ascend_parser.py

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import paddle.fluid.core as core
import numpy as np
from functools import reduce

__all__ = []

registerd_op = {  # forwards
    "elementwise_add": "AddParser",
    "matmul": "MatMulParser",
    "mul": "MulParser",
    "relu": "ReluParser",
    "softmax_with_cross_entropy": "SoftmaxWithCrossEntropyParser",
    "shape": "ShapeParser",
    "fill_constant": "FillConstantParser",
    "reduce_sum": "ReduceSumParser",
    "elementwise_mul": "DotMulParser",
    "elementwise_div": "DotDivParser",
    "elementwise_pow": "DotPowParser",
    "elementwise_max": "MaxParser",
    "elementwise_min": "MinParser",
    "elementwise_sub": "DotSubParser",
    "pow": "PowParser",
    "gelu": "GeluParser",
    "sqrt": "SqrtParser",
    "log": "LogParser",
    "sum": "SumParser",
    "logical_not": "LogicalNotParser",
    "gather": "GatherParser",
    "scatter": "ScatterParser",
    "cast": "CastParser",
    "tanh": "TanhParser",
    "stack": "StackParser",
    "square": "SquareParser",
    "unsqueeze2": "UnSqueezeParser",
    "assign": "AssignParser",
    "softmax": "SoftMaxParser",
    "reshape2": "ReshapeParser",
    "transpose2": "TransposeParser",
    "layer_norm": "LayerNormParser",
    "less_than": "LessParser",
    "mean": "MeanParser",
    "scale": "ScaleParser",
    "slice": "SliceParser",
    "top_k": "TopkParser",
    "accuracy": "AccuracyParser",
    # "increment": "IncrementParser",
    "lookup_table": "LookupTableParser",
    "truncated_gaussian_random": "TruncatedNormalParser",
    "c_allgather": "AllGatherParser",
    "c_allreduce_sum": "AllReduceSumParser",
    "c_allreduce_max": "AllReduceMaxParser",
    "c_broadcast": "BroadcastParser",
    "c_reduce_scatter": "ReduceScatterParser",
    "c_send": "SendParser",
    "c_receive": "ReceiveParser",
    "uniform_random": "UniformRandomParser",
    "range": "RangeParser",
    "equal": "EqualParser",
    "expand": "ExpandParser",
    "squeeze2": "SqueezeParser",
    ## backwords
    "matmul_grad": "MatMulGradParser",
    "mul_grad": "MulGradParser",
    "relu_grad": "ReluGradParser",
    "reduce_sum_grad": "ReduceSumGradParser",
    "softmax_with_cross_entropy_grad": "SoftmaxWithCrossEntropyGradParser",
    "tanh_grad": "TanhGradParser",
    "log_grad": "LogGradParser",
    "pow_grad": "PowGradParser",
    "sqrt_grad": "SqrtGradParser",
    "gelu_grad": "GeluGradParser",
    "mean_grad": "MeanGradParser",
    'lookup_table_grad': "LookUpTableGradParser",
    "elementwise_mul_grad": "DotMulGradParser",
    "elementwise_add_grad": "DotAddGradParser",
    "elementwise_div_grad": "DotDivGradParser",
    "softmax_grad": "SoftmaxGradParser",
    "slice_grad": "SliceGradParser",
    "reshape2_grad": "ReshapeGradParser",
    "gather_grad": "GatherGradParser",
    "transpose2_grad": "TransposeGradParser",
    "layer_norm_grad": "LayerNormGradParser",
    ## opt
    "sgd": "SGDParser",
    # "adam": "AdamParser",
}
global_cnt = -1
global_input_cnt = -1


class AscendHelper(object):
    def __init__(self):
        self.dtype2ge_map = {
            0: core.GEDataType.DT_BOOL,
            1: core.GEDataType.DT_INT16,
            2: core.GEDataType.DT_INT32,
            3: core.GEDataType.DT_INT64,
            4: core.GEDataType.DT_FLOAT16,
            5: core.GEDataType.DT_FLOAT,
            6: core.GEDataType.DT_DOUBLE,
        }
        self.dtype2np_map = {
            0: "bool",
            1: "int16",
            2: "int32",
            3: "int64",
            4: "float16",
            5: "float32",
            6: "float64",
        }
        self.dtype2paddle_inv_map = {"VarType.FP32": 0, "VarType.FP16": 1}

    def dtype2ge(self, dtype):
        assert dtype in self.dtype2ge_map, "dtype[%d] is not supported %d" % (
            dtype
        )
        return self.dtype2ge_map[dtype]

    def dtype2np(self, index):
        assert index in self.dtype2np_map, "index[%d] is not supported %d" % (
            index
        )
        return self.dtype2np_map[index]


class AscendParserFactory(object):
    def __init__(self, graph, var2geop):
        self.graph = graph
        self.var2geop = var2geop

    def create_parse(self, parser_class):
        try:
            parser = globals()[parser_class](self.graph, self.var2geop)
            return parser
        except:
            raise ValueError("parser class %s does not exist" % parser_class)


class AscendParserBase(object):
    def __init__(self, graph, var2geop):
        self.graph = graph
        self.var2geop = var2geop
        self.op = None
        self.ascend_helper = AscendHelper()

    def _get_ge_input(self, input_var_name):
        assert input_var_name in self.var2geop, "var %s not created before" % (
            input_var_name
        )
        return self.var2geop[input_var_name]

    def update_output(self, geop_list, index_list):
        output_num = len(self.op.output_names)
        assert output_num == len(index_list), (
            "Parser[%s]'s output number[%d] is not equal to parameters number[%d]"
            % (self.parser_name, len(index_list), output_num)
        )
        for output_id in range(output_num):
            arguments = self.op.output(self.op.output_names[output_id])
            if len(arguments) > 0:
                assert len(arguments) == len(index_list[output_id]), (
                    "Parser[%s]'s %dth argument number[%d] is not equal to paddle's number[%d]"
                    % (
                        self.parser_name,
                        output_id,
                        len(index_list[output_id]),
                        len(arguments),
                    )
                )
                for i in range(len(arguments)):
                    self.var2geop[arguments[i]] = geop_list[
                        index_list[output_id][i]
                    ]

        for geop in geop_list:
            self.graph.add_op(geop)

    def apply(self, op):
        self.op = op
        assert (
            self.op.type == self.parser_name
        ), "op [%s] != parser_name[%s]" % (self.op.type, self.parser_name)
        # print("begin to parse op %s" % (self.parser_name))
        geop_list, index_list = self._apply()
        self.update_output(geop_list, index_list)

    def _mark_as_input(self, ge_tensor):
        global global_input_cnt
        global_input_cnt += 1
        self.var2geop["geinput." + str(global_input_cnt)] = ge_tensor

    def _accumulated_op_id(self):
        global global_cnt
        global_cnt += 1
        name = "." + str(global_cnt)
        return name

    def _create_ge_tensor(self, shape, dtype, value):
        tensor_desc = core.GETensorDesc(
            core.GEShape(shape),
            core.GEFormat.FORMAT_ND,
            self.ascend_helper.dtype2ge(dtype),
        )
        tensor = core.GETensor(tensor_desc)

        data = (
            (value * np.ones((shape)))
            .reshape(shape)
            .astype(self.ascend_helper.dtype2np(dtype))
        )
        buf = data.tobytes()
        data_8 = np.frombuffer(buf, dtype=np.uint8)
        tensor.set_data(data_8)
        return tensor

    def _get_ge_tensor(self, shape, dtype, value_list):
        tensor_desc = core.GETensorDesc(
            core.GEShape(shape),
            core.GEFormat.FORMAT_ND,
            self.ascend_helper.dtype2ge(dtype),
        )
        tensor = core.GETensor(tensor_desc)

        data = (
            np.array(value_list)
            .reshape(shape)
            .astype(self.ascend_helper.dtype2np(dtype))
        )
        buf = data.tobytes()
        data_8 = np.frombuffer(buf, dtype=np.uint8)
        tensor.set_data(data_8)

        tensor_const = core.GEOperatorFactory.create_operator(
            "const" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", tensor)

        return tensor_const

    def _get_variable(self, shape, dtype, tensor):
        if dtype == "int32":
            type = core.GEDataType.DT_INT32
        elif dtype == "float32":
            type = core.GEDataType.DT_FLOAT

        var = core.GEOperatorFactory.create_operator(
            "variable" + self._accumulated_op_id(), "Variable"
        )
        var.update_output_desc(
            "y",
            core.GETensorDesc(
                core.GEShape(shape), core.GEFormat.FORMAT_ND, type
            ),
        )
        assign = (
            core.GEOperatorFactory.create_operator(
                "assign" + self._accumulated_op_id(), "Assign"
            )
            .set_input("value", tensor)
            .set_input("ref", var)
        )

        return assign

    def _create_shape_tensor(self):
        tensor_desc = core.GETensorDesc(
            core.GEShape([2]), core.GEFormat.FORMAT_ND, core.GEDataType.DT_INT32
        )
        tensor = core.GETensor(tensor_desc)

        data = np.ones((2)).astype("int32").reshape([2])
        data[0] = 64
        buf = data.tobytes()
        data_8 = np.frombuffer(buf, dtype=np.uint8)
        tensor.set_data(data_8)
        return tensor

    def _get_GEtensor_shape(self, tensor):
        tensor_shape = core.GEOperatorFactory.create_operator(
            "shape" + self._accumulated_op_id(), "Shape"
        ).set_input("x", tensor)
        tensor_shape = (
            core.GEOperatorFactory.create_operator(
                "cast" + self._accumulated_op_id(), "Cast"
            )
            .set_input("x", tensor_shape)
            .set_attr_int32("dst_type", 0)
        )
        return tensor_shape


class AddParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "elementwise_add"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        y = self._get_ge_input(self.op.input_arg_names[1])
        add = (
            core.GEOperatorFactory.create_operator(
                "add" + self._accumulated_op_id(), "Add"
            )
            .set_input("x1", x)
            .set_input("x2", y)
        )
        return [add], [[0]]


class DotSubParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "elementwise_sub"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        y = self._get_ge_input(self.op.input_arg_names[1])
        sub = (
            core.GEOperatorFactory.create_operator(
                "sub" + self._accumulated_op_id(), "Sub"
            )
            .set_input("x1", x)
            .set_input("x2", y)
        )
        return [sub], [[0]]


class DotMulParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "elementwise_mul"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        y = self._get_ge_input(self.op.input_arg_names[1])
        mul = (
            core.GEOperatorFactory.create_operator(
                "dotmul" + self._accumulated_op_id(), "Mul"
            )
            .set_input("x1", x)
            .set_input("x2", y)
        )
        return [mul], [[0]]


class DotDivParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "elementwise_div"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        y = self._get_ge_input(self.op.input_arg_names[1])
        div = (
            core.GEOperatorFactory.create_operator(
                "dotdiv" + self._accumulated_op_id(), "Div"
            )
            .set_input("x1", x)
            .set_input("x2", y)
        )
        return [div], [[0]]


class DotPowParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "elementwise_pow"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        y = self._get_ge_input(self.op.input_arg_names[1])
        pow = (
            core.GEOperatorFactory.create_operator(
                "dotpow" + self._accumulated_op_id(), "Pow"
            )
            .set_input("x1", x)
            .set_input("x2", y)
        )
        return [pow], [[0]]


class LessParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "less_than"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        y = self._get_ge_input(self.op.input_arg_names[1])
        less_than = (
            core.GEOperatorFactory.create_operator(
                "less_than" + self._accumulated_op_id(), "Less"
            )
            .set_input("x1", x)
            .set_input("x2", y)
        )
        return [less_than], [[0]]


class MaxParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "elementwise_max"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        y = self._get_ge_input(self.op.input_arg_names[1])
        max_out = (
            core.GEOperatorFactory.create_operator(
                "max" + self._accumulated_op_id(), "Maximum"
            )
            .set_input("x1", x)
            .set_input("x2", y)
        )
        return [max_out], [[0]]


class MinParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "elementwise_min"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        y = self._get_ge_input(self.op.input_arg_names[1])
        min_out = (
            core.GEOperatorFactory.create_operator(
                "min" + self._accumulated_op_id(), "Minimum"
            )
            .set_input("x1", x)
            .set_input("x2", y)
        )
        return [min_out], [[0]]


## cal
class LogParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "log"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        log = core.GEOperatorFactory.create_operator(
            "log" + self._accumulated_op_id(), "Log"
        ).set_input("x", x)
        return [log], [[0]]


class SqrtParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "sqrt"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        sqrt = core.GEOperatorFactory.create_operator(
            "sqrt" + self._accumulated_op_id(), "Sqrt"
        ).set_input("x", x)
        return [sqrt], [[0]]


class PowParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "pow"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        factor = self.op.attr("factor")
        pow_value = (
            core.GEOperatorFactory.create_operator(
                "pow" + self._accumulated_op_id(), "Power"
            )
            .set_input("x", x)
            .set_attr_float("power", factor)
            .set_attr_float("scale", 1.0)
            .set_attr_float("shift", 0.0)
        )
        return [pow_value], [[0]]


class SquareParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "square"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        square = core.GEOperatorFactory.create_operator(
            "square" + self._accumulated_op_id(), "Square"
        ).set_input("x", x)
        return [square], [[0]]


class SumParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "sum"

    def _apply(self):
        len_list = len(self.op.input_arg_names)
        if len_list < 2:
            assert False, "the size of input list must large or equal 2"
        x = self._get_ge_input(self.op.input_arg_names[0])
        y = self._get_ge_input(self.op.input_arg_names[1])
        sum = (
            core.GEOperatorFactory.create_operator(
                "sum" + self._accumulated_op_id(), "Add"
            )
            .set_input("x1", x)
            .set_input("x2", y)
        )
        for i in range(2, len_list):
            y = self._get_ge_input(self.op.input_arg_names[i])
            sum = (
                core.GEOperatorFactory.create_operator(
                    "sum" + self._accumulated_op_id(), "Add"
                )
                .set_input("x1", sum)
                .set_input("x2", y)
            )
        return [sum], [[0]]


class LogicalNotParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "logical_not"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        logical_not = core.GEOperatorFactory.create_operator(
            "logical_not" + self._accumulated_op_id(), "LogicalNot"
        ).set_input("x", x)
        return [logical_not], [[0]]


class MeanParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "mean"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        mean = (
            core.GEOperatorFactory.create_operator(
                "mean" + self._accumulated_op_id(), "ReduceMeanD"
            )
            .set_input("x", x)
            .set_attr_bool("keep_dims", False)
            .set_attr_vec_int32("axes", [])
        )
        return [mean], [[0]]


class ReduceSumParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "reduce_sum"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        axes = self.op.attr("dim")
        keep_dims = self.op.attr("keep_dim")
        reduce_all = self.op.attr("reduce_all")
        x_shape = self.op.block.var(self.op.input_arg_names[0]).shape
        if reduce_all:
            axes = list(range(len(x_shape)))
        reduce_sum = (
            core.GEOperatorFactory.create_operator(
                "reduce_sum" + self._accumulated_op_id(), "ReduceSumD"
            )
            .set_input("x", x, 0)
            .set_attr_vec_int32("axes", axes)
            .set_attr_bool("keep_dims", keep_dims)
        )
        return [reduce_sum], [[0]]


# class IncrementParser(AscendParserBase):
#    def __init__(self, graph, var2geop):
#        super().__init__(graph, var2geop)
#        self.parser_name = "increment"
#
#    def _apply(self):
#        x = self._get_ge_input(self.op.input_arg_names[0])
#        step = self.op.attr("step") #self._get_ge_input(self.op.input_arg_names[1])
#        print("step: ", step)
#
#        increment = core.GEOperatorFactory.create_operator("adds" + self._accumulated_op_id(), "Adds").set_input("x", x).set_attr_float("value", step) #set_input("x2", bias)
#
#        return [increment]


## matrix cal
class MatMulParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "matmul"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        y = self._get_ge_input(self.op.input_arg_names[1])
        transpose_x = self.op.attr("transpose_X")
        transpose_y = self.op.attr("transpose_Y")

        x1_shape = self.op.block.var(self.op.input_arg_names[0]).shape
        x2_shape = self.op.block.var(self.op.input_arg_names[1]).shape

        if len(x1_shape) > 2:
            matmul = (
                core.GEOperatorFactory.create_operator(
                    "matmul" + self._accumulated_op_id(), "BatchMatMul"
                )
                .set_input("x1", x)
                .set_input("x2", y)
                .set_attr_bool("adj_x1", transpose_x)
                .set_attr_bool("adj_x2", transpose_y)
            )
        elif len(x1_shape) == 2:
            matmul = (
                core.GEOperatorFactory.create_operator(
                    "matmul" + self._accumulated_op_id(), "MatMul"
                )
                .set_input("x1", x)
                .set_input("x2", y)
                .set_attr_bool("transpose_x1", transpose_x)
                .set_attr_bool("transpose_x2", transpose_y)
            )
        else:
            assert False, "not support"
        return [matmul], [[0]]


class MulParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "mul"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        y = self._get_ge_input(self.op.input_arg_names[1])
        x_num_col_dims = self.op.attr("x_num_col_dims")
        y_num_col_dims = self.op.attr("y_num_col_dims")
        shape_x1 = self.op.block.var(self.op.input_arg_names[0]).shape
        shape_x2 = self.op.block.var(self.op.input_arg_names[1]).shape

        if x_num_col_dims == 1 and y_num_col_dims == 1:
            if len(shape_x1) == 2 and len(shape_x2) == 2:
                matmul = (
                    core.GEOperatorFactory.create_operator(
                        "mul" + self._accumulated_op_id(), "MatMul"
                    )
                    .set_input("x1", x)
                    .set_input("x2", y)
                )
            elif len(shape_x1) == 3 and len(shape_x2) == 2:
                flatten_x1 = core.GEOperatorFactory.create_operator(
                    "flatten" + self._accumulated_op_id(), "Flatten"
                ).set_input("x", x)
                matmul = (
                    core.GEOperatorFactory.create_operator(
                        "mul" + self._accumulated_op_id(), "MatMul"
                    )
                    .set_input("x1", flatten_x1, 0)
                    .set_input("x2", y, 0)
                )
            else:
                assert False, "not support"
        else:
            if len(shape_x1) == 3 and len(shape_x2) == 2:
                assert x_num_col_dims == 2, "only support 2"
                flatten_x1 = (
                    core.GEOperatorFactory.create_operator(
                        "flatten" + self._accumulated_op_id(), "FlattenV2"
                    )
                    .set_input("x", x)
                    .set_attr_int32("axis", 0)
                    .set_attr_int32("end_axis", 1)
                )
                matmul_m = (
                    core.GEOperatorFactory.create_operator(
                        "mul" + self._accumulated_op_id(), "MatMul"
                    )
                    .set_input("x1", flatten_x1, 0)
                    .set_input("x2", y, 0)
                )
                matmul_transpose = (
                    core.GEOperatorFactory.create_operator(
                        "transpose" + self._accumulated_op_id(), "TransposeD"
                    )
                    .set_input("x", matmul_m)
                    .set_attr_vec_int32("perm", [1, 0])
                )
                tensor = self._create_ge_tensor(
                    [3], 2, [shape_x2[1], shape_x1[0], shape_x1[1]]
                )
                const_shape = core.GEOperatorFactory.create_operator(
                    "shape" + self._accumulated_op_id(), "Const"
                ).set_attr_tensor("value", tensor)
                reshape_matmul = (
                    core.GEOperatorFactory.create_operator(
                        "reshape" + self._accumulated_op_id(), "Reshape"
                    )
                    .set_input("x", matmul_transpose)
                    .set_input("shape", const_shape)
                    .set_attr_int32("axis", 0)
                )
                matmul = (
                    core.GEOperatorFactory.create_operator(
                        "transpose" + self._accumulated_op_id(), "TransposeD"
                    )
                    .set_input("x", reshape_matmul)
                    .set_attr_vec_int32("perm", [1, 2, 0])
                )
            else:
                assert False, "not support"

        return [matmul], [[0]]


class LayerNormParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "layer_norm"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[2])
        scale = self._get_ge_input(self.op.input_arg_names[1])
        bias = self._get_ge_input(self.op.input_arg_names[0])
        epsilon = self.op.attr("epsilon")
        begin_norm_axis = self.op.attr("begin_norm_axis")
        x_dtype = self.op.block.var(self.op.input_arg_names[2]).dtype

        shape_tensor = core.GEOperatorFactory.create_operator(
            "shape" + self._accumulated_op_id(), "Shape"
        ).set_input("x", x)
        scale_expand = (
            core.GEOperatorFactory.create_operator(
                "broadcast_to_d" + self._accumulated_op_id(), "BroadcastTo"
            )
            .set_input("x", scale)
            .set_input("shape", shape_tensor)
        )
        bias_expand = (
            core.GEOperatorFactory.create_operator(
                "broadcast_to_d" + self._accumulated_op_id(), "BroadcastTo"
            )
            .set_input("x", bias)
            .set_input("shape", shape_tensor)
        )
        layer_norm = (
            core.GEOperatorFactory.create_operator(
                "layer_norm" + self._accumulated_op_id(), "LayerNorm"
            )
            .set_input("x", x)
            .set_input("gamma", scale_expand)
            .set_input("beta", bias_expand)
            .set_attr_int32("begin_norm_axis", begin_norm_axis)
            .set_attr_int32("begin_params_axis", begin_norm_axis)
            .set_attr_float("epsilon", epsilon)
        )

        cast_dtype = (
            0
            if self.ascend_helper.dtype2paddle_inv_map[str(x_dtype)] == 0
            else 1
        )
        y = (
            core.GEOperatorFactory.create_operator(
                "cast" + self._accumulated_op_id(), "Cast"
            )
            .set_input("x", layer_norm, 0)
            .set_attr_int32("dst_type", cast_dtype)
        )
        mean = (
            core.GEOperatorFactory.create_operator(
                "cast" + self._accumulated_op_id(), "Cast"
            )
            .set_input("x", layer_norm, 1)
            .set_attr_int32("dst_type", cast_dtype)
        )
        variance = (
            core.GEOperatorFactory.create_operator(
                "cast" + self._accumulated_op_id(), "Cast"
            )
            .set_input("x", layer_norm, 2)
            .set_attr_int32("dst_type", cast_dtype)
        )
        return [y, mean, variance], [[1], [2], [0]]


## activate function
class ReluParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "relu"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        relu = core.GEOperatorFactory.create_operator(
            "relu" + self._accumulated_op_id(), "Relu"
        ).set_input("x", x)
        return [relu], [[0]]


class GeluParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "gelu"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        gelu = core.GEOperatorFactory.create_operator(
            "gelu" + self._accumulated_op_id(), "Gelu"
        ).set_input("x", x)
        return [gelu], [[0]]


class TanhParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "tanh"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        tanh = core.GEOperatorFactory.create_operator(
            "tanh" + self._accumulated_op_id(), "Tanh"
        ).set_input("x", x)
        return [tanh], [[0]]


## loss function
class SoftmaxWithCrossEntropyParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "softmax_with_cross_entropy"

    def _apply(self):
        label = self._get_ge_input(self.op.input_arg_names[0])
        logits = self._get_ge_input(self.op.input_arg_names[1])
        cls_num = self.op.block.var(self.op.input_arg_names[1]).shape[1]

        softmax = core.GEOperatorFactory.create_operator(
            "softmax" + self._accumulated_op_id(), "SoftmaxV2"
        ).set_input("x", logits)
        label = (
            core.GEOperatorFactory.create_operator(
                "cast" + self._accumulated_op_id(), "Cast"
            )
            .set_input("x", label)
            .set_attr_int32("dst_type", 3)
        )

        tensoron = self._create_ge_tensor([1], 5, 1)
        on = core.GEOperatorFactory.create_operator(
            "const" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", tensoron)
        tensoroff = self._create_ge_tensor([1], 5, 0)
        off = core.GEOperatorFactory.create_operator(
            "const" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", tensoroff)
        self._mark_as_input(on)
        self._mark_as_input(off)
        onehot = (
            core.GEOperatorFactory.create_operator(
                "onehot" + self._accumulated_op_id(), "OneHotD"
            )
            .set_input("x", label)
            .set_input("on_value", on)
            .set_input("off_value", off)
            .set_attr_int32("depth", cls_num)
        )
        squeeze = core.GEOperatorFactory.create_operator(
            "mul" + self._accumulated_op_id(), "Squeeze"
        ).set_input("x", onehot)

        loss_all = (
            core.GEOperatorFactory.create_operator(
                "loss" + self._accumulated_op_id(),
                "SoftmaxCrossEntropyWithLogits",
            )
            .set_input("features", logits)
            .set_input("labels", squeeze)
        )
        loss = (
            core.GEOperatorFactory.create_operator(
                "cast" + self._accumulated_op_id(), "Cast"
            )
            .set_input("x", loss_all, 0)
            .set_attr_int32("dst_type", 0)
        )
        loss_expand = (
            core.GEOperatorFactory.create_operator(
                "unsqueeze" + self._accumulated_op_id(), "Unsqueeze"
            )
            .set_input("x", loss)
            .set_attr_vec_int32("axes", [1])
        )
        return [label, softmax, loss_expand], [[2], [1]]


class SoftMaxParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "softmax"

    def _apply(self):
        logits = self._get_ge_input(self.op.input_arg_names[0])
        axes = self.op.attr("axis")

        softmax = (
            core.GEOperatorFactory.create_operator(
                "softmax" + self._accumulated_op_id(), "SoftmaxV2"
            )
            .set_input("x", logits)
            .set_attr_vec_int32("axes", [axes])
        )
        return [softmax], [[0]]


## general
class ShapeParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "shape"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        shape = core.GEOperatorFactory.create_operator(
            "shape" + self._accumulated_op_id(), "Shape"
        ).set_input("x", x)
        return [shape], [[0]]


class FillConstantParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "fill_constant"

    def _apply(self):
        shape = self.op.attr("shape")
        dtype = self.op.attr("dtype")
        value = self.op.attr("value")

        tensor = self._create_ge_tensor(shape, dtype, value)
        const = core.GEOperatorFactory.create_operator(
            "const" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", tensor)
        self._mark_as_input(const)
        if self.op.block.var(self.op.output('Out')[0]).persistable:
            # print("%s is Persistable in fill_constant" %
            #      (self.op.output('Out')[0]))
            var = core.GEOperatorFactory.create_operator(
                self.op.output('Out')[0], "Variable"
            )
            var.update_output_desc(
                "y",
                core.GETensorDesc(
                    core.GEShape(shape),
                    core.GEFormat.FORMAT_ND,
                    core.GEDataType.DT_FLOAT,
                ),
            )
            assign = (
                core.GEOperatorFactory.create_operator(
                    "assign" + self._accumulated_op_id(), "Assign"
                )
                .set_input("value", const)
                .set_input("ref", var)
            )
            return [const], [[0]]
        return [const], [[0]]


class TruncatedNormalParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "truncated_gaussian_random"

    def _apply(self):
        shape = self.op.attr("shape")
        dtype = self.op.attr("dtype")
        mean = self.op.attr("mean")
        std = self.op.attr("std")
        seed = self.op.attr("seed")

        tensor1 = self._create_ge_tensor([len(shape)], 2, shape)
        shape_tensor = core.GEOperatorFactory.create_operator(
            "const" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", tensor1)
        tensor2 = self._create_ge_tensor([1], dtype, mean)
        mean_tensor = core.GEOperatorFactory.create_operator(
            "const" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", tensor2)
        tensor3 = self._create_ge_tensor([1], dtype, std)
        std_tensor = core.GEOperatorFactory.create_operator(
            "const" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", tensor3)
        tensor4 = self._create_ge_tensor([1], dtype, mean - 2 * std)
        min_tensor = core.GEOperatorFactory.create_operator(
            "const" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", tensor4)
        tensor5 = self._create_ge_tensor([1], dtype, mean + 2 * std)
        max_tensor = core.GEOperatorFactory.create_operator(
            "const" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", tensor5)

        self._mark_as_input(shape_tensor)
        self._mark_as_input(mean_tensor)
        self._mark_as_input(std_tensor)
        self._mark_as_input(min_tensor)
        self._mark_as_input(max_tensor)

        truncated_normal = (
            core.GEOperatorFactory.create_operator(
                "truncated_normal" + self._accumulated_op_id(),
                "ParameterizedTruncatedNormal",
            )
            .set_input("shape", shape_tensor)
            .set_input("means", mean_tensor)
            .set_input("stdevs", std_tensor)
            .set_input("min", min_tensor)
            .set_input("max", max_tensor)
            .set_attr_int32("seed", 0)
        )

        ## wirte the output of truncatedNormal from startup_program to main_program
        if self.op.block.var(self.op.output('Out')[0]).persistable:
            # print("%s is Persistable in truncated_normal" %
            #      (self.op.output('Out')[0]))
            var = core.GEOperatorFactory.create_operator(
                self.op.output('Out')[0], "Variable"
            )
            var.update_output_desc(
                "y",
                core.GETensorDesc(
                    core.GEShape(shape),
                    core.GEFormat.FORMAT_ND,
                    core.GEDataType.DT_FLOAT,
                ),
            )
            assign = (
                core.GEOperatorFactory.create_operator(
                    "assign" + self._accumulated_op_id(), "Assign"
                )
                .set_input("value", truncated_normal)
                .set_input("ref", var)
            )
            return [
                shape_tensor,
                mean_tensor,
                std_tensor,
                min_tensor,
                max_tensor,
                truncated_normal,
            ], [[-1]]
        # else:
        #    print(
        #        "self.op.output('Out')[0] is not persistable in truncated_noraml"
        #    )
        return [truncated_normal], [[0]]


class GatherParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "gather"

    def _apply(self):
        index = self._get_ge_input(self.op.input_arg_names[0])
        x = self._get_ge_input(self.op.input_arg_names[1])
        clo = self.op.block.var(self.op.input_arg_names[1]).shape[-1]

        gather = (
            core.GEOperatorFactory.create_operator(
                "gather" + self._accumulated_op_id(), "Gather"
            )
            .set_input("x", x)
            .set_input("indices", index)
            .set_attr_bool("validate_indices", True)
        )
        return [gather], [[0]]


class ScatterParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "scatter"

    def _apply(self):
        index = self._get_ge_input(self.op.input_arg_names[0])
        x = self._get_ge_input(self.op.input_arg_names[1])
        updates = self._get_ge_input(self.op.input_arg_names[2])
        overwrite = self.op.attr("overwrite")
        index_shape = self.op.block.var(self.op.input_arg_names[0]).shape

        if len(index_shape) == 1:
            index = (
                core.GEOperatorFactory.create_operator(
                    "unsqueeze" + self.getid(), "Unsqueeze"
                )
                .set_input("x", index)
                .set_attr_vec_int32("axes", [1])
            )
        if not overwrite:
            scatter_value = (
                core.GEOperatorFactory.create_operator(
                    "scatter" + self._accumulated_op_id(), "TensorScatterAdd"
                )
                .set_input("x", x)
                .set_input("indices", index)
                .set_input("updates", updates)
            )
        else:
            scatter_value = (
                core.GEOperatorFactory.create_operator(
                    "scatter" + self._accumulated_op_id(), "TensorScatterUpdate"
                )
                .set_input("x", x)
                .set_input("indices", index)
                .set_input("updates", updates)
            )
        return [x, index, updates, scatter_value], [[-1]]


class CastParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "cast"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        dtype = self.op.attr("out_dtype")
        cast = (
            core.GEOperatorFactory.create_operator(
                "cast" + self._accumulated_op_id(), "Cast"
            )
            .set_input("x", x)
            .set_attr_int32("dst_type", dtype)
        )
        return [cast], [[0]]


class AssignParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "assign"

    def _apply(self):
        const = self._get_ge_input(self.op.input_arg_names[0])
        var = self._get_ge_input(self.op.input_arg_names[1])
        assign = (
            core.GEOperatorFactory.create_operator(
                "assign" + self._accumulated_op_id(), "Assign"
            )
            .set_input("value", const)
            .set_input("ref", var)
        )
        return [assign], [[0]]


class ScaleParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "scale"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        scale = self.op.attr("scale")
        bias = self.op.attr("bias")
        bias_after_scale = self.op.attr("bias_after_scale")

        if bias_after_scale:
            scale_value = (
                core.GEOperatorFactory.create_operator(
                    "scale" + self._accumulated_op_id(), "Power"
                )
                .set_input("x", x)
                .set_attr_float("power", 1.0)
                .set_attr_float("scale", scale)
                .set_attr_float("shift", bias)
            )
        else:
            x_add_bias = (
                core.GEOperatorFactory.create_operator(
                    "adds" + self._accumulated_op_id(), "Adds"
                )
                .set_input("x", x)
                .set_attr_float("value", bias)
            )
            scale_value = (
                core.GEOperatorFactory.create_operator(
                    "scale" + self._accumulated_op_id(), "Power"
                )
                .set_input("x", x_add_bias)
                .set_attr_float("power", 1.0)
                .set_attr_float("scale", scale)
                .set_attr_float("shift", 0.0)
            )
        return [scale_value], [[0]]


class SliceParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "slice"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        axes = self.op.attr("axes")
        starts = self.op.attr("starts")
        ends = self.op.attr("ends")

        x_shape = self.op.block.var(self.op.input_arg_names[0]).shape
        len_shape = len(x_shape)
        axes_cor = list(range(len_shape))
        starts_cor, ends_cor = [], []
        cnt = 0
        for i in range(len_shape):
            starts_cor.append(starts[cnt] if i in axes else 0)
            if i in axes and ends[cnt] <= x_shape[i]:
                ends_cor.append(ends[cnt])
            else:
                ends_cor.append(x_shape[i])
            if i in axes:
                cnt += 1
        size = [ends_cor[i] - starts_cor[i] for i in range(len(axes_cor))]

        assert (
            len(axes_cor) == len(starts_cor) == len(ends_cor)
        ), "the three fields must have same size"
        slice_value = (
            core.GEOperatorFactory.create_operator(
                "slice" + self._accumulated_op_id(), "SliceD"
            )
            .set_input("x", x)
            .set_attr_vec_int32("offsets", starts_cor)
            .set_attr_vec_int32("size", size)
        )

        return [slice_value], [[0]]


class ReshapeParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "reshape2"

    def _apply(self):
        org_shape = self.op.block.var(self.op.input_arg_names[0]).shape
        assert org_shape.count(-1) == 0, "do not allow the dim is -1"
        shape = self.op.attr("shape")
        for cnt in range(len(shape)):
            if shape[cnt] == 0:
                shape[cnt] = org_shape[cnt]

        if -1 in shape:
            assert shape.count(-1) == 1, "only allow one dim is -1"
            mul_res_org = reduce(lambda x, y: x * y, org_shape)
            mul_res_refine = reduce(lambda x, y: x * y, shape) * -1
            idx = shape.index(-1)
            shape[idx] = mul_res_org // mul_res_refine

        x = self._get_ge_input(self.op.input_arg_names[0])
        tensor = self._create_ge_tensor([len(shape)], 2, shape)
        const_shape = core.GEOperatorFactory.create_operator(
            "shape" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", tensor)
        reshape = (
            core.GEOperatorFactory.create_operator(
                "reshape" + self._accumulated_op_id(), "Reshape"
            )
            .set_input("x", x)
            .set_input("shape", const_shape)
            .set_attr_int32("axis", 0)
        )
        x_shape = core.GEOperatorFactory.create_operator(
            "shape" + self._accumulated_op_id(), "Shape"
        ).set_input("x", x)

        return [x_shape, reshape], [[1], [0]]


class TransposeParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "transpose2"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        perm = self.op.attr("axis")
        transpose = (
            core.GEOperatorFactory.create_operator(
                "transpose" + self._accumulated_op_id(), "TransposeD"
            )
            .set_input("x", x)
            .set_attr_vec_int32("perm", perm)
        )
        x_shape = core.GEOperatorFactory.create_operator(
            "shape" + self._accumulated_op_id(), "Shape"
        ).set_input("x", x)

        return [x_shape, transpose], [[1], [0]]


class AccuracyParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "accuracy"

    def _apply(self):
        pred = self._get_ge_input(self.op.input_arg_names[0])
        label = self._get_ge_input(self.op.input_arg_names[1])
        logits = self._get_ge_input(self.op.input_arg_names[2])

        pred = (
            core.GEOperatorFactory.create_operator(
                "cast" + self._accumulated_op_id(), "Cast"
            )
            .set_input("x", pred)
            .set_attr_int32("dst_type", 3)
        )
        label = (
            core.GEOperatorFactory.create_operator(
                "cast" + self._accumulated_op_id(), "Cast"
            )
            .set_input("x", label)
            .set_attr_int32("dst_type", 3)
        )
        equal = (
            core.GEOperatorFactory.create_operator(
                "equal" + self._accumulated_op_id(), "Equal"
            )
            .set_input("x1", pred)
            .set_input("x2", label)
        )
        cast = (
            core.GEOperatorFactory.create_operator(
                "cast" + self._accumulated_op_id(), "Cast"
            )
            .set_input("x", equal)
            .set_attr_int32("dst_type", 0)
        )
        acc = (
            core.GEOperatorFactory.create_operator(
                "mean" + self._accumulated_op_id(), "ReduceMeanD"
            )
            .set_input("x", cast)
            .set_attr_bool("keep_dims", False)
            .set_attr_vec_int32("axes", [])
        )
        correct = (
            core.GEOperatorFactory.create_operator(
                "sum" + self._accumulated_op_id(), "ReduceSumD"
            )
            .set_input("x", cast)
            .set_attr_bool("keep_dims", False)
            .set_attr_vec_int32("axes", [])
        )
        ones_tensor = core.GEOperatorFactory.create_operator(
            "oneslike" + self._accumulated_op_id(), "OnesLike"
        ).set_input("x", label)
        ones_tensor = (
            core.GEOperatorFactory.create_operator(
                "cast" + self._accumulated_op_id(), "Cast"
            )
            .set_input("x", ones_tensor)
            .set_attr_int32("dst_type", 0)
        )
        total = (
            core.GEOperatorFactory.create_operator(
                "sum" + self._accumulated_op_id(), "ReduceSumD"
            )
            .set_input("x", ones_tensor)
            .set_attr_bool("keep_dims", False)
            .set_attr_vec_int32("axes", [])
        )

        return [acc, correct, total], [[0], [1], [2]]


class TopkParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "top_k"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        k = self.op.attr("k")

        tensor = self._create_ge_tensor([1], 2, k)
        const_k = core.GEOperatorFactory.create_operator(
            "const" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", tensor)
        cast_x = (
            core.GEOperatorFactory.create_operator(
                "cast" + self._accumulated_op_id(), "Cast"
            )
            .set_input("x", x)
            .set_attr_int32("dst_type", 1)
        )
        topk = (
            core.GEOperatorFactory.create_operator(
                "topk" + self._accumulated_op_id(), "TopK"
            )
            .set_input("x", cast_x)
            .set_input("k", const_k)
        )
        value = (
            core.GEOperatorFactory.create_operator(
                "cast" + self._accumulated_op_id(), "Cast"
            )
            .set_input("x", topk, 0)
            .set_attr_int32("dst_type", 0)
        )
        index = (
            core.GEOperatorFactory.create_operator(
                "cast" + self._accumulated_op_id(), "Cast"
            )
            .set_input("x", topk, 1)
            .set_attr_int32("dst_type", 0)
        )
        return [value, index], [[1], [0]]


class LookupTableParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "lookup_table"

    def _apply(self):
        ids = self._get_ge_input(self.op.input_arg_names[0])
        w = self._get_ge_input(self.op.input_arg_names[1])

        ids_squeeze = (
            core.GEOperatorFactory.create_operator(
                "squeeze" + self._accumulated_op_id(), "Squeeze"
            )
            .set_input("x", ids)
            .set_attr_vec_int32("axes", [-1])
        )
        out = (
            core.GEOperatorFactory.create_operator(
                "lookup" + self._accumulated_op_id(), "Gather"
            )
            .set_input("x", w)
            .set_input("indices", ids_squeeze)
        )
        return [out], [[0]]


class StackParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "stack"

    def _apply(self):
        tiles = len(self.op.input_arg_names)
        data_x_lst = []
        for index in range(tiles):
            data_x_lst.append(
                self._get_ge_input(self.op.input_arg_names[index])
            )
        axis = self.op.attr("axis")

        data_x = data_x_lst[0]
        tensor = self._create_ge_tensor([1], 2, axis)
        tensor_axis = core.GEOperatorFactory.create_operator(
            "axis" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", tensor)
        expand = (
            core.GEOperatorFactory.create_operator(
                "expand" + self._accumulated_op_id(), "ExpandDims"
            )
            .set_input("x", data_x)
            .set_input("axis", tensor_axis)
        )

        stack = (
            core.GEOperatorFactory.create_operator(
                "stack" + self._accumulated_op_id(), "TileWithAxis"
            )
            .set_input("x", expand)
            .set_attr_int32("axis", axis)
            .set_attr_int32("tiles", tiles)
        )

        return [stack], [[0]]


class UnSqueezeParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "unsqueeze2"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        axes = self.op.attr('axes')

        output = (
            core.GEOperatorFactory.create_operator(
                "unsqueeze" + self._accumulated_op_id(), "Unsqueeze"
            )
            .set_input("x", x)
            .set_attr_vec_int32("axes", axes)
        )
        shape = core.GEOperatorFactory.create_operator(
            "shape" + self._accumulated_op_id(), "Shape"
        ).set_input("x", output)
        return [shape, output], [[1], [0]]


