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# 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 __future__ import annotations
import warnings
from typing import Any, Optional
import bitsandbytes as bnb
import torch
from peft.import_utils import is_bnb_4bit_available, is_bnb_available
from peft.tuners.tuners_utils import BaseTunerLayer, check_adapters_to_merge
from peft.utils.integrations import dequantize_bnb_weight
from peft.utils.other import transpose
from .layer import LoraLayer
if is_bnb_available():
class Linear8bitLt(torch.nn.Module, LoraLayer):
# Lora implemented in a dense layer
def __init__(
self,
base_layer: torch.nn.Module,
adapter_name: str,
r: int = 0,
lora_alpha: int = 1,
lora_dropout: float = 0.0,
init_lora_weights: bool = True,
use_rslora: bool = False,
use_dora: bool = False,
**kwargs,
) -> None:
super().__init__()
LoraLayer.__init__(self, base_layer)
self.fan_in_fan_out = False
self._active_adapter = adapter_name
self.update_layer(
adapter_name,
r,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
init_lora_weights=init_lora_weights,
use_rslora=use_rslora,
use_dora=use_dora,
)
def merge(self, safe_merge: bool = False, adapter_names: Optional[list[str]] = None) -> None:
"""
Merge the active adapter weights into the base weights
Args:
safe_merge (`bool`, *optional*):
If True, the merge operation will be performed in a copy of the original weights and check for NaNs
before merging the weights. This is useful if you want to check if the merge operation will produce
NaNs. Defaults to `False`.
adapter_names (`list[str]`, *optional*):
The list of adapter names that should be merged. If None, all active adapters will be merged.
Defaults to `None`.
"""
adapter_names = check_adapters_to_merge(self, adapter_names)
if not adapter_names:
# no adapter to merge
return
for active_adapter in adapter_names:
if active_adapter not in self.lora_A.keys():
continue
warnings.warn(
"Merge lora module to 8-bit linear may get different generations due to rounding errors."
)
lora_data = self.get_delta_weight(active_adapter)
weight = self.get_base_layer().weight
state = self.get_base_layer().state
if state.SCB is None:
state.SCB = weight.SCB
# Dequantize the result of identity matrix and int8 weight because bitsandbytes does not support int8
# dequantization directly
output = dequantize_bnb_weight(weight, state=state)
if not self.use_dora[active_adapter]:
w_data = output.to(lora_data.dtype).to(lora_data.device) + lora_data
else:
# handle dora
# since output already includes scaling, set it to 1 here
weight_norm = self._get_weight_norm(output, lora_data, scaling=1).detach()
# We need to cache weight_norm because it has to be based on the original weights. We
# cannot calculate it on the fly based on the merged weights when unmerging because its a
# different value
self._cache_store(f"{active_adapter}-weight_norm", weight_norm)
dora_factor = self.lora_magnitude_vector[active_adapter] / weight_norm
w_data = dora_factor.view(-1, 1) * (output + lora_data)
if safe_merge and not torch.isfinite(w_data).all():
raise ValueError(
f"NaNs detected in the merged weights. The adapter {active_adapter} seems to be broken"
)
self.get_base_layer().weight = bnb.nn.Int8Params(
w_data.to("cpu"), requires_grad=False, has_fp16_weights=weight.has_fp16_weights
).to(weight.device)
state.reset_grads()
self.merged_adapters.append(active_adapter)
def unmerge(self) -> None:
"""
This method unmerges all merged adapter layers from the base weights.
"""
if not self.merged:
warnings.warn("Already unmerged. Nothing to do.")
return
while len(self.merged_adapters) > 0:
active_adapter = self.merged_adapters.pop()
if active_adapter not in self.lora_A.keys():
continue
warnings.warn(
"Unmerge lora module to 8-bit linear may get different generations due to rounding errors."
