Skip to content

[TorchToTosa] Lower bool bitwise ops to logical ops #4490

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
catcor01 wants to merge 6 commits into
base: main
Choose a base branch
from
Open

[TorchToTosa] Lower bool bitwise ops to logical ops #4490

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
6 commits
Select commit Hold shift + click to select a range
e64b255
[TorchToTosa] Lower bool bitwise ops to logical ops
catcor01 Mar 4, 2026
e7d5e2d
Move isI1Type into utils func
catcor01 Mar 10, 2026
8911a85
Add elementwise python e2e tests
catcor01 Mar 10, 2026
27b6230
Merge branch 'main' into bitwise_to_logical
catcor01 Mar 12, 2026
15b71f1
Format fix
catcor01 Mar 12, 2026
2660cf0
Small review updates
catcor01 Mar 12, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
3 changes: 3 additions & 0 deletions include/torch-mlir/Conversion/TorchToTosa/TosaLegalizeUtils.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -119,6 +119,9 @@ FailureOr<Value> getZeroPointValue(PatternRewriter &rewriter, Operation *op,
// Check if a shaped type has any dimension with size 0.
bool typeHasZeroDim(ShapedType type);

// Check if a type is i1 or a shaped type with i1 element type.
bool isI1Type(Type type);

// Compute scale/offset/border parameters for TOSA resize on one dimension.
void computeResizeParams(int inputSize, int outputSize, bool alignCorners,
tosa::ResizeMode mode, int &scaleN, int &scaleD,
Expand Down
21 changes: 21 additions & 0 deletions lib/Conversion/TorchToTosa/TorchToTosa.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -748,6 +748,27 @@ class ConvertAtenCompareOp : public TorchToTosaOpConversionPattern<AtenOpT> {
OpConversionPattern<AtenOpT>::getTypeConverter()->convertType(
op.getType()));
if (isBitwiseOp) {
// TOSA bitwise ops do not support i1. Use logical ops for bool tensors.
if (tosa::isI1Type(resultTy)) {
auto lhsBool =
tosa::tosaCastTensorToType(rewriter, lhs, resultTy).value();
auto rhsBool =
tosa::tosaCastTensorToType(rewriter, rhsTensor, resultTy).value();
Comment on lines +753 to +756
Copy link
Member

@sahas3 sahas3 Mar 9, 2026

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Are these casts necessary? Won't the operand types already be same as the result type for the ops under consideration?

if constexpr (std::is_same<AtenOpT, AtenBitwiseAndTensorOp>() ||
std::is_same<AtenOpT, AtenBitwiseAndScalarOp>()) {
rewriter.replaceOpWithNewOp<tosa::LogicalAndOp>(op, resultTy, lhsBool,
rhsBool);
return success();
} else if constexpr (std::is_same<AtenOpT, AtenBitwiseOrTensorOp>()) {
rewriter.replaceOpWithNewOp<tosa::LogicalOrOp>(op, resultTy, lhsBool,
rhsBool);
return success();
} else if constexpr (std::is_same<AtenOpT, AtenBitwiseXorTensorOp>()) {
rewriter.replaceOpWithNewOp<tosa::LogicalXorOp>(op, resultTy, lhsBool,
rhsBool);
return success();
}
}
lhs = tosa::tosaCastTensorToType(rewriter, lhs, resultTy).value();
rhsTensor =
tosa::tosaCastTensorToType(rewriter, rhsTensor, resultTy).value();
Expand Down
8 changes: 8 additions & 0 deletions lib/Conversion/TorchToTosa/TosaLegalizeUtils.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -586,6 +586,14 @@ bool typeHasZeroDim(ShapedType type) {
return llvm::any_of(outShape, [](int64_t dim) { return dim == 0; });
}

bool isI1Type(Type type) {
if (auto shapedTy = dyn_cast<ShapedType>(type))
type = shapedTy.getElementType();
if (auto intTy = dyn_cast<IntegerType>(type))
return intTy.getWidth() == 1;
return false;
}

void computeResizeParams(int inputSize, int outputSize, bool alignCorners,
tosa::ResizeMode mode, int &scaleN, int &scaleD,
int &offset, int &border) {
Expand Down
54 changes: 54 additions & 0 deletions projects/pt1/python/torch_mlir_e2e_test/test_suite/elementwise.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -4899,6 +4899,33 @@ def ElementwiseBitwiseAndModule_basic(module, tu: TestUtils):
# ==============================================================================


class ElementwiseBitwiseAndBoolModule(torch.nn.Module):
def __init__(self):
super().__init__()

