-
Notifications
You must be signed in to change notification settings - Fork 516
/
Copy pathutils_onnxruntime_tests.py
235 lines (215 loc) · 11.7 KB
/
utils_onnxruntime_tests.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
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
# coding=utf-8
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# 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 shutil
import tempfile
import unittest
from typing import Dict
import numpy as np
import torch
from transformers import set_seed
from optimum.exporters import TasksManager
MODEL_NAMES = {
"albert": "hf-internal-testing/tiny-random-AlbertModel",
"audio_spectrogram_transformer": "Ericwang/tiny-random-ast",
"beit": "hf-internal-testing/tiny-random-BeitForImageClassification",
"bert": "hf-internal-testing/tiny-random-BertModel",
"bart": "hf-internal-testing/tiny-random-bart",
# "big_bird": "hf-internal-testing/tiny-random-BigBirdModel",
# "bigbird_pegasus": "hf-internal-testing/tiny-random-bigbird_pegasus",
"blenderbot_small": "hf-internal-testing/tiny-random-BlenderbotModel",
"blenderbot": "hf-internal-testing/tiny-random-BlenderbotModel",
"bloom": "hf-internal-testing/tiny-random-BloomModel",
"camembert": "hf-internal-testing/tiny-random-camembert",
"clip": "hf-internal-testing/tiny-random-CLIPModel",
"convbert": "hf-internal-testing/tiny-random-ConvBertModel",
"convnext": "hf-internal-testing/tiny-random-convnext",
"convnextv2": "hf-internal-testing/tiny-random-ConvNextV2Model",
"codegen": "hf-internal-testing/tiny-random-CodeGenForCausalLM",
"data2vec_text": "hf-internal-testing/tiny-random-Data2VecTextModel",
"data2vec_vision": "hf-internal-testing/tiny-random-Data2VecVisionModel",
"data2vec_audio": "hf-internal-testing/tiny-random-Data2VecAudioModel",
"deberta": "hf-internal-testing/tiny-random-DebertaModel",
"deberta_v2": "hf-internal-testing/tiny-random-DebertaV2Model",
"default-timm-config": {
"timm/inception_v3.tf_adv_in1k": ["image-classification"],
"timm/tf_efficientnet_b0.in1k": ["image-classification"],
"timm/resnetv2_50x1_bit.goog_distilled_in1k": ["image-classification"],
"timm/cspdarknet53.ra_in1k": ["image-classification"],
"timm/cspresnet50.ra_in1k": ["image-classification"],
"timm/cspresnext50.ra_in1k": ["image-classification"],
"timm/densenet121.ra_in1k": ["image-classification"],
"timm/dla102.in1k": ["image-classification"],
"timm/dpn107.mx_in1k": ["image-classification"],
"timm/ecaresnet101d.miil_in1k": ["image-classification"],
"timm/efficientnet_b1_pruned.in1k": ["image-classification"],
"timm/inception_resnet_v2.tf_ens_adv_in1k": ["image-classification"],
"timm/fbnetc_100.rmsp_in1k": ["image-classification"],
"timm/xception41.tf_in1k": ["image-classification"],
"timm/senet154.gluon_in1k": ["image-classification"],
"timm/seresnext26d_32x4d.bt_in1k": ["image-classification"],
"timm/hrnet_w18.ms_aug_in1k": ["image-classification"],
"timm/inception_v3.gluon_in1k": ["image-classification"],
"timm/inception_v4.tf_in1k": ["image-classification"],
"timm/mixnet_s.ft_in1k": ["image-classification"],
"timm/mnasnet_100.rmsp_in1k": ["image-classification"],
"timm/mobilenetv2_100.ra_in1k": ["image-classification"],
"timm/mobilenetv3_small_050.lamb_in1k": ["image-classification"],
"timm/nasnetalarge.tf_in1k": ["image-classification"],
"timm/tf_efficientnet_b0.ns_jft_in1k": ["image-classification"],
"timm/pnasnet5large.tf_in1k": ["image-classification"],
"timm/regnetx_002.pycls_in1k": ["image-classification"],
