!293 Register all tbe op info with new registration mode

Merge pull request !293 from zjun/Modify_all_tbe_op

mindspore/ops/_op_impl/tbe/add.py

@@ -14,71 +14,28 @@
 # ============================================================================
 
 """Add op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+add_op_info = TBERegOp("Add") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("add.so") \
+    .compute_cost(10) \
+    .kernel_name("add") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Add",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "add.so",
-    "compute_cost": 10,
-    "kernel_name": "add",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float",
-                "float", "int32", "int32", "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(add_op_info)
 def _add_tbe():
     """Add TBE register"""
     return

mindspore/ops/_op_impl/tbe/add_n.py

@@ -14,61 +14,33 @@
 # ============================================================================
 
 """AddN op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+add_n_op_info = TBERegOp("AddN") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("add_n.so") \
+    .compute_cost(10) \
+    .kernel_name("add_n") \
+    .partial_flag(True) \
+    .attr("n", "required", "int", "all") \
+    .input(0, "x", False, "dynamic", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.I32_FracZ, DataType.I32_FracZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "AddN",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "add_n.so",
-    "compute_cost": 10,
-    "kernel_name": "add_n",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "n",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float","int32","int32","int32"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","FracZ","FRACTAL_NZ",
-                "DefaultFormat","NC1HWC0","FracZ","FRACTAL_NZ","DefaultFormat","NC1HWC0","FracZ"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "dynamic",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float","int32","int32","int32"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","FracZ","FRACTAL_NZ",
-                "DefaultFormat","NC1HWC0","FracZ","FRACTAL_NZ","DefaultFormat","NC1HWC0","FracZ"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(add_n_op_info)
 def _add_n_tbe():
     """AddN TBE register"""
     return

mindspore/ops/_op_impl/tbe/apply_adam.py

@@ -14,214 +14,66 @@
 # ============================================================================
 
 """ApplyAdam op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+apply_adam_op_info = TBERegOp("Adam") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("apply_adam.so") \
+    .compute_cost(10) \
+    .kernel_name("apply_adam") \
+    .partial_flag(True) \
+    .attr("use_locking", "optional", "bool", "true,false", "false") \
+    .attr("use_nesterov", "optional", "bool", "true,false", "false") \
+    .input(0, "var", False, "required", "all") \
+    .input(1, "m", False, "required", "all") \
+    .input(2, "v", False, "required", "all") \
+    .input(3, "beta1_power", False, "required", "all") \
+    .input(4, "beta2_power", False, "required", "all") \
+    .input(5, "lr", False, "required", "all") \
+    .input(6, "beta1", False, "required", "all") \
+    .input(7, "beta2", False, "required", "all") \
+    .input(8, "epsilon", False, "required", "all") \
+    .input(9, "grad", False, "required", "all") \
+    .output(0, "var", False, "required", "all") \
+    .output(1, "m", False, "required", "all") \
+    .output(2, "v", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0,
+                  DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD,
+                  DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ,
+                  DataType.F16_FracZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0,
+                  DataType.F32_C1HWNCoC0) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ,
+                  DataType.F32_FracZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Adam",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "apply_adam.so",
-    "compute_cost": 10,
-    "kernel_name": "apply_adam",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "use_locking",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "true,false",
-            "default_value":"false"
-        },
-        {
-            "name": "use_nesterov",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "true,false",
-            "default_value":"false"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
-            ],
-            "name": "var",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
-            ],
-            "name": "m",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
-            ],
-            "name": "v",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "beta1_power",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-                {
-            "index": 4,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "beta2_power",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 5,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "lr",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 6,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "beta1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 7,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "beta2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 8,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "epsilon",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 9,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float", "float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
-            ],
-            "name": "grad",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
-            ],
-            "name": "var",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
-            ],
-            "name": "m",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ"
-            ],
-            "name": "v",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(apply_adam_op_info)
 def _apply_adam_tbe():
     """ApplyAdam TBE register"""
     return

mindspore/ops/_op_impl/tbe/apply_momentum.py

@@ -14,112 +14,42 @@
 # ============================================================================
 
 """ApplyMomentum op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+apply_momentum_op_info = TBERegOp("ApplyMomentum") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("apply_momentum.so") \
+    .compute_cost(10) \
+    .kernel_name("apply_momentum") \
+    .partial_flag(True) \
+    .attr("use_nesterov", "optional", "bool", "true,false", "false") \
+    .input(0, "var", False, "required", "all") \
+    .input(1, "accum", False, "required", "all") \
+    .input(2, "lr", False, "required", "all") \
+    .input(3, "grad", False, "required", "all") \
+    .input(4, "momentum", False, "required", "all") \
+    .output(0, "var", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_Default, DataType.F16_5HD,
+                  DataType.F16_Default, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_Default, DataType.F16_C1HWNCoC0,
+                  DataType.F16_Default, DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_Default, DataType.F16_FracZ,
+                  DataType.F16_Default, DataType.F16_FracZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_Default, DataType.F32_5HD,
+                  DataType.F32_Default, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_Default, DataType.F32_C1HWNCoC0,
+                  DataType.F32_Default, DataType.F32_C1HWNCoC0) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_Default, DataType.F32_FracZ,
+                  DataType.F32_Default, DataType.F32_FracZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ApplyMomentum",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "apply_momentum.so",
-    "compute_cost": 10,
-    "kernel_name": "apply_momentum",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "use_nesterov",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "true,false",
-            "default_value":"false"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "DefaultFormat", "FracZ", "C1HWNCoC0"
-            ],
-            "name": "var",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "DefaultFormat", "FracZ", "C1HWNCoC0"
-            ],
-            "name": "accum",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "lr",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "DefaultFormat", "FracZ", "C1HWNCoC0"
-            ],
-            "name": "grad",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "momentum",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0", "C1HWNCoC0", "DefaultFormat", "FracZ", "NC1HWC0", "DefaultFormat", "FracZ", "C1HWNCoC0"
-            ],
-            "name": "var",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(apply_momentum_op_info)
 def _apply_momentum_tbe():
     """ApplyMomentum TBE register"""
     return

mindspore/ops/_op_impl/tbe/arg_max_with_value.py

@@ -14,70 +14,25 @@
 # ============================================================================
 
 """ArgMaxWithValue op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+arg_max_with_value_op_info = TBERegOp("ArgMaxWithValue") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("arg_max_with_value.so") \
+    .compute_cost(10) \
+    .kernel_name("arg_max_with_value") \
+    .partial_flag(True) \
+    .attr("axis", "required", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "indice", False, "required", "all") \
+    .output(1, "values", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.I32_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ArgMaxWithValue",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "arg_max_with_value.so",
-    "compute_cost": 10,
-    "kernel_name": "arg_max_with_value",
-    "partial_flag": true,
-    "attr": [
-            {
-            "name": "axis",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "indice",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "values",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(arg_max_with_value_op_info)
 def _arg_max_with_value_tbe():
     """ArgMaxWithValue TBE register"""
     return

mindspore/ops/_op_impl/tbe/arg_min_with_value.py

@@ -14,70 +14,25 @@
 # ============================================================================
 
 """ArgMinWithValue op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+arg_min_with_value_op_info = TBERegOp("ArgMaxWithValue") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("arg_min_with_value.so") \
+    .compute_cost(10) \
+    .kernel_name("arg_min_with_value") \
+    .partial_flag(True) \
+    .attr("axis", "required", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "indice", False, "required", "all") \
+    .output(1, "values", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.I32_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ArgMinWithValue",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "arg_min_with_value.so",
-    "compute_cost": 10,
-    "kernel_name": "arg_min_with_value",
-    "partial_flag": true,
-    "attr": [
-            {
-            "name": "axis",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "indice",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "values",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(arg_min_with_value_op_info)
 def _arg_min_with_value_tbe():
     """ArgMinWithValue TBE register"""
     return

mindspore/ops/_op_impl/tbe/assign.py

@@ -14,93 +14,43 @@
 # ============================================================================
 
 """Assign op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+assign_op_info = TBERegOp("Assign") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("assign.so") \
+    .compute_cost(10) \
+    .kernel_name("assign") \
+    .partial_flag(True) \
+    .input(0, "resource", False, "required", "all") \
+    .input(1, "value", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.I16_Default, DataType.I16_Default, DataType.I16_Default) \
+    .dtype_format(DataType.I16_5HD, DataType.I16_5HD, DataType.I16_5HD) \
+    .dtype_format(DataType.U16_Default, DataType.U16_Default, DataType.U16_Default) \
+    .dtype_format(DataType.U16_5HD, DataType.U16_5HD, DataType.U16_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.U32_Default, DataType.U32_Default, DataType.U32_Default) \
+    .dtype_format(DataType.U32_5HD, DataType.U32_5HD, DataType.U32_5HD) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.I64_Default) \
+    .dtype_format(DataType.I64_5HD, DataType.I64_5HD, DataType.I64_5HD) \
+    .dtype_format(DataType.U64_Default, DataType.U64_Default, DataType.U64_Default) \
+    .dtype_format(DataType.U64_5HD, DataType.U64_5HD, DataType.U64_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Assign",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "assign.so",
-    "compute_cost": 10,
-    "kernel_name": "assign",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32", "uint32", "uint32", "uint32", "uint32", "int8",
-                "int8", "int8", "int8", "uint8", "uint8", "uint8", "uint8", "int16", "int16", "int16",
-                "int16", "uint16", "uint16", "uint16", "uint16", "int64", "int64", "int64", "int64",
-                "uint64", "uint64", "uint64", "uint64", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "FRACTAL_NZ"
-            ],
-            "name": "resource",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32", "int32",
-                "int32", "int32", "uint32", "uint32", "uint32", "uint32", "int8", "int8", "int8", "int8", "uint8",
-                "uint8", "uint8", "uint8", "int16", "int16", "int16", "int16", "uint16", "uint16", "uint16",
-                "uint16", "int64", "int64", "int64", "int64", "uint64", "uint64", "uint64", "uint64", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "FRACTAL_NZ"
-            ],
-            "name": "value",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32", "uint32", "uint32", "uint32", "uint32", "int8", "int8", "int8",
-                "int8", "uint8", "uint8", "uint8", "uint8", "int16", "int16", "int16", "int16", "uint16",
-                "uint16", "uint16", "uint16", "int64", "int64", "int64", "int64",
-                "uint64", "uint64", "uint64", "uint64", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "FRACTAL_NZ"
-            ],
-            "name": "y",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(assign_op_info)
 def _assign_tbe():
     """Assign TBE register"""
     return

mindspore/ops/_op_impl/tbe/assign_add.py

@@ -14,80 +14,34 @@
 # ============================================================================
 
 """AssignAdd op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+assign_add_op_info = TBERegOp("AssignAdd") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("assignadd.so") \
+    .compute_cost(10) \
+    .kernel_name("assignadd") \
+    .partial_flag(True) \
+    .input(0, "ref", False, "required", "all") \
+    .input(1, "value", False, "required", "all") \
+    .output(0, "output_ref", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.I64_Default) \
+    .dtype_format(DataType.I64_5HD, DataType.I64_5HD, DataType.I64_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "AssignAdd",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "assignadd.so",
-    "compute_cost": 10,
-    "kernel_name": "assignadd",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype":[
-            "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32", "int32",
-            "int32", "int32", "int8", "int8", "int8", "int8", "uint8", "uint8", "uint8", "uint8", "int64",
-            "int64", "int64", "int64"
-            ],
-            "format":[
-            "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-            "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-            "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-            "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "ref",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype":[
-            "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32", "int32",
-            "int32", "int32", "int8", "int8", "int8", "int8", "uint8", "uint8", "uint8", "uint8", "int64",
-            "int64", "int64", "int64"
-            ],
-            "format":[
-            "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-            "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-            "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-            "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "value",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype":[
-            "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32", "int32",
-            "int32", "int32", "int8", "int8", "int8", "int8", "uint8", "uint8", "uint8", "uint8", "int64",
-            "int64", "int64", "int64"
-            ],
-            "format":[
-            "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-            "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-            "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-            "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output_ref",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(assign_add_op_info)
 def _assign_add_tbe():
     """AssignAdd TBE register"""
     return

mindspore/ops/_op_impl/tbe/assign_sub.py

@@ -14,65 +14,27 @@
 # ============================================================================
 
 """AssignSub op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+assign_sub_op_info = TBERegOp("AssignSub") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("assign_sub.so") \
+    .compute_cost(10) \
+    .kernel_name("assign_sub") \
+    .partial_flag(True) \
+    .input(0, "var", False, "required", "all") \
+    .input(1, "value", False, "required", "all") \
+    .output(0, "output_ref", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "AssignSub",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "assign_sub.so",
-    "compute_cost": 10,
-    "kernel_name": "assign_sub",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float", "int32", "int8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "var",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float", "int32", "int8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "value",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float", "int32", "int8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "out_ref",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(assign_sub_op_info)
 def _assign_sub_tbe():
     """AssignSub TBE register"""
     return

mindspore/ops/_op_impl/tbe/atomic_addr_clean.py

@@ -14,31 +14,20 @@
 # ============================================================================
 
 """AtomicAddrClean op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp
 
+atomic_addr_clean_op_info = TBERegOp("AtomicAddrClean") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("atomic_addr_clean.so") \
+    .compute_cost(10) \
+    .kernel_name("atomic_addr_clean") \
+    .partial_flag(True) \
+    .attr("automic_add_mem_size", "required", "listInt", "all") \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "AtomicAddrClean",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "atomic_addr_clean.so",
-    "compute_cost": 10,
-    "kernel_name": "atomic_addr_clean",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "automic_add_mem_size",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-    ],
-    "outputs": [
-    ]
-}""")
+
+@op_info_register(atomic_addr_clean_op_info)
 def _atomic_addr_clean_tbe():
     """AtomicAddrClean TBE register"""
     return

mindspore/ops/_op_impl/tbe/batch_matmul.py

@@ -14,88 +14,29 @@
 # ============================================================================
 
 """BatchMatMul op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+batch_matmul_op_info = TBERegOp("BatchMatMul") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("batch_matmul.so") \
+    .compute_cost(10) \
+    .kernel_name("batch_matmul") \
+    .attr("transpose_x1", "required", "bool", "all") \
+    .attr("transpose_x2", "required", "bool", "all") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .input(2, "bias", False, "optional", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_Default, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "BatchMatMul",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "batch_matmul.so",
-    "compute_cost": 10,
-    "kernel_name": "batch_matmul",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "transpose_x1",
-            "param_type": "required",
-            "type": "bool",
-            "value": "all"
-        },
-        {
-            "name": "transpose_x2",
-            "param_type": "required",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","int32","int32"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","FRACTAL_NZ","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float","float","int32","int32"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","FRACTAL_NZ","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float","float","int32","int32"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "bias",
-            "need_compile": false,
-            "param_type": "optional",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","int32","int32"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","FRACTAL_NZ","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(batch_matmul_op_info)
 def _batch_matmul_tbe():
     """BatchMatMul TBE register"""
     return

mindspore/ops/_op_impl/tbe/batchnorm.py

@@ -14,174 +14,45 @@
 # ============================================================================
 
