Add profiler module

cmake/package.cmake

@@ -213,6 +213,7 @@ install(
         ${CMAKE_SOURCE_DIR}/mindspore/common
         ${CMAKE_SOURCE_DIR}/mindspore/ops
         ${CMAKE_SOURCE_DIR}/mindspore/communication
+        ${CMAKE_SOURCE_DIR}/mindspore/profiler
     DESTINATION ${INSTALL_PY_DIR}
     COMPONENT mindspore
 )

mindspore/profiler/__init__.py

+# 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
+#
+# 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.
+# ============================================================================
+"""
+Profiler Module Introduction.
+
+This module provides Python APIs to enable the profiling of MindSpore neural networks.
+Users can import the mindspore.profiler.Profiler, initialize the Profiler object to start profiling,
+and use Profiler.analyse() to stop profiling and analyse the results.
+To visualize the profiling results, users can open mindspore Web, find the corresponding run
+and click the profile link.
+Now, Profiler supports the AICore operator analysis.
+"""
+from mindspore.profiler.profiling import Profiler
+
+__all__ = ["Profiler"]

mindspore/profiler/common/__init__.py

+# 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
+#
+# 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.
+# ============================================================================

mindspore/profiler/common/exceptions/__init__.py

+# 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
+#
+# 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.
+# ============================================================================

mindspore/profiler/common/exceptions/error_code.py

+# 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
+#
+# 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.
+# ============================================================================
+"""Profiler error code and messages."""
+from enum import unique, Enum
+
+
+_GENERAL_MASK = 0b00001 << 7
+_PARSER_MASK = 0b00010 << 7
+_ANALYSER_MASK = 0b00011 << 7
+
+
+class ProfilerMgrErrors(Enum):
+    """Enum definition for profiler errors"""
+
+@unique
+class ProfilerErrors(ProfilerMgrErrors):
+    """Profiler error codes."""
+    # general error code
+    PARAM_VALUE_ERROR = 0 | _GENERAL_MASK
+    PATH_ERROR = 1 | _GENERAL_MASK
+    PARAM_TYPE_ERROR = 2 | _GENERAL_MASK
+    DIR_NOT_FOUND_ERROR = 3 | _GENERAL_MASK
+    FILE_NOT_FOUND_ERROR = 4 | _GENERAL_MASK
+    IO_ERROR = 5 | _GENERAL_MASK
+
+    # parser error code
+    DEVICE_ID_MISMATCH_ERROR = 0 | _PARSER_MASK
+    RAW_FILE_ERROR = 1 | _PARSER_MASK
+    STEP_NUM_NOT_SUPPORTED_ERROR = 2 | _PARSER_MASK
+    JOB_ID_MISMATCH_ERROR = 3 | _PARSER_MASK
+
+    # analyser error code
+    COLUMN_NOT_EXIST_ERROR = 0 | _ANALYSER_MASK
+    ANALYSER_NOT_EXIST_ERROR = 1 | _ANALYSER_MASK
+    DEVICE_ID_ERROR = 2 | _ANALYSER_MASK
+    OP_TYPE_ERROR = 3 | _ANALYSER_MASK
+    GROUP_CONDITION_ERROR = 4 | _ANALYSER_MASK
+    SORT_CONDITION_ERROR = 5 | _ANALYSER_MASK
+    FILTER_CONDITION_ERROR = 6 | _ANALYSER_MASK
+    COLUMN_NOT_SUPPORT_SORT_ERROR = 7 | _ANALYSER_MASK
+    PIPELINE_OP_NOT_EXIST_ERROR = 8 | _ANALYSER_MASK
+
+
+
+
+@unique
+class ProfilerErrorMsg(Enum):
+    """Profiler error messages."""
+    # general error msg
+    PARAM_VALUE_ERROR = 'Param value error. {}'
+    PATH_ERROR = 'Path error. {}'
+    PARAM_TYPE_ERROR = 'Param type error. {}'
+    DIR_NOT_FOUND_ERROR = 'The dir <{}> not found.'
+    FILE_NOT_FOUND_ERROR = 'The file <{}> not found.'
+    IO_ERROR = 'Read or write file fail.'
+
+    # parser error msg
+    DEVICE_ID_MISMATCH_ERROR = 'The device ID mismatch.'
+    RAW_FILE_ERROR = 'Raw file error. {}'
+    STEP_NUM_NOT_SUPPORTED_ERROR = 'The step num must be in {}'
+    JOB_ID_MISMATCH_ERROR = 'The job id in the parameter is not the same as ' \
+                            'in the training trace file. '
+
+    # analyser error msg
+    COLUMN_NOT_EXIST_ERROR = 'The column {} does not exist.'
+    ANALYSER_NOT_EXIST_ERROR = 'The analyser {} does not exist.'
+    DEIVICE_ID_ERROR = 'The device_id in search_condition error, {}'
+    FILTER_CONDITION_ERROR = 'The filter_condition in search_condition error, {}'
+    OP_TYPE_ERROR = 'The op_type in search_condition error, {}'
+    GROUP_CONDITION_ERROR = 'The group_condition in search_condition error, {}'
+    SORT_CONDITION_ERROR = 'The sort_condition in search_condition error, {}'
+    COLUMN_NOT_SUPPORT_SORT_ERROR = 'The column {} does not support to sort.'
+    PIPELINE_OP_NOT_EXIST_ERROR = 'The minddata pipeline operator {} does not exist.'

mindspore/profiler/common/exceptions/exceptions.py

+# 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
+#
+# 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.
+# ============================================================================
+"""Definition of error code and relative messages in profiler module."""
+from mindspore.profiler.common.exceptions.error_code import ProfilerErrors, \
+    ProfilerErrorMsg
+
+
+class ProfilerException(Exception):
+    """
+    Base class for Profilier exception.
+
+    Examples:
+        >>> raise ProfilerException(GeneralErrors.PATH_NOT_EXISTS_ERROR, 'path not exists')
+    """
+
+    RUNTIME = 1
+    TYPE = 1
+    LEVEL = 0
+    SYSID = 42
+
+    def __init__(self, error, message, http_code=500):
+        """
+        Initialization of ProfilerException.
+
+        Args:
+            error (Enum): Error value for specified case.
+            message (str): Description for exception.
+            http_code (int): Http code for exception. Default is 500.
+        """
+        if isinstance(message, str):
+            message = ' '.join(message.split())
+        super(ProfilerException, self).__init__(message)
+        self.error = error
+        self.message = message
+        self.http_code = http_code
+
+
+    @property
+    def error_code(self):
+        """
+        Transform exception no to Profiler error code.
+
+        code compose(4bytes):
+        runtime 2bits, type 2bits, level 3bits, sysid 8bits, modid 5bits, value 12bits.
+
+        num = ((0xFF & runtime) << 30) \
+                | ((0xFF & type) << 28) \
+                | ((0xFF & level) << 25) \
+                | ((0xFF & sysid) << 17) \
+                | ((0xFF & modid) << 12) \
+                | (0x0FFF & value)
+
+        Returns:
+            str, Hex string representing the composed Profiler error code.
+        """
+        num = (((0xFF & self.RUNTIME) << 30)
+               | ((0xFF & self.TYPE) << 28)
+               | ((0xFF & self.LEVEL) << 25)
+               | ((0xFF & self.SYSID) << 17)
+               | ((0xFF & 6) << 12)
+               | (0x0FFF & self.error.value))
+
+        return hex(num)[2:].zfill(8).upper()
+
+    def __str__(self):
+        return '[{}] code: {}, msg: {}'.format(self.__class__.__name__, self.error_code, self.message)
+
+
+class ProfilerParamValueErrorException(ProfilerException):
+    """The parameter value error in profiler module."""
+
+    def __init__(self, msg):
+        super(ProfilerParamValueErrorException, self).__init__(
+            error=ProfilerErrors.PARAM_VALUE_ERROR,
+            message=ProfilerErrorMsg.PARAM_VALUE_ERROR.value.format(msg),
+            http_code=400
+        )
+
+
+class ProfilerPathErrorException(ProfilerException):
+    """The path error in profiler module."""
+
+    def __init__(self, msg):
+        super(ProfilerPathErrorException, self).__init__(
+            error=ProfilerErrors.PATH_ERROR,
+            message=ProfilerErrorMsg.PATH_ERROR.value.format(msg),
+            http_code=400
+        )
+
+
+class ProfilerParamTypeErrorException(ProfilerException):
+    """The parameter type error in profiler module."""
+
+    def __init__(self, msg):
+        super(ProfilerParamTypeErrorException, self).__init__(
+            error=ProfilerErrors.PARAM_TYPE_ERROR,
+            message=ProfilerErrorMsg.PARAM_TYPE_ERROR.value.format(msg),
+            http_code=400
+        )
+
+
+class ProfilerDirNotFoundException(ProfilerException):
+    """The dir not found exception in profiler module."""
+
+    def __init__(self, msg):
+        super(ProfilerDirNotFoundException, self).__init__(
+            error=ProfilerErrors.DIR_NOT_FOUND_ERROR,
+            message=ProfilerErrorMsg.DIR_NOT_FOUND_ERROR.value.format(msg),
+            http_code=400
+        )
+
+
+class ProfilerFileNotFoundException(ProfilerException):
+    """The file not found exception in profiler module."""
+
+    def __init__(self, msg):
+        super(ProfilerFileNotFoundException, self).__init__(
+            error=ProfilerErrors.FILE_NOT_FOUND_ERROR,
+            message=ProfilerErrorMsg.FILE_NOT_FOUND_ERROR.value.format(msg),
+            http_code=400
+        )
+
+
+class ProfilerIOException(ProfilerException):
+    """The IO exception in profiler module."""
+
+    def __init__(self):
+        super(ProfilerIOException, self).__init__(
+            error=ProfilerErrors.IO_ERROR,
+            message=ProfilerErrorMsg.IO_ERROR.value,
+            http_code=400
+        )
+
+
+class ProfilerDeviceIdMismatchException(ProfilerException):
+    """The device id mismatch exception in profiler module."""
+
+    def __init__(self):
+        super(ProfilerDeviceIdMismatchException, self).__init__(
+            error=ProfilerErrors.DEVICE_ID_MISMATCH_ERROR,
+            message=ProfilerErrorMsg.DEVICE_ID_MISMATCH_ERROR.value,
+            http_code=400
+        )
+
+
+class ProfilerRawFileException(ProfilerException):
+    """The raw file exception in profiler module."""
+
+    def __init__(self, msg):
+        super(ProfilerRawFileException, self).__init__(
+            error=ProfilerErrors.RAW_FILE_ERROR,
+            message=ProfilerErrorMsg.RAW_FILE_ERROR.value.format(msg),
+            http_code=400
+        )
+
+
+class ProfilerColumnNotExistException(ProfilerException):
+    """The column does not exist exception in profiler module."""
+
+    def __init__(self, msg):
+        super(ProfilerColumnNotExistException, self).__init__(
+            error=ProfilerErrors.COLUMN_NOT_EXIST_ERROR,
+            message=ProfilerErrorMsg.COLUMN_NOT_EXIST_ERROR.value.format(msg),
+            http_code=400
+        )
+
+
+class ProfilerAnalyserNotExistException(ProfilerException):
+    """The analyser in profiler module."""
+
+    def __init__(self, msg):
+        super(ProfilerAnalyserNotExistException, self).__init__(
+            error=ProfilerErrors.ANALYSER_NOT_EXIST_ERROR,
+            message=ProfilerErrorMsg.ANALYSER_NOT_EXIST_ERROR.value.format(msg),
+            http_code=400
+        )
+
+
+class ProfilerDeviceIdException(ProfilerException):
+    """The parameter device_id error in profiler module."""
+
+    def __init__(self, msg):
+        super(ProfilerDeviceIdException, self).__init__(
+            error=ProfilerErrors.DEVICE_ID_ERROR,
+            message=ProfilerErrorMsg.DEIVICE_ID_ERROR.value.format(msg),
+            http_code=400
+        )
+
+
+class ProfilerOpTypeException(ProfilerException):
+    """The parameter op_type error in profiler module."""
+
+    def __init__(self, msg):
+        super(ProfilerOpTypeException, self).__init__(
+            error=ProfilerErrors.OP_TYPE_ERROR,
+            message=ProfilerErrorMsg.OP_TYPE_ERROR.value.format(msg),
+            http_code=400
+        )
+
+
+class ProfilerSortConditionException(ProfilerException):
+    """The parameter sort_condition error in profiler module."""
+
+    def __init__(self, msg):
+        super(ProfilerSortConditionException, self).__init__(
+            error=ProfilerErrors.SORT_CONDITION_ERROR,
+            message=ProfilerErrorMsg.SORT_CONDITION_ERROR.value.format(msg),
+            http_code=400
+        )
+
+
+class ProfilerFilterConditionException(ProfilerException):
+    """The parameter filer_condition error in profiler module."""
+
+    def __init__(self, msg):
+        super(ProfilerFilterConditionException, self).__init__(
+            error=ProfilerErrors.FILTER_CONDITION_ERROR,
+            message=ProfilerErrorMsg.FILTER_CONDITION_ERROR.value.format(msg),
+            http_code=400
+        )
+
+
+class ProfilerGroupConditionException(ProfilerException):
+    """The parameter group_condition error in profiler module."""
+
+    def __init__(self, msg):
+        super(ProfilerGroupConditionException, self).__init__(
+            error=ProfilerErrors.GROUP_CONDITION_ERROR,
+            message=ProfilerErrorMsg.GROUP_CONDITION_ERROR.value.format(msg),
+            http_code=400
+        )
+
+
+class ProfilerColumnNotSupportSortException(ProfilerException):
+    """The column does not support to sort error in profiler module."""
+
+    def __init__(self, msg):
+        super(ProfilerColumnNotSupportSortException, self).__init__(
+            error=ProfilerErrors.COLUMN_NOT_SUPPORT_SORT_ERROR,
+            message=ProfilerErrorMsg.COLUMN_NOT_SUPPORT_SORT_ERROR.value.format(msg),
+            http_code=400
+        )
+
+
+class StepNumNotSupportedException(ProfilerException):
+    """The step number error in profiler module."""
+
+    def __init__(self, msg):
+        super(StepNumNotSupportedException, self).__init__(
+            error=ProfilerErrors.STEP_NUM_NOT_SUPPORTED_ERROR,
+            message=ProfilerErrorMsg.STEP_NUM_NOT_SUPPORTED_ERROR.value.format(msg),
+            http_code=400
+        )
+
+
+class JobIdMismatchException(ProfilerException):
+    """The Job ID mismatch error in profiler module."""
+
+    def __init__(self):
+        super(JobIdMismatchException, self).__init__(
+            error=ProfilerErrors.JOB_ID_MISMATCH_ERROR,
+            message=ProfilerErrorMsg.JOB_ID_MISMATCH_ERROR.value,
+            http_code=400
+        )
+
+
+class ProfilerPipelineOpNotExistException(ProfilerException):
+    """The minddata pipeline operator does not exist error in profiler module."""
+
+    def __init__(self, msg):
+        super(ProfilerPipelineOpNotExistException, self).__init__(
+            error=ProfilerErrors.PIPELINE_OP_NOT_EXIST_ERROR,
+            message=ProfilerErrorMsg.PIPELINE_OP_NOT_EXIST_ERROR.value.format(msg),
+            http_code=400
+        )

