[MLIR][TORCH] E2E support for [ge|ceil].float, [ge|ne|gt].float_int op

This commit adds lowering of `aten.ge.float`, `aten.ge.float_int`,
`aten.ne.float_int`, `aten.gt.float_int` and `aten.ceil.float` op.
This commit also fixes formatting for the file scalar.py and scalar_comparison.py.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
pull/821/head snapshot-20220505.433
Vivek Khandelwal 2022-04-25 18:42:45 +05:30
parent e682b1d0f3
commit 96fabc0036
8 changed files with 462 additions and 9 deletions

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@ -6678,6 +6678,31 @@ def Torch_AtenGtFloatOp : Torch_Op<"aten.gt.float", [
let hasFolder = 1;
}
def Torch_AtenGeFloatOp : Torch_Op<"aten.ge.float", [
AllowsTypeRefinement,
HasValueSemantics,
ReadOnly
]> {
let summary = "Generated op for `aten::ge.float : (float, float) -> (bool)`";
let arguments = (ins
Torch_FloatType:$a,
Torch_FloatType:$b
);
let results = (outs
Torch_BoolType:$result
);
let hasCustomAssemblyFormat = 1;
let extraClassDefinition = [{
ParseResult AtenGeFloatOp::parse(OpAsmParser &parser, OperationState &result) {
return parseDefaultTorchOp(parser, result, 2, 1);
}
void AtenGeFloatOp::print(OpAsmPrinter &printer) {
printDefaultTorchOp(printer, *this, 2, 1);
}
}];
let hasFolder = 1;
}
def Torch_AtenLtFloatOp : Torch_Op<"aten.lt.float", [
AllowsTypeRefinement,
HasValueSemantics,
@ -6727,6 +6752,78 @@ def Torch_AtenLtFloatIntOp : Torch_Op<"aten.lt.float_int", [
}];
}
def Torch_AtenGeFloatIntOp : Torch_Op<"aten.ge.float_int", [
AllowsTypeRefinement,
HasValueSemantics,
ReadOnly
]> {
let summary = "Generated op for `aten::ge.float_int : (float, int) -> (bool)`";
let arguments = (ins
Torch_FloatType:$a,
Torch_IntType:$b
);
let results = (outs
Torch_BoolType:$result
);
let hasCustomAssemblyFormat = 1;
let extraClassDefinition = [{
ParseResult AtenGeFloatIntOp::parse(OpAsmParser &parser, OperationState &result) {
return parseDefaultTorchOp(parser, result, 2, 1);
}
void AtenGeFloatIntOp::print(OpAsmPrinter &printer) {
printDefaultTorchOp(printer, *this, 2, 1);
}
}];
}
def Torch_AtenNeFloatIntOp : Torch_Op<"aten.ne.float_int", [
AllowsTypeRefinement,
HasValueSemantics,
ReadOnly
]> {
let summary = "Generated op for `aten::ne.float_int : (float, int) -> (bool)`";
let arguments = (ins
Torch_FloatType:$a,
Torch_IntType:$b
);
let results = (outs
Torch_BoolType:$result
);
let hasCustomAssemblyFormat = 1;
let extraClassDefinition = [{
ParseResult AtenNeFloatIntOp::parse(OpAsmParser &parser, OperationState &result) {
return parseDefaultTorchOp(parser, result, 2, 1);
}
void AtenNeFloatIntOp::print(OpAsmPrinter &printer) {
printDefaultTorchOp(printer, *this, 2, 1);
}
}];
}
def Torch_AtenGtFloatIntOp : Torch_Op<"aten.gt.float_int", [
AllowsTypeRefinement,
HasValueSemantics,
ReadOnly
]> {
let summary = "Generated op for `aten::gt.float_int : (float, int) -> (bool)`";
let arguments = (ins
Torch_FloatType:$a,
Torch_IntType:$b
);
let results = (outs
Torch_BoolType:$result
);
let hasCustomAssemblyFormat = 1;
let extraClassDefinition = [{
ParseResult AtenGtFloatIntOp::parse(OpAsmParser &parser, OperationState &result) {
return parseDefaultTorchOp(parser, result, 2, 1);
