Do the device_map more automaticlly for multi-GPUs

The accelerate lib provide some functions to do the device_map
automaticlly. This patch use infer_auto_device_map() to relpace the
hard codes which splite the mode by hand.

It works well with my two GPUs(6GB-RTX3060 + 12GB-RTX3060).

Signed-off-by: Alex He <heye_dev@163.com>

utils.py

@@ -5,43 +5,6 @@ from torch.nn import Module
 from transformers import AutoModel
 
 
-def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
-    # transformer.word_embeddings 占用1层
-    # transformer.final_layernorm 和 lm_head 占用1层
-    # transformer.layers 占用 28 层
-    # 总共30层分配到num_gpus张卡上
-    num_trans_layers = 28
-    per_gpu_layers = 30 / num_gpus
-
-    # bugfix: 在linux中调用torch.embedding传入的weight,input不在同一device上,导致RuntimeError
-    # windows下 model.device 会被设置成 transformer.word_embeddings.device
-    # linux下 model.device 会被设置成 lm_head.device
-    # 在调用chat或者stream_chat时,input_ids会被放到model.device上
-    # 如果transformer.word_embeddings.device和model.device不同,则会导致RuntimeError
-    # 因此这里将transformer.word_embeddings,transformer.final_layernorm,lm_head都放到第一张卡上
-    # 本文件来源于https://github.com/THUDM/ChatGLM-6B/blob/main/utils.py
-    # 仅此处做少许修改以支持ChatGLM2
-    device_map = {
-        'transformer.embedding.word_embeddings': 0,
-        'transformer.encoder.final_layernorm': 0,
-        'transformer.output_layer': 0,
-        'transformer.rotary_pos_emb': 0,
-        'lm_head': 0
-    }
-
-    used = 2
-    gpu_target = 0
-    for i in range(num_trans_layers):
-        if used >= per_gpu_layers:
-            gpu_target += 1
-            used = 0
-        assert gpu_target < num_gpus
-        device_map[f'transformer.encoder.layers.{i}'] = gpu_target
-        used += 1
-
-    return device_map
-
-
 def load_model_on_gpus(checkpoint_path: Union[str, os.PathLike], num_gpus: int = 2,
                        device_map: Optional[Dict[str, int]] = None, **kwargs) -> Module:
     if num_gpus < 2 and device_map is None:
@@ -52,7 +15,13 @@ def load_model_on_gpus(checkpoint_path: Union[str, os.PathLike], num_gpus: int =
         model = AutoModel.from_pretrained(checkpoint_path, trust_remote_code=True, **kwargs).half()
 
         if device_map is None:
-            device_map = auto_configure_device_map(num_gpus)
+            from accelerate import infer_auto_device_map
+
+            device_map = infer_auto_device_map(model, no_split_module_classes=["GLMBlock"])
+            # e.g. Use max_memory to set the upper limit memory size of each device.
+            # Huggingface suggest to save some memory of gpu0 for some reasons.
+            #device_map = infer_auto_device_map(model, max_memory={0: "4GiB", 1: "10GiB", "cpu": "30GiB"}, no_split_module_classes=["GLMBlock"])
+            #print(device_map)
 
         model = dispatch_model(model, device_map=device_map)