Feature/vitb16

README.md

@@ -42,26 +42,18 @@ pip install -v --no-cache-dir --global-option="--cuda_ext" .
 
 ```python
 import colossalai
-from colossalai.engine import Engine
 from colossalai.trainer import Trainer
 from colossalai.core import global_context as gpc
 
-model, train_dataloader, test_dataloader, criterion, optimizer, schedule, lr_scheduler = colossalai.initialize()
-engine = Engine(
-    model=model,
-    criterion=criterion,
-    optimizer=optimizer,
-    lr_scheduler=lr_scheduler,
-    schedule=schedule
-)
+engine, train_dataloader, test_dataloader = colossalai.initialize()
 
 trainer = Trainer(engine=engine,
-                  hooks_cfg=gpc.config.hooks,
                   verbose=True)
 trainer.fit(
     train_dataloader=train_dataloader,
     test_dataloader=test_dataloader,
-    max_epochs=gpc.config.num_epochs,
+    epochs=gpc.config.num_epochs,
+    hooks_cfg=gpc.config.hooks,
     display_progress=True,
     test_interval=5
 )

colossalai/builder/__init__.py

@@ -1,2 +1,10 @@
-from .builder import *
+from .builder import (build_schedule, build_lr_scheduler, build_model, build_optimizer, build_optimizer_wrapper,
+                      build_layer, build_loss, build_hooks, build_dataset, build_transform, build_data_sampler,
+                      build_gradient_handler)
 from .pipeline import ModelInitializer
+
+__all__ = [
+    'build_schedule', 'build_lr_scheduler', 'build_model', 'build_optimizer', 'build_optimizer_wrapper',
+    'build_layer', 'build_loss', 'build_hooks', 'build_dataset', 'build_transform', 'build_data_sampler',
+    'build_gradient_handler', 'ModelInitializer'
+]

colossalai/builder/builder.py

@@ -181,18 +181,6 @@ def build_transform(config):
     return build_from_registry(config, TRANSFORMS)
 
 
-def build_pipe_alloc_policy(config):
-    """Returns a pipeline allocation policy object 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: A pipeline allocation policy object
-    :rtype: 
-    """
-    return build_from_registry(config, PIPE_ALLOC_POLICY)
-
-
 def build_data_sampler(config, dataset):
     """Returns a data sampler object of :class:`colossalai.nn.data.sampler.BaseSampler`
     constructed from `config`.
@@ -235,7 +223,7 @@ def build_optimizer_wrapper(config, optimizer, model=None):
         return OPTIMIZER_WRAPPERS.get_module(mod_type)(optimizer, **config_)
 
 
-def build_lr_scheduler(config, optimizer, total_steps, num_steps_per_epoch):
+def build_lr_scheduler(config, optimizer):
     """Returns a learning rate scheduler object of :class:`torch.optim.lr_scheduler` 
     constructed from `config`, `optimizer`, `total_steps` and `num_steps_per_epoch`.
 
@@ -254,9 +242,16 @@ def build_lr_scheduler(config, optimizer, total_steps, num_steps_per_epoch):
     """
     config_ = config.copy()
     mod_type = config_.pop('type')
-    # warmup epochs will overwrite warmup steps
-    if 'warmup_epochs' in config_:
-        warmup_epochs = config_.pop('warmup_epochs')
-        config_['warmup_steps'] = int(num_steps_per_epoch * warmup_epochs)
-    return LR_SCHEDULERS.get_module(mod_type)(optimizer, total_steps, num_steps_per_epoch=num_steps_per_epoch,
-                                              **config_)
+    return LR_SCHEDULERS.get_module(mod_type)(optimizer, **config_)
+
+
+def build_schedule(config):
+    """Returns a schedule of :class:`colossalai.engine.schedule.BaseSchedule`.
+
+    :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.engine.schedule.BaseSchedule`
+    :rtype: :class:`colossalai.engine.schedule.BaseSchedule`
+    """
+    return build_from_registry(config, SCHEDULE)

colossalai/engine/__init__.py

@@ -1,7 +1,7 @@
-from .amp_type import AMP_TYPE
 from ._base_engine import Engine
 from .gradient_handler import *
 from .schedule import *
+from .amp import *
 
 
 __all__ = ['Engine']

colossalai/engine/_base_engine.py

@@ -1,170 +1,176 @@
 #!/usr/bin/env python
 # -*- encoding: utf-8 -*-
 
-from typing import Optional
+from torch.nn import Module
+from torch.nn.modules.loss import _Loss
+from torch.optim import Optimizer
 
 from colossalai.builder import build_gradient_handler
 from colossalai.context import ParallelMode
 from colossalai.core import global_context as gpc
 from colossalai.logging import get_global_dist_logger
 from colossalai.nn import (ZeroRedundancyOptimizer_Level_2,
                            ZeroRedundancyOptimizer_Level_3)
-from torch.nn import Module
-from torch.nn.modules.loss import _Loss
-from torch.optim import Optimizer
-from torch.optim.lr_scheduler import _LRScheduler
-from torch.utils.data import DataLoader
-
-from .schedule import BaseSchedule, NoPipelineSchedule
+from .schedule import BaseSchedule
 
 
 class Engine:
     """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.
 
-    :param train_dataloader: Dataloader in training
-    :param test_dataloader: Dataloader in evaluation
     :param model: The neural network model
-    :param criterion: Criterion for calculating loss
     :param optimizer: Optimizer for updating the parameters
-    :param lr_scheduler: Learning rate scheduler ajusting learning rate during the training or evaluation
-    :param schedule: Running schedule in :meth:`step`
-    :type train_dataloader: DataLoader, optional
-    :type test_dataloader: DataLoader, optional
+    :param step_schedule: Running schedule in :meth:`step`
+    :param gradient_accumulation: Steps of gradient accumulation
+    :param gradient_clipping: The norm of gradient clipping
     :type model: Module
-    :type criterion: _Loss, optional
-    :type optimizer: Optimizer, optional
-    :type lr_scheduler: _LRScheduler, optional
-    :type schedule: BaseSchedule, optional
+    :type optimizer: Optimizer
+    :type step_schedule: BaseSchedule, optional
+    :type gradient_accumulation: int, optional
+    :type gradient_clipping: float, optional
     """
+
     def __init__(self,
-                 train_dataloader: Optional[DataLoader] = None,
-                 test_dataloader: Optional[DataLoader] = None,
-                 model: Module = None,
-                 criterion: _Loss = None,
-                 optimizer: Optimizer = None,
-                 lr_scheduler: Optional[_LRScheduler] = None,
-                 schedule: BaseSchedule = None):
-        self.train_dataloader = train_dataloader
-        self.test_dataloader = test_dataloader
-        assert model is not None, "Engine requires a model"
-        self.model = model
-        self.criterion = criterion
-        self.optimizer = optimizer
-        self.lr_scheduler = lr_scheduler
-        self.schedule = schedule if schedule is not None \
-            else NoPipelineSchedule()
+                 model: Module,
+                 optimizer: Optimizer,
+                 criterion: _Loss,
+                 step_schedule: BaseSchedule,
+                 gradient_handlers: list = None,
+                 gradient_accumulation: int = 1,
+                 gradient_clipping: float = 0.0,
+                 ):
+        self._model = model
+        self._optimizer = optimizer
+        self._criterion = criterion
+        self._schedule = step_schedule
+
+        # schedule initialize
+        self._schedule.initialize(model, optimizer)
+
+        # state
+        self.training = True  # default
+
+        # gradient accumulation
+        assert gradient_accumulation > 0, 'gradient accumulation size must be larger than 0'
+        self._grad_accum_size = gradient_accumulation
+        self._grad_clip = gradient_clipping
         self._logger = get_global_dist_logger()
 
         # build gradient handler
         self._gradient_handlers = []
-        gradient_handler_cfg = []
 
-        if hasattr(gpc.config, 'gradient_handler'):
-            assert isinstance(gpc.config.gradient_handler, list), \
+        if gradient_handlers is not None:
+            assert isinstance(gradient_handlers, list), \
                 f'argument gradient_handler_cfg expected type list, ' \
-                f'but got type {type(gpc.config.gradient_handler)}'
-            gradient_handler_cfg = gpc.config.gradient_handler
-        elif isinstance(self.optimizer, (ZeroRedundancyOptimizer_Level_2,
-                                         ZeroRedundancyOptimizer_Level_3)):
-            gradient_handler_cfg = [dict(type='ZeROGradientHandler')]
+                f'but got type {type(gradient_handlers)}'
+        elif isinstance(optimizer, (ZeroRedundancyOptimizer_Level_2,
+                                    ZeroRedundancyOptimizer_Level_3)):
+            gradient_handlers = [dict(type='ZeROGradientHandler')]
             self._logger.info(
                 "Training with zero is detected, ZeROGradientHandler is automatically "
                 "added even though not specified in the configuration",
                 ranks=[0])
         elif gpc.is_initialized(ParallelMode.DATA) and gpc.get_world_size(
                 ParallelMode.DATA) > 1:
-            gradient_handler_cfg = [dict(type='DataParallelGradientHandler')]
+            gradient_handlers = [dict(type='DataParallelGradientHandler')]
             self._logger.info(
                 "Data parallel training is detected, DataParallelGradientHandler is automatically "
                 "added even though not specified in the configuration",
                 ranks=[0])
-        if len(gradient_handler_cfg) == 0:
+
+        if gradient_handlers is None:
             self._logger.warning(
                 "No gradient handler is set up, please make sure you do not need "
                 "to all-reduce the gradients after a training step.",
                 ranks=[0])
-        for cfg in gradient_handler_cfg:
-            handler = build_gradient_handler(cfg, self.model, self.optimizer)
-            self._gradient_handlers.append(handler)
+        else:
+            for cfg in gradient_handlers:
+                handler = build_gradient_handler(cfg, model, optimizer)
+                self._gradient_handlers.append(handler)
 
-        self.schedule.initialize(self.train_dataloader, self.model,
-                                 self.criterion, self.optimizer,
-                                 self.lr_scheduler)
-        self.forward_only = False
+    @property
+    def model(self):
+        return self._model
 
-    def handle_gradient(self):
-        """Handles all-reduce operations of gradients across different parallel groups.
