[mlir][math] Rename math.abs -> math.absf

[Mogball]

Summary

To make room for introducing math.absi.

[Mogball]

fix name

[Mogball]

fix another comment name

[Mogball]

remove unrelated change

[Mogball]

fix diff

[aartbik]

LGTM for sparse part

flang/lib/Lower/IntrinsicCall.cpp

 }
 
 /// Mapping between mathematical intrinsic operations and MLIR operations
 /// of some appropriate dialect (math, complex, etc.) or libm calls.
 /// TODO: support remaining Fortran math intrinsics.
 ///       See https://gcc.gnu.org/onlinedocs/gcc-12.1.0/gfortran/\
 ///       Intrinsic-Procedures.html for a reference.
 static constexpr MathOperation mathOperations[] = {
-    {"abs", "fabsf", genF32F32FuncType, genMathOp<mlir::math::AbsOp>},
-    {"abs", "fabs", genF64F64FuncType, genMathOp<mlir::math::AbsOp>},
+    {"abs", "fabsf", genF32F32FuncType, genMathOp<mlir::math::AbsFOp>},
+    {"abs", "fabs", genF64F64FuncType, genMathOp<mlir::math::AbsFOp>},
     {"abs", "llvm.fabs.f128", genF128F128FuncType,
-     genMathOp<mlir::math::AbsOp>},
+     genMathOp<mlir::math::AbsFOp>},
     // llvm.trunc behaves the same way as libm's trunc.
     {"aint", "llvm.trunc.f32", genF32F32FuncType, genLibCall},
     {"aint", "llvm.trunc.f64", genF64F64FuncType, genLibCall},
     {"aint", "llvm.trunc.f80", genF80F80FuncType, genLibCall},
     // llvm.round behaves the same way as libm's round.
     {"anint", "llvm.round.f32", genF32F32FuncType,
      genMathOp<mlir::LLVM::RoundOp>},
     {"anint", "llvm.round.f64", genF64F64FuncType,

flang/test/Intrinsics/math-codegen.fir

 // CHECK: {{%[A-Za-z0-9._]+}} = llvm.call @hypotf({{%[A-Za-z0-9._]+}}, {{%[A-Za-z0-9._]+}}) : (f32, f32) -> f32
 
 // CHECK: @_QPtest_complex8
 // CHECK: {{%[A-Za-z0-9._]+}} = llvm.call @hypot({{%[A-Za-z0-9._]+}}, {{%[A-Za-z0-9._]+}}) : (f64, f64) -> f64
 
 func.func @_QPtest_real4(%arg0: !fir.ref<f32> {fir.bindc_name = "x"}) -> f32 {
   %0 = fir.alloca f32 {bindc_name = "test_real4", uniq_name = "_QFtest_real4Etest_real4"}
   %1 = fir.load %arg0 : !fir.ref<f32>
-  %2 = math.abs %1 : f32
+  %2 = math.absf %1 : f32
   fir.store %2 to %0 : !fir.ref<f32>
   %3 = fir.load %0 : !fir.ref<f32>
   return %3 : f32
 }
 func.func @_QPtest_real8(%arg0: !fir.ref<f64> {fir.bindc_name = "x"}) -> f64 {
   %0 = fir.alloca f64 {bindc_name = "test_real8", uniq_name = "_QFtest_real8Etest_real8"}
   %1 = fir.load %arg0 : !fir.ref<f64>
-  %2 = math.abs %1 : f64
+  %2 = math.absf %1 : f64
   fir.store %2 to %0 : !fir.ref<f64>
   %3 = fir.load %0 : !fir.ref<f64>
   return %3 : f64
 }
 func.func @_QPtest_real16(%arg0: !fir.ref<f128> {fir.bindc_name = "x"}) -> f128 {
   %0 = fir.alloca f128 {bindc_name = "test_real16", uniq_name = "_QFtest_real16Etest_real16"}
   %1 = fir.load %arg0 : !fir.ref<f128>
-  %2 = math.abs %1 : f128
+  %2 = math.absf %1 : f128
   fir.store %2 to %0 : !fir.ref<f128>
   %3 = fir.load %0 : !fir.ref<f128>
   return %3 : f128
 }
 func.func @_QPtest_complex4(%arg0: !fir.ref<!fir.complex<4>> {fir.bindc_name = "c"}) -> !fir.complex<4> {
   %0 = fir.alloca !fir.complex<4> {bindc_name = "test_complex4", uniq_name = "_QFtest_complex4Etest_complex4"}
   %1 = fir.load %arg0 : !fir.ref<!fir.complex<4>>
   %2 = fir.extract_value %1, [0 : index] : (!fir.complex<4>) -> f32

 // CHECK: {{%[A-Za-z0-9._]+}} = llvm.call @hypotf({{%[A-Za-z0-9._]+}}, {{%[A-Za-z0-9._]+}}) : (f32, f32) -> f32
 
 // CHECK: @_QPtest_complex8
 // CHECK: {{%[A-Za-z0-9._]+}} = llvm.call @hypot({{%[A-Za-z0-9._]+}}, {{%[A-Za-z0-9._]+}}) : (f64, f64) -> f64
 
 func.func @_QPtest_real4(%arg0: !fir.ref<f32> {fir.bindc_name = "x"}) -> f32 {
   %0 = fir.alloca f32 {bindc_name = "test_real4", uniq_name = "_QFtest_real4Etest_real4"}
   %1 = fir.load %arg0 : !fir.ref<f32>
-  %2 = math.abs %1 : f32
+  %2 = math.absf %1 : f32
   fir.store %2 to %0 : !fir.ref<f32>
   %3 = fir.load %0 : !fir.ref<f32>
   return %3 : f32
 }
 func.func @_QPtest_real8(%arg0: !fir.ref<f64> {fir.bindc_name = "x"}) -> f64 {
   %0 = fir.alloca f64 {bindc_name = "test_real8", uniq_name = "_QFtest_real8Etest_real8"}
   %1 = fir.load %arg0 : !fir.ref<f64>
-  %2 = math.abs %1 : f64
+  %2 = math.absf %1 : f64
   fir.store %2 to %0 : !fir.ref<f64>
   %3 = fir.load %0 : !fir.ref<f64>
   return %3 : f64
 }
 func.func @_QPtest_real16(%arg0: !fir.ref<f128> {fir.bindc_name = "x"}) -> f128 {
   %0 = fir.alloca f128 {bindc_name = "test_real16", uniq_name = "_QFtest_real16Etest_real16"}
   %1 = fir.load %arg0 : !fir.ref<f128>
-  %2 = math.abs %1 : f128
+  %2 = math.absf %1 : f128
   fir.store %2 to %0 : !fir.ref<f128>
   %3 = fir.load %0 : !fir.ref<f128>
   return %3 : f128
 }
 func.func @_QPtest_complex4(%arg0: !fir.ref<!fir.complex<4>> {fir.bindc_name = "c"}) -> !fir.complex<4> {
   %0 = fir.alloca !fir.complex<4> {bindc_name = "test_complex4", uniq_name = "_QFtest_complex4Etest_complex4"}
   %1 = fir.load %arg0 : !fir.ref<!fir.complex<4>>
   %2 = fir.extract_value %1, [0 : index] : (!fir.complex<4>) -> f32
func.func @_QPtest_real8(%arg0: !fir.ref<f64> {fir.bindc_name = "x"}) -> f64 {
   %1 = fir.load %arg0 : !fir.ref<f64>
   %2 = fir.call @tan(%1) : (f64) -> f64
   fir.store %2 to %0 : !fir.ref<f64>
   %3 = fir.load %0 : !fir.ref<f64>
   return %3 : f64
 }
 func.func private @tanf(f32) -> f32
 func.func private @tan(f64) -> f64
-

flang/test/Lower/Intrinsics/abs.f90

! CHECK:  return
   b = abs(a)
 end subroutine
 
 ! CHECK-LABEL: func @_QPabs_testh(
 ! CHECK-SAME:  %[[VAL_0:.*]]: !fir.ref<f16>{{.*}}, %[[VAL_1:.*]]: !fir.ref<f16>{{.*}}) {
 subroutine abs_testh(a, b)
 ! CHECK: %[[VAL_2:.*]] = fir.load %[[VAL_0]] : !fir.ref<f16>
 ! CHECK: %[[VAL_2_1:.*]] = fir.convert %[[VAL_2]] : (f16) -> f32
-! CHECK: %[[VAL_3:.*]] = math.abs %[[VAL_2_1]] : f32
+! CHECK: %[[VAL_3:.*]] = math.absf %[[VAL_2_1]] : f32
 ! CHECK: %[[VAL_3_1:.*]] = fir.convert %[[VAL_3]] : (f32) -> f16
 ! CHECK: fir.store %[[VAL_3_1]] to %[[VAL_1]] : !fir.ref<f16>
 ! CHECK: return
   real(kind=2) :: a, b
   b = abs(a)
 end subroutine
 
 ! CHECK-LABEL: func @_QPabs_testb(
 ! CHECK-SAME:  %[[VAL_0:.*]]: !fir.ref<bf16>{{.*}}, %[[VAL_1:.*]]: !fir.ref<bf16>{{.*}}) {
 subroutine abs_testb(a, b)
 ! CHECK: %[[VAL_2:.*]] = fir.load %[[VAL_0]] : !fir.ref<bf16>
 ! CHECK: %[[VAL_2_1:.*]] = fir.convert %[[VAL_2]] : (bf16) -> f32
-! CHECK: %[[VAL_3:.*]] = math.abs %[[VAL_2_1]] : f32
+! CHECK: %[[VAL_3:.*]] = math.absf %[[VAL_2_1]] : f32
 ! CHECK: %[[VAL_3_1:.*]] = fir.convert %[[VAL_3]] : (f32) -> bf16
 ! CHECK: fir.store %[[VAL_3_1]] to %[[VAL_1]] : !fir.ref<bf16>
 ! CHECK: return
   real(kind=3) :: a, b
   b = abs(a)
 end subroutine
 
 ! CHECK-LABEL: func @_QPabs_testr(
 ! CHECK-SAME:  %[[VAL_0:.*]]: !fir.ref<f32>{{.*}}, %[[VAL_1:.*]]: !fir.ref<f32>{{.*}}) {
 subroutine abs_testr(a, b)
 ! CHECK: %[[VAL_2:.*]] = fir.load %[[VAL_0]] : !fir.ref<f32>
-! CHECK: %[[VAL_3:.*]] = math.abs %[[VAL_2]] : f32
+! CHECK: %[[VAL_3:.*]] = math.absf %[[VAL_2]] : f32
 ! CHECK: fir.store %[[VAL_3]] to %[[VAL_1]] : !fir.ref<f32>
 ! CHECK: return
   real :: a, b
   b = abs(a)
 end subroutine
 
 ! CHECK-LABEL: func @_QPabs_testd(
 ! CHECK-SAME:  %[[VAL_0:.*]]: !fir.ref<f64>{{.*}}, %[[VAL_1:.*]]: !fir.ref<f64>{{.*}}) {
 subroutine abs_testd(a, b)
 ! CHECK: %[[VAL_2:.*]] = fir.load %[[VAL_0]] : !fir.ref<f64>
-! CHECK: %[[VAL_3:.*]] = math.abs %[[VAL_2]] : f64
+! CHECK: %[[VAL_3:.*]] = math.absf %[[VAL_2]] : f64
 ! CHECK: fir.store %[[VAL_3]] to %[[VAL_1]] : !fir.ref<f64>
 ! CHECK: return
   real(kind=8) :: a, b
   b = abs(a)
 end subroutine
 
 ! CHECK-LABEL: func @_QPabs_testr16(
 ! CHECK-SAME:  %[[VAL_0:.*]]: !fir.ref<f128>{{.*}}, %[[VAL_1:.*]]: !fir.ref<f128>{{.*}}) {
 subroutine abs_testr16(a, b)
 ! CHECK: %[[VAL_2:.*]] = fir.load %[[VAL_0]] : !fir.ref<f128>
-! CHECK: %[[VAL_3:.*]] = math.abs %[[VAL_2]] : f128
+! CHECK: %[[VAL_3:.*]] = math.absf %[[VAL_2]] : f128
 ! CHECK: fir.store %[[VAL_3]] to %[[VAL_1]] : !fir.ref<f128>
 ! CHECK: return
   real(kind=16) :: a, b
   b = abs(a)
 end subroutine
 
 ! CHECK-LABEL: func @_QPabs_testzr(
 ! CHECK-SAME:  %[[VAL_0:.*]]: !fir.ref<!fir.complex<4>>{{.*}}, %[[VAL_1:.*]]: !fir.ref<f32>{{.*}}) {

flang/test/Lower/array-expression.f90

!  real, pointer, intent(in) :: b(:)
   ! CHECK: fir.array_merge_store %[[A]], %{{.*}} to %arg0
 end subroutine test5
 
