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[TOSA] Handle float<->bool cast via i8 in tosaCastTensorToType #4257

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[TOSA] Handle float<->bool cast via i8 in tosaCastTensorToType #4257

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1649e9a
[TOSA] Handle float<->bool cast via i8 in tosaCastTensorToType
Lallapallooza Jul 7, 2025
941521b
Merge branch 'llvm:main' into tosa_progressive_cast
Lallapallooza Jul 17, 2025
b80a57c
fix the float<->i1 flow and add tests
Lallapallooza Jul 17, 2025
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24 changes: 24 additions & 0 deletions lib/Conversion/TorchToTosa/TosaLegalizeUtils.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -434,6 +434,30 @@ std::optional<Value> tosaCastTensorToType(PatternRewriter &rewriter, Value src,
// if (failed(checkValidityOfCast(srcElemTy, destElemTy)))
// return std::nullopt;

if (llvm::isa<FloatType>(srcElemTy) && destElemTy.isInteger(1)) {
// TOSA does not support casting from float->i1.
// In PyTorch the bool value will be True if any element is non-zero
Value zeroValue = *getConstTensor<float>(rewriter, op, 0.0f, {}, srcElemTy);
if (mlir::tosa::EqualizeRanks(rewriter, op->getLoc(), src, zeroValue)
.failed())
return std::nullopt;

auto cmpTy = srcType.clone(rewriter.getIntegerType(1));
Value isEq =
rewriter.create<tosa::EqualOp>(op->getLoc(), cmpTy, src, zeroValue);
return rewriter.create<tosa::LogicalNotOp>(op->getLoc(),
srcType.clone(destElemTy), isEq);
}

if (srcElemTy.isInteger(1) && llvm::isa<FloatType>(destElemTy)) {
// TOSA does not support casting from i1->float.
// Instead, we cast to i8 and then to the float.
TensorType midType = srcType.clone(rewriter.getIntegerType(8));
Value mid = rewriter.create<tosa::CastOp>(op->getLoc(), midType, src);
return rewriter.create<tosa::CastOp>(op->getLoc(),
srcType.clone(destElemTy), mid);
}

if (srcElemTy == destElemTy)
return src;

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15 changes: 15 additions & 0 deletions projects/pt1/python/torch_mlir_e2e_test/test_suite/type_conversion.py
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Expand Up @@ -27,6 +27,21 @@ def TypeConversionF32ToF64Module_basic(module, tu: TestUtils):
module.forward(tu.rand(3, 5))


class TypeConversionF32ToI1Module(torch.nn.Module):
def __init__(self):
super().__init__()

@export
@annotate_args([None, ([-1, -1], torch.float32, True)])
def forward(self, x):
return x.to(torch.bool)


@register_test_case(module_factory=lambda: TypeConversionF32ToI1Module())
def TypeConversionF32ToI1Module_basic(module, tu: TestUtils):
module.forward(tu.rand(3, 5))


class TypeConversionF64ToF32Module(torch.nn.Module):
def __init__(self):
super().__init__()
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44 changes: 44 additions & 0 deletions test/Conversion/TorchToTosa/basic.mlir
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Expand Up @@ -1169,6 +1169,50 @@ func.func @torch.aten.to.dtype$floatToInt(%arg0: !torch.vtensor<[3,5],f32>) -> !
return %0 : !torch.vtensor<[3,5],si64>
}

// -----
// CHECK-LABEL: func.func @torch.aten.to.dtype$floatToBool(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[3,5],f32>) -> !torch.vtensor<[3,5],i1> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[3,5],f32> -> tensor<3x5xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.int 11
// CHECK: %[[VAL_3:.*]] = torch.constant.bool false
// CHECK: %[[VAL_4:.*]] = torch.constant.none
// CHECK: %[[VAL_5:.*]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<f32>}> : () -> tensor<f32>
// CHECK: %[[VAL_6:.*]] = tosa.const_shape {values = dense<1> : tensor<2xindex>} : () -> !tosa.shape<2>
// CHECK: %[[VAL_7:.*]] = tosa.reshape %[[VAL_5]], %[[VAL_6]] : (tensor<f32>, !tosa.shape<2>) -> tensor<1x1xf32>
// CHECK: %[[VAL_8:.*]] = tosa.equal %[[VAL_1]], %[[VAL_7]] : (tensor<3x5xf32>, tensor<1x1xf32>) -> tensor<3x5xi1>
// CHECK: %[[VAL_9:.*]] = tosa.logical_not %[[VAL_8]] : (tensor<3x5xi1>) -> tensor<3x5xi1>
// CHECK: %[[VAL_10:.*]] = torch_c.from_builtin_tensor %[[VAL_9]] : tensor<3x5xi1> -> !torch.vtensor<[3,5],i1>
// CHECK: return %[[VAL_10]] : !torch.vtensor<[3,5],i1>
// CHECK: }
func.func @torch.aten.to.dtype$floatToBool(%arg0: !torch.vtensor<[3,5],f32>) -> !torch.vtensor<[3,5],i1> {
%int11 = torch.constant.int 11
%false = torch.constant.bool false
%none = torch.constant.none
%0 = torch.aten.to.dtype %arg0, %int11, %false, %false, %none : !torch.vtensor<[3,5],f32>, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[3,5],i1>
return %0 : !torch.vtensor<[3,5],i1>
}

// -----
// CHECK-LABEL: func.func @torch.aten.to.dtype$boolToFloat(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[3,4],i1>) -> !torch.vtensor<[3,4],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[3,4],i1> -> tensor<3x4xi1>
// CHECK: %[[VAL_2:.*]] = torch.constant.int 6
// CHECK: %[[VAL_3:.*]] = torch.constant.bool false
// CHECK: %[[VAL_4:.*]] = torch.constant.none
// CHECK: %[[VAL_5:.*]] = tosa.cast %[[VAL_1]] : (tensor<3x4xi1>) -> tensor<3x4xi8>
// CHECK: %[[VAL_6:.*]] = tosa.cast %[[VAL_5]] : (tensor<3x4xi8>) -> tensor<3x4xf32>
// CHECK: %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<3x4xf32> -> !torch.vtensor<[3,4],f32>
// CHECK: return %[[VAL_7]] : !torch.vtensor<[3,4],f32>
// CHECK: }
func.func @torch.aten.to.dtype$boolToFloat(%arg0: !torch.vtensor<[3,4],i1>) -> !torch.vtensor<[3,4],f32> {
%int6 = torch.constant.int 6
%false = torch.constant.bool false
%none = torch.constant.none
%0 = torch.aten.to.dtype %arg0, %int6, %false, %false, %none : !torch.vtensor<[3,4],i1>, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[3,4],f32>
return %0 : !torch.vtensor<[3,4],f32>
}


// -----
// CHECK-LABEL: func.func @torch.aten.gather(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[1,4,3],f32>,
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