# Copyright (c) Alibaba, Inc. and its affiliates. # Copyright 2023-present the HuggingFace Inc. team. import hashlib import os import shutil import tempfile import threading from dataclasses import asdict, dataclass, field from types import FunctionType from typing import Dict, Optional, Union import json import numpy as np import torch from modelscope import snapshot_download from modelscope.hub.utils.utils import get_cache_dir from packaging import version from peft.utils import CONFIG_NAME from peft.utils import ModulesToSaveWrapper as _ModulesToSaveWrapper from peft.utils import _get_submodules from swift.llm import MODEL_ARCH_MAPPING, ModelKeys from swift.utils import gc_collect from swift.utils.constants import BIN_EXTENSIONS from swift.utils.logger import get_logger logger = get_logger() @dataclass class SwiftConfig: swift_type: str = field(default=None) model_key_mapping: Optional[Union[dict, ModelKeys]] = field(default=None) @property def __dict__(self): return asdict(self) def to_dict(self): return self.__dict__ def save_pretrained(self, save_directory, **kwargs): r""" This method saves the configuration of your adapter model in a directory. Args: save_directory (`str`): The directory where the configuration will be saved. """ if os.path.isfile(save_directory): raise AssertionError(f'Provided path ({save_directory}) should be a directory, not a file') os.makedirs(save_directory, exist_ok=True) output_dict = self.__dict__ output_dict.update(kwargs) output_path = os.path.join(save_directory, CONFIG_NAME) # save it with open(output_path, 'w', encoding='utf-8') as writer: writer.write(json.dumps(output_dict, indent=2, sort_keys=True)) @classmethod def from_pretrained(cls, pretrained_model_name_or_path, **kwargs): r""" This method loads the configuration of your adapter model from a directory. Args: pretrained_model_name_or_path (`str`): The directory or the hub-id where the configuration is saved. **kwargs: Additional keyword arguments passed along to the child class initialization. """ if os.path.isfile(os.path.join(pretrained_model_name_or_path, CONFIG_NAME)): config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME) else: try: model_dir = snapshot_download(pretrained_model_name_or_path, ignore_patterns=BIN_EXTENSIONS) config_file = os.path.join(model_dir, CONFIG_NAME) except Exception: raise ValueError(f"Can't find config.json at '{pretrained_model_name_or_path}'") loaded_attributes = cls.from_json_file(config_file) from .mapping import SWIFT_MAPPING assert loaded_attributes.get('swift_type', '') in SWIFT_MAPPING config = SWIFT_MAPPING[loaded_attributes['swift_type']][0](**kwargs) for key, value in loaded_attributes.items(): if hasattr(config, key): setattr(config, key, value) return config @classmethod def from_json_file(cls, path_json_file, **kwargs): r""" Loads a configuration file from a json file. Args: path_json_file (`str`): The path to the json file. """ with open(path_json_file, 'r', encoding='utf-8') as file: json_object = json.load(file) return json_object @dataclass class SwiftOutput: """The output class returned by all tuners. Args: model (`torch.nn.Module`): The model wrapped config (`SwiftConfig`): The swift config instance. state_dict_callback (`FunctionType`): A callback returned by the tuner which is used to get the tuner's state dict among the model's state dict. This callback should receive a state dict, and returns a created state dict. Examples: >>> def state_dict_callback(state_dict, adapter_name): >>> return { >>> key: value >>> for key, value in state_dict.items() if adapter_name in key >>> } save_callback (`FunctionType`): A callback used to save trained model. mark_trainable_callback (`FunctionType`): A callback returned by the tuner which is used to mark the tuner's adapter's parameters to trainable. This callback should receive a model instance, and returns nothing. Examples: >>> def mark_trainable_callback(model): >>> mark_lora_as_trainable(model, config.bias) optimizer_group_callback (`FunctionType`): A callback returned the param group cared by the tuner. load_state_dict_callback (`FunctionType`): A callback called before load_state_dict of the tuner. load_callback (`FunctionType`): A callback used to load trained model. """ model: torch.nn.Module = None config: SwiftConfig = None state_dict_callback: FunctionType = None save_callback: FunctionType = None mark_trainable_callback: FunctionType = None optimizer_group_callback: FunctionType = None load_state_dict_callback: FunctionType = None load_callback: FunctionType = None class ActivationMixin: USE_UNIQUE_THREAD = 'USE_UNIQUE_THREAD' REMINEDED = False def __init__(self, module_key): self.module_key = module_key