# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Profiling api file."""
import os
import stat
import time
import json
from enum import Enum

from mindspore import log as logger, context
from mindspore.communication.management import GlobalComm, release, get_rank, get_group_size
import mindspore._c_expression as c_expression
from mindspore.dataset.core.config import _stop_dataset_profiler
from mindspore.profiler.common.exceptions.exceptions import ProfilerFileNotFoundException, \
    ProfilerIOException, ProfilerException, ProfilerRawFileException
from mindspore.profiler.common.util import get_file_path, fwrite_format
from mindspore.profiler.common.validator.validate_path import \
    validate_and_normalize_path
from mindspore.profiler.parser.aicpu_data_parser import DataPreProcessParser
from mindspore.profiler.parser.framework_parser import FrameworkParser
from mindspore.profiler.parser.hwts_log_parser import HWTSLogParser
from mindspore.profiler.parser.integrator import Integrator
from mindspore.profiler.parser.integrator import GpuTimelineGenerator, AscendTimelineGenerator
from mindspore.profiler.parser.memory_usage_parser import MemoryUsageParser
from mindspore.profiler.parser.minddata_parser import MinddataParser
from mindspore.profiler.parser.minddata_analyzer import MinddataProfilingAnalyzer
from mindspore.profiler.parser.flops_parser import FlopsParser
from mindspore.profiler.parser.minddata_pipeline_parser import \
    MinddataPipelineParser
from mindspore.profiler.parser.optime_parser import OPComputeTimeParser
from mindspore.profiler.parser.step_trace_parser import GpuStepTraceParser, AscendStepTraceParser
from mindspore.profiler.parser.hccl_parser import HcclParser
from mindspore.nn.cell import Cell

INIT_OP_NAME = 'Default/InitDataSetQueue'


def _environment_check():
    if c_expression.security.enable_security():
        raise RuntimeError("Profiler is not supported if compiled with \'-s on\'")
    if context.get_context("mode") == context.PYNATIVE_MODE:
        raise RuntimeError("Profiler is not supported in pynative mode currently, "
                           "and it is only supported in graph mode.")


class ProfileOption(Enum):
    """
    Profile Option Enum which be used in Profiler.profile.
    """
    trainable_parameters = 0


class Profiler:
    """
    Performance profiling API.

    This API enables MindSpore users to profile the performance of neural network.
    Profiler supports Ascend and GPU, both of them are used in the same way,
    but only output_path in args works on GPU.

    Args:
        output_path (str): Output data path.
        optypes_not_deal (str): (Ascend only) Op type names, determine the data of which optype should be collected
            and analysed, will deal with all op if null. Different op types should be separated by comma.
        ascend_job_id (str): (Ascend only) The directory where the profiling files to be parsed are located.
            This parameter is used to support offline parsing.
        profile_communication (bool): Whether to collect communication performance data in a multi devices training,
            collect when True. Default is False. Setting this parameter has no effect during single device training.
        profile_memory (bool): Whether to collect tensor memory data, collect when True. Default is False.

    Examples:
        >>> import numpy as np
        >>> from mindspore import nn, context
        >>> from mindspore import Model
        >>> import mindspore.dataset as ds
        >>> from mindspore.profiler import Profiler
        >>>
        >>>
        >>> class Net(nn.Cell):
        ...     def __init__(self):
        ...         super(Net, self).__init__()
        ...         self.fc = nn.Dense(2,2)
        ...     def construct(self, x):
        ...         return self.fc(x)
        >>>
        >>> def generator():
        ...     for i in range(2):
        ...         yield (np.ones([2, 2]).astype(np.float32), np.ones([2]).astype(np.int32))
        >>>
        >>> def train(net):
        ...     optimizer = nn.Momentum(net.trainable_params(), 1, 0.9)
        ...     loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
        ...     data = ds.GeneratorDataset(generator, ["data", "label"])
        ...     model = Model(net, loss, optimizer)
        ...     model.train(1, data)
        >>>
        >>> if __name__ == '__main__':
        ...     # If the device_target is GPU, set the device_target to "GPU"
        ...     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
        ...
        ...     # Init Profiler
        ...     # Note that the Profiler should be initialized after context.set_context and before model.train
        ...     # If you are running in parallel mode on Ascend, the Profiler should be initialized before HCCL
        ...     # initialized.
        ...     profiler = Profiler()
        ...
        ...     # Train Model
        ...     net = Net()
        ...     train(net)
        ...
        ...     # Profiler end
        ...     profiler.analyse()
    """

