diff --git a/mlir/include/mlir/Dialect/Linalg/IR/Linalg.h b/mlir/include/mlir/Dialect/Linalg/IR/Linalg.h index bb0ac414bcc2d..62c04bb2ee1ab 100644 --- a/mlir/include/mlir/Dialect/Linalg/IR/Linalg.h +++ b/mlir/include/mlir/Dialect/Linalg/IR/Linalg.h @@ -10,6 +10,7 @@ #define MLIR_DIALECT_LINALG_IR_LINALG_H #include "mlir/Bytecode/BytecodeOpInterface.h" +#include "mlir/Dialect/Tensor/IR/Tensor.h" #include "mlir/Dialect/Utils/ReshapeOpsUtils.h" #include "mlir/Dialect/Utils/StructuredOpsUtils.h" #include "mlir/IR/AffineExpr.h" @@ -144,4 +145,17 @@ std::pair getFmrFromWinogradConv2DFmr(WinogradConv2DFmr fmr); #define GET_OP_CLASSES #include "mlir/Dialect/Linalg/IR/LinalgRelayoutOps.h.inc" +namespace mlir { +namespace linalg { + +/// Returns the outer shape in the packed domain before applying the +/// transposition. +template || + std::is_same_v>> +SmallVector getPackedOuterShapeWithoutTransposition(OpTy packOrUnPack); + +} // namespace linalg +} // namespace mlir + #endif // MLIR_DIALECT_LINALG_IR_LINALG_H diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td b/mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td index c384e8b638382..fa572024ff72b 100644 --- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td +++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgRelayoutOps.td @@ -360,6 +360,10 @@ def Linalg_UnPackOp : Linalg_RelayoutOp<"unpack"> { ArrayRef innerPermutation, ArrayRef outerPermutation); + /// Returns true if it is statically known that the `sliceOp` result shape + /// is compatible with the `unPackOp`. I.e., it does not drop any tile. + bool canFoldSliceOp(tensor::ExtractSliceOp sliceOp); + /// Check if this UnPackOp is like a simple unpad operation. /// In other words, this operation: /// 1. drops useless dimensions (dimension of size 1), and diff --git a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp index 3aa6ac3ea0918..3f7756e79210d 100644 --- a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp +++ b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp @@ -4490,6 +4490,29 @@ Speculation::Speculatability ElementwiseOp::getSpeculatability() { //===----------------------------------------------------------------------===// // PackOp/UnPackOp Common //===----------------------------------------------------------------------===// + +template +SmallVector +getPackedOuterShapeWithoutTransposition(OpTy packOrUnPack) { + RankedTensorType packedType = (std::is_same::value) + ? packOrUnPack.getDestType() + : packOrUnPack.getSourceType(); + RankedTensorType unpackedType = (std::is_same::value) + ? packOrUnPack.getSourceType() + : packOrUnPack.getDestType(); + SmallVector result( + packedType.getShape().take_front(unpackedType.getRank())); + if (!packOrUnPack.getOuterDimsPerm().empty()) { + applyPermutationToVector( + result, invertPermutationVector(packOrUnPack.getOuterDimsPerm())); + } + return result; +} +template SmallVector + getPackedOuterShapeWithoutTransposition(PackOp); +template SmallVector + getPackedOuterShapeWithoutTransposition(UnPackOp); + // Given the (potentially) updated packed type, `newPackedTy`, generates an // updated mixed-tile-sizes attribute. A tile size is updated only // when: @@ -5447,11 +5470,7 @@ LogicalResult UnPackOp::canonicalize(UnPackOp unPackOp, if (unPackOp->hasOneUse()) { auto