}
void AtenGtFloatIntOp::print(OpAsmPrinter &printer) {
printDefaultTorchOp(printer, *this, 2, 1);
}
}];
}
def Torch_Aten__And__BoolOp : Torch_Op<"aten.__and__.bool", [
AllowsTypeRefinement,
HasValueSemantics,
@ -6970,6 +7067,30 @@ def Torch_AtenEqDeviceOp : Torch_Op<"aten.eq.device", [
}];
}
def Torch_AtenCeilFloatOp : Torch_Op<"aten.ceil.float", [
AllowsTypeRefinement,
HasValueSemantics,
ReadOnly
]> {
let summary = "Generated op for `aten::ceil.float : (float) -> (int)`";
let arguments = (ins
Torch_FloatType:$a
);
let results = (outs
Torch_IntType:$result
);
let hasCustomAssemblyFormat = 1;
let extraClassDefinition = [{
ParseResult AtenCeilFloatOp::parse(OpAsmParser &parser, OperationState &result) {
return parseDefaultTorchOp(parser, result, 1, 1);
}
void AtenCeilFloatOp::print(OpAsmPrinter &printer) {
printDefaultTorchOp(printer, *this, 1, 1);
}
}];
let hasFolder = 1;
}
def Torch_Aten_SoftmaxBackwardDataOp : Torch_Op<"aten._softmax_backward_data", [
AllowsTypeRefinement,
HasValueSemantics,

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@ -13,9 +13,11 @@
#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
#include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Dialect/Math/IR/Math.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Traits.h"
#include "mlir/Transforms/DialectConversion.h"
#include "torch-mlir/Conversion/Utils/Utils.h"
#include "torch-mlir/Dialect/Torch/IR/TorchDialect.h"
#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
#include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionDialect.h"
@ -76,6 +78,25 @@ public:
};
} // namespace
namespace {
template <typename AtenOp, typename UnaryOp>
class ConvertAtenUnaryOp : public OpConversionPattern<AtenOp> {
public:
using OpConversionPattern<AtenOp>::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenOp op,
typename OpConversionPattern<AtenOp>::OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Type resultType =
this->getTypeConverter()->convertType(op->getResult(0).getType());
Value result = rewriter.create<UnaryOp>(op.getLoc(), adaptor.a());
rewriter.replaceOp(
op, convertScalarToDtype(rewriter, op.getLoc(), result, resultType));
return success();
}
};
} // namespace
namespace {
// Lowers aten integer comparison ops.
template <typename AtenOp, arith::CmpIPredicate Pred>
@ -93,6 +114,24 @@ public:
};
} // namespace
namespace {
// Lowers aten float and float_int comparison ops.
template <typename AtenOp, arith::CmpFPredicate Pred>
class ConvertAtenFloatComparisonOp : public OpConversionPattern<AtenOp> {
public:
using OpConversionPattern<AtenOp>::OpConversionPattern;
LogicalResult
matchAndRewrite(AtenOp op,
typename OpConversionPattern<AtenOp>::OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Value lhs = adaptor.a(), rhs = adaptor.b();
rhs = convertScalarToDtype(rewriter, op.getLoc(), rhs, lhs.getType());
rewriter.replaceOpWithNewOp<arith::CmpFOp>(op, Pred, lhs, rhs);
return success();
}
};
} // namespace
// Tensors with integer types need to be converted to signless integer
// element type. All tensors with element types other than integer can reuse
// existing elements attribute.
