# coding=utf-8 # Copyright 2018 Google T5 Authors and HuggingFace Inc. team. # # 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 T5Config, is_tf_available from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow from transformers.utils import cached_property from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import ByT5Tokenizer, T5Tokenizer, TFT5EncoderModel, TFT5ForConditionalGeneration, TFT5Model class TFT5ModelTester: def __init__( self, parent, ): self.parent = parent self.batch_size = 13 self.seq_length = 7 self.is_training = True self.use_input_mask = True self.use_labels = True self.vocab_size = 99 self.n_positions = 14 self.hidden_size = 32 self.num_hidden_layers = 2 self.num_attention_heads = 4 self.d_ff = 37 self.relative_attention_num_buckets = 8 self.dropout_rate = 0.1 self.initializer_factor = 0.002 self.eos_token_id = 1 self.pad_token_id = 0 self.scope = None 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_labels = None if self.use_labels: token_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) config = T5Config( vocab_size=self.vocab_size, n_positions=self.n_positions, d_model=self.hidden_size, d_ff=self.d_ff, d_kv=self.hidden_size // self.num_attention_heads, num_layers=self.num_hidden_layers, num_heads=self.num_attention_heads, relative_attention_num_buckets=self.relative_attention_num_buckets, dropout_rate=self.dropout_rate, initializer_factor=self.initializer_factor, eos_token_id=self.eos_token_id, bos_token_id=self.pad_token_id, pad_token_id=self.pad_token_id, decoder_start_token_id=self.pad_token_id, ) return (config, input_ids, input_mask, token_labels) def create_and_check_t5_model(self, config, input_ids, input_mask, token_labels): model = TFT5Model(config=config) inputs = { "input_ids": input_ids, "decoder_input_ids": input_ids, "decoder_attention_mask": input_mask, } result = model(inputs) result = model(input_ids, decoder_attention_mask=input_mask, decoder_input_ids=input_ids) decoder_output = result.last_hidden_state decoder_past = result.past_key_values encoder_output = result.encoder_last_hidden_state self.parent.assertListEqual(list(encoder_output.shape), [self.batch_size, self.seq_length, self.hidden_size]) self.parent.assertListEqual(list(decoder_output.shape), [self.batch_size, self.seq_length, self.hidden_size]) # There should be `num_layers` key value embeddings stored in decoder_past[1] self.parent.assertEqual(len(decoder_past), config.num_layers) # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past[1] tuple self.parent.assertEqual(len(decoder_past[0]), 4) def create_and_check_t5_with_lm_head(self, config, input_ids, input_mask, token_labels): model = TFT5ForConditionalGeneration(config=config) inputs_dict = { "input_ids": input_ids, "decoder_input_ids": input_ids, "decoder_attention_mask": input_mask, } result = model(inputs_dict) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_t5_decoder_model_past(self, config, input_ids, decoder_input_ids, attention_mask): model = TFT5Model(config=config).get_decoder() input_ids = input_ids[:1, :] self.batch_size = 1 # first forward pass outputs = model(input_ids, use_cache=True) outputs_use_cache_conf = model(input_ids) outputs_no_past = model(input_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) output_from_no_past = model(next_input_ids)[0] output_from_past = model(next_tokens, past_key_values=outputs.past_key_values)[0] # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-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-3) def create_and_check_t5_decoder_model_attention_mask_past( self, config, input_ids, decoder_input_ids, attention_mask ): model = TFT5Model(config=config).get_decoder() # 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 outputs = model(input_ids, attention_mask=attn_mask, use_cache=True) # 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((attn_mask.shape[0], 1), dtype=tf.int32)], axis=1, ) # get two different outputs output_from_no_past = model(next_input_ids, attention_mask=attn_mask)[0] output_from_past = model(next_tokens, past_key_values=outputs.past_key_values, attention_mask=attn_mask)[0] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).numpy().item() 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-3) def create_and_check_t5_decoder_model_past_large_inputs( self, config, input_ids, decoder_input_ids, attention_mask ): model = TFT5Model(config=config).get_decoder() input_ids = input_ids[:1, :] attention_mask = attention_mask[:1, :] self.batch_size = 1 # first forward pass outputs = model(input_ids, attention_mask=attention_mask, use_cache=True) # 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) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)[0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, past_key_values=outputs.past_key_values )[0] self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1]) # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-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 prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() (config, input_ids, input_mask, token_labels) = config_and_inputs