## parallel
class AllGatherParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "c_allgather"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        rank_size = self.op.attr("rank_size")
        group = self.op.attr("group")

        allgather = (
            core.GEOperatorFactory.create_operator(
                "allgather" + self._accumulated_op_id(), "HcomAllGather"
            )
            .set_input("x", x)
            .set_attr_int32("rank_size", rank_size)
            .set_attr_string("group", group)
        )
        return [allgather], [[0]]


class AllReduceParser(AscendParserBase):
    def __init__(self, graph, var2geop, reduction):
        super().__init__(graph, var2geop)
        self.parser_name = "c_allreduce_" + reduction
        self.reduction = reduction

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        reduction = self.reduction
        ring_id = self.op.attr("ring_id")
        group = "hcom_group_" + str(ring_id)
        fusion = None  # self.op.attr("fusion")
        fusion_id = None  # self.op.attr("fusion_id")

        allreduce = (
            core.GEOperatorFactory.create_operator(
                "allreduce" + self._accumulated_op_id(), "HcomAllReduce"
            )
            .set_input("x", x)
            .set_attr_string("reduction", reduction)
            .set_attr_string("group", group)
        )
        if fusion is not None:
            allreduce.set_attr_int32("fusion", fusion)

        if fusion_id is not None:
            allreduce.set_attr_int32("fusion_id", fusion_id)
        return [allreduce], [[0]]


class AllReduceSumParser(AllReduceParser):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop, 'sum')


class AllReduceMaxParser(AllReduceParser):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop, 'max')


class BroadcastParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "c_broadcast"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        root_rank = self.op.attr("root_rank")
        group = self.op.attr("group")

        broadcast = (
            core.GEOperatorFactory.create_operator(
                "broadcast" + self._accumulated_op_id(), "HcomBroadcast"
            )
            .set_input("x", x)
            .set_attr_int32("root_rank", root_rank)
            .set_attr_string("group", group)
        )
        return [broadcast], [[0]]


class ReduceScatterParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "c_reduce_scatter"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        reduction = self.op.attr("reduction")
        group = self.op.attr("group")
        rank_size = self.op.attr("rank_size")

        reduce_scatter = (
            core.GEOperatorFactory.create_operator(
                "reducescatter" + self._accumulated_op_id(), "HcomReduceScatter"
            )
            .set_input("x", x)
            .set_attr_string("reduction", reduction)
            .set_attr_string("group", group)
            .set_attr_int32("rank_size", rank_size)
        )
        return [reduce_scatter], [[0]]


class SendParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "c_send"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        sr_tag = self.op.attr("sr_tag")
        dest_rank = self.op.attr("dest_rank")
        group = self.op.attr("group")

        send = (
            core.GEOperatorFactory.create_operator(
                "send" + self._accumulated_op_id(), "HcomSend"
            )
            .set_input("x", x)
            .set_attr_int32("sr_tag", sr_tag)
            .set_attr_int32("dest_rank", dest_rank)
            .set_attr_string("group", group)
        )
        return [send], [[0]]


class ReceiveParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "c_receive"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        sr_tag = self.op.attr("sr_tag")
        src_rank = self.op.attr("src_rank")
        group = self.op.attr("group")
        shape = self.op.attr("shape")
        dtype = self.op.attr("dtype")

        receive = (
            core.GEOperatorFactory.create_operator(
                "receive" + self._accumulated_op_id(), "HcomReceive"
            )
            .set_input("x", x)
            .set_attr_int32("sr_tag", sr_tag)
            .set_attr_int32("src_rank", src_rank)
            .set_attr_string("group", group)
            .set_attr_vec_int32("shape", shape)
            .set_attr_int32("dtype", dtype)
        )
        return [receive], [[0]]


class RangeParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "range"

    def _apply(self):
        # TODO not support range type yet
        start = self._get_ge_input(self.op.input_arg_names[0])
        end = self._get_ge_input(self.op.input_arg_names[1])
        delta = self._get_ge_input(self.op.input_arg_names[2])

        ge_range = (
            core.GEOperatorFactory.create_operator(
                "range" + self._accumulated_op_id(), "Range"
            )
            .set_input("start", end)
            .set_input("limit", start)
            .set_input("delta", delta)
        )

        return [ge_range], [[0]]


class UniformRandomParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "uniform_random"

    def _apply(self):
        shape = self.op.attr("shape")

        min_v = self.op.attr("min")
        max_v = self.op.attr("max")
        seed = self.op.attr("seed")
        dtype = self.op.attr("dtype")
        assert max_v > min_v, (
            "assert max_v > min_v, but received "
            + "as max_v={}, min_v={} ".format(max_v, min_v)
        )

        tensor1 = self._create_ge_tensor([len(shape)], 2, shape)
        shape_tensor = core.GEOperatorFactory.create_operator(
            "const" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", tensor1)

        ge_ur = (
            core.GEOperatorFactory.create_operator(
                "uniform_random" + self._accumulated_op_id(), "RandomUniform"
            )
            .set_input("shape", shape_tensor)
            .set_attr_dtype("dtype", self.ascend_helper.dtype2ge(dtype))
            .set_attr_int32("seed", seed)
            .set_attr_int32("seed2", seed)
        )

        scale = max_v - min_v

        scale_value = (
            core.GEOperatorFactory.create_operator(
                "scale" + self._accumulated_op_id(), "Power"
            )
            .set_input("x", ge_ur)
            .set_attr_float("power", 1.0)
            .set_attr_float("scale", scale)
            .set_attr_float("shift", min_v)
        )

        return [scale_value], [[0]]


class EqualParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "equal"

    def _apply(self):
        data_x1 = self._get_ge_input(self.op.input_arg_names[0])
        data_x2 = self._get_ge_input(self.op.input_arg_names[1])
        equal = (
            core.GEOperatorFactory.create_operator(
                "equal" + self._accumulated_op_id(), "Equal"
            )
            .set_input("x1", data_x1)
            .set_input("x2", data_x2)
        )
        return [equal], [[0]]


class ExpandParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "expand"

    def _apply(self):
        data_x1_shape = self._get_ge_input(self.op.input_arg_names[0])
        expand_times = self.op.attr('expand_times')

        tensor = self._create_ge_tensor([len(expand_times)], 2, expand_times)
        expand_tensor = core.GEOperatorFactory.create_operator(
            "const" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", tensor)

        assign = (
            core.GEOperatorFactory.create_operator(
                "tile" + self._accumulated_op_id(), "Tile"
            )
            .set_input("x", data_x1_shape)
            .set_input("multiples", expand_tensor)
        )
        return [assign], [[0]]


class SqueezeParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "squeeze2"

    def _apply(self):
        tensor = self._get_ge_input(self.op.input_arg_names[0])
        axes = self.op.attr("axes")

        data_squeezed = (
            core.GEOperatorFactory.create_operator(
                "squeeze" + self._accumulated_op_id(), "Squeeze"
            )
            .set_input("x", tensor)
            .set_attr_vec_int32("axes", axes)
        )
        shape = core.GEOperatorFactory.create_operator(
            "shape" + self._accumulated_op_id(), "Shape"
        ).set_input("x", data_squeezed)
        return [shape, data_squeezed], [[1], [0]]


# ****************************************************************#
# ***************************            *************************#
# ***************************            *************************#
# *************************** GradParser *************************#
# ***************************            *************************#
# ***************************            *************************#
# ****************************************************************#
## grad
class ReduceSumGradParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "reduce_sum_grad"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        input = self._get_ge_input(self.op.input_arg_names[1])

        shape_tensor = core.GEOperatorFactory.create_operator(
            "shape" + self._accumulated_op_id(), "Shape"
        ).set_input("x", input, 0)
        tensoron = self._create_ge_tensor([1], 2, -1)
        const = core.GEOperatorFactory.create_operator(
            "const" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", tensoron)
        self._mark_as_input(const)

        reduce_sum = (
            core.GEOperatorFactory.create_operator(
                "broadcast_to_d" + self._accumulated_op_id(), "BroadcastTo"
            )
            .set_input("x", x)
            .set_input("shape", shape_tensor)
        )
        # reduce_sum = core.GEOperatorFactory.create_operator("expand" + self._accumulated_op_id(), "ExpandDims").set_input("x", reduce_sum).set_input("axis", const)

        return [reduce_sum], [[0]]


class MatMulGradParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "matmul_grad"

    def _apply(self):
        out_grad = self._get_ge_input(self.op.input_arg_names[0])
        x = self._get_ge_input(self.op.input_arg_names[1])
        y = self._get_ge_input(self.op.input_arg_names[2])
        transpose_x = self.op.attr("transpose_X")
        transpose_y = self.op.attr("transpose_Y")

        out_grad_shape = self.op.block.var(self.op.input_arg_names[0]).shape
        x_shape = self.op.block.var(self.op.input_arg_names[1]).shape
        y_shape = self.op.block.var(self.op.input_arg_names[2]).shape

