value. The set of expected attributes, their
   ///   structure, and their interpretation are all contextually dependent on
   ///   what they are attached to.
   ///
   /// Example, the source level statement:
   ///   var a<2, 3> = [[1, 2, 3], [4, 5, 6]];
   /// will be converted to:
   ///   %0 = "toy.constant"() {value: dense<tensor<2x3xf64>,
   ///     [[1.000000e+00, 2.000000e+00, 3.000000e+00],
   ///      [4.000000e+00, 5.000000e+00, 6.000000e+00]]>} : () -> tensor<2x3xf64>
   ///
   mlir::Value *mlirGen(LiteralExprAST &lit) {
     auto type = getType(lit.getDims());

     // The attribute is a vector with a floating point value per element
     // (number) in the array, see `collectData()` below for more details.
     std::vector<double> data;
     data.reserve(std::accumulate(lit.getDims().begin(), lit.getDims().end(), 1,
                                  std::multiplies<int>()));
     collectData(lit, data);

     // The type of this attribute is tensor of 64-bit floating-point with the
     // shape of the literal.
     mlir::Type elementType = builder.getF64Type();
     auto dataType = builder.getTensorType(lit.getDims(), elementType);

     // This is the actual attribute that holds the list of values for this
     // tensor literal.
     auto dataAttribute =
         mlir::DenseElementsAttr::get(dataType, llvm::makeArrayRef(data));

     // Build the MLIR op `toy.constant`, only boilerplate below.
     mlir::OperationState result(loc(lit.loc()), "toy.constant");
     result.addTypes(type);
     result.addAttribute("value", dataAttribute);
     return builder.createOperation(result)->getResult(0);
   }

   /// Recursive helper function to accumulate the data that compose an array
   /// literal. It flattens the nested structure in the supplied vector. For
   /// example with this array:
   ///  [[1, 2], [3, 4]]
   /// we will generate:
   ///  [ 1, 2, 3, 4 ]
   /// Individual numbers are represented as doubles.
   /// Attributes are the way MLIR attaches constant to operations.
   void collectData(ExprAST &expr, std::vector<double> &data) {
     if (auto *lit = dyn_cast<LiteralExprAST>(&expr)) {
       for (auto &value : lit->getValues())
         collectData(*value, data);
       return;
     }

     assert(isa<NumberExprAST>(expr) && "expected literal or number expr");
     data.push_back(cast<NumberExprAST>(expr).getValue());
   }

   /// Emit a call expression. It emits specific operations for the `transpose`
   /// builtin. Other identifiers are assumed to be user-defined functions.
   mlir::Value *mlirGen(CallExprAST &call) {
     llvm::StringRef callee = call.getCallee();

     // Codegen the operands first.
     SmallVector<mlir::Value *, 4> operands;
     for (auto &expr : call.getArgs()) {
       auto *arg = mlirGen(*expr);
       if (!arg)
         return nullptr;
       operands.push_back(arg);
     }

     // Builting calls have their custom operation, meaning this is a
     // straightforward emission.
     if (callee == "transpose") {
       mlir::OperationState result(loc(call.loc()), "toy.transpose");
       result.addTypes(getType(VarType{}));
       result.operands = std::move(operands);
       return builder.createOperation(result)->getResult(0);
     }

     // Otherwise this is a call to a user-defined function. Calls to
     // user-defined functions are mapped to a custom call that takes the callee
     // name as an attribute.
     mlir::OperationState result(loc(call.loc()), "toy.generic_call");
     result.addTypes(getType(VarType{}));
     result.operands = std::move(operands);
     result.addAttribute("callee", builder.getSymbolRefAttr(callee));
     return builder.createOperation(result)->getResult(0);
   }

   /// Emit a print expression. It emits specific operations for two builtins:
   /// transpose(x) and print(x).
   mlir::LogicalResult mlirGen(PrintExprAST &call) {
     auto *arg = mlirGen(*call.getArg());
     if (!arg)
       return mlir::failure();

     mlir::OperationState result(loc(call.loc()), "toy.print");
     result.addOperands(arg);
     builder.createOperation(result);
     return mlir::success();
   }

   /// Emit a constant for a single number (FIXME: semantic? broadcast?)
   mlir::Value *mlirGen(NumberExprAST &num) {
     mlir::OperationState result(loc(num.loc()), "toy.constant");
     mlir::Type elementType = builder.getF64Type();
     result.addTypes(builder.getTensorType({}, elementType));
     result.addAttribute("value", builder.getF64FloatAttr(num.getValue()));
     return builder.createOperation(result)->getResult(0);
   }