 ! CHECK-LABEL: func @_QPtest6(
 ! CHECK-SAME:    %[[VAL_0:.*]]: !fir.ref<!fir.array<?xf32>>{{.*}}, %[[VAL_1:.*]]: !fir.ref<!fir.array<?xf32>>{{.*}}, %[[VAL_2:.*]]: !fir.ref<f32>{{.*}}, %[[VAL_3:.*]]: !fir.ref<i32>{{.*}}, %[[VAL_4:.*]]: !fir.ref<i32>{{.*}}) {
 ! CHECK:         %[[VAL_5:.*]] = fir.load %[[VAL_3]] : !fir.ref<i32>
 ! CHECK:         %[[VAL_6:.*]] = fir.convert %[[VAL_5]] : (i32) -> i64
 ! CHECK:         %[[VAL_7A:.*]] = fir.convert %[[VAL_6]] : (i64) -> index
-! CHECK:         %[[C0:.*]] = arith.constant 0 : index 
-! CHECK:         %[[CMP:.*]] = arith.cmpi sgt, %[[VAL_7A]], %[[C0]] : index 
-! CHECK:         %[[VAL_7:.*]] = arith.select %[[CMP]], %[[VAL_7A]], %[[C0]] : index 
+! CHECK:         %[[C0:.*]] = arith.constant 0 : index
+! CHECK:         %[[CMP:.*]] = arith.cmpi sgt, %[[VAL_7A]], %[[C0]] : index
+! CHECK:         %[[VAL_7:.*]] = arith.select %[[CMP]], %[[VAL_7A]], %[[C0]] : index
 ! CHECK:         %[[VAL_8:.*]] = fir.load %[[VAL_4]] : !fir.ref<i32>
 ! CHECK:         %[[VAL_9:.*]] = fir.convert %[[VAL_8]] : (i32) -> i64
 ! CHECK:         %[[VAL_10A:.*]] = fir.convert %[[VAL_9]] : (i64) -> index
-! CHECK:         %[[C0_2:.*]] = arith.constant 0 : index 
-! CHECK:         %[[CMP_2:.*]] = arith.cmpi sgt, %[[VAL_10A]], %[[C0_2]] : index 
-! CHECK:         %[[VAL_10:.*]] = arith.select %[[CMP_2]], %[[VAL_10A]], %[[C0_2]] : index 
+! CHECK:         %[[C0_2:.*]] = arith.constant 0 : index
+! CHECK:         %[[CMP_2:.*]] = arith.cmpi sgt, %[[VAL_10A]], %[[C0_2]] : index
+! CHECK:         %[[VAL_10:.*]] = arith.select %[[CMP_2]], %[[VAL_10A]], %[[C0_2]] : index
 ! CHECK:         %[[VAL_11:.*]] = arith.constant 3 : i64
 ! CHECK:         %[[VAL_12:.*]] = fir.convert %[[VAL_11]] : (i64) -> index
 ! CHECK:         %[[VAL_13:.*]] = arith.constant 4 : i64
 ! CHECK:         %[[VAL_14:.*]] = fir.convert %[[VAL_13]] : (i64) -> index
 ! CHECK:         %[[VAL_15:.*]] = fir.load %[[VAL_3]] : !fir.ref<i32>
 ! CHECK:         %[[VAL_16:.*]] = fir.convert %[[VAL_15]] : (i32) -> i64
 ! CHECK:         %[[VAL_17:.*]] = fir.convert %[[VAL_16]] : (i64) -> index
 ! CHECK:         %[[VAL_18:.*]] = arith.constant 0 : index
subroutine test6b(a,b)
   a(4,41:50) = b
 end subroutine test6b
 
 ! CHECK-LABEL: func @_QPtest7(
 ! CHECK-SAME:    %[[VAL_0:.*]]: !fir.ref<!fir.array<?xf32>>{{.*}}, %[[VAL_1:.*]]: !fir.ref<!fir.array<?xf32>>{{.*}}, %[[VAL_2:.*]]: !fir.ref<i32>{{.*}}) {
 ! CHECK:         %[[VAL_3:.*]] = fir.load %[[VAL_2]] : !fir.ref<i32>
 ! CHECK:         %[[VAL_4:.*]] = fir.convert %[[VAL_3]] : (i32) -> i64
 ! CHECK:         %[[VAL_5A:.*]] = fir.convert %[[VAL_4]] : (i64) -> index
-! CHECK:         %[[C0:.*]] = arith.constant 0 : index 
-! CHECK:         %[[CMP:.*]] = arith.cmpi sgt, %[[VAL_5A]], %[[C0]] : index 
-! CHECK:         %[[VAL_5:.*]] = arith.select %[[CMP]], %[[VAL_5A]], %[[C0]] : index 
+! CHECK:         %[[C0:.*]] = arith.constant 0 : index
+! CHECK:         %[[CMP:.*]] = arith.cmpi sgt, %[[VAL_5A]], %[[C0]] : index
+! CHECK:         %[[VAL_5:.*]] = arith.select %[[CMP]], %[[VAL_5A]], %[[C0]] : index
 ! CHECK:         %[[VAL_6:.*]] = fir.load %[[VAL_2]] : !fir.ref<i32>
 ! CHECK:         %[[VAL_7:.*]] = fir.convert %[[VAL_6]] : (i32) -> i64
 ! CHECK:         %[[VAL_8A:.*]] = fir.convert %[[VAL_7]] : (i64) -> index
-! CHECK:         %[[C0_2:.*]] = arith.constant 0 : index 
-! CHECK:         %[[CMP_2:.*]] = arith.cmpi sgt, %[[VAL_8A]], %[[C0_2]] : index 
-! CHECK:         %[[VAL_8:.*]] = arith.select %[[CMP_2]], %[[VAL_8A]], %[[C0_2]] : index 
+! CHECK:         %[[C0_2:.*]] = arith.constant 0 : index
+! CHECK:         %[[CMP_2:.*]] = arith.cmpi sgt, %[[VAL_8A]], %[[C0_2]] : index
+! CHECK:         %[[VAL_8:.*]] = arith.select %[[CMP_2]], %[[VAL_8A]], %[[C0_2]] : index
 ! CHECK:         %[[VAL_9:.*]] = fir.shape %[[VAL_5]] : (index) -> !fir.shape<1>
 ! CHECK:         %[[VAL_10:.*]] = fir.array_load %[[VAL_0]](%[[VAL_9]]) : (!fir.ref<!fir.array<?xf32>>, !fir.shape<1>) -> !fir.array<?xf32>
 ! CHECK:         %[[VAL_11:.*]] = fir.shape %[[VAL_5]] : (index) -> !fir.shape<1>
 ! CHECK:         %[[VAL_12:.*]] = fir.array_load %[[VAL_0]](%[[VAL_11]]) : (!fir.ref<!fir.array<?xf32>>, !fir.shape<1>) -> !fir.array<?xf32>
 ! CHECK:         %[[VAL_13:.*]] = fir.shape %[[VAL_8]] : (index) -> !fir.shape<1>
 ! CHECK:         %[[VAL_14:.*]] = fir.array_load %[[VAL_1]](%[[VAL_13]]) : (!fir.ref<!fir.array<?xf32>>, !fir.shape<1>) -> !fir.array<?xf32>
 ! CHECK:         %[[VAL_15:.*]] = arith.constant 1 : index
 ! CHECK:         %[[VAL_16:.*]] = arith.constant 0 : index

 ! CHECK-LABEL: func @_QPtest15(
 ! CHECK-SAME: %[[a:.*]]: !fir.ref<!fir.array<100xf32>>{{.*}}, %[[b:.*]]: !fir.ref<!fir.array<100xf32>>{{.*}})
 subroutine test15(a,b)
   ! CHECK-DAG: %[[barr:.*]] = fir.array_load %[[b]](%{{.*}}) : (!fir.ref<!fir.array<100xf32>>, !fir.shape<1>) -> !fir.array<100xf32>
   ! CHECK-DAG: %[[aarr:.*]] = fir.array_load %[[a]](%{{.*}}) : (!fir.ref<!fir.array<100xf32>>, !fir.shape<1>) -> !fir.array<100xf32>
   real :: a(100), b(100)
   ! CHECK: %[[loop:.*]] = fir.do_loop %[[i:.*]] = %{{.*}} to %{{.*}} step %{{.*}} iter_args(%[[bth:.*]] = %[[barr]]) -> (!fir.array<100xf32>) {
   ! CHECK: %[[val:.*]] = fir.array_fetch %[[aarr]], %[[i]] : (!fir.array<100xf32>, index) -> f32
-  ! CHECK: %[[fres:.*]] = math.abs %[[val]] : f32
+  ! CHECK: %[[fres:.*]] = math.absf %[[val]] : f32
   ! CHECK: %[[res:.*]] = fir.array_update %[[bth]], %[[fres]], %[[i]] : (!fir.array<100xf32>, f32, index) -> !fir.array<100xf32>
   ! CHECK: fir.result %[[res]] : !fir.array<100xf32>
   ! CHECK: fir.array_merge_store %[[barr]], %[[loop]] to %[[b]]
   b = abs(a)
 end subroutine test15
 
 ! Test elemental call to function f2 with VALUE attribute
 ! CHECK-LABEL: func @_QPtest16(

 ! CHECK:         %[[VAL_8:.*]] = arith.select %[[VAL_7]], %[[VAL_5]], %[[VAL_6]] : i32
 ! CHECK:         %[[VAL_9:.*]] = fir.convert %[[VAL_4]]#0 : (!fir.ref<!fir.char<1,?>>) -> !fir.ref<!fir.array<70x!fir.char<1,?>>>
 ! CHECK:         %[[VAL_10:.*]] = arith.constant 70 : index
 ! CHECK:         %[[VAL_11:.*]]:2 = fir.unboxchar %[[VAL_1]] : (!fir.boxchar<1>) -> (!fir.ref<!fir.char<1,?>>, index)
 ! CHECK:         %[[VAL_12:.*]] = fir.convert %[[VAL_11]]#0 : (!fir.ref<!fir.char<1,?>>) -> !fir.ref<!fir.array<?x!fir.char<1,?>>>
 ! CHECK:         %[[VAL_13:.*]] = fir.load %[[VAL_3]] : !fir.ref<i32>
 ! CHECK:         %[[VAL_14:.*]] = fir.convert %[[VAL_13]] : (i32) -> i64
 ! CHECK:         %[[VAL_15A:.*]] = fir.convert %[[VAL_14]] : (i64) -> index
-! CHECK:         %[[C0:.*]] = arith.constant 0 : index 
-! CHECK:         %[[CMP:.*]] = arith.cmpi sgt, %[[VAL_15A]], %[[C0]] : index 
-! CHECK:         %[[VAL_15:.*]] = arith.select %[[CMP]], %[[VAL_15A]], %[[C0]] : index 
+! CHECK:         %[[C0:.*]] = arith.constant 0 : index
+! CHECK:         %[[CMP:.*]] = arith.cmpi sgt, %[[VAL_15A]], %[[C0]] : index
+! CHECK:         %[[VAL_15:.*]] = arith.select %[[CMP]], %[[VAL_15A]], %[[C0]] : index
 ! CHECK:         %[[VAL_16:.*]] = fir.shape %[[VAL_10]] : (index) -> !fir.shape<1>
 ! CHECK:         %[[VAL_17:.*]] = fir.array_load %[[VAL_9]](%[[VAL_16]]) typeparams %[[VAL_8]] : (!fir.ref<!fir.array<70x!fir.char<1,?>>>, !fir.shape<1>, i32) -> !fir.array<70x!fir.char<1,?>>
 ! CHECK:         %[[VAL_18:.*]] = arith.constant 1 : i64
 ! CHECK:         %[[VAL_19:.*]] = fir.convert %[[VAL_18]] : (i64) -> index
 ! CHECK:         %[[VAL_20:.*]] = arith.constant 2 : i64
 ! CHECK:         %[[VAL_21:.*]] = fir.convert %[[VAL_20]] : (i64) -> index
 ! CHECK:         %[[VAL_22:.*]] = arith.constant 140 : i64
 ! CHECK:         %[[VAL_23:.*]] = fir.convert %[[VAL_22]] : (i64) -> index

flang/test/Lower/math-lowering.f90

 ! RUN: split-file %s %t
 
 //--- abs.f90
 ! RUN: bbc -emit-fir %t/abs.f90 -o - --math-runtime=fast | FileCheck --check-prefixes=ALL,FAST %t/abs.f90
 ! RUN: %flang_fc1 -emit-fir -mllvm -math-runtime=fast %t/abs.f90 -o - | FileCheck --check-prefixes=ALL,FAST %t/abs.f90
 ! RUN: bbc -emit-fir %t/abs.f90 -o - --math-runtime=relaxed | FileCheck --check-prefixes=ALL,RELAXED %t/abs.f90
 ! RUN: %flang_fc1 -emit-fir -mllvm -math-runtime=relaxed %t/abs.f90 -o - | FileCheck --check-prefixes=ALL,RELAXED %t/abs.f90
 ! RUN: bbc -emit-fir %t/abs.f90 -o - --math-runtime=precise | FileCheck --check-prefixes=ALL,PRECISE %t/abs.f90
 ! RUN: %flang_fc1 -emit-fir -mllvm -math-runtime=precise %t/abs.f90 -o - | FileCheck --check-prefixes=ALL,PRECISE %t/abs.f90
 
 function test_real4(x)
   real :: x, test_real4
   test_real4 = abs(x)
 end function
 
