mirror of
https://github.com/wassname/lora-lite.git
synced 2026-06-27 20:21:55 +08:00
wassname
fdb4c77d6c
- 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)
463 lines
19 KiB
Python
463 lines
19 KiB
Python
# Copyright 2024-present the HuggingFace Inc. team.
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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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import math
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import warnings
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from typing import Any, Optional
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from peft.tuners.tuners_utils import BaseTunerLayer, check_adapters_to_merge
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from .config import HRAConfig
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class HRALayer(BaseTunerLayer):
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# All names of layers that may contain (trainable) adapter weights
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adapter_layer_names = ("hra_u",)
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# All names of other parameters that may contain adapter-related parameters
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other_param_names = ("hra_r", "hra_apply_GS")
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def __init__(self, base_layer: nn.Module, **kwargs) -> None:
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self.base_layer = base_layer
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self.hra_r = {}
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self.hra_apply_GS = {}
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self.hra_u = nn.ParameterDict({})
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# Mark the weight as unmerged
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self._disable_adapters = False
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self.merged_adapters = []
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# flag to enable/disable casting of input to weight dtype during forward call
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self.cast_input_dtype_enabled = True
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self.kwargs = kwargs
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base_layer = self.get_base_layer()
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if isinstance(base_layer, nn.Linear):
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self.in_features, self.out_features = base_layer.in_features, base_layer.out_features
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elif isinstance(base_layer, nn.Conv2d):
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self.in_features, self.out_features = base_layer.in_channels, base_layer.out_channels
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else:
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raise ValueError(f"Unsupported layer type {type(base_layer)}")
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def update_layer(
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self,
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adapter_name: str,
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r: int,
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config: HRAConfig,
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**kwargs,
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) -> None:
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"""Internal function to create hra adapter
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Args:
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adapter_name (`str`): Name for the adapter to add.
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r (`int`): Rank for the added adapter.
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config (`HRAConfig`): The adapter configuration for this layer.
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"""
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apply_GS = config.apply_GS
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init_weights = config.init_weights
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inference_mode = config.inference_mode
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if r <= 0:
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raise ValueError(f"`r` should be a positive integer value but the value passed is {r}")
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self.hra_r[adapter_name] = r
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self.hra_apply_GS[adapter_name] = apply_GS
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# Determine shape of HRA weights
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base_layer = self.get_base_layer()
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if isinstance(base_layer, nn.Linear):
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self.hra_u[adapter_name] = nn.Parameter(torch.empty(self.in_features, r), requires_grad=True)
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elif isinstance(base_layer, nn.Conv2d):
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self.hra_u[adapter_name] = nn.Parameter(
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torch.empty(self.in_features * base_layer.kernel_size[0] * base_layer.kernel_size[0], r),
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requires_grad=True,
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)
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else:
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raise TypeError(f"HRA is not implemented for base layers of type {type(base_layer).__name__}")
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# Initialize weights
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if init_weights:
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self.reset_hra_parameters(adapter_name)
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else:
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self.reset_hra_parameters_random(adapter_name)
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# Move new weights to device
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self._move_adapter_to_device_of_base_layer(adapter_name)
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self.set_adapter(self.active_adapters, inference_mode=inference_mode)
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def reset_hra_parameters(self, adapter_name: str):
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if self.hra_r[adapter_name] % 2 != 0:
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warnings.warn("The symmetric initialization can NOT be performed when r is odd!")
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nn.init.kaiming_uniform_(self.hra_u[adapter_name], a=math.sqrt(5))
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else:
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shape = self.hra_u[adapter_name].shape
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half_u = torch.zeros(shape[0], shape[1] // 2)
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nn.init.kaiming_uniform_(half_u, a=math.sqrt(5))
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self.hra_u[adapter_name] = nn.Parameter(torch.repeat_interleave(half_u, 2, dim=1))
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def reset_hra_parameters_random(self, adapter_name: str):
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nn.init.kaiming_uniform_(self.hra_u[adapter_name], a=math.sqrt(5))
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def scale_layer(self, scale: float) -> None:
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if scale == 1:
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return
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for active_adapter in self.active_adapters:
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if active_adapter not in self.hra_u.keys():
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continue
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warnings.warn("Scaling operation for HRA not supported! Automatically set scale to 1.")