   /// Dispatch codegen for the right expression subclass using RTTI.
   mlir::Value *mlirGen(ExprAST &expr) {
     switch (expr.getKind()) {
     case toy::ExprAST::Expr_BinOp:
       return mlirGen(cast<BinaryExprAST>(expr));
     case toy::ExprAST::Expr_Var:
       return mlirGen(cast<VariableExprAST>(expr));
     case toy::ExprAST::Expr_Literal:
       return mlirGen(cast<LiteralExprAST>(expr));
     case toy::ExprAST::Expr_Call:
       return mlirGen(cast<CallExprAST>(expr));
     case toy::ExprAST::Expr_Num:
       return mlirGen(cast<NumberExprAST>(expr));
     default:
       emitError(loc(expr.loc()))
           << "MLIR codegen encountered an unhandled expr kind '"
           << Twine(expr.getKind()) << "'";
       return nullptr;
     }
   }

   /// Handle a variable declaration, we'll codegen the expression that forms the
   /// initializer and record the value in the symbol table before returning it.
   /// Future expressions will be able to reference this variable through symbol
   /// table lookup.
   mlir::Value *mlirGen(VarDeclExprAST &vardecl) {
     auto init = vardecl.getInitVal();
     if (!init) {
       emitError(loc(vardecl.loc()),
                 "missing initializer in variable declaration");
       return nullptr;
     }

     mlir::Value *value = mlirGen(*init);
     if (!value)
       return nullptr;

     // We have the initializer value, but in case the variable was declared
     // with specific shape, we emit a "reshape" operation. It will get
     // optimized out later as needed.
     if (!vardecl.getType().shape.empty()) {
       mlir::OperationState result(loc(vardecl.loc()), "toy.reshape");
       result.addTypes(getType(vardecl.getType()));
       result.addOperands(value);
       value = builder.createOperation(result)->getResult(0);
     }

     // Register the value in the symbol table
     if (failed(declare(vardecl.getName(), value)))
       return nullptr;
     return value;
   }

   /// Codegen a list of expression, return failure if one of them hit an error.
   mlir::LogicalResult mlirGen(ExprASTList &blockAST) {
     ScopedHashTableScope<llvm::StringRef, mlir::Value *> var_scope(symbolTable);
     for (auto &expr : blockAST) {
       // Specific handling for variable declarations, return statement, and
       // print. These can only appear in block list and not in nested
       // expressions.
       if (auto *vardecl = dyn_cast<VarDeclExprAST>(expr.get())) {
         if (!mlirGen(*vardecl))
           return mlir::failure();
         continue;
       }
       if (auto *ret = dyn_cast<ReturnExprAST>(expr.get()))
         return mlirGen(*ret);
       if (auto *print = dyn_cast<PrintExprAST>(expr.get())) {
         if (mlir::failed(mlirGen(*print)))
           return mlir::success();
         continue;
       }

       // Generic expression dispatch codegen.
       if (!mlirGen(*expr))
         return mlir::failure();
     }
     return mlir::success();
   }

   /// Build a tensor type from a list of shape dimensions.
   mlir::Type getType(llvm::ArrayRef<int64_t> shape) {
     // If the shape is empty, then this type is unranked.
     if (shape.empty())
       return builder.getTensorType(builder.getF64Type());

     // Otherwise, we use the given shape.
     return builder.getTensorType(shape, builder.getF64Type());
   }

   /// Build an MLIR type from a Toy AST variable type
   /// (forward to the generic getType(T) above).
   mlir::Type getType(const VarType &type) { return getType(type.shape); }
 };

 } // namespace

 namespace toy {

 // The public API for codegen.
 mlir::OwningModuleRef mlirGen(mlir::MLIRContext &context,
                               ModuleAST &moduleAST) {
   return MLIRGenImpl(context).mlirGen(moduleAST);
 }

 } // namespace toy
	//===- MLIRGen.cpp - MLIR Generation from a Toy AST -----------------------===//
	//
	// Copyright 2019 The MLIR Authors.
	//
	// Licensed under the Apache License, Version 2.0 (the "License");
	// you may not use this file except in compliance with the License.
	// You may obtain a copy of the License at
	//
	// http://www.apache.org/licenses/LICENSE-2.0
	//
	// Unless required by applicable law or agreed to in writing, software
	// distributed under the License is distributed on an "AS IS" BASIS,
	// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	// See the License for the specific language governing permissions and
	// limitations under the License.
	// =============================================================================
	//
	// This file implements a simple IR generation targeting MLIR from a Module AST
	// for the Toy language.
	//
	//===----------------------------------------------------------------------===//

