[Mlir-commits] [mlir] aa7ae1b - [mlir][tensor] Fold producer linalg transpose with consumer unpack an… (#86795)
llvmlistbot at llvm.org
llvmlistbot at llvm.org
Thu Mar 28 10:43:38 PDT 2024
Author: Prashant Kumar
Date: 2024-03-28T23:13:33+05:30
New Revision: aa7ae1ba0bfc3421cc933c9d0e236e5f642d23ac
URL: https://github.com/llvm/llvm-project/commit/aa7ae1ba0bfc3421cc933c9d0e236e5f642d23ac
DIFF: https://github.com/llvm/llvm-project/commit/aa7ae1ba0bfc3421cc933c9d0e236e5f642d23ac.diff
LOG: [mlir][tensor] Fold producer linalg transpose with consumer unpack an… (#86795)
…d viceversa
-- Adds folding of producer linalg transpose op with consumer unpack op,
also adds folding of producer unpack op and consumer transpose op.
-- Minor bug fixes w.r.t. to the test cases.
Added:
Modified:
mlir/lib/Dialect/Tensor/Transforms/PackAndUnpackPatterns.cpp
mlir/test/Dialect/Tensor/fold-into-pack-and-unpack.mlir
Removed:
################################################################################
diff --git a/mlir/lib/Dialect/Tensor/Transforms/PackAndUnpackPatterns.cpp b/mlir/lib/Dialect/Tensor/Transforms/PackAndUnpackPatterns.cpp
index 55fd387496690a..666ac56c6cd5cd 100644
--- a/mlir/lib/Dialect/Tensor/Transforms/PackAndUnpackPatterns.cpp
+++ b/mlir/lib/Dialect/Tensor/Transforms/PackAndUnpackPatterns.cpp
@@ -11,7 +11,6 @@
#include "mlir/Dialect/Tensor/Transforms/Transforms.h"
#include "mlir/Dialect/Utils/IndexingUtils.h"
#include "mlir/IR/PatternMatch.h"
-#include "llvm/Support/Debug.h"
namespace mlir {
namespace tensor {
@@ -224,6 +223,33 @@ struct FoldUnpackWithExtractSliceOp : public OpRewritePattern<ExtractSliceOp> {
}
};
+// Applies 'permutation' on 'inVec' and stores the result in resVec.
+// 'inVec' may be empty, in that case it's one-to-one mapping with permutation.
+// `rank` sets the boundary for permutation i.e., the permutation dim can't be
+// greater than the rank specified. If it's so then return false.
+// For e.g., permutation {1, 0, 3, 2} with rank 2 is allowed since the values in
+// permutation[:rank] doesn't exceed rank, whereas, permutation {1, 3, 0, 2} is
+// not allowed since `3` exceeds the value of the rank in the given range.
+static bool checkAndPermute(ArrayRef<int64_t> permutation,
+ ArrayRef<int64_t> inVec,
+ SmallVectorImpl<int64_t> &resVec, int64_t rank) {
+
+ for (unsigned int i = 0; i < rank; ++i) {
+ int64_t remappedPosition = permutation[i];
+
+ if (!inVec.empty()) {
+ if (remappedPosition >= rank) {
+ return false;
+ }
+ remappedPosition = inVec[remappedPosition];
+ }
+
+ resVec.push_back(remappedPosition);
+ }
+
+ return true;
+}
+
/// Fold 'pack' -> 'transpose' into 'pack' since 'pack' already has transpose
/// semantics.
struct FoldProducerPackWithConsumerLinalgTransposeOp
@@ -246,24 +272,12 @@ struct FoldProducerPackWithConsumerLinalgTransposeOp
SmallVector<OpFoldResult> newMixedInnerTilesVec;
int64_t srcRank = packOp.getSourceRank();
- // Process transpose operation for non-tiled outer dimensions
- for (unsigned int i = 0; i < srcRank; ++i) {
- int64_t remappedPosition = transposePerm[i];
-
- // If tensor.pack has outer_dims_perm attribute, then consider it during
- // index remapping.
