# 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. from __future__ import annotations import unittest from transformers import GPT2Config, is_tf_available from transformers.testing_utils import require_tf, require_tf2onnx, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin if is_tf_available(): import tensorflow as tf from transformers import GPT2Tokenizer from transformers.models.gpt2.modeling_tf_gpt2 import ( TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST, TFGPT2DoubleHeadsModel, TFGPT2ForSequenceClassification, TFGPT2LMHeadModel, TFGPT2Model, ) from transformers.tf_utils import shape_list class TFGPT2ModelTester: def __init__( self, parent, ): self.parent = parent self.batch_size = 13 self.seq_length = 7 self.is_training = True self.use_token_type_ids = True self.use_input_mask = True self.use_labels = True self.use_mc_token_ids = True self.vocab_size = 99 self.hidden_size = 32 self.num_hidden_layers = 2 self.num_attention_heads = 4 self.intermediate_size = 37 self.hidden_act = "gelu" self.hidden_dropout_prob = 0.1 self.attention_probs_dropout_prob = 0.1 self.max_position_embeddings = 512 self.type_vocab_size = 16 self.type_sequence_label_size = 2 self.initializer_range = 0.02 self.num_labels = 3 self.num_choices = 4 self.scope = None self.bos_token_id = self.vocab_size - 1 self.eos_token_id = self.vocab_size - 1 self.pad_token_id = self.vocab_size - 1 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 = random_attention_mask([self.batch_size, self.seq_length]) 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, # type_vocab_size=self.type_vocab_size, # initializer_range=self.initializer_range bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, return_dict=True, ) 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 prepare_config_and_inputs_for_decoder(self): ( config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels, ) = self.prepare_config_and_inputs() encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) return ( config, input_ids, input_mask, head_mask, token_type_ids, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) 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, } result = model(inputs) inputs = [input_ids, None, input_mask] # None is the input for 'past' result = model(inputs) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_gpt2_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = TFGPT2Model(config=config) # first forward pass outputs = model(input_ids, token_type_ids=token_type_ids, use_cache=True) outputs_use_cache_conf = model(input_ids, token_type_ids=token_type_ids) outputs_no_past = model(input_ids, token_type_ids=token_type_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) output, past_key_values = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) next_token_types = ids_tensor([self.batch_size, 1], self.type_vocab_size) # append to next input_ids and token_type_ids next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-1) output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"] output_from_past = model(next_tokens, token_type_ids=next_token_types, past_key_values=past_key_values)[ "last_hidden_state" ] # select random slice random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1])) output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx] output_from_past_slice = output_from_past[:, 0, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6) def create_and_check_gpt2_model_attention_mask_past( self, config, input_ids, input_mask, head_mask, token_type_ids, *args ): model = TFGPT2Model(config=config) # create attention mask half_seq_length = self.seq_length // 2 attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32) attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32) attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1) # first forward pass output, past_key_values = model(input_ids, attention_mask=attn_mask).to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy() + 1 random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size) vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change) condition = tf.transpose( tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size)) ) input_ids = tf.where(condition, random_other_next_tokens, input_ids) # append to next input_ids and attn_mask next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) attn_mask = tf.concat([attn_mask, tf.ones((shape_list(attn_mask)[0], 1), dtype=tf.int32)], axis=1) # get two different outputs output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past_key_values, attention_mask=attn_mask)[ "last_hidden_state" ] # select random slice random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1])) output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx] output_from_past_slice = output_from_past[:, 0, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-12) def create_and_check_gpt2_model_past_large_inputs( self, config, input_ids, input_mask, head_mask, token_type_ids, *args ): model = TFGPT2Model(config=config) input_ids = input_ids[:1, :] input_mask = input_mask[:1, :] token_type_ids = token_type_ids[:1, :] self.batch_size = 1 # first forward pass outputs = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) next_token_types = ids_tensor((self.batch_size, 3), self.type_vocab_size) # append to next input_ids and token_type_ids next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1) next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-1) output_from_no_past = model( next_input_ids, token_type_ids=next_token_type_ids, attention_mask=next_attention_mask )["last_hidden_state"] output_from_past = model( next_tokens, token_type_ids=next_token_types, attention_mask=next_attention_mask, past_key_values=past_key_values, )["last_hidden_state"] self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]) # select random slice random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3) 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, } result = model(inputs) self.parent.assertEqual(result.logits.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, } result = model(inputs) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_choices, self.seq_length, self.vocab_size) ) self.parent.assertEqual(result.mc_logits.shape, (self.batch_size, self.num_choices)) def create_and_check_gpt2_for_sequence_classification( self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args ): config.num_labels = self.num_labels inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, "labels": sequence_labels, } model = TFGPT2ForSequenceClassification(config) result = model(inputs) 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, 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 @require_tf class TFGPT2ModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( (TFGPT2Model, TFGPT2LMHeadModel, TFGPT2ForSequenceClassification, TFGPT2DoubleHeadsModel) if is_tf_available() else () ) all_generative_model_classes = (TFGPT2LMHeadModel,) if is_tf_available() else () pipeline_model_mapping = ( { "feature-extraction": TFGPT2Model, "text-classification": TFGPT2ForSequenceClassification, "text-generation": TFGPT2LMHeadModel, "zero-shot": TFGPT2ForSequenceClassification, } if is_tf_available() else {} ) test_head_masking = False test_onnx = True onnx_min_opset = 10 def setUp(self): self.model_tester = 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_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_model_past(*config_and_inputs) def test_gpt2_model_att_mask_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_model_attention_mask_past(*config_and_inputs) def test_gpt2_model_past_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_model_past_large_inputs(*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) def test_gpt2_sequence_classification_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_for_sequence_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFGPT2Model.from_pretrained(model_name) self.assertIsNotNone(model) # overwrite from common since ONNX runtime optimization doesn't work with tf.gather() when the argument # `batch_dims` > 0" @require_tf2onnx @slow def test_onnx_runtime_optimize(self): if not self.test_onnx: return import onnxruntime import tf2onnx config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: # Skip these 2 classes which uses `tf.gather` with `batch_dims=1` if model_class in [TFGPT2ForSequenceClassification, TFGPT2DoubleHeadsModel]: continue model = model_class(config) model.build() onnx_model_proto, _ = tf2onnx.convert.from_keras(model, opset=self.onnx_min_opset) onnxruntime.InferenceSession(onnx_model_proto.SerializeToString()) # TODO (Joao): fix me @unittest.skip("Onnx compliancy broke with TF 2.10") def test_onnx_compliancy(self): pass @require_tf class TFGPT2ModelLanguageGenerationTest(unittest.TestCase): @slow def test_lm_generate_greedy_distilgpt2_batch_special(self): model = TFGPT2LMHeadModel.from_pretrained("distilgpt2") tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2") tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "left" sentences = ["Today is a beautiful day and", "Yesterday was"] input_ids = tokenizer(sentences, return_tensors="tf", padding=True) generation_kwargs = { "bad_words_ids": [tokenizer("is").input_ids, tokenizer("angry about").input_ids], "no_repeat_ngram_size": 2, "do_sample": False, "repetition_penalty": 1.3, } output_ids = model.generate(**input_ids, **generation_kwargs) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) expected_output_string = [ "Today is a beautiful day and I am so happy to be able take part in this amazing event.", "Yesterday was a very interesting time for the world to see how much of this is", ] self.assertListEqual(output_strings, expected_output_string) @slow def test_lm_generate_sample_distilgpt2_batch_special(self): model = TFGPT2LMHeadModel.from_pretrained("distilgpt2") tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2") tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "left" sentences = ["Today is a beautiful day and", "Yesterday was"] input_ids = tokenizer(sentences, return_tensors="tf", padding=True) generation_kwargs = { "do_sample": True, "bad_words_ids": [tokenizer("is").input_ids, tokenizer("angry