[utils] add synchronized cuda memory monitor (#740)

colossalai/trainer/hooks/_mem_tracer_hook.py

@@ -1,9 +1,8 @@
-from cgitb import Hook
 from colossalai.registry import HOOKS
 from torch import Tensor
 from colossalai.trainer.hooks import BaseHook
 from colossalai.utils.memory_tracer import AsyncMemoryMonitor
-from ._metric_hook import LearningRateMetric, MetricHook
+
 
 @HOOKS.register_module
 class MemTraceHook(BaseHook):
@@ -11,6 +10,7 @@ class MemTraceHook(BaseHook):
     This hook is used to record memory usage info, and pass to trainer.states
     You can use it as other trainer hook and fetch data from trainer.states['metrics][mode]
     """
+
     def __init__(
         self,
         priority: int = 0,
@@ -36,9 +36,9 @@ class MemTraceHook(BaseHook):
     def before_test_iter(self, trainer):
         self._memory_monitor.start()
         return super().before_test(trainer)
-    
+
     def after_test_iter(self, trainer, output: Tensor, label: Tensor, loss: Tensor):
         self._memory_monitor.finish()
         trainer.states['metrics']['train'] = self._memory_monitor.state_dict
         trainer.states['metrics']['test'] = self._memory_monitor.state_dict
-        return super().after_test_iter(trainer, output, label, loss)
+        return super().after_test_iter(trainer, output, label, loss)

colossalai/utils/memory_tracer/__init__.py

@@ -1,4 +1,4 @@
-from .async_memtracer import AsyncMemoryMonitor
+from .memory_monitor import AsyncMemoryMonitor, SyncCudaMemoryMonitor
 from .memstats_collector import MemStatsCollector
 
-__all__ = ['AsyncMemoryMonitor', 'MemStatsCollector']
+__all__ = ['AsyncMemoryMonitor', 'SyncCudaMemoryMonitor', 'MemStatsCollector']

