[booster] fixed the torch ddp plugin with the new checkpoint api

colossalai/booster/plugin/gemini_plugin.py

@@ -13,6 +13,7 @@
 from torch.utils.data.distributed import DistributedSampler
 
 from colossalai.checkpoint_io import CheckpointIO, GeneralCheckpointIO
+from colossalai.checkpoint_io.utils import save_state_dict
 from colossalai.cluster import DistCoordinator
 from colossalai.interface import ModelWrapper, OptimizerWrapper
 from colossalai.tensor.colo_parameter import ColoParameter
@@ -83,22 +84,22 @@ def load_unsharded_model(self, model: GeminiDDP, checkpoint: str, strict: bool =
         # the model should be unwrapped in self.load_model via ModelWrapper.unwrap
         return super().load_unsharded_model(model, checkpoint, strict=strict)
 
-    def save_unsharded_model(self, model: GeminiDDP, checkpoint: str):
+    def save_unsharded_model(self, model: GeminiDDP, checkpoint: str, gather_dtensor: bool, use_safetensors: bool):
         """
         Save model to checkpoint but only on master process.
         """
         # the model should be unwrapped in self.load_model via ModelWrapper.unwrap
         # as there is communication when get state dict, this must be called on all processes
         state_dict = model.state_dict(only_rank_0=True)
         if self.coordinator.is_master():
-            self.save_checkpoint(state_dict, checkpoint)
+            save_state_dict(state_dict, checkpoint, use_safetensors)
 
-    def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str):
+    def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool):
         """
         Save optimizer to checkpoint but only on master process.
         """
         # TODO(ver217): optimizer state dict is sharded
-        super().save_unsharded_optimizer(optimizer, checkpoint)
+        super().save_unsharded_optimizer(optimizer, checkpoint, gather_dtensor)
 
     def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str):
         """

colossalai/booster/plugin/torch_ddp_plugin.py

@@ -33,20 +33,20 @@ def load_unsharded_model(self, model: nn.Module, checkpoint: str, strict: bool =
         # the model should be unwrapped in self.load_model via ModelWrapper.unwrap
         return super().load_unsharded_model(model, checkpoint, strict=strict)
 
-    def save_unsharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor: bool):
+    def save_unsharded_model(self, model: nn.Module, checkpoint: str, gather_dtensor: bool, use_safetensors: bool):
         """
         Save model to checkpoint but only on master process.
         """
         # the model should be unwrapped in self.load_model via ModelWrapper.unwrap
         if self.coordinator.is_master():
-            super().save_unsharded_model(model, checkpoint)
+            super().save_unsharded_model(model, checkpoint, gather_dtensor, use_safetensors)
 
     def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool):
         """
         Save optimizer to checkpoint but only on master process.
         """
         if self.coordinator.is_master():
-            super().save_unsharded_optimizer(optimizer, checkpoint)
+            super().save_unsharded_optimizer(optimizer, checkpoint, gather_dtensor)
 
     def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str):
         """

examples/tutorial/new_api/torch_ddp/README.md

@@ -2,10 +2,10 @@
 
 ## 🚀 Quick Start
 
-This example provides a training script and and evaluation script. The training script provides a an example of training ResNet on CIFAR10 dataset from scratch.
+This example provides a training script and an evaluation script. The training script provides an example of training ResNet on CIFAR10 dataset from scratch.
 
 - Training Arguments
-  - `-r, `--resume`: resume from checkpoint file path
+  - `-r`, `--resume`: resume from checkpoint file path
   - `-c`, `--checkpoint`: the folder to save checkpoints
   - `-i`, `--interval`: epoch interval to save checkpoints
   - `-f`, `--fp16`: use fp16
@@ -41,4 +41,4 @@ Expected accuracy performance will be:
 | --------- | ------------------------ | --------------------- | --------------------- |
 | ResNet-18 | 85.85%                   | 85.03%                | 85.12%                |
 
-**Note: the baseline is a adapted from the [script](https://pytorch-tutorial.readthedocs.io/en/latest/tutorial/chapter03_intermediate/3_2_2_cnn_resnet_cifar10/) to use `torchvision.models.resnet18`**
+**Note: the baseline is adapted from the [script](https://pytorch-tutorial.readthedocs.io/en/latest/tutorial/chapter03_intermediate/3_2_2_cnn_resnet_cifar10/) to use `torchvision.models.resnet18`**