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def unscale_layer(self, scale=None) -> None:
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for active_adapter in self.active_adapters:
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if active_adapter not in self.hra_u.keys():
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continue
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warnings.warn("Unscaling operation for HRA not supported! Keeping scale at 1.")
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class HRALinear(nn.Module, HRALayer):
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"""
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HRA implemented in a dense layer.
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"""
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def __init__(
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self,
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base_layer,
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adapter_name: str,
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config: HRAConfig,
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r: int = 0,
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**kwargs,
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) -> None:
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super().__init__()
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HRALayer.__init__(self, base_layer, **kwargs)
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self._active_adapter = adapter_name
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self.update_layer(adapter_name, r, config=config, **kwargs)
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def merge(self, safe_merge: bool = False, adapter_names: Optional[list[str]] = None) -> None:
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"""
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Merge the active adapter weights into the base weights
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Args:
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safe_merge (`bool`, *optional*):
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If `True`, the merge operation will be performed in a copy of the original weights and check for NaNs
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before merging the weights. This is useful if you want to check if the merge operation will produce
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NaNs. Defaults to `False`.
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adapter_names (`List[str]`, *optional*):
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The list of adapter names that should be merged. If `None`, all active adapters will be merged.
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Defaults to `None`.
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"""
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adapter_names = check_adapters_to_merge(self, adapter_names)
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if not adapter_names:
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# no adapter to merge
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return
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for active_adapter in adapter_names:
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if active_adapter in self.hra_u.keys():
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base_layer = self.get_base_layer()
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orig_dtype = base_layer.weight.dtype
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if safe_merge:
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# Note that safe_merge will be slower than the normal merge
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# because of the copy operation.
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orig_weight = base_layer.weight.data.clone()
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delta_weight = self.get_delta_weight(active_adapter)
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orig_weight = torch.mm(orig_weight.to(delta_weight.dtype), delta_weight)
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if not torch.isfinite(orig_weight).all():
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raise ValueError(
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f"NaNs detected in the merged weights. The adapter {active_adapter} seems to be broken"
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)
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base_layer.weight.data = orig_weight.to(orig_dtype)
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else:
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delta_weight = self.get_delta_weight(active_adapter)
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new_weight = torch.mm(base_layer.weight.data.to(delta_weight.dtype), delta_weight)
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base_layer.weight.data = new_weight.to(orig_dtype)
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self.merged_adapters.append(active_adapter)
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def unmerge(self) -> None:
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"""
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This method unmerges all merged adapter layers from the base weights.
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"""
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if not self.merged:
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warnings.warn("Already unmerged. Nothing to do.")
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return
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while len(self.merged_adapters) > 0:
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active_adapter = self.merged_adapters.pop()
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base_layer = self.get_base_layer()
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orig_dtype = base_layer.weight.dtype
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if active_adapter in self.hra_u.keys():
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orig_weight = base_layer.weight.data.clone()
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delta_weight = self.get_delta_weight(active_adapter, reverse=True)
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new_weight = torch.mm(orig_weight.to(delta_weight.dtype), delta_weight)
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base_layer.weight.data = new_weight.to(orig_dtype)
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def get_delta_weight(self, adapter_name: str, reverse: bool = False) -> torch.Tensor:
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rank = self.hra_r[adapter_name]
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apply_GS = self.hra_apply_GS[adapter_name]
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opt_u = self.hra_u[adapter_name]
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shape = opt_u.shape
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if apply_GS:
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weight = [(opt_u[:, 0] / opt_u[:, 0].norm()).view(-1, 1)]
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for i in range(1, rank):
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ui = opt_u[:, i].view(-1, 1)
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for j in range(i):
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ui = ui - (weight[j].t() @ ui) * weight[j]
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weight.append((ui / ui.norm()).view(-1, 1))
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weight = torch.cat(weight, dim=1)
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weight = torch.eye(shape[0], device=opt_u.device, dtype=opt_u.dtype) - 2 * weight @ weight.t()
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else:
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opt_u = opt_u / opt_u.norm(dim=0)
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weight = torch.eye(shape[0], device=opt_u.device, dtype=opt_u.dtype)
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if reverse:
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indices = range(rank - 1, -1, -1)
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else:
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indices = range(rank)
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for i in indices:
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ui = opt_u[:, i].view(-1, 1)
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weight = weight - 2 * weight @ ui @ ui.t()
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return weight
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def forward(self, x: torch.Tensor, *args: Any, **kwargs: Any) -> torch.Tensor:
