mirror of
https://github.com/wassname/lora-lite.git
synced 2026-09-12 12:31:37 +08:00
Add reference-impl URLs to variant docstrings + V2 external review
- Fetch canonical reference impls for offline review:
* peft_{lora,hra,delora,ia3}_layer.py + peft_lora_{dora,variants}.py
* orig_pissa_init.py (MuLabPKU/PiSSA)
* orig_hra_layer.py (DaShenZi721/HRA)
* orig_delora.py (ExplainableML/DeLoRA author fork)
- Add reference-impl URLs to all 6 variant docstrings
- Document HRA gate=0 dead-grad issue and DoRA detach-omission in their docstrings
- Re-run external review (codex) with refs available -> docs/audit/variants_review_v2.md
Major NEW findings vs paper-only review:
* DeLoRA: scalar W.norm() should be per-input-channel norm(dim=0)
* HRA: PEFT uses symmetric repeated-column init (no dead grad), not zero gate
* IA3: FFN targets need input-side gating, not output, our up_proj advice wrong
* All LoRA-family: cfg.dropout silently ignored (no-op)
* DeLoRA: wnorm should be persistent buffer, not Parameter
HRA and DeLoRA upgraded to BUGGY (from Partial)
This commit is contained in:
wassname
@@ -0,0 +1,462 @@
|
||||
# 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, Optional
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from peft.tuners.tuners_utils import BaseTunerLayer, check_adapters_to_merge
|
||||
|
||||
from .config import HRAConfig
|
||||
|
||||
|
||||
class HRALayer(BaseTunerLayer):
|
||||
# All names of layers that may contain (trainable) adapter weights
|
||||
adapter_layer_names = ("hra_u",)
|
||||
# All names of other parameters that may contain adapter-related parameters
|
||||
other_param_names = ("hra_r", "hra_apply_GS")
|
||||
|
||||
def __init__(self, base_layer: nn.Module, **kwargs) -> None:
|
||||
self.base_layer = base_layer
|
||||
self.hra_r = {}
|
||||
self.hra_apply_GS = {}
|
||||
self.hra_u = nn.ParameterDict({})
|
||||
# Mark the weight as unmerged
|
||||
self._disable_adapters = False
|
||||
self.merged_adapters = []
|
||||
# flag to enable/disable casting of input to weight dtype during forward call
|
||||
self.cast_input_dtype_enabled = True
|
||||
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
|
||||
elif isinstance(base_layer, nn.Conv2d):
|
||||
self.in_features, self.out_features = base_layer.in_channels, base_layer.out_channels
|
||||
else:
|
||||
raise ValueError(f"Unsupported layer type {type(base_layer)}")
|
||||
|
||||
def update_layer(
|
||||
self,
|
||||
adapter_name: str,
|
||||
r: int,
|
||||
config: HRAConfig,
|
||||
**kwargs,
|
||||
) -> None:
|
||||
"""Internal function to create hra adapter
|
||||
|
||||
Args:
|
||||
adapter_name (`str`): Name for the adapter to add.
|
||||
r (`int`): Rank for the added adapter.
|
||||
config (`HRAConfig`): The adapter configuration for this layer.
|
||||
"""
|
||||
apply_GS = config.apply_GS
|
||||
init_weights = config.init_weights
|
||||
inference_mode = config.inference_mode
|
||||
|
||||
if r <= 0:
|
||||
raise ValueError(f"`r` should be a positive integer value but the value passed is {r}")
|
||||
|
||||
self.hra_r[adapter_name] = r
|
||||
self.hra_apply_GS[adapter_name] = apply_GS
|
||||
|
||||
# Determine shape of HRA weights
|
||||
base_layer = self.get_base_layer()
|
||||
if isinstance(base_layer, nn.Linear):
|
||||
self.hra_u[adapter_name] = nn.Parameter(torch.empty(self.in_features, r), requires_grad=True)
|
||||
elif isinstance(base_layer, nn.Conv2d):
|
||||
self.hra_u[adapter_name] = nn.Parameter(
|
||||
torch.empty(self.in_features * base_layer.kernel_size[0] * base_layer.kernel_size[0], r),
|
||||
requires_grad=True,
|
||||
)
|
||||
else:
|
||||
raise TypeError(f"HRA is not implemented for base layers of type {type(base_layer).__name__}")
|
||||
|
||||
# Initialize weights
|
||||
if init_weights:
|
||||
self.reset_hra_parameters(adapter_name)
|
||||
else:
|
||||
self.reset_hra_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, inference_mode=inference_mode)
|
||||
|
||||
def reset_hra_parameters(self, adapter_name: str):
|
||||
if self.hra_r[adapter_name] % 2 != 0:
|
||||
warnings.warn("The symmetric initialization can NOT be performed when r is odd!")
