File size: 14,211 Bytes
3b609b9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 |
# Copyright 2024-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 warnings
from typing import Any, List, Optional, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from peft.tuners.tuners_utils import BaseTunerLayer, check_adapters_to_merge
class BoneLayer(BaseTunerLayer):
# All names of layers that may contain (trainable) adapter weights
adapter_layer_names = ("bone_block",)
# All names of other parameters that may contain adapter-related parameters
other_param_names = ("bone_r",)
def __init__(self, base_layer: nn.Module, **kwargs) -> None:
self.base_layer = base_layer
self.bone_r = {}
self.bone_block = nn.ParameterDict({})
# Mark the weight as unmerged
self._disable_adapters = False
self.merged_adapters = []
self.kwargs = kwargs
base_layer = self.get_base_layer()
if isinstance(base_layer, nn.Linear):
self.in_features, self.out_features = base_layer.in_features, base_layer.out_features
else:
raise ValueError(f"Unsupported layer type {type(base_layer)}")
def update_layer(
self,
adapter_name: str,
r: int,
init_weights: bool,
**kwargs,
) -> None:
"""Internal function to create bone adapter
Args:
adapter_name (`str`): Name for the adapter to add.
r (`int`): Rank for the added adapter.
init_weights (`bool`): Whether to initialize weights.
"""
if r <= 0:
raise ValueError(f"`r` should be a positive integer value but the value passed is {r}")
self.bone_r[adapter_name] = r
# Determine shape of Bone weights
base_layer = self.get_base_layer()
if isinstance(base_layer, nn.Linear):
self.bone_block[adapter_name] = nn.Parameter(torch.zeros(r, self.out_features), requires_grad=True)
else:
raise TypeError(f"Bone is not implemented for base layers of type {type(base_layer).__name__}")
# Initialize weights
if init_weights == "bat":
if self.in_features % r != 0 or self.out_features % r != 0:
raise ValueError("The weight matrix must be fully divisible into [r, r] blocks.")
self.reset_bat_parameters(adapter_name, r)
elif init_weights:
self.reset_bone_parameters(adapter_name, r)
else:
self.reset_bone_parameters_random(adapter_name)
# Move new weights to device
self._move_adapter_to_device_of_base_layer(adapter_name)
self.set_adapter(self.active_adapters)
def reset_bone_parameters(self, adapter_name: str, r):
self.bone_block[adapter_name] = nn.Parameter(torch.zeros(r, self.out_features), requires_grad=True)
def reset_bat_parameters(self, adapter_name: str, r):
self.bone_block[adapter_name] = nn.Parameter(torch.zeros(self.out_features // r, r, r), requires_grad=True)
def reset_bone_parameters_random(self, adapter_name: str):
nn.init.kaiming_uniform_(self.bone_block[adapter_name], a=math.sqrt(5))
def scale_layer(self, scale: float) -> None:
if scale == 1:
return
for active_adapter in self.active_adapters:
if active_adapter not in self.bone_block.keys():
continue
warnings.warn("Scaling operation for Bone not supported! Automatically set scale to 1.")
def unscale_layer(self, scale=None) -> None:
for active_adapter in self.active_adapters:
if active_adapter not in self.bone_block.keys():
continue
warnings.warn("Unscaling operation for Bone not supported! Keeping scale at 1.")
class BoneLinear(nn.Module, BoneLayer):
"""
Bone implemented in a dense layer.
"""
def __init__(
self,
base_layer,
adapter_name: str,
r: int = 0,
init_weights: Union[bool, str] = True,
**kwargs,
) -> None:
super().__init__()
BoneLayer.__init__(self, base_layer, **kwargs)
self._active_adapter = adapter_name
self.update_layer(adapter_name, r, init_weights, **kwargs)
self.bone_fn = init_weights
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 in self.bone_block.keys():
base_layer = self.get_base_layer()
if safe_merge:
# Note that safe_merge will be slower than the normal merge
# because of the copy operation.
orig_weight = base_layer.weight.data.clone()
if self.bone_fn == "bat":
delta_weight = self.get_delta_weight(active_adapter, orig_weight)
orig_weight += delta_weight
else:
delta_weight = self.get_delta_weight_bone(active_adapter, self.base_layer.weight.data)
orig_weight = delta_weight
if not torch.isfinite(orig_weight).all():
raise ValueError(
f"NaNs detected in the merged weights. The adapter {active_adapter} seems to be broken"
)
self.base_layer.weight.data = orig_weight
else:
if self.bone_fn == "bat":
delta_weight = self.get_delta_weight(active_adapter, self.base_layer.weight.data)
self.base_layer.weight.data += delta_weight
else:
delta_weight = self.get_delta_weight_bone(active_adapter, self.base_layer.weight.data)
self.base_layer.weight.data = delta_weight
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 in self.bone_block.keys():
orig_weight = self.get_base_layer().weight.data.clone()
if self.bone_fn == "bat":
delta_weight = self.get_delta_weight(active_adapter, orig_weight, re=True)
else:
delta_weight = self.get_delta_weight_bone(active_adapter, orig_weight, re=True)
self.get_base_layer().weight.data = delta_weight
def get_delta_weight(self, adapter, orig_weight, re: bool = False) -> torch.Tensor:
"""
Compute the delta weight for the given adapter.
Args:
adapter (str):
The name of the adapter for which the delta weight should be computed.
