| //===- ShapeToStandard.cpp - conversion from Shape to Standard dialect ----===// |
| // |
| // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. |
| // See https://llvm.org/LICENSE.txt for license information. |
| // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception |
| // |
| //===----------------------------------------------------------------------===// |
| |
| #include "mlir/Conversion/ShapeToStandard/ShapeToStandard.h" |
| |
| #include "../PassDetail.h" |
| #include "mlir/Dialect/Arithmetic/IR/Arithmetic.h" |
| #include "mlir/Dialect/SCF/SCF.h" |
| #include "mlir/Dialect/Shape/IR/Shape.h" |
| #include "mlir/Dialect/StandardOps/IR/Ops.h" |
| #include "mlir/Dialect/Tensor/IR/Tensor.h" |
| #include "mlir/IR/BlockAndValueMapping.h" |
| #include "mlir/IR/ImplicitLocOpBuilder.h" |
| #include "mlir/Transforms/DialectConversion.h" |
| #include "llvm/ADT/STLExtras.h" |
| |
| using namespace mlir; |
| using namespace mlir::shape; |
| using namespace mlir::scf; |
| |
| /// Conversion patterns. |
| namespace { |
| class AnyOpConversion : public OpConversionPattern<AnyOp> { |
| public: |
| using OpConversionPattern<AnyOp>::OpConversionPattern; |
| |
| LogicalResult |
| matchAndRewrite(AnyOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const override; |
| }; |
| } // namespace |
| |
| LogicalResult |
| AnyOpConversion::matchAndRewrite(AnyOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const { |
| // Replace `any` with its first operand. |
| // Any operand would be a valid substitution. |
| rewriter.replaceOp(op, {adaptor.getInputs().front()}); |
| return success(); |
| } |
| |
| namespace { |
| template <typename SrcOpTy, typename DstOpTy> |
| class BinaryOpConversion : public OpConversionPattern<SrcOpTy> { |
| public: |
| using OpConversionPattern<SrcOpTy>::OpConversionPattern; |
| |
| LogicalResult |
| matchAndRewrite(SrcOpTy op, typename SrcOpTy::Adaptor adaptor, |
| ConversionPatternRewriter &rewriter) const override { |
| // For now, only error-free types are supported by this lowering. |
| if (op.getType().template isa<SizeType>()) |
| return failure(); |
| |
| rewriter.replaceOpWithNewOp<DstOpTy>(op, adaptor.getLhs(), |
| adaptor.getRhs()); |
| return success(); |
| } |
| }; |
| } // namespace |
| |
| namespace { |
| struct BroadcastOpConverter : public OpConversionPattern<BroadcastOp> { |
| using OpConversionPattern<BroadcastOp>::OpConversionPattern; |
| |
| LogicalResult |
| matchAndRewrite(BroadcastOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const override; |
| }; |
| |
| // Get the resulting extent in a given dimension. This is computed with any |
| // number of extent tensors and shifted offsets into them. |
| Value getBroadcastedDim(ImplicitLocOpBuilder lb, ValueRange extentTensors, |
| ValueRange rankDiffs, Value outputDimension) { |
| Value one = lb.create<arith::ConstantIndexOp>(1); |
| Value broadcastedDim = one; |
| for (auto tup : llvm::zip(extentTensors, rankDiffs)) { |
| Value shape = std::get<0>(tup); |
| Value rankDiff = std::get<1>(tup); |
| Value outOfBounds = lb.create<arith::CmpIOp>(arith::CmpIPredicate::ult, |
| outputDimension, rankDiff); |
| Type indexTy = lb.getIndexType(); |
| broadcastedDim = |
| lb.create<IfOp>( |
| TypeRange{indexTy}, outOfBounds, |
| [&](OpBuilder &b, Location loc) { |
| b.create<scf::YieldOp>(loc, broadcastedDim); |
| }, |
| [&](OpBuilder &b, Location loc) { |
| // The broadcasting logic is: |
| // - if one extent (here we arbitrarily choose the |
| // extent from the greater-rank operand) is equal to 1, |
| // then take the extent from the other operand |
