28 lines
1.8 KiB
MLIR
28 lines
1.8 KiB
MLIR
// RUN: onnx-mlir-opt %s -split-input-file | FileCheck %s
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//===----------------------------------------------------------------------===//
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// CHECK-LABEL: @check_map1(%arg0: tuple<i64, f32>) -> tensor<*xf32> {
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func @check_map1(%arg0: tuple<i64, f32>) -> tensor<*xf32> {
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%0 = "onnx.CastMap"(%arg0) {cast_to = "TO_FLOAT", map_form = "DENSE", max_map = 1 : si64} : (tuple<i64, f32>) -> tensor<*xf32>
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return %0 : tensor<*xf32>
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// CHECK-NEXT: %0 = "onnx.CastMap"(%arg0) {cast_to = "TO_FLOAT", map_form = "DENSE", max_map = 1 : si64} : (tuple<i64, f32>) -> tensor<*xf32>
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}
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// CHECK-LABEL: @check_string(%arg0: tensor<10x20x!onnx.String>) -> tensor<10x20x!onnx.String> {
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func @check_string(%arg0: tensor<10x20x!onnx.String>) -> tensor<10x20x!onnx.String> {
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return %arg0 : tensor<10x20x!onnx.String>
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// CHECK-NEXT: return %arg0 : tensor<10x20x!onnx.String>
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}
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// CHECK-LABEL: @check_seq(%arg0: tensor<10x20xf32>, %arg1: tensor<5x20xf32>) -> tensor<*xf32> {
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func @check_seq(%arg0: tensor<10x20xf32>, %arg1: tensor<5x20xf32>) -> tensor<*xf32> {
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%cst = "onnx.Constant"() {value = dense<[0]> : tensor<1xi32>} : () -> tensor<1xi32>
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%0 = "onnx.SequenceConstruct"(%arg0, %arg1) : (tensor<10x20xf32>, tensor<5x20xf32>) -> !onnx.Seq<tensor<10x20xf32>, tensor<5x20xf32>>
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%1 = "onnx.SequenceAt"(%0, %cst) : (!onnx.Seq<tensor<10x20xf32>, tensor<5x20xf32>>, tensor<1xi32>) -> tensor<*xf32>
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return %1 : tensor<*xf32>
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// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
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// CHECK-NEXT: %1 = "onnx.SequenceConstruct"(%arg0, %arg1) : (tensor<10x20xf32>, tensor<5x20xf32>) -> !onnx.Seq<tensor<10x20xf32>, tensor<5x20xf32>>
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// CHECK-NEXT: %2 = "onnx.SequenceAt"(%1, %0) : (!onnx.Seq<tensor<10x20xf32>, tensor<5x20xf32>>, tensor<1xi32>) -> tensor<*xf32>
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}
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