mindspore/profiler/common/util.py

+# 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
+#
+# 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.
+# ============================================================================
+"""
+Profiler util.
+
+This module provides the utils.
+"""
+import os
+
+
+# one sys count takes 10 ns, 1 ms has 100000 system count
+import re
+
+PER_MS_SYSCNT = 100000
+
+
+def to_int(param, param_name):
+    """
+    Transfer param to int type.
+
+    Args:
+        param (Any): A param transformed.
+        param_name (str): Param name.
+
+    Returns:
+        int, value after transformed.
+
+    """
+    try:
+        param = int(param)
+    except ValueError:
+        raise TypeError('Must be Integer: ' + param_name)
+    return param
+
+
+def fwrite_format(output_data_path, data_source=None, is_print=False, is_start=False):
+    """
+    Write data to the output file.
+
+    Args:
+         output_data_path (str): The output file path of the data.
+         data_source (str, list, tuple): The data to write.
+         is_print (bool): whether to print the data to stdout.
+         is_start (bool): Whether is the first line of the output file, will remove the old file if True."
+    """
+
+    if is_start is True and os.path.exists(output_data_path):
+        os.remove(output_data_path)
+
+    if isinstance(data_source, str) and data_source.startswith("title:"):
+        title_label = '=' * 20
+        data_source = title_label + data_source[6:] + title_label
+
+    with open(output_data_path, 'a+') as f:
+        if isinstance(data_source, (list, tuple)):
+            for raw_data in data_source:
+                if isinstance(raw_data, (list, tuple)):
+                    raw_data = map(str, raw_data)
+                    raw_data = " ".join(raw_data)
+                f.write(raw_data)
+                f.write("\n")
+        else:
+            f.write(data_source)
+            f.write("\n")
+
+    if is_print:
+        if isinstance(data_source, (list, tuple)):
+            for raw_data in data_source:
+                if isinstance(raw_data, (list, tuple)):
+                    raw_data = map(str, raw_data)
+                    raw_data = " ".join(raw_data)
+                print(raw_data)
+        else:
+            print(data_source)
+
+
+def get_log_slice_id(file_name):
+    pattern = re.compile(r'(?<=slice_)\d+')
+    slice_list = pattern.findall(file_name)
+    index = re.findall(r'\d+', slice_list[0])
+    return int(index[0])
+
+
+def get_file_join_name(input_path, file_name):
+    """
+    Search files under the special path, and will join all the files to one file.
+
+    Args:
+        input_path (str): The source path, will search files under it.
+        file_name (str): The target of the filename, such as 'hwts.log.data.45.dev'.
+
+    Returns:
+        str, the join file name.
+    """
+    name_list = []
+    file_join_name = ''
+    input_path = os.path.realpath(input_path)
+    if os.path.exists(input_path):
+        files = os.listdir(input_path)
+        for f in files:
+            if file_name in f and not f.endswith('.done') and not f.endswith('.join') \
+                    and not f.endswith('.zip'):
+                name_list.append(f)
+
+        # resort name_list
+        name_list.sort(key=get_log_slice_id)
+
+    if len(name_list) == 1:
+        file_join_name = os.path.join(input_path, name_list[0])
+    elif len(name_list) > 1:
+        file_join_name = os.path.join(input_path, '%s.join' % file_name)
+        if os.path.exists(file_join_name):
+            os.remove(file_join_name)
+        with open(file_join_name, 'ab') as bin_data:
+            for i in name_list:
+                file = input_path + os.sep + i
+                with open(file, 'rb') as txt:
+                    bin_data.write(txt.read())
+    return file_join_name
+
+def get_file_names(input_path, file_name):
+    """
+    Search files under the special path.
+
+    Args:
+        input_path (str): The source path, will search files under it.
+        file_name (str): The target of the filename, such as 'host_start_log'.
+
+    Returns:
+        list, file name list.
+    """
+
+    input_path = os.path.realpath(input_path)
+    name_list = []
+    if os.path.exists(input_path):
+        files = os.listdir(input_path)
+        for f in files:
+            if file_name in f and not f.endswith('.done') \
+                    and not f.endswith('.zip'):
+                name_list.append(f)
+                break
+
+    return name_list
+
+
+def analyse_device_list_from_profiler_dir(profiler_dir):
+    """
+    Analyse device list from profiler dir.
+
+    Args:
+        profiler_dir (str): The profiler data dir.
+
+    Returns:
+        list, the device_id list.
+    """
+    profiler_file_prefix = ["timeline_display", "output_op_compute_time"]
+
+    device_id_list = set()
+    for _, _, filenames in os.walk(profiler_dir):
+        for filename in filenames:
+            if filename.startswith("step_trace_raw"):
+                items = filename.split("_")
+                device_num = ""
+                if len(items) > 3:
+                    device_num = items[3]
+            else:
+                items = filename.split("_")
+                device_num = items[-1].split(".")[0] if items[-1].split(".") else ""
+
+            if device_num.isdigit() and '_'.join(items[:-1]) in profiler_file_prefix:
+                device_id_list.add(device_num)
+
+    return sorted(list(device_id_list))
+
+
+def query_latest_trace_time_file(profiler_dir, device_id=0):
+    """
+    Query the latest trace time file.
+
+    Args:
+        profiler_dir (str): The profiler directory.
+        device_id (int): The id of device.
+
+    Returns:
+        str, the latest trace time file path.
+    """
+    files = os.listdir(profiler_dir)
+    target_file = f'step_trace_raw_{device_id}_detail_time.csv'
+    try:
+        latest_file = max(
+            filter(
+                lambda file: file == target_file,
+                files
+            ),
+            key=lambda file: os.stat(os.path.join(profiler_dir, file)).st_mtime
+        )
+    except ValueError:
+        return None
+    return os.path.join(profiler_dir, latest_file)
+
+
+def query_step_trace_file(profiler_dir):
+    """
+    Query for all step trace file.
+
+    Args:
+        profiler_dir (str): The directory that contains all step trace files.
+
+    Returns:
+        str, the file path of step trace time.
+    """
+    files = os.listdir(profiler_dir)
+    training_trace_file = list(
+        filter(
+            lambda file: file.startswith('training_trace') and not file.endswith('.done'),
+            files
+        )
+    )
+    if training_trace_file:
+        return os.path.join(profiler_dir, training_trace_file[0])
+    return None
+
+
+def get_summary_for_step_trace(average_info, header):
+    """The property of summary info."""
+    if not average_info or not header:
+        return {}
+    total_time = get_field_value(average_info, 'total', header)
+    iteration_interval = get_field_value(average_info, 'iteration_interval',
+                                         header)
+    fp_and_bp = get_field_value(average_info, 'fp_and_bp', header)
+    tail = get_field_value(average_info, 'tail', header)
+    summary = {
+        'total_time': total_time,
+        'iteration_interval': iteration_interval,
+        'iteration_interval_percent': calculate_percent(iteration_interval, total_time),
+        'fp_and_bp': fp_and_bp,
+        'fp_and_bp_percent': calculate_percent(fp_and_bp, total_time),
+        'tail': tail,
+        'tail_percent': calculate_percent(tail, total_time)
+    }
+    return summary
+
+
+def calculate_percent(partial, total):
+    """Calculate percent value."""
+    if total:
+        percent = round(partial / total * 100, 2)
+    else:
+        percent = 0
+    return f'{percent}%'
+
+
+def to_millisecond(sys_count, limit=4):
+    """Translate system count to millisecond."""
+    return round(sys_count / PER_MS_SYSCNT, limit)
+
+
+def get_field_value(row_info, field_name, header, time_type='realtime'):
+    """
+    Extract basic info through row_info.
+
+    Args:
+        row_info (list): The list of data info in one row.
+        field_name (str): The name in header.
+        header (list[str]): The list of field names.
+        time_type (str): The type of value, `realtime` or `systime`. Default: `realtime`.
+
+    Returns:
+        dict, step trace info in dict format.
+    """
+    field_index = header.index(field_name)
+    value = row_info[field_index]
+    value = to_int(value, field_name)
+    if time_type == 'realtime':
+        value = to_millisecond(value)
+
+    return value
+
+def get_options(options):
+    if options is None:
+        options = {}
+    return options