inputs_dict = { "input_ids": input_ids, "decoder_input_ids": input_ids, "decoder_attention_mask": input_mask, } return config, inputs_dict @require_tf class TFT5ModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): is_encoder_decoder = True all_model_classes = (TFT5Model, TFT5ForConditionalGeneration) if is_tf_available() else () all_generative_model_classes = (TFT5ForConditionalGeneration,) if is_tf_available() else () pipeline_model_mapping = ( { "conversational": TFT5ForConditionalGeneration, "feature-extraction": TFT5Model, "summarization": TFT5ForConditionalGeneration, "text2text-generation": TFT5ForConditionalGeneration, "translation": TFT5ForConditionalGeneration, } if is_tf_available() else {} ) test_onnx = False def setUp(self): self.model_tester = TFT5ModelTester(self) self.config_tester = ConfigTester(self, config_class=T5Config, d_model=37) def test_config(self): self.config_tester.run_common_tests() def test_t5_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_t5_model(*config_and_inputs) def test_t5_model_v1_1(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() config = config_and_inputs[0] config.tie_word_embeddings = False config.feed_forward_proj = "gated-gelu" self.model_tester.create_and_check_t5_model(config, *config_and_inputs[1:]) def test_with_lm_head(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_t5_with_lm_head(*config_and_inputs) def test_t5_decoder_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_t5_decoder_model_past(*config_and_inputs) def test_t5_decoder_model_past_with_attn_mask(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_t5_decoder_model_attention_mask_past(*config_and_inputs) def test_t5_decoder_model_past_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() # `create_and_check_t5_decoder_model_past_large_inputs` has special inputs: # (config, input_ids, decoder_input_ids, attention_mask) # and we have to prepare it correctly here. config, input_ids, input_mask, token_labels = config_and_inputs config_and_inputs = (config, input_ids, None, input_mask) self.model_tester.create_and_check_t5_decoder_model_past_large_inputs(*config_and_inputs) @slow def test_model_from_pretrained(self): model = TFT5Model.from_pretrained("t5-small") self.assertIsNotNone(model) def test_generate_with_headmasking(self): # TODO: Fix head-masking according to PyTorch T5 model pass # This test is run in `TFT5EncoderOnlyModelTest`, where the main layer has the same inputs as the model @unittest.skip(reason="The inputs of the Main Layer are different.") def test_keras_save_load(self): pass class TFT5EncoderOnlyModelTester: def __init__( self, parent, vocab_size=99, batch_size=13, encoder_seq_length=7, # For common tests use_attention_mask=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, d_ff=37, relative_attention_num_buckets=8, is_training=False, dropout_rate=0.1, initializer_factor=0.002, is_encoder_decoder=False, eos_token_id=1, pad_token_id=0, scope=None, ): self.parent = parent self.batch_size = batch_size self.encoder_seq_length = encoder_seq_length # For common tests self.seq_length = self.encoder_seq_length self.use_attention_mask = use_attention_mask 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.d_ff = d_ff self.relative_attention_num_buckets = relative_attention_num_buckets self.dropout_rate = dropout_rate self.initializer_factor = initializer_factor self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.is_encoder_decoder = is_encoder_decoder self.scope = None self.is_training = is_training def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size) attention_mask = None if self.use_attention_mask: attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2) config = T5Config( vocab_size=self.vocab_size, d_model=self.hidden_size, d_ff=self.d_ff, d_kv=self.hidden_size // self.num_attention_heads, num_layers=self.num_hidden_layers, num_heads=self.num_attention_heads, relative_attention_num_buckets=self.relative_attention_num_buckets, dropout_rate=self.dropout_rate, initializer_factor=self.initializer_factor, eos_token_id=self.eos_token_id, bos_token_id=self.pad_token_id, pad_token_id=self.pad_token_id, is_encoder_decoder=self.is_encoder_decoder, ) return ( config, input_ids, attention_mask, ) def create_and_check_model( self, config, input_ids, attention_mask, ): model = TFT5EncoderModel(config=config) result = model( input_ids=input_ids, attention_mask=attention_mask, ) result = model(input_ids=input_ids) encoder_output = result.last_hidden_state self.parent.assertEqual(encoder_output.shape, (self.batch_size, self.encoder_seq_length, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, attention_mask, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict class TFT5EncoderOnlyModelTest(TFModelTesterMixin, unittest.TestCase): is_encoder_decoder = False all_model_classes = (TFT5EncoderModel,) if is_tf_available() else () test_onnx = False def setUp(self): self.model_tester = TFT5EncoderOnlyModelTester(self) self.config_tester = ConfigTester(self, config_class=T5Config, d_model=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) # is not able