[Mlir-commits] [mlir] 93d038a - Revert "[MLIR][Linalg] Add more arith named ops to linalg"
Renato Golin
llvmlistbot at llvm.org
Wed Jul 5 14:02:50 PDT 2023
Author: Renato Golin
Date: 2023-07-05T22:02:23+01:00
New Revision: 93d038a0ea86ff1010bae0b27570d426d29978d5
URL: https://github.com/llvm/llvm-project/commit/93d038a0ea86ff1010bae0b27570d426d29978d5
DIFF: https://github.com/llvm/llvm-project/commit/93d038a0ea86ff1010bae0b27570d426d29978d5.diff
LOG: Revert "[MLIR][Linalg] Add more arith named ops to linalg"
This reverts commit eda47fdd258ca666815122a931b82699a0629b87.
It failed on NVidia, AMD and Windows bots. Investigating.
Added:
Modified:
mlir/include/mlir/Dialect/Linalg/IR/LinalgEnums.td
mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
mlir/test/Dialect/Linalg/generalize-named-ops.mlir
mlir/test/Dialect/Linalg/named-ops-fail.mlir
mlir/test/Dialect/Linalg/named-ops.mlir
Removed:
################################################################################
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgEnums.td b/mlir/include/mlir/Dialect/Linalg/IR/LinalgEnums.td
index a778f305b6c1b0..6d50cda9718625 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgEnums.td
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgEnums.td
@@ -31,12 +31,10 @@ def BinaryFn : I32EnumAttr<"BinaryFn", "", [
I32EnumAttrCase<"add", 0>,
I32EnumAttrCase<"sub", 1>,
I32EnumAttrCase<"mul", 2>,
- I32EnumAttrCase<"div", 3>,
- I32EnumAttrCase<"div_unsigned", 4>,
- I32EnumAttrCase<"max_signed", 5>,
- I32EnumAttrCase<"min_signed", 6>,
- I32EnumAttrCase<"max_unsigned", 7>,
- I32EnumAttrCase<"min_unsigned", 8>
+ I32EnumAttrCase<"max_signed", 3>,
+ I32EnumAttrCase<"min_signed", 4>,
+ I32EnumAttrCase<"max_unsigned", 5>,
+ I32EnumAttrCase<"min_unsigned", 6>
]> {
let genSpecializedAttr = 0;
let cppNamespace = "::mlir::linalg";
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
index 74ee6227a8ba0f..86c4d2dd10711e 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
@@ -205,204 +205,6 @@ structured_op: !LinalgStructuredOpConfig
- !ScalarExpression
scalar_arg: rhs
--- !LinalgOpConfig
-metadata: !LinalgOpMetadata
- name: sub
- cpp_class_name: SubOp
- doc: |-
- Subtracts two tensors elementwise.
-
- The shapes and element types must be identical. The appropriate casts,
- broadcasts and reductions should be done previously to calling this op.
-
- This means reduction/broadcast/element cast semantics is explicit. Further
- passes can take that into account when lowering this code. For example,
- a `linalg.broadcast` + `linalg.sub` sequence can be lowered to a
- `linalg.generic` with
diff erent affine maps for the two operands.
-structured_op: !LinalgStructuredOpConfig
- args:
- - !LinalgOperandDefConfig
- name: lhs
- kind: input_tensor
- type_var: T
- shape_map: affine_map<() -> ()>
- - !LinalgOperandDefConfig
- name: rhs
- kind: input_tensor
- type_var: T
- shape_map: affine_map<() -> ()>
- - !LinalgOperandDefConfig
- name: out
- kind: output_tensor
- type_var: T
- shape_map: affine_map<() -> ()>
- indexing_maps: !LinalgIndexingMapsConfig
- static_indexing_maps:
- - affine_map<() -> ()>
- - affine_map<() -> ()>
- - affine_map<() -> ()>
- iterator_types: []
- assignments:
- - !ScalarAssign
- arg: out
- value: !ScalarExpression
- scalar_fn:
- kind: binary
- fn_name: sub
- operands:
- - !ScalarExpression
- scalar_arg: lhs
- - !ScalarExpression
- scalar_arg: rhs
---- !LinalgOpConfig
-metadata: !LinalgOpMetadata
- name: mul
- cpp_class_name: MulOp
- doc: |-
- Multiply two tensors elementwise.
