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lora.py
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# coding=utf-8
# 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.
import math
import re
import warnings
from dataclasses import asdict, dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.pytorch_utils import Conv1D
from ..import_utils import is_bnb_available
from ..utils import (
TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING,
ModulesToSaveWrapper,
PeftConfig,
PeftType,
_freeze_adapter,
_get_submodules,
transpose,
)
if is_bnb_available():
import bitsandbytes as bnb
@dataclass
class LoraConfig(PeftConfig):
"""
This is the configuration class to store the configuration of a [`LoraModel`].
Args:
r (`int`): Lora attention dimension.
target_modules (`Union[List[str],str]`): The names of the modules to apply Lora to.
lora_alpha (`float`): The alpha parameter for Lora scaling.
lora_dropout (`float`): The dropout probability for Lora layers.
fan_in_fan_out (`bool`): Set this to True if the layer to replace stores weight like (fan_in, fan_out).
For example, gpt-2 uses `Conv1D` which stores weights like (fan_in, fan_out) and hence this should be set to `True`.:
bias (`str`): Bias type for Lora. Can be 'none', 'all' or 'lora_only'
modules_to_save (`List[str]`):List of modules apart from LoRA layers to be set as trainable
and saved in the final checkpoint.
"""
r: int = field(default=8, metadata={"help": "Lora attention dimension"})
target_modules: Optional[Union[List[str], str]] = field(
default=None,
metadata={
"help": "List of module names or regex expression of the module names to replace with Lora."
"For example, ['q', 'v'] or '.*decoder.*(SelfAttention|EncDecAttention).*(q|v)$' "
},
)
lora_alpha: int = field(default=None, metadata={"help": "Lora alpha"})
lora_dropout: float = field(default=None, metadata={"help": "Lora dropout"})
fan_in_fan_out: bool = field(
default=False,
metadata={"help": "Set this to True if the layer to replace stores weight like (fan_in, fan_out)"},
)
bias: str = field(default="none", metadata={"help": "Bias type for Lora. Can be 'none', 'all' or 'lora_only'"})
modules_to_save: Optional[List[str]] = field(
default=None,
metadata={
"help": "List of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. "
"For example, in Sequence Classification or Token Classification tasks, "
"the final layer `classifier/score` are randomly initialized and as such need to be trainable and saved."
},
)
init_lora_weights: bool = field(
default=True,
metadata={"help": "Whether to initialize the weights of the Lora layers."},
)
def __post_init__(self):
self.peft_type = PeftType.LORA
class LoraModel(torch.nn.Module):
"""
Creates Low Rank Adapter (Lora) model from a pretrained transformers model.
Args:
model ([`~transformers.PreTrainedModel`]): The model to be adapted.
config ([`LoraConfig`]): The configuration of the Lora model.
Returns:
`torch.nn.Module`: The Lora model.
Example:
```py
>>> from transformers import AutoModelForSeq2SeqLM, LoraConfig
>>> from peft import LoraModel, LoraConfig
>>> config = LoraConfig(
... peft_type="LORA",
... task_type="SEQ_2_SEQ_LM",
... r=8,
... lora_alpha=32,
... target_modules=["q", "v"],
... lora_dropout=0.01,
... )
>>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
>>> lora_model = LoraModel(config, model)
```
**Attributes**:
- **model** ([`~transformers.PreTrainedModel`]) -- The model to be adapted.
- **peft_config** ([`LoraConfig`]): The configuration of the Lora model.
"""
def __init__(self, model, config, adapter_name):
super().__init__()
self.model = model
self.forward = self.model.forward
self.peft_config = config
self.add_adapter(adapter_name, self.peft_config[adapter_name])
def add_adapter(self, adapter_name, config=None):
if config is not None:
model_config = self.model.config.to_dict() if hasattr(self.model.config, "to_dict") else self.model.config
config = self._prepare_lora_config(config, model_config)
self.peft_config[adapter_name] = config
self._find_and_replace(adapter_name)
if len(self.peft_config) > 1 and self.peft_config[adapter_name].bias != "none":
raise ValueError(
"LoraModel supports only 1 adapter with bias. When using multiple adapters, set bias to 'none' for all adapters."
