Automerge: [MLIR][Linalg] Remove elemwise_unary and elemwise_binary (#147082)

RFC:
https://discourse.llvm.org/t/rfc-deprecate-linalg-elemwise-unary-and-elemwise-binary/87144

Remove the two operations and fix the tests by:
* Cleaning simple operation tests of the old ops
* Changing `linalg.elemwise_{u|bi}nary` with `linalg.{exp|add}` on
transform tests
* Changing some of the tests with `linalg.elementwise` instead, to
broaden test coverage
* Surgically removing the `elemwise_*` part in the Python tests
* Update MLIR transform examples (text and tests) with
`linalg.elementwise` instead

Nothing else changed.

Author: rengolin

mlir/docs/Tutorials/transform/Ch1.md

@@ -19,13 +19,13 @@ func.func @fc_relu(%lhs: tensor<512x512xf32>, %rhs: tensor<512x512xf32>,
                           outs(%output: tensor<512x512xf32>) -> tensor<512x512xf32>
 
   // Elementwise addition.
-  %biased = linalg.elemwise_binary { fun = #linalg.binary_fn<add> }
+  %biased = linalg.elementwise kind=#linalg.elementwise_kind<add>
     ins(%matmul, %bias : tensor<512x512xf32>, tensor<512x512xf32>)
     outs(%output : tensor<512x512xf32>) -> tensor<512x512xf32>
 
   // Elementwise max with 0 (ReLU).
   %c0f = arith.constant 0.0 : f32
-  %relued = linalg.elemwise_binary { fun = #linalg.binary_fn<max_signed> }
+  %relued = linalg.elementwise kind=#linalg.elementwise_kind<max_signed>
     ins(%biased, %c0f : tensor<512x512xf32>, f32)
     outs(%output : tensor<512x512xf32>) -> tensor<512x512xf32>
   func.return %relued : tensor<512x512xf32>
@@ -41,7 +41,7 @@ module attributes {transform.with_named_sequence} {
   transform.named_sequence @__transform_main(
       %arg0: !transform.any_op,
       %arg1: !transform.op<"linalg.matmul">,
-      %arg2: !transform.op<"linalg.elemwise_binary">):
+      %arg2: !transform.op<"linalg.elementwise">):
     transform.yield
   }
 }
@@ -72,11 +72,11 @@ To check or debug a transform sequence, it is possible to print various entities
 transform.sequence failures(propagate) {
 ^bb0(%arg0: !transform.any_op,
      %arg1: !transform.op<"linalg.matmul">,
-     %arg2: !transform.op<"linalg.elemwise_binary">):
+     %arg2: !transform.op<"linalg.elementwise">):
   transform.debug.emit_remark_at %arg1, "matmul"
       : !transform.op<"linalg.matmul">
   transform.debug.emit_remark_at %arg2, "elemwise_binaries"
-      : !transform.op<"linalg.elemwise_binary">
+      : !transform.op<"linalg.elementwise">
   transform.yield
 }
 ```
@@ -89,24 +89,24 @@ Since we don’t want to recompile the compiler every time we change a transform
 ```sh
 $ mlir-opt sequence.mlir --pass-pipeline="
     builtin.module(transform-interpreter{
-        debug-bind-trailing-args=linalg.matmul,linalg.elemwise_binary})"
+        debug-bind-trailing-args=linalg.matmul,linalg.elementwise})"
 ```
 
-The `sequence.mlir` file contains _both_ the payload IR function _and_ the transform IR sequence nested in the same module. The transform interpreter pass will apply the `@__transform_main` named sequence to the anchor operation of the pass. In our case, we also asked the interpreter pass to associate the two extra arguments of the top-level sequence with all `linalg.matmul` and `linalg.elemwise_binary` payload operations through the respective pass options. Running this pass results in the expected remarks:
+The `sequence.mlir` file contains _both_ the payload IR function _and_ the transform IR sequence nested in the same module. The transform interpreter pass will apply the `@__transform_main` named sequence to the anchor operation of the pass. In our case, we also asked the interpreter pass to associate the two extra arguments of the top-level sequence with all `linalg.matmul` and `linalg.elementwise` payload operations through the respective pass options. Running this pass results in the expected remarks:
 