to be part of a pipeline def test_train_pipeline_custom_model(self): pass @require_tf @require_sentencepiece @require_tokenizers class TFT5GenerationIntegrationTests(unittest.TestCase): @slow def test_greedy_xla_generate_simple(self): model = TFT5ForConditionalGeneration.from_pretrained("t5-small") tokenizer = T5Tokenizer.from_pretrained("t5-small") # two examples with different lengths to confirm that attention masks are operational in XLA sentences = [ "Translate English to German: Today is a beautiful day.", "Translate English to German: I have four cats, three dogs, two birds, and a horse.", ] input_ids = tokenizer(sentences, return_tensors="tf", padding=True).input_ids xla_generate = tf.function(model.generate, jit_compile=True) output_ids = model.generate(input_ids) output_ids_xla = xla_generate(input_ids) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) output_strings_xla = tokenizer.batch_decode(output_ids_xla, skip_special_tokens=True) expected_output_string = [ "Heute ist ein schöner Tag.", "Ich habe vier Katzen, drei Hunde, zwei Vögel und ein Pferd.", ] self.assertListEqual(expected_output_string, output_strings) self.assertListEqual(expected_output_string, output_strings_xla) @slow def test_greedy_generate(self): model = TFT5ForConditionalGeneration.from_pretrained("t5-small") tokenizer = T5Tokenizer.from_pretrained("t5-small") sentences = ["Yesterday, my name was", "Today is a beautiful day and"] input_ids = tokenizer(sentences, return_tensors="tf", padding=True).input_ids generation_kwargs = { "bad_words_ids": [tokenizer("my").input_ids, tokenizer("ein schöner").input_ids], "no_repeat_ngram_size": 3, "do_sample": False, "repetition_penalty": 2.2, } output_ids = model.generate(input_ids, **generation_kwargs) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) expected_output_string = ["Yesterday, my name was", "Heute ist ein schöne Tag und"] self.assertListEqual(expected_output_string, output_strings) @slow def test_sample_xla_generate_simple(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 = TFT5ForConditionalGeneration.from_pretrained("t5-small") tokenizer = T5Tokenizer.from_pretrained("t5-small") sentence = "Translate English to German: I have two bananas" input_ids = tokenizer(sentence, return_tensors="tf", padding=True).input_ids expected_output_string = ["Ich habe zwei Bananen"] expected_output_string_xla = ["Ich habe 2 Bananen"] # seed set -> deterministic sampling sequence -> deterministic generation output_ids = model.generate(input_ids, do_sample=True, seed=[42, 0]) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) self.assertListEqual(expected_output_string, output_strings) xla_generate = tf.function(model.generate, jit_compile=True) # seed set -> deterministic sampling sequence -> deterministic generation output_ids_xla = xla_generate(input_ids, do_sample=True, seed=[42, 0]) output_strings_xla = tokenizer.batch_decode(output_ids_xla, skip_special_tokens=True) self.assertListEqual(expected_output_string_xla, output_strings_xla) @slow def test_sample_generate(self): model = TFT5ForConditionalGeneration.from_pretrained("t5-small") tokenizer = T5Tokenizer.from_pretrained("t5-small") sentences = ["I really love my", "Translate English to German: the transformers are truly amazing"] input_ids = tokenizer(sentences, return_tensors="tf", padding=True).input_ids generation_kwargs = { "do_sample": True, "bad_words_ids": [tokenizer("my").input_ids, tokenizer("ein schöner").input_ids], "no_repeat_ngram_size": 3, "repetition_penalty": 2.2, "temperature": 0.8, "top_k": 500, "top_p": 0.9, "seed": [20, 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 = ["- I really love my way of this.", "die Transformatoren sind wirklich erstaunlich"] self.assertListEqual(expected_output_string, output_strings) # TODO (ydshieh): undo skip once a fix is done on TF side. @unittest.skip("Skip for now as TF 2.13 breaks it on GPU") @slow def test_beam_search_xla_generate_simple(self): model = TFT5ForConditionalGeneration.from_pretrained("t5-small") tokenizer = T5Tokenizer.from_pretrained("t5-small") # tests XLA with task specific arguments task_specific_config = getattr(model.config, "task_specific_params", {}) translation_config = task_specific_config.get("translation_en_to_fr", {}) model.config.update(translation_config) # two examples with different lengths to confirm that attention masks are operational in XLA sentences = [ model.config.prefix + "Today is a beautiful day.", model.config.prefix + "I have four cats, three dogs, two birds, and a horse.", ] input_ids = tokenizer(sentences, return_tensors="tf", padding=True).input_ids xla_generate = tf.function(model.generate, jit_compile=True) output_ids = model.generate(input_ids, num_beams=2) output_ids_xla = xla_generate(input_ids, num_beams=2) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) output_strings_xla = tokenizer.batch_decode(output_ids_xla, skip_special_tokens=True) expected_output_string = [ "Aujourd'hui est une belle journée.", "J'ai quatre chats, trois chiens, deux oiseaux et un