diff --git a/mindspore/_extends/graph_kernel/expanders/bias_add_grad.py b/mindspore/_extends/graph_kernel/expanders/bias_add_grad.py
index 9d9163efc9..161f33c07c 100644
--- a/mindspore/_extends/graph_kernel/expanders/bias_add_grad.py
+++ b/mindspore/_extends/graph_kernel/expanders/bias_add_grad.py
@@ -20,24 +20,30 @@ from ._utils import Expander, ExpanderInfoValidator as VLD
 @VLD.add_format(DF.DEFAULT)
 @VLD.add_format(DF.NHWC)
 @VLD.add_format(DF.NCHW)
+@VLD.add_format(DF.FRAC_NZ)
 class BiasAddGrad(Expander):
     """BiasAddGrad expander"""
 
     def _expand(self, graph_builder):
-        input_x = self.inputs[0]
+        x = self.inputs[0]
 
         reduce_axis = ()
-        if input_x.data_format == 'NHWC':
+        if x.data_format == DF.NHWC:
             reduce_axis = (0, 1, 2)
-        elif input_x.data_format == 'NCHW':
+        elif x.data_format == DF.NCHW:
             reduce_axis = (0, 2, 3)
-        # DefaultFormat shape's length should be from 2 to 4
+        elif x.data_format == DF.FRAC_NZ:
+            reduce_axis = (-2, -3)
         else:
-            if len(input_x.shape) == 2:
+            # DefaultFormat shape's length should be from 2 to 4
+            if len(x.shape) == 2:
                 reduce_axis = (0,)
-            elif len(input_x.shape) == 3:
+            elif len(x.shape) == 3:
                 reduce_axis = (0, 1)
             else:
                 reduce_axis = (0, 2, 3)
-        result = graph_builder.emit('ReduceSum', [input_x], attrs={'reduce_axis': reduce_axis, 'keep_dims': False})
+        result = graph_builder.emit('ReduceSum', [x], attrs={'reduce_axis': reduce_axis, 'keep_dims': False})
+        if x.data_format == DF.FRAC_NZ:
+            out_shape = x.shape[:-4] + [x.shape[-1] * x.shape[-4]]
+            result = graph_builder.emit('Reshape', [result], attrs={'shape': out_shape})
         return result
diff --git a/mindspore/_extends/graph_kernel/expanders/tile.py b/mindspore/_extends/graph_kernel/expanders/tile.py
index 45e881f550..23c2584105 100644
--- a/mindspore/_extends/graph_kernel/expanders/tile.py
+++ b/mindspore/_extends/graph_kernel/expanders/tile.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 # ===========================================================================
 """generate json desc for Tile"""
-from mindspore._extends.graph_kernel.model import model_builder as builder
+from mindspore._extends.graph_kernel.model.op_infer import Tile as TileInfer
 from mindspore._extends.graph_kernel.model.model import DataFormat as DF
 from ._utils import Expander, ExpanderInfoValidator as VLD
 
@@ -27,8 +27,9 @@ class Tile(Expander):
         input_x = self.inputs[0]
         multiples = self.attrs['multiples']
 
-        output_shape, _, _, shape_compatible = builder.get_tile_output_shape(self.inputs[0].shape, multiples)
-        if shape_compatible:
+        tile_infer = TileInfer(self.name, self.inputs, self.attrs)
+        output_shape, _, _ = tile_infer.infer()
+        if tile_infer.broadcast_compatible:
             result = graph_builder.emit('BroadcastTo', [input_x], attrs={'shape': output_shape})
         else:
             result = graph_builder.emit('Tile', [input_x], attrs={'multiples': multiples})
diff --git a/mindspore/_extends/graph_kernel/model/model_builder.py b/mindspore/_extends/graph_kernel/model/model_builder.py
index cdcd4dc92e..4125b75e1b 100644
--- a/mindspore/_extends/graph_kernel/model/model_builder.py
+++ b/mindspore/_extends/graph_kernel/model/model_builder.py
@@ -15,139 +15,8 @@
 """GraphKernel model builder"""
 
 import copy
-from .model import PrimLib, Tensor, Value, Operator, Graph, AlignShape, AddControlBuddy, DataFormat
-
-
-def get_tile_output_shape(shape, multiples):
-    """compute output shape of tile"""
-
-    if multiples is None:
-        return shape
-    if not isinstance(shape, (list, tuple)):
-        raise TypeError("Input shape of Tile must be of type list or tuple")
-    if not isinstance(multiples, (list, tuple)):
-        raise TypeError("multiples of Tile must be of type list or tuple")
-
-    shape = list(shape)
-    multiples = list(multiples)
-    diff_len = len(multiples) - len(shape)
-    if diff_len < 0:
-        raise ValueError("Dimensions of multiples{} < dimensions of input{} in Tile".format(multiples, shape))
-    if diff_len > 0:
-        for _ in range(diff_len):
-            shape.insert(0, 1)
-
-    shape_compatible = True
-    output_shape = []
-    input_reshape = []
-    output_reshape = []
-    for sh, mul in list(zip(shape, multiples)):
-        dim = sh * mul
-        output_shape.append(dim)
-        if sh == 1 or mul == 1:
-            input_reshape.append(sh)
-            output_reshape.append(dim)
-        else:
-            shape_compatible = False
-            input_reshape.append(1)
-            input_reshape.append(sh)
-            output_reshape.append(mul)
-            output_reshape.append(sh)
-
-    return output_shape, input_reshape, output_reshape, shape_compatible
-
-
-class OpInfer:
-    """Op infer"""
-    @staticmethod
-    def default_reduce_infer(inputs, attrs):
-        """Default reduce infer"""
-        shape = copy.deepcopy(inputs[0].shape)
-        if attrs['keep_dims']:
-            for i in attrs['reduce_axis']:
-                shape[i] = 1
-            return shape
-
-        real_shape = []
-        for i, _ in enumerate(shape):
-            if i not in attrs['reduce_axis'] and i - len(shape) not in attrs['reduce_axis']:
-                real_shape.append(shape[i])
-        return real_shape
-
-    @staticmethod
-    def default_elementwise_infer(inputs, attrs):
-        """Default elementwise infer"""
-        shape = (1,)
-        max_flatten_shape = 1
-        for t in inputs:
-            flatten_shape = 1
-            for s in t.shape:
-                flatten_shape *= s
-            if flatten_shape >= max_flatten_shape:
-                max_flatten_shape = flatten_shape
-                shape = t.shape
-        return shape
-
-    default_infer_shape_func = [
-        None,
-        None,
-        default_elementwise_infer.__func__,
-        lambda inputs, attrs: max([t.shape for t in inputs]),
-        default_reduce_infer.__func__,
-        None,
-        lambda inputs, attrs: [1],  # control op
-    ]
-
-    @staticmethod
-    def default_infer_dtype_func(inputs, attrs):
-        """Infer dtype"""
-        return inputs[0].dtype
-
-    @staticmethod
-    def default_infer_format_func(inputs, attrs):
-        """Infer format"""
-        result = inputs[0].data_format
-        # default_format and other_format results in other_format
-        for input_tensor in inputs[1:]:
-            data_format = input_tensor.data_format
-            if data_format != DataFormat.DEFAULT:
-                if result not in [DataFormat.DEFAULT, data_format]:
-                    raise RuntimeError("Incompatible data format %s and %s" % (data_format, result))
-                result = data_format
-        return result
-
-    infer_shape_func = {
-        # add special infer func here
-        'InplaceAssign': lambda inputs, attrs: inputs[2].shape,
-        'Reshape': lambda inputs, attrs: attrs["shape"],
-        'BroadcastTo': lambda inputs, attrs: attrs["shape"],
-        'Tile': lambda inputs, attrs: get_tile_output_shape(inputs[0].shape, attrs["multiples"])[0],
-        'ExpandDims': lambda inputs, attrs: list(inputs[0].shape).insert(attrs["axis"], 1),
-    }
-    infer_dtype_func = {
-        # add special infer func here
-        'Cast': lambda inputs, attrs: attrs['dst_type'],
-        'Less': lambda inputs, attrs: "bool",
-        'LessEqual': lambda inputs, attrs: "bool",
-        'Equal': lambda inputs, attrs: "bool",
-        'Greater': lambda inputs, attrs: "bool",
-        'GreaterEqual': lambda inputs, attrs: "bool",
-    }
-    infer_format_func = {
-        # add special infer func here
-        'Reshape': lambda inputs, attrs: "DefaultFormat",
-    }
-
-    @classmethod
-    def infer(cls, prim_name, inputs, attrs):
-        prim = PrimLib.primtives[prim_name]
-        infer_shape = cls.infer_shape_func.get(
-            prim_name, cls.default_infer_shape_func[prim.iter_type])
-        infer_dtype = cls.infer_dtype_func.get(
-            prim_name, cls.default_infer_dtype_func)
-        infer_format = cls.infer_format_func.get(
-            prim_name, cls.default_infer_format_func)
-        return infer_shape(inputs, attrs), infer_dtype(inputs, attrs), infer_format(inputs, attrs)
+from . import op_infer
+from .model import Tensor, Value, Operator, Graph, AlignShape, AddControlBuddy
 
 
 class GraphBuilder:
@@ -229,7 +98,7 @@ class GraphBuilder:
         if isinstance(inputs, (Tensor, Value)):
             inputs = [inputs]
         tensor_inputs = [t for t in inputs if isinstance(t, (Tensor, Value))]
-        out_shape, out_dtype, out_format = OpInfer.infer(prim, tensor_inputs, attrs)
+        out_shape, out_dtype, out_format = op_infer.infer(prim, tensor_inputs, attrs)
         output = self.tensor(out_shape, out_dtype, out_format, name)
         self.op(prim, output, inputs, attrs)
         return output
diff --git a/mindspore/_extends/graph_kernel/model/op_infer.py b/mindspore/_extends/graph_kernel/model/op_infer.py