colossalai/utils/memory_tracer/memory_monitor.py

@@ -1,103 +1,142 @@
-from concurrent.futures import ThreadPoolExecutor
-from time import sleep, time
-import pickle
-
-import torch
-
-from colossalai.utils.memory import colo_device_memory_used
-from colossalai.utils import get_current_device
-
-
-class AsyncMemoryMonitor:
-    """
-    An Async Memory Monitor runing during computing. Sampling memory usage of the current GPU
-    at interval of `1/(10**power)` sec.
-
-    The idea comes from Runtime Memory Tracer of PatrickStar
-    `PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management`_
-
-    Usage::
-
-        async_mem_monitor = AsyncMemoryMonitor()
-        input = torch.randn(2, 20).cuda()
-        OP1 = torch.nn.Linear(20, 30).cuda()
-        OP2 = torch.nn.Linear(30, 40).cuda()
-
-        async_mem_monitor.start()
-        output = OP1(input)
-        async_mem_monitor.finish()
-        async_mem_monitor.start()
-        output = OP2(output)
-        async_mem_monitor.finish()
-        async_mem_monitor.save('log.pkl')
-
-
-    Args:
-        power (int, optional): the power of time interva. Defaults to 10.
-
-    .. _PatrickStar\: Parallel Training of Pre-trained Models via Chunk-based Memory Management:
-        https://arxiv.org/abs/2108.05818
-    """
-
-    def __init__(self, power: int = 10):
-        self.keep_measuring = False
-
-        current_device = get_current_device()
-
-        def _set_cuda_device():
-            torch.cuda.set_device(current_device)
-
-        self.executor = ThreadPoolExecutor(max_workers=1, initializer=_set_cuda_device)
-        self.monitor_thread = None
-        self.interval = 1 / (10**power)
-        self.time_stamps = []
-        self.mem_stats = []
-
-    def __len__(self):
-        return len(self.mem_stats)
-
-    def set_interval(self, power: int):
-        self.clear()
-        self.interval = 1 / (10**power)
-
-    def is_measuring(self):
-        return self.keep_measuring
-
-    def start(self):
-        self.keep_measuring = True
-        self.monitor_thread = self.executor.submit(self._measure_usage)
-
-    def finish(self):
-        if self.keep_measuring is False:
-            return 0
-        self.keep_measuring = False
-        max_usage = self.monitor_thread.result()
-        self.monitor_thread = None
-        self.time_stamps.append(time())
-        self.mem_stats.append(max_usage)
-        return max_usage
-
-    def _measure_usage(self):
-        max_usage = 0
-        while self.keep_measuring:
-            max_usage = max(
-                max_usage,
-                colo_device_memory_used(get_current_device()),
-            )
-            sleep(self.interval)
-        return max_usage
-
-    @property
-    def state_dict(self):
-        return {
-            "time_stamps": self.time_stamps,
-            "mem_stats": self.mem_stats,
-        }
-
-    def save(self, filename):
-        with open(filename, "wb") as f:
-            pickle.dump(self.state_dict(), f)
-
-    def clear(self):
-        self.mem_stats.clear()
-        self.time_stamps.clear()
+from abc import abstractmethod
+from concurrent.futures import ThreadPoolExecutor
+from time import sleep, time
+import json
+
+import torch
+
+from colossalai.utils.memory import colo_device_memory_used
+from colossalai.utils import get_current_device
+
+
+class MemoryMonitor:
+    """Base class for all types of memory monitor.
+    All monitors should have a list called `time_stamps` and a list called `mem_stats`.
+    """
+
+    def __init__(self):
+        self.time_stamps = []
+        self.mem_stats = []
+
+    def __len__(self):
+        return len(self.mem_stats)
+
+    @abstractmethod
+    def start(self):
+        pass
+
+    @abstractmethod
+    def finish(self):
+        pass
+
+    def state_dict(self):
+        return {
+            "time_stamps": self.time_stamps,
+            "mem_stats": self.mem_stats,
+        }
+
+    def save(self, filename):
+        with open(filename, "w") as f:
+            json.dump(self.state_dict(), f)
+
+    def clear(self):
+        self.mem_stats.clear()
+        self.time_stamps.clear()
+
+
+class AsyncMemoryMonitor(MemoryMonitor):
+    """
+    An Async Memory Monitor runing during computing. Sampling memory usage of the current GPU
+    at interval of `1/(10**power)` sec.
+
+    The idea comes from Runtime Memory Tracer of PatrickStar
+    `PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management`_
+
+    Usage::
+
+        async_mem_monitor = AsyncMemoryMonitor()
+        input = torch.randn(2, 20).cuda()
+        OP1 = torch.nn.Linear(20, 30).cuda()
+        OP2 = torch.nn.Linear(30, 40).cuda()
+
+        async_mem_monitor.start()
+        output = OP1(input)
+        async_mem_monitor.finish()
+        async_mem_monitor.start()
+        output = OP2(output)
+        async_mem_monitor.finish()
+        async_mem_monitor.save('log.pkl')
+
+    Args:
+        power (int, optional): the power of time interva. Defaults to 10.
+
+    .. _PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management:
+        https://arxiv.org/abs/2108.05818
+    """
+
+    def __init__(self, power: int = 10):
+        super().__init__()
+        self.keep_measuring = False
+
+        current_device = get_current_device()
+
+        def _set_cuda_device():
+            torch.cuda.set_device(current_device)
+
+        self.executor = ThreadPoolExecutor(max_workers=1, initializer=_set_cuda_device)
+        self.monitor_thread = None
+        self.interval = 1 / (10**power)
+
+    def set_interval(self, power: int):
+        self.clear()
+        self.interval = 1 / (10**power)
+
+    def is_measuring(self):
+        return self.keep_measuring
+
+    def start(self):
+        self.keep_measuring = True
+        self.monitor_thread = self.executor.submit(self._measure_usage)
+
+    def finish(self):
+        if self.keep_measuring is False:
+            return 0
+
+        self.keep_measuring = False
+        max_usage = self.monitor_thread.result()
+
+        self.monitor_thread = None
+        self.time_stamps.append(time())
+        self.mem_stats.append(max_usage)
+        return max_usage
+
+    def _measure_usage(self):
+        max_usage = 0
+        while self.keep_measuring:
+            max_usage = max(
+                max_usage,
+                colo_device_memory_used(get_current_device()),
+            )
+            sleep(self.interval)
+        return max_usage
+
+
+class SyncCudaMemoryMonitor(MemoryMonitor):
+    """
+    A synchronized cuda memory monitor.
+    It only record the maximum allocated cuda memory from start point to finish point.
+    """
+
+    def __init__(self, power: int = 10):
+        super().__init__()
+
+    def start(self):
+        torch.cuda.synchronize()
+        torch.cuda.reset_peak_memory_stats()
+
+    def finish(self):
+        torch.cuda.synchronize()
+        self.time_stamps.append(time())
+        max_usage = torch.cuda.max_memory_allocated()
+        self.mem_stats.append(max_usage)
+        return max_usage

colossalai/utils/memory_tracer/memstats_collector.py

@@ -1,6 +1,6 @@
 from colossalai.utils.memory_tracer.model_data_memtracer import GLOBAL_MODEL_DATA_TRACER
 from colossalai.utils.memory import colo_device_memory_used
-from colossalai.utils.memory_tracer.async_memtracer import AsyncMemoryMonitor
+from colossalai.utils.memory_tracer import AsyncMemoryMonitor
 import torch
 import time
 from typing import List