cheval.", ] self.assertListEqual(expected_output_string, output_strings) self.assertListEqual(expected_output_string, output_strings_xla) @slow def test_beam_search_generate(self): model = TFT5ForConditionalGeneration.from_pretrained("t5-small") tokenizer = T5Tokenizer.from_pretrained("t5-small") sentences = ["I really love my", "Translate English to German: the transformers are truly amazing"] input_ids = tokenizer(sentences, return_tensors="tf", padding=True).input_ids generation_kwargs = { "bad_words_ids": [tokenizer("my").input_ids, tokenizer("ein schöner").input_ids], "no_repeat_ngram_size": 3, "do_sample": False, "repetition_penalty": 2.2, "num_beams": 4, } output_ids = model.generate(input_ids, **generation_kwargs) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) expected_output_string = ["Ich liebe es so sehr!", "die Transformatoren sind wirklich erstaunlich"] self.assertListEqual(expected_output_string, output_strings) @require_tf @require_sentencepiece @require_tokenizers class TFT5ModelIntegrationTests(unittest.TestCase): @cached_property def model(self): return TFT5ForConditionalGeneration.from_pretrained("t5-base") @slow def test_small_integration_test(self): """ For comparision run: >>> import t5 # pip install t5==0.7.1 >>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary >>> path_to_mtf_small_t5_checkpoint = '' >>> path_to_mtf_small_spm_model_path = '' >>> t5_model = t5.models.MtfModel(model_dir=path_to_mtf_small_t5_checkpoint, batch_size=1, tpu=None) >>> vocab = SentencePieceVocabulary(path_to_mtf_small_spm_model_path, extra_ids=100) >>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab) """ model = TFT5ForConditionalGeneration.from_pretrained("t5-small") tokenizer = T5Tokenizer.from_pretrained("t5-small") input_ids = tokenizer("Hello there", return_tensors="tf").input_ids labels = tokenizer("Hi I am", return_tensors="tf").input_ids loss = model(input_ids, labels=labels).loss mtf_score = -tf.math.reduce_mean(loss).numpy() EXPECTED_SCORE = -4.771147 self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4) @slow def test_small_v1_1_integration_test(self): """ For comparision run: >>> import t5 # pip install t5==0.7.1 >>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary >>> path_to_mtf_small_t5_v1.1_checkpoint = '' >>> path_to_mtf_small_spm_model_path = '' >>> t5_model = t5.models.MtfModel(model_dir=path_to_mtf_small_t5_v1.1_checkpoint, batch_size=1, tpu=None) >>> vocab = SentencePieceVocabulary(path_to_mtf_small_spm_model_path, extra_ids=100) >>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab) """ model = TFT5ForConditionalGeneration.from_pretrained("google/t5-v1_1-small") tokenizer = T5Tokenizer.from_pretrained("google/t5-v1_1-small") input_ids = tokenizer("Hello there", return_tensors="tf").input_ids labels = tokenizer("Hi I am", return_tensors="tf").input_ids loss = model(input_ids, labels=labels).loss mtf_score = -tf.math.reduce_mean(loss).numpy() EXPECTED_SCORE = -14.757326 self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4) @slow def test_small_byt5_integration_test(self): """ For comparision run: >>> import t5 # pip install t5==0.9.1 >>> path_to_byt5_small_checkpoint = '' >>> t5_model = t5.models.MtfModel(model_dir=path_to_tf_checkpoint, batch_size=1, tpu=None) >>> vocab = t5.data.ByteVocabulary() >>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab) """ model = TFT5ForConditionalGeneration.from_pretrained("google/byt5-small") tokenizer = ByT5Tokenizer.from_pretrained("google/byt5-small") input_ids = tokenizer("Hello there", return_tensors="tf").input_ids labels = tokenizer("Hi I am", return_tensors="tf").input_ids loss = model(input_ids, labels=labels).loss mtf_score = -tf.math.reduce_mean(loss).numpy() EXPECTED_SCORE = -7.592465 self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4) @slow def test_summarization(self): model = self.model tok = T5Tokenizer.from_pretrained("t5-base") FRANCE_ARTICLE = ( # @noqa "Marseille, France (CNN)The French prosecutor leading an investigation into the crash of Germanwings" " Flight 9525 insisted Wednesday that he was not aware of any video footage from on board the plane." ' Marseille prosecutor Brice Robin told CNN that "so far no videos were used in the crash investigation."' ' He added, "A person who has such a video needs to immediately give it to the investigators." Robin\'s' " comments follow claims by two magazines, German daily Bild and French Paris Match, of a cell phone video" " showing the harrowing final seconds from on board Germanwings Flight 9525 as it crashed into the French" " Alps. All 150 on board were killed. Paris Match and Bild reported that the video was recovered from a" " phone at the wreckage site. The two publications described the supposed video, but did not post it on" " their websites. The publications said that they watched the video, which was found by a source close to" " the investigation. \"One can hear cries of 'My God' in several languages,\" Paris Match reported." ' "Metallic banging can also be heard more than three times, perhaps of the pilot trying to open the' " cockpit door with a heavy object. Towards the end, after a