Reorder mmt4d r.h.s operand layout

[asaadaldien]

Summary

Switch r.h.s operand layout (n1, k1, n0, k0) -> (n1, k1, k0, n0)
which is more consistant with scalar-vector products vectorization
and elementates operand transpose.

[asaadaldien]

update TensorDef

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

- !LinalgOperandDefConfig
     name: lhs
     usage: InputOperand
     type_var: LhsType
     shape_map: affine_map<()[s0, s1, s2, s3, s4, s5] -> (s0, s1, s2, s3)>
   - !LinalgOperandDefConfig
     name: rhs
     usage: InputOperand
     type_var: RhsType
-    shape_map: affine_map<()[s0, s1, s2, s3, s4, s5] -> (s4, s1, s5, s3)>
+    shape_map: affine_map<()[s0, s1, s2, s3, s4, s5] -> (s4, s1, s3, s5)>
   - !LinalgOperandDefConfig
     name: accum
     usage: OutputOperand
     type_var: AccumType
     shape_map: affine_map<()[s0, s1, s2, s3, s4, s5] -> (s0, s4, s2, s5)>
   indexing_maps: !LinalgIndexingMapsConfig
     static_indexing_maps:
     - affine_map<(d0, d1, d2, d3, d4, d5)[s0, s1, s2, s3, s4, s5] -> (d0, d4, d2,
       d5)>
-    - affine_map<(d0, d1, d2, d3, d4, d5)[s0, s1, s2, s3, s4, s5] -> (d1, d4, d3,
-      d5)>
+    - affine_map<(d0, d1, d2, d3, d4, d5)[s0, s1, s2, s3, s4, s5] -> (d1, d4, d5,
+      d3)>
     - affine_map<(d0, d1, d2, d3, d4, d5)[s0, s1, s2, s3, s4, s5] -> (d0, d1, d2,
       d3)>
   iterator_types:
   - parallel
   - parallel
   - parallel
   - parallel
   - reduction

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

def quantized_matmul(
   includes zero-point adjustments for the left and right operands of the
   matmul.
   """
   domain(D.m, D.n, D.k)
   C[D.m, D.n] += (cast(U, A[D.m, D.k]) - cast(U, AZp)) * (cast(U, B[D.k, D.n]) - cast(U, BZp))
 
 @linalg_structured_op
 def mmt4d(lhs=TensorDef(TV.LhsType, S.M, S.K, S.M0, S.K0),
-          rhs=TensorDef(TV.RhsType, S.N, S.K, S.N0, S.K0),
+          rhs=TensorDef(TV.RhsType, S.N, S.K, S.K0, S.N0),
           accum=TensorDef(TV.AccumType, S.M, S.N, S.M0, S.N0,
                                   output=True)):
   """Performs a matrix-matrix-transpose multiplication of two 4D inputs.
 
     Differences from linalg.matmul:
     * The right hand side is transposed, whence the 't' in 'mmt'.
     * The input and output tensors have a 4D shape instead of a 2D shape. They
       are interpreted as 2D matrices with one level of 2D tile subdivision,
       whence the 2+2=4 dimensions. The inner tile dimensions are identified with
       '0' suffixes below, for instance the LHS matrix shape (M, K, M0, K0) reads
       as: MxK tiles, each of shape M0xK0.
   """
   domain(D.m, D.n, D.m0, D.n0, D.k, D.k0)
   implements(ContractionOpInterface)
-  accum[D.m, D.n, D.m0, D.n0] += cast(TV.AccumType, lhs[D.m, D.k, D.m0, D.k0]) * cast(TV.AccumType, rhs[D.n, D.k, D.n0, D.k0])
+  accum[D.m, D.n, D.m0, D.n0] += cast(TV.AccumType, lhs[D.m, D.k, D.m0, D.k0]) * cast(TV.AccumType, rhs[D.n, D.k, D.k0, D.n0])
 
 @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.