# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors. # # 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 unittest from transformers import GPT2Config, is_tf_available from .test_configuration_common import ConfigTester from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor from .utils import CACHE_DIR, require_tf, slow if is_tf_available(): import tensorflow as tf from transformers.modeling_tf_gpt2 import ( TFGPT2Model, TFGPT2LMHeadModel, TFGPT2DoubleHeadsModel, TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP, ) @require_tf class TFGPT2ModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = (TFGPT2Model, TFGPT2LMHeadModel, TFGPT2DoubleHeadsModel) if is_tf_available() else () # all_model_classes = (TFGPT2Model, TFGPT2LMHeadModel) if is_tf_available() else () class TFGPT2ModelTester(object): def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_token_type_ids=True, use_input_mask=True, use_labels=True, use_mc_token_ids=True, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_token_type_ids = use_token_type_ids self.use_input_mask = use_input_mask self.use_labels = use_labels self.use_mc_token_ids = use_mc_token_ids self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) mc_token_ids = None if self.use_mc_token_ids: mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = GPT2Config( vocab_size=self.vocab_size, n_embd=self.hidden_size, n_layer=self.num_hidden_layers, n_head=self.num_attention_heads, # intermediate_size=self.intermediate_size, # hidden_act=self.hidden_act, # hidden_dropout_prob=self.hidden_dropout_prob, # attention_probs_dropout_prob=self.attention_probs_dropout_prob, n_positions=self.max_position_embeddings, n_ctx=self.max_position_embeddings # type_vocab_size=self.type_vocab_size, # initializer_range=self.initializer_range ) head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2) return ( config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels, ) def create_and_check_gpt2_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = TFGPT2Model(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} sequence_output = model(inputs)[0] inputs = [input_ids, None, input_mask] # None is the input for 'past' sequence_output = model(inputs)[0] sequence_output = model(input_ids)[0] result = { "sequence_output": sequence_output.numpy(), } self.parent.assertListEqual( list(result["sequence_output"].shape), [self.batch_size, self.seq_length, self.hidden_size] ) def create_and_check_gpt2_lm_head(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = TFGPT2LMHeadModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} prediction_scores = model(inputs)[0] result = { "prediction_scores": prediction_scores.numpy(), } self.parent.assertListEqual( list(result["prediction_scores"].shape), [self.batch_size, self.seq_length, self.vocab_size] ) def create_and_check_gpt2_double_head( self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, *args ): model = TFGPT2DoubleHeadsModel(config=config) multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1)) multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1)) multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1)) inputs = { "input_ids": multiple_choice_inputs_ids, "mc_token_ids": mc_token_ids, "attention_mask": multiple_choice_input_mask, "token_type_ids": multiple_choice_token_type_ids, } lm_logits, mc_logits = model(inputs)[:2] result = {"lm_logits": lm_logits.numpy(), "mc_logits": mc_logits.numpy()} self.parent.assertListEqual( list(result["lm_logits"].shape), [self.batch_size, self.num_choices, self.seq_length, self.vocab_size] ) self.parent.assertListEqual(list(result["mc_logits"].shape), [self.batch_size, self.num_choices]) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict def setUp(self): self.model_tester = TFGPT2ModelTest.TFGPT2ModelTester(self) self.config_tester = ConfigTester(self, config_class=GPT2Config, n_embd=37) def test_config(self): self.config_tester.run_common_tests() def test_gpt2_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_model(*config_and_inputs) def test_gpt2_lm_head(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_lm_head(*config_and_inputs) def test_gpt2_double_head(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_double_head(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in list(TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: model = TFGPT2Model.from_pretrained(model_name, cache_dir=CACHE_DIR) self.assertIsNotNone(model)