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[mlir][tosa] Require operand/result tensors of at least rank 1 for some operations #131335

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Mar 17, 2025
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[mlir][tosa] Require operand/result tensors of at least rank 1 for some operations #131335

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50 changes: 25 additions & 25 deletions mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
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Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,7 @@ def Tosa_ArgMaxOp : Tosa_InferShapedTypeOp<"argmax"> {
}];

let arguments = (ins
Tosa_Tensor: $input,
Tosa_TensorAtLeast1D: $input,
I32Attr: $axis,
DefaultValuedAttr<Tosa_NanPropagationAttr, "\"PROPAGATE\"">:$nan_mode
);
Expand Down Expand Up @@ -1629,12 +1629,12 @@ def Tosa_ReduceAllOp : Tosa_InferTensorTypeOp<"reduce_all"> {
}];

let arguments = (ins
Tosa_Tensor:$input,
Tosa_TensorAtLeast1D:$input,
I32Attr:$axis
);

let results = (outs
Tosa_Tensor:$output
Tosa_TensorAtLeast1D:$output
);

list<Availability> availability = [
Expand Down Expand Up @@ -1668,12 +1668,12 @@ def Tosa_ReduceAnyOp : Tosa_InferTensorTypeOp<"reduce_any"> {
}];

let arguments = (ins
Tosa_Tensor:$input,
Tosa_TensorAtLeast1D:$input,
I32Attr:$axis
);

let results = (outs
Tosa_Tensor:$output
Tosa_TensorAtLeast1D:$output
);

list<Availability> availability = [
Expand Down Expand Up @@ -1707,13 +1707,13 @@ def Tosa_ReduceMaxOp : Tosa_InferTensorTypeOp<"reduce_max"> {
}];

let arguments = (ins
Tosa_Tensor:$input,
Tosa_TensorAtLeast1D:$input,
I32Attr:$axis,
DefaultValuedAttr<Tosa_NanPropagationAttr, "\"PROPAGATE\"">:$nan_mode
);

let results = (outs
Tosa_Tensor:$output
Tosa_TensorAtLeast1D:$output
);

list<Availability> availability = [
Expand Down Expand Up @@ -1748,13 +1748,13 @@ def Tosa_ReduceMinOp : Tosa_InferTensorTypeOp<"reduce_min"> {
}];

let arguments = (ins
Tosa_Tensor:$input,
Tosa_TensorAtLeast1D:$input,
I32Attr:$axis,
DefaultValuedAttr<Tosa_NanPropagationAttr, "\"PROPAGATE\"">:$nan_mode
);

let results = (outs
Tosa_Tensor:$output
Tosa_TensorAtLeast1D:$output
);

list<Availability> availability = [
Expand Down Expand Up @@ -1789,12 +1789,12 @@ def Tosa_ReduceProductOp : Tosa_InferTensorTypeOp<"reduce_product"> {
}];

let arguments = (ins
Tosa_Tensor:$input,
Tosa_TensorAtLeast1D:$input,
I32Attr:$axis
);

let results = (outs
Tosa_Tensor:$output
Tosa_TensorAtLeast1D:$output
);

list<Availability> availability = [
Expand Down Expand Up @@ -1828,12 +1828,12 @@ def Tosa_ReduceSumOp : Tosa_InferTensorTypeOp<"reduce_sum"> {
}];

let arguments = (ins
Tosa_Tensor:$input,
Tosa_TensorAtLeast1D:$input,
I32Attr:$axis
);

let results = (outs
Tosa_Tensor:$output
Tosa_TensorAtLeast1D:$output
);

list<Availability> availability = [
Expand Down Expand Up @@ -1872,12 +1872,12 @@ def Tosa_ConcatOp : Tosa_InferTensorTypeOp<"concat"> {
}];

let arguments = (ins
Variadic<Tosa_Tensor>:$input1,
Variadic<Tosa_TensorAtLeast1D>:$input1,
I32Attr:$axis
);

