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Zero
| # 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 typing import Any | |
| import torch | |
| from peft.import_utils import is_bnb_4bit_available, is_bnb_available | |
| from .layer import AdaLoraLayer | |
| if is_bnb_available(): | |
| class SVDLinear8bitLt(torch.nn.Module, AdaLoraLayer): | |
| # Low-rank matrix for SVD-based adaptation | |
| 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, | |
| **kwargs, | |
| ) -> None: | |
| super().__init__() | |
| AdaLoraLayer.__init__(self, base_layer) | |
| # Freezing the pre-trained weight matrix | |
| self.get_base_layer().weight.requires_grad = False | |
| self._active_adapter = adapter_name | |
| self.update_layer(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights) | |
| def forward(self, x: torch.Tensor) -> torch.Tensor: | |
| # note: no check for self.merged because merging is not supported (yet) | |
| result = self.base_layer(x) | |
| if self.disable_adapters: | |
| return result | |
| for active_adapter in self.active_adapters: | |
| if active_adapter not in self.lora_A.keys(): | |
| continue | |
| requires_conversion = not torch.is_autocast_enabled() | |
| if requires_conversion: | |
| expected_dtype = result.dtype | |
| if x.dtype != torch.float32: | |
| x = x.float() | |
| lora_A = self.lora_A[active_adapter] | |
| lora_B = self.lora_B[active_adapter] | |
| lora_E = self.lora_E[active_adapter] | |
| dropout = self.lora_dropout[active_adapter] | |
| scaling = self.scaling[active_adapter] | |
| ranknum = self.ranknum[active_adapter] + 1e-5 | |
| output = dropout(x) @ (lora_A * lora_E).T @ lora_B.T | |
| if requires_conversion: | |
| output = output.to(expected_dtype) | |
| output = output * scaling / ranknum | |
| # inplace operation on view is forbidden for MatMul8bitLtBackward, so avoid it | |
| result = result + output | |
| return result | |
| def __repr__(self) -> str: | |
| rep = super().__repr__() | |
| return "adalora." + rep | |
| if is_bnb_4bit_available(): | |
| class SVDLinear4bit(torch.nn.Module, AdaLoraLayer): | |
| # Low-rank matrix for SVD-based adaptation | |
| 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, | |
| **kwargs, | |
| ) -> None: | |
| super().__init__() | |
| AdaLoraLayer.__init__(self, base_layer) | |
| # Freezing the pre-trained weight matrix | |
| self.get_base_layer().weight.requires_grad = False | |
| self._active_adapter = adapter_name | |
| self.update_layer(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights) | |
| def forward(self, x: torch.Tensor, *args: Any, **kwargs: Any) -> torch.Tensor: | |
| # note: no check for self.merged because merging is not supported (yet) | |
| result = self.base_layer(x, *args, **kwargs) | |
| if self.disable_adapters: | |
| return result | |
| # 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] | |
| lora_E = self.lora_E[active_adapter] | |
| dropout = self.lora_dropout[active_adapter] | |
| scaling = self.scaling[active_adapter] | |
| ranknum = self.ranknum[active_adapter] + 1e-5 | |
| requires_conversion = not torch.is_autocast_enabled() | |
| if requires_conversion: | |
| expected_dtype = result.dtype | |
| compute_dtype = lora_A.dtype | |
| if x.dtype != compute_dtype: | |
| x = x.to(compute_dtype) | |
| output = dropout(x) @ (lora_A * lora_E).T @ lora_B.T | |
| if requires_conversion: | |
| output = output.to(expected_dtype) | |
| output = output * scaling / ranknum | |
| result += output | |
| return result | |
| def __repr__(self) -> str: | |
| rep = super().__repr__() | |
| return "adalora." + rep | |