emit SGIR based on onnx model, following the toy/Ch2 example (#345)
* emit SGIR based on onnx model, following the toy/Ch2 example * fix 1) code style 2) multiple output of a node * Update sgir.cpp
This commit is contained in:
TONG CHEN
Doru Bercea
parent
63427030ca
commit
50ac6e7bce
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@ -6,6 +6,8 @@
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//
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//===----------------------------------------------------------------------===//
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#include <regex>
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#include <tuple>
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#include <numeric>
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#include "mlir/Analysis/Verifier.h"
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@ -18,51 +20,192 @@
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#include "mlir/IR/Module.h"
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#include "mlir/IR/StandardTypes.h"
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#include "mlir/IR/Types.h"
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#include "llvm/ADT/STLExtras.h"
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#include "llvm/ADT/ScopedHashTable.h"
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#include "llvm/Support/raw_ostream.h"
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#include "sgir.hpp"
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using llvm::cast;
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using llvm::dyn_cast;
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using llvm::isa;
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using llvm::ScopedHashTableScope;
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using llvm::SmallVector;
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using llvm::StringRef;
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using llvm::Twine;
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namespace onnf {
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namespace {
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struct OnnxOnnfSymbolMapping {
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/*!
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* Get MLIR tensor by onnx tensor name.
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* @param name onnx tensor name.
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* @return onnf tensor corresponding to `name`.
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*/
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mlir::Value* GetTensorByOnnxName(std::string name) {
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return onnx_name2onnf_tensor.at(legalize_name(name));
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}
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/*!
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* Add a new mapping from onnx tensor name to MLIR symbol.
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* @param name onnx tensor name.
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* @param tensor MLIR Value* pointer.
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*/
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void AddMapping(std::string name, mlir::Value* tensor) {
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onnx_name2onnf_tensor.emplace(legalize_name(name), tensor);
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}
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bool ContainKey(std::string name) {
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return onnx_name2onnf_tensor.count(name) != 0;
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}
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private:
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/*!
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* mapping from onnx tensor names to MLIR tensor.
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*/
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std::map<std::string, mlir::Value*> onnx_name2onnf_tensor;
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};
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class SGIRGenImpl {
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public :
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SGIRGenImpl(mlir::MLIRContext &context)
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: context(context), builder(&context) {}
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: context_(context), builder_(&context) {
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module_ = mlir::ModuleOp::create(mlir::UnknownLoc::get(&context));
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}
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mlir::ModuleOp mlirGen() {
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theModule = mlir::ModuleOp::create(mlir::UnknownLoc::get(&context));
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return theModule;
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mlir::ModuleOp ImportModel(onnx::ModelProto model) {
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ImportGraph(model.graph());
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return module_;
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}
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private:
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mlir::MLIRContext &context;
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mlir::ModuleOp theModule;
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mlir::OpBuilder builder;
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mlir::MLIRContext &context_;
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mlir::ModuleOp module_;
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mlir::OpBuilder builder_;
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// mapping between string name and symbol
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OnnxOnnfSymbolMapping sgir_symbols_;
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} ;
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mlir::Location UnknownLoc() {
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return mlir::UnknownLoc::get(&context_);
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}
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mlir::Type TypeConvert(onnx::TensorProto_DataType intype) {
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return builder_.getF32Type();
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}
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void ImportInputTensor(onnx::ValueInfoProto& input) {
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std::vector<int64_t> dims;
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auto shape_proto = input.type().tensor_type().shape();
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auto input_tensor_legalized_name = legalize_name(input.name());
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for (int i = 0; i < shape_proto.dim_size(); i++) {
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if (shape_proto.dim()[i].dim_value()) {
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int dim_numeric_size = shape_proto.dim()[i].dim_value();
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if (dim_numeric_size > 0) {
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dims.push_back(dim_numeric_size);
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}else { // If dim_value < 0, then dim is parametric.