self._thread_inf: Dict[int, Dict[str, bool]] = {} self._unique_thread = bool(int(os.environ.get(ActivationMixin.USE_UNIQUE_THREAD, '1'))) if not self._unique_thread and not ActivationMixin.REMINEDED: ActivationMixin.REMINEDED = True logger.warn('Using multiple thread mode, gradient checkpointing is not supported.') def mark_all_sub_modules_as_plugin(self: torch.nn.Module): self.plugin = True for name, module in self.named_modules(): if 'base_layer' not in name: module.plugin = True @property def indent(self): return 0 if self.unique_thread else threading.get_ident() @property def unique_thread(self): return self._unique_thread def set_activation(self, adapter_name, activate=True): tid = self.indent if tid not in self._thread_inf: self._thread_inf[tid] = {} self._thread_inf[tid][adapter_name] = activate def is_activated(self, adapter_name): tid = self.indent return self._thread_inf.get(tid, {}).get(adapter_name, False) def get_activated_adapters(self): return [key for key, value in self._thread_inf.get(self.indent, {}).items() if value] class OffloadHelper: def __init__(self): cache_dir = os.path.join(get_cache_dir(), 'offload_cache') os.makedirs(cache_dir, exist_ok=True) tmp_dir = tempfile.TemporaryDirectory(dir=cache_dir) self.cache_dir = tmp_dir.name self._tmp_dir = tmp_dir self.index = {} @staticmethod def offload_weight(weight, weight_name, offload_folder, index=None): dtype = None if str(weight.dtype) == 'torch.bfloat16': weight = weight.view(torch.int16) dtype = 'bfloat16' array = weight.cpu().numpy() tensor_file = os.path.join(offload_folder, f'{weight_name}.dat') if index is not None: if dtype is None: dtype = str(array.dtype) index[weight_name] = {'dtype': dtype, 'shape': list(array.shape)} if array.ndim == 0: array = array[None] file_array = np.memmap(tensor_file, dtype=array.dtype, mode='w+', shape=array.shape) file_array[:] = array[:] file_array.flush() return index @staticmethod def load_offloaded_weight(weight_file, weight_info): shape = tuple(weight_info['shape']) if shape == (): shape = (1, ) dtype = weight_info['dtype'] if dtype == 'bfloat16': dtype = 'int16' weight = np.memmap(weight_file, dtype=dtype, shape=shape, mode='r') if len(weight_info['shape']) == 0: weight = weight[0] weight = torch.tensor(weight) if weight_info['dtype'] == 'bfloat16': weight = weight.view(torch.bfloat16) return weight def offload_disk(self, module: torch.nn.Module, adapter_name, module_key): key = adapter_name + ':' + module_key md5 = hashlib.md5(key.encode('utf-8')).hexdigest() sub_folder = os.path.join(self.cache_dir, md5) os.makedirs(sub_folder, exist_ok=True) state_dict = module.state_dict() self.index[md5] = {} for key, tensor in state_dict.items(): OffloadHelper.offload_weight(tensor, key, sub_folder, self.index[md5]) def load_disk(self, module: torch.nn.Module, adapter_name, module_key): key = adapter_name + ':' + module_key md5 = hashlib.md5(key.encode('utf-8')).hexdigest() sub_folder = os.path.join(self.cache_dir, md5) state_dict = {} for key, value in self.index[md5].items(): file = os.path.join(sub_folder, f'{key}.dat') state_dict[key] = OffloadHelper.load_offloaded_weight(file, self.index[md5][key]) if version.parse(torch.__version__) >= version.parse('2.1.0'): module.load_state_dict(state_dict, assign=True) else: for name, _module in module.named_modules(): if len(list(_module.modules())) > 1: continue buffers = {} prefix = name if not name else name + '.' for sub_name, buffer in _module.named_buffers(): buffer_cls = type(buffer) buffers[sub_name] = buffer_cls(state_dict[prefix + sub_name]) _module._buffers.update(buffers) params = {} for sub_name, param in _module.named_parameters(): param_cls = type(param) params[sub_name] = param_cls(state_dict[prefix + sub_name], requires_grad=param.requires_grad) _module._parameters.update(params) shutil.rmtree(sub_folder, ignore_errors=True) class SwiftAdapter: offload_helper = None @staticmethod def prepare_model(model: torch.nn.Module, config: SwiftConfig, adapter_name: str) -> SwiftOutput: raise NotImplementedError @staticmethod def activate_adapter(module: torch.nn.Module, adapter_name: str, activate: bool, offload: str = None): raise NotImplementedError @staticmethod def save_memory(module: torch.nn.Module, adapter_name: str, module_key: str, activate: bool, offload: str = None): if not isinstance(module, torch.nn.Module): return if activate: SwiftAdapter.load(module, adapter_name, module_key) else: SwiftAdapter.offload(module, adapter_name, module_key, offload=offload) @staticmethod def offload(module: torch.nn.Module, adapter_name, module_key, offload: str): if not offload: return device = next(iter(module.parameters())).device if hasattr(module, 'origin_device') and