mindspore/profiler/common/validator/__init__.py

+# 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
+#
+# 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.
+# ============================================================================

mindspore/profiler/common/validator/checkparam.py

+# 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
+#
+# 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.
+# ============================================================================
+"""Profiler check parameters."""
+def check_bool(input_param, param_name):
+    """Bool type judgment."""
+    if isinstance(input_param, bool):
+        return input_param
+    raise TypeError("Parameter {}: input type must be bool!".format(param_name))
+
+def check_subgraph(subgraph):
+    """Check subgraph."""
+    if subgraph in ("all", "Default", "Gradients"):
+        return subgraph
+    raise ValueError("subgraph must be all or Default or Gradients, but got {}.".format(subgraph))

mindspore/profiler/common/validator/validate.py

+# 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
+#
+# 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.
+# ============================================================================
+"""Validate the profiler parameters."""
+import os
+import sys
+
+
+from mindspore.profiler.common.exceptions.exceptions import ProfilerParamTypeErrorException, \
+    ProfilerDeviceIdException, ProfilerOpTypeException, \
+    ProfilerSortConditionException, ProfilerFilterConditionException, \
+    ProfilerGroupConditionException, ProfilerParamValueErrorException
+from mindspore import log
+from mindspore.profiler.common.util import to_int
+
+AICORE_TYPE_COL = ["op_type", "execution_time", "execution_frequency", "precent"]
+AICORE_DETAIL_COL = ["op_name", "op_type", "avg_execution_time", "subgraph", "full_op_name"]
+AICPU_COL = ["serial_number", "op_type", "total_time", "dispatch_time", "run_start",
+             "run_end"]
+MINDDATA_PIPELINE_COL = [
+    'op_id', 'op_type', 'num_workers', 'output_queue_average_size',
+    'output_queue_length', 'output_queue_usage_rate', 'sample_interval',
+    'parent_id'
+]
+
+
+def validate_condition(search_condition):
+    """
+    Verify the param in search_condition is valid or not.
+
+    Args:
+        search_condition (dict): The search condition.
+
+    Raises:
+        ProfilerParamTypeErrorException: If the type of the param in search_condition is invalid.
+        ProfilerDeviceIdException: If the device_id param in search_condition is invalid.
+        ProfilerOpTypeException: If the op_type param in search_condition is invalid.
+        ProfilerGroupConditionException: If the group_condition param in search_condition is invalid.
+        ProfilerSortConditionException: If the sort_condition param in search_condition is invalid.
+        ProfilerFilterConditionException: If the filter_condition param in search_condition is invalid.
+    """
+    if not isinstance(search_condition, dict):
+        log.error("Invalid search_condition type, it should be dict.")
+        raise ProfilerParamTypeErrorException(
+            "Invalid search_condition type, it should be dict.")
+
+    if "device_id" in search_condition:
+        device_id = search_condition.get("device_id")
+        if not isinstance(device_id, str):
+            raise ProfilerDeviceIdException("Invalid device_id type, it should be str.")
+
+    if "op_type" in search_condition:
+        op_type = search_condition.get("op_type")
+        if op_type == "aicpu":
+            search_scope = AICPU_COL
+        elif op_type == "aicore_type":
+            search_scope = AICORE_TYPE_COL
+        elif op_type == "aicore_detail":
+            search_scope = AICORE_DETAIL_COL
+        else:
+            raise ProfilerOpTypeException("The op_type must in ['aicpu', 'aicore_type', 'aicore_detail']")
+    else:
+        raise ProfilerOpTypeException("The op_type must in ['aicpu', 'aicore_type', 'aicore_detail']")
+
+    if "group_condition" in search_condition:
+        validate_group_condition(search_condition)
+
+    if "sort_condition" in search_condition:
+        validate_sort_condition(search_condition, search_scope)
+
+    if "filter_condition" in search_condition:
+        validate_filter_condition(search_condition)
+
+
+def validate_group_condition(search_condition):
+    """
+    Verify the group_condition in search_condition is valid or not.
+
+    Args:
+        search_condition (dict): The search condition.
+
+    Raises:
+        ProfilerGroupConditionException: If the group_condition param in search_condition is invalid.
+    """
+    group_condition = search_condition.get("group_condition")
+    if not isinstance(group_condition, dict):
+        raise ProfilerGroupConditionException("The group condition must be dict.")
+    if "limit" in group_condition:
+        limit = group_condition.get("limit", 10)
+        if isinstance(limit, bool) \
+                or not isinstance(group_condition.get("limit"), int):
+            log.error("The limit must be int.")
+            raise ProfilerGroupConditionException("The limit must be int.")
+        if limit < 1 or limit > 100:
+            raise ProfilerGroupConditionException("The limit must in [1, 100].")
+
+    if "offset" in group_condition:
+        offset = group_condition.get("offset", 0)
+        if isinstance(offset, bool) \
+                or not isinstance(group_condition.get("offset"), int):
+            log.error("The offset must be int.")
+            raise ProfilerGroupConditionException("The offset must be int.")
+        if offset < 0:
+            raise ProfilerGroupConditionException("The offset must ge 0.")
+
+        if offset > 1000000:
+            raise ProfilerGroupConditionException("The offset must le 1000000.")
+
+
+def validate_sort_condition(search_condition, search_scope):
+    """
+    Verify the sort_condition in search_condition is valid or not.
+
+    Args:
+        search_condition (dict): The search condition.
+        search_scope (list): The search scope.
+
+    Raises:
+        ProfilerSortConditionException: If the sort_condition param in search_condition is invalid.
+    """
+    sort_condition = search_condition.get("sort_condition")
+    if not isinstance(sort_condition, dict):
+        raise ProfilerSortConditionException("The sort condition must be dict.")
+    if "name" in sort_condition:
+        sorted_name = sort_condition.get("name", "")
+        err_msg = "The sorted_name must be in {}".format(search_scope)
+        if not isinstance(sorted_name, str):
+            log.error("Wrong sorted name type.")
+            raise ProfilerSortConditionException("Wrong sorted name type.")
+        if sorted_name not in search_scope:
+            log.error(err_msg)
+            raise ProfilerSortConditionException(err_msg)
+
+    if "type" in sort_condition:
+        sorted_type_param = ['ascending', 'descending']
+        sorted_type = sort_condition.get("type")
+        if sorted_type and sorted_type not in sorted_type_param:
+            err_msg = "The sorted type must be ascending or descending."
+            log.error(err_msg)
+            raise ProfilerSortConditionException(err_msg)
+
+
+def validate_op_filter_condition(op_condition, value_type=str, value_type_msg='str'):
+    """
+    Verify the op_condition in filter_condition is valid or not.
+
+    Args:
+        op_condition (dict): The op_condition in search_condition.
+        value_type (type): The value type. Default: str.
+        value_type_msg (str): The value type message. Default: 'str'.
+
+    Raises:
+        ProfilerFilterConditionException: If the filter_condition param in search_condition is invalid.
+    """
+    filter_key = ["in", "not_in", "partial_match_str_in"]
+    if not isinstance(op_condition, dict):
+        raise ProfilerFilterConditionException("The filter condition value must be dict.")
+    for key, value in op_condition.items():
+        if not isinstance(key, str):
+            raise ProfilerFilterConditionException("The filter key must be str")
+        if not isinstance(value, list):
+            raise ProfilerFilterConditionException("The filter value must be list")
+        if key not in filter_key:
+            raise ProfilerFilterConditionException("The filter key must in {}.".format(filter_key))
+        for item in value:
+            if not isinstance(item, value_type):
+                raise ProfilerFilterConditionException(
+                    "The item in filter value must be {}.".format(value_type_msg)
+                )
+
+
+def validate_filter_condition(search_condition):
+    """
+    Verify the filter_condition in search_condition is valid or not.
+
+    Args:
+        search_condition (dict): The search condition.
+
+    Raises:
+        ProfilerFilterConditionException: If the filter_condition param in search_condition is invalid.
+    """
+    filter_condition = search_condition.get("filter_condition")
+    if not isinstance(filter_condition, dict):
+        raise ProfilerFilterConditionException("The filter condition must be dict.")
+    if filter_condition:
+        if "op_type" in filter_condition:
+            op_type_condition = filter_condition.get("op_type")
+            validate_op_filter_condition(op_type_condition)
+        if "op_name" in filter_condition:
+            op_name_condition = filter_condition.get("op_name")
+            validate_op_filter_condition(op_name_condition)
+        if "op_type" not in filter_condition and "op_name" not in filter_condition:
+            raise ProfilerFilterConditionException("The key of filter_condition is not support")
+
+
+def validate_and_set_job_id_env(job_id_env):
+    """
+    Validate the job id and set it in environment.
+
+    Args:
+        job_id_env (str): The id that to be set in environment parameter `JOB_ID`.
+
+    Returns:
+        int, the valid job id env.
+    """
+    if job_id_env is None:
+        return job_id_env
+    # get job_id_env in int type
+    valid_id = to_int(job_id_env, 'job_id_env')
+    # check the range of valid_id
+    if valid_id and 255 < valid_id < sys.maxsize:
+        os.environ['JOB_ID'] = job_id_env
+    else:
+        log.warning("Invalid job_id_env %s. The value should be int and between 255 and %s. Use"
+                    "default job id env instead.",
+                    job_id_env, sys.maxsize)
+    return valid_id
+
+
+def validate_ui_proc(proc_name):
+    """
+    Validate proc name in restful request.
+
+    Args:
+        proc_name (str): The proc name to query. Acceptable value is in
+        [`iteration_interval`, `fp_and_bp`, `tail`].
+
+    Raises:
+        ProfilerParamValueErrorException: If the proc_name is invalid.
+    """
+    accept_names = ['iteration_interval', 'fp_and_bp', 'tail']
+    if proc_name not in accept_names:
+        log.error("Invalid proc_name. The proc_name for restful api is in %s", accept_names)
+        raise ProfilerParamValueErrorException(f'proc_name should be in {accept_names}.')
+
+
+def validate_minddata_pipeline_condition(condition):
+    """
+    Verify the minddata pipeline search condition is valid or not.
+
+    Args:
+        condition (dict): The minddata pipeline search condition.
+
+    Raises:
+        ProfilerParamTypeErrorException: If the type of the search condition is
+            invalid.
+        ProfilerDeviceIdException: If the device_id param in the search
+            condition is invalid.
+        ProfilerGroupConditionException: If the group_condition param in the
+            search condition is invalid.
+        ProfilerSortConditionException: If the sort_condition param in the
+            search condition is invalid.
+        ProfilerFilterConditionException: If the filter_condition param in the
+            search condition is invalid.
+    """
+    if not isinstance(condition, dict):
+        log.error("Invalid condition type, it should be dict.")
+        raise ProfilerParamTypeErrorException(
+            "Invalid condition type, it should be dict."
+        )
+
+    if "device_id" in condition:
+        device_id = condition.get("device_id")
+        if not isinstance(device_id, str):
+            raise ProfilerDeviceIdException(
+                "Invalid device_id type, it should be str."
+            )
+
+    if "group_condition" in condition:
+        validate_group_condition(condition)
+
+    if "sort_condition" in condition:
+        validate_sort_condition(condition, MINDDATA_PIPELINE_COL)
+
+    if "filter_condition" in condition:
+        filter_condition = condition.get('filter_condition')
+        if not isinstance(filter_condition, dict):
+            raise ProfilerFilterConditionException(
+                "The filter condition must be dict."
+            )
+        for key, value in filter_condition.items():
+            if key == 'op_id':
+                validate_op_filter_condition(
+                    value, value_type=int, value_type_msg='int'
+                )
+            elif key == 'op_type':
+                validate_op_filter_condition(value)
+            elif key == 'is_display_op_detail':
+                if not isinstance(value, bool):
+                    raise ProfilerFilterConditionException(
+                        "The condition must be bool."
+                    )
+            else:
+                raise ProfilerFilterConditionException(
+                    "The key {} of filter_condition is not support.".format(key)
+                )