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previous_dtype = x.dtype
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if self.disable_adapters:
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if self.merged:
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self.unmerge()
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result = self.base_layer(x, *args, **kwargs)
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elif self.merged:
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result = self.base_layer(x, *args, **kwargs)
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else:
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new_weight = torch.eye(self.in_features, device=x.device)
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for active_adapter in self.active_adapters:
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if active_adapter not in self.hra_u.keys():
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continue
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delta_weight = self.get_delta_weight(active_adapter)
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new_weight = torch.mm(new_weight.to(delta_weight.dtype), delta_weight)
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orig_weight = self.get_base_layer().weight.data
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orig_weight = self._cast_input_dtype(orig_weight, new_weight.dtype)
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new_weight = torch.mm(orig_weight, new_weight)
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bias = self._cast_input_dtype(self.base_layer.bias, new_weight.dtype)
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if self.cast_input_dtype_enabled:
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x = self._cast_input_dtype(x, new_weight.dtype)
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else:
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x = x.to(self.get_base_layer().weight.data.dtype)
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result = F.linear(input=x, weight=new_weight, bias=bias)
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result = result.to(previous_dtype)
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return result
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def __repr__(self) -> str:
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rep = super().__repr__()
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return "hra." + rep
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class HRAConv2d(nn.Module, HRALayer):
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"""HRA implemented in Conv2d layer"""
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def __init__(
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self,
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base_layer,
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adapter_name: str,
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config: HRAConfig,
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r: int = 0,
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**kwargs,
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):
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super().__init__()
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HRALayer.__init__(self, base_layer)
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self._active_adapter = adapter_name
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self.update_layer(adapter_name, r, config=config, **kwargs)
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def merge(self, safe_merge: bool = False, adapter_names: Optional[list[str]] = None) -> None:
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"""
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Merge the active adapter weights into the base weights
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Args:
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safe_merge (`bool`, *optional*):
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If `True`, the merge operation will be performed in a copy of the original weights and check for NaNs
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before merging the weights. This is useful if you want to check if the merge operation will produce
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NaNs. Defaults to `False`.
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adapter_names (`List[str]`, *optional*):
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The list of adapter names that should be merged. If `None`, all active adapters will be merged.
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Defaults to `None`.
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"""
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adapter_names = check_adapters_to_merge(self, adapter_names)
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if not adapter_names:
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# no adapter to merge
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return
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for active_adapter in adapter_names:
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if active_adapter in self.hra_u.keys():
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base_layer = self.get_base_layer()
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orig_dtype = base_layer.weight.dtype
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if safe_merge:
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# Note that safe_merge will be slower than the normal merge
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# because of the copy operation.
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orig_weight = base_layer.weight.data.clone()
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orig_weight = orig_weight.view(
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self.out_features,
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self.in_features * base_layer.kernel_size[0] * self.base_layer.kernel_size[0],
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)
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delta_weight = self.get_delta_weight(active_adapter)
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orig_weight = torch.mm(orig_weight.to(delta_weight.dtype), delta_weight)
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orig_weight = orig_weight.view(
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self.out_features,
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self.in_features,
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base_layer.kernel_size[0],
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base_layer.kernel_size[0],
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)
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if not torch.isfinite(orig_weight).all():
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raise ValueError(
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f"NaNs detected in the merged weights. The adapter {active_adapter} seems to be broken"
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)
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base_layer.weight.data = orig_weight.to(orig_dtype)
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else:
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orig_weight = base_layer.weight.data
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orig_weight = orig_weight.view(
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self.out_features,
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self.in_features * self.base_layer.kernel_size[0] * self.base_layer.kernel_size[0],
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)
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delta_weight = self.get_delta_weight(active_adapter)
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orig_weight = torch.mm(orig_weight.to(delta_weight.dtype), delta_weight)
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orig_weight = orig_weight.view(
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self.out_features,
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self.in_features,
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base_layer.kernel_size[0],
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base_layer.kernel_size[0],
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)
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base_layer.weight.data = orig_weight.to(orig_dtype)
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self.merged_adapters.append(active_adapter)
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def unmerge(self) -> None:
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"""
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This method unmerges all merged adapter layers from the base weights.