    _hwts_output_filename_target = "output_format_data_hwts_"
    _opcompute_output_filename_target = "output_op_compute_time_"
    _aicpu_op_output_filename_target = "output_data_preprocess_aicpu_"
    _has_analysed = False

    def __init__(self, **kwargs):
        _environment_check()
        # get device_id and device_target
        self._get_devid_rankid_and_devtarget()
        self._get_output_path(kwargs)
        self._profile_communication = False

        os.environ['PROFILING_MODE'] = 'true'
        os.environ['MINDDATA_PROFILING_DIR'] = self._output_path

        if self._device_target:
            cpu_profiler = c_expression.CPUProfiler
            self._cpu_profiler = cpu_profiler.get_instance()
            self._cpu_profiler.init(self._output_path)
            self._cpu_profiler.step_profiling_enable(True)
        if self._device_target and self._device_target == "GPU":
            gpu_profiler = c_expression.GPUProfiler
            self._gpu_profiler = gpu_profiler.get_instance()
            self._gpu_profiler.init(self._output_path)
            self._gpu_profiler.step_profiling_enable(True)
            if GlobalComm.WORLD_COMM_GROUP == "nccl_world_group":
                self._dev_id = str(get_rank())
            os.environ['DEVICE_ID'] = self._dev_id

            if kwargs:
                logger.warning("Params not be supported yet on GPU.")
        elif self._device_target and self._device_target == "Ascend":
            self._parse_parameter_for_ascend(**kwargs)
            os.environ['DEVICE_ID'] = self._dev_id

            profiling_options = json.dumps(self._construct_profiling_options())
            # Characters longer than 2048 are ignored, resulting in profiling option resolution errors
            if len(profiling_options) > 2048:
                msg = "The parameter length exceeds the limit (2048), please input valid parameters."
                logger.error(msg)
                raise ValueError(msg)
            # use context interface to open profiling, for the new mindspore version(after 2020.5.21)
            self._ascend_profiler = c_expression.AscendProfiler.get_instance()
            self._ascend_profiler.start(profiling_options)
            base_profiling_container_path = os.path.join(self._output_path, "container")
            container_path = os.path.join(base_profiling_container_path, self._dev_id)
            data_path = os.path.join(container_path, "data")
            data_path = validate_and_normalize_path(data_path)
            if not os.path.exists(data_path):
                os.makedirs(data_path, exist_ok=True)

            # add job id env through user input later
            self._job_id_env = 0
            self._start_time = int(time.time() * 10000000)
            logger.info("Profiling: profiling start time: %d", self._start_time)

    def _construct_profiling_options(self):
        """
        Construct profiling options to determine which profiling data should be collected.
        """
        profile_memory = "off"
        if self._profile_memory:
            profile_memory = "on"

        fp_point = os.environ.get("PROFILING_FP_START", "")
        bp_point = os.environ.get("PROFILING_BP_END", "")

        profiling_options = {
            "output": self._output_path,
            "fp_point": fp_point,
            "bp_point": bp_point,
            "training_trace": "on",
            "task_trace": "on",
            "aic_metrics": "ArithmeticUtilization",
            "aicpu": "on",
            "profile_memory": profile_memory
        }

        return profiling_options

    def _parse_parameter_for_ascend(self, **kwargs):
        """Parse parameter in Proflier when the device target is Ascend."""
        optypes_not_deal = kwargs.pop("optypes_not_deal", "Variable")
        if not isinstance(optypes_not_deal, str):
            raise TypeError("The parameter optypes_not_deal must be str.")
        self._filt_optype_names = optypes_not_deal.split(",") if optypes_not_deal else []
        job_dir = kwargs.pop("ascend_job_id", "")
        if job_dir:
            job_dir = validate_and_normalize_path(job_dir)
            if not os.path.exists(job_dir):
                msg = f"Invalid ascend_job_id: {job_dir}, Please pass the absolute path of the JOB dir"
                logger.error(msg)
                raise ValueError(msg)
            self._output_path, _ = os.path.split(job_dir)

