we are providing a way to do fp16 static quantization in fx graph mode quantization as well, it is ready in master. you can find an example of related tests:

  
      github.com
  
  
    pytorch/pytorch/blob/master/test/quantization/test_quantize_fx.py#L2145 2

            # weight
            ns.call_method("to"): 1 if is_reference else 0
        }
        self.checkGraphModeFxOp(
            model, data, QuantType.DYNAMIC, qlinear_fun,
            is_reference=is_reference,
            custom_qconfig_dict={"": float16_dynamic_qconfig},
            prepare_expected_node_occurrence=prepare_node_occurrence,
            expected_node_occurrence=convert_node_occurrence)

def test_linear_static_fp16(self):
    class FuncLinear(torch.nn.Module):
        def __init__(self, use_bias, has_relu, f_relu):
            super(FuncLinear, self).__init__()
            self.w = torch.randn(4, 30)
            self.b = torch.randn(4)
            self.use_bias = use_bias
            if has_relu:
                if f_relu:
                    self.relu = F.relu
                else: