269 lines
9.9 KiB
Python
269 lines
9.9 KiB
Python
# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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PEFT utilities: Utilities related to peft library
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"""
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import collections
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import importlib
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from typing import Optional
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from packaging import version
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from .import_utils import is_peft_available, is_torch_available
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if is_torch_available():
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import torch
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def recurse_remove_peft_layers(model):
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r"""
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Recursively replace all instances of `LoraLayer` with corresponding new layers in `model`.
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"""
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from peft.tuners.tuners_utils import BaseTunerLayer
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has_base_layer_pattern = False
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for module in model.modules():
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if isinstance(module, BaseTunerLayer):
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has_base_layer_pattern = hasattr(module, "base_layer")
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break
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if has_base_layer_pattern:
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from peft.utils import _get_submodules
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key_list = [key for key, _ in model.named_modules() if "lora" not in key]
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for key in key_list:
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try:
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parent, target, target_name = _get_submodules(model, key)
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except AttributeError:
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continue
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if hasattr(target, "base_layer"):
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setattr(parent, target_name, target.get_base_layer())
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else:
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# This is for backwards compatibility with PEFT <= 0.6.2.
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# TODO can be removed once that PEFT version is no longer supported.
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from peft.tuners.lora import LoraLayer
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for name, module in model.named_children():
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if len(list(module.children())) > 0:
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## compound module, go inside it
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recurse_remove_peft_layers(module)
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module_replaced = False
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if isinstance(module, LoraLayer) and isinstance(module, torch.nn.Linear):
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new_module = torch.nn.Linear(module.in_features, module.out_features, bias=module.bias is not None).to(
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module.weight.device
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)
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new_module.weight = module.weight
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if module.bias is not None:
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new_module.bias = module.bias
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module_replaced = True
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elif isinstance(module, LoraLayer) and isinstance(module, torch.nn.Conv2d):
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new_module = torch.nn.Conv2d(
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module.in_channels,
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module.out_channels,
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module.kernel_size,
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module.stride,
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module.padding,
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module.dilation,
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module.groups,
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).to(module.weight.device)
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new_module.weight = module.weight
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if module.bias is not None:
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new_module.bias = module.bias
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module_replaced = True
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if module_replaced:
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setattr(model, name, new_module)
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del module
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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return model
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def scale_lora_layers(model, weight):
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"""
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Adjust the weightage given to the LoRA layers of the model.
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Args:
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model (`torch.nn.Module`):
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The model to scale.
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weight (`float`):
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The weight to be given to the LoRA layers.
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"""
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from peft.tuners.tuners_utils import BaseTunerLayer
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for module in model.modules():
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if isinstance(module, BaseTunerLayer):
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module.scale_layer(weight)
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def unscale_lora_layers(model, weight: Optional[float] = None):
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"""
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Removes the previously passed weight given to the LoRA layers of the model.
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Args:
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model (`torch.nn.Module`):
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The model to scale.
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weight (`float`, *optional*):
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The weight to be given to the LoRA layers. If no scale is passed the scale of the lora layer will be
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re-initialized to the correct value. If 0.0 is passed, we will re-initialize the scale with the correct
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value.
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"""
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from peft.tuners.tuners_utils import BaseTunerLayer
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for module in model.modules():
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if isinstance(module, BaseTunerLayer):
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if weight is not None and weight != 0:
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module.unscale_layer(weight)
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elif weight is not None and weight == 0:
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for adapter_name in module.active_adapters:
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# if weight == 0 unscale should re-set the scale to the original value.
