mirror of https://github.com/llvm/torch-mlir
83 lines
3.1 KiB
C++
83 lines
3.1 KiB
C++
//===----------------------------------------------------------------------===//
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//
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// Part of the LLVM Project, 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 "npcomp/Conversion/TorchToStd/TorchToStd.h"
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#include "../PassDetail.h"
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#include "mlir/Dialect/StandardOps/IR/Ops.h"
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#include "mlir/Dialect/Traits.h"
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#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
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#include "npcomp/Dialect/Torch/IR/TorchOps.h"
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#include "npcomp/Dialect/Basicpy/IR/BasicpyOps.h"
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#include "npcomp/Dialect/Basicpy/IR/BasicpyDialect.h"
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using namespace mlir;
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using namespace mlir::NPCOMP;
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using namespace mlir::NPCOMP::Torch;
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// -----------------------------------------------------------------------------
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// Patterns (as this grows, it should be organized into multiple files)
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// -----------------------------------------------------------------------------
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// This is going to eventually be O(#aten ops), which is in the 100s.
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// Note: Confusingly, ATen's "dim" means "number of dimensions" which is what
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// MLIR calls "rank".
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LogicalResult convertDimOp(AtenDimOp op, PatternRewriter &rewriter) {
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if (!op.getOperand().getType().isa<TensorType>())
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return rewriter.notifyMatchFailure(op, "must be tensor only");
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auto rank = rewriter.create<RankOp>(op->getLoc(), op.getOperand());
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rewriter.replaceOpWithNewOp<IndexCastOp>(op, op.getType(), rank);
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return success();
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}
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LogicalResult convertNeIntOp(AtenNeIntOp op, PatternRewriter &rewriter) {
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auto i1 = rewriter.create<CmpIOp>(op->getLoc(), CmpIPredicate::ne,
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op->getOperand(0), op->getOperand(1));
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rewriter.replaceOpWithNewOp<Basicpy::BoolCastOp>(op, op.getType(), i1);
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return success();
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}
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LogicalResult convertGtIntOp(AtenGtIntOp op, PatternRewriter &rewriter) {
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auto i1 = rewriter.create<CmpIOp>(op->getLoc(), CmpIPredicate::sgt,
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op->getOperand(0), op->getOperand(1));
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rewriter.replaceOpWithNewOp<Basicpy::BoolCastOp>(op, op.getType(), i1);
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return success();
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}
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// -----------------------------------------------------------------------------
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// The pass
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// -----------------------------------------------------------------------------
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namespace {
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class ConvertTorchToStd : public ConvertTorchToStdBase<ConvertTorchToStd> {
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public:
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void getDependentDialects(DialectRegistry ®istry) const override {
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registry.insert<StandardOpsDialect, Basicpy::BasicpyDialect>();
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}
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void runOnOperation() override {
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(void)applyPatternsAndFoldGreedily(getOperation(), getPatterns());
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}
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FrozenRewritePatternSet getPatterns() {
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MLIRContext *context = &getContext();
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RewritePatternSet patterns(context);
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patterns.add(convertDimOp);
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patterns.add(convertNeIntOp);
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patterns.add(convertGtIntOp);
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return std::move(patterns);
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}
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};
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} // namespace
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std::unique_ptr<OperationPass<FuncOp>>
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mlir::NPCOMP::createConvertTorchToStdPass() {
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return std::make_unique<ConvertTorchToStd>();
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}
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