mirror of https://github.com/llvm/torch-mlir
163 lines
6.0 KiB
C++
163 lines
6.0 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/TCFToStd/TCFToStd.h"
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#include "../PassDetail.h"
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#include "mlir/Dialect/Math/IR/Math.h"
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#include "mlir/Dialect/Shape/IR/Shape.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/DialectConversion.h"
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#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
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#include "npcomp/Dialect/TCF/IR/TCFOps.h"
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#include "npcomp/Dialect/TCP/IR/TCPDialect.h"
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#include "npcomp/Dialect/TCP/IR/TCPOps.h"
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using namespace mlir;
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using namespace mlir::NPCOMP;
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static RankedTensorType getExtentTensorType(Builder &builder) {
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return RankedTensorType::get({ShapedType::kDynamicSize},
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builder.getIndexType());
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}
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// Non-templated version of the body of ConvertBinaryElementwise to keep things
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// simple.
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static LogicalResult
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matchAndRewriteBinaryElementwise(Operation *op, PatternRewriter &rewriter) {
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Value lhs = op->getOperand(0);
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Value rhs = op->getOperand(1);
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Location loc = op->getLoc();
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Value result = op->getResult(0);
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auto lhsType = lhs.getType().dyn_cast<RankedTensorType>();
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auto rhsType = rhs.getType().dyn_cast<RankedTensorType>();
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if (!lhsType || !rhsType)
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return rewriter.notifyMatchFailure(op, "requires ranked tensors");
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Value lhsShape = rewriter.create<shape::ShapeOfOp>(loc, lhs);
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Value rhsShape = rewriter.create<shape::ShapeOfOp>(loc, rhs);
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// Create the constraints, and the assuming region.
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Value witness =
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rewriter.create<shape::CstrBroadcastableOp>(loc, lhsShape, rhsShape);
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auto assuming = rewriter.create<shape::AssumingOp>(
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loc, ArrayRef<Type>{result.getType()}, witness);
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// Start building the region body.
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rewriter.createBlock(&assuming.doRegion());
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Value broadcastedShape = rewriter.create<shape::BroadcastOp>(
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loc, getExtentTensorType(rewriter), lhsShape, rhsShape,
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/*error=*/nullptr);
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// TODO: It's annoying to do the dynamic broadcast above then
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// do the static transfer function here. Would be nice if they could
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// somehow be unified.
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SmallVector<int64_t, 6> broadcastedStaticShape;
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OpTrait::util::getBroadcastedShape(lhsType.getShape(), rhsType.getShape(),
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broadcastedStaticShape);
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auto resultType =
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RankedTensorType::get(broadcastedStaticShape, lhsType.getElementType());
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Value lhsBroadcasted = rewriter.create<tcp::BroadcastToOp>(
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loc, resultType, lhs, broadcastedShape);
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Value rhsBroadcasted = rewriter.create<tcp::BroadcastToOp>(
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loc, resultType, rhs, broadcastedShape);
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Value binaryOpResult;
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if (isa<tcf::AddOp>(op)) {
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binaryOpResult = rewriter.create<AddFOp>(loc, result.getType(),
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lhsBroadcasted, rhsBroadcasted);
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} else if (isa<tcf::MaxOp>(op)) {
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// XXX: remove TCP dep
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// XXX: remove TCP ops from TCP
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auto pred = rewriter.create<CmpFOp>(loc, CmpFPredicate::OGT, lhsBroadcasted,
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rhsBroadcasted);
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binaryOpResult =
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rewriter.create<SelectOp>(loc, pred, lhsBroadcasted, rhsBroadcasted);
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} else if (isa<tcf::MulOp>(op)) {
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binaryOpResult = rewriter.create<MulFOp>(loc, result.getType(),
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lhsBroadcasted, rhsBroadcasted);
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} else {
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op->dump();
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llvm::report_fatal_error(
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"unhandled op (see dump above): TCF->Std binary elementwise");
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}
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rewriter.create<shape::AssumingYieldOp>(loc, binaryOpResult);
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// Finally, replace with the results of the shape.assuming
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rewriter.replaceOp(op, assuming.getResults());
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return success();
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}
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namespace {
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template <typename SourceOp>
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class ConvertBinaryElementwise : public OpRewritePattern<SourceOp> {
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public:
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using OpRewritePattern<SourceOp>::OpRewritePattern;
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LogicalResult matchAndRewrite(SourceOp op,
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PatternRewriter &rewriter) const override {
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return matchAndRewriteBinaryElementwise(op, rewriter);
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}
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};
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} // namespace
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static LogicalResult
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matchAndRewriteUnaryElementwise(Operation *op, PatternRewriter &rewriter) {
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if (isa<tcf::ExpOp>(op)) {
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rewriter.replaceOpWithNewOp<math::ExpOp>(op, op->getOperand(0));
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} else if (isa<tcf::TanhOp>(op)) {
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rewriter.replaceOpWithNewOp<math::TanhOp>(op, op->getOperand(0));
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} else {
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op->dump();
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llvm::report_fatal_error(
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"unhandled op (see dump above): TCF->TCP unary elementwise");
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}
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return success();
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}
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namespace {
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template <typename SourceOp>
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class ConvertUnaryElementwise : public OpRewritePattern<SourceOp> {
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public:
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using OpRewritePattern<SourceOp>::OpRewritePattern;
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LogicalResult matchAndRewrite(SourceOp op,
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PatternRewriter &rewriter) const override {
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return matchAndRewriteUnaryElementwise(op, rewriter);
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}
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};
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} // namespace
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namespace {
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class ConvertTCFToStd : public ConvertTCFToStdBase<ConvertTCFToStd> {
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public:
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void getDependentDialects(DialectRegistry ®istry) const override {
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registry.insert<math::MathDialect, shape::ShapeDialect, tcp::TCPDialect>();
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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<ConvertUnaryElementwise<tcf::ExpOp>,
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ConvertUnaryElementwise<tcf::TanhOp>>(context);
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patterns.add<ConvertBinaryElementwise<tcf::AddOp>,
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ConvertBinaryElementwise<tcf::MaxOp>,
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ConvertBinaryElementwise<tcf::MulOp>>(context);
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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::createConvertTCFToStdPass() {
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return std::make_unique<ConvertTCFToStd>();
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}
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