| // - otherwise, take the extent as-is. |
| // Note that this logic remains correct in the presence |
| // of dimensions of zero extent. |
| Value lesserRankOperandDimension = b.create<arith::SubIOp>( |
| loc, indexTy, outputDimension, rankDiff); |
| Value lesserRankOperandExtent = b.create<tensor::ExtractOp>( |
| loc, shape, ValueRange{lesserRankOperandDimension}); |
| |
| Value dimIsOne = |
| b.create<arith::CmpIOp>(loc, arith::CmpIPredicate::eq, |
| lesserRankOperandExtent, one); |
| Value dim = b.create<SelectOp>(loc, dimIsOne, broadcastedDim, |
| lesserRankOperandExtent); |
| b.create<scf::YieldOp>(loc, dim); |
| }) |
| .getResult(0); |
| } |
| return broadcastedDim; |
| } |
| } // namespace |
| |
| LogicalResult BroadcastOpConverter::matchAndRewrite( |
| BroadcastOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const { |
| // For now, this lowering is only defined on `tensor<?xindex>` operands, not |
| // on shapes. |
| if (op.getType().isa<ShapeType>()) |
| return failure(); |
| |
| auto loc = op.getLoc(); |
| ImplicitLocOpBuilder lb(loc, rewriter); |
| |
| Value zero = lb.create<arith::ConstantIndexOp>(0); |
| Type indexTy = lb.getIndexType(); |
| |
| // Save all the ranks for bounds checking. Because this is a tensor |
| // representing the shape extents, the rank is the extent of the only |
| // dimension in the tensor. |
| SmallVector<Value> ranks, rankDiffs; |
| llvm::append_range(ranks, llvm::map_range(adaptor.getShapes(), [&](Value v) { |
| return lb.create<tensor::DimOp>(v, zero); |
| })); |
| |
| // Find the maximum rank |
| Value maxRank = ranks.front(); |
| for (Value v : llvm::drop_begin(ranks, 1)) { |
| Value rankIsGreater = |
| lb.create<arith::CmpIOp>(arith::CmpIPredicate::ugt, v, maxRank); |
| maxRank = lb.create<SelectOp>(rankIsGreater, v, maxRank); |
| } |
| |
| // Calculate the difference of ranks and the maximum rank for later offsets. |
| llvm::append_range(rankDiffs, llvm::map_range(ranks, [&](Value v) { |
| return lb.create<arith::SubIOp>(indexTy, maxRank, v); |
| })); |
| |
| Value replacement = lb.create<tensor::GenerateOp>( |
| getExtentTensorType(lb.getContext()), ValueRange{maxRank}, |
| [&](OpBuilder &b, Location loc, ValueRange args) { |
| Value broadcastedDim = |
| getBroadcastedDim(ImplicitLocOpBuilder(loc, b), adaptor.getShapes(), |
| rankDiffs, args[0]); |
| |
| b.create<tensor::YieldOp>(loc, broadcastedDim); |
| }); |
| if (replacement.getType() != op.getType()) |
| replacement = lb.create<tensor::CastOp>(op.getType(), replacement); |
| rewriter.replaceOp(op, replacement); |
| return success(); |
| } |
| |
| namespace { |
| class ConstShapeOpConverter : public OpConversionPattern<ConstShapeOp> { |
| public: |
| using OpConversionPattern<ConstShapeOp>::OpConversionPattern; |
| |
| LogicalResult |
| matchAndRewrite(ConstShapeOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const override; |
| }; |
| } // namespace |
| |
| LogicalResult ConstShapeOpConverter::matchAndRewrite( |
| ConstShapeOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const { |
| |
| // For now, this lowering supports only extent tensors, not `shape.shape` |
| // types. |
| if (op.getType().isa<ShapeType>()) |
| return failure(); |
| |
| auto loc = op.getLoc(); |
| SmallVector<Value, 4> extentOperands; |
| for (auto extent : op.getShape()) { |
| extentOperands.push_back( |
| rewriter.create<arith::ConstantIndexOp>(loc, extent.getLimitedValue())); |
| } |
| Type indexTy = rewriter.getIndexType(); |
| Value tensor = |
| rewriter.create<tensor::FromElementsOp>(loc, indexTy, extentOperands); |