 ! ALL-LABEL: @_QPtest_real4
-! FAST: {{%[A-Za-z0-9._]+}} = math.abs {{%[A-Za-z0-9._]+}} : f32
-! RELAXED: {{%[A-Za-z0-9._]+}} = math.abs {{%[A-Za-z0-9._]+}} : f32
+! FAST: {{%[A-Za-z0-9._]+}} = math.absf {{%[A-Za-z0-9._]+}} : f32
+! RELAXED: {{%[A-Za-z0-9._]+}} = math.absf {{%[A-Za-z0-9._]+}} : f32
 ! PRECISE: {{%[A-Za-z0-9._]+}} = fir.call @fabsf({{%[A-Za-z0-9._]+}}) : (f32) -> f32
 
 function test_real8(x)
   real(8) :: x, test_real8
   test_real8 = abs(x)
 end function
 
 ! ALL-LABEL: @_QPtest_real8
-! FAST: {{%[A-Za-z0-9._]+}} = math.abs {{%[A-Za-z0-9._]+}} : f64
-! RELAXED: {{%[A-Za-z0-9._]+}} = math.abs {{%[A-Za-z0-9._]+}} : f64
+! FAST: {{%[A-Za-z0-9._]+}} = math.absf {{%[A-Za-z0-9._]+}} : f64
+! RELAXED: {{%[A-Za-z0-9._]+}} = math.absf {{%[A-Za-z0-9._]+}} : f64
 ! PRECISE: {{%[A-Za-z0-9._]+}} = fir.call @fabs({{%[A-Za-z0-9._]+}}) : (f64) -> f64
 
 function test_real16(x)
   real(16) :: x, test_real16
   test_real16 = abs(x)
 end function
 ! ALL-LABEL: @_QPtest_real16
-! FAST: {{%[A-Za-z0-9._]+}} = math.abs {{%[A-Za-z0-9._]+}} : f128
-! RELAXED: {{%[A-Za-z0-9._]+}} = math.abs {{%[A-Za-z0-9._]+}} : f128
+! FAST: {{%[A-Za-z0-9._]+}} = math.absf {{%[A-Za-z0-9._]+}} : f128
+! RELAXED: {{%[A-Za-z0-9._]+}} = math.absf {{%[A-Za-z0-9._]+}} : f128
 ! PRECISE: {{%[A-Za-z0-9._]+}} = fir.call @llvm.fabs.f128({{%[A-Za-z0-9._]+}}) : (f128) -> f128
 
 function test_complex4(c)
   complex(4) :: c, test_complex4
   test_complex4 = abs(c)
 end function
 
 ! ALL-LABEL: @_QPtest_complex4

mlir/docs/Bindings/Python.md

 
 Note: In order to avoid naming conflicts, all internal names used by the wrapper
 module are prefixed by `_ods_`.
 
 Each concrete `OpView` subclass further defines several public-intended
 attributes:
 
 *   `OPERATION_NAME` attribute with the `str` fully qualified operation name
-    (i.e. `math.abs`).
+    (i.e. `math.absf`).
 *   An `__init__` method for the *default builder* if one is defined or inferred
     for the operation.
 *   `@property` getter for each operand or result (using an auto-generated name
     for unnamed of each).
 *   `@property` getter, setter and deleter for each declared attribute.
 
 It further emits additional private-intended attributes meant for subclassing
 and customization (default cases omit these attributes in favor of the defaults

 
 Dialect functionality other than IR objects or passes, such as helper functions,
 can be exposed to Python similarly to attributes and types. C API is expected to
 exist for this functionality, which can then be wrapped using pybind11 and
 `[include/mlir/Bindings/Python/PybindAdaptors.h](https://github.com/llvm/llvm-project/blob/main/mlir/include/mlir/Bindings/Python/PybindAdaptors.h)`
 utilities to connect to the rest of Python API. The bindings can be located in a
 separate pybind11 module or in the same module as attributes and types, and
 loaded along with the dialect.
-

mlir/include/mlir/Dialect/Math/IR/MathBase.td

let description = [{
     The math dialect is intended to hold mathematical operations on integer and
     floating types beyond simple arithmetics. Each operation works on scalar, vector
     or tensor type. On vector and tensor type operations apply elementwise unless
     explicitly specified otherwise. As an example, the floating point absolute value
     can be expressed as:
 
     ```mlir
     // Scalar absolute value.
-    %a = math.abs %b : f64
-    
+    %a = math.absf %b : f64
     // Vector elementwise absolute value.
-    %f = math.abs %g : vector<4xf32>
+    %f = math.absf %g : vector<4xf32>
 
     // Tensor elementwise absolute value.
-    %x = math.abs %y : tensor<4x?xf8>
+    %x = math.absf %y : tensor<4x?xf8>
     ```
   }];
   let hasConstantMaterializer = 1;
   let emitAccessorPrefix = kEmitAccessorPrefix_Prefixed;
 }
 #endif // MATH_BASE

mlir/include/mlir/Dialect/Math/IR/MathOps.td

class Math_FloatTernaryOp<string mnemonic, list<Trait> traits = []> :
     Math_Op<mnemonic, traits # [SameOperandsAndResultType]> {
   let arguments = (ins FloatLike:$a, FloatLike:$b, FloatLike:$c);
   let results = (outs FloatLike:$result);
 
   let assemblyFormat = "$a `,` $b `,` $c attr-dict `:` type($result)";
 }
 
 //===----------------------------------------------------------------------===//
-// AbsOp
+// AbsFOp
 //===----------------------------------------------------------------------===//
 
-def Math_AbsOp : Math_FloatUnaryOp<"abs"> {
+def Math_AbsFOp : Math_FloatUnaryOp<"absf"> {
   let summary = "floating point absolute-value operation";
   let description = [{
-    The `abs` operation computes the absolute value. It takes one operand of
-    floating point type (i.e., scalar, tensor or vector) and returns one result of
-    the same type.
+    The `absf` operation computes the absolute value. It takes one operand of
+    floating point type (i.e., scalar, tensor or vector) and returns one result
+    of the same type.
 
     Example:
 
     ```mlir
     // Scalar absolute value.
-    %a = math.abs %b : f64
+    %a = math.absf %b : f64
     ```
   }];
   let hasFolder = 1;
 }
 
 //===----------------------------------------------------------------------===//
 // AtanOp
 //===----------------------------------------------------------------------===//
let description = [{
     The `floor` operation computes the floor of a given value. It takes one
     operand of floating point type (i.e., scalar, tensor or vector) and returns one
     result of the same type.  It has no standard attributes.
 
     Example:
 
     ```mlir
     // Scalar floor value.
-    %a = math.floor %b : f64 
+    %a = math.floor %b : f64
     ```
   }];
 }
 
 //===----------------------------------------------------------------------===//
 // FmaOp
 //===----------------------------------------------------------------------===//
 

 def Math_RsqrtOp : Math_FloatUnaryOp<"rsqrt"> {
   let summary = "reciprocal of sqrt (1 / sqrt of the specified value)";
   let description = [{
     The `rsqrt` operation computes the reciprocal of the square root. It takes
     one operand of floating point type (i.e., scalar, tensor or vector) and returns
     one result of the same type. It has no standard attributes.
 