let results = (outs
Tosa_Tensor:$output
Tosa_TensorAtLeast1D:$output
);

list<Availability> availability = [
Expand Down Expand Up @@ -1923,13 +1923,13 @@ def Tosa_PadOp : Tosa_InferShapedTypeOp<"pad"> {
}];

let arguments = (ins
Tosa_RankedTensor:$input1,
Tosa_TensorAtLeast1D:$input1,
Tosa_Shape:$padding,
Tosa_ScalarTensor:$pad_const
);

let results = (outs
Tosa_RankedTensor:$output
Tosa_TensorAtLeast1D:$output
);

list<Availability> availability = [
Expand Down Expand Up @@ -1996,12 +1996,12 @@ def Tosa_ReverseOp: Tosa_Op<"reverse", [
}];

let arguments = (ins
Tosa_Tensor:$input1,
Tosa_TensorAtLeast1D:$input1,
I32Attr:$axis
);

let results = (outs
Tosa_Tensor:$output
Tosa_TensorAtLeast1D:$output
);

list<Availability> availability = [
Expand All @@ -2028,13 +2028,13 @@ def Tosa_SliceOp : Tosa_InferShapedTypeOp<"slice"> {
}];

let arguments = (ins
Tosa_Tensor:$input1,
Tosa_TensorAtLeast1D:$input1,
Tosa_Shape:$start,
Tosa_Shape:$size
);

let results = (outs
Tosa_Tensor:$output
Tosa_TensorAtLeast1D:$output
);

list<Availability> availability = [
Expand All @@ -2058,11 +2058,11 @@ def Tosa_TileOp : Tosa_InferShapedTypeOp<"tile"> {
}];

let arguments = (ins
Tosa_Tensor:$input1,
Tosa_TensorAtLeast1D:$input1,
Tosa_Shape:$multiples);

let results = (outs
Tosa_Tensor:$output
Tosa_TensorAtLeast1D:$output
);

list<Availability> availability = [
Expand Down Expand Up @@ -2093,12 +2093,12 @@ def Tosa_TransposeOp : Tosa_InferShapedTypeOp<"transpose",
}];

let arguments = (ins
Tosa_Tensor:$input1,
Tosa_TensorAtLeast1D:$input1,
DenseI32ArrayAttr:$perms
);

let results = (
outs Tosa_Tensor:$output
outs Tosa_TensorAtLeast1D:$output
);

list<Availability> availability = [
Expand Down
7 changes: 7 additions & 0 deletions mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
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Original file line number Diff line number Diff line change
Expand Up @@ -101,6 +101,10 @@ def AllDimensionsAreSizeOne : And<[
IsRankedTensorTypePred,
CPred<"::llvm::all_of(::llvm::cast<::mlir::RankedTensorType>($_self).getShape(), [](auto v) { return v == 1; })">]>;

def AtLeastRankOne : And<[
IsRankedTensorTypePred,
CPred<"::llvm::cast<::mlir::RankedTensorType>($_self).getRank() >= 1">]>;

class TosaTensorOf<
list<Type> allowedTypes, string summary = "tosa-conformant tensor">
: TensorOf<allowedTypes, [Or<[HasNo0Dimensions, IsUnrankedTensorTypePred]>], summary>;
Expand Down Expand Up @@ -183,6 +187,9 @@ def Tosa_TensorUpto4D : AnyTypeOf<[
def Tosa_Int32TensorUpto4D : AnyTypeOf<[
Tosa_UnrankedTensor, TosaTensorRankOf<[Tosa_Int32], [0,1,2,3,4]>]>;

def Tosa_TensorAtLeast1D : AnyTypeOf<[
Tosa_UnrankedTensor, TosaRankedTensorOf<[Tosa_AnyNumber], [AtLeastRankOne]>], "tosa-conformant tensor of at least rank 1", "::mlir::TensorType">;