@ -163,6 +202,7 @@ public:
registry.insert<arith::ArithmeticDialect>();
registry.insert<tensor::TensorDialect>();
registry.insert<cf::ControlFlowDialect>();
registry.insert<math::MathDialect>();
TorchConversion::getBackendTypeConversionDependentDialects(registry);
}
@ -171,7 +211,7 @@ public:
ConversionTarget target(*context);
target.addLegalDialect<Torch::TorchDialect, func::FuncDialect,
arith::ArithmeticDialect, tensor::TensorDialect,
cf::ControlFlowDialect>();
cf::ControlFlowDialect, math::MathDialect>();
TypeConverter typeConverter;
typeConverter.addConversion([](Type type) { return type; });
@ -192,6 +232,20 @@ public:
patterns.add<
ConvertAtenIntComparisonOp<AtenGtIntOp, arith::CmpIPredicate::sgt>>(
typeConverter, context);
target.addIllegalOp<AtenGeFloatOp, AtenGeFloatIntOp, AtenNeFloatIntOp,
AtenGtFloatIntOp>();
patterns.add<
ConvertAtenFloatComparisonOp<AtenGeFloatOp, arith::CmpFPredicate::UGE>>(
typeConverter, context);
patterns.add<ConvertAtenFloatComparisonOp<AtenGeFloatIntOp,
arith::CmpFPredicate::UGE>>(
typeConverter, context);
patterns.add<ConvertAtenFloatComparisonOp<AtenNeFloatIntOp,
arith::CmpFPredicate::UNE>>(
typeConverter, context);
patterns.add<ConvertAtenFloatComparisonOp<AtenGtFloatIntOp,
arith::CmpFPredicate::UGT>>(
typeConverter, context);
target.addIllegalOp<ValueTensorLiteralOp>();
patterns.add<ConvertTorchTensorLiteralOp>(typeConverter, context);
@ -217,6 +271,9 @@ public:
target.addIllegalOp<AtenDivFloatOp>();
patterns.add<ConvertAtenBinaryOp<AtenDivFloatOp, arith::DivFOp>>(
typeConverter, context);
target.addIllegalOp<AtenCeilFloatOp>();
patterns.add<ConvertAtenUnaryOp<AtenCeilFloatOp, math::CeilOp>>(
typeConverter, context);
if (failed(applyPartialConversion(getOperation(), target,
std::move(patterns))))

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@ -857,6 +857,15 @@ OpFoldResult AtenGtFloatOp::fold(ArrayRef<Attribute> operands) {
[](double a, double b) { return a > b; });
}
//===----------------------------------------------------------------------===//
// AtenGeFloatOp
//===----------------------------------------------------------------------===//
OpFoldResult AtenGeFloatOp::fold(ArrayRef<Attribute> operands) {
return floatComparatorFoldHelper(*this,
[](double a, double b) { return a >= b; });
}
//===----------------------------------------------------------------------===//
// AtenEqFloatOp
//===----------------------------------------------------------------------===//
@ -1604,6 +1613,16 @@ OpFoldResult AtenDivFloatOp::fold(ArrayRef<Attribute> operands) {
return nullptr;
}
// AtenCeilFloatOp
//===----------------------------------------------------------------------===//
OpFoldResult AtenCeilFloatOp::fold(ArrayRef<Attribute> operands) {
double c;
if (matchPattern(getOperand(), m_TorchConstantFloat(&c)))
return getI64IntegerAttr(getContext(), std::ceil(c));
return nullptr;
}
//===----------------------------------------------------------------------===//
//===----------------------------------------------------------------------===//

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@ -501,8 +501,12 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
emit("aten::neg.float : (float) -> (float)")
emit("aten::eq.float : (float, float) -> (bool)", has_folder=True)
emit("aten::gt.float : (float, float) -> (bool)", has_folder=True)
emit("aten::ge.float : (float, float) -> (bool)", has_folder=True)
emit("aten::lt.float : (float, float) -> (bool)", has_folder=True)
emit("aten::lt.float_int : (float, int) -> (bool)")
emit("aten::ge.float_int : (float, int) -> (bool)")
emit("aten::ne.float_int : (float, int) -> (bool)")
emit("aten::gt.float_int : (float, int) -> (bool)")
emit("aten::__and__.bool : (bool, bool) -> (bool)")
emit("aten::ne.bool : (bool, bool) -> (bool)", has_folder=True)
emit("aten::__is__ : (t1, t2) -> (bool)", has_folder=True)
@ -515,6 +519,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
emit("aten::_set_item.t : (t[], int, t) -> (t[])")
emit("aten::div : (Scalar, Scalar) -> (float)")
emit("aten::eq.device : (Device, Device) -> (bool)")