     Example:
-  
     ```mlir
     // Scalar reciprocal square root value.
     %a = math.rsqrt %b : f64
     ```
   }];
 }
 
 //===----------------------------------------------------------------------===//

mlir/lib/Conversion/ComplexToStandard/ComplexToStandard.cpp

Value imagNumerator2 = rewriter.create<arith::SubFOp>(
         rewriter.create<arith::MulFOp>(loc, lhsReal, rhsImagRealRatio));
     Value resultImag2 =
         rewriter.create<arith::DivFOp>(loc, imagNumerator2, rhsImagRealDenom);
 
     // Consider corner cases.
     // Case 1. Zero denominator, numerator contains at most one NaN value.
     Value zero = rewriter.create<arith::ConstantOp>(
         loc, elementType, rewriter.getZeroAttr(elementType));
-    Value rhsRealAbs = rewriter.create<math::AbsOp>(loc, rhsReal);
+    Value rhsRealAbs = rewriter.create<math::AbsFOp>(loc, rhsReal);
     Value rhsRealIsZero = rewriter.create<arith::CmpFOp>(
         loc, arith::CmpFPredicate::OEQ, rhsRealAbs, zero);
-    Value rhsImagAbs = rewriter.create<math::AbsOp>(loc, rhsImag);
+    Value rhsImagAbs = rewriter.create<math::AbsFOp>(loc, rhsImag);
     Value rhsImagIsZero = rewriter.create<arith::CmpFOp>(
         loc, arith::CmpFPredicate::OEQ, rhsImagAbs, zero);
     Value lhsRealIsNotNaN = rewriter.create<arith::CmpFOp>(
         loc, arith::CmpFPredicate::ORD, lhsReal, zero);
     Value lhsImagIsNotNaN = rewriter.create<arith::CmpFOp>(
         loc, arith::CmpFPredicate::ORD, lhsImag, zero);
     Value lhsContainsNotNaNValue =
         rewriter.create<arith::OrIOp>(loc, lhsRealIsNotNaN, lhsImagIsNotNaN);
matchAndRewrite(complex::DivOp op, OpAdaptor adaptor,
 
     // Case 2. Infinite numerator, finite denominator.
     Value rhsRealFinite = rewriter.create<arith::CmpFOp>(
         loc, arith::CmpFPredicate::ONE, rhsRealAbs, inf);
     Value rhsImagFinite = rewriter.create<arith::CmpFOp>(
         loc, arith::CmpFPredicate::ONE, rhsImagAbs, inf);
     Value rhsFinite =
         rewriter.create<arith::AndIOp>(loc, rhsRealFinite, rhsImagFinite);
-    Value lhsRealAbs = rewriter.create<math::AbsOp>(loc, lhsReal);
+    Value lhsRealAbs = rewriter.create<math::AbsFOp>(loc, lhsReal);
     Value lhsRealInfinite = rewriter.create<arith::CmpFOp>(
         loc, arith::CmpFPredicate::OEQ, lhsRealAbs, inf);
-    Value lhsImagAbs = rewriter.create<math::AbsOp>(loc, lhsImag);
+    Value lhsImagAbs = rewriter.create<math::AbsFOp>(loc, lhsImag);
     Value lhsImagInfinite = rewriter.create<arith::CmpFOp>(
         loc, arith::CmpFPredicate::OEQ, lhsImagAbs, inf);
     Value lhsInfinite =
         rewriter.create<arith::OrIOp>(loc, lhsRealInfinite, lhsImagInfinite);
     Value infNumFiniteDenom =
         rewriter.create<arith::AndIOp>(loc, lhsInfinite, rhsFinite);
     Value one = rewriter.create<arith::ConstantOp>(
         loc, elementType, rewriter.getFloatAttr(elementType, 1));
struct MulOpConversion : public OpConversionPattern<complex::MulOp> {
   LogicalResult
   matchAndRewrite(complex::MulOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
     mlir::ImplicitLocOpBuilder b(op.getLoc(), rewriter);
     auto type = adaptor.getLhs().getType().cast<ComplexType>();
     auto elementType = type.getElementType().cast<FloatType>();
 
     Value lhsReal = b.create<complex::ReOp>(elementType, adaptor.getLhs());
-    Value lhsRealAbs = b.create<math::AbsOp>(lhsReal);
+    Value lhsRealAbs = b.create<math::AbsFOp>(lhsReal);
     Value lhsImag = b.create<complex::ImOp>(elementType, adaptor.getLhs());
-    Value lhsImagAbs = b.create<math::AbsOp>(lhsImag);
+    Value lhsImagAbs = b.create<math::AbsFOp>(lhsImag);
     Value rhsReal = b.create<complex::ReOp>(elementType, adaptor.getRhs());
-    Value rhsRealAbs = b.create<math::AbsOp>(rhsReal);
+    Value rhsRealAbs = b.create<math::AbsFOp>(rhsReal);
     Value rhsImag = b.create<complex::ImOp>(elementType, adaptor.getRhs());
-    Value rhsImagAbs = b.create<math::AbsOp>(rhsImag);
+    Value rhsImagAbs = b.create<math::AbsFOp>(rhsImag);
 
     Value lhsRealTimesRhsReal = b.create<arith::MulFOp>(lhsReal, rhsReal);
-    Value lhsRealTimesRhsRealAbs = b.create<math::AbsOp>(lhsRealTimesRhsReal);
+    Value lhsRealTimesRhsRealAbs = b.create<math::AbsFOp>(lhsRealTimesRhsReal);
     Value lhsImagTimesRhsImag = b.create<arith::MulFOp>(lhsImag, rhsImag);
-    Value lhsImagTimesRhsImagAbs = b.create<math::AbsOp>(lhsImagTimesRhsImag);
+    Value lhsImagTimesRhsImagAbs = b.create<math::AbsFOp>(lhsImagTimesRhsImag);
     Value real =
         b.create<arith::SubFOp>(lhsRealTimesRhsReal, lhsImagTimesRhsImag);
 
     Value lhsImagTimesRhsReal = b.create<arith::MulFOp>(lhsImag, rhsReal);
-    Value lhsImagTimesRhsRealAbs = b.create<math::AbsOp>(lhsImagTimesRhsReal);
+    Value lhsImagTimesRhsRealAbs = b.create<math::AbsFOp>(lhsImagTimesRhsReal);
     Value lhsRealTimesRhsImag = b.create<arith::MulFOp>(lhsReal, rhsImag);
-    Value lhsRealTimesRhsImagAbs = b.create<math::AbsOp>(lhsRealTimesRhsImag);
+    Value lhsRealTimesRhsImagAbs = b.create<math::AbsFOp>(lhsRealTimesRhsImag);
     Value imag =
         b.create<arith::AddFOp>(lhsImagTimesRhsReal, lhsRealTimesRhsImag);
 
     // Handle cases where the "naive" calculation results in NaN values.
     Value realIsNan =
         b.create<arith::CmpFOp>(arith::CmpFPredicate::UNO, real, real);
     Value imagIsNan =
         b.create<arith::CmpFOp>(arith::CmpFPredicate::UNO, imag, imag);
matchAndRewrite(complex::SqrtOp op, OpAdaptor adaptor,
     Value arg = adaptor.getComplex();
 
     Value zero =
         b.create<arith::ConstantOp>(elementType, b.getZeroAttr(elementType));
 
     Value real = b.create<complex::ReOp>(elementType, adaptor.getComplex());
     Value imag = b.create<complex::ImOp>(elementType, adaptor.getComplex());
 
-    Value absLhs = b.create<math::AbsOp>(real);
+    Value absLhs = b.create<math::AbsFOp>(real);
     Value absArg = b.create<complex::AbsOp>(elementType, arg);
     Value addAbs = b.create<arith::AddFOp>(absLhs, absArg);
 
     Value half = b.create<arith::ConstantOp>(elementType,
                                              b.getFloatAttr(elementType, 0.5));
     Value halfAddAbs = b.create<arith::MulFOp>(addAbs, half);
     Value sqrtAddAbs = b.create<math::SqrtOp>(halfAddAbs);
 

mlir/lib/Conversion/GPUToNVVM/LowerGpuOpsToNVVMOps.cpp

void mlir::populateGpuToNVVMConversionPatterns(LLVMTypeConverter &converter,
   // Explicitly drop memory space when lowering private memory
   // attributions since NVVM models it as `alloca`s in the default
   // memory space and does not support `alloca`s with addrspace(5).
   patterns.add<GPUFuncOpLowering>(
       converter, /*allocaAddrSpace=*/0,
       StringAttr::get(&converter.getContext(),
                       NVVM::NVVMDialect::getKernelFuncAttrName()));
 
-  patterns.add<OpToFuncCallLowering<math::AbsOp>>(converter, "__nv_fabsf",
+  patterns.add<OpToFuncCallLowering<math::AbsFOp>>(converter, "__nv_fabsf",
                                                    "__nv_fabs");
   patterns.add<OpToFuncCallLowering<math::AtanOp>>(converter, "__nv_atanf",
                                                    "__nv_atan");
   patterns.add<OpToFuncCallLowering<math::Atan2Op>>(converter, "__nv_atan2f",
                                                     "__nv_atan2");
   patterns.add<OpToFuncCallLowering<math::CeilOp>>(converter, "__nv_ceilf",
                                                    "__nv_ceil");
   patterns.add<OpToFuncCallLowering<math::CosOp>>(converter, "__nv_cosf",
                                                   "__nv_cos");

mlir/lib/Conversion/GPUToROCDL/LowerGpuOpsToROCDLOps.cpp

patterns.add<GPUFuncOpLowering>(
                       ROCDL::ROCDLDialect::getKernelFuncAttrName()));
   if (Runtime::HIP == runtime) {
     patterns.add<GPUPrintfOpToHIPLowering>(converter);
   } else if (Runtime::OpenCL == runtime) {
     // Use address space = 4 to match the OpenCL definition of printf()
     patterns.add<GPUPrintfOpToLLVMCallLowering>(converter, /*addressSpace=*/4);
   }
 
-  patterns.add<OpToFuncCallLowering<math::AbsOp>>(converter, "__ocml_fabs_f32",
+  patterns.add<OpToFuncCallLowering<math::AbsFOp>>(converter, "__ocml_fabs_f32",
                                                    "__ocml_fabs_f64");
   patterns.add<OpToFuncCallLowering<math::AtanOp>>(converter, "__ocml_atan_f32",
                                                    "__ocml_atan_f64");
   patterns.add<OpToFuncCallLowering<math::Atan2Op>>(
       converter, "__ocml_atan2_f32", "__ocml_atan2_f64");
   patterns.add<OpToFuncCallLowering<math::CeilOp>>(converter, "__ocml_ceil_f32",
                                                    "__ocml_ceil_f64");
   patterns.add<OpToFuncCallLowering<math::CosOp>>(converter, "__ocml_cos_f32",
                                                   "__ocml_cos_f64");

mlir/lib/Conversion/MathToLLVM/MathToLLVM.cpp

 #include "mlir/Conversion/LLVMCommon/VectorPattern.h"
 #include "mlir/Dialect/LLVMIR/LLVMDialect.h"
 #include "mlir/Dialect/Math/IR/Math.h"
 #include "mlir/IR/TypeUtilities.h"
 
 using namespace mlir;
 
 namespace {
-using AbsOpLowering = VectorConvertToLLVMPattern<math::AbsOp, LLVM::FAbsOp>;
+using AbsFOpLowering = VectorConvertToLLVMPattern<math::AbsFOp, LLVM::FAbsOp>;
 using CeilOpLowering = VectorConvertToLLVMPattern<math::CeilOp, LLVM::FCeilOp>;
 using CopySignOpLowering =
     VectorConvertToLLVMPattern<math::CopySignOp, LLVM::CopySignOp>;
 using CosOpLowering = VectorConvertToLLVMPattern<math::CosOp, LLVM::CosOp>;
 using CtPopFOpLowering =
     VectorConvertToLLVMPattern<math::CtPopOp, LLVM::CtPopOp>;
 using Exp2OpLowering = VectorConvertToLLVMPattern<math::Exp2Op, LLVM::Exp2Op>;
 using ExpOpLowering = VectorConvertToLLVMPattern<math::ExpOp, LLVM::ExpOp>;
struct ConvertMathToLLVMPass
   }
 };
 } // namespace
 
 void mlir::populateMathToLLVMConversionPatterns(LLVMTypeConverter &converter,
                                                 RewritePatternSet &patterns) {
   // clang-format off
   patterns.add<
-    AbsOpLowering,
+    AbsFOpLowering,
     CeilOpLowering,
     CopySignOpLowering,
     CosOpLowering,
     CountLeadingZerosOpLowering,
