mirror of https://github.com/llvm/torch-mlir
112 lines
4.0 KiB
C++
112 lines
4.0 KiB
C++
//===- PrepareForGlobalizeObjectGraph.cpp ------------------------*- 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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// Also available under a BSD-style license. See LICENSE.
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//
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//===----------------------------------------------------------------------===//
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#include "PassDetail.h"
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#include "mlir/IR/BuiltinOps.h"
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#include "mlir/Transforms/DialectConversion.h"
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#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
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#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
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#include "torch-mlir/Dialect/Torch/Transforms/Passes.h"
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using namespace mlir;
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using namespace mlir::torch;
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using namespace mlir::torch::Torch;
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namespace {
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class ConvertPrimCallMethodToCall : public OpRewritePattern<PrimCallMethodOp> {
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public:
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ConvertPrimCallMethodToCall(MLIRContext *context, SymbolTable &symbolTable)
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: OpRewritePattern(context), symbolTable(symbolTable) {}
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LogicalResult matchAndRewrite(PrimCallMethodOp op,
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PatternRewriter &rewriter) const override {
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auto classType = symbolTable.lookup<ClassTypeOp>(
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cast<NnModuleType>(op.getReceiver().getType()).getClassName());
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assert(classType && "malformed module -- missing ClassTypeOp");
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func::FuncOp func;
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for (auto method : classType.getOps<MethodOp>()) {
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if (method.getName() == op.getName()) {
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func = symbolTable.lookup<func::FuncOp>(method.getFunction());
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break;
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}
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}
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assert(func);
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rewriter.replaceOpWithNewOp<func::CallOp>(op, func, op->getOperands());
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return success();
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}
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private:
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SymbolTable &symbolTable;
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};
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} // namespace
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namespace {
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class EraseUnusedConstantOp : public OpRewritePattern<func::ConstantOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(func::ConstantOp op,
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PatternRewriter &rewriter) const override {
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if (op.use_empty()) {
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rewriter.eraseOp(op);
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return success();
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}
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return failure();
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}
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};
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} // namespace
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namespace {
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class PrepareForGlobalizeObjectGraphPass
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: public PrepareForGlobalizeObjectGraphBase<
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PrepareForGlobalizeObjectGraphPass> {
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void runOnOperation() override {
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SymbolTable symbolTable(getOperation());
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MLIRContext *context = &getContext();
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RewritePatternSet patterns(context);
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patterns.add<ConvertPrimCallMethodToCall>(context, symbolTable);
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func::CallIndirectOp::getCanonicalizationPatterns(patterns, context);
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patterns.add<EraseUnusedConstantOp>(context);
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// Use applyPatternsAndFoldGreedily because the CallIndirectOp folding
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// makes the ConstantOp unused, which does not work with the visitation
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// order of the dialect conversion infrastructure.
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// TODO: Do this with the dialect conversion infrastructure to avoid doing
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// folding as part of this. Or avoid folding during greedy pattern
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// application. See: https://llvm.org/PR49502
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if (failed(applyPatternsAndFoldGreedily(getOperation(),
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std::move(patterns)))) {
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return signalPassFailure();
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}
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// Do a dummy full conversion to ensure that the program has been converted
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// to the form we want.
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ConversionTarget target(*context);
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target.addIllegalOp<PrimCallMethodOp>();
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target.addDynamicallyLegalOp<func::ConstantOp>(
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[](func::ConstantOp op) { return !isa<FunctionType>(op.getType()); });
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target.addIllegalOp<func::CallIndirectOp>();
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target.markUnknownOpDynamicallyLegal([](Operation *) { return true; });
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RewritePatternSet dummyPatterns(context);
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if (failed(applyFullConversion(getOperation(), target,
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std::move(dummyPatterns)))) {
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return signalPassFailure();
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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::torch::Torch::createPrepareForGlobalizeObjectGraphPass() {
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return std::make_unique<PrepareForGlobalizeObjectGraphPass>();
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}
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