// RUN: mlir-opt -transform-interpreter -split-input-file --cse %s | FileCheck %s
func.func @simple_matmul(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>,
%arg2 : tensor<?x?xf32>) -> tensor<?x?xf32> {
%0 = linalg.matmul
ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%arg2 : tensor<?x?xf32>) -> tensor<?x?xf32>
return %0 : tensor<?x?xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {
%matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1
: (!transform.any_op) -> !transform.any_op
%a, %b = transform.test.tile_using_forall %matmul [10, 20] mapping = [#gpu.block<y>, #gpu.block<x>]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 10)>
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 20)>
// CHECK: func.func @simple_matmul(
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: tensor<?x?xf32>
// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index
// CHECK-DAG: %[[M:.+]] = tensor.dim %[[ARG0]], %[[C0]]
// CHECK-DAG: %[[K:.+]] = tensor.dim %[[ARG0]], %[[C1]]
// CHECK-DAG: %[[N:.+]] = tensor.dim %[[ARG1]], %[[C1]]
// CHECK: %[[RESULT:.+]] = scf.forall (%[[IV0:[a-zA-Z0-9]+]], %[[IV1:[a-zA-Z0-9]+]]) =
// CHECK-SAME: (0, 0) to (%[[M]], %[[N]]) step (10, 20) shared_outs(%[[INIT:.+]] = %[[ARG2]])
// CHECK: %[[TS_Y:.+]] = affine.min #[[MAP0]](%[[IV0]])[%[[M]]]
// CHECK: %[[TS_X:.+]] = affine.min #[[MAP1]](%[[IV1]])[%[[N]]]
// CHECK: %[[LHS_TILE:.+]] = tensor.extract_slice %[[ARG0]]
// CHECK-SAME: [%[[IV0]], 0] [%[[TS_Y]], %[[K]]] [1, 1]
// CHECK: %[[RHS_TILE:.+]] = tensor.extract_slice %[[ARG1]]
// CHECK-SAME: [0, %[[IV1]]] [%[[K]], %[[TS_X]]] [1, 1]
// CHECK: %[[INIT_TILE:.+]] = tensor.extract_slice %[[INIT]]
// CHECK-SAME: [%[[IV0]], %[[IV1]]] [%[[TS_Y]], %[[TS_X]]] [1, 1]
// CHECK: %[[GEMM_TILE:.+]] = linalg.matmul
// CHECK-SAME: ins(%[[LHS_TILE]], %[[RHS_TILE]] :
// CHECK-SAME: outs(%[[INIT_TILE]] :
// CHECK: scf.forall.in_parallel {
// CHECK: tensor.parallel_insert_slice %[[GEMM_TILE]] into %[[INIT]]
// CHECK-SAME: [%[[IV0]], %[[IV1]]] [%[[TS_Y]], %[[TS_X]]] [1, 1]
// CHECK: mapping = [#gpu.block<y>, #gpu.block<x>]
// CHECK: return %[[RESULT]]
// -----
func.func @simple_matmul_memref(%arg0 : memref<?x?xf32>, %arg1 : memref<?x?xf32>,
%arg2 : memref<?x?xf32>) {
linalg.matmul ins(%arg0, %arg1 : memref<?x?xf32>, memref<?x?xf32>)
outs(%arg2 : memref<?x?xf32>)
return
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {
%matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1
: (!transform.any_op) -> !transform.any_op
%a, %b = transform.test.tile_using_forall %matmul [10, 20]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 10)>
// CHECK-DAG: #[[$MAP1:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 20)>
// CHECK-LABEL: func.func @simple_matmul_memref(
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: memref<?x?xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: memref<?x?xf32>
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: memref<?x?xf32>
// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index
// CHECK-DAG: %[[M:.+]] = memref.dim %[[ARG0]], %[[C0]]
// CHECK-DAG: %[[K:.+]] = memref.dim %[[ARG0]], %[[C1]]
// CHECK-DAG: %[[N:.+]] = memref.dim %[[ARG1]], %[[C1]]
// CHECK: scf.forall (%[[IV0:[a-zA-Z0-9]+]], %[[IV1:[a-zA-Z0-9]+]]) = (0, 0) to (%[[M]], %[[N]]) step (10, 20) {
// CHECK-DAG: %[[TS_M:.+]] = affine.min #[[$MAP0]](%[[IV0]])[%[[M]]]
// CHECK-DAG: %[[TS_N:.+]] = affine.min #[[$MAP1]](%[[IV1]])[%[[N]]]
// CHECK-DAG: %[[LHS_TILE:.+]] = memref.subview %[[ARG0]]
// CHECK-SAME: [%[[IV0]], 0] [%[[TS_M]], %[[K]]] [1, 1]
// CHECK-DAG: %[[RHS_TILE:.+]] = memref.subview %[[ARG1]]
// CHECK-SAME: [0, %[[IV1]]] [%[[K]], %[[TS_N]]] [1, 1]
// CHECK-DAG: %[[OUT_TILE:.+]] = memref.subview %[[ARG2]]
// CHECK-SAME: [%[[IV0]], %[[IV1]]] [%[[TS_M]], %[[TS_N]]] [1, 1]
// CHECK: linalg.matmul
// CHECK-SAME: ins(%[[LHS_TILE]], %[[RHS_TILE]] :
// CHECK-SAME: outs(%[[OUT_TILE]] :
// -----
#map0 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
#map1 = affine_map<(d0, d1, d2) -> (d0, d2, d1)>
#map2 = affine_map<(d0, d1, d2) -> (d2, d0, d1)>
func.func @multi_result(%arg0 : tensor<128x200x300xf32>) -> (tensor<128x300x200xf32>, tensor<300x128x200xf32>) {
%init0 = tensor.empty() : tensor<128x300x200xf32>
%init1 = tensor.empty() : tensor<300x128x200xf32>
%0:2 = linalg.generic {
indexing_maps = [#map0, #map1, #map2],
iterator_types = ["parallel", "parallel", "parallel"]}
ins(%arg0 : tensor<128x200x300xf32>)
outs(%init0, %init1 : tensor<128x300x200xf32>, tensor<300x128x200xf32>) {
^bb0(%b0 : f32, %b1 : f32, %b2 : f32):
linalg.yield %b0, %b0 : f32, f32
} -> (tensor<128x300x200xf32>, tensor<300x128x200xf32>)
return %0#0, %0#1 : tensor<128x300x200xf32>, tensor<300x128x200xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {
%generic = transform.structured.match ops{["linalg.generic"]} in %arg1
: (!transform.any_op) -> !transform.any_op
%a, %b = transform.test.tile_using_forall %generic [10, 0, 20]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<(d0) -> (-d0 + 128, 10)>
// CHECK-LABEL: func.func @multi_result(
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<128x200x300xf32>)
// CHECK-DAG: %[[INIT0:.+]] = tensor.empty()
// CHECK-DAG: %[[INIT1:.+]] = tensor.empty()
// CHECK: %[[OUTER:[a-zA-Z0-9]+]]:2 = scf.forall (%[[IV0:[a-zA-Z0-9]+]], %[[IV1:[a-zA-Z0-9]+]]) = (0, 0) to (128, 300) step (10, 20)
// CHECK-SAME: shared_outs(%[[ARG1:[a-zA-Z0-9]+]] = %[[INIT0]], %[[ARG2:[a-zA-Z0-9]+]] = %[[INIT1]])
// CHECK: %[[TS_Y:.+]] = affine.min #[[$MAP0]](%[[IV0]])
// CHECK: %[[ARG_TILE:.+]] = tensor.extract_slice %[[ARG0]]
// CHECK-SAME: [%[[IV0]], 0, %[[IV1]]] [%[[TS_Y]], 200, 20] [1, 1, 1]
// CHECK-DAG: %[[INIT0_TILE:.+]] = tensor.extract_slice %[[ARG1]]
// CHECK-SAME: [%[[IV0]], %[[IV1]], 0] [%[[TS_Y]], 20, 200] [1, 1, 1]
// CHECK-DAG: %[[INIT1_TILE:.+]] = tensor.extract_slice %[[ARG2]]
// CHECK-SAME: [%[[IV1]], %[[IV0]], 0] [20, %[[TS_Y]], 200] [1, 1, 1]