     CountTrailingZerosOpLowering,
     CtPopFOpLowering,
     Exp2OpLowering,
     ExpM1OpLowering,

mlir/lib/Conversion/MathToSPIRV/MathToSPIRV.cpp

void populateMathToSPIRVPatterns(SPIRVTypeConverter &typeConverter,
                                  RewritePatternSet &patterns) {
   // Core patterns
   patterns.add<CopySignPattern>(typeConverter, patterns.getContext());
 
   // GLSL patterns
   patterns
       .add<CountLeadingZerosPattern, Log1pOpPattern<spirv::GLLogOp>,
            ExpM1OpPattern<spirv::GLExpOp>, PowFOpPattern, RoundOpPattern,
-           spirv::ElementwiseOpPattern<math::AbsOp, spirv::GLFAbsOp>,
+           spirv::ElementwiseOpPattern<math::AbsFOp, spirv::GLFAbsOp>,
            spirv::ElementwiseOpPattern<math::CeilOp, spirv::GLCeilOp>,
            spirv::ElementwiseOpPattern<math::CosOp, spirv::GLCosOp>,
            spirv::ElementwiseOpPattern<math::ExpOp, spirv::GLExpOp>,
            spirv::ElementwiseOpPattern<math::FloorOp, spirv::GLFloorOp>,
            spirv::ElementwiseOpPattern<math::FmaOp, spirv::GLFmaOp>,
            spirv::ElementwiseOpPattern<math::LogOp, spirv::GLLogOp>,
            spirv::ElementwiseOpPattern<math::RsqrtOp, spirv::GLInverseSqrtOp>,
            spirv::ElementwiseOpPattern<math::SinOp, spirv::GLSinOp>,
            spirv::ElementwiseOpPattern<math::SqrtOp, spirv::GLSqrtOp>,
            spirv::ElementwiseOpPattern<math::TanhOp, spirv::GLTanhOp>>(
           typeConverter, patterns.getContext());
 
   // OpenCL patterns
   patterns.add<Log1pOpPattern<spirv::CLLogOp>, ExpM1OpPattern<spirv::CLExpOp>,
-               spirv::ElementwiseOpPattern<math::AbsOp, spirv::CLFAbsOp>,
+               spirv::ElementwiseOpPattern<math::AbsFOp, spirv::CLFAbsOp>,
                spirv::ElementwiseOpPattern<math::CeilOp, spirv::CLCeilOp>,
                spirv::ElementwiseOpPattern<math::CosOp, spirv::CLCosOp>,
                spirv::ElementwiseOpPattern<math::ErfOp, spirv::CLErfOp>,
                spirv::ElementwiseOpPattern<math::ExpOp, spirv::CLExpOp>,
                spirv::ElementwiseOpPattern<math::FloorOp, spirv::CLFloorOp>,
                spirv::ElementwiseOpPattern<math::FmaOp, spirv::CLFmaOp>,
                spirv::ElementwiseOpPattern<math::LogOp, spirv::CLLogOp>,
                spirv::ElementwiseOpPattern<math::PowFOp, spirv::CLPowOp>,

mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp

createLinalgBodyCalculationForElementwiseOp(Operation *op, ValueRange args,
                                             ArrayRef<Type> resultTypes,
                                             PatternRewriter &rewriter) {
   Location loc = op->getLoc();
   auto elementTy =
       op->getOperand(0).getType().cast<ShapedType>().getElementType();
 
   // tosa::AbsOp
   if (isa<tosa::AbsOp>(op) && elementTy.isa<FloatType>())
-    return rewriter.create<math::AbsOp>(loc, resultTypes, args);
+    return rewriter.create<math::AbsFOp>(loc, resultTypes, args);
 
   if (isa<tosa::AbsOp>(op) && elementTy.isa<IntegerType>()) {
     auto zero = rewriter.create<arith::ConstantOp>(
         loc, rewriter.getZeroAttr(elementTy));
     auto cmp = rewriter.create<arith::CmpIOp>(loc, arith::CmpIPredicate::sgt,
                                               args[0], zero);
     auto neg = rewriter.create<arith::SubIOp>(loc, zero, args[0]);
     return rewriter.create<arith::SelectOp>(loc, cmp, args[0], neg);

mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp

if (!isFloatingPoint(arg))
       llvm_unreachable("unsupported non numeric type");
     OpBuilder builder = getBuilder();
     switch (unaryFn) {
     case UnaryFn::exp:
       return builder.create<math::ExpOp>(arg.getLoc(), arg);
     case UnaryFn::log:
       return builder.create<math::LogOp>(arg.getLoc(), arg);
     case UnaryFn::abs:
-      return builder.create<math::AbsOp>(arg.getLoc(), arg);
+      return builder.create<math::AbsFOp>(arg.getLoc(), arg);
     case UnaryFn::ceil:
       return builder.create<math::CeilOp>(arg.getLoc(), arg);
     case UnaryFn::floor:
       return builder.create<math::FloorOp>(arg.getLoc(), arg);
     case UnaryFn::negf:
       return builder.create<arith::NegFOp>(arg.getLoc(), arg);
     }
     llvm_unreachable("unsupported unary function");

mlir/lib/Dialect/Math/IR/MathOps.cpp

 //===----------------------------------------------------------------------===//
 // TableGen'd op method definitions
 //===----------------------------------------------------------------------===//
 
 #define GET_OP_CLASSES
 #include "mlir/Dialect/Math/IR/MathOps.cpp.inc"
 
 //===----------------------------------------------------------------------===//
-// AbsOp folder
+// AbsFOp folder
 //===----------------------------------------------------------------------===//
 
-OpFoldResult math::AbsOp::fold(ArrayRef<Attribute> operands) {
-  return constFoldUnaryOp<FloatAttr>(operands, [](const APFloat &a) {
-    const APFloat &result(a);
-    return abs(result);
-  });
+OpFoldResult math::AbsFOp::fold(ArrayRef<Attribute> operands) {
+  return constFoldUnaryOp<FloatAttr>(operands,
+                                     [](const APFloat &a) { return abs(a); });
 }
 
 //===----------------------------------------------------------------------===//
 // AtanOp folder
 //===----------------------------------------------------------------------===//
 
 OpFoldResult math::AtanOp::fold(ArrayRef<Attribute> operands) {
   return constFoldUnaryOpConditional<FloatAttr>(

mlir/lib/Dialect/Math/Transforms/PolynomialApproximation.cpp

static std::pair<Value, Value> frexp(ImplicitLocOpBuilder &builder, Value arg,
   Value i32Arg = builder.create<arith::BitcastOp>(i32Vec, arg);
 
   // Compute normalized fraction.
   Value tmp0 = builder.create<arith::AndIOp>(i32Arg, bcast(i32InvMantMask));
   Value tmp1 = builder.create<arith::OrIOp>(tmp0, bcast(i32Half));
   Value normalizedFraction = builder.create<arith::BitcastOp>(f32Vec, tmp1);
 
   // Compute exponent.
-  Value arg0 = isPositive ? arg : builder.create<math::AbsOp>(arg);
+  Value arg0 = isPositive ? arg : builder.create<math::AbsFOp>(arg);
   Value biasedExponentBits = builder.create<arith::ShRUIOp>(
       builder.create<arith::BitcastOp>(i32Vec, arg0),
       bcast(i32Cst(builder, 23)));
   Value biasedExponent =
       builder.create<arith::SIToFPOp>(f32Vec, biasedExponentBits);
   Value exponent =
       builder.create<arith::SubFOp>(biasedExponent, bcast(cst126f));
 
AtanApproximation::matchAndRewrite(math::AtanOp op,
 
   ArrayRef<int64_t> shape = vectorShape(op.getOperand());
 
   ImplicitLocOpBuilder builder(op->getLoc(), rewriter);
   auto one = broadcast(builder, f32Cst(builder, 1.0f), shape);
 
   // Remap the problem over [0.0, 1.0] by looking at the absolute value and the
   // handling symmetry.
-  Value abs = builder.create<math::AbsOp>(operand);
+  Value abs = builder.create<math::AbsFOp>(operand);
   Value reciprocal = builder.create<arith::DivFOp>(one, abs);
   Value compare =
       builder.create<arith::CmpFOp>(arith::CmpFPredicate::OLT, abs, reciprocal);
   Value x = builder.create<arith::SelectOp>(compare, abs, reciprocal);
 
   // Perform the Taylor series approximation for atan over the range
   // [-1.0, 1.0].
   auto n1 = broadcast(builder, f32Cst(builder, 0.14418283f), shape);
TanhApproximation::matchAndRewrite(math::TanhOp op,
   // Clamp operand into [plusClamp, minusClamp] range.
   Value minusClamp = bcast(f32Cst(builder, -7.99881172180175781f));
   Value plusClamp = bcast(f32Cst(builder, 7.99881172180175781f));
   Value x = clamp(builder, op.getOperand(), minusClamp, plusClamp);
 
   // Mask for tiny values that are approximated with `operand`.
   Value tiny = bcast(f32Cst(builder, 0.0004f));
   Value tinyMask = builder.create<arith::CmpFOp>(
-      arith::CmpFPredicate::OLT, builder.create<math::AbsOp>(op.getOperand()),
+      arith::CmpFPredicate::OLT, builder.create<math::AbsFOp>(op.getOperand()),
       tiny);
 
   // The monomial coefficients of the numerator polynomial (odd).
   Value alpha1 = bcast(f32Cst(builder, 4.89352455891786e-03f));
   Value alpha3 = bcast(f32Cst(builder, 6.37261928875436e-04f));
   Value alpha5 = bcast(f32Cst(builder, 1.48572235717979e-05f));
   Value alpha7 = bcast(f32Cst(builder, 5.12229709037114e-08f));
   Value alpha9 = bcast(f32Cst(builder, -8.60467152213735e-11f));

mlir/lib/Dialect/SparseTensor/Utils/Merger.cpp

if (def->getNumOperands() == 0) {
     if (auto indexOp = dyn_cast<linalg::IndexOp>(def))
       return addExp(kIndex, indexOp.getDim());
   }
   // Construct unary operations if subexpression can be built.
   if (def->getNumOperands() == 1) {
     auto x = buildTensorExp(op, def->getOperand(0));
     if (x.has_value()) {
       unsigned e = x.value();
-      if (isa<math::AbsOp>(def))
+      if (isa<math::AbsFOp>(def))
         return addExp(kAbsF, e);
       if (isa<complex::AbsOp>(def))
         return addExp(kAbsC, e);
       if (isa<math::CeilOp>(def))
         return addExp(kCeilF, e);
       if (isa<math::FloorOp>(def))
         return addExp(kFloorF, e);
       if (isa<math::SqrtOp>(def))
Value Merger::buildExp(RewriterBase &rewriter, Location loc, unsigned e,
   switch (tensorExps[e].kind) {
   // Leaf.
   case kTensor:
   case kInvariant:
   case kIndex:
     llvm_unreachable("unexpected non-op");
   // Unary operations.
   case kAbsF:
-    return rewriter.create<math::AbsOp>(loc, v0);
+    return rewriter.create<math::AbsFOp>(loc, v0);
   case kAbsC: {
     auto type = v0.getType().cast<ComplexType>();
     auto eltType = type.getElementType().cast<FloatType>();
     return rewriter.create<complex::AbsOp>(loc, eltType, v0);
   }
   case kCeilF:
     return rewriter.create<math::CeilOp>(loc, v0);
   case kFloorF:

mlir/test/Conversion/ComplexToStandard/convert-to-standard.mlir

 // CHECK: %[[REAL_NUMERATOR_2:.*]] = arith.addf %[[LHS_REAL]], %[[LHS_IMAG_TIMES_RHS_IMAG_REAL_RATIO]] : f32
 // CHECK: %[[RESULT_REAL_2:.*]] = arith.divf %[[REAL_NUMERATOR_2]], %[[RHS_IMAG_REAL_DENOM]] : f32
 // CHECK: %[[LHS_REAL_TIMES_RHS_IMAG_REAL_RATIO:.*]] = arith.mulf %[[LHS_REAL]], %[[RHS_IMAG_REAL_RATIO]] : f32
 // CHECK: %[[IMAG_NUMERATOR_2:.*]] = arith.subf %[[LHS_IMAG]], %[[LHS_REAL_TIMES_RHS_IMAG_REAL_RATIO]] : f32
