fix small bugs of convert2hf;remove low_cpu_usage

tools/transformers/README.md

@@ -4,10 +4,10 @@
 
 ## 权重转换
 
-`convert2hf.py` 可以将训练保存的权重一键转换为 transformers 格式。
+`convert2hf.py` 可以将训练保存的权重一键转换为 transformers 格式。在根目录下执行：
 
 ```bash
-python convert2hf.py --src_folder origin_ckpt/ --tgt_folder hf_ckpt/ --tokenizer tokenizes/tokenizer.model
+python tools/transformers/convert2hf.py --src_folder origin_ckpt/ --tgt_folder hf_ckpt/ --tokenizer tokenizes/tokenizer.model
 ```
 
 然后可以使用 `from_pretrained` 接口加载：

tools/transformers/convert2hf.py

@@ -85,7 +85,7 @@ def convert2hf(model_config, states_tp_pps):
         config.save_pretrained(folder)
         torch.save(current_states, os.path.join(folder, "pytorch_model.bin"))
 
-        model = InternLMForCausalLM.from_pretrained(folder, torch_dtype=torch.float16, low_cpu_mem_usage=True)
+        model = InternLMForCausalLM.from_pretrained(folder, torch_dtype=torch.float16)
         del model.config._name_or_path
 
     return config, model

tools/transformers/modeling_internlm.py

@@ -31,7 +31,7 @@ from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutpu
 from transformers.modeling_utils import PreTrainedModel
 from transformers.generation.streamers import BaseStreamer
 from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
-from .configuration_internlm import InternLMConfig
+from configuration_internlm import InternLMConfig
 
 
 logger = logging.get_logger(__name__)