mirror of https://github.com/llvm/torch-mlir
42 lines
3.0 KiB
MLIR
42 lines
3.0 KiB
MLIR
// RUN: torch-mlir-opt <%s --split-input-file -convert-torch-onnx-to-torch | FileCheck %s
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// CHECK-LABEL: func.func @test_ifop_basic
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// CHECK: %[[IF:.*]] = torch.prim.If %{{.*}} -> (!torch.vtensor<[1],f32>)
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// CHECK-DAG: %[[SUB:.*]] = torch.aten.sub.Tensor %arg1, %arg2, %{{.*}} : !torch.vtensor<[1],f32>, !torch.vtensor<[1],f32>, !torch.int -> !torch.vtensor<[1],f32>
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// CHECK-DAG: torch.prim.If.yield %[[SUB]] : !torch.vtensor<[1],f32>
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// CHECK-DAG: } else {
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// CHECK-DAG: %[[ADD:.*]] = torch.aten.add.Tensor %arg1, %arg2, %{{.*}} : !torch.vtensor<[1],f32>, !torch.vtensor<[1],f32>, !torch.int -> !torch.vtensor<[1],f32>
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// CHECK-DAG: torch.prim.If.yield %[[ADD]] : !torch.vtensor<[1],f32>
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func.func @test_ifop_basic(%arg0: !torch.vtensor<[1],i1>, %arg1: !torch.vtensor<[1],f32>, %arg2: !torch.vtensor<[1],f32>) -> !torch.vtensor<[1],f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "conditional_example", torch.onnx_meta.producer_version = ""} {
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%none = torch.constant.none
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%0 = torch.operator "onnx.If"(%arg0) : (!torch.vtensor<[1],i1>) -> !torch.vtensor<[1],f32> {
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%1 = torch.operator "onnx.Add"(%arg1, %arg2) : (!torch.vtensor<[1],f32>, !torch.vtensor<[1],f32>) -> !torch.vtensor<[1],f32>
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torch.operator_terminator %1 : !torch.vtensor<[1],f32>
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}, {
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%1 = torch.operator "onnx.Sub"(%arg1, %arg2) : (!torch.vtensor<[1],f32>, !torch.vtensor<[1],f32>) -> !torch.vtensor<[1],f32>
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torch.operator_terminator %1 : !torch.vtensor<[1],f32>
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}
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return %0 : !torch.vtensor<[1],f32>
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}
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// -----
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// CHECK-LABEL: func.func @test_ifop_cast_shape
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// CHECK: %[[IF:.*]] = torch.prim.If %{{.*}} -> (!torch.vtensor<[?],si64>)
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// CHECK-DAG: %[[CAST:.*]] = torch.tensor_static_info_cast %{{.*}} : !torch.vtensor<[0],si64> to !torch.vtensor<[?],si64>
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// CHECK-DAG: torch.prim.If.yield %[[CAST]] : !torch.vtensor<[?],si64>
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// CHECK-DAG: } else {
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// CHECK-DAG: %[[SQUEEZE:.*]] = torch.prims.squeeze %arg1, %{{.*}} : !torch.vtensor<[?,1],si64>, !torch.list<int> -> !torch.vtensor<[?],si64>
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// CHECK-DAG: torch.prim.If.yield %[[SQUEEZE]] : !torch.vtensor<[?],si64>
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func.func @test_ifop_cast_shape(%arg0: !torch.vtensor<[1],i1>, %arg1: !torch.vtensor<[?,1],si64>) -> !torch.vtensor<[?],si64> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "conditional_example", torch.onnx_meta.producer_version = ""} {
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%0 = torch.operator "onnx.If"(%arg0) : (!torch.vtensor<[1],i1>) -> !torch.vtensor<[?],si64> {
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%1 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<1> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64>
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%2 = torch.operator "onnx.Squeeze"(%arg1, %1) : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si64>
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torch.operator_terminator %2 : !torch.vtensor<[?],si64>
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}, {
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%1 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0> : tensor<0xsi64>} : () -> !torch.vtensor<[0],si64>
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torch.operator_terminator %1 : !torch.vtensor<[0],si64>
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}
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return %0 : !torch.vtensor<[?],si64>
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}
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