- if (!outerDimsPerm.empty()) {
- if (transposePerm[i] >= srcRank) {
- return rewriter.notifyMatchFailure(
- transposeOp,
- "Cannot fold in tensor.pack if a tile dimension was transposed "
- "with a non-tile dimension in linalg.transpose.");
- }
- remappedPosition = outerDimsPerm[remappedPosition];
- }
-
- newOuterDimsPermVec.push_back(remappedPosition);
- }
+ if (!checkAndPermute(transposePerm, outerDimsPerm, newOuterDimsPermVec,
+ srcRank))
+ return rewriter.notifyMatchFailure(
+ transposeOp,
+ "Cannot fold in tensor.pack if a tile dimension was transposed "
+ "with a non-tile dimension in linalg.transpose.");
// Process transpose operation for tiled inner dimensions
for (unsigned int i = srcRank; i < transposePerm.size(); ++i) {
@@ -323,12 +337,103 @@ struct FoldConsumerPackWithProducerLinalgTransposeOp
return success();
}
};
+
+/// Fold 'unpack' -> 'transpose' into 'unpack' since 'unpack' already has
+/// transpose semantics.
+struct FoldProducerUnPackWithConsumerLinalgTransposeOp
+ : public OpRewritePattern<linalg::TransposeOp> {
+ using OpRewritePattern<linalg::TransposeOp>::OpRewritePattern;
+
+ LogicalResult matchAndRewrite(linalg::TransposeOp transposeOp,
+ PatternRewriter &rewriter) const override {
+ auto unPackOp = transposeOp.getOperand(0).getDefiningOp<UnPackOp>();
+
+ if (!unPackOp)
+ return failure();
+
+ auto transposePermutation = transposeOp.getPermutation();
+ auto outerDimsPerm = unPackOp.getOuterDimsPerm();
+ auto innerDimsPos = unPackOp.getInnerDimsPos();
+ SmallVector<int64_t> newInnerDimsPosVec;
+ SmallVector<int64_t> newOuterDimsPermVec =
+ llvm::to_vector(transposePermutation);
+
+ if (!outerDimsPerm.empty())
+ applyPermutationToVector(newOuterDimsPermVec, outerDimsPerm);
+
+ // Can't use applyPermutationToVector for newInnerDimsPosVec since input and
+ // permutation rank won't necessarily be equal in all cases.
+ for (auto dim : innerDimsPos)
+ newInnerDimsPosVec.push_back(transposePermutation[dim]);
+
+ Value output = unPackOp.createDestinationTensor(
+ rewriter, transposeOp.getLoc(), unPackOp.getSource(),
+ unPackOp.getMixedTiles(), newInnerDimsPosVec, newOuterDimsPermVec);
+
+ rewriter.replaceOpWithNewOp<UnPackOp>(
+ transposeOp, unPackOp.getSource(), output, newInnerDimsPosVec,
+ unPackOp.getMixedTiles(), newOuterDimsPermVec);
+
+ return success();
+ }
+};
+
+/// Fold 'transpose' -> 'unpack' into 'unpack' since 'unpack' already has
+/// transpose semantics.
