add pytorch hooks (#179)

* add pytorch hooks
fix #175

* remove licenses in src code

* add gpu memory tracer

* replacing print with logger in ophooks.

colossalai/builder/__init__.py

@@ -1,10 +1,12 @@
-from .builder import (build_schedule, build_lr_scheduler, build_model, build_optimizer, build_layer,
-                      build_loss, build_hooks, build_dataset, build_transform, build_data_sampler,
-                      build_gradient_handler)
+from .builder import (build_schedule, build_lr_scheduler, build_model,
+                      build_optimizer, build_layer, build_loss, build_hooks,
+                      build_dataset, build_transform, build_data_sampler,
+                      build_gradient_handler, build_ophooks)
 from .pipeline import build_pipeline_model, build_pipeline_model_from_cfg
 
 __all__ = [
     'build_schedule', 'build_lr_scheduler', 'build_model', 'build_optimizer',
-    'build_layer', 'build_loss', 'build_hooks', 'build_dataset', 'build_transform', 'build_data_sampler',
-    'build_gradient_handler', 'build_pipeline_model', 'build_pipeline_model_from_cfg'
+    'build_layer', 'build_loss', 'build_hooks', 'build_dataset',
+    'build_transform', 'build_data_sampler', 'build_gradient_handler',
+    'build_pipeline_model', 'build_pipeline_model_from_cfg', 'build_ophooks'
 ]

colossalai/builder/builder.py

@@ -27,7 +27,7 @@ def build_from_registry(config, registry: Registry):
     """Returns an object constructed from `config`, the type of the object
     is specified by `registry`.
 
-    :param config: A python dict or a :class:`colossalai.context.Config` object 
+    :param config: A python dict or a :class:`colossalai.context.Config` object
         containing information used in the construction of the return object
     :type config: dict or :class:`colossalai.context.colossalai.context.Config`
     :param registry: A registry specifying the type of the return object
@@ -50,7 +50,8 @@ def build_from_registry(config, registry: Registry):
         obj = registry.get_module(mod_type)(**config_)
     except Exception as e:
         print(
-            f'An error occurred when building {mod_type} from registry {registry.name}', flush=True)
+            f'An error occurred when building {mod_type} from registry {registry.name}',
+            flush=True)
         raise e
 
     return obj
@@ -69,7 +70,7 @@ def build_layer(config):
 
 
 def build_loss(config):
-    """Returns a loss function object of :class:`torch.autograd.Function` constructed 
+    """Returns a loss function object of :class:`torch.autograd.Function` constructed
     from `config`.
 
     :param config: A python dict or a :class:`colossalai.context.Config` object
@@ -94,7 +95,7 @@ def build_model(config):
 
 
 def build_dataset(config):
-    """Returns a dataset object of :class:`torch.utils.data.Dataset` constructed 
+    """Returns a dataset object of :class:`torch.utils.data.Dataset` constructed
     from `config`.
 
     :param config: A python dict or a :class:`colossalai.context.Config` object
@@ -107,13 +108,13 @@ def build_dataset(config):
 
 
 def build_optimizer(config, model):
-    """Returns an optimizer object of :class:`torch.optim.Optimizer` constructed from `config`, 
+    """Returns an optimizer object of :class:`torch.optim.Optimizer` constructed from `config`,
     'model' and 'params'.
 
-    :param config: A python dict or a :class:`colossalai.context.Config` object 
+    :param config: A python dict or a :class:`colossalai.context.Config` object
         containing information used in the construction of the return object
     :type config: dict or :class:`colossalai.context.Config`
-    :param model: A model containing parameters for the optimizer 
+    :param model: A model containing parameters for the optimizer
     :type model: :class:`nn.Module`
     :return: An object of :class:`torch.optim.Optimizer`
     :rtype: :class:`torch.optim.Optimizer`
@@ -159,6 +160,19 @@ def build_hooks(config, trainer):
     return build_from_registry(config_, HOOKS)
 
