Remove redundant linalg.matmul_signed (#98615)

`linalg.matmul` already has an attribute for casts, defaults to signed
but allowed unsigned, so the operation `linalg.matmul_unsigned` is
redundant. The generalization test has an example on how to lower to
unsigned matmul in linalg.

This is the first PR in a list of many that will simplify the linalg
operations by using similar attributes.

Ref:
https://discourse.llvm.org/t/rfc-transpose-attribute-for-linalg-matmul-operations/80092

Author: rengolin

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

@@ -1137,74 +1137,6 @@ structured_op: !LinalgStructuredOpConfig
                 - !ScalarExpression
                   scalar_arg: B
 --- !LinalgOpConfig
-metadata: !LinalgOpMetadata
-  name: matmul_unsigned
-  cpp_class_name: MatmulUnsignedOp
-  doc: |-
-    Performs an unsigned matrix multiplication of two 2D inputs.
-
-    Numeric casting is performed on the operands to the inner multiply, promoting
-    them to the same data type as the accumulator/output.
-  implements:
-  - LinalgContractionOpInterface
-structured_op: !LinalgStructuredOpConfig
-  args:
-  - !LinalgOperandDefConfig
-    name: A
-    kind: input_tensor
-    type_var: T1
-    shape_map: affine_map<()[s0, s1, s2] -> (s0, s1)>
-  - !LinalgOperandDefConfig
-    name: B
-    kind: input_tensor
-    type_var: T2
-    shape_map: affine_map<()[s0, s1, s2] -> (s1, s2)>
-  - !LinalgOperandDefConfig
-    name: C
-    kind: output_tensor
-    type_var: U
-    shape_map: affine_map<()[s0, s1, s2] -> (s0, s2)>
-  indexing_maps: !LinalgIndexingMapsConfig
-    static_indexing_maps:
-    - affine_map<(d0, d1, d2)[s0, s1, s2] -> (d0, d2)>
-    - affine_map<(d0, d1, d2)[s0, s1, s2] -> (d2, d1)>
-    - affine_map<(d0, d1, d2)[s0, s1, s2] -> (d0, d1)>
-  iterator_types:
-  - parallel
-  - parallel
-  - reduction
-  assignments:
-  - !ScalarAssign
-    arg: C
-    value: !ScalarExpression
-      scalar_fn:
-        kind: binary
-        fn_name: add
-        operands:
-        - !ScalarExpression
-          scalar_arg: C
-        - !ScalarExpression
-          scalar_fn:
-            kind: binary
-            fn_name: mul
-            operands:
-            - !ScalarExpression
-              scalar_fn:
-                kind: type
-                fn_name: cast_unsigned
-                type_var: U
-                operands:
-                - !ScalarExpression
-                  scalar_arg: A
-            - !ScalarExpression
-              scalar_fn:
-                kind: type
-                fn_name: cast_unsigned
-                type_var: U
-                operands:
-                - !ScalarExpression
-                  scalar_arg: B
---- !LinalgOpConfig
 metadata: !LinalgOpMetadata
   name: quantized_matmul
   cpp_class_name: QuantizedMatmulOp

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

@@ -388,24 +388,6 @@ def matmul(
     C[D.m, D.n] += cast(U, A[D.m, D.k]) * cast(U, B[D.k, D.n])
 
 
-@linalg_structured_op
-def matmul_unsigned(
-    A=TensorDef(T1, S.M, S.K),
-    B=TensorDef(T2, S.K, S.N),
-    C=TensorDef(U, S.M, S.N, output=True),
-):
-    """Performs an unsigned matrix multiplication of two 2D inputs.
-
-    Numeric casting is performed on the operands to the inner multiply, promoting
-    them to the same data type as the accumulator/output.
-    """
-    domain(D.m, D.n, D.k)
-    implements(ContractionOpInterface)
-    C[D.m, D.n] += TypeFn.cast_unsigned(U, A[D.m, D.k]) * TypeFn.cast_unsigned(
-        U, B[D.k, D.n]
-    )
-
-
 @linalg_structured_op
 def quantized_matmul(
     A=TensorDef(T1, S.M, S.K),

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

@@ -79,8 +79,9 @@ func.func @generalize_matmul_tensor_f16f64i32(%A : tensor<16x8xf16>, %B: tensor<
 // -----
 
 func.func @generalize_matmul_unsigned_tensor_i16i64i32(%A : tensor<16x8xi16>, %B: tensor<8x32xi64>, %C: tensor<16x32xi32>) -> tensor<16x32xi32> {
-  %0 = linalg.matmul_unsigned ins(%A, %B: tensor<16x8xi16>, tensor<8x32xi64>)
-                              outs(%C: tensor<16x32xi32>) -> tensor<16x32xi32>
+  %0 = linalg.matmul { cast = #linalg.type_fn<cast_unsigned> }
+                       ins(%A, %B: tensor<16x8xi16>, tensor<8x32xi64>)
+                       outs(%C: tensor<16x32xi32>) -> tensor<16x32xi32>
   return %0: tensor<16x32xi32>
 }
 
@@ -92,8 +93,9 @@ func.func @generalize_matmul_unsigned_tensor_i16i64i32(%A : tensor<16x8xi16>, %B
 // -----
 
 func.func @generalize_matmul_unsigned_tensor_i16i64f32(%A : tensor<16x8xi16>, %B: tensor<8x32xi64>, %C: tensor<16x32xf32>) -> tensor<16x32xf32> {
-  %0 = linalg.matmul_unsigned ins(%A, %B: tensor<16x8xi16>, tensor<8x32xi64>)
-                              outs(%C: tensor<16x32xf32>) -> tensor<16x32xf32>
+  %0 = linalg.matmul { cast = #linalg.type_fn<cast_unsigned> }
+                       ins(%A, %B: tensor<16x8xi16>, tensor<8x32xi64>)
+                       outs(%C: tensor<16x32xf32>) -> tensor<16x32xf32>
   return %0: tensor<16x32xf32>
 }
 
@@ -105,8 +107,9 @@ func.func @generalize_matmul_unsigned_tensor_i16i64f32(%A : tensor<16x8xi16>, %B
 // -----
 
 func.func @generalize_matmul_unsigned_tensor_f16f64i32(%A : tensor<16x8xf16>, %B: tensor<8x32xf64>, %C: tensor<16x32xi32>) -> tensor<16x32xi32> {
-  %0 = linalg.matmul_unsigned ins(%A, %B: tensor<16x8xf16>, tensor<8x32xf64>)
-                              outs(%C: tensor<16x32xi32>) -> tensor<16x32xi32>
+  %0 = linalg.matmul { cast = #linalg.type_fn<cast_unsigned> }
+                       ins(%A, %B: tensor<16x8xf16>, tensor<8x32xf64>)
+                       outs(%C: tensor<16x32xi32>) -> tensor<16x32xi32>
   return %0: tensor<16x32xi32>
 }