about").input_ids], "no_repeat_ngram_size": 2, "repetition_penalty": 1.3, "temperature": 1.5, "top_k": 500, "top_p": 0.9, "seed": [42, 0], # seed set -> deterministic sampling sequence -> deterministic generation } # forces the generation to happen on CPU, to avoid GPU-related quirks with tf.device(":/CPU:0"): output_ids = model.generate(**input_ids, **generation_kwargs) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) expected_output_string = [ "Today is a beautiful day and we will make you feel very hot/terrific in all your", "Yesterday was known by national television networks as Le Big Show or Wild Dog Jeopard", ] self.assertListEqual(output_strings, expected_output_string) @slow def test_lm_generate_greedy_distilgpt2_beam_search_special(self): model = TFGPT2LMHeadModel.from_pretrained("distilgpt2") tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2") tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "left" sentences = ["Today is a beautiful day and", "Yesterday was"] input_ids = tokenizer(sentences, return_tensors="tf", padding=True) generation_kwargs = { "bad_words_ids": [tokenizer("is").input_ids, tokenizer("angry about").input_ids], "no_repeat_ngram_size": 2, "do_sample": False, "num_beams": 2, } output_ids = model.generate(**input_ids, **generation_kwargs) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) expected_output_string = [ "Today is a beautiful day and a great day for all of us.\n\nI’m", "Yesterday was the first time that a person has been arrested in the United States for", ] self.assertListEqual(output_strings, expected_output_string) @slow def test_lm_generate_distilgpt2_left_padding(self): """Tests that the generated text is the same, regarless of left padding""" model = TFGPT2LMHeadModel.from_pretrained("distilgpt2") tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2") tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "left" generation_kwargs = { "bad_words_ids": [tokenizer("is").input_ids, tokenizer("angry about").input_ids], "no_repeat_ngram_size": 2, "do_sample": False, "repetition_penalty": 1.3, } expected_output_string = ( "Today is a beautiful day and I am so happy to be able take part in this amazing event." ) sentences = ["Today is a beautiful day and"] input_ids = tokenizer(sentences, return_tensors="tf", padding=True) # using default length output_ids = model.generate(**input_ids, **generation_kwargs) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) self.assertEqual(output_strings[0], expected_output_string) sentences = ["Today is a beautiful day and", "This is a very long input that we absolutely don't care about"] input_ids = tokenizer(sentences, return_tensors="tf", padding=True) # longer max length to capture the full length (remember: it is left padded) output_ids = model.generate(**input_ids, **generation_kwargs, max_length=27) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) self.assertEqual(output_strings[0], expected_output_string) @slow def test_lm_generate_gpt2_greedy_xla(self): model = TFGPT2LMHeadModel.from_pretrained("gpt2") tokenizer = GPT2Tokenizer.from_pretrained("gpt2") tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "left" sentences = ["The dog", "The flying machine"] expected_output_strings = [ "The dog was found in a field near the intersection of West and West Streets.\n\nThe", "The flying machine is a small, lightweight, and lightweight aircraft that can be used for any type of", ] input_ids = tokenizer(sentences, return_tensors="tf", padding=True) output_ids = model.generate(**input_ids, do_sample=False) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) self.assertListEqual(output_strings, expected_output_strings) xla_generate = tf.function(model.generate, jit_compile=True) output_ids = xla_generate(**input_ids, do_sample=False) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) self.assertListEqual(output_strings, expected_output_strings) @slow def test_lm_generate_gpt2_sample_xla(self): # NOTE: due to the small numerical differences that are natural when we compile to XLA, sampling the same # output out of the same seed is far from guaranteed. We can, however, confirm that the results are sensible # and that we can seed both versions. # forces the generation to happen on CPU, to avoid GPU-related quirks with tf.device(":/CPU:0"): model = TFGPT2LMHeadModel.from_pretrained("gpt2") tokenizer = GPT2Tokenizer.from_pretrained("gpt2") tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "left" sentence = ["The dog", "The flying machine"] expected_output_string = [ "The dog owner asked why did our vet decide there needed to be extra ventilation inside because most" " puppies", "The flying machine was made by an artist who found it difficult to control it as it did not use", ] expected_output_string_xla = [ "The dog has been named in connection with the murder of a 20-year-old man in", "The flying machine is a new and improved system to operate and operate a new system and system " "system system", ] input_ids = tokenizer(sentence, return_tensors="tf", padding=True) output_ids = model.generate(**input_ids, do_sample=True, seed=[7, 