# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # 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 random import unittest from transformers import TransfoXLConfig, is_tf_available from transformers.testing_utils import require_tf, slow from .test_configuration_common import ConfigTester from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor if is_tf_available(): import tensorflow as tf from transformers import ( TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST, TFTransfoXLForSequenceClassification, TFTransfoXLLMHeadModel, TFTransfoXLModel, ) class TFTransfoXLModelTester: def __init__( self, parent, ): self.parent = parent self.batch_size = 13 self.seq_length = 7 self.mem_len = 30 self.key_length = self.seq_length + self.mem_len self.clamp_len = 15 self.is_training = True self.use_labels = True self.vocab_size = 99 self.cutoffs = [10, 50, 80] self.hidden_size = 32 self.d_embed = 32 self.num_attention_heads = 4 self.d_head = 8 self.d_inner = 128 self.div_val = 2 self.num_hidden_layers = 5 self.scope = None self.seed = 1 self.eos_token_id = 0 self.num_labels = 3 self.pad_token_id = self.vocab_size - 1 self.init_range = 0.01 def prepare_config_and_inputs(self): input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) lm_labels = None if self.use_labels: lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) config = TransfoXLConfig( vocab_size=self.vocab_size, mem_len=self.mem_len, clamp_len=self.clamp_len, cutoffs=self.cutoffs, d_model=self.hidden_size, d_embed=self.d_embed, n_head=self.num_attention_heads, d_head=self.d_head, d_inner=self.d_inner, div_val=self.div_val, n_layer=self.num_hidden_layers, eos_token_id=self.eos_token_id, pad_token_id=self.vocab_size - 1, init_range=self.init_range, num_labels=self.num_labels, ) return (config, input_ids_1, input_ids_2, lm_labels) def set_seed(self): random.seed(self.seed) tf.random.set_seed(self.seed) def create_and_check_transfo_xl_model(self, config, input_ids_1, input_ids_2, lm_labels): model = TFTransfoXLModel(config) hidden_states_1, mems_1 = model(input_ids_1).to_tuple() inputs = {"input_ids": input_ids_2, "mems": mems_1} hidden_states_2, mems_2 = model(inputs).to_tuple() self.parent.assertEqual(hidden_states_1.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(hidden_states_2.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertListEqual( [mem.shape for mem in mems_1], [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers, ) self.parent.assertListEqual( [mem.shape for mem in mems_2], [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers, ) def create_and_check_transfo_xl_lm_head(self, config, input_ids_1, input_ids_2, lm_labels): model = TFTransfoXLLMHeadModel(config) lm_logits_1, mems_1 = model(input_ids_1).to_tuple() inputs = {"input_ids": input_ids_1, "labels": lm_labels} _, mems_1 = model(inputs).to_tuple() lm_logits_2, mems_2 = model([input_ids_2, mems_1]).to_tuple() inputs = {"input_ids": input_ids_1, "mems": mems_1, "labels": lm_labels} _, mems_2 = model(inputs).to_tuple() self.parent.assertEqual(lm_logits_1.shape, (self.batch_size, self.seq_length, self.vocab_size)) self.parent.assertListEqual( [mem.shape for mem in mems_1], [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers, ) self.parent.assertEqual(lm_logits_2.shape, (self.batch_size, self.seq_length, self.vocab_size)) self.parent.assertListEqual( [mem.shape for mem in mems_2], [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers, ) def create_and_check_transfo_xl_for_sequence_classification(self, config, input_ids_1, input_ids_2, lm_labels): model = TFTransfoXLForSequenceClassification(config) result = model(input_ids_1) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() (config, input_ids_1, input_ids_2, lm_labels) = config_and_inputs inputs_dict = {"input_ids": input_ids_1} return config, inputs_dict @require_tf class TFTransfoXLModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = ( (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else () ) all_generative_model_classes = () if is_tf_available() else () # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented test_resize_embeddings = False test_head_masking = False test_onnx = False test_mismatched_shapes = False def setUp(self): self.model_tester = TFTransfoXLModelTester(self) self.config_tester = ConfigTester(self, config_class=TransfoXLConfig, d_embed=37) def test_config(self): self.config_tester.run_common_tests() def test_transfo_xl_model(self): self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_transfo_xl_model(*config_and_inputs) def test_transfo_xl_lm_head(self): self.model_tester.set_seed() config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_transfo_xl_lm_head(*config_and_inputs) def test_transfo_xl_sequence_classification_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*config_and_inputs) def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() list_other_models_with_output_ebd = [TFTransfoXLForSequenceClassification] for model_class in self.all_model_classes: model = model_class(config) assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer) if model_class in list_other_models_with_output_ebd: x = model.get_output_embeddings() assert isinstance(x, tf.keras.layers.Layer) name = model.get_bias() assert name is None else: x = model.get_output_embeddings() assert x is None name = model.get_bias() assert name is None def test_xla_mode(self): # TODO JP: Make TransfoXL XLA compliant pass @slow def test_model_from_pretrained(self): for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFTransfoXLModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_tf class TFTransfoXLModelLanguageGenerationTest(unittest.TestCase): @unittest.skip("Skip test until #12651 is resolved.") @slow def test_lm_generate_transfo_xl_wt103(self): model = TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103") # fmt: off input_ids = tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]],dtype=tf.int32) # noqa: E231 # fmt: on # In 1991 , the remains of Russian Tsar Nicholas II and his family # ( except for Alexei and Maria ) are discovered . # The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the # remainder of the story . 1883 Western Siberia , # a young Grigori Rasputin is asked by his father and a group of men to perform magic . # Rasputin has a vision and denounces one of the men as a horse thief . Although his # father initially slaps him for making such an accusation , Rasputin watches as the # man is chased outside and beaten . Twenty years later , Rasputin sees a vision of # the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous , # with people , even a bishop , begging for his blessing . # fmt: off expected_output_ids = [33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0] # noqa: E231 # fmt: on # In 1991, the remains of Russian Tsar Nicholas II and his family ( # except for Alexei and Maria ) are discovered. The voice of young son, # Tsarevich Alexei Nikolaevich, narrates the remainder of the story. # 1883 Western Siberia, a young Grigori Rasputin is asked by his father # and a group of men to perform magic. Rasputin has a vision and # denounces one of the men as a horse thief. Although his father initially # slaps him for making such an accusation, Rasputin watches as the man # is chased outside and beaten. Twenty years later, Rasputin sees a vision # of the Virgin Mary, prompting him to become a priest. # Rasputin quickly becomes famous, with people, even a bishop, begging for # his blessing. In the 1990s, the remains of Russian Tsar # Nicholas II and his family were discovered. The voice of young son, # Tsarevich Alexei Nikolaevich, narrates the remainder of the story. output_ids = model.generate(input_ids, max_length=200, do_sample=False) self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)