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93 lines
3.8 KiB
Python
93 lines
3.8 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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import unittest
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import torch
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from parameterized import parameterized
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from transformers import AutoModelForSeq2SeqLM
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from .testing_common import PeftCommonTester, PeftTestConfigManager
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PEFT_ENCODER_DECODER_MODELS_TO_TEST = [
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"ybelkada/tiny-random-T5ForConditionalGeneration-calibrated",
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"hf-internal-testing/tiny-random-BartForConditionalGeneration",
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]
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FULL_GRID = {"model_ids": PEFT_ENCODER_DECODER_MODELS_TO_TEST, "task_type": "SEQ_2_SEQ_LM"}
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def skip_non_lora_or_pt(test_list):
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r"""
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Skip tests that are not lora or prefix tuning
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"""
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return [test for test in test_list if ("lora" in test[0] or "prefix_tuning" in test[0])]
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class PeftEncoderDecoderModelTester(unittest.TestCase, PeftCommonTester):
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r"""
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Test if the PeftModel behaves as expected. This includes:
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- test if the model has the expected methods
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We use parametrized.expand for debugging purposes to test each model individually.
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"""
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transformers_class = AutoModelForSeq2SeqLM
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def prepare_inputs_for_testing(self):
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input_ids = torch.tensor([[1, 1, 1], [1, 2, 1]]).to(self.torch_device)
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decoder_input_ids = torch.tensor([[1, 1, 1], [1, 2, 1]]).to(self.torch_device)
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attention_mask = torch.tensor([[1, 1, 1], [1, 0, 1]]).to(self.torch_device)
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input_dict = {
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"input_ids": input_ids,
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"decoder_input_ids": decoder_input_ids,
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"attention_mask": attention_mask,
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}
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return input_dict
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@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
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def test_attributes_parametrized(self, test_name, model_id, config_cls, config_kwargs):
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self._test_model_attr(model_id, config_cls, config_kwargs)
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@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
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def test_prepare_for_training_parametrized(self, test_name, model_id, config_cls, config_kwargs):
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self._test_prepare_for_training(model_id, config_cls, config_kwargs)
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@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
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def test_save_pretrained(self, test_name, model_id, config_cls, config_kwargs):
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self._test_save_pretrained(model_id, config_cls, config_kwargs)
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@parameterized.expand(
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PeftTestConfigManager.get_grid_parameters(
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{
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"model_ids": PEFT_ENCODER_DECODER_MODELS_TO_TEST,
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"lora_kwargs": {"init_lora_weights": [False]},
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"task_type": "SEQ_2_SEQ_LM",
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},
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)
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)
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def test_merge_layers(self, test_name, model_id, config_cls, config_kwargs):
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self._test_merge_layers(model_id, config_cls, config_kwargs)
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# skip non lora models - generate does not work for prefix tuning, prompt tuning
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@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID, filter_params_func=skip_non_lora_or_pt))
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def test_generate(self, test_name, model_id, config_cls, config_kwargs):
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self._test_generate(model_id, config_cls, config_kwargs)
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@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
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def test_generate_half_prec(self, test_name, model_id, config_cls, config_kwargs):
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self._test_generate_half_prec(model_id, config_cls, config_kwargs)
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