fix lora (#7824)

Author: lugimzzz

paddlenlp/peft/lora/lora_model.py

@@ -230,12 +230,12 @@ def save_pretrained(self, save_directory: str, merge_tensor_parallel: bool = Fal
 
         if self.is_pipelinemodel:
             self.model._single_to_pp_mapping = None
-        if self.quantized and merge_tensor_parallel and self.lora_config.tensor_parallel_degre > 1:
+        if self.quantized and merge_tensor_parallel and self.lora_config.tensor_parallel_degree > 1:
             merge_tensor_parallel = False
             logger.warning(
                 "Quantized strategy does not support merge_tensor_parallel. Set merge_tensor_parallel to False."
             )
-        if self.is_pipelinemodel and merge_tensor_parallel and self.lora_config.tensor_parallel_degre > 1:
+        if self.is_pipelinemodel and merge_tensor_parallel and self.lora_config.tensor_parallel_degree > 1:
             merge_tensor_parallel = False
             logger.warning(
                 "Pipeline parallism does not support merge_tensor_parallel. Set merge_tensor_parallel to False."

paddlenlp/quantization/quantization_utils.py

@@ -109,9 +109,12 @@ def convert_to_quantize_state_dict_with_check(state_dict, quantization_linear_li
                 raise ValueError(
                     f"{quant_weight_name} should be {paddle.int8} in state_dict but received dtype {state_dict[quant_weight_name].dtype}."
                 )
-            if state_dict[quant_scale_name].dtype != paddle.float32:
+            if (
+                state_dict[quant_scale_name].dtype != paddle.float16
+                and state_dict[quant_scale_name].dtype != paddle.bfloat16
+            ):
                 raise ValueError(
-                    f"{quant_scale_name} should be {paddle.float32} in state_dict but received dtype {state_dict[quant_scale_name].dtype}."
+                    f"{quant_scale_name} should be {paddle.float16} or {paddle.bfloat16} in state_dict but received dtype {state_dict[quant_scale_name].dtype}."
                 )
         elif weight_name in state_dict:
             target_weight = state_dict.pop(weight_name).cast(dtype)

paddlenlp/transformers/model_utils.py

@@ -1776,9 +1776,15 @@ def _load_pretrained_model(
                     loaded_keys, quantization_linear_list, config.quantization_config
                 )
             if keep_in_fp32_modules is None:
-                keep_in_fp32_modules = ["quant_scale"]
+                keep_in_fp32_modules = (
+                    ["quant_scale"] if config.quantization_config.weight_quantize_algo in ["nf4", "fp4"] else None
+                )
             else:
-                keep_in_fp32_modules += ["quant_scale"]
+                keep_in_fp32_modules = (
+                    keep_in_fp32_modules + ["quant_scale"]
+                    if config.quantization_config.weight_quantize_algo in ["nf4", "fp4"]
+                    else keep_in_fp32_modules
+                )
 
         missing_keys = list(set(expected_keys) - set(loaded_keys))
         unexpected_keys = list(set(loaded_keys) - set(expected_keys))
@@ -2200,7 +2206,9 @@ def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
             logger.info("Loaded weights file from disk, setting weights to model.")
 
         # Check if `_keep_in_fp32_modules` is not None
-        use_keep_in_fp32_modules = (cls._keep_in_fp32_modules is not None) and dtype == "float16"
+        use_keep_in_fp32_modules = (cls._keep_in_fp32_modules is not None) and (
+            dtype == "float16" or dtype == "bfloat16"
+        )
 
         if is_sharded:
             loaded_state_dict_keys = sharded_metadata["all_checkpoint_keys"]