File size: 10,008 Bytes
c668e80 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 |
import unittest
from onmt.translate.translation_server import ServerModel, TranslationServer
import os
from textwrap import dedent
import torch
from onmt.translate.translator import Translator
TEST_DIR = os.path.dirname(os.path.abspath(__file__))
class TestServerModel(unittest.TestCase):
def test_deferred_loading_model_and_unload(self):
model_id = 0
opt = {"models": ["test_model.pt"]}
model_root = TEST_DIR
sm = ServerModel(opt, model_id, model_root=model_root, load=False)
self.assertFalse(sm.loaded)
sm.load()
self.assertTrue(sm.loaded)
self.assertIsInstance(sm.translator, Translator)
sm.unload()
self.assertFalse(sm.loaded)
def test_load_model_on_init_and_unload(self):
model_id = 0
opt = {"models": ["test_model.pt"]}
model_root = TEST_DIR
sm = ServerModel(opt, model_id, model_root=model_root, load=True)
self.assertTrue(sm.loaded)
self.assertIsInstance(sm.translator, Translator)
sm.unload()
self.assertFalse(sm.loaded)
def test_tokenizing_with_no_tokenizer_fails(self):
model_id = 0
opt = {"models": ["test_model.pt"]}
model_root = TEST_DIR
sm = ServerModel(opt, model_id, model_root=model_root, load=True)
with self.assertRaises(ValueError):
sm.tokenize("hello world")
def test_detokenizing_with_no_tokenizer_fails(self):
model_id = 0
opt = {"models": ["test_model.pt"]}
model_root = TEST_DIR
sm = ServerModel(opt, model_id, model_root=model_root, load=True)
with self.assertRaises(ValueError):
sm.detokenize("hello world")
def test_source_features(self):
model_id = 0
opt = {"models": ["test_model.pt"]}
model_root = TEST_DIR
sm = ServerModel(
opt,
model_id,
model_root=model_root,
load=True,
features_opt={
"n_src_feats": 1,
"src_feats_defaults": "0",
"reversible_tokenization": "joiner",
},
)
feats = sm.maybe_transform_feats("hello world.", "hello world ￭.", ["0 1"])
self.assertEqual(feats, ["0 1 1"])
preprocessed = sm.maybe_preprocess({"src": "hello￨0 world￨1"})
self.assertEqual(preprocessed["seg"], ["hello world"])
self.assertEqual(preprocessed["src_feats"], [["0 1"]])
def test_passing_source_features_without_proper_configuration(self):
model_id = 0
opt = {"models": ["test_model.pt"]}
model_root = TEST_DIR
sm = ServerModel(opt, model_id, model_root=model_root, load=True)
with self.assertRaises(AssertionError):
sm.maybe_preprocess({"src": "hello￨0 world￨1"})
sm = ServerModel(
opt,
model_id,
model_root=model_root,
load=True,
features_opt={
"n_src_feats": 2,
"src_feats_defaults": "0￨0",
"reversible_tokenization": "joiner",
},
)
with self.assertRaises(AssertionError):
sm.maybe_preprocess({"src": "hello￨0 world￨1"})
if torch.cuda.is_available():
def test_moving_to_gpu_and_back(self):
torch.cuda.set_device(torch.device("cuda", 0))
model_id = 0
opt = {"models": ["test_model.pt"]}
model_root = TEST_DIR
sm = ServerModel(opt, model_id, model_root=model_root, load=True)
for p in sm.translator.model.parameters():
self.assertEqual(p.device.type, "cpu")
sm.to_gpu()
for p in sm.translator.model.parameters():
self.assertEqual(p.device.type, "cuda")
self.assertEqual(p.device.index, 0)
sm.to_cpu()
for p in sm.translator.model.parameters():
self.assertEqual(p.device.type, "cpu")
def test_initialize_on_gpu_and_move_back(self):
torch.cuda.set_device(torch.device("cuda", 0))
model_id = 0
opt = {"models": ["test_model.pt"], "gpu": 0}
model_root = TEST_DIR
sm = ServerModel(opt, model_id, model_root=model_root, load=True)
for p in sm.translator.model.parameters():
self.assertEqual(p.device.type, "cuda")
self.assertEqual(p.device.index, 0)
sm.to_gpu()
for p in sm.translator.model.parameters():