"timm/regnety_002.pycls_in1k": ["image-classification"],
"timm/res2net101_26w_4s.in1k": ["image-classification"],
"timm/res2next50.in1k": ["image-classification"],
"timm/resnest101e.in1k": ["image-classification"],
"timm/spnasnet_100.rmsp_in1k": ["image-classification"],
"timm/resnet18.fb_swsl_ig1b_ft_in1k": ["image-classification"],
"timm/wide_resnet101_2.tv_in1k": ["image-classification"],
"timm/tresnet_l.miil_in1k": ["image-classification"],
},
"deit": "hf-internal-testing/tiny-random-DeiTModel",
"donut": "fxmarty/tiny-doc-qa-vision-encoder-decoder",
"detr": "hf-internal-testing/tiny-random-detr",
"dpt": "hf-internal-testing/tiny-random-DPTModel",
"distilbert": "hf-internal-testing/tiny-random-DistilBertModel",
"electra": "hf-internal-testing/tiny-random-ElectraModel",
"encoder-decoder": {
"hf-internal-testing/tiny-random-EncoderDecoderModel-bert-bert": [
"text2text-generation",
],
"mohitsha/tiny-random-testing-bert2gpt2": ["text2text-generation", "text2text-generation-with-past"],
},
"falcon": "fxmarty/really-tiny-falcon-testing",
"flaubert": "hf-internal-testing/tiny-random-flaubert",
"gemma": "fxmarty/tiny-random-GemmaForCausalLM",
"gpt2": "hf-internal-testing/tiny-random-gpt2",
"gpt_bigcode": "hf-internal-testing/tiny-random-GPTBigCodeModel",
"gpt_neo": "hf-internal-testing/tiny-random-GPTNeoModel",
"gpt_neox": "hf-internal-testing/tiny-random-GPTNeoXForCausalLM",
"gptj": "hf-internal-testing/tiny-random-GPTJForCausalLM",
"groupvit": "hf-internal-testing/tiny-random-groupvit",
"hubert": "hf-internal-testing/tiny-random-HubertModel",
"ibert": "hf-internal-testing/tiny-random-IBertModel",
"levit": "hf-internal-testing/tiny-random-LevitModel",
"latent-consistency": "echarlaix/tiny-random-latent-consistency",
"layoutlm": "hf-internal-testing/tiny-random-LayoutLMModel",
"layoutlmv3": "hf-internal-testing/tiny-random-LayoutLMv3Model",
"longt5": "hf-internal-testing/tiny-random-LongT5Model",
"llama": "fxmarty/tiny-llama-fast-tokenizer",
"m2m_100": "hf-internal-testing/tiny-random-m2m_100",
"marian": "sshleifer/tiny-marian-en-de", # hf-internal-testing ones are broken
"mbart": "hf-internal-testing/tiny-random-mbart",
"mistral": "echarlaix/tiny-random-mistral",
"mobilebert": "hf-internal-testing/tiny-random-MobileBertModel",
"mobilenet_v1": "google/mobilenet_v1_0.75_192",
"mobilenet_v2": "hf-internal-testing/tiny-random-MobileNetV2Model",
"mobilevit": "hf-internal-testing/tiny-random-mobilevit",
"mpnet": "hf-internal-testing/tiny-random-MPNetModel",
"mpt": "hf-internal-testing/tiny-random-MptForCausalLM",
"mt5": "lewtun/tiny-random-mt5",
"nystromformer": "hf-internal-testing/tiny-random-NystromformerModel",
"pegasus": "hf-internal-testing/tiny-random-PegasusModel",
"perceiver_text": "hf-internal-testing/tiny-random-language_perceiver",
"perceiver_vision": "hf-internal-testing/tiny-random-vision_perceiver_conv",
"phi": "hf-internal-testing/tiny-random-PhiForCausalLM",
"pix2struct": "fxmarty/pix2struct-tiny-random",
"poolformer": "hf-internal-testing/tiny-random-PoolFormerModel",
"qwen2": "fxmarty/tiny-dummy-qwen2",
"resnet": "hf-internal-testing/tiny-random-resnet",
"roberta": "hf-internal-testing/tiny-random-RobertaModel",
"roformer": "hf-internal-testing/tiny-random-RoFormerModel",
"segformer": "hf-internal-testing/tiny-random-SegformerModel",
"sew": "hf-internal-testing/tiny-random-SEWModel",
"sew_d": "asapp/sew-d-tiny-100k-ft-ls100h",
"squeezebert": "hf-internal-testing/tiny-random-SqueezeBertModel",
