-
Notifications
You must be signed in to change notification settings - Fork 1.7k
/
Copy pathtest_feature_extraction_models.py
204 lines (166 loc) · 9.19 KB
/
test_feature_extraction_models.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
# 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.
import unittest
import torch
from parameterized import parameterized
from transformers import AutoModel
from peft import LoraConfig, PrefixTuningConfig, PromptLearningConfig
from .testing_common import PeftCommonTester, PeftTestConfigManager
PEFT_FEATURE_EXTRACTION_MODELS_TO_TEST = [
"hf-internal-testing/tiny-random-BertModel",
"hf-internal-testing/tiny-random-RobertaModel",
"hf-internal-testing/tiny-random-DebertaModel",
"hf-internal-testing/tiny-random-DebertaV2Model",
]
FULL_GRID = {
"model_ids": PEFT_FEATURE_EXTRACTION_MODELS_TO_TEST,
"task_type": "FEATURE_EXTRACTION",
}
def skip_non_prompt_tuning(test_list):
"""Skip tests that are not prompt tuning"""
return [
test for test in test_list if issubclass(test[2], PromptLearningConfig) and (test[2] != PrefixTuningConfig)
]
def skip_deberta_lora_tests(test_list):
r"""
Skip tests that are checkpointing with lora/ia3/boft/vera/fourierft for Deberta models (couldn't find much info on
the error)
"""
to_skip = ["lora", "ia3", "boft", "vera", "fourierft", "hra", "bone"]
return [test for test in test_list if not (any(k in test[0] for k in to_skip) and "Deberta" in test[0])]
def skip_deberta_pt_tests(test_list):
r"""
Skip tests that are checkpointing with lora/ia3 tests for Deberta models (couldn't find much info on the error)
"""
return [test for test in test_list if not ("prefix_tuning" in test[0] and "Deberta" in test[0])]
class PeftFeatureExtractionModelTester(unittest.TestCase, PeftCommonTester):
r"""
Test if the PeftModel behaves as expected. This includes:
- test if the model has the expected methods
We use parametrized.expand for debugging purposes to test each model individually.
"""
transformers_class = AutoModel
def prepare_inputs_for_testing(self):
input_ids = torch.tensor([[1, 1, 1], [1, 2, 1]]).to(self.torch_device)
attention_mask = torch.tensor([[1, 1, 1], [1, 0, 1]]).to(self.torch_device)
input_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return input_dict
@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
def test_attributes_parametrized(self, test_name, model_id, config_cls, config_kwargs):
self._test_model_attr(model_id, config_cls, config_kwargs)
@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
def test_adapter_name(self, test_name, model_id, config_cls, config_kwargs):
self._test_adapter_name(model_id, config_cls, config_kwargs)
@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
def test_prepare_for_training_parametrized(self, test_name, model_id, config_cls, config_kwargs):
self._test_prepare_for_training(model_id, config_cls, config_kwargs)
@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
def test_save_pretrained(self, test_name, model_id, config_cls, config_kwargs):
self._test_save_pretrained(model_id, config_cls, config_kwargs)
@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
def test_save_pretrained_selected_adapters(self, test_name, model_id, config_cls, config_kwargs):
self._test_save_pretrained_selected_adapters(model_id, config_cls, config_kwargs)
def test_load_model_low_cpu_mem_usage(self):
self._test_load_model_low_cpu_mem_usage(PEFT_FEATURE_EXTRACTION_MODELS_TO_TEST[0], LoraConfig, {})
@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
def test_from_pretrained_config_construction(self, test_name, model_id, config_cls, config_kwargs):
self._test_from_pretrained_config_construction(model_id, config_cls, config_kwargs)
@parameterized.expand(
PeftTestConfigManager.get_grid_parameters(
{
"model_ids": PEFT_FEATURE_EXTRACTION_MODELS_TO_TEST,
"lora_kwargs": {"init_lora_weights": [False]},