emit("aten::ceil.float : (float) -> (int)", has_folder=True)
# backprop ops
emit("aten::_softmax_backward_data : (Tensor, Tensor, int, int) -> (Tensor)")

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@ -11,7 +11,9 @@ from torch_mlir_e2e_test.torchscript.annotations import annotate_args, export
# ==============================================================================
class AddIntModule(torch.nn.Module):
def __init__(self):
super().__init__()
@ -22,16 +24,19 @@ class AddIntModule(torch.nn.Module):
([], torch.int64, True),
])
def forward(self, lhs, rhs):
return int(lhs)+int(rhs)
return int(lhs) + int(rhs)
@register_test_case(module_factory=lambda: AddIntModule())
def AddIntModule_basic(module, tu: TestUtils):
module.forward(torch.randint(-100, 100,()), torch.randint(-100, 100,()))
module.forward(torch.randint(-100, 100, ()), torch.randint(-100, 100, ()))
# ==============================================================================
class SubIntModule(torch.nn.Module):
def __init__(self):
super().__init__()
@ -42,16 +47,19 @@ class SubIntModule(torch.nn.Module):
([], torch.int64, True),
])
def forward(self, lhs, rhs):
return int(lhs)-int(rhs)
return int(lhs) - int(rhs)
@register_test_case(module_factory=lambda: SubIntModule())
def SubIntModule_basic(module, tu: TestUtils):
module.forward(torch.randint(-100, 100,()), torch.randint(-100, 100,()))
module.forward(torch.randint(-100, 100, ()), torch.randint(-100, 100, ()))
# ==============================================================================
class SubFloatModule(torch.nn.Module):
def __init__(self):
super().__init__()
@ -62,16 +70,19 @@ class SubFloatModule(torch.nn.Module):
([], torch.float64, True),
])
def forward(self, lhs, rhs):
return float(lhs)-float(rhs)
return float(lhs) - float(rhs)
@register_test_case(module_factory=lambda: SubFloatModule())
def SubFloatModule_basic(module, tu: TestUtils):
module.forward(torch.rand(()).double(), torch.rand(()).double())
# ==============================================================================
class MulIntModule(torch.nn.Module):
def __init__(self):
super().__init__()
@ -82,16 +93,19 @@ class MulIntModule(torch.nn.Module):
([], torch.int64, True),
])
def forward(self, lhs, rhs):
return int(lhs)*int(rhs)
return int(lhs) * int(rhs)
@register_test_case(module_factory=lambda: MulIntModule())
def MulIntModule_basic(module, tu: TestUtils):
module.forward(torch.randint(-100, 100,()), torch.randint(-100, 100,()))
module.forward(torch.randint(-100, 100, ()), torch.randint(-100, 100, ()))
# ==============================================================================
class DivFloatModule(torch.nn.Module):
def __init__(self):
super().__init__()
@ -102,9 +116,33 @@ class DivFloatModule(torch.nn.Module):
([], torch.float64, True),
])
def forward(self, lhs, rhs):
return float(lhs)/float(rhs)
return float(lhs) / float(rhs)
@register_test_case(module_factory=lambda: DivFloatModule())
def DivFloatModule_basic(module, tu: TestUtils):
module.forward(torch.rand(()).double(), torch.rand(()).double())
# ==============================================================================
class CeilFloatModule(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args([
None,
([], torch.float64, True),
([], torch.float64, True),
])
def forward(self, lhs, rhs):
sub = float(lhs) - float(rhs)
return torch.ops.aten.ceil(float(sub))
@register_test_case(module_factory=lambda: CeilFloatModule())
def CeilFloatModule_basic(module, tu: TestUtils):
module.forward(torch.rand(()).double(), torch.rand(()).double())

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@ -11,7 +11,9 @@ from torch_mlir_e2e_test.torchscript.annotations import annotate_args, export
# ==============================================================================
class NeIntModule(torch.nn.Module):
def __init__(self):
super().__init__()
@ -29,9 +31,12 @@ class NeIntModule(torch.nn.Module):
def NeIntModule_basic(module, tu: TestUtils):
module.forward(torch.randint(-100, 100, ()), torch.randint(-100, 100, ()))