 """BatchNorm op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+batch_norm_op_info = TBERegOp("BatchNorm") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("batch_norm.so") \
+    .compute_cost(10) \
+    .kernel_name("batch_norm") \
+    .partial_flag(True) \
+    .attr("epsilon", "optional", "float", "all") \
+    .attr("data_format", "optional", "str", "all") \
+    .attr("is_training", "optional", "bool", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "scale", False, "required", "all") \
+    .input(2, "offset", False, "required", "all") \
+    .input(3, "mean", False, "optional", "all") \
+    .input(4, "variance", False, "optional", "all") \
+    .output(0, "y", False, "required", "all") \
+    .output(1, "batch_mean", False, "required", "all") \
+    .output(2, "batch_variance", False, "required", "all") \
+    .output(3, "reserve_space_1", False, "optional", "all") \
+    .output(4, "reserve_space_2", False, "optional", "all") \
+    .output(5, "reserve_space_3", False, "optional", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F16_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F16_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "BatchNorm",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "batch_norm.so",
-    "compute_cost": 10,
-    "kernel_name": "batch_norm",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "epsilon",
-            "param_type": "required",
-            "type": "float",
-            "value": "all"
-        },
-        {
-            "name": "data_format",
-            "param_type": "required",
-            "type": "str",
-            "value": "all"
-        },
-        {
-            "name": "is_training",
-            "param_type": "required",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0", "DefaultFormat","NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat", "NC1HWC0"
-            ],
-            "name": "scale",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "offset",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "mean",
-            "need_compile": false,
-            "param_type": "optional",
-            "shape": "all"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "variance",
-            "need_compile": false,
-            "param_type": "optional",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0", "DefaultFormat","NC1HWC0"
-            ],
-            "name": "y",
-            "param_type": "required"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "batch_mean",
-            "param_type": "required"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float", "float", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "batch_variance",
-            "param_type": "required"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float", "float", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "reserve_space_1",
-            "param_type": "optional"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float", "float", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "reserve_space_2",
-            "param_type": "optional"
-        },
-        {
-            "index": 5,
-            "dtype": [
-                "float", "float", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "reserve_space_3",
-            "param_type": "optional"
-        }
-    ]
-}""")
+
+@op_info_register(batch_norm_op_info)
 def _batch_norm_tbe():
     """BatchNorm TBE register"""
     return

mindspore/ops/_op_impl/tbe/batchnorm_grad.py

@@ -14,181 +14,45 @@
 # ============================================================================
 
 """BatchNormGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+batch_norm_grad_op_info = TBERegOp("BatchNormGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("batchnormgrad.so") \
+    .compute_cost(10) \
+    .kernel_name("batchnormgrad") \
+    .partial_flag(True) \
+    .attr("epsilon", "optional", "float", "all") \
+    .attr("data_format", "optional", "str", "all") \
+    .attr("is_training", "optional", "bool", "all") \
+    .input(0, "y_backprop", False, "required", "all") \
+    .input(1, "x", False, "required", "all") \
+    .input(2, "scale", False, "required", "all") \
+    .input(3, "reserve_space_1", False, "required", "all") \
+    .input(4, "reserve_space_2", False, "required", "all") \
+    .input(5, "reserve_space_3", False, "required", "all") \
+    .output(0, "x_backprop", False, "required", "all") \
+    .output(1, "scale_backprop", False, "required", "all") \
+    .output(2, "offset_backprop", False, "required", "all") \
+    .output(3, "reserve_space_4", False, "optional", "all") \
+    .output(4, "reserve_space_5", False, "optional", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F16_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F16_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "BatchNormGrad",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "batchnormgrad.so",
-    "compute_cost": 10,
-    "kernel_name": "batchnormgrad",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "epsilon",
-            "param_type": "optional",
-            "type": "float",
-            "value": "all"
-        },
-        {
-            "name": "data_format",
-            "param_type": "optional",
-            "type": "str",
-            "value": "all"
-        },
-        {
-            "name": "is_training",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "y_backprop",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float","float","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "scale",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float","float","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "reserve_space_1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float","float","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "reserve_space_2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 5,
-            "dtype": [
-                "float","float","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "reserve_space_3",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x_backprop",
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float","float","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "scale_backprop",
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float","float","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "offset_backprop",
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float","float","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "reserve_space_4",
-            "param_type": "optional",
-            "shape": "all"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float","float","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "reserve_space_5",
-            "param_type": "optional",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(batch_norm_grad_op_info)
 def _batch_norm_grad_tbe():
     """BatchNormGrad TBE register"""
     return

mindspore/ops/_op_impl/tbe/bias_add.py

@@ -14,70 +14,26 @@
 # ============================================================================
 
 """BiasAdd op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+bias_add_grad_op_info = TBERegOp("BiasAdd") \
+    .fusion_type("COMMREDUCE") \
+    .async_flag(False) \
+    .binfile_name("bias_add.so") \
+    .compute_cost(10) \
+    .kernel_name("bias_add") \
+    .partial_flag(True) \
+    .attr("data_format", "required", "str", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "bias", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "BiasAdd",
-    "imply_type": "TBE",
-    "fusion_type": "COMMREDUCE",
-    "async_flag": false,
-    "binfile_name": "bias_add.so",
-    "compute_cost": 10,
-    "kernel_name": "bias_add",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "data_format",
-            "param_type": "required",
-            "type": "str",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32", "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "int32", "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "bias",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32", "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(bias_add_grad_op_info)
 def _bias_add_tbe():
     """BiasAdd TBE register"""
     return

mindspore/ops/_op_impl/tbe/bias_add_grad.py

@@ -14,57 +14,26 @@
 # ============================================================================
 
 """BiasAddGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+bias_add_grad_op_info = TBERegOp("BiasAddGrad") \
+    .fusion_type("COMMREDUCE") \
+    .async_flag(False) \
+    .binfile_name("biasaddgrad.so") \
+    .compute_cost(10) \
+    .kernel_name("biasaddgrad") \
+    .partial_flag(True) \
+    .attr("data_format", "required", "str", "all") \
+    .input(0, "output_backprop", False, "required", "all") \
+    .output(0, "output", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "BiasAddGrad",
-    "imply_type": "TBE",
-    "fusion_type": "COMMREDUCE",
-    "async_flag": false,
-    "binfile_name": "biasaddgrad.so",
-    "compute_cost": 10,
-    "kernel_name": "biasaddgrad",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "data_format",
-            "param_type": "required",
-            "type": "str",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","FRACTAL_NZ","DefaultFormat"
-            ],
-            "name": "out_backprop",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "output",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(bias_add_grad_op_info)
 def _bias_add_grad_tbe():
     """BiasAddGrad TBE register"""
     return

mindspore/ops/_op_impl/tbe/bn_training_reduce.py

@@ -14,60 +14,24 @@
 # ============================================================================
 
 """BatchNorm op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+bn_training_reduce_op_info = TBERegOp("BNTrainingReduce") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("bn_training_reduce.so") \
+    .compute_cost(10) \
+    .kernel_name("bn_training_reduce") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "sum", False, "required", "all") \
+    .output(1, "square_sum", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "BNTrainingReduce",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "bn_training_reduce.so",
-    "compute_cost": 10,
-    "kernel_name": "bn_training_reduce",
-    "partial_flag": true,
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float"
-            ],
-            "format": [
-                "NC1HWC0", "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float","float"
-            ],
-            "format": [
-                "NC1HWC0", "NC1HWC0"
-            ],
-            "name": "sum",
-            "param_type": "required"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float","float"
-            ],
-            "format": [
-                "NC1HWC0", "NC1HWC0"
-            ],
-            "name": "square_sum",
-            "param_type": "required"
-        }
-    ]
-}""")
+
+@op_info_register(bn_training_reduce_op_info)
 def _bn_training_reduce_tbe():
     """BNTrainingReduce TBE register"""
     return

mindspore/ops/_op_impl/tbe/bn_training_reduce_grad.py

@@ -14,134 +14,32 @@
 # ============================================================================
 
 """BatchNormGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+bn_training_reduce_grad_op_info = TBERegOp("BNTrainingReduceGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("bn_training_reduce_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("bn_training_reduce_grad") \
+    .partial_flag(True) \
+    .attr("epsilon", "optional", "float", "all") \
+    .input(0, "grads", False, "required", "all") \
+    .input(1, "x_norm", False, "required", "all") \
+    .input(2, "diff_scale", False, "required", "all") \
+    .input(3, "diff_offset", False, "required", "all") \
+    .input(4, "scale", False, "required", "all") \
+    .input(5, "batch_mean", False, "required", "all") \
+    .input(6, "batch_variance", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "BNTrainingReduceGrad",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "bn_training_reduce_grad.so",
-    "compute_cost": 10,
-    "kernel_name": "bn_training_reduce_grad",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "epsilon",
-            "param_type": "optional",
-            "type": "float",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "grads",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "x_norm",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "diff_scale",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "diff_offset",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "scale",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 5,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "batch_mean",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 6,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "batch_variance",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "y",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(bn_training_reduce_grad_op_info)
 def _bn_training_reduce_grad_tbe():
     """BNTrainingReduceGrad TBE register"""
     return

mindspore/ops/_op_impl/tbe/bn_training_update.py

@@ -14,200 +14,40 @@
 # ============================================================================
 
 """BatchNormGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+bn_training_update_op_info = TBERegOp("BNTrainingUpdate") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("bn_training_update.so") \
+    .compute_cost(10) \
+    .kernel_name("bn_training_update") \
+    .partial_flag(True) \
+    .attr("factor", "optional", "float", "all") \
+    .attr("epsilon", "optional", "float", "all") \
+    .attr("isRef", "optional", "bool", "all", "true") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "sum", False, "required", "all") \
+    .input(2, "square_sum", False, "required", "all") \
+    .input(3, "scale", False, "required", "all") \
+    .input(4, "offset", False, "required", "all") \
+    .input(5, "mean", False, "required", "all") \
+    .input(6, "variance", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .output(1, "mean", False, "required", "all") \
+    .output(2, "variance", False, "required", "all") \
+    .output(3, "batch_mean", False, "required", "all") \
+    .output(4, "batch_variance", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F16_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "BNTrainingUpdate",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "bn_training_update.so",
-    "compute_cost": 10,
-    "kernel_name": "bn_training_update",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "factor",
-            "param_type": "optional",
-            "type": "float",
-            "value": "all"
-        },
-        {
-            "name": "epsilon",
-            "param_type": "optional",
-            "type": "float",
-            "value": "all"
-        },
-        {
-            "name": "isRef",
-            "param_type": "optional",
-            "type": "bool",
-            "default_value":"true",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "sum",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "square_sum",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "scale",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "offset",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 5,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "mean",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 6,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "variance",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "mean",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "variance",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "batch_mean",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "batch_variance",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(bn_training_update_op_info)
 def _bn_training_update_tbe():
     """BNTrainingUpdate TBE register"""
     return

mindspore/ops/_op_impl/tbe/bn_training_update_grad.py

@@ -14,109 +14,30 @@
 # ============================================================================
 
 """BatchNormGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+bn_training_update_grad_op_info = TBERegOp("BNTrainingUpdateGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("bn_training_update_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("bn_training_update_grad") \
+    .partial_flag(True) \
+    .attr("epsilon", "optional", "float", "all") \
+    .input(0, "grads", False, "required", "all") \
+    .input(1, "x", False, "required", "all") \
+    .input(2, "batch_mean", False, "required", "all") \
+    .input(3, "batch_variance", False, "required", "all") \
+    .output(0, "diff_scale", False, "required", "all") \
+    .output(1, "diff_offset", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "BNTrainingUpdateGrad",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "bn_training_update_grad.so",
-    "compute_cost": 10,
-    "kernel_name": "bn_training_update_grad",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "epsilon",
-            "param_type": "optional",
-            "type": "float",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "grads",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "batch_mean",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "batch_variance",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "diff_scale",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float", "float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0"
-            ],
-            "name": "diff_offset",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(bn_training_update_grad_op_info)
 def _bn_training_update_grad_tbe():
     """BNTrainingUpdateGrad TBE register"""
     return

mindspore/ops/_op_impl/tbe/cast.py

@@ -14,69 +14,42 @@
 # ============================================================================
 
 """Cast op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+cast_op_info = TBERegOp("Cast") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("cast.so") \
+    .compute_cost(10) \
+    .kernel_name("cast") \
+    .partial_flag(True) \
+    .attr("dst_type", "required", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.BOOL_Default, DataType.F16_Default) \
+    .dtype_format(DataType.BOOL_Default, DataType.U8_Default) \
+    .dtype_format(DataType.BOOL_Default, DataType.F32_Default) \
+    .dtype_format(DataType.BOOL_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I8_Default, DataType.F16_Default) \
+    .dtype_format(DataType.I8_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I8_Default, DataType.I32_Default) \
+    .dtype_format(DataType.U8_Default, DataType.F16_Default) \
+    .dtype_format(DataType.U8_Default, DataType.F32_Default) \
+    .dtype_format(DataType.U8_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I32_Default, DataType.F16_Default) \
+    .dtype_format(DataType.I32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.U8_Default) \
+    .dtype_format(DataType.F16_Default, DataType.U8_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.I32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Cast",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "cast.so",
-    "compute_cost": 10,
-    "kernel_name": "cast",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "dst_type",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float", 
-                "int32", "int32", "int32", "int32", "int32", 
-                "int8", "int8", "int8", "uint8", "uint8", "uint8", 
-                "bool", "bool", "bool", "bool", "float16"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float", "int32", "float16", "int32",
-                 "float16", "float", "int8", "uint8", "bool", 
-                 "float16", "float", "int32", "float16", "float", "int32", 
-                 "float16", "float", "int32", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(cast_op_info)
 def _cast_tbe():
     """Cast TBE register"""
     return

mindspore/ops/_op_impl/tbe/clip_by_norm_no_div_sum.py

@@ -14,90 +14,28 @@
 # ============================================================================
 
 """ClipByNormNoDivSum op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+clip_by_norm_no_div_sum_op_info = TBERegOp("ClipByNormNoDivSum") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("clip_by_norm_no_div_sum.so") \
+    .compute_cost(10) \
+    .kernel_name("clip_by_norm_no_div_sum") \
+    .partial_flag(True) \
+    .input(0, "input_x", False, "required", "all") \
+    .input(1, "input1", False, "required", "all") \
+    .input(2, "input2", False, "required", "all") \
+    .input(3, "input3", False, "required", "all") \
+    .output(0, "output_y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ClipByNormNoDivSum",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "clip_by_norm_no_div_sum.so",
-    "compute_cost": 10,
-    "kernel_name": "clip_by_norm_no_div_sum",
-    "partial_flag": true,
-    "attr":[
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float32"
-            ],
-            "format": [
-               "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "input_x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float32"
-            ],
-            "format": [
-               "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "input1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16", "float32"
-            ],
-            "format": [
-               "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "input2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float16", "float32"
-            ],
-            "format": [
-               "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "input3",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output_y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(clip_by_norm_no_div_sum_op_info)
 def _clip_by_norm_no_div_sum_tbe():
     """ClipByNormNoDivSum TBE register"""
     return

mindspore/ops/_op_impl/tbe/clip_by_value.py

@@ -14,85 +14,30 @@
 # ============================================================================
 
 """ClipByValue op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+clip_by_value_op_info = TBERegOp("ClipByValue") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("clip_by_value.so") \
+    .compute_cost(10) \
+    .kernel_name("clip_by_value") \
+    .partial_flag(True) \
+    .attr("dst_type", "required", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "clip_value_min", False, "required", "all") \
+    .input(2, "clip_value_max", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ClipByValue",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "clip_by_value.so",
-    "compute_cost": 10,
-    "kernel_name": "clip_by_value",
-    "partial_flag": true,
-    "attr":[
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32"
-            ],
-            "format": [
-               "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-               "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32"
-            ],
-            "format": [
-               "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-               "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "clip_value_min",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32"
-            ],
-            "format": [
-               "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-               "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "clip_value_max",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32"
-            ],
-            "format": [
-               "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-               "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(clip_by_value_op_info)
 def _clip_by_value_tbe():
     """ClipByValue TBE register"""
     return

mindspore/ops/_op_impl/tbe/concat.py

@@ -14,141 +14,44 @@
 # ============================================================================
 
