# 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. import copy import os import pickle import tempfile import unittest from transformers import T5Config, is_torch_available from transformers.models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES from transformers.testing_utils import ( require_accelerate, require_sentencepiece, require_tokenizers, require_torch, slow, torch_device, ) from transformers.utils import cached_property, is_torch_fx_available from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_fx_available(): from transformers.utils.fx import symbolic_trace if is_torch_available(): import torch from transformers import ( AutoTokenizer, ByT5Tokenizer, T5EncoderModel, T5ForConditionalGeneration, T5ForQuestionAnswering, T5ForSequenceClassification, T5Model, T5Tokenizer, ) from transformers.models.t5.modeling_t5 import T5_PRETRAINED_MODEL_ARCHIVE_LIST class T5ModelTester: def __init__( self, parent, vocab_size=99, batch_size=13, encoder_seq_length=7, decoder_seq_length=7, # For common tests is_training=True, use_attention_mask=True, use_labels=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, d_ff=37, relative_attention_num_buckets=8, dropout_rate=0.1, initializer_factor=0.002, eos_token_id=1, pad_token_id=0, decoder_start_token_id=0, scope=None, decoder_layers=None, ): self.parent = parent self.batch_size = batch_size self.encoder_seq_length = encoder_seq_length self.decoder_seq_length = decoder_seq_length # For common tests self.seq_length = self.decoder_seq_length self.is_training = is_training self.use_attention_mask = use_attention_mask self.use_labels = use_labels 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.decoder_start_token_id = decoder_start_token_id self.scope = None self.decoder_layers = decoder_layers def get_large_model_config(self): return T5Config.from_pretrained("t5-base") def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size).clamp(2) input_ids[:, -1] = self.eos_token_id # Eos Token decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) attention_mask = None decoder_attention_mask = None if self.use_attention_mask: attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2) decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2) lm_labels = None if self.use_labels: lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) config = self.get_config() return ( config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ) def get_pipeline_config(self): return T5Config( vocab_size=166, # t5 forces 100 extra tokens 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_decoder_layers=self.decoder_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.decoder_start_token_id, ) def get_config(self): return 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_decoder_layers=self.decoder_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.decoder_start_token_id, ) def check_prepare_lm_labels_via_shift_left( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = T5Model(config=config) model.to(torch_device) model.eval() # make sure that lm_labels are correctly padded from the right lm_labels.masked_fill_((lm_labels == self.decoder_start_token_id), self.eos_token_id) # add casaul pad token mask triangular_mask = torch.tril(lm_labels.new_ones(lm_labels.shape)).logical_not() lm_labels.masked_fill_(triangular_mask, self.pad_token_id) decoder_input_ids = model._shift_right(lm_labels) for i, (decoder_input_ids_slice, lm_labels_slice) in enumerate(zip(decoder_input_ids, lm_labels)): # first item self.parent.assertEqual(decoder_input_ids_slice[0].item(), self.decoder_start_token_id) if i < decoder_input_ids_slice.shape[-1]: if i < decoder_input_ids.shape[-1] - 1: # items before diagonal self.parent.assertListEqual( decoder_input_ids_slice[1 : i + 1].tolist(), lm_labels_slice[:i].tolist() ) # pad items after diagonal if i < decoder_input_ids.shape[-1] - 2: self.parent.assertListEqual( decoder_input_ids_slice[i + 2 :].tolist(), lm_labels_slice[i + 1 : -1].tolist() ) else: # all items after square self.parent.assertListEqual(decoder_input_ids_slice[1:].tolist(), lm_labels_slice[:-1].tolist()) def create_and_check_model( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = T5Model(config=config) model.to(torch_device) model.eval() result = model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, ) result = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids) decoder_output = result.last_hidden_state decoder_past = result.past_key_values encoder_output = result.encoder_last_hidden_state self.parent.assertEqual(encoder_output.size(), (self.batch_size, self.encoder_seq_length, self.hidden_size)) self.parent.assertEqual(decoder_output.size(), (self.batch_size, self.decoder_seq_length, self.hidden_size)) # There