heavy shake, stronger than the others, the" ' screaming intensifies. Then nothing." "It is a very disturbing scene," said Julian Reichelt,' " editor-in-chief of Bild online. An official with France's accident investigation agency, the BEA, said" " the agency is not aware of any such video. Lt. Col. Jean-Marc Menichini, a French Gendarmerie spokesman" " in charge of communications on rescue efforts around the Germanwings crash site, told CNN that the" ' reports were "completely wrong" and "unwarranted." Cell phones have been collected at the site, he said,' ' but that they "hadn\'t been exploited yet." Menichini said he believed the cell phones would need to be' " sent to the Criminal Research Institute in Rosny sous-Bois, near Paris, in order to be analyzed by" " specialized technicians working hand-in-hand with investigators. But none of the cell phones found so" " far have been sent to the institute, Menichini said. Asked whether staff involved in the search could" ' have leaked a memory card to the media, Menichini answered with a categorical "no." Reichelt told "Erin' ' Burnett: Outfront" that he had watched the video and stood by the report, saying Bild and Paris Match' ' are "very confident" that the clip is real. He noted that investigators only revealed they\'d recovered' ' cell phones from the crash site after Bild and Paris Match published their reports. "That is something' " we did not know before. ... Overall we can say many things of the investigation weren't revealed by the" ' investigation at the beginning," he said. What was mental state of Germanwings co-pilot? German airline' " Lufthansa confirmed Tuesday that co-pilot Andreas Lubitz had battled depression years before he took the" " controls of Germanwings Flight 9525, which he's accused of deliberately crashing last week in the" ' French Alps. Lubitz told his Lufthansa flight training school in 2009 that he had a "previous episode of' ' severe depression," the airline said Tuesday. Email correspondence between Lubitz and the school' " discovered in an internal investigation, Lufthansa said, included medical documents he submitted in" " connection with resuming his flight training. The announcement indicates that Lufthansa, the parent" " company of Germanwings, knew of Lubitz's battle with depression, allowed him to continue training and" " ultimately put him in the cockpit. Lufthansa, whose CEO Carsten Spohr previously said Lubitz was 100%" ' fit to fly, described its statement Tuesday as a "swift and seamless clarification" and said it was' " sharing the information and documents -- including training and medical records -- with public" " prosecutors. Spohr traveled to the crash site Wednesday, where recovery teams have been working for the" " past week to recover human remains and plane debris scattered across a steep mountainside. He saw the" " crisis center set up in Seyne-les-Alpes, laid a wreath in the village of Le Vernet, closer to the crash" " site, where grieving families have left flowers at a simple stone memorial. Menichini told CNN late" " Tuesday that no visible human remains were left at the site but recovery teams would keep searching." " French President Francois Hollande, speaking Tuesday, said that it should be possible to identify all" " the victims using DNA analysis by the end of the week, sooner than authorities had previously suggested." " In the meantime, the recovery of the victims' personal belongings will start Wednesday, Menichini said." " Among those personal belongings could be more cell phones belonging to the 144 passengers and six crew" " on board. Check out the latest from our correspondents . The details about Lubitz's correspondence with" " the flight school during his training were among several developments as investigators continued to" " delve into what caused the crash and Lubitz's possible motive for downing the jet. A Lufthansa" " spokesperson told CNN on Tuesday that Lubitz had a valid medical certificate, had passed all his" ' examinations and "held all the licenses required." Earlier, a spokesman for the prosecutor\'s office in' " Dusseldorf, Christoph Kumpa, said medical records reveal Lubitz suffered from suicidal tendencies at" " some point before his aviation career and underwent psychotherapy before he got his pilot's license." " Kumpa emphasized there's no evidence suggesting Lubitz was suicidal or acting aggressively before the" " crash. Investigators are looking into whether Lubitz feared his medical condition would cause him to" " lose his pilot's license, a European government official briefed on the investigation told CNN on" ' Tuesday. While flying was "a big part of his life," the source said, it\'s only one theory being' " considered. Another source, a law enforcement official briefed on the investigation, also told CNN that" " authorities believe the primary motive for Lubitz to bring down the plane was that he feared he would" " not be allowed to fly because of his medical problems. Lubitz's girlfriend told investigators he had" " seen an eye doctor and a neuropsychologist, both of