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[mlir][tosa] Check for 0-ranked-tensors during fold #68512

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Merged
merged 1 commit into from
Oct 20, 2023
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[mlir][tosa] Check for 0-ranked-tensors during fold #68512

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5 changes: 3 additions & 2 deletions mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -771,7 +771,7 @@ OpFoldResult ConstOp::fold(FoldAdaptor adaptor) { return getValueAttr(); }
ShapedType inputTy = llvm::cast<ShapedType>(getInput().getType()); \
if (!inputTy.hasRank()) \
return {}; \
if (inputTy.getDimSize(getAxis()) == 1) \
if (inputTy.getRank() == 0 || inputTy.getDimSize(getAxis()) == 1) \
return getInput(); \
return {}; \
}
Expand Down Expand Up @@ -874,7 +874,8 @@ OpFoldResult ReverseOp::fold(FoldAdaptor adaptor) {
return operandAttr;

// If the dim-length is 1, tosa.reverse is a no-op.
if (operandTy.hasRank() && operandTy.getDimSize(axis) == 1)
if (operandTy.hasRank() &&
(operandTy.getRank() == 0 || operandTy.getDimSize(axis) == 1))
return operand;

return {};
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2 changes: 1 addition & 1 deletion mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
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Expand Up @@ -1015,7 +1015,7 @@ LogicalResult tosa::ScatterOp::inferReturnTypeComponents(
static LogicalResult ReduceInferReturnTypes(
ShapeAdaptor operandShape, Type inputType, IntegerAttr axis,
SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
if (!operandShape.hasRank()) {
if (!operandShape.hasRank() || operandShape.getRank() == 0) {
inferredReturnShapes.push_back(ShapedTypeComponents(inputType));
return success();
}
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12 changes: 12 additions & 0 deletions mlir/test/Dialect/Tosa/canonicalize.mlir
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Expand Up @@ -591,3 +591,15 @@ func.func @fold_abs_abs(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
%1 = tosa.abs %0 : (tensor<?x1xf32>) -> tensor<?x1xf32>
return %1 : tensor<?x1xf32>
}

// -----

// CHECK-LABEL: @fold_reduce_rank_zero
func.func nested @fold_reduce_rank_zero() {
// CHECK-NOT: tosa.reduce_min
// CHECK-NOT: tosa.reverse
%0 = tensor.empty() : tensor<i32>
%1 = tosa.reduce_min %0 {axis = 0 : i32} : (tensor<i32>) -> tensor<1x10xi32>
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Also worth adding a CHECK-NOT label and ensure that the reduce_min and reverse ops are not present after the canonicalization

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Added the CHECK-NOT labels for both the ops.

%2 = tosa.reverse %0 {axis = 0 : i32} : (tensor<i32>) -> tensor<1x10xi32>
return
}