mindspore/profiler/common/validator/validate_path.py

+# Copyright 2019 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.
+# ============================================================================
+"""Validate the input path."""
+import os
+
+
+def validate_and_normalize_path(
+        path,
+        check_absolute_path=False,
+        allow_parent_dir=False,
+):
+    """
+    Validates path and returns its normalized form.
+
+    If path has a valid scheme, treat path as url, otherwise consider path a
+    unix local path.
+
+    Note:
+        File scheme (rfc8089) is currently not supported.
+
+    Args:
+        path (str): Path to be normalized.
+        check_absolute_path (bool): Whether check path scheme is supported.
+        allow_parent_dir (bool): Whether allow parent dir in path.
+
+    Returns:
+        str, normalized path.
+    """
+    if not path:
+        raise RuntimeError("The path is invalid!")
+
+    path_str = str(path)
+    if not allow_parent_dir:
+        path_components = path_str.split("/")
+        if ".." in path_components:
+            raise RuntimeError("The path is invalid!")
+
+    # path does not have valid schema, treat it as unix local path.
+    if check_absolute_path:
+        if not path_str.startswith("/"):
+            raise RuntimeError("The path is invalid!")
+    try:
+        # most unix systems allow
+        normalized_path = os.path.realpath(path)
+    except ValueError:
+        raise RuntimeError("The path is invalid!")
+
+    return normalized_path

mindspore/profiler/parser/__init__.py

+# 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
+#
+# 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.
+# ============================================================================

mindspore/profiler/parser/aicpu_data_parser.py

+# 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
+#
+# 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.
+# ============================================================================
+"""
+The parser for AI CPU preprocess data.
+"""
+import os
+
+from mindspore.profiler.common.util import fwrite_format, get_file_join_name
+from mindspore import log as logger
+
+
+class DataPreProcessParser:
+    """
+    The Parser for AI CPU preprocess data.
+
+    Args:
+         input_path(str): The profiling job path.
+         output_filename(str): The output data path and name.
+
+    """
+
+    _source_file_target = 'DATA_PREPROCESS.dev.AICPU.'
+    _dst_file_title = 'title:DATA_PREPROCESS AICPU'
+    _dst_file_column_title = ['serial_number', 'node_type_name', 'total_time(ms)',
+                              'dispatch_time(ms)', 'run_start', 'run_end']
+    _ms_unit = 1000
+
+    def __init__(self, input_path, output_filename):
+        self._input_path = input_path
+        self._output_filename = output_filename
+        self._source_file_name = self._get_source_file()
+        self._ms_kernel_flag = 3
+        self._other_kernel_flag = 6
+        self._thread_flag = 7
+        self._ms_kernel_run_end_index = 2
+        self._other_kernel_run_end_index = 5
+        self._result_list = []
+        self._min_cycle_counter = float('inf')
+
+    def _get_source_file(self):
+        """Get log file name, which was created by ada service."""
+        file_name = get_file_join_name(self._input_path, self._source_file_target)
+        if not file_name:
+            data_path = os.path.join(self._input_path, "data")
+            file_name = get_file_join_name(data_path, self._source_file_target)
+        return file_name
+
+    def _get_kernel_result(self, number, node_list, thread_list):
+        """Get the profiling data form different aicpu kernel"""
+        try:
+            if len(node_list) == self._ms_kernel_flag and len(thread_list) == self._thread_flag:
+                node_type_name = node_list[0].split(':')[-1]
+                run_end_index = self._ms_kernel_run_end_index
+            elif len(node_list) == self._other_kernel_flag and len(thread_list) == self._thread_flag:
+                node_type_name = node_list[0].split(':')[-1].split('/')[-1].split('-')[0]
+                run_end_index = self._other_kernel_run_end_index
+            else:
+                logger.warning("the data format can't support 'node_list':%s", str(node_list))
+                return None
+
+            run_start = node_list[1].split(':')[-1].split(' ')[0]
+            run_end = node_list[run_end_index].split(':')[-1].split(' ')[0]
+            total_time = float(thread_list[-1].split('=')[-1].split()[0]) / self._ms_unit
+            dispatch_time = float(thread_list[-2].split('=')[-1].split()[0]) / self._ms_unit
+
+            return [number, node_type_name, total_time, dispatch_time,
+                    run_start, run_end]
+        except IndexError as e:
+            logger.error(e)
+            return None
+
+    def execute(self):
+        """Execute the parser, get result data, and write it to the output file."""
+
+        if not os.path.exists(self._source_file_name):
+            logger.info("Did not find the aicpu profiling source file")
+            return
+
+        with open(self._source_file_name, 'rb') as ai_cpu_data:
+            ai_cpu_str = str(ai_cpu_data.read().replace(b'\n\x00', b' ___ ')
+                             .replace(b'\x00', b' ___ '))[2:-1]
+            ai_cpu_lines = ai_cpu_str.split(" ___ ")
+
+        result_list = list()
+        ai_cpu_total_time_summary = 0
+        # Node serial number.
+        serial_number = 1
+        for i in range(len(ai_cpu_lines) - 1):
+            node_line = ai_cpu_lines[i]
+            thread_line = ai_cpu_lines[i + 1]
+            if "Node" in node_line and "Thread" in thread_line:
+                # Get the node data from node_line
+                node_list = node_line.split(',')
+                thread_list = thread_line.split(',')
+                result = self._get_kernel_result(serial_number, node_list, thread_list)
+
+                if result is None:
+                    continue
+
+                result_list.append(result)
+                # Calculate the total time.
+                total_time = result[2]
+                ai_cpu_total_time_summary += total_time
+                # Increase node serial number.
+                serial_number += 1
+            elif "Node" in node_line and "Thread" not in thread_line:
+                node_type_name = node_line.split(',')[0].split(':')[-1]
+                logger.warning("The node type:%s cannot find thread data", node_type_name)
+
+        if result_list:
+            ai_cpu_total_time = format(ai_cpu_total_time_summary, '.6f')
+            result_list.append(["AI CPU Total Time(ms):", ai_cpu_total_time])
+            fwrite_format(self._output_filename, " ".join(self._dst_file_column_title), is_start=True, is_print=True)
+            fwrite_format(self._output_filename, result_list, is_print=True)
+
+        # For timeline display.
+        self._result_list = result_list
+
+    def query_aicpu_data(self):
+        """
+        Get execution time of AI CPU operator.
+
+        Returns:
+            a dict, the metadata of AI CPU operator execution time.
+        """
+        stream_id = 0  # Default stream id for AI CPU.
+        pid = 9000  # Default pid for AI CPU.
+        factor = 1000  # Convert time unit from 1us to 1ms
+        total_time = 0
+        min_cycle_counter = float('inf')
+        aicpu_info = []
+        op_count_list = []
+        for aicpu_item in self._result_list:
+            if "AI CPU Total Time(ms):" in aicpu_item:
+                total_time = aicpu_item[-1]
+                continue
+
+            op_name = aicpu_item[1]
+            start_time = float(aicpu_item[4]) / factor
+            min_cycle_counter = min(min_cycle_counter, start_time)
+            end_time = float(aicpu_item[5]) / factor
+            duration = end_time - start_time
+            aicpu_info.append([op_name, stream_id, start_time, duration, pid])
+
+            # Record the number of operator types.
+            if op_name not in op_count_list:
+                op_count_list.append(op_name)
+
+        self._min_cycle_counter = min_cycle_counter
+        aicpu_dict = {
+            'info': aicpu_info,
+            'total_time': float(total_time),
+            'op_exe_times': len(aicpu_info),
+            'num_of_ops': len(op_count_list),
+            'num_of_streams': 1
+        }
+
+        return aicpu_dict
+
+    @property
+    def min_cycle_counter(self):
+        """Get minimum cycle counter in AI CPU."""
+        return self._min_cycle_counter