// CHECK: %[[RESULT_TILE:.+]]:2 = linalg.generic
// CHECK-SAME: ins(%[[ARG_TILE]] :
// CHECK-SAME: outs(%[[INIT0_TILE]], %[[INIT1_TILE]] :
// CHECK: scf.forall.in_parallel {
// CHECK-DAG: tensor.parallel_insert_slice %[[RESULT_TILE]]#0 into %[[ARG1]][%[[IV0]], %[[IV1]], 0] [%[[TS_Y]], 20, 200] [1, 1, 1]
// CHECK-DAG: tensor.parallel_insert_slice %[[RESULT_TILE]]#1 into %[[ARG2]][%[[IV1]], %[[IV0]], 0] [20, %[[TS_Y]], 200] [1, 1, 1]
// CHECK: }
// CHECK: return %[[OUTER]]#0, %[[OUTER]]#1
// -----
func.func @conv2D(%arg0 : tensor<?x?x?x?xf32>, %arg1 : tensor<?x?x?x?xf32>,
%arg2 : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
%0 = linalg.conv_2d_nhwc_hwcf {
strides = dense<[2, 3]> : tensor<2xi64>,
dilation = dense<[4, 5]> : tensor<2xi64>}
ins(%arg0, %arg1 : tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>)
outs(%arg2 : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
return %0 : tensor<?x?x?x?xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {
%conv = transform.structured.match ops{["linalg.conv_2d_nhwc_hwcf"]} in %arg1
: (!transform.any_op) -> !transform.any_op
%a, %b = transform.test.tile_using_forall %conv [0, 0, 0, 0, 10, 20, 30]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 10)>
// CHECK-DAG: #[[$MAP1:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 20)>
// CHECK-DAG: #[[$MAP2:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 30)>
// CHECK-DAG: #[[$MAP3:.+]] = affine_map<(d0)[s0] -> (d0 + s0 * 2 - 2)>
// CHECK-DAG: #[[$MAP4:.+]] = affine_map<(d0)[s0] -> (d0 + s0 * 3 - 3)>
// CHECK-LABEL: func.func @conv2D(
// CHECK-SAME: %[[INPUT:[a-zA-Z0-9]+]]: tensor<?x?x?x?xf32>
// CHECK-SAME: %[[FILTER:[a-zA-Z0-9]+]]: tensor<?x?x?x?xf32>
// CHECK-SAME: %[[INIT:[a-zA-Z0-9]+]]: tensor<?x?x?x?xf32>
// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index
// CHECK-DAG: %[[C2:.+]] = arith.constant 2 : index
// CHECK-DAG: %[[C3:.+]] = arith.constant 3 : index
// CHECK-DAG: %[[N:.+]] = tensor.dim %[[INPUT]], %[[C0]]
// CHECK-DAG: %[[C:.+]] = tensor.dim %[[INPUT]], %[[C3]]
// CHECK-DAG: %[[P:.+]] = tensor.dim %[[FILTER]], %[[C0]]
// CHECK-DAG: %[[Q:.+]] = tensor.dim %[[FILTER]], %[[C1]]
// CHECK-DAG: %[[F:.+]] = tensor.dim %[[FILTER]], %[[C3]]
// CHECK-DAG: %[[R:.+]] = tensor.dim %[[INIT]], %[[C1]]
// CHECK-DAG: %[[S:.+]] = tensor.dim %[[INIT]], %[[C2]]
// CHECK: %[[RESULT:.+]] = scf.forall (%[[IV0:[a-zA-Z0-9]+]], %[[IV1:[a-zA-Z0-9]+]], %[[IV2:[a-zA-Z0-9]+]]) =
// CHECK-SAME: (0, 0, 0) to (%[[P]], %[[Q]], %[[C]]) step (10, 20, 30) shared_outs(%[[INIT0:.+]] = %[[INIT]])
// CHECK-DAG: %[[TS_P:.+]] = affine.min #[[$MAP0]](%[[IV0]])[%[[P]]]
// CHECK-DAG: %[[TS_Q:.+]] = affine.min #[[$MAP1]](%[[IV1]])[%[[Q]]]
// CHECK-DAG: %[[TS_C:.+]] = affine.min #[[$MAP2]](%[[IV2]])[%[[C]]]
// CHECK-DAG: %[[TS_H:.+]] = affine.apply #[[$MAP3]](%[[TS_P]])[%[[R]]]
// CHECK-DAG: %[[TS_W:.+]] = affine.apply #[[$MAP4]](%[[TS_Q]])[%[[S]]]
// CHECK-DAG: %[[INPUT_TILE:.+]] = tensor.extract_slice %[[INPUT]]