"speech_to_text": "hf-internal-testing/tiny-random-Speech2TextModel",
"stable-diffusion": "hf-internal-testing/tiny-stable-diffusion-torch",
"stable-diffusion-xl": "echarlaix/tiny-random-stable-diffusion-xl",
"swin": "hf-internal-testing/tiny-random-SwinModel",
"t5": "hf-internal-testing/tiny-random-t5",
"table-transformer": "hf-internal-testing/tiny-random-TableTransformerModel",
"trocr": "microsoft/trocr-small-handwritten",
"unispeech": "hf-internal-testing/tiny-random-unispeech",
"unispeech_sat": "hf-internal-testing/tiny-random-UnispeechSatModel",
"vision-encoder-decoder": "hf-internal-testing/tiny-random-VisionEncoderDecoderModel-vit-gpt2",
"vit": "hf-internal-testing/tiny-random-vit",
"whisper": "openai/whisper-tiny.en", # hf-internal-testing ones are broken
"wav2vec2": "hf-internal-testing/tiny-random-Wav2Vec2Model",
"wav2vec2-conformer": "hf-internal-testing/tiny-random-wav2vec2-conformer",
"wavlm": "hf-internal-testing/tiny-random-WavlmModel",
"xlm": "hf-internal-testing/tiny-random-XLMModel",
"xlm_qa": "hf-internal-testing/tiny-random-XLMForQuestionAnsweringSimple", # issue with default hf-internal-testing in transformers QA pipeline post-processing
"xlm_roberta": "hf-internal-testing/tiny-xlm-roberta",
"yolos": "hf-internal-testing/tiny-random-YolosModel",
}
SEED = 42
class ORTModelTestMixin(unittest.TestCase):
TENSOR_ALIAS_TO_TYPE = {
"pt": torch.Tensor,
"np": np.ndarray,
}
@classmethod
def setUpClass(cls):
cls.onnx_model_dirs = {}
def _setup(self, model_args: Dict):
"""
Exports the PyTorch models to ONNX ahead of time to avoid multiple exports during the tests.
We don't use unittest setUpClass, in order to still be able to run individual tests.
"""
model_arch = model_args["model_arch"]
model_arch_and_params = model_args["test_name"]
model_ids = MODEL_NAMES[model_arch]
if isinstance(model_ids, dict):
model_ids = list(model_ids.keys())
else:
model_ids = [model_ids]
# TODO: this should actually be checked in ORTModel!
task = self.TASK
if "use_cache" in model_args and model_args["use_cache"] is True:
task = task + "-with-past"
library_name = TasksManager.infer_library_from_model(model_ids[0])
if "use_cache" in model_args and task not in TasksManager.get_supported_tasks_for_model_type(
model_arch.replace("_", "-"), exporter="onnx", library_name=library_name
):
self.skipTest("Unsupported export case")
if model_arch_and_params not in self.onnx_model_dirs:
self.onnx_model_dirs[model_arch_and_params] = {}
# model_args will contain kwargs to pass to ORTModel.from_pretrained()
model_args.pop("test_name")
model_args.pop("model_arch")
for idx, model_id in enumerate(model_ids):
if model_arch == "encoder-decoder" and task not in MODEL_NAMES[model_arch][model_id]:
# The model with use_cache=True is not supported for bert as a decoder")
continue
set_seed(SEED)
onnx_model = self.ORTMODEL_CLASS.from_pretrained(
model_id, **model_args, use_io_binding=False, export=True
)
model_dir = tempfile.mkdtemp(
prefix=f"{model_arch_and_params}_{self.TASK}_{model_id.replace('/', '_')}"
)
onnx_model.save_pretrained(model_dir)
if isinstance(MODEL_NAMES[model_arch], dict):
self.onnx_model_dirs[model_arch_and_params][model_id] = model_dir
else:
self.onnx_model_dirs[model_arch_and_params] = model_dir
@classmethod
def tearDownClass(cls):
for _, dir_path in cls.onnx_model_dirs.items():
if isinstance(dir_path, dict):
for _, sec_dir_path in dir_path.items():
shutil.rmtree(sec_dir_path)
else:
shutil.rmtree(dir_path)