mindspore/profiler/parser/container.py

+# 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
+#
+# 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.
+# ============================================================================
+"""The container of metadata used in profiler parser."""
+
+
+class HWTSContainer:
+    """
+    HWTS output container.
+
+    Args:
+        split_list (list): The split list of metadata in HWTS output file.
+    """
+    def __init__(self, split_list):
+        self._op_name = ''
+        self._duration = None
+        self._status = split_list[0]
+        self._task_id = split_list[6]
+        self._cycle_counter = float(split_list[7])
+        self._stream_id = split_list[8]
+
+    @property
+    def status(self):
+        """Get the status of the operator, i.e. Start or End."""
+        return self._status
+
+    @property
+    def task_id(self):
+        """Get the task id of the operator."""
+        return self._task_id
+
+    @property
+    def cycle_counter(self):
+        """Get the cycle counter."""
+        return self._cycle_counter
+
+    @property
+    def stream_id(self):
+        """Get the stream id of the operator."""
+        return self._stream_id
+
+    @property
+    def op_name(self):
+        """Get the name of the operator."""
+        return self._op_name
+
+    @op_name.setter
+    def op_name(self, name):
+        """Set the name of the operator."""
+        self._op_name = name
+
+    @property
+    def duration(self):
+        """Get the duration of the operator execution."""
+        return self._duration
+
+    @duration.setter
+    def duration(self, value):
+        """Set the duration of the operator execution."""
+        self._duration = value
+
+
+class TimelineContainer:
+    """
+    A container of operator computation metadata.
+
+    Args:
+        split_list (list): The split list of metadata in op_compute output file.
+    """
+    def __init__(self, split_list):
+        self._op_name = split_list[0]
+        self._stream_id = int(split_list[1])
+        self._start_time = float(split_list[2])
+        self._duration = float(split_list[3])
+        self._pid = None
+        if len(split_list) == 5:
+            self._pid = int(split_list[4])
+
+    @property
+    def op_name(self):
+        """Get the name of the operator."""
+        return self._op_name
+
+    @property
+    def stream_id(self):
+        """Get the stream id of the operator."""
+        return self._stream_id
+
+    @property
+    def start_time(self):
+        """Get the execution start time of the operator."""
+        return self._start_time
+
+    @property
+    def duration(self):
+        """Get the duration of the operator execution."""
+        return self._duration
+
+    @property
+    def pid(self):
+        """Get the pid of the operator execution."""
+        return self._pid

mindspore/profiler/parser/hwts_log_parser.py

+# 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
+#
+# 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.
+# ============================================================================
+"""The parser for hwts log file."""
+import os
+import struct
+from mindspore.profiler.common.util import fwrite_format, get_file_join_name
+from mindspore import log as logger
+
+
+class HWTSLogParser:
+    """
+    The Parser for hwts log files.
+
+    Args:
+         input_path (str): The profiling job path. Such as: '/var/log/npu/profiling/JOBAIFGJEJFEDCBAEADIFJAAAAAAAAAA".
+         output_filename (str): The output data path and name. Such as: './output_format_data_hwts_0.txt'.
+    """
+
+    _source_file_target = 'hwts.log.data.45.dev.profiler_default_tag'
+    _dst_file_title = 'title:45 HWTS data'
+    _dst_file_column_title = 'Type           cnt  Core_ID  Block_ID  Task_ID  Cycle_counter   Stream_ID'
+
+    def __init__(self, input_path, output_filename):
+        self._input_path = input_path
+        self._output_filename = output_filename
+        self._source_flie_name = self._get_source_file()
+
+    def _get_source_file(self):
+        """Get hwts log file name, which was created by ada service."""
+
+        file_name = get_file_join_name(self._input_path, self._source_file_target)
+        if not file_name:
+            data_path = os.path.join(self._input_path, "data")
+            file_name = get_file_join_name(data_path, self._source_file_target)
+            if not file_name:
+                msg = "Fail to find hwts log file, under profiling directory"
+                raise RuntimeError(msg)
+
+        return file_name
+
+    def execute(self):
+        """
+        Execute the parser, get result data, and write it to the output file.
+
+        Returns:
+            bool, whether succeed to analyse hwts log.
+        """
+
+        content_format = ['QIIIIIIIIIIII', 'QIIQIIIIIIII', 'IIIIQIIIIIIII']
+        log_type = ['Start of task', 'End of task', 'Start of block', 'End of block', 'Block PMU']
+
+        result_data = ""
+
+        with open(self._source_flie_name, 'rb') as hwts_data:
+            while True:
+                line = hwts_data.read(64)
+                if line:
+                    if not line.strip():
+                        continue
+                else:
+                    break
+                byte_first_four = struct.unpack('BBHHH', line[0:8])
+                byte_first = bin(byte_first_four[0]).replace('0b', '').zfill(8)
+                ms_type = byte_first[-3:]
+                is_warn_res0_ov = byte_first[4]
+                cnt = int(byte_first[0:4], 2)
+                core_id = byte_first_four[1]
+                blk_id, task_id = byte_first_four[3], byte_first_four[4]
+                if ms_type in ['000', '001', '010']:  # log type 0,1,2
+                    result = struct.unpack(content_format[0], line[8:])
+                    syscnt = result[0]
+                    stream_id = result[1]
+                elif ms_type == '011':  # log type 3
+                    result = struct.unpack(content_format[1], line[8:])
+                    syscnt = result[0]
+                    stream_id = result[1]
+                elif ms_type == '100':  # log type 4
+                    result = struct.unpack(content_format[2], line[8:])
+                    stream_id = result[2]
+                    if is_warn_res0_ov == '0':
+                        syscnt = result[4]
+                    else:
+                        syscnt = None
+                else:
+                    logger.info("Profiling: invalid hwts log record type %s", ms_type)
+                    continue
+
+                if int(task_id) < 25000:
+                    task_id = str(stream_id) + "_" + str(task_id)
+                result_data += ("%-14s %-4s %-8s %-9s %-8s %-15s %s\n" %(log_type[int(ms_type, 2)], cnt, core_id,
+                                                                         blk_id, task_id, syscnt, stream_id))
+
+        fwrite_format(self._output_filename, data_source=self._dst_file_title, is_start=True)
+        fwrite_format(self._output_filename, data_source=self._dst_file_column_title)
+        fwrite_format(self._output_filename, data_source=result_data)
+
+        return True

mindspore/profiler/parser/minddata_parser.py

+# 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
+#
+# 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.
+# ============================================================================
+"""Minddata aicpu parser."""
+import os
+
+from mindspore.profiler.common.util import get_file_join_name, fwrite_format
+from mindspore import log as logger
+
+
+class MinddataParser:
+    """Minddata Aicpu Parser."""
+    @staticmethod
+    def parse_minddata_aicpu_data(minddata_aicpu_source_path):
+        """
+        Parse minddata get_next info which contains queue size and execute time.
+
+        Args:
+            minddata_aicpu_source_path (str): the source file path.
+
+        Returns:
+            list[Union[str, float]], the converted data.
+        """
+        result = list()
+        try:
+            with open(minddata_aicpu_source_path) as source_data_file:
+                source_data = source_data_file.read()
+                step_data = source_data.split("\x00")
+                for one_step in step_data:
+                    if one_step:
+                        node_info = one_step.split(", ")
+                        node_name, node_start, node_end, queue_size = "", 0, 0, 0
+                        if node_info:
+                            node_name = node_info[0].replace("Node:", "")
+                        if len(node_info) > 2:
+                            node_start = node_info[1].replace("Run start:", "")
+                            if node_start.isdigit():
+                                node_start = int(node_start)
+                            node_end = node_info[2].replace("Run end:", "")
+                            if node_end.isdigit():
+                                node_end = int(node_end)
+                        if len(node_info) > 3:
+                            queue_size = node_info[3].replace("queue size:", "")
+                            if queue_size.isdigit():
+                                queue_size = int(queue_size)
+
+                        one_step_list = [node_name, node_start, node_end, queue_size]
+                        result.append(one_step_list)
+        except OSError:
+            logger.error("Open get_next profiling file error.")
+
+        return result
+
+    @staticmethod
+    def execute(source_path, output_path, device_id):
+        """
+        Execute the parser.
+
+        Args:
+            source_path (str): the source file path.
+            output_path (str): the output file path.
+            device_id (str): the device id.
+        """
+        col_names = ["node_name", "start_time", "end_time", "queue_size"]
+        minddata_aicpu_source_path = get_file_join_name(
+            input_path=source_path, file_name='DATA_PREPROCESS.dev.AICPUMI')
+        if not minddata_aicpu_source_path:
+            minddata_aicpu_source_path = get_file_join_name(
+                input_path=os.path.join(source_path, "data"), file_name='DATA_PREPROCESS.dev.AICPUMI')
+            if not minddata_aicpu_source_path:
+                return
+        minddata_aicpu_output_path = os.path.join(output_path, "minddata_aicpu_" + device_id + ".txt")
+
+        minddata_aicpu_data = MinddataParser.parse_minddata_aicpu_data(minddata_aicpu_source_path)
+        if minddata_aicpu_data:
+            fwrite_format(minddata_aicpu_output_path, " ".join(col_names), is_start=True)
+            fwrite_format(minddata_aicpu_output_path, minddata_aicpu_data, is_start=True)

