Split toy dialect using static registration

1 files changed, 480 insertions, 0 deletions

diff --git a/toy/MLIRGen.cpp b/toy/MLIRGen.cpp
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+//===- 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 "toy/Dialect.h"
+
+#include "mlir/IR/Attributes.h"
+#include "mlir/IR/Builders.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 "mlir/StandardOps/Ops.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::make_unique;
+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 semantic is associated with
+/// them natively by MLIR.
+class MLIRGenImpl {
+public:
+  MLIRGenImpl(mlir::MLIRContext &context) : context(context) {}
+
+  /// Public API: convert the AST for a Toy module (source file) to an MLIR
+  /// Module.
+  std::unique_ptr<mlir::Module> mlirGen(ModuleAST &moduleAST) {
+    // We create an empty MLIR module and codegen functions one at a time and
+    // add them to the module.
+    theModule = make_unique<mlir::Module>(&context);
+
+    for (FunctionAST &F : moduleAST) {
+      auto func = mlirGen(F);
+      if (!func)
+        return nullptr;
+      theModule->getFunctions().push_back(func.release());
+    }
+
+    // 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.
+    if (failed(theModule->verify())) {
+      context.emitError(mlir::UnknownLoc::get(&context),
+                        "Module verification error");
+      return nullptr;
+    }
+
+    return std::move(theModule);
+  }
+
+private:
+  /// In MLIR (like in LLVM) a "context" object holds the memory allocation and
+  /// the ownership of many internal structure of the IR and provide a level
+  /// of "uniquing" across multiple modules (types for instance).
+  mlir::MLIRContext &context;
+
+  /// A "module" matches a source file: it contains a list of functions.
+  std::unique_ptr<mlir::Module> theModule;
+
+  /// The builder is a helper class to create IR inside a function. It is
+  /// re-initialized every time we enter a function and kept around as a
+  /// convenience for emitting individual operations.
+  /// The builder is stateful, in particular it keeeps an "insertion point":
+  /// this is where the next operations will be introduced.
+  std::unique_ptr<mlir::FuncBuilder> 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::FileLineColLoc loc(Location loc) {
+    return mlir::FileLineColLoc::get(
+        mlir::UniquedFilename::get(*loc.file, &context), loc.line, loc.col,
+        &context);
+  }
+
+  /// Declare a variable in the current scope, return true if the variable
+  /// wasn't declared yet.
+  bool declare(llvm::StringRef var, mlir::Value *value) {
+    if (symbolTable.count(var)) {
+      return false;
+    }
+    symbolTable.insert(var, value);
+    return true;
+  }
+
+  /// Create the prototype for an MLIR function with as many arguments as the
+  /// provided Toy AST prototype.
+  mlir::Function *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 = mlir::FunctionType::get(arg_types, ret_types, &context);
+    auto *function = new mlir::Function(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", mlir::BoolAttr::get(true, &context));
+
+    return function;
+  }
+
+  /// Emit a new function and add it to the MLIR module.
+  std::unique_ptr<mlir::Function> 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.
+    std::unique_ptr<mlir::Function> 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.
+    function->addEntryBlock();
+
+    auto &entryBlock = function->front();
+    auto &protoArgs = funcAST.getProto()->getArgs();
+    // Declare all the function arguments in the symbol table.
+    for (const auto &name_value :
+         llvm::zip(protoArgs, entryBlock.getArguments())) {
+      declare(std::get<0>(name_value)->getName(), std::get<1>(name_value));
+    }
+
+    // Create a builder for the function, it will be used throughout the codegen
+    // to create operations in this function.
+    builder = llvm::make_unique<mlir::FuncBuilder>(function.get());
+
+    // Emit the body of the function.
+    if (!mlirGen(*funcAST.getBody()))
+      return nullptr;
+
+    // Implicitly return void if no return statement was emited.
+    // FIXME: we may fix the parser instead to always return the last expression
+    // (this would possibly help the REPL case later)
+    if (function->getBlocks().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 '*'.
+    switch (binop.getOp()) {
+    case '+':
+      return builder->create<AddOp>(location, L, R).getResult();
+      break;
+    case '*':
+      return builder->create<MulOp>(location, L, R).getResult();
+    default:
+      context.emitError(loc(binop.loc()),
+                        Twine("Error: invalid binary operator '") +
+                            Twine(binop.getOp()) + "'");
+      return nullptr;
+    }
+  }
+
+  // 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 (symbolTable.count(expr.getName()))
+      return symbolTable.lookup(expr.getName());
+    context.emitError(loc(expr.loc()), Twine("Error: unknown variable '") +
+                                           expr.getName() + "'");
+    return nullptr;
+  }
+
+  // Emit a return operation, return true on success.
+  bool mlirGen(ReturnExprAST &ret) {
+    auto location = loc(ret.loc());
+    // `return` takes an optional expression, we need to account for it here.
+    if (!ret.getExpr().hasValue()) {
+      builder->create<ReturnOp>(location);
+      return true;
+    }
+    auto *expr = mlirGen(*ret.getExpr().getValue());
+    if (!expr)
+      return false;
+    builder->create<ReturnOp>(location, expr);
+    return true;
+  }
+
+  // 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](#attribute-values). It is possible to
+  //   attach attributes to operations, functions, and function arguments. 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]]>} : () -> memref<2x3xf64>
+  //
+  mlir::Value *mlirGen(LiteralExprAST &lit) {
+    auto location = loc(lit.loc());
+    // The attribute is a vector with an attribute per element (number) in the
+    // array, see `collectData()` below for more details.
