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8f63f565c6
* add merge_lora utility function * forward contrib credits from original script * some changes * make style * fix tets * finally fix tests * Update tests/test_peft_model.py * adapt from suggestions * adapt * Update src/peft/tuners/lora.py Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> * fix 8bit * Update src/peft/tuners/lora.py Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> --------- Co-authored-by: edbeeching <edbeeching@users.noreply.github.com> Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com>
118 lines
4.2 KiB
Python
118 lines
4.2 KiB
Python
# coding=utf-8
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# Copyright 2023-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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from collections import OrderedDict
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from peft import (
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LoraConfig,
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PrefixTuningConfig,
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PromptEncoderConfig,
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PromptTuningConfig,
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)
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CONFIG_CLASSES = (
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LoraConfig,
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PrefixTuningConfig,
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PromptEncoderConfig,
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PromptTuningConfig,
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)
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CONFIG_TESTING_KWARGS = (
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{
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"r": 8,
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"lora_alpha": 32,
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"target_modules": None,
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"lora_dropout": 0.05,
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"bias": "none",
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"task_type": "CAUSAL_LM",
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},
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{
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"num_virtual_tokens": 10,
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"task_type": "CAUSAL_LM",
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},
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{
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"num_virtual_tokens": 10,
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"encoder_hidden_size": 32,
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"task_type": "CAUSAL_LM",
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},
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{
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"num_virtual_tokens": 10,
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"task_type": "CAUSAL_LM",
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},
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)
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CLASSES_MAPPING = {
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"lora": (LoraConfig, CONFIG_TESTING_KWARGS[0]),
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"prefix_tuning": (PrefixTuningConfig, CONFIG_TESTING_KWARGS[1]),
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"prompt_encoder": (PromptEncoderConfig, CONFIG_TESTING_KWARGS[2]),
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"prompt_tuning": (PromptTuningConfig, CONFIG_TESTING_KWARGS[3]),
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}
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# Adapted from https://github.com/huggingface/transformers/blob/48327c57182fdade7f7797d1eaad2d166de5c55b/src/transformers/activations.py#LL166C7-L166C22
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class ClassInstantier(OrderedDict):
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def __getitem__(self, key, *args, **kwargs):
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# check if any of the kwargs is inside the config class kwargs
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if any([kwarg in self[key][1] for kwarg in kwargs]):
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new_config_kwargs = self[key][1].copy()
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new_config_kwargs.update(kwargs)
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return (self[key][0], new_config_kwargs)
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return super().__getitem__(key, *args, **kwargs)
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def get_grid_parameters(self, grid_parameters, filter_params_func=None):
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r"""
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Returns a list of all possible combinations of the parameters in the config classes.
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Args:
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grid_parameters (`dict`):
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A dictionary containing the parameters to be tested. There should be at least the key "model_ids" which
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contains a list of model ids to be tested. The other keys should be the name of the config class
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post-fixed with "_kwargs" and the value should be a dictionary containing the parameters to be tested
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for that config class.
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filter_params_func (`callable`, `optional`):
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A function that takes a list of tuples and returns a list of tuples. This function is used to filter
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out the tests that needs for example to be skipped.
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Returns:
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generated_tests (`list`):
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A list of tuples containing the name of the test, the model id, the config class and the config class
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kwargs.
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"""
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generated_tests = []
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model_list = grid_parameters["model_ids"]
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for model_id in model_list:
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for key, value in self.items():
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if "{}_kwargs".format(key) in grid_parameters:
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peft_configs = []
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current_peft_config = value[1].copy()
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for current_key, current_value in grid_parameters[f"{key}_kwargs"].items():
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for kwarg in current_value:
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current_peft_config.update({current_key: kwarg})
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peft_configs.append(current_peft_config)
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else:
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peft_configs = [value[1].copy()]
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for peft_config in peft_configs:
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generated_tests.append((f"test_{model_id}_{key}", model_id, value[0], peft_config))
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if filter_params_func is not None:
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generated_tests = filter_params_func(generated_tests)
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return generated_tests
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PeftTestConfigManager = ClassInstantier(CLASSES_MAPPING)
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