[MLIR][Linalg] Remove elemwise_unary and elemwise_binary

mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml

@@ -44,56 +44,6 @@ structured_op: !LinalgStructuredOpConfig
         - !ScalarExpression
           scalar_arg: I
 --- !LinalgOpConfig
-metadata: !LinalgOpMetadata
-  name: elemwise_unary
-  cpp_class_name: ElemwiseUnaryOp
-  doc: |-
-    Applies the unary function fun elementwise.
-
-    Numeric casting is performed on the input operand, promoting it to the same
-    data type as the accumulator/output.
-structured_op: !LinalgStructuredOpConfig
-  args:
-  - !LinalgOperandDefConfig
-    name: I
-    kind: input_tensor
-    type_var: T1
-    shape_map: affine_map<() -> ()>
-  - !LinalgOperandDefConfig
-    name: O
-    kind: output_tensor
-    type_var: U
-    shape_map: affine_map<() -> ()>
-  - !LinalgOperandDefConfig
-    name: fun
-    kind: unary_fn_attr
-    default_fn: exp
-  - !LinalgOperandDefConfig
-    name: cast
-    kind: type_fn_attr
-    default_fn: cast_signed
-  indexing_maps: !LinalgIndexingMapsConfig
-    static_indexing_maps:
-    - affine_map<() -> ()>
-    - affine_map<() -> ()>
-  iterator_types: []
-  assignments:
-  - !ScalarAssign
-    arg: O
-    value: !ScalarExpression
-      scalar_fn:
-        kind: unary
-        attr_name: fun
-        operands:
-        - !ScalarExpression
-          scalar_fn:
-            kind: type
-            attr_name: cast
-            type_var: U
-            operands:
-            - !ScalarExpression
-              scalar_arg: I
---- !LinalgOpConfig
 metadata: !LinalgOpMetadata
   name: exp
   cpp_class_name: ExpOp
@@ -549,70 +499,6 @@ structured_op: !LinalgStructuredOpConfig
         - !ScalarExpression
           scalar_arg: I
 --- !LinalgOpConfig
-metadata: !LinalgOpMetadata
-  name: elemwise_binary
-  cpp_class_name: ElemwiseBinaryOp
-  doc: |-
-    Applies the binary function fun elementwise.
-
-    Numeric casting is performed on the input operand, promoting it to the same
-    data type as the accumulator/output.
-structured_op: !LinalgStructuredOpConfig
-  args:
-  - !LinalgOperandDefConfig
-    name: lhs
-    kind: input_tensor
-    type_var: T1
-    shape_map: affine_map<() -> ()>
-  - !LinalgOperandDefConfig
-    name: rhs
-    kind: input_tensor
-    type_var: T2
-    shape_map: affine_map<() -> ()>
-  - !LinalgOperandDefConfig
-    name: O
-    kind: output_tensor
-    type_var: U
-    shape_map: affine_map<() -> ()>
-  - !LinalgOperandDefConfig
-    name: fun
-    kind: binary_fn_attr
-    default_fn: add
-  - !LinalgOperandDefConfig
-    name: cast
-    kind: type_fn_attr
-    default_fn: cast_signed
-  indexing_maps: !LinalgIndexingMapsConfig
-    static_indexing_maps:
-    - affine_map<() -> ()>
-    - affine_map<() -> ()>
-    - affine_map<() -> ()>
-  iterator_types: []
-  assignments:
-  - !ScalarAssign
-    arg: O
-    value: !ScalarExpression
-      scalar_fn:
-        kind: binary
-        attr_name: fun
-        operands:
-        - !ScalarExpression
-          scalar_fn:
-            kind: type
-            attr_name: cast
-            type_var: U
-            operands:
-            - !ScalarExpression
-              scalar_arg: lhs
-        - !ScalarExpression
-          scalar_fn:
-            kind: type
-            attr_name: cast
-            type_var: U
-            operands:
-            - !ScalarExpression
-              scalar_arg: rhs
---- !LinalgOpConfig
 metadata: !LinalgOpMetadata
   name: add
   cpp_class_name: AddOp

mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py

@@ -21,21 +21,6 @@ def copy(
     O[None] = cast(U, I[None])
 
