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[mlir][linalg] Retain Op Type of linalg ops in fuseWithReshapeByExpansion pattern #129128

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Merged
merged 3 commits into from
Mar 3, 2025
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[mlir][linalg] Retain Op Type of linalg ops in fuseWithReshapeByExpansion pattern #129128

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620f2a8
[mlir][linalg] Retain named ops in fuseWithReshapeByExpansion pattern
nirvedhmeshram Feb 27, 2025
3526164
Address reviewer comments
nirvedhmeshram Feb 28, 2025
0e75e3e
Further refactor
nirvedhmeshram Mar 3, 2025
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73 changes: 55 additions & 18 deletions mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -814,6 +814,55 @@ validateDynamicDimExpansion(LinalgOp linalgOp,
}
return success();
}
// Create an expanded fused op that retains the name for certain ops
// such as fill, copy and transpose and produce a generic op for
// rest of linalg ops.
Operation *createFusedOpForReshapeByExpansion(
PatternRewriter &rewriter, LinalgOp linalgOp, TypeRange resultTypes,
ArrayRef<Value> expandedOpOperands, ArrayRef<Value> outputs,
ArrayRef<AffineMap> expandedOpIndexingMaps, ExpansionInfo &expansionInfo,
SmallVector<ReassociationIndices> reassociation) {

return TypeSwitch<Operation *, Operation *>(linalgOp.getOperation())
.Case<TransposeOp>([&](TransposeOp op) {
applyPermutationToVector(reassociation, op.getPermutation());
SmallVector<int64_t> newPerm;
for (auto reassoc : reassociation) {
for (auto dim : reassoc) {
newPerm.push_back(dim);
}
}
return rewriter.create<TransposeOp>(
linalgOp.getLoc(), expandedOpOperands[0], outputs[0], newPerm);
})
.Case<FillOp, CopyOp>([&](Operation *op) {
return clone(rewriter, linalgOp, resultTypes,
llvm::to_vector(llvm::concat<Value>(
llvm::to_vector(expandedOpOperands),
llvm::to_vector(outputs))));
})
.Default([&](Operation *op) {
// The iterator types of the expanded op are all parallel.
SmallVector<utils::IteratorType> iteratorTypes(
expansionInfo.getExpandedOpNumDims(),
utils::IteratorType::parallel);
for (auto [i, type] : llvm::enumerate(linalgOp.getIteratorTypesArray()))
for (auto j : expansionInfo.getExpandedDims(i))
iteratorTypes[j] = type;
Operation *fused = rewriter.create<GenericOp>(
linalgOp.getLoc(), resultTypes, expandedOpOperands, outputs,
expandedOpIndexingMaps, iteratorTypes);
Region &fusedRegion = fused->getRegion(0);
Region &originalRegion = linalgOp->getRegion(0);
rewriter.cloneRegionBefore(originalRegion, fusedRegion,
fusedRegion.begin());

// Update the index accesses after the expansion.
updateExpandedGenericOpRegion(rewriter, linalgOp.getLoc(), fusedRegion,
expansionInfo);
return fused;
});
}

/// Implements the fusion of a tensor.collapse_shape or a tensor.expand_shape op
/// and a generic op as explained in `isFusableWithReshapeByExpansion`. Assumes
Expand Down Expand Up @@ -919,25 +968,13 @@ fuseWithReshapeByExpansion(LinalgOp linalgOp, Operation *reshapeOp,
}
}

// The iterator types of the expanded op are all parallel.
SmallVector<utils::IteratorType> iteratorTypes(
expansionInfo.getExpandedOpNumDims(), utils::IteratorType::parallel);
for (auto [i, type] : llvm::enumerate(linalgOp.getIteratorTypesArray()))
for (auto j : expansionInfo.getExpandedDims(i))
iteratorTypes[j] = type;

TypeRange resultTypes = ValueRange(outputs).getTypes();
auto fusedOp =
rewriter.create<GenericOp>(linalgOp.getLoc(), resultTypes,
/*inputs=*/expandedOpOperands, outputs,
expandedOpIndexingMaps, iteratorTypes);
Region &fusedRegion = fusedOp->getRegion(0);
Region &originalRegion = linalgOp->getRegion(0);
rewriter.cloneRegionBefore(originalRegion, fusedRegion, fusedRegion.begin());

// Update the index accesses after the expansion.
updateExpandedGenericOpRegion(rewriter, loc, fusedRegion, expansionInfo);

SmallVector<ReassociationIndices> reassociationBeforeExpansion =
isExpanding ? expandingReshapeOp.getReassociationIndices()
: collapsingReshapeOp.getReassociationIndices();
Operation *fusedOp = createFusedOpForReshapeByExpansion(
rewriter, linalgOp, resultTypes, expandedOpOperands, outputs,
expandedOpIndexingMaps, expansionInfo, reassociationBeforeExpansion);
// Reshape the result values to their original shape if this is a collapsing
// reshape folded into its consumer.
SmallVector<Value> resultVals;
Expand Down
70 changes: 67 additions & 3 deletions mlir/test/Dialect/Linalg/reshape_fusion.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -783,9 +783,6 @@ func.func @linalg_add_reshape_consumer_fusion(%arg0 : tensor<?x?xf32>,

