mlir-development Skill: Разработка и оптимизация компиляции

--- name: mlir-development description: Expertise in MLIR (Multi-Level Intermediate Representation) and CIR (Clang IR) development for domain-specific compilation and high-level optimizations. Use this skill when building ML compilers, domain-specific languages, or working with multi-level compilation pipelines. --- # MLIR Development Skill This skill covers MLIR (Multi-Level Intermediate Representation) development for building domain-specific compilers and high-level optimization pipelines. ## MLIR Overview ### What is MLIR? MLIR is a compiler infrastructure that enables building reusable and extensible compiler components. It provides: - Hierarchical, multi-level IR representation - Extensible operation and type system - Progressive lowering between abstraction levels - Rich transformation infrastructure ### Architecture ``` High-Level DSL ↓ Domain-Specific Dialects (e.g., TensorFlow, PyTorch) ↓ Mid-Level Dialects (e.g., Linalg, Affine) ↓ Low-Level Dialects (e.g., LLVM, GPU) ↓ Target Code ``` ## Core Concepts ### Dialects Dialects are groupings of operations, types, and attributes: ```cpp // Define a custom dialect class MyDialect : public mlir::Dialect { public: explicit MyDialect(mlir::MLIRContext *context) : Dialect("my_dialect", context, mlir::TypeID::get<MyDialect>()) { addOperations< MyAddOp, MyMulOp, MyFuncOp >(); addTypes<MyTensorType>(); } static llvm::StringRef getDialectNamespace() { return "my_dialect"; } }; ``` ### Operations ```cpp // Define using ODS (Operation Definition Specification) // In TableGen file (.td) def MyAddOp : Op<MyDialect, "add", [Pure]> { let summary = "Add two tensors"; let description = [{ Performs element-wise addition of two tensors. }]; let arguments = (ins AnyTensor:$lhs, AnyTensor:$rhs ); let results = (outs AnyTensor:$result ); let assemblyFormat = [{ $lhs `,` $rhs attr-dict `:` type($result) }]; } ``` ### Types and Attributes ```cpp // Custom type definition class MyTensorType : public mlir::Type::TypeBase< MyTensorType, mlir::Type, MyTensorTypeStorage> { public: using Base::Base; static MyTensorType get(mlir::MLIRContext *context, llvm::ArrayRef<int64_t> shape, mlir::Type elementType) { return Base::get(context, shape, elementType); } llvm::ArrayRef<int64_t> getShape() const; mlir::Type getElementType() const; }; ``` ## Writing MLIR Passes ### Transform Pass ```cpp #include "mlir/Pass/Pass.h" #include "mlir/IR/PatternMatch.h" struct MyOptimizationPass : public mlir::PassWrapper<MyOptimizationPass, mlir::OperationPass<mlir::func::FuncOp>> { void runOnOperation() override { mlir::func::FuncOp func = getOperation(); // Walk all operations func.walk([](mlir::Operation *op) { // Transform operations if (auto addOp = llvm::dyn_cast<MyAddOp>(op)) { optimizeAdd(addOp); } }); } llvm::StringRef getArgument() const final { return "my-optimization"; } llvm::StringRef getDescription() const final { return "My custom optimization pass"; } }; ``` ### Pattern-Based Rewriting ```cpp // Define rewrite pattern struct SimplifyRedundantAdd : public mlir::OpRewritePattern<MyAddOp> { using OpRewritePattern<MyAddOp>::OpRewritePattern; mlir::LogicalResult matchAndRewrite( MyAddOp op, mlir::PatternRewriter &rewriter) const override { // Match: add(x, 0) -> x if (auto constOp = op.getRhs().getDefiningOp<ConstantOp>()) { if (isZero(constOp)) { rewriter.replaceOp(op, op.getLhs()); return mlir::success(); } } return mlir::failure(); } }; // Apply patterns void runOnOperation() override { mlir::RewritePatternSet patterns(&getContext()); patterns.add<SimplifyRedundantAdd>(&getContext()); if (mlir::failed(mlir::applyPatternsAndFoldGreedily( getOperation(), std::move(patterns)))) { signalPassFailure(); } } ``` ## Dialect Conversion ### Lowering Between Dialects ```cpp // Convert high-level ops to lower-level ops struct MyAddOpLowering : public mlir::OpConversionPattern<MyAddOp> { using OpConversionPattern<MyAddOp>::OpConversionPattern; mlir::LogicalResult matchAndRewrite( MyAddOp op, OpAdaptor adaptor, mlir::ConversionPatternRewriter &rewriter) const override { // Lower to arith dialect rewriter.replaceOpWithNewOp<mlir::arith::AddFOp>( op, adaptor.getLhs(), adaptor.getRhs()); return mlir::success(); } }; // Conversion