onnx-mlir/test/mlir/onnx/onnx_structure.mlir

28 lines
1.8 KiB
MLIR

// RUN: onnx-mlir-opt %s -split-input-file | FileCheck %s
//===----------------------------------------------------------------------===//
// CHECK-LABEL: @check_map1(%arg0: tuple<i64, f32>) -> tensor<*xf32> {
func @check_map1(%arg0: tuple<i64, f32>) -> tensor<*xf32> {
%0 = "onnx.CastMap"(%arg0) {cast_to = "TO_FLOAT", map_form = "DENSE", max_map = 1 : i64} : (tuple<i64, f32>) -> tensor<*xf32>
return %0 : tensor<*xf32>
// CHECK-NEXT: %0 = "onnx.CastMap"(%arg0) {cast_to = "TO_FLOAT", map_form = "DENSE", max_map = 1 : i64} : (tuple<i64, f32>) -> tensor<*xf32>
}
// CHECK-LABEL: @check_string(%arg0: tensor<10x20x!onnx.String>) -> tensor<10x20x!onnx.String> {
func @check_string(%arg0: tensor<10x20x!onnx.String>) -> tensor<10x20x!onnx.String> {
return %arg0 : tensor<10x20x!onnx.String>
// CHECK-NEXT: return %arg0 : tensor<10x20x!onnx.String>
}
// CHECK-LABEL: @check_seq(%arg0: tensor<10x20xf32>, %arg1: tensor<5x20xf32>) -> tensor<*xf32> {
func @check_seq(%arg0: tensor<10x20xf32>, %arg1: tensor<5x20xf32>) -> tensor<*xf32> {
%cst = "onnx.Constant"() {value = dense<[0]> : tensor<1xi32>} : () -> tensor<1xi32>
%0 = "onnx.SequenceConstruct"(%arg0, %arg1) : (tensor<10x20xf32>, tensor<5x20xf32>) -> !onnx.Seq<tensor<10x20xf32>, tensor<5x20xf32>>
%1 = "onnx.SequenceAt"(%0, %cst) : (!onnx.Seq<tensor<10x20xf32>, tensor<5x20xf32>>, tensor<1xi32>) -> tensor<*xf32>
return %1 : tensor<*xf32>
// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
// CHECK-NEXT: %1 = "onnx.SequenceConstruct"(%arg0, %arg1) : (tensor<10x20xf32>, tensor<5x20xf32>) -> !onnx.Seq<tensor<10x20xf32>, tensor<5x20xf32>>
// CHECK-NEXT: %2 = "onnx.SequenceAt"(%1, %0) : (!onnx.Seq<tensor<10x20xf32>, tensor<5x20xf32>>, tensor<1xi32>) -> tensor<*xf32>
}