[MLIR][Linalg] Introduce broadcast/transpose semantic to batch_matmul (#122275)

Goals:
1. To add syntax and semantic to 'batch_matmul' without changing any of
the existing syntax expectations for current usage. batch_matmul is
still just batch_matmul.

2. Move the definition of batch_matmul from linalg OpDsl to tablegen ODS
infra.

Scope of this patch:
To expose broadcast and transpose semantics on the 'batch_matmul'.

The broadcast and transpose semantic are as follows:

By default, 'linalg.batch_matmul' behavior will remain as is. Broadcast
and Transpose semantics can be applied by specifying the explicit
attribute 'indexing_maps' as shown below. This is a list attribute, so
the list must include all the maps if specified.

    Example Transpose:
    ```
    linalg.batch_matmul indexing_maps = [
affine_map< (d0, d1, d2, d3) -> (d0, d3, d1)>, //transpose
                   affine_map< (d0, d1, d2, d3) -> (d0, d3, d2)>,
                   affine_map< (d0, d1, d2, d3) -> (d0, d1, d2)>
                   ]
ins (%arg0, %arg1: memref<2x5x3xf32>,memref<2x5x7xf32>)
                   outs (%arg2: memref<2x3x7xf32>)
    ```

    Example Broadcast:
    ```
    linalg.batch_matmul indexing_maps = [
affine_map< (d0, d1, d2, d3) -> (d3)>, //broadcast
                       affine_map< (d0, d1, d2, d3) -> (d0, d3, d2)>,
                       affine_map< (d0, d1, d2, d3) -> (d0, d1, d2)>
                     ]
                     ins (%arg0, %arg1: memref<5xf32>,memref<2x5x7xf32>)
                     outs (%arg2: memref<2x3x7xf32>)
    ```

    Example Broadcast and transpose:
    ```
    linalg.batch_matmul indexing_maps = [
affine_map< (d0, d1, d2, d3) -> (d1, d3)>, //broadcast
affine_map< (d0, d1, d2, d3) -> (d0, d2, d3)>, //transpose
                       affine_map< (d0, d1, d2, d3) -> (d0, d1, d2)>
                     ]
ins (%arg0, %arg1: memref<3x5xf32>, memref<2x7x5xf32>)
                     outs (%arg2: memref<2x3x7xf32>)
    ```

RFCs and related PR:

https://discourse.llvm.org/t/rfc-linalg-opdsl-constant-list-attribute-definition/80149
https://discourse.llvm.org/t/rfc-op-explosion-in-linalg/82863
https://discourse.llvm.org/t/rfc-mlir-linalg-operation-tree/83586
llvm/llvm-project#115319

Author: shahidact

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

@@ -484,24 +484,6 @@ 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(
-    A=TensorDef(T1, Batch, S.M, S.K),
-    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.
-
-    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.k, D.n]
-    )
-
-
 @linalg_structured_op
 def batch_matmul_transpose_a(
     A=TensorDef(T1, Batch, S.K, S.M),