[checkpoint] make unitest faster (#1217)

colossalai/utils/checkpoint/module_checkpoint.py

@@ -5,7 +5,8 @@ import collections
 from torch.optim.lr_scheduler import CosineAnnealingLR as _CosineAnnealingLR
 from colossalai.utils.model.colo_init_context import colo_state_dict
 
-def save_checkpoint(dire,
+
+def save_checkpoint(dire: str,
                     epoch: int,
                     model: torch.nn.Module,
                     optimizer: torch.optim.Optimizer = None,
@@ -15,30 +16,21 @@ def save_checkpoint(dire,
     """save_checkpoint 
     save a model, whose parameters are `ColoTensor`s.
     Args:
-        dire (_type_): _description_
-        epoch (int): _description_
-        model (torch.nn.Module): _description_
-        optimizer (torch.optim.Optimizer, optional): _description_. Defaults to None.
-        lr_scheduler (torch.optim.lr_scheduler._LRScheduler, optional): _description_. Defaults to None.
+        dire (str): directory to save the checkpoint files.
+        epoch (int): the number of epoch
+        model (torch.nn.Module): a torch module initialized by ColoInitContext
+        optimizer (torch.optim.Optimizer, optional): optimizers. Defaults to None.
+        lr_scheduler (torch.optim.lr_scheduler._LRScheduler, optional): lr schedule. Defaults to None.
     """
-    model_state = {
-        'epoch': epoch,
-        'model': colo_state_dict(model, state_dict_func=nn.Module.state_dict)
-    }
+    model_state = {'epoch': epoch, 'model': colo_state_dict(model, state_dict_func=nn.Module.state_dict)}
     if dist.get_rank() == 0:
         torch.save(model_state, dire + '/epoch_{}_model.pth'.format(epoch))
     lr_scheduler_dict = lr_scheduler.state_dict()
     lr_scheduler_dict['after_scheduler'] = lr_scheduler_dict['after_scheduler'].state_dict()
-    optim_state = {
-        'epoch': epoch,
-        'optimizer': optimizer.state_dict(),
-        'lr_scheduler': lr_scheduler_dict
-    }
+    optim_state = {'epoch': epoch, 'optimizer': optimizer.state_dict(), 'lr_scheduler': lr_scheduler_dict}
     torch.save(optim_state, dire + '/epoch_{}_optim_rank_{}.pth'.format(epoch, dist.get_rank()))
 
 
-
-
 def load_checkpoint(dire,
                     epoch: int,
                     rank: int,
@@ -64,10 +56,7 @@ def load_checkpoint(dire,
     optimizer.load_state_dict(optim_state['optimizer'])
     lr_scheduler_dict = optim_state['lr_scheduler']
     after_scheduler_dict = lr_scheduler_dict['after_scheduler']
-    lr_scheduler_dict['after_scheduler'] = _CosineAnnealingLR(
-        optimizer, 
-        after_scheduler_dict['T_max'],
-        after_scheduler_dict['eta_min'],
-        after_scheduler_dict['last_epoch']
-        )
+    lr_scheduler_dict['after_scheduler'] = _CosineAnnealingLR(optimizer, after_scheduler_dict['T_max'],
+                                                              after_scheduler_dict['eta_min'],
+                                                              after_scheduler_dict['last_epoch'])
     lr_scheduler.load_state_dict(lr_scheduler_dict)

tests/test_utils/test_colo_checkpoint.py

 from abc import ABC, abstractmethod
-import os, sys, shutil
+import os, shutil
 import torch
 import torch.nn as nn
 import pytest
 import copy
-import operator
-import colossalai
-from colossalai.context.parallel_mode import ParallelMode
+from functools import partial
+
 import torch.multiprocessing as mp
 import torch.distributed as dist
+
+import colossalai
 from colossalai.testing import rerun_if_address_is_in_use
 from colossalai.utils.cuda import get_current_device
 from colossalai.utils import free_port
 from colossalai.utils.model.colo_init_context import ColoInitContext
-from colossalai.tensor import ColoTensorSpec, ComputePattern, ComputeSpec, DistSpecManager, distspec, ProcessGroup, ColoTensor
-from colossalai.core import global_context as gpc
-from functools import partial
+from colossalai.tensor import ComputePattern, ComputeSpec, DistSpecManager, distspec, ProcessGroup
 from colossalai.nn.parallel.data_parallel import ColoDDP
 from colossalai.utils.checkpoint import save_checkpoint, load_checkpoint
 from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR
@@ -46,15 +45,17 @@ class DummyDataGenerator(ABC):
 
 
 class DummyDataLoader(DummyDataGenerator):
-    batch_size = 128
-    category = 16
-    feature_size = 256
+
+    def __init__(self, batch_size, category, feature_size, length=10):
+        super().__init__(length)
+        self.batch_size = batch_size
+        self.category = category
+        self.feature_size = feature_size
 
     def generate(self):
         image_dict = {}
-        image_dict['pixel_values'] = torch.rand(
-            DummyDataLoader.batch_size, DummyDataLoader.feature_size, device=get_current_device()) * 2 - 1
-        image_dict['label'] = torch.randint(DummyDataLoader.category, (DummyDataLoader.batch_size,),
+        image_dict['pixel_values'] = torch.rand(self.batch_size, self.feature_size, device=get_current_device()) * 2 - 1
+        image_dict['label'] = torch.randint(self.category, (self.batch_size,),
                                             dtype=torch.int64,
                                             device=get_current_device())
         return image_dict
@@ -102,11 +103,15 @@ def remove(path):
 
 
 def run_checkpoint(init_spec_func, use_ddp, test_epoch, pg):
-    train_dataloader = DummyDataLoader(length=16)
+    batch = 3
+    feature = 32
+    category = 16
+    train_dataloader = DummyDataLoader(batch, category, feature, length=16)
     with ColoInitContext(device=get_current_device()):
-        model = MLP(256, 16, 64)
-        model_reload = MLP(256, 16, 64)
-        model_ref = MLP(256, 16, 64)
+        model = MLP(feature, category)
+        model_reload = MLP(feature, category)
+        model_ref = MLP(feature, category)
+
     model = model.cuda()
     model_reload = model_reload.cuda()
     model_ref = model_ref.cuda()