forked from huawei/mindspore2022
273 lines
12 KiB
Python
273 lines
12 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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Module to provide conversion capabalities from .timestamp async dump files to .npy.
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It's an internal module for debugger backend but not exposed to users.
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"""
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import os
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import glob
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import stat
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import sys
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from pathlib import Path
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from importlib import import_module
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from collections import namedtuple
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import numpy as np
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class ConvertToolLoader:
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"""Module to load CANN conversion tool."""
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def __init__(self):
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self.utils = None
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self.common = None
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self.dump_data_parser = None
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self.format_conversion = None
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self.progress = None
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self.log = None
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self.compare_none_error = None
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self.compare_exception = None
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self.load_convert_tool()
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@staticmethod
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def find_toolkit_path():
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"""Find the path to Ascend toolkit."""
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ascend_toolkit_path = os.getenv("ASCEND_TOOLKIT_PATH")
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if not ascend_toolkit_path:
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ascend_toolkit_path = "/usr/local/Ascend"
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if not os.path.exists(ascend_toolkit_path):
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raise ValueError(
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"Path {} does not exist. Please install Ascend run packages " \
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"and set the environment variable $ASCEND_TOOLKIT_PATH correctly.".format(ascend_toolkit_path))
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toolkit_search_path = Path(ascend_toolkit_path).resolve()
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msaccucmp_file_list = list(toolkit_search_path.rglob('msaccucmp.py*'))
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if not msaccucmp_file_list:
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toolkit_search_path = toolkit_search_path / 'tools'
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msaccucmp_file_list = list(toolkit_search_path.rglob('msaccucmp.py*'))
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if not msaccucmp_file_list:
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raise ValueError("Failed to find msaccucmp.py or msaccucmp.pyc file under {}. " \
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"Please install Ascend toolkit.".format(ascend_toolkit_path))
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return msaccucmp_file_list[0].parent
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def load_convert_tool(self):
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"""load CANN conversion tool from the toolkit path."""
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toolkit_path = self.find_toolkit_path()
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# add toolkit path to system searching module path
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if str(toolkit_path) not in sys.path:
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sys.path.append(str(toolkit_path))
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try:
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self.utils = import_module('utils')
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self.common = import_module('common')
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self.dump_data_parser = import_module(
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'dump_data_parser').DumpDataParser
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self.format_conversion = import_module(
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'shape_conversion').FormatConversionMain
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except ModuleNotFoundError:
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# restore system searching module path
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if str(toolkit_path) in sys.path:
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sys.path.remove(str(toolkit_path))
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raise ModuleNotFoundError(
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"Failed to load CANN conversion tools under {}. Please make sure Ascend " \
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"toolkit has been installed properly.".format(toolkit_path))
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try:
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self.progress = import_module("progress").Progress
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except (ModuleNotFoundError, AttributeError):
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self.progress = self.utils.Progress
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try:
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self.log = import_module("log")
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if not hasattr(self.log, "print_error_log"):
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raise ModuleNotFoundError
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except ModuleNotFoundError:
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self.log = self.utils
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try:
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compare_error = import_module("compare_error")
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self.compare_none_error = compare_error.CompareError.MSACCUCMP_NONE_ERROR
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self.compare_exception = compare_error.CompareError
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except ModuleNotFoundError:
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self.compare_none_error = self.utils.VECTOR_COMPARISON_NONE_ERROR
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self.compare_exception = self.utils.CompareError
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# restore system searching module path
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if str(toolkit_path) in sys.path:
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sys.path.remove(str(toolkit_path))
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def parse_args(file_list, output_path):
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"""Helper function to parse the input argument for the conversion configuration."""
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args_dict = dict()
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args_dict['dump_version'] = '2.0'
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args_dict['format'] = 'NCHW'
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args_dict['output_file_type'] = 'npy'
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args_dict['dump_path'] = output_path
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args_dict['output_path'] = output_path
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args_dict['file_list'] = file_list
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args_dict['input'] = None
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args_dict['output'] = None
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args_dict['shape'] = None
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args_dict['custom_script_path'] = None
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args_parser = namedtuple("args_parser", args_dict.keys())
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return args_parser(**args_dict)
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class AsyncDumpConverter:
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"""Convert the target async dump data into npy files."""
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def __init__(self, file_list, output_path):
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# check input path
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for file_item in file_list:
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file_item = os.path.realpath(file_item)
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output_path = os.path.realpath(output_path)
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self.convert_tool = ConvertToolLoader()
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self.args = parse_args(file_list, output_path)
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self.files_to_convert = self.args.file_list
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self.output_path = self.args.output_path
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self.failed_file_path = os.path.join(
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self.output_path, 'convert_failed_file_list.txt')
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self.clear_failed_list_file()
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def clear_failed_list_file(self):
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"""Remove existing failed txt file."""
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if self.failed_file_path and os.path.exists(self.failed_file_path):
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os.remove(self.failed_file_path)
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def convert_files(self):
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"""Main entry of the converter to convert async dump files into npy format."""
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self.convert_tool.log.print_info_log('Start to convert async dump files.')
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ret_code = self.convert_tool.compare_none_error
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if self.args.format is not None:
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convert = self.convert_tool.format_conversion(self.args)
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else:
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convert = self.convert_tool.dump_data_parser(self.args)
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ret_code = self.handle_multi_process(convert, self.files_to_convert)
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self._rename_generated_npy_files()
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if ret_code != self.convert_tool.compare_none_error:
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if os.path.exists(self.failed_file_path):
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self.convert_failed_tensors()
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self.convert_tool.log.print_info_log('Finish to convert async dump files.')
