adapt new version of Yi

finetune/utils/utils.py

@@ -90,12 +90,6 @@ def save_hf_format(model, tokenizer, args, sub_folder=""):
     print(os.listdir(output_dir))
     print(os.getcwd())
 
-    source = args.model_name_or_path
-    target = os.path.abspath(os.path.join(os.getcwd(), args.output_dir))
-    copy(os.path.join(source, "configuration_yi.py"), target)
-    copy(os.path.join(source, "modeling_yi.py"), target)
-    copy(os.path.join(source, "tokenization_yi.py"), target)
-
 
 def get_all_reduce_mean(tensor):
     torch.distributed.all_reduce(tensor, op=torch.distributed.ReduceOp.SUM)
@@ -259,31 +253,3 @@ def save_zero_three_model(model_ema, global_rank, save_dir, zero_stage=0):
             torch.save(output_state_dict, output_model_file)
         del output_state_dict
 
-
-def save_zero_three_model(model_ema, global_rank, save_dir, zero_stage=0):
-    zero_stage_3 = (zero_stage == 3)
-    os.makedirs(save_dir, exist_ok=True)
-    WEIGHTS_NAME = "pytorch_model.bin"
-    output_model_file = os.path.join(save_dir, WEIGHTS_NAME)
-
-    model_to_save = model_ema.module if hasattr(model_ema,
-                                                'module') else model_ema
-    if not zero_stage_3:
-        if global_rank == 0:
-            torch.save(model_to_save.state_dict(), output_model_file)
-    else:
-        output_state_dict = {}
-        for k, v in model_to_save.named_parameters():
-            if hasattr(v, 'ds_id'):
-                with deepspeed.zero.GatheredParameters(_z3_params_to_fetch([v]),
-                                                       enabled=zero_stage_3):
-                    v_p = v.data.cpu()
-            else:
-                v_p = v.cpu()
-            if global_rank == 0 and "lora" not in k:
-                output_state_dict[k] = v_p
-
-        if global_rank == 0:
-
-            torch.save(output_state_dict, output_model_file)
-        del output_state_dict