)
mark_only_lora_as_trainable(self.model, self.peft_config[adapter_name].bias)
if self.peft_config[adapter_name].inference_mode:
_freeze_adapter(self.model, adapter_name)
def _find_and_replace(self, adapter_name):
lora_config = self.peft_config[adapter_name]
loaded_in_8bit = getattr(self.model, "is_loaded_in_8bit", False)
if loaded_in_8bit and not is_bnb_available():
raise ImportError(
"To use Lora with 8-bit quantization, please install the `bitsandbytes` package. "
"You can install it with `pip install bitsandbytes`."
)
is_target_modules_in_base_model = False
kwargs = {
"r": lora_config.r,
"lora_alpha": lora_config.lora_alpha,
"lora_dropout": lora_config.lora_dropout,
"fan_in_fan_out": lora_config.fan_in_fan_out,
"init_lora_weights": lora_config.init_lora_weights,
}
key_list = [key for key, _ in self.model.named_modules()]
for key in key_list:
if isinstance(lora_config.target_modules, str):
target_module_found = re.fullmatch(lora_config.target_modules, key)
else:
target_module_found = any(key.endswith(target_key) for target_key in lora_config.target_modules)
if target_module_found:
if not is_target_modules_in_base_model:
is_target_modules_in_base_model = True
parent, target, target_name = _get_submodules(self.model, key)
bias = target.bias is not None
if isinstance(target, LoraLayer):
target.update_layer(
adapter_name,
lora_config.r,
lora_config.lora_alpha,
lora_config.lora_dropout,
lora_config.init_lora_weights,
)
else:
if loaded_in_8bit and isinstance(target, bnb.nn.Linear8bitLt):
eightbit_kwargs = kwargs.copy()
eightbit_kwargs.update(
{
"has_fp16_weights": target.state.has_fp16_weights,
"memory_efficient_backward": target.state.memory_efficient_backward,
"threshold": target.state.threshold,
"index": target.index,
}
)
new_module = Linear8bitLt(
adapter_name, target.in_features, target.out_features, bias=bias, **eightbit_kwargs
)
else:
if isinstance(target, torch.nn.Linear):
in_features, out_features = target.in_features, target.out_features
if kwargs["fan_in_fan_out"]:
warnings.warn(
"fan_in_fan_out is set to True but the target module is `torch.nn.Linear`. "
"Setting fan_in_fan_out to False."
)
kwargs["fan_in_fan_out"] = lora_config.fan_in_fan_out = False
elif isinstance(target, Conv1D):
in_features, out_features = (
target.weight.ds_shape if hasattr(target.weight, "ds_shape") else target.weight.shape
)
if not kwargs["fan_in_fan_out"]:
warnings.warn(
"fan_in_fan_out is set to False but the target module is `Conv1D`. "
"Setting fan_in_fan_out to True."
)
kwargs["fan_in_fan_out"] = lora_config.fan_in_fan_out = True
else:
raise ValueError(
f"Target module {target} is not supported. "
f"Currently, only `torch.nn.Linear` and `Conv1D` are supported."
)
new_module = Linear(adapter_name, in_features, out_features, bias=bias, **kwargs)
self._replace_module(parent, target_name, new_module, target)
if not is_target_modules_in_base_model:
raise ValueError(
f"Target modules {lora_config.target_modules} not found in the base model. "
f"Please check the target modules and try again."
)
def _replace_module(self, parent_module, child_name, new_module, old_module):
setattr(parent_module, child_name, new_module)
new_module.weight = old_module.weight
if old_module.bias is not None:
new_module.bias = old_module.bias
if getattr(old_module, "state", None) is not None:
new_module.state = old_module.state
new_module.to(old_module.weight.device)
# dispatch to correct device
for name, module in new_module.named_modules():
if "lora_" in name:
module.to(old_module.weight.device)
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, LoraLayer):
module.disable_adapters = False if enabled else True
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, LoraLayer):
if module.merged:
warnings.warn("Adapter cannot be set when the model is merged. Unmerging the model first.")
module.unmerge()
module.active_adapter = adapter_name
def merge_adapter(self):
for module in self.model.modules():
if isinstance(module, LoraLayer):
module.merge()
def unmerge_adapter(self):
for module in self.model.modules():
if isinstance(module, LoraLayer):
module.unmerge()
@staticmethod
def _prepare_lora_config(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 = TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING[model_config["model_type"]]
if peft_config.inference_mode:
peft_config.merge_weights = True
return peft_config
def merge_and_unload(self):
r"""
This method merges the LoRa layers into the base model. This is needed if someone wants to use the base model
as a standalone model.