	#include "toy/MLIRGen.h"
	#include "toy/AST.h"

	#include "mlir/Analysis/Verifier.h"
	#include "mlir/Dialect/StandardOps/Ops.h"
	#include "mlir/IR/Attributes.h"
	#include "mlir/IR/Builders.h"
	#include "mlir/IR/Function.h"
	#include "mlir/IR/Location.h"
	#include "mlir/IR/MLIRContext.h"
	#include "mlir/IR/Module.h"
	#include "mlir/IR/StandardTypes.h"
	#include "mlir/IR/Types.h"

	#include "llvm/ADT/STLExtras.h"
	#include "llvm/ADT/ScopedHashTable.h"
	#include "llvm/Support/raw_ostream.h"
	#include <numeric>

	using namespace toy;
	using llvm::cast;
	using llvm::dyn_cast;
	using llvm::isa;
	using llvm::ScopedHashTableScope;
	using llvm::SmallVector;
	using llvm::StringRef;
	using llvm::Twine;

	namespace {

	/// Implementation of a simple MLIR emission from the Toy AST.
	///
	/// This will emit operations that are specific to the Toy language, preserving
	/// the semantics of the language and (hopefully) allow to perform accurate
	/// analysis and transformation based on these high level semantics.
	///
	/// At this point we take advantage of the "raw" MLIR APIs to create operations
	/// that haven't been registered in any way with MLIR. These operations are
	/// unknown to MLIR, custom passes could operate by string-matching the name of
	/// these operations, but no other type checking or semantics are associated
	/// with them natively by MLIR.
	class MLIRGenImpl {
	public:
	MLIRGenImpl(mlir::MLIRContext &context)
	: context(context), builder(&context) {}

	/// Public API: convert the AST for a Toy module (source file) to an MLIR
	/// Module operation.
	mlir::ModuleOp mlirGen(ModuleAST &moduleAST) {
	// We create an empty MLIR module and codegen functions one at a time and
	// add them to the module.
	theModule = mlir::ModuleOp::create(mlir::UnknownLoc::get(&context));

	for (FunctionAST &F : moduleAST) {
	auto func = mlirGen(F);
	if (!func)
	return nullptr;
	theModule.push_back(func);
	}

	// FIXME: (in the next chapter...) without registering a dialect in MLIR,
	// this won't do much, but it should at least check some structural
	// properties of the generated MLIR module.
	if (failed(mlir::verify(theModule))) {
	theModule.emitError("module verification error");
	return nullptr;
	}

	return theModule;
	}

	private:
	/// In MLIR (like in LLVM) a "context" object holds the memory allocation and
	/// ownership of many internal structures of the IR and provides a level of
	/// "uniquing" across multiple modules (types for instance).
	mlir::MLIRContext &context;

	/// A "module" matches a Toy source file: containing a list of functions.
	mlir::ModuleOp theModule;

	/// The builder is a helper class to create IR inside a function. The builder
	/// is stateful, in particular it keeeps an "insertion point": this is where
	/// the next operations will be introduced.
	mlir::OpBuilder builder;

	/// The symbol table maps a variable name to a value in the current scope.
	/// Entering a function creates a new scope, and the function arguments are
	/// added to the mapping. When the processing of a function is terminated, the
	/// scope is destroyed and the mappings created in this scope are dropped.
	llvm::ScopedHashTable<StringRef, mlir::Value *> symbolTable;

	/// Helper conversion for a Toy AST location to an MLIR location.
	mlir::Location loc(Location loc) {
	return builder.getFileLineColLoc(builder.getIdentifier(*loc.file), loc.line,
	loc.col);
	}

	/// Declare a variable in the current scope, return success if the variable
	/// wasn't declared yet.
	mlir::LogicalResult declare(llvm::StringRef var, mlir::Value *value) {
	if (symbolTable.count(var))
	return mlir::failure();
	symbolTable.insert(var, value);
	return mlir::success();
	}

	/// Create the prototype for an MLIR function with as many arguments as the
	/// provided Toy AST prototype.
	mlir::FuncOp mlirGen(PrototypeAST &proto) {
	// This is a generic function, the return type will be inferred later.
	llvm::SmallVector<mlir::Type, 4> ret_types;
	// Arguments type is uniformly a generic array.
	llvm::SmallVector<mlir::Type, 4> arg_types(proto.getArgs().size(),
	getType(VarType{}));
	auto func_type = builder.getFunctionType(arg_types, ret_types);
	auto function = mlir::FuncOp::create(loc(proto.loc()), proto.getName(),
	func_type, /* attrs = */ {});

