import torch, os from safetensors import safe_open from contextlib import contextmanager import hashlib @contextmanager def init_weights_on_device(device = torch.device("meta"), include_buffers :bool = False): old_register_parameter = torch.nn.Module.register_parameter if include_buffers: old_register_buffer = torch.nn.Module.register_buffer def register_empty_parameter(module, name, param): old_register_parameter(module, name, param) if param is not None: param_cls = type(module._parameters[name]) kwargs = module._parameters[name].__dict__ kwargs["requires_grad"] = param.requires_grad module._parameters[name] = param_cls(module._parameters[name].to(device), **kwargs) def register_empty_buffer(module, name, buffer, persistent=True): old_register_buffer(module, name, buffer, persistent=persistent) if buffer is not None: module._buffers[name] = module._buffers[name].to(device) def patch_tensor_constructor(fn): def wrapper(*args, **kwargs): kwargs["device"] = device return fn(*args, **kwargs) return wrapper if include_buffers: tensor_constructors_to_patch = { torch_function_name: getattr(torch, torch_function_name) for torch_function_name in ["empty", "zeros", "ones", "full"] } else: tensor_constructors_to_patch = {} try: torch.nn.Module.register_parameter = register_empty_parameter if include_buffers: torch.nn.Module.register_buffer = register_empty_buffer for torch_function_name in tensor_constructors_to_patch.keys(): setattr(torch, torch_function_name, patch_tensor_constructor(getattr(torch, torch_function_name))) yield finally: torch.nn.Module.register_parameter = old_register_parameter if include_buffers: torch.nn.Module.register_buffer = old_register_buffer for torch_function_name, old_torch_function in tensor_constructors_to_patch.items(): setattr(torch, torch_function_name, old_torch_function) def load_state_dict_from_folder(file_path, torch_dtype=None): state_dict = {} for file_name in os.listdir(file_path): if "." in file_name and file_name.split(".")[-1] in [ "safetensors", "bin", "ckpt", "pth", "pt" ]: state_dict.update(load_state_dict(os.path.join(file_path, file_name), torch_dtype=torch_dtype)) return state_dict def load_state_dict(file_path, torch_dtype=None, device="cpu"): if file_path.endswith(".safetensors"): return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype, device=device) else: return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype, device=device) def load_state_dict_from_safetensors(file_path, torch_dtype=None, device="cpu"): state_dict = {} with safe_open(file_path, framework="pt", device=str(device)) as f: for k in f.keys(): state_dict[k] = f.get_tensor(k) if torch_dtype is not None: state_dict[k] = state_dict[k].to(torch_dtype) return state_dict def load_state_dict_from_bin(file_path, torch_dtype=None, device="cpu"): state_dict = torch.load(file_path, map_location=device, weights_only=True) if torch_dtype is not None: for i in state_dict: if isinstance(state_dict[i], torch.Tensor): state_dict[i] = state_dict[i].to(torch_dtype) return state_dict def search_for_embeddings(state_dict): embeddings = [] for k in state_dict: if isinstance(state_dict[k], torch.Tensor): embeddings.append(state_dict[k]) elif isinstance(state_dict[k], dict): embeddings += search_for_embeddings(state_dict[k]) return embeddings def search_parameter(param, state_dict): for name, param_ in state_dict.items(): if param.numel() == param_.numel(): if param.shape == param_.shape: if torch.dist(param, param_) < 1e-3: return name else: if torch.dist(param.flatten(), param_.flatten()) < 1e-3: return name return None def build_rename_dict(source_state_dict, target_state_dict, split_qkv=False): matched_keys = set() with torch.no_grad(): for name in source_state_dict: rename = search_parameter(source_state_dict[name], target_state_dict) if rename is not None: print(f'"{name}": "{rename}",') matched_keys.add(rename) elif split_qkv and len(source_state_dict[name].shape)>=1 and source_state_dict[name].shape[0]%3==0: length = source_state_dict[name].shape[0] // 3 rename = [] for i in range(3): rename.append(search_parameter(source_state_dict[name][i*length: i*length+length], target_state_dict)) if None not in rename: print(f'"{name}": {rename},') for rename_ in rename: matched_keys.add(rename_) for name in target_state_dict: if name not in matched_keys: print("Cannot find", name, target_state_dict[name].shape) def search_for_files(folder, extensions): files = [] if os.path.isdir(folder): for file in sorted(os.listdir(folder)): files += search_for_files(os.path.join(folder, file), extensions) elif os.path.isfile(folder): for extension in extensions: if folder.endswith(extension): files.append(folder) break return files def convert_state_dict_keys_to_single_str(state_dict, with_shape=True): keys = [] for key, value in state_dict.items(): # "encoder.layer1.weight": tensor(...) if isinstance(key, str): if isinstance(value, torch.Tensor): if with_shape: shape = "_".join(map(str, list(value.shape))) keys.append(key + ":" + shape) # "encoder.weight:1024_4096" keys.append(key) elif isinstance(value, dict): keys.append(key + "|" + convert_state_dict_keys_to_single_str(value, with_shape=with_shape)) keys.sort() keys_str = ",".join(keys) return keys_str # b'linear.bias,linear.bias:4,linear.weight,linear.weight:4_8' def split_state_dict_with_prefix(state_dict): keys = sorted([key for key in state_dict if isinstance(key, str)]) prefix_dict = {} for key in keys: prefix = key if "." not in key else key.split(".")[0] if prefix not in prefix_dict: prefix_dict[prefix] = [] prefix_dict[prefix].append(key) state_dicts = [] for prefix, keys in prefix_dict.items(): sub_state_dict = {key: state_dict[key] for key in keys} state_dicts.append(sub_state_dict) return state_dicts def hash_state_dict_keys(state_dict, with_shape=True): keys_str = convert_state_dict_keys_to_single_str(state_dict, with_shape=with_shape) keys_str = keys_str.encode(encoding="UTF-8") return hashlib.md5(keys_str).hexdigest()