+struct FoldConsumerUnPackWithProducerLinalgTransposeOp
+ : public OpRewritePattern<UnPackOp> {
+ using OpRewritePattern<UnPackOp>::OpRewritePattern;
+
+ LogicalResult matchAndRewrite(UnPackOp unPackOp,
+ PatternRewriter &rewriter) const override {
+ auto transposeOp =
+ unPackOp.getSource().getDefiningOp<linalg::TransposeOp>();
+
+ if (!transposeOp)
+ return failure();
+
+ auto transposePermutation = transposeOp.getPermutation();
+ auto outerDimsPerm = unPackOp.getOuterDimsPerm();
+ auto innerDimsPos = unPackOp.getInnerDimsPos();
+ int64_t destRank = unPackOp.getSourceRank() - innerDimsPos.size();
+ auto mixedInnerTilesVec = unPackOp.getMixedTiles();
+ SmallVector<int64_t> newOuterDimsPermVec;
+ SmallVector<int64_t> newInnerDimsPosVec;
+ SmallVector<OpFoldResult> newMixedInnerTilesVec;
+
+ if (!checkAndPermute(transposePermutation, outerDimsPerm,
+ newOuterDimsPermVec, destRank))
+ return rewriter.notifyMatchFailure(
+ unPackOp,
+ "Cannot fold in tensor.unpack if a tile dimension was transposed "
+ "with a non-tile dimension in linalg.transpose.");
+
+ // Process transpose operation for tiled inner dimensions
+ for (unsigned int i = destRank; i < transposePermutation.size(); ++i) {
+ int64_t remappedPosition = transposePermutation[i] - destRank;
+ newMixedInnerTilesVec.push_back(mixedInnerTilesVec[remappedPosition]);
+ newInnerDimsPosVec.push_back(innerDimsPos[remappedPosition]);
+ }
+
+ Value output = unPackOp.createDestinationTensor(
+ rewriter, unPackOp.getLoc(), transposeOp.getOperand(0),
+ newMixedInnerTilesVec, newInnerDimsPosVec, newOuterDimsPermVec);
+
+ rewriter.replaceOpWithNewOp<UnPackOp>(
+ unPackOp, transposeOp.getOperand(0), output, newInnerDimsPosVec,
+ newMixedInnerTilesVec, newOuterDimsPermVec);
+
+ return success();
+ }
+};
} // namespace
void populateFoldIntoPackAndUnpackPatterns(RewritePatternSet &patterns) {
patterns.insert<FoldUnpackWithExtractSliceOp, FoldPadWithPackOp,
FoldProducerPackWithConsumerLinalgTransposeOp,
- FoldConsumerPackWithProducerLinalgTransposeOp>(
+ FoldConsumerPackWithProducerLinalgTransposeOp,
+ FoldConsumerUnPackWithProducerLinalgTransposeOp,
+ FoldProducerUnPackWithConsumerLinalgTransposeOp>(
patterns.getContext());
}
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 682107dbebbfae..aa860e33cf3517 100644
--- a/mlir/test/Dialect/Tensor/fold-into-pack-and-unpack.mlir
+++ b/mlir/test/Dialect/Tensor/fold-into-pack-and-unpack.mlir
@@ -290,22 +290,21 @@ func.func @tensor_pack_linalg_transpose_fold_dynamic_outer_and_tile_dims(%arg0:
%return_value = tensor.cast %transposed : tensor<12x4x56x9x32x8x2xf32> to tensor<?x?x56x9x32x8x2xf32>
return %return_value : tensor<?x?x56x9x32x8x2xf32>
}
-// CHECK: #[[map:.+]] = affine_map<()[s0] -> (s0 ceildiv 8)>
-// CHECK: #[[map1:.+]] = affine_map<()[s0] -> (s0 ceildiv 2)>