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module.set_scale(adapter_name, 1.0)
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def get_peft_kwargs(rank_dict, network_alpha_dict, peft_state_dict, is_unet=True):
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rank_pattern = {}
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alpha_pattern = {}
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r = lora_alpha = list(rank_dict.values())[0]
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if len(set(rank_dict.values())) > 1:
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# get the rank occuring the most number of times
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r = collections.Counter(rank_dict.values()).most_common()[0][0]
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# for modules with rank different from the most occuring rank, add it to the `rank_pattern`
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rank_pattern = dict(filter(lambda x: x[1] != r, rank_dict.items()))
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rank_pattern = {k.split(".lora_B.")[0]: v for k, v in rank_pattern.items()}
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if network_alpha_dict is not None and len(network_alpha_dict) > 0:
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if len(set(network_alpha_dict.values())) > 1:
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# get the alpha occuring the most number of times
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lora_alpha = collections.Counter(network_alpha_dict.values()).most_common()[0][0]
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# for modules with alpha different from the most occuring alpha, add it to the `alpha_pattern`
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alpha_pattern = dict(filter(lambda x: x[1] != lora_alpha, network_alpha_dict.items()))
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if is_unet:
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alpha_pattern = {
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".".join(k.split(".lora_A.")[0].split(".")).replace(".alpha", ""): v
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for k, v in alpha_pattern.items()
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}
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else:
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alpha_pattern = {".".join(k.split(".down.")[0].split(".")[:-1]): v for k, v in alpha_pattern.items()}
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else:
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lora_alpha = set(network_alpha_dict.values()).pop()
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# layer names without the Diffusers specific
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target_modules = list({name.split(".lora")[0] for name in peft_state_dict.keys()})
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lora_config_kwargs = {
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"r": r,
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"lora_alpha": lora_alpha,
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"rank_pattern": rank_pattern,
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"alpha_pattern": alpha_pattern,
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"target_modules": target_modules,
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}
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return lora_config_kwargs
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def get_adapter_name(model):
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from peft.tuners.tuners_utils import BaseTunerLayer
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for module in model.modules():
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if isinstance(module, BaseTunerLayer):
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return f"default_{len(module.r)}"
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return "default_0"
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def set_adapter_layers(model, enabled=True):
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from peft.tuners.tuners_utils import BaseTunerLayer
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for module in model.modules():
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if isinstance(module, BaseTunerLayer):
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# The recent version of PEFT needs to call `enable_adapters` instead
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if hasattr(module, "enable_adapters"):
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module.enable_adapters(enabled=enabled)
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else:
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module.disable_adapters = not enabled
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def delete_adapter_layers(model, adapter_name):
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from peft.tuners.tuners_utils import BaseTunerLayer
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for module in model.modules():
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if isinstance(module, BaseTunerLayer):
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if hasattr(module, "delete_adapter"):
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module.delete_adapter(adapter_name)
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else:
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raise ValueError(
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"The version of PEFT you are using is not compatible, please use a version that is greater than 0.6.1"
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)
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# For transformers integration - we need to pop the adapter from the config
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if getattr(model, "_hf_peft_config_loaded", False) and hasattr(model, "peft_config"):
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model.peft_config.pop(adapter_name, None)
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# In case all adapters are deleted, we need to delete the config
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# and make sure to set the flag to False
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if len(model.peft_config) == 0:
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del model.peft_config
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model._hf_peft_config_loaded = None
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def set_weights_and_activate_adapters(model, adapter_names, weights):
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from peft.tuners.tuners_utils import BaseTunerLayer
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# iterate over each adapter, make it active and set the corresponding scaling weight
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for adapter_name, weight in zip(adapter_names, weights):
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for module in model.modules():
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if isinstance(module, BaseTunerLayer):
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# For backward compatbility with previous PEFT versions
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if hasattr(module, "set_adapter"):
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module.set_adapter(adapter_name)
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else:
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module.active_adapter = adapter_name
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module.set_scale(adapter_name, weight)
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# set multiple active adapters
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for module in model.modules():
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if isinstance(module, BaseTunerLayer):
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# For backward compatbility with previous PEFT versions
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if hasattr(module, "set_adapter"):
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module.set_adapter(adapter_names)
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else:
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module.active_adapter = adapter_names
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def check_peft_version(min_version: str) -> None:
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r"""
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Checks if the version of PEFT is compatible.
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Args:
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version (`str`):
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The version of PEFT to check against.
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"""
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if not is_peft_available():
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raise ValueError("PEFT is not installed. Please install it with `pip install peft`")
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is_peft_version_compatible = version.parse(importlib.metadata.version("peft")) > version.parse(min_version)
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if not is_peft_version_compatible:
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raise ValueError(
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f"The version of PEFT you are using is not compatible, please use a version that is greater"
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f" than {min_version}"
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)
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