 
-@linalg_structured_op
-def elemwise_unary(
-    I=TensorDef(T1),
-    O=TensorDef(U, output=True),
-    fun=UnaryFnAttrDef(default=UnaryFn.exp),
-    cast=TypeFnAttrDef(default=TypeFn.cast_signed),
-):
-    """Applies the unary function fun elementwise.
-
-    Numeric casting is performed on the input operand, promoting it to the same
-    data type as the accumulator/output.
-    """
-    O[None] = fun(cast(U, I[None]))
-
-
 @linalg_structured_op
 def exp(
     I=TensorDef(T1),
@@ -192,22 +177,6 @@ def erf(
     O[None] = UnaryFn.erf(I[None])
 
 
-@linalg_structured_op
-def elemwise_binary(
-    lhs=TensorDef(T1),
-    rhs=TensorDef(T2),
-    O=TensorDef(U, output=True),
-    fun=BinaryFnAttrDef(default=BinaryFn.add),
-    cast=TypeFnAttrDef(default=TypeFn.cast_signed),
-):
-    """Applies the binary function fun elementwise.
-
-    Numeric casting is performed on the input operand, promoting it to the same
-    data type as the accumulator/output.
-    """
-    O[None] = fun(cast(U, lhs[None]), cast(U, rhs[None]))
-
-
 @linalg_structured_op
 def add(
     lhs=TensorDef(T1),

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

@@ -434,117 +434,6 @@ func.func @generalize_const(%min: f64, %max: f64, %seed: i32, %O: tensor<16x32xf
 
 // -----
 
-// Verifies the default value of the fun attribute is an exp op.
-func.func @generalize_elemwise_exp(%lhs : tensor<4x8xf32>, %output : tensor<4x8xf32>) -> tensor<4x8xf32> {
-  %0 = linalg.elemwise_unary ins(%lhs: tensor<4x8xf32>) outs(%output: tensor<4x8xf32>) -> tensor<4x8xf32>
-  return %0: tensor<4x8xf32>
-}
-
-// CHECK-LABEL: @generalize_elemwise_exp
-// CHECK:        = math.exp
-
-// -----
-
-// Verifies the fun attribute controls the unary function used.
-func.func @generalize_elemwise_log(%lhs : tensor<4x8xf32>, %output : tensor<4x8xf32>) -> tensor<4x8xf32> {
-  %0 = linalg.elemwise_unary {fun = #linalg.unary_fn<log>}
-                              ins(%lhs: tensor<4x8xf32>) outs(%output: tensor<4x8xf32>) -> tensor<4x8xf32>
-  return %0: tensor<4x8xf32>
-}
-
-// CHECK-LABEL: @generalize_elemwise_log
-// CHECK:        = math.log
-
-// -----
-
-// 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.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>
-}
-
-// CHECK-LABEL: @generalize_elemwise_ceil
-// CHECK:        = math.ceil
-
-// -----
-
-// Verifies the fun attribute controls the unary function used.
-func.func @generalize_elemwise_floor(%lhs : tensor<4x8xf32>, %output : tensor<4x8xf32>) -> tensor<4x8xf32> {
-  %0 = linalg.elemwise_unary {fun = #linalg.unary_fn<floor>}