new file mode 100644
index 0000000000..8ae890364e
--- /dev/null
+++ b/mindspore/_extends/graph_kernel/model/op_infer.py
@@ -0,0 +1,275 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ===========================================================================
+"""GraphKernel Op Infer"""
+
+
+import copy
+import sys
+from functools import reduce
+from .model import GraphKernelUnsupportedException as GKException
+from .model import PrimLib, DataFormat as DF
+
+
+def infer(op_name, inputs, attrs):
+    """infer shape dtype and format"""
+    def _create_opinfer():
+        if hasattr(sys.modules[__name__], op_name):
+            op_cls = getattr(sys.modules[__name__], op_name)
+            return op_cls(op_name, inputs, attrs)
+        # common infer
+        class_name_map = {
+            PrimLib.ELEMWISE: "_Elemwise",
+            PrimLib.REDUCE: "_Reduce",
+        }
+        cls_name = class_name_map.get(PrimLib.primtives.get(op_name, PrimLib.default_primtive).iter_type, None)
+        if not cls_name:
+            raise GKException("OpInfo does not support op {}".format(op_name))
+        op_cls = getattr(sys.modules[__name__], cls_name)
+        return op_cls(op_name, inputs, attrs)
+    return _create_opinfer().infer()
+
+
+class OpInfer:
+    """
+    OpInfer is the base class for inferring operator info in GraphKernel model builder.
+
+    There are three methods should be overridden to define the infer logic of the operator:
+    _infer_shape(), _infer_type() and _infer_format().
+    """
+
+    def __init__(self, name, inputs, attrs):
+        self.name = name
+        self.inputs = inputs
+        self.attrs = attrs
+
+    def infer(self):
+        """Infer shape, type and format by op inputs"""
+        self._check()
+        return self._infer_shape(), self._infer_type(), self._infer_format()
+
+    def _infer_shape(self):
+        return self.inputs[0].shape
+
+    def _infer_type(self):
+        return self.inputs[0].dtype
+
+    def _infer_format(self):
+        return self.inputs[0].data_format
+
+    def _check(self):
+        self._check_shape()
+        self._check_type()
+        self._check_format()
+
+    def _check_shape(self):
+        pass
+
+    def _check_type(self):
+        """check all dtypes are same"""
+        dtype = self.inputs[0].dtype
+        for i, t in enumerate(self.inputs[1:]):
+            if t.dtype != dtype:
+                raise GKException(
+                    "Incompatible dtype between input {}({}) and {}({})".format(0, dtype, i + 1, t.dtype))
+
+    def _check_format(self):
+        """check formats are compatible. only DefaultFormat is compatible with others"""
+        result = self.inputs[0].data_format
+        i = 0
+        for j, t in enumerate(self.inputs[1:]):
+            if t.data_format != result:
+                if DF.DEFAULT not in (result, t.data_format):
+                    raise GKException("Incompatible format between input {}({}) and {}({})".format(
+                        i, result, j + 1, t.data_format))
+                if result == DF.DEFAULT:
+                    result = t.data_format
+                    i = j + 1
+
+
+class _Elemwise(OpInfer):
+    """Common infer for elementwise operators"""
+
+    def _infer_shape(self):
+        """returns the input shape with largest flatten size"""
+        shape = (1,)
+        max_flatten_size = 1
+        for t in self.inputs:
+            flatten_size = reduce(lambda x, y: x * y, t.shape)
+            if flatten_size >= max_flatten_size:
+                max_flatten_size = flatten_size
+                shape = t.shape
+        return shape
+
+    def _infer_format(self):
+        for tensor in self.inputs:
+            if tensor.data_format != DF.DEFAULT:
+                return tensor.data_format
+        return DF.DEFAULT
+
+
+class _Reduce(OpInfer):
+    """Common infer for reduction operators"""
+
+    def _check(self):
+        super()._check()
+        # check reduce axis in the range [-len, len)
+        shape_len = len(self.inputs[0].shape)
+        axis = self.attrs['reduce_axis']
+        if isinstance(axis, int):
+            axis = [axis]
+        if not all([(-shape_len <= i < shape_len) for i in axis]):
+            raise GKException(
+                "reduce_axis should be in range [{},{}) but got {}".format(-shape_len, shape_len, axis))
+
+    def _infer_shape(self):
+        shape = copy.deepcopy(self.inputs[0].shape)
+        axis = self.attrs['reduce_axis']
+
+        if isinstance(axis, int):
+            axis = [axis]
+        if any([i < 0 for i in axis]):
+            # change the axis to non-negative number.
