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Running
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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 IA3Layer | |
| if is_bnb_available(): | |
| class Linear8bitLt(torch.nn.Module, IA3Layer): | |
| # (IA)^3 implemented in a dense layer | |
| def __init__( | |
| self, | |
| base_layer: torch.nn.Module, | |
| adapter_name: str, | |
| is_feedforward: bool, | |
| init_ia3_weights: bool = True, | |
| **kwargs, | |
| ) -> None: | |
| super().__init__() | |
| IA3Layer.__init__(self, base_layer, is_feedforward=is_feedforward) | |
| # Freezing the pre-trained weight matrix | |
| self.get_base_layer().weight.requires_grad = False | |
| self._active_adapter = adapter_name | |
| self.update_layer(adapter_name, init_ia3_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) | |
| if self.disable_adapters: | |
| return self.base_layer(x) | |
| ia3_scaling = 1 | |
| for active_adapter in self.active_adapters: | |
| if active_adapter not in self.ia3_l.keys(): | |
| continue | |
| ia3_scaling *= self.ia3_l[active_adapter].flatten() | |
| requires_conversion = (not torch.is_autocast_enabled()) and (x.dtype != torch.float32) | |
| if requires_conversion: | |
| x = x.float() | |
| if self.is_feedforward: | |
| result = self.base_layer(x * ia3_scaling) | |
| expected_dtype = result.dtype | |
| else: | |
| result = self.base_layer(x) | |
| expected_dtype = result.dtype | |
| result = result * ia3_scaling | |
| if requires_conversion: | |
| result = result.to(expected_dtype) | |
| return result | |
| def __repr__(self) -> str: | |
| rep = super().__repr__() | |
| return "ia3." + rep | |
| if is_bnb_4bit_available(): | |
| class Linear4bit(torch.nn.Module, IA3Layer): | |
| # IA3 implemented in a dense layer | |
| def __init__( | |
| self, | |
| base_layer: torch.nn.Module, | |
| adapter_name: str, | |
| is_feedforward: bool, | |
| init_ia3_weights: bool = True, | |
| **kwargs, | |
| ) -> None: | |
| super().__init__() | |
| IA3Layer.__init__(self, base_layer, is_feedforward=is_feedforward) | |
| # Freezing the pre-trained weight matrix | |
| self.get_base_layer().weight.requires_grad = False | |
| self._active_adapter = adapter_name | |
| self.update_layer(adapter_name, init_ia3_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) | |
| if self.disable_adapters: | |
| return self.base_layer(x) | |
| ia3_scaling = 1 | |
| for active_adapter in self.active_adapters: | |
| if active_adapter not in self.ia3_l.keys(): | |
| continue | |
| ia3_scaling *= self.ia3_l[active_adapter].flatten() | |
| requires_conversion = (not torch.is_autocast_enabled()) and (x.dtype != torch.float32) | |
| if requires_conversion: | |
| x = x.float() | |
| if self.is_feedforward: | |
| result = self.base_layer(x * ia3_scaling) | |
| expected_dtype = result.dtype | |
| else: | |
| result = self.base_layer(x) | |
| expected_dtype = result.dtype | |
| result = result * ia3_scaling | |
| result = result.clone() | |
| # adalora.py and lora.py both suggest that this is necessary for 4-bit training on older versions of Pytorch. | |
| # This has been duplicated here. | |
| if requires_conversion: | |
| result = result.to(expected_dtype) | |
| return result | |
| def __repr__(self) -> str: | |
| rep = super().__repr__() | |
| return "ia3." + rep | |