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[mlir][tensor] Fold producer linalg transpose with consumer tensor pack (#75658)

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Successor to https://github.com/llvm/llvm-project/pull/74206 

Partial fix to https://github.com/openxla/iree/issues/15367
This commit is contained in:
Prathamesh Tagore
2024-01-10 20:25:27 +05:30
committed by GitHub
parent 9bde5becb4
commit 113bce0c79
2 changed files with 220 additions and 1 deletions
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44
mlir/lib/Dialect/Tensor/Transforms/PackAndUnpackPatterns.cpp
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@@ -9,6 +9,7 @@
#include "mlir/Dialect/Linalg/IR/Linalg.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Tensor/Transforms/Transforms.h"
#include "mlir/Dialect/Utils/IndexingUtils.h"
#include "mlir/IR/PatternMatch.h"
#include "llvm/Support/Debug.h"
@@ -223,11 +224,52 @@ struct FoldProducerPackWithConsumerLinalgTransposeOp
return success();
}
};
/// Fold 'transpose' -> 'pack' into 'pack' since 'pack' already has transpose
/// semantics.
struct FoldConsumerPackWithProducerLinalgTransposeOp
: public OpRewritePattern<PackOp> {
using OpRewritePattern<PackOp>::OpRewritePattern;
LogicalResult matchAndRewrite(PackOp packOp,
PatternRewriter &rewriter) const override {
auto transposeOp = packOp.getSource().getDefiningOp<linalg::TransposeOp>();
if (!transposeOp)
return failure();
auto transposePermutation = transposeOp.getPermutation();
auto outerDimsPerm = packOp.getOuterDimsPerm();
auto innerDimsPos = packOp.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 = packOp.createDestinationTensor(
rewriter, packOp.getLoc(), transposeOp.getOperand(0),
packOp.getMixedTiles(), newInnerDimsPosVec, newOuterDimsPermVec);
rewriter.replaceOpWithNewOp<PackOp>(
packOp, transposeOp.getOperand(0), output, newInnerDimsPosVec,
packOp.getMixedTiles(), packOp.getPaddingValue(), newOuterDimsPermVec);
return success();
}
};
} // namespace
void populateFoldIntoPackAndUnpackPatterns(RewritePatternSet &patterns) {
patterns.insert<FoldUnpackWithExtractSliceOp, FoldPadWithPackOp,
FoldProducerPackWithConsumerLinalgTransposeOp>(
FoldProducerPackWithConsumerLinalgTransposeOp,
FoldConsumerPackWithProducerLinalgTransposeOp>(
patterns.getContext());
}

177
mlir/test/Dialect/Tensor/fold-into-pack-and-unpack.mlir
View File

@@ -345,3 +345,180 @@ func.func @tensor_pack_linalg_transpose_fold_dynamic_outer_dims_tile_dims_tile_s
// 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>
// CHECK: }
// -----
func.func @linalg_transpose_tensor_pack_fold(%arg0: tensor<56x57x1x64xf32>) -> tensor<1x57x56x2x32xf32> {
%0 = tensor.empty() : tensor<1x56x57x64xf32>
%transposed = linalg.transpose
ins(%arg0 : tensor<56x57x1x64xf32>)
outs(%0 : tensor<1x56x57x64xf32>)
permutation = [2, 0, 1, 3]
%1 = tensor.empty() : tensor<1x57x56x2x32xf32>
%pack = tensor.pack %transposed
outer_dims_perm = [0, 2, 1, 3]
inner_dims_pos = [3]
inner_tiles = [32]