 """Concat op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+concat_op_info = TBERegOp("Concat") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("concat_d.so") \
+    .compute_cost(10) \
+    .kernel_name("concat_d") \
+    .partial_flag(True) \
+    .attr("axis", "required", "int", "all") \
+    .input(0, "input_values", False, "dynamic", "all") \
+    .output(0, "output_data", False, "required", "all") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.BOOL_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.I16_Default, DataType.I16_Default) \
+    .dtype_format(DataType.I16_5HD, DataType.I16_5HD) \
+    .dtype_format(DataType.U16_Default, DataType.U16_Default) \
+    .dtype_format(DataType.U16_5HD, DataType.U16_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.U32_Default, DataType.U32_Default) \
+    .dtype_format(DataType.U32_5HD, DataType.U32_5HD) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default) \
+    .dtype_format(DataType.I64_5HD, DataType.I64_5HD) \
+    .dtype_format(DataType.U64_Default, DataType.U64_Default) \
+    .dtype_format(DataType.U64_5HD, DataType.U64_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Concat",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "concat_d.so",
-    "compute_cost": 10,
-    "kernel_name": "concat_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16",
-                "float16",
-                "float",
-                "float",
-                "int32",
-                "int32",
-                "int8",
-                "int8",
-                "int16",
-                "int16",
-                "int64",
-                "int64",
-                "uint8",
-                "uint8",
-                "uint16",
-                "uint16",
-                "uint32",
-                "uint32",
-                "uint64",
-                "uint64",
-                "bool",
-                "bool"
-            ],
-            "format": [
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0"
-            ],
-            "name": "input_values",
-            "need_compile": false,
-            "param_type": "dynamic",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16",
-                "float16",
-                "float",
-                "float",
-                "int32",
-                "int32",
-                "int8",
-                "int8",
-                "int16",
-                "int16",
-                "int64",
-                "int64",
-                "uint8",
-                "uint8",
-                "uint16",
-                "uint16",
-                "uint32",
-                "uint32",
-                "uint64",
-                "uint64",
-                "bool",
-                "bool"
-            ],
-            "format": [
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "NC1HWC0"
-            ],
-            "name": "output_data",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(concat_op_info)
 def _concat_tbe():
     """Concat TBE register"""
     return

mindspore/ops/_op_impl/tbe/confusion_softmax_grad.py

@@ -14,65 +14,28 @@
 # ============================================================================
 
 """ConfusionSoftmaxGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+confusion_softmax_grad_op_info = TBERegOp("ConfusionSoftmaxGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("confusion_softmax_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("confusion_softmax_grad") \
+    .partial_flag(True) \
+    .input(0, "grad", False, "required", "all") \
+    .input(1, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ConfusionSoftmaxGrad",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "confusion_softmax_grad.so",
-    "compute_cost": 10,
-    "kernel_name": "confusion_softmax_grad",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float", "float", "float"
-            ],
-            "format": [
-               "FRACTAL_NZ", "DefaultFormat", "NC1HWC0", "FRACTAL_NZ", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "grad",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float16", "float", "float", "float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "NC1HWC0", "FRACTAL_NZ", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float", "float", "float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "NC1HWC0", "FRACTAL_NZ", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(confusion_softmax_grad_op_info)
 def _confusion_softmax_grad_tbe():
     """ConfusionSoftmaxGrad TBE register"""
     return

mindspore/ops/_op_impl/tbe/confusion_transpose_d.py

@@ -14,79 +14,44 @@
 # ============================================================================
 
 """ConfusionTransposeD op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+confusion_transpose_d_op_info = TBERegOp("ConfusionTransposeD") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("confusion_transpose_d.so") \
+    .compute_cost(10) \
+    .kernel_name("confusion_transpose_d") \
+    .partial_flag(True) \
+    .attr("perm", "required", "listInt", "all") \
+    .attr("shape", "required", "listInt", "all") \
+    .attr("transpose_first", "required", "bool", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_FracNZ, DataType.I8_FracNZ) \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_FracNZ, DataType.U8_FracNZ) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I16_FracNZ, DataType.I16_FracNZ) \
+    .dtype_format(DataType.I16_Default, DataType.I16_Default) \
+    .dtype_format(DataType.U16_FracNZ, DataType.U16_FracNZ) \
+    .dtype_format(DataType.U16_Default, DataType.U16_Default) \
+    .dtype_format(DataType.I32_FracNZ, DataType.I32_FracNZ) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.U32_FracNZ, DataType.U32_FracNZ) \
+    .dtype_format(DataType.U32_Default, DataType.U32_Default) \
+    .dtype_format(DataType.I64_FracNZ, DataType.I64_FracNZ) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default) \
+    .dtype_format(DataType.U64_FracNZ, DataType.U64_FracNZ) \
+    .dtype_format(DataType.U64_Default, DataType.U64_Default) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ConfusionTransposeD",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "confusion_transpose_d.so",
-    "compute_cost": 10,
-    "kernel_name": "confusion_transpose_d",
-    "partial_flag": true,
-    "attr":[
-        {
-            "name":"perm",
-            "param_type":"required",
-            "type":"listInt",
-            "value":"all"
-        },
-        {
-            "name":"shape",
-            "param_type":"required",
-            "type":"listInt",
-            "value":"all"
-        },
-        {
-            "name":"transpose_first",
-            "param_type":"required",
-            "type":"bool",
-            "value":"all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float", "int8", "int16", "int32", "int64", "uint8", "uint16", "uint32",
-                "uint64", "float16", "float", "int8", "int16", "int32", "int64", "uint8", "uint16",
-                "uint32", "uint64"
-            ],
-            "format": [
-               "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ",
-               "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ", "DefaultFormat", "DefaultFormat",
-               "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-               "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float", "int8", "int16", "int32", "int64", "uint8", "uint16", "uint32",
-                "uint64", "float16", "float", "int8", "int16", "int32", "int64", "uint8", "uint16",
-                "uint32", "uint64"
-            ],
-            "format": [
-               "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ",
-               "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ", "FRACTAL_NZ", "DefaultFormat", "DefaultFormat",
-               "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-               "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(confusion_transpose_d_op_info)
 def _confusion_transpose_d_tbe():
     """ConfusionTransposeD TBE register"""
     return

mindspore/ops/_op_impl/tbe/conv2d.py

@@ -14,114 +14,30 @@
 # ============================================================================
 
 """Conv2D op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+conv2d_op_info = TBERegOp("Conv2D") \
+    .fusion_type("CONVLUTION") \
+    .async_flag(False) \
+    .binfile_name("conv2d.so") \
+    .compute_cost(10) \
+    .kernel_name("conv2d") \
+    .partial_flag(True) \
+    .attr("stride", "required", "listInt", "all") \
+    .attr("pad_list", "required", "listInt", "all") \
+    .attr("dilation", "required", "listInt", "all") \
+    .attr("offset_a", "optional", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "filter", False, "required", "all") \
+    .input(2, "bias", False, "optional", "all") \
+    .input(3, "offset_w", False, "optional", "all") \
+    .output(0, "y", True, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_FracZ, DataType.F16_Default, DataType.I8_Default,
+                  DataType.F16_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Conv2D",
-    "imply_type": "TBE",
-    "fusion_type": "CONVLUTION",
-    "async_flag": false,
-    "binfile_name": "conv2d.so",
-    "compute_cost": 10,
-    "kernel_name": "conv2d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "stride",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "pad_list",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "dilation",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "offset_a",
-            "param_type": "optional",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "FracZ"
-            ],
-            "name": "filter",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "DefaultFormat"
-            ],
-            "name": "bias",
-            "need_compile": false,
-            "param_type": "optional",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "int8"
-            ],
-            "format": [
-                "DefaultFormat"
-            ],
-            "name": "offset_w",
-            "need_compile": false,
-            "param_type": "optional",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(conv2d_op_info)
 def _conv2d_tbe():
     """Conv2D TBE register"""
     return

mindspore/ops/_op_impl/tbe/conv2d_backprop_filter.py

@@ -14,89 +14,27 @@
 # ============================================================================
 
 """Conv2DBackpropFilter op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+conv2d_backprop_filter_op_info = TBERegOp("Conv2DBackpropFilter") \
+    .fusion_type("CONVLUTION") \
+    .async_flag(False) \
+    .binfile_name("conv2d_backprop_filter_d.so") \
+    .compute_cost(10) \
+    .kernel_name("conv2d_backprop_filter_d") \
+    .partial_flag(True) \
+    .attr("filter_sizes", "required", "listInt", "all") \
+    .attr("stride", "required", "listInt", "all") \
+    .attr("pad_mode", "required", "str", "all") \
+    .attr("dilation", "required", "listInt", "all") \
+    .input(0, "out_backprop", False, "required", "all") \
+    .input(1, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F32_FracZ) \
+    .get_op_info()
 
-# map to tbe kernel name conv2d_backprop_filter_d
-@op_info_register("""{
-    "op_name": "Conv2DBackpropFilter",
-    "imply_type": "TBE",
-    "fusion_type": "CONVLUTION",
-    "async_flag": false,
-    "binfile_name": "conv2d_backprop_filter_d.so",
-    "compute_cost": 10,
-    "kernel_name": "conv2d_backprop_filter_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "filter_sizes",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "stride",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "pad_mode",
-            "param_type": "required",
-            "type": "str",
-            "value": "all"
-        },
-        {
-            "name": "dilation",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "out_backprop",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float32"
-            ],
-            "format": [
-                "FracZ"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(conv2d_backprop_filter_op_info)
 def _conv2d_backprop_filter_tbe():
     """Conv2DBackpropFilter TBE register"""
     return

mindspore/ops/_op_impl/tbe/conv2d_backprop_input.py

@@ -14,88 +14,27 @@
 # ============================================================================
 
 """Conv2DBackpropInput op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+conv2d_backprop_input_op_info = TBERegOp("Conv2DBackpropInput") \
+    .fusion_type("CONVLUTION") \
+    .async_flag(False) \
+    .binfile_name("conv2d_backprop_input_d.so") \
+    .compute_cost(10) \
+    .kernel_name("conv2d_backprop_input_d") \
+    .partial_flag(True) \
+    .attr("input_sizes", "required", "listInt", "all") \
+    .attr("stride", "required", "listInt", "all") \
+    .attr("pad_mode", "required", "str", "all") \
+    .attr("dilation", "required", "listInt", "all") \
+    .input(0, "out_backprop", False, "required", "all") \
+    .input(1, "filter", False, "required", "all") \
+    .output(0, "y", True, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_FracZ, DataType.F16_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Conv2DBackpropInput",
-    "imply_type": "TBE",
-    "fusion_type": "CONVLUTION",
-    "async_flag": false,
-    "binfile_name": "conv2d_backprop_input_d.so",
-    "compute_cost": 10,
-    "kernel_name": "conv2d_backprop_input_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "input_sizes",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "stride",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "pad_mode",
-            "param_type": "required",
-            "type": "str",
-            "value": "all"
-        },
-        {
-            "name": "dilation",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "out_backprop",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "FracZ"
-            ],
-            "name": "filter",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(conv2d_backprop_input_op_info)
 def _conv2d_backprop_input_tbe():
     """Conv2DBackpropInput TBE register"""
     return

mindspore/ops/_op_impl/tbe/div.py

@@ -14,71 +14,32 @@
 # ============================================================================
 
 """Div op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+div_op_info = TBERegOp("Div") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("div.so") \
+    .compute_cost(10) \
+    .kernel_name("div") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Div",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "div.so",
-    "compute_cost": 10,
-    "kernel_name": "div",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float", "int32", "int32", "int8", "int8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float", "int32", "int32", "int8", "int8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float", "int32", "int32", "int8", "int8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(div_op_info)
 def _div_tbe():
     """Div TBE register"""
     return

mindspore/ops/_op_impl/tbe/dropout_do_mask.py

@@ -14,76 +14,25 @@
 # ============================================================================
 
 """DropoutdoMask op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+drop_out_do_mask_op_info = TBERegOp("DropoutDoMask") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("drop_out_do_mask.so") \
+    .compute_cost(10) \
+    .kernel_name("drop_out_do_mask") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "mask", False, "required", "all") \
+    .input(2, "keep_prob", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.U8_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.U8_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "DropoutDoMask",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "drop_out_do_mask.so",
-    "compute_cost": 10,
-    "kernel_name": "drop_out_do_mask",
-    "partial_flag": true,
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "uint8","uint8","uint8","uint8","uint8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "mask",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "keep_prob",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(drop_out_do_mask_op_info)
 def _dropout_do_mask_tbe():
     """DropoutdoMask TBE register"""
     return

mindspore/ops/_op_impl/tbe/equal.py

@@ -14,66 +14,32 @@
 # ============================================================================
 
 """Equal op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+equal_op_info = TBERegOp("Equal") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("equal.so") \
+    .compute_cost(10) \
+    .kernel_name("equal") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.BOOL_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Equal",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "equal.so",
-    "compute_cost": 10,
-    "kernel_name": "equal",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float","int32","int32","int8","int8","uint8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float","float","int32","int32","int8","int8","uint8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-         {
-            "index": 0,
-            "dtype": [
-                "bool","bool","bool","bool","bool","bool","bool","bool","bool","bool"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "y",
-            "param_type": "required"
-        }
-    ]
-}""")
+@op_info_register(equal_op_info)
 def _equal_tbe():
     """Equal TBE register"""
     return

mindspore/ops/_op_impl/tbe/exp.py

@@ -14,52 +14,25 @@
 # ============================================================================
 
 """Exp op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+exp_op_info = TBERegOp("Exp") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("exp.so") \
+    .compute_cost(10) \
+    .kernel_name("exp") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Exp",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "exp.so",
-    "compute_cost": 10,
-    "kernel_name": "exp",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(exp_op_info)
 def _exp_tbe():
     """Exp TBE register"""
     return

mindspore/ops/_op_impl/tbe/expand_dims.py

@@ -14,57 +14,25 @@
 # ============================================================================
 
 """ExpandDims op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+expand_dims_op_info = TBERegOp("ExpandDims") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("expand_dims.so") \
+    .compute_cost(10) \
+    .kernel_name("expand_dims") \
+    .partial_flag(True) \
+    .attr("axis", "required", "listInt", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ExpandDims",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "expand_dims.so",
-    "compute_cost": 10,
-    "kernel_name": "expand_dims",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(expand_dims_op_info)
 def _expand_dims_tbe():
     """ExpandDims TBE register"""
     return

mindspore/ops/_op_impl/tbe/floor_div.py

@@ -14,64 +14,27 @@
 # ============================================================================
 
 """FloorDiv op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+floordiv_op_info = TBERegOp("FloorDiv") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("floordiv.so") \
+    .compute_cost(10) \
+    .kernel_name("floordiv") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "FloorDiv",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "floordiv.so",
-    "compute_cost": 10,
-    "kernel_name": "floordiv",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-         {
-            "index": 0,
-            "dtype": [
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(floordiv_op_info)
 def _floor_div_tbe():
     """FloorDiv TBE register"""
     return

mindspore/ops/_op_impl/tbe/fused_mul_add.py

@@ -14,93 +14,38 @@
 # ============================================================================
 
