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// RUN: mlir-opt %s --lower-sparse-ops-to-foreach="enable-runtime-library=false enable-convert=false" \
// RUN: --lower-sparse-foreach-to-scf | FileCheck %s
#CSR = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : dense, d1 : compressed)
}>
#CSC = #sparse_tensor.encoding<{
map = (d0, d1) -> (d1 : dense, d0 : compressed)
}>
#COO = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton)
}>
// CHECK-LABEL: func.func @sparse_new(
// CHECK-SAME: %[[A:.*]]: !llvm.ptr) -> tensor<?x?xf32, #sparse{{[0-9]*}}> {
// CHECK: %[[COO:.*]] = sparse_tensor.new %[[A]] : !llvm.ptr to tensor<?x?xf32, #sparse{{[0-9]*}}>
// CHECK: %[[R:.*]] = sparse_tensor.convert %[[COO]]
// CHECK: bufferization.dealloc_tensor %[[COO]]
// CHECK: return %[[R]]
func.func @sparse_new(%arg0: !llvm.ptr) -> tensor<?x?xf32, #CSR> {
%0 = sparse_tensor.new %arg0 : !llvm.ptr to tensor<?x?xf32, #CSR>
return %0 : tensor<?x?xf32, #CSR>
}
// CHECK-LABEL: func.func @sparse_new_csc(
// CHECK-SAME: %[[A:.*]]: !llvm.ptr) -> tensor<?x?xf32, #sparse{{[0-9]*}}> {
// CHECK: %[[COO:.*]] = sparse_tensor.new %[[A]] : !llvm.ptr to tensor<?x?xf32, #sparse{{[0-9]*}}>
// CHECK: %[[R:.*]] = sparse_tensor.convert %[[COO]]
// CHECK: bufferization.dealloc_tensor %[[COO]]
// CHECK: return %[[R]]
func.func @sparse_new_csc(%arg0: !llvm.ptr) -> tensor<?x?xf32, #CSC> {
%0 = sparse_tensor.new %arg0 : !llvm.ptr to tensor<?x?xf32, #CSC>
return %0 : tensor<?x?xf32, #CSC>
}
// CHECK-LABEL: func.func @sparse_new_coo(
// CHECK-SAME: %[[A:.*]]: !llvm.ptr) -> tensor<?x?xf32, #sparse{{[0-9]*}}> {
// CHECK: %[[COO:.*]] = sparse_tensor.new %[[A]] : !llvm.ptr to tensor<?x?xf32, #sparse{{[0-9]*}}>
// CHECK: return %[[COO]]
func.func @sparse_new_coo(%arg0: !llvm.ptr) -> tensor<?x?xf32, #COO> {
%0 = sparse_tensor.new %arg0 : !llvm.ptr to tensor<?x?xf32, #COO>
return %0 : tensor<?x?xf32, #COO>
}
// CHECK-LABEL: func.func @sparse_out(
// CHECK-SAME: %[[A:.*]]: tensor<10x20xf32, #sparse{{[0-9]*}}>,
// CHECK-SAME: %[[B:.*]]: !llvm.ptr) {
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[C10:.*]] = arith.constant 10 : index
// CHECK-DAG: %[[C20:.*]] = arith.constant 20 : index
// CHECK: %[[NNZ:.*]] = sparse_tensor.number_of_entries %[[A]]
// CHECK: %[[DS:.*]] = memref.alloca(%[[C2]]) : memref<?xindex>
// CHECK: memref.store %[[C10]], %[[DS]]{{\[}}%[[C0]]] : memref<?xindex>
// CHECK: memref.store %[[C20]], %[[DS]]{{\[}}%[[C1]]] : memref<?xindex>
// CHECK: %[[W:.*]] = call @createSparseTensorWriter(%[[B]])
// CHECK: call @outSparseTensorWriterMetaData(%[[W]], %[[C2]], %[[NNZ]], %[[DS]])
// CHECK: %[[V:.*]] = memref.alloca() : memref<f32>
// CHECK: scf.for %{{.*}} = %[[C0]] to %[[C10]] step %[[C1]] {
// CHECK: scf.for {{.*}} {
// CHECK: func.call @outSparseTensorWriterNextF32(%[[W]], %[[C2]], %[[DS]], %[[V]])
// CHECK: }
// CHECK: }
// CHECK: call @delSparseTensorWriter(%[[W]])
// CHECK: return
// CHECK: }
func.func @sparse_out( %arg0: tensor<10x20xf32, #CSR>, %arg1: !llvm.ptr) -> () {
sparse_tensor.out %arg0, %arg1 : tensor<10x20xf32, #CSR>, !llvm.ptr
return
}
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