-                              ins(%lhs: tensor<4x8xf32>) outs(%output: tensor<4x8xf32>) -> tensor<4x8xf32>
-  return %0: tensor<4x8xf32>
-}
-
-// CHECK-LABEL: @generalize_elemwise_floor
-// CHECK:        = math.floor
-
-// -----
-
-// Verifies the fun attribute controls the unary function used.
-func.func @generalize_elemwise_negf(%lhs : tensor<4x8xf32>, %output : tensor<4x8xf32>) -> tensor<4x8xf32> {
-  %0 = linalg.elemwise_unary {fun = #linalg.unary_fn<negf>}
-                              ins(%lhs: tensor<4x8xf32>) outs(%output: tensor<4x8xf32>) -> tensor<4x8xf32>
-  return %0: tensor<4x8xf32>
-}
-
-// CHECK-LABEL: @generalize_elemwise_negf
-// CHECK:        = arith.negf
-
-// -----
-
-// Verifies the default value of the fun attribute is an add op.
-func.func @generalize_elemwise_add(%lhs : tensor<4x8xf32>, %rhs : tensor<4x8xf32>, %output : tensor<4x8xf32>) -> tensor<4x8xf32> {
-  %0 = linalg.elemwise_binary ins(%lhs, %rhs: tensor<4x8xf32>, tensor<4x8xf32>)
-                              outs(%output: tensor<4x8xf32>) -> tensor<4x8xf32>
-  return %0: tensor<4x8xf32>
-}
-
-// CHECK-LABEL: @generalize_elemwise_add
-// CHECK:        = arith.addf
-
-// -----
-
-// Verifies the fun attribute controls the binary function used.
-func.func @generalize_elemwise_mul(%lhs : tensor<4x8xf32>, %rhs : tensor<4x8xf32>, %output : tensor<4x8xf32>) -> tensor<4x8xf32> {
-  %0 = linalg.elemwise_binary {fun = #linalg.binary_fn<mul>}
-                              ins(%lhs, %rhs: tensor<4x8xf32>, tensor<4x8xf32>)
-                              outs(%output: tensor<4x8xf32>) -> tensor<4x8xf32>
-  return %0: tensor<4x8xf32>
-}
-
-// CHECK-LABEL: @generalize_elemwise_mul
-// CHECK:        = arith.mulf
-
-// -----
-
-// Verifies pointwise ops support rank zero input tensors
-func.func @generalize_elemwise_rank_zero(%lhs : tensor<f32>, %rhs : tensor<f32>, %output : tensor<4x8xf32>) -> tensor<4x8xf32> {
-  %0 = linalg.elemwise_binary {fun = #linalg.binary_fn<sub>}
-                              ins(%lhs, %rhs: tensor<f32>, tensor<f32>)
-                              outs(%output: tensor<4x8xf32>) -> tensor<4x8xf32>
-  return %0: tensor<4x8xf32>
-}
-
-// CHECK-LABEL: @generalize_elemwise_rank_zero
-// CHECK:       linalg.generic
-// CHECK-SAME:  iterator_types = ["parallel", "parallel"]
-// CHECK:        = arith.subf
-
-// -----
-
 // Verifies the fun attribute controls the binary function used.
 func.func @generalize_copy(%lhs : tensor<4x8xf32>, %output : tensor<4x8xf32>) -> tensor<4x8xf32> {
   %0 = linalg.copy ins(%lhs: tensor<4x8xf32>) outs(%output: tensor<4x8xf32>) -> tensor<4x8xf32>

