[model] fix kv cache (#7564)

src/llamafactory/model/adapter.py

@@ -18,7 +18,6 @@ from typing import TYPE_CHECKING
 import torch
 from peft import LoraConfig, LoraModel, PeftModel, TaskType, get_peft_model
 from transformers.integrations import is_deepspeed_zero3_enabled
-from transformers.modeling_utils import is_fsdp_enabled
 
 from ..extras import logging
 from .model_utils.misc import find_all_linear_modules, find_expanded_modules
@@ -277,14 +276,14 @@ def init_adapter(
 
     # cast trainable parameters to float32 if:
     # 1. is_trainable and not pure_bf16 and not badam and quantization_bit is not None (qlora)
-    # 2. is_trainable and not pure_bf16 and not badam and not zero3 and not fsdp (zero3 or fsdp already in fp32)
+    # 2. is_trainable and not pure_bf16 and not badam and not zero3 (zero3 already in fp32)
     cast_trainable_params_to_fp32 = False
     if not is_trainable:
         pass
     elif finetuning_args.pure_bf16 or finetuning_args.use_badam:
         logger.info_rank0("Pure bf16 / BAdam detected, remaining trainable params in half precision.")
-    elif model_args.quantization_bit is None and (is_deepspeed_zero3_enabled() or is_fsdp_enabled()):
-        logger.info_rank0("ZeRO3 / FSDP detected, remaining trainable params in float32.")
+    elif model_args.quantization_bit is None and is_deepspeed_zero3_enabled():
+        logger.info_rank0("DeepSpeed ZeRO3 detected, remaining trainable params in float32.")
     else:
         logger.info_rank0("Upcasting trainable params to float32.")
         cast_trainable_params_to_fp32 = True