感谢你提供的代码实现，它对我的帮助很大。然而，在使用load_from_name函数时，我发现它并不支持flash-attn,因此我自己实现了这部分代码。尽管它可以正常运行，但我仍然不确定实现是否正确。如果作者有空，能否帮我检查一下这个实现是否正确呢？如果是正确的，作者可以将我的实现加入到仓库中。非常感谢！

以下是代码片段：

------- ps: add use_flash_attention keyword -------

def load_from_name(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu",
download_root: str = None, vision_model_name: str = None, text_model_name: str = None,
input_resolution: int = None, use_flash_attention: bool = False):
if name in _MODELS:
model_path = _download(_MODELS[name], download_root or os.path.expanduser("~/.cache/clip"))
model_name, model_input_resolution = _MODEL_INFO[name]['struct'], _MODEL_INFO[name]['input_resolution']
elif os.path.isfile(name):
assert vision_model_name and text_model_name and input_resolution, "Please specify specific 'vision_model_name', 'text_model_name', and 'input_resolution'"
model_path = name
model_name, model_input_resolution = f'{vision_model_name}@{text_model_name}', input_resolution
else:
raise RuntimeError(f"Model {name} not found; available models = {available_models()}")

with open(model_path, 'rb') as opened_file:
    # loading saved checkpoint
    checkpoint = torch.load(opened_file, map_location="cpu")

model = create_model(model_name, checkpoint, use_flash_attention=use_flash_attention)
if str(device) == "cpu":
    model.float()
else:
    model.to(device)
return model, image_transform(model_input_resolution)

------- ps: convert flash_attention weight -------

def create_model(model_name, checkpoint=None, use_flash_attention=False):
vision_model, text_model = model_name.split('@')
# Initialize the model.
vision_model_config_file = Path(
file).parent / f"model_configs/{vision_model.replace('/', '-')}.json"
print('Loading vision model config from', vision_model_config_file)
assert os.path.exists(vision_model_config_file)

text_model_config_file = Path(
    __file__).parent / f"model_configs/{text_model.replace('/', '-')}.json"
print('Loading text model config from', text_model_config_file)
assert os.path.exists(text_model_config_file)

with open(vision_model_config_file, 'r') as fv, open(text_model_config_file, 'r') as ft:
    model_info = json.load(fv)
    for k, v in json.load(ft).items():
        model_info[k] = v
if isinstance(model_info['vision_layers'], str):
    model_info['vision_layers'] = eval(model_info['vision_layers'])
print('Model info', model_info)
if use_flash_attention:
    model_info['use_flash_attention'] = use_flash_attention
model = CLIP(**model_info)
convert_weights(model)
        
if checkpoint:
    if use_flash_attention:
        sd = checkpoint["state_dict"]
        sd = {k: v for k, v in sd.items() if "bert.pooler" not in k}
        if next(iter(sd.items()))[0].startswith('module'):
            sd = {k[len('module.'):]: v for k, v in sd.items() if "bert.pooler" not in k}
        # Resize the positional embedding by interpolation, if needed
        resize_pos_embed(sd, model, prefix="module.")
        # Adapt flash attention
        sd = convert_state_dict(sd)
        # Load the state dict
    else:
        sd = checkpoint["state_dict"]
        if next(iter(sd.items()))[0].startswith('module'):
            sd = {k[len('module.'):]: v for k, v in sd.items() if "bert.pooler" not in k}
    model.load_state_dict(sd)
return model