[refactor] pipeline, put runtime schedule into engine. (#627)

colossalai/engine/_base_engine.py

@@ -2,7 +2,7 @@
 # -*- encoding: utf-8 -*-
 
 from asyncio.log import logger
-from typing import List
+from typing import List, Iterable
 from torch.nn import Module
 from torch.nn.modules.loss import _Loss
 from torch.optim import Optimizer
@@ -10,6 +10,7 @@ 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 colossalai.engine.schedule import BaseSchedule, NonPipelineSchedule, PipelineSchedule, InterleavedPipelineSchedule
 from typing import Optional, Type
 from colossalai.engine.gradient_handler import BaseGradientHandler
 from colossalai.logging import get_dist_logger
@@ -27,6 +28,7 @@ class Engine:
         clip_grad_norm (float, optional): The norm of gradient clipping.
         ophook_list (list): List of ophook.
         verbose (bool): whether to display log info.
+        schedule (''BaseSchedule''): Runtime schedule.
 
     Examples:
         >>> # define model, criterion, optimizer, lr_scheduler, train_dataloader for your training
@@ -59,7 +61,8 @@ class Engine:
                  gradient_handlers: Optional[List[BaseGradientHandler]] = None,
                  clip_grad_norm: float = 0.0,
                  ophook_list: Optional[List[BaseOpHook]] = None,
-                 verbose: bool = True):
+                 verbose: bool = True,
+                 schedule: Optional[BaseSchedule] = None):
         self._model = model
         self._optimizer = optimizer
         self._criterion = criterion
@@ -80,6 +83,14 @@ class Engine:
             self._ophook_list = []
         else:
             self._ophook_list = ophook_list
+        
+        # build schedule
+        if schedule:
+            self._schedule = schedule
+        else:
+            self._schedule = NonPipelineSchedule()
+        if self.uses_pipeline:
+            self._schedule.pre_processing(self)
         register_ophooks_recursively(self._model, self._ophook_list)
 
     @property
@@ -102,6 +113,16 @@ class Engine:
         """Criterion attached to the engine"""
         return self._criterion
 
+    @property
+    def schedule(self):
+        """Schedule attached to the engine"""
+        return self._schedule
+
+    @property
+    def uses_pipeline(self):
+        """show the pipeline parallel used or not"""
+        return isinstance(self._schedule, (PipelineSchedule, InterleavedPipelineSchedule))
+
     def add_hook(self, ophook: Type[BaseOpHook]) -> None:
         """add necessary hook"""
         # whether this hook exist
@@ -165,6 +186,16 @@ class Engine:
         """
         for handler in self._gradient_handlers:
             handler.handle_gradient()
+    
+    def execute_schedule(self, data_iter: Iterable, **kwargs):
+        """Run the forward, loss computation, and backward for the model.
+        Returns a tuple of (output, label, loss).
+
+        Returns:
+            Tuple[:class:`torch.Tensor`]: A tuple of (output, label, loss).
+        """
+        output, label, loss = self._schedule.forward_backward_step(self, data_iter, **kwargs)
+        return output, label, loss
 
     def train(self):
         """Sets the model to training mode.
@@ -176,4 +207,4 @@ class Engine:
         """Sets the model to evaluation mode.
         """
         self.training = False
-        self._model.eval()
+        self._model.eval()
\ No newline at end of file

colossalai/engine/schedule/_base_schedule.py

@@ -6,7 +6,6 @@ from abc import ABC, abstractmethod
 import torch
 
 from typing import Iterable, Callable
-from .._base_engine import Engine
 from colossalai.logging import get_dist_logger
 from colossalai.utils import get_current_device
 
@@ -75,14 +74,14 @@ class BaseSchedule(ABC):
             return self._move_to_device(data), self._move_to_device(label)
         return data, label
 
-    def pre_processing(self, engine: Engine):
+    def pre_processing(self, engine):
         """To perform actions before running the schedule.
         """
         pass
 
     @abstractmethod
     def forward_backward_step(self,
-                              engine: Engine,
+                              engine,
                               data_iter: Iterable,
                               forward_only: bool,
                               return_loss: bool = True,

colossalai/engine/schedule/_non_pipeline_schedule.py

@@ -5,7 +5,6 @@ from typing import Iterable
 
 import torch
 
-from colossalai.engine import Engine
 from ._base_schedule import BaseSchedule
 from colossalai.utils import conditional_context
 