)
lora_data = self.get_delta_weight(active_adapter)
weight = self.get_base_layer().weight
state = self.get_base_layer().state
if state.SCB is None:
state.SCB = weight.SCB
output = dequantize_bnb_weight(weight, state=state)
if not self.use_dora[active_adapter]:
w_data = output.to(lora_data.dtype).to(lora_data.device) - lora_data
else:
weight_norm = self._cache_pop(f"{active_adapter}-weight_norm")
dora_factor = self.lora_magnitude_vector[active_adapter] / weight_norm
w_data = output.data / dora_factor.view(-1, 1) - lora_data
self.get_base_layer().weight = bnb.nn.Int8Params(
w_data.to("cpu"), requires_grad=False, has_fp16_weights=weight.has_fp16_weights
).to(weight.device)
state.reset_grads()
def get_delta_weight(self, adapter):
return (
transpose(
self.lora_B[adapter].weight @ self.lora_A[adapter].weight,
False,
)
* self.scaling[adapter]
)
def _mixed_batch_forward(
self, x: torch.Tensor, *args: Any, adapter_names: list[str], **kwargs: Any
) -> torch.Tensor:
# This is a special method that handles the case when users pass the argument `adapter_names`. This is an
# extra argument that allows mixing different adapters in the same batch at inference time.
result = self.base_layer(x, *args, **kwargs)
unique_adapters = set(adapter_names)
sub_batch_indices_list = []
for adapter in unique_adapters:
sub_batch_indices_list.append([index for index, item in enumerate(adapter_names) if item == adapter])
for i, active_adapter in enumerate(unique_adapters):
if active_adapter == "__base__":
continue
if active_adapter not in self.lora_A.keys():
continue
lora_A = self.lora_A[active_adapter]
lora_B = self.lora_B[active_adapter]
dropout = self.lora_dropout[active_adapter]
scaling = self.scaling[active_adapter]
requires_conversion = not torch.is_autocast_enabled()
if requires_conversion:
expected_dtype = result.dtype
compute_dtype = lora_A.weight.dtype
if x.dtype != compute_dtype:
x = x.to(compute_dtype)
# getting the sub-batch, passing it to LoRA layers and updating the corresponding indices of the linear
# layer output
sub_batch = x[sub_batch_indices_list[i]]
output = lora_B(lora_A(dropout(sub_batch))) * scaling
if requires_conversion:
output = output.to(expected_dtype)
result[sub_batch_indices_list[i]] += output
return result
def forward(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor:
self._check_forward_args(x, *args, **kwargs)
adapter_names = kwargs.pop("adapter_names", None)
if self.disable_adapters:
if self.merged:
self.unmerge()
result = self.base_layer(x, *args, **kwargs)
elif adapter_names is not None:
result = self._mixed_batch_forward(x, *args, adapter_names=adapter_names, **kwargs)
elif self.merged:
result = self.base_layer(x, *args, **kwargs)
else:
result = self.base_layer(x, *args, **kwargs)
for active_adapter in self.active_adapters:
if active_adapter not in self.lora_A.keys():
continue
lora_A = self.lora_A[active_adapter]
lora_B = self.lora_B[active_adapter]
dropout = self.lora_dropout[active_adapter]
scaling = self.scaling[active_adapter]
requires_conversion = not torch.is_autocast_enabled()
if requires_conversion:
expected_dtype = result.dtype
compute_dtype = lora_A.weight.dtype
if x.dtype != compute_dtype:
x = x.to(compute_dtype)
if not self.use_dora[active_adapter]:
output = lora_B(lora_A(dropout(x))) * scaling
else:
output = self._apply_dora(x, lora_A, lora_B, scaling, active_adapter)
if requires_conversion:
output = output.to(expected_dtype)
result = result + output
return result
def __repr__(self) -> str:
rep = super().__repr__()
return "lora." + rep
def dispatch_bnb_8bit(target: torch.nn.Module, adapter_name: str, **kwargs):
new_module = None
if isinstance(target, BaseTunerLayer):
target_base_layer = target.get_base_layer()
else:
target_base_layer = target
loaded_in_8bit = kwargs.get("loaded_in_8bit", False)
if loaded_in_8bit and isinstance(target_base_layer, 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(target, adapter_name, **eightbit_kwargs)
return new_module
if is_bnb_4bit_available():
class Linear4bit(torch.nn.Module, LoraLayer):
# Lora implemented in a dense layer
def __init__(
self,
base_layer: torch.nn.Module,
adapter_name: str,
r: int = 0,
lora_alpha: int = 1,
lora_dropout: float = 0.0,
init_lora_weights: bool = True,
use_rslora: bool = False,
use_dora: bool = False,
**kwargs,
) -> None:
super().__init__()
LoraLayer.__init__(self, base_layer)
self.fan_in_fan_out = False
self._active_adapter = adapter_name
self.update_layer(
adapter_name,
r,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
init_lora_weights=init_lora_weights,
use_rslora=use_rslora,
use_dora=use_dora,
)
def merge(self, safe_merge: bool = False, adapter_names: Optional[list[str]] = None) -> None:
"""
Merge the active adapter weights into the base weights
Args:
safe_merge (`bool`, *optional*):
If True, the merge operation will be performed in a copy of the original weights and check for NaNs
before merging the weights. This is useful if you want to check if the merge operation will produce
NaNs. Defaults to `False`.