whom deemed him unfit to work recently and concluded" " he had psychological issues, the European government official said. But no matter what details emerge" " about his previous mental health struggles, there's more to the story, said Brian Russell, a forensic" ' psychologist. "Psychology can explain why somebody would turn rage inward on themselves about the fact' " that maybe they weren't going to keep doing their job and they're upset about that and so they're" ' suicidal," he said. "But there is no mental illness that explains why somebody then feels entitled to' " also take that rage and turn it outward on 149 other people who had nothing to do with the person's" ' problems." Germanwings crash compensation: What we know . Who was the captain of Germanwings Flight' " 9525? CNN's Margot Haddad reported from Marseille and Pamela Brown from Dusseldorf, while Laura" " Smith-Spark wrote from London. CNN's Frederik Pleitgen, Pamela Boykoff, Antonia Mortensen, Sandrine" " Amiel and Anna-Maja Rappard contributed to this report." ) SHORTER_ARTICLE = ( "(CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on" " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The" " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based." " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its" ' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East' ' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the' " situation in Palestinian territories, paving the way for possible war crimes investigations against" " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and" " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the" " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a" ' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the' ' world is also a step closer to ending a long era of impunity and injustice," he said, according to an' ' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge' " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the" ' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine' " acquires all the rights as well as responsibilities that come with being a State Party to the Statute." ' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights' ' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should' " immediately end their pressure, and countries that support universal acceptance of the court's treaty" ' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the' " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's" ' decision to join a treaty to which over 100 countries around the world are members." In January, when' " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an" ' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"' " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a" ' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in' ' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We' ' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"' " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the" ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the' " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou" ' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war' " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry" " will include alleged war crimes committed since June. The International Criminal Court was set up in" " 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder" " and Faith Karimi contributed to this report." ) IRAN_ARTICLE = ( "(CNN)The United States and its negotiating partners reached a very strong framework agreement with Iran" " in Lausanne, Switzerland, on Thursday that limits Iran's nuclear program in such a way as to effectively" " block it from building a nuclear weapon. Expect pushback anyway, if the recent past is any harbinger." " Just last month, in an attempt to head off such an agreement, House Speaker John Boehner invited Israeli" " Prime Minister Benjamin Netanyahu to preemptively blast it before Congress, and 47 senators sent a" " letter to the Iranian leadership warning them away from a deal. The debate that has already begun since" " the announcement of the new framework will likely result in more heat than light. It will not be helped" " by the gathering swirl of dubious assumptions and doubtful assertions. Let us address some of these: ." " The most misleading assertion, despite universal rejection by experts, is that the negotiations'" " objective at the outset was the total elimination of any nuclear program in Iran. That is the position" " of Netanyahu and his acolytes in the U.S. Congress. But that is not and never was the objective. If it" " had been, there would have been no Iranian team at the negotiating table. Rather, the objective has" " always been to structure an agreement or series of agreements so that Iran could not covertly develop a" " nuclear arsenal before the United States and its allies could respond. The new framework has exceeded" " expectations in achieving that goal. It would reduce Iran's low-enriched uranium stockpile, cut by" " two-thirds its number of installed centrifuges and implement