Add linalg.batch_matvec named op

[asaadaldien]

Summary

Similarly to batch_mat vec outer most dim is a batching dim
and this op does |b| matrix-vector-products :
C[b, i] = sum_k(A[b, i, k] * B[b, k])

[asaadaldien]

Rebase...

[asaadaldien]

Generate YAML changes by running mlir.dialects.linalg.opdsl.dump

[asaadaldien]

Rebase...

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

value: !ScalarExpression
             - !ScalarExpression
               symbolic_cast:
                 type_var: U
                 operands:
                 - !ScalarExpression
                   scalar_arg: A
 --- !LinalgOpConfig
 metadata: !LinalgOpMetadata
+  name: batch_matvec
+  cpp_class_name: BatchMatvecOp
+  doc: |-
+    Performs a batched matrix-vector multiplication.
+
+    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
+    usage: InputOperand
+    type_var: T1
+    shape_map: affine_map<()[s0, s1, s2] -> (s0, s1, s2)>
+  - !LinalgOperandDefConfig
+    name: B
+    usage: InputOperand
+    type_var: T2
+    shape_map: affine_map<()[s0, s1, s2] -> (s0, s2)>
+  - !LinalgOperandDefConfig
+    name: C
+    usage: OutputOperand
+    type_var: U
+    shape_map: affine_map<()[s0, s1, s2] -> (s0, s1)>
+  indexing_maps: !LinalgIndexingMapsConfig
+    static_indexing_maps:
+    - affine_map<(d0, d1, d2)[s0, s1, s2] -> (d0, d1, d2)>
+    - affine_map<(d0, d1, d2)[s0, s1, s2] -> (d0, d2)>
+    - affine_map<(d0, d1, d2)[s0, s1, s2] -> (d0, d1)>
+  iterator_types:
+  - parallel
+  - parallel
+  - reduction
+  assignments:
+  - !ScalarAssign
+    arg: C
+    value: !ScalarExpression
+      scalar_apply:
+        fn_name: add
+        operands:
+        - !ScalarExpression
+          scalar_arg: C
+        - !ScalarExpression
+          scalar_apply:
+            fn_name: mul
+            operands:
+            - !ScalarExpression
+              symbolic_cast:
+                type_var: U
+                operands:
+                - !ScalarExpression
+                  scalar_arg: A
+            - !ScalarExpression
+              symbolic_cast:
+                type_var: U
+                operands:
+                - !ScalarExpression
+                  scalar_arg: B
+--- !LinalgOpConfig
+metadata: !LinalgOpMetadata
   name: dot
   cpp_class_name: DotOp
   doc: |-
     Performs a dot product of two vectors to a scalar result.
 
     Numeric casting is performed on the operands to the inner multiply, promoting
     them to the same data type as the accumulator/output.
   implements:

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

def vecmat(
   them to the same data type as the accumulator/output.
   """
   domain(D.n, D.m)
   implements(ContractionOpInterface)
   x[D.n] += cast(U, y[D.m]) * cast(U, A[D.m, D.n])
 
 
 @linalg_structured_op
+def batch_matvec(
+    A=TensorDef(T1, Batch, S.M, S.K),
+    B=TensorDef(T2, Batch, S.K),
+    C=TensorDef(U,  Batch, S.M, output=True)):
+  """Performs a batched matrix-vector multiplication.
+
+  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.k)
+  implements(ContractionOpInterface)
+  C[D.b, D.m] += cast(U, A[D.b, D.m, D.k]) * cast(U, B[D.b, D.k])
+
+
+@linalg_structured_op
 def dot(
     A=TensorDef(T1, S.M), B=TensorDef(T2, S.M), C=TensorDef(U, output=True)):
   """Performs a dot product of two vectors to a scalar result.
 
   Numeric casting is performed on the operands to the inner multiply, promoting
   them to the same data type as the accumulator/output.
   """
   implements(ContractionOpInterface)

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

 // CHECK: linalg.generic
 // CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]]]
 // CHECK-SAME: iterator_types = ["parallel", "parallel"]}
 // CHECK-SAME: ins(%[[VAL]] : f32)
 // CHECK-SAME: outs(%{{.+}} : memref<?x?xf32>)
 
 // CHECK:         ^{{.+}}(%[[BBARG0:.+]]: f32, %[[BBARG1:.+]]: f32)
 // CHECK-NEXT:      linalg.yield %[[BBARG0]] : f32
+
+// -----
+
+func @generalize_batch_matm_vec(%lhs : memref<?x?x?xi8>, %rhs: memref<?x?xi8>,  %out: memref<?x?xf32>) {
+  linalg.batch_matvec ins(%lhs, %rhs: memref<?x?x?xi8>, memref<?x?xi8>)
+                     outs(%out: memref<?x?xf32>)
+  return
+}
+// CHECK: #[[MAP0:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
+// CHECK: #[[MAP1:.+]] = affine_map<(d0, d1, d2) -> (d0, d2)>
+// CHECK: #[[MAP2:.+]] = affine_map<(d0, d1, d2) -> (d0, d1)>
+
+// CHECK: @generalize_batch_matm_vec
+
+// CHECK: linalg.generic
+// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]
+// CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"]}
+// CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<?x?x?xi8>, memref<?x?xi8>)
+// CHECK-SAME: outs(%{{.+}} : memref<?x?xf32>)
+// CHECK:         ^{{.+}}(%[[BBARG0:.+]]: i8, %[[BBARG1:.+]]: i8, %[[BBARG2:.+]]: f32)
+// CHECK:            %[[BBARG0_F32:.+]] = sitofp %[[BBARG0]] : i8 to f32
+// CHECK:            %[[BBARG1_F32:.+]] = sitofp %[[BBARG1]] : i8 to f32
+// CHECK:            %[[MUL:.+]] = mulf %[[BBARG0_F32]], %[[BBARG1_F32]]
+// CHECK:            %[[ADD:.+]] = addf %[[BBARG2]], %[[MUL]]
+// CHECK:            linalg.yield %[[ADD]] : f32