Source code for mindinsight.profiler.profiling

# Copyright 2020 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
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# ============================================================================
"""Profiling api file."""
import os
import time
from tabulate import tabulate
from mindinsight.profiler.parser.hwts_log_parser import HWTSLogParser
from mindinsight.profiler.parser.framework_parser import FrameworkParser
from mindinsight.profiler.parser.optime_parser import OPComputeTimeParser
from mindinsight.profiler.parser.aicpu_data_parser import DataPreProcessParser
from mindinsight.profiler.analyser.analyser_factory import AnalyserFactory
from mindinsight.profiler.analyser.integrator import Integrator
from mindinsight.profiler.common._utils import get_file_names, fwrite_format
from mindinsight.profiler.common.validator.validate_path import \
from mindinsight.profiler.common.validator.checkparam import \
    check_bool, check_subgraph
from mindinsight.profiler.common.log import logger
from mindinsight.utils.exceptions import MindInsightException

PROFILING_LOG_BASE_PATH = "/var/log/npu/profiling"

[docs]class Profiler: """ Performance profiling API. Enable MindSpore users to profile the performance of neural network. Args: subgraph (str): Define which subgraph to monitor and analyse, can be 'all', 'Default', 'Gradients'. is_detail (bool): Whether to show profiling data for op_instance level, only show optype level if False. is_show_op_path (bool): Whether to save the full path for each op instance. output_path (str): Output data path. optypes_to_deal (list[str]): Op type names, the data of which optype should be collected and analysed, will deal with all op if null. optypes_not_deal (list[str]): Op type names, the data of which optype will not be collected and analysed. Examples: >>> from mindinsight.profiler import Profiler >>> context.set_context(mode=context.GRAPH_MODE, device_target=“Ascend”, >>> device_id=int(os.environ["DEVICE_ID"])) >>> profiler = Profiler(subgraph='all', is_detail=True, is_show_op_path=False, output_path='./data') >>> model = Model(train_network) >>> dataset = get_dataset() >>> model.train(2, dataset) >>> profiler.analyse() """ _base_profiling_container_path = "/var/log/npu/profiling/container" _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_" def __init__(self, subgraph='all', is_detail=True, is_show_op_path=False, output_path='./data', optypes_to_deal='', optypes_not_deal='Variable', job_id=""): # get device_id and device_target device_target = "" try: import mindspore.context as context dev_id = str(context.get_context("device_id")) device_target = context.get_context("device_target") except ImportError: logger.error("Profiling: fail to import context from mindspore.") except ValueError as err: logger.error("Profiling: fail to get context %s", err.message) if not dev_id: dev_id = str(os.getenv('DEVICE_ID')) if not dev_id: dev_id = "0" logger.error("Fail to get DEVICE_ID, use 0 instead.") if device_target and device_target != "Davinci" \ and device_target != "Ascend": msg = ("Profiling: unsupport backend: %s" \ % device_target) raise RuntimeError(msg) self._dev_id = dev_id self._container_path = os.path.join(self._base_profiling_container_path, dev_id) data_path = os.path.join(self._container_path, "data") if not os.path.exists(data_path): os.makedirs(data_path) self._output_path = validate_and_normalize_path(output_path, 'Profiler output 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) os.environ['PROFILING_MODE'] = 'true' os.environ['PROFILING_OPTIONS'] = 'training_trace:task_trace' # use context interface to open profiling, for the new mindspore version(after 2020.5.21) try: import mindspore.context as context context.set_context(enable_profiling=True, profiling_options="training_trace:task_trace") except ImportError: logger.error("Profiling: fail to import context from mindspore.") except ValueError as err: logger.error("Profiling: fail to set context, %s", err.message) os.environ['AICPU_PROFILING_MODE'] = 'true' os.environ['PROFILING_DIR'] = str(self._container_path) self._subgraph = check_subgraph(subgraph) self._valid_optype_name = optypes_to_deal.split(",") if optypes_to_deal else [] self._filt_optype_names = optypes_not_deal.split(",") if optypes_not_deal else [] self._detail = check_bool(is_detail, 'is_detail') self._withfullpath = check_bool(is_show_op_path, 'is_show_op_path') self._profiling_job_id = job_id self._start_time = int(time.time() * 10000000)"Profiling: profiling start time: %d", self._start_time)