 
+def build_ophooks(config):
+    """Returns a hook object of :class:`BaseOpHook` constructed from `config`.
+
+    :param config: A python dict or a :class:`colossalai.context.Config` object
+        containing information used in the construction of the return object
+    :type config: dict or :class:`colossalai.context.Config`
+    :return: An object of :class:`colossalai.trainer.hooks.BaseOpHook`
+    :rtype: :class:`colossalai.trainer.hooks.BaseOpHook`
+    """
+    config_ = config.copy()
+    return build_from_registry(config_, OPHOOKS)
+
+
 def build_transform(config):
     """Returns a transformation object of :class:`torchvision.transforms` constructed
     from `config`.
@@ -191,10 +205,10 @@ def build_data_sampler(config, dataset):
 
 
 def build_lr_scheduler(config, optimizer):
-    """Returns a learning rate scheduler object of :class:`torch.optim.lr_scheduler` 
+    """Returns a learning rate scheduler object of :class:`torch.optim.lr_scheduler`
     constructed from `config`, `optimizer`, `total_steps` and `num_steps_per_epoch`.
 
-    :param config: A python dict or a :class:`colossalai.context.Config` object 
+    :param config: A python dict or a :class:`colossalai.context.Config` object
         containing information used in the construction of the return object
     :type config: dict or :class:`colossalai.context.Config`
     :param optimizer: An optimizer object containing parameters for the learning rate

colossalai/engine/_base_engine.py

@@ -1,7 +1,6 @@
 #!/usr/bin/env python
 # -*- encoding: utf-8 -*-
 
-
 from typing import List
 from torch.nn import Module
 from torch.nn.modules.loss import _Loss
@@ -9,10 +8,11 @@ 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
 
 
 class Engine:
-    """Basic engine class for training and evaluation. It runs a specific process method 
+    """Basic engine class for training and evaluation. It runs a specific process method
     :meth:`step` which is based on the given :attr:`schedule` over each batch of a dataset.
     It controls a iteration in training.
 
@@ -29,15 +29,14 @@ class Engine:
     :param verbose: whether to display log info
     :type verbose: bool
     """
-
     def __init__(self,
                  model: Module,
                  optimizer: Optimizer,
                  criterion: _Loss,
                  gradient_handlers: List = None,
                  clip_grad_norm: float = 0.0,
-                 verbose: bool = True
-                 ):
+                 ophook_list: List[BaseOpHook] = [],
+                 verbose: bool = True):
         self._model = model
         self._optimizer = optimizer
         self._criterion = criterion
@@ -54,6 +53,9 @@ class Engine:
         else:
             self._gradient_handlers = []
 
+        self._ophook_list = ophook_list
+        register_ophooks_recursively(self._model, self._ophook_list)
+
     @property
     def model(self):
         """Model attached to the engine"""
@@ -87,7 +89,10 @@ class Engine:
         :param loss: Loss value computed by a loss function
         :type loss: :class:`torch.Tensor`
         """
-        return self.optimizer.backward(loss)
+        ret = self.optimizer.backward(loss)
+        for ophook in self._ophook_list:
+            ophook.post_iter()
+        return ret
 
     def backward_by_grad(self, tensor, grad):
         """Start backward propagation given the gradient of the output tensor
@@ -97,7 +102,10 @@ class Engine:
         :param grad: Gradient passed back to the output
         :type grad: :class:`torch.Tensor`
         """
-        return self.optimizer.backward_by_grad(tensor, grad)
+        ret = self.optimizer.backward_by_grad(tensor, grad)
+        for ophook in self._ophook_list:
+            ophook.post_iter()
+        return ret
 