-// CHECK: module {
-// CHECK: func.func @tensor_pack_linalg_transpose_fold_dynamic_outer_and_tile_dims(
-// CHECK-SAME: %[[ARG0:.+]]: tensor<56x?x?x128xf32>)
-// CHECK-DAG: %[[c1:.+]] = arith.constant 1 : index
-// CHECK-DAG: %[[c2:.+]] = arith.constant 2 : index
-// CHECK: %[[dim:.+]] = tensor.dim %[[ARG0]], %[[c1]] : tensor<56x?x?x128xf32>
-// CHECK: %[[dim_0:.+]] = tensor.dim %[[ARG0]], %[[c2]] : tensor<56x?x?x128xf32>
-// CHECK: %[[mapped_dim1:.+]] = affine.apply #[[map:.+]]()[%[[dim]]]
-// CHECK: %[[mapped_dim2:.+]] = affine.apply #[[map1:.+]]()[%[[dim_0]]]
-// CHECK: %[[INIT:.+]] = tensor.empty(%[[mapped_dim2]], %[[mapped_dim1]]) : tensor<?x4x56x?x32x8x2xf32>
-// CHECK: %[[PACK:.+]] = tensor.pack %[[ARG0]] outer_dims_perm = [2, 3, 0, 1] inner_dims_pos = [3, 1, 2] inner_tiles = [32, 8, 2] into %[[INIT]] : tensor<56x?x?x128xf32> -> tensor<?x4x56x?x32x8x2xf32>
-// CHECK: %[[CAST:.+]] = tensor.cast %[[PACK]] : tensor<?x4x56x?x32x8x2xf32> to tensor<?x?x56x9x32x8x2xf32>
-// CHECK: return %[[CAST]] : tensor<?x?x56x9x32x8x2xf32>
-// CHECK: }
+// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 8)>
+// CHECK-DAG: #[[$MAP1:.+]] = affine_map<()[s0] -> (s0 ceildiv 2)>
+// CHECK-LABEL: func.func @tensor_pack_linalg_transpose_fold_dynamic_outer_and_tile_dims(
+// CHECK-SAME: %[[ARG0:.+]]: tensor<56x?x?x128xf32>)
+// CHECK-DAG: %[[c1:.+]] = arith.constant 1 : index
+// CHECK-DAG: %[[c2:.+]] = arith.constant 2 : index
+// CHECK: %[[dim:.+]] = tensor.dim %[[ARG0]], %[[c1]] : tensor<56x?x?x128xf32>
+// CHECK: %[[dim_0:.+]] = tensor.dim %[[ARG0]], %[[c2]] : tensor<56x?x?x128xf32>
+// CHECK: %[[mapped_dim1:.+]] = affine.apply #[[$MAP0]]()[%[[dim]]]
+// CHECK: %[[mapped_dim2:.+]] = affine.apply #[[$MAP1]]()[%[[dim_0]]]
+// CHECK: %[[INIT:.+]] = tensor.empty(%[[mapped_dim2]], %[[mapped_dim1]]) : tensor<?x4x56x?x32x8x2xf32>
+// CHECK: %[[PACK:.+]] = tensor.pack %[[ARG0]] outer_dims_perm = [2, 3, 0, 1] inner_dims_pos = [3, 1, 2] inner_tiles = [32, 8, 2] into %[[INIT]] : tensor<56x?x?x128xf32> -> tensor<?x4x56x?x32x8x2xf32>
+// CHECK: %[[CAST:.+]] = tensor.cast %[[PACK]] : tensor<?x4x56x?x32x8x2xf32> to tensor<?x?x56x9x32x8x2xf32>
+// CHECK: return %[[CAST]] : tensor<?x?x56x9x32x8x2xf32>
+// CHECK: }
// -----
@@ -323,7 +322,7 @@ func.func @tensor_pack_linalg_transpose_fold_dynamic_outer_dims_tile_dims_tile_s
return %transposed : tensor<?x?x?x?x?x?x?xf32>
}
-// CHECK: #[[map:.+]] = affine_map<()[s0, s1] -> (s0 ceildiv s1)>
+// CHECK: #[[$MAP:.+]] = affine_map<()[s0, s1] -> (s0 ceildiv s1)>
// CHECK: module {
// CHECK: func.func @tensor_pack_linalg_transpose_fold_dynamic_outer_dims_tile_dims_tile_sizes(
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?x?x?xf32>,
@@ -338,9 +337,9 @@ func.func @tensor_pack_linalg_transpose_fold_dynamic_outer_dims_tile_dims_tile_s