 """FusedMulAdd op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+fused_mul_add_op_info = TBERegOp("FusedMulAdd") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("fused_mul_add.so") \
+    .compute_cost(10) \
+    .kernel_name("fused_mul_add") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .input(2, "x3", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.I32_FracZ, DataType.I32_FracZ, DataType.I32_FracZ, DataType.I32_FracZ) \
+    .dtype_format(DataType.I32_FracNZ, DataType.I32_FracNZ, DataType.I32_FracNZ, DataType.I32_FracNZ) \
+    .dtype_format(DataType.I32_C1HWNCoC0, DataType.I32_C1HWNCoC0, DataType.I32_C1HWNCoC0, DataType.I32_C1HWNCoC0) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "FusedMulAdd",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "fused_mul_add.so",
-    "compute_cost": 10,
-    "kernel_name": "fused_mul_add",
-    "partial_flag": true,
-    "attr": [
-    
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32", "int32", "int32", "int32", "int32",
-                "float16", "float16", "float16", "float16", "float16",
-                "float", "float", "float", "float", "float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0",   
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0", 
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "int32", "int32", "int32", "int32", "int32",
-                "float16", "float16", "float16", "float16", "float16",
-                "float", "float", "float", "float", "float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0",   
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0", 
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "int32", "int32", "int32", "int32", "int32",
-                "float16", "float16", "float16", "float16", "float16",
-                "float", "float", "float", "float", "float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0",   
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0", 
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "x3",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32", "int32", "int32", "int32", "int32",
-                "float16", "float16", "float16", "float16", "float16",
-                "float", "float", "float", "float", "float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0",   
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0", 
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "y",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(fused_mul_add_op_info)
 def _fused_mul_add_tbe():
     """FusedMulAdd TBE register"""
     return

mindspore/ops/_op_impl/tbe/fused_mul_add_n.py

@@ -14,86 +14,31 @@
 # ============================================================================
 
 """FusedMulAddN op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+fused_mul_add_n_op_info = TBERegOp("FusedMulAddN") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("fused_mul_add_n.so") \
+    .compute_cost(10) \
+    .kernel_name("fused_mul_add_n") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .input(2, "x3", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_Default, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_Default, DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_Default, DataType.F16_FracZ) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_Default, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_Default, DataType.F32_C1HWNCoC0) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_Default, DataType.F32_FracZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "FusedMulAddN",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "fused_mul_add_n.so",
-    "compute_cost": 10,
-    "kernel_name": "fused_mul_add_n",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ",
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ",
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x3",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ",
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(fused_mul_add_n_op_info)
 def _fused_mul_add_n_tbe():
     """FusedMulAddN TBE register"""
     return

mindspore/ops/_op_impl/tbe/fused_mul_apply_momentum.py

@@ -14,137 +14,43 @@
 # ============================================================================
 
 """FusedMulApplyMomentum op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+fused_mul_apply_momentum_op_info = TBERegOp("FusedMulApplyMomentum") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("fused_mul_apply_momentum.so") \
+    .compute_cost(10) \
+    .kernel_name("fused_mul_apply_momentum") \
+    .partial_flag(True) \
+    .attr("use_nesterov", "optional", "bool", "true,false", "false") \
+    .input(0, "var", False, "required", "all") \
+    .input(1, "accum", False, "required", "all") \
+    .input(2, "lr", False, "required", "all") \
+    .input(3, "x1", False, "required", "all") \
+    .input(4, "momentum", False, "required", "all") \
+    .input(5, "x2", False, "required", "all") \
+    .output(0, "var", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_Default, DataType.F16_5HD,
+                  DataType.F16_Default, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_Default, DataType.F16_C1HWNCoC0,
+                  DataType.F16_Default, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_Default, DataType.F16_FracZ,
+                  DataType.F16_Default, DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_Default, DataType.F32_5HD,
+                  DataType.F32_Default, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_Default, DataType.F32_C1HWNCoC0,
+                  DataType.F32_Default, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_Default, DataType.F32_FracZ,
+                  DataType.F32_Default, DataType.F32_FracZ, DataType.F32_FracZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "FusedMulApplyMomentum",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "fused_mul_apply_momentum.so",
-    "compute_cost": 10,
-    "kernel_name": "fused_mul_apply_momentum",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "use_nesterov",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "true,false",
-            "default_value":"false"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ",
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ"
-            ],
-            "name": "var",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ",
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ"
-            ],
-            "name": "accum",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "lr",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ",
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "momentum",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 5,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ",
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16",
-                "float","float","float","float"
-            ],
-            "format": [
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ",
-                "NC1HWC0","C1HWNCoC0","DefaultFormat","FracZ"
-            ],
-            "name": "var",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(fused_mul_apply_momentum_op_info)
 def _fused_mul_apply_momentum_tbe():
     """FusedMulApplyMomentum TBE register"""
     return

mindspore/ops/_op_impl/tbe/gather_v2.py

@@ -14,94 +14,53 @@
 # ============================================================================
 
 """AddN op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+gather_v2_op_info = TBERegOp("GatherV2") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("gather_v2_d.so") \
+    .compute_cost(10) \
+    .kernel_name("gather_v2_d") \
+    .partial_flag(True) \
+    .attr("axis", "optional", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "indices", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I32_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_Default, DataType.I64_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I32_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.I8_5HD, DataType.I64_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.I8_FracZ, DataType.I32_FracZ, DataType.I8_FracZ) \
+    .dtype_format(DataType.I8_FracZ, DataType.I64_FracZ, DataType.I8_FracZ) \
+    .dtype_format(DataType.U8_Default, DataType.I32_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.I64_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.I32_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.U8_5HD, DataType.I64_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.U8_FracZ, DataType.I32_FracZ, DataType.U8_FracZ) \
+    .dtype_format(DataType.U8_FracZ, DataType.I64_FracZ, DataType.U8_FracZ) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I64_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.I32_5HD, DataType.I64_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.I32_FracZ, DataType.I32_FracZ, DataType.I32_FracZ) \
+    .dtype_format(DataType.I32_FracZ, DataType.I64_FracZ, DataType.I32_FracZ) \
+    .dtype_format(DataType.F16_Default, DataType.I32_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_Default, DataType.I64_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.I32_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_5HD, DataType.I64_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.I32_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_FracZ, DataType.I64_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_Default, DataType.I64_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.I32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_5HD, DataType.I64_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.I32_FracZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_FracZ, DataType.I64_FracZ, DataType.F32_FracZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "GatherV2",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "gather_v2_d.so",
-    "compute_cost": 10,
-    "kernel_name": "gather_v2_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "optional",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16","float16","float16",
-                "float","float","float","float","float","float",
-                "int32","int32","int32", "int32","int32","int32",
-                "uint8","uint8","uint8","uint8","uint8","uint8",
-                "int8","int8", "int8","int8","int8", "int8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "int32","int32","int32","int64","int64","int64",
-                "int32","int32","int32","int64","int64","int64",
-                "int32","int32","int32","int64","int64","int64",
-                "int32","int32","int32","int64","int64","int64",
-                "int32","int32","int32","int64","int64","int64"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ"
-            ],
-            "name": "indices",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-         {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float16","float16","float16",
-                "float","float","float","float","float","float",
-                "int32","int32","int32", "int32","int32","int32",
-                "uint8","uint8","uint8","uint8","uint8","uint8",
-                "int8","int8", "int8","int8","int8", "int8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ",
-                "DefaultFormat","NC1HWC0","FracZ","DefaultFormat","NC1HWC0","FracZ"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(gather_v2_op_info)
 def _gather_v2_tbe():
     """GatherV2 TBE register"""
     return

mindspore/ops/_op_impl/tbe/gelu.py

@@ -14,51 +14,29 @@
 # ============================================================================
 
 """Gelu op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+gelu_op_info = TBERegOp("Gelu") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("gelu.so") \
+    .compute_cost(10) \
+    .kernel_name("gelu") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Gelu",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "gelu.so",
-    "compute_cost": 10,
-    "kernel_name": "gelu",
-    "partial_flag": true,
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-               "float16","float","float16","float","float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","FRACTAL_NZ","FracZ","FracZ","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-               "float16","float","float16","float","float16","float16","float16","float16","float","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","FRACTAL_NZ","FracZ","FracZ","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(gelu_op_info)
 def _gelu_tbe():
     """Gelu TBE register"""
     return

mindspore/ops/_op_impl/tbe/gelu_grad.py

@@ -14,77 +14,29 @@
 # ============================================================================
 
 """GeluGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+gelu_grad_op_info = TBERegOp("GeluGrad") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("gelu_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("gelu_grad") \
+    .partial_flag(True) \
+    .input(0, "dy", False, "required", "all") \
+    .input(1, "x", False, "required", "all") \
+    .input(2, "y", False, "required", "all") \
+    .output(0, "z", True, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "GeluGrad",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "gelu_grad.so",
-    "compute_cost": 10,
-    "kernel_name": "gelu_grad",
-    "partial_flag": true,
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "dy",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "z",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(gelu_grad_op_info)
 def _gelu_grad_tbe():
     """GeluGrad TBE register"""
     return

mindspore/ops/_op_impl/tbe/greater.py

@@ -14,68 +14,32 @@
 # ============================================================================
 
 """Greater op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+greater_op_info = TBERegOp("Greater") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("greater.so") \
+    .compute_cost(10) \
+    .kernel_name("greater") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.BOOL_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Greater",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "greater.so",
-    "compute_cost": 10,
-    "kernel_name": "greater",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float","int32","int32","int8","int8","uint8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float","float","int32","int32","int8","int8","uint8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "bool","bool","bool","bool","bool","bool","bool","bool","bool","bool"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(greater_op_info)
 def _greater_tbe():
     """Greater TBE register"""
     return

mindspore/ops/_op_impl/tbe/lamb_next_mv.py

@@ -14,279 +14,46 @@
 # ============================================================================
 
 """LambNextMV op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+lamb_next_mv_op_info = TBERegOp("LambNextMV") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("lamb_next_m_v.so") \
+    .compute_cost(10) \
+    .kernel_name("lamb_next_m_v") \
+    .partial_flag(True) \
+    .input(0, "input1", False, "required", "all") \
+    .input(1, "input2", False, "required", "all") \
+    .input(2, "input3", False, "required", "all") \
+    .input(3, "input4", False, "required", "all") \
+    .input(4, "input5", False, "required", "all") \
+    .input(5, "input6", False, "required", "all") \
+    .input(6, "input7", False, "required", "all") \
+    .input(7, "input8", False, "required", "all") \
+    .input(8, "input9", False, "required", "all") \
+    .input(9, "inputx0", False, "required", "all") \
+    .input(10, "inputx1", False, "required", "all") \
+    .input(11, "inputx2", False, "required", "all") \
+    .input(12, "inputx3", False, "required", "all") \
+    .output(0, "output1", False, "required", "all") \
+    .output(1, "output2", False, "required", "all") \
+    .output(2, "output3", False, "required", "all") \
+    .output(3, "output4", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name":"LambNextMV",
-    "imply_type":"TBE",
-    "fusion_type":"ELEMWISE",
-    "async_flag":false,
-    "binfile_name":"lamb_next_m_v.so",
-    "compute_cost":10,
-    "kernel_name":"lamb_next_m_v",
-    "partial_flag":true,
-    "attr":[],
-    "inputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":2,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input3",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":3,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input4",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":4,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input5",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":5,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input6",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":6,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input7",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":7,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input8",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":8,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input9",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":9,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"inputx0",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":10,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"inputx1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":11,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"inputx2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":12,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"inputx3",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ],
-    "outputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"output1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"output2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":2,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"output3",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":3,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"output4",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ]
-}""")
+
+@op_info_register(lamb_next_mv_op_info)
 def _lamb_next_mv_tbe():
     """LambNextMV TBE register"""
     return

mindspore/ops/_op_impl/tbe/lamb_next_mv_with_decay_v1.py

@@ -14,279 +14,46 @@
 # ============================================================================
 
 """LambNextMVWithDecayV1 op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+lamb_next_m_v_with_decay_v1_op_info = TBERegOp("LambNextMVWithDecayV1") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("lamb_next_m_v_with_decay_v1.so") \
+    .compute_cost(10) \
+    .kernel_name("lamb_next_m_v_with_decay_v1") \
+    .partial_flag(True) \
+    .input(0, "input1", False, "required", "all") \
+    .input(1, "input2", False, "required", "all") \
+    .input(2, "input3", False, "required", "all") \
+    .input(3, "input4", False, "required", "all") \
+    .input(4, "input5", False, "required", "all") \
+    .input(5, "input6", False, "required", "all") \
+    .input(6, "input7", False, "required", "all") \
+    .input(7, "input8", False, "required", "all") \
+    .input(8, "input9", False, "required", "all") \
+    .input(9, "inputx0", False, "required", "all") \
+    .input(10, "inputx1", False, "required", "all") \
+    .input(11, "inputx2", False, "required", "all") \
+    .input(12, "inputx3", False, "required", "all") \
+    .output(0, "output1", False, "required", "all") \
+    .output(1, "output2", False, "required", "all") \
+    .output(2, "output3", False, "required", "all") \
+    .output(3, "output4", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name":"LambNextMVWithDecayV1",
-    "imply_type":"TBE",
-    "fusion_type":"OPAQUE",
-    "async_flag":false,
-    "binfile_name":"lamb_next_m_v_with_decay_v1.so",
-    "compute_cost":10,
-    "kernel_name":"lamb_next_m_v_with_decay_v1",
-    "partial_flag":true,
-    "attr":[],
-    "inputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":2,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input3",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":3,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input4",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":4,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input5",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":5,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input6",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":6,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input7",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":7,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input8",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":8,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input9",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":9,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"inputx0",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":10,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"inputx1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":11,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"inputx2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":12,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"inputx3",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ],
-    "outputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"output1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"output2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":2,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"output3",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":3,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"output4",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ]
-}""")
+
+@op_info_register(lamb_next_m_v_with_decay_v1_op_info)
 def _lamb_next_mv_with_decay_v1_tbe():
     """LambNextMVWithDecayV1 TBE register"""
     return

mindspore/ops/_op_impl/tbe/lamb_update_with_lr.py

@@ -14,174 +14,35 @@
 # ============================================================================
 
 """LambUpdateWithLr op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+lamb_update_with_lr_op_info = TBERegOp("LambUpdateWithLR") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("lamb_update_with_lr.so") \
+    .compute_cost(10) \
+    .kernel_name("lamb_update_with_lr") \
+    .partial_flag(True) \
+    .input(0, "input1", False, "required", "all") \
+    .input(1, "input2", False, "required", "all") \
+    .input(2, "input3", False, "required", "all") \
+    .input(3, "input4", False, "required", "all") \
+    .input(4, "input5", False, "required", "all") \
+    .input(5, "input6", False, "required", "all") \
+    .input(6, "input7", False, "required", "all") \
+    .input(7, "input8", False, "required", "all") \
+    .input(8, "input9", False, "required", "all") \
+    .output(0, "output_y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name":"LambUpdateWithLR",
-    "imply_type":"TBE",
-    "fusion_type":"ELEMWISE",
-    "async_flag":false,
-    "binfile_name":"lamb_update_with_lr.so",
-    "compute_cost":10,
-    "kernel_name":"lamb_update_with_lr",
-    "partial_flag":true,
-    "attr":[],
-    "inputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":2,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input3",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":3,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input4",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":4,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input5",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":5,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input6",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":6,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input7",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":7,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input8",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":8,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"input9",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ],
-    "outputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"output_y",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ]
-}""")
+
+@op_info_register(lamb_update_with_lr_op_info)
 def _lamb_update_with_lr_tbe():
     """LambUpdateWithLr TBE register"""
     return

mindspore/ops/_op_impl/tbe/lamb_update_with_lr_v2.py

@@ -14,144 +14,31 @@
 # ============================================================================
 