	// Mark the function as generic: it'll require type specialization for every
	// call site.
	if (function.getNumArguments())
	function.setAttr("toy.generic", builder.getUnitAttr());
	return function;
	}

	/// Emit a new function and add it to the MLIR module.
	mlir::FuncOp mlirGen(FunctionAST &funcAST) {
	// Create a scope in the symbol table to hold variable declarations.
	ScopedHashTableScope<llvm::StringRef, mlir::Value *> var_scope(symbolTable);

	// Create an MLIR function for the given prototype.
	mlir::FuncOp function(mlirGen(*funcAST.getProto()));
	if (!function)
	return nullptr;

	// Let's start the body of the function now!
	// In MLIR the entry block of the function is special: it must have the same
	// argument list as the function itself.
	auto &entryBlock = *function.addEntryBlock();
	auto &protoArgs = funcAST.getProto()->getArgs();

	// Declare all the function arguments in the symbol table.
	for (const auto &name_value :
	llvm::zip(protoArgs, entryBlock.getArguments())) {
	if (failed(declare(std::get<0>(name_value)->getName(),
	std::get<1>(name_value))))
	return nullptr;
	}

	// Set the insertion point in the builder to the beginning of the function
	// body, it will be used throughout the codegen to create operations in this
	// function.
	builder.setInsertionPointToStart(&entryBlock);

	// Emit the body of the function.
	if (mlir::failed(mlirGen(*funcAST.getBody()))) {
	function.erase();
	return nullptr;
	}

	// Implicitly return void if no return statement was emitted.
	// FIXME: we may fix the parser instead to always return the last expression
	// (this would possibly help the REPL case later)
	if (function.getBody().back().back().getName().getStringRef() !=
	"toy.return") {
	ReturnExprAST fakeRet(funcAST.getProto()->loc(), llvm::None);
	mlirGen(fakeRet);
	}

	return function;
	}

	/// Emit a binary operation
	mlir::Value *mlirGen(BinaryExprAST &binop) {
	// First emit the operations for each side of the operation before emitting
	// the operation itself. For example if the expression is `a + foo(a)`
	// 1) First it will visiting the LHS, which will return a reference to the
	// value holding `a`. This value should have been emitted at declaration
	// time and registered in the symbol table, so nothing would be
	// codegen'd. If the value is not in the symbol table, an error has been
	// emitted and nullptr is returned.
	// 2) Then the RHS is visited (recursively) and a call to `foo` is emitted
	// and the result value is returned. If an error occurs we get a nullptr
	// and propagate.
	//
	mlir::Value L = mlirGen(binop.getLHS());
	if (!L)
	return nullptr;
	mlir::Value R = mlirGen(binop.getRHS());
	if (!R)
	return nullptr;
	auto location = loc(binop.loc());

	// Derive the operation name from the binary operator. At the moment we only
	// support '+' and '*'.
	const char *op_name = nullptr;
	switch (binop.getOp()) {
	case '+':
	op_name = "toy.add";
	break;
	case '*':
	op_name = "toy.mul";
	break;
	default:
	emitError(location, "error: invalid binary operator '")
	<< binop.getOp() << "'";
	return nullptr;
	}

	// Build the MLIR operation from the name and the two operands. The return
	// type is always a generic array for binary operators.
	mlir::OperationState result(location, op_name);
	result.addTypes(getType(VarType{}));
	result.addOperands({L, R});
	return builder.createOperation(result)->getResult(0);
	}

	/// This is a reference to a variable in an expression. The variable is
	/// expected to have been declared and so should have a value in the symbol
	/// table, otherwise emit an error and return nullptr.
	mlir::Value *mlirGen(VariableExprAST &expr) {
	if (auto *variable = symbolTable.lookup(expr.getName()))
	return variable;

	emitError(loc(expr.loc()), "error: unknown variable '")
	<< expr.getName() << "'";
	return nullptr;
	}

	/// Emit a return operation. This will return failure if any generation fails.
	mlir::LogicalResult mlirGen(ReturnExprAST &ret) {
	mlir::OperationState result(loc(ret.loc()), "toy.return");