"""
if getattr(self.config, "model_type", None) == "gpt2":
raise ValueError("GPT2 models are not supported for merging LORA layers")
if getattr(self.model, "is_loaded_in_8bit", False):
raise ValueError("Cannot merge LORA layers when the model is loaded in 8-bit mode")
key_list = [key for key, _ in self.model.named_modules() if "lora" not in key]
for key in key_list:
try:
parent, target, target_name = _get_submodules(self.model, key)
except AttributeError:
continue
if isinstance(target, LoraLayer):
bias = target.bias is not None
new_module = torch.nn.Linear(target.in_features, target.out_features, bias=bias)
target.merge()
self._replace_module(parent, target_name, new_module, target)
# save any additional trainable modules part of `modules_to_save`
if isinstance(target, ModulesToSaveWrapper):
setattr(parent, target_name, target.modules_to_save[target.active_adapter])
return self.model
def add_weighted_adapter(self, adapters, weights, adapter_name):
if len({self.peft_config[adapter].r for adapter in adapters}) != 1:
raise ValueError("All adapters must have the same r value")
self.peft_config[adapter_name] = self.peft_config[adapters[0]]
self.peft_config[adapter_name].lora_alpha = self.peft_config[adapters[0]].r
self._find_and_replace(adapter_name)
mark_only_lora_as_trainable(self.model, self.peft_config[adapter_name].bias)
_freeze_adapter(self.model, adapter_name)
key_list = [key for key, _ in self.model.named_modules() if "lora" not in key]
for key in key_list:
_, target, _ = _get_submodules(self.model, key)
if isinstance(target, LoraLayer):
target.lora_A[adapter_name].weight.data = target.lora_A[adapter_name].weight.data * 0.0
target.lora_B[adapter_name].weight.data = target.lora_B[adapter_name].weight.data * 0.0
for adapter, weight in zip(adapters, weights):
if adapter not in target.lora_A:
continue
target.lora_A[adapter_name].weight.data += (
target.lora_A[adapter].weight.data * weight * target.scaling[adapter]
)
target.lora_B[adapter_name].weight.data += target.lora_B[adapter].weight.data * weight
# Below code is based on https://github.com/microsoft/LoRA/blob/main/loralib/layers.py
# and modified to work with PyTorch FSDP
# ------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
# ------------------------------------------------------------------------------------------
# had to adapt it for `lora_only` to work
def mark_only_lora_as_trainable(model: nn.Module, bias: str = "none") -> None:
for n, p in model.named_parameters():
if "lora_" not in n:
p.requires_grad = False
if bias == "none":
return
elif bias == "all":
for n, p in model.named_parameters():
if "bias" in n:
p.requires_grad = True
elif bias == "lora_only":
for m in model.modules():
if isinstance(m, LoraLayer) and hasattr(m, "bias") and m.bias is not None:
m.bias.requires_grad = True
else:
raise NotImplementedError
class LoraLayer:
def __init__(
self,
in_features: int,
out_features: int,
):
self.r = {}
self.lora_alpha = {}
self.scaling = {}
self.lora_dropout = nn.ModuleDict({})
self.lora_A = nn.ModuleDict({})
self.lora_B = nn.ModuleDict({})
# Mark the weight as unmerged
self.merged = False
self.disable_adapters = False
self.in_features = in_features
self.out_features = out_features
def update_layer(self, adapter_name, r, lora_alpha, lora_dropout, init_lora_weights):
self.r[adapter_name] = r
self.lora_alpha[adapter_name] = lora_alpha
if lora_dropout > 0.0:
lora_dropout_layer = nn.Dropout(p=lora_dropout)
else:
lora_dropout_layer = nn.Identity()
self.lora_dropout.update(nn.ModuleDict({adapter_name: lora_dropout_layer}))
# Actual trainable parameters
if r > 0:
self.lora_A.update(nn.ModuleDict({adapter_name: nn.Linear(self.in_features, r, bias=False)}))
self.lora_B.update(nn.ModuleDict({adapter_name: nn.Linear(r, self.out_features, bias=False)}))
self.scaling[adapter_name] = lora_alpha / r
if init_lora_weights:
self.reset_lora_parameters(adapter_name)
self.to(self.weight.device)
def reset_lora_parameters(self, adapter_name):
if adapter_name in self.lora_A.keys():
# initialize A the same way as the default for nn.Linear and B to zero
nn.init.kaiming_uniform_(self.lora_A[adapter_name].weight, a=math.sqrt(5))
nn.init.zeros_(self.lora_B[adapter_name].weight)
class Linear(nn.Linear, LoraLayer):
# Lora implemented in a dense layer
def __init__(
self,
adapter_name: str,
in_features: int,
out_features: int,
r: int = 0,
lora_alpha: int = 1,
lora_dropout: float = 0.0,
fan_in_fan_out: bool = False, # Set this to True if the layer to replace stores weight like (fan_in, fan_out)
**kwargs,
):
init_lora_weights = kwargs.pop("init_lora_weights", True)
nn.Linear.__init__(self, in_features, out_features, **kwargs)
LoraLayer.__init__(self, in_features=in_features, out_features=out_features)
# Freezing the pre-trained weight matrix
self.weight.requires_grad = False
self.fan_in_fan_out = fan_in_fan_out
if fan_in_fan_out:
self.weight.data = self.weight.data.T
nn.Linear.reset_parameters(self)
self.update_layer(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights)
self.active_adapter = adapter_name
def merge(self):
if self.active_adapter not in self.lora_A.keys():
return
if self.merged:
warnings.warn("Already merged. Nothing to do.")