| Type resultTy = RankedTensorType::get({op.getShape().size()}, indexTy); |
| rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultTy, tensor); |
| return success(); |
| } |
| |
| namespace { |
| class ConstSizeOpConversion : public OpConversionPattern<ConstSizeOp> { |
| public: |
| using OpConversionPattern<ConstSizeOp>::OpConversionPattern; |
| |
| LogicalResult |
| matchAndRewrite(ConstSizeOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const override; |
| }; |
| } // namespace |
| |
| LogicalResult ConstSizeOpConversion::matchAndRewrite( |
| ConstSizeOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const { |
| rewriter.replaceOpWithNewOp<arith::ConstantIndexOp>( |
| op, op.getValue().getSExtValue()); |
| return success(); |
| } |
| |
| namespace { |
| struct IsBroadcastableOpConverter |
| : public OpConversionPattern<IsBroadcastableOp> { |
| using OpConversionPattern<IsBroadcastableOp>::OpConversionPattern; |
| |
| LogicalResult |
| matchAndRewrite(IsBroadcastableOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const override; |
| }; |
| } // namespace |
| |
| LogicalResult IsBroadcastableOpConverter::matchAndRewrite( |
| IsBroadcastableOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const { |
| // For now, this lowering is only defined on `tensor<?xindex>` operands, not |
| // on shapes. |
| if (!llvm::all_of(op.getShapes(), |
| [](Value v) { return !v.getType().isa<ShapeType>(); })) |
| return failure(); |
| |
| auto loc = op.getLoc(); |
| ImplicitLocOpBuilder lb(loc, rewriter); |
| Value zero = lb.create<arith::ConstantIndexOp>(0); |
| Value one = lb.create<arith::ConstantIndexOp>(1); |
| Type indexTy = lb.getIndexType(); |
| |
| // Save all the ranks for bounds checking. Because this is a tensor |
| // representing the shape extents, the rank is the extent of the only |
| // dimension in the tensor. |
| SmallVector<Value> ranks, rankDiffs; |
| llvm::append_range(ranks, llvm::map_range(adaptor.getShapes(), [&](Value v) { |
| return lb.create<tensor::DimOp>(v, zero); |
| })); |
| |
| // Find the maximum rank |
| Value maxRank = ranks.front(); |
| for (Value v : llvm::drop_begin(ranks, 1)) { |
| Value rankIsGreater = |
| lb.create<arith::CmpIOp>(arith::CmpIPredicate::ugt, v, maxRank); |
| maxRank = lb.create<SelectOp>(rankIsGreater, v, maxRank); |
| } |
| |
| // Calculate the difference of ranks and the maximum rank for later offsets. |
| llvm::append_range(rankDiffs, llvm::map_range(ranks, [&](Value v) { |
| return lb.create<arith::SubIOp>(indexTy, maxRank, v); |
| })); |
| |
| Type i1Ty = rewriter.getI1Type(); |
| Value trueVal = |
| rewriter.create<arith::ConstantOp>(loc, i1Ty, rewriter.getBoolAttr(true)); |
| |
| auto reduceResult = lb.create<ForOp>( |
| loc, zero, maxRank, one, ValueRange{trueVal}, |
| [&](OpBuilder &b, Location loc, Value iv, ValueRange iterArgs) { |
| // Find a non-1 dim, if it exists. Note that the first part of this |
| // could reuse the Broadcast lowering entirely, but we redo the work |
| // here to make optimizations easier between the two loops. |
| Value broadcastedDim = getBroadcastedDim( |
| ImplicitLocOpBuilder(loc, b), adaptor.getShapes(), rankDiffs, iv); |
| |
| Value broadcastable = iterArgs[0]; |
| for (auto tup : llvm::zip(adaptor.getShapes(), rankDiffs)) { |
| Value shape, rankDiff; |
| std::tie(shape, rankDiff) = tup; |
| Value outOfBounds = b.create<arith::CmpIOp>( |
| loc, arith::CmpIPredicate::ult, iv, rankDiff); |
| broadcastable = |
| b.create<IfOp>( |
| loc, TypeRange{i1Ty}, outOfBounds, |
| [&](OpBuilder &b, Location loc) { |
| // Non existent dimensions are always broadcastable |