adapter_names (`list[str]`, *optional*):
The list of adapter names that should be merged. If None, all active adapters will be merged.
Defaults to `None`.
"""
adapter_names = check_adapters_to_merge(self, adapter_names)
if not adapter_names:
# no adapter to merge
return
for active_adapter in adapter_names:
if active_adapter not in self.lora_A.keys():
continue
warnings.warn(
"Merge lora module to 4-bit linear may get different generations due to rounding errors."
)
# Refer to https://gist.github.com/ChrisHayduk/1a53463331f52dca205e55982baf9930
weight = self.get_base_layer().weight
kwargs = weight.__dict__
lora_data = self.get_delta_weight(active_adapter)
output = dequantize_bnb_weight(weight, state=weight.quant_state)
if not self.use_dora[active_adapter]:
w_data = output + lora_data
else:
# handle dora
# since output already includes scaling, set it to 1 here
weight_norm = self._get_weight_norm(output, lora_data, scaling=1).detach()
# We need to cache weight_norm because it has to be based on the original weights. We
# cannot calculate it on the fly based on the merged weights when unmerging because its a
# different value
self._cache_store(f"{active_adapter}-weight_norm", weight_norm)
dora_factor = self.lora_magnitude_vector[active_adapter] / weight_norm
w_data = dora_factor.view(-1, 1) * (output + lora_data)
if safe_merge and not torch.isfinite(w_data).all():
raise ValueError(
f"NaNs detected in the merged weights. The adapter {active_adapter} seems to be broken"
)
if "bnb_quantized" in kwargs:
kwargs["bnb_quantized"] = False
self.get_base_layer().weight = bnb.nn.Params4bit(w_data.to("cpu"), requires_grad=False, **kwargs).to(
weight.device
)
self.merged_adapters.append(active_adapter)
def unmerge(self) -> None:
"""
This method unmerges all merged adapter layers from the base weights.
"""
if not self.merged:
warnings.warn("Already unmerged. Nothing to do.")
return
while len(self.merged_adapters) > 0:
active_adapter = self.merged_adapters.pop()
if active_adapter not in self.lora_A.keys():
continue
warnings.warn(
"Unmerge lora module to 4-bit linear may get different generations due to rounding errors."
)
lora_data = self.get_delta_weight(active_adapter)
weight = self.get_base_layer().weight
kwargs = weight.__dict__
output = dequantize_bnb_weight(weight, state=weight.quant_state)
if not self.use_dora[active_adapter]:
w_data = output - lora_data
else:
weight_norm = self._cache_pop(f"{active_adapter}-weight_norm")
dora_factor = self.lora_magnitude_vector[active_adapter] / weight_norm
w_data = output.data / dora_factor.view(-1, 1) - lora_data
if "bnb_quantized" in kwargs:
kwargs["bnb_quantized"] = False
self.get_base_layer().weight = bnb.nn.Params4bit(w_data.to("cpu"), requires_grad=False, **kwargs).to(
weight.device
)
def get_delta_weight(self, adapter):
return (
transpose(
self.lora_B[adapter].weight @ self.lora_A[adapter].weight,
False,
)
* self.scaling[adapter]
)
def _mixed_batch_forward(
self, x: torch.Tensor, *args: Any, adapter_names: list[str], **kwargs: Any
) -> torch.Tensor:
# This is a special method that handles the case when users pass the argument `adapter_names`. This is an
# extra argument that allows mixing different adapters in the same batch at inference time.
result = self.base_layer(x, *args, **kwargs)
unique_adapters = set(adapter_names)
sub_batch_indices_list = []
for adapter in unique_adapters:
sub_batch_indices_list.append([index for index, item in enumerate(adapter_names) if item == adapter])
for i, active_adapter in enumerate(unique_adapters):
if active_adapter == "__base__":
continue
if active_adapter not in self.lora_A.keys():
continue
lora_A = self.lora_A[active_adapter]
lora_B = self.lora_B[active_adapter]
dropout = self.lora_dropout[active_adapter]
scaling = self.scaling[active_adapter]
requires_conversion = not torch.is_autocast_enabled()
if requires_conversion:
expected_dtype = result.dtype
x = x.to(lora_A.weight.dtype)
# getting the sub-batch, passing it to LoRA layers and updating the corresponding indices of the linear
# layer output
sub_batch = x[sub_batch_indices_list[i]]
output = lora_B(lora_A(dropout(sub_batch))) * scaling
if requires_conversion:
output = output.to(expected_dtype)
result[sub_batch_indices_list[i]] += output
return result
def forward(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor:
self._check_forward_args(x, *args, **kwargs)
adapter_names = kwargs.pop("adapter_names", None)
if self.disable_adapters:
if self.merged:
self.unmerge()
result = self.base_layer(x, *args, **kwargs)
elif adapter_names is not None:
result = self._mixed_batch_forward(x, *args, adapter_names=adapter_names, **kwargs)
elif self.merged:
result = self.base_layer(x, *args, **kwargs)
else:
result = self.base_layer(x, *args, **kwargs)
# As per Tim Dettmers, for 4bit, we need to defensively clone here.
# The reason is that in some cases, an error can occur that backprop
# does not work on a manipulated view. This issue may be solved with
# newer PyTorch versions but this would need extensive testing to be
# sure.
result = result.clone()
for active_adapter in self.active_adapters:
if active_adapter not in self.lora_A.keys():
continue
lora_A = self.lora_A[active_adapter]
lora_B = self.lora_B[active_adapter]
dropout = self.lora_dropout[active_adapter]
scaling = self.scaling[active_adapter]
requires_conversion = not torch.is_autocast_enabled()
if requires_conversion:
expected_dtype = result.dtype
x = x.to(lora_A.weight.dtype)
if not self.use_dora[active_adapter]:
output = lora_B(lora_A(dropout(x))) * scaling
else:
output = self._apply_dora(x, lora_A, lora_B, scaling, active_adapter)
if requires_conversion:
output = output.to(expected_dtype)
result = result + output
return result
def __repr__(self) -> str:
rep = super().__repr__()
return "lora." + rep
def dispatch_bnb_4bit(target: torch.nn.Module, adapter_name: str, **kwargs):
new_module = None
if isinstance(target, BaseTunerLayer):
target_base_layer = target.get_base_layer()
else:
target_base_layer = target
loaded_in_4bit = kwargs.get("loaded_in_4bit", False)
if loaded_in_4bit and is_bnb_4bit_available() and isinstance(target_base_layer, bnb.nn.Linear4bit):
fourbit_kwargs = kwargs.copy()
fourbit_kwargs.update(
{
"compute_dtype": target_base_layer.compute_dtype,
"compress_statistics": target_base_layer.weight.compress_statistics,
"quant_type": target_base_layer.weight.quant_type,
}
)
new_module = Linear4bit(target, adapter_name, **fourbit_kwargs)
return new_module