     def calc_loss(self, *args, **kwargs):
         """Compute the loss value

colossalai/engine/ophooks/__init__.py

@@ -0,0 +1,115 @@
+from ._base_ophook import BaseOpHook
+from ._memtracer_ophook import MemTracerOpHook
+import torch
+from typing import List
+
+all = ["BaseOpHook", "MemTracerOpHook", "register_ophooks_recursively"]
+
+
+# apply torch.autograd.Function that calls a backward_function to tensors in output
+def _apply_to_tensors_only(module, functional, backward_function, outputs):
+    if type(outputs) is tuple:
+        touched_outputs = []
+        for output in outputs:
+            touched_output = _apply_to_tensors_only(module, functional,
+                                                    backward_function, output)
+            touched_outputs.append(touched_output)
+        return tuple(touched_outputs)
+    elif type(outputs) is torch.Tensor:
+        return functional.apply(module, backward_function, outputs)
+    else:
+        return outputs
+
+
+class PreBackwardFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, module, pre_backward_function, outputs):
+        ctx.module = module
+        ctx.pre_backward_function = pre_backward_function
+        module.applied_pre_backward = False
+        outputs = outputs.detach()
+        return outputs
+
+    @staticmethod
+    def backward(ctx, *args):
+        ctx.pre_backward_function(ctx.module)
+        return (None, None) + args
+
+
+class PostBackwardFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, module, pre_backward_function, output):
+        ctx.module = module
+        output = output.detach()
+        ctx.pre_backward_function = pre_backward_function
+        return output
+
+    @staticmethod
+    def backward(ctx, *args):
+        """
+        Args:
+            activation_grad of the next layer.
+        Returns:
+            grad of the input activation.
+        """
+        ctx.pre_backward_function(ctx.module)
+        return (None, None) + args
+
+
+def register_ophooks_recursively(module: torch.nn.Module,
+                                 ophook_list: List[BaseOpHook] = None,
+                                 name: str = ""):
+    r"""Recursilvely register pre/post hooks for all submodules in the module in FWD and BWD."""
+    assert isinstance(module, torch.nn.Module)
+    has_children = False
+    for child_name, child in module.named_children():
+        register_ophooks_recursively(child, ophook_list, name + child_name)
+        has_children = True
+
+    # Early return on modules with no parameters or buffers that
+    # are not in their children.
+    if (len(list(module.named_parameters(recurse=False))) == 0
+            and len(list(module.named_buffers(recurse=False))) == 0):
+        return
+
+    # return if the module has not childern.
+    if has_children:
+        return
+
+    if ophook_list is not None:
+        for hook in ophook_list:
+            assert (isinstance(hook, BaseOpHook))
+
+    def _pre_forward_module_hook(submodule, *args):
+        for hook in ophook_list:
+            assert isinstance(submodule, torch.nn.Module)
+            hook.pre_fwd_exec(submodule, *args)
+
+    def _post_forward_module_hook(submodule, *args):
+        for hook in ophook_list:
+            assert isinstance(submodule, torch.nn.Module)
+            hook.post_fwd_exec(submodule, *args)
+
+    def _pre_backward_module_hook(submodule, inputs, output):
+        def _run_before_backward_function(submodule):
+            for hook in ophook_list:
+                assert isinstance(submodule, torch.nn.Module)
+                hook.pre_bwd_exec(submodule, inputs, output)
+
+        return _apply_to_tensors_only(submodule, PreBackwardFunction,
+                                      _run_before_backward_function, output)
+
+    def _post_backward_module_hook(submodule, inputs):
+        def _run_after_backward_function(submodule):
+            for hook in ophook_list:
+                assert isinstance(submodule, torch.nn.Module)
+                hook.post_bwd_exec(submodule, inputs)
+
+        return _apply_to_tensors_only(submodule, PostBackwardFunction,
+                                      _run_after_backward_function, inputs)
+
+    module.register_forward_pre_hook(_pre_forward_module_hook)
+    module.register_forward_hook(_post_forward_module_hook)
+
+    module.register_forward_hook(_pre_backward_module_hook)
+    module.register_forward_pre_hook(_post_backward_module_hook)

colossalai/engine/ophooks/_base_ophook.py

@@ -0,0 +1,29 @@
+from abc import ABC, abstractmethod
+import torch
+
+
+class BaseOpHook(ABC):
+    """This class allows users to add customized operations
+    before and after the execution of a PyTorch submodule"""
+    def __init__(self):
+        pass
+
+    @abstractmethod
+    def pre_fwd_exec(self, module: torch.nn.Module, *args):
+        pass
+
+    @abstractmethod
+    def post_fwd_exec(self, module: torch.nn.Module, *args):
+        pass
+
+    @abstractmethod
+    def pre_bwd_exec(self, module: torch.nn.Module, input, output):
+        pass
+
+    @abstractmethod
+    def post_bwd_exec(self, module: torch.nn.Module, input):
+        pass
+
+    @abstractmethod
+    def post_iter(self):
+        pass