-
- The shapes and element types must be identical. The appropriate casts,
- broadcasts and reductions should be done previously to calling this op.
-
- This means reduction/broadcast/element cast semantics is explicit. Further
- passes can take that into account when lowering this code. For example,
- a `linalg.broadcast` + `linalg.mul` sequence can be lowered to a
- `linalg.generic` with
diff erent affine maps for the two operands.
-structured_op: !LinalgStructuredOpConfig
- args:
- - !LinalgOperandDefConfig
- name: lhs
- kind: input_tensor
- type_var: T
- shape_map: affine_map<() -> ()>
- - !LinalgOperandDefConfig
- name: rhs
- kind: input_tensor
- type_var: T
- shape_map: affine_map<() -> ()>
- - !LinalgOperandDefConfig
- name: out
- kind: output_tensor
- type_var: T
- shape_map: affine_map<() -> ()>
- indexing_maps: !LinalgIndexingMapsConfig
- static_indexing_maps:
- - affine_map<() -> ()>
- - affine_map<() -> ()>
- - affine_map<() -> ()>
- iterator_types: []
- assignments:
- - !ScalarAssign
- arg: out
- value: !ScalarExpression
- scalar_fn:
- kind: binary
- fn_name: mul
- operands:
- - !ScalarExpression
- scalar_arg: lhs
- - !ScalarExpression
- scalar_arg: rhs
---- !LinalgOpConfig
-metadata: !LinalgOpMetadata
- name: div
- cpp_class_name: DivOp
- doc: |-
- Divides the first tensor by the second tensor, elementwise. For integer
- types, performs a signed division.
-
- The shapes and element types must be identical. The appropriate casts,
- broadcasts and reductions should be done previously to calling this op.
-
- This means reduction/broadcast/element cast semantics is explicit. Further
- passes can take that into account when lowering this code. For example,
- a `linalg.broadcast` + `linalg.div` sequence can be lowered to a
- `linalg.generic` with
diff erent affine maps for the two operands.
-structured_op: !LinalgStructuredOpConfig
- args:
- - !LinalgOperandDefConfig
- name: lhs
- kind: input_tensor
- type_var: T
- shape_map: affine_map<() -> ()>
- - !LinalgOperandDefConfig
- name: rhs
- kind: input_tensor
- type_var: T
- shape_map: affine_map<() -> ()>
- - !LinalgOperandDefConfig
- name: out
- kind: output_tensor
- type_var: T
- shape_map: affine_map<() -> ()>
- indexing_maps: !LinalgIndexingMapsConfig
- static_indexing_maps:
- - affine_map<() -> ()>
- - affine_map<() -> ()>
- - affine_map<() -> ()>
- iterator_types: []
- assignments:
- - !ScalarAssign
- arg: out
- value: !ScalarExpression
- scalar_fn:
- kind: binary
- fn_name: div
- operands:
- - !ScalarExpression
- scalar_arg: lhs
- - !ScalarExpression
- scalar_arg: rhs
---- !LinalgOpConfig
-metadata: !LinalgOpMetadata
- name: div_unsigned
- cpp_class_name: DivUOp
- doc: |-
- Divides the first tensor by the second tensor, elementwise. For integer
- types, performs an unsigned division.
-
- The shapes and element types must be identical. The appropriate casts,
- broadcasts and reductions should be done previously to calling this op.
-
- This means reduction/broadcast/element cast semantics is explicit. Further
- passes can take that into account when lowering this code. For example,
- a `linalg.broadcast` + `linalg.div` sequence can be lowered to a
- `linalg.generic` with
diff erent affine maps for the two operands.