pass struct LowerToArithPass : public mlir::PassWrapper< LowerToArithPass, mlir::OperationPass<mlir::ModuleOp>> { void runOnOperation() override { mlir::ConversionTarget target(getContext()); target.addLegalDialect<mlir::arith::ArithDialect>(); target.addIllegalDialect<MyDialect>(); mlir::RewritePatternSet patterns(&getContext()); patterns.add<MyAddOpLowering>(&getContext()); if (mlir::failed(mlir::applyPartialConversion( getOperation(), target, std::move(patterns)))) { signalPassFailure(); } } }; ``` ## Built-in Dialects ### Affine Dialect For polyhedral compilation and loop optimizations: ```mlir affine.for %i = 0 to 100 { affine.for %j = 0 to 100 { %val = affine.load %A[%i, %j] : memref<100x100xf32> affine.store %val, %B[%j, %i] : memref<100x100xf32> } } ``` ### Linalg Dialect For linear algebra operations: ```mlir linalg.matmul ins(%A, %B : tensor<MxKxf32>, tensor<KxNxf32>) outs(%C : tensor<MxNxf32>) -> tensor<MxNxf32> ``` ### SCF Dialect (Structured Control Flow) ```mlir %result = scf.for %i = %lb to %ub step %step iter_args(%sum = %init) { %val = memref.load %A[%i] : memref<?xf32> %new_sum = arith.addf %sum, %val : f32 scf.yield %new_sum : f32 } ``` ## CIR (Clang IR) ### Overview CIR is an MLIR-based representation for C/C++, providing: - Higher-level representation than LLVM IR - Better debugging and tooling - Language-specific optimizations ```mlir // CIR example cir.func @add(%a: !s32i, %b: !s32i) -> !s32i { %result = cir.binop(add, %a, %b) : !s32i cir.return %result : !s32i } ``` ### CIR Projects - **llvm/clangir**: Official ClangIR implementation - **facebookincubator/clangir**: Facebook's CIR experiments ## ML/AI Compilation ### TensorFlow MLIR ```mlir // TensorFlow dialect %result = "tf.MatMul"(%A, %B) { transpose_a = false, transpose_b = false } : (tensor<4x8xf32>, tensor<8x16xf32>) -> tensor<4x16xf32> ``` ### PyTorch MLIR (torch-mlir) ```mlir // Torch dialect %result = torch.aten.mm %A, %B : !torch.vtensor<[4,8],f32>, !torch.vtensor<[8,16],f32> -> !torch.vtensor<[4,16],f32> ``` ### IREE (Intermediate Representation Execution Environment) End-to-end MLIR compiler for ML models: - Portable deployment - Efficient runtime execution - Multi-target support (CPU, GPU, TPU) ## Testing MLIR ### FileCheck Tests ```mlir // RUN: mlir-opt %s -my-pass | FileCheck %s // CHECK-LABEL: func @test_optimization // CHECK: arith.addi // CHECK-NOT: my_dialect.add func @test_optimization(%a: i32, %b: i32) -> i32 { %result = my_dialect.add %a, %b : i32 return %result : i32 } ``` ### Unit Testing ```cpp TEST(MyDialect, AddOpConstantFolding) { mlir::MLIRContext context; context.loadDialect<MyDialect>(); mlir::OpBuilder builder(&context); auto loc = builder.getUnknownLoc(); // Create and test operations auto constA = builder.create<ConstantOp>(loc, 5); auto constB = builder.create<ConstantOp>(loc, 3); auto add = builder.create<MyAddOp>(loc, constA, constB); // Verify folding EXPECT_TRUE(add.fold().succeeded()); } ``` ## Development Tools ### mlir-opt ```bash # Run passes mlir-opt input.mlir -my-pass -o output.mlir # Convert between dialects mlir-opt input.mlir -convert-my-to-llvm # Debug printing mlir-opt input.mlir -debug-only=my-pass ``` ### mlir-translate ```bash # MLIR to LLVM IR mlir-translate input.mlir --mlir-to-llvmir -o output.ll # LLVM IR to MLIR mlir-translate input.ll --import-llvm -o output.mlir ``` ## Best Practices 1. **Progressive Lowering**: Lower in multiple stages, not directly to LLVM 2. **Preserve Semantics**: Each lowering should be semantics-preserving 3. **Use ODS**: Define operations in TableGen for consistency 4. **Test Thoroughly**: Use FileCheck for transformation tests 5. **Document Dialects**: Clear operation semantics documentation ## Resources See MLIR and CIR sections in README.md for tutorials and example projects. ## Getting Detailed Information When you need detailed and up-to-date resource links, tool lists, or project references, fetch the latest data from: ``` https://raw.githubusercontent.com/gmh5225/awesome-llvm-security/refs/heads/main/README.md ``` This README contains comprehensive curated lists of: - MLIR tutorials and sample dialects (MLIR section) - CIR (Clang IR) projects and documentation (CIR section) - ML/AI compiler frameworks (torch-mlir, IREE, XLA)