// CHECK: %[[dim_0:.+]] = tensor.dim %[[ARG0]], %[[c1]] : tensor<?x?x?x?xf32>
// CHECK: %[[dim_1:.+]] = tensor.dim %[[ARG0]], %[[c2]] : tensor<?x?x?x?xf32>
// CHECK: %[[dim_2:.+]] = tensor.dim %[[ARG0]], %[[c3]] : tensor<?x?x?x?xf32>
-// CHECK: %[[mapped_dim0:.+]] = affine.apply #[[map:.+]]()[%[[dim_2]], %[[ARG3]]]
-// CHECK: %[[mapped_dim1:.+]] = affine.apply #[[map:.+]]()[%[[dim_0]], %[[ARG1]]]
-// CHECK: %[[mapped_dim2:.+]] = affine.apply #[[map:.+]]()[%[[dim_1]], %[[ARG2]]]
+// CHECK: %[[mapped_dim0:.+]] = affine.apply #[[$MAP]]()[%[[dim_2]], %[[ARG3]]]
+// CHECK: %[[mapped_dim1:.+]] = affine.apply #[[$MAP]]()[%[[dim_0]], %[[ARG1]]]
+// CHECK: %[[mapped_dim2:.+]] = affine.apply #[[$MAP]]()[%[[dim_1]], %[[ARG2]]]
// CHECK: %[[INIT:.+]] = tensor.empty(%[[mapped_dim2]], %[[mapped_dim1]], %[[mapped_dim0]], %[[dim]], %[[ARG3]], %[[ARG1]], %[[ARG2]]) : tensor<?x?x?x?x?x?x?xf32>
// CHECK: %[[PACK:.+]] = tensor.pack %[[ARG0]] outer_dims_perm = [2, 1, 3, 0] inner_dims_pos = [3, 1, 2] inner_tiles = [%[[ARG3]], %[[ARG1]], %[[ARG2]]] into %[[INIT]] : tensor<?x?x?x?xf32> -> tensor<?x?x?x?x?x?x?xf32>
// CHECK: return %[[PACK]] : tensor<?x?x?x?x?x?x?xf32>
@@ -465,7 +464,7 @@ func.func @linalg_transpose_tensor_pack_fold_dynamic_outer_dims_tile_dims_tile_s
into %pack_dest : tensor<?x?x?x?xf32> -> tensor<?x?x?x?x?x?x?xf32>
return %pack : tensor<?x?x?x?x?x?x?xf32>
}
-// CHECK: #[[map:.+]] = affine_map<()[s0, s1] -> (s0 ceildiv s1)>
+// CHECK: #[[$MAP:.+]] = affine_map<()[s0, s1] -> (s0 ceildiv s1)>
//CHECK-LABEL: func.func @linalg_transpose_tensor_pack_fold_dynamic_outer_dims_tile_dims_tile_sizes(
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?x?x?xf32>, %[[ARG1:.+]]: tensor<?x?x?x?xf32>,
// CHECK-SAME: %[[ARG2:.+]]: tensor<?x?x?x?x?x?x?xf32>, %[[ARG3:.+]]: index, %[[ARG4:.+]]: index, %[[ARG5:.+]]: index) -> tensor<?x?x?x?x?x?x?xf32> {
@@ -477,9 +476,9 @@ func.func @linalg_transpose_tensor_pack_fold_dynamic_outer_dims_tile_dims_tile_s
// CHECK: %[[DIM0:.+]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<?x?x?x?xf32>
// CHECK: %[[DIM1:.+]] = tensor.dim %[[ARG0]], %[[C2]] : tensor<?x?x?x?xf32>
// CHECK: %[[DIM2:.+]] = tensor.dim %[[ARG0]], %[[C3]] : tensor<?x?x?x?xf32>
-// CHECK: %[[VAL0:.+]] = affine.apply #[[map:.+]]()[%[[DIM2]], %[[ARG3]]]
-// CHECK: %[[VAL1:.+]] = affine.apply #[[map:.+]]()[%[[DIM0]], %[[ARG4]]]
-// CHECK: %[[VAL2:.+]] = affine.apply #[[map:.+]]()[%[[DIM]], %[[ARG5]]]
+// CHECK: %[[VAL0:.+]] = affine.apply #[[$MAP]]()[%[[DIM2]], %[[ARG3]]]
+// CHECK: %[[VAL1:.+]] = affine.apply #[[$MAP]]()[%[[DIM0]], %[[ARG4]]]
+// CHECK: %[[VAL2:.+]] = affine.apply #[[$MAP]]()[%[[DIM]], %[[ARG5]]]
// CHECK: %[[VAL3:.+]] = tensor.empty(%[[VAL1]], %[[DIM1]], %[[VAL2]], %[[VAL0]], %[[ARG3]], %[[ARG4]], %[[ARG5]]) : tensor<?x?x?x?x?x?x?xf32>