mlir/test/Dialect/Linalg/invalid.mlir

@@ -1909,14 +1909,6 @@ module {
 
 // -----
 
-func.func @elemwise_unary_invalid_mixed_types(%arg0 : tensor<?xi32>) -> tensor<?xi32> {
-  // expected-error @below {{unsupported non numeric type}}
-  %0 = linalg.elemwise_unary ins(%arg0 : tensor<?xi32>) outs(%arg0 : tensor<?xi32>) -> tensor<?xi32>
-  return %0 : tensor<?xi32>
-}
-
-// -----
-
 func.func @matmul_invalid_mixed_types(%t: tensor<?xf16>, %f: vector<4xf16>)
   -> (tensor<?xf16>, vector<4xf16>)
 {

mlir/test/Dialect/Linalg/library-calls.mlir

@@ -59,43 +59,3 @@ module {
     return
   }
 }
-
-
-// -----
-
-// CHECK: func.func private @linalg_elemwise_unary_negf_view16x8xf32_view16x8xf32(memref<16x8xf32, strided<[?, ?], offset: ?>>, memref<16x8xf32, strided<[?, ?], offset: ?>>) attributes {llvm.emit_c_interface}
-// CHECK: func.func private @linalg_elemwise_unary_negf_view16xf32_view16xf32(memref<16xf32, strided<[?], offset: ?>>, memref<16xf32, strided<[?], offset: ?>>) attributes {llvm.emit_c_interface}
-
-func.func @test_neg(%A : memref<16x8xf32>, %B: memref<16x8xf32>, %C: memref<16xf32>, %D: memref<16xf32>) {
-  linalg.elemwise_unary {fun = #linalg.unary_fn<negf>}
-                              ins(%A: memref<16x8xf32>) outs(%B: memref<16x8xf32>)
-  linalg.elemwise_unary {fun = #linalg.unary_fn<negf>}
-                              ins(%C: memref<16xf32>) outs(%D: memref<16xf32>)
-  return
-}
-
-// -----
-
-// CHECK: func.func private @linalg_elemwise_unary_exp_view16x8xf32_view16x8xf32(memref<16x8xf32, strided<[?, ?], offset: ?>>, memref<16x8xf32, strided<[?, ?], offset: ?>>) attributes {llvm.emit_c_interface}
-// CHECK: func.func private @linalg_elemwise_unary_exp_view16xf32_view16xf32(memref<16xf32, strided<[?], offset: ?>>, memref<16xf32, strided<[?], offset: ?>>) attributes {llvm.emit_c_interface}
-
-func.func @test_exp(%A : memref<16x8xf32>, %B: memref<16x8xf32>, %C: memref<16xf32>, %D: memref<16xf32>) {
-  linalg.elemwise_unary {fun = #linalg.unary_fn<exp>}
-                              ins(%A: memref<16x8xf32>) outs(%B: memref<16x8xf32>)
-  linalg.elemwise_unary {fun = #linalg.unary_fn<exp>}
-                              ins(%C: memref<16xf32>) outs(%D: memref<16xf32>)
-  return
-}
-
-// -----
-
-// CHECK: func.func private @linalg_elemwise_binary_add_view16x8xf32_view16x8xf32_view16x8xf32(memref<16x8xf32, strided<[?, ?], offset: ?>>, memref<16x8xf32, strided<[?, ?], offset: ?>>, memref<16x8xf32, strided<[?, ?], offset: ?>>) attributes {llvm.emit_c_interface}
-// CHECK: func.func private @linalg_elemwise_binary_add_view16xf32_view16xf32_view16xf32(memref<16xf32, strided<[?], offset: ?>>, memref<16xf32, strided<[?], offset: ?>>, memref<16xf32, strided<[?], offset: ?>>) attributes {llvm.emit_c_interface}
-
-func.func @test_add(%A : memref<16x8xf32>, %B: memref<16x8xf32>, %C: memref<16x8xf32>, %D: memref<16xf32>, %E: memref<16xf32>, %F: memref<16xf32>) {
-  linalg.elemwise_binary {fun = #linalg.binary_fn<add>}
-                              ins(%A, %B: memref<16x8xf32>, memref<16x8xf32>) outs(%C: memref<16x8xf32>)
-  linalg.elemwise_binary {fun = #linalg.binary_fn<add>}
-                              ins(%D, %E: memref<16xf32>, memref<16xf32>) outs(%F: memref<16xf32>)
-  return
-}

mlir/test/Dialect/Linalg/match-ops-interpreter.mlir

@@ -842,15 +842,15 @@ module attributes { transform.with_named_sequence } {
     // expected-remark @below {{op result}}
     // expected-note @below {{value handle points to an op result #0}}
     // expected-remark @below {{single user}}
-    linalg.elemwise_unary {fun = #linalg.unary_fn<negf>} ins(%2 : tensor<42x42xf32>) outs(%0 : tensor<42x42xf32>) -> tensor<42x42xf32>
+    linalg.negf ins(%2 : tensor<42x42xf32>) outs(%0 : tensor<42x42xf32>) -> tensor<42x42xf32>
     // expected-remark @below {{matched result value}}
     // expected-remark @below {{op result}}
     // expected-note @below {{value handle points to an op result #0}}
-    linalg.elemwise_unary {fun = #linalg.unary_fn<exp>} ins(%3 : tensor<42x42xf32>) outs(%0 : tensor<42x42xf32>) -> tensor<42x42xf32>
+    linalg.exp ins(%3 : tensor<42x42xf32>) outs(%0 : tensor<42x42xf32>) -> tensor<42x42xf32>
     // expected-remark @below {{matched result value}}
     // expected-remark @below {{op result}}
     // expected-note @below {{value handle points to an op result #0}}
-    linalg.elemwise_unary {fun = #linalg.unary_fn<exp>} ins(%3 : tensor<42x42xf32>) outs(%0 : tensor<42x42xf32>) -> tensor<42x42xf32>
+    linalg.exp ins(%3 : tensor<42x42xf32>) outs(%0 : tensor<42x42xf32>) -> tensor<42x42xf32>
     return
   }
 }