[MLIR][Linalg] Remove elemwise_unary and elemwise_binary #147082
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@llvm/pr-subscribers-mlir @llvm/pr-subscribers-mlir-linalg Author: Renato Golin (rengolin) ChangesRFC: https://discourse.llvm.org/t/rfc-deprecate-linalg-elemwise-unary-and-elemwise-binary/87144 Remove the two operations and fix the tests by:
Nothing else changed. Patch is 54.07 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/147082.diff 15 Files Affected:
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
index 6344861c53ac5..3637147c5a90d 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml
+++ b/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
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 48e724d80c926..1b359da40a291 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
@@ -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),
diff --git a/mlir/test/Dialect/Linalg/generalize-named-polymorphic-ops.mlir b/mlir/test/Dialect/Linalg/generalize-named-polymorphic-ops.mlir
index bbd6e0fc8e2cc..290c6c7c36f76 100644
--- a/mlir/test/Dialect/Linalg/generalize-named-polymorphic-ops.mlir
+++ b/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>
diff --git a/mlir/test/Dialect/Linalg/invalid.mlir b/mlir/test/Dialect/Linalg/invalid.mlir
index 964681d7dcd92..da1dfc7b6a624 100644
--- a/mlir/test/Dialect/Linalg/invalid.mlir
+++ b/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>)
{
diff --git a/mlir/test/Dialect/Linalg/library-calls.mlir b/mlir/test/Dialect/Linalg/library-calls.mlir
index 1fa675d8b4b68..77c9d4a911447 100644
--- a/mlir/test/Dialect/Linalg/library-calls.mlir
+++ b/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
-}
diff --git a/mlir/test/Dialect/Linalg/match-ops-interpreter.mlir b/mlir/test/Dialect/Linalg/match-ops-interpreter.mlir
index bfe7a07cb38a5..618ba3402ff52 100644
--- a/mlir/test/Dialect/Linalg/match-ops-interpreter.mlir
+++ b/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
}
}
diff --git a/mlir/test/Dialect/Linalg/one-shot-bufferize-analysis.mlir b/mlir/test/Dialect/Linalg/one-shot-bufferize-analysis.mlir
index 5b7c2baf9d84f..a0922bdfcfbe4 100644
--- a/mlir/test/Dialect/Linalg/one-shot-bufferize-analysis.mlir
+++ b/mlir/test/Dialect/Linalg/one-shot-bufferize-analysis.mlir
@@ -1,43 +1,5 @@
// RUN: mlir-opt %s -one-shot-bufferize="bufferize-function-boundaries test-analysis-only" -split-input-file | FileCheck %s
-// CHECK-LABEL: @elementwise_no_conflict
-func.func @elementwise_no_conflict(%a: tensor<5xf32>,
- %b: tensor<5xf32>) -> tensor<5xf32> {
- // CHECK: linalg.elemwise_binary
- // CHECK-SAME: {__inplace_operands_attr__ = ["true", "true", "true"], fun = #linalg.binary_fn<add>}
- %0 = linalg.elemwise_binary {fun = #linalg.binary_fn<add>}
- ins(%a, %b : tensor<5xf32>, tensor<5xf32>)
- outs(%a : tensor<5xf32>) -> tensor<5xf32>
- return %0 : tensor<5xf32>
-}
-
-// -----
-
-// CHECK-LABEL: @elementwise_no_conflict_2
-func.func @elementwise_no_conflict_2(%a: tensor<5xf32>) -> tensor<5xf32> {
- // CHECK: linalg.elemwise_binary
- // CHECK-SAME: {__inplace_operands_attr__ = ["true", "true", "true"], fun = #linalg.binary_fn<add>}
- %0 = linalg.elemwise_binary {fun = #linalg.binary_fn<add>}
- ins(%a, %a : tensor<5xf32>, tensor<5xf32>)
- outs(%a : tensor<5xf32>) -> tensor<5xf32>
- return %0 : tensor<5xf32>
-}
-
-// -----
-
-// CHECK-LABEL: @elementwise_no_conflict_3
-func.func @elementwise_no_conflict_3(%a: tensor<5xf32>) -> tensor<5xf32> {
- %c0f = arith.constant 1.0 : f32
- // CHECK: linalg.elemwise_binary
- // CHECK-SAME: {__inplace_operands_attr__ = ["true", "none", "true"], fun = #linalg.binary_fn<add>}
- %0 = linalg.elemwise_binary {fun = #linalg.binary_fn<add>}
- ins(%a, %c0f : tensor<5xf32>, f32)
- outs(%a : tensor<5xf32>) -> tensor<5xf32>
- return %0 : tensor<5xf32>
-}
-
-// -----
-