0]) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) self.assertListEqual(output_strings, expected_output_string) xla_generate = tf.function(model.generate, jit_compile=True) output_ids = xla_generate(**input_ids, do_sample=True, seed=[7, 0]) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) self.assertListEqual(output_strings, expected_output_string_xla) @slow def test_lm_generate_gpt2_beam_search_xla(self): model = TFGPT2LMHeadModel.from_pretrained("gpt2") tokenizer = GPT2Tokenizer.from_pretrained("gpt2") tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "left" sentences = ["The dog", "The flying machine"] expected_output_strings = [ "The dog was found in the backyard of a home in the 6500 block of South Main Street", "The flying machine is a very powerful machine, but it's not a very powerful machine. It's", ] input_ids = tokenizer(sentences, return_tensors="tf", padding=True) output_ids = model.generate(**input_ids, do_sample=False, num_beams=2) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) self.assertListEqual(output_strings, expected_output_strings) xla_generate = tf.function(model.generate, jit_compile=True) output_ids = xla_generate(**input_ids, do_sample=False, num_beams=2) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) self.assertListEqual(output_strings, expected_output_strings) @slow def test_contrastive_search_gpt2(self): article = ( "DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and research " "laboratory founded in 2010. DeepMind was acquired by Google in 2014. The company is based" ) gpt2_tokenizer = GPT2Tokenizer.from_pretrained("gpt2-large") gpt2_model = TFGPT2LMHeadModel.from_pretrained("gpt2-large") input_ids = gpt2_tokenizer(article, return_tensors="tf") outputs = gpt2_model.generate(**input_ids, penalty_alpha=0.6, top_k=4, max_length=256) generated_text = gpt2_tokenizer.batch_decode(outputs, skip_special_tokens=True) self.assertListEqual( generated_text, [ "DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and research " "laboratory founded in 2010. DeepMind was acquired by Google in 2014. The company is based in London, " "United Kingdom\n\nGoogle has a lot of data on its users and uses it to improve its products, such as " "Google Now, which helps users find the information they're looking for on the web. But the company " "is not the only one to collect data on its users. Facebook, for example, has its own facial " "recognition technology, as well as a database of millions of photos that it uses to personalize its " "News Feed.\n\nFacebook's use of data is a hot topic in the tech industry, with privacy advocates " "concerned about the company's ability to keep users' information private. In a blog post last " 'year, Facebook CEO Mark Zuckerberg said his company would "do our best to be transparent about our ' 'data use and how we use it."\n\n"We have made it clear that we do not sell or share your data with ' 'third parties," Zuckerberg wrote. "If you have questions or concerns, please reach out to us at ' 'privacy@facebook.com."\n\nGoogle declined to comment on the privacy implications of its use of data, ' "but said in a statement to The Associated Press that" ], ) @slow def test_contrastive_search_gpt2_xla(self): article = ( "DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and research " "laboratory founded in 2010. DeepMind was acquired by Google in 2014. The company is based" ) gpt2_tokenizer = GPT2Tokenizer.from_pretrained("gpt2-large") gpt2_model = TFGPT2LMHeadModel.from_pretrained("gpt2-large") input_ids = gpt2_tokenizer(article, return_tensors="tf") xla_generate = tf.function(gpt2_model.generate, jit_compile=True) outputs = xla_generate(**input_ids, penalty_alpha=0.6, top_k=4, max_length=256) generated_text = gpt2_tokenizer.batch_decode(outputs, skip_special_tokens=True) self.assertListEqual( generated_text, [ "DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and research " "laboratory founded in 2010. DeepMind was acquired by Google in 2014. The company is based in London, " "United Kingdom\n\nGoogle has a lot of data on its users and uses it to improve its products, such as " "Google Now, which helps users find the information they're looking for on the web. But the company " "is not the only one to collect data on its users. Facebook, for example, has its own facial " "recognition technology, as well as a database of millions of photos that it uses to personalize its " "News Feed.\n\nFacebook's use of data is a hot topic in the tech industry, with privacy advocates " "concerned about the company's ability to keep users' information private. In a blog post last " 'year, Facebook CEO Mark Zuckerberg said his company would "do our best to be transparent about our ' 'data use and how we use it."\n\n"We have made it clear that we do not sell or share your data with ' 'third parties," Zuckerberg wrote. "If you have questions or concerns, please reach out to us at ' 'privacy@facebook.com."\n\nGoogle declined to comment on the privacy implications of its use of data, ' "but said in a statement to The Associated Press that" ], )