本章节中的单算子是基于Ascend IR定义的，描述文件为json格式。关于单算子的定义请参见《算子清单》。

单算子生成的离线模型文件命名规则为：序号_opType_输入的描述(dataType_format_shape)_输出的描述(dataType_format_shape)。

dataType以及format对应枚举值请从${INSTALL_DIR}/include/graph/types.h文件中查看，枚举值从0开始依次递增。其中，${INSTALL_DIR}请替换为CANN软件安装后文件存储路径。例如，若安装的Ascend-cann-toolkit软件包，则安装后文件存储路径为：$HOME/Ascend/ascend-toolkit/latest。

format为ND：

[
{
  "op": "GEMM",
  "input_desc": [
    {
      "format": "ND",
      "shape": [16, 16],
      "type": "float16"
    },
    {
      "format": "ND",
      "shape": [16, 16],
      "type": "float16"
    },
    {
      "format": "ND",
      "shape": [16, 16],
      "type": "float16"
    },
    {
      "format": "ND",
      "shape": [],
      "type": "float16"
    },
    {
      "format": "ND",
      "shape": [],
      "type": "float16"
    }
  ],
  "output_desc": [
    {
      "format": "ND",
      "shape": [16, 16],
      "type": "float16"
    }
  ],
  "attr": [
  {
    "name": "transpose_a",
    "type": "bool",
    "value": false
  },
  {
    "name": "transpose_b",
    "type": "bool",
    "value": false
    }
  ]
}
]

上述单算子转换后的离线模型为：0_GEMM_1_2_16_16_1_2_16_16_1_2_16_16_1_2_1_2_1_2_16_16.om

format为NCHW：

[
 {
   "op": "Conv2D",  
   "input_desc": [     
     {        
       "format": "NCHW",      
       "shape": [1, 3, 16, 16],     
       "type": "float16"     
     },      
     {       
       "format": "NCHW",       
       "shape": [3, 3, 3, 3],        
       "type": "float16"       
     }     
   ],    
   "output_desc": [     
     {     
       "format": "NCHW",        
       "shape": [1, 3, 16, 16],     
       "type": "float16"      
     }    
   ],   
   "attr": [      
       {       
         "name": "strides",       
         "type": "list_int",       
         "value": [1, 1, 1, 1]    
       },     
       {       
         "name": "pads",      
         "type": "list_int",      
         "value": [1, 1, 1, 1]     
       },      
       {        
         "name": "dilations",      
         "type": "list_int",     
         "value": [1, 1, 1, 1]    
       }  
   ]  
 } 
 ]

上述单算子转换后的离线模型为：0_Conv2D_1_0_1_3_16_16_1_0_3_3_3_3_1_0_1_3_16_16.om

Tensor的实现format与原始format不同

ATC模型转换时，会将origin_format与origin_shape转成离线模型需要的format与shape。

[
  {
    "op": "Add",
    "input_desc": [
      {
        "format": "NC1HWC0",
        "origin_format": "NCHW",
        "shape": [8, 1, 16, 4, 16],
        "origin_shape": [8, 16, 16, 4],
        "type": "float16"
      },
      {
        "format": "NC1HWC0",
        "origin_format": "NCHW",
        "shape": [8, 1, 16, 4, 16],
        "origin_shape": [8, 16, 16, 4],
        "type": "float16"
      }
    ],
    "output_desc": [
      {
        "format": "NC1HWC0",
        "origin_format": "NCHW",
        "shape": [8, 1, 16, 4, 16],
        "origin_shape": [8, 16, 16, 4],
        "type": "float16"
      }
    ]
  }
]

上述单算子转换后的离线模型为：0_Add_1_3_8_1_16_4_16_1_3_8_1_16_4_16_1_3_8_1_16_4_16.om

输入指定为常量

该场景下，支持设置为常量的输入，新增is_const和const_value两个参数，分别表示是否为常量以及常量取值，const_value当前仅支持一维list配置，具体配置个数由shape取值决定，例如，如下样例中shape为2，则const_value中列表个数为2；const_value中取值类型由type决定，假设type取值为float16，则单算子编译时会自动将const_value中的取值转换为float16格式的取值。

[
  {
    "op": "ResizeBilinearV2",
    "input_desc": [
      {
        "format": "NHWC",
        "name": "x",
        "shape": [
          4,
          16,
          16,
          16
        ],
        "type": "float16"
      },
      {
        "format": "NHWC",
        "is_const": true,
        "const_value": [49, 49],
        "name": "size",
        "shape": [
          2
        ],
        "type": "int32",
	"test": [7, 7.0]
      }
    ],
    "output_desc": [
      {
        "format": "NHWC",
        "name": "y",
        "shape": [
          4,
          48,
          48,
          16
        ],
        "type": "float"
      }
    ],
	"attr": [
      {
        "name": "align_corners",
        "type": "bool",
        "value": false
      },
      {
        "name": "half_pixel_centers",
        "type": "bool",
        "value": false
      }
    ]
  }
]