+    std::vector<mlir::Attribute> data;
+    data.reserve(std::accumulate(lit.getDims().begin(), lit.getDims().end(), 1,
+                                 std::multiplies<int>()));
+    collectData(lit, data);
+
+    // FIXME: using a tensor type is a HACK here.
+    // Can we do differently without registering a dialect? Using a string blob?
+    mlir::Type elementType = mlir::FloatType::getF64(&context);
+    auto dataType = builder->getTensorType(lit.getDims(), elementType);
+
+    // This is the actual attribute that actually hold the list of values for
+    // this array literal.
+    auto dataAttribute = builder->getDenseElementsAttr(dataType, data)
+                             .cast<mlir::DenseElementsAttr>();
+
+    // Build the MLIR op `toy.constant`, only boilerplate below.
+    return builder->create<ConstantOp>(location, lit.getDims(), dataAttribute)
+        .getResult();
+  }
+
+  // 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 wrapped in a light wrapper `mlir::FloatAttr`.
+  // Attributes are the way MLIR attaches constant to operations and functions.
+  void collectData(ExprAST &expr, std::vector<mlir::Attribute> &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");
+    mlir::Type elementType = mlir::FloatType::getF64(&context);
+    auto attr = mlir::FloatAttr::getChecked(
+        elementType, cast<NumberExprAST>(expr).getValue(), loc(expr.loc()));
+    data.push_back(attr);
+  }
+
+  // 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) {
+    auto location = loc(call.loc());
+    std::string callee = call.getCallee();
+    if (callee == "transpose") {
+      if (call.getArgs().size() != 1) {
+        context.emitError(
+            location, Twine("MLIR codegen encountered an error: toy.transpose "
+                            "does not accept multiple arguments"));
+        return nullptr;
+      }
+      mlir::Value *arg = mlirGen(*call.getArgs()[0]);
+      return builder->create<TransposeOp>(location, arg).getResult();
+    }
+
+    // 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);
+    }
+    // Calls to user-defined function are mapped to a custom call that takes
+    // the callee name as an attribute.
+    return builder->create<GenericCallOp>(location, call.getCallee(), operands)
+        .getResult();
+  }
+
+  // Emit a call expression. It emits specific operations for two builtins:
+  // transpose(x) and print(x). Other identifiers are assumed to be user-defined
+  // functions. Return false on failure.
+  bool mlirGen(PrintExprAST &call) {
+    auto *arg = mlirGen(*call.getArg());
+    if (!arg)
+      return false;
+    auto location = loc(call.loc());
+    builder->create<PrintOp>(location, arg);
+    return true;
+  }
+
+  // Emit a constant for a single number (FIXME: semantic? broadcast?)
+  mlir::Value *mlirGen(NumberExprAST &num) {
+    auto location = loc(num.loc());
+    mlir::Type elementType = mlir::FloatType::getF64(&context);
+    auto attr = mlir::FloatAttr::getChecked(elementType, num.getValue(),
+                                            loc(num.loc()));
+    return builder->create<ConstantOp>(location, attr).getResult();
+  }
+
+  // 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:
+      context.emitError(
+          loc(expr.loc()),
+          Twine("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) {
+    mlir::Value *value = nullptr;
+    auto location = loc(vardecl.loc());
+    if (auto init = vardecl.getInitVal()) {
+      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()) {
+        value = builder
+                    ->create<ReshapeOp>(
+                        location, value,
+                        getType(vardecl.getType()).cast<ToyArrayType>())
+                    .getResult();
+      }
+    } else {
+      context.emitError(loc(vardecl.loc()),
+                        "Missing initializer in variable declaration");
+      return nullptr;
+    }
+    // Register the value in the symbol table
+    declare(vardecl.getName(), value);
+    return value;
+  }
+
+  /// Codegen a list of expression, return false if one of them hit an error.
+  bool 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 false;
+        continue;
+      }
+      if (auto *ret = dyn_cast<ReturnExprAST>(expr.get())) {
+        if (!mlirGen(*ret))
+          return false;
+        return true;
+      }
+      if (auto *print = dyn_cast<PrintExprAST>(expr.get())) {
+        if (!mlirGen(*print))
+          return false;
+        continue;
+      }
+      // Generic expression dispatch codegen.
+      if (!mlirGen(*expr))
+        return false;
+    }
+    return true;
+  }
+
+  /// Build a type from a list of shape dimensions. Types are `array` followed
+  /// by an optional dimension list, example: array<2, 2>
+  /// They are wrapped in a `toy` dialect (see next chapter) and get printed:
+  ///   !toy.array<2, 2>
+  template <typename T> mlir::Type getType(T shape) {
+    SmallVector<int64_t, 8> shape64(shape.begin(), shape.end());
+    return ToyArrayType::get(&context, shape64);
+  }
+
+  /// 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.
+std::unique_ptr<mlir::Module> mlirGen(mlir::MLIRContext &context,
+                                      ModuleAST &moduleAST) {
+  return MLIRGenImpl(context).mlirGen(moduleAST);
+}
+
+} // namespace toy