should be `num_layers` key value embeddings stored in decoder_past 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 tuple self.parent.assertEqual(len(decoder_past[0]), 4) def create_and_check_with_lm_head( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = T5ForConditionalGeneration(config=config).to(torch_device).eval() outputs = model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, labels=lm_labels, ) self.parent.assertEqual(len(outputs), 4) self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.decoder_seq_length, self.vocab_size)) self.parent.assertEqual(outputs["loss"].size(), ()) def create_and_check_with_sequence_classification_head( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): labels = torch.tensor([1] * self.batch_size, dtype=torch.long, device=torch_device) model = T5ForSequenceClassification(config=config).to(torch_device).eval() outputs = model( input_ids=input_ids, decoder_input_ids=input_ids, labels=labels, ) # self.parent.assertEqual(len(outputs), 4) self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, config.num_labels)) self.parent.assertEqual(outputs["loss"].size(), ()) def create_and_check_decoder_model_past( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = T5Model(config=config).get_decoder().to(torch_device).eval() # 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) 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) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) output_from_no_past = model(next_input_ids)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def create_and_check_decoder_model_attention_mask_past( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = T5Model(config=config).get_decoder() model.to(torch_device) model.eval() # create attention mask attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device) half_seq_length = input_ids.shape[-1] // 2 attn_mask[:, half_seq_length:] = 0 # first forward pass output, past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True).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).item() + 1 random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1) input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens # append to next input_ids and attn_mask next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) attn_mask = torch.cat( [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=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 = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def create_and_check_decoder_model_past_large_inputs( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = T5Model(config=config).get_decoder().to(torch_device).eval() # first forward pass outputs = model(input_ids, attention_mask=attention_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_mask = ids_tensor((self.batch_size, 3), vocab_size=2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([attention_mask, next_mask], dim=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "last_hidden_state" ] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def create_and_check_generate_with_past_key_values( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = T5ForConditionalGeneration(config=config).to(torch_device).eval() torch.manual_seed(0) output_without_past_cache = model.generate( input_ids[:1], num_beams=2, max_length=5, do_sample=True, use_cache=False ) torch.manual_seed(0) output_with_past_cache = model.generate(input_ids[:1], num_beams=2, max_length=5, do_sample=True) self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache)) def create_and_check_model_fp16_forward( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = T5Model(config=config).to(torch_device).half().eval() output = model(input_ids, decoder_input_ids=input_ids, attention_mask=attention_mask)["last_hidden_state"] self.parent.assertFalse(torch.isnan(output).any().item()) def create_and_check_encoder_decoder_shared_weights( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): for model_class in [T5Model, T5ForConditionalGeneration]: torch.manual_seed(0) model = model_class(config=config).to(torch_device).eval() # load state dict copies weights but does not tie them model.encoder.load_state_dict(model.decoder.state_dict(), strict=False) torch.manual_seed(0) tied_config = copy.deepcopy(config) tied_config.tie_encoder_decoder = True tied_model = model_class(config=tied_config).to(torch_device).eval() model_result = model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, ) tied_model_result = tied_model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, ) # check