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[mlir][Tensor] Add pattern to fold concats of empty. #98994

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Jul 17, 2024
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[mlir][Tensor] Add pattern to fold concats of empty. #98994

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37 changes: 35 additions & 2 deletions mlir/lib/Dialect/Tensor/Transforms/EmptyOpPatterns.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -136,6 +136,38 @@ struct FoldEmptyTensorWithUnPackOp : public OpRewritePattern<UnPackOp> {
}
};

// Fold concat operation where all the operands are empty.
struct FoldConcatsOfEmpty : public OpRewritePattern<ConcatOp> {
using OpRewritePattern<ConcatOp>::OpRewritePattern;

LogicalResult matchAndRewrite(tensor::ConcatOp concatOp,
PatternRewriter &rewriter) const override {
auto concatOperands = concatOp.getInputs();
if (concatOperands.empty()) {
return failure();
}
auto firstEmptyOp = concatOperands.front().getDefiningOp<tensor::EmptyOp>();
if (!firstEmptyOp) {
return failure();
}
auto isDefinedByEmptyOp = [](Value v) -> bool {
return v.getDefiningOp<tensor::EmptyOp>();
};
if (!llvm::all_of(concatOperands.drop_front(), isDefinedByEmptyOp)) {
return rewriter.notifyMatchFailure(
concatOp, "not all operands are defined by an empty op");
}
Comment on lines +149 to +159
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Why is the first operand special? Can't we just write something like below?

if (!llvm::all_of(concatOperands, isDefinedByEmptyOp)) {
  ....
}

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Just adding an early exit if the first operand is itself is not an empty op.

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I thought that the llvm::all_of should already consider the case? I imagine that it also does early-exit if the first operand is not an empty op. It could be different in multi-threads though, idk.

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+1 I don't quite understand the code here: llvm::all_of should already be having the early exit.

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I was just trying to avoid even doing the all_of at all if even the first operand is not empty. But ok, I'll change it.

SmallVector<SmallVector<OpFoldResult>> resultShape;
if (failed(concatOp.reifyResultShapes(rewriter, resultShape))) {
return rewriter.notifyMatchFailure(concatOp,
"failed to get result shape");
}
rewriter.replaceOpWithNewOp<tensor::EmptyOp>(
concatOp, resultShape[0], concatOp.getResultType().getElementType());
return success();
}
};

} // namespace

void mlir::tensor::populateFoldTensorEmptyPatterns(RewritePatternSet &patterns,
Expand All @@ -144,6 +176,7 @@ void mlir::tensor::populateFoldTensorEmptyPatterns(RewritePatternSet &patterns,
FoldEmptyTensorWithReshapeOp<tensor::ExpandShapeOp>,
FoldEmptyTensorWithReshapeOp<tensor::CollapseShapeOp>>(
patterns.getContext(), /*benefit=*/1, foldSingleUseOnly);
patterns.add<FoldEmptyTensorWithPackOp, FoldEmptyTensorWithUnPackOp>(
patterns.getContext(), /*benefit=*/1);
patterns.add<FoldConcatsOfEmpty, FoldEmptyTensorWithPackOp,
FoldEmptyTensorWithUnPackOp>(patterns.getContext(),
/*benefit=*/1);
}
38 changes: 38 additions & 0 deletions mlir/test/Dialect/Tensor/fold-empty-op.mlir
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Expand Up @@ -164,3 +164,41 @@ func.func @double_use_of_tensor_empty(%arg0: index, %arg1: index)
// CHECK: tensor.empty{{.*}} : tensor<?x10x40xf32>
// CHECK: tensor.extract_slice
// CHECK: tensor.extract_slice

// -----

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%root : !transform.any_op {transform.readonly}) {
%func_op = transform.structured.match ops{["func.func"]} in %root : (!transform.any_op) -> !transform.op<"func.func">
transform.apply_patterns to %func_op {
transform.apply_patterns.tensor.fold_tensor_empty
} : !transform.op<"func.func">
transform.yield
}
}

func.func @concats_of_empty(
%arg0 : index, %arg1 : index, %arg2 : index, %arg3 : index)
-> tensor<5x?x?xf32>
{
%0 = tensor.empty(%arg0, %arg1) : tensor<5x?x?xf32>
%1 = tensor.empty(%arg2, %arg3) : tensor<5x?x?xf32>
%2 = tensor.concat dim(1) %0, %1 : (tensor<5x?x?xf32>, tensor<5x?x?xf32>) -> tensor<5x?x?xf32>
return %2 : tensor<5x?x?xf32>
}
// CHECK: #[[MAP:.+]] = affine_map<()[s0, s1] -> (s0 + s1)>
// CHECK: func @concats_of_empty(
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: index,
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: index,
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: index,
// CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]]: index)
// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index
// CHECK-DAG: %[[C2:.+]] = arith.constant 2 : index
// CHECK-DAG: %[[EMPTY0:.+]] = tensor.empty(%[[ARG0]], %[[ARG1]])
// CHECK-DAG: %[[EMPTY1:.+]] = tensor.empty(%[[ARG2]], %[[ARG3]])
// CHECK: %[[D2:.+]] = tensor.dim %[[EMPTY0]], %[[C2]]
// CHECK-DAG: %[[D0_1:.+]] = tensor.dim %[[EMPTY0]], %[[C1]]
// CHECK-DAG: %[[D1_1:.+]] = tensor.dim %[[EMPTY1]], %[[C1]]
// CHECK-DAG: %[[SUM:.+]] = affine.apply #[[MAP]]()[%[[D0_1]], %[[D1_1]]]
// CHECK: %[[NEW_EMPTY:.+]] = tensor.empty(%[[SUM]], %[[D2]])
// CHECK: return %[[NEW_EMPTY]]
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