colossalai/engine/ophooks/_memtracer_ophook.py

@@ -0,0 +1,131 @@
+import torch
+from . import BaseOpHook
+from concurrent.futures import ThreadPoolExecutor
+from colossalai.registry import OPHOOKS
+from colossalai.logging import get_dist_logger
+from time import sleep, time
+import psutil
+import pickle
+
+
+def get_cuda_memory_used(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.
+    """
+    ret = torch.cuda.memory_allocated()
+    # get the peak memory to report correct data, so reset the counter for the next call
+    if hasattr(torch.cuda, "reset_peak_memory_stats"):  # pytorch 1.4+
+        torch.cuda.reset_peak_memory_stats()
+    return ret
+
+
+class AsyncMemoryMonitor:
+    def __init__(self, power=10):
+        """
+        An Async Mem Monitor runing during computing.
+        Sampling GPU memory usage of the current GPU dev
+        at interval of 1/(10**power) sec.
+        """
+        self.keep_measuring = False
+        self.executor = ThreadPoolExecutor(max_workers=1)
+        self.monitor_thread = None
+        self.interval = 1 / (10**power)
+        self.time_stamps = []
+        self.mem_stats = []
+
+    def set_interval(self, power: int):
+        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
+        dev = torch.device(f"cuda:{torch.cuda.current_device()}")
+        while self.keep_measuring:
+            max_usage = max(
+                max_usage,
+                get_cuda_memory_used(dev),
+            )
+            sleep(self.interval)
+        return max_usage
+
+    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)
+
+
+@OPHOOKS.register_module
+class MemTracerOpHook(BaseOpHook):
+    def __init__(self, niter=5):
+        super().__init__()
+        self.async_mem_monitor = AsyncMemoryMonitor()
+        self._niter = niter
+        self._curiter = 0
+        self._logger = get_dist_logger()
+
+    def _isvalid(self, module):
+        return module.training and self._curiter < self._niter
+
+    def niter(self):
+        return self._niter
+
+    def pre_fwd_exec(self, module: torch.nn.Module, *args):
+        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):
+        assert isinstance(module, torch.nn.Module)
+        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):
+        assert isinstance(module, torch.nn.Module)
+        if self._isvalid(module):
+            self.async_mem_monitor.finish()
+            self._logger.debug(f'BWD POST {module.__class__.__name__}')
+
+    def pre_iter(self):
+        pass
+
+    def post_iter(self):
+        self.async_mem_monitor.finish()
+        if self._curiter == self._niter:
+            self._logger.info(
+                f'dump a memory statistics as pickle to ./memstats.pkl')
+            self.save_results("memstats.pkl")
+        self._curiter += 1
+
+    def save_results(self, filename):
+        self.async_mem_monitor.save(filename)

colossalai/registry/__init__.py

@@ -7,16 +7,17 @@ from torchvision import transforms
 
 from .registry import Registry
 
-LAYERS = Registry('layers', third_party_library=[nn])
-LOSSES = Registry('losses')
-MODELS = Registry('models', third_party_library=[tv_models])
-OPTIMIZERS = Registry('optimizers', third_party_library=[optim, dist_optim])
-DATASETS = Registry('datasets', third_party_library=[tv_datasets])
-DIST_GROUP_INITIALIZER = Registry('dist_group_initializer')
-GRADIENT_HANDLER = Registry('gradient_handler')
-LOSSES = Registry('losses', third_party_library=[nn])
-HOOKS = Registry('hooks')
-TRANSFORMS = Registry('transforms', third_party_library=[transforms])
-DATA_SAMPLERS = Registry('data_samplers')
-LR_SCHEDULERS = Registry('lr_schedulers')
-SCHEDULE = Registry('schedules')
+LAYERS = Registry("layers", third_party_library=[nn])
+LOSSES = Registry("losses")
+MODELS = Registry("models", third_party_library=[tv_models])
+OPTIMIZERS = Registry("optimizers", third_party_library=[optim, dist_optim])
+DATASETS = Registry("datasets", third_party_library=[tv_datasets])
+DIST_GROUP_INITIALIZER = Registry("dist_group_initializer")
+GRADIENT_HANDLER = Registry("gradient_handler")
+LOSSES = Registry("losses", third_party_library=[nn])
+HOOKS = Registry("hooks")
+TRANSFORMS = Registry("transforms", third_party_library=[transforms])
+DATA_SAMPLERS = Registry("data_samplers")
+LR_SCHEDULERS = Registry("lr_schedulers")
+SCHEDULE = Registry("schedules")
+OPHOOKS = Registry("ophooks")