| b.create<scf::YieldOp>(loc, broadcastable); |
| }, |
| [&](OpBuilder &b, Location loc) { |
| // Every value needs to be either 1, or the same non-1 |
| // value to be broadcastable in this dim. |
| Value operandDimension = |
| b.create<arith::SubIOp>(loc, indexTy, iv, rankDiff); |
| Value dimensionExtent = b.create<tensor::ExtractOp>( |
| loc, shape, ValueRange{operandDimension}); |
| |
| Value equalOne = b.create<arith::CmpIOp>( |
| loc, arith::CmpIPredicate::eq, dimensionExtent, one); |
| Value equalBroadcasted = b.create<arith::CmpIOp>( |
| loc, arith::CmpIPredicate::eq, dimensionExtent, |
| broadcastedDim); |
| Value result = b.create<arith::AndIOp>( |
| loc, broadcastable, |
| b.create<arith::OrIOp>(loc, equalOne, |
| equalBroadcasted)); |
| b.create<scf::YieldOp>(loc, result); |
| }) |
| .getResult(0); |
| } |
| |
| b.create<scf::YieldOp>(loc, broadcastable); |
| }); |
| |
| rewriter.replaceOp(op, reduceResult.results().front()); |
| return success(); |
| } |
| |
| namespace { |
| class GetExtentOpConverter : public OpConversionPattern<GetExtentOp> { |
| using OpConversionPattern<GetExtentOp>::OpConversionPattern; |
| |
| LogicalResult |
| matchAndRewrite(GetExtentOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const override; |
| }; |
| } // namespace |
| |
| LogicalResult GetExtentOpConverter::matchAndRewrite( |
| GetExtentOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const { |
| // For now, only error-free types are supported by this lowering. |
| if (op.getType().isa<SizeType>()) |
| return failure(); |
| |
| // Derive shape extent directly from shape origin if possible. This |
| // circumvents the necessity to materialize the shape in memory. |
| if (auto shapeOfOp = op.getShape().getDefiningOp<ShapeOfOp>()) { |
| if (shapeOfOp.getArg().getType().isa<ShapedType>()) { |
| rewriter.replaceOpWithNewOp<tensor::DimOp>(op, shapeOfOp.getArg(), |
| adaptor.getDim()); |
| return success(); |
| } |
| } |
| |
| rewriter.replaceOpWithNewOp<tensor::ExtractOp>(op, rewriter.getIndexType(), |
| adaptor.getShape(), |
| ValueRange{adaptor.getDim()}); |
| return success(); |
| } |
| |
| namespace { |
| class RankOpConverter : public OpConversionPattern<shape::RankOp> { |
| public: |
| using OpConversionPattern<shape::RankOp>::OpConversionPattern; |
| |
| LogicalResult |
| matchAndRewrite(shape::RankOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const override; |
| }; |
| } // namespace |
| |
| LogicalResult |
| RankOpConverter::matchAndRewrite(shape::RankOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const { |
| // For now, this lowering supports only error-free types. |
| if (op.getType().isa<SizeType>()) |
| return failure(); |
| |
| rewriter.replaceOpWithNewOp<tensor::DimOp>(op, adaptor.getShape(), 0); |
| return success(); |
| } |
| |
| namespace { |
| /// Converts `shape.reduce` to `scf.for`. |
| struct ReduceOpConverter : public OpConversionPattern<shape::ReduceOp> { |
| public: |
| using OpConversionPattern::OpConversionPattern; |
| |
| LogicalResult |
| matchAndRewrite(shape::ReduceOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const final; |
| }; |
| } // namespace |
| |
| LogicalResult |
| ReduceOpConverter::matchAndRewrite(shape::ReduceOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const { |
| // For now, this lowering is only defined on `tensor<?xindex>` operands. |
| if (op.getShape().getType().isa<ShapeType>()) |
| return failure(); |
| |
| auto loc = op.getLoc(); |
| |
| Value zero = rewriter.create<arith::ConstantIndexOp>(loc, 0); |
| Value one = rewriter.create<arith::ConstantIndexOp>(loc, 1); |