@export
@annotate_args(
[
None,
([-1, -1], torch.bool, True),
([-1, -1], torch.bool, True),
]
)
def forward(self, x, y):
return torch.bitwise_and(x, y)


@register_test_case(module_factory=lambda: ElementwiseBitwiseAndBoolModule())
def ElementwiseBitwiseAndBoolModule_basic(module, tu: TestUtils):
module.forward(
tu.randint(3, 4, low=0, high=2).to(torch.bool),
tu.randint(3, 4, low=0, high=2).to(torch.bool),
)


# ==============================================================================


class ElementwiseBitwiseAndStaticShapeModule(torch.nn.Module):
def __init__(self):
super().__init__()
Expand Down Expand Up @@ -4953,6 +4980,33 @@ def ElementwiseBitwiseOrModule_basic(module, tu: TestUtils):
# ==============================================================================


class ElementwiseBitwiseOrBoolModule(torch.nn.Module):
def __init__(self):
super().__init__()

@export
@annotate_args(
[
None,
([-1, -1], torch.bool, True),
([-1, -1], torch.bool, True),
]
)
def forward(self, x, y):
return torch.bitwise_or(x, y)


@register_test_case(module_factory=lambda: ElementwiseBitwiseOrBoolModule())
def ElementwiseBitwiseOrBoolModule_basic(module, tu: TestUtils):
module.forward(
tu.randint(3, 4, low=0, high=2).to(torch.bool),
tu.randint(3, 4, low=0, high=2).to(torch.bool),
)


# ==============================================================================


class ElementwiseBitwiseOrStaticShapeModule(torch.nn.Module):
def __init__(self):
super().__init__()
Expand Down
17 changes: 17 additions & 0 deletions test/Conversion/TorchToTosa/basic.mlir
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -895,6 +895,23 @@ func.func @torch.aten.bitwise_and.Tensor$basic(%arg0: !torch.vtensor<[?,?],si32>

// -----

// CHECK-LABEL: func.func @torch.aten.bitwise_and.Tensor$bool(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],i1>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],i1>) -> !torch.vtensor<[?,?],i1> {
// CHECK-DAG: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],i1> -> tensor<?x?xi1>
// CHECK-DAG: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],i1> -> tensor<?x?xi1>
// CHECK-NOT: tosa.bitwise_and
// CHECK: %[[VAL_4:.*]] = tosa.logical_and %[[VAL_2]], %[[VAL_3]] : (tensor<?x?xi1>, tensor<?x?xi1>) -> tensor<?x?xi1>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[?,?],i1>
// CHECK: }
func.func @torch.aten.bitwise_and.Tensor$bool(%arg0: !torch.vtensor<[?,?],i1>, %arg1: !torch.vtensor<[?,?],i1>) -> !torch.vtensor<[?,?],i1> {
%0 = torch.aten.bitwise_and.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],i1>, !torch.vtensor<[?,?],i1> -> !torch.vtensor<[?,?],i1>
return %0 : !torch.vtensor<[?,?],i1>
}

// -----

// CHECK-LABEL: func.func @torch.aten.log2$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
Expand Down
11 changes: 11 additions & 0 deletions test/Conversion/TorchToTosa/torch-backend-to-tosa-backend-pipeline.mlir
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -91,6 +91,17 @@ func.func @torch.aten.bitwise_and.Tensor$mixed_type(%arg0: !torch.vtensor<[?,?],

// -----

// CHECK-LABEL: torch.aten.bitwise_xor.Tensor$bool
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xi1>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?xi1>
// CHECK: %[[VAL_2:.*]] = tosa.logical_xor %[[VAL_0]], %[[VAL_1]] : (tensor<?x?xi1>, tensor<?x?xi1>) -> tensor<?x?xi1>
func.func @torch.aten.bitwise_xor.Tensor$bool(%arg0: !torch.vtensor<[?,?],i1>, %arg1: !torch.vtensor<[?,?],i1>) -> !torch.vtensor<[?,?],i1> {
%0 = torch.aten.bitwise_xor.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],i1>, !torch.vtensor<[?,?],i1> -> !torch.vtensor<[?,?],i1>
return %0 : !torch.vtensor<[?,?],i1>
}

// -----

// CHECK-LABEL: func.func @torch.aten.div.Tensor$mixed_type_fp(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf32>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?xi32>) -> tensor<?x?xf32> {
Expand Down
Loading