// CHECK-SAME: [0, %[[IV0]], %[[IV1]], %[[IV2]]] [%[[N]], %[[TS_H]], %[[TS_W]], %[[TS_C]]]
// CHECK-DAG: %[[FILTER_TILE:.+]] = tensor.extract_slice %[[FILTER]]
// CHECK-SAME: [%[[IV0]], %[[IV1]], %[[IV2]], 0] [%[[TS_P]], %[[TS_Q]], %[[TS_C]], %[[F]]]
// CHECK-DAG: %[[INIT_TILE:.+]] = tensor.extract_slice %[[INIT0]]
// CHECK-SAME: [0, 0, 0, 0] [%[[N]], %[[R]], %[[S]], %[[F]]]
// CHECK: %[[CONV_TILE:.+]] = linalg.conv_2d_nhwc_hwcf
// CHECK-SAME: dilation = dense<[4, 5]> : tensor<2xi64>, strides = dense<[2, 3]> : tensor<2xi64>
// CHECK-SAME: ins(%[[INPUT_TILE]], %[[FILTER_TILE]] :
// CHECK-SAME: outs(%[[INIT_TILE]] :
// CHECK: scf.forall.in_parallel
// CHECK: tensor.parallel_insert_slice %[[CONV_TILE]] into %[[INIT0]]
// CHECK-SAME: [0, 0, 0, 0] [%[[N]], %[[R]], %[[S]], %[[F]]] [1, 1, 1, 1]
// CHECK: return %[[RESULT]]
// -----
// CHECK: #[[$MAP_ADD:.+]] = affine_map<(d0, d1) -> (d0 + d1)>
func.func @indexed_semantics(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> {
// Check that we correctly amend "linalg.index" results.
%0 = linalg.generic {
indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>],
iterator_types = ["parallel", "parallel"]}
ins(%arg0: tensor<?x?xf32>)
outs(%arg1: tensor<?x?xf32>) {
^bb0(%arg2: f32, %arg3: f32):
%1 = linalg.index 0 : index
%2 = linalg.index 1 : index
%3 = arith.addi %1, %2 : index
%4 = arith.index_cast %3 : index to i64
%5 = arith.uitofp %4 : i64 to f32
%6 = arith.addf %5, %arg2 : f32
linalg.yield %6 : f32
} -> (tensor<?x?xf32>)
return %0 : tensor<?x?xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {
%generic = transform.structured.match ops{["linalg.generic"]} in %arg1
: (!transform.any_op) -> !transform.any_op
%a, %b = transform.test.tile_using_forall %generic [10, 20]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK-LABEL: @indexed_semantics
// CHECK: scf.forall (%[[I0:.+]], %[[I1:.+]]) =
// CHECK: %[[INDEX0:.+]] = linalg.index 0
// CHECK: %[[INDEX0_AMENDED:.+]] = affine.apply #[[$MAP_ADD]](%[[INDEX0]], %[[I0]])
// CHECK: %[[INDEX1:.+]] = linalg.index 1
// CHECK: %[[INDEX1_AMENDED:.+]] = affine.apply #[[$MAP_ADD]](%[[INDEX1]], %[[I1]])
// CHECK: arith.addi %[[INDEX0_AMENDED]], %[[INDEX1_AMENDED]]
// -----
func.func @interchange_matmul(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>,
%arg2 : tensor<?x?xf32>) -> tensor<?x?xf32> {
%0 = linalg.matmul ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%arg2 : tensor<?x?xf32>) -> tensor<?x?xf32>
return %0 : tensor<?x?xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {
%matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1
: (!transform.any_op) -> !transform.any_op
%a, %b = transform.test.tile_using_forall %matmul [10, 20] interchange = [1, 0] mapping = [#gpu.block<y>, #gpu.block<x>]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 20)>
// CHECK-DAG: #[[$MAP2:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 10)>
// CHECK-LABEL: func.func @interchange_matmul(