 """LambUpdateWithLrV2 op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+lamb_update_with_lr_v2_op_info = TBERegOp("LambUpdateWithLrV2") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("lamb_update_with_lr_v2.so") \
+    .compute_cost(10) \
+    .kernel_name("lamb_update_with_lr_v2") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .input(2, "x3", False, "required", "all") \
+    .input(3, "x4", False, "required", "all") \
+    .input(4, "x5", False, "required", "all") \
+    .input(5, "greater_y", False, "required", "all") \
+    .input(6, "select_e", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name":"LambUpdateWithLrV2",
-    "imply_type":"TBE",
-    "fusion_type":"ELEMWISE",
-    "async_flag":false,
-    "binfile_name":"lamb_update_with_lr_v2.so",
-    "compute_cost":10,
-    "kernel_name":"lamb_update_with_lr_v2",
-    "partial_flag":true,
-    "attr":[],
-    "inputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"x1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"x2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":2,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"x3",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":3,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"x4",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":4,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"x5",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":5,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"greater_y",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":6,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"select_e",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ],
-    "outputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float32"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"y",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ]
-}""")
+
+@op_info_register(lamb_update_with_lr_v2_op_info)
 def _lamb_update_with_lr_v2_tbe():
     """LambUpdateWithLrV2 TBE register"""
     return

mindspore/ops/_op_impl/tbe/layer_norm.py

@@ -14,111 +14,39 @@
 # ============================================================================
 
 """LayerNorm op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+layer_norm_op_info = TBERegOp("LayerNorm") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("layer_norm.so") \
+    .compute_cost(10) \
+    .kernel_name("layer_norm") \
+    .partial_flag(True) \
+    .attr("begin_norm_axis", "required", "int", "all") \
+    .attr("begin_params_axis", "required", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "gamma", False, "required", "all") \
+    .input(2, "beta", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .output(1, "mean", False, "required", "all") \
+    .output(2, "variance", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD,
+                  DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_Default, DataType.F16_Default, DataType.F16_FracNZ,
+                  DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_Default, DataType.F32_Default, DataType.F32_FracNZ,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "LayerNorm",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "layer_norm.so",
-    "compute_cost": 10,
-    "kernel_name": "layer_norm",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "begin_norm_axis",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        },
-        {
-            "name": "begin_params_axis",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "gamma",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "beta",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "y",
-            "param_type": "required"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
 
-            ],
-            "name": "mean",
-            "param_type": "required"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-
-            ],
-            "name": "variance",
-            "param_type": "required"
-        }
-    ]
-}""")
+@op_info_register(layer_norm_op_info)
 def _layer_norm_tbe():
     """LayerNorm TBE register"""
     return

mindspore/ops/_op_impl/tbe/layer_norm_beta_gamma_backprop.py

@@ -14,105 +14,38 @@
 # ============================================================================
 
 """LayerNormBetaGammaBackprop op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+layer_norm_beta_gamma_backprop_op_info = TBERegOp("LayerNormBetaGammaBackprop") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("layer_norm_beta_gamma_backprop.so") \
+    .compute_cost(10) \
+    .kernel_name("layer_norm_beta_gamma_backprop") \
+    .partial_flag(True) \
+    .attr("shape_gamma", "required", "listInt", "all") \
+    .input(0, "dy", False, "required", "all") \
+    .input(1, "x", False, "required", "all") \
+    .input(2, "variance", False, "required", "all") \
+    .input(3, "mean", False, "required", "all") \
+    .output(0, "pd_gamma", False, "required", "all") \
+    .output(1, "pd_beta", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "LayerNormBetaGammaBackprop",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "layer_norm_beta_gamma_backprop.so",
-    "compute_cost": 10,
-    "kernel_name": "layer_norm_beta_gamma_backprop",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "shape_gamma",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "dy",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "variance",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "mean",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-         {
-            "index": 0,
-            "dtype": [
-                "float","float","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "pd_gamma",
-            "param_type": "required"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float","float","float","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "pd_beta",
-            "param_type": "required"
-        }
-    ]
-}""")
+
+@op_info_register(layer_norm_beta_gamma_backprop_op_info)
 def _layer_norm_beta_gamma_backprop_tbe():
     """LayerNormBetaGammaBackprop TBE register"""
     return

mindspore/ops/_op_impl/tbe/layer_norm_grad.py

@@ -14,124 +14,35 @@
 # ============================================================================
 
 """LayerNormGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+layer_norm_grad_op_info = TBERegOp("LayerNormGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("layer_norm_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("layer_norm_grad") \
+    .partial_flag(True) \
+    .input(0, "dy", False, "required", "all") \
+    .input(1, "x", False, "required", "all") \
+    .input(2, "variance", False, "required", "all") \
+    .input(3, "mean", False, "required", "all") \
+    .input(4, "gamma", False, "required", "all") \
+    .output(0, "pd_x", False, "required", "all") \
+    .output(1, "pd_gamma", False, "required", "all") \
+    .output(2, "pd_beta", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD,
+                  DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "LayerNormGrad",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "layer_norm_grad.so",
-    "compute_cost": 10,
-    "kernel_name": "layer_norm_grad",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "dy",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "variance",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "mean",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "gamma",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-         {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "pd_x",
-            "param_type": "required"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "pd_gamma",
-            "param_type": "required"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float","float"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "pd_beta",
-            "param_type": "required"
-        }
-    ]
-}""")
+@op_info_register(layer_norm_grad_op_info)
 def _layer_norm_grad_tbe():
     """LayerNormGrad TBE register"""
     return

mindspore/ops/_op_impl/tbe/layer_norm_x_backprop.py

@@ -14,102 +14,37 @@
 # ============================================================================
 
 """LayerNormXBackprop op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+layer_norm_x_backprop_op_info = TBERegOp("LayerNormXBackprop") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("layer_norm_x_backprop.so") \
+    .compute_cost(10) \
+    .kernel_name("layer_norm_x_backprop") \
+    .partial_flag(True) \
+    .input(0, "dy", False, "required", "all") \
+    .input(1, "x", False, "required", "all") \
+    .input(2, "variance", False, "required", "all") \
+    .input(3, "mean", False, "required", "all") \
+    .input(4, "gamma", False, "required", "all") \
+    .output(0, "pd_x", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD,
+                  DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_FracNZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "LayerNormXBackprop",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "layer_norm_x_backprop.so",
-    "compute_cost": 10,
-    "kernel_name": "layer_norm_x_backprop",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "dy",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "variance",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 3,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "mean",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 4,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","NC1HWC0","DefaultFormat","DefaultFormat","NC1HWC0"
-            ],
-            "name": "gamma",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-         {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float16","float","float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ","DefaultFormat","NC1HWC0","FRACTAL_NZ","DefaultFormat","NC1HWC0"
-            ],
-            "name": "pd_x",
-            "param_type": "required"
-        }
-    ]
-}""")
+@op_info_register(layer_norm_x_backprop_op_info)
 def _layer_norm_x_backprop_tbe():
     """LayerNormXBackprop TBE register"""
     return

mindspore/ops/_op_impl/tbe/less.py

@@ -14,67 +14,32 @@
 # ============================================================================
 
 """Less op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+less_op_info = TBERegOp("Less") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("less.so") \
+    .compute_cost(10) \
+    .kernel_name("less") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.BOOL_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Less",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "less.so",
-    "compute_cost": 10,
-    "kernel_name": "less",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float","int32","int32","int8","int8","uint8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float","float","int32","int32","int8","int8","uint8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-         {
-            "index": 0,
-            "dtype": [
-                "bool","bool","bool","bool","bool","bool","bool","bool","bool","bool"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "y",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(less_op_info)
 def _less_tbe():
     """Less TBE register"""
     return

mindspore/ops/_op_impl/tbe/less_equal.py

@@ -14,67 +14,34 @@
 # ============================================================================
 
 """LessEqual op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+less_equal_op_info = TBERegOp("LessEqual") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("less_equal.so") \
+    .compute_cost(10) \
+    .kernel_name("less_equal") \
+    .partial_flag(True) \
+    .attr("begin_norm_axis", "required", "int", "all") \
+    .attr("begin_params_axis", "required", "int", "all") \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.BOOL_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.BOOL_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "LessEqual",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "less_equal.so",
-    "compute_cost": 10,
-    "kernel_name": "less_equal",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","float","int32","int32","int8","int8","uint8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float","float","int32","int32","int8","int8","uint8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-         {
-            "index": 0,
-            "dtype": [
-                "bool","bool","bool","bool","bool","bool","bool","bool","bool","bool"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat",
-                "NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "y",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(less_equal_op_info)
 def _less_equal_tbe():
     """LessEqual TBE register"""
     return

mindspore/ops/_op_impl/tbe/log.py

@@ -14,52 +14,25 @@
 # ============================================================================
 
 """Log op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+log_op_info = TBERegOp("Log") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("log.so") \
+    .compute_cost(10) \
+    .kernel_name("log") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Log",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "log.so",
-    "compute_cost": 10,
-    "kernel_name": "log",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(log_op_info)
 def _log_tbe():
     """Log TBE register"""
     return

mindspore/ops/_op_impl/tbe/logical_and.py

@@ -14,65 +14,26 @@
 # ============================================================================
 
 """LogicalAnd op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+logical_and_op_info = TBERegOp("LogicalAnd") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("logical_and.so") \
+    .compute_cost(10) \
+    .kernel_name("logical_and") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", True, "required", "all") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.BOOL_FracZ, DataType.BOOL_FracZ, DataType.BOOL_FracZ) \
+    .dtype_format(DataType.BOOL_C1HWNCoC0, DataType.BOOL_C1HWNCoC0, DataType.BOOL_C1HWNCoC0) \
+    .dtype_format(DataType.BOOL_5HD, DataType.BOOL_5HD, DataType.BOOL_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "LogicalAnd",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "logical_and.so",
-    "compute_cost": 10,
-    "kernel_name": "logical_and",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "bool", "bool", "bool", "bool"
-            ],
-            "format": [
-                "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "bool", "bool", "bool", "bool"
-            ],
-            "format": [
-                "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "bool", "bool", "bool", "bool"
-            ],
-            "format": [
-                "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(logical_and_op_info)
 def _logical_and_tbe():
     """LogicalAnd TBE register"""
     return

mindspore/ops/_op_impl/tbe/logical_not.py

@@ -14,52 +14,25 @@
 # ============================================================================
 
 """LogicalNot op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+logical_not_op_info = TBERegOp("LogicalNot") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("logical_not.so") \
+    .compute_cost(10) \
+    .kernel_name("logical_not") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", True, "required", "all") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.BOOL_FracZ, DataType.BOOL_FracZ) \
+    .dtype_format(DataType.BOOL_C1HWNCoC0, DataType.BOOL_C1HWNCoC0) \
+    .dtype_format(DataType.BOOL_5HD, DataType.BOOL_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "LogicalNot",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "logical_not.so",
-    "compute_cost": 10,
-    "kernel_name": "logical_not",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "bool", "bool", "bool", "bool"
-            ],
-            "format": [
-                "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "bool", "bool", "bool", "bool"
-            ],
-            "format": [
-                "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(logical_not_op_info)
 def _logical_not_tbe():
     """LogicalNot TBE register"""
     return

mindspore/ops/_op_impl/tbe/logical_or.py

@@ -14,65 +14,26 @@
 # ============================================================================
 
 """LogicalOr op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+logical_or_op_info = TBERegOp("LogicalOr") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("logical_or.so") \
+    .compute_cost(10) \
+    .kernel_name("logical_or") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", True, "required", "all") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.BOOL_FracZ, DataType.BOOL_FracZ, DataType.BOOL_FracZ) \
+    .dtype_format(DataType.BOOL_C1HWNCoC0, DataType.BOOL_C1HWNCoC0, DataType.BOOL_C1HWNCoC0) \
+    .dtype_format(DataType.BOOL_5HD, DataType.BOOL_5HD, DataType.BOOL_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "LogicalOr",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "logical_or.so",
-    "compute_cost": 10,
-    "kernel_name": "logical_or",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "bool", "bool", "bool", "bool"
-            ],
-            "format": [
-                "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "bool", "bool", "bool", "bool"
-            ],
-            "format": [
-                "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "bool", "bool", "bool", "bool"
-            ],
-            "format": [
-                "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(logical_or_op_info)
 def _logical_or_tbe():
     """LogicalOr TBE register"""
     return

mindspore/ops/_op_impl/tbe/logsoftmax.py

@@ -14,57 +14,24 @@
 # ============================================================================
 
 """LogSoftmax op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+log_softmax_op_info = TBERegOp("LogSoftmax") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("log_softmax.so") \
+    .compute_cost(10) \
+    .kernel_name("log_softmax") \
+    .partial_flag(True) \
+    .attr("axis", "optional", "listInt", "all") \
+    .input(0, "logits", False, "required", "all") \
+    .output(0, "logsoftmax", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "LogSoftmax",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "log_softmax.so",
-    "compute_cost": 10,
-    "kernel_name": "log_softmax",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                    "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "logits",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                    "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "logsoftmax",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(log_softmax_op_info)
 def _logsoftmax_tbe():
     """LogSoftMaxGrad TBE register"""
     return

mindspore/ops/_op_impl/tbe/logsoftmax_grad.py

@@ -14,70 +14,25 @@
 # ============================================================================
 
 """LogSoftmaxGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+log_softmax_grad_op_info = TBERegOp("LogSoftmaxGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("log_softmax_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("log_softmax_grad") \
+    .partial_flag(True) \
+    .attr("axis", "optional", "listInt", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "grad", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "LogSoftmaxGrad",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "log_softmax_grad.so",
-    "compute_cost": 10,
-    "kernel_name": "log_softmax_grad",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                    "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                    "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "grad",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                    "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(log_softmax_grad_op_info)
 def _logsoftmax_grad_tbe():
     """LogSoftMaxGrad TBE register"""
     return

mindspore/ops/_op_impl/tbe/matmul.py

@@ -14,89 +14,29 @@
 # ============================================================================
 
 """MatMul op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+matmul_op_info = TBERegOp("MatMul") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("matmul.so") \
+    .compute_cost(10) \
+    .kernel_name("matmul") \
+    .partial_flag(True) \
+    .attr("transpose_a", "required", "bool", "all") \
+    .attr("transpose_b", "required", "bool", "all") \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .input(2, "x3", False, "optional", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_Default, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F32_Default, DataType.F32_FracNZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "MatMul",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "matmul.so",
-    "compute_cost": 10,
-    "kernel_name": "matmul",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "transpose_a",
-            "param_type": "required",
-            "type": "bool",
-            "value": "all"
-        },
-        {
-            "name": "transpose_b",
-            "param_type": "required",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float16","float","int32"
-            ],
-            "format": [
-                "FRACTAL_NZ","FRACTAL_NZ","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float16","float","int32"
-            ],
-            "format": [
-                "FRACTAL_NZ","FRACTAL_NZ","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-               "float16","float","float","int32"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x3",
-            "need_compile": false,
-            "param_type": "optional",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","float","int32"
-            ],
-            "format": [
-                "FRACTAL_NZ","FRACTAL_NZ","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(matmul_op_info)
 def _matmul_tbe():
     """Mul TBE register"""
     return

mindspore/ops/_op_impl/tbe/max_pool.py

@@ -14,74 +14,26 @@
 # ============================================================================
 
 """MaxPool op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+max_pool_op_info = TBERegOp("MaxPool") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("max_pool.so") \
+    .compute_cost(10) \
+    .kernel_name("max_pool") \
+    .partial_flag(True) \
+    .attr("ksize", "required", "listInt", "all") \
+    .attr("strides", "required", "listInt", "all") \
+    .attr("padding", "required", "str", "all") \
+    .attr("data_format", "required", "str", "all") \
+    .input(0, "input_data", False, "required", "all") \
+    .output(0, "output_data", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "MaxPool",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "max_pool.so",
-    "compute_cost": 10,
-    "kernel_name": "max_pool",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "ksize",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "strides",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "padding",
-            "param_type": "required",
-            "type": "str",
-            "value": "all"
-        },
-        {
-            "name": "data_format",
-            "param_type": "required",
-            "type": "str",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "input_data",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "output_data",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(max_pool_op_info)
 def _max_pool_tbe():
     """MaxPool TBE register"""
     return

mindspore/ops/_op_impl/tbe/max_pool_grad.py

@@ -14,93 +14,27 @@
 # ============================================================================
 