// CHECK: %[[PACK:.+]] = tensor.pack %[[ARG0]] outer_dims_perm = [1, 2, 0, 3] inner_dims_pos = [3, 1, 0] inner_tiles = [%[[ARG3]], %[[ARG4]], %[[ARG5]]] into %[[VAL3]] : tensor<?x?x?x?xf32> -> tensor<?x?x?x?x?x?x?xf32>
// CHECK: return %[[PACK]] : tensor<?x?x?x?x?x?x?xf32>
@@ -506,13 +505,13 @@ func.func @linalg_transpose_tensor_pack_multiple_tiles(%arg0: tensor<?x32x128xbf
into %2 : tensor<32x128x?xbf16> -> tensor<32x?x64x16x2xbf16>
return %pack : tensor<32x?x64x16x2xbf16>
}
-// CHECK: #[[map:.+]] = affine_map<()[s0] -> (s0 ceildiv 16)>
+// CHECK: #[[$MAP:.+]] = affine_map<()[s0] -> (s0 ceildiv 16)>
//CHECK-LABEL: func.func @linalg_transpose_tensor_pack_multiple_tiles(
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x32x128xbf16>) -> tensor<32x?x64x16x2xbf16> {
// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
// CHECK-DAG: %[[CST:.+]] = arith.constant 0.000000e+00 : bf16
// CHECK: %[[DIM:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x32x128xbf16>
-// CHECK: %[[VAL0:.+]] = affine.apply #[[map:.+]]()[%[[DIM]]]
+// CHECK: %[[VAL0:.+]] = affine.apply #[[$MAP]]()[%[[DIM]]]
// CHECK: %[[VAL1:.+]] = tensor.empty(%[[VAL0]]) : tensor<32x?x64x16x2xbf16>
// CHECK: %[[PACK:.+]] = tensor.pack %[[ARG0]]
// CHECK-SAME: padding_value(%[[CST]] : bf16)
@@ -522,3 +521,91 @@ func.func @linalg_transpose_tensor_pack_multiple_tiles(%arg0: tensor<?x32x128xbf
// CHECK-SAME: into %[[VAL1]] : tensor<?x32x128xbf16> -> tensor<32x?x64x16x2xbf16>
// CHECK: return %[[PACK]] : tensor<32x?x64x16x2xbf16>
// CHECK: }
+
+// -----
+
+func.func @linalg_transpose_tensor_unpack_fold(%arg0: tensor<1x1x4x16xi32>) -> tensor<16x4xi32> {
+ %0 = tensor.empty() : tensor<1x1x16x4xi32>
+ %transposed = linalg.transpose ins(%arg0 : tensor<1x1x4x16xi32>)
+ outs(%0 : tensor<1x1x16x4xi32>)
+ permutation = [1, 0, 3, 2]
+ %1 = tensor.empty() : tensor<16x4xi32>
+ %unpack = tensor.unpack %transposed
+ outer_dims_perm = [0, 1]
+ inner_dims_pos = [0, 1]
+ inner_tiles = [16, 4] into
+ %1 : tensor<1x1x16x4xi32> -> tensor<16x4xi32>
+ return %unpack : tensor<16x4xi32>
+}
+//CHECK-LABEL: func.func @linalg_transpose_tensor_unpack_fold(
+// CHECK-SAME: %[[ARG0:.+]]: tensor<1x1x4x16xi32>) -> tensor<16x4xi32> {
+// CHECK: %[[OUT:.+]] = tensor.empty() : tensor<16x4xi32>
+// CHECK: %[[UNPACK:.+]] = tensor.unpack %[[ARG0]]
+// CHECK-SAME: outer_dims_perm = [1, 0]
+// CHECK-SAME: inner_dims_pos = [1, 0]
+// CHECK-SAME: inner_tiles = [4, 16]
+// CHEKC-SAME: into %[[OUT]] : tensor<1x1x4x16xi32> -> tensor<16x4xi32>
+// CHECK: return %[[UNPACK]] : tensor<16x4xi32>
+// CHECK: }
+
+// -----
+
+func.func @linalg_transpose_tensor_unpack_fold_dynamic_outer_dims_tile_dims_tile_sizes(%arg0: tensor<?x?x?x?xf32>, %transpose_dest: tensor<?x?x?x?xf32>, %unpack_dest: tensor<?x?xf32>, %tile_p : index, %tile_q : index) -> tensor<?x?xf32> {