 // CHECK: %[[RESULT_IMAG_2:.*]] = arith.divf %[[IMAG_NUMERATOR_2]], %[[RHS_IMAG_REAL_DENOM]] : f32
 
 // Case 1. Zero denominator, numerator contains at most one NaN value.
 // CHECK: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %[[RHS_REAL_ABS:.*]] = math.abs %[[RHS_REAL]] : f32
+// CHECK: %[[RHS_REAL_ABS:.*]] = math.absf %[[RHS_REAL]] : f32
 // CHECK: %[[RHS_REAL_ABS_IS_ZERO:.*]] = arith.cmpf oeq, %[[RHS_REAL_ABS]], %[[ZERO]] : f32
-// CHECK: %[[RHS_IMAG_ABS:.*]] = math.abs %[[RHS_IMAG]] : f32
+// CHECK: %[[RHS_IMAG_ABS:.*]] = math.absf %[[RHS_IMAG]] : f32
 // CHECK: %[[RHS_IMAG_ABS_IS_ZERO:.*]] = arith.cmpf oeq, %[[RHS_IMAG_ABS]], %[[ZERO]] : f32
 // CHECK: %[[LHS_REAL_IS_NOT_NAN:.*]] = arith.cmpf ord, %[[LHS_REAL]], %[[ZERO]] : f32
 // CHECK: %[[LHS_IMAG_IS_NOT_NAN:.*]] = arith.cmpf ord, %[[LHS_IMAG]], %[[ZERO]] : f32
 // CHECK: %[[LHS_CONTAINS_NOT_NAN_VALUE:.*]] = arith.ori %[[LHS_REAL_IS_NOT_NAN]], %[[LHS_IMAG_IS_NOT_NAN]] : i1
 // CHECK: %[[RHS_IS_ZERO:.*]] = arith.andi %[[RHS_REAL_ABS_IS_ZERO]], %[[RHS_IMAG_ABS_IS_ZERO]] : i1
 // CHECK: %[[RESULT_IS_INFINITY:.*]] = arith.andi %[[LHS_CONTAINS_NOT_NAN_VALUE]], %[[RHS_IS_ZERO]] : i1
 // CHECK: %[[INF:.*]] = arith.constant 0x7F800000 : f32
 // CHECK: %[[INF_WITH_SIGN_OF_RHS_REAL:.*]] = math.copysign %[[INF]], %[[RHS_REAL]] : f32
 // CHECK: %[[INFINITY_RESULT_REAL:.*]] = arith.mulf %[[INF_WITH_SIGN_OF_RHS_REAL]], %[[LHS_REAL]] : f32
 // CHECK: %[[INFINITY_RESULT_IMAG:.*]] = arith.mulf %[[INF_WITH_SIGN_OF_RHS_REAL]], %[[LHS_IMAG]] : f32
 
 // Case 2. Infinite numerator, finite denominator.
 // CHECK: %[[RHS_REAL_FINITE:.*]] = arith.cmpf one, %[[RHS_REAL_ABS]], %[[INF]] : f32
 // CHECK: %[[RHS_IMAG_FINITE:.*]] = arith.cmpf one, %[[RHS_IMAG_ABS]], %[[INF]] : f32
 // CHECK: %[[RHS_IS_FINITE:.*]] = arith.andi %[[RHS_REAL_FINITE]], %[[RHS_IMAG_FINITE]] : i1
-// CHECK: %[[LHS_REAL_ABS:.*]] = math.abs %[[LHS_REAL]] : f32
+// CHECK: %[[LHS_REAL_ABS:.*]] = math.absf %[[LHS_REAL]] : f32
 // CHECK: %[[LHS_REAL_INFINITE:.*]] = arith.cmpf oeq, %[[LHS_REAL_ABS]], %[[INF]] : f32
-// CHECK: %[[LHS_IMAG_ABS:.*]] = math.abs %[[LHS_IMAG]] : f32
+// CHECK: %[[LHS_IMAG_ABS:.*]] = math.absf %[[LHS_IMAG]] : f32
 // CHECK: %[[LHS_IMAG_INFINITE:.*]] = arith.cmpf oeq, %[[LHS_IMAG_ABS]], %[[INF]] : f32
 // CHECK: %[[LHS_IS_INFINITE:.*]] = arith.ori %[[LHS_REAL_INFINITE]], %[[LHS_IMAG_INFINITE]] : i1
 // CHECK: %[[INF_NUM_FINITE_DENOM:.*]] = arith.andi %[[LHS_IS_INFINITE]], %[[RHS_IS_FINITE]] : i1
 // CHECK: %[[ONE:.*]] = arith.constant 1.000000e+00 : f32
 // CHECK: %[[LHS_REAL_IS_INF:.*]] = arith.select %[[LHS_REAL_INFINITE]], %[[ONE]], %[[ZERO]] : f32
 // CHECK: %[[LHS_REAL_IS_INF_WITH_SIGN:.*]] = math.copysign %[[LHS_REAL_IS_INF]], %[[LHS_REAL]] : f32
 // CHECK: %[[LHS_IMAG_IS_INF:.*]] = arith.select %[[LHS_IMAG_INFINITE]], %[[ONE]], %[[ZERO]] : f32
 // CHECK: %[[LHS_IMAG_IS_INF_WITH_SIGN:.*]] = math.copysign %[[LHS_IMAG_IS_INF]], %[[LHS_IMAG]] : f32

 
 // CHECK-LABEL: func @complex_mul
 // CHECK-SAME: (%[[LHS:.*]]: complex<f32>, %[[RHS:.*]]: complex<f32>)
 func.func @complex_mul(%lhs: complex<f32>, %rhs: complex<f32>) -> complex<f32> {
   %mul = complex.mul %lhs, %rhs : complex<f32>
   return %mul : complex<f32>
 }
 // CHECK: %[[LHS_REAL:.*]] = complex.re %[[LHS]] : complex<f32>
-// CHECK: %[[LHS_REAL_ABS:.*]] = math.abs %[[LHS_REAL]] : f32
+// CHECK: %[[LHS_REAL_ABS:.*]] = math.absf %[[LHS_REAL]] : f32
 // CHECK: %[[LHS_IMAG:.*]] = complex.im %[[LHS]] : complex<f32>
-// CHECK: %[[LHS_IMAG_ABS:.*]] = math.abs %[[LHS_IMAG]] : f32
+// CHECK: %[[LHS_IMAG_ABS:.*]] = math.absf %[[LHS_IMAG]] : f32
 // CHECK: %[[RHS_REAL:.*]] = complex.re %[[RHS]] : complex<f32>
-// CHECK: %[[RHS_REAL_ABS:.*]] = math.abs %[[RHS_REAL]] : f32
+// CHECK: %[[RHS_REAL_ABS:.*]] = math.absf %[[RHS_REAL]] : f32
 // CHECK: %[[RHS_IMAG:.*]] = complex.im %[[RHS]] : complex<f32>
-// CHECK: %[[RHS_IMAG_ABS:.*]] = math.abs %[[RHS_IMAG]] : f32
+// CHECK: %[[RHS_IMAG_ABS:.*]] = math.absf %[[RHS_IMAG]] : f32
 
 // CHECK: %[[LHS_REAL_TIMES_RHS_REAL:.*]] = arith.mulf %[[LHS_REAL]], %[[RHS_REAL]] : f32
-// CHECK: %[[LHS_REAL_TIMES_RHS_REAL_ABS:.*]] = math.abs %[[LHS_REAL_TIMES_RHS_REAL]] : f32
+// CHECK: %[[LHS_REAL_TIMES_RHS_REAL_ABS:.*]] = math.absf %[[LHS_REAL_TIMES_RHS_REAL]] : f32
 // CHECK: %[[LHS_IMAG_TIMES_RHS_IMAG:.*]] = arith.mulf %[[LHS_IMAG]], %[[RHS_IMAG]] : f32
-// CHECK: %[[LHS_IMAG_TIMES_RHS_IMAG_ABS:.*]] = math.abs %[[LHS_IMAG_TIMES_RHS_IMAG]] : f32
+// CHECK: %[[LHS_IMAG_TIMES_RHS_IMAG_ABS:.*]] = math.absf %[[LHS_IMAG_TIMES_RHS_IMAG]] : f32
 // CHECK: %[[REAL:.*]] = arith.subf %[[LHS_REAL_TIMES_RHS_REAL]], %[[LHS_IMAG_TIMES_RHS_IMAG]] : f32
 
 // CHECK: %[[LHS_IMAG_TIMES_RHS_REAL:.*]] = arith.mulf %[[LHS_IMAG]], %[[RHS_REAL]] : f32
-// CHECK: %[[LHS_IMAG_TIMES_RHS_REAL_ABS:.*]] = math.abs %[[LHS_IMAG_TIMES_RHS_REAL]] : f32
+// CHECK: %[[LHS_IMAG_TIMES_RHS_REAL_ABS:.*]] = math.absf %[[LHS_IMAG_TIMES_RHS_REAL]] : f32
 // CHECK: %[[LHS_REAL_TIMES_RHS_IMAG:.*]] = arith.mulf %[[LHS_REAL]], %[[RHS_IMAG]] : f32
-// CHECK: %[[LHS_REAL_TIMES_RHS_IMAG_ABS:.*]] = math.abs %[[LHS_REAL_TIMES_RHS_IMAG]] : f32
+// CHECK: %[[LHS_REAL_TIMES_RHS_IMAG_ABS:.*]] = math.absf %[[LHS_REAL_TIMES_RHS_IMAG]] : f32
 // CHECK: %[[IMAG:.*]] = arith.addf %[[LHS_IMAG_TIMES_RHS_REAL]], %[[LHS_REAL_TIMES_RHS_IMAG]] : f32
 
 // Handle cases where the "naive" calculation results in NaN values.
 // CHECK: %[[REAL_IS_NAN:.*]] = arith.cmpf uno, %[[REAL]], %[[REAL]] : f32
 // CHECK: %[[IMAG_IS_NAN:.*]] = arith.cmpf uno, %[[IMAG]], %[[IMAG]] : f32
 // CHECK: %[[IS_NAN:.*]] = arith.andi %[[REAL_IS_NAN]], %[[IMAG_IS_NAN]] : i1
 // CHECK: %[[INF:.*]] = arith.constant 0x7F800000 : f32
 

 // CHECK: %[[REAL_NUMERATOR_2:.*]] = arith.addf %[[LHS_REAL]], %[[LHS_IMAG_TIMES_RHS_IMAG_REAL_RATIO]] : f32
 // CHECK: %[[RESULT_REAL_2:.*]] = arith.divf %[[REAL_NUMERATOR_2]], %[[RHS_IMAG_REAL_DENOM]] : f32
 // CHECK: %[[LHS_REAL_TIMES_RHS_IMAG_REAL_RATIO:.*]] = arith.mulf %[[LHS_REAL]], %[[RHS_IMAG_REAL_RATIO]] : f32
 // CHECK: %[[IMAG_NUMERATOR_2:.*]] = arith.subf %[[LHS_IMAG]], %[[LHS_REAL_TIMES_RHS_IMAG_REAL_RATIO]] : f32
 // CHECK: %[[RESULT_IMAG_2:.*]] = arith.divf %[[IMAG_NUMERATOR_2]], %[[RHS_IMAG_REAL_DENOM]] : f32
 
 // Case 1. Zero denominator, numerator contains at most one NaN value.
 // CHECK: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %[[RHS_REAL_ABS:.*]] = math.abs %[[RHS_REAL]] : f32
+// CHECK: %[[RHS_REAL_ABS:.*]] = math.absf %[[RHS_REAL]] : f32
 // CHECK: %[[RHS_REAL_ABS_IS_ZERO:.*]] = arith.cmpf oeq, %[[RHS_REAL_ABS]], %[[ZERO]] : f32
-// CHECK: %[[RHS_IMAG_ABS:.*]] = math.abs %[[RHS_IMAG]] : f32
+// CHECK: %[[RHS_IMAG_ABS:.*]] = math.absf %[[RHS_IMAG]] : f32
 // CHECK: %[[RHS_IMAG_ABS_IS_ZERO:.*]] = arith.cmpf oeq, %[[RHS_IMAG_ABS]], %[[ZERO]] : f32
 // CHECK: %[[LHS_REAL_IS_NOT_NAN:.*]] = arith.cmpf ord, %[[LHS_REAL]], %[[ZERO]] : f32
 // CHECK: %[[LHS_IMAG_IS_NOT_NAN:.*]] = arith.cmpf ord, %[[LHS_IMAG]], %[[ZERO]] : f32
 // CHECK: %[[LHS_CONTAINS_NOT_NAN_VALUE:.*]] = arith.ori %[[LHS_REAL_IS_NOT_NAN]], %[[LHS_IMAG_IS_NOT_NAN]] : i1
 // CHECK: %[[RHS_IS_ZERO:.*]] = arith.andi %[[RHS_REAL_ABS_IS_ZERO]], %[[RHS_IMAG_ABS_IS_ZERO]] : i1
 // CHECK: %[[RESULT_IS_INFINITY:.*]] = arith.andi %[[LHS_CONTAINS_NOT_NAN_VALUE]], %[[RHS_IS_ZERO]] : i1
 // CHECK: %[[INF:.*]] = arith.constant 0x7F800000 : f32
 // CHECK: %[[INF_WITH_SIGN_OF_RHS_REAL:.*]] = math.copysign %[[INF]], %[[RHS_REAL]] : f32
 // CHECK: %[[INFINITY_RESULT_REAL:.*]] = arith.mulf %[[INF_WITH_SIGN_OF_RHS_REAL]], %[[LHS_REAL]] : f32
 // CHECK: %[[INFINITY_RESULT_IMAG:.*]] = arith.mulf %[[INF_WITH_SIGN_OF_RHS_REAL]], %[[LHS_IMAG]] : f32
 
 // Case 2. Infinite numerator, finite denominator.
 // CHECK: %[[RHS_REAL_FINITE:.*]] = arith.cmpf one, %[[RHS_REAL_ABS]], %[[INF]] : f32
 // CHECK: %[[RHS_IMAG_FINITE:.*]] = arith.cmpf one, %[[RHS_IMAG_ABS]], %[[INF]] : f32
 // CHECK: %[[RHS_IS_FINITE:.*]] = arith.andi %[[RHS_REAL_FINITE]], %[[RHS_IMAG_FINITE]] : i1
-// CHECK: %[[LHS_REAL_ABS:.*]] = math.abs %[[LHS_REAL]] : f32
+// CHECK: %[[LHS_REAL_ABS:.*]] = math.absf %[[LHS_REAL]] : f32
 // CHECK: %[[LHS_REAL_INFINITE:.*]] = arith.cmpf oeq, %[[LHS_REAL_ABS]], %[[INF]] : f32
-// CHECK: %[[LHS_IMAG_ABS:.*]] = math.abs %[[LHS_IMAG]] : f32
+// CHECK: %[[LHS_IMAG_ABS:.*]] = math.absf %[[LHS_IMAG]] : f32
 // CHECK: %[[LHS_IMAG_INFINITE:.*]] = arith.cmpf oeq, %[[LHS_IMAG_ABS]], %[[INF]] : f32
 // CHECK: %[[LHS_IS_INFINITE:.*]] = arith.ori %[[LHS_REAL_INFINITE]], %[[LHS_IMAG_INFINITE]] : i1
 // CHECK: %[[INF_NUM_FINITE_DENOM:.*]] = arith.andi %[[LHS_IS_INFINITE]], %[[RHS_IS_FINITE]] : i1
 // CHECK: %[[ONE:.*]] = arith.constant 1.000000e+00 : f32
 // CHECK: %[[LHS_REAL_IS_INF:.*]] = arith.select %[[LHS_REAL_INFINITE]], %[[ONE]], %[[ZERO]] : f32
 // CHECK: %[[LHS_REAL_IS_INF_WITH_SIGN:.*]] = math.copysign %[[LHS_REAL_IS_INF]], %[[LHS_REAL]] : f32
 // CHECK: %[[LHS_IMAG_IS_INF:.*]] = arith.select %[[LHS_IMAG_INFINITE]], %[[ONE]], %[[ZERO]] : f32
 // CHECK: %[[LHS_IMAG_IS_INF_WITH_SIGN:.*]] = math.copysign %[[LHS_IMAG_IS_INF]], %[[LHS_IMAG]] : f32

mlir/test/Conversion/GPUToNVVM/gpu-to-nvvm.mlir

 
 // -----
 
 gpu.module @test_module {
   // CHECK: llvm.func @__nv_fabsf(f32) -> f32
   // CHECK: llvm.func @__nv_fabs(f64) -> f64
   // CHECK-LABEL: func @gpu_fabs
   func.func @gpu_fabs(%arg_f32 : f32, %arg_f64 : f64) -> (f32, f64) {
-    %result32 = math.abs %arg_f32 : f32
+    %result32 = math.absf %arg_f32 : f32
     // CHECK: llvm.call @__nv_fabsf(%{{.*}}) : (f32) -> f32
-    %result64 = math.abs %arg_f64 : f64
+    %result64 = math.absf %arg_f64 : f64
     // CHECK: llvm.call @__nv_fabs(%{{.*}}) : (f64) -> f64
     func.return %result32, %result64 : f32, f64
   }
 }
 
 // -----
 
 gpu.module @test_module {
gpu.module @test_module {
   // CHECK-LABEL: @kernel_func
   // CHECK: attributes
   // CHECK: gpu.kernel
   // CHECK: nvvm.kernel
   gpu.func @kernel_func() kernel {
     gpu.return
   }
 }
-

mlir/test/Conversion/GPUToROCDL/gpu-to-rocdl.mlir

 
 // -----
 
 gpu.module @test_module {
   // CHECK: llvm.func @__ocml_fabs_f32(f32) -> f32
   // CHECK: llvm.func @__ocml_fabs_f64(f64) -> f64
   // CHECK-LABEL: func @gpu_fabs