into %1 : tensor<1x56x57x64xf32> -> tensor<1x57x56x2x32xf32>
return %pack : tensor<1x57x56x2x32xf32>
}
//CHECK-LABEL: func @linalg_transpose_tensor_pack_fold(
// CHECK-SAME: %[[ARG0:.+]]: tensor<56x57x1x64xf32>)
// CHECK: %[[INIT:.+]] = tensor.empty() : tensor<1x57x56x2x32xf32>
// CHECK: %[[PACK:.+]] = tensor.pack %[[ARG0]]
// CHECK-SAME: outer_dims_perm = [2, 1, 0, 3]
// CHECK-SAME: inner_dims_pos = [3] inner_tiles = [32]
// CHECK-SAME: into %[[INIT]]
// CHECK: return %[[PACK]]
// -----
func.func @linalg_transpose_tensor_pack_fold_with_padding(%arg0: tensor<56x57x1x55xf32>, %padding: f32) -> tensor<1x57x56x2x32xf32> {
%0 = tensor.empty() : tensor<1x56x57x55xf32>
%transpose = linalg.transpose
ins(%arg0 : tensor<56x57x1x55xf32>)
outs(%0 : tensor<1x56x57x55xf32>)
permutation = [2, 0, 1, 3]
%1 = tensor.empty() : tensor<1x57x56x2x32xf32>
%pack = tensor.pack %transpose padding_value(%padding : f32)
outer_dims_perm = [0, 2, 1, 3]
inner_dims_pos = [3]
inner_tiles = [32]
into %1 : tensor<1x56x57x55xf32> -> tensor<1x57x56x2x32xf32>
return %pack : tensor<1x57x56x2x32xf32>
}
//CHECK-LABEL: func @linalg_transpose_tensor_pack_fold_with_padding(
// CHECK-SAME: %[[ARG0:.+]]: tensor<56x57x1x55xf32>, %[[PADDING:.+]]: f32)
// CHECK: %[[INIT:.+]] = tensor.empty() : tensor<1x57x56x2x32xf32>
// CHECK: %[[PACK:.+]] = tensor.pack %[[ARG0]] padding_value(%[[PADDING]] : f32)
// CHECK-SAME: outer_dims_perm = [2, 1, 0, 3]
// CHECK-SAME: inner_dims_pos = [3] inner_tiles = [32]
// CHECK-SAME: into %[[INIT]]
// CHECK: return %[[PACK]]
// -----
func.func @linalg_transpose_tensor_pack_fold_no_outer_dims_perm(%arg0: tensor<56x57x1x64xf32>) -> tensor<1x56x57x2x32xf32> {
%0 = tensor.empty() : tensor<1x56x57x64xf32>
%transposed = linalg.transpose
ins(%arg0 : tensor<56x57x1x64xf32>)
outs(%0 : tensor<1x56x57x64xf32>)
permutation = [2, 0, 1, 3]
%1 = tensor.empty() : tensor<1x56x57x2x32xf32>
%pack = tensor.pack %transposed
inner_dims_pos = [3]
inner_tiles = [32]
into %1 : tensor<1x56x57x64xf32> -> tensor<1x56x57x2x32xf32>
return %pack : tensor<1x56x57x2x32xf32>
}
//CHECK-LABEL: func @linalg_transpose_tensor_pack_fold_no_outer_dims_perm(
// CHECK-SAME: %[[ARG0:.+]]: tensor<56x57x1x64xf32>)
// CHECK: %[[INIT:.+]] = tensor.empty() : tensor<1x56x57x2x32xf32>
// CHECK: %[[PACK:.+]] = tensor.pack %[[ARG0]]
// CHECK-SAME: outer_dims_perm = [2, 0, 1, 3]
// CHECK-SAME: inner_dims_pos = [3] inner_tiles = [32]
// CHECK-SAME: into %[[INIT]]
// CHECK: return %[[PACK]]
// -----
func.func @linalg_transpose_tensor_pack_fold_complex_inner_dims_change(%arg0: tensor<25x30x35x40xf32>, %transpose_dest: tensor<35x40x25x30xf32>, %pack_dest: tensor<3x35x5x8x5x10x5xf32>) -> tensor<3x35x5x8x5x10x5xf32> {
%transposed = linalg.transpose
ins(%arg0 : tensor<25x30x35x40xf32>)
outs(%transpose_dest : tensor<35x40x25x30xf32>)
permutation = [2, 3, 0, 1]
%pack = tensor.pack %transposed
outer_dims_perm = [3, 0, 2, 1]
inner_dims_pos = [1, 3, 2]
inner_tiles = [5, 10, 5]