        if len(x_shape) > 2:
            if transpose_y:
                x_grad = (
                    core.GEOperatorFactory.create_operator(
                        self.parser_name + self._accumulated_op_id(),
                        "BatchMatMul",
                    )
                    .set_input("x1", out_grad)
                    .set_input("x2", y)
                    .set_attr_bool("adj_x1", False)
                    .set_attr_bool("adj_x2", False)
                )
                y_grad = (
                    core.GEOperatorFactory.create_operator(
                        self.parser_name + self._accumulated_op_id(),
                        "BatchMatMul",
                    )
                    .set_input("x1", out_grad)
                    .set_input("x2", x)
                    .set_attr_bool("adj_x1", True)
                    .set_attr_bool("adj_x2", False)
                )
            else:
                x_grad = (
                    core.GEOperatorFactory.create_operator(
                        self.parser_name + self._accumulated_op_id(),
                        "BatchMatMul",
                    )
                    .set_input("x1", out_grad)
                    .set_input("x2", y)
                    .set_attr_bool("adj_x1", False)
                    .set_attr_bool("adj_x2", True)
                )
                y_grad = (
                    core.GEOperatorFactory.create_operator(
                        self.parser_name + self._accumulated_op_id(),
                        "BatchMatMul",
                    )
                    .set_input("x1", x)
                    .set_input("x2", out_grad)
                    .set_attr_bool("adj_x1", True)
                    .set_attr_bool("adj_x2", False)
                )
        else:
            if transpose_y:
                x_grad = (
                    core.GEOperatorFactory.create_operator(
                        self.parser_name + self._accumulated_op_id(), "MatMul"
                    )
                    .set_input("x1", out_grad)
                    .set_input("x2", y)
                    .set_attr_bool("transpose_x1", False)
                    .set_attr_bool("transpose_x2", False)
                )
                y_grad = (
                    core.GEOperatorFactory.create_operator(
                        self.parser_name + self._accumulated_op_id(), "MatMul"
                    )
                    .set_input("x1", out_grad)
                    .set_input("x2", x)
                    .set_attr_bool("transpose_x1", True)
                    .set_attr_bool("transpose_x2", False)
                )
            else:
                x_grad = (
                    core.GEOperatorFactory.create_operator(
                        self.parser_name + self._accumulated_op_id(), "MatMul"
                    )
                    .set_input("x1", out_grad)
                    .set_input("x2", y)
                    .set_attr_bool("transpose_x1", False)
                    .set_attr_bool("transpose_x2", True)
                )
                y_grad = (
                    core.GEOperatorFactory.create_operator(
                        self.parser_name + self._accumulated_op_id(), "MatMul"
                    )
                    .set_input("x1", x)
                    .set_input("x2", out_grad)
                    .set_attr_bool("transpose_x1", True)
                    .set_attr_bool("transpose_x2", False)
                )

        return [x_grad, y_grad], [[0], [1]]


class MulGradParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "mul_grad"

    def _apply(self):
        out_grad = self._get_ge_input(self.op.input_arg_names[0])
        x = self._get_ge_input(self.op.input_arg_names[1])
        y = self._get_ge_input(self.op.input_arg_names[2])
        x_num_col_dims = self.op.attr("x_num_col_dims")
        y_num_col_dims = self.op.attr("y_num_col_dims")

        shape_out_grad = self.op.block.var(self.op.input_arg_names[0]).shape
        shape_x = self.op.block.var(self.op.input_arg_names[1]).shape
        shape_y = self.op.block.var(self.op.input_arg_names[2]).shape

        if x_num_col_dims == 1 and y_num_col_dims == 1:
            if len(shape_x) == 2 and len(shape_y) == 2:
                x_grad = (
                    core.GEOperatorFactory.create_operator(
                        self.parser_name + self._accumulated_op_id(), "MatMul"
                    )
                    .set_input("x1", out_grad)
                    .set_input("x2", y)
                    .set_attr_bool("transpose_x1", False)
                    .set_attr_bool("transpose_x2", True)
                )
                y_grad = (
                    core.GEOperatorFactory.create_operator(
                        self.parser_name + self._accumulated_op_id(), "MatMul"
                    )
                    .set_input("x1", x)
                    .set_input("x2", out_grad)
                    .set_attr_bool("transpose_x1", True)
                    .set_attr_bool("transpose_x2", False)
                )
            elif len(shape_x) == 3 and len(shape_y) == 2:
                flatten_x = core.GEOperatorFactory.create_operator(
                    "flatten" + self._accumulated_op_id(), "Flatten"
                ).set_input("x", x)
                x_grad = (
                    core.GEOperatorFactory.create_operator(
                        self.parser_name + self._accumulated_op_id(), "MatMul"
                    )
                    .set_input("x1", out_grad)
                    .set_input("x2", y)
                    .set_attr_bool("transpose_x1", False)
                    .set_attr_bool("transpose_x2", True)
                )
                if len(shape_out_grad) == 2:
                    x_grad = (
                        core.GEOperatorFactory.create_operator(
                            "unsqueeze" + self._accumulated_op_id(), "Unsqueeze"
                        )
                        .set_input("x", x_grad)
                        .set_attr_vec_int32("axes", [1])
                    )

                y_grad = (
                    core.GEOperatorFactory.create_operator(
                        self.parser_name + self._accumulated_op_id(), "MatMul"
                    )
                    .set_input("x1", flatten_x)
                    .set_input("x2", out_grad)
                    .set_attr_bool("transpose_x1", True)
                    .set_attr_bool("transpose_x2", False)
                )
        else:
            if len(shape_x) == 3 and len(shape_y) == 2:
                assert x_num_col_dims == 2, "only support 2"
                flatten_x = (
                    core.GEOperatorFactory.create_operator(
                        "flatten" + self._accumulated_op_id(), "FlattenV2"
                    )
                    .set_input("x", x)
                    .set_attr_int32("axis", 0)
                    .set_attr_int32("end_axis", 1)
                )
                flatten_out_grad = (
                    core.GEOperatorFactory.create_operator(
                        "flatten" + self._accumulated_op_id(), "FlattenV2"
                    )
                    .set_input("x", out_grad)
                    .set_attr_int32("axis", 0)
                    .set_attr_int32("end_axis", 1)
                )

                y_unsqueeze = (
                    core.GEOperatorFactory.create_operator(
                        "unsqueeze" + self._accumulated_op_id(), "Unsqueeze"
                    )
                    .set_input("x", y)
                    .set_attr_vec_int32("axes", [0])
                )
                y_stack = (
                    core.GEOperatorFactory.create_operator(
                        "stack" + self._accumulated_op_id(), "TileWithAxis"
                    )
                    .set_input("x", y_unsqueeze)
                    .set_attr_int32("axis", 0)
                    .set_attr_int32("tiles", shape_out_grad[0])
                )
                x_grad = (
                    core.GEOperatorFactory.create_operator(
                        self.parser_name + self._accumulated_op_id(),
                        "BatchMatMul",
                    )
                    .set_input("x1", out_grad)
                    .set_input("x2", y_stack)
                    .set_attr_bool("adj_x1", False)
                    .set_attr_bool("adj_x2", True)
                )
                y_grad = (
                    core.GEOperatorFactory.create_operator(
                        self.parser_name + self._accumulated_op_id(), "MatMul"
                    )
                    .set_input("x1", flatten_x)
                    .set_input("x2", flatten_out_grad)
                    .set_attr_bool("transpose_x1", True)
                    .set_attr_bool("transpose_x2", False)
                )

        return [x_grad, y_grad], [[0], [1]]


class ReluGradParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "relu_grad"

    def _apply(self):
        out = self._get_ge_input(self.op.input_arg_names[0])
        out_grad = self._get_ge_input(self.op.input_arg_names[1])
        relu_grad = (
            core.GEOperatorFactory.create_operator(
                self.parser_name + self._accumulated_op_id(), "ReluGrad"
            )
            .set_input("gradients", out_grad)
            .set_input("features", out)
        )
        return [relu_grad], [[0]]


class SoftmaxWithCrossEntropyGradParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "softmax_with_cross_entropy_grad"

    def _apply(self):
        label = self._get_ge_input(self.op.input_arg_names[0])
        loss_grad = self._get_ge_input(self.op.input_arg_names[1])
        softmax = self._get_ge_input(self.op.input_arg_names[2])
        cls_num = self.op.block.var(self.op.input_arg_names[2]).shape[1]

        label_shape = self.op.block.var(self.op.input_arg_names[0]).shape
        loss_grad_shape = self.op.block.var(self.op.input_arg_names[1]).shape
        softmax_shape = self.op.block.var(self.op.input_arg_names[2]).shape

        tensoron = self._create_ge_tensor([1], 5, 1)
        on = core.GEOperatorFactory.create_operator(
            "const" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", tensoron)
        tensoroff = self._create_ge_tensor([1], 5, 0)
        off = core.GEOperatorFactory.create_operator(
            "const" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", tensoroff)
        self._mark_as_input(on)
        self._mark_as_input(off)

        label = (
            core.GEOperatorFactory.create_operator(
                "cast" + self._accumulated_op_id(), "Cast"
            )
            .set_input("x", label)
            .set_attr_int32("dst_type", 3)
        )
        onehot = (
            core.GEOperatorFactory.create_operator(
                "onehot" + self._accumulated_op_id(), "OneHotD"
            )
            .set_input("x", label)
            .set_input("on_value", on)
            .set_input("off_value", off)
            .set_attr_int32("depth", cls_num)
        )
        squeeze = core.GEOperatorFactory.create_operator(
            "suqeeze" + self._accumulated_op_id(), "Squeeze"
        ).set_input("x", onehot)
        sub = (
            core.GEOperatorFactory.create_operator(
                "sub" + self._accumulated_op_id(), "Sub"
            )
            .set_input("x1", softmax)
            .set_input("x2", squeeze)
        )
        grad = (
            core.GEOperatorFactory.create_operator(
                "mul" + self._accumulated_op_id(), "Mul"
            )
            .set_input("x1", loss_grad)
            .set_input("x2", sub)
        )

        return [on, off, label, onehot, grad], [[-1]]


class DotMulGradParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "elementwise_mul_grad"

    def _apply(self):
        out_grad = self._get_ge_input(self.op.input_arg_names[0])
        out_1 = self._get_ge_input(self.op.input_arg_names[1])
        out_2 = self._get_ge_input(self.op.input_arg_names[2])

        x_grad = (
            core.GEOperatorFactory.create_operator(
                self.parser_name + self._accumulated_op_id(), "Mul"
            )
            .set_input("x1", out_grad)
            .set_input("x2", out_2)
        )
        y_grad = (
            core.GEOperatorFactory.create_operator(
                self.parser_name + self._accumulated_op_id(), "Mul"
            )
            .set_input("x1", out_1)
            .set_input("x2", out_grad)
        )

        return [x_grad, y_grad], [[0], [1]]


class DotAddGradParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "elementwise_add_grad"

    def _apply(self):
        out_grad = self._get_ge_input(self.op.input_arg_names[0])
        out_1 = self._get_ge_input(self.op.input_arg_names[1])
        out_2 = self._get_ge_input(self.op.input_arg_names[2])
        out_grad_shape = self.op.block.var(self.op.input_arg_names[0]).shape
        out_1_shape = self.op.block.var(self.op.input_arg_names[1]).shape
        out_2_shape = self.op.block.var(self.op.input_arg_names[2]).shape

        x_grad = out_grad
        cur_time_x = len(out_grad_shape) - len(out_1_shape)
        for i in range(cur_time_x):
            x_grad = (
                core.GEOperatorFactory.create_operator(
                    self.parser_name + self._accumulated_op_id(), "ReduceSumD"
                )
                .set_input("x", x_grad)
                .set_attr_vec_int32("axes", [0])
                .set_attr_bool("keep_dims", False)
            )
        for axis, size in enumerate(out_1_shape):
            if size == 1:
                x_grad = (
                    core.GEOperatorFactory.create_operator(
                        self.parser_name + self._accumulated_op_id(),
                        "ReduceSumD",
                    )
                    .set_input("x", x_grad)
                    .set_attr_vec_int32("axes", [axis])
                    .set_attr_bool("keep_dims", True)
                )

        y_grad = out_grad
        cur_time_y = len(out_grad_shape) - len(out_2_shape)
        for i in range(cur_time_y):
            y_grad = (
                core.GEOperatorFactory.create_operator(
                    self.parser_name + self._accumulated_op_id(), "ReduceSumD"
                )
                .set_input("x", y_grad)
                .set_attr_vec_int32("axes", [0])
                .set_attr_bool("keep_dims", False)
            )
        for axis, size in enumerate(out_2_shape):
            if size == 1:
                y_grad = (
                    core.GEOperatorFactory.create_operator(
                        self.parser_name + self._accumulated_op_id(),
                        "ReduceSumD",
                    )
                    .set_input("x", y_grad)
                    .set_attr_vec_int32("axes", [axis])
                    .set_attr_bool("keep_dims", True)
                )

        return [x_grad, y_grad], [[0], [1]]


class DotDivGradParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "elementwise_div_grad"

    def _apply(self):
        out = self._get_ge_input(self.op.input_arg_names[0])
        out_grad = self._get_ge_input(self.op.input_arg_names[1])
        x = self._get_ge_input(self.op.input_arg_names[2])
        y = self._get_ge_input(self.op.input_arg_names[3])

        y_power = (
            core.GEOperatorFactory.create_operator(
                "power" + self._accumulated_op_id(), "Power"
            )
            .set_input("x", y)
            .set_attr_float("power", -1)
        )

        tensor_zeros = core.GEOperatorFactory.create_operator(
            "zeroslike" + self._accumulated_op_id(), "ZerosLike"
        ).set_input("x", x)
        x_zero = (
            core.GEOperatorFactory.create_operator(
                "equal" + self._accumulated_op_id(), "Equal"
            )
            .set_input("x1", x)
            .set_input("x2", tensor_zeros)
        )
        x_nozero = core.GEOperatorFactory.create_operator(
            "logical_not" + self._accumulated_op_id(), "LogicalNot"
        ).set_input("x", x_zero)
        x_nozero_f = (
            core.GEOperatorFactory.create_operator(
                "cast" + self._accumulated_op_id(), "Cast"
            )
            .set_input("x", x_nozero)
            .set_attr_int32("dst_type", 0)
        )
        x_grad_w = (
            core.GEOperatorFactory.create_operator(
                "mul" + self._accumulated_op_id(), "Mul"
            )
            .set_input("x1", x_nozero_f)
            .set_input("x2", y_power)
        )
        x_grad = (
            core.GEOperatorFactory.create_operator(
                self.parser_name + self._accumulated_op_id(), "Mul"
            )
            .set_input("x1", x_grad_w)
            .set_input("x2", out_grad)
        )

        y_grad_w = (
            core.GEOperatorFactory.create_operator(
                "mul" + self._accumulated_op_id(), "Mul"
            )
            .set_input("x1", out)
            .set_input("x2", y_power)
        )
        y_grad = (
            core.GEOperatorFactory.create_operator(
                "mul" + self._accumulated_op_id(), "Mul"
            )
            .set_input("x1", y_grad_w)
            .set_input("x2", out_grad)
        )

        return [x_grad, y_grad], [[0], [1]]


class SoftmaxGradParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "softmax_grad"

    def _apply(self):
        out = self._get_ge_input(self.op.input_arg_names[0])
        out_grad = self._get_ge_input(self.op.input_arg_names[1])

        x_grad = (
            core.GEOperatorFactory.create_operator(
                self.parser_name + self._accumulated_op_id(), "SoftmaxGrad"
            )
            .set_input("softmax", out)
            .set_input("grad_softmax", out_grad)
        )
        return [x_grad], [[0]]


class ReshapeGradParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "reshape2_grad"

    def _apply(self):
        out_grad = self._get_ge_input(self.op.input_arg_names[0])
        x_shape = self._get_ge_input(self.op.input_arg_names[1])
        x_shape_list = self.op.block.var(self.op.input_arg_names[1]).shape

        if x_shape_list[0] == 0:
            x_shape_delzero = x_shape_list[1:]
        tensor = self._create_ge_tensor(
            [len(x_shape_delzero)], 2, x_shape_delzero
        )
        const_shape = core.GEOperatorFactory.create_operator(
            "shape" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", tensor)
        x_grad = (
            core.GEOperatorFactory.create_operator(
                "reshape" + self._accumulated_op_id(), "Reshape"
            )
            .set_input("x", out_grad)
            .set_input("shape", const_shape)
        )

        return [x_grad], [[0]]


class GatherGradParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "gather_grad"

    def _apply(self):
        index = self._get_ge_input(self.op.input_arg_names[0])
        out_grad = self._get_ge_input(self.op.input_arg_names[1])
        x = self._get_ge_input(self.op.input_arg_names[2])

        index_shape = self.op.block.var(self.op.input_arg_names[0]).shape
        out_grad_shape = self.op.block.var(self.op.input_arg_names[1]).shape
        x_shape = self.op.block.var(self.op.input_arg_names[2]).shape

        if len(index_shape) == 1:
            index = (
                core.GEOperatorFactory.create_operator(
                    "unsqueeze" + self._accumulated_op_id(), "Unsqueeze"
                )
                .set_input("x", index)
                .set_attr_vec_int32("axes", [1])
            )

        tensor_zeros = core.GEOperatorFactory.create_operator(
            "zeroslike" + self._accumulated_op_id(), "ZerosLike"
        ).set_input("x", x)
        x_grad = (
            core.GEOperatorFactory.create_operator(
                "scatter" + self._accumulated_op_id(), "TensorScatterUpdate"
            )
            .set_input("x", tensor_zeros)
            .set_input("indices", index)
            .set_input("updates", out_grad)
        )

        return [tensor_zeros, x_grad], [[-1]]


class TransposeGradParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "transpose2_grad"

    def _apply(self):
        out_grad = self._get_ge_input(self.op.input_arg_names[0])
        x = self._get_ge_input(self.op.input_arg_names[1])
        perm = self.op.attr("axis")

        x_shape = self.op.block.var(self.op.input_arg_names[1]).shape[1:]
        out_grad_shape = self.op.block.var(self.op.input_arg_names[0]).shape
        assert list(map(lambda x: out_grad_shape[x], perm)) == list(x_shape)

        x_grad = (
            core.GEOperatorFactory.create_operator(
                "transpose" + self._accumulated_op_id(), "TransposeD"
            )
            .set_input("x", out_grad)
            .set_attr_vec_int32("perm", perm)
        )

        return [x_grad], [[0]]


class LayerNormGradParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "layer_norm_grad"

    def _apply(self):
        bias = self._get_ge_input(self.op.input_arg_names[0])
        mean = self._get_ge_input(self.op.input_arg_names[1])
        scale = self._get_ge_input(self.op.input_arg_names[2])
        variance = self._get_ge_input(self.op.input_arg_names[3])
        x = self._get_ge_input(self.op.input_arg_names[4])
        out_grad = self._get_ge_input(self.op.input_arg_names[5])
        x_dtype = self.op.block.var(self.op.input_arg_names[4]).dtype

        x_grad = (
            core.GEOperatorFactory.create_operator(
                self.parser_name + self._accumulated_op_id(), "LayerNormGrad"
            )
            .set_input("dy", out_grad)
            .set_input("x", x)
            .set_input("variance", variance)
            .set_input("mean", mean)
            .set_input("gamma", scale)
        )

        cast_dtype = (
            0
            if self.ascend_helper.dtype2paddle_inv_map[str(x_dtype)] == 0
            else 1
        )
        out_x_grad = (
            core.GEOperatorFactory.create_operator(
                "cast" + self._accumulated_op_id(), "Cast"
            )
            .set_input("x", x_grad, 0)
            .set_attr_int32("dst_type", cast_dtype)
        )
        out_scale_grad = (
            core.GEOperatorFactory.create_operator(
                "cast" + self._accumulated_op_id(), "Cast"
            )
            .set_input("x", x_grad, 1)
            .set_attr_int32("dst_type", cast_dtype)
        )
        out_bias_grad = (
            core.GEOperatorFactory.create_operator(
                "cast" + self._accumulated_op_id(), "Cast"
            )
            .set_input("x", x_grad, 2)
            .set_attr_int32("dst_type", cast_dtype)
        )