-structured_op: !LinalgStructuredOpConfig
- args:
- - !LinalgOperandDefConfig
- name: lhs
- kind: input_tensor
- type_var: T
- shape_map: affine_map<() -> ()>
- - !LinalgOperandDefConfig
- name: rhs
- kind: input_tensor
- type_var: T
- shape_map: affine_map<() -> ()>
- - !LinalgOperandDefConfig
- name: out
- kind: output_tensor
- type_var: T
- shape_map: affine_map<() -> ()>
- indexing_maps: !LinalgIndexingMapsConfig
- static_indexing_maps:
- - affine_map<() -> ()>
- - affine_map<() -> ()>
- - affine_map<() -> ()>
- iterator_types: []
- assignments:
- - !ScalarAssign
- arg: out
- value: !ScalarExpression
- scalar_fn:
- kind: binary
- fn_name: div_unsigned
- operands:
- - !ScalarExpression
- scalar_arg: lhs
- - !ScalarExpression
- scalar_arg: rhs
---- !LinalgOpConfig
metadata: !LinalgOpMetadata
name: matmul
cpp_class_name: MatmulOp
diff --git a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
index 3f3d5afa32d246..77380edc2022e7 100644
--- a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
+++ b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
@@ -434,22 +434,6 @@ class RegionBuilderHelper {
if (allBool)
return builder.create<arith::AndIOp>(arg0.getLoc(), arg0, arg1);
return builder.create<arith::MulIOp>(arg0.getLoc(), arg0, arg1);
- case BinaryFn::div:
- if (allComplex)
- return builder.create<complex::DivOp>(arg0.getLoc(), arg0, arg1);
- if (allFloatingPoint)
- return builder.create<arith::DivFOp>(arg0.getLoc(), arg0, arg1);
- if (allBool)
- llvm_unreachable("unsupported operation: div with bools");
- return builder.create<arith::DivSIOp>(arg0.getLoc(), arg0, arg1);
- case BinaryFn::div_unsigned:
- if (allComplex)
- return builder.create<complex::DivOp>(arg0.getLoc(), arg0, arg1);
- if (allFloatingPoint)
- return builder.create<arith::DivFOp>(arg0.getLoc(), arg0, arg1);
- if (allBool)
- llvm_unreachable("unsupported operation: div with bools");
- return builder.create<arith::DivUIOp>(arg0.getLoc(), arg0, arg1);
case BinaryFn::max_signed:
assert(!allComplex);
if (allFloatingPoint)
diff --git a/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py b/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
index 5c591085e74e62..063165faf31dee 100644
--- a/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
+++ b/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py
@@ -57,7 +57,7 @@ def add(
rhs=TensorDef(T1),
O=TensorDef(T1, output=True),
):
- """Adds two tensors elementwise.
+ """ Adds two tensors elementwise.
The shapes and element types must be identical. The appropriate casts,
broadcasts and reductions should be done previously to calling this op.
@@ -70,63 +70,6 @@ def add(
O[None] = lhs[None] + rhs[None]
- at linalg_structured_op
-def sub(
- lhs=TensorDef(T1),
- rhs=TensorDef(T1),
- O=TensorDef(T1, output=True),
-):
- """Subtracts two tensors elementwise.
-
- The shapes and element types must be identical. The appropriate casts,
- broadcasts and reductions should be done previously to calling this op.
-
- This means reduction/broadcast/element cast semantics is explicit. Further
- passes can take that into account when lowering this code. For example,
- a `linalg.broadcast` + `linalg.sub` sequence can be lowered to a
- `linalg.generic` with
diff erent affine maps for the two operands.
- """
- O[None] = lhs[None] - rhs[None]
-
-
- at linalg_structured_op
-def mul(
- lhs=TensorDef(T1),
- rhs=TensorDef(T1),
- O=TensorDef(T1, output=True),
-):
- """Multiplies two tensors elementwise.
-
- The shapes and element types must be identical. The appropriate casts,
- broadcasts and reductions should be done previously to calling this op.