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"""
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if not self.merged:
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warnings.warn("Already unmerged. Nothing to do.")
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return
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while len(self.merged_adapters) > 0:
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active_adapter = self.merged_adapters.pop()
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base_layer = self.get_base_layer()
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orig_dtype = base_layer.weight.dtype
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if active_adapter in self.hra_u.keys():
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orig_weight = base_layer.weight.data.clone()
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orig_weight = orig_weight.view(
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self.out_features,
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self.in_features * base_layer.kernel_size[0] * base_layer.kernel_size[0],
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)
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delta_weight = self.get_delta_weight(active_adapter, reverse=True)
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orig_weight = torch.mm(orig_weight.to(delta_weight.dtype), delta_weight)
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orig_weight = orig_weight.view(
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self.out_features, self.in_features, base_layer.kernel_size[0], base_layer.kernel_size[0]
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)
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base_layer.weight.data = orig_weight.to(orig_dtype)
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def get_delta_weight(self, adapter_name: str, reverse: bool = False) -> torch.Tensor:
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rank = self.hra_r[adapter_name]
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apply_GS = self.hra_apply_GS[adapter_name]
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opt_u = self.hra_u[adapter_name]
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shape = opt_u.shape
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if apply_GS:
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weight = [(opt_u[:, 0] / opt_u[:, 0].norm()).view(-1, 1)]
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for i in range(1, rank):
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ui = opt_u[:, i].view(-1, 1)
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for j in range(i):
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ui = ui - (weight[j].t() @ ui) * weight[j]
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weight.append((ui / ui.norm()).view(-1, 1))
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weight = torch.cat(weight, dim=1)
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weight = torch.eye(shape[0], device=opt_u.device, dtype=opt_u.dtype) - 2 * weight @ weight.t()
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else:
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opt_u = opt_u / opt_u.norm(dim=0)
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weight = torch.eye(shape[0], device=opt_u.device, dtype=opt_u.dtype)
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if reverse:
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indices = range(rank - 1, -1, -1)
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else:
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indices = range(rank)
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for i in indices:
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ui = opt_u[:, i].view(-1, 1)
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weight = weight - 2 * weight @ ui @ ui.t()
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return weight
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def forward(self, x: torch.Tensor, *args: Any, **kwargs: Any) -> torch.Tensor:
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previous_dtype = x.dtype
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if self.disable_adapters:
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if self.merged:
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self.unmerge()
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result = self.base_layer(x, *args, **kwargs)
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elif self.merged:
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result = self.base_layer(x, *args, **kwargs)
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else:
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new_weight = torch.eye(
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self.in_features * self.base_layer.kernel_size[0] * self.base_layer.kernel_size[0],
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device=x.device,
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)
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for active_adapter in self.active_adapters:
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if active_adapter not in self.hra_u.keys():
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continue
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delta_weight = self.get_delta_weight(active_adapter)
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new_weight = torch.mm(new_weight.to(delta_weight.dtype), delta_weight)
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orig_weight = self.base_layer.weight.data
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orig_weight = orig_weight.view(
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self.out_features,
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self.in_features * self.base_layer.kernel_size[0] * self.base_layer.kernel_size[0],
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)
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orig_weight = self._cast_input_dtype(orig_weight, new_weight.dtype)
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bias = self._cast_input_dtype(self.base_layer.bias, new_weight.dtype)
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new_weight = torch.mm(orig_weight, new_weight)
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new_weight = new_weight.view(
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self.out_features,
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self.in_features,
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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
|