extractSliceUser = dyn_cast(*unPackOp->getUsers().begin()); - if (extractSliceUser && - areAllConstantIntValue(extractSliceUser.getMixedOffsets(), 0) && - areAllConstantIntValue(extractSliceUser.getMixedStrides(), 1) && - extractSliceUser.getSourceType().getRank() == - extractSliceUser.getResultType().getRank()) { + if (extractSliceUser && unPackOp.canFoldSliceOp(extractSliceUser)) { OpBuilder::InsertionGuard g(rewriter); rewriter.setInsertionPoint(unPackOp); auto newDest = rewriter.create( @@ -5494,6 +5513,32 @@ LogicalResult UnPackOp::canonicalize(UnPackOp unPackOp, return failure(); } +bool UnPackOp::canFoldSliceOp(tensor::ExtractSliceOp sliceOp) { + // Rank-reduced folding is not supported. + if (sliceOp.getResultType().getRank() != this->getDestType().getRank()) + return false; + if (!areAllConstantIntValue(sliceOp.getMixedOffsets(), 0) || + !areAllConstantIntValue(sliceOp.getMixedStrides(), 1)) + return false; + RankedTensorType unpackedTypeAfterFold = sliceOp.getResultType(); + SmallVector outerShapeWithoutTranspose = + getPackedOuterShapeWithoutTransposition(*this); + for (auto [pos, tileSize] : + llvm::zip_equal(this->getInnerDimsPos(), this->getStaticInnerTiles())) { + if (unpackedTypeAfterFold.isDynamicDim(pos)) + return false; + if (ShapedType::isDynamic(outerShapeWithoutTranspose[pos])) + return false; + if (ShapedType::isDynamic(tileSize)) + return false; + int64_t paddingSize = outerShapeWithoutTranspose[pos] * tileSize - + unpackedTypeAfterFold.getDimSize(pos); + if (paddingSize >= tileSize) + return false; + } + return true; +} + bool UnPackOp::isLikeUnPad() { RankedTensorType packedTensorType = getSourceType(); return isLikePadUnPad(*this, packedTensorType); diff --git a/mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp b/mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp index 2afa2f9b71c2a..0b5eb449ffa2f 100644 --- a/mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp +++ b/mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp @@ -220,6 +220,33 @@ struct FoldPadWithPackOp : public OpRewritePattern { if (!isEqualConstantIntOrValue(paddingValue, constantPaddingValue)) return failure(); + // Folding is not allowed if it were to introduce artificial padding. + // Folding is also disabled in the case of dynamic dimensions and/or tile + // sizes - that is because it would be impossible to compute the padding + // size and hence to establish whether "artificial" padding would be + // created. + RankedTensorType unpackedType = packOp.getSourceType(); + SmallVector outerShapeWithoutTranspose = + getPackedOuterShapeWithoutTransposition(packOp); + for (auto [pos, tileSize, high] : + llvm::zip_equal(packOp.getInnerDimsPos(), packOp.getStaticInnerTiles(), + padOp.getMixedHighPad())) { + if (unpackedType.isDynamicDim(pos)) + return failure(); + if (ShapedType::isDynamic(outerShapeWithoutTranspose[pos])) + return failure(); + if (ShapedType::isDynamic(tileSize)) + return failure(); + std::optional cstHigh = getConstantIntValue(high); + if (!cstHigh) + return failure(); + int64_t paddingSize = outerShapeWithoutTranspose[pos] * tileSize - + unpackedType.getDimSize(pos); + // Do not fold the op if it requires artificial padding. + if (paddingSize + cstHigh.value() >= tileSize) + return