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: tensor<?x?xf32>
// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index
// CHECK-DAG: %[[M:.+]] = tensor.dim %[[ARG0]], %[[C0]]
// CHECK-DAG: %[[K:.+]] = tensor.dim %[[ARG0]], %[[C1]]
// CHECK-DAG: %[[N:.+]] = tensor.dim %[[ARG1]], %[[C1]]
// CHECK: %[[OUTER:[a-zA-Z0-9]+]] = scf.forall (%[[IV0:[a-zA-Z0-9]+]], %[[IV1:[a-zA-Z0-9]+]]
// CHECK-SAME: (0, 0) to (%[[N]], %[[M]]) step (20, 10)
// CHECK-SAME: shared_outs(%[[INIT0:.+]] = %[[ARG2]])
// CHECK-DAG: %[[TS_N:.+]] = affine.min #[[$MAP0]](%[[IV0]])[%[[N]]]
// CHECK-DAG: %[[TS_M:.+]] = affine.min #[[$MAP2]](%[[IV1]])[%[[M]]]
// CHECK-DAG: %[[LHS_TILE:.+]] = tensor.extract_slice %[[ARG0]]
// CHECK-SAME: [%[[IV1]], 0] [%[[TS_M]], %[[K]]] [1, 1]
// CHECK-DAG: %[[RHS_TILE:.+]] = tensor.extract_slice %[[ARG1]]
// CHECK-SAME: [0, %[[IV0]]] [%[[K]], %[[TS_N]]] [1, 1]
// CHECK-DAG: %[[INIT_TILE:.+]] = tensor.extract_slice %[[INIT0]]
// CHECK-SAME: [%[[IV1]], %[[IV0]]] [%[[TS_M]], %[[TS_N]]] [1, 1]
// CHECK: %[[GEMM_TILE:.+]] = linalg.matmul
// CHECK-SAME: ins(%[[LHS_TILE]], %[[RHS_TILE]] :
// CHECK-SAME: outs(%[[INIT_TILE]] :
// CHECK: scf.forall.in_parallel {
// CHECK: tensor.parallel_insert_slice %[[GEMM_TILE]] into %[[INIT0]]
// CHECK-SAME: [%[[IV1]], %[[IV0]]] [%[[TS_M]], %[[TS_N]]] [1, 1]
// CHECK: } {mapping = [#gpu.block<y>, #gpu.block<x>]}
// CHECK: return %[[OUTER]]
// -----
func.func @check_scalar_operation(%arg0 : tensor<f32>) -> tensor<f32> {
%init = tensor.empty() : tensor<f32>
%0 = linalg.generic {
indexing_maps = [affine_map<() -> ()>, affine_map<() -> ()>],
iterator_types = []}
ins(%arg0 : tensor<f32>) outs(%init : tensor<f32>){
^bb0(%b0 : f32, %b1 : f32):
%1 = arith.mulf %b0, %b0 : f32
linalg.yield %1 : f32
} -> tensor<f32>
return %0 : tensor<f32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {
%generic = transform.structured.match ops{["linalg.generic"]} in %arg1
: (!transform.any_op) -> !transform.any_op
%a = transform.test.tile_using_forall %generic []
: (!transform.any_op) -> (!transform.any_op)
transform.yield
}
}
// CHECK-LABEL: func @check_scalar_operation
// CHECK-NOT: scf.for
// CHECK: linalg.generic
// -----
func.func @check_scalar_memref_operation(%arg0 : memref<f32>, %arg1 : memref<f32>){
linalg.generic {
indexing_maps = [affine_map<() -> ()>, affine_map<() -> ()>],
iterator_types = []}
ins(%arg0 : memref<f32>) outs(%arg1 : memref<f32>){
^bb0(%b0 : f32, %b1 : f32):
%1 = arith.mulf %b0, %b0 : f32
linalg.yield %1 : f32
}
return
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {
%generic = transform.structured.match ops{["linalg.generic"]} in %arg1
: (!transform.any_op) -> !transform.any_op
%a = transform.test.tile_using_forall %generic []
: (!transform.any_op) -> (!transform.any_op)
transform.yield
}
}
// CHECK-LABEL: func @check_scalar_memref_operation
// CHECK-NOT: scf.for
// CHECK: linalg.generic
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