a rigorous inspection regime. Another" " dubious assumption of opponents is that the Iranian nuclear program is a covert weapons program. Despite" " sharp accusations by some in the United States and its allies, Iran denies having such a program, and" " U.S. intelligence contends that Iran has not yet made the decision to build a nuclear weapon. Iran's" " continued cooperation with International Atomic Energy Agency inspections is further evidence on this" " point, and we'll know even more about Iran's program in the coming months and years because of the deal." " In fact, the inspections provisions that are part of this agreement are designed to protect against any" " covert action by the Iranians. What's more, the rhetoric of some members of Congress has implied that" " the negotiations have been between only the United States and Iran (i.e., the 47 senators' letter" " warning that a deal might be killed by Congress or a future president). This of course is not the case." " The talks were between Iran and the five permanent members of the U.N. Security Council (United States," " United Kingdom, France, China and Russia) plus Germany, dubbed the P5+1. While the United States has" " played a leading role in the effort, it negotiated the terms alongside its partners. If the agreement" " reached by the P5+1 is rejected by Congress, it could result in an unraveling of the sanctions on Iran" " and threaten NATO cohesion in other areas. Another questionable assertion is that this agreement" " contains a sunset clause, after which Iran will be free to do as it pleases. Again, this is not the" " case. Some of the restrictions on Iran's nuclear activities, such as uranium enrichment, will be eased" " or eliminated over time, as long as 15 years. But most importantly, the framework agreement includes" " Iran's ratification of the Additional Protocol, which allows IAEA inspectors expanded access to nuclear" " sites both declared and nondeclared. This provision will be permanent. It does not sunset. Thus, going" " forward, if Iran decides to enrich uranium to weapons-grade levels, monitors will be able to detect such" " a move in a matter of days and alert the U.N. Security Council. Many in Congress have said that the" ' agreement should be a formal treaty requiring the Senate to "advise and consent." But the issue is not' " suited for a treaty. Treaties impose equivalent obligations on all signatories. For example, the New" " START treaty limits Russia and the United States to 1,550 deployed strategic warheads. But any agreement" " with Iran will not be so balanced. The restrictions and obligations in the final framework agreement" " will be imposed almost exclusively on Iran. The P5+1 are obligated only to ease and eventually remove" " most but not all economic sanctions, which were imposed as leverage to gain this final deal. Finally" " some insist that any agreement must address Iranian missile programs, human rights violations or support" " for Hamas or Hezbollah. As important as these issues are, and they must indeed be addressed, they are" " unrelated to the most important aim of a nuclear deal: preventing a nuclear Iran. To include them in" " the negotiations would be a poison pill. This agreement should be judged on its merits and on how it" " affects the security of our negotiating partners and allies, including Israel. Those judgments should be" " fact-based, not based on questionable assertions or dubious assumptions." ) ARTICLE_SUBWAY = ( "New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A" " year later, she got married again in Westchester County, but to a different man and without divorcing" " her first husband. Only 18 days after that marriage, she got hitched yet again. Then, Barrientos" ' declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married' " once more, this time in the Bronx. In an application for a marriage license, she stated it was her" ' "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false' ' instrument for filing in the first degree," referring to her false statements on the 2010 marriage' " license application, according to court documents. Prosecutors said the marriages were part of an" " immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to" " her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was" " arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New" " York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total," " Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. All" " occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be" " married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors" " said the immigration scam involved some of her husbands, who filed for permanent residence status" " shortly after the marriages. Any divorces happened only after such filings were approved. It was" " unclear whether any of the men will be prosecuted. The case was referred to the Bronx District" " Attorney's Office by Immigration and Customs Enforcement and the Department of Homeland