mindspore/profiler/parser/minddata_pipeline_parser.py

+# 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
+#
+# 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.
+# ============================================================================
+"""Thr parser for parsing minddata pipeline files."""
+import csv
+import json
+import os
+from queue import Queue
+
+from mindspore.profiler.common.exceptions.exceptions import \
+    ProfilerPathErrorException, ProfilerFileNotFoundException, \
+    ProfilerDirNotFoundException, ProfilerRawFileException
+from mindspore import log as logger
+from mindspore.profiler.common.validator.validate_path import \
+    validate_and_normalize_path
+
+
+class MinddataPipelineParser:
+    """
+    Thr parser for parsing minddata pipeline files.
+
+    Args:
+        source_dir (str): The minddata pipeline source dir.
+        device_id (str): The device ID.
+        output_path (str): The directory of the parsed file. Default: `./`.
+
+    Raises:
+        ProfilerPathErrorException: If the minddata pipeline file path or
+            the output path is invalid.
+        ProfilerFileNotFoundException: If the minddata pipeline file or
+            the output dir does not exist.
+    """
+    _raw_pipeline_file_name = 'pipeline_profiling_{}.json'
+    _parsed_pipeline_file_name = 'minddata_pipeline_raw_{}.csv'
+    _col_names = [
+        'op_id', 'op_type', 'num_workers', 'output_queue_size',
+        'output_queue_average_size', 'output_queue_length',
+        'output_queue_usage_rate', 'sample_interval', 'parent_id', 'children_id'
+    ]
+
+    def __init__(self, source_dir, device_id, output_path='./'):
+        self._device_id = device_id
+        self._pipeline_path = self._get_pipeline_path(source_dir)
+        self._save_path = self._get_save_path(output_path)
+
+    @property
+    def save_path(self):
+        """
+        The property of save path.
+
+        Returns:
+            str, the save path.
+        """
+        return self._save_path
+
+    def parse(self):
+        """
+        Parse the minddata pipeline files.
+
+        Raises:
+            ProfilerRawFileException: If fails to parse the raw file of
+                minddata pipeline or the file is empty.
+        """
+        with open(self._pipeline_path, 'r') as file:
+            try:
+                pipeline_info = json.load(file)
+            except (json.JSONDecodeError, TypeError) as err:
+                logger.warning(err)
+                raise ProfilerRawFileException(
+                    'Fail to parse minddata pipeline file.'
+                )
+        if not pipeline_info:
+            logger.warning('The minddata pipeline file is empty.')
+            raise ProfilerRawFileException(
+                'The minddata pipeline file is empty.'
+            )
+
+        self._parse_and_save(pipeline_info)
+
+    def _get_pipeline_path(self, source_dir):
+        """
+        Get the minddata pipeline file path.
+
+        Args:
+            source_dir (str): The minddata pipeline source dir.
+
+        Returns:
+            str, the minddata pipeline file path.
+        """
+        pipeline_path = os.path.join(
+            source_dir,
+            self._raw_pipeline_file_name.format(self._device_id)
+        )
+
+        try:
+            pipeline_path = validate_and_normalize_path(pipeline_path)
+        except RuntimeError:
+            logger.warning('Minddata pipeline file is invalid.')
+            raise ProfilerPathErrorException('Minddata pipeline file is invalid.')
+        if not os.path.isfile(pipeline_path):
+            logger.warning(
+                'The minddata pipeline file <%s> not found.', pipeline_path
+            )
+            raise ProfilerFileNotFoundException(pipeline_path)
+
+        return pipeline_path
+
+    def _get_save_path(self, output_path):
+        """
+        Get the save path.
+
+        Args:
+            output_path (str): The output dir.
+
+        Returns:
+            str, the save path.
+        """
+        try:
+            output_dir = validate_and_normalize_path(output_path)
+        except ValidationError:
+            logger.warning('Output path is invalid.')
+            raise ProfilerPathErrorException('Output path is invalid.')
+        if not os.path.isdir(output_dir):
+            logger.warning('The output dir <%s> not found.', output_dir)
+            raise ProfilerDirNotFoundException(output_dir)
+        return os.path.join(
+            output_dir, self._parsed_pipeline_file_name.format(self._device_id)
+        )
+
+    def _parse_and_save(self, pipeline_info):
+        """
+        Parse and save the parsed minddata pipeline file.
+
+        Args:
+            pipeline_info (dict): The pipeline info reads from the raw file of
+                the minddata pipeline.
+
+        Raises:
+            ProfilerRawFileException: If the format of minddata pipeline raw
+                file is wrong.
+        """
+        sample_interval = pipeline_info.get('sampling_interval')
+        op_info = pipeline_info.get('op_info')
+        if sample_interval is None or not op_info:
+            raise ProfilerRawFileException(
+                'The format of minddata pipeline raw file is wrong.'
+            )
+
+        op_id_info_cache = {}
+        for item in op_info:
+            op_id_info_cache[item.get('op_id')] = item
+
+        with open(self._save_path, 'w') as save_file:
+            csv_writer = csv.writer(save_file)
+            csv_writer.writerow(self._col_names)
+            self._parse_and_save_op_info(
+                csv_writer, op_id_info_cache, sample_interval
+            )
+
+    def _parse_and_save_op_info(self, csv_writer, op_id_info_cache,
+                                sample_interval):
+        """
+        Parse and save the minddata pipeline operator information.
+
+        Args:
+            csv_writer (csv.writer): The csv writer.
+            op_id_info_cache (dict): The operator id and information cache.
+            sample_interval (int): The sample interval.
+
+        Raises:
+            ProfilerRawFileException: If the operator that id is 0 does not exist.
+        """
+        queue = Queue()
+        root_node = op_id_info_cache.get(0)
+        if not root_node:
+            raise ProfilerRawFileException(
+                'The format of minddata pipeline raw file is wrong, '
+                'the operator that id is 0 does not exist.'
+            )
+        root_node['parent_id'] = None
+        queue.put_nowait(root_node)
+
+        while not queue.empty():
+            node = queue.get_nowait()
+            self._update_child_node(node, op_id_info_cache)
+            csv_writer.writerow(self._get_op_info(node, sample_interval))
+
+            op_id = node.get('op_id')
+            children_ids = node.get('children')
+            if not children_ids:
+                continue
+            for child_op_id in children_ids:
+                sub_node = op_id_info_cache.get(child_op_id)
+                sub_node['parent_id'] = op_id
+                queue.put_nowait(sub_node)
+
+    def _update_child_node(self, node, op_id_info_cache):
+        """
+        Updates the child node information of the operator.
+
+        Args:
+            node (dict): The node represents an operator.
+            op_id_info_cache (dict): The operator id and information cache.
+        """
+        child_op_ids = node.get('children')
+        if not child_op_ids:
+            return
+
+        queue = Queue()
+        self._cp_list_item_to_queue(child_op_ids, queue)
+
+        new_child_op_ids = []
+        while not queue.empty():
+            child_op_id = queue.get_nowait()
+            child_node = op_id_info_cache.get(child_op_id)
+            if child_node is None:
+                continue
+            metrics = child_node.get('metrics')
+            if not metrics or not metrics.get('output_queue'):
+                op_ids = child_node.get('children')
+                if op_ids:
+                    self._cp_list_item_to_queue(op_ids, queue)
+            else:
+                new_child_op_ids.append(child_op_id)
+
+        node['children'] = new_child_op_ids
+
+    def _get_op_info(self, op_node, sample_interval):
+        """
+        Get the operator information.
+
+        Args:
+            op_node (dict): The node represents an operator.
+            sample_interval (int): The sample interval.
+
+        Returns:
+            list[str, int, float], the operator information.
+        """
+        queue_size = None
+        queue_average_size = None
+        queue_length = None
+        queue_usage_rate = None
+        metrics = op_node.get('metrics')
+        if metrics:
+            output_queue = metrics.get('output_queue')
+            if output_queue:
+                queue_size = output_queue.get('size')
+                queue_average_size = sum(queue_size) / len(queue_size)
+                queue_length = output_queue.get('length')
+                queue_usage_rate = queue_average_size / queue_length
+
+        children_id = op_node.get('children')
+        op_info = [
+            op_node.get('op_id'),
+            op_node.get('op_type'),
+            op_node.get('num_workers'),
+            queue_size,
+            queue_average_size,
+            queue_length,
+            queue_usage_rate,
+            sample_interval,
+            op_node.get('parent_id'),
+            children_id if children_id else None
+        ]
+        return op_info
+
+    def _cp_list_item_to_queue(self, inner_list, queue):
+        """
+        Copy the contents of a list to a queue.
+
+        Args:
+            inner_list (list): The list.
+            queue (Queue): The target queue.
+        """
+        for item in inner_list:
+            queue.put_nowait(item)

mindspore/profiler/parser/optime_parser.py

+# 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
+#
+# 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.
+# ============================================================================
+"""Op compute time files parser."""
+import os
+from mindspore.profiler.common.util import fwrite_format
+from mindspore.profiler.common.exceptions.exceptions import ProfilerFileNotFoundException, \
+    ProfilerIOException
+from mindspore import log as logger
+from mindspore.profiler.common.validator.validate_path import validate_and_normalize_path
+from mindspore.profiler.parser.container import HWTSContainer
+
+TIMELINE_FILE_COLUMN_TITLE = 'op_name, stream_id, start_time(ms), duration(ms)'
+
+class OPComputeTimeParser:
+    """
+    Join hwts info and framework info, get op time info, and output to the result file.
+
+    Args:
+         hwts_output_file (str): The file path of hwts_output_file. Such as: './output_format_data_hwts_0.txt".
+         output_filename (str): The output data file path and name. Such as: './output_op_compute_time_0.txt'.
+         op_task_info (dict): The task and op relation info. The format: {task_id, [opname, stream_id, block dim]}.
+    """
+
+    _dst_file_title = 'title:op compute time'
+    _dst_file_column_title = 'op_name       compute_time(ms) stream_id'
+    _dst_file_column_title += '\n------------  ---------------  ---------'
+
+    def __init__(self, hwts_output_file, output_filename, op_task_info,
+                 output_path, device_id):
+        hwts_output_file = validate_and_normalize_path(hwts_output_file)
+        self._hwts_output_file = hwts_output_file
+        self._output_filename = output_filename
+        self._op_task_info = op_task_info
+        self._output_path = output_path
+        self._device_id = device_id
+        self._min_cycle_counter = float("inf")
+
+    def _get_op_task_id_map(self):
+        """
+        Read hwts data file, get the task time info.
+
+        Returns:
+           list: all hwts task time info.
+        """
+
+        op_map_result = []
+        hwts_list = []
+
+        if not os.path.exists(self._hwts_output_file):
+            logger.error('The hwts output file does not exist.')
+            raise ProfilerFileNotFoundException('hwts output file')
+
+        with open(self._hwts_output_file, 'r') as data_file:
+            lines = data_file.readlines()
+            for line in lines:
+                if line.startswith("Start of task") or line.startswith("End of task"):
+                    line_split = line.split()
+                    container = HWTSContainer(line_split)
+                    hwts_list.append(container)
+
+        # hwts op map by taskId
+        for hwts in hwts_list:
+            if hwts.task_id in self._op_task_info.keys():
+                hwts.op_name = self._op_task_info[hwts.task_id]
+                op_map_result.append(hwts)
+
+        return op_map_result
+
+    def execute(self):
+        """Execute the parser, compute all op, get op time, and write it to the output file."""
+        # Calculate the execution time of operators,
+        # and update the minimum cycle counter.
+        tmp_result_data = self._calculate_op_execution_time()
+
+        # Convert time units from nanoseconds to milliseconds.
+        # The unit of the cycle counter is 10 nanoseconds.
+        op_name_time_dict = {}
+        op_name_stream_dict = {}
+        op_name_count_dict = {}
+        op_name_task_dict = {}
+        op_name_start_time = {}
+        self._convert_op_time_unit(
+            tmp_result_data, op_name_time_dict, op_name_stream_dict,
+            op_name_count_dict, op_name_task_dict, op_name_start_time
+        )
+
+        result_data = ""
+        total_time = 0
+        for op_name, time in op_name_time_dict.items():
+            if op_name in op_name_stream_dict.keys():
+                stream_id = op_name_stream_dict[op_name]
+                avg_time = time / op_name_count_dict[op_name]
+                total_time += avg_time
+                result_data += ("%s %s  %s\n" %(op_name, str(avg_time), stream_id))
+        result_data += ("total op  %s 0" %(str(total_time)))
+
+        timeline_data = []
+        for op_name, time in op_name_time_dict.items():
+            if op_name in op_name_stream_dict.keys():
+                stream_id = op_name_stream_dict[op_name]
+                start_time_list = op_name_start_time.get(op_name)
+                for (start_time, duration) in start_time_list:
+                    timeline_data.append([op_name, stream_id, start_time, duration])
+
+        # Write the metadata of operators into the file,
+        # including operator name, average time, and stream id.
+        self._write_op_time_into_file(result_data)
+        # Write the timeline data into file,
+        # including operator name, stream id, start time, and duration.
+        self._write_timeline_data_into_file(timeline_data)
+
+    def _write_op_time_into_file(self, result_data):
+        """
+        Write the metadata of operators into the file, including
+            op name, average time, and stream id.
+
+        Args:
+            result_data (str): The metadata to be written into the file.
+                    'op_name_1', 'avg_time_1', 'stream_id_1',
+                    'op_name_2', 'avg_time_2', 'stream_id_2',
+                    ...
+        """
+
+        fwrite_format(self._output_filename, data_source=self._dst_file_title, is_start=True)
+        fwrite_format(self._output_filename, data_source=self._dst_file_column_title)
+        fwrite_format(self._output_filename, data_source=result_data)
+
+    def _write_timeline_data_into_file(self, timeline_data):
+        """
+        Write the timeline information into the file, including
+            operator name, stream id, start time and duration.
+
+        Args:
+            timeline_data (list): The metadata to be written into the file.
+                [
+                    ['op_name_1', 'stream_id_1', 'start_time_1', 'durarion_1'],
+                    ['op_name_2', 'stream_id_2', 'start_time_2', 'durarion_2'],
+                    [...]
+                ]
+        """
+        # sorted by start times
+        timeline_data.sort(key=lambda x: float(x[2]))
+        filename = 'output_timeline_data_{}.txt'.format(self._device_id)
+        file_path = os.path.join(self._output_path, filename)
+        file_path = validate_and_normalize_path(file_path)
+
+        # write to file
+        try:
+            with open(file_path, 'w') as f_obj:
+                f_obj.write(TIMELINE_FILE_COLUMN_TITLE + '\n')
+                for timeline in timeline_data:
+                    timeline = [str(item) for item in timeline]
+                    f_obj.write(','.join(timeline) + '\n')
+        except (IOError, OSError) as err:
+            logger.error('Error occurred when writing intermediate timeline file: %s', err)
+            raise ProfilerIOException
+
+    def _calculate_op_execution_time(self):
+        """
+        Calculate the execution time of each operator.
+
+        Returns:
+            list, including the intermediate data of op execution time.
+        """
+        tmp_result_data = []
+        op_map_list = self._get_op_task_id_map()
+
+        cur_index = 0
+        length = len(op_map_list)
+        min_cycle_counter = float("inf")
+        while cur_index < length:
+            if cur_index + 1 == length:
+                break
+
+            op_start = op_map_list[cur_index]
+            op_end = op_map_list[cur_index + 1]
+            if op_start.status == "Start" and op_end.status == "End" \
+                    and op_start.op_name == op_end.op_name:
+                op_start.duration = op_end.cycle_counter - op_start.cycle_counter
+                tmp_result_data.append(op_start)
+                cur_index += 2
+                if not op_start.op_name.startswith("assign"):
+                    min_cycle_counter = min(min_cycle_counter, op_start.cycle_counter)
+            else:
+                cur_index += 1
+
+        # Update the value of minimum cycle counter.
+        self._min_cycle_counter = min_cycle_counter / 1e5  # Convert the time unit from 10ns to 1ms
+
+        return tmp_result_data
+
+    def _convert_op_time_unit(self, op_data_list, op_name_time_dict, op_name_stream_dict,
+                              op_name_count_dict, op_name_task_dict, op_name_start_time):
+        """
+        Calculate the execution time of operator and convert it into millisecond.
+
+        Args:
+            op_data_list (list): The list of operator metadata.
+            op_name_time_dict (dict): The mapping relation of operator name and its execution time.
+            op_name_stream_dict (dict): The mapping relation of operator name and its stream id.
+            op_name_count_dict (dict): The mapping relation of operator name and its count.
+            op_name_task_dict (dict): The mapping relation of operator name and its task id.
+            op_name_start_time (dict): The mapping relation of operator name and its start time.
+        """
+        factor = 1e5
+        for item in op_data_list:
+            op_name = item.op_name
+            # Unit conversion: converting the cycle counter into ms.
+            op_start_time_str = str(item.cycle_counter / factor)
+            op_duration = item.duration / factor
+            op_duration_str = str(item.duration / factor)
+            if op_name in op_name_time_dict.keys():
+                op_name_time_dict[op_name] += op_duration
+                if item.task_id == op_name_task_dict[op_name]:
+                    op_name_count_dict[op_name] += 1
+                op_name_start_time[op_name].append(
+                    (op_start_time_str, op_duration_str)
+                )
+
+            else:
+                op_name_time_dict[op_name] = op_duration
+                op_name_stream_dict[op_name] = item.stream_id
+                op_name_task_dict[op_name] = item.task_id
+                op_name_count_dict[op_name] = 1
+                op_name_start_time[op_name] = []
+                op_name_start_time[op_name].append(
+                    (op_start_time_str, op_duration_str)
+                )
+
+    @property
+    def min_cycle_counter(self):
+        """Get minimum cycle counter."""
+        return self._min_cycle_counter