return
if self.r[self.active_adapter] > 0:
self.weight.data += (
transpose(
self.lora_B[self.active_adapter].weight @ self.lora_A[self.active_adapter].weight,
self.fan_in_fan_out,
)
* self.scaling[self.active_adapter]
)
self.merged = True
def unmerge(self):
if self.active_adapter not in self.lora_A.keys():
return
if not self.merged:
warnings.warn("Already unmerged. Nothing to do.")
return
if self.r[self.active_adapter] > 0:
self.weight.data -= (
transpose(
self.lora_B[self.active_adapter].weight @ self.lora_A[self.active_adapter].weight,
self.fan_in_fan_out,
)
* self.scaling[self.active_adapter]
)
self.merged = False
def forward(self, x: torch.Tensor):
previous_dtype = x.dtype
if self.active_adapter not in self.lora_A.keys():
return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
if self.disable_adapters:
if self.r[self.active_adapter] > 0 and self.merged:
self.unmerge()
result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
elif self.r[self.active_adapter] > 0 and not self.merged:
result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
x = x.to(self.lora_A[self.active_adapter].weight.dtype)
result += (
self.lora_B[self.active_adapter](
self.lora_A[self.active_adapter](self.lora_dropout[self.active_adapter](x))
)
* self.scaling[self.active_adapter]
)
else:
result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
result = result.to(previous_dtype)
return result
if is_bnb_available():
class Linear8bitLt(bnb.nn.Linear8bitLt, LoraLayer):
# Lora implemented in a dense layer
def __init__(
self,
adapter_name,
in_features,
out_features,
r: int = 0,
lora_alpha: int = 1,
lora_dropout: float = 0.0,
**kwargs,
):
bnb.nn.Linear8bitLt.__init__(
self,
in_features,
out_features,
bias=kwargs.get("bias", True),
has_fp16_weights=kwargs.get("has_fp16_weights", True),
memory_efficient_backward=kwargs.get("memory_efficient_backward", False),
threshold=kwargs.get("threshold", 0.0),
index=kwargs.get("index", None),
)
LoraLayer.__init__(self, in_features=in_features, out_features=out_features)
# Freezing the pre-trained weight matrix
self.weight.requires_grad = False
init_lora_weights = kwargs.pop("init_lora_weights", True)
self.update_layer(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights)
self.active_adapter = adapter_name
def forward(self, x: torch.Tensor):
result = super().forward(x)
if self.disable_adapters or self.active_adapter not in self.lora_A.keys():
return result
elif self.r[self.active_adapter] > 0:
if not torch.is_autocast_enabled():
expected_dtype = result.dtype
if x.dtype != torch.float32:
x = x.float()
output = (
self.lora_B[self.active_adapter](
self.lora_A[self.active_adapter](self.lora_dropout[self.active_adapter](x))
).to(expected_dtype)
* self.scaling[self.active_adapter]
)
else:
output = (
self.lora_B[self.active_adapter](
self.lora_A[self.active_adapter](self.lora_dropout[self.active_adapter](x))
)
* self.scaling[self.active_adapter]
)
result += output
return result