failure(); + } + rewriter.replaceOpWithNewOp( packOp, padOp.getSource(), packOp.getDest(), packOp.getInnerDimsPos(), packOp.getMixedTiles(), constantPaddingValue, @@ -251,17 +278,8 @@ struct FoldUnpackWithExtractSliceOp if (controlFn && !controlFn(&sliceOp.getSourceMutable())) return failure(); - if (sliceOp.getResultType().getRank() != unpackOp.getDestType().getRank()) { - return rewriter.notifyMatchFailure( - sliceOp, "rank-reduced folding is not supported"); - } - - // Check all offsets are zeros, and all strides are ones. - if (!areAllConstantIntValue(sliceOp.getMixedOffsets(), 0) || - !areAllConstantIntValue(sliceOp.getMixedStrides(), 1)) { - return rewriter.notifyMatchFailure( - sliceOp, "expects offsets to be 0s and strides to be 1s"); - } + if (!unpackOp.canFoldSliceOp(sliceOp)) + return failure(); // Create a new empty output tensor. Type elementType = unpackOp.getDestType().getElementType(); diff --git a/mlir/test/Dialect/Linalg/canonicalize.mlir b/mlir/test/Dialect/Linalg/canonicalize.mlir index 7284ae7dbd673..9cbb56e4de884 100644 --- a/mlir/test/Dialect/Linalg/canonicalize.mlir +++ b/mlir/test/Dialect/Linalg/canonicalize.mlir @@ -1889,31 +1889,84 @@ func.func @fold_cast_unpack_dynamic_tile_size( // linalg.unpack + tensor.extract_slice //===----------------------------------------------------------------------===// -func.func @fold_extract_slice_into_unpack( - %src : tensor<28x2x?x16x16xf32>, %dest : tensor<28x32x?xf32>, %size : index -) -> tensor<28x28x?xf32> { +func.func @fold_extract_slice_into_unpack_slicing_trailing_dim(%src : tensor<28x2x1x16x16xf32>, %dest : tensor<28x28x15xf32>) -> tensor<28x28x10xf32> { %unpack = linalg.unpack %src outer_dims_perm = [0, 1, 2] inner_dims_pos = [1, 2] inner_tiles = [16, 16] - into %dest : tensor<28x2x?x16x16xf32> -> tensor<28x32x?xf32> + into %dest : tensor<28x2x1x16x16xf32> -> tensor<28x28x15xf32> %extracted_slice = tensor.extract_slice %unpack - [0, 0, 0] [28, 28, %size] [1, 1, 1] : tensor<28x32x?xf32> to tensor<28x28x?xf32> - return %extracted_slice : tensor<28x28x?xf32> + [0, 0, 0] [28, 28, 10] [1, 1, 1] : tensor<28x28x15xf32> to tensor<28x28x10xf32> + return %extracted_slice : tensor<28x28x10xf32> } +// CHECK-LABEL: func @fold_extract_slice_into_unpack_slicing_trailing_dim +// CHECK-SAME: %[[SRC:[a-zA-Z0-9]+]] +// CHECK-SAME: %[[DEST:[a-zA-Z0-9]+]] +// CHECK: %[[DEST_SLICE:.+]] = tensor.extract_slice %[[DEST]] +// CHECK-SAME: [0, 0, 0] [28, 28, 10] [1, 1, 1] +// CHECK: %[[UNPACK:.+]] = linalg.unpack %[[SRC]] +// CHECK-SAME: into %[[DEST_SLICE]] +// CHECK: return %[[UNPACK]] + +// ----- + +// The available dimension size is [17, 32], because CeilDiv(%d1, 16) == 2. -// CHECK-LABEL: func @fold_extract_slice_into_unpack -// CHECK-SAME: %[[SRC:.+]]: tensor<28x2x?x16x16xf32> -// CHECK-SAME: %[[DEST:.+]]: tensor<28x32x?xf32> -// CHECK-SAME: %[[SIZE:.+]]: index +func.func @fold_extract_slice_into_unpack_slicing_dim_1(%src : tensor<28x2x1x16x16xf32>, %dest : tensor<28x28x15xf32>) -> tensor<28x17x15xf32> { + %unpack = linalg.unpack %src + inner_dims_pos = [1, 2] + inner_tiles = [16, 16] + into %dest : tensor<28x2x1x16x16xf32> -> tensor<28x28x15xf32> + %extracted_slice = tensor.extract_slice %unpack + [0, 0, 0] [28, 17, 15] [1, 1, 1] : tensor<28x28x15xf32> to tensor<28x17x15xf32> + return %extracted_slice : tensor<28x17x15xf32> +} +// CHECK-LABEL: func @fold_extract_slice_into_unpack_slicing_dim_1( +// CHECK-SAME: %[[SRC:[a-zA-Z0-9]+]] +// CHECK-SAME: %[[DEST:[a-zA-Z0-9]+]] // CHECK: %[[DEST_SLICE:.