[chat] set default zero2 strategy

applications/Chat/examples/README.md

@@ -69,7 +69,7 @@ torchrun --standalone --nproc_per_node=4 train_sft.py \
     --grad_checkpoint
 ```
 ### Arg List
-- --strategy:          the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='naive'
+- --strategy:          the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2'
 - --model:             model type, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom'
 - --pretrain:          pretrain model, type=str, default=None
 - --max_datasets_size: the max size of dataset, type=int, default=None
@@ -117,7 +117,7 @@ Model performance in [Anthropics paper](https://arxiv.org/abs/2204.05862):
 <div align=left>We also train the reward model based on LLaMA-7B, which reaches the ACC of 72.06% after 1 epoch, performing almost the same as Anthropic's best RM.
 
 ### Arg List
-- --strategy:          the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='naive'
+- --strategy:          the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2'
 - --model:             model type, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom'
 - --pretrain:          pretrain model, type=str, default=None
 - --model_path:        the path of rm model(if continue to train), type=str, default=None
@@ -158,7 +158,7 @@ Prompt dataset: the instruction dataset mentioned in the above figure which incl
 Pretrain dataset: the pretrain dataset including the instruction and corresponding response, e.g. you can use the [InstructWild Data](https://github.com/XueFuzhao/InstructionWild/tree/main/data) in stage 1 supervised instructs tuning.
 
 ### Arg List
-- --strategy:          the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='naive'
+- --strategy:          the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='colossalai_zero2'
 - --model:             model type of actor, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom'
 - --pretrain:          pretrain model, type=str, default=None
 - --rm_model:          reward model type, type=str, choices=['gpt2', 'bloom', 'opt', 'llama'], default=None

applications/Chat/examples/train_prompts.py

@@ -208,7 +208,7 @@ def main(args):
     parser.add_argument('--pretrain_dataset', type=str, default=None, help='path to the pretrained dataset')
     parser.add_argument('--strategy',
                         choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'],
-                        default='naive',
+                        default='colossalai_zero2',
                         help='strategy to use')
     parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama', 'roberta'])
     parser.add_argument('--pretrain', type=str, default=None)

applications/Chat/examples/train_reward_model.py

@@ -189,7 +189,7 @@ def train(args):
     parser = argparse.ArgumentParser()
     parser.add_argument('--strategy',
                         choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'],
-                        default='naive')
+                        default='colossalai_zero2')
     parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'deberta', 'llama', 'roberta'], default='bloom')
     parser.add_argument('--pretrain', type=str, default=None)
     parser.add_argument('--model_path', type=str, default=None)

applications/Chat/examples/train_sft.py

@@ -176,7 +176,7 @@ def train(args):
     parser = argparse.ArgumentParser()
     parser.add_argument('--strategy',
                         choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_zero2_cpu'],
-                        default='naive')
+                        default='colossalai_zero2')
     parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom')
     parser.add_argument('--pretrain', type=str, default=None)
     parser.add_argument('--dataset', type=str, default=None)