mindspore/profiler/parser/step_trace_parser.py

+# 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
+#
+# 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.
+# ============================================================================
+"""The parser for step trace data."""
+import csv
+import json
+import os
+import stat
+import struct
+from collections import namedtuple
+from decimal import Decimal
+
+from mindspore.profiler.common.exceptions.exceptions import ProfilerPathErrorException, \
+    JobIdMismatchException, ProfilerIOException
+from mindspore import log
+from mindspore.profiler.common.util import get_summary_for_step_trace
+
+StepTraceStruct = namedtuple(
+    'TrainingTraceStruct', ['tag_id', 'task_id', 'stream_id', 'sys_count']
+)
+
+
+class StepTraceParser:
+    """
+    The parser for step trace data.
+
+    Args:
+        input_dir (str): The directory that contains original step trace data.
+        output_file_path (str): The output file path.
+        job_id (int): The job id used to define the start of new step. Default: 0.
+        skip_first_step (bool): Whether skip the first step or not.
+    """
+    _event_size = 20
+    _fp_tag = 1
+    _bp_tag = 2
+    _end_tag = 255
+
+    def __init__(self, input_dir, output_file_path, job_id=0, skip_first_step=False):
+        self._input_dir = input_dir
+        self._output_path = output_file_path
+        self._job_id = job_id
+        self._skip_first_step = skip_first_step
+        self._result = []
+        self._header = []
+        self._step_num = 0
+        self._tag_map = {}
+
+    @property
+    def output_file(self):
+        """The property of step trace header."""
+        file_name = self._output_path.rsplit('/', 2)
+        return file_name[-1] if len(file_name) == 3 else ''
+
+    def show(self):
+        """The property of step trace info."""
+        summary_info = {}
+        if self._result:
+            summary_info = get_summary_for_step_trace(self._result[-1], self._header)
+            summary_info['total_steps'] = len(self._result) - 1
+        print('\nStep trace summary info (unit: syscnt):')
+        print(summary_info)
+        print('\nThe step trace parse result saves under ${summary_dir}/profiler/%s'
+              % self.output_file)
+
+    def parse_and_save(self):
+        """Parse step trace files and save the result."""
+        try:
+            source_files = self._get_step_trace_files()
+            self._parse(source_files)
+            self._save()
+        except IOError as err:
+            log.warning(err)
+            raise ProfilerIOException()
+        else:
+            log.info("Finish to save intermediate result for step trace file.")
+
+    def record_point_info(self, point_info, output_path):
+        """
+        Record point info into json.
+
+        Args:
+            point_info (dict): The point info about tag id and relative op name.
+            output_path (str): The output path for saving point info.
+
+        Returns:
+            dict, parsed point info.
+        """
+        points = {
+            'fp_start': point_info.get(self._fp_tag, ''),
+            'bp_end': point_info.get(self._bp_tag, '')
+        }
+        try:
+            with open(output_path, 'w') as json_file:
+                json.dump(points, json_file)
+            os.chmod(output_path, stat.S_IREAD)
+        except (IOError, OSError) as err:
+            log.warning('Failed to save point info. %s', err)
+            raise ProfilerIOException
+        return points
+
+    def update_tag_op_type_map(self, point_info):
+        """
+        update the map from tag id to op type.
+
+        Args:
+            point_info (dict): The point info about tag id and relative op name.
+        """
+        tag_map = {}
+        for tag, op_name in point_info.items():
+            op_type = self._get_op_type(tag, op_name)
+            tag_map[tag] = op_type
+        log.info("Get tag types for step trace analysis: %s", tag_map)
+        self._tag_map = tag_map
+
+    def _get_op_type(self, tag, name):
+        """
+        Get op type from tag and name.
+
+        Args:
+            tag (int): The tag id.
+            name (str): The op name.
+
+        Returns:
+            str, the op type.
+        """
+        tag_map = {self._fp_tag: 'fp', self._bp_tag: 'bp', self._end_tag: 'end'}
+        # get solid tag type
+        op_type = tag_map.get(tag, '')
+        if op_type:
+            return op_type
+        # check if the tag is step tag.
+        if tag > self._end_tag or tag == 0:
+            return 'start'
+        # analyze the reduce tag
+        op_type = name.rsplit('/', 1)[-1].split('-')[0]
+        if not op_type:
+            log.warning("Unexpected op name:%s", name)
+
+        return op_type
+
+    def _get_step_trace_files(self):
+        """Get step trace files."""
+        # step trace files may under $profiler_dir or $profiler_dir/data
+        profiler_dir = self._input_dir
+        step_trace_files = self._search_file(profiler_dir)
+        if not step_trace_files:
+            # try to find step trace files under $profiler_dir/data
+            profiler_dir = os.path.join(profiler_dir, 'data')
+            step_trace_files = self._search_file(profiler_dir)
+        if not step_trace_files:
+            raise ProfilerPathErrorException('Training trace file does not exist.')
+
+        return step_trace_files
+
+    @staticmethod
+    def _search_file(input_dir):
+        """Search step trace file under specific input directory."""
+        # validate input_dir
+        if not os.path.isdir(input_dir):
+            raise ProfilerPathErrorException(
+                '{} does not exist or is not a dir'.format(input_dir)
+            )
+        # get step trace files
+        files = os.listdir(input_dir)
+        step_trace_files = list(
+            filter(
+                lambda file: file.startswith('training_trace') and not file.endswith('.done'),
+                files
+            )
+        )
+        # validate result
+        if len(step_trace_files) > 1:
+            # the format of file name is like
+            # `training_trace.46.dev.profiler_default_tag.$id.slice_$number`
+            # use the $number as the sorted key
+            try:
+                step_trace_files.sort(key=lambda path: int(path.rsplit('_', 1)[-1]))
+            except ValueError as err:
+                log.warning("Unable to parse file names: %s. %s", step_trace_files, err)
+                step_trace_files = []
+
+        file_paths = [os.path.join(input_dir, file) for file in step_trace_files]
+        log.info("Find %d step trace files.", len(file_paths))
+        return file_paths
+
+    def _parse(self, source_files):
+        """Parse source step trace files."""
+        log.info("Start to parse step trace file.")
+        event_info = {}
+        for source_file in source_files:
+            with open(source_file, 'rb') as handler:
+                content = handler.read()
+                for step_trace in self._get_next_step_trace(content, event_info):
+                    if self._skip_first_step:
+                        self._skip_first_step = False
+                        continue
+                    self._record_trace_event(step_trace)
+        self._record_average_info()
+        log.info("Finish to parse step trace file.")
+
+    def _get_next_step_trace(self, content, event_info):
+        """
+        Get next step trace info.
+
+        Args:
+            content (bytes): The input step trace info.
+            event_info (dict): The event info.
+
+        Returns:
+            Generator, return the step trace one by one.
+        """
+        for pos in range(0, len(content), 20):
+            next_event = self._get_trace_struct(content[pos:pos + self._event_size])
+            self._construct_event_info(next_event, event_info)
+            if event_info.get('end'):
+                yield event_info
+
+    def _get_trace_struct(self, bin_info):
+        """Translate event info to StepTraceStruct."""
+        if len(bin_info) == self._event_size:
+            parsed_info = struct.unpack('=QHHQ', bin_info)
+            return StepTraceStruct(*parsed_info)
+        return None
+
+    def _construct_event_info(self, next_event, event_info):
+        """Construct event info according to next_event."""
+        min_job_id = 255
+        step_flag: bool = lambda tag: tag > min_job_id or tag == 0
+        end_flag: bool = lambda tag: tag == min_job_id
+        fp_flag: bool = lambda tag: tag == self._fp_tag
+        bp_flag: bool = lambda tag: tag == self._bp_tag
+
+        def _on_step_event():
+            """Handle step event."""
+            self._validate_tag_id(tag_id)
+            start_time = event_info.get('end', '-')
+            event_info.clear()
+            event_info['start'] = start_time
+            event_info['reduce'] = {}
+
+        def _on_reduce_event(reduce_tag_id):
+            """Handle reduce event."""
+            stream_id = next_event.stream_id
+            if event_info['reduce'].get(stream_id):
+                event_info['reduce'][stream_id].append((reduce_tag_id, sys_count))
+            else:
+                event_info['reduce'][stream_id] = [(reduce_tag_id, sys_count)]
+
+        tag_id = next_event.tag_id
+        sys_count = next_event.sys_count
+        if end_flag(tag_id):
+            event_info['end'] = sys_count
+        elif step_flag(tag_id):
+            _on_step_event()
+        elif fp_flag(tag_id):
+            event_info['fp'] = sys_count
+        elif bp_flag(tag_id):
+            event_info['bp'] = sys_count
+        else:
+            _on_reduce_event(tag_id)
+
+    def _validate_tag_id(self, job_id):
+        """Check the job id in source step trace file is same as user set."""
+        if not self._job_id:
+            self._job_id = job_id
+        elif self._job_id != job_id:
+            raise JobIdMismatchException()
+
+    def _record_trace_event(self, step_trace):
+        """Record trace event."""
+        self._step_num += 1
+        start_time = step_trace.get('start')
+        end_time = step_trace.get('end')
+        fp_time = step_trace.get('fp')
+        bp_time = step_trace.get('bp')
+        if not (start_time and end_time and fp_time and bp_time):
+            log.warning("The step %d lacks basic time.", self._step_num)
+            return
+        if start_time == '-':
+            start_time = fp_time
+        row_data = {
+            'step_num': self._step_num,
+            'start_point': start_time,
+            'end_point': end_time,
+            'total': end_time - start_time,
+            'fp_point': fp_time,
+            'bp_point': bp_time,
+            'iteration_interval': fp_time - start_time,
+            'fp_and_bp': bp_time - fp_time,
+            'tail': end_time - bp_time
+        }
+        # update reduce info
+        self._update_reduce_info(step_trace, row_data)
+        # save the row data
+        if not self._header:
+            self._header = list(row_data.keys())
+        row_data_list = [row_data.get(header_name, 0) for header_name in self._header]
+        self._result.append(row_data_list)
+
+    def _update_reduce_info(self, step_trace, row_data):
+        """Extract reduce info."""
+        reduce_time = step_trace.get('reduce', {})
+        for stream_id, time_points in reduce_time.items():
+            time_point_num = len(time_points)
+            if time_point_num % 2:
+                log.warning("Stream %d has %d reduce time points.", stream_id, time_point_num)
+                continue
+            for index, point_id in enumerate(range(0, time_point_num, 2)):
+                field_name = f'stream_{stream_id}_{index}'
+                reduce_info = self._get_single_reduce_event_info(
+                    field_name, time_points[point_id], time_points[point_id + 1])
+                row_data.update(reduce_info)
+
+    def _get_single_reduce_event_info(self, field_name, start_point, end_point):
+        """
+        Get single reduce info.
+
+        Args:
+            field_name (str): The field name.
+            start_point (Tuple[int, int]): Start point time info, including (tag_id, sys_count).
+            end_point (Tuple[int, int]): End point time info, including (tag_id, sys_count).
+
+        Returns:
+            dict, reduce info.
+        """
+        reduce_info = {}
+        if end_point[0] - start_point[0] != 1 or end_point[0] % 2:
+            log.warning("Unmatched reduce event <%s, %s>.", start_point, end_point)
+            return reduce_info
+        op_type = self._tag_map.get(start_point[0])
+        # append field name with op type.
+        if not op_type:
+            log.warning("Can't recognize the inner type for point tag: %d.", start_point[0])
+            field_name += '_parallel'
+        else:
+            field_name += '_' + op_type
+        reduce_info[field_name] = end_point[1] - start_point[1]
+        reduce_info[field_name + '_start_point'] = start_point[1]
+        reduce_info[field_name + '_end_point'] = end_point[1]
+
+        return reduce_info
+
+    def _record_average_info(self):
+        """Calculate average info."""
+        result_size = len(self._result)
+        # calculate average data for each column in result data
+        average_data = [0] * len(self._header)
+        if result_size >= 2:
+            for row_info in self._result[1:]:
+                average_data = [
+                    Decimal(i) + Decimal(j) for i, j in zip(row_info, average_data)
+                ]
+            average_data = [
+                round((item / (result_size - 1))) for item in average_data
+            ]
+            # change step num info in average_data to None
+            step_num_index = self._header.index('step_num')
+            average_data[step_num_index] = '-'
+        self._result.append(average_data)
+        log.info("Finish add average info for step trace.")
+
+    def _save(self):
+        log.info("Start to save step trace file.")
+        if not self._header:
+            return
+        with open(self._output_path, 'w') as file_handle:
+            csv_writer = csv.writer(file_handle)
+            csv_writer.writerow(self._header)
+            for row_data in self._result:
+                csv_writer.writerow(row_data)
+        os.chmod(self._output_path, stat.S_IREAD)