| Type indexTy = rewriter.getIndexType(); |
| Value rank = |
| rewriter.create<tensor::DimOp>(loc, indexTy, adaptor.getShape(), zero); |
| |
| auto loop = rewriter.create<scf::ForOp>( |
| loc, zero, rank, one, op.getInitVals(), |
| [&](OpBuilder &b, Location loc, Value iv, ValueRange args) { |
| Value extent = b.create<tensor::ExtractOp>(loc, adaptor.getShape(), iv); |
| |
| SmallVector<Value, 2> mappedValues{iv, extent}; |
| mappedValues.append(args.begin(), args.end()); |
| |
| BlockAndValueMapping mapping; |
| Block *reduceBody = op.getBody(); |
| mapping.map(reduceBody->getArguments(), mappedValues); |
| for (auto &nested : reduceBody->without_terminator()) |
| b.clone(nested, mapping); |
| |
| SmallVector<Value, 2> mappedResults; |
| for (auto result : reduceBody->getTerminator()->getOperands()) |
| mappedResults.push_back(mapping.lookup(result)); |
| b.create<scf::YieldOp>(loc, mappedResults); |
| }); |
| |
| rewriter.replaceOp(op, loop.getResults()); |
| return success(); |
| } |
| |
| namespace { |
| /// Converts `shape.shape_eq` to an `scf.for` loop. For now, the lowering is |
| /// only defined on `tensor<?xindex>` operands. The test for equality first |
| /// compares their size and, if equal, checks every extent for equality. |
| /// |
| /// Example: |
| /// |
| /// %result = shape.shape_eq %a, %b : tensor<?xindex>, tensor<?xindex> |
| /// |
| /// becomes |
| /// |
| /// %c0 = arith.constant 0 : index |
| /// %0 = dim %arg0, %c0 : tensor<?xindex> |
| /// %1 = dim %arg1, %c0 : tensor<?xindex> |
| /// %2 = arith.cmpi "eq", %0, %1 : index |
| /// %result = scf.if %2 -> (i1) { |
| /// %c1 = arith.constant 1 : index |
| /// %true = arith.constant true |
| /// %4 = scf.for %arg2 = %c0 to %0 step %c1 iter_args(%arg3 = %true) -> (i1) { |
| /// %5 = tensor.extract %arg0[%arg2] : tensor<?xindex> |
| /// %6 = tensor.extract %arg1[%arg2] : tensor<?xindex> |
| /// %7 = arith.cmpi "eq", %5, %6 : index |
| /// %8 = arith.andi %arg3, %7 : i1 |
| /// scf.yield %8 : i1 |
| /// } |
| /// scf.yield %4 : i1 |
| /// } else { |
| /// %false = arith.constant false |
| /// scf.yield %false : i1 |
| /// } |
| /// |
| struct ShapeEqOpConverter : public OpConversionPattern<ShapeEqOp> { |
| using OpConversionPattern<ShapeEqOp>::OpConversionPattern; |
| |
| LogicalResult |
| matchAndRewrite(ShapeEqOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const override; |
| }; |
| } // namespace |
| |
| LogicalResult |
| ShapeEqOpConverter::matchAndRewrite(ShapeEqOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const { |
| if (!llvm::all_of(op.getShapes(), |
| [](Value v) { return !v.getType().isa<ShapeType>(); })) |
| return failure(); |
| |
| Type i1Ty = rewriter.getI1Type(); |
| if (op.getShapes().size() <= 1) { |
| rewriter.replaceOpWithNewOp<arith::ConstantOp>(op, i1Ty, |
| rewriter.getBoolAttr(true)); |
| return success(); |
| } |
| |
| auto loc = op.getLoc(); |
| Type indexTy = rewriter.getIndexType(); |
| Value zero = rewriter.create<arith::ConstantIndexOp>(loc, 0); |
| Value firstShape = adaptor.getShapes().front(); |
| Value firstRank = |
| rewriter.create<tensor::DimOp>(loc, indexTy, firstShape, zero); |
| Value result = nullptr; |
| // Generate a linear sequence of compares, all with firstShape as lhs. |
| for (Value shape : adaptor.getShapes().drop_front(1)) { |
| Value rank = rewriter.create<tensor::DimOp>(loc, indexTy, shape, zero); |
| Value eqRank = rewriter.create<arith::CmpIOp>(loc, arith::CmpIPredicate::eq, |
| firstRank, rank); |
| auto same = rewriter.create<IfOp>( |
| loc, i1Ty, eqRank, |
| [&](OpBuilder &b, Location loc) { |
