Add MeanVarNorm lit tests

Signed-off-by: Zahid Wakeel <[email protected]>

Author: zahidwx

test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir

@@ -1553,6 +1553,32 @@ func.func @test_mod_int64_no_fmod(%arg0: !torch.vtensor<[6],si64>, %arg1: !torch
 
 // -----
 
+// CHECK-LABEL:   func.func @test_meanvar_norm(
+func.func @test_meanvar_norm(%arg0: !torch.vtensor<[3,5,2,2],f32>) -> !torch.vtensor<[3,5,2,2],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK-SAME:      %[[ARG0:.*]]: !torch.vtensor<[3,5,2,2],f32>) -> !torch.vtensor<[3,5,2,2],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK:           %[[VAL_0:.*]] = torch.constant.bool true
+  // CHECK:           %[[VAL_1:.*]] = torch.constant.bool false
+  // CHECK:           %[[VAL_2:.*]] = torch.constant.none
+  // CHECK:           %[[VAL_3:.*]] = torch.constant.int 0
+  // CHECK:           %[[VAL_4:.*]] = torch.constant.int 2
+  // CHECK:           %[[VAL_5:.*]] = torch.constant.int 3
+  // CHECK:           %[[VAL_6:.*]] = torch.prim.ListConstruct %[[VAL_3]], %[[VAL_4]], %[[VAL_5]] : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
+  // CHECK:           %[[VAL_7:.*]] = torch.aten.mean.dim %[[ARG0]], %[[VAL_6]], %[[VAL_0]], %[[VAL_2]] : !torch.vtensor<[3,5,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[1,5,1,1],f32>
+  // CHECK:           %[[VAL_8:.*]] = torch.aten.var.dim %[[ARG0]], %[[VAL_6]], %[[VAL_1]], %[[VAL_0]] : !torch.vtensor<[3,5,2,2],f32>, !torch.list<int>, !torch.bool, !torch.bool -> !torch.vtensor<[1,5,1,1],f32>
+  // CHECK:           %[[VAL_9:.*]] = torch.constant.int 1
+  // CHECK:           %[[VAL_10:.*]] = torch.constant.float 1.000000e-09
+  // CHECK:           %[[VAL_11:.*]] = torch.aten.add.Scalar %[[VAL_8]], %[[VAL_10]], %[[VAL_9]] : !torch.vtensor<[1,5,1,1],f32>, !torch.float, !torch.int -> !torch.vtensor<[1,5,1,1],f32>
+  // CHECK:           %[[VAL_12:.*]] = torch.aten.sqrt %[[VAL_11]] : !torch.vtensor<[1,5,1,1],f32> -> !torch.vtensor<[1,5,1,1],f32>
+  // CHECK:           %[[VAL_13:.*]] = torch.aten.sub.Tensor %[[ARG0]], %[[VAL_7]], %[[VAL_9]] : !torch.vtensor<[3,5,2,2],f32>, !torch.vtensor<[1,5,1,1],f32>, !torch.int -> !torch.vtensor<[3,5,2,2],f32>
+  // CHECK:           %[[VAL_14:.*]] = torch.aten.div.Tensor %[[VAL_13]], %[[VAL_12]] : !torch.vtensor<[3,5,2,2],f32>, !torch.vtensor<[1,5,1,1],f32> -> !torch.vtensor<[3,5,2,2],f32>
+  // CHECK:           return %[[VAL_14]] : !torch.vtensor<[3,5,2,2],f32>
+  // CHECK:         }
+  %0 = torch.operator "onnx.MeanVarianceNormalization"(%arg0) {torch.onnx.axes = [0 : si64, 2 : si64, 3 : si64]} : (!torch.vtensor<[3,5,2,2],f32>) -> !torch.vtensor<[3,5,2,2],f32>
+  return %0 : !torch.vtensor<[3,5,2,2],f32>
+}
+
+// -----
+
 // CHECK-LABEL: func.func @test_not_2d
 func.func @test_not_2d(%arg0: !torch.vtensor<[3,4],i1>) -> !torch.vtensor<[3,4],i1> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 1 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
     // CHECK: torch.aten.bitwise_not %arg0 : !torch.vtensor<[3,4],i1> -> !torch.vtensor<[3,4],i1>