 """MaxPoolGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+max_pool_grad_op_info = TBERegOp("MaxPoolGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("max_pool_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("max_pool_grad") \
+    .partial_flag(True) \
+    .attr("ksize", "required", "listInt", "all") \
+    .attr("strides", "required", "listInt", "all") \
+    .attr("padding", "required", "str", "all") \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .input(2, "grad", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "MaxPoolGrad",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "max_pool_grad.so",
-    "compute_cost": 10,
-    "kernel_name": "max_pool_grad",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "ksize",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "strides",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "padding",
-            "param_type": "required",
-            "type": "str",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "grad",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "y",
-            "param_type": "required"
-        }
-    ]
-}""")
+
+@op_info_register(max_pool_grad_op_info)
 def _max_pool_grad_tbe():
     """MaxPoolGrad TBE register"""
     return

mindspore/ops/_op_impl/tbe/max_pool_grad_with_argmax.py

@@ -14,95 +14,28 @@
 # ============================================================================
 
 """MaxPoolGradWithArgmax op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+max_pool_grad_with_argmax_op_info = TBERegOp("MaxPoolGradWithArgmax") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("max_pool_grad_with_argmax.so") \
+    .compute_cost(10) \
+    .kernel_name("max_pool_grad_with_argmax") \
+    .partial_flag(True) \
+    .attr("ksize", "required", "listInt", "all") \
+    .attr("strides", "required", "listInt", "all") \
+    .attr("padding", "required", "str", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "grad", False, "required", "all") \
+    .input(2, "argmax", False, "optional", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.U16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.I64_5HD, DataType.F16_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "MaxPoolGradWithArgmax",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "max_pool_grad_with_argmax.so",
-    "compute_cost": 10,
-    "kernel_name": "max_pool_grad_with_argmax",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "ksize",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "strides",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "padding",
-            "param_type": "required",
-            "type": "str",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16"
-            ],
-            "format": [
-                "NC1HWC0", "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16"
-            ],
-            "format": [
-                "NC1HWC0", "NC1HWC0"
-            ],
-            "name": "grad",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "uint16", "int64"
-            ],
-            "format": [
-                "NC1HWC0", "NC1HWC0"
-            ],
-            "name": "argmax",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16"
-            ],
-            "format": [
-                "NC1HWC0", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(max_pool_grad_with_argmax_op_info)
 def _max_pool_grad_with_argmax_tbe():
     """MaxPoolGradWithArgmax TBE register"""
     return

mindspore/ops/_op_impl/tbe/max_pool_with_argmax.py

@@ -14,82 +14,26 @@
 # ============================================================================
 
 """MaxPoolWithArgmax op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+max_pool_with_argmax_op_info = TBERegOp("MaxPoolWithArgmax") \
+    .fusion_type("CONVLUTION") \
+    .async_flag(False) \
+    .binfile_name("max_pool_with_argmax.so") \
+    .compute_cost(10) \
+    .kernel_name("max_pool_with_argmax") \
+    .partial_flag(True) \
+    .attr("ksize", "required", "listInt", "all") \
+    .attr("strides", "required", "listInt", "all") \
+    .attr("padding", "required", "str", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .output(1, "argmax", False, "required", "all") \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.U16_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "MaxPoolWithArgmax",
-    "imply_type": "TBE",
-    "fusion_type": "CONVLUTION",
-    "async_flag": false,
-    "binfile_name": "max_pool_with_argmax.so",
-    "compute_cost": 10,
-    "kernel_name": "max_pool_with_argmax",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "ksize",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "strides",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "padding",
-            "param_type": "required",
-            "type": "str",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "uint16"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "argmax",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(max_pool_with_argmax_op_info)
 def _max_pool_with_argmax_tbe():
     """MaxPoolWithArgmax TBE register"""
     return

mindspore/ops/_op_impl/tbe/maximum.py

@@ -14,69 +14,28 @@
 # ============================================================================
 
 """Maximum op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+maximum_op_info = TBERegOp("Maximum") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("maximum.so") \
+    .compute_cost(10) \
+    .kernel_name("maximum") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name":"Maximum",
-    "imply_type":"TBE",
-    "fusion_type":"ELEMWISE",
-    "async_flag":false,
-    "binfile_name":"maximum.so",
-    "compute_cost":10,
-    "kernel_name":"maximum",
-    "partial_flag":true,
-    "attr":[],
-    "inputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"x1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"x2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ],
-    "outputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"y",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ]
-}""")
+
+@op_info_register(maximum_op_info)
 def _maximum_tbe():
     """Maximum TBE register"""
     return

mindspore/ops/_op_impl/tbe/maximum_grad.py

@@ -14,112 +14,38 @@
 # ============================================================================
 
 """MaximumGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+maximum_grad_op_info = TBERegOp("MaximumGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("maximum_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("maximum_grad") \
+    .partial_flag(True) \
+    .attr("grad_x", "optional", "bool", "all") \
+    .attr("grad_y", "optional", "bool", "all") \
+    .input(0, "grads", False, "required", "all") \
+    .input(1, "x1", False, "required", "all") \
+    .input(2, "x2", False, "required", "all") \
+    .output(0, "y1", False, "required", "all") \
+    .output(1, "y2", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default,
+                  DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD,
+                  DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD,
+                  DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name":"MaximumGrad",
-    "imply_type":"TBE",
-    "fusion_type":"OPAQUE",
-    "async_flag":false,
-    "binfile_name":"maximum_grad.so",
-    "compute_cost":10,
-    "kernel_name":"maximum_grad",
-    "partial_flag":true,
-    "attr":[
-        {
-            "name":"grad_x",
-            "param_type":"optional",
-            "type":"bool",
-            "value":"all"
-        },
-        {
-            "name":"grad_y",
-            "param_type":"optional",
-            "type":"bool",
-            "value":"all"
-        }
-    ],
-    "inputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"grads",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"x1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":2,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"x2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ],
-    "outputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"y1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"y2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ]
-}""")
+
+@op_info_register(maximum_grad_op_info)
 def _maximum_grad_tbe():
     """MaximumGrad TBE register"""
     return

mindspore/ops/_op_impl/tbe/minimum.py

@@ -15,74 +15,28 @@
 
 
 """Minimum op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+minimum_op_info = TBERegOp("Minimum") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("minimum.so") \
+    .compute_cost(10) \
+    .kernel_name("minimum") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD)  \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Minimum",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "minimum.so",
-    "compute_cost": 10,
-    "kernel_name": "minimum",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format": [
-               "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-               "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-               "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-                "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(minimum_op_info)
 def _minimum_tbe():
     """Minimum TBE register"""
     return

mindspore/ops/_op_impl/tbe/minimum_grad.py

@@ -14,112 +14,38 @@
 # ============================================================================
 
 """MinimumGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+minimum_grad_op_info = TBERegOp("MinimumGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("minimum_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("minimum_grad") \
+    .partial_flag(True) \
+    .attr("grad_x", "optional", "bool", "all") \
+    .attr("grad_y", "optional", "bool", "all") \
+    .input(0, "grads", False, "required", "all") \
+    .input(1, "x1", False, "required", "all") \
+    .input(2, "x2", False, "required", "all") \
+    .output(0, "y1", False, "required", "all") \
+    .output(1, "y2", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default,
+                  DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD,
+                  DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD,
+                  DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name":"MinimumGrad",
-    "imply_type":"TBE",
-    "fusion_type":"OPAQUE",
-    "async_flag":false,
-    "binfile_name":"minimum_grad.so",
-    "compute_cost":10,
-    "kernel_name":"minimum_grad",
-    "partial_flag":true,
-    "attr":[
-        {
-            "name":"grad_x",
-            "param_type":"optional",
-            "type":"bool",
-            "value":"all"
-        },
-        {
-            "name":"grad_y",
-            "param_type":"optional",
-            "type":"bool",
-            "value":"all"
-        }
-    ],
-    "inputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"grads",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"x1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":2,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"x2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ],
-    "outputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"y1",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        },
-        {
-            "index":1,
-            "dtype":[
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32"
-            ],
-            "format":[
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name":"y2",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ]
-}""")
+
+@op_info_register(minimum_grad_op_info)
 def _minimum_grad_tbe():
     """MinimumGrad TBE register"""
     return

mindspore/ops/_op_impl/tbe/mul.py

@@ -14,77 +14,37 @@
 # ============================================================================
 
 """Mul op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+mul_op_info = TBERegOp("Mul") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("mul.so") \
+    .compute_cost(10) \
+    .kernel_name("mul") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "y", False, "required", "all") \
+    .output(0, "output", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.I32_FracZ, DataType.I32_FracZ, DataType.I32_FracZ) \
+    .dtype_format(DataType.I32_FracNZ, DataType.I32_FracNZ, DataType.I32_FracNZ) \
+    .dtype_format(DataType.I32_C1HWNCoC0, DataType.I32_C1HWNCoC0, DataType.I32_C1HWNCoC0) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ, DataType.F32_FracNZ) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Mul",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "mul.so",
-    "compute_cost": 10,
-    "kernel_name": "mul",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32", "int32", "int32", "int32", "int32",
-                "float16", "float16", "float16", "float16", "float16",
-                "float", "float", "float", "float", "float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0",
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0",
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "int32", "int32", "int32", "int32", "int32",
-                "float16", "float16", "float16", "float16", "float16",
-                "float", "float", "float", "float", "float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0",
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0",
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32", "int32", "int32", "int32", "int32",
-                "float16", "float16", "float16", "float16", "float16",
-                "float", "float", "float", "float","float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0",
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0",
-                "FRACTAL_NZ", "DefaultFormat", "FracZ", "C1HWNCoC0", "NC1HWC0"
-            ],
-            "name": "output",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(mul_op_info)
 def _mul_tbe():
     """Mul TBE register"""
     return

mindspore/ops/_op_impl/tbe/neg.py

@@ -14,51 +14,29 @@
 # ============================================================================
 
 """Neg op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+neg_op_info = TBERegOp("Neg") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("neg.so") \
+    .compute_cost(10) \
+    .kernel_name("neg") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Neg",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "neg.so",
-    "compute_cost": 10,
-    "kernel_name": "neg",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float","float","float16","float16","int32","int32","int8","int8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-         {
-            "index": 0,
-            "dtype": [
-                "float","float","float16","float16","int32","int32","int8","int8"
-            ],
-            "format": [
-                "DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0","DefaultFormat","NC1HWC0"
-            ],
-            "name": "y",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(neg_op_info)
 def _neg_tbe():
     """Neg TBE register"""
     return

mindspore/ops/_op_impl/tbe/npu_alloc_float_status.py

@@ -14,39 +14,21 @@
 # ============================================================================
 
 """NPUAllocFloatStatus op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+npu_alloc_float_status_op_info = TBERegOp("NPUAllocFloatStatus") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("n_p_u_alloc_float_status.so") \
+    .compute_cost(10) \
+    .kernel_name("n_p_u_alloc_float_status") \
+    .partial_flag(True) \
+    .output(0, "data", False, "required", "all") \
+    .dtype_format(DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "NPUAllocFloatStatus",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "n_p_u_alloc_float_status.so",
-    "compute_cost": 10,
-    "kernel_name": "n_p_u_alloc_float_status",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float"
-            ],
-            "format": [
-                "DefaultFormat"
-            ],
-            "name": "data",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(npu_alloc_float_status_op_info)
 def _npu_alloc_float_status_tbe():
     """NPUAllocFloatStatus TBE register"""
     return

mindspore/ops/_op_impl/tbe/npu_clear_float_status.py

@@ -14,52 +14,22 @@
 # ============================================================================
 
 """NPUClearFloatStatus op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+npu_clear_float_status_op_info = TBERegOp("NPUClearFloatStatus") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("n_p_u_clear_float_status.so") \
+    .compute_cost(10) \
+    .kernel_name("n_p_u_clear_float_status") \
+    .partial_flag(True) \
+    .input(0, "addr", False, "required", "all") \
+    .output(0, "data", False, "required", "all") \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "NPUClearFloatStatus",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "n_p_u_clear_float_status.so",
-    "compute_cost": 10,
-    "kernel_name": "n_p_u_clear_float_status",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float"
-            ],
-            "format": [
-                "DefaultFormat"
-            ],
-            "name": "addr",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float"
-            ],
-            "format": [
-                "DefaultFormat"
-            ],
-            "name": "data",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(npu_clear_float_status_op_info)
 def _npu_clear_float_status_tbe():
     """NPUClearFloatStatus TBE register"""
     return

mindspore/ops/_op_impl/tbe/npu_get_float_status.py

@@ -14,52 +14,22 @@
 # ============================================================================
 
 """NPUGetFloatStatus op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+npu_get_float_status_op_info = TBERegOp("NPUGetFloatStatus") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("n_p_u_get_float_status.so") \
+    .compute_cost(10) \
+    .kernel_name("n_p_u_get_float_status") \
+    .partial_flag(True) \
+    .input(0, "addr", False, "required", "all") \
+    .output(0, "data", False, "required", "all") \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "NPUGetFloatStatus",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "n_p_u_get_float_status.so",
-    "compute_cost": 10,
-    "kernel_name": "n_p_u_get_float_status",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float"
-            ],
-            "format": [
-                "DefaultFormat"
-            ],
-            "name": "addr",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float"
-            ],
-            "format": [
-                "DefaultFormat"
-            ],
-            "name": "data",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(npu_get_float_status_op_info)
 def _npu_get_float_status_tbe():
     """NPUGetFloatStatus TBE register"""
     return

mindspore/ops/_op_impl/tbe/one_hot.py

@@ -14,96 +14,35 @@
 # ============================================================================
 
 """OneHot op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+one_hot_op_info = TBERegOp("OneHot") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("one_hot.so") \
+    .compute_cost(10) \
+    .kernel_name("one_hot") \
+    .partial_flag(True) \
+    .attr("depth", "required", "int", "all") \
+    .attr("axis", "required", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "on_value", False, "required", "all") \
+    .input(2, "off_value", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.U8_Default, DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.U8_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.U8_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.I32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "OneHot",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "one_hot.so",
-    "compute_cost": 10,
-    "kernel_name": "one_hot",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "depth",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        },
-        {
-            "name": "axis",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32","int32","int32","int32","int32",
-                "uint8","uint8","uint8","uint8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float32","int32","int8","uint8",
-                "float16","float32","int32","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "on_value",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16","float32","int32","int8","uint8",
-                "float16","float32","int32","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "off_value",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-         {
-            "index": 0,
-            "dtype": [
-                "float16","float32","int32","int8","uint8",
-                "float16","float32","int32","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(one_hot_op_info)
 def _one_hot_tbe():
     """OneHot TBE register"""
     return

mindspore/ops/_op_impl/tbe/pad_d.py

@@ -14,57 +14,27 @@
 # ============================================================================
 
 """Pad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+pad_d_op_info = TBERegOp("Pad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("pad_d.so") \
+    .compute_cost(10) \
+    .kernel_name("pad_d") \
+    .partial_flag(True) \
+    .attr("paddings", "optional", "listListInt", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Pad",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "pad_d.so",
-    "compute_cost": 10,
-    "kernel_name": "pad_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "paddings",
-            "param_type": "optional",
-            "type": "listListInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","int8","uint8","int32"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","int8","uint8","int32"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(pad_d_op_info)
 def _pad_d_tbe():
     """Pad TBE register"""
     return

mindspore/ops/_op_impl/tbe/pow.py

@@ -14,65 +14,27 @@
 # ============================================================================
 