-
- This means reduction/broadcast/element cast semantics is explicit. Further
- passes can take that into account when lowering this code. For example,
- a `linalg.broadcast` + `linalg.mul` sequence can be lowered to a
- `linalg.generic` with
diff erent affine maps for the two operands.
- """
- O[None] = lhs[None] * rhs[None]
-
-
- at linalg_structured_op
-def div(
- lhs=TensorDef(T1),
- rhs=TensorDef(T1),
- O=TensorDef(T1, output=True),
-):
- """Divides the first tensor by the second tensor, elementwise.
-
- The shapes and element types must be identical. The appropriate casts,
- broadcasts and reductions should be done previously to calling this op.
-
- This means reduction/broadcast/element cast semantics is explicit. Further
- passes can take that into account when lowering this code. For example,
- a `linalg.broadcast` + `linalg.div` sequence can be lowered to a
- `linalg.generic` with
diff erent affine maps for the two operands.
- """
- O[None] = lhs[None] / rhs[None]
-
-
@linalg_structured_op
def matmul(
A=TensorDef(T1, S.M, S.K),
diff --git a/mlir/test/Dialect/Linalg/generalize-named-ops.mlir b/mlir/test/Dialect/Linalg/generalize-named-ops.mlir
index b6d6e11f455b36..204db8e1f0f9f9 100644
--- a/mlir/test/Dialect/Linalg/generalize-named-ops.mlir
+++ b/mlir/test/Dialect/Linalg/generalize-named-ops.mlir
@@ -311,103 +311,3 @@ func.func @generalize_add(%lhs: memref<7x14x21xf32>, %rhs: memref<7x14x21xf32>,
// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32, %[[BBARG2:.+]]: f32)
// CHECK-NEXT: %[[SUM:.+]] = arith.addf %[[BBARG0]], %[[BBARG1]] : f32
// CHECK-NEXT: linalg.yield %[[SUM]] : f32
-
-// -----
-
-func.func @generalize_sub(%lhs: memref<7x14x21xf32>, %rhs: memref<7x14x21xf32>,
- %out: memref<7x14x21xf32>) {
- linalg.sub ins(%lhs, %rhs : memref<7x14x21xf32>, memref<7x14x21xf32>)
- outs(%out : memref<7x14x21xf32>)
- return
-}
-
-// CHECK-DAG: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
-
-// CHECK: func @generalize_sub
-// CHECK-SAME: (%[[LHS:.+]]: memref<7x14x21xf32>, %[[RHS:.+]]: memref<7x14x21xf32>,
-// CHECK-SAME: %[[OUT:.+]]: memref<7x14x21xf32>)
-
-// CHECK: linalg.generic
-// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]], #[[MAP]]]
-// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]}
-// CHECK-SAME: ins(%[[LHS]], %[[RHS]] : memref<7x14x21xf32>, memref<7x14x21xf32>)
-// CHECK-SAME: outs(%[[OUT]] : memref<7x14x21xf32>)
-
-// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32, %[[BBARG2:.+]]: f32)
-// CHECK-NEXT: %[[SUM:.+]] = arith.subf %[[BBARG0]], %[[BBARG1]] : f32
-// CHECK-NEXT: linalg.yield %[[SUM]] : f32
-
-// -----
-
-func.func @generalize_mul(%lhs: memref<7x14x21xf32>, %rhs: memref<7x14x21xf32>,
- %out: memref<7x14x21xf32>) {
- linalg.mul ins(%lhs, %rhs : memref<7x14x21xf32>, memref<7x14x21xf32>)
- outs(%out : memref<7x14x21xf32>)
- return
-}
-
-// CHECK-DAG: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