+]] = tensor.extract_slice %[[DEST]] -// CHECK-SAME: [0, 0, 0] [28, 28, %[[SIZE]]] [1, 1, 1] +// CHECK-SAME: [0, 0, 0] [28, 17, 15] [1, 1, 1] // CHECK: %[[UNPACK:.+]] = linalg.unpack %[[SRC]] // CHECK-SAME: into %[[DEST_SLICE]] // CHECK: return %[[UNPACK]] // ----- +// The available dimension size is [17, 32], because CeilDiv(%d1, 16) == 2. + +func.func @no_fold_extract_slice_into_unpack_artificial_padding(%src : tensor<28x2x1x16x16xf32>, %dest : tensor<28x28x15xf32>) -> tensor<28x16x15xf32> { + %unpack = linalg.unpack %src + inner_dims_pos = [1, 2] + inner_tiles = [16, 16] + into %dest : tensor<28x2x1x16x16xf32> -> tensor<28x28x15xf32> + %extracted_slice = tensor.extract_slice %unpack + [0, 0, 0] [28, 16, 15] [1, 1, 1] : tensor<28x28x15xf32> to tensor<28x16x15xf32> + return %extracted_slice : tensor<28x16x15xf32> +} +// CHECK-LABEL: func @no_fold_extract_slice_into_unpack_artificial_padding +// CHECK: linalg.unpack +// CHECK: tensor.extract_slice + +// ----- + +func.func @no_fold_extract_slice_into_unpack_dynamic( + %src : tensor<28x2x?x16x16xf32>, %dest : tensor<28x32x?xf32>, %size : index +) -> tensor<28x28x?xf32> { + %unpack = linalg.unpack %src + outer_dims_perm = [0, 1, 2] + inner_dims_pos = [1, 2] + inner_tiles = [16, 16] + into %dest : tensor<28x2x?x16x16xf32> -> tensor<28x32x?xf32> + %extracted_slice = tensor.extract_slice %unpack + [0, 0, 0] [28, 28, %size] [1, 1, 1] : tensor<28x32x?xf32> to tensor<28x28x?xf32> + return %extracted_slice : tensor<28x28x?xf32> +} +// CHECK-LABEL: func @no_fold_extract_slice_into_unpack_dynamic +// CHECK: linalg.unpack +// CHECK: tensor.extract_slice + +// ----- + func.func @no_fold_extract_slice_into_unpack_rank_reducing( %src : tensor<28x2x16xf32>, %dest : tensor<28x32xf32> ) -> tensor<28xf32> { diff --git a/mlir/test/Dialect/Tensor/fold-into-pack-and-unpack.mlir b/mlir/test/Dialect/Tensor/fold-into-pack-and-unpack.mlir index 16efa73f87a2a..9da2dea0bbd3c 100644 --- a/mlir/test/Dialect/Tensor/fold-into-pack-and-unpack.mlir +++ b/mlir/test/Dialect/Tensor/fold-into-pack-and-unpack.mlir @@ -1,22 +1,92 @@ // RUN: mlir-opt -split-input-file -test-linalg-transform-patterns=test-fold-into-pack-and-unpack %s | FileCheck %s // RUN: mlir-opt -split-input-file -test-linalg-transform-patterns=test-fold-into-pack-and-unpack-control %s | FileCheck %s --check-prefix=CONTROL -func.func @fold_unpack_slice(%arg0 : tensor, %arg1 : tensor, +func.func @fold_extract_slice_into_unpack_slicing_trailing_dim(%arg0 : tensor<28x2x1x16x16xf32>) -> tensor<28x28x10xf32> { + %empty = tensor.empty() : tensor<28x28x15xf32> + %unpack = linalg.unpack %arg0 + inner_dims_pos = [1, 2] + inner_tiles = [16, 16] + into %empty : tensor<28x2x1x16x16xf32> -> tensor<28x28x15xf32> + %extracted_slice = tensor.extract_slice %unpack + [0, 0, 0] [28, 28, 10] [1, 1, 1] : tensor<28x28x15xf32> to tensor<28x28x10xf32> + return %extracted_slice : tensor<28x28x10xf32> +} +// CHECK-LABEL: func @fold_extract_slice_into_unpack_slicing_trailing_dim +// CHECK-SAME: %[[SRC:[a-zA-Z0-9]+]] +// CHECK: %[[DEST_SLICE:.+]] = tensor.empty() : tensor<28x28x10xf32> +// CHECK: %[[UNPACK:.