"adalora_kwargs": {"init_lora_weights": [False]},
"ia3_kwargs": {"init_ia3_weights": [False]},
"boft_kwargs": {"init_weights": [False]},
"oft_kwargs": {"init_weights": [False]},
"vera_kwargs": {"init_weights": [False]},
"hra_kwargs": {"init_weights": [False]},
"bone_kwargs": {"init_weights": [False]},
"task_type": "FEATURE_EXTRACTION",
},
)
)
def test_merge_layers(self, test_name, model_id, config_cls, config_kwargs):
self._test_merge_layers(model_id, config_cls, config_kwargs)
@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
def test_training(self, test_name, model_id, config_cls, config_kwargs):
self._test_training(model_id, config_cls, config_kwargs)
@parameterized.expand(
PeftTestConfigManager.get_grid_parameters(FULL_GRID, filter_params_func=skip_deberta_pt_tests)
)
def test_training_prompt_learning_tasks(self, test_name, model_id, config_cls, config_kwargs):
self._test_training_prompt_learning_tasks(model_id, config_cls, config_kwargs)
@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
def test_training_layer_indexing(self, test_name, model_id, config_cls, config_kwargs):
self._test_training_layer_indexing(model_id, config_cls, config_kwargs)
@parameterized.expand(
PeftTestConfigManager.get_grid_parameters(FULL_GRID, filter_params_func=skip_deberta_lora_tests)
)
def test_training_gradient_checkpointing(self, test_name, model_id, config_cls, config_kwargs):
self._test_training_gradient_checkpointing(model_id, config_cls, config_kwargs)
@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
def test_inference_safetensors(self, test_name, model_id, config_cls, config_kwargs):
self._test_inference_safetensors(model_id, config_cls, config_kwargs)
@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
def test_peft_model_device_map(self, test_name, model_id, config_cls, config_kwargs):
self._test_peft_model_device_map(model_id, config_cls, config_kwargs)
@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
def test_delete_adapter(self, test_name, model_id, config_cls, config_kwargs):
self._test_delete_adapter(model_id, config_cls, config_kwargs)
@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
def test_delete_inactive_adapter(self, test_name, model_id, config_cls, config_kwargs):
self._test_delete_inactive_adapter(model_id, config_cls, config_kwargs)
@parameterized.expand(
PeftTestConfigManager.get_grid_parameters(
{
"model_ids": PEFT_FEATURE_EXTRACTION_MODELS_TO_TEST,
"lora_kwargs": {"init_lora_weights": [False]},
"adalora_kwargs": {"init_lora_weights": [False]},
"ia3_kwargs": {"init_ia3_weights": [False]},
"boft_kwargs": {"init_weights": [False]},
"oft_kwargs": {"init_weights": [False]},
"vera_kwargs": {"init_weights": [False]},
"hra_kwargs": {"init_weights": [False]},
"bone_kwargs": {"init_weights": [False]},
"task_type": "FEATURE_EXTRACTION",
},
)
)
def test_unload_adapter(self, test_name, model_id, config_cls, config_kwargs):
self._test_unload_adapter(model_id, config_cls, config_kwargs)
@parameterized.expand(
PeftTestConfigManager.get_grid_parameters(
{
"model_ids": PEFT_FEATURE_EXTRACTION_MODELS_TO_TEST,
"lora_kwargs": {"init_lora_weights": [False]},
"ia3_kwargs": {"init_ia3_weights": [False]},
"boft_kwargs": {"init_weights": [False]},
"oft_kwargs": {"init_weights": [False]},
"hra_kwargs": {"init_weights": [False]},
"bone_kwargs": {"init_weights": [False]},
"task_type": "FEATURE_EXTRACTION",
},
)
)
def test_weighted_combination_of_adapters(self, test_name, model_id, config_cls, config_kwargs):
self._test_weighted_combination_of_adapters(model_id, config_cls, config_kwargs)
@parameterized.expand(
PeftTestConfigManager.get_grid_parameters(FULL_GRID, filter_params_func=skip_non_prompt_tuning)
)
def test_passing_input_embeds_works(self, test_name, model_id, config_cls, config_kwargs):
self._test_passing_input_embeds_works(test_name, model_id, config_cls, config_kwargs)