        self._profile_communication = kwargs.pop("profile_communication", False)
        if not isinstance(self._profile_communication, bool):
            raise TypeError("The parameter profile_communication must be bool.")
        if self._profile_communication:
            hccl_option = {"output": self._output_path, "task_trace": "on"}
            os.environ['PROFILING_OPTIONS'] = json.dumps(hccl_option)
        self._profile_memory = kwargs.pop("profile_memory", False)
        if not isinstance(self._profile_memory, bool):
            raise TypeError("The parameter profile_memory must be bool")
        if kwargs:
            logger.warning("There are invalid params which don't work.")
        task_sink = os.getenv("GRAPH_OP_RUN")
        if task_sink and task_sink == "1":
            logger.warning("Profiling is not supported when task is not sink.")

    def analyse(self):
        """
        Collect and analyse performance data, called after training or during training. The example shows above.
        """
        if Profiler._has_analysed:
            msg = "Do not analyze twice in the profiler."
            raise RuntimeError(msg)
        Profiler._has_analysed = True
        _environment_check()
        self._cpu_profiler.stop()
        _stop_dataset_profiler()
        if self._device_target and self._device_target == "GPU":
            self._gpu_analyse()

        elif self._device_target and self._device_target == "Ascend":
            self._ascend_analyse()

    def _ascend_analyse(self):
        """Collect and analyse ascend performance data"""
        self._rank_size = 1
        if self._profile_communication and not GlobalComm.INITED:
            self._profile_communication = False

        if GlobalComm.INITED:
            self._rank_size = get_group_size()

        release()

        job_id = self._get_profiling_job_id()
        logger.info("Profiling: job id is %s ", job_id)

        source_path = os.path.join(self._output_path, job_id)
        # parse hwts.log.data.45.dev file, and get task profiling data
        hwts_output_filename = self._hwts_output_filename_target + self._rank_id + ".txt"
        hwts_output_filename = os.path.join(self._output_path, hwts_output_filename)
        source_path = validate_and_normalize_path(source_path)
        hwts_output_filename = validate_and_normalize_path(hwts_output_filename)
        hwtslog_parser = HWTSLogParser(source_path, hwts_output_filename)
        hwtslog_parser.execute()

        # parse Framework file, and get the relation of op and tasks
        framework_parser = FrameworkParser(job_id, self._dev_id, self._rank_id, self._output_path)
        framework_parser.parse()
        op_task_dict = framework_parser.to_task_id_full_op_name_dict()
        if not op_task_dict:
            logger.error("Profiling: fail to parse framework files.")
            return

        # get op compute time from hwts data and framework data, write output_op_compute_time.txt
        opcompute_output_filename = self._opcompute_output_filename_target + self._rank_id + ".txt"
        opcompute_output_filename = os.path.join(self._output_path, opcompute_output_filename)
        opcompute_output_filename = validate_and_normalize_path(opcompute_output_filename)
        optime_parser = OPComputeTimeParser(
            hwts_output_filename, opcompute_output_filename,
            op_task_dict, self._output_path, self._rank_id
        )
        optime_parser.execute()

        # parse DATA_PREPROCESS.dev.AICPU file, write output_data_preprocess_aicpu_x.txt
        output_data_preprocess_aicpu = self._aicpu_op_output_filename_target + self._rank_id + ".txt"
        output_data_preprocess_aicpu = os.path.join(self._output_path, output_data_preprocess_aicpu)
        output_data_preprocess_aicpu = validate_and_normalize_path(output_data_preprocess_aicpu)
        aicpu_data_parser = DataPreProcessParser(source_path, output_data_preprocess_aicpu)
        aicpu_data_parser.execute()

        # Parsing minddata AICPU profiling
        MinddataParser.execute(source_path, self._output_path, self._rank_id)

        # parse minddata pipeline operator and queue
        try:
            pipeline_parser = MinddataPipelineParser(self._output_path, self._rank_id, self._output_path)
            pipeline_parser.parse()
        except ProfilerException as err:
            logger.warning(err.message)

        # Analyze minddata information
        try:
            md_analyzer = MinddataProfilingAnalyzer(self._output_path, self._rank_id, self._output_path)
            md_analyzer.analyze()
        except ProfilerException as err:
            logger.warning(err.message)