colossalai/trainer/_trainer.py

@@ -1,8 +1,4 @@
-#!/usr/bin/env python
-# -*- encoding: utf-8 -*-
-
 from typing import Union, List
-from colossalai import engine
 from colossalai.context.parallel_mode import ParallelMode
 
 import torch
@@ -11,17 +7,18 @@ from torch.utils.data import DataLoader
 from tqdm import tqdm
 
 from colossalai.core import global_context as gpc
+
 from colossalai.engine import Engine
 from colossalai.engine.schedule import NonPipelineSchedule, BaseSchedule
 from colossalai.logging import DistributedLogger
 from colossalai.utils import MultiTimer
 from colossalai.utils import is_dp_rank_0, is_tp_rank_0, is_no_pp_or_last_stage
-from .hooks import BaseHook
+from colossalai.trainer.hooks import BaseHook
 
 
 class Trainer:
-    """This a class tending for easy deployments of users' training and evaluation instead of 
-    writing their own scripts. It is similar with ``ignite.engine`` and ``keras.engine``, but is 
+    """This a class tending for easy deployments of users' training and evaluation instead of
+    writing their own scripts. It is similar with ``ignite.engine`` and ``keras.engine``, but is
     called `Trainer`.
 
     :param engine: Engine responsible for the process function
@@ -33,12 +30,13 @@ class Trainer:
     :param logger: Logger used to record the whole training
     :type logger: :class:`colossalai.logging.DistributedLogger`, optional
     """
-
-    def __init__(self,
-                 engine: Engine,
-                 schedule: BaseSchedule = None,
-                 timer: MultiTimer = None,
-                 logger: DistributedLogger = None):
+    def __init__(
+            self,
+            engine: Engine,
+            schedule: BaseSchedule = None,
+            timer: MultiTimer = None,
+            logger: DistributedLogger = None,
+    ):
         # training-ralated params
         self._engine = engine
         self._max_epochs = 0
@@ -63,29 +61,28 @@ class Trainer:
         # set schedule which specifies the training iteration for the engine
         if schedule is None:
             schedule = NonPipelineSchedule()
-        if gpc.is_initialized(ParallelMode.PIPELINE) and gpc.get_world_size(ParallelMode.PIPELINE) > 1:
-            assert not isinstance(schedule, NonPipelineSchedule), \
-                'NonPipelineSchedule cannot be used for pipeline parallel training, please use PipelineSchedule instead.'
+        if (gpc.is_initialized(ParallelMode.PIPELINE)
+                and gpc.get_world_size(ParallelMode.PIPELINE) > 1):
+            assert not isinstance(
+                schedule, NonPipelineSchedule
+            ), "NonPipelineSchedule cannot be used for pipeline parallel training, please use PipelineSchedule instead."
         self._schedule = schedule
         self._schedule.pre_processing(engine)
 
     @property
     def cur_epoch(self):
-        """Returns the index of the current epoch.
-        """
+        """Returns the index of the current epoch."""
         return self._cur_epoch
 
     @cur_epoch.setter
     def cur_epoch(self, epoch: int):
-        """Set how many epochs have been processed.
-        """
+        """Set how many epochs have been processed."""
         # allow setter for training resumption
         self._cur_epoch = epoch
 
     @property
     def cur_step(self):
-        """Returns how many iteration steps have been processed.
-        """
+        """Returns how many iteration steps have been processed."""
         return self._cur_step
 