   func.func @gpu_fabs(%arg_f32 : f32, %arg_f64 : f64) -> (f32, f64) {
-    %result32 = math.abs %arg_f32 : f32
+    %result32 = math.absf %arg_f32 : f32
     // CHECK: llvm.call @__ocml_fabs_f32(%{{.*}}) : (f32) -> f32
-    %result64 = math.abs %arg_f64 : f64
+    %result64 = math.absf %arg_f64 : f64
     // CHECK: llvm.call @__ocml_fabs_f64(%{{.*}}) : (f64) -> f64
     func.return %result32, %result64 : f32, f64
   }
 }
 
 // -----
 
 gpu.module @test_module {

mlir/test/Conversion/MathToSPIRV/math-to-gl-spirv.mlir

func.func @float32_unary_scalar(%arg0: f32) {
   %5 = math.rsqrt %arg0 : f32
   // CHECK: spv.GL.Sqrt %{{.*}}: f32
   %6 = math.sqrt %arg0 : f32
   // CHECK: spv.GL.Tanh %{{.*}}: f32
   %7 = math.tanh %arg0 : f32
   // CHECK: spv.GL.Sin %{{.*}}: f32
   %8 = math.sin %arg0 : f32
   // CHECK: spv.GL.FAbs %{{.*}}: f32
-  %9 = math.abs %arg0 : f32
+  %9 = math.absf %arg0 : f32
   // CHECK: spv.GL.Ceil %{{.*}}: f32
   %10 = math.ceil %arg0 : f32
   // CHECK: spv.GL.Floor %{{.*}}: f32
   %11 = math.floor %arg0 : f32
   return
 }
 
 // CHECK-LABEL: @float32_unary_vector

mlir/test/Conversion/MathToSPIRV/math-to-opencl-spirv.mlir

func.func @float32_unary_scalar(%arg0: f32) {
   %5 = math.rsqrt %arg0 : f32
   // CHECK: spv.CL.sqrt %{{.*}}: f32
   %6 = math.sqrt %arg0 : f32
   // CHECK: spv.CL.tanh %{{.*}}: f32
   %7 = math.tanh %arg0 : f32
   // CHECK: spv.CL.sin %{{.*}}: f32
   %8 = math.sin %arg0 : f32
   // CHECK: spv.CL.fabs %{{.*}}: f32
-  %9 = math.abs %arg0 : f32
+  %9 = math.absf %arg0 : f32
   // CHECK: spv.CL.ceil %{{.*}}: f32
   %10 = math.ceil %arg0 : f32
   // CHECK: spv.CL.floor %{{.*}}: f32
   %11 = math.floor %arg0 : f32
   // CHECK: spv.CL.erf %{{.*}}: f32
   %12 = math.erf %arg0 : f32
   // CHECK: spv.CL.round %{{.*}}: f32
   %13 = math.round %arg0 : f32

mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir

 // RUN: mlir-opt --split-input-file -pass-pipeline="func.func(tosa-to-linalg)" %s -verify-diagnostics -o -| FileCheck %s
 
 // CHECK: #[[$MAP0:.*]] = affine_map<() -> ()>
 
 // CHECK-LABEL: @test_abs
 func.func @test_abs(%arg0: tensor<f32>) -> tensor<f32> {
   // CHECK: [[INIT:%.+]] = linalg.init_tensor [] : tensor<f32>
   // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = []} ins(%arg0 : tensor<f32>) outs([[INIT]] : tensor<f32>) {
   // CHECK: ^bb0(%arg1: f32, %arg2: f32):
-  // CHECK:   [[ELEMENT:%.+]] = math.abs %arg1
+  // CHECK:   [[ELEMENT:%.+]] = math.absf %arg1
   // CHECK:   linalg.yield [[ELEMENT]] : f32
   // CHECK: } -> tensor<f32>
 
   %0 = "tosa.abs"(%arg0) : (tensor<f32>) -> tensor<f32>
 
   // CHECK: return [[GENERIC]]
   return %0 : tensor<f32>
 }
 
 // -----
 
 // CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> (d0)>
 
 // CHECK-LABEL: @test_abs
 func.func @test_abs(%arg0: tensor<2xf32>) -> tensor<2xf32> {
   // CHECK: [[INIT:%.+]] = linalg.init_tensor [2] : tensor<2xf32>
   // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel"]} ins(%arg0 : tensor<2xf32>) outs([[INIT]] : tensor<2xf32>) {
   // CHECK: ^bb0(%arg1: f32, %arg2: f32):
-  // CHECK:   [[ELEMENT:%.+]] = math.abs %arg1
+  // CHECK:   [[ELEMENT:%.+]] = math.absf %arg1
   // CHECK:   linalg.yield [[ELEMENT]] : f32
   // CHECK: } -> tensor<2xf32>
   %0 = "tosa.abs"(%arg0) : (tensor<2xf32>) -> tensor<2xf32>
 
   // CHECK: return [[GENERIC]]
   return %0 : tensor<2xf32>
 }
 
 // -----
 
 // CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
 
 // CHECK-LABEL: @test_abs
 func.func @test_abs(%arg0: tensor<2x3xf32>) -> tensor<2x3xf32> {
   // CHECK: [[INIT:%.+]] = linalg.init_tensor [2, 3] : tensor<2x3xf32>
   // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel", "parallel"]} ins(%arg0 : tensor<2x3xf32>) outs([[INIT]] : tensor<2x3xf32>) {
   // CHECK: ^bb0(%arg1: f32, %arg2: f32):
-  // CHECK:   [[ELEMENT:%.+]] = math.abs %arg1
+  // CHECK:   [[ELEMENT:%.+]] = math.absf %arg1
   // CHECK:   linalg.yield [[ELEMENT]] : f32
   // CHECK: } -> tensor<2x3xf32>
   %0 = "tosa.abs"(%arg0) : (tensor<2x3xf32>) -> tensor<2x3xf32>
 
   // CHECK: return [[GENERIC]]
   return %0 : tensor<2x3xf32>
 }
 
 // -----
 
 // CHECK-LABEL: @test_abs
 func.func @test_abs(%arg0: tensor<?xf32>) -> tensor<?xf32> {
   // CHECK: %[[C0:.+]] = arith.constant 0
   // CHECK: %[[DIM:.+]] = tensor.dim %arg0, %[[C0]]
   // CHECK: %[[INIT:.+]] = linalg.init_tensor [%[[DIM]]]
   // CHECK: linalg.generic
-  // CHECK: math.abs
+  // CHECK: math.absf
   %0 = "tosa.abs"(%arg0) : (tensor<?xf32>) -> tensor<?xf32>
   return %0 : tensor<?xf32>
 }
 
 // -----
 
 // CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
 
 // CHECK-LABEL: @test_abs_dyn
 func.func @test_abs_dyn(%arg0: tensor<2x?xf32>) -> tensor<2x?xf32> {
   // CHECK: %[[C1:.+]] = arith.constant 1
   // CHECK: %[[DIM:.+]] = tensor.dim %arg0, %[[C1]]
   // CHECK: %[[INIT:.+]] = linalg.init_tensor [2, %[[DIM]]]
   // CHECK: linalg.generic
-  // CHECK: math.abs
+  // CHECK: math.absf
   %0 = "tosa.abs"(%arg0) : (tensor<2x?xf32>) -> tensor<2x?xf32>
   return %0 : tensor<2x?xf32>
 }
 // -----
 
 
 // CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> ()>
 // CHECK: #[[$MAP1:.*]] = affine_map<(d0) -> (d0)>

 
 // CHECK-LABEL: @test_simple_f32
 func.func @test_simple_f32(%arg0: tensor<1xf32>) -> () {
   // CHECK: linalg.generic
   // CHECK: tanh
   %0 = "tosa.tanh"(%arg0) : (tensor<1xf32>) -> tensor<1xf32>
 
   // CHECK: linalg.generic
-  // CHECK: math.abs
+  // CHECK: math.absf
   %1 = "tosa.abs"(%arg0) : (tensor<1xf32>) -> tensor<1xf32>
 
   // CHECK: linalg.generic
   // CHECK: arith.addf
   %2 = "tosa.add"(%0, %0) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
 
   // CHECK: linalg.generic
   // CHECK: arith.subf
func.func @pad_float(%arg0 : tensor<1x2xf32>) -> (tensor<4x9xf32>) {
   %0 = arith.constant dense<[[1, 2], [3, 4]]> : tensor<2x2xi32>
   // TODO: Output contains multiple "arith.constant 1 : index".
   // CHECK-DAG: [[INDEX1:%.+]] = arith.constant 1 : index
   // CHECK-DAG: [[INDEX2:%.+]] = arith.constant 2 : index
   // CHECK-DAG: [[INDEX3:%.+]] = arith.constant 3 : index
   // CHECK-DAG: [[INDEX4:%.+]] = arith.constant 4 : index
   // CHECK-DAG: [[CST:%.+]] = arith.constant 0.000000e+00 : f32
   // CHECK: tensor.pad %arg0 low{{\[}}%{{.*}}, [[INDEX3]]] high{{\[}}[[INDEX2]], [[INDEX4]]]  {
-  // CHECK: ^bb0(%arg1: index, %arg2: index):  
+  // CHECK: ^bb0(%arg1: index, %arg2: index):
   // CHECK:   tensor.yield [[CST]]
   // CHECK: } : tensor<1x2xf32> to tensor<4x9xf32>
   %1 = "tosa.pad"(%arg0, %0)  : (tensor<1x2xf32>, tensor<2x2xi32>)  -> (tensor<4x9xf32>)
   return %1 : tensor<4x9xf32>
 }
 
 func.func @pad_int(%arg0 : tensor<1x2xi32>) -> (tensor<4x9xi32>) {
   %0 = arith.constant dense<[[1, 2], [3, 4]]> : tensor<2x2xi32>
func.func @pad_float_explicit(%arg0 : tensor<1x2xf32>) -> (tensor<4x9xf32>) {
   %0 = arith.constant dense<[[1, 2], [3, 4]]> : tensor<2x2xi32>
   // TODO: Output contains multiple "arith.constant 1 : index".
   // CHECK-DAG: [[INDEX1:%.+]] = arith.constant 1 : index
   // CHECK-DAG: [[INDEX2:%.+]] = arith.constant 2 : index
   // CHECK-DAG: [[INDEX3:%.+]] = arith.constant 3 : index
   // CHECK-DAG: [[INDEX4:%.+]] = arith.constant 4 : index
   // CHECK-DAG: [[CST:%.+]] = arith.constant 4.200000e+01 : f32
   // CHECK: tensor.pad %arg0 low{{\[}}%{{.*}}, [[INDEX3]]] high{{\[}}[[INDEX2]], [[INDEX4]]]  {
-  // CHECK: ^bb0(%arg1: index, %arg2: index):  
+  // CHECK: ^bb0(%arg1: index, %arg2: index):
   // CHECK:   tensor.yield [[CST]]
   // CHECK: } : tensor<1x2xf32> to tensor<4x9xf32>
   %1 = arith.constant dense<42.0> : tensor<f32>
   %2 = "tosa.pad"(%arg0, %0, %1)  : (tensor<1x2xf32>, tensor<2x2xi32>, tensor<f32>)  -> (tensor<4x9xf32>)
   return %2 : tensor<4x9xf32>
 }
 
 // -----

mlir/test/Dialect/Linalg/generalize-named-polymorphic-ops.mlir

 // Verifies the fun attribute controls the unary function used.
 func.func @generalize_elemwise_abs(%lhs : tensor<4x8xf32>, %output : tensor<4x8xf32>) -> tensor<4x8xf32> {
   %0 = linalg.elemwise_unary {fun = #linalg.unary_fn<abs>}
                               ins(%lhs: tensor<4x8xf32>) outs(%output: tensor<4x8xf32>) -> tensor<4x8xf32>
   return %0: tensor<4x8xf32>
 }
 
 // CHECK-LABEL: @generalize_elemwise_abs
-// CHECK:        = math.abs
+// CHECK:        = math.absf
 
 // -----
 
 // Verifies the fun attribute controls the unary function used.
 func.func @generalize_elemwise_ceil(%lhs : tensor<4x8xf32>, %output : tensor<4x8xf32>) -> tensor<4x8xf32> {
   %0 = linalg.elemwise_unary {fun = #linalg.unary_fn<ceil>}
                               ins(%lhs: tensor<4x8xf32>) outs(%output: tensor<4x8xf32>) -> tensor<4x8xf32>
   return %0: tensor<4x8xf32>

mlir/test/Dialect/Math/canonicalize.mlir

-// RUN: mlir-opt %s -canonicalize | FileCheck %s 
+// RUN: mlir-opt %s -canonicalize | FileCheck %s
 
 // CHECK-LABEL: @ceil_fold
 // CHECK: %[[cst:.+]] = arith.constant 1.000000e+00 : f32
 // CHECK: return %[[cst]]
 func.func @ceil_fold() -> f32 {
   %c = arith.constant 0.3 : f32
   %r = math.ceil %c : f32
   return %r : f32
func.func @sqrt_fold_vec() -> (vector<4xf32>) {
   return %0 : vector<4xf32>
 }
 
 // CHECK-LABEL: @abs_fold
 // CHECK: %[[cst:.+]] = arith.constant 4.000000e+00 : f32
 // CHECK: return %[[cst]]
 func.func @abs_fold() -> f32 {
   %c = arith.constant -4.0 : f32
-  %r = math.abs %c : f32
+  %r = math.absf %c : f32
   return %r : f32
 }
 
 // CHECK-LABEL: @copysign_fold
 // CHECK: %[[cst:.+]] = arith.constant -4.000000e+00 : f32
 // CHECK: return %[[cst]]
 func.func @copysign_fold() -> f32 {
   %c1 = arith.constant 4.0 : f32

mlir/test/Dialect/Math/polynomial-approximation.mlir

 // CHECK:           %[[VAL_11:.*]] = arith.constant 0.00489352504 : f32
 // CHECK:           %[[VAL_12:.*]] = arith.constant 0.00226843474 : f32
 // CHECK:           %[[VAL_13:.*]] = arith.constant 1.18534706E-4 : f32
 // CHECK:           %[[VAL_14:.*]] = arith.constant 1.19825836E-6 : f32
 // CHECK:           %[[VAL_15:.*]] = arith.cmpf ult, %[[VAL_0]], %[[VAL_2]] : f32
 // CHECK:           %[[VAL_16:.*]] = arith.select %[[VAL_15]], %[[VAL_0]], %[[VAL_2]] : f32
 // CHECK:           %[[VAL_17:.*]] = arith.cmpf ugt, %[[VAL_16]], %[[VAL_1]] : f32