-        """
-        for handler in self._gradient_handlers:
-            handler.handle_gradient()
+    @property
+    def optimizer(self):
+        return self._optimizer
 
-    def set_dataloader(self, data: DataLoader, train: bool = True):
-        """Sets dataloader in training or evaluation.
+    @property
+    def criterion(self):
+        return self._criterion
 
-        :param data: Dataloader to be set
-        :param train: Set training dataloader if True, otherwise evaluation dataloader
-        :type data: DataLoader
-        :type train: bool
-        """
-        if train:
-            self.train_dataloader = data
-        else:
-            self.test_dataloader = data
+    @property
+    def schedule(self):
+        return self._schedule
 
-    def get_model(self):
-        """Returns the neural network model in the engine.
-        """
-        return self.model
-    def get_optimizer(self):
-        """Returns optimizier in the engine.
-        """
-        return self.optimizer
+    @property
+    def gradient_accumulation(self):
+        return self._grad_accum_size
 
-    def get_lr_scheduler(self):
-        """Returns the learning rate scheduler in the engine.
+    def handle_gradient(self):
+        """Handles all-reduce operations of gradients across different parallel groups.
         """
-        return self.lr_scheduler
+        for handler in self._gradient_handlers:
+            handler.handle_gradient()
 
     def train(self):
         """Sets the model to training mode.
         """
-        self.forward_only = False
-        self.schedule.train(dataloader=self.train_dataloader, mode=True)
+        self.training = True
+        self._model.train()
 
     def eval(self):
         """Sets the model to evaluation mode.
         """
-        self.forward_only = True
-        self.schedule.train(dataloader=self.test_dataloader, mode=False)
+        self.training = False
+        self._model.eval()
 
-    def is_train(self):
-        """Returns True if it is in training, otherwise False.
-        """
-        return not self.forward_only
-
-    def get_lr(self):
-        """Gets current learning rate.
-        """
-        return self.schedule.get_lr()
-
-    def step(self, return_loss=True):
+    def step(self,
+             data_iter,
+             is_last_iteration: bool = False,
+             return_loss=True):
         """A running step based on the schedule. Usually, it runs a training or
         evaluation over a batch of dataset.
 
+        :param data_iter: Data iterator of the dataset
+        :param is_last_iteration: If True, this iteration is the last iteration in the epoch
         :param return_loss: loss will be returned if True
-        :type return_loss: bool
+        :type data_iter: Iterator
+        :type is_last_iteration: bool, optional
+        :type return_loss: bool, optional
         :return: (output, lablel, loss)
         """
-        self.schedule.zero_grad(forward_only=self.forward_only)
-
-        output, label, loss = self.schedule.forward_backward_step(
-            forward_only=self.forward_only, return_loss=return_loss)
-
-        if not self.forward_only:
-            # all reduce gradients
-            self.handle_gradient()
-
-            self.schedule.step()
+        if self.training:
+            self._optimizer.zero_grad()
+
+        # differentiate training and eval with grad accum
+        if self.training:
+            for i in range(self._grad_accum_size):
+                output, label, loss = self._schedule.forward_backward_step(
+                    data_iter, self._model, self._criterion, self._optimizer,
+                    forward_only=False,
+                    grad_accum_size=self._grad_accum_size,
+                    return_loss=return_loss)
+
+                if i == self._grad_accum_size - 1:
+                    # all reduce gradients
+                    self.handle_gradient()
+                    self._schedule.optimizer_step(self._model, self._optimizer, self._grad_clip)
+        else:
+            output, label, loss = self._schedule.forward_backward_step(
+                data_iter, self._model, self._criterion, self._optimizer,
+                forward_only=True,
+                grad_accum_size=1,
+                return_loss=return_loss)
+
+        # consume the remaining dataset left out due to gradient accumulation
+        if is_last_iteration:
+            while True:
+                try:
+                    _ = next(data_iter)
+                except StopIteration:
+                    break
 
         return output, label, loss

colossalai/engine/amp/__init__.py

@@ -0,0 +1,2 @@
+from .grad_scaler import GradScaler
+from .amp_type import AMP_TYPE