text: /*
   * Licensed to the Apache Software Foundation (ASF) under one
   * or more contributor license agreements.  See the NOTICE file
   * distributed with this work for additional information
   * regarding copyright ownership.  The ASF licenses this file
   * to you under the Apache License, Version 2.0 (the
   * License); you may not use this file except in compliance
   * with the License.  You may obtain a copy of the License at
   *
   *   http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing,
   * software distributed under the License is distributed on an
   * AS IS BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
   * KIND, either express or implied.  See the License for the
   * specific language governing permissions and limitations
   * under the License.
   */
  
  /*
   * Copyright (c) 2021, OPEN AI LAB
   * Author: qtang@openailab.com
   */
  
  #include "graph/tensor.h"
  #include "graph/node.h"
  #include "graph/graph.h"
  #include "utility/sys_port.h"
  #include "utility/float.h"
  #include "utility/log.h"
  #include "device/cpu/cpu_node.h"
  #include "device/cpu/cpu_graph.h"
  #include "device/cpu/cpu_module.h"
  
  #include <math.h>
  
  int ref_squareddifference_fp32(struct tensor* input_tensor_0, struct tensor* input_tensor_1,
                                 struct tensor* output_tensor, int num_thread)
  {
      // dims size = 2 or 3
      if (input_tensor_0->dim_num < 4)
      {
          float* input0 = (float*)input_tensor_0->data;
          float* input1 = (float*)input_tensor_1->data;
          float* output = (float*)output_tensor->data;
          int total_size = output_tensor->elem_num;
  
          for (int i = 0; i < total_size; i++)
          {
              output[i] = powf((input0[i] - input1[i]), 2);
          }
  
          return 0;
      }
      // dims size 3
      else if (output_tensor->dim_num == 4)
      {
          int w = output_tensor->dims[3];
          int h = output_tensor->dims[2];
          int channels = output_tensor->dims[1];
          int size = h * w;
          int c_step = h * w;
  
          float* input0 = (float*)input_tensor_0->data;
          float* input1 = (float*)input_tensor_1->data;
          float* output = (float*)output_tensor->data;
  
  #pragma omp parallel for num_threads(num_thread)
          for (int q = 0; q < channels; q++)
          {
              float* src0 = input0 + c_step * q;
              float* src1 = input1 + c_step * q;
              float* dst = output + c_step * q;
  
              for (int i = 0; i < size; i++)
              {
                  dst[i] = powf((src0[i] - src1[i]), 2);
              }
          }
  
          return 0;
      }
  
      return -1;
  }
  
  int ref_squareddifference_uint8(struct tensor* input_tensor_0, struct tensor* input_tensor_1,
                                  struct tensor* output_tensor, int num_thread)
  {
      /* dequant */
      uint8_t* input0_uint8 = (uint8_t*)input_tensor_0->data;
      uint8_t* input1_uint8 = (uint8_t*)input_tensor_1->data;
      uint8_t* output_uint8 = (uint8_t*)output_tensor->data;
      float input0_scale = input_tensor_0->scale;
      float input1_scale = input_tensor_1->scale;
      float output_scale = output_tensor->scale;
      int32_t input0_zero = input_tensor_0->zero_point;
      int32_t input1_zero = input_tensor_1->zero_point;
      int32_t output_zero = output_tensor->zero_point;
      int input0_size = input_tensor_0->elem_num;
      int input1_size = input_tensor_1->elem_num;
      int output_size = output_tensor->elem_num;
  
      float* input0 = (float*)sys_malloc(input0_size * sizeof(float));
      float* input1 = (float*)sys_malloc(input1_size * sizeof(float));
      float* output = (float*)sys_malloc(output_size * sizeof(float));
  
      for (int i = 0; i < input0_size; i++)
      {
          input0[i] = ((float)input0_uint8[i] - (float)input0_zero) * input0_scale;
      }
      for (int i = 0; i < input1_size; i++)
      {
          input1[i] = ((float)input1_uint8[i] - (float)input1_zero) * input1_scale;
      }
  
