[Unified Checkpoint] Fix tie_weights save and load

paddlenlp/trainer/plugins/unified_checkpoint.py

@@ -902,6 +902,8 @@ def save_single_card_checkpoint(args, model_to_save, output_dir):
         sharded_index_json["type"] = "lora"
     elif isinstance(model_to_save, PrefixModelForCausalLM):
         sharded_index_json["type"] = "ptuning"
+
+    os.makedirs(output_dir, exist_ok=True)
     path = os.path.join(output_dir, index_filename)
     with open(path, "w") as f:
         json.dump(sharded_index_json, f, indent=4)
@@ -911,6 +913,13 @@ def save_single_card_checkpoint(args, model_to_save, output_dir):
 
     if isinstance(model_to_save, PrefixModelForCausalLM):
         save_prefix_past_key_value(model_to_save, output_dir)
+        model_to_save.prefix_config.save_pretrained(output_dir)
+    if isinstance(model_to_save, LoRAModel):
+        model_to_save.lora_config.save_pretrained(output_dir)
+
+    config_to_save = save_config(model_to_save)
+    config_to_save.architectures = [model_to_save.__class__.__name__]
+    config_to_save.save_pretrained(output_dir)
 
 
 def save_single_card_optimizer(args, model, optimizer, output_dir):
@@ -1003,6 +1012,11 @@ def get_expected_state_dict(model_to_save):
         state_dict = model_to_save.get_trainable_state_dict()
     elif isinstance(model_to_save, PrefixModelForCausalLM):
         state_dict = model_to_save.prefix_encoder.state_dict()
+
+    if hasattr(model_to_save, "_tied_weights_keys") and model_to_save._tied_weights_keys is not None:
+        for key in model_to_save._tied_weights_keys:
+            if key in state_dict:
+                state_dict.pop(key)
     return state_dict
 
 

paddlenlp/trainer/trainer.py

@@ -2457,6 +2457,7 @@ def _load_optimizer_and_scheduler(self, checkpoint):
             # Load in optimizer and scheduler states
             self.optimizer.set_state_dict(opt_state_dict)
         else:
+            optimizer_name = _add_variant(OPTIMIZER_NAME, self.args.optimizer_name_suffix)
             raise ValueError(f"optimizer-state-dict not found, opt: {os.path.join(checkpoint, optimizer_name)}.")
 
         if not self.args.ignore_load_lr_and_optim:

paddlenlp/transformers/chatglm/modeling.py

@@ -789,7 +789,7 @@ def forward(self, hidden_states):
 
 class ChatGLMForCausalLM(ChatGLMPretrainedModel):
     _keys_to_ignore_on_save = [r"lm_head.decoder_weight"]
-    _tied_weights_keys = ["lm_head.weight"]
+    _tied_weights_keys = ["lm_head.decoder_weight"]
 
     def __init__(self, config: ChatGLMConfig):
         super(ChatGLMForCausalLM, self).__init__(config)