Large scale MIMO FDD systems are often hampered by bandwidth required to feedback downlink CSI. Previous works have made notable progresses in efficient CSI encoding and recovery by taking advantage of FDD uplink/downlink reciprocity between their CSI magnitudes. Such framework separately encodes CSI phase and magnitude. To further enhance feedback efficiency, we propose a new deep learning architecture for phase encoding based on limited CSI feedback and magnitude-aided information. Our contribution features a framework with a modified loss function to enable end-to-end joint optimization of CSI magnitude and phase recovery. Our test results show superior performance in indoor/outdoor scenarios.