Incorrect quantize output with fp16 scales

[parthchadha]

module @ins_input_outs_t5_1 {
  func.func @main(%arg0: tensor<2x2xf16> {tensorrt.shape_profile = #tensorrt.shape_profile<min = [2, 2], opt = [2, 2], max = [2, 2]>}) -> tensor<?x?xi8> {
    %cst = stablehlo.constant dense<[1.999510e-01, 9.997550e-02]> : tensor<2xf16>
    %cst_0 = stablehlo.constant dense<-1.280000e+02> : tensor<f16>
    %c = stablehlo.constant dense<2> : tensor<i32>
    %c_1 = stablehlo.constant dense<1> : tensor<1xi32>
    %c_2 = stablehlo.constant dense<2> : tensor<1xi32>
    %c_3 = stablehlo.constant dense<2> : tensor<i32>
    %c_4 = stablehlo.constant dense<2> : tensor<1xi32>
    %0 = stablehlo.concatenate %c_2, %c_4, dim = 0 : (tensor<1xi32>, tensor<1xi32>) -> tensor<2xi32>
    %1 = stablehlo.dynamic_broadcast_in_dim %cst, %0, dims = [0] : (tensor<2xf16>, tensor<2xi32>) -> tensor<2x2xf16>
    %2 = stablehlo.divide %arg0, %1 : tensor<2x2xf16>
    %3 = stablehlo.round_nearest_even %2 : tensor<2x2xf16>
    %cst_5 = stablehlo.constant dense<1.270000e+02> : tensor<f16>
    %4 = stablehlo.clamp %cst_0, %3, %cst_5 : (tensor<f16>, tensor<2x2xf16>, tensor<f16>) -> tensor<2x2xf16>
    %5 = stablehlo.convert %4 : (tensor<2x2xf16>) -> tensor<?x?xi8>
    return %5 : tensor<?x?xi8>
  }
}

the output of ^ network is incorrect when input is:

tensor(
    [[1.0000, 2.0000],
     [3.0000, 4.0000]], 
    dtype=float16, loc=gpu:0, shape=(2, 2))

output:

tensor(
    [[0, 127],
     [0, 0]], 
    dtype=int8, loc=gpu:0, shape=(2, 2))

[christopherbate]

Thanks, I will investigate