        # analyse op compute time info
        try:
            self._analyser_op_info()
        except ProfilerException as err:
            logger.warning(err.message)

        # analyse step trace info
        points = None
        is_training_mode_flag = False

        try:
            points, is_training_mode_flag = self._analyse_step_trace(source_path, framework_parser)
        except ProfilerException as err:
            logger.warning(err.message)

        # analyse timeline info
        try:
            self._analyse_timeline(aicpu_data_parser, optime_parser, source_path)
        except (ProfilerIOException, ProfilerFileNotFoundException, RuntimeError) as err:
            logger.warning('Fail to write timeline data: %s', err)

        # analyse memory usage info
        if self._profile_memory:
            try:
                self._analyse_memory_usage(points)
            except (ProfilerIOException, ProfilerFileNotFoundException, ProfilerRawFileException) as err:
                logger.warning(err.message)

        # analyse hccl profiler info
        if self._profile_communication:
            try:
                self._analyse_hccl_info()
            except (ProfilerIOException, ProfilerFileNotFoundException, ProfilerRawFileException) as err:
                logger.warning(err.message)

        # get op FLOPs from aicore.data.x.slice.0 file, and compute FLOPS, write output_op_flops_x.txt
        flops_parser = FlopsParser(source_path, self._output_path, op_task_dict,
                                   self._dev_id, self._rank_id, is_training_mode_flag)
        flops_parser.execute()

        os.environ['PROFILING_MODE'] = str("false")
        self._ascend_profiler.stop()

    def _gpu_analyse(self):
        """Collect and analyse gpu performance data"""
        self._dev_id = context.get_context("device_id")
        if GlobalComm.WORLD_COMM_GROUP == "nccl_world_group":
            self._dev_id = str(get_rank())
        self._gpu_profiler.stop()
        timeline_generator = self._generate_timeline()

        # parse minddata pipeline operator and queue for GPU
        try:
            pipeline_parser = MinddataPipelineParser(self._output_path, self._dev_id, self._output_path)
            pipeline_parser.parse()
        except ProfilerException as err:
            logger.warning(err.message)

        # Analyze minddata information
        try:
            md_analyzer = MinddataProfilingAnalyzer(self._output_path, self._dev_id, self._output_path)
            md_analyzer.analyze()
        except ProfilerException as err:
            logger.warning(err.message)

        # analyse step trace info
        try:
            self._analyse_step_trace(
                is_training_mode_flag=timeline_generator.check_op_name('Gradients'),
                is_gpu_kernel_async_launch_flag=timeline_generator.is_gpu_kernel_async_launch()
            )
        except ProfilerException as err:
            logger.warning(err.message)

        os.environ['PROFILING_MODE'] = str("false")

        logger.warning(
            '\nMemory Usage is not supported on GPU currently.\n'
            'Please running on Ascend if you would like to see memory analysis, '
            'otherwise, this warning can be ignored.'
        )

    def _analyse_step_trace(self, source_path=None, framework_parser=None, is_training_mode_flag=True,
                            is_gpu_kernel_async_launch_flag=False):
        """
        Analyse step trace data and save the result.

        Args:
            source_path (str): The directory that contains the step trace original data.
            framework_parser (FrameworkParser): The framework parse instance.
            is_training_mode_flag (bool): Whether in training mode or not.
        """
        logger.info("Begin to parse step trace.")
        # construct output path
        dev_id = self._rank_id if self._device_target == "Ascend" else self._dev_id
        step_trace_intermediate_file_path = os.path.join(
            self._output_path,
            f'step_trace_raw_{dev_id}_detail_time.csv'
        )
        point_info_file_path = os.path.join(
            self._output_path,
            f'step_trace_point_info_{dev_id}.json'
        )
        step_trace_intermediate_file_path = validate_and_normalize_path(step_trace_intermediate_file_path)
        point_info_file_path = validate_and_normalize_path(point_info_file_path)