+]] = linalg.unpack %[[SRC]] +// CHECK-SAME: into %[[DEST_SLICE]] +// CHECK: return %[[UNPACK]] + +// ----- + +// The available dimension size is [17, 32], because CeilDiv(%d1, 16) == 2. + +func.func @fold_extract_slice_into_unpack_slicing_dim_1(%arg0 : tensor<28x2x1x16x16xf32>) -> tensor<28x17x15xf32> { + %empty = tensor.empty() : tensor<28x28x15xf32> + %unpack = linalg.unpack %arg0 + inner_dims_pos = [1, 2] + inner_tiles = [16, 16] + into %empty : tensor<28x2x1x16x16xf32> -> tensor<28x28x15xf32> + %extracted_slice = tensor.extract_slice %unpack + [0, 0, 0] [28, 17, 15] [1, 1, 1] : tensor<28x28x15xf32> to tensor<28x17x15xf32> + return %extracted_slice : tensor<28x17x15xf32> +} +// CHECK-LABEL: func @fold_extract_slice_into_unpack_slicing_dim_1( +// CHECK-SAME: %[[SRC:[a-zA-Z0-9]+]] +// CHECK: %[[DEST_SLICE:.+]] = tensor.empty() : tensor<28x17x15xf32> +// CHECK: %[[UNPACK:.+]] = linalg.unpack %[[SRC]] +// CHECK-SAME: into %[[DEST_SLICE]] +// CHECK: return %[[UNPACK]] + +// ----- + +// The available dimension size is [17, 32], because CeilDiv(%d1, 16) == 2. + +func.func @no_fold_extract_slice_into_unpack_artificial_padding(%arg0 : tensor<28x2x1x16x16xf32>) -> tensor<28x16x15xf32> { + %empty = tensor.empty() : tensor<28x28x15xf32> + %unpack = linalg.unpack %arg0 + inner_dims_pos = [1, 2] + inner_tiles = [16, 16] + into %empty : tensor<28x2x1x16x16xf32> -> tensor<28x28x15xf32> + %extracted_slice = tensor.extract_slice %unpack + [0, 0, 0] [28, 16, 15] [1, 1, 1] : tensor<28x28x15xf32> to tensor<28x16x15xf32> + return %extracted_slice : tensor<28x16x15xf32> +} +// CHECK-LABEL: func @no_fold_extract_slice_into_unpack_artificial_padding +// CHECK: linalg.unpack +// CHECK: tensor.extract_slice + +// ----- + +func.func @no_fold_extract_slice_into_unpack_dynamic( + %src : tensor<28x2x?x16x16xf32>, %dest : tensor<28x32x?xf32>, %size : index +) -> tensor<28x28x?xf32> { + %unpack = linalg.unpack %src + outer_dims_perm = [0, 1, 2] + inner_dims_pos = [1, 2] + inner_tiles = [16, 16] + into %dest : tensor<28x2x?x16x16xf32> -> tensor<28x32x?xf32> + %extracted_slice = tensor.extract_slice %unpack + [0, 0, 0] [28, 28, %size] [1, 1, 1] : tensor<28x32x?xf32> to tensor<28x28x?xf32> + return %extracted_slice : tensor<28x28x?xf32> +} +// CHECK-LABEL: func @no_fold_extract_slice_into_unpack_dynamic +// CHECK: linalg.unpack +// CHECK: tensor.extract_slice + +// ----- + +func.func @nofold_dynamic_unpack_slice(%arg0 : tensor, %arg1 : tensor, %arg2 : index, %arg3 : index) -> tensor { %0 = linalg.unpack %arg0 inner_dims_pos = [0, 1] inner_tiles = [8, 4] into %arg1 : tensor -> tensor %1 = tensor.extract_slice %0[0, 0] [%arg2, %arg3] [1, 1] : tensor to tensor return %1 : tensor } -// CHECK: func @fold_unpack_slice( -// CHECK-SAME: %[[ARG0:.+]]: tensor -// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor -// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: index -// CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]]: index -// CHECK: %[[INIT:.+]] = tensor.empty(%[[ARG2]], %[[ARG3]]) : tensor -// CHECK: %[[UNPACK:.