[profiler] add MemProfiler (#356)

* add memory trainer hook

* fix bug

* add memory trainer hook

* fix import bug

* fix import bug

* add trainer hook

* fix #370 git log bug

* modify `to_tensorboard` function to support better output

* remove useless output

* change the name of `MemProfiler`

* complete memory profiler

* replace error with warning

* finish trainer hook

* modify interface of MemProfiler

* modify `__init__.py` in profiler

* remove unnecessary pass statement

* add usage to doc string

* add usage to trainer hook

* new location to store temp data file

README-zh-Hans.md

@@ -267,4 +267,4 @@ class MLP_2D(nn.Module):
 }
 ```
 
-<p align="right">(<a href="#top">返回顶端</a>)</p>
+<p align="right">(<a href="#top">返回顶端</a>)</p>

README.md

@@ -270,4 +270,4 @@ class MLP_2D(nn.Module):
 }
 ```
 
-<p align="right">(<a href="#top">back to top</a>)</p>
+<p align="right">(<a href="#top">back to top</a>)</p>

colossalai/engine/_base_engine.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python
 # -*- encoding: utf-8 -*-
 
+from asyncio.log import logger
 from typing import List
 from torch.nn import Module
 from torch.nn.modules.loss import _Loss
@@ -9,9 +10,9 @@ from torch.optim import Optimizer
 from colossalai.logging import get_dist_logger
 from torch import Tensor
 from colossalai.engine.ophooks import register_ophooks_recursively, BaseOpHook
-from typing import Optional
+from typing import Optional, Type
 from colossalai.engine.gradient_handler import BaseGradientHandler
-
+from colossalai.logging import get_dist_logger
 
 class Engine:
     """Basic engine class for training and evaluation. It runs a specific process method
@@ -64,6 +65,11 @@ class Engine:
             self._ophook_list = ophook_list
         register_ophooks_recursively(self._model, self._ophook_list)
 
+    @property
+    def ophooks(self):
+        """show current activated ophooks"""
+        return self._ophook_list
+
     @property
     def model(self):
         """Model attached to the engine"""
@@ -79,6 +85,21 @@ class Engine:
         """Criterion attached to the engine"""
         return self._criterion
 
+    def add_hook(self, ophook: Type[BaseOpHook]) -> None:
+        """add necessary hook"""
+        # whether this hook exist
+        for h in self._ophook_list:
+            if type(h) == type(ophook):
+                logger = get_dist_logger()
+                logger.warning(f"duplicate hooks, at least two instance of {type(ophook)}")
+        self._ophook_list.append(ophook)
+        register_ophooks_recursively(self._model, self._ophook_list)
+
+    def remove_hook(self, ophook: Type[BaseOpHook]) -> None:
+        """remove hook"""
+        logger = get_dist_logger()
+        logger.warning(f"removing hooks is currently not supported")
+
     def zero_grad(self):
         """Set the gradient of parameters to zero
         """
@@ -150,4 +171,4 @@ class Engine:
         """Sets the model to evaluation mode.
         """
         self.training = False
-        self._model.eval()
+        self._model.eval()

colossalai/engine/ophooks/_memtracer_ophook.py

@@ -1,12 +1,15 @@
+import json
+import pickle
+from pathlib import Path
 from colossalai.context.parallel_mode import ParallelMode
 import torch
 from colossalai.engine.ophooks import BaseOpHook
 from colossalai.registry import OPHOOKS
 from colossalai.logging import get_dist_logger
 from colossalai.core import global_context as gpc
-
+from typing import Union
 from colossalai.utils.memory_tracer import AsyncMemoryMonitor
-
+import os
 import math
 
 
@@ -103,12 +106,14 @@ class MemTracerOpHook(BaseOpHook):
             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._data_prefix}-{self._rank}.pkl")
-                self.save_results()
+                home_dir = Path.home()
+                with open (home_dir.joinpath(f".cache/colossal/mem-{self._rank}.pkl"), "wb") as f:
+                    pickle.dump(self.async_mem_monitor.state_dict, f)
                 self._count += 1
                 self._logger.debug(f"data file has been refreshed {self._count} times")
         # finish a iteration
         self._curiter += 1
 
-    def save_results(self):
-        datafile = f"{self._data_prefix}-{self._rank}.pkl"
-        self.async_mem_monitor.save(datafile)
+    def save_results(self, data_file: Union[str, Path]):
+        with open(data_file, "w") as f:
+            f.write(json.dumps(self.async_mem_monitor.state_dict))

colossalai/trainer/hooks/_mem_tracer_hook.py

@@ -0,0 +1,44 @@
+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):
+    """Save memory stats and pass it to states
+    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,
+    ) -> None:
+        super().__init__(priority=priority)
+        self._memory_monitor = AsyncMemoryMonitor()
+
+    def after_hook_is_attached(self, trainer):
+        # Initialize the data
+        trainer.states['metrics']['train'] = self._memory_monitor.state_dict
+        trainer.states['metrics']['test'] = self._memory_monitor.state_dict
+
+    def before_train_iter(self, trainer):
+        self._memory_monitor.start()
+        return super().before_train_iter(trainer)
+
+    def after_train_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_train_iter(trainer, output, label, loss)
+
+    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)

colossalai/utils/memory_tracer/async_memtracer.py

@@ -86,6 +86,7 @@ class AsyncMemoryMonitor:
             sleep(self.interval)
         return max_usage
 
+    @property
     def state_dict(self):
         return {
             "time_stamps": self.time_stamps,
@@ -94,7 +95,6 @@ class AsyncMemoryMonitor:
 
     def save(self, filename):
         with open(filename, "wb") as f:
-            print(self.state_dict())
             pickle.dump(self.state_dict(), f)
 
     def clear(self):

colossalai/utils/profiler/__init__.py

@@ -1,3 +1,6 @@
 from .comm_profiler import CommProfiler
 from .pcie_profiler import PcieProfiler
-from .prof_utils import ProfilerContext
+from .prof_utils import ProfilerContext, BaseProfiler
+from .mem_profiler import MemProfiler
+
+__all__ = ['BaseProfiler', 'CommProfiler', 'PcieProfiler', 'MemProfiler', 'ProfilerContext']

colossalai/utils/profiler/mem_profiler.py

@@ -0,0 +1,50 @@
+from pathlib import Path
+from typing import Union
+from colossalai.engine import Engine
+from torch.utils.tensorboard import SummaryWriter
+from colossalai.engine.ophooks import MemTracerOpHook
+from colossalai.utils.profiler import BaseProfiler
+
+
+class MemProfiler(BaseProfiler):
+    """Wraper of MemOpHook, used to show GPU memory usage through each iteration
+    
+    To use this profiler, you need to pass an `engine` instance. And the usage is same like
+    CommProfiler.
+
+        mm_prof = MemProfiler(engine)
+        with ProfilerContext([mm_prof]) as prof:
+            writer = SummaryWriter("mem")
+            engine.train()
+            ...
+            prof.to_file("./log")
+            prof.to_tensorboard(writer)
+
+    """
+
+    def __init__(self, engine: Engine, warmup: int = 50, refreshrate: int = 10) -> None:
+        super().__init__(profiler_name="MemoryProfiler", priority=0)
+        self._mem_tracer = MemTracerOpHook(warmup=warmup, refreshrate=refreshrate)
+        self._engine = engine
+
+    def enable(self) -> None:
+        self._engine.add_hook(self._mem_tracer)
+
+    def disable(self) -> None:
+        self._engine.remove_hook(self._mem_tracer)
+
+    def to_tensorboard(self, writer: SummaryWriter) -> None:
+        stats = self._mem_tracer.async_mem_monitor.state_dict['mem_stats']
+        for info, i in enumerate(stats):
+            writer.add_scalar(
+                "memory_usage/GPU",
+                info,
+                i
+            )
+
+    def to_file(self, data_file: Path) -> None:
+        self._mem_tracer.save_results(data_file)
+
+    def show(self) -> None:
+        stats = self._mem_tracer.async_mem_monitor.state_dict['mem_stats'] 
+        print(stats)