support ccl (MPI) distributed mode in alpaca_qlora_finetuning_cpu (#9507)

python/llm/example/CPU/QLoRA-FineTuning/alpaca-qlora/alpaca_qlora_finetuning_cpu.py

@@ -91,8 +91,7 @@ def train(
     wandb_log_model: str = "",  # options: false | true
     resume_from_checkpoint: str = None,  # either training checkpoint or final adapter
     prompt_template_name: str = "alpaca",  # The prompt template to use, will default to alpaca.
-    gradient_checkpointing: bool = False,
-    deepspeed: str = None,
+    gradient_checkpointing: bool = False
 ):
     if int(os.environ.get("LOCAL_RANK", 0)) == 0:
         print(
@@ -279,11 +278,6 @@ def train(
     else:
         train_data = data["train"].shuffle().map(generate_and_tokenize_prompt)
         val_data = None
-
-    trainer = transformers.Trainer(
-        model=model,
-        train_dataset=train_data,
-        eval_dataset=val_data,
     args = transformers.TrainingArguments(
             per_device_train_batch_size=micro_batch_size,
             gradient_accumulation_steps=gradient_accumulation_steps,
@@ -309,9 +303,17 @@ def train(
             report_to="wandb" if use_wandb else None,
             run_name=wandb_run_name if use_wandb else None,
             gradient_checkpointing=gradient_checkpointing,
-            ddp_backend="ccl",
-            deepspeed=deepspeed,
-        ),
+            ddp_backend="ccl" if ddp else None,
+    )
+    if ddp:
+        from accelerate.state import PartialState
+        args.distributed_state = PartialState(cpu=True, backend=args.ddp_backend)
+
+    trainer = transformers.Trainer(
+        model=model,
+        train_dataset=train_data,
+        eval_dataset=val_data,
+        args=args,
         # Inputs are dynamically padded to the maximum length among all inputs
         data_collator=transformers.DataCollatorForSeq2Seq(
             tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True