into %pack_dest : tensor<35x40x25x30xf32> -> tensor<3x35x5x8x5x10x5xf32>
return %pack : tensor<3x35x5x8x5x10x5xf32>
}
//CHECK-LABEL: func.func @linalg_transpose_tensor_pack_fold_complex_inner_dims_change(
// CHECK-SAME: %[[ARG0:.+]]: tensor<25x30x35x40xf32>,
// CHECK-SAME: %[[ARG1:.+]]: tensor<35x40x25x30xf32>,
// CHECK-SAME: %[[ARG2:.+]]: tensor<3x35x5x8x5x10x5xf32>) -> tensor<3x35x5x8x5x10x5xf32> {
// CHECK: %[[VAL0:.+]] = tensor.empty() : tensor<3x35x5x8x5x10x5xf32>
// CHECK: %[[PACK:.+]] = tensor.pack %[[ARG0]]
// CHECK-SAME: outer_dims_perm = [1, 2, 0, 3]
// CHECK-SAME: inner_dims_pos = [3, 1, 0]
// CHECK-SAME: inner_tiles = [5, 10, 5]
// CHECK-SAME: into %[[VAL0]]
// CHECK: return %[[PACK]]
// -----
func.func @linalg_transpose_tensor_pack_fold_dynamic_outer_dims_tile_dims_tile_sizes(%arg0: tensor<?x?x?x?xf32>, %transpose_dest: tensor<?x?x?x?xf32>, %pack_dest: tensor<?x?x?x?x?x?x?xf32>, %tile_p : index, %tile_q : index, %tile_r : index) -> tensor<?x?x?x?x?x?x?xf32> {
%transposed = linalg.transpose
ins(%arg0 : tensor<?x?x?x?xf32>)
outs(%transpose_dest : tensor<?x?x?x?xf32>)
permutation = [2, 3, 0, 1]
%pack = tensor.pack %transposed
outer_dims_perm = [3, 0, 2, 1]
inner_dims_pos = [1, 3, 2]
inner_tiles = [%tile_p, %tile_q, %tile_r]
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-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> {
// CHECK: %[[C0:.+]] = arith.constant 0 : index
// CHECK: %[[C1:.+]] = arith.constant 1 : index
// CHECK: %[[C2:.+]] = arith.constant 2 : index
// CHECK: %[[C3:.+]] = arith.constant 3 : index
// CHECK: %[[DIM:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?x?x?xf32>
// 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: %[[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>
// -----
func.func @linalg_transpose_tensor_pack_multiple_tiles(%arg0: tensor<?x32x128xbf16>) -> tensor<32x?x64x16x2xbf16> {
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : bf16
%dim = tensor.dim %arg0, %c0 : tensor<?x32x128xbf16>
%0 = tensor.empty(%dim) : tensor<32x128x?xbf16>
%transposed = linalg.transpose
ins(%arg0 : tensor<?x32x128xbf16>)
outs(%0 : tensor<32x128x?xbf16>)
permutation = [1, 2, 0]
%2 = tensor.empty(%dim) : tensor<32x?x64x16x2xbf16>
%pack = tensor.pack %transposed
padding_value(%cst : bf16)
outer_dims_perm = [0, 2, 1]
inner_dims_pos = [2, 1]
inner_tiles = [16, 2]
into %2 : tensor<32x128x?xbf16> -> tensor<32x?x64x16x2xbf16>
return %pack : tensor<32x?x64x16x2xbf16>
}
// 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: %[[C0:.+]] = arith.constant 0 : index
// CHECK: %[[CST:.+]] = arith.constant 0.000000e+00 : bf16
// CHECK: %[[DIM:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x32x128xbf16>
// 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)
// CHECK-SAME: outer_dims_perm = [1, 0, 2]
// CHECK-SAME: inner_dims_pos = [0, 2]
// CHECK-SAME: inner_tiles = [16, 2]
// CHECK-SAME: into %[[VAL1]] : tensor<?x32x128xbf16> -> tensor<32x?x64x16x2xbf16>
// CHECK: return %[[PACK]] : tensor<32x?x64x16x2xbf16>
// CHECK: }