-
-// CHECK: func @generalize_mul
-// CHECK-SAME: (%[[LHS:.+]]: memref<7x14x21xf32>, %[[RHS:.+]]: memref<7x14x21xf32>,
-// CHECK-SAME: %[[OUT:.+]]: memref<7x14x21xf32>)
-
-// CHECK: linalg.generic
-// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]], #[[MAP]]]
-// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]}
-// CHECK-SAME: ins(%[[LHS]], %[[RHS]] : memref<7x14x21xf32>, memref<7x14x21xf32>)
-// CHECK-SAME: outs(%[[OUT]] : memref<7x14x21xf32>)
-
-// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32, %[[BBARG2:.+]]: f32)
-// CHECK-NEXT: %[[SUM:.+]] = arith.mulf %[[BBARG0]], %[[BBARG1]] : f32
-// CHECK-NEXT: linalg.yield %[[SUM]] : f32
-
-// -----
-
-func.func @generalize_div(%lhs: memref<7x14x21xf32>, %rhs: memref<7x14x21xf32>,
- %out: memref<7x14x21xf32>) {
- linalg.div ins(%lhs, %rhs : memref<7x14x21xf32>, memref<7x14x21xf32>)
- outs(%out : memref<7x14x21xf32>)
- return
-}
-
-// CHECK-DAG: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
-
-// CHECK: func @generalize_div
-// CHECK-SAME: (%[[LHS:.+]]: memref<7x14x21xf32>, %[[RHS:.+]]: memref<7x14x21xf32>,
-// CHECK-SAME: %[[OUT:.+]]: memref<7x14x21xf32>)
-
-// CHECK: linalg.generic
-// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]], #[[MAP]]]
-// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]}
-// CHECK-SAME: ins(%[[LHS]], %[[RHS]] : memref<7x14x21xf32>, memref<7x14x21xf32>)
-// CHECK-SAME: outs(%[[OUT]] : memref<7x14x21xf32>)
-
-// CHECK: ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32, %[[BBARG2:.+]]: f32)
-// CHECK-NEXT: %[[SUM:.+]] = arith.divf %[[BBARG0]], %[[BBARG1]] : f32
-// CHECK-NEXT: linalg.yield %[[SUM]] : f32
-
-// -----
-
-func.func @generalize_divu(%lhs: memref<7x14x21xi32>, %rhs: memref<7x14x21xi32>,
- %out: memref<7x14x21xi32>) {
- linalg.div_unsigned ins(%lhs, %rhs : memref<7x14x21xi32>, memref<7x14x21xi32>)
- outs(%out : memref<7x14x21xi32>)
- return
-}
-
-// CHECK-DAG: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
-
-// CHECK: func @generalize_divu
-// CHECK-SAME: (%[[LHS:.+]]: memref<7x14x21xi32>, %[[RHS:.+]]: memref<7x14x21xi32>,
-// CHECK-SAME: %[[OUT:.+]]: memref<7x14x21xi32>)
-
-// CHECK: linalg.generic
-// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]], #[[MAP]]]
-// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]}
-// CHECK-SAME: ins(%[[LHS]], %[[RHS]] : memref<7x14x21xi32>, memref<7x14x21xi32>)
-// CHECK-SAME: outs(%[[OUT]] : memref<7x14x21xi32>)
-
-// CHECK: ^{{.+}}(%[[BBARG0:.+]]: i32, %[[BBARG1:.+]]: i32, %[[BBARG2:.+]]: i32)
-// CHECK-NEXT: %[[SUM:.+]] = arith.divui %[[BBARG0]], %[[BBARG1]] : i32
-// CHECK-NEXT: linalg.yield %[[SUM]] : i32
diff --git a/mlir/test/Dialect/Linalg/named-ops-fail.mlir b/mlir/test/Dialect/Linalg/named-ops-fail.mlir
index f4682a588ed21f..2e3b752371ab76 100644
--- a/mlir/test/Dialect/Linalg/named-ops-fail.mlir
+++ b/mlir/test/Dialect/Linalg/named-ops-fail.mlir
