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[CI] Add ci tests (#203)
* add ci tests * fix some tests * fix tests * rename * fix * update tests * try * temp hotfix * refactor tests * Update .github/workflows/tests.yml * fix test
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# 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, model_list):
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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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"""
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grid_parameters = []
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for model_tuple in model_list:
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model_id, lora_kwargs, prefix_tuning_kwargs, prompt_encoder_kwargs, prompt_tuning_kwargs = model_tuple
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for key, value in self.items():
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if key == "lora":
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# update value[1] if necessary
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if lora_kwargs is not None:
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value[1].update(lora_kwargs)
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elif key == "prefix_tuning":
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# update value[1] if necessary
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if prefix_tuning_kwargs is not None:
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value[1].update(prefix_tuning_kwargs)
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elif key == "prompt_encoder":
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# update value[1] if necessary
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if prompt_encoder_kwargs is not None:
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value[1].update(prompt_encoder_kwargs)
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else:
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# update value[1] if necessary
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if prompt_tuning_kwargs is not None:
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value[1].update(prompt_tuning_kwargs)
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grid_parameters.append((f"test_{model_id}_{key}", model_id, value[0], value[1]))
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return grid_parameters
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PeftTestConfigManager = ClassInstantier(CLASSES_MAPPING)
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