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def convert_failed_tensors(self):
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"""Convert the failed tensor recorded in the failed txt file."""
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self.convert_tool.log.print_info_log(
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'Start to convert failed tensors recorded in ' + self.failed_file_path + '.')
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with open(self.failed_file_path) as failed_lines:
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for failed_line in failed_lines:
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try:
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failed_line_list = failed_line.rstrip().split(',')
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self.convert_one_failed_tensor(failed_line_list)
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except (ValueError, OSError, AttributeError, self.convert_tool.compare_exception) as err:
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self.convert_tool.log.print_error_log(
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'Failed to convert ' + failed_line + ' to Host format: ' + str(err))
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def convert_one_failed_tensor(self, failed_tensor):
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"""Convert failed operator one by one."""
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if len(failed_tensor) <= 1:
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raise ValueError(
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"Invalid tensor info in convert_failed_file_list.txt")
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file_path = failed_tensor[0]
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type_index = failed_tensor[1:]
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op_data = self.convert_tool.utils.parse_dump_file(
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file_path, self.args.dump_version)
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for type_index_item in type_index:
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tensor_type, index = type_index_item.split(':')
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index = int(index)
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tensor = getattr(op_data, tensor_type)[index]
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dump_data_array = self.convert_tool.utils.deserialize_dump_data_to_array(tensor)
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array = dump_data_array.reshape(tensor.shape.dim)
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self._save_tensor_to_npy_file(
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file_path, tensor_type, index, tensor.format, array)
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def handle_multi_process(self, convert_obj, files):
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"""Convert async format files to npy in a multithreaded manner."""
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return_code = self.convert_tool.compare_none_error
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# try looking for function in compatibility with the toolkit package version.
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progress = self.convert_tool.progress(len(files))
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if hasattr(convert_obj, 'multi_process'):
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_ = setattr(convert_obj.multi_process, '_progress', progress)
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else:
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_ = setattr(convert_obj, 'progress', progress)
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multi_process_file_list = []
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big_file_list = []
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max_file_size = 0
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if hasattr(convert_obj, 'multi_process'):
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max_file_size = getattr(convert_obj.multi_process, 'get_max_file_size')()
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else:
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max_file_size = getattr(convert_obj, '_get_max_file_size')()
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for cur_file in files:
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cur_path = cur_file
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if os.path.isfile(cur_path):
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if os.path.getsize(cur_path) > max_file_size:
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big_file_list.append(cur_path)
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else:
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multi_process_file_list.append(cur_path)
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if multi_process_file_list:
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ret_mp = self.convert_tool.compare_none_error
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if hasattr(convert_obj, 'multi_process'):
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ret_mp = getattr(convert_obj.multi_process, '_do_multi_process')(multi_process_file_list)
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else:
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ret_mp = getattr(convert_obj, '_do_multi_process')(multi_process_file_list)
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if ret_mp != self.convert_tool.compare_none_error:
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return_code = ret_mp
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for big_file in big_file_list:
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ret_bf = self.convert_tool.compare_none_error
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if hasattr(convert_obj, '_convert_format_for_one_file'):
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ret_bf, _ = getattr(convert_obj, '_convert_format_for_one_file')(big_file)
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else:
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ret_bf, _ = getattr(convert_obj, 'convert_format_for_one_file')(big_file)
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if hasattr(convert_obj, 'multi_process'):
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getattr(convert_obj.multi_process, '_handle_result_callback')([ret_bf, big_file])
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else:
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getattr(convert_obj, '_handle_result_callback')([ret_bf, big_file])
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if ret_bf != self.convert_tool.compare_none_error:
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return_code = ret_bf
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if return_code != self.convert_tool.compare_none_error:
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if os.path.exists(self.failed_file_path):
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self.convert_tool.log.print_info_log(
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'The list of file that failed to convert has been written to "'
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+ self.failed_file_path + '".')
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return return_code
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def _save_tensor_to_npy_file(self, file_path, tensor_type, idx, tensor_format, dump_data_array):
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"""Save tensor file into npy format."""
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file_name = os.path.basename(file_path)
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name_splits = file_name.split('.')
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name_splits[1] = name_splits[1].split('_')[-1]
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file_name_no_scope = '.'.join(name_splits)
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out_file_name = "%s.%s.%d.%s.npy" % (
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file_name_no_scope,
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tensor_type,
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idx,
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self.convert_tool.common.get_format_string(tensor_format)
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)
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out_path = os.path.join(self.output_path, out_file_name)
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np.save(out_path, dump_data_array)
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os.chmod(out_path, stat.S_IRUSR)
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def _rename_generated_npy_files(self):
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"""In order to follow dump naming convention, rename npy files generated by CANN conversion tool."""
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target_file_list = []
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for in_file in self.files_to_convert:
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target_file_list.extend(glob.glob(in_file + "*.npy"))
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for target_file in target_file_list:
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old_filename = os.path.basename(target_file)
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name_splits = old_filename.split('.')
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name_splits[1] = name_splits[1].split('_')[-1]
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name_splits[-2] = self.args.format
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new_file_name = '.'.join(name_splits)
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out_path = os.path.join(self.output_path, new_file_name)
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os.rename(target_file, out_path)
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os.chmod(out_path, stat.S_IRUSR)
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self.convert_tool.log.print_info_log("Rename file " + target_file + " to " + out_path)
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