# ==============================================================================
class EqIntModule(torch.nn.Module):
def __init__(self):
super().__init__()
@ -49,9 +54,12 @@ class EqIntModule(torch.nn.Module):
def EqIntModule_basic(module, tu: TestUtils):
module.forward(torch.randint(-100, 100, ()), torch.randint(-100, 100, ()))
# ==============================================================================
class GtIntModule(torch.nn.Module):
def __init__(self):
super().__init__()
@ -69,3 +77,94 @@ class GtIntModule(torch.nn.Module):
def GtIntModule_basic(module, tu: TestUtils):
module.forward(torch.randint(-100, 100, ()), torch.randint(-100, 100, ()))
# ==============================================================================
class GeFloatModule(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args([
None,
([], torch.float64, True),
([], torch.float64, True),
])
def forward(self, lhs, rhs):
return float(lhs) >= float(rhs)
@register_test_case(module_factory=lambda: GeFloatModule())
def GeFloatModule_basic(module, tu: TestUtils):
module.forward(torch.randn(()).double(), torch.randn(()).double())
# ==============================================================================
class GeFloatIntModule(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args([
None,
([], torch.float64, True),
([], torch.int64, True),
])
def forward(self, lhs, rhs):
return float(lhs) >= int(rhs)
@register_test_case(module_factory=lambda: GeFloatIntModule())
def GeFloatIntModule_basic(module, tu: TestUtils):
module.forward(torch.randn(()).double(), torch.randint(-100, 100, ()))
# ==============================================================================
class NeFloatIntModule(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args([
None,
([], torch.float64, True),
([], torch.int64, True),
])
def forward(self, lhs, rhs):
return float(lhs) != int(rhs)
@register_test_case(module_factory=lambda: NeFloatIntModule())
def NeFloatIntModule_basic(module, tu: TestUtils):
module.forward(torch.randn(()).double(), torch.randint(-100, 100, ()))
# ==============================================================================
class GtFloatIntModule(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args([
None,
([], torch.float64, True),
([], torch.int64, True),
])
def forward(self, lhs, rhs):
return float(lhs) > int(rhs)
@register_test_case(module_factory=lambda: GtFloatIntModule())
def GtFloatIntModule_basic(module, tu: TestUtils):
module.forward(torch.randn(()).double(), torch.randint(-100, 100, ()))

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@ -166,3 +166,70 @@ func @torch.aten.div.float(%arg0: !torch.float, %arg1: !torch.float) -> !torch.f
%0 = torch.aten.div.float %arg0, %arg1 : !torch.float, !torch.float -> !torch.float
return %0 : !torch.float
}
// CHECK-LABEL: func @torch.aten.ge.float(
// CHECK-SAME: %[[LHS:.*]]: !torch.float,
// CHECK-SAME: %[[RHS:.*]]: !torch.float) -> !torch.bool {
// CHECK: %[[LHS_F64:.*]] = torch_c.to_f64 %[[LHS]]
// CHECK: %[[RHS_F64:.*]] = torch_c.to_f64 %[[RHS]]
// CHECK: %[[CMP:.*]] = arith.cmpf uge, %[[LHS_F64]], %[[RHS_F64]] : f64
// CHECK: %[[CMP_TORCH_BOOL:.*]] = torch_c.from_i1 %[[CMP]]
// CHECK: return %[[CMP_TORCH_BOOL]] : !torch.bool
func @torch.aten.ge.float(%arg0: !torch.float, %arg1: !torch.float) -> !torch.bool {
%0 = torch.aten.ge.float %arg0, %arg1 : !torch.float, !torch.float -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.ge.float_int(
// CHECK-SAME: %[[LHS:.*]]: !torch.float,
// CHECK-SAME: %[[RHS:.*]]: !torch.int) -> !torch.bool {
// CHECK: %[[LHS_F64:.*]] = torch_c.to_f64 %[[LHS]]
// CHECK: %[[RHS_I64:.*]] = torch_c.to_i64 %[[RHS]]
// CHECK: %[[RHS_F64:.*]] = arith.sitofp %[[RHS_I64]] : i64 to f64
// CHECK: %[[CMP:.*]] = arith.cmpf uge, %[[LHS_F64]], %[[RHS_F64]] : f64
// CHECK: %[[CMP_TORCH_BOOL:.*]] = torch_c.from_i1 %[[CMP]]
// CHECK: return %[[CMP_TORCH_BOOL]] : !torch.bool
func @torch.aten.ge.float_int(%arg0: !torch.float, %arg1: !torch.int) -> !torch.bool {
%0 = torch.aten.ge.float_int %arg0, %arg1 : !torch.float, !torch.int -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.ne.float_int(