"""
device = self.bone_block[adapter].device
dtype = self.bone_block[adapter].dtype
# In case users wants to merge the adapter weights that are in
# (b)float16 while being on CPU, we need to cast the weights to float32, perform the merge and then cast back to
# (b)float16 because some CPUs have slow bf16/fp16 matmuls.
cast_to_fp32 = device.type == "cpu" and (dtype == torch.float16 or dtype == torch.bfloat16)
weight_bone = self.bone_block[adapter]
if cast_to_fp32:
weight_bone = weight_bone.float()
r = weight_bone.size(-1)
if re:
o = orig_weight.reshape(orig_weight.size(0) // r, r, orig_weight.size(1) // r, r).permute(2, 0, 1, 3)
one = torch.eye(weight_bone.size(-1)).to(weight_bone.device)
inv_I_plus_b = torch.inverse(one + weight_bone)
w = (o - weight_bone) @ inv_I_plus_b
output_tensor = w.permute(1, 2, 0, 3).reshape(*orig_weight.shape)
else:
w = (
orig_weight.reshape(orig_weight.size(0) // r, r, orig_weight.size(1) // r, r).permute(2, 0, 1, 3)
@ weight_bone
+ weight_bone
)
output_tensor = w.permute(1, 2, 0, 3).reshape(*orig_weight.shape)
if cast_to_fp32:
output_tensor = output_tensor.to(dtype=dtype)
# cast back the weights
self.bone_block[adapter].data = weight_bone.to(dtype)
return output_tensor
def get_delta_weight_bone(self, adapter, orig_weight, re: bool = False) -> torch.Tensor:
"""
Compute the delta weight for the given adapter.
Args:
adapter (str):
The name of the adapter for which the delta weight should be computed.
"""
device = self.bone_block[adapter].device
dtype = self.bone_block[adapter].dtype
# In case users wants to merge the adapter weights that are in
# (b)float16 while being on CPU, we need to cast the weights to float32, perform the merge and then cast back to
# (b)float16 because some CPUs have slow bf16/fp16 matmuls.
cast_to_fp32 = device.type == "cpu" and (dtype == torch.float16 or dtype == torch.bfloat16)
weight_bone = self.bone_block[adapter]
if cast_to_fp32:
weight_bone = weight_bone.float()
in_features = orig_weight.size(-1)
r = weight_bone.size(0)
if in_features % r != 0:
last_size = in_features % r
n_block = in_features // r
n_block_size = n_block * r
if re:
orig_weight[:, :n_block_size] = (
(orig_weight[:, :n_block_size].reshape(-1, n_block, r).permute(1, 2, 0) - weight_bone)
.permute(2, 0, 1)
.reshape(*orig_weight[:, :n_block_size].shape)
)
orig_weight[:, n_block_size:] = (
orig_weight[:, n_block_size:] - (weight_bone.transpose(0, 1))[:, :last_size]
)
else:
orig_weight[:, :n_block_size] = (
(orig_weight[:, :n_block_size].reshape(-1, n_block, r).permute(1, 2, 0) + weight_bone)
.permute(2, 0, 1)
.reshape(*orig_weight[:, :n_block_size].shape)
)
orig_weight[:, n_block_size:] = (
orig_weight[:, n_block_size:] + (weight_bone.transpose(0, 1))[:, :last_size]
)
output_tensor = orig_weight
else:
if re:
w = orig_weight.reshape(-1, orig_weight.size(1) // r, r).permute(1, 2, 0) - weight_bone
output_tensor = w.permute(2, 0, 1).reshape(*orig_weight.shape)
else:
w = orig_weight.reshape(-1, orig_weight.size(1) // r, r).permute(1, 2, 0) + weight_bone
output_tensor = w.permute(2, 0, 1).reshape(*orig_weight.shape)
if cast_to_fp32:
output_tensor = output_tensor.to(dtype=dtype)
# cast back the weights
self.bone_block[adapter].data = weight_bone.to(dtype)
return output_tensor
def forward(self, x: torch.Tensor, *args: Any, **kwargs: Any) -> torch.Tensor:
previous_dtype = x.dtype
if self.disable_adapters:
if self.merged:
self.unmerge()
result = self.base_layer(x, *args, **kwargs)
elif self.merged:
result = self.base_layer(x, *args, **kwargs)
else:
if self.bone_fn == "bat":
orig_weight = self.base_layer.weight.data.clone()
for active_adapter in self.active_adapters:
if active_adapter not in self.bone_block.keys():
continue
delta_weight = self.get_delta_weight(active_adapter, orig_weight)
orig_weight = orig_weight + delta_weight
result = F.linear(input=x, weight=orig_weight, bias=self.base_layer.bias)
else:
result = self.base_layer(x, *args, **kwargs)
for active_adapter in self.active_adapters:
if active_adapter not in self.bone_block.keys():
continue
bone = self.bone_block[active_adapter]
r = bone.size(0)
if x.size(-1) % r != 0:
padding_size = (r - x.size(-1) % r) % r
x = F.pad(x, (0, padding_size))
result = result + torch.sum(x.reshape(*x.shape[:-1], x.size(-1) // r, r), dim=-2) @ bone
result = result.to(previous_dtype)
return result
def __repr__(self) -> str:
rep = super().__repr__()
return "bone." + rep
|