 """Pow op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+pow_op_info = TBERegOp("Pow") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("pow.so") \
+    .compute_cost(10) \
+    .kernel_name("pow") \
+    .partial_flag(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Pow",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "pow.so",
-    "compute_cost": 10,
-    "kernel_name": "pow",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float", "int32", "int8", "uint8"
-            ],
-            "format": [
-               "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float", "int32", "int8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float", "int32", "int8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(pow_op_info)
 def _pow_tbe():
     """Pow TBE register"""
     return

mindspore/ops/_op_impl/tbe/real_div.py

@@ -14,64 +14,26 @@
 # ============================================================================
 
 """RealDiv op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+realdiv_op_info = TBERegOp("RealDiv") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("realdiv.so") \
+    .compute_cost(10) \
+    .kernel_name("realdiv") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "y", False, "required", "all") \
+    .output(0, "z", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "RealDiv",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "realdiv.so",
-    "compute_cost": 10,
-    "kernel_name": "realdiv",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "z",
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(realdiv_op_info)
 def _real_div_tbe():
     """RealDiv TBE register"""
     return

mindspore/ops/_op_impl/tbe/reciprocal.py

@@ -14,52 +14,27 @@
 # ============================================================================
 
 """Add op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+reciprocal_op_info = TBERegOp("Reciprocal") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("reciprocal.so") \
+    .compute_cost(10) \
+    .kernel_name("reciprocal") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_NHWC, DataType.F16_NHWC) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_NHWC, DataType.F32_NHWC) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Reciprocal",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "reciprocal.so",
-    "compute_cost": 10,
-    "kernel_name": "reciprocal",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float32", "float32", "float32"
-            ],
-            "format": [
-                    "DefaultFormat", "NC1HWC0", "NHWC", "DefaultFormat", "NC1HWC0", "NHWC"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float32", "float32", "float32"
-            ],
-            "format": [
-                    "DefaultFormat", "NC1HWC0", "NHWC", "DefaultFormat", "NC1HWC0", "NHWC"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(reciprocal_op_info)
 def _reciprocal_tbe():
     """Add TBE register"""
     return

mindspore/ops/_op_impl/tbe/reduce_max.py

@@ -14,63 +14,29 @@
 # ============================================================================
 
 """ReduceMax op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+reduce_max_d_op_info = TBERegOp("ReduceMax") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("reduce_max_d.so") \
+    .compute_cost(10) \
+    .kernel_name("reduce_max_d") \
+    .partial_flag(True) \
+    .attr("axis", "optional", "listInt", "all") \
+    .attr("keep_dims", "optional", "bool", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ReduceMax",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "reduce_max_d.so",
-    "compute_cost": 10,
-    "kernel_name": "reduce_max_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "keep_dims",
-            "param_type": "required",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float", "int8", "uint8", "bool", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float", "int8", "uint8", "bool", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(reduce_max_d_op_info)
 def _reduce_max_tbe():
     """ReduceMax TBE register"""
     return

mindspore/ops/_op_impl/tbe/reduce_mean.py

@@ -14,63 +14,27 @@
 # ============================================================================
 
 """ReduceMean op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+reduce_mean_op_info = TBERegOp("ReduceMean") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("reduce_mean.so") \
+    .compute_cost(10) \
+    .kernel_name("reduce_mean") \
+    .partial_flag(True) \
+    .attr("axis", "optional", "listInt", "all") \
+    .attr("keep_dims", "optional", "bool", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ReduceMean",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "reduce_mean.so",
-    "compute_cost": 10,
-    "kernel_name": "reduce_mean",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "keep_dims",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","float16","int8","uint8"
-            ],
-            "format": [
-                "NC1HWC0","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","float16","int8","uint8"
-            ],
-            "format": [
-                "NC1HWC0","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(reduce_mean_op_info)
 def _reduce_mean_tbe():
     """ReduceMean TBE register"""
     return

mindspore/ops/_op_impl/tbe/reduce_mean_d.py

@@ -14,63 +14,27 @@
 # ============================================================================
 
 """ReduceMeanD op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+reduce_mean_d_op_info = TBERegOp("ReduceMeanD") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("reduce_mean_d.so") \
+    .compute_cost(10) \
+    .kernel_name("reduce_mean_d") \
+    .partial_flag(True) \
+    .attr("axis", "optional", "listInt", "all") \
+    .attr("keep_dims", "optional", "bool", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ReduceMeanD",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "reduce_mean_d.so",
-    "compute_cost": 10,
-    "kernel_name": "reduce_mean_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "keep_dims",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float","float16","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float","float16","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(reduce_mean_d_op_info)
 def _reduce_mean_d_tbe():
     """Conv2D TBE register"""
     return

mindspore/ops/_op_impl/tbe/reduce_min.py

@@ -14,63 +14,31 @@
 # ============================================================================
 
 """ReduceMin op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+reduce_min_op_info = TBERegOp("ReduceMin") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("reduce_min_d.so") \
+    .compute_cost(10) \
+    .kernel_name("reduce_min_d") \
+    .partial_flag(True) \
+    .attr("axis", "required", "listInt", "all") \
+    .attr("keep_dims", "required", "bool", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_FracZ, DataType.I8_FracZ) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_FracZ, DataType.U8_FracZ) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ReduceMin",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "reduce_min_d.so",
-    "compute_cost": 10,
-    "kernel_name": "reduce_min_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "keep_dims",
-            "param_type": "required",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float", "int8", "int8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float", "int8", "int8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ", "DefaultFormat", "FracZ"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(reduce_min_op_info)
 def _reduce_min_tbe():
     """ReduceMin TBE register"""
     return

mindspore/ops/_op_impl/tbe/reduce_sum.py

@@ -14,63 +14,25 @@
 # ============================================================================
 
 """ReduceSum op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+reduce_sum_op_info = TBERegOp("ReduceSum") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("reduce_sum_d.so") \
+    .compute_cost(10) \
+    .kernel_name("reduce_sum_d") \
+    .partial_flag(True) \
+    .attr("axis", "optional", "listInt", "all") \
+    .attr("keep_dims", "optional", "bool", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ReduceSum",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "reduce_sum_d.so",
-    "compute_cost": 10,
-    "kernel_name": "reduce_sum_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "keep_dims",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(reduce_sum_op_info)
 def _reduce_sum_tbe():
     """ReduceSum TBE register"""
     return

mindspore/ops/_op_impl/tbe/relu.py

@@ -14,54 +14,29 @@
 # ============================================================================
 
 """ReLU op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+relu_op_info = TBERegOp("ReLU") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("relu.so") \
+    .compute_cost(10) \
+    .kernel_name("relu") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ReLU",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "relu.so",
-    "compute_cost": 10,
-    "kernel_name": "relu",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float","int32", "int32", "int8", "int8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float", "int32", "int32", "int8", "int8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(relu_op_info)
 def _relu_tbe():
     """Relu TBE register"""
     return

mindspore/ops/_op_impl/tbe/relu_grad.py

@@ -14,68 +14,32 @@
 # ============================================================================
 
 """ReluGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+relugrad_op_info = TBERegOp("ReluGrad") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("relugrad.so") \
+    .compute_cost(10) \
+    .kernel_name("relugrad") \
+    .partial_flag(True) \
+    .input(0, "gradients", False, "required", "all") \
+    .input(1, "features", False, "required", "all") \
+    .output(0, "backprops", True, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ReluGrad",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "relugrad.so",
-    "compute_cost": 10,
-    "kernel_name": "relugrad",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float", "int32", "int32", "int8", "int8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0","DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "gradients",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float", "float", "int32", "int32", "int8", "int8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "features",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float", "int32", "int32", "int8", "int8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "NC1HWC0"
-            ],
-            "name": "backprops",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(relugrad_op_info)
 def _relu_grad_tbe():
     """ReluGrad TBE register"""
     return

mindspore/ops/_op_impl/tbe/reshape.py

@@ -14,57 +14,25 @@
 # ============================================================================
 
 """Reshape op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+reshape_op_info = TBERegOp("Reshape") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("reshape.so") \
+    .compute_cost(10) \
+    .kernel_name("reshape") \
+    .partial_flag(True) \
+    .attr("shape", "required", "listInt", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Reshape",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "reshape.so",
-    "compute_cost": 10,
-    "kernel_name": "reshape",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "shape",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float32", "int32"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(reshape_op_info)
 def _reshape_tbe():
     """Reshape TBE register"""
     return

mindspore/ops/_op_impl/tbe/resize_nearest_neighbor.py

@@ -14,67 +14,33 @@
 # ============================================================================
 
 """ResizeNearestNeighbor op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+resize_nearest_neighbor_op_info = TBERegOp("ResizeNearestNeighbor") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("resize_nearest_neighbor_d.so") \
+    .compute_cost(10) \
+    .kernel_name("resize_nearest_neighbor_d") \
+    .partial_flag(True) \
+    .attr("size", "required", "listInt", "all") \
+    .attr("align_corners", "optional", "bool", "all") \
+    .input(0, "images", False, "required", "all") \
+    .output(0, "y", True, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ResizeNearestNeighbor",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "resize_nearest_neighbor_d.so",
-    "compute_cost": 10,
-    "kernel_name": "resize_nearest_neighbor_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "size",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "align_corners",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","int32","int8","uint8",
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0","NC1HWC0","NC1HWC0","NC1HWC0",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "images",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","int32","int8","uint8",
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0","NC1HWC0","NC1HWC0","NC1HWC0",
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(resize_nearest_neighbor_op_info)
 def _resize_nearest_neighbor_d_tbe():
     """ResizeNearestNeighbor TBE register"""
     return

mindspore/ops/_op_impl/tbe/resize_nearest_neighbor_d.py

@@ -14,63 +14,28 @@
 # ============================================================================
 
 """ResizeNearestNeighbor op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+resize_nearest_neighbor_d_op_info = TBERegOp("ResizeNearestNeighbor") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("resize_nearest_neighbor_d.so") \
+    .compute_cost(10) \
+    .kernel_name("resize_nearest_neighbor_d") \
+    .partial_flag(True) \
+    .attr("size", "required", "listInt", "all") \
+    .attr("align_corners", "optional", "bool", "all") \
+    .input(0, "images", False, "required", "all") \
+    .output(0, "y", True, "required", "all") \
+    .dtype_format(DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ResizeNearestNeighbor",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "resize_nearest_neighbor_d.so",
-    "compute_cost": 10,
-    "kernel_name": "resize_nearest_neighbor_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "size",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "align_corners",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0","NC1HWC0","NC1HWC0","NC1HWC0"
-            ],
-            "name": "images",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0","NC1HWC0","NC1HWC0","NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(resize_nearest_neighbor_d_op_info)
 def _resize_nearest_neighbor_d_tbe():
     """ResizeNearestNeighbor TBE register"""
     return

mindspore/ops/_op_impl/tbe/resize_nearest_neighbor_grad_d.py

@@ -14,63 +14,24 @@
 # ============================================================================
 
 """ResizeNearestNeighborgrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+resize_nearest_neighbor_grad_d_op_info = TBERegOp("ResizeNearestNeighborGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("resize_nearest_neighbor_grad_d.so") \
+    .compute_cost(10) \
+    .kernel_name("resize_nearest_neighbor_grad_d") \
+    .partial_flag(True) \
+    .attr("size", "required", "listInt", "all") \
+    .attr("align_corners", "optional", "bool", "all") \
+    .input(0, "grads", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ResizeNearestNeighborGrad",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "resize_nearest_neighbor_grad_d.so",
-    "compute_cost": 10,
-    "kernel_name": "resize_nearest_neighbor_grad_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "size",
-            "param_type": "required",
-            "type": "listInt",
-            "value": "all"
-        },
-        {
-            "name": "align_corners",
-            "param_type": "optional",
-            "type": "bool",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "grads",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float"
-            ],
-            "format": [
-                "NC1HWC0"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(resize_nearest_neighbor_grad_d_op_info)
 def _resize_nearest_neighbor_grad_d_tbe():
     """ResizeNearestNeighborGrad TBE register"""
     return

mindspore/ops/_op_impl/tbe/round.py

@@ -14,52 +14,27 @@
 # ============================================================================
 
 """Round op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+round_op_info = TBERegOp("Round") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("round.so") \
+    .compute_cost(10) \
+    .kernel_name("round") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Round",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "round.so",
-    "compute_cost": 10,
-    "kernel_name": "round",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "FracZ", "DefaultFormat", "NC1HWC0", "FracZ"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float", "float", "float"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "FracZ", "DefaultFormat", "NC1HWC0", "FracZ"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(round_op_info)
 def _round_tbe():
     """Round TBE register"""
     return

mindspore/ops/_op_impl/tbe/rsqrt.py

@@ -14,94 +14,29 @@
 # ============================================================================
 
 """Rsqrt op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+rsqrt_op_info = TBERegOp("Rsqrt") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("rsqrt.so") \
+    .compute_cost(10) \
+    .kernel_name("rsqrt") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name":"Rsqrt",
-    "imply_type":"TBE",
-    "fusion_type":"OPAQUE",
-    "async_flag":false,
-    "binfile_name":"rsqrt.so",
-    "compute_cost":10,
-    "kernel_name":"rsqrt",
-    "partial_flag":true,
-    "attr":[],
-    "inputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float16",
-                "float16",
-                "float16",
-                "float16",
-                "float16",
-                "float",
-                "float",
-                "float",
-                "float",
-                "float",
-                "float"
-            ],
-            "format":[
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "FracZ",
-                "C1HWNCoC0",
-                "DefaultFormat",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "DefaultFormat",
-                "FracZ",
-                "C1HWNCoC0"
-            ],
-            "name":"x",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ],
-    "outputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float16",
-                "float16",
-                "float16",
-                "float16",
-                "float16",
-                "float16",
-                "float",
-                "float",
-                "float",
-                "float",
-                "float",
-                "float"
-            ],
-            "format":[
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "FracZ",
-                "C1HWNCoC0",
-                "DefaultFormat",
-                "DefaultFormat",
-                "NC1HWC0",
-                "DefaultFormat",
-                "DefaultFormat",
-                "FracZ",
-                "C1HWNCoC0"
-            ],
-            "name":"y",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ]
-}""")
+
+@op_info_register(rsqrt_op_info)
 def _rsqrt_tbe():
     """Rsqrt TBE register"""
     return

mindspore/ops/_op_impl/tbe/scatter_nd.py

@@ -14,71 +14,28 @@
 # ============================================================================
 
 """ScatterNd op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+scatter_nd_op_info = TBERegOp("ScatterNd") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("scatter_nd_d.so") \
+    .compute_cost(10) \
+    .kernel_name("scatter_nd_d") \
+    .partial_flag(True) \
+    .attr("shape", "optional", "listInt", "all") \
+    .input(0, "indices", False, "required", "all") \
+    .input(1, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.I32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-# map to tbe kernel name scatter_nd_d
-@op_info_register("""{
-    "op_name": "ScatterNd",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "scatter_nd_d.so",
-    "compute_cost": 10,
-    "kernel_name": "scatter_nd_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "shape",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32", "int32", "int32", "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "indices",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(scatter_nd_op_info)
 def _scatter_nd_tbe():
     """Conv2D TBE register"""
     return

mindspore/ops/_op_impl/tbe/scatter_nd_d.py

@@ -14,70 +14,28 @@
 # ============================================================================
 
 """ScatterNdD op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+scatter_nd_d_op_info = TBERegOp("ScatterNdD") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("scatter_nd_d.so") \
+    .compute_cost(10) \
+    .kernel_name("scatter_nd_d") \
+    .partial_flag(True) \
+    .attr("shape", "optional", "listInt", "all") \
+    .input(0, "indices", False, "required", "all") \
+    .input(1, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.I32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "ScatterNdD",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "scatter_nd_d.so",
-    "compute_cost": 10,
-    "kernel_name": "scatter_nd_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "shape",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "int32", "int32", "int32", "int32", "int32"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "indices",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","int32","int8","uint8"
-            ],
-            "format": [
-                "DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(scatter_nd_d_op_info)
 def _scatter_nd_d_tbe():
     """ScatterNdD TBE register"""
     return

mindspore/ops/_op_impl/tbe/select.py

@@ -14,94 +14,33 @@
 # ============================================================================
 
