Yw22's picture
init demo
d711508
raw
history blame
6.79 kB
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from contextlib import contextmanager
from dataclasses import asdict
from enum import Enum
from typing import Any
import torch
from torch import nn
from peft.tuners.tuners_utils import BaseTuner, BaseTunerLayer, check_target_module_exists
from peft.utils import (
TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING,
ModulesToSaveWrapper,
)
from .config import PolyConfig
from .layer import Linear, PolyLayer
class PolyModel(BaseTuner):
prefix: str = "poly_"
def __init__(self, model, config, adapter_name) -> None:
super().__init__(model, config, adapter_name)
@staticmethod
def _check_target_module_exists(poly_config, key):
return check_target_module_exists(poly_config, key)
def _create_and_replace(
self,
poly_config: PolyConfig,
adapter_name: str,
target: nn.Module,
target_name: str,
parent: nn.Module,
**optional_kwargs: Any,
):
if isinstance(target, PolyLayer):
target.update_layer(adapter_name, poly_config)
else:
new_module = self._create_new_module(
poly_config,
adapter_name,
target,
)
if adapter_name not in self.active_adapters:
# adding an additional adapter: it is not automatically trainable
new_module.requires_grad_(False)
self._replace_module(parent, target_name, new_module, target)
def _replace_module(self, parent, child_name, new_module, child):
setattr(parent, child_name, new_module)
# It's not necessary to set requires_grad here, as that is handled by
# _mark_only_adapters_as_trainable
# child layer wraps the original module, unpack it
if hasattr(child, "base_layer"):
child = child.base_layer
if not hasattr(new_module, "base_layer"):
new_module.weight = child.weight
if hasattr(child, "bias"):
new_module.bias = child.bias
if getattr(child, "state", None) is not None:
if hasattr(new_module, "base_layer"):
new_module.base_layer.state = child.state
else:
new_module.state = child.state
new_module.to(child.weight.device)
# dispatch to correct device
for name, module in new_module.named_modules():
if (self.prefix in name) or ("ranknum" in name):
weight = child.qweight if hasattr(child, "qweight") else child.weight
module.to(weight.device)
def _mark_only_adapters_as_trainable(self, model: nn.Module) -> None:
for n, p in model.named_parameters():
if self.prefix not in n:
p.requires_grad = False
@staticmethod
def _create_new_module(poly_config, adapter_name, target, **kwargs):
if isinstance(target, BaseTunerLayer):
target_base_layer = target.get_base_layer()
else:
target_base_layer = target
if isinstance(target_base_layer, torch.nn.Linear):
return Linear(target, adapter_name, poly_config, **kwargs)
else:
raise ValueError(
f"Target module {target} is not supported. Currently, only the following modules are supported: "
"`torch.nn.Linear`."
)
def __getattr__(self, name: str):
"""Forward missing attributes to the wrapped module."""
try:
return super().__getattr__(name) # defer to nn.Module's logic
except AttributeError:
return getattr(self.model, name)
def get_peft_config_as_dict(self, inference: bool = False):
config_dict = {}
for key, value in self.peft_config.items():
config = {k: v.value if isinstance(v, Enum) else v for k, v in asdict(value).items()}
if inference:
config["inference_mode"] = True
config_dict[key] = config
return config
def _set_adapter_layers(self, enabled=True):
for module in self.model.modules():
if isinstance(module, (PolyLayer, ModulesToSaveWrapper)):
module.enable_adapters(enabled)
def enable_adapter_layers(self):
self._set_adapter_layers(enabled=True)
def disable_adapter_layers(self):
self._set_adapter_layers(enabled=False)
def set_adapter(self, adapter_name):
for module in self.model.modules():
if isinstance(module, PolyLayer):
module.set_adapter(adapter_name)
def _prepare_adapter_config(self, peft_config, model_config):
if peft_config.target_modules is None:
if model_config["model_type"] not in TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING:
raise ValueError("Please specify `target_modules` in `peft_config`")
peft_config.target_modules = set(
TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING[model_config["model_type"]]
)
return peft_config
def _register_pre_hooks(self, task_ids):
"""Helper method to register pre hooks."""
if task_ids is None:
return []
def pre_hook(_, args, kwargs):
kwargs["task_ids"] = task_ids
return args, kwargs
handles = []
for module in self.model.modules():
if isinstance(module, Linear):
handle = module.register_forward_pre_hook(pre_hook, with_kwargs=True)
handles.append(handle)
return handles
@contextmanager
def _manage_pre_hooks(self, task_ids):
"""Context manager to handle the lifecycle of pre hooks."""
handles = self._register_pre_hooks(task_ids)
try:
yield
finally:
for handle in handles:
handle.remove()
def forward(self, *args, task_ids=None, **kwargs):
with self._manage_pre_hooks(task_ids):
return self.model(*args, **kwargs)
def generate(self, *args, task_ids=None, **kwargs):
with self._manage_pre_hooks(task_ids):
return self.model.generate(*args, **kwargs)