--convert-onnx-to-stablehlo bombs with garbage SmallVector size

[PerMildner]

The attached input file crashes onnx-mlir-opt --convert-onnx-to-stablehlo (but not `--convert-onnx-to-tosa).

module attributes {llvm.data_layout = "e-m:e-p270:32:32-p271:32:32-p272:64:64-i64:64-i128:128-f80:128-n8:16:32:64-S128", llvm.target_triple = "x86_64-unknown-linux-gnu", "onnx-mlir.symbol-postfix" = "model.onnx-basic.mlir"} {
  func.func @main_graph(%arg0: tensor<2x3x4x5xf32> {onnx.name = "X"}, %arg1: tensor<4x5xf32> {onnx.name = "W"}, %arg2: tensor<4x5xf32> {onnx.name = "B"}) -> (tensor<2x3x4x5xf32> {onnx.name = "Y"}, tensor<2x3x1x1xf32> {onnx.name = "Mean"}, tensor<2x3x1x1xf32> {onnx.name = "InvStdDev"}) {
    %0 = onnx.Constant dense<9.99999974E-6> : tensor<f32>
    %1 = "onnx.Cast"(%0) {saturate = 1 : si64, to = f32} : (tensor<f32>) -> tensor<f32>
    %2 = "onnx.Shape"(%arg0) {start = 0 : si64} : (tensor<2x3x4x5xf32>) -> tensor<4xi64>
    %3 = "onnx.Size"(%2) : (tensor<4xi64>) -> tensor<i64>
    %4 = onnx.Constant dense<0> : tensor<1xi64>
    %5 = onnx.Constant dense<-2> : tensor<1xi64>
    %6 = "onnx.NoValue"() {value} : () -> none
    %7 = "onnx.NoValue"() {value} : () -> none
    %8 = "onnx.Slice"(%2, %4, %5, %6, %7) : (tensor<4xi64>, tensor<1xi64>, tensor<1xi64>, none, none) -> tensor<2xi64>
    %9 = "onnx.Neg"(%5) : (tensor<1xi64>) -> tensor<1xi64>
    %10 = onnx.ConstantOfShape(%9) {value = dense<1> : tensor<1xi64>} : (tensor<1xi64>) -> tensor<?xi64>
    %11 = "onnx.Concat"(%8, %10) {axis = 0 : si64} : (tensor<2xi64>, tensor<?xi64>) -> tensor<?xi64>
    %12 = "onnx.Flatten"(%arg0) {axis = -2 : si64} : (tensor<2x3x4x5xf32>) -> tensor<6x20xf32>
    %13 = "onnx.Cast"(%12) {saturate = 1 : si64, to = f32} : (tensor<6x20xf32>) -> tensor<6x20xf32>
    %14 = onnx.Constant dense<1> : tensor<1xi64>
    %15 = "onnx.ReduceMean"(%13, %14) {keepdims = 1 : si64, noop_with_empty_axes = 0 : si64} : (tensor<6x20xf32>, tensor<1xi64>) -> tensor<6x1xf32>
    %16 = "onnx.Mul"(%13, %13) : (tensor<6x20xf32>, tensor<6x20xf32>) -> tensor<6x20xf32>
    %17 = "onnx.ReduceMean"(%16, %14) {keepdims = 1 : si64, noop_with_empty_axes = 0 : si64} : (tensor<6x20xf32>, tensor<1xi64>) -> tensor<6x1xf32>
    %18 = "onnx.Mul"(%15, %15) : (tensor<6x1xf32>, tensor<6x1xf32>) -> tensor<6x1xf32>
    %19 = "onnx.Sub"(%17, %18) : (tensor<6x1xf32>, tensor<6x1xf32>) -> tensor<6x1xf32>
    %20 = "onnx.Add"(%19, %1) : (tensor<6x1xf32>, tensor<f32>) -> tensor<6x1xf32>
    %21 = "onnx.Sqrt"(%20) : (tensor<6x1xf32>) -> tensor<6x1xf32>
    %22 = "onnx.Sub"(%13, %15) : (tensor<6x20xf32>, tensor<6x1xf32>) -> tensor<6x20xf32>
    %23 = "onnx.Div"(%22, %21) : (tensor<6x20xf32>, tensor<6x1xf32>) -> tensor<6x20xf32>
    %24 = "onnx.Cast"(%23) {saturate = 1 : si64, to = f32} : (tensor<6x20xf32>) -> tensor<6x20xf32>
    %25 = "onnx.Flatten"(%arg1) {axis = 0 : si64} : (tensor<4x5xf32>) -> tensor<1x20xf32>
    %26 = "onnx.Mul"(%24, %25) : (tensor<6x20xf32>, tensor<1x20xf32>) -> tensor<6x20xf32>
    %27 = "onnx.Flatten"(%arg2) {axis = 0 : si64} : (tensor<4x5xf32>) -> tensor<1x20xf32>
    %28 = "onnx.Add"(%26, %27) : (tensor<6x20xf32>, tensor<1x20xf32>) -> tensor<6x20xf32>
    %29 = "onnx.Reshape"(%28, %2) {allowzero = 0 : si64} : (tensor<6x20xf32>, tensor<4xi64>) -> tensor<2x3x4x5xf32>
    %30 = "onnx.Reciprocal"(%21) : (tensor<6x1xf32>) -> tensor<6x1xf32>
    %31 = "onnx.Reshape"(%15, %11) {allowzero = 0 : si64} : (tensor<6x1xf32>, tensor<?xi64>) -> tensor<2x3x1x1xf32>
    %32 = "onnx.Reshape"(%30, %11) {allowzero = 0 : si64} : (tensor<6x1xf32>, tensor<?xi64>) -> tensor<2x3x1x1xf32>
    onnx.Return %29, %31, %32 : tensor<2x3x4x5xf32>, tensor<2x3x1x1xf32>, tensor<2x3x1x1xf32>
  }
  "onnx.EntryPoint"() {func = @main_graph} : () -> ()
}

The problem happens in

// src/Dialect/ONNX/ONNXOps/Tensor/Reshape.cpp:26
template <>
LogicalResult ONNXReshapeOpShapeHelper::computeShape() {
  ONNXReshapeOpAdaptor operandAdaptor(operands);
  DimsExpr outputDims;

  // Get info about input data operand.
  Value data = operandAdaptor.getData();
  int64_t dataRank = data.getType().cast<ShapedType>().getShape().size();

  // Get info about shape operand.
  Value shape = operandAdaptor.getShape();
  int64_t outputRank = createIE->getShape(shape, 0);
  assert(outputRank != -1 && "Shape tensor must have constant shape");
  // !!!! Here outputRank is outputRank = 0x8000000000000000, i.e. -9223372036854775808
  // Initialize context and results.
  outputDims.resize(outputRank); // <--- !!!! This crashes
  ...
}

Neither ASan nor valgrind complains.

Commit 4400cbc.

Full transcript attached.

bad_onnx.txt