 """Select op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+select_op_info = TBERegOp("Select") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("select.so") \
+    .compute_cost(10) \
+    .kernel_name("select") \
+    .partial_flag(True) \
+    .input(0, "condition", False, "required", "all") \
+    .input(1, "x1", False, "required", "all") \
+    .input(2, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.BOOL_Default, DataType.I8_Default, DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.BOOL_Default, DataType.U8_Default, DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.BOOL_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.BOOL_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.BOOL_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.BOOL_5HD, DataType.I8_5HD, DataType.I8_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.BOOL_5HD, DataType.U8_5HD, DataType.U8_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.BOOL_5HD, DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.BOOL_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.BOOL_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Select",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "select.so",
-    "compute_cost": 10,
-    "kernel_name": "select",
-    "partial_flag": true,
-    "attr":[
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "bool", "bool", "bool", "bool", "bool", "bool", "bool", "bool", "bool", "bool",
-                "bool", "bool", "bool", "bool", "bool", "bool", "bool", "bool", "bool", "bool"
-            ],
-            "format": [
-               "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat",
-               "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat",
-               "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-               "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "condition",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float",
-                "int32", "int32", "int32", "int32", "int8", "int8", "int8", "int8", "uint8",
-                "uint8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat",
-                "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x1",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32", "int8", "int8", "int8", "int8", "uint8", "uint8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "x2",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float16", "float", "float", "float", "float", "int32",
-                "int32", "int32", "int32", "int8", "int8", "int8", "int8", "uint8", "uint8", "uint8", "uint8"
-            ],
-            "format": [
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat",
-                "DefaultFormat", "NC1HWC0", "DefaultFormat", "DefaultFormat", "DefaultFormat", "NC1HWC0",
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(select_op_info)
 def _select_tbe():
     """Select TBE register"""
     return

mindspore/ops/_op_impl/tbe/sigmoid.py

@@ -14,67 +14,31 @@
 # ============================================================================
 
 """Sigmoid op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+sigmoid_op_info = TBERegOp("Sigmoid") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("sigmoid.so") \
+    .compute_cost(10) \
+    .kernel_name("sigmoid") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ) \
+    .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Sigmoid",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "Sigmoid.so",
-    "compute_cost": 10,
-    "kernel_name": "sigmoid",
-    "partial_flag": true,
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float",
-                "float16","float",
-                "float16","float",
-                "float16","float",
-                "float16","float"
-            ],
-            "format": [
-                "FracZ","FracZ",
-                "FRACTAL_NZ","FRACTAL_NZ",
-                "C1HWNCoC0","C1HWNCoC0",
-                "NC1HWC0","NC1HWC0",
-                "DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float",
-                "float16","float",
-                "float16","float",
-                "float16","float",
-                "float16","float"
-            ],
-            "format": [
-                "FracZ","FracZ",
-                "FRACTAL_NZ","FRACTAL_NZ",
-                "C1HWNCoC0","C1HWNCoC0",
-                "NC1HWC0","NC1HWC0",
-                "DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(sigmoid_op_info)
 def _sigmoid_tbe():
     """Sigmoid TBE register"""
     return

mindspore/ops/_op_impl/tbe/sigmoid_cross_entropy_with_logits.py

@@ -14,64 +14,26 @@
 # ============================================================================
 
 """SigmoidCrossEntropyWithLogits op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+sigmoid_cross_entropy_with_logits_op_info = TBERegOp("SigmoidCrossEntropyWithLogits") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("sigmoid_cross_entropy_with_logits.so") \
+    .compute_cost(10) \
+    .kernel_name("sigmoid_cross_entropy_with_logits") \
+    .partial_flag(True) \
+    .input(0, "predict", False, "required", "all") \
+    .input(1, "target", False, "required", "all") \
+    .output(0, "loss", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "SigmoidCrossEntropyWithLogits",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "sigmoid_cross_entropy_with_logits.so",
-    "compute_cost": 10,
-    "kernel_name": "sigmoid_cross_entropy_with_logits",
-    "partial_flag": true,
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat"
-            ],
-            "name": "predict",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat"
-            ],
-            "name": "target",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat"
-            ],
-            "name": "loss",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(sigmoid_cross_entropy_with_logits_op_info)
 def _sigmoid_cross_entropy_with_logits_tbe():
     """SigmoidCrossEntropyWithLogits TBE register"""
     return

mindspore/ops/_op_impl/tbe/sigmoid_cross_entropy_with_logits_grad.py

@@ -14,77 +14,27 @@
 # ============================================================================
 
 """SigmoidCrossEntropyWithLogitsGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+sigmoid_cross_entropy_with_logits_grad_op_info = TBERegOp("SigmoidCrossEntropyWithLogitsGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("sigmoid_cross_entropy_with_logits_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("sigmoid_cross_entropy_with_logits_grad") \
+    .partial_flag(True) \
+    .input(0, "predict", False, "required", "all") \
+    .input(1, "target", False, "required", "all") \
+    .input(2, "dout", False, "required", "all") \
+    .output(0, "gradient", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "SigmoidCrossEntropyWithLogitsGrad",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "sigmoid_cross_entropy_with_logits_grad.so",
-    "compute_cost": 10,
-    "kernel_name": "sigmoid_cross_entropy_with_logits_grad",
-    "partial_flag": true,
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat"
-            ],
-            "name": "predict",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat"
-            ],
-            "name": "target",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 2,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat"
-            ],
-            "name": "dout",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "NC1HWC0", "DefaultFormat", "NC1HWC0", "DefaultFormat"
-            ],
-            "name": "gradient",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(sigmoid_cross_entropy_with_logits_grad_op_info)
 def _sigmoid_cross_entropy_with_logits_grad_tbe():
     """SigmoidCrossEntropyWithLogitsGrad TBE register"""
     return

mindspore/ops/_op_impl/tbe/sigmoid_grad.py

@@ -14,64 +14,26 @@
 # ============================================================================
 
 """SigmoidGrad op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+sigmoid_cross_entropy_with_logits_op_info = TBERegOp("SigmoidGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("sigmoid_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("sigmoid_grad") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "y", False, "required", "all") \
+    .output(0, "z", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "SigmoidGrad",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "sigmoid_grad.so",
-    "compute_cost": 10,
-    "kernel_name": "sigmoid_grad",
-    "partial_flag": true,
-    "attr": [
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","float16","float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0","DefaultFormat","DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16","float","float16","float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0","DefaultFormat","DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16","float","float16","float"
-            ],
-            "format": [
-                "NC1HWC0","NC1HWC0","DefaultFormat","DefaultFormat"
-            ],
-            "name": "z",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(sigmoid_cross_entropy_with_logits_op_info)
 def _sigmoid_grad_tbe():
     """SigmoidGrad TBE register"""
     return

mindspore/ops/_op_impl/tbe/slice.py

@@ -14,99 +14,33 @@
 # ============================================================================
 
 """Slice op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+slice_op_info = TBERegOp("Slice") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("slice_d.so") \
+    .compute_cost(10) \
+    .kernel_name("slice_d") \
+    .partial_flag(True) \
+    .attr("begin", "required", "listInt", "all") \
+    .attr("size", "required", "listInt", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.I16_Default, DataType.I16_Default) \
+    .dtype_format(DataType.U16_Default, DataType.U16_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default) \
+    .dtype_format(DataType.U32_Default, DataType.U32_Default) \
+    .dtype_format(DataType.U64_Default, DataType.U64_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name":"Slice",
-    "imply_type":"TBE",
-    "fusion_type":"OPAQUE",
-    "async_flag":false,
-    "binfile_name":"slice_d.so",
-    "compute_cost":10,
-    "kernel_name":"slice_d",
-    "partial_flag":true,
-    "attr":[
-        {
-            "name":"begin",
-            "param_type":"required",
-            "type":"listInt",
-            "value":"all"
-        },
-        {
-            "name":"size",
-            "param_type":"required",
-            "type":"listInt",
-            "value":"all"
-        }
-    ],
-    "inputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float",
-                "float16",
-                "int8",
-                "int16",
-                "int32",
-                "int64",
-                "uint8",
-                "uint16",
-                "uint32",
-                "uint64"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"x",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ],
-    "outputs":[
-        {
-            "index":0,
-            "dtype":[
-                "float",
-                "float16",
-                "int8",
-                "int16",
-                "int32",
-                "int64",
-                "uint8",
-                "uint16",
-                "uint32",
-                "uint64"
-            ],
-            "format":[
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat",
-                "DefaultFormat"
-            ],
-            "name":"y",
-            "need_compile":false,
-            "param_type":"required",
-            "shape":"all"
-        }
-    ]
-}""")
+
+@op_info_register(slice_op_info)
 def _slice_tbe():
     """Slice TBE register"""
     return

mindspore/ops/_op_impl/tbe/softmax.py

@@ -14,57 +14,27 @@
 # ============================================================================
 
 """Softmax op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+softmax_op_info = TBERegOp("Softmax") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("softmax.so") \
+    .compute_cost(10) \
+    .kernel_name("softmax") \
+    .partial_flag(True) \
+    .attr("axis", "optional", "listInt", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracNZ, DataType.F16_FracNZ) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_FracNZ, DataType.F32_FracNZ) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Softmax",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "softmax.so",
-    "compute_cost": 10,
-    "kernel_name": "softmax",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "optional",
-            "type": "listInt",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "NC1HWC0", "FRACTAL_NZ", "DefaultFormat"
-            ],
-            "name": "x",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16", "float16", "float", "float"
-            ],
-            "format": [
-                "FRACTAL_NZ", "DefaultFormat", "NC1HWC0", "FRACTAL_NZ", "DefaultFormat"
-            ],
-            "name": "y",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(softmax_op_info)
 def _softmax_tbe():
     """Softmax TBE register"""
     return

mindspore/ops/_op_impl/tbe/softmax_cross_entropy_with_logits.py

@@ -14,78 +14,25 @@
 # ============================================================================
 
 """SoftmaxCrossEntropyWithLogits op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+softmax_cross_entropy_with_logits_op_info = TBERegOp("SoftmaxCrossEntropyWithLogits") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("softmax_cross_entropy_with_logits.so") \
+    .compute_cost(10) \
+    .kernel_name("softmax_cross_entropy_with_logits") \
+    .partial_flag(True) \
+    .input(0, "input_features", False, "required", "all") \
+    .input(1, "input_labels", False, "required", "all") \
+    .output(0, "output_loss", True, "required", "all") \
+    .output(1, "output_backprop", True, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "SoftmaxCrossEntropyWithLogits",
-    "imply_type": "TBE",
-    "fusion_type": "OPAQUE",
-    "async_flag": false,
-    "binfile_name": "softmax_cross_entropy_with_logits.so",
-    "compute_cost": 10,
-    "kernel_name": "softmax_cross_entropy_with_logits",
-    "partial_flag": true,
-    "attr": [
 
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "input_features",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "input_labels",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output_loss",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        },
-        {
-            "index": 1,
-            "dtype": [
-                "float16", "float"
-            ],
-            "format": [
-                "DefaultFormat", "DefaultFormat"
-            ],
-            "name": "output_backprop",
-            "need_compile": true,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ]
-}""")
+@op_info_register(softmax_cross_entropy_with_logits_op_info)
 def _softmax_cross_entropy_with_logits_tbe():
     """SoftmaxCrossEntropyWithLogits TBE register"""
     return

mindspore/ops/_op_impl/tbe/split_d.py

@@ -14,71 +14,45 @@
 # ============================================================================
 
 """Add op"""
-from mindspore.ops.op_info_register import op_info_register
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
+split_d_op_info = TBERegOp("Split") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("split_d.so") \
+    .compute_cost(10) \
+    .kernel_name("split_d") \
+    .partial_flag(True) \
+    .attr("axis", "required", "int", "all") \
+    .attr("output_num", "required", "int", "all") \
+    .input(0, "value", False, "required", "all") \
+    .output(0, "output", False, "dynamic", "all") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.BOOL_NHWC, DataType.BOOL_NHWC) \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_NHWC, DataType.I8_NHWC) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_NHWC, DataType.U8_NHWC) \
+    .dtype_format(DataType.I16_Default, DataType.I16_Default) \
+    .dtype_format(DataType.I16_NHWC, DataType.I16_NHWC) \
+    .dtype_format(DataType.U16_Default, DataType.U16_Default) \
+    .dtype_format(DataType.U16_NHWC, DataType.U16_NHWC) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_NHWC, DataType.I32_NHWC) \
+    .dtype_format(DataType.U32_Default, DataType.U32_Default) \
+    .dtype_format(DataType.U32_NHWC, DataType.U32_NHWC) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default) \
+    .dtype_format(DataType.I64_NHWC, DataType.I64_NHWC) \
+    .dtype_format(DataType.U64_Default, DataType.U64_Default) \
+    .dtype_format(DataType.U64_NHWC, DataType.U64_NHWC) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_NHWC, DataType.F16_NHWC) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_NHWC, DataType.F32_NHWC) \
+    .get_op_info()
 
-@op_info_register("""{
-    "op_name": "Split",
-    "imply_type": "TBE",
-    "fusion_type": "ELEMWISE",
-    "async_flag": false,
-    "binfile_name": "split_d.so",
-    "compute_cost": 10,
-    "kernel_name": "split_d",
-    "partial_flag": true,
-    "attr": [
-        {
-            "name": "axis",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        },
-        {
-            "name": "output_num",
-            "param_type": "required",
-            "type": "int",
-            "value": "all"
-        }
-    ],
-    "inputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16","float32", "float32", "int32", "int32", "int8", "int8",
-                "int16", "int16", "int64", "int64", "uint8", "uint8", "uint16", "uint16",
-                "uint32", "uint32", "uint64", "uint64", "bool", "bool"
-            ],
-            "format": [
-                "DefaultFormat", "NHWC", "DefaultFormat", "NHWC", "DefaultFormat", "NHWC", "DefaultFormat", "NHWC"
-                , "DefaultFormat", "NHWC", "DefaultFormat", "NHWC", "DefaultFormat", "NHWC", "DefaultFormat", "NHWC"
-                , "DefaultFormat", "NHWC", "DefaultFormat", "NHWC", "DefaultFormat", "NHWC"
-            ],
-            "name": "value",
-            "need_compile": false,
-            "param_type": "required",
-            "shape": "all"
-        }
-    ],
-    "outputs": [
-        {
-            "index": 0,
-            "dtype": [
-                "float16", "float16","float32", "float32", "int32", "int32", "int8", "int8",
-                "int16", "int16", "int64", "int64", "uint8", "uint8", "uint16", "uint16",
-                "uint32", "uint32", "uint64", "uint64", "bool", "bool"
-            ],
-            "format": [
-                "DefaultFormat", "NHWC", "DefaultFormat", "NHWC", "DefaultFormat", "NHWC", "DefaultFormat", "NHWC"
-                , "DefaultFormat", "NHWC", "DefaultFormat", "NHWC", "DefaultFormat", "NHWC", "DefaultFormat", "NHWC"
-                , "DefaultFormat", "NHWC", "DefaultFormat", "NHWC", "DefaultFormat", "NHWC"
-            ],
-            "name": "output",
-            "need_compile": false,
-            "param_type": "dynamic",
-            "shape": "all"
-        }
-    ]
-}""")
+
+@op_info_register(split_d_op_info)
 def _split_d_tbe():
     """Add TBE register"""
     return