module.origin_device != str(device): return module.origin_device = str(device) if offload == 'cpu': if str(device) != 'cpu': module.to('cpu') elif offload == 'meta': if str(device) != 'meta': if SwiftAdapter.offload_helper is None: SwiftAdapter.offload_helper = OffloadHelper() SwiftAdapter.offload_helper.offload_disk(module, adapter_name=adapter_name, module_key=module_key) module.to('meta') else: raise NotImplementedError gc_collect() @staticmethod def load(module: torch.nn.Module, adapter_name, module_key): device = next(iter(module.parameters())).device if not hasattr(module, 'origin_device') or module.origin_device == str(device): return if str(device) == 'cpu': module.to(module.origin_device) delattr(module, 'origin_device') elif str(device) == 'meta': SwiftAdapter.offload_helper.load_disk(module, adapter_name=adapter_name, module_key=module_key) module.to(module.origin_device) delattr(module, 'origin_device') @classmethod def get_model_key_mapping(cls, model_type, config) -> ModelKeys: if model_type in MODEL_ARCH_MAPPING.keys(): model_key_mapping = MODEL_ARCH_MAPPING[model_type] else: model_key_mapping = config.model_key_mapping if model_key_mapping is None: raise ValueError(f'{model_type} is not defined in MODEL_KEYS_MAPPING, ' f'please consider pass the information through the config.model_key_mapping') if isinstance(model_key_mapping, dict): model_key_mapping: ModelKeys = ModelKeys(**model_key_mapping) return model_key_mapping @staticmethod def state_dict_load_hook(model: torch.nn.Module, state_dict: Dict[str, torch.Tensor]): pass @staticmethod def has_additional_modules(): return True class ModulesToSaveWrapper(ActivationMixin, _ModulesToSaveWrapper): def __init__(self, *args, module_key, **kwargs): super(ModulesToSaveWrapper, self).__init__(module_key) super(ActivationMixin, self).__init__(*args, **kwargs) SwiftAdapter.save_memory(self.original_module, 'original_module', self.module_key, False, offload='cpu') @property def active_adapter(self): active_adapters = self.get_activated_adapters() if not active_adapters: return None elif len(active_adapters) > 1: raise ValueError('ModulesToSaveWrapper does not support multiple active adapters') return active_adapters[0] def set_adapter(self, adapter_name: str, offload: str = None): if adapter_name not in self.modules_to_save: raise ValueError(f'Adapter {adapter_name} not found in {self.modules_to_save.keys()}') self.modules_to_save[adapter_name].requires_grad_(True) self.set_activation(adapter_name, True) SwiftAdapter.save_memory(self.modules_to_save[adapter_name], adapter_name, self.module_key, True) SwiftAdapter.save_memory(self.original_module, 'original_module', self.module_key, False, offload=offload) def deactivate_adapter(self, adapter_name: str, offload: str = None): if adapter_name in self.modules_to_save and self.unique_thread: self.modules_to_save[adapter_name].requires_grad_(False) self.set_activation(adapter_name, False) SwiftAdapter.save_memory( self.modules_to_save[adapter_name], adapter_name, self.module_key, False, offload=offload) if not self.get_activated_adapters(): SwiftAdapter.save_memory(self.original_module, 'original_module', self.module_key, True) def enable_adapters(self, enabled: bool): super().enable_adapters(enabled) if not enabled: SwiftAdapter.save_memory(self.original_module, 'original_module', self.module_key, False, offload='meta') else: SwiftAdapter.save_memory(self.original_module, 'original_module', self.module_key, True) def set_adapter(model, adapter_name, activate, offload): for module in model.modules(): if isinstance(module, ModulesToSaveWrapper): if activate: module.set_adapter(adapter_name, offload) else: module.deactivate_adapter(adapter_name, offload) def set_trainable(model, adapter_name): key_list = [key for key, _ in model.named_modules()] for key in key_list: target_module_found = any(key.endswith(target_key) for target_key in model.modules_to_save) if target_module_found: parent, target, target_name = _get_submodules(model, key) if isinstance(target, ModulesToSaveWrapper): target.update(adapter_name) target.set_adapter(target.active_adapter) else: new_module = ModulesToSaveWrapper(target, module_key=key, adapter_name=adapter_name) new_module.set_adapter(adapter_name) setattr(parent, target_name, new_module) def swift_to_peft_format(ckpt_dir: str, output_dir: str) -> str: if 'default' in os.listdir(ckpt_dir): # swift_backend from swift import Swift Swift.save_to_peft_format(ckpt_dir, output_dir) ckpt_dir = output_dir logger.info(f'Converting the swift format checkpoint to peft format, and saving it to: `{output_dir}`') else: logger.info('The format of the checkpoint is already in peft format.') return ckpt_dir