func.func @not_elementwise(%a: tensor<5x6xf32>) -> tensor<5x6xf32> {
%cst = arith.constant 5.0 : f32
// CHECK: tensor.extract_slice
diff --git a/mlir/test/Dialect/Linalg/transform-op-fuse-into-containing.mlir b/mlir/test/Dialect/Linalg/transform-op-fuse-into-containing.mlir
index 5bdb5073ee865..312468970ae6d 100644
--- a/mlir/test/Dialect/Linalg/transform-op-fuse-into-containing.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-fuse-into-containing.mlir
@@ -26,8 +26,8 @@ module {
// CHECK: %[[T1:.*]] = linalg.fill {{.*}} outs(%[[T0]]
%6 = tensor.extract_slice %0[%3] [%4] [1] : tensor<?xf32> to tensor<?xf32>
- // CHECK: %[[T2:.*]] = linalg.elemwise_unary ins(%[[T1]]
- %7 = linalg.elemwise_unary ins(%6 : tensor<?xf32>) outs(%5 : tensor<?xf32>) -> tensor<?xf32>
+ // CHECK: %[[T2:.*]] = linalg.exp ins(%[[T1]]
+ %7 = linalg.exp ins(%6 : tensor<?xf32>) outs(%5 : tensor<?xf32>) -> tensor<?xf32>
scf.forall.in_parallel {
tensor.parallel_insert_slice %7 into %o[%3] [%4] [1] : tensor<?xf32> into tensor<?xf32>
}
@@ -76,8 +76,8 @@ module {
%4 = affine.min #map2(%arg3)[%arg0]
%5 = tensor.extract_slice %o[%3] [%4] [1] : tensor<64xf32> to tensor<?xf32>
- // CHECK: %[[T2:.*]] = linalg.elemwise_unary ins(%[[INIT_TENSOR]]
- %7 = linalg.elemwise_unary ins(%0 : tensor<?xf32>) outs(%5 : tensor<?xf32>) -> tensor<?xf32>
+ // CHECK: %[[T2:.*]] = linalg.exp ins(%[[INIT_TENSOR]]
+ %7 = linalg.exp ins(%0 : tensor<?xf32>) outs(%5 : tensor<?xf32>) -> tensor<?xf32>
scf.forall.in_parallel {
tensor.parallel_insert_slice %7 into %o[%3] [%4] [1] : tensor<?xf32> into tensor<64xf32>
}
@@ -177,8 +177,8 @@ module {
// CHECK: %[[T1:.*]] = linalg.fill {{.*}} outs(%[[T0]]
%6 = tensor.extract_slice %arg1[%3] [%4] [1] : tensor<?xf32> to tensor<?xf32>
- // CHECK: %[[T2:.*]] = linalg.elemwise_unary {{.*}} outs(%[[T1]]
- %7 = linalg.elemwise_unary ins(%6 : tensor<?xf32>) outs(%5 : tensor<?xf32>) -> tensor<?xf32>
+ // CHECK: %[[T2:.*]] = linalg.exp {{.*}} outs(%[[T1]]
+ %7 = linalg.exp ins(%6 : tensor<?xf32>) outs(%5 : tensor<?xf32>) -> tensor<?xf32>
scf.forall.in_parallel {
tensor.parallel_insert_slice %7 into %o[%3] [%4] [1] : tensor<?xf32> into tensor<?xf32>
}
@@ -228,8 +228,8 @@ module {
// CHECK: %[[T2:.*]] = linalg.fill {{.*}} outs(%[[T1]]
%6 = tensor.extract_slice %0[%3] [%4] [1] : tensor<?xf32> to tensor<?xf32>
- // CHECK: %[[T3:.*]] = linalg.elemwise_unary ins(%[[T2]] : tensor<?xf32>) outs(%[[T0]] : tensor<?xf32>)
- %7 = linalg.elemwise_unary ins(%6 : tensor<?xf32>) outs(%5 : tensor<?xf32>) -> tensor<?xf32>
+ // CHECK: %[[T3:.*]] = linalg.exp ins(%[[T2]] : tensor<?xf32>) outs(%[[T0]] : tensor<?xf32>)
+ %7 = linalg.exp ins(%6 : tensor<?xf32>) outs(%5 : tensor<?xf32>) -> tensor<?xf32>
scf.forall.in_parallel {
tensor.parallel_insert_slice %7 into %o[%3] [%4] [1] : tensor<?xf32> into tensor<?xf32>
}
@@ -261,7 +261,7 @@ module {
%c2 = arith.constant...
[truncated]
|
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That's what's failing the CI on both Linux and Windows. I'm happy that it caught the issue. :) |
Examples moved to |
|
@makslevental @rolfmorel how do I change the Python interfaces to not try to create these instructions anymore? |
Just delete them from https://github.com/llvm/llvm-project/blob/main/mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py |
|
Ok, that's all done. Hopefully no one else is using them, but I'll wait a few days just in case. |
Looks like just me and that's fine (I'll remove too). |
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RFC: https://discourse.llvm.org/t/rfc-deprecate-linalg-elemwise-unary-and-elemwise-binary/87144
Remove the two operations and fix the tests by:
linalg.elemwise_{u|bi}narywithlinalg.{exp|add}on transform testslinalg.elementwiseinstead, to broaden test coverageelemwise_*part in the Python testslinalg.elementwiseinsteadNothing else changed.