	// `return` takes an optional expression, we need to account for it here.
	if (ret.getExpr().hasValue()) {
	auto expr = mlirGen(ret.getExpr().getValue());
	if (!expr)
	return mlir::failure();
	result.addOperands(expr);
	}

	builder.createOperation(result);
	return mlir::success();
	}

	/// Emit a literal/constant array. It will be emitted as a flattened array of
	/// data in an Attribute attached to a `toy.constant` operation.
	/// See documentation on [Attributes](LangRef.md#attributes) for more details.
	/// Here is an excerpt:
	///
	/// Attributes are the mechanism for specifying constant data in MLIR in
	/// places where a variable is never allowed [...]. They consist of a name
	/// and a concrete attribute value. The set of expected attributes, their
	/// structure, and their interpretation are all contextually dependent on
	/// what they are attached to.
	///
	/// Example, the source level statement:
	/// var a<2, 3> = [[1, 2, 3], [4, 5, 6]];
	/// will be converted to:
	/// %0 = "toy.constant"() {value: dense<tensor<2x3xf64>,
	/// [[1.000000e+00, 2.000000e+00, 3.000000e+00],
	/// [4.000000e+00, 5.000000e+00, 6.000000e+00]]>} : () -> tensor<2x3xf64>
	///
	mlir::Value *mlirGen(LiteralExprAST &lit) {
	auto type = getType(lit.getDims());

	// The attribute is a vector with a floating point value per element
	// (number) in the array, see `collectData()` below for more details.
	std::vector<double> data;
	data.reserve(std::accumulate(lit.getDims().begin(), lit.getDims().end(), 1,
	std::multiplies<int>()));
	collectData(lit, data);

	// The type of this attribute is tensor of 64-bit floating-point with the
	// shape of the literal.
	mlir::Type elementType = builder.getF64Type();
	auto dataType = builder.getTensorType(lit.getDims(), elementType);

	// This is the actual attribute that holds the list of values for this
	// tensor literal.
	auto dataAttribute =
	mlir::DenseElementsAttr::get(dataType, llvm::makeArrayRef(data));

	// Build the MLIR op `toy.constant`, only boilerplate below.
	mlir::OperationState result(loc(lit.loc()), "toy.constant");
	result.addTypes(type);
	result.addAttribute("value", dataAttribute);
	return builder.createOperation(result)->getResult(0);
	}

	/// Recursive helper function to accumulate the data that compose an array
	/// literal. It flattens the nested structure in the supplied vector. For
	/// example with this array:
	/// [[1, 2], [3, 4]]
	/// we will generate:
	/// [ 1, 2, 3, 4 ]
	/// Individual numbers are represented as doubles.
	/// Attributes are the way MLIR attaches constant to operations.
	void collectData(ExprAST &expr, std::vector<double> &data) {
	if (auto *lit = dyn_cast<LiteralExprAST>(&expr)) {
	for (auto &value : lit->getValues())
	collectData(*value, data);
	return;
	}

	assert(isa<NumberExprAST>(expr) && "expected literal or number expr");
	data.push_back(cast<NumberExprAST>(expr).getValue());
	}

	/// Emit a call expression. It emits specific operations for the `transpose`
	/// builtin. Other identifiers are assumed to be user-defined functions.
	mlir::Value *mlirGen(CallExprAST &call) {
	llvm::StringRef callee = call.getCallee();

	// Codegen the operands first.
	SmallVector<mlir::Value *, 4> operands;
	for (auto &expr : call.getArgs()) {
	auto arg = mlirGen(expr);
	if (!arg)
	return nullptr;
	operands.push_back(arg);
	}

	// Builting calls have their custom operation, meaning this is a
	// straightforward emission.
	if (callee == "transpose") {
	mlir::OperationState result(loc(call.loc()), "toy.transpose");
	result.addTypes(getType(VarType{}));
	result.operands = std::move(operands);
	return builder.createOperation(result)->getResult(0);
	}

	// Otherwise this is a call to a user-defined function. Calls to
	// user-defined functions are mapped to a custom call that takes the callee
	// name as an attribute.
	mlir::OperationState result(loc(call.loc()), "toy.generic_call");
	result.addTypes(getType(VarType{}));
	result.operands = std::move(operands);
	result.addAttribute("callee", builder.getSymbolRefAttr(callee));
	return builder.createOperation(result)->getResult(0);
	}

	/// Emit a print expression. It emits specific operations for two builtins:
	/// transpose(x) and print(x).
	mlir::LogicalResult mlirGen(PrintExprAST &call) {
	auto arg = mlirGen(call.getArg());
	if (!arg)
	return mlir::failure();

	mlir::OperationState result(loc(call.loc()), "toy.print");
	result.addOperands(arg);
	builder.createOperation(result);
	return mlir::success();
	}

