optimize profiling data analysis logic

mindinsight/profiler/parser/hwts_log_parser.py

@@ -13,8 +13,8 @@
 # limitations under the License.
 # ============================================================================
 """The parser for hwts log file."""
+import os
 import struct
-from tabulate import tabulate
 from mindinsight.profiler.common._utils import fwrite_format, get_file_join_name
 from mindinsight.profiler.common.log import logger
 
@@ -30,8 +30,7 @@ class HWTSLogParser:
 
     _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']
+    _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
@@ -43,9 +42,11 @@ class HWTSLogParser:
 
         file_name = get_file_join_name(self._input_path, self._source_file_target)
         if not file_name:
-            msg = ("Fail to find hwts log file, under directory %s"
-                   % self._input_path)
-            raise RuntimeError(msg)
+            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
 
@@ -60,7 +61,7 @@ class HWTSLogParser:
         content_format = ['QIIIIIIIIIIII', 'QIIQIIIIIIII', 'IIIIQIIIIIIII']
         log_type = ['Start of task', 'End of task', 'Start of block', 'End of block', 'Block PMU']
 
-        result_data = []
+        result_data = ""
 
         with open(self._source_flie_name, 'rb') as hwts_data:
             while True:
@@ -73,6 +74,7 @@ class HWTSLogParser:
                 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]
@@ -80,22 +82,28 @@ class HWTSLogParser:
                     result = struct.unpack(content_format[0], line[8:])
                     syscnt = result[0]
                     stream_id = result[1]
-                    result_data.append((log_type[int(ms_type, 2)], cnt, core_id, blk_id, task_id, syscnt, stream_id))
-
                 elif ms_type == '011':  # log type 3
                     result = struct.unpack(content_format[1], line[8:])
                     syscnt = result[0]
                     stream_id = result[1]
-                    result_data.append((log_type[int(ms_type, 2)], cnt, core_id, blk_id, task_id, syscnt, stream_id))
                 elif ms_type == '100':  # log type 4
                     result = struct.unpack(content_format[2], line[8:])
                     stream_id = result[2]
-                    result_data.append((log_type[int(ms_type, 2)], cnt, core_id, blk_id, task_id, total_cyc, stream_id))
+                    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 = stream_id + "_" + 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=tabulate(result_data,
-                                                                  self._dst_file_column_title,
-                                                                  tablefmt='simple'))
+        fwrite_format(self._output_filename, data_source=self._dst_file_column_title)
+        fwrite_format(self._output_filename, data_source=result_data)
+
         return True

mindinsight/profiler/parser/optime_parser.py

@@ -14,8 +14,6 @@
 # ============================================================================
 """Op compute time files parser."""
 import os
-
-from tabulate import tabulate
 from mindinsight.profiler.common._utils import fwrite_format
 from mindinsight.profiler.common.exceptions.exceptions import ProfilerFileNotFoundException, \
     ProfilerIOException
@@ -25,7 +23,6 @@ from mindinsight.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.
@@ -37,7 +34,8 @@ class OPComputeTimeParser:
     """
 
     _dst_file_title = 'title:op compute time'
-    _dst_file_column_title = ['op_name', 'compute_time(ms)', 'stream_id']
+    _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):
@@ -100,12 +98,15 @@ class OPComputeTimeParser:
             op_name_count_dict, op_name_task_dict, op_name_start_time
         )
 
-        result_data = []
+        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]
-                result_data.append([op_name, avg_time, stream_id])
+                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():
@@ -130,23 +131,15 @@ class OPComputeTimeParser:
             op name, average time, and stream id.
 
         Args:
-            result_data (list): 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'],
-                    [...]
-                ]
+            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',
+                    ...
         """
-        result_data.sort(key=lambda x: x[0])
-        total_time = 0
-        for item in result_data:
-            total_time += item[1]
-        result_data.append(["total op", total_time, 0])
 
         fwrite_format(self._output_filename, data_source=self._dst_file_title, is_start=True)
-        fwrite_format(self._output_filename, data_source=tabulate(result_data,
-                                                                  self._dst_file_column_title,
-                                                                  tablefmt='simple'))
+        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):
         """

mindinsight/profiler/profiling.py

@@ -89,9 +89,9 @@ class Profiler:
         except ValueError as err:
             logger.error("Profiling: fail to get context, %s", err)
 
-        if not dev_id:
+        if not dev_id or not dev_id.isdigit():
             dev_id = os.getenv('DEVICE_ID')
-        if not dev_id:
+        if not dev_id or not dev_id.isdigit():
             dev_id = "0"
             logger.error("Fail to get DEVICE_ID, use 0 instead.")
 
@@ -105,12 +105,12 @@ class Profiler:
         self._container_path = os.path.join(self._base_profiling_container_path, dev_id)
         data_path = os.path.join(self._container_path, "data")
         if not os.path.exists(data_path):
-            os.makedirs(data_path)
+            os.makedirs(data_path, exist_ok=True)
         self._output_path = validate_and_normalize_path(output_path,
                                                         'Profiler output path (' + output_path + ')')
         self._output_path = os.path.join(self._output_path, "profiler")
         if not os.path.exists(self._output_path):
-            os.makedirs(self._output_path)
+            os.makedirs(self._output_path, exist_ok=True)
 
         os.environ['PROFILING_MODE'] = 'true'
         os.environ['PROFILING_OPTIONS'] = 'training_trace:task_trace'
@@ -121,8 +121,8 @@ class Profiler:
             context.set_context(enable_profiling=True, profiling_options="training_trace:task_trace")
         except ImportError:
             logger.error("Profiling: fail to import context from mindspore.")
-        except ValueError as err:
-            logger.error("Profiling: fail to set context, %s", err.message)
+        except ValueError:
+            logger.error("Profiling: fail to set context enable_profiling")
 
         os.environ['AICPU_PROFILING_MODE'] = 'true'
         os.environ['PROFILING_DIR'] = str(self._container_path)
@@ -162,8 +162,7 @@ class Profiler:
 
         job_id = self._get_profiling_job_id()
         if not job_id:
-            msg = ("Fail to get profiling job, please check whether job dir was generated under path %s" \
-                   % PROFILING_LOG_BASE_PATH)
+            msg = ("Fail to get profiling job, please check whether job dir was generated")
             raise RuntimeError(msg)
 
         logger.info("Profiling: job id is %s ", job_id)
@@ -296,6 +295,13 @@ class Profiler:
         """Disable the profiling collection service, called after training."""
 
         os.environ['PROFILING_MODE'] = str("false")
+        try:
+            import mindspore.context as context
+            context.set_context(enable_profiling=False)
+        except ImportError:
+            logger.error("Profiling: fail to import context from mindspore.")
+        except ValueError:
+            logger.error("Profiling: fail to set context enable_profiling")
 
     def _get_profiling_job_id(self):
         """Get profiling job id, which was generated by ada service.