[MLIR][Linalg] Remove matmul_transpose variants (#147961)

Removes the `(batch_)matmul_transpose_{a|b}` variants from OpDSL and
replace it with `matmul affine_maps [...]` whenever appropriate. This is
in line with the
[plan](https://discourse.llvm.org/t/rfc-op-explosion-in-linalg/82863),
and can be done since #104783 merged.

See:
https://discourse.llvm.org/t/deprecate-batch-matmul-transpose-a-b-linalg-operations/87245

Issues investigated:
* pad transform tests that could use `matmul` instead, so change to
that.
* ArmSME test using transpose actually needed it, so changed to `matmul`
+ affine maps.

Arm tests validated by @banach-space (thanks!!).

Author: rengolin

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

@@ -373,42 +373,6 @@ def quantized_matmul(
     )
 
 
-@linalg_structured_op
-def matmul_transpose_a(
-    A=TensorDef(T1, S.K, S.N),
-    B=TensorDef(T2, S.K, S.M),
-    C=TensorDef(U, S.M, S.N, output=True),
-    cast=TypeFnAttrDef(default=TypeFn.cast_signed),
-):
-    """Performs a matrix multiplication of two 2D inputs with lhs operand
-    transposed.
-
-    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] += cast(U, A[D.k, D.m]) * cast(U, B[D.k, D.n])
-
-
-@linalg_structured_op
-def matmul_transpose_b(
-    A=TensorDef(T1, S.M, S.K),
-    B=TensorDef(T2, S.N, S.K),
-    C=TensorDef(U, S.M, S.N, output=True),
-    cast=TypeFnAttrDef(default=TypeFn.cast_signed),
-):
-    """Performs a matrix multiplication of two 2D inputs with rhs operand
-    transposed.
-
-    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] += cast(U, A[D.m, D.k]) * cast(U, B[D.n, D.k])
-
-
 @linalg_structured_op
 def mmt4d(
     lhs=TensorDef(TV.LhsType, S.M, S.K, S.M0, S.K0),
@@ -453,44 +417,6 @@ def batch_mmt4d(
     ) * TypeFn.cast_signed(TV.AccumType, rhs[D.b, D.n, D.k, D.n0, D.k0])
 
 
-@linalg_structured_op
-def batch_matmul_transpose_a(
-    A=TensorDef(T1, Batch, S.K, S.M),
-    B=TensorDef(T2, Batch, S.K, S.N),
-    C=TensorDef(U, Batch, S.M, S.N, output=True),
-):
-    """Performs a batched matrix multiplication of two 3D inputs where lhs operand
-    has its non-batch dimensions transposed.
-
-    Numeric casting is performed on the operands to the inner multiply, promoting
-    them to the same data type as the accumulator/output.
-    """
-    domain(D.b, D.m, D.n, D.k)
-    implements(ContractionOpInterface)
-    C[D.b, D.m, D.n] += TypeFn.cast_signed(U, A[D.b, D.k, D.m]) * TypeFn.cast_signed(
-        U, B[D.b, D.k, D.n]
-    )
-
-
-@linalg_structured_op
-def batch_matmul_transpose_b(
-    A=TensorDef(T1, Batch, S.M, S.K),
-    B=TensorDef(T2, Batch, S.N, S.K),
-    C=TensorDef(U, Batch, S.M, S.N, output=True),
-):
-    """Performs a batched matrix multiplication of two 3D inputs where rhs operand
-    has its non-batch dimensions transposed.
-
-    Numeric casting is performed on the operands to the inner multiply, promoting
-    them to the same data type as the accumulator/output.
-    """
-    domain(D.b, D.m, D.n, D.k)
-    implements(ContractionOpInterface)
-    C[D.b, D.m, D.n] += TypeFn.cast_signed(U, A[D.b, D.m, D.k]) * TypeFn.cast_signed(
-        U, B[D.b, D.n, D.k]
-    )
-
-
 @linalg_structured_op
 def quantized_batch_matmul(
     A=TensorDef(T1, Batch, S.M, S.K),
@@ -512,25 +438,6 @@ def quantized_batch_matmul(
     ) * (TypeFn.cast_signed(U, B[D.b, D.k, D.n]) - TypeFn.cast_signed(U, BZp))
 
 
-@linalg_structured_op
-def batch_reduce_matmul(
-    A=TensorDef(T1, Batch, S.M, S.K),
-    B=TensorDef(T2, Batch, S.K, S.N),
-    C=TensorDef(U, S.M, S.N, output=True),
-):
-    """Performs a batch-reduce matrix multiplication of two 3D inputs.
-    The partial multiplication results are reduced into a 2D output.
-
-    Numeric casting is performed on the operands to the inner multiply, promoting
-    them to the same data type as the accumulator/output.
-    """
-    domain(D.b, D.m, D.n, D.k)
-    implements(ContractionOpInterface)
-    C[D.m, D.n] += TypeFn.cast_signed(U, A[D.b, D.m, D.k]) * TypeFn.cast_signed(
-        U, B[D.b, D.k, D.n]
-    )
-
-
 @linalg_structured_op
 def matvec(
     A=TensorDef(T1, S.M, S.N), y=TensorDef(T2, S.N), x=TensorDef(U, S.M, output=True)