removed unused function

Signed-off-by: 严照东 <[email protected]>

flagai/model/aquila2/modeling_aquila.py

@@ -765,57 +765,6 @@ def __init__(self, config):
         # Initialize weights and apply final processing
         self.post_init()
 
-    @classmethod
-    def from_pretrain(self, model_dir, model_name, **kwargs):
-        download_path = os.path.join(model_dir, model_name)
-        if os.path.exists(download_path):
-            return self.from_pretrained(download_path, **kwargs)
-
-
-        config_path = os.path.join(download_path, "config.json")
-        checkpoint_path = os.path.join(download_path, "pytorch_model.bin")
-        from flagai.model.file_utils import _get_model_id
-        model_id = _get_model_id(model_name)
-        if model_id and model_id != "null":
-            model_files = eval(_get_model_files(model_name))
-            print("model files:" + str(model_files))
-            for file_name in model_files:
-                if not file_name.endswith("bin"):
-                    _get_vocab_path(download_path, file_name, model_id)
-
-            if os.path.exists(
-                    os.path.join(download_path, 'config.json')):
-                if os.getenv('ENV_TYPE') == 'deepspeed+mpu':
-                    model_parallel_size = int(os.getenv("MODEL_PARALLEL_SIZE"))
-                    if model_parallel_size > 1:
-                        # if gpus == nums_of_modelhub_models
-                        # can load
-                        # else need to download the pytorch_model.bin and to recut.
-                        model_hub_parallel_size = 0
-                        for f in model_files:
-                            if "pytorch_model_" in f:
-                                model_hub_parallel_size += 1
-                else:
-                    model_parallel_size = 1
-
-                if "pytorch_model_01.bin" in model_files and model_parallel_size > 1 and model_hub_parallel_size == model_parallel_size:
-                    # Only to download the model slices(megatron-lm).
-                    for file_to_load in model_files:
-                        if "pytorch_model_" in file_to_load:
-                            _get_checkpoint_path(download_path, file_to_load,
-                                                 model_id)
-
-                elif 'pytorch_model.bin' in model_files:
-                    checkpoint_path = _get_checkpoint_path(
-                        download_path, 'pytorch_model.bin', model_id)
-                else:
-                    checkpoint_merge = {}
-                    # maybe multi weights files
-                    for file_to_load in model_files:
-                        if "pytorch_model-0" in file_to_load:
-                            _get_checkpoint_path(download_path, file_to_load,
-                                                 model_id)
-
     def get_input_embeddings(self):
         return self.model.embed_tokens