that models has less parameters self.parent.assertLess( sum(p.numel() for p in tied_model.parameters()), sum(p.numel() for p in model.parameters()) ) random_slice_idx = ids_tensor((1,), model_result[0].shape[-1]).item() # check that outputs are equal self.parent.assertTrue( torch.allclose( model_result[0][0, :, random_slice_idx], tied_model_result[0][0, :, random_slice_idx], atol=1e-4 ) ) # check that outputs after saving and loading are equal with tempfile.TemporaryDirectory() as tmpdirname: tied_model.save_pretrained(tmpdirname) tied_model = model_class.from_pretrained(tmpdirname) tied_model.to(torch_device) tied_model.eval() # check that models has less parameters self.parent.assertLess( sum(p.numel() for p in tied_model.parameters()), sum(p.numel() for p in model.parameters()) ) random_slice_idx = ids_tensor((1,), model_result[0].shape[-1]).item() tied_model_result = tied_model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, ) # check that outputs are equal self.parent.assertTrue( torch.allclose( model_result[0][0, :, random_slice_idx], tied_model_result[0][0, :, random_slice_idx], atol=1e-4, ) ) def check_resize_embeddings_t5_v1_1( self, config, ): prev_vocab_size = config.vocab_size config.tie_word_embeddings = False model = T5ForConditionalGeneration(config=config).to(torch_device).eval() model.resize_token_embeddings(prev_vocab_size - 10) self.parent.assertEqual(model.get_input_embeddings().weight.shape[0], prev_vocab_size - 10) self.parent.assertEqual(model.get_output_embeddings().weight.shape[0], prev_vocab_size - 10) self.parent.assertEqual(model.config.vocab_size, prev_vocab_size - 10) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": decoder_attention_mask, "use_cache": False, } return config, inputs_dict @require_torch class T5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( (T5Model, T5ForConditionalGeneration, T5ForSequenceClassification, T5ForQuestionAnswering) if is_torch_available() else () ) all_generative_model_classes = (T5ForConditionalGeneration,) if is_torch_available() else () pipeline_model_mapping = ( { "conversational": T5ForConditionalGeneration, "feature-extraction": T5Model, "question-answering": T5ForQuestionAnswering, "summarization": T5ForConditionalGeneration, "text-classification": T5ForSequenceClassification, "text2text-generation": T5ForConditionalGeneration, "translation": T5ForConditionalGeneration, "zero-shot": T5ForSequenceClassification, } if is_torch_available() else {} ) all_parallelizable_model_classes = (T5Model, T5ForConditionalGeneration) if is_torch_available() else () fx_compatible = True test_pruning = False test_resize_embeddings = True test_model_parallel = True is_encoder_decoder = True # The small T5 model needs higher percentages for CPU/MP tests model_split_percents = [0.8, 0.9] def setUp(self): self.model_tester = T5ModelTester(self) self.config_tester = ConfigTester(self, config_class=T5Config, d_model=37) # `QAPipelineTests` is not working well with slow tokenizers (for some models) and we don't want to touch the file # `src/transformers/data/processors/squad.py` (where this test fails for this model) def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): if pipeline_test_casse_name == "QAPipelineTests" and not tokenizer_name.endswith("Fast"): return True return False def _create_and_check_torch_fx_tracing(self, config, inputs_dict, output_loss=False): if not is_torch_fx_available() or not self.fx_compatible: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.return_dict = False for model_class in self.all_model_classes: if model_class.__name__ == "T5ForSequenceClassification": continue model = model_class(config=configs_no_init) model.to(torch_device) model.eval() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=output_loss) try: if model.config.is_encoder_decoder: model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward labels = inputs.get("labels", None) input_names = [ "attention_mask", "decoder_attention_mask", "decoder_input_ids", "input_features", "input_ids", "input_values", ] if labels is not None: input_names.append("labels") filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names} input_names = list(filtered_inputs.keys()) model_output = model(**filtered_inputs) traced_model = symbolic_trace(model, input_names) traced_output = traced_model(**filtered_inputs) else: input_names = [ "attention_mask", "bbox", "input_features", "input_ids", "input_values", "pixel_values", "token_type_ids", "visual_feats", "visual_pos", ] labels = inputs.get("labels", None) start_positions = inputs.get("start_positions", None) end_positions = inputs.get("end_positions", None) if labels is not None: input_names.append("labels") if start_positions is not None: input_names.append("start_positions") if end_positions is not None: input_names.append("end_positions") filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names} input_names = list(filtered_inputs.keys()) if model.