|
||||
nn.init.kaiming_uniform_(self.hra_u[adapter_name], a=math.sqrt(5))
|
||||
else:
|
||||
shape = self.hra_u[adapter_name].shape
|
||||
half_u = torch.zeros(shape[0], shape[1] // 2)
|
||||
nn.init.kaiming_uniform_(half_u, a=math.sqrt(5))
|
||||
self.hra_u[adapter_name] = nn.Parameter(torch.repeat_interleave(half_u, 2, dim=1))
|
||||
|
||||
def reset_hra_parameters_random(self, adapter_name: str):
|
||||
nn.init.kaiming_uniform_(self.hra_u[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.hra_u.keys():
|
||||
continue
|
||||
|
||||
warnings.warn("Scaling operation for HRA 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.hra_u.keys():
|
||||
continue
|
||||
|
||||
warnings.warn("Unscaling operation for HRA not supported! Keeping scale at 1.")
|
||||
|
||||
|
||||
class HRALinear(nn.Module, HRALayer):
|
||||
"""
|
||||
HRA implemented in a dense layer.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
base_layer,
|
||||
adapter_name: str,
|
||||
config: HRAConfig,
|
||||
r: int = 0,
|
||||
**kwargs,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
HRALayer.__init__(self, base_layer, **kwargs)
|
||||
self._active_adapter = adapter_name
|
||||
self.update_layer(adapter_name, r, config=config, **kwargs)
|
||||
|
||||
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.hra_u.keys():
|
||||
base_layer = self.get_base_layer()
|
||||
orig_dtype = base_layer.weight.dtype
|
||||
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()
|
||||
delta_weight = self.get_delta_weight(active_adapter)
|
||||
orig_weight = torch.mm(orig_weight.to(delta_weight.dtype), 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"
|
||||
)
|
||||
|
||||
base_layer.weight.data = orig_weight.to(orig_dtype)
|
||||
else:
|
||||
delta_weight = self.get_delta_weight(active_adapter)
|
||||
new_weight = torch.mm(base_layer.weight.data.to(delta_weight.dtype), delta_weight)
|
||||
base_layer.weight.data = new_weight.to(orig_dtype)
|
||||
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()
|
||||
base_layer = self.get_base_layer()
|
||||
orig_dtype = base_layer.weight.dtype
|
||||
if active_adapter in self.hra_u.keys():
|
||||
orig_weight = base_layer.weight.data.clone()
|
||||
delta_weight = self.get_delta_weight(active_adapter, reverse=True)
|
||||
new_weight = torch.mm(orig_weight.to(delta_weight.dtype), delta_weight)
|
||||
base_layer.weight.data = new_weight.to(orig_dtype)
|
||||
|
||||
def get_delta_weight(self, adapter_name: str, reverse: bool = False) -> torch.Tensor:
|
||||
rank = self.hra_r[adapter_name]
|
||||
apply_GS = self.hra_apply_GS[adapter_name]
|
||||
opt_u = self.hra_u[adapter_name]
|
||||
shape = opt_u.shape
|
||||
|
||||
if apply_GS:
|
||||
weight = [(opt_u[:, 0] / opt_u[:, 0].norm()).view(-1, 1)]
|
||||
for i in range(1, rank):
|
||||
ui = opt_u[:, i].view(-1, 1)
|
||||
for j in range(i):
|
||||
ui = ui - (weight[j].t() @ ui) * weight[j]
|
||||
weight.append((ui / ui.norm()).view(-1, 1))
|
||||
weight = torch.cat(weight, dim=1)
|
||||
weight = torch.eye(shape[0], device=opt_u.device, dtype=opt_u.dtype) - 2 * weight @ weight.t()
|
||||
|
||||
else:
|
||||
opt_u = opt_u / opt_u.norm(dim=0)
|
||||
weight = torch.eye(shape[0], device=opt_u.device, dtype=opt_u.dtype)
|
||||
if reverse:
|
||||
indices = range(rank - 1, -1, -1)
|
||||
else:
|
||||
indices = range(rank)
|
||||
|
||||
