batch_vecmat

Author: bjacob

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

@@ -1796,6 +1796,74 @@ structured_op: !LinalgStructuredOpConfig
  - !ScalarExpression
  scalar_arg: B
 --- !LinalgOpConfig
+metadata: !LinalgOpMetadata
+ name: batch_vecmat
+ cpp_class_name: BatchVecmatOp
+ 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
+ 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] -> (s0, 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] -> (d0, 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_signed
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: A
+ - !ScalarExpression
+ scalar_fn:
+ kind: type
+ fn_name: cast_signed
+ type_var: U
+ operands:
+ - !ScalarExpression
+ scalar_arg: B
+--- !LinalgOpConfig
 metadata: !LinalgOpMetadata
  name: dot
  cpp_class_name: DotOp

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

@@ -516,6 +516,23 @@ def batch_matvec(
  U, B[D.b, D.k]
  )
 
+@linalg_structured_op
+def batch_vecmat(
+ A=TensorDef(T1, Batch, S.K),
+ B=TensorDef(T2, Batch, S.K, S.N),
+ C=TensorDef(U, Batch, S.N, 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.n, D.k)
+ implements(ContractionOpInterface)
+ C[D.b, D.n] += TypeFn.cast_signed(U, A[D.b, D.k]) * TypeFn.cast_signed(
+ U, B[D.b, D.k, D.n]
+ )
+
 
 @linalg_structured_op
 def dot(A=TensorDef(T1, S.M), B=TensorDef(T2, S.M), C=TensorDef(U, output=True)):

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

@@ -251,6 +251,31 @@ func.func @generalize_batch_matm_vec(%lhs : memref<?x?x?xi8>, %rhs: memref<?x?xi
 
 // -----
 
+func.func @generalize_batch_vecmat(%lhs : memref<?x?xi8>, %rhs: memref<?x?x?xi8>, %out: memref<?x?xf32>) {
+ linalg.batch_vecmat ins(%lhs, %rhs: memref<?x?xi8>, memref<?x?x?xi8>)
+ outs(%out: memref<?x?xf32>)
+ return
+}
+// CHECK: #[[MAP0:.+]] = affine_map<(d0, d1, d2) -> (d0, d2)>
+// CHECK: #[[MAP1:.+]] = affine_map<(d0, d1, d2) -> (d0, d2, d1)>
+// CHECK: #[[MAP2:.+]] = affine_map<(d0, d1, d2) -> (d0, d1)>
+
+// CHECK: @generalize_batch_vecmat
+
+// CHECK: linalg.generic
+// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]
+// CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"]}
+// CHECK-SAME: ins(%{{.+}}, %{{.+}} : memref<?x?xi8>, memref<?x?x?xi8>)
+// CHECK-SAME: outs(%{{.+}} : memref<?x?xf32>)
+// CHECK: ^{{.+}}(%[[BBARG0:.+]]: i8, %[[BBARG1:.+]]: i8, %[[BBARG2:.+]]: f32)
+// CHECK: %[[BBARG0_F32:.+]] = arith.sitofp %[[BBARG0]] : i8 to f32
+// CHECK: %[[BBARG1_F32:.+]] = arith.sitofp %[[BBARG1]] : i8 to f32
+// CHECK: %[[MUL:.+]] = arith.mulf %[[BBARG0_F32]], %[[BBARG1_F32]]
+// CHECK: %[[ADD:.+]] = arith.addf %[[BBARG2]], %[[MUL]]
+// CHECK: linalg.yield %[[ADD]] : f32
+
+// -----
+
 func.func @batch_reduce_gemm(%lhs: memref<7x8x9xf32>, %rhs: memref<7x9x8xf32>, %out: memref<8x8xf32>) {
  linalg.batch_reduce_matmul ins(%lhs, %rhs: memref<7x8x9xf32>, memref<7x9x8xf32>)
  outs(%out: memref<8x8xf32>)