        return [out_x_grad, out_scale_grad, out_bias_grad], [[2], [1], [0]]


class TanhGradParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = 'tanh_grad'

    def _apply(self):
        y = self._get_ge_input(self.op.input_arg_names[0])
        out_grad = self._get_ge_input(self.op.input_arg_names[1])
        tanh_grad = (
            core.GEOperatorFactory.create_operator(
                "tanh_grad" + self._accumulated_op_id(), "TanhGrad"
            )
            .set_input("y", y)
            .set_input("dy", out_grad)
        )

        return [tanh_grad], [[0]]


class LogGradParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = 'log_grad'

    def _apply(self):
        grad = self._get_ge_input(self.op.input_arg_names[0])
        input = self._get_ge_input(self.op.input_arg_names[1])
        log_grad = (
            core.GEOperatorFactory.create_operator(
                "log_grad" + self._accumulated_op_id(), "DivNoNan"
            )
            .set_input("x1", grad)
            .set_input("x2", input)
        )
        return [log_grad], [[0]]


class SqrtGradParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "sqrt_grad"

    def _apply(self):
        y = self._get_ge_input(self.op.input_arg_names[0])
        out_grad = self._get_ge_input(self.op.input_arg_names[1])
        sqrt_grad = (
            core.GEOperatorFactory.create_operator(
                "sqrt_grad" + self._accumulated_op_id(), "SqrtGrad"
            )
            .set_input("y", y)
            .set_input("dy", out_grad)
        )
        return [sqrt_grad]


class PowGradParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "pow_grad"

    def _apply(self):
        grad = self._get_ge_input(self.op.input_arg_names[0])
        x = self._get_ge_input(self.op.input_arg_names[1])
        factor = self.op.attr("factor")

        shape_tensor = self._create_shape_tensor()
        shape_tensor = core.GEOperatorFactory.create_operator(
            "shape" + self._accumulated_op_id(), "Shape"
        ).set_input("x", x)
        factor_scale = self._create_ge_tensor([1], 5, factor)
        factor_scale = core.GEOperatorFactory.create_operator(
            "const" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", factor_scale)
        factor_tensor = (
            core.GEOperatorFactory.create_operator(
                "broadcast_to_d" + self._accumulated_op_id(), "BroadcastTo"
            )
            .set_input("x", factor_scale)
            .set_input("shape", shape_tensor)
        )

        x_power = (
            core.GEOperatorFactory.create_operator(
                "x_power" + self._accumulated_op_id(), "Power"
            )
            .set_input("x", x)
            .set_attr_float("power", factor - 1)
        )
        x_power_mul_factor = (
            core.GEOperatorFactory.create_operator(
                "x_power_mul_factor" + self._accumulated_op_id(), "Mul"
            )
            .set_input("x1", x)
            .set_input("x2", factor_tensor)
        )
        x_power_mul_factor_grad = (
            core.GEOperatorFactory.create_operator(
                "x_power_mul_factor_grad" + self._accumulated_op_id(), "Mul"
            )
            .set_input("x1", x_power_mul_factor)
            .set_input("x2", grad)
        )

        return [x_power_mul_factor_grad], [[0]]


class GeluGradParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "gelu_grad"

    def _apply(self):
        grad = self._get_ge_input(self.op.input_arg_names[0])
        x = self._get_ge_input(self.op.input_arg_names[1])

        y = core.GEOperatorFactory.create_operator(
            "gelu" + self._accumulated_op_id(), "Gelu"
        ).set_input("x", x)
        gelu_grad = (
            core.GEOperatorFactory.create_operator(
                "gelu_grad" + self._accumulated_op_id(), "GeluGrad"
            )
            .set_input("x", x)
            .set_input("dy", grad)
            .set_input("y", y)
        )

        return [gelu_grad], [[0]]


class MeanGradParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "mean_grad"

    def _apply(self):
        grad = self._get_ge_input(self.op.input_arg_names[0])
        x = self._get_ge_input(self.op.input_arg_names[1])

        ones_tensor = core.GEOperatorFactory.create_operator(
            "one_tensor" + self._accumulated_op_id(), "OnesLike"
        ).set_input("x", x)
        sum = (
            core.GEOperatorFactory.create_operator(
                "mean" + self._accumulated_op_id(), "ReduceSumD"
            )
            .set_input("x", ones_tensor)
            .set_attr_bool("keep_dims", False)
            .set_attr_vec_int32("axes", [])
        )
        mean = (
            core.GEOperatorFactory.create_operator(
                "x_power" + self._accumulated_op_id(), "Power"
            )
            .set_input("x", sum)
            .set_attr_float("power", -1)
        )

        mean_grad = (
            core.GEOperatorFactory.create_operator(
                "mean_grad" + self._accumulated_op_id(), "Mul"
            )
            .set_input("x1", mean)
            .set_input("x2", grad)
        )

        return [mean_grad], [[0]]


class SliceGradParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "slice_grad"

    def _apply(self):
        x = self._get_ge_input(self.op.input_arg_names[0])
        grad = self._get_ge_input(self.op.input_arg_names[1])
        axes = self.op.attr("axes")
        starts = self.op.attr("starts")
        ends = self.op.attr("ends")

        x_shape = self.op.block.var(self.op.input_arg_names[0]).shape
        grad_shape = self.op.block.var(self.op.input_arg_names[1]).shape

        len_shape = len(x_shape)
        axes_cor = list(range(len_shape))
        starts_cor, ends_cor = [], []
        cnt = 0
        for i in range(len_shape):
            starts_cor.append(starts[cnt] if i in axes else 0)
            if i in axes and ends[cnt] <= x_shape[i]:
                ends_cor.append(x_shape[i] - ends[cnt])
            else:
                ends_cor.append(0)
            if i in axes:
                cnt += 1

        starts_cor[0] = 0
        ends_cor[0] = 0
        paddings = [[s, e] for (s, e) in zip(starts_cor, ends_cor)]
        slice_value = (
            core.GEOperatorFactory.create_operator(
                "slice_grad" + self._accumulated_op_id(), "PadD"
            )
            .set_input("x", grad)
            .set_attr_vec_vec_int64("paddings", paddings)
        )

        return [slice_value], [[0]]


class LookUpTableGradParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "lookup_table_grad"

    def _apply(self):
        ids = self._get_ge_input(self.op.input_arg_names[0])
        grad = self._get_ge_input(self.op.input_arg_names[1])
        embedding = self._get_ge_input(self.op.input_arg_names[2])

        shape_ids = self.op.block.var(self.op.input_arg_names[0]).shape
        shape_grad = self.op.block.var(self.op.input_arg_names[1]).shape
        shape_embedding = self.op.block.var(self.op.input_arg_names[2]).shape

        ids_flatten = (
            core.GEOperatorFactory.create_operator(
                "flatten" + self._accumulated_op_id(), "FlattenV2"
            )
            .set_input("x", ids)
            .set_attr_int32("axis", 0)
            .set_attr_int32("end_axis", 1)
        )
        grad_flatten = (
            core.GEOperatorFactory.create_operator(
                "flatten" + self._accumulated_op_id(), "FlattenV2"
            )
            .set_input("x", grad)
            .set_attr_int32("axis", 0)
            .set_attr_int32("end_axis", 1)
        )

        tensor_zeros = core.GEOperatorFactory.create_operator(
            "zeroslike" + self._accumulated_op_id(), "ZerosLike"
        ).set_input("x", embedding)
        embedding_grad = (
            core.GEOperatorFactory.create_operator(
                "scatteradd" + self._accumulated_op_id(), "TensorScatterAdd"
            )
            .set_input("x", tensor_zeros)
            .set_input("indices", ids_flatten)
            .set_input("updates", grad_flatten)
        )

        return [embedding_grad], [[0]]


class SGDParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "sgd"

    def _apply(self):
        grad = self._get_ge_input(self.op.input_arg_names[0])
        lr = self._get_ge_input(self.op.input_arg_names[1])
        param = self._get_ge_input(self.op.input_arg_names[2])
        sgd = (
            core.GEOperatorFactory.create_operator(
                "momentum" + self._accumulated_op_id(), "ApplyGradientDescent"
            )
            .set_input("var", param)
            .set_input("alpha", lr)
            .set_input("delta", grad)
        )
        return [sgd], [[0]]


class AdamParser(AscendParserBase):
    def __init__(self, graph, var2geop):
        super().__init__(graph, var2geop)
        self.parser_name = "adam"

    def _apply(self):
        beta1_power = self._get_ge_input(self.op.input_arg_names[0])
        beta2_power = self._get_ge_input(self.op.input_arg_names[1])
        grad = self._get_ge_input(self.op.input_arg_names[2])
        lr = self._get_ge_input(self.op.input_arg_names[3])
        moment1 = self._get_ge_input(self.op.input_arg_names[4])
        moment2 = self._get_ge_input(self.op.input_arg_names[5])
        param = self._get_ge_input(self.op.input_arg_names[6])
        beta1 = self.op.attr('beta1')
        beta2 = self.op.attr('beta2')
        epsilon = self.op.attr('epsilon')

        beta1 = core.GEOperatorFactory.create_operator(
            "const" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", self._create_ge_tensor([1], 5, beta1))
        beta2 = core.GEOperatorFactory.create_operator(
            "const" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", self._create_ge_tensor([1], 5, beta2))
        epsilon = core.GEOperatorFactory.create_operator(
            "const" + self._accumulated_op_id(), "Const"
        ).set_attr_tensor("value", self._create_ge_tensor([1], 5, epsilon))

        adam = (
            core.GEOperatorFactory.create_operator(
                "adam" + self._accumulated_op_id(), "ApplyAdam"
            )
            .set_input("var", param)
            .set_input("m", moment1)
            .set_input("v", moment2)
            .set_input("beta1_power", beta1_power)
            .set_input("beta2_power", beta2_power)
            .set_input("lr", lr)
            .set_input("beta1", beta1)
            .set_input("beta2", beta2)
            .set_input("epsilon", epsilon)
            .set_input("grad", grad)
        )

        return [adam], [[0]]