__class__.__name__ in set(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES.values()) and ( not hasattr(model.config, "problem_type") or model.config.problem_type is None ): model.config.problem_type = "single_label_classification" traced_model = symbolic_trace(model, input_names) traced_output = traced_model(**filtered_inputs) model_output = model(**filtered_inputs) except Exception as e: self.fail(f"Couldn't trace module: {e}") def flatten_output(output): flatten = [] for x in output: if isinstance(x, (tuple, list)): flatten += flatten_output(x) elif not isinstance(x, torch.Tensor): continue else: flatten.append(x) return flatten model_output = flatten_output(model_output) traced_output = flatten_output(traced_output) num_outputs = len(model_output) for i in range(num_outputs): self.assertTrue( torch.allclose(model_output[i], traced_output[i]), f"traced {i}th output doesn't match model {i}th output for {model_class}", ) # Test that the model can be serialized and restored properly with tempfile.TemporaryDirectory() as tmp_dir_name: pkl_file_name = os.path.join(tmp_dir_name, "model.pkl") try: with open(pkl_file_name, "wb") as f: pickle.dump(traced_model, f) with open(pkl_file_name, "rb") as f: loaded = pickle.load(f) except Exception as e: self.fail(f"Couldn't serialize / deserialize the traced model: {e}") loaded_output = loaded(**filtered_inputs) loaded_output = flatten_output(loaded_output) for i in range(num_outputs): self.assertTrue( torch.allclose(model_output[i], loaded_output[i]), f"serialized model {i}th output doesn't match model {i}th output for {model_class}", ) # Avoid memory leak. Without this, each call increase RAM usage by ~20MB. # (Even with this call, there are still memory leak by ~0.04MB) self.clear_torch_jit_class_registry() def test_config(self): self.config_tester.run_common_tests() def test_shift_right(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_prepare_lm_labels_via_shift_left(*config_and_inputs) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_v1_1(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() # check that gated gelu feed forward and different word embeddings work config = config_and_inputs[0] config.tie_word_embeddings = False config.feed_forward_proj = "gated-gelu" self.model_tester.create_and_check_model(config, *config_and_inputs[1:]) # T5ForSequenceClassification does not support inputs_embeds def test_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in (T5Model, T5ForConditionalGeneration, T5ForQuestionAnswering): model = model_class(config) model.to(torch_device) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) if not self.is_encoder_decoder: input_ids = inputs["input_ids"] del inputs["input_ids"] else: encoder_input_ids = inputs["input_ids"] decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids) del inputs["input_ids"] inputs.pop("decoder_input_ids", None) wte = model.get_input_embeddings() if not self.is_encoder_decoder: inputs["inputs_embeds"] = wte(input_ids) else: inputs["inputs_embeds"] = wte(encoder_input_ids) inputs["decoder_inputs_embeds"] = wte(decoder_input_ids) with torch.no_grad(): model(**inputs)[0] def test_config_and_model_silu_gated(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() config = config_and_inputs[0] config.feed_forward_proj = "gated-silu" self.model_tester.create_and_check_model(*config_and_inputs) def test_with_lm_head(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_with_lm_head(*config_and_inputs) def test_with_sequence_classification_head(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_with_sequence_classification_head(*config_and_inputs) def test_decoder_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past(*config_and_inputs) def test_decoder_model_past_with_attn_mask(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs) def test_decoder_model_past_with_3d_attn_mask(self): ( config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ) = self.model_tester.prepare_config_and_inputs() attention_mask = ids_tensor( [self.model_tester.batch_size, self.model_tester.encoder_seq_length, self.model_tester.encoder_seq_length], vocab_size=2, ) decoder_attention_mask = ids_tensor( [self.model_tester.batch_size, self.model_tester.decoder_seq_length, self.model_tester.decoder_seq_length], vocab_size=2, ) self.model_