for i in indices:
|
||||
ui = opt_u[:, i].view(-1, 1)
|
||||
weight = weight - 2 * weight @ ui @ ui.t()
|
||||
|
||||
return weight
|
||||
|
||||
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:
|
||||
new_weight = torch.eye(self.in_features, device=x.device)
|
||||
|
||||
for active_adapter in self.active_adapters:
|
||||
if active_adapter not in self.hra_u.keys():
|
||||
continue
|
||||
delta_weight = self.get_delta_weight(active_adapter)
|
||||
new_weight = torch.mm(new_weight.to(delta_weight.dtype), delta_weight)
|
||||
|
||||
orig_weight = self.get_base_layer().weight.data
|
||||
orig_weight = self._cast_input_dtype(orig_weight, new_weight.dtype)
|
||||
new_weight = torch.mm(orig_weight, new_weight)
|
||||
bias = self._cast_input_dtype(self.base_layer.bias, new_weight.dtype)
|
||||
|
||||
if self.cast_input_dtype_enabled:
|
||||
x = self._cast_input_dtype(x, new_weight.dtype)
|
||||
else:
|
||||
x = x.to(self.get_base_layer().weight.data.dtype)
|
||||
result = F.linear(input=x, weight=new_weight, bias=bias)
|
||||
|
||||
result = result.to(previous_dtype)
|
||||
return result
|
||||
|
||||
def __repr__(self) -> str:
|
||||
rep = super().__repr__()
|
||||
return "hra." + rep
|
||||
|
||||
|
||||
class HRAConv2d(nn.Module, HRALayer):
|
||||
"""HRA implemented in Conv2d layer"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
base_layer,
|
||||
adapter_name: str,
|
||||
config: HRAConfig,
|
||||
r: int = 0,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__()
|
||||
HRALayer.__init__(self, base_layer)
|
||||
self._active_adapter = adapter_name
|
||||
self.update_layer(adapter_name, r, config=config, **kwargs)
|
||||
|
||||
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.hra_u.keys():
|
||||
base_layer = self.get_base_layer()
|
||||
orig_dtype = base_layer.weight.dtype
|
||||
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()
|
||||
orig_weight = orig_weight.view(
|
||||
self.out_features,
|
||||
self.in_features * base_layer.kernel_size[0] * self.base_layer.kernel_size[0],
|
||||
)
|
||||
delta_weight = self.get_delta_weight(active_adapter)
|
||||
orig_weight = torch.mm(orig_weight.to(delta_weight.dtype), delta_weight)
|
||||
orig_weight = orig_weight.view(
|
||||
self.out_features,
|
||||
self.in_features,
|
||||
base_layer.kernel_size[0],
|
||||
base_layer.kernel_size[0],
|
||||
)
|
||||
|
||||
if not torch.isfinite(orig_weight).all():
|
||||
raise ValueError(
|
||||
f"NaNs detected in the merged weights. The adapter {active_adapter} seems to be broken"
|
||||
)
|
||||
|
||||
base_layer.weight.data = orig_weight.to(orig_dtype)
|
||||
else:
|
||||
orig_weight = base_layer.weight.data
|
||||
orig_weight = orig_weight.view(
|
||||
self.out_features,
|
||||
self.in_features * self.base_layer.kernel_size[0] * self.base_layer.kernel_size[0],
|
||||
)
|
||||
delta_weight = self.get_delta_weight(active_adapter)
|
||||
orig_weight = torch.mm(orig_weight.to(delta_weight.dtype), delta_weight)
|
||||
orig_weight = orig_weight.view(
|
||||
self.out_features,
|
||||
self.in_features,
|
||||
base_layer.kernel_size[0],
|
||||
base_layer.kernel_size[0],
|
||||
)
|
||||
|
||||
base_layer.weight.data = orig_weight.to(orig_dtype)
|
||||
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()
|
||||
base_layer = self.get_base_layer()
|
||||
orig_dtype = base_layer.weight.dtype