        if self._device_target and self._device_target == 'GPU':
            input_file_path = os.path.join(
                self._output_path,
                f'step_trace_profiling_{self._dev_id}.txt'
            )
            parser = GpuStepTraceParser(input_dir=input_file_path,
                                        output_file_path=step_trace_intermediate_file_path,
                                        is_training_mode=is_training_mode_flag,
                                        is_gpu_kernel_async_launch=is_gpu_kernel_async_launch_flag)
            parser.parse_and_save()
            point_info = parser.record_point_info(input_file_path, point_info_file_path)
        else:
            # whether keep the first step
            skip_first_step_flag = framework_parser.check_op_name(INIT_OP_NAME)
            point_info = framework_parser.point_info
            # recognize inference or training mode
            is_training_mode_flag = framework_parser.check_op_name("Gradients")
            # parser the step trace files and save the result to disk
            source_path = validate_and_normalize_path(source_path)
            parser = AscendStepTraceParser(input_dir=source_path,
                                           output_file_path=step_trace_intermediate_file_path,
                                           job_id=self._job_id_env,
                                           skip_first_step=skip_first_step_flag,
                                           is_training_mode=is_training_mode_flag)
            parser.update_tag_op_type_map(point_info)
            parser.parse_and_save()
            point_info = parser.record_point_info(point_info, point_info_file_path)
        # print parser result
        parser.show()
        logger.info("Finish saving the intermediate result: %s", step_trace_intermediate_file_path)
        logger.info("The point info is: %s", point_info)

        return point_info, is_training_mode_flag

    def _analyse_timeline(self, aicpu_parser, optime_parser, source_path):
        """
        Analyse and parse timeline info.

        Args:
            aicpu_parser (DataPreProcessParser): The parser instance for AI CPU operator
                execution time calculation.
            optime_parser (OPComputeTimeParserParser): The parser instance for AI Core
                operator execution time calculation.
        """
        timeline_analyser = AscendTimelineGenerator(self._output_path, self._dev_id, self._rank_id, self._rank_size)
        # Get framework info
        integrator = Integrator(self._output_path, self._rank_id)
        aicore_detail_data = integrator.get_aicore_detail_data()
        aicore_detail_data_size = len(aicore_detail_data)
        col_names = ['op_name', 'op_type', 'avg_execution_time', 'subgraph',
                     'full_op_name', 'op_info']
        framework_info = {
            'col_name': col_names,
            'object': aicore_detail_data,
            'size': aicore_detail_data_size
        }

        all_reduce_info = integrator.query_for_all_reduce()

        # Get timeline info
        logger.info('Start writing timeline info...')
        logger.info('Warm Prompt: It could take a few minutes if you are training '
                    'with a complex network or more than 10 steps.')
        # Add info into timeline, such as AI CPU, AllReduce, framework info.
        aicpu_info = aicpu_parser.query_aicpu_data()
        min_cycle_counter = min(aicpu_parser.min_cycle_counter, optime_parser.min_cycle_counter)
        timeline_analyser.init_timeline(all_reduce_info, framework_info, aicpu_info,
                                        min_cycle_counter, source_path)
        size_limit = 100 * 1024 * 1024  # 100MB
        timeline_analyser.write_timeline(size_limit)
        timeline_analyser.write_timeline_summary()

    def _generate_timeline(self):
        """Used for gpu, generate timeline info, write to json format file."""
        try:
            size_limit = 100 * 1024 * 1024  # 100MB
            timeline_generator = GpuTimelineGenerator(self._output_path, self._dev_id)
            timeline_generator.init_timeline()
            timeline_generator.write_timeline(size_limit)
            timeline_generator.write_timeline_summary()
            return timeline_generator
        except (ProfilerIOException, ProfilerFileNotFoundException, RuntimeError) as err:
            logger.warning('Fail to write timeline data: %s', err)
            raise RuntimeError('Fail to write timeline data.')

    def _analyse_memory_usage(self, points):
        """Analyse memory usage data."""
        integrator = Integrator(self._output_path, self._rank_id)
        aicore_detail_data = integrator.get_aicore_detail_data()
        memory_parser = MemoryUsageParser(self._output_path, self._rank_id)
        memory_parser.init_memory_usage_info(aicore_detail_data, points)
        memory_parser.write_memory_files()

    def _get_profiling_job_id(self):
        """Get profiling job id, which was generated by ada service.