      // dims size = 2 or 3
      if (input_tensor_0->dim_num < 4)
      {
          int total_size = output_tensor->elem_num;
  
          for (int i = 0; i < total_size; i++)
          {
              output[i] = powf((input0[i] - input1[i]), 2);
          }
  
          return 0;
      }
      // dims size 3
      else if (output_tensor->dim_num == 4)
      {
          int w = output_tensor->dims[3];
          int h = output_tensor->dims[2];
          int channels = output_tensor->dims[1];
          int size = h * w;
          int c_step = h * w;
  
  #pragma omp parallel for num_threads(num_thread)
          for (int q = 0; q < channels; q++)
          {
              float* src0 = input0 + c_step * q;
              float* src1 = input1 + c_step * q;
              float* dst = output + c_step * q;
  
              for (int i = 0; i < size; i++)
              {
                  dst[i] = powf((src0[i] - src1[i]), 2);
              }
          }
  
          return 0;
      }
  
      /* quant */
      for (int i = 0; i < output_size; i++)
      {
          int udata = round(output[i] / output_scale + output_zero);
          if (udata > 255)
              udata = 255;
          else if (udata < 0)
              udata = 0;
          output_uint8[i] = udata;
      }
  
      sys_free(input0);
      sys_free(input1);
      sys_free(output);
  
      return -1;
  }
  
  static int init_node(struct node_ops* node_ops, struct exec_node* exec_node, struct exec_graph* exec_graph)
  {
      return 0;
  }
  
  static int release_node(struct node_ops* node_ops, struct exec_node* exec_node, struct exec_graph* exec_graph)
  {
      return 0;
  }
  
  static int prerun(struct node_ops* node_ops, struct exec_node* exec_node, struct exec_graph* exec_graph)
  {
      return 0;
  }
  
  static int run(struct node_ops* node_ops, struct exec_node* exec_node, struct exec_graph* exec_graph)
  {
      struct node* ir_node = exec_node->ir_node;
      struct graph* ir_graph = ir_node->graph;
      struct tensor* input_tensor_0;
      struct tensor* input_tensor_1;
      struct tensor* output_tensor;
      int layout = ir_graph->graph_layout;
  
      input_tensor_0 = get_ir_graph_tensor(ir_graph, ir_node->input_tensors[0]);
      input_tensor_1 = get_ir_graph_tensor(ir_graph, ir_node->input_tensors[1]);
      output_tensor = get_ir_graph_tensor(ir_graph, ir_node->output_tensors[0]);
  
      int ret = -1;
      if (input_tensor_0->data_type == TENGINE_DT_FP32)
          ret = ref_squareddifference_fp32(input_tensor_0, input_tensor_1, output_tensor, exec_graph->num_thread);
      else if (input_tensor_0->data_type == TENGINE_DT_UINT8)
          ret = ref_squareddifference_uint8(input_tensor_0, input_tensor_1, output_tensor, exec_graph->num_thread);
  
      return ret;
  }
  
  static int score(struct node_ops* node_ops, struct exec_graph* exec_graph, struct node* exec_node)
  {
      return OPS_SCORE_CANDO;
  }
  
  static struct node_ops hcl_node_ops = {.prerun = prerun,
                                         .run = run,
                                         .reshape = NULL,
                                         .postrun = NULL,
                                         .init_node = init_node,
                                         .release_node = release_node,
                                         .score = score};
  
  int register_squareddifference_ref_op()
  {
      return register_builtin_node_ops(OP_SQUAREDDIFFERENCE, &hcl_node_ops);
  }
  
  int unregister_squareddifference_ref_op()
  {
      return unregister_builtin_node_ops(OP_SQUAREDDIFFERENCE, &hcl_node_ops);
  }
  