+ %transposed = linalg.transpose
+ ins(%arg0 : tensor<?x?x?x?xf32>)
+ outs(%transpose_dest : tensor<?x?x?x?xf32>)
+ permutation = [1, 0, 3, 2]
+
+ %unpack = tensor.unpack %transposed
+ outer_dims_perm = [1, 0]
+ inner_dims_pos = [0, 1]
+ inner_tiles = [%tile_p, %tile_q]
+ into %unpack_dest : tensor<?x?x?x?xf32> -> tensor<?x?xf32>
+ return %unpack : tensor<?x?xf32>
+}
+// CHECK: #[[$MAP:.+]] = affine_map<()[s0, s1] -> (s0 * s1)>
+// CHECK-LABEL: func.func @linalg_transpose_tensor_unpack_fold_dynamic_outer_dims_tile_dims_tile_sizes(
+// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?x?x?xf32>, %[[ARG1:.+]]: tensor<?x?x?x?xf32>, %[[ARG2:.+]]: tensor<?x?xf32>,
+// CHECK-SAME: %[[IDX1:.+]]: index, %[[IDX2:.+]]: index) -> tensor<?x?xf32> {
+// CHECK-DAG: %[[CST1:.+]] = arith.constant 1 : index
+// CHECK-DAG: %[[CST0:.+]] = arith.constant 0 : index
+// CHECK-DAG: %[[DIM0:.+]] = tensor.dim %[[ARG0]], %[[CST0]] : tensor<?x?x?x?xf32>
+// CHECK-DAG: %[[DIM1:.+]] = tensor.dim %[[ARG0]], %[[CST1]] : tensor<?x?x?x?xf32>
+// CHECK-DAG: %[[AMAP0:.+]] = affine.apply #[[$MAP]]()[%[[DIM1]], %[[IDX2]]]
+// CHECK-DAG: %[[AMAP1:.+]] = affine.apply #[[$MAP]]()[%[[DIM0]], %[[IDX1]]]
+// CHECK: %[[OUT:.+]] = tensor.empty(%[[AMAP1]], %[[AMAP0]]) : tensor<?x?xf32>
+// CHECK: %[[UNPACK:.+]] = tensor.unpack %[[ARG0]]
+// CHECK-SAME: outer_dims_perm = [0, 1]
+// CHECK-SAME: inner_dims_pos = [1, 0]
+// CHECK-SAME: inner_tiles = [%[[IDX2]], %[[IDX1]]]
+// CHECK-SAME: into %[[OUT]] : tensor<?x?x?x?xf32> -> tensor<?x?xf32>
+// CHECK: return %[[UNPACK]] : tensor<?x?xf32>
+// CHECK: }
+
+// -----
+
+func.func @tensor_unpack_linalg_transpose_fold(%arg0: tensor<56x57x1x64xf32>) -> tensor<3648x56xf32> {
+ %0 = tensor.empty() : tensor<56x3648xf32>
+ %pack = tensor.unpack %arg0
+ outer_dims_perm = [0, 1]
+ inner_dims_pos = [0, 1]
+ inner_tiles = [1, 64]
+ into %0 : tensor<56x57x1x64xf32> -> tensor<56x3648xf32>
+
+ %1 = tensor.empty() : tensor<3648x56xf32>
+ %transposed = linalg.transpose
+ ins(%pack : tensor<56x3648xf32>)
+ outs(%1 : tensor<3648x56xf32>)
+ permutation = [1,0]
+ return %transposed : tensor<3648x56xf32>
+}
+// CHECK-LABEL: func.func @tensor_unpack_linalg_transpose_fold(
+// CHECK-SAME: %[[ARG0:.+]]: tensor<56x57x1x64xf32>) -> tensor<3648x56xf32> {
+// CHECK: %[[OUT:.+]] = tensor.empty() : tensor<3648x56xf32>
+// CHECK: %[[UNPACK:.+]] = tensor.unpack %[[ARG0]]
+// CHECK-SAME: outer_dims_perm = [1, 0]
+// CHECK-SAME: inner_dims_pos = [1, 0]
+// CHECK-SAME: inner_tiles = [1, 64]
+// CHECK-SAME: into %[[OUT:.+]] : tensor<56x57x1x64xf32> -> tensor<3648x56xf32>
+// CHECK: return %[[UNPACK]] : tensor<3648x56xf32>
+// CHECK: }
More information about the Mlir-commits
mailing list