// -----

#map0 = affine_map<(d0, d1, d2) -> (d2, d0)>
#map1 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
#map2 = affine_map<(d0, d1, d2) -> (d0, d2)>
func.func @linalg_add_reshape_producer_fusion(%arg0 : tensor<?x7x?x8xf32>,
%arg1 : tensor<?x?xf32>,
%arg2 : tensor<?x?xf32>) ->
Expand Down Expand Up @@ -829,6 +826,73 @@ func.func @linalg_add_reshape_producer_fusion(%arg0 : tensor<?x7x?x8xf32>,

// -----

func.func @linalg_copy_reshape_producer_fusion(%arg0 : tensor<?x7x?x8xf32>,
%arg1 : tensor<?x?xf32>) ->
tensor<?x?xf32>
{
%0 = tensor.collapse_shape %arg0 [[0, 1], [2, 3]] :
tensor<?x7x?x8xf32> into tensor<?x?xf32>
%1 = linalg.copy ins(%0 : tensor<?x?xf32>)
outs(%arg1 : tensor<?x?xf32>) -> tensor<?x?xf32>
return %1 : tensor<?x?xf32>
}

// CHECK: func @linalg_copy_reshape_producer_fusion
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x7x?x8xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
// CHECK: %[[C8:.+]] = arith.constant 8 : index
// CHECK: %[[C7:.+]] = arith.constant 7 : index
// CHECK: %[[C1:.+]] = arith.constant 1 : index
// CHECK: %[[C0:.+]] = arith.constant 0 : index
// CHECK: %[[DIM:.+]] = tensor.dim %[[ARG1]], %[[C0]] : tensor<?x?xf32>
// CHECK: %[[DIM_0:.+]] = tensor.dim %[[ARG1]], %[[C1]] : tensor<?x?xf32>
// CHECK: %[[VAL_0:.+]] = arith.divsi %[[DIM]], %[[C7]] : index
// CHECK: %[[VAL_1:.+]] = arith.divsi %[[DIM_0]], %[[C8]] : index
// CHECK: %[[T1:.+]] = tensor.expand_shape %[[ARG1]] {{\[\[}}0, 1], [2, 3]] output_shape [%[[VAL_0]], 7, %[[VAL_1]], 8] : tensor<?x?xf32> into tensor<?x7x?x8xf32>
// CHECK: %[[T2:.+]] = linalg.copy
// CHECK-SAME: ins(%[[ARG0]] : tensor<?x7x?x8xf32>)
// CHECK-SAME: outs(%[[T1]] : tensor<?x7x?x8xf32>)
// CHECK: %[[T3:.+]] = tensor.collapse_shape %[[T2]]
// CHECK-SAME: [0, 1], [2, 3]
// CHECK-SAME: tensor<?x7x?x8xf32> into tensor<?x?xf32>
// CHECK: return %[[T3]]

// -----

func.func @linalg_transpose_reshape_producer_fusion(%arg0 : tensor<?x7x?x8xf32>,
%arg1 : tensor<?x?xf32>) ->
tensor<?x?xf32>
{
%0 = tensor.collapse_shape %arg0 [[0, 1], [2, 3]] :
tensor<?x7x?x8xf32> into tensor<?x?xf32>
%1 = linalg.transpose ins(%0 : tensor<?x?xf32>)
outs(%arg1 : tensor<?x?xf32>) permutation = [1, 0]
return %1 : tensor<?x?xf32>
}

// CHECK: func @linalg_transpose_reshape_producer_fusion
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x7x?x8xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
// CHECK-DAG: %[[C8:.+]] = arith.constant 8 : index
// CHECK-DAG: %[[C7:.+]] = arith.constant 7 : index
// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index
// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
// CHECK-DAG: %[[DIM:.+]] = tensor.dim %[[ARG1]], %[[C0]] : tensor<?x?xf32>
// CHECK-DAG: %[[DIM_0:.+]] = tensor.dim %[[ARG1]], %[[C1]] : tensor<?x?xf32>
// CHECK-DAG: %[[VAL_0:.+]] = arith.divsi %[[DIM_0]], %[[C7]] : index
// CHECK-DAG: %[[VAL_1:.+]] = arith.divsi %[[DIM]], %[[C8]] : index
// CHECK: %[[T1:.+]] = tensor.expand_shape %[[ARG1]] {{\[\[}}0, 1], [2, 3]] output_shape [%[[VAL_1]], 8, %[[VAL_0]], 7] : tensor<?x?xf32> into tensor<?x8x?x7xf32>
// CHECK: %[[T2:.+]] = linalg.transpose
// CHECK-SAME: ins(%[[ARG0]] : tensor<?x7x?x8xf32>)
// CHECK-SAME: outs(%[[T1]] : tensor<?x8x?x7xf32>)
// CHECK-SAME: permutation = [2, 3, 0, 1]
// CHECK: %[[T3:.+]] = tensor.collapse_shape %[[T2]]
// CHECK-SAME: [0, 1], [2, 3]
// CHECK-SAME: tensor<?x8x?x7xf32> into tensor<?x?xf32>
// CHECK: return %[[T3]]

// -----

func.func @fuse_by_expanding_pad(%arg0 : tensor<2x3x4x5x6x7x8x9xi32>) -> tensor<8x12x17x336x14xi32> {
%collapse = tensor.collapse_shape %arg0 [[0], [1, 2], [3], [4, 5, 6], [7]] : tensor<2x3x4x5x6x7x8x9xi32> into tensor<2x12x5x336x9xi32>
%cst = arith.constant 0 : i32
Expand Down