self.assertEqual(p.device.type, "cuda")
self.assertEqual(p.device.index, 0)
sm.to_cpu()
for p in sm.translator.model.parameters():
self.assertEqual(p.device.type, "cpu")
if torch.cuda.device_count() > 1:
def test_initialize_on_nonzero_gpu_and_back(self):
torch.cuda.set_device(torch.device("cuda", 1))
model_id = 0
opt = {"models": ["test_model.pt"], "gpu": 1}
model_root = TEST_DIR
sm = ServerModel(opt, model_id, model_root=model_root, load=True)
for p in sm.translator.model.parameters():
self.assertEqual(p.device.type, "cuda")
self.assertEqual(p.device.index, 1)
sm.to_gpu()
for p in sm.translator.model.parameters():
self.assertEqual(p.device.type, "cuda")
self.assertEqual(p.device.index, 1)
sm.to_cpu()
for p in sm.translator.model.parameters():
self.assertEqual(p.device.type, "cpu")
def test_run(self):
model_id = 0
opt = {"models": ["test_model.pt"]}
model_root = TEST_DIR
sm = ServerModel(opt, model_id, model_root=model_root, load=True)
inp = [{"src": "hello how are you today"}, {"src": "good morning to you ."}]
results, scores, n_best, time, aligns, align_scores = sm.run(inp)
self.assertIsInstance(results, list)
for sentence_string in results:
self.assertIsInstance(sentence_string, str)
self.assertIsInstance(scores, list)
for elem in scores:
self.assertIsInstance(elem, float)
self.assertIsInstance(aligns, list)
for align_list in aligns:
for align_string in align_list:
if align_string is not None:
self.assertIsInstance(align_string, str)
for align_scores_list in align_scores:
for score_string in align_scores_list:
if score_string is not None:
self.assertIsInstance(align_string, str)
self.assertEqual(len(results), len(scores))
self.assertEqual(len(scores), len(inp) * n_best)
self.assertEqual(len(time), 1)
self.assertIsInstance(time, dict)
self.assertIn("translation", time)
class TestTranslationServer(unittest.TestCase):
# this could be considered an integration test because it touches
# the filesystem for the config file (and the models)
CFG_F = os.path.join(TEST_DIR, "test_translation_server_config_file.json")
def tearDown(self):
if os.path.exists(self.CFG_F):
os.remove(self.CFG_F)
def write(self, cfg):
with open(self.CFG_F, "w") as f:
f.write(cfg)
CFG_NO_LOAD = dedent(
"""\
{
"models_root": "%s",
"models": [
{
"id": 100,
"model": "test_model.pt",
"timeout": -1,
"on_timeout": "to_cpu",
"load": false,
"opt": {
"beam_size": 5
}
}
]
}
"""
% TEST_DIR
)
def test_start_without_initial_loading(self):
self.write(self.CFG_NO_LOAD)
sv = TranslationServer()
sv.start(self.CFG_F)
self.assertFalse(sv.models[100].loaded)
self.assertEqual(set(sv.models.keys()), {100})
CFG_LOAD = dedent(
"""\
{
"models_root": "%s",
"models": [
{
"id": 100,
"model": "test_model.pt",
"timeout": -1,
"on_timeout": "to_cpu",
"load": true,
"opt": {
"beam_size": 5
}
}
]
}
"""
% TEST_DIR
)
def test_start_with_initial_loading(self):
self.write(self.CFG_LOAD)
sv = TranslationServer()
sv.start(self.CFG_F)
self.assertTrue(sv.models[100].loaded)
self.assertEqual(set(sv.models.keys()), {100})
CFG_2_MODELS = dedent(
"""\
{
"models_root": "%s",
"models": [
{
"id": 100,
"model": "test_model.pt",
"timeout": -1,
"on_timeout": "to_cpu",
"load": true,
"opt": {
"beam_size": 5
}
},
{
"id": 1000,
"model": "test_model2.pt",
"timeout": -1,
"on_timeout": "to_cpu",
"load": false,
"opt": {
"beam_size": 5
}
}
]
}
"""
% TEST_DIR
)
def test_start_with_two_models(self):
self.write(self.CFG_2_MODELS)
sv = TranslationServer()
sv.start(self.CFG_F)
self.assertTrue(sv.models[100].loaded)
self.assertFalse(sv.models[1000].loaded)
self.assertEqual(set(sv.models.keys()), {100, 1000})
|