# 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 warnings from typing import Any, Optional, Union import torch import torch.nn as nn import torch.nn.functional as F from transformers.pytorch_utils import Conv1D from peft.tuners.tuners_utils import BaseTunerLayer, check_adapters_to_merge class FourierFTLayer(BaseTunerLayer): # All names of layers that may contain (trainable) adapter weights adapter_layer_names = ("fourierft_spectrum",) # All names of other parameters that may contain adapter-related parameters other_param_names = ("fourierft_n_frequency", "fourierft_scaling", "fourierft_random_loc_seed") def __init__(self, base_layer: nn.Module, **kwargs) -> None: self.base_layer = base_layer self.fourierft_n_frequency = {} self.fourierft_scaling = {} self.fourierft_spectrum = nn.ParameterDict({}) self.indices = {} self.fourierft_random_loc_seed = {} # 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 elif isinstance(base_layer, Conv1D): self.in_features, self.out_features = ( base_layer.weight.ds_shape if hasattr(base_layer.weight, "ds_shape") else base_layer.weight.shape ) else: raise ValueError(f"Unsupported layer type {type(base_layer)}") def update_layer(self, adapter_name, n_frequency, scaling, init_weights, random_loc_seed): if n_frequency <= 0: raise ValueError(f"`n_frequency` should be a positive integer value but the value passed is {n_frequency}") if n_frequency > self.in_features * self.out_features: raise ValueError( f"`n_frequency` should be less than or equal to the product of the input and output dimensions " f"but the value passed is {n_frequency} and the product is {self.in_features * self.out_features}" ) self.fourierft_n_frequency[adapter_name] = n_frequency self.fourierft_random_loc_seed[adapter_name] = random_loc_seed self.indices[adapter_name] = torch.randperm( self.out_features * self.in_features, generator=torch.Generator().manual_seed(self.fourierft_random_loc_seed[adapter_name]), )[:n_frequency] self.indices[adapter_name] = torch.stack( [self.indices[adapter_name] // self.in_features, self.indices[adapter_name] % self.in_features], dim=0 ) self.fourierft_scaling[adapter_name] = scaling # Actual trainable parameters self.fourierft_spectrum[adapter_name] = nn.Parameter(torch.randn(n_frequency), requires_grad=True) if init_weights: self.reset_fourier_parameters(adapter_name) self._move_adapter_to_device_of_base_layer(adapter_name) self.set_adapter(self.active_adapters) @torch.no_grad() def reset_fourier_parameters(self, adapter_name): if adapter_name in self.fourierft_spectrum.keys(): nn.init.zeros_(self.fourierft_spectrum[adapter_name]) def get_delta_weight(self, adapter) -> torch.Tensor: # careful: ifft2 does not work with float16 or bfloat16 spectrum = self.fourierft_spectrum[adapter] indices = self.indices[adapter].to(spectrum.device) dense_spectrum = torch.zeros(self.out_features, self.in_features, device=spectrum.device) dense_spectrum[indices[0, :], indices[1, :]] = spectrum.float() delta_weight = torch.fft.ifft2(dense_spectrum).real * self.fourierft_scaling[adapter] return delta_weight.to(spectrum.dtype) class FourierFTLinear(nn.Module, FourierFTLayer): # FourierFT implemented in a dense layer def __init__( self, base_layer, adapter_name: str, n_frequency: int = 1000, scaling: float = 150.0, fan_in_fan_out: bool = False, # Set this to True if the layer to replace stores weight like (fan_in, fan_out) init_weights: Union[bool, str] = False, random_loc_seed: int = 777, **kwargs, ) -> None: super().__init__() FourierFTLayer.__init__(self, base_layer, **kwargs) self.fan_in_fan_out = fan_in_fan_out self._active_adapter = adapter_name self.update_layer(adapter_name, n_frequency, scaling, init_weights, random_loc_seed) 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.fourierft_spectrum.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_weights = base_layer.weight.data.clone() orig_weights += self.get_delta_weight(active_adapter) if not torch.isfinite(orig_weights).all(): raise ValueError( f"NaNs detected in the merged weights. The adapter {active_adapter} seems to be broken" ) base_layer.weight.data = orig_weights else: base_layer.weight.data += self.get_delta_weight(active_adapter) 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.fourierft_spectrum.keys(): self.get_base_layer().weight.data -= self.get_delta_weight(active_adapter) def get_delta_weight(self, adapter) -> torch.Tensor: return super().get_delta_weight(adapter) 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: result = self.base_layer(x, *args, **kwargs) for active_adapter in self.active_adapters: if active_adapter not in self.fourierft_spectrum.keys(): continue delta_w = self.get_delta_weight(active_adapter) x = x.to(delta_w.dtype) result = result + F.linear(x, delta_w) result = result.to(previous_dtype) return result def __repr__(self) -> str: rep = super().__repr__() return "fourierft." + rep