# Copyright (c) Alibaba, Inc. and its affiliates.
# Copyright 2023-present the HuggingFace Inc. team.
import hashlib
import os
import shutil
import threading
import uuid
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.utils.constants import BIN_EXTENSIONS
from swift.utils.logger import get_logger
from swift.utils.module_mapping import MODEL_KEYS_MAPPING, ModelKeys
logger = get_logger()
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@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__
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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') as writer:
writer.write(json.dumps(output_dict, indent=2, sort_keys=True))
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@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_file_pattern=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
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@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') as file:
json_object = json.load(file)
return json_object
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@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):
sub_dir = os.path.join('offload_cache', str(uuid.uuid4().hex))
self.cache_dir = os.path.join(get_cache_dir(), sub_dir)
shutil.rmtree(self.cache_dir, ignore_errors=True)
os.makedirs(self.cache_dir, exist_ok=True)
self.index = {}
def __del__(self):
shutil.rmtree(self.cache_dir, ignore_errors=True)
@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 = OffloadHelper()
@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':
SwiftAdapter.offload_helper.offload_disk(module, adapter_name=adapter_name, module_key=module_key)
module.to('meta')
else:
raise NotImplementedError
torch.cuda.empty_cache()
@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_KEYS_MAPPING.keys():
model_key_mapping = MODEL_KEYS_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)