// CHECK-SAME: %[[LHS:.*]]: !torch.float,
// CHECK-SAME: %[[RHS:.*]]: !torch.int) -> !torch.bool {
// CHECK: %[[LHS_F64:.*]] = torch_c.to_f64 %[[LHS]]
// CHECK: %[[RHS_I64:.*]] = torch_c.to_i64 %[[RHS]]
// CHECK: %[[RHS_F64:.*]] = arith.sitofp %[[RHS_I64]] : i64 to f64
// CHECK: %[[CMP:.*]] = arith.cmpf une, %[[LHS_F64]], %[[RHS_F64]] : f64
// CHECK: %[[CMP_TORCH_BOOL:.*]] = torch_c.from_i1 %[[CMP]]
// CHECK: return %[[CMP_TORCH_BOOL]] : !torch.bool
func @torch.aten.ne.float_int(%arg0: !torch.float, %arg1: !torch.int) -> !torch.bool {
%0 = torch.aten.ne.float_int %arg0, %arg1 : !torch.float, !torch.int -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.ceil.float(
// CHECK-SAME: %[[ARG:.*]]: !torch.float) -> !torch.int {
// CHECK: %[[ARG_F64:.*]] = torch_c.to_f64 %[[ARG]]
// CHECK: %[[CEIL:.*]] = math.ceil %[[ARG_F64]] : f64
// CHECK: %[[CEIL_I64:.*]] = arith.fptosi %[[CEIL]] : f64 to i64
// CHECK: %[[OUT:.*]] = torch_c.from_i64 %[[CEIL_I64]]
// CHECK: return %[[OUT]] : !torch.int
func @torch.aten.ceil.float(%arg0: !torch.float) -> !torch.int {
%0 = torch.aten.ceil.float %arg0 : !torch.float -> !torch.int
return %0 : !torch.int
}
// CHECK-LABEL: func @torch.aten.gt.float_int(
// CHECK-SAME: %[[LHS:.*]]: !torch.float,
// CHECK-SAME: %[[RHS:.*]]: !torch.int) -> !torch.bool {
// CHECK: %[[LHS_F64:.*]] = torch_c.to_f64 %[[LHS]]
// CHECK: %[[RHS_I64:.*]] = torch_c.to_i64 %[[RHS]]
// CHECK: %[[RHS_F64:.*]] = arith.sitofp %[[RHS_I64]] : i64 to f64
// CHECK: %[[CMP:.*]] = arith.cmpf ugt, %[[LHS_F64]], %[[RHS_F64]] : f64
// CHECK: %[[CMP_TORCH_BOOL:.*]] = torch_c.from_i1 %[[CMP]]
// CHECK: return %[[CMP_TORCH_BOOL]] : !torch.bool
func @torch.aten.gt.float_int(%arg0: !torch.float, %arg1: !torch.int) -> !torch.bool {
%0 = torch.aten.gt.float_int %arg0, %arg1 : !torch.float, !torch.int -> !torch.bool
return %0 : !torch.bool
}

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@ -1186,3 +1186,50 @@ func @torch.aten.to.dtype_layout$same_dtype(%arg0: !torch.tensor<[?,?],f32>) ->
%0 = torch.aten.to.dtype_layout %arg0, %int6, %none, %none, %none, %false, %false, %none : !torch.tensor<[?,?],f32>, !torch.int, !torch.none, !torch.none, !torch.none, !torch.bool, !torch.bool, !torch.none -> !torch.tensor<[?,?],f32>
return %0 : !torch.tensor<[?,?],f32>
}
// CHECK-LABEL: func @torch.aten.ge.float$same_operand(
// CHECK-SAME: %{{.*}}: !torch.float) -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func @torch.aten.ge.float$same_operand(%arg0: !torch.float) -> !torch.bool {
%2 = torch.aten.ge.float %arg0, %arg0: !torch.float, !torch.float -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.ge.float$same_value() -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func @torch.aten.ge.float$same_value() -> !torch.bool {
%float4 = torch.constant.float 4.0
%float4_0 = torch.constant.float 4.0
%2 = torch.aten.ge.float %float4, %float4_0: !torch.float, !torch.float -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.ge.float$different_value() -> !torch.bool {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func @torch.aten.ge.float$different_value() -> !torch.bool {
%float4 = torch.constant.float 4.0
%float4_0 = torch.constant.float 5.0
%2 = torch.aten.ge.float %float4, %float4_0: !torch.float, !torch.float -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.ceil.float$fold_cst() -> !torch.int {
// CHECK: %[[CST2:.*]] = torch.constant.int 2
// CHECK: return %[[CST2]] : !torch.int
func @torch.aten.ceil.float$fold_cst() -> !torch.int {
%float = torch.constant.float 1.5
%1 = torch.aten.ceil.float %float : !torch.float -> !torch.int
return %1 : !torch.int
}
// CHECK-LABEL: func @torch.aten.ceil.float$no_fold(
// CHECK-SAME: %[[ARG:.*]]: !torch.float) -> !torch.int {
// CHECK: %[[RESULT:.*]] = torch.aten.ceil.float %[[ARG]] : !torch.float -> !torch.int
// CHECK: return %[[RESULT]] : !torch.int
func @torch.aten.ceil.float$no_fold(%arg0 : !torch.float) -> !torch.int {
%1 = torch.aten.ceil.float %arg0 : !torch.float -> !torch.int
return %1 : !torch.int
}