//===----------------------------------------------------------------------===//
// Generic scalar, vector, or tensor of a particular type.
//===----------------------------------------------------------------------===//
Expand Down
9 changes: 7 additions & 2 deletions mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1354,8 +1354,13 @@ LogicalResult tosa::PadOp::verify() {
}
}

RankedTensorType inputType = getInput1().getType();
RankedTensorType outputType = getOutput().getType();
RankedTensorType inputType =
llvm::dyn_cast<RankedTensorType>(getInput1().getType());
RankedTensorType outputType =
llvm::dyn_cast<RankedTensorType>(getOutput().getType());
if (!inputType || !outputType)
return success();

auto paddingRank = cast<tosa::shapeType>(getPadding().getType()).getRank();

if (inputType.getRank() != outputType.getRank())
Expand Down
33 changes: 5 additions & 28 deletions mlir/test/Dialect/Tosa/canonicalize.mlir
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Expand Up @@ -915,29 +915,6 @@ func.func @fold_abs_abs(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {

// -----

// CHECK-LABEL: @fold_reduce_rank_zero
func.func @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<i32>
%2 = tosa.reverse %0 {axis = 0 : i32} : (tensor<i32>) -> tensor<i32>
return
}

// -----

// CHECK-LABEL: @fold_tile_rank_zero
func.func nested @fold_tile_rank_zero() -> tensor<i32> {
// CHECK-NOT: tosa.tile
%0 = tensor.empty() : tensor<i32>
%cst = tosa.const_shape { values = dense<> : tensor<0xindex> } : () -> !tosa.shape<0>
%1 = tosa.tile %0, %cst : (tensor<i32>, !tosa.shape<0>) -> tensor<i32>
return %1 : tensor<i32>
}

// -----

// CHECK-LABEL: @reshape_quant_nofold
// check that segfault is fixed
func.func @reshape_quant_nofold() -> tensor<1x1x1x1xi32> {
Expand Down Expand Up @@ -1015,12 +992,12 @@ func.func @cast_quant_nofold() -> tensor<!quant.uniform<i8:f32, 3.07574046018999
// -----

// CHECK-LABEL: @reverse_quant_fold
func.func @reverse_quant_fold() -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>> {
// CHECK: %[[CST:.*]] = "tosa.const"() <{values = dense<0> : tensor<i8>}> : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
func.func @reverse_quant_fold() -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>> {
// CHECK: %[[CST:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
// CHECK: return %[[CST]]
%0 = "tosa.const"() {values = dense<0> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
%1 = "tosa.reverse"(%0) { axis = 0 : i32 } : (tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
return %1 : tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
%0 = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
%1 = "tosa.reverse"(%0) { axis = 0 : i32 } : (tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
return %1 : tensor<1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
}

// -----
Expand Down
73 changes: 71 additions & 2 deletions mlir/test/Dialect/Tosa/invalid.mlir
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Expand Up @@ -452,9 +452,9 @@ func.func @test_reduce_sum_invalid_axis(%arg0 : tensor<2x3x4xf32>) -> () {

// -----

func.func @test_reduce_min_invalid_output_rank(%arg0 : tensor<i32>) -> () {
func.func @test_reduce_min_invalid_output_rank(%arg0 : tensor<1xi32>) -> () {
// expected-error@+1 {{'tosa.reduce_min' op expect output tensor rank to be equal to input tensor rank}}
%0 = tosa.reduce_min %arg0 {axis = 0 : i32} : (tensor<i32>) -> tensor<1x10xi32>
%0 = tosa.reduce_min %arg0 {axis = 0 : i32} : (tensor<1xi32>) -> tensor<1x10xi32>
return
}

Expand Down Expand Up @@ -1852,3 +1852,72 @@ func.func @test_maxpool2d_unexpected_output_width(%arg0: tensor<1x32x32x8xf32>)
(tensor<1x32x32x8xf32>) -> tensor<1x32x2x8xf32>
return %0 : tensor<1x32x2x8xf32>
}