     @property
@@ -131,8 +128,7 @@ class Trainer:
             getattr(self._timer, action)(item, *args, **kwargs)
 
     def _reset_states(self) -> None:
-        """Clear trainer states
-        """
+        """Clear trainer states"""
         self.states = dict()
 
     def _call_hooks(self, func, output=None):
@@ -152,99 +148,122 @@ class Trainer:
 
     @staticmethod
     def _should_display_progress(display_progress: bool):
-        """ Only display progress on DP rank 0, TP rank 0 and PP last rank
-        """
-        return display_progress and is_dp_rank_0() and is_tp_rank_0() and is_no_pp_or_last_stage()
+        """Only display progress on DP rank 0, TP rank 0 and PP last rank"""
+        return (display_progress and is_dp_rank_0() and is_tp_rank_0()
+                and is_no_pp_or_last_stage())
 
-    def _train_epoch(self,
-                     train_dataloader: DataLoader,
-                     epoch: int = None,
-                     display_progress: bool = False,
-                     return_output_label: bool = True):
+    def _train_epoch(
+            self,
+            train_dataloader: DataLoader,
+            epoch: int = None,
+            display_progress: bool = False,
+            return_output_label: bool = True,
+    ):
         # set training state
         self._engine.train()
         data_iter = iter(train_dataloader)
         progress = range(self._steps_per_epoch)
         if display_progress:
             if epoch is None:
-                progress = tqdm(progress, desc='[Train]')
+                progress = tqdm(progress, desc="[Train]")
             else:
-                progress = tqdm(progress, desc=f'[Epoch {epoch} / Train]')
+                progress = tqdm(progress, desc=f"[Epoch {epoch} / Train]")
 
-        self._call_hooks('before_train_epoch')
-        self._call_timer(action='start', item='Train-epoch')
+        self._call_hooks("before_train_epoch")
+        self._call_timer(action="start", item="Train-epoch")
         for i in progress:
-            self._call_hooks('before_train_iter')
-            self._call_timer(action='start', item='Train-step')
+            self._call_hooks("before_train_iter")
+            self._call_timer(action="start", item="Train-step")
 
             # run 1 training step
             self.engine.zero_grad()
             logits, label, loss = self.schedule.forward_backward_step(
-                self.engine, data_iter, forward_only=False, return_loss=True, return_output_label=return_output_label)
+                self.engine,
+                data_iter,
+                forward_only=False,
+                return_loss=True,
+                return_output_label=return_output_label,
+            )
             self.engine.step()
-            self._call_timer(action='stop', item='Train-step', keep_in_history=True)
-            self._call_hooks('after_train_iter', output=(logits, label, loss))
+            self._call_timer(action="stop",
+                             item="Train-step",
+                             keep_in_history=True)
+            self._call_hooks("after_train_iter", output=(logits, label, loss))
 
             self._cur_step += 1
 
             if display_progress:
-                if 'step_metrics' in self.states:
-                    progress.set_postfix(**self.states['step_metrics'])
+                if "step_metrics" in self.states:
+                    progress.set_postfix(**self.states["step_metrics"])
 
             # stop when max iter is reached
             if self._exceed_max_step():
                 break
 
-        self._call_timer(action='stop', item='Train-epoch', keep_in_history=True)
-        self._call_hooks('after_train_epoch')
-        self._call_timer(action='reset', item='Train-epoch')
+        self._call_timer(action="stop",
+                         item="Train-epoch",
+                         keep_in_history=True)
+        self._call_hooks("after_train_epoch")
+        self._call_timer(action="reset", item="Train-epoch")
 
-    def _eval(self,
-              test_dataloader: DataLoader,
-              epoch: int = None,
-              display_progress: bool = False,
-              return_output_label: bool = True):
+    def _eval(
+            self,
+            test_dataloader: DataLoader,
+            epoch: int = None,
+            display_progress: bool = False,
+            return_output_label: bool = True,
+    ):
         # switch engine status
         self._engine.eval()
 
         data_iter = iter(test_dataloader)
         num_steps = len(test_dataloader)
 
-        self._call_hooks('before_test')
+        self._call_hooks("before_test")
         # prepare progress bar
         progress = range(num_steps)
         if display_progress:
-            desc = 'Evaluation'
+            desc = "Evaluation"
             if epoch is not None:
-                desc = '[Epoch %d / Test]' % epoch
+                desc = "[Epoch %d / Test]" % epoch
             progress = tqdm(progress, desc=desc)
 