+            axis = list(map(lambda i: i + len(shape) if i < 0 else i, axis))
+        self.attrs['reduce_axis'] = sorted(axis)
+
+        if self.attrs['keep_dims']:
+            for i in axis:
+                shape[i] = 1
+            return shape
+
+        real_shape = []
+        for i, s in enumerate(shape):
+            if i not in axis:
+                real_shape.append(s)
+        return real_shape
+
+    def _infer_format(self):
+        return DF.DEFAULT
+
+
+class _Reshape(OpInfer):
+    """Common infer for reshape operators, should not be instantiated"""
+
+    def _infer_shape(self):
+        raise GKException("_infer_shape should be implemented by subclass")
+
+    def _infer_format(self):
+        return DF.DEFAULT
+
+
+class Reshape(_Reshape):
+    def _infer_shape(self):
+        return self.attrs["shape"]
+
+
+class ExpandDims(_Reshape):
+    def _infer_shape(self):
+        return list(self.inputs[0].shape).insert(self.attrs["axis"], 1)
+
+
+class Cast(_Elemwise):
+    def _infer_type(self):
+        return self.attrs["dst_type"]
+
+
+class InplaceAssign(_Elemwise):
+    def _infer_shape(self):
+        return [1] if self.attrs["fake_output"] else self.inputs[2].shape
+
+    def _infer_type(self):
+        return self.inputs[2].dtype
+
+    def _infer_format(self):
+        return DF.DEFAULT if self.attrs["fake_output"] else self.inputs[2].data_format
+
+
+class BroadcastTo(OpInfer):
+    def _infer_shape(self):
+        return self.attrs["shape"]
+
+    def _infer_format(self):
+        return self.inputs[0].data_format
+
+
+class Tile(OpInfer):
+    """Op Tile"""
+
+    def __init__(self, op_name, inputs, attrs):
+        super().__init__(op_name, inputs, attrs)
+        self.input_reshape = None
+        self.output_reshape = None
+        self.broadcast_compatible = True
+
+    def _infer_shape(self):
+        shape = self.inputs[0].shape
+        multiples = self.attrs["multiples"]
+
+        shape = list(shape)
+        multiples = list(multiples)
+        diff_len = len(multiples) - len(shape)
+        if diff_len < 0:
+            raise ValueError("Dimensions of multiples{} < dimensions of input{} in Tile".format(multiples, shape))
+        if diff_len > 0:
+            for _ in range(diff_len):
+                shape.insert(0, 1)
+
+        self.broadcast_compatible = True
+        output_shape = []
+        self.input_reshape = []
+        self.output_reshape = []
+        for sh, mul in list(zip(shape, multiples)):
+            dim = sh * mul
+            output_shape.append(dim)
+            if sh == 1 or mul == 1:
+                self.input_reshape.append(sh)
+                self.output_reshape.append(dim)
+            else:
+                self.broadcast_compatible = False
+                self.input_reshape.append(1)
+                self.input_reshape.append(sh)
+                self.output_reshape.append(mul)
+                self.output_reshape.append(sh)
+
+        return output_shape
+
+    def _infer_format(self):
+        return DF.DEFAULT
+
+
+class _CompareOp(_Elemwise):
+    """Compare operators"""
+
+    def _infer_type(self):
+        return "bool"
+
+
+class Less(_CompareOp):
+    pass
+
+
+class LessEqual(_CompareOp):
+    pass
+
+
+class Equal(_CompareOp):
+    pass
+
+
+class Greater(_CompareOp):
+    pass
+
+
+class GreaterEqual(_CompareOp):
+    pass