        Returns:
            str, profiling job id.
        """

        job_id = ""
        job_dirs = filter(lambda item: item.startswith('JOB') and os.path.isdir(os.path.join(self._output_path, item)),
                          os.listdir(self._output_path))
        sorted_job_dirs = sorted(job_dirs, key=lambda x: os.path.getmtime(os.path.join(self._output_path, x)),
                                 reverse=True)

        for dir_name in sorted_job_dirs:
            job_dir = os.path.join(self._output_path, dir_name)
            host_start_file_path = get_file_path(job_dir, "host_start.log")
            if host_start_file_path is None:
                logger.warning("Find profiling job path %s, but host_start.log not exist, "
                               "profiler will ignore this job dir.", job_dir)
                continue

            training_device_id = host_start_file_path.split('.')[-1]
            if self._dev_id != training_device_id:
                logger.warning("Find profiling find job path %s, but not current training device id. "
                               "Current training device id %s, but job path device id: %s, "
                               "profiler will ignore this job dir.", job_dir, self._dev_id, training_device_id)
                continue

            job_start_time = self._parse_host_start_log(host_start_file_path)
            if not job_start_time:
                logger.warning("Find profiling job path %s, but fail to get job start info, "
                               "profiler will ignore this job dir.", job_start_time)
                continue

            if int(job_start_time) < self._start_time:
                logger.warning("Find profiling job path %s, but start_time(%d) is earlier than this training "
                               "start_time(%d), profiler will ignore this job dir.",
                               job_dir, job_start_time, self._start_time)
                continue

            job_id = dir_name
            break

        if not job_id:
            msg = "Fail to get profiling job, output path is {}, " \
                  "please check whether job dir(name startswith JOB) in output path was generated, " \
                  "or may be the device id from job dir dismatch the " \
                  "device_id in current process.".format(self._output_path)
            raise RuntimeError(msg)

        return job_id

    @staticmethod
    def _parse_host_start_log(input_file):
        """
        Parse host start log file, get the start time of the job.

        Args:
             input_file (str): The file path of the host start log file.

        Returns:
            str, job start time.
        """

        job_start_time = ""
        with open(input_file) as f:
            for line in f.readlines():
                if "clock_realtime" in line:
                    # 16 means the first digit of the timestamp, len(line)-3 means the last.
                    job_start_time = line[16:len(line) - 3]

        return job_start_time

    def _analyser_op_info(self):
        """Analyse the operator information."""
        integrator = Integrator(self._output_path, self._rank_id)
        integrator.integrate()

        aicore_type_result = self._query_op_type_info()
        detail_file_path = os.path.join(
            self._output_path,
            'output_op_compute_time_detail_{}.txt'.format(self._rank_id)
        )
        fwrite_format(detail_file_path, data_source='title:op compute time')
        display_names = [
            'optype_name', 'compute_time(ms, per-step)',
            'called_times(per-step)', 'percent'
        ]
        fwrite_format(detail_file_path, data_source=" ".join(display_names), is_print=True)
        fwrite_format(detail_file_path, data_source=aicore_type_result, is_print=True)

        op_type_order = [item[0] for item in aicore_type_result]
        aicore_detail_result = self._query_op_detail_info(op_type_order)

        fwrite_format(detail_file_path, data_source='', is_print=True)
        fwrite_format(detail_file_path, data_source='Detail:', is_print=True)
        fwrite_format(detail_file_path, data_source=" ".join(aicore_detail_result.get('col_name_detail')),
                      is_print=True)
        fwrite_format(detail_file_path, data_source=aicore_detail_result.get('object'), is_print=True)

    def _query_op_type_info(self):
        """
        Query AICORE operator type information.

        Returns:
            list[list], the AICORE operator type and execution time information.
        """
        integrator = Integrator(self._output_path, self._rank_id)
        return integrator.get_aicore_data()

    def _query_op_detail_info(self, op_type_order):
        """
        Query AICORE operator detail information.

        Args:
            op_type_order(list): The name of the op type in order.

        Returns:
            dict, the AICORE operator detail information.
        """

        op_type_condition = {}
        if self._filt_optype_names:
            op_type_condition['not_in'] = self._filt_optype_names

        filter_condition = {
            'op_type': op_type_condition,
            'is_display_detail': False,
        }
        integrator = Integrator(self._output_path, self._rank_id)
        return integrator.query_and_sort_by_op_type(filter_condition, op_type_order)

    def _get_devid_rankid_and_devtarget(self):
        """Get device id and rank id and target of this training."""

        device_target = ""
        dev_id = ""
        rank_id = ""
        try:
            dev_id = str(context.get_context("device_id"))
            device_target = context.get_context("device_target")
        except ValueError as err:
            logger.error("Profiling: fail to get context, %s", err)

        if not dev_id or not dev_id.isdigit():
            dev_id = os.getenv('DEVICE_ID')
        if not dev_id or not dev_id.isdigit():
            dev_id = "0"
            logger.warning("Fail to get DEVICE_ID, use 0 instead.")

        if device_target and device_target not in ["Ascend", "GPU", "CPU"]:
            msg = "Profiling: unsupported backend: %s" % device_target
            raise RuntimeError(msg)

        rank_id = os.getenv("RANK_ID")
        if not rank_id or not rank_id.isdigit():
            rank_id = "0"
            logger.info("Fail to get RANK_ID, use 0 instead.")

        self._dev_id = dev_id
        self._device_target = device_target
        self._rank_id = rank_id

    def _get_output_path(self, kwargs):
        """Get output path of profiling data."""
        if os.getenv("MS_DIAGNOSTIC_DATA_PATH") and kwargs.get("output_path") is not None:
            logger.warning("Both parameter output_path and environment variable MS_DIAGNOSTIC_DATA_PATH"
                           " have values set, and the profiling data saving path is the value set "
                           "in parameter output_path")
        if kwargs.get("output_path") is None:
            if "output_path" in kwargs:
                kwargs.pop("output_path")
            # Environment variables are mainly set for the convenience of cloud profiler.
            output_path = os.getenv("MS_DIAGNOSTIC_DATA_PATH")
            if output_path:
                self._output_path = validate_and_normalize_path(output_path)
            else:
                output_path = "data"
                self._output_path = validate_and_normalize_path(output_path)
        else:
            output_path = kwargs.pop("output_path")
            self._output_path = validate_and_normalize_path(output_path)
        self._output_path = os.path.join(self._output_path, "profiler")
        if not os.path.exists(self._output_path):
            os.makedirs(self._output_path, exist_ok=True)
            os.chmod(self._output_path, stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR)
        else:
            logger.warning("The target dir already exists. "
                           "There may be some old profiling data, and they will be rewritten in the end.")

    def _analyse_hccl_info(self):
        """Analyse hccl info."""
        hccl_path = os.path.join(self._output_path, "hccl_info_{}".format(self._rank_id))
        if not os.path.exists(hccl_path):
            os.makedirs(hccl_path, exist_ok=True)
            os.chmod(hccl_path, stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR)
        logger.info("Start call the interface HCCLParseOP parsing hccl info...")
        logger.info('Warm Prompt: It could take a few minutes if you are training '
                    'with a complex network or more than 10 steps.')
        # Call the interface HCCLParseOP parsing hccl info.
        try:
            from hccl_parser.entry import hccl_parse_op
            hccl_parse_op(self._dev_id, self._output_path, hccl_path, op_type='all')
        except ImportError as err:
            logger.error("%s,please check if the hccl_parser-{version}-py3-none-any.whl is installed."
                         "The hccl_parser-{version}-py3-none-any.whl package is usually located "
                         "in the /usr/local/Ascend/tools Directory", err)
            raise ImportError(err)
        logger.info("Parse hccl info successfully.")
        logger.info("Start analyse hccl info.")
        hccl_parse = HcclParser(hccl_path, self._dev_id, self._rank_id, self._output_path)
        hccl_parse.parse()
        logger.info("Analyse hccl info successfully.")

    @staticmethod
    def profile(network, profile_option):
        """
        Get the number of trainable parameters in the training network.

        Args:
            network (Cell): The training network.
            profile_option (ProfileOption): The profile option.

        Returns:
            dict, the key is the option name, the value is the result of option.
        """
        result = dict()
        if not profile_option:
            raise ValueError("The parameter profile_option must pass a value using ProfileOption.")

        if profile_option == ProfileOption.trainable_parameters:
            if not isinstance(network, Cell):
                msg = "Profiling: The network should be an object of nn.Cell"
                raise ValueError(msg)
            param_nums = len(network.parameters_dict())
            result = {"trainable_parameters": param_nums}
        else:
            raise ValueError("Wrong options.")

        return result