|
||||
if active_adapter in self.hra_u.keys():
|
||||
orig_weight = base_layer.weight.data.clone()
|
||||
orig_weight = orig_weight.view(
|
||||
self.out_features,
|
||||
self.in_features * base_layer.kernel_size[0] * base_layer.kernel_size[0],
|
||||
)
|
||||
delta_weight = self.get_delta_weight(active_adapter, reverse=True)
|
||||
orig_weight = torch.mm(orig_weight.to(delta_weight.dtype), delta_weight)
|
||||
orig_weight = orig_weight.view(
|
||||
self.out_features, self.in_features, base_layer.kernel_size[0], base_layer.kernel_size[0]
|
||||
)
|
||||
|
||||
base_layer.weight.data = orig_weight.to(orig_dtype)
|
||||
|
||||
def get_delta_weight(self, adapter_name: str, reverse: bool = False) -> torch.Tensor:
|
||||
rank = self.hra_r[adapter_name]
|
||||
apply_GS = self.hra_apply_GS[adapter_name]
|
||||
opt_u = self.hra_u[adapter_name]
|
||||
shape = opt_u.shape
|
||||
|
||||
if apply_GS:
|
||||
weight = [(opt_u[:, 0] / opt_u[:, 0].norm()).view(-1, 1)]
|
||||
for i in range(1, rank):
|
||||
ui = opt_u[:, i].view(-1, 1)
|
||||
for j in range(i):
|
||||
ui = ui - (weight[j].t() @ ui) * weight[j]
|
||||
weight.append((ui / ui.norm()).view(-1, 1))
|
||||
weight = torch.cat(weight, dim=1)
|
||||
weight = torch.eye(shape[0], device=opt_u.device, dtype=opt_u.dtype) - 2 * weight @ weight.t()
|
||||
|
||||
else:
|
||||
opt_u = opt_u / opt_u.norm(dim=0)
|
||||
weight = torch.eye(shape[0], device=opt_u.device, dtype=opt_u.dtype)
|
||||
if reverse:
|
||||
indices = range(rank - 1, -1, -1)
|
||||
else:
|
||||
indices = range(rank)
|
||||
|
||||
for i in indices:
|
||||
ui = opt_u[:, i].view(-1, 1)
|
||||
weight = weight - 2 * weight @ ui @ ui.t()
|
||||
|
||||
return weight
|
||||
|
||||
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:
|
||||
new_weight = torch.eye(
|
||||
self.in_features * self.base_layer.kernel_size[0] * self.base_layer.kernel_size[0],
|
||||
device=x.device,
|
||||
)
|
||||
for active_adapter in self.active_adapters:
|
||||
if active_adapter not in self.hra_u.keys():
|
||||
continue
|
||||
delta_weight = self.get_delta_weight(active_adapter)
|
||||
new_weight = torch.mm(new_weight.to(delta_weight.dtype), delta_weight)
|
||||
|
||||
orig_weight = self.base_layer.weight.data
|
||||
orig_weight = orig_weight.view(
|
||||
self.out_features,
|
||||
self.in_features * self.base_layer.kernel_size[0] * self.base_layer.kernel_size[0],
|
||||
)
|
||||
orig_weight = self._cast_input_dtype(orig_weight, new_weight.dtype)
|
||||
bias = self._cast_input_dtype(self.base_layer.bias, new_weight.dtype)
|
||||
|
||||
new_weight = torch.mm(orig_weight, new_weight)
|
||||
new_weight = new_weight.view(
|
||||
self.out_features,
|
||||
self.in_features,
|
||||
self.base_layer.kernel_size[0],
|
||||
self.base_layer.kernel_size[0],
|
||||
)
|
||||
|
||||
if self.cast_input_dtype_enabled:
|
||||
x = self._cast_input_dtype(x, new_weight.dtype)
|
||||
else:
|
||||
x = x.to(self.get_base_layer().weight.data.dtype)
|
||||
result = F.conv2d(
|
||||
input=x,
|
||||
weight=new_weight,
|
||||
bias=bias,
|
||||
padding=self.base_layer.padding[0],
|
||||
stride=self.base_layer.stride[0],
|
||||
)
|
||||
|
||||
result = result.to(previous_dtype)
|
||||
return result
|
||||
|
||||
def __repr__(self) -> str:
|
||||
rep = super().__repr__()
|
||||
return "hra." + rep
|
||||
Reference in New Issue
Block a user