 ```sh
 sequence.mlir:7:13: remark: matmul
   %matmul = linalg.matmul ins(%lhs, %rhs: tensor<512x512xf32>, tensor<512x512xf32>)
             ^
 sequence.mlir:7:13: note: see current operation: %0 = linalg.matmul ins(%arg0, %arg1 : tensor<512x512xf32>, tensor<512x512xf32>) outs(%arg3 : tensor<512x512xf32>) -> tensor<512x512xf32>
 sequence.mlir:10:13: remark: elemwise_binaries
-  %biased = linalg.elemwise_binary { fun = #linalg.binary_fn<add> }
+  %biased = linalg.elementwise kind=#linalg.elementwise_kind<add>
             ^
-sequence.mlir:10:13: note: see current operation: %1 = linalg.elemwise_binary {fun = #linalg.binary_fn<add>} ins(%0, %arg2 : tensor<512x512xf32>, tensor<512x512xf32>) outs(%arg3 : tensor<512x512xf32>) -> tensor<512x512xf32>
+sequence.mlir:10:13: note: see current operation: %1 = linalg.elementwise kind=#linalg.elementwise_kind<add>> ins(%0, %arg2 : tensor<512x512xf32>, tensor<512x512xf32>) outs(%arg3 : tensor<512x512xf32>) -> tensor<512x512xf32>
 sequence.mlir:14:13: remark: elemwise_binaries
-  %relued = linalg.elemwise_binary { fun = #linalg.binary_fn<max_signed> }
+  %relued = linalg.elementwise kind=#linalg.elementwise_kind<max_signed>
             ^
-sequence.mlir:14:13: note: see current operation: %2 = linalg.elemwise_binary {fun = #linalg.binary_fn<max_signed>} ins(%1, %cst : tensor<512x512xf32>, f32) outs(%arg3 : tensor<512x512xf32>) -> tensor<512x512xf32>
+sequence.mlir:14:13: note: see current operation: %2 = linalg.elementwise kind=#linalg.elementwise_kind<max_signed>> ins(%1, %cst : tensor<512x512xf32>, f32) outs(%arg3 : tensor<512x512xf32>) -> tensor<512x512xf32>
 ```
 
 Note that `%arg2` is associated with both elementwise payload operations. Any handle is associated with a list of entities. Individual transformations may or may not care about the order of elements in that list.
@@ -121,7 +121,7 @@ module attributes {transform.with_named_sequence} {
   transform.named_sequence @__transform_main(
        %arg0: !transform.any_op,
        %arg1: !transform.op<"linalg.matmul">,
-       %arg2: !transform.op<"linalg.elemwise_binary">) {
+       %arg2: !transform.op<"linalg.elementwise">) {
     // The actual tiling transformation takes tile sizes as attributes.
     %loop, %tiled = transform.structured.tile_using_forall %arg1
                     tile_sizes [4, 32]
@@ -163,10 +163,10 @@ func.func @fc_relu(%arg0: tensor<512x512xf32>,
           : tensor<4x32xf32> into tensor<512x512xf32>
     }
   }
-  %1 = linalg.elemwise_binary {fun = #linalg.binary_fn<add>}
+  %1 = linalg.elementwise kind=#linalg.elementwise_kind<add>>
     ins(%0, %arg2 : tensor<512x512xf32>, tensor<512x512xf32>)
     outs(%arg3 : tensor<512x512xf32>) -> tensor<512x512xf32>
-  %2 = linalg.elemwise_binary {fun = #linalg.binary_fn<max_signed>}
+  %2 = linalg.elementwise kind=#linalg.elementwise_kind<max_signed>>
     ins(%1, %cst : tensor<512x512xf32>, f32)
     outs(%arg3 : tensor<512x512xf32>) -> tensor<512x512xf32>
   return %2 : tensor<512x512xf32>
@@ -185,7 +185,7 @@ module attributes {transform.with_named_sequence} {
   transform.named_sequence @__transform_main(
        %arg0: !transform.any_op,
        %arg1: !transform.op<"linalg.matmul">,
-       %arg2: !transform.op<"linalg.elemwise_binary">) {
+       %arg2: !transform.op<"linalg.elementwise">) {
     // The actual tiling transformation takes tile sizes as attributes.
     %loop, %tiled = transform.structured.tile_using_forall %arg1 tile_sizes [4, 32]
         : (!transform.op<"linalg.matmul">) -> (!transform.any_op, !transform.any_op)
@@ -219,7 +219,7 @@ module attributes {transform.with_named_sequence} {
   transform.named_sequence @__transform_main
        %arg0: !transform.any_op,
        %arg1: !transform.op<"linalg.matmul">,
-       %arg2: !transform.op<"linalg.elemwise_binary">) {
+       %arg2: !transform.op<"linalg.elementwise">) {
     // We can cast one type to another as long as operations are compatible
     // with both types. This creates "aliasing" handles.
     %casted = transform.cast %arg1 : !transform.op<"linalg.matmul">
@@ -248,7 +248,7 @@ sequence.mlir:28:3: error: op uses a handle invalidated by a previously executed
   transform.debug.emit_remark_at %matmul, "elemwise_binaries" : !transform.op<"linalg.matmul">
   ^
 sequence.mlir:21:29: note: handle to invalidated ops
