[chatgpt] fix torch version

applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py

@@ -9,6 +9,10 @@
 from chatgpt.models.lora import LoraLinear
 from torch.optim import Optimizer
 
+
+from transformers.modeling_utils import PreTrainedModel
+from transformers.tokenization_utils_base import PreTrainedTokenizerBase
+
 import colossalai
 from colossalai.nn.optimizer import CPUAdam, HybridAdam
 from colossalai.nn.parallel import ZeroDDP, zero_model_wrapper, zero_optim_wrapper
@@ -143,7 +147,7 @@ def _unwrap_actor(actor: Actor) -> nn.Module:
             return model.module
         return model
 
-    def save_model(self, model: nn.Module, path: str, only_rank0: bool = False) -> None:
+    def save_model(self, model: nn.Module, path: str, only_rank0: bool = False, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None:
         unwrapped_model = self._unwrap_model(model)
         # TODO : better way to get torch model from gemini model
         # to get torch model from gemini model
@@ -159,10 +163,16 @@ def save_model(self, model: nn.Module, path: str, only_rank0: bool = False) -> N
                 module.merge_weights=True
                 module.eval()
         # get state_dict and save
-        state_dict = unwrapped_model.state_dict()
-        if only_rank0 and dist.get_rank() != 0:
-            return
-        torch.save(state_dict, path)
+
+        if not isinstance(self.model, PreTrainedModel):
+            state_dict = unwrapped_model.state_dict()
+            if only_rank0 and dist.get_rank() != 0:
+                return
+            torch.save(state_dict, path)
+        else:
+            self.model.save_pretrained(path)
+            if tokenizer is not None:
+                tokenizer.save_pretrained(path)
 
     def save_optimizer(self, optimizer: Optimizer, path: str, only_rank0: bool = False) -> None:
         if only_rank0:

applications/ChatGPT/requirements.txt

@@ -3,5 +3,5 @@ tqdm
 datasets
 loralib
 colossalai>=0.2.4
-torch
+torch==1.12.1
 langchain