[profiler] add adaptive sampling to memory profiler (#330)

* fix merge conflict

modify unit test

remove unnessesary log info

reformat file

* remove unused module

* remove unnecessary sync function

* change doc string style from Google to Sphinx

colossalai/engine/ophooks/_memtracer_ophook.py

@@ -8,13 +8,18 @@ from time import sleep, time
 import pickle
 from typing import Optional
 from colossalai.core import global_context as gpc
+import math
 
 
 def get_cuda_memory_used(device: Optional[torch.device]) -> int:
-    """
-    Get the free memory info of device.
+    """Get the free memory info of device.
     Notice that for CPU, this function will return 1/N of the total free memory,
     where N is the world size.
+
+    :param device: device id
+    :type device: torch.device
+    :return: current memory usage, sized by MB
+    :rtype: int
     """
     ret: int = torch.cuda.memory_allocated(device)
     # get the peak memory to report correct data, so reset the counter for the next call
@@ -24,13 +29,16 @@ def get_cuda_memory_used(device: Optional[torch.device]) -> int:
 
 
 class AsyncMemoryMonitor:
-
-    def __init__(self, power=10):
-        """
-        An Async Mem Monitor runing during computing.
-        Sampling GPU memory usage of the current GPU dev
+    """
+        An Async Mem Monitor runing during computing. Sampling GPU memory usage of the current GPU
         at interval of 1/(10**power) sec.
+
+        :param power: the power of time interval, defaults to 10
+        :type power: int
         """
+
+    def __init__(self, power: int = 10):
+
         self.keep_measuring = False
         self.executor = ThreadPoolExecutor(max_workers=1)
         self.monitor_thread = None
@@ -42,6 +50,7 @@ class AsyncMemoryMonitor:
         return len(self.mem_stats)
 
     def set_interval(self, power: int):
+        self.clear()
         self.interval = 1 / (10**power)
 
     def is_measuring(self):
@@ -89,23 +98,16 @@ class AsyncMemoryMonitor:
 
 @OPHOOKS.register_module
 class MemTracerOpHook(BaseOpHook):
-    '''
+    """
     Collect GPU memory usage information
 
-    Args:
-        warmup (int): This parameter indicates how many iterations to truncate
-        before profiling, e.g. set to 5 and the data will start from 6-th iteration
-        refreshrate (int): This parameter decides the frequency of write file.
-        datafile(string): the name of the stats data file
-    Attributes:
-        _warmup (int): warmup iterations
-        _refreshrate(int): how many iterations we shall refresh the file
-        _logger (colossalai.logging.logger): output log file
-        _curiter (int): current iteration number
-        _count (int): the number of times the data file was written
-        _data_prefix (string): the prefix of the stats data file
-        _rank (int): the rank of current node
-    '''
+    :param warmup: This parameter indicates how many iterations to truncate before profiling, defaults to 50
+    :type warmup: int
+    :param refreshrate: This parameter decides the frequency of write file, defaults to 10
+    :type refreshrate: int
+    :param data_prefix: The prefix of the stats data file, defaults to "memstats"
+    :type data_prefix: string
+    """
 
     def __init__(self, warmup: int = 50, refreshrate: int = 10, data_prefix: str = "memstats"):
         super().__init__()
@@ -126,6 +128,16 @@ class MemTracerOpHook(BaseOpHook):
         assert isinstance(module, torch.nn.Module)
         return module.training
 
+    def _resample(self):
+        # calculate the average iteration time
+        total_time = (self.async_mem_monitor.time_stamps[-1] - self.async_mem_monitor.time_stamps[0])
+        avg_it_time = total_time / self.warmup
+        self._logger.debug(f"total time for {self.warmup} iterations is {total_time}s")
+        # adjust the sampling power
+        power: int = round(-math.log(avg_it_time, 10)) + 1
+        self._logger.debug(f"the power is {power}")
+        self.async_mem_monitor.set_interval(power)
+
     @property
     def refreshrate(self) -> int:
         return self._refreshrate
@@ -146,23 +158,19 @@ class MemTracerOpHook(BaseOpHook):
         if self._isvalid(module):
             self.async_mem_monitor.finish()
             self.async_mem_monitor.start()
-            self._logger.debug(f'FWD PRE {module.__class__.__name__}')
 
     def post_fwd_exec(self, module: torch.nn.Module, *args):
         if self._isvalid(module):
             self.async_mem_monitor.finish()
-            self._logger.debug(f'FWD POST {module.__class__.__name__}')
 
     def pre_bwd_exec(self, module: torch.nn.Module, input, output):
         if self._isvalid(module):
             self.async_mem_monitor.finish()
             self.async_mem_monitor.start()
-            self._logger.debug(f'BWD PRE {module.__class__.__name__}')
 
     def post_bwd_exec(self, module: torch.nn.Module, input):
         if self._isvalid(module):
             self.async_mem_monitor.finish()
-            self._logger.debug(f'BWD POST {module.__class__.__name__}')
 
     def pre_iter(self):
         pass
@@ -170,19 +178,21 @@ class MemTracerOpHook(BaseOpHook):
     def post_iter(self):
         self.async_mem_monitor.finish()
         # in the warmup stage
-        if self._curiter < self.warmup:
-            # TODO: record time and adaptively change sampling rate
+        if self.curiter < self.warmup:
             pass
-        elif self._curiter == self._warmup:
-            self.async_mem_monitor.clear()
+        # adjust the sampling rate
+        elif self.curiter == self.warmup:
+            # use adaptive sample rate
+            self._resample()
+        # record data to log file
         else:
             # every `refreshrate` times, refresh the file
             if self.valid_iter != 0 and self.valid_iter % self.refreshrate == 0:
                 # output file info
-                self._logger.info(f'dump a memory statistics as pickle to {self._dataprefix}-{self._rank}.pkl')
+                self._logger.info(f"dump a memory statistics as pickle to {self._data_prefix}-{self._rank}.pkl")
                 self.save_results()
                 self._count += 1
-                self._logger.debug(f'data file has been refreshed {self._count} times')
+                self._logger.debug(f"data file has been refreshed {self._count} times")
         # finish a iteration
         self._curiter += 1