Security's" ' Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt,' " Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his" " native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces" " up to four years in prison. Her next court appearance is scheduled for May 18." ) expected_summaries = [ 'prosecutor: "so far no videos were used in the crash investigation" two magazines claim to have found a' " cell phone video of the final seconds . \"one can hear cries of 'My God' in several languages,\" one" " magazine says .", "the formal accession was marked by a ceremony at The Hague, in the Netherlands . the ICC opened a" " preliminary examination into the situation in the occupied Palestinian territory . as members of the" " court, Palestinians may be subject to counter-charges as well .", "the u.s. and its negotiating partners reached a very strong framework agreement with Iran . aaron miller:" " the debate that has already begun since the announcement of the new framework will likely result in more" " heat than light . the deal would reduce Iran's low-enriched uranium stockpile, cut centrifuges and" " implement a rigorous inspection regime .", "prosecutors say the marriages were part of an immigration scam . if convicted, barrientos faces two" ' criminal counts of "offering a false instrument for filing in the first degree" she has been married 10' " times, with nine of her marriages occurring between 1999 and 2002 .", ] task_specific_config = getattr(model.config, "task_specific_params", {}) summarization_config = task_specific_config.get("summarization", {}) model.config.update(summarization_config) dct = tok( [model.config.prefix + x for x in [FRANCE_ARTICLE, SHORTER_ARTICLE, IRAN_ARTICLE, ARTICLE_SUBWAY]], max_length=512, padding="max_length", truncation=True, return_tensors="tf", ) self.assertEqual(512, dct["input_ids"].shape[1]) hypotheses_batch = model.generate( input_ids=dct["input_ids"], attention_mask=dct["attention_mask"], num_beams=4, length_penalty=2.0, max_length=142, min_length=56, no_repeat_ngram_size=3, do_sample=False, early_stopping=True, ) decoded = [ tok.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in hypotheses_batch ] self.assertListEqual( expected_summaries, decoded, ) @slow def test_translation_en_to_de(self): tok = T5Tokenizer.from_pretrained("t5-base") model = self.model task_specific_config = getattr(model.config, "task_specific_params", {}) translation_config = task_specific_config.get("translation_en_to_de", {}) self.model.config.update(translation_config) original_input = '"Luigi often said to me that he never wanted the brothers to end up in court", she wrote.' expected_translation = ( '"Luigi sagte mir oft, dass er nie wollte, dass die Brüder am Gericht sitzen", schrieb sie.' ) input_ids = tok.encode(model.config.prefix + original_input, return_tensors="tf") output = model.generate( input_ids=input_ids, num_beams=4, length_penalty=2.0, max_length=50, no_repeat_ngram_size=3, do_sample=False, early_stopping=True, ) translation = tok.decode(output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False) self.assertEqual(translation, expected_translation) @slow def test_translation_en_to_fr(self): model = self.model tok = T5Tokenizer.from_pretrained("t5-base") task_specific_config = getattr(model.config, "task_specific_params", {}) translation_config = task_specific_config.get("translation_en_to_fr", {}) model.config.update(translation_config) en_text = ( ' This image section from an infrared recording by the Spitzer telescope shows a "family portrait" of' " countless generations of stars: the oldest stars are seen as blue dots. " ) new_truncated_translation = ( "Cette section d'images provenant de l'enregistrement infrarouge effectué par le télescope Spitzer montre " "un " "« portrait familial » de générations innombrables d’étoiles : les plus anciennes sont observées " "sous forme " "de points bleus." ) input_ids = tok(model.config.prefix + en_text, return_tensors="tf").input_ids output = model.generate( input_ids=input_ids, num_beams=4, length_penalty=2.0, max_length=100, no_repeat_ngram_size=3, do_sample=False, early_stopping=True, ) translation = tok.decode(output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False) self.assertEqual(translation, new_truncated_translation) @slow def test_translation_en_to_ro(self): model = self.model tok = T5Tokenizer.from_pretrained("t5-base") task_specific_config = getattr(model.config, "task_specific_params", {}) translation_config = task_specific_config.get("translation_en_to_ro", {}) model.config.update(translation_config) original_input = "Taco Bell said it plans to add 2,000 locations in the US by 2022." expected_translation = "Taco Bell a declarat că intenţionează să adauge 2 000 de locaţii în SUA până în 2022." input_ids = tok.encode(model.config.prefix + original_input, return_tensors="tf") output = model.generate( input_ids=input_ids, num_beams=4, length_penalty=2.0, max_length=50, no_repeat_ngram_size=3, do_sample=False, early_stopping=True, ) translation = tok.decode(output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False) self.assertEqual(translation, expected_translation)