 // CHECK:           %[[VAL_18:.*]] = arith.select %[[VAL_17]], %[[VAL_16]], %[[VAL_1]] : f32
-// CHECK:           %[[VAL_19:.*]] = math.abs %[[VAL_0]] : f32
+// CHECK:           %[[VAL_19:.*]] = math.absf %[[VAL_0]] : f32
 // CHECK:           %[[VAL_20:.*]] = arith.cmpf olt, %[[VAL_19]], %[[VAL_3]] : f32
 // CHECK:           %[[VAL_21:.*]] = arith.mulf %[[VAL_18]], %[[VAL_18]] : f32
 // CHECK:           %[[VAL_22:.*]] = math.fma %[[VAL_21]], %[[VAL_10]], %[[VAL_9]] : f32
 // CHECK:           %[[VAL_23:.*]] = math.fma %[[VAL_21]], %[[VAL_22]], %[[VAL_8]] : f32
 // CHECK:           %[[VAL_24:.*]] = math.fma %[[VAL_21]], %[[VAL_23]], %[[VAL_7]] : f32
 // CHECK:           %[[VAL_25:.*]] = math.fma %[[VAL_21]], %[[VAL_24]], %[[VAL_6]] : f32
 // CHECK:           %[[VAL_26:.*]] = math.fma %[[VAL_21]], %[[VAL_25]], %[[VAL_5]] : f32
 // CHECK:           %[[VAL_27:.*]] = math.fma %[[VAL_21]], %[[VAL_26]], %[[VAL_4]] : f32

 
 // CHECK-LABEL: @atan_scalar
 // CHECK-DAG:  %[[ONE:.+]] = arith.constant 1.000000e+00
 // CHECK-DAG:  %[[N1:.+]] = arith.constant 0.144182831
 // CHECK-DAG:  %[[N2:.+]] = arith.constant -0.349992335
 // CHECK-DAG:  %[[N3:.+]] = arith.constant -0.0106783099
 // CHECK-DAG:  %[[N4:.+]] = arith.constant 1.00209987
 // CHECK-DAG:  %[[HALF_PI:.+]] = arith.constant 1.57079637
-// CHECK-DAG:  %[[ABS:.+]] = math.abs %arg0
+// CHECK-DAG:  %[[ABS:.+]] = math.absf %arg0
 // CHECK-DAG:  %[[DIV:.+]] = arith.divf %cst, %[[ABS]]
 // CHECK-DAG:  %[[CMP:.+]] = arith.cmpf olt, %[[ABS]], %[[DIV]]
 // CHECK-DAG:  %[[SEL:.+]] = arith.select %[[CMP]], %[[ABS]], %[[DIV]]
 // CHECK-DAG:  %[[P0:.+]] = math.fma %[[SEL]], %[[N1]], %[[N2]]
 // CHECK-DAG:  %[[P1:.+]] = math.fma %[[SEL]], %[[P0]], %[[N3]]
 // CHECK-DAG:  %[[P2:.+]] = math.fma %[[SEL]], %[[P1]], %[[N4]]
 // CHECK-DAG:  %[[P3:.+]] = arith.mulf %[[SEL]], %[[P2]]
 // CHECK-DAG:  %[[SUB:.+]] = arith.subf %[[HALF_PI]], %[[P3]]

 // CHECK-DAG:  %[[N1:.+]] = arith.constant 0.144182831
 // CHECK-DAG:  %[[N2:.+]] = arith.constant -0.349992335
 // CHECK-DAG:  %[[N3:.+]] = arith.constant -0.0106783099
 // CHECK-DAG:  %[[N4:.+]] = arith.constant 1.00209987
 // CHECK-DAG:  %[[HALF_PI:.+]] = arith.constant 1.57079637
 // CHECK-DAG:  %[[ARG0:.+]] = arith.extf %arg0 : f16 to f32
 // CHECK-DAG:  %[[ARG1:.+]] = arith.extf %arg1 : f16 to f32
 // CHECK-DAG:  %[[RATIO:.+]] = arith.divf %[[ARG0]], %[[ARG1]]
-// CHECK-DAG:  %[[ABS:.+]] = math.abs %[[RATIO]]
+// CHECK-DAG:  %[[ABS:.+]] = math.absf %[[RATIO]]
 // CHECK-DAG:  %[[DIV:.+]] = arith.divf %cst, %[[ABS]]
 // CHECK-DAG:  %[[CMP:.+]] = arith.cmpf olt, %[[ABS]], %[[DIV]]
 // CHECK-DAG:  %[[SEL:.+]] = arith.select %[[CMP]], %[[ABS]], %[[DIV]]
 // CHECK-DAG:  %[[P0:.+]] = math.fma %[[SEL]], %[[N1]], %[[N2]]
 // CHECK-DAG:  %[[P1:.+]] = math.fma %[[SEL]], %[[P0]], %[[N3]]
 // CHECK-DAG:  %[[P2:.+]] = math.fma %[[SEL]], %[[P1]], %[[N4]]
 // CHECK-DAG:  %[[P3:.+]] = arith.mulf %[[SEL]], %[[P2]]
 // CHECK-DAG:  %[[SUB:.+]] = arith.subf %[[HALF_PI]], %[[P3]]

 // CHECK-DAG: %[[EDGE3:.+]] = arith.select %[[X_Y_ZERO]], %[[NAN]], %[[EDGE2]]
 // CHECK: %[[RET:.+]] = arith.truncf %[[EDGE3]]
 // CHECK: return %[[RET]]
 
 func.func @atan2_scalar(%arg0: f16, %arg1: f16) -> f16 {
   %0 = math.atan2 %arg0, %arg1 : f16
   return %0 : f16
 }
-

mlir/test/Dialect/SparseTensor/sparse_fp_ops.mlir

 // CHECK:         %[[VAL_5:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_2]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:         %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
 // CHECK:         %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf64>
 // CHECK:         %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:         %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:         scf.for %[[VAL_10:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] {
 // CHECK:           %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_10]]] : memref<?xindex>
 // CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref<?xf64>
-// CHECK:           %[[VAL_13:.*]] = math.abs %[[VAL_12]] : f64
+// CHECK:           %[[VAL_13:.*]] = math.absf %[[VAL_12]] : f64
 // CHECK:             memref.store %[[VAL_13]], %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<32xf64>
 // CHECK:         }
 // CHECK:         %[[VAL_14:.*]] = bufferization.to_tensor %[[VAL_7]] : memref<32xf64>
 // CHECK:         return %[[VAL_14]] : tensor<32xf64>
 // CHECK:       }
 func.func @abs(%arga: tensor<32xf64, #SV>,
                %argx: tensor<32xf64>) -> tensor<32xf64> {
   %0 = linalg.generic #trait1
      ins(%arga: tensor<32xf64, #SV>)
     outs(%argx: tensor<32xf64>) {
       ^bb(%a: f64, %x: f64):
-        %0 = math.abs %a : f64
+        %0 = math.absf %a : f64
         linalg.yield %0 : f64
   } -> tensor<32xf64>
   return %0 : tensor<32xf64>
 }
 
 // CHECK-LABEL: func @ceil(
 // CHECK-SAME:    %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:    %[[VAL_1:.*]]: tensor<32xf64>) -> tensor<32xf64> {

 // CHECK:         %[[VAL_8:.*]] = memref.alloca(%[[VAL_2]]) : memref<?xindex>
 // CHECK:         %[[BUF:.*]] = memref.alloca() : memref<f64>
 // CHECK:         %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_1]]] : memref<?xindex>
 // CHECK:         %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:         scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_2]] {
 // CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
 // CHECK:           memref.store %[[VAL_12]], %[[VAL_8]]{{\[}}%[[VAL_1]]] : memref<?xindex>
 // CHECK:           %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<?xf64>
-// CHECK:           %[[VAL_14:.*]] = math.abs %[[VAL_13]] : f64
+// CHECK:           %[[VAL_14:.*]] = math.absf %[[VAL_13]] : f64
 // CHECK:           %[[VAL_15:.*]] = math.ceil %[[VAL_14]] : f64
 // CHECK:           %[[VAL_16:.*]] = math.floor %[[VAL_15]] : f64
 // CHECK:           %[[VAL_17:.*]] = math.sqrt %[[VAL_16]] : f64
 // CHECK:           %[[VAL_18:.*]] = math.expm1 %[[VAL_17]] : f64
 // CHECK:           %[[VAL_19:.*]] = math.log1p %[[VAL_18]] : f64
 // CHECK:           %[[VAL_20:.*]] = math.sin %[[VAL_19]] : f64
 // CHECK:           %[[VAL_21:.*]] = math.tanh %[[VAL_20]] : f64
 // CHECK:           memref.store %[[VAL_21]], %[[BUF]][] : memref<f64>
 // CHECK:           sparse_tensor.lex_insert %[[VAL_4]], %[[VAL_8]], %[[BUF]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>, memref<?xindex>, memref<f64>
 // CHECK:         }
 // CHECK:         %[[VAL_22:.*]] = sparse_tensor.load %[[VAL_4]] hasInserts : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:         return %[[VAL_22]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:       }
 func.func @zero_preserving_math(%arga: tensor<32xf64, #SV>) -> tensor<32xf64, #SV> {
   %c32 = arith.constant 32 : index
   %xinp = bufferization.alloc_tensor() : tensor<32xf64, #SV>
   %0 = linalg.generic #trait1
      ins(%arga: tensor<32xf64, #SV>)
     outs(%xinp: tensor<32xf64, #SV>) {
       ^bb(%a: f64, %x: f64):
-  %0 = math.abs %a : f64
+  %0 = math.absf %a : f64
         %1 = math.ceil %0 : f64
         %2 = math.floor %1 : f64
         %3 = math.sqrt %2 : f64
         %4 = math.expm1 %3 : f64
         %5 = math.log1p %4 : f64
         %6 = math.sin %5 : f64
         %7 = math.tanh %6 : f64
         linalg.yield %7 : f64

mlir/test/IR/core-ops.mlir

^bb42(%t: tensor<4x4x?xf32>, %f: f32, %i: i32, %idx : index, %j: i64, %half: f16):
   %70 = arith.cmpf oeq, %vcf32, %vcf32 : vector<4 x f32>
 
   // CHECK: arith.constant true
   %74 = arith.constant true
 
   // CHECK: arith.constant false
   %75 = arith.constant false
 
-  // CHECK: %{{.*}} = math.abs %arg1 : f32
-  %100 = "math.abs"(%f) : (f32) -> f32
+  // CHECK: %{{.*}} = math.absf %arg1 : f32
+  %100 = "math.absf"(%f) : (f32) -> f32
 
-  // CHECK: %{{.*}} = math.abs %arg1 : f32
-  %101 = math.abs %f : f32
+  // CHECK: %{{.*}} = math.absf %arg1 : f32
+  %101 = math.absf %f : f32
 
-  // CHECK: %{{.*}} = math.abs %{{.*}}: vector<4xf32>
-  %102 = math.abs %vcf32 : vector<4xf32>
+  // CHECK: %{{.*}} = math.absf %{{.*}}: vector<4xf32>
+  %102 = math.absf %vcf32 : vector<4xf32>
 
-  // CHECK: %{{.*}} = math.abs %arg0 : tensor<4x4x?xf32>
-  %103 = math.abs %t : tensor<4x4x?xf32>
+  // CHECK: %{{.*}} = math.absf %arg0 : tensor<4x4x?xf32>
+  %103 = math.absf %t : tensor<4x4x?xf32>
 
   // CHECK: %{{.*}} = math.ceil %arg1 : f32
   %104 = "math.ceil"(%f) : (f32) -> f32
 
   // CHECK: %{{.*}} = math.ceil %arg1 : f32
   %105 = math.ceil %f : f32
 
   // CHECK: %{{.*}} = math.ceil %{{.*}}: vector<4xf32>

mlir/test/Target/Cpp/invalid.mlir

 // RUN: mlir-translate -split-input-file -mlir-to-cpp -verify-diagnostics %s
 
 // expected-error@+1 {{'func.func' op with multiple blocks needs variables declared at top}}
 func.func @multiple_blocks() {
 ^bb1:
     cf.br ^bb2
 ^bb2:
     return
 }
 
 // -----
 
 func.func @unsupported_std_op(%arg0: f64) -> f64 {
-  // expected-error@+1 {{'math.abs' op unable to find printer for op}}
-  %0 = math.abs %arg0 : f64
+  // expected-error@+1 {{'math.absf' op unable to find printer for op}}
+  %0 = math.absf %arg0 : f64
   return %0 : f64
 }
 
 // -----
 
 // expected-error@+1 {{cannot emit integer type 'i80'}}
 func.func @unsupported_integer_type(%arg0 : i80) {
   return

mlir/test/python/dialects/linalg/opdsl/emit_misc.py

with InsertionPoint(module.body):
     # CHECK-NEXT: -> tensor<4x16xf32>
     @func.FuncOp.from_py_func(
         RankedTensorType.get((4, 16), f32), RankedTensorType.get((4, 16), f32))
     def test_f32_elemwise_log(input, init_result):
       return elemwise_unary_poly(input, outs=[init_result], fun=UnaryFn.log)
 
     # CHECK-LABEL: @test_f32_elemwise_abs
     # CHECK:      ^{{.*}}(%[[IN:.+]]: f32, %[[OUT:.+]]: f32)
-    # CHECK-NEXT:   %[[EXP:.+]] = math.abs %[[IN]] : f32
+    # CHECK-NEXT:   %[[EXP:.+]] = math.absf %[[IN]] : f32
     # CHECK-NEXT:   linalg.yield %[[EXP]] : f32
     # CHECK-NEXT: -> tensor<4x16xf32>
     @func.FuncOp.from_py_func(
         RankedTensorType.get((4, 16), f32), RankedTensorType.get((4, 16), f32))
     def test_f32_elemwise_abs(input, init_result):
       return elemwise_unary_poly(input, outs=[init_result], fun=UnaryFn.abs)
 
     # CHECK-LABEL: @test_f32_elemwise_ceil