NFC: Various code cleanups for Ch3.

This change refactors the toyc driver to be much cleaner and easier to extend. It also cleans up a few comments in the combiner.

PiperOrigin-RevId: 274973808

mlir/examples/toy/Ch3/mlir/ToyCombine.cpp

@@ -15,8 +15,8 @@
 // limitations under the License.
 // =============================================================================
 //
-// This file implements a simple combiner for optimizing pattern in the Toy
-// dialect.
+// This file implements a set of simple combiners for optimizing operations in
+// the Toy dialect.
 //
 //===----------------------------------------------------------------------===//
 
@@ -32,7 +32,8 @@ namespace {
 #include "ToyCombine.inc"
 } // end anonymous namespace
 
-/// Fold transpose(transpose(x) -> transpose(x)
+/// 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> {
   /// We register this pattern to match every toy.transpose in the IR.
   /// The "benefit" is used by the framework to order the patterns and process
@@ -41,8 +42,8 @@ struct SimplifyRedundantTranspose : public mlir::OpRewritePattern<TransposeOp> {
       : OpRewritePattern<TransposeOp>(context, /*benefit=*/1) {}
 
   /// This method attempts to match a pattern and rewrite it. The rewriter
-  /// argument is the orchestrator of the sequence of rewrites. It is expected
-  /// to interact with it to perform any changes to the IR from here.
+  /// argument is the orchestrator of the sequence of rewrites. The pattern is
+  /// expected to interact with it to perform any changes to the IR from here.
   mlir::PatternMatchResult
   matchAndRewrite(TransposeOp op,
                   mlir::PatternRewriter &rewriter) const override {
@@ -55,19 +56,21 @@ struct SimplifyRedundantTranspose : public mlir::OpRewritePattern<TransposeOp> {
     if (!transposeInputOp)
       return matchFailure();
 
-    // Use the rewriter to perform the replacement
+    // Use the rewriter to perform the replacement.
     rewriter.replaceOp(op, {transposeInputOp.getOperand()}, {transposeInputOp});
     return matchSuccess();
   }
 };
 
-/// Register our patterns for rewrite by the Canonicalization framework.
+/// Register our patterns as "canonicalization" patterns on the TransposeOp so
+/// that they can be picked up by the Canonicalization framework.
 void TransposeOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
                                               MLIRContext *context) {
   results.insert<SimplifyRedundantTranspose>(context);
 }
 
-/// Register our patterns for rewrite by the Canonicalization framework.
+/// Register our patterns as "canonicalization" patterns on the ReshapeOp so
+/// that they can be picked up by the Canonicalization framework.
 void ReshapeOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
                                             MLIRContext *context) {
   results.insert<ReshapeReshapeOptPattern, RedundantReshapeOptPattern,

mlir/examples/toy/Ch3/mlir/ToyCombine.td

@@ -27,12 +27,13 @@
 include "toy/Ops.td"
 #endif // OP_BASE
 
-/* Pattern-Match and Rewrite using DRR:
-class Pattern<
-    dag sourcePattern, list<dag> resultPatterns,
-    list<dag> additionalConstraints = [],
-    dag benefitsAdded = (addBenefit 0)>;
-*/
+/// Note: The DRR definition used for defining patterns is shown below:
+///
+/// class Pattern<
+///    dag sourcePattern, list<dag> resultPatterns,
+///    list<dag> additionalConstraints = [],
+///    dag benefitsAdded = (addBenefit 0)
+/// >;
 
 //===----------------------------------------------------------------------===//
 // Basic Pattern-Match and Rewrite

mlir/examples/toy/Ch3/toyc.cpp

@@ -79,29 +79,24 @@ std::unique_ptr<toy::ModuleAST> parseInputFile(llvm::StringRef filename) {
   return parser.ParseModule();
 }
 
-mlir::LogicalResult optimize(mlir::ModuleOp module) {
-  mlir::PassManager pm(module.getContext());
-  pm.addPass(mlir::createCanonicalizerPass());
-
-  // Apply any generic pass manager command line options and run the pipeline.
-  applyPassManagerCLOptions(pm);
-  return pm.run(module);
+int loadMLIR(mlir::MLIRContext &context, mlir::OwningModuleRef &module) {
+  // Handle '.toy' input to the compiler.
+  if (inputType != InputType::MLIR &&
+      !llvm::StringRef(inputFilename).endswith(".mlir")) {
+    auto moduleAST = parseInputFile(inputFilename);
+    module = mlirGen(context, *moduleAST);
+    return !module ? 1 : 0;
   }
 
-int dumpMLIR() {
-  // Register our Dialect with MLIR
-  mlir::registerDialect<mlir::toy::ToyDialect>();
-
-  mlir::MLIRContext context;
-  mlir::OwningModuleRef module;
-  if (inputType == InputType::MLIR ||
-      llvm::StringRef(inputFilename).endswith(".mlir")) {
+  // Otherwise, the input is '.mlir'.
   llvm::ErrorOr<std::unique_ptr<llvm::MemoryBuffer>> fileOrErr =
       llvm::MemoryBuffer::getFileOrSTDIN(inputFilename);
   if (std::error_code EC = fileOrErr.getError()) {
     llvm::errs() << "Could not open input file: " << EC.message() << "\n";
     return -1;
   }
+
+  // Parse the input mlir.
   llvm::SourceMgr sourceMgr;
   sourceMgr.AddNewSourceBuffer(std::move(*fileOrErr), llvm::SMLoc());
   module = mlir::parseSourceFile(sourceMgr, &context);
@@ -109,22 +104,29 @@ int dumpMLIR() {
     llvm::errs() << "Error can't load file " << inputFilename << "\n";
     return 3;
   }
-    if (failed(mlir::verify(*module))) {
-      llvm::errs() << "Error verifying MLIR module\n";
+  return 0;
+}
+
+int dumpMLIR() {
+  // Register our Dialect with MLIR.
+  mlir::registerDialect<mlir::toy::ToyDialect>();
+
+  mlir::MLIRContext context;
+  mlir::OwningModuleRef module;
+  if (int error = loadMLIR(context, module))
+    return error;
+
+  if (EnableOpt) {
+    mlir::PassManager pm(&context);
+    // 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());
+    if (mlir::failed(pm.run(*module)))
       return 4;
   }
-  } else {
-    auto moduleAST = parseInputFile(inputFilename);
-    module = mlirGen(context, *moduleAST);
-  }
-  if (!module)
-    return 1;
-  if (EnableOpt) {
-    if (failed(optimize(*module))) {
-      llvm::errs() << "Module optimization failed\n";
-      return 7;
-    }
-  }
+
   module->dump();
   return 0;
 }