Spaces:
Sleeping
Sleeping
| # coding=utf-8 | |
| # 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 collections import OrderedDict | |
| from peft import ( | |
| LoraConfig, | |
| PrefixTuningConfig, | |
| PromptEncoderConfig, | |
| PromptTuningConfig, | |
| ) | |
| CONFIG_CLASSES = ( | |
| LoraConfig, | |
| PrefixTuningConfig, | |
| PromptEncoderConfig, | |
| PromptTuningConfig, | |
| ) | |
| CONFIG_TESTING_KWARGS = ( | |
| { | |
| "r": 8, | |
| "lora_alpha": 32, | |
| "target_modules": None, | |
| "lora_dropout": 0.05, | |
| "bias": "none", | |
| "task_type": "CAUSAL_LM", | |
| }, | |
| { | |
| "num_virtual_tokens": 10, | |
| "task_type": "CAUSAL_LM", | |
| }, | |
| { | |
| "num_virtual_tokens": 10, | |
| "encoder_hidden_size": 32, | |
| "task_type": "CAUSAL_LM", | |
| }, | |
| { | |
| "num_virtual_tokens": 10, | |
| "task_type": "CAUSAL_LM", | |
| }, | |
| ) | |
| CLASSES_MAPPING = { | |
| "lora": (LoraConfig, CONFIG_TESTING_KWARGS[0]), | |
| "prefix_tuning": (PrefixTuningConfig, CONFIG_TESTING_KWARGS[1]), | |
| "prompt_encoder": (PromptEncoderConfig, CONFIG_TESTING_KWARGS[2]), | |
| "prompt_tuning": (PromptTuningConfig, CONFIG_TESTING_KWARGS[3]), | |
| } | |
| # Adapted from https://github.com/huggingface/transformers/blob/48327c57182fdade7f7797d1eaad2d166de5c55b/src/transformers/activations.py#LL166C7-L166C22 | |
| class ClassInstantier(OrderedDict): | |
| def __getitem__(self, key, *args, **kwargs): | |
| # check if any of the kwargs is inside the config class kwargs | |
| if any([kwarg in self[key][1] for kwarg in kwargs]): | |
| new_config_kwargs = self[key][1].copy() | |
| new_config_kwargs.update(kwargs) | |
| return (self[key][0], new_config_kwargs) | |
| return super().__getitem__(key, *args, **kwargs) | |
| def get_grid_parameters(self, model_list): | |
| r""" | |
| Returns a list of all possible combinations of the parameters in the config classes. | |
| """ | |
| grid_parameters = [] | |
| for model_tuple in model_list: | |
| model_id, lora_kwargs, prefix_tuning_kwargs, prompt_encoder_kwargs, prompt_tuning_kwargs = model_tuple | |
| for key, value in self.items(): | |
| if key == "lora": | |
| # update value[1] if necessary | |
| if lora_kwargs is not None: | |
| value[1].update(lora_kwargs) | |
| elif key == "prefix_tuning": | |
| # update value[1] if necessary | |
| if prefix_tuning_kwargs is not None: | |
| value[1].update(prefix_tuning_kwargs) | |
| elif key == "prompt_encoder": | |
| # update value[1] if necessary | |
| if prompt_encoder_kwargs is not None: | |
| value[1].update(prompt_encoder_kwargs) | |
| else: | |
| # update value[1] if necessary | |
| if prompt_tuning_kwargs is not None: | |
| value[1].update(prompt_tuning_kwargs) | |
| grid_parameters.append((f"test_{model_id}_{key}", model_id, value[0], value[1])) | |
| return grid_parameters | |
| PeftTestConfigManager = ClassInstantier(CLASSES_MAPPING) | |