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aa3f41f752
See #2283 Right now, using mixed adapter batches with beam search generations does not work. This is because users need to pass the adapter names associated with each sample, i.e. the number of adapter names should be identical to the number of samples in the input. When applying beam search, transformers internally repeats the samples once per beam (or so it looks like). Therefore, we have more samples during generation than samples in the input. Consequently, the adapter names have to be extended accordingly. This is now taken care of.
264 lines
13 KiB
Python
264 lines
13 KiB
Python
# 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 tempfile
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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, AutoModelForTokenClassification
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from peft import LoraConfig, PromptEncoderConfig, TaskType, get_peft_model
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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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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_adapter_name(self, test_name, model_id, config_cls, config_kwargs):
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self._test_adapter_name(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(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
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def test_save_pretrained_pickle(self, test_name, model_id, config_cls, config_kwargs):
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self._test_save_pretrained(model_id, config_cls, config_kwargs, safe_serialization=False)
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@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
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def test_save_pretrained_selected_adapters(self, test_name, model_id, config_cls, config_kwargs):
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self._test_save_pretrained_selected_adapters(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_selected_adapters_pickle(self, test_name, model_id, config_cls, config_kwargs):
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self._test_save_pretrained_selected_adapters(model_id, config_cls, config_kwargs, safe_serialization=False)
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def test_load_model_low_cpu_mem_usage(self):
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self._test_load_model_low_cpu_mem_usage(PEFT_ENCODER_DECODER_MODELS_TO_TEST[0], LoraConfig, {})
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@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
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def test_from_pretrained_config_construction(self, test_name, model_id, config_cls, config_kwargs):
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self._test_from_pretrained_config_construction(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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"adalora_kwargs": {"init_lora_weights": [False]},
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"ia3_kwargs": {"init_ia3_weights": [False]},
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"vera_kwargs": {"init_weights": [False]},
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"hra_kwargs": {"init_weights": [False]},
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"bone_kwargs": {"init_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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@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_mixed_adapter_batches(self, test_name, model_id, config_cls, config_kwargs):
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self._test_mixed_adapter_batches(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_generate_with_mixed_adapter_batches(self, test_name, model_id, config_cls, config_kwargs):
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self._test_generate_with_mixed_adapter_batches_and_beam_search(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))
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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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# 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))
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def test_generate_pos_args(self, test_name, model_id, config_cls, config_kwargs):
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# positional arguments are not supported for PeftModelForSeq2SeqLM
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self._test_generate_pos_args(model_id, config_cls, config_kwargs, raises_err=True)
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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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@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
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def test_prefix_tuning_half_prec_conversion(self, test_name, model_id, config_cls, config_kwargs):
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self._test_prefix_tuning_half_prec_conversion(model_id, config_cls, config_kwargs)
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@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
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def test_training_encoder_decoders(self, test_name, model_id, config_cls, config_kwargs):
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self._test_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_training_encoder_decoders_layer_indexing(self, test_name, model_id, config_cls, config_kwargs):
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self._test_training_layer_indexing(model_id, config_cls, config_kwargs)
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@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
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def test_training_encoder_decoders_gradient_checkpointing(self, test_name, model_id, config_cls, config_kwargs):
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self._test_training_gradient_checkpointing(model_id, config_cls, config_kwargs)
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@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
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def test_inference_safetensors(self, test_name, model_id, config_cls, config_kwargs):
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self._test_inference_safetensors(model_id, config_cls, config_kwargs)
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@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
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def test_peft_model_device_map(self, test_name, model_id, config_cls, config_kwargs):
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self._test_peft_model_device_map(model_id, config_cls, config_kwargs)
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@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
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def test_delete_adapter(self, test_name, model_id, config_cls, config_kwargs):
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self._test_delete_adapter(model_id, config_cls, config_kwargs)
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@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
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def test_delete_inactive_adapter(self, test_name, model_id, config_cls, config_kwargs):
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self._test_delete_inactive_adapter(model_id, config_cls, config_kwargs)
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@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
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def test_adding_multiple_adapters_with_bias_raises(self, test_name, model_id, config_cls, config_kwargs):
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self._test_adding_multiple_adapters_with_bias_raises(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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"adalora_kwargs": {"init_lora_weights": [False]},
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"ia3_kwargs": {"init_ia3_weights": [False]},
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"boft_kwargs": {"init_weights": [False]},
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"oft_kwargs": {"init_weights": [False]},
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"vera_kwargs": {"init_weights": [False]},
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"hra_kwargs": {"init_weights": [False]},
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"bone_kwargs": {"init_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_unload_adapter(self, test_name, model_id, config_cls, config_kwargs):
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self._test_unload_adapter(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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"ia3_kwargs": {"init_ia3_weights": [False]},
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"bone_kwargs": {"init_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_weighted_combination_of_adapters(self, test_name, model_id, config_cls, config_kwargs):
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self._test_weighted_combination_of_adapters(model_id, config_cls, config_kwargs)
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@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
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def test_training_prompt_learning_tasks(self, test_name, model_id, config_cls, config_kwargs):
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self._test_training_prompt_learning_tasks(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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"adalora_kwargs": {"init_lora_weights": [False]},
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"ia3_kwargs": {"init_ia3_weights": [False]},
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"boft_kwargs": {"init_weights": [False]},
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"oft_kwargs": {"init_weights": [False]},
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"vera_kwargs": {"init_weights": [False]},
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"hra_kwargs": {"init_weights": [False]},
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"bone_kwargs": {"init_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_disable_adapter(self, test_name, model_id, config_cls, config_kwargs):
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self._test_disable_adapter(model_id, config_cls, config_kwargs)
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def test_active_adapters_prompt_learning(self):
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# see issue https://github.com/huggingface/transformers/pull/30790#issuecomment-2253808249
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model = AutoModelForSeq2SeqLM.from_pretrained("hf-internal-testing/tiny-random-BartForConditionalGeneration")
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# any prompt learning method would work here
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config = PromptEncoderConfig(task_type=TaskType.SEQ_2_SEQ_LM, num_virtual_tokens=10)
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model = get_peft_model(model, config)
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assert model.active_adapters == ["default"]
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class PeftEncoderDecoderCustomModelTester(unittest.TestCase):
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"""
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A custom class to write any custom test related with Enc-Dec models
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"""
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def test_save_shared_tensors(self):
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model_id = "hf-internal-testing/tiny-random-RobertaModel"
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peft_config = LoraConfig(
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task_type=TaskType.TOKEN_CLS, inference_mode=False, r=16, lora_alpha=16, lora_dropout=0.1, bias="all"
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)
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model = AutoModelForTokenClassification.from_pretrained(model_id, num_labels=11)
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model = get_peft_model(model, peft_config)
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with tempfile.TemporaryDirectory() as tmp_dir:
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# This should work fine
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model.save_pretrained(tmp_dir, safe_serialization=True)
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