160 lines
5.6 KiB
C++
160 lines
5.6 KiB
C++
//===--------------------------- main.cpp ---------------------------------===//
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//
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// Copyright 2019 The IBM Research Authors.
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//
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// =============================================================================
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//
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//===----------------------------------------------------------------------===//
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#include <cmath>
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#include <iostream>
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#include "llvm/Bitcode/BitcodeWriter.h"
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#include "llvm/Support/CommandLine.h"
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#include "llvm/Support/FileUtilities.h"
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#include "llvm/Support/InitLLVM.h"
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#include "llvm/Support/Regex.h"
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#include "llvm/Support/SourceMgr.h"
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#include "src/Builder/FrontendDialectTransformer.hpp"
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#include "src/Dialect/Krnl/KrnlOps.hpp"
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#include "src/Dialect/ONNX/ONNXOps.hpp"
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#include "src/Pass/Passes.hpp"
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#include "mlir/Conversion/LoopToStandard/ConvertLoopToStandard.h"
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#include "mlir/ExecutionEngine/ExecutionEngine.h"
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#include "mlir/ExecutionEngine/OptUtils.h"
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#include "mlir/InitAllDialects.h"
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#include "mlir/IR/MLIRContext.h"
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#include "mlir/IR/Module.h"
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#include "mlir/Parser.h"
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#include "mlir/Pass/Pass.h"
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#include "mlir/Pass/PassManager.h"
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#include "mlir/Target/LLVMIR.h"
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#include "mlir/Transforms/Passes.h"
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void EmitLLVMBitCode(const mlir::OwningModuleRef &module);
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using namespace std;
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using namespace onnx_mlir;
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void LoadMLIR(string inputFilename, mlir::MLIRContext &context,
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mlir::OwningModuleRef &module) {
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// Handle '.mlir' input to the ONNX MLIR frontend.
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// The mlir format indicates that one or more of the supported
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// representations are used in the file.
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llvm::ErrorOr<std::unique_ptr<llvm::MemoryBuffer>> fileOrErr =
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llvm::MemoryBuffer::getFileOrSTDIN(inputFilename);
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if (std::error_code EC = fileOrErr.getError()) {
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llvm::errs() << "Could not open input file: " << EC.message() << "\n";
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return;
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}
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// Parse the input mlir.
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llvm::SourceMgr sourceMgr;
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sourceMgr.AddNewSourceBuffer(std::move(*fileOrErr), llvm::SMLoc());
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module = mlir::parseSourceFile(sourceMgr, &context);
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if (!module) {
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llvm::errs() << "Error can't load file " << inputFilename << "\n";
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return;
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}
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}
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void EmitLLVMBitCode(const mlir::OwningModuleRef &module) {
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error_code error;
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llvm::raw_fd_ostream moduleBitcodeStream("model.bc", error,
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llvm::sys::fs::F_None);
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llvm::WriteBitcodeToFile(*mlir::translateModuleToLLVMIR(*module),
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moduleBitcodeStream);
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moduleBitcodeStream.flush();
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}
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int main(int argc, char *argv[]) {
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mlir::registerDialect<mlir::AffineOpsDialect>();
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mlir::registerDialect<mlir::LLVM::LLVMDialect>();
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mlir::registerDialect<mlir::loop::LoopOpsDialect>();
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mlir::registerDialect<mlir::StandardOpsDialect>();
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mlir::registerDialect<mlir::ONNXOpsDialect>();
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mlir::registerDialect<mlir::KrnlOpsDialect>();
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llvm::cl::OptionCategory OnnxMlirOptions("ONNX MLIR Options",
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"These are frontend options.");
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llvm::cl::opt<string> inputFilename(
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llvm::cl::Positional, llvm::cl::desc("<input file>"), llvm::cl::init("-"),
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llvm::cl::cat(OnnxMlirOptions));
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enum EmissionTargetType {
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EmitONNXIR,
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EmitMLIR,
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EmitLLVMIR,
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EmitLLVMBC,
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};
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llvm::cl::opt<EmissionTargetType> emissionTarget(
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llvm::cl::desc("Choose target to emit:"),
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llvm::cl::values(
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clEnumVal(EmitONNXIR,
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"Ingest ONNX and emit corresponding ONNX dialect."),
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clEnumVal(EmitMLIR,
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"Lower model to MLIR built-in transformation dialect."),
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clEnumVal(EmitLLVMIR, "Lower model to LLVM IR (LLVM dialect)."),
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clEnumVal(EmitLLVMBC, "Lower model to LLVM IR and emit (to file) "
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"LLVM bitcode for model.")),
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llvm::cl::init(EmitLLVMBC), llvm::cl::cat(OnnxMlirOptions));
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llvm::cl::HideUnrelatedOptions(OnnxMlirOptions);
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llvm::cl::ParseCommandLineOptions(argc, argv,
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"ONNX MLIR modular optimizer driver\n");
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// Decide if the input file is an ONNX model or a model specified
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// in MLIR. The extension of the file is the decider.
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string extension = inputFilename.substr(inputFilename.find_last_of(".") + 1);
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bool inputIsONNX = (extension == "onnx");
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bool inputIsMLIR = (extension == "mlir");
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assert(inputIsONNX != inputIsMLIR &&
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"Either ONNX model or MLIR file needs to be provided.");
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mlir::MLIRContext context;
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mlir::OwningModuleRef module;
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if (inputIsONNX) {
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ImportFrontendModelFile(inputFilename, context, module);
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} else {
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LoadMLIR(inputFilename, context, module);
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}
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mlir::PassManager pm(&context);
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pm.addPass(mlir::createDecomposeONNXToONNXPass());
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pm.addPass(mlir::createShapeInferencePass());
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pm.addPass(mlir::createCanonicalizerPass());
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pm.addPass(mlir::createShapeInferencePass());
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pm.addPass(mlir::createAttributePromotionPass());
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if (emissionTarget >= EmitMLIR) {
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pm.addPass(mlir::createLowerToKrnlPass());
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// An additional pass of canonicalization is helpful because lowering
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// from ONNX dialect to Standard dialect exposes additional canonicalization
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// oppertunities.
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pm.addPass(mlir::createCanonicalizerPass());
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pm.addPass(mlir::createLowerKrnlPass());
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}
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if (emissionTarget >= EmitLLVMIR) {
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pm.addPass(mlir::createLowerAffinePass());
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pm.addPass(mlir::createLowerToCFGPass());
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pm.addPass(mlir::createKrnlLowerToLLVMPass());
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pm.addPass(mlir::createCanonicalizerPass());
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}
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if (mlir::failed(pm.run(*module)))
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return 4;
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if (emissionTarget == EmitLLVMBC) {
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// Write LLVM bitcode to disk.
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EmitLLVMBitCode(module);
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printf("LLVM bitcode written to ./model.bc");
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} else
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module->dump();
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return 0;
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}
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