上述单算子转换后的离线模型为：0_ResizeBilinearV2_1_1_4_16_16_16_3_1_2_0_1_4_48_48_16.om

可选输入（optional input）：

[
  {
    "op": "MatMulV2",
    "input_desc": [
      {
        "format": "ND",
        "shape": [16, 16],
        "type": "float"
      },
      {
        "format": "ND",
        "shape": [16, 16],
        "type": "float"
      },
      {
        "format": "RESERVED",
        "shape": [16, 16],
        "type": "UNDEFINED"
      },
      {
        "format": "RESERVED",
        "shape": [16, 16],
        "type": "UNDEFINED"
      }
    ],
    "attr": [
    {  
        "name": "transpose_x1",
        "type": "bool",
        "value": false
    },
    {
        "name": "transpose_x2",
        "type": "bool",
        "value": false
    }
    ],
    "output_desc": [
      {
        "format": "ND",
        "shape": [16, 16],
        "type": "float"
      }
	]
  }
]

上述单算子转换后的离线模型为：0_MatMulV2_0_2_16_16_0_2_16_16_26_40_16_16_26_40_16_16_0_2_16_16.om

输入个数不确定（动态输入场景）：

该场景下，单算子的输入个数不确定。比如AddN单算子：

构造的单算子json文件使用动态输入dynamic_input参数，而不使用Tensor的名称name参数，该场景下构造的描述文件为：

该场景下算子的dynamic_input取值必须和算子信息库中该算子定义的输入name的取值相同。

具体设置几个输入，由AddN单算子描述文件属性参数中N的取值决定，用户可以自行修改输入的个数，但是必须和属性中N的取值匹配。（该说明仅针对AddN算子生效，其他动态输入算子的约束以具体算子为准。）

[
    {
        "op": "AddN",
        "input_desc": [
            {
                "dynamic_input": "x",
                "format": "NCHW",
                "shape": [1,3,166,166],
                "type": "float32"
            },
            {
                "dynamic_input": "x",
                "format": "NCHW",
                "shape": [1,3,166,166],
                "type": "int32"
            },
            {
                "dynamic_input": "x",
                "format": "NCHW",
                "shape": [1,3,166,166],
                "type": "float32"
            }
        ],
        "output_desc": [
            {
                "format": "NCHW",
                "shape": [1,3,166,166],
                "type": "float32"
            }
        ],
        "attr": [
            {
                "name": "N",
                "type": "int",
                "value": 3
            }
        ]
    }
]

构造的单算子json文件使用Tensor的名称name参数，而不使用动态输入dynamic_input参数，该场景下构造的描述文件为：

该场景下算子的name取值必须和算子原型定义中算子的输入名称相同，根据输入的个数自动生成x0、x1、x2……。

具体设置几个Tensor名称，由AddN单算子描述文件属性参数中N的取值决定，用户可以自行修改Tensor名称的个数，但是必须和属性中N的取值匹配，例如N取值为3，则name取值分别设置为x0、x1、x2。（该说明仅针对AddN算子生效，其他动态输入算子的约束以具体算子为准。）

[
    {
        "op": "AddN",
        "input_desc": [
            {
		"name":"x0",
                "format": "NCHW",
                "shape": [1,3,166,166],
                "type": "float32"
            },
            {
		"name":"x1",
                "format": "NCHW",
                "shape": [1,3,166,166],
                "type": "int32"
            },
            {
		"name":"x2"
                "format": "NCHW",
                "shape": [1,3,166,166],
                "type": "float32",

            }
        ],
        "output_desc": [
            {
                "format": "NCHW",
                "shape": [1,3,166,166],
                "type": "float32"
            }
        ],
        "attr": [
            {
                "name": "N",
                "type": "int",
                "value": 3
            }
        ]
    }
]

上述单算子转换后的离线模型为：0_AddN_0_0_1_3_166_166_3_0_1_3_166_166_0_0_1_3_166_166_0_0_1_3_166_166.om

模糊编译场景

如果用户无法获取算子的shape范围，又想编译一次达到多次执行推理的目的时，可以使用该特性。该场景下构造的单算子描述文件如下，当前仅支持Transformer网络模型涉及的算子：

[   
    {
     "compile_flag":1
      "op": "Add",
      "input_desc": [
        {
          "format": "ND",
          "shape": [3,3],
          "type": "int32"
        },
        {
          "format": "ND",
          "shape": [3,3],
          "type": "int32"
        }
      ],
      "output_desc": [
        {
          "format": "ND",
          "shape": [3,3],
          "type": "int32"
        }
      ]
    }
  ]

单算子描述文件配置