// -----

func.func @test_scalar_argmax(%arg0: tensor<i32>) -> tensor<i32> {
// expected-error@+1 {{'tosa.argmax' op operand #0 must be tosa-conformant tensor of at least rank 1, but got 'tensor<i32>'}}
%0 = tosa.argmax %arg0 {axis = 0 : i32} : (tensor<i32>) -> tensor<i32>
return %0 : tensor<i32>
}

// -----

func.func @test_scalar_reduce_all(%arg0: tensor<i1>) -> tensor<i1> {
// expected-error@+1 {{'tosa.reduce_all' op operand #0 must be tosa-conformant tensor of at least rank 1, but got 'tensor<i1>'}}
%0 = tosa.reduce_all %arg0 {axis = 0 : i32} : (tensor<i1>) -> tensor<i1>
return %0 : tensor<i1>
}

// -----

func.func @test_scalar_inputs_concat(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<2xf32> {
// expected-error@+1 {{'tosa.concat' op operand #0 must be variadic of tosa-conformant tensor of at least rank 1, but got 'tensor<f32>'}}
%0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<f32>, tensor<f32>) -> tensor<2xf32>
return %0 : tensor<2xf32>
}

// -----

func.func @test_scalar_pad(%arg0: tensor<f32>) -> tensor<f32> {
%0 = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
%padding = tosa.const_shape {values = dense<0> : tensor<6xindex>} : () -> !tosa.shape<6>
// expected-error@+1 {{'tosa.pad' op operand #0 must be tosa-conformant tensor of at least rank 1, but got 'tensor<f32>'}}
%1 = tosa.pad %arg0, %padding, %0 : (tensor<f32>, !tosa.shape<6>, tensor<1xf32>) -> tensor<f32>
return %1 : tensor<f32>
}

// -----

func.func @test_scalar_reverse(%arg0: tensor<f32>) -> tensor<f32> {
// expected-error@+1 {{'tosa.reverse' op operand #0 must be tosa-conformant tensor of at least rank 1, but got 'tensor<f32>'}}
%0 = tosa.reverse %arg0 {axis = 0: i32} : (tensor<f32>) -> tensor<f32>
return %arg0 : tensor<f32>
}

// -----

func.func @test_scalar_slice(%arg0: tensor<f32>) -> tensor<f32> {
%0 = tosa.const_shape {values = dense<[]> : tensor<0xindex>} : () -> !tosa.shape<0>
%1 = tosa.const_shape {values = dense<[]> : tensor<0xindex>} : () -> !tosa.shape<0>
// expected-error@+1 {{'tosa.slice' op operand #0 must be tosa-conformant tensor of at least rank 1, but got 'tensor<f32>'}}
%2 = tosa.slice %arg0, %0, %1 : (tensor<f32>, !tosa.shape<0>, !tosa.shape<0>) -> tensor<f32>
return %2 : tensor<f32>
}

// -----

func.func @test_scalar_tile(%arg0: tensor<f32>) -> tensor<*xf32> {
%cst = tosa.const_shape { values = dense<[]> : tensor<0xindex> } : () -> !tosa.shape<0>
// expected-error@+1 {{'tosa.tile' op operand #0 must be tosa-conformant tensor of at least rank 1, but got 'tensor<f32>'}}
%0 = tosa.tile %arg0, %cst: (tensor<f32>, !tosa.shape<0>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}

// -----

func.func @test_scalar_output_transpose(%arg0: tensor<*xf32>) -> tensor<f32> {
// expected-error@+1 {{'tosa.transpose' op result #0 must be tosa-conformant tensor of at least rank 1, but got 'tensor<f32>'}}
%1 = tosa.transpose %arg0 {perms = array<i32: 2, 0, 1>} : (tensor<*xf32>) -> tensor<f32>
return %1 : tensor<f32>
}