!136 Support AICPU profiling data parser

Merge pull request !136 from 治愈系潇洒哥/master

mindinsight/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.
+"""
+
+from tabulate import tabulate
+
+from mindinsight.profiler.common._utils import fwrite_format, get_file_join_name
+from mindinsight.profiler.common.log import logger
+
+_source_file_target = 'DATA_PREPROCESS.dev.AICPU'
+_dst_file_title = 'title:DATA_PREPROCESS AICPU'
+_dst_file_column_title = ['serial_number', 'node_name', 'total_time(us)', 'dispatch_time(us)',
+                          'RunV2_start', 'compute_start', 'memcpy_start', 'memcpy_end', 'RunV2_end']
+
+
+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.
+
+    """
+
+    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()
+
+    def _get_source_file(self):
+        """Get log file name, which was created by ada service."""
+
+        return get_file_join_name(self._input_path, _source_file_target)
+
+    def execute(self):
+        """Execute the parser, get result data, and write it to the output file."""
+
+        if self._source_file_name is None:
+            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(" ___ ")
+
+        node_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_name = node_line.split(',')[0].split(':')[-1]
+                run_v2_start = node_line.split(',')[1].split(':')[-1]
+                compute_start = node_line.split(',')[2].split(':')[-1]
+                mercy_start = node_line.split(',')[3].split(':')[-1]
+                mercy_end = node_line.split(',')[4].split(':')[-1]
+                run_v2_end = node_line.split(',')[5].split(':')[-1]
+                # get total_time and dispatch_time from thread line
+                total_time = thread_line.split(',')[-1].split('=')[-1].split()[0]
+                dispatch_time = thread_line.split(',')[-2].split('=')[-1].split()[0]
+
+                node_data = [serial_number, node_name, total_time, dispatch_time, run_v2_start, compute_start,
+                             mercy_start, mercy_end, run_v2_end]
+
+                node_list.append(node_data)
+                # calculate the total time
+                ai_cpu_total_time_summary += int(total_time)
+                # increase node serial number
+                serial_number += 1
+            elif "Node" in node_line and "Thread" not in thread_line:
+                node_name = node_line.split(',')[0].split(':')[-1]
+                logger.warning("The node:%s cannot find thread data", node_name)
+
+        node_list.append(["AI CPU Total Time(us):", ai_cpu_total_time_summary])
+
+        if node_list:
+            fwrite_format(self._output_filename, data_source=_dst_file_title, is_print=True,
+                          is_start=True)
+            fwrite_format(self._output_filename,
+                          data_source=tabulate(node_list, _dst_file_column_title,
+                                               tablefmt='simple'),
+                          is_start=True, is_print=True)