	/// Emit a constant for a single number (FIXME: semantic? broadcast?)
	mlir::Value *mlirGen(NumberExprAST &num) {
	mlir::OperationState result(loc(num.loc()), "toy.constant");
	mlir::Type elementType = builder.getF64Type();
	result.addTypes(builder.getTensorType({}, elementType));
	result.addAttribute("value", builder.getF64FloatAttr(num.getValue()));
	return builder.createOperation(result)->getResult(0);
	}

	/// Dispatch codegen for the right expression subclass using RTTI.
	mlir::Value *mlirGen(ExprAST &expr) {
	switch (expr.getKind()) {
	case toy::ExprAST::Expr_BinOp:
	return mlirGen(cast<BinaryExprAST>(expr));
	case toy::ExprAST::Expr_Var:
	return mlirGen(cast<VariableExprAST>(expr));
	case toy::ExprAST::Expr_Literal:
	return mlirGen(cast<LiteralExprAST>(expr));
	case toy::ExprAST::Expr_Call:
	return mlirGen(cast<CallExprAST>(expr));
	case toy::ExprAST::Expr_Num:
	return mlirGen(cast<NumberExprAST>(expr));
	default:
	emitError(loc(expr.loc()))
	<< "MLIR codegen encountered an unhandled expr kind '"
	<< Twine(expr.getKind()) << "'";
	return nullptr;
	}
	}

	/// Handle a variable declaration, we'll codegen the expression that forms the
	/// initializer and record the value in the symbol table before returning it.
	/// Future expressions will be able to reference this variable through symbol
	/// table lookup.
	mlir::Value *mlirGen(VarDeclExprAST &vardecl) {
	auto init = vardecl.getInitVal();
	if (!init) {
	emitError(loc(vardecl.loc()),
	"missing initializer in variable declaration");
	return nullptr;
	}

	mlir::Value value = mlirGen(init);
	if (!value)
	return nullptr;

	// We have the initializer value, but in case the variable was declared
	// with specific shape, we emit a "reshape" operation. It will get
	// optimized out later as needed.
	if (!vardecl.getType().shape.empty()) {
	mlir::OperationState result(loc(vardecl.loc()), "toy.reshape");
	result.addTypes(getType(vardecl.getType()));
	result.addOperands(value);
	value = builder.createOperation(result)->getResult(0);
	}

	// Register the value in the symbol table
	if (failed(declare(vardecl.getName(), value)))
	return nullptr;
	return value;
	}

	/// Codegen a list of expression, return failure if one of them hit an error.
	mlir::LogicalResult mlirGen(ExprASTList &blockAST) {
	ScopedHashTableScope<llvm::StringRef, mlir::Value *> var_scope(symbolTable);
	for (auto &expr : blockAST) {
	// Specific handling for variable declarations, return statement, and
	// print. These can only appear in block list and not in nested
	// expressions.
	if (auto *vardecl = dyn_cast<VarDeclExprAST>(expr.get())) {
	if (!mlirGen(*vardecl))
	return mlir::failure();
	continue;
	}
	if (auto *ret = dyn_cast<ReturnExprAST>(expr.get()))
	return mlirGen(*ret);
	if (auto *print = dyn_cast<PrintExprAST>(expr.get())) {
	if (mlir::failed(mlirGen(*print)))
	return mlir::success();
	continue;
	}

	// Generic expression dispatch codegen.
	if (!mlirGen(*expr))
	return mlir::failure();
	}
	return mlir::success();
	}

	/// Build a tensor type from a list of shape dimensions.
	mlir::Type getType(llvm::ArrayRef<int64_t> shape) {
	// If the shape is empty, then this type is unranked.
	if (shape.empty())
	return builder.getTensorType(builder.getF64Type());

	// Otherwise, we use the given shape.
	return builder.getTensorType(shape, builder.getF64Type());
	}

	/// Build an MLIR type from a Toy AST variable type
	/// (forward to the generic getType(T) above).
	mlir::Type getType(const VarType &type) { return getType(type.shape); }
	};

	} // namespace

	namespace toy {

	// The public API for codegen.
	mlir::OwningModuleRef mlirGen(mlir::MLIRContext &context,
	ModuleAST &moduleAST) {
	return MLIRGenImpl(context).mlirGen(moduleAST);
	}

	} // namespace toy