mindspore/profiler/profiling.py

+# 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
+#
+# 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 time
+
+from mindspore import log as logger, context
+from mindspore.communication.management import release
+from mindspore.profiler.common.exceptions.exceptions import ProfilerFileNotFoundException, \
+    ProfilerIOException, ProfilerException
+from mindspore.profiler.common.util import get_file_names, fwrite_format
+from mindspore.profiler.common.validator.checkparam import \
+    check_bool, check_subgraph
+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 TimelineAnalyser
+from mindspore.profiler.parser.minddata_parser import MinddataParser
+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 StepTraceParser
+
+PROFILING_LOG_BASE_PATH = "/var/log/npu/profiling"
+INIT_OP_NAME = 'Default/InitDataSetQueue'
+
+
+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 (str): Op type names, the data of which optype should be collected and analysed,
+            will deal with all op if null; Different op types should be seperated by comma.
+        optypes_not_deal (str): Op type names, the data of which optype will not be collected and analysed;
+            Different op types should be seperated by comma.
+
+    Examples:
+        >>> from mindspore.profiler import Profiler
+        >>> import mindspore.context
+        >>> 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()
+        >>> model.train()
+        >>> 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
+        self._get_devid_and_devtarget()
+        self._container_path = os.path.join(self._base_profiling_container_path, self._dev_id)
+        data_path = os.path.join(self._container_path, "data")
+        if not os.path.exists(data_path):
+            os.makedirs(data_path, exist_ok=True)
+        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.environ['PROFILING_MODE'] = 'true'
+        os.environ['PROFILING_OPTIONS'] = 'training_trace:task_trace'
+        os.environ['MINDDATA_PROFILING_DIR'] = self._output_path
+        os.environ['DEVICE_ID'] = self._dev_id
+        os.environ['AICPU_PROFILING_MODE'] = 'true'
+        os.environ['PROFILING_DIR'] = str(self._container_path)
+
+        # use context interface to open profiling, for the new mindspore version(after 2020.5.21)
+        context.set_context(enable_profiling=True, profiling_options="training_trace:task_trace")
+
+        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
+        # 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 analyse(self):
+        """
+        Collect and analyse performance data, called after training or during training.
+
+        Examples:
+            >>> from mindspore.profiler import Profiler
+            >>> import mindspore.context
+            >>> 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()
+            >>> model.train()
+            >>> profiler.analyse()
+        """
+        release()
+
+        job_id = self._get_profiling_job_id()
+        logger.info("Profiling: job id is %s ", job_id)
+
+        source_path = os.path.join(PROFILING_LOG_BASE_PATH, job_id)
+        # parse hwts.log.data.45.dev 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, self._output_path, self._dev_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._dev_id + ".txt"
+        output_data_preprocess_aicpu = os.path.join(self._output_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._dev_id)
+
+        # parse minddata pipeline operator and queue
+        try:
+            pipeline_parser = MinddataPipelineParser(self._output_path, self._dev_id, self._output_path)
+            pipeline_parser.parse()
+        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
+        try:
+            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)
+        except (ProfilerIOException, ProfilerFileNotFoundException, RuntimeError) as err:
+            logger.warning('Fail to write timeline data: %s', err)
+
+    def _analyse_step_trace(self, source_path, framework_parser):
+        """
+        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.
+        """
+        logger.info("Begin to parse step trace.")
+        # construct output path
+        step_trace_intermediate_file_path = os.path.join(
+            self._output_path,
+            f'step_trace_raw_{self._dev_id}_detail_time.csv'
+        )
+        point_info_file_path = os.path.join(
+            self._output_path,
+            'step_trace_point_info.json'
+        )
+        # whether keep the first step
+        skip_first_step_flag = framework_parser.check_op_name(INIT_OP_NAME)
+        point_info = framework_parser.point_info
+        # parser the step trace files and save the result to disk
+        parser = StepTraceParser(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)
+        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)
+
+    def _analyse_timeline(self, aicpu_parser, optime_parser):
+        """
+        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 = TimelineAnalyser(self._output_path, self._dev_id)
+        # Get framework info
+        integrator = Integrator(self._output_path, self._dev_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)
+        timeline_analyser.write_timeline()
+        timeline_analyser.write_timeline_summary()
+
+    def __del__(self):
+        """Disable the profiling collection service, called after training."""
+        os.environ['PROFILING_MODE'] = str("false")
+        context.set_context(enable_profiling=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"]):
+                logger.info("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"]:
+                logger.info("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
+
+        if not job_id:
+            msg = "Fail to get profiling job, please check whether job dir was generated"
+            raise RuntimeError(msg)
+
+        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'
+        ]
+        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)
+
+        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)
+            col_names = ['op_name', 'op_type', 'avg_execution_time', 'subgraph',
+                         'full_op_name', 'op_info']
+            fwrite_format(detail_file_path, data_source=" ".join(col_names), is_print=True)
+            fwrite_format(detail_file_path, data_source=aicore_detail_result, 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._dev_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._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]
+
+        integrator = Integrator(self._output_path, self._dev_id)
+        return integrator.get_aicore_detail_data()
+
+
+    def _get_devid_and_devtarget(self):
+        """Get device id and target of this training."""
+
+        device_target = ""
+        dev_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.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