@@ -14,66 +14,3 @@ func.func @add_broadcast(%arg0: memref<8x16xf32>, %arg1: memref<4x8x16xf32>, %ar
return
}
-// -----
-
-func.func @sub_type_cast(%arg0: memref<4x8x16xf32>, %arg1: memref<4x8x16xf16>, %arg2: memref<4x8x16xf32>) {
- // CHECK: op requires the same type for all operands and results
- linalg.sub ins(%arg0, %arg1 : memref<4x8x16xf32>, memref<4x8x16xf16>) outs(%arg2: memref<4x8x16xf32>)
- return
-}
-
-// -----
-
-func.func @sub_broadcast(%arg0: memref<8x16xf32>, %arg1: memref<4x8x16xf32>, %arg2: memref<4x8x16xf32>) {
- // CHECK: op expected operand rank (2) to match the result rank of indexing_map #0 (3)
- linalg.sub ins(%arg0, %arg1 : memref<8x16xf32>, memref<4x8x16xf32>) outs(%arg2: memref<4x8x16xf32>)
- return
-}
-
-// -----
-
-func.func @mul_type_cast(%arg0: memref<4x8x16xf32>, %arg1: memref<4x8x16xf16>, %arg2: memref<4x8x16xf32>) {
- // CHECK: op requires the same type for all operands and results
- linalg.mul ins(%arg0, %arg1 : memref<4x8x16xf32>, memref<4x8x16xf16>) outs(%arg2: memref<4x8x16xf32>)
- return
-}
-
-// -----
-
-func.func @mul_broadcast(%arg0: memref<8x16xf32>, %arg1: memref<4x8x16xf32>, %arg2: memref<4x8x16xf32>) {
- // CHECK: op expected operand rank (2) to match the result rank of indexing_map #0 (3)
- linalg.mul ins(%arg0, %arg1 : memref<8x16xf32>, memref<4x8x16xf32>) outs(%arg2: memref<4x8x16xf32>)
- return
-}
-
-// -----
-
-func.func @div_type_cast(%arg0: memref<4x8x16xf32>, %arg1: memref<4x8x16xf16>, %arg2: memref<4x8x16xf32>) {
- // CHECK: op requires the same type for all operands and results
- linalg.div ins(%arg0, %arg1 : memref<4x8x16xf32>, memref<4x8x16xf16>) outs(%arg2: memref<4x8x16xf32>)
- return
-}
-
-// -----
-
-func.func @div_broadcast(%arg0: memref<8x16xf32>, %arg1: memref<4x8x16xf32>, %arg2: memref<4x8x16xf32>) {
- // CHECK: op expected operand rank (2) to match the result rank of indexing_map #0 (3)
- linalg.div ins(%arg0, %arg1 : memref<8x16xf32>, memref<4x8x16xf32>) outs(%arg2: memref<4x8x16xf32>)
- return
-}
-
-// -----
-
-func.func @divu_type_cast(%arg0: memref<4x8x16xf32>, %arg1: memref<4x8x16xf16>, %arg2: memref<4x8x16xf32>) {
- // CHECK: op requires the same type for all operands and results
- linalg.div_unsigned ins(%arg0, %arg1 : memref<4x8x16xf32>, memref<4x8x16xf16>) outs(%arg2: memref<4x8x16xf32>)
- return
-}
-
-// -----
-
-func.func @divu_broadcast(%arg0: memref<8x16xf32>, %arg1: memref<4x8x16xf32>, %arg2: memref<4x8x16xf32>) {
- // CHECK: op expected operand rank (2) to match the result rank of indexing_map #0 (3)
- linalg.div_unsigned ins(%arg0, %arg1 : memref<8x16xf32>, memref<4x8x16xf32>) outs(%arg2: memref<4x8x16xf32>)
- return
-}
diff --git a/mlir/test/Dialect/Linalg/named-ops.mlir b/mlir/test/Dialect/Linalg/named-ops.mlir
index 13256f97c5aba4..466d0cf0bb0e9f 100644
--- a/mlir/test/Dialect/Linalg/named-ops.mlir
+++ b/mlir/test/Dialect/Linalg/named-ops.mlir
@@ -1218,139 +1218,3 @@ func.func @add_tensor(%arg0: tensor<4x8x16xf32>, %arg1: tensor<4x8x16xf32>) -> t