| Value one = b.create<arith::ConstantIndexOp>(loc, 1); |
| Value init = |
| b.create<arith::ConstantOp>(loc, i1Ty, b.getBoolAttr(true)); |
| auto loop = b.create<scf::ForOp>( |
| loc, zero, firstRank, one, ValueRange{init}, |
| [&](OpBuilder &b, Location nestedLoc, Value iv, ValueRange args) { |
| Value conj = args[0]; |
| Value lhsExtent = |
| b.create<tensor::ExtractOp>(loc, firstShape, iv); |
| Value rhsExtent = b.create<tensor::ExtractOp>(loc, shape, iv); |
| Value eqExtent = b.create<arith::CmpIOp>( |
| loc, arith::CmpIPredicate::eq, lhsExtent, rhsExtent); |
| Value conjNext = b.create<arith::AndIOp>(loc, conj, eqExtent); |
| b.create<scf::YieldOp>(loc, ValueRange({conjNext})); |
| }); |
| b.create<scf::YieldOp>(loc, loop.getResults()); |
| }, |
| [&](OpBuilder &b, Location loc) { |
| Value result = |
| b.create<arith::ConstantOp>(loc, i1Ty, b.getBoolAttr(false)); |
| b.create<scf::YieldOp>(loc, result); |
| }); |
| result = !result ? same.getResult(0) |
| : rewriter.create<arith::AndIOp>(loc, result, |
| same.getResult(0)); |
| } |
| rewriter.replaceOp(op, result); |
| return success(); |
| } |
| |
| namespace { |
| class ShapeOfOpConversion : public OpConversionPattern<ShapeOfOp> { |
| public: |
| using OpConversionPattern<ShapeOfOp>::OpConversionPattern; |
| |
| LogicalResult |
| matchAndRewrite(ShapeOfOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const override; |
| }; |
| } // namespace |
| |
| LogicalResult ShapeOfOpConversion::matchAndRewrite( |
| ShapeOfOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const { |
| |
| // For now, only error-free types are supported by this lowering. |
| if (op.getType().isa<ShapeType>()) |
| return failure(); |
| |
| // For ranked tensor arguments, lower to `tensor.from_elements`. |
| auto loc = op.getLoc(); |
| Value tensor = adaptor.getArg(); |
| Type tensorTy = tensor.getType(); |
| if (tensorTy.isa<RankedTensorType>()) { |
| |
| // Build values for individual extents. |
| SmallVector<Value, 8> extentValues; |
| RankedTensorType rankedTensorTy = tensorTy.cast<RankedTensorType>(); |
| int64_t rank = rankedTensorTy.getRank(); |
| for (int64_t i = 0; i < rank; i++) { |
| if (rankedTensorTy.isDynamicDim(i)) { |
| Value extent = rewriter.create<tensor::DimOp>(loc, tensor, i); |
| extentValues.push_back(extent); |
| } else { |
| Value extent = rewriter.create<arith::ConstantIndexOp>( |
| loc, rankedTensorTy.getDimSize(i)); |
| extentValues.push_back(extent); |
| } |
| } |
| |
| // Materialize extent tensor. |
| Value staticExtentTensor = rewriter.create<tensor::FromElementsOp>( |
| loc, rewriter.getIndexType(), extentValues); |
| rewriter.replaceOpWithNewOp<tensor::CastOp>(op, op.getType(), |
| staticExtentTensor); |
| return success(); |
| } |
| |
| // Lower to `tensor.generate` otherwise. |
| auto *ctx = rewriter.getContext(); |
| Value rank = rewriter.create<mlir::RankOp>(loc, tensor); |
| rewriter.replaceOpWithNewOp<tensor::GenerateOp>( |
| op, getExtentTensorType(ctx), ValueRange{rank}, |
| [&](OpBuilder &b, Location loc, ValueRange args) { |
| Value dim = args.front(); |
| Value extent = b.create<tensor::DimOp>(loc, tensor, dim); |
| b.create<tensor::YieldOp>(loc, extent); |
| }); |
| |
| return success(); |
| } |
| |
| namespace { |
| class SplitAtOpConversion : public OpConversionPattern<SplitAtOp> { |
| public: |
| using OpConversionPattern<SplitAtOp>::OpConversionPattern; |
| |
| LogicalResult |
| matchAndRewrite(SplitAtOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const override; |
| }; |
| } // namespace |
| |
| LogicalResult SplitAtOpConversion::matchAndRewrite( |
| SplitAtOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const { |
| // Error conditions are not implemented, only lower if all operands and |
| // results are extent tensors. |
| if (llvm::any_of(ValueRange{op.getOperand(), op.getHead(), op.getTail()}, |
| [](Value v) { return v.getType().isa<ShapeType>(); })) |
| return failure(); |
| |
| ImplicitLocOpBuilder b(op.getLoc(), rewriter); |
| Value zero = b.create<arith::ConstantIndexOp>(0); |
| Value rank = b.create<tensor::DimOp>(adaptor.getOperand(), zero); |
| |
| // index < 0 ? index + rank : index |
| Value originalIndex = adaptor.getIndex(); |
| Value add = b.create<arith::AddIOp>(originalIndex, rank); |
| Value indexIsNegative = |
| b.create<arith::CmpIOp>(arith::CmpIPredicate::slt, originalIndex, zero); |
| Value index = b.create<SelectOp>(indexIsNegative, add, originalIndex); |
| |
| Value one = b.create<arith::ConstantIndexOp>(1); |
| Value head = |
| b.create<tensor::ExtractSliceOp>(adaptor.getOperand(), zero, index, one); |
| Value tailSize = b.create<arith::SubIOp>(rank, index); |
| Value tail = b.create<tensor::ExtractSliceOp>(adaptor.getOperand(), index, |
| tailSize, one); |
| rewriter.replaceOp(op, {head, tail}); |
| return success(); |
| } |
| |
| namespace { |
| class ToExtentTensorOpConversion |
| : public OpConversionPattern<ToExtentTensorOp> { |
| public: |
| using OpConversionPattern<ToExtentTensorOp>::OpConversionPattern; |
| |
| LogicalResult |
| matchAndRewrite(ToExtentTensorOp op, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const override { |
| if (!adaptor.getInput().getType().isa<RankedTensorType>()) |
| return rewriter.notifyMatchFailure(op, "input needs to be a tensor"); |
| |
| rewriter.replaceOpWithNewOp<tensor::CastOp>(op, op.getType(), |
| adaptor.getInput()); |
| return success(); |
| } |
| }; |
| } // namespace |
| |
| namespace { |
| /// Import the Shape Ops to Std Patterns. |
| #include "ShapeToStandard.cpp.inc" |
| } // namespace |
| |
| namespace { |
| /// Conversion pass. |
| class ConvertShapeToStandardPass |
| : public ConvertShapeToStandardBase<ConvertShapeToStandardPass> { |
| |
| void runOnOperation() override; |
| }; |
| } // namespace |
| |
| void ConvertShapeToStandardPass::runOnOperation() { |
| // Setup target legality. |
| MLIRContext &ctx = getContext(); |
| ConversionTarget target(ctx); |
| target.addLegalDialect<arith::ArithmeticDialect, StandardOpsDialect, |
| SCFDialect, tensor::TensorDialect>(); |
| target.addLegalOp<CstrRequireOp, FuncOp, ModuleOp>(); |
| |
| // Setup conversion patterns. |
| RewritePatternSet patterns(&ctx); |
| populateShapeToStandardConversionPatterns(patterns); |
| |
| // Apply conversion. |
| auto module = getOperation(); |
| if (failed(applyPartialConversion(module, target, std::move(patterns)))) |
| signalPassFailure(); |
| } |
| |
| void mlir::populateShapeToStandardConversionPatterns( |
| RewritePatternSet &patterns) { |
| // clang-format off |
| populateWithGenerated(patterns); |
| patterns.add< |
| AnyOpConversion, |
| BinaryOpConversion<AddOp, arith::AddIOp>, |
| BinaryOpConversion<MulOp, arith::MulIOp>, |
| BroadcastOpConverter, |
| ConstShapeOpConverter, |
| ConstSizeOpConversion, |
| IsBroadcastableOpConverter, |
| GetExtentOpConverter, |
| RankOpConverter, |
| ReduceOpConverter, |
| ShapeEqOpConverter, |
| ShapeOfOpConversion, |
| SplitAtOpConversion, |
| ToExtentTensorOpConversion>(patterns.getContext()); |
| // clang-format on |
| } |
| |
| std::unique_ptr<OperationPass<ModuleOp>> |
| mlir::createConvertShapeToStandardPass() { |
| return std::make_unique<ConvertShapeToStandardPass>(); |
| } |