+]] = linalg.unpack %[[ARG0]] inner_dims_pos = [0, 1] inner_tiles = [8, 4] -// CHECK-SAME: into %[[INIT]] -// CHECK: return %[[UNPACK]] +// CHECK-LABEL: func @nofold_dynamic_unpack_slice( +// CHECK: linalg.unpack +// CHECK: tensor.extract_slice // ----- @@ -59,48 +129,62 @@ func.func @nofold_unpack_slice_rank_reduced(%arg0 : tensor, %arg1 : // ----- -func.func @pad_pack(%src: tensor<16641x16xf32>) -> tensor<2082x1x8x32xf32> { - %c0 = arith.constant 0 : index +func.func @fold_pad_pack(%src: tensor<9x16xf32>) -> tensor<2x1x8x32xf32> { %cst = arith.constant 0.000000e+00 : f32 - %padded = tensor.pad %src low[0, 0] high[15, 0] { + %padded = tensor.pad %src low[0, 0] high[7, 0] { ^bb0(%arg0: index, %arg1: index): tensor.yield %cst : f32 - } : tensor<16641x16xf32> to tensor<16656x16xf32> - %empty = tensor.empty() : tensor<2082x1x8x32xf32> + } : tensor<9x16xf32> to tensor<16x16xf32> + %empty = tensor.empty() : tensor<2x1x8x32xf32> %pack = linalg.pack %padded padding_value(%cst : f32) inner_dims_pos = [0, 1] inner_tiles = [8, 32] into %empty - : tensor<16656x16xf32> -> tensor<2082x1x8x32xf32> - return %pack : tensor<2082x1x8x32xf32> + : tensor<16x16xf32> -> tensor<2x1x8x32xf32> + return %pack : tensor<2x1x8x32xf32> } -// CHECK-LABEL: func.func @pad_pack +// CHECK-LABEL: func.func @fold_pad_pack // CHECK-SAME: %[[SRC:[a-zA-Z0-9]+]] // CHECK: %[[PAD_VAL:.+]] = arith.constant 0.000000e+00 : f32 -// CHECK: %[[DEST:.+]] = tensor.empty() : tensor<2082x1x8x32xf32> +// CHECK: %[[DEST:.+]] = tensor.empty() : tensor<2x1x8x32xf32> // CHECK: %[[PACK:.+]] = linalg.pack %[[SRC]] // CHECK-SAME: padding_value(%[[PAD_VAL]] : f32) // CHECK-SAME: inner_dims_pos = [0, 1] inner_tiles = [8, 32] into %[[DEST]] // ----- -func.func @nofold_pad_pack(%src: tensor<16641x16xf32>) -> tensor<2082x1x8x32xf32> { - %c0 = arith.constant 0 : index +func.func @nofold_pad_pack_artificial_padding(%src: tensor<9x16xf32>) -> tensor<3x1x8x32xf32> { %cst = arith.constant 0.000000e+00 : f32 - %padded = tensor.pad %src nofold low[0, 0] high[15, 0] { + %padded = tensor.pad %src low[0, 0] high[8, 0] { ^bb0(%arg0: index, %arg1: index): tensor.yield %cst : f32 - } : tensor<16641x16xf32> to tensor<16656x16xf32> + } : tensor<9x16xf32> to tensor<17x16xf32> + %empty = tensor.empty() : tensor<3x1x8x32xf32> + %pack = linalg.pack %padded padding_value(%cst : f32) inner_dims_pos = [0, 1] inner_tiles = [8, 32] into %empty + : tensor<17x16xf32> -> tensor<3x1x8x32xf32> + return %pack : tensor<3x1x8x32xf32> +} +// CHECK-LABLE: func.func @nofold_pad_pack_artificial_padding( +// CHECK: tensor.pad +// CHECK: linalg.pack + +// ----- + +func.func @nofold_pad_pack_with_nofold_attribute(%src: tensor<16649x16xf32>) -> tensor<2082x1x8x32xf32> { + %cst = arith.constant 0.000000e+00 : f32 + %padded = tensor.pad %src nofold low[0, 0] high[7, 0] { + ^bb0(%arg0: index, %arg1: index): + tensor.yield %cst : f32 + } : tensor<16649x16xf32> to tensor<16656x16xf32> %empty = tensor.empty() : tensor<2082x1x8x32xf32> %pack = linalg.pack %padded padding_value(%cst : f32) inner_dims_pos = [0, 1] inner_tiles = [8, 32] into %empty : tensor<16656x16xf32> -> tensor<2082x1x8x32xf32> return %pack : tensor<2082x1x8x32xf32> } -// CHECK-LABEL: func.func @nofold_pad_pack +// CHECK-LABEL: func.func @nofold_pad_pack_with_nofold_attribute( // CHECK: tensor.pad // CHECK: linalg.pack // ----- func.func @pad_pack_different_padding_value(%src: tensor<16641x16xf32>) -> tensor<2082x1x8x32xf32> { - %c0 = arith.constant 0 : index %cst0 = arith.constant 0.000000e+00 : f32 %cst1 = arith.constant 1.000000e+00 : f32 %padded = tensor.pad %src low[0, 0] high[15, 0] {