mirror of https://github.com/llvm/torch-mlir
99 lines
3.3 KiB
C++
99 lines
3.3 KiB
C++
//===- PublicFunctionToTensor.cpp - Type inference passes --------*- C++-*-===//
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//
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// This file is licensed under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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#include "PassDetail.h"
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#include "mlir/Dialect/StandardOps/IR/Ops.h"
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#include "mlir/IR/Builders.h"
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#include "mlir/IR/Module.h"
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#include "npcomp/Dialect/Numpy/IR/NumpyDialect.h"
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#include "npcomp/Dialect/Numpy/IR/NumpyOps.h"
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#include "npcomp/Dialect/Numpy/Transforms/Passes.h"
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using namespace mlir;
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using namespace mlir::NPCOMP::Numpy;
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namespace {
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class PublicFunctionsToTensorPass
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: public NumpyPublicFunctionsToTensorBase<PublicFunctionsToTensorPass> {
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void runOnOperation() override {
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auto module = getOperation();
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module.walk([&](FuncOp func) {
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if (func.getVisibility() != SymbolTable::Visibility::Public)
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return;
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if (func.isExternal())
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return;
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auto uses = SymbolTable::getSymbolUses(func, module);
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if (!uses || uses->begin() != uses->end()) {
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func.emitWarning() << "unimplemented: cannot convert ndarray->tensor "
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<< "signature for public function with uses";
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return;
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}
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rewriteSignature(func);
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});
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}
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void rewriteSignature(FuncOp func) {
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auto &entryBlock = func.getBlocks().front();
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auto funcType = func.getType();
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auto loc = func.getLoc();
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// Rewrite inputs.
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auto builder = OpBuilder::atBlockBegin(&entryBlock);
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auto inputTypes = llvm::to_vector<4>(funcType.getInputs());
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for (unsigned i = 0; i < inputTypes.size(); ++i) {
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auto arrayType = inputTypes[i].dyn_cast<NdArrayType>();
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if (!arrayType)
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continue;
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Type tensorType = arrayType.toTensorType();
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BlockArgument argument = entryBlock.getArgument(i);
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argument.setType(tensorType);
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auto createOp =
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builder.create<CreateArrayFromTensorOp>(loc, arrayType, argument);
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argument.replaceAllUsesExcept(createOp,
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SmallPtrSet<Operation *, 1>{createOp});
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inputTypes[i] = tensorType;
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}
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// Rewrite result signature.
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auto resultTypes = llvm::to_vector<4>(funcType.getResults());
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for (auto &resultType : resultTypes) {
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auto arrayType = resultType.dyn_cast<NdArrayType>();
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if (arrayType)
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resultType = arrayType.toTensorType();
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}
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// Update signature.
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funcType =
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FunctionType::get(inputTypes, resultTypes, funcType.getContext());
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func.setType(funcType);
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// Rewrite all return terminators.
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func.walk([&](ReturnOp term) {
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OpBuilder builder(term);
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for (unsigned i = 0; i < term.getNumOperands(); ++i) {
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Value operand = term.getOperand(i);
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auto arrayType = operand.getType().dyn_cast<NdArrayType>();
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if (!arrayType)
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continue;
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Type tensorType = arrayType.toTensorType();
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auto copyOp = builder.create<CopyToTensorOp>(loc, tensorType, operand);
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term.setOperand(i, copyOp);
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}
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});
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}
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};
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} // namespace
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std::unique_ptr<OperationPass<ModuleOp>>
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mlir::NPCOMP::Numpy::createPublicFunctionsToTensorPass() {
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return std::make_unique<PublicFunctionsToTensorPass>();
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}
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