%1 = linalg.add ins(%arg0, %arg1 : tensor<4x8x16xf32>, tensor<4x8x16xf32>) outs(%0: tensor<4x8x16xf32>) -> tensor<4x8x16xf32>
return %1 : tensor<4x8x16xf32>
}
-
-// -----
-
-// CHECK-LABEL: func @sub_dynamic
-func.func @sub_dynamic(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) {
- // CHECK: linalg.sub
- // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<?x?x?xf32>, memref<?x?x?xf32>)
- // CHECK-SAME: outs(%{{.+}} : memref<?x?x?xf32>)
- linalg.sub ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>) outs(%arg2: memref<?x?x?xf32>)
- return
-}
-
-// -----
-
-// CHECK-LABEL: func @sub_static
-func.func @sub_static(%arg0: memref<4x8x16xf32>, %arg1: memref<4x8x16xf32>, %arg2: memref<4x8x16xf32>) {
- // CHECK: linalg.sub
- // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<4x8x16xf32>, memref<4x8x16xf32>)
- // CHECK-SAME: outs(%{{.+}} : memref<4x8x16xf32>)
- linalg.sub ins(%arg0, %arg1 : memref<4x8x16xf32>, memref<4x8x16xf32>) outs(%arg2: memref<4x8x16xf32>)
- return
-}
-
-// -----
-
-// CHECK-LABEL: func @sub_tensor
-func.func @sub_tensor(%arg0: tensor<4x8x16xf32>, %arg1: tensor<4x8x16xf32>) -> tensor<4x8x16xf32> {
- %0 = tensor.empty() : tensor<4x8x16xf32>
- // CHECK: linalg.sub
- // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<4x8x16xf32>, tensor<4x8x16xf32>)
- // CHECK-SAME: outs(%{{.+}} : tensor<4x8x16xf32>)
- %1 = linalg.sub ins(%arg0, %arg1 : tensor<4x8x16xf32>, tensor<4x8x16xf32>) outs(%0: tensor<4x8x16xf32>) -> tensor<4x8x16xf32>
- return %1 : tensor<4x8x16xf32>
-}
-
-// -----
-
-// CHECK-LABEL: func @mul_dynamic
-func.func @mul_dynamic(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) {
- // CHECK: linalg.mul
- // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<?x?x?xf32>, memref<?x?x?xf32>)
- // CHECK-SAME: outs(%{{.+}} : memref<?x?x?xf32>)
- linalg.mul ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>) outs(%arg2: memref<?x?x?xf32>)
- return
-}
-
-// -----
-
-// CHECK-LABEL: func @mul_static
-func.func @mul_static(%arg0: memref<4x8x16xf32>, %arg1: memref<4x8x16xf32>, %arg2: memref<4x8x16xf32>) {
- // CHECK: linalg.mul
- // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<4x8x16xf32>, memref<4x8x16xf32>)
- // CHECK-SAME: outs(%{{.+}} : memref<4x8x16xf32>)
- linalg.mul ins(%arg0, %arg1 : memref<4x8x16xf32>, memref<4x8x16xf32>) outs(%arg2: memref<4x8x16xf32>)
- return
-}
-
-// -----
-
-// CHECK-LABEL: func @mul_tensor
-func.func @mul_tensor(%arg0: tensor<4x8x16xf32>, %arg1: tensor<4x8x16xf32>) -> tensor<4x8x16xf32> {
- %0 = tensor.empty() : tensor<4x8x16xf32>
- // CHECK: linalg.mul
- // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<4x8x16xf32>, tensor<4x8x16xf32>)
- // CHECK-SAME: outs(%{{.+}} : tensor<4x8x16xf32>)
- %1 = linalg.mul ins(%arg0, %arg1 : tensor<4x8x16xf32>, tensor<4x8x16xf32>) outs(%0: tensor<4x8x16xf32>) -> tensor<4x8x16xf32>
- return %1 : tensor<4x8x16xf32>
