upgrade peft, fix #1088 #1411

src/llmtuner/tuner/core/loader.py

@@ -25,9 +25,8 @@ except ImportError: # https://github.com/huggingface/transformers/releases/tag/v
 from llmtuner.extras.logging import reset_logging, get_logger
 from llmtuner.extras.misc import count_parameters, infer_optim_dtype
 from llmtuner.extras.patches import llama_patch as LlamaPatches
-from llmtuner.extras.save_and_load import load_valuehead_params
 from llmtuner.hparams import FinetuningArguments
-from llmtuner.tuner.core.adapter import init_adapter
+from llmtuner.tuner.core.adapter import init_adapter, load_valuehead_params
 from llmtuner.tuner.core.utils import prepare_model_for_training
 
 if TYPE_CHECKING:
@@ -41,7 +40,7 @@ logger = get_logger(__name__)
 require_version("transformers>=4.31.0,<4.35.0", "To fix: pip install \"transformers>=4.31.0,<4.35.0\"")
 require_version("datasets>=2.12.0", "To fix: pip install datasets>=2.12.0")
 require_version("accelerate>=0.21.0", "To fix: pip install accelerate>=0.21.0")
-require_version("peft>=0.4.0", "To fix: pip install peft>=0.4.0")
+require_version("peft>=0.6.0", "To fix: pip install peft>=0.6.0")
 require_version("trl>=0.7.2", "To fix: pip install trl>=0.7.2")
 
 
@@ -64,7 +63,7 @@ def load_model_and_tokenizer(
         "trust_remote_code": True,
         "cache_dir": model_args.cache_dir,
         "revision": model_args.model_revision,
-        "use_auth_token": True if model_args.use_auth_token else None,
+        "token": model_args.hf_hub_token
     }
 
     tokenizer = AutoTokenizer.from_pretrained(
@@ -99,15 +98,9 @@ def load_model_and_tokenizer(
 
     # Set RoPE scaling
     if model_args.rope_scaling is not None:
-        if hasattr(config, "use_dynamic_ntk"): # for Qwen models
-            if is_trainable:
-                logger.warning("Qwen model does not support RoPE scaling in training.")
-            else:
-                setattr(config, "use_dynamic_ntk", True)
-                setattr(config, "use_logn_attn", True)
-                logger.info("Using dynamic NTK scaling.")
-
-        elif hasattr(config, "rope_scaling"): # for LLaMA and Falcon models
+        if not hasattr(config, "rope_scaling"):
+            logger.warning("Current model does not support RoPE scaling.")
+        else:
             if is_trainable:
                 if model_args.rope_scaling == "dynamic":
                     logger.warning(
@@ -129,9 +122,6 @@ def load_model_and_tokenizer(
                 model_args.rope_scaling, scaling_factor
             ))
 
-        else:
-            logger.warning("Current model does not support RoPE scaling.")
-
     # Set FlashAttention-2
     if model_args.flash_attn:
         if getattr(config, "model_type", None) == "llama":
@@ -155,7 +145,6 @@ def load_model_and_tokenizer(
             logger.warning("Current model does not support shift short attention.")
 
     # Quantization configurations (using bitsandbytes library).
-    is_mergeable = True
     if model_args.quantization_bit is not None:
         if is_deepspeed_zero3_enabled():
             raise ValueError("DeepSpeed ZeRO-3 is incompatible with quantization.")
@@ -165,7 +154,7 @@ def load_model_and_tokenizer(
             config_kwargs["load_in_8bit"] = True
             config_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)
 
-        elif model_args.quantization_bit == 4:
+        if model_args.quantization_bit == 4:
             require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
             config_kwargs["load_in_4bit"] = True
             config_kwargs["quantization_config"] = BitsAndBytesConfig(
@@ -175,7 +164,6 @@ def load_model_and_tokenizer(
                 bnb_4bit_quant_type=model_args.quantization_type
             )
 
-        is_mergeable = False
         config_kwargs["device_map"] = {"": int(os.environ.get("LOCAL_RANK", "0"))} if is_trainable else "auto"
         logger.info("Quantizing model to {} bit.".format(model_args.quantization_bit))
 
@@ -207,7 +195,7 @@ def load_model_and_tokenizer(
 
     # Initialize adapters
     model = prepare_model_for_training(model=model, finetuning_args=finetuning_args) if is_trainable else model
-    model = init_adapter(model, model_args, finetuning_args, is_trainable, is_mergeable)
+    model = init_adapter(model, model_args, finetuning_args, is_trainable)
     model = model.train() if is_trainable else model.eval()
 
     # Prepare model with valuehead for RLHF
@@ -226,7 +214,7 @@ def load_model_and_tokenizer(
             logger.info("Load reward model from {}".format(model_args.reward_model))
             if getattr(model, "is_peft_model", False):
                 model.pretrained_model.load_adapter(model_args.reward_model, "reward")
-            assert load_valuehead_params(model, model_args.reward_model), "Reward model is not correctly loaded."
+            load_valuehead_params(model, model_args)
 
     # Prepare model for inference
     if not is_trainable: