def test_lmhead_model_from_pretrained(self):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelWithLMHead.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForMaskedLM)
def test_question_answering_model_from_pretrained(self):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForQuestionAnswering.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForQuestionAnswering)
def test_model_for_causal_lm(self):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForCausalLM.from_pretrained(model_name)
model, loading_info = TFAutoModelForCausalLM.from_pretrained(
model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertLMHeadModel)
def test_sequence_classification_model_from_pretrained(self):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForSequenceClassification.from_pretrained(
model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForSequenceClassification)
def test_model_for_pretraining_from_pretrained(self):
import h5py
self.assertTrue(h5py.version.hdf5_version.startswith("1.10"))
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForPreTraining.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForPreTraining)
def test_inference_with_configs_graph(self):
MODEL_ID = "albert-base-v2"
config = AutoConfig.from_pretrained(MODEL_ID)
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
training=False,
no_inference=False,
sequence_lengths=[8],
batch_sizes=[1],
no_multi_process=True,
)
benchmark = TensorFlowBenchmark(benchmark_args, [config])
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def __init__(self,
args: BenchmarkArguments = None,
configs: PretrainedConfig = None):
self.args = args
if configs is None:
self.config_dict = {
model_name: AutoConfig.from_pretrained(model_name)
for model_name in self.args.model_names
}
else:
self.config_dict = {
model_name: config
for model_name, config in zip(self.args.model_names, configs)
}
if not self.args.no_memory and os.getenv(
"TRANSFORMERS_USE_MULTIPROCESSING") == 0:
logger.warning(
"Memory consumption will not be measured accurately if `args.no_multi_process` is set to `True.` The flag 'TRANSFORMERS_USE_MULTIPROCESSING' should only be disabled for debugging / testing."
)
self._print_fn = None
self._framework_version = None
self._environment_info = None