Remove zero3 context manager from LoRA (intel#11346)

Author: Uxito-Ada

python/llm/example/GPU/LLM-Finetuning/LoRA/alpaca_lora_finetuning.py

@@ -158,51 +158,28 @@ def train(
     # Check if parameter passed or if set within environ
     use_wandb = wandb_check(wandb_project, wandb_watch, wandb_log_model)
 
-    if deepspeed_zero3:
-        deepspeed = deepspeed if deepspeed is not None else "./deepspeed_zero3_config.json"
-
     if saved_low_bit_model is not None:
         # Load the low bit optimized model if provide the saved path
-        if deepspeed_zero3:
-            import deepspeed as ds
-            with ds.zero.Init(config_dict_or_path=deepspeed):
-                model = AutoModelForCausalLM.load_low_bit(
-                    saved_low_bit_model,
-                    optimize_model=False,
-                    torch_dtype=torch.bfloat16,
-                    modules_to_not_convert=["lm_head"],
-                    trust_remote_code=True,
-                )
-        else:
-            model = AutoModelForCausalLM.load_low_bit(
-                saved_low_bit_model,
-                optimize_model=False,
-                torch_dtype=torch.bfloat16,
-                modules_to_not_convert=["lm_head"],
-                trust_remote_code=True,
-            )
+        model = AutoModelForCausalLM.load_low_bit(
+            saved_low_bit_model,
+            optimize_model=False,
+            torch_dtype=torch.bfloat16,
+            modules_to_not_convert=["lm_head"],
+            trust_remote_code=True,
+        )
+    else:
+        model = AutoModelForCausalLM.from_pretrained(
+            base_model,
+            load_in_low_bit="bf16",
+            optimize_model=False,
+            torch_dtype=torch.bfloat16,
+             modules_to_not_convert=["lm_head"],
+            trust_remote_code=True,
+        )
+
+    if deepspeed_zero3:
+        deepspeed = deepspeed if deepspeed is not None else "./deepspeed_zero3_config.json"
     else:
-        if deepspeed_zero3:
-            import deepspeed as ds
-            with ds.zero.Init(config_dict_or_path=deepspeed):
-                model = AutoModelForCausalLM.from_pretrained(
-                    base_model,
-                    load_in_low_bit="bf16",
-                    optimize_model=False,
-                    torch_dtype=torch.bfloat16,
-                    modules_to_not_convert=["lm_head"],
-                    trust_remote_code=True,
-                )
-        else:
-            model = AutoModelForCausalLM.from_pretrained(
-                base_model,
-                load_in_low_bit="bf16",
-                optimize_model=False,
-                torch_dtype=torch.bfloat16,
-                modules_to_not_convert=["lm_head"],
-                trust_remote_code=True,
-            )
-    if not deepspeed_zero3:
         print(f"Model loaded on rank {os.environ.get('LOCAL_RANK')}")
         model = model.to(f'xpu:{os.environ.get("LOCAL_RANK", 0)}')
         print(f"Model moved to rank {os.environ.get('LOCAL_RANK')}")