[docs] def analyse(self): """ Collect and analyse performance data, called after training or during training. Examples: >>> from mindinsight.profiler import Profiler >>> context.set_context(mode=context.GRAPH_MODE, device_target=“Ascend”, >>> device_id=int(os.environ["DEVICE_ID"])) >>> profiler = Profiler(subgraph='all', is_detail=True, is_show_op_path=False, output_path='./data') >>> model = Model(train_network) >>> dataset = get_dataset() >>> model.train(2, dataset) >>> profiler.analyse() """ try: from import release release() except ImportError: logger.error("Profiling: fail to import release from mindspore.")"begin profiler analyse") job_id = self._get_profiling_job_id() if not job_id: msg = ("Fail to get profiling job, please check whether job dir was generated under path %s" \ % PROFILING_LOG_BASE_PATH) raise RuntimeError(msg)"Profiling: job id is %s ", job_id) source_path = os.path.join(PROFILING_LOG_BASE_PATH, job_id) # parse file, and get task profiling data hwts_output_filename = self._hwts_output_filename_target + self._dev_id + ".txt" hwts_output_filename = os.path.join(self._output_path, hwts_output_filename) hwtslog_parser = HWTSLogParser(source_path, hwts_output_filename) result = hwtslog_parser.execute() if not result: logger.error("Profiling: fail to parse hwts log file.") return # parse Framework file, and get the relation of op and tasks framework_parser = FrameworkParser(job_id, self._dev_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._dev_id + ".txt" opcompute_output_filename = os.path.join(self._output_path, opcompute_output_filename) optime_parser = OPComputeTimeParser(hwts_output_filename, opcompute_output_filename, op_task_dict) optime_parser.execute() # parse file, write output_data_preprocess_aicpu_x.txt output_data_preprocess_aicpu = self._aicpu_op_output_filename_target + self._dev_id + ".txt" output_data_preprocess_aicpu = os.path.join(self._output_path, output_data_preprocess_aicpu) try: aicpu_data_parser = DataPreProcessParser(source_path, output_data_preprocess_aicpu) aicpu_data_parser.execute() except FileNotFoundError as err: logger.exception(err) # analyse op compute time info try: self._analyser_op_info() except MindInsightException as err: logger.error(err.message)
def __del__(self): """Disable the profiling collection service, called after training.""" os.environ['PROFILING_MODE'] = str("false") def _get_profiling_job_id(self): """Get profiling job id, which was generated by ada service. Returns: str: profiling jon id. """ if self._profiling_job_id: return self._profiling_job_id job_id = "" cmd = "ls -t " + PROFILING_LOG_BASE_PATH + "|grep JOB|awk '{print $1}'" r = os.popen(cmd) profiling_job_dirs = r.readlines() r.close() for item in profiling_job_dirs: path = os.path.join(PROFILING_LOG_BASE_PATH, item.strip()) log_file = get_file_names(path, "host_start.log") if not log_file: logger.error("Profiling: job path %s, host_start.log not exist.", path) continue log_file = os.path.join(path, log_file[0]) item_dict = self._parse_host_start_log(log_file) if not item_dict: logger.error("Profiling: job path %s, fail to get job start info.", path) continue if self._start_time > int(item_dict["start_time"]):"Profiling: job path %s, start_time %s, training start_time %d.", path, item_dict["start_time"], self._start_time) break if self._dev_id != item_dict["device_id"]:"Profiling: job path %s, dev id %s, training device id %s.", path, item_dict["device_id"], self._dev_id) continue job_id = item.strip() break return job_id def _parse_host_start_log(self, input_file): """ Parse host start log file, get the device id and start time of the job. Args: input_file (str): The file path of the host start log file. Returns: dict, job start time and device id. """ item_dict = {} for line in open(input_file): if "Device" in line: item_dict["device_id"] = line[7:len(line)-2] elif "clock_realtime" in line: item_dict["start_time"] = line[16:len(line)-3] return item_dict def _analyser_op_info(self): """Analyse the operator information.""" integrator = Integrator(self._output_path, self._dev_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._dev_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' ] data_source = tabulate(aicore_type_result, display_names) fwrite_format(detail_file_path, data_source=data_source, is_print=True) if self._detail: 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) data_source = tabulate( aicore_detail_result.get('object'), aicore_detail_result.get('col_name') ) fwrite_format(detail_file_path, data_source=data_source, 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. """ condition = { 'sort_condition': { 'name': 'execution_time', 'type': 'descending' } } analyser = AnalyserFactory.instance().get_analyser( 'aicore_type', self._output_path, self._dev_id ) result = analyser.query(condition) return result.get('object') 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._valid_optype_name: op_type_condition['in'] = self._valid_optype_name if self._filt_optype_names: op_type_condition['not_in'] = self._filt_optype_names subgraph_condition = {} if self._subgraph != 'all': subgraph_condition['in'] = [self._subgraph] filter_condition = { 'op_type': op_type_condition, 'subgraph': subgraph_condition, 'is_display_detail': False, 'is_display_full_op_name': self._withfullpath } analyser = AnalyserFactory.instance().get_analyser( 'aicore_detail', self._output_path, self._dev_id ) result = analyser.query_and_sort_by_op_type( filter_condition, op_type_order ) return result