-^bb0(%root: !transform.any_op, %matmul: !transform.op<"linalg.matmul">, %elemwise: !transform.op<"linalg.elemwise_binary">):
+^bb0(%root: !transform.any_op, %matmul: !transform.op<"linalg.matmul">, %elemwise: !transform.op<"linalg.elementwise">):
                             ^
 sequence.mlir:27:19: note: invalidated by this transform op that consumes its operand #0 and invalidates all handles to payload IR entities associated with this operand and entities nested in them
   %loop, %tiled = transform.structured.tile_using_forall %mm tile_sizes [4, 32]
@@ -263,12 +263,12 @@ module attributes {transform.with_named_sequence} {
   transform.named_sequence @__transform_main(
        %arg0: !transform.any_op,
        %arg1: !transform.op<"linalg.matmul">,
-       %arg2: !transform.op<"linalg.elemwise_binary">) {
+       %arg2: !transform.op<"linalg.elementwise">) {
     // Since the %arg2 handle is associated with both elementwise operations,
     // we need to split it into two handles so we can target only the second
     // elementwise operation.
     %add, %max = transform.split_handle %arg2
-        : (!transform.op<"linalg.elemwise_binary">)
+        : (!transform.op<"linalg.elementwise">)
         -> (!transform.any_op, !transform.any_op)
 
     // The actual tiling transformation takes tile sizes as attributes. It
@@ -308,12 +308,12 @@ module attributes {transform.with_named_sequence} {
   transform.named_sequence @__transform_main(
        %arg0: !transform.any_op,
        %arg1: !transform.op<"linalg.matmul">,
-       %arg2: !transform.op<"linalg.elemwise_binary">) {
+       %arg2: !transform.op<"linalg.elementwise">) {
     // Since the %arg2 handle is associated with both elementwise operations,
     // we need to split it into two handles so we can target only the second
     // elementwise operation.
     %add, %max = transform.split_handle %arg2
-        : (!transform.op<"linalg.elemwise_binary">)
+        : (!transform.op<"linalg.elementwise">)
           -> (!transform.any_op, !transform.any_op)
 
     // The actual tiling transformation takes tile sizes as attributes. It
@@ -384,7 +384,7 @@ test/Examples/transform/Ch1/invalidation-2.mlir:106:18: note: invalidated by thi
   %func, %call = transform.loop.outline %outline_target {func_name = "outlined"}
                  ^
 test/Examples/transform/Ch1/invalidation-2.mlir:24:13: note: ancestor payload op
-  %biased = linalg.elemwise_binary { fun = #linalg.binary_fn<add> }
+  %biased = linalg.elementwise kind=#linalg.elementwise_kind<add>
             ^
 test/Examples/transform/Ch1/invalidation-2.mlir:24:13: note: nested payload op
   %matmul = linalg.matmul ins(%lhs, %rhs: tensor<512x512xf32>, tensor<512x512xf32>)

mlir/docs/Tutorials/transform/Ch2.md

@@ -290,12 +290,12 @@ module attributes {transform.with_named_sequence} {
   transform.named_sequence @__transform_main(
       %arg0: !transform.any_op,
       %arg1: !transform.op<"linalg.matmul">,
-      %arg2: !transform.op<"linalg.elemwise_binary">) {
+      %arg2: !transform.op<"linalg.elementwise">) {
     // Since the %arg2 handle is associated with both elementwise operations,
     // we need to split it into two handles so we can target only the second
     // elementwise operation.
     %add, %max = transform.split_handle %arg2
-        : (!transform.op<"linalg.elemwise_binary">)
+        : (!transform.op<"linalg.elementwise">)
         -> (!transform.any_op, !transform.any_op)
 
     // The actual tiling transformation takes tile sizes as attributes. It

mlir/docs/Tutorials/transform/Ch4.md

@@ -42,13 +42,13 @@ func.func @fc_relu(%lhs: tensor<512x512xf32>, %rhs: tensor<512x512xf32>,
             outs(%output: tensor<512x512xf32>) -> tensor<512x512xf32>
 
   // Elementwise addition.
-  %biased = linalg.elemwise_binary { fun = #linalg.binary_fn<add> }
+  %biased = linalg.elementwise kind=#linalg.elementwise_kind<add>
     ins(%matmul, %bias : tensor<512x512xf32>, tensor<512x512xf32>)
     outs(%output : tensor<512x512xf32>) -> tensor<512x512xf32>
 
   // Elementwise max with 0 (ReLU).
   %c0f = arith.constant 0.0 : f32
-  %relued = linalg.elemwise_binary { fun = #linalg.binary_fn<max_signed> }
+  %relued = linalg.elementwise kind=#linalg.elementwise_kind<max_signed>
     ins(%biased, %c0f : tensor<512x512xf32>, f32)