-}
-
-// -----
-
-// CHECK-LABEL: func @div_dynamic
-func.func @div_dynamic(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) {
- // CHECK: linalg.div
- // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<?x?x?xf32>, memref<?x?x?xf32>)
- // CHECK-SAME: outs(%{{.+}} : memref<?x?x?xf32>)
- linalg.div ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>) outs(%arg2: memref<?x?x?xf32>)
- return
-}
-
-// -----
-
-// CHECK-LABEL: func @div_static
-func.func @div_static(%arg0: memref<4x8x16xf32>, %arg1: memref<4x8x16xf32>, %arg2: memref<4x8x16xf32>) {
- // CHECK: linalg.div
- // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<4x8x16xf32>, memref<4x8x16xf32>)
- // CHECK-SAME: outs(%{{.+}} : memref<4x8x16xf32>)
- linalg.div ins(%arg0, %arg1 : memref<4x8x16xf32>, memref<4x8x16xf32>) outs(%arg2: memref<4x8x16xf32>)
- return
-}
-
-// -----
-
-// CHECK-LABEL: func @div_tensor
-func.func @div_tensor(%arg0: tensor<4x8x16xf32>, %arg1: tensor<4x8x16xf32>) -> tensor<4x8x16xf32> {
- %0 = tensor.empty() : tensor<4x8x16xf32>
- // CHECK: linalg.div
- // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<4x8x16xf32>, tensor<4x8x16xf32>)
- // CHECK-SAME: outs(%{{.+}} : tensor<4x8x16xf32>)
- %1 = linalg.div ins(%arg0, %arg1 : tensor<4x8x16xf32>, tensor<4x8x16xf32>) outs(%0: tensor<4x8x16xf32>) -> tensor<4x8x16xf32>
- return %1 : tensor<4x8x16xf32>
-}
-
-// -----
-
-// CHECK-LABEL: func @div_unsigned_dynamic
-func.func @div_unsigned_dynamic(%arg0: memref<?x?x?xf32>, %arg1: memref<?x?x?xf32>, %arg2: memref<?x?x?xf32>) {
- // CHECK: linalg.div_unsigned
- // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<?x?x?xf32>, memref<?x?x?xf32>)
- // CHECK-SAME: outs(%{{.+}} : memref<?x?x?xf32>)
- linalg.div_unsigned ins(%arg0, %arg1 : memref<?x?x?xf32>, memref<?x?x?xf32>) outs(%arg2: memref<?x?x?xf32>)
- return
-}
-
-// -----
-
-// CHECK-LABEL: func @div_unsigned_static
-func.func @div_unsigned_static(%arg0: memref<4x8x16xf32>, %arg1: memref<4x8x16xf32>, %arg2: memref<4x8x16xf32>) {
- // CHECK: linalg.div_unsigned
- // CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<4x8x16xf32>, memref<4x8x16xf32>)
- // CHECK-SAME: outs(%{{.+}} : memref<4x8x16xf32>)
- linalg.div_unsigned ins(%arg0, %arg1 : memref<4x8x16xf32>, memref<4x8x16xf32>) outs(%arg2: memref<4x8x16xf32>)
- return
-}
-
-// -----
-
-// CHECK-LABEL: func @div_unsigned_tensor
-func.func @div_unsigned_tensor(%arg0: tensor<4x8x16xf32>, %arg1: tensor<4x8x16xf32>) -> tensor<4x8x16xf32> {
- %0 = tensor.empty() : tensor<4x8x16xf32>
- // CHECK: linalg.div_unsigned
- // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<4x8x16xf32>, tensor<4x8x16xf32>)
- // CHECK-SAME: outs(%{{.+}} : tensor<4x8x16xf32>)
- %1 = linalg.div_unsigned ins(%arg0, %arg1 : tensor<4x8x16xf32>, tensor<4x8x16xf32>) outs(%0: tensor<4x8x16xf32>) -> tensor<4x8x16xf32>
- return %1 : tensor<4x8x16xf32>
-}
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