-        self._call_hooks('before_test_epoch')
-        self._call_timer(action='start', item='Test-epoch')
+        self._call_hooks("before_test_epoch")
+        self._call_timer(action="start", item="Test-epoch")
         with torch.no_grad():
             for _ in progress:
-                self._call_hooks('before_test_iter')
-                self._call_timer(action='start', item='Test-step')
+                self._call_hooks("before_test_iter")
+                self._call_timer(action="start", item="Test-step")
                 logits, label, loss = self.schedule.forward_backward_step(
-                    self.engine, data_iter, forward_only=True, return_loss=True, return_output_label=return_output_label)
-                self._call_timer(action='stop', item='Test-step', keep_in_history=True)
-                self._call_hooks('after_test_iter',
+                    self.engine,
+                    data_iter,
+                    forward_only=True,
+                    return_loss=True,
+                    return_output_label=return_output_label,
+                )
+                self._call_timer(action="stop",
+                                 item="Test-step",
+                                 keep_in_history=True)
+                self._call_hooks("after_test_iter",
                                  output=(logits, label, loss))
 
                 if display_progress:
-                    if 'step_metrics' in self.states:
-                        progress.set_postfix(**self.states['step_metrics'])
+                    if "step_metrics" in self.states:
+                        progress.set_postfix(**self.states["step_metrics"])
 
-        self._call_timer(action='stop', item='Test-epoch', keep_in_history=True)
-        self._call_hooks('after_test_epoch')
-        self._call_hooks('after_test')
-        self._call_timer(action='reset', item='Test-step')
-        self._call_timer(action='reset', item='Test-epoch')
+        self._call_timer(action="stop",
+                         item="Test-epoch",
+                         keep_in_history=True)
+        self._call_hooks("after_test_epoch")
+        self._call_hooks("after_test")
+        self._call_timer(action="reset", item="Test-step")
+        self._call_timer(action="reset", item="Test-epoch")
 
     def _exceed_max_step(self):
         return self._max_steps is not None and self._cur_step >= self._max_steps
 
-    def fit(self,
+    def fit(
+            self,
             train_dataloader: DataLoader,
             epochs: int,
             max_steps: int = None,
@@ -253,7 +272,7 @@ class Trainer:
             hooks: List[BaseHook] = None,
             display_progress: bool = False,
             return_output_label: bool = True,
-            ):
+    ):
         """Trains the model to fit training data.
 
         :param train_dataloader: DataLoader in training
@@ -290,7 +309,9 @@ class Trainer:
         # reset hooks
         self._reset_states()
         if hooks is not None:
-            assert isinstance(hooks, list), f'expected argument hooks be to list, but got {type(hooks)}'
+            assert isinstance(
+                hooks, list
+            ), f"expected argument hooks be to list, but got {type(hooks)}"
         else:
             hooks = []
         self.hooks = hooks
@@ -298,13 +319,16 @@ class Trainer:
         if self._verbose:
             for hook in self.hooks:
                 self._logger.info(
-                    f'Using {hook.__class__.__name__} for training, priority = {hook.priority}', ranks=[0])
-            self._logger.info("Lower value means higher priority for calling hook function", ranks=[0])
-        self._call_hooks('after_hook_is_attached')
+                    f"Using {hook.__class__.__name__} for training, priority = {hook.priority}",
+                    ranks=[0],
+                )
+            self._logger.info(
+                "Lower value means higher priority for calling hook function",
+                ranks=[0])
+        self._call_hooks("after_hook_is_attached")
 
-        # start train
         self._engine.train()
-        self._call_hooks('before_train')
+        self._call_hooks("before_train")
 
         # recover step value if resuming training
         last_epoch = self._cur_epoch
@@ -317,16 +341,17 @@ class Trainer:
                 train_dataloader=train_dataloader,
                 epoch=epoch,
                 display_progress=display_progress,
-                return_output_label=return_output_label
+                return_output_label=return_output_label,
             )
 
