def test_TFOpenAIGPTModel(self):
from transformers import OpenAIGPTConfig, TFOpenAIGPTModel
keras.backend.clear_session()
# pretrained_weights = 'openai-gpt'
tokenizer_file = 'openai_openai-gpt.pickle'
tokenizer = self._get_tokenzier(tokenizer_file)
text, inputs, inputs_onnx = self._prepare_inputs(tokenizer)
config = OpenAIGPTConfig()
model = TFOpenAIGPTModel(config)
predictions = model.predict(inputs)
onnx_model = keras2onnx.convert_keras(model, model.name)
self.assertTrue(run_onnx_runtime(onnx_model.graph.name, onnx_model, inputs_onnx, predictions, self.model_files))
def __init__(
self,
reduce_output='sum',
pretrained_model_name_or_path='openai-gpt',
trainable=True,
num_tokens=None,
**kwargs
):
super(GPTEncoder, self).__init__()
try:
from transformers import TFOpenAIGPTModel
except ModuleNotFoundError:
logger.error(
' transformers is not installed. '
'In order to install all text feature dependencies run '
'pip install ludwig[text]'
)
sys.exit(-1)
self.transformer = TFOpenAIGPTModel.from_pretrained(
pretrained_model_name_or_path
)
self.reduce_output = reduce_output
self.reduce_sequence = SequenceReducer(reduce_mode=reduce_output)
self.transformer.trainable = trainable
self.transformer.resize_token_embeddings(num_tokens)
def test_TFOpenAIGPTModel(self):
from transformers import OpenAIGPTTokenizer, TFOpenAIGPTModel
pretrained_weights = 'openai-gpt'
tokenizer = OpenAIGPTTokenizer.from_pretrained(pretrained_weights)
text, inputs, inputs_onnx = self._prepare_inputs(tokenizer)
model = TFOpenAIGPTModel.from_pretrained(pretrained_weights)
predictions = model.predict(inputs)
onnx_model = keras2onnx.convert_keras(model, model.name)
self.assertTrue(
run_onnx_runtime(onnx_model.graph.name, onnx_model, inputs_onnx,
predictions, self.model_files))