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//TODO Verify the unknown dim size in MLIR
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dims.push_back(-1);
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}
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} else {
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//TODO How to represent variable length
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dims.push_back(-1);
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}
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}
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if (!sgir_symbols_.ContainKey(input_tensor_legalized_name)) {
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mlir::Type elementType = TypeConvert(input.type().tensor_type().elem_type());
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llvm::ArrayRef<int64_t> llvmdimsAR(dims.data(), dims.size());
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auto dataType = builder_.getTensorType(llvmdimsAR, elementType);
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mlir::OperationState result(UnknownLoc(), "sgir.input "+input_tensor_legalized_name);
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result.addTypes(dataType);
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auto op = builder_.createOperation(result);
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auto value = op->getResult(0);
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sgir_symbols_.AddMapping(input_tensor_legalized_name, value);
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} else {
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//TODO Should not happen
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}
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}
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void ImportNode(onnx::NodeProto node) {
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std::vector<mlir::Value*> inputs;
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for (auto item : node.input()) {
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if (sgir_symbols_.ContainKey(legalize_name(item))) {
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inputs.push_back(sgir_symbols_.GetTensorByOnnxName(item));
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}
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}
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mlir::OperationState result(UnknownLoc(), "SGIR."+node.op_type());
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for (auto item : node.output()) {
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result.addTypes(builder_.getTensorType(builder_.getF32Type()));
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}
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result.addOperands(inputs);
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auto op = builder_.createOperation(result);
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for (int i=0 ; i< node.output().size(); i++) {
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auto r = builder_.createOperation(result)->getResult(i);
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sgir_symbols_.AddMapping(legalize_name(node.output()[i]), r);
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}
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//TODO more info from node: attributes
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}
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void ImportOutputTensor(onnx::ValueInfoProto& output) {
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if(sgir_symbols_.ContainKey(legalize_name(output.name()))) {
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mlir::OperationState result(UnknownLoc(), "sgir.output "+output.name());
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result.addTypes(builder_.getTensorType(builder_.getF32Type()));
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result.addOperands(sgir_symbols_.GetTensorByOnnxName(output.name()));
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builder_.createOperation(result);
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} else {
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//TODO: Why not in the symbol table? something is wrong
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}
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}
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void ImportGraph(onnx::GraphProto graph) {
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//create a function for the graph
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//TODO:
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// * get name and type for the function.
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// * maintain a list of the defined graph
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llvm::SmallVector<mlir::Type, 4> ret_types;
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llvm::SmallVector<mlir::Type, 4> arg_types;
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auto func_type = builder_.getFunctionType(arg_types, ret_types);
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auto llvmfunction = mlir::FuncOp::create(UnknownLoc(),
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graph.name(), func_type, /* attrs = */ {});
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auto &entryBlock = *llvmfunction.addEntryBlock();
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builder_.setInsertionPointToStart(&entryBlock);
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module_.push_back(llvmfunction);
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//TODO: import the initializer
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//
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//import the input tensors
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for (auto input : graph.input()) {
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ImportInputTensor(input);
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}
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//import nodes in the graph
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auto node = graph.node();
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for (auto item: node) {
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ImportNode(item);
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}
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//import the output tensors
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for (auto output : graph.output()) {
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ImportOutputTensor(output);
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}
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}
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} ; //SGIRGenImpl class
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} //namespace
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} //namespace onnf
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namespace onnf {
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int SGIRTest() {
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/*!
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* Generate SGIR with MLIR for a onnx model
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* @param model onnx model.
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* @return module mlir module generated for the onnx model
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*/
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mlir::OwningModuleRef SGIRImportModel(onnx::ModelProto model) {
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mlir::MLIRContext context;
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SGIRGenImpl mySGIRGen(context);
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auto module = mySGIRGen.ImportModel(model);
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module.dump();
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mlir::OwningModuleRef module = SGIRGenImpl(context).mlirGen();
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if (!module)
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return 1;
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module->dump();
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return 0;
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return module;
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}
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} //namespace onnf
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@ -8,7 +8,15 @@
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#pragma once
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#include <fstream>
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#include <functional>
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#include <map>
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#include <memory>
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#include <sstream>
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#include <string>
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#include <vector>
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#include "onnx/onnx_pb.h"
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namespace mlir {
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class MLIRContext;
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@ -17,10 +25,11 @@ class OwningModuleRef;
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namespace onnf {
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/*!
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* Test dummy
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* @return status, 0 for success, otherwise failure
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**/
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int SGIRTest();
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* Import an ONNX Model into SGIR
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* @param model onnx model.
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* @return MLIR::module generated for the ONNX model
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*/
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mlir::OwningModuleRef SGIRImportModel(onnx::ModelProto model);
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} //namespace onnf
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