@@ -22,7 +21,7 @@ class NonPipelineSchedule(BaseSchedule):
     """
 
     def forward_backward_step(self,
-                              engine: Engine,
+                              engine,
                               data_iter: Iterable,
                               forward_only: bool = False,
                               return_loss: bool = True,

colossalai/initialize.py

@@ -20,6 +20,7 @@ from colossalai.amp.naive_amp import NaiveAMPModel
 from colossalai.builder.builder import build_gradient_handler
 from colossalai.context import Config, ConfigException, ParallelMode
 from colossalai.core import global_context as gpc
+from colossalai.engine.schedule import NonPipelineSchedule, PipelineSchedule, InterleavedPipelineSchedule
 
 from colossalai.context.moe_context import MOE_CONTEXT
 from colossalai.engine import Engine
@@ -388,6 +389,20 @@ def initialize(model: nn.Module,
     if isinstance(model, DDP) and isinstance(model.module, NaiveAMPModel):
         model.module.sync_buffer = False
 
+    # initialize schedule for engine
+    if is_using_pp():
+        tensor_shape = getattr(gpc.config, 'TENSOR_SHAPE', None)
+        use_interleaved = hasattr(gpc.config, 'model') and hasattr(gpc.config.model, 'num_chunks')
+        if use_interleaved:
+            schedule = InterleavedPipelineSchedule(gpc.config.NUM_MICRO_BATCHES, 
+                                                   gpc.config.model.num_chunks, tensor_shape=tensor_shape, scatter_gather_tensors=True)
+        else:
+            schedule = PipelineSchedule(gpc.config.NUM_MICRO_BATCHES,
+                                        tensor_shape=tensor_shape, scatter_gather_tensors=True)
+    else:
+        schedule = NonPipelineSchedule()
+
+
     if gradient_handler_cfg is None:
         gradient_handlers = None
         if verbose and not isinstance(model, DDP):
@@ -418,6 +433,7 @@ def initialize(model: nn.Module,
                     criterion=criterion,
                     gradient_handlers=gradient_handlers,
                     clip_grad_norm=clip_grad_norm,
-                    ophook_list=ophooks)
+                    ophook_list=ophooks,
+                    schedule=schedule)
 
     return engine, train_dataloader, test_dataloader, lr_scheduler

colossalai/trainer/_trainer.py

@@ -9,7 +9,6 @@ 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
@@ -23,13 +22,9 @@ class Trainer:
 
     Args:
         engine (:class:`Engine`): Engine responsible for the process function.
-        schedule (:class:`BaseSchedule`, optional): Schedule responsible for forward and backward steps.
         timer (:class:`MultiTimer`, optional): Timer used to monitor the whole training.
         logger (:class:`colossalai.logging.DistributedLogger`, optional): Logger used to record the whole training log.
 
-    Note:
-        when `schedule` is None, the ``NonPipelineSchedule`` would be used. If you would like to use pipeline,
-        you should choose ``PipelineSchedule`` or ``InterleavedPipelineSchedule`` for the `schedule`
 
     Examples:
         >>> # define model, criterion, optimizer, lr_scheduler, train_dataloader for your training
@@ -42,7 +37,7 @@ class Trainer:
         >>> # Beginning training progress
         >>> timier = ...
         >>> logger = ...
-        >>> trainer = Trainer(engine=engine, logger=logger, schedule=schedule, timer=timier)
+        >>> trainer = Trainer(engine=engine, logger=logger, timer=timier)
         >>> # add hooks you would like to use here.
         >>> hook_list = []
         >>> trainer.fit(
@@ -61,7 +56,6 @@ class Trainer:
     def __init__(
             self,
             engine: Engine,
-            schedule: BaseSchedule = None,
             timer: MultiTimer = None,
             logger: DistributedLogger = None,
     ):
@@ -86,17 +80,6 @@ class Trainer:
         # multi-timer for time benchmarking
         self._timer = timer
 
-        # 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."
-        self._schedule = schedule
-        self._schedule.pre_processing(engine)
-
     @property
     def cur_epoch(self):
         """Returns the index of the current epoch."""
@@ -129,10 +112,6 @@ class Trainer:
     def engine(self):
         return self._engine
 
-    @property
-    def schedule(self):
-        return self._schedule
-
     def _set_current_step(self, epoch: int):
         """Sets current step number.
 
