forked from OpenNMT/CTranslate2
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathtest_opennmt_tf.py
More file actions
219 lines (176 loc) · 6.8 KB
/
Copy pathtest_opennmt_tf.py
File metadata and controls
219 lines (176 loc) · 6.8 KB
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
import copy
import os
import opennmt
import pytest
import tensorflow as tf
import test_utils
import ctranslate2
from ctranslate2.converters import opennmt_tf
@pytest.mark.parametrize("model_path", ["v1/checkpoint", "v2/checkpoint"])
def test_opennmt_tf_model_conversion(tmp_dir, model_path):
model_path = os.path.join(
test_utils.get_data_dir(),
"models",
"transliteration-aren-all",
"opennmt_tf",
model_path,
)
config = {
"model_dir": model_path,
"data": {
"source_vocabulary": os.path.join(model_path, "ar.vocab"),
"target_vocabulary": os.path.join(model_path, "en.vocab"),
},
}
original_config = copy.deepcopy(config)
converter = ctranslate2.converters.OpenNMTTFConverter.from_config(config)
# auto_config should not update the configuration in place.
assert config == original_config
output_dir = str(tmp_dir.join("ctranslate2_model"))
converter.convert(output_dir)
translator = ctranslate2.Translator(output_dir)
output = translator.translate_batch([["آ", "ت", "ز", "م", "و", "ن"]])
assert output[0].hypotheses[0] == ["a", "t", "z", "m", "o", "n"]
@pytest.mark.parametrize("quantization", ["float16", "int16", "int8", "int8_float16"])
def test_opennmt_tf_model_quantization(tmp_dir, quantization):
model_path = os.path.join(
test_utils.get_data_dir(),
"models",
"transliteration-aren-all",
"opennmt_tf",
"v2",
"checkpoint",
)
config = {
"model_dir": model_path,
"data": {
"source_vocabulary": os.path.join(model_path, "ar.vocab"),
"target_vocabulary": os.path.join(model_path, "en.vocab"),
},
}
converter = ctranslate2.converters.OpenNMTTFConverter.from_config(config)
output_dir = str(tmp_dir.join("ctranslate2_model"))
converter.convert(output_dir, quantization=quantization)
translator = ctranslate2.Translator(output_dir)
output = translator.translate_batch([["آ", "ت", "ز", "م", "و", "ن"]])
assert output[0].hypotheses[0] == ["a", "t", "z", "m", "o", "n"]
def test_opennmt_tf_model_conversion_invalid_vocab():
model_path = os.path.join(
test_utils.get_data_dir(),
"models",
"transliteration-aren-all",
"opennmt_tf",
"v2",
"checkpoint",
)
# Swap source and target vocabularies.
config = {
"model_dir": model_path,
"data": {
"source_vocabulary": os.path.join(model_path, "en.vocab"),
"target_vocabulary": os.path.join(model_path, "ar.vocab"),
},
}
with pytest.raises(ValueError, match="not compatible"):
ctranslate2.converters.OpenNMTTFConverter.from_config(config)
def _create_vocab(tmp_dir, name="vocab", size=10):
vocab = opennmt.data.Vocab()
for i in range(size):
vocab.add(str(i))
vocab_path = str(tmp_dir.join("%s.txt" % name))
vocab.serialize(vocab_path)
return vocab_path
def test_opennmt_tf_model_conversion_invalid_dir(tmp_dir):
model_path = str(tmp_dir.join("model").ensure(dir=1))
vocab_path = _create_vocab(tmp_dir)
config = {
"model_dir": model_path,
"data": {"source_vocabulary": vocab_path, "target_vocabulary": vocab_path},
}
with pytest.raises(RuntimeError, match="checkpoint"):
ctranslate2.converters.OpenNMTTFConverter.from_config(
config, model="TransformerBase"
)
def test_opennmt_tf_shared_embeddings_conversion(tmp_dir):
# Issue https://github.com/OpenNMT/CTranslate2/issues/118
model = opennmt.models.Transformer(
opennmt.inputters.WordEmbedder(32),
opennmt.inputters.WordEmbedder(32),
num_layers=3,
num_units=32,
num_heads=4,
ffn_inner_dim=64,
share_embeddings=opennmt.models.EmbeddingsSharingLevel.ALL,
)
vocab_path = _create_vocab(tmp_dir)
model.initialize({"source_vocabulary": vocab_path, "target_vocabulary": vocab_path})
model.create_variables()
converter = ctranslate2.converters.OpenNMTTFConverter(model)
output_dir = str(tmp_dir.join("ctranslate2_model"))
converter.convert(output_dir)
assert os.path.isfile(os.path.join(output_dir, "shared_vocabulary.json"))
# Check that the translation runs.
translator = ctranslate2.Translator(output_dir)
translator.translate_batch([["1", "2", "3"]], max_decoding_length=10)
@pytest.mark.parametrize("encoder_only", [True, False])
def test_opennmt_tf_postnorm_transformer_conversion(tmp_dir, encoder_only):
model = opennmt.models.Transformer(
opennmt.inputters.WordEmbedder(32),
opennmt.inputters.WordEmbedder(32),
num_layers=3,
num_units=32,
num_heads=4,
ffn_inner_dim=64,
pre_norm=encoder_only,
)
if encoder_only:
model.encoder = opennmt.encoders.SelfAttentionEncoder(
num_layers=3,
num_units=32,
num_heads=4,
ffn_inner_dim=64,
pre_norm=False,
)
vocab_path = _create_vocab(tmp_dir)
model.initialize({"source_vocabulary": vocab_path, "target_vocabulary": vocab_path})
model.create_variables()
converter = ctranslate2.converters.OpenNMTTFConverter(model)
output_dir = str(tmp_dir.join("ctranslate2_model"))
converter.convert(output_dir)
def test_opennmt_tf_gpt_conversion(tmp_dir):
vocabulary = _create_vocab(tmp_dir, "vocab")
model = opennmt.models.GPT2Small()
model.initialize(dict(vocabulary=vocabulary))
model.create_variables()
output_dir = str(tmp_dir.join("ctranslate2_model"))
converter = ctranslate2.converters.OpenNMTTFConverter(model)
converter.convert(output_dir)
assert os.path.isfile(os.path.join(output_dir, "vocabulary.json"))
def test_opennmt_tf_multi_features(tmp_dir):
model = opennmt.models.Transformer(
opennmt.inputters.ParallelInputter(
[
opennmt.inputters.WordEmbedder(24),
opennmt.inputters.WordEmbedder(8),
],
reducer=opennmt.layers.ConcatReducer(),
),
opennmt.inputters.WordEmbedder(32),
num_layers=3,
num_units=32,
num_heads=4,
ffn_inner_dim=64,
)
model.initialize(
{
"source_1_vocabulary": _create_vocab(tmp_dir, "source_1", 50),
"source_2_vocabulary": _create_vocab(tmp_dir, "source_2", 10),
"target_vocabulary": _create_vocab(tmp_dir, "target", 60),
}
)
model.create_variables()
converter = ctranslate2.converters.OpenNMTTFConverter(model)
output_dir = str(tmp_dir.join("ctranslate2_model"))
converter.convert(output_dir)
assert os.path.isfile(os.path.join(output_dir, "source_1_vocabulary.json"))
assert os.path.isfile(os.path.join(output_dir, "source_2_vocabulary.json"))