     outs(%output : tensor<512x512xf32>) -> tensor<512x512xf32>
   func.return %relued : tensor<512x512xf32>
@@ -59,7 +59,7 @@ func.func @fc_relu(%lhs: tensor<512x512xf32>, %rhs: tensor<512x512xf32>,
 
 In Chapter 1, we were calling the test transform interpreter pass with
 additional arguments, `bind-first-extra-to-ops=linalg.matmul
-bind-second-extra-to-ops=linalg.elemwise_binary`, to provide initial
+bind-second-extra-to-ops=linalg.elementwise`, to provide initial
 associations for operation handles. Instead, we can use match operations to
 discover relevant operations in the payload IR. Match operations can be combined
 with “regular” transform operations using, e.g., the
@@ -97,7 +97,7 @@ module @transforms attributes { transform.with_named_sequence } {
   // rewriter sequence on success.
   transform.named_sequence @match_elemwise(
       %entry: !transform.any_op {transform.readonly}) -> !transform.any_op {
-    transform.match.operation_name %entry ["linalg.elemwise_binary"]
+    transform.match.operation_name %entry ["linalg.elementwise"]
       : !transform.any_op
     transform.yield %entry : !transform.any_op
   }
@@ -127,7 +127,7 @@ module @transforms attributes { transform.with_named_sequence } {
 This script can be executed using the non-test interpreter pass running on the
 root operation of the translation unit without additional flags: `mlir-opt
 --transform-interpreter`. It will emit corresponding remarks at
-`linalg.elemwise_binary` and `linalg.matmul` operations. In debug builds, the
+`linalg.elementwise` and `linalg.matmul` operations. In debug builds, the
 infrastructure provides a convenient method to understand the matching process
 by passing `-debug-only=transform-matcher` to `mlir-opt` or a derived tool. It
 will print the silenceable failure messages produced by the match operations
@@ -169,7 +169,7 @@ transform.named_sequence @match_matmul_elemwise(
     %last: !transform.any_op {transform.readonly})
     -> (!transform.any_op, !transform.any_op, !transform.any_op) {
   // The last operation must be an elementwise binary.
-  transform.match.operation_name %last ["linalg.elemwise_binary"]
+  transform.match.operation_name %last ["linalg.elementwise"]
     : !transform.any_op
   // Its first operand must be defined by another operation, to which we
   // will get a handle here. We are guaranteed that the first operand exists
@@ -179,7 +179,7 @@ transform.named_sequence @match_matmul_elemwise(
   %middle = transform.get_producer_of_operand %last[0]
     : (!transform.any_op) -> !transform.any_op
   // The defining operation must itself be an elementwise binary.
-  transform.match.operation_name %middle ["linalg.elemwise_binary"]
+  transform.match.operation_name %middle ["linalg.elementwise"]
     : !transform.any_op
   // And the first operand of that operation must be defined by yet another
   // operation.
@@ -399,7 +399,7 @@ transform.named_sequence @match_matmul_elemwise(
     -> (!transform.any_op, !transform.any_op, !transform.any_op,
         !transform.param<i32>) {
   // The last operation must be an elementwise binary.
-  transform.match.operation_name %last ["linalg.elemwise_binary"]
+  transform.match.operation_name %last ["linalg.elementwise"]
     : !transform.any_op
 
   // One of its operands must be defined by another operation, to which we
@@ -413,7 +413,7 @@ transform.named_sequence @match_matmul_elemwise(
     %def = transform.get_defining_op %operand
       : (!transform.any_value) -> !transform.any_op
     // The defining operation must itself be an elementwise binary.
-    transform.match.operation_name %def ["linalg.elemwise_binary"]
+    transform.match.operation_name %def ["linalg.elementwise"]
       : !transform.any_op
     transform.yield %def : !transform.any_op
   }

mlir/docs/Tutorials/transform/ChH.md

@@ -290,7 +290,7 @@ scf.forall (%co) in (2) {
   scf.forall (%n, %y, %xo) in (5, 80, 20) {
     tensor.extract_slice
     // Implicit dimensions [ni=0:1, y=0:1, xi=0:5, ci=0:64]
-    %relued = linalg.elemwise_binary { fun = #linalg.binary_fn<max_signed> } // ...
+    %relued = linalg.elementwise kind=#linalg.elementwise_kind<max_signed> // ...
     scf.forall.in_parallel {
       tensor.parallel_insert_slice // ...
     }

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