@@ -203,8 +182,7 @@ class Trainer:
 
             # run 1 training step
             self.engine.zero_grad()
-            logits, label, loss = self.schedule.forward_backward_step(
-                self.engine,
+            logits, label, loss = self.engine.execute_schedule(
                 data_iter,
                 forward_only=False,
                 return_loss=True,
@@ -260,8 +238,7 @@ class Trainer:
             for _ in progress:
                 self._call_hooks("before_test_iter")
                 self._call_timer(action="start", item="Test-step")
-                logits, label, loss = self.schedule.forward_backward_step(
-                    self.engine,
+                logits, label, loss = self.engine.execute_schedule(
                     data_iter,
                     forward_only=True,
                     return_loss=True,
@@ -449,8 +426,7 @@ 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.engine.execute_schedule(data_iter,
+                                                    forward_only=True,
+                                                    return_loss=False)
         return output

tests/test_data_pipeline_tensor_parallel/test_cifar_with_data_pipeline_tensor.py

@@ -23,9 +23,9 @@ from torchvision.datasets import CIFAR10
 BATCH_SIZE = 4
 NUM_EPOCHS = 60
 WARMUP_EPOCHS = 5
-CONFIG = dict(parallel=dict(pipeline=2, tensor=dict(size=2, mode='1d')),
-              fp16=dict(mode=AMP_TYPE.NAIVE),
-              gradient_accumulation=2)
+CONFIG = dict(NUM_MICRO_BATCHES=2, parallel=dict(pipeline=2, tensor=dict(size=2, mode='1d')),
+                fp16=dict(mode=AMP_TYPE.NAIVE),
+                gradient_accumulation=2)
 
 
 def run_trainer(rank, world_size, port):
@@ -63,10 +63,9 @@ def run_trainer(rank, world_size, port):
                                                                       train_dataloader,
                                                                       lr_scheduler=lr_scheduler)
 
-    schedule = PipelineSchedule(num_microbatches=2)
     logger = get_dist_logger()
 
-    trainer = Trainer(engine=engine, logger=logger, schedule=schedule)
+    trainer = Trainer(engine=engine, logger=logger)
 
     hook_list = [
         hooks.LRSchedulerHook(lr_scheduler=lr_scheduler, by_epoch=False),

tests/test_trainer/test_pipeline/resnet_config.py

@@ -7,6 +7,7 @@ IMG_SIZE = 224
 DIM = 768
 NUM_CLASSES = 10
 NUM_ATTN_HEADS = 12
+NUM_MICRO_BATCHES = 2
 
 # resnet 18
 model = dict(type='VanillaResNet',

tests/test_trainer/test_pipeline/test_pipeline_schedule.py

@@ -19,7 +19,6 @@ from torchvision import transforms
 from torchvision.datasets import CIFAR10
 
 BATCH_SIZE = 4
-NUM_MICRO = 2
 
 DIR_PATH = osp.dirname(osp.realpath(__file__))
 CONFIG_PATH = osp.join(DIR_PATH, './resnet_config.py')
@@ -57,7 +56,7 @@ def run_schedule(rank, world_size, port):
     engine, train_dataloader, _, _ = colossalai.initialize(model, optimizer, criterion, train_dataloader)
 
     # build pipeline schedule
-    schedule = PipelineSchedule(num_microbatches=NUM_MICRO)
+    schedule = engine.schedule
 
     # run schedule
     data_iter = iter(train_dataloader)

tests/test_trainer/test_trainer_with_pipe_schedule.py

@@ -23,7 +23,7 @@ BATCH_SIZE = 4
 IMG_SIZE = 32
 NUM_EPOCHS = 200
 
-CONFIG = dict(parallel=dict(pipeline=2),)
+CONFIG = dict(NUM_MICRO_BATCHES=2, parallel=dict(pipeline=2),)
 
 
 def run_trainer_with_pipeline(rank, world_size, port):
@@ -69,9 +69,8 @@ def run_trainer_with_pipeline(rank, world_size, port):
 
     logger = get_dist_logger()
     logger.info("engine is built", ranks=[0])
-    pipe_schedule = PipelineSchedule(num_microbatches=2)
     timer = MultiTimer()
-    trainer = Trainer(engine=engine, schedule=pipe_schedule, logger=logger, timer=timer)
+    trainer = Trainer(engine=engine, logger=logger, timer=timer)
     logger.info("trainer is built", ranks=[0])
 
     logger.info("start training", ranks=[0])