             # start eval
             if should_test and epoch % test_interval == 0:
-                self._eval(test_dataloader=test_dataloader,
-                           display_progress=display_progress,
-                           epoch=epoch,
-                           return_output_label=return_output_label
-                           )
+                self._eval(
+                    test_dataloader=test_dataloader,
+                    display_progress=display_progress,
+                    epoch=epoch,
+                    return_output_label=return_output_label,
+                )
 
             self._cur_epoch += 1
 
@@ -334,16 +359,19 @@ class Trainer:
             if self._exceed_max_step():
                 self._logger.info(
                     f"Max number of steps {max_steps} has been reached, training is stopped automatically",
-                    ranks=[0])
+                    ranks=[0],
+                )
                 break
-        self._call_hooks('after_train')
-        self._call_timer('reset', 'Train-epoch')
+        self._call_hooks("after_train")
+        self._call_timer("reset", "Train-epoch")
 
-    def evaluate(self,
-                 test_dataloader: DataLoader,
-                 hooks: List[BaseHook] = None,
-                 display_progress: bool = False,
-                 return_output_label: bool = True):
+    def evaluate(
+            self,
+            test_dataloader: DataLoader,
+            hooks: List[BaseHook] = None,
+            display_progress: bool = False,
+            return_output_label: bool = True,
+    ):
         """Evaluates the model with testing data.
 
         :param test_dataloader: DataLoader in testing
@@ -362,7 +390,9 @@ class Trainer:
         # reset hooks
         self._reset_states()
         if hooks is not None:
-            assert isinstance(hooks, list), f'expected argument hooks be to list, but got {type(hooks)}'
+            assert isinstance(
+                hooks, list
+            ), f"expected argument hooks be to list, but got {type(hooks)}"
         else:
             hooks = []
         self.hooks = hooks
@@ -370,15 +400,20 @@ class Trainer:
         if self._verbose:
             for hook in self.hooks:
                 self._logger.info(
-                    f'Using {hook.__class__.__name__} for training, priority = {hook.priority}', ranks=[0])
-            self._logger.info("Lower value means higher priority for calling hook function", ranks=[0])
-        self._call_hooks('after_hook_is_attached')
+                    f"Using {hook.__class__.__name__} for training, priority = {hook.priority}",
+                    ranks=[0],
+                )
+            self._logger.info(
+                "Lower value means higher priority for calling hook function",
+                ranks=[0])
+        self._call_hooks("after_hook_is_attached")
 
         # eval
-        self._eval(test_dataloader=test_dataloader,
-                   display_progress=display_progress,
-                   return_output_label=return_output_label
-                   )
+        self._eval(
+            test_dataloader=test_dataloader,
+            display_progress=display_progress,
+            return_output_label=return_output_label,
+        )
 
     def predict(self, data: Union[Tensor, List[Tensor]]):
         """Uses trained model to make a prediction for a tensor or a tensor list.
@@ -399,6 +434,8 @@ class Trainer:
         # for compatibility with schedule
         simple_dataloader = [(data, None)]
         data_iter = iter(simple_dataloader)
-        output, _, _ = self.schedule.forward_backward_step(
-            self.engine, data_iter, forward_only=True, return_loss=False)
+        output, _, _ = self.schedule.forward_backward_step(self.engine,
+                                                           data_iter,
+                                                           forward_only=True,
+                                                           return_loss=False)
         return output

tests/test_config/test_load_config.py

@@ -6,6 +6,7 @@ from pathlib import Path
 import pytest
 
 from colossalai.context.config import Config
+from colossalai.builder import build_ophooks
 
 
 @pytest.mark.cpu
@@ -17,3 +18,10 @@ def test_load_config():
     assert config.train_data.dataset, 'cannot access grandchild attribute'
     assert isinstance(config.train_data.dataset.transform_pipeline[0], dict), \
         f'expected attribute transform_pipeline elements to be a dict, but found {type(config.train_data.dataset.transform_pipeline)}'
+
+
+@pytest.mark.cpu
+def test_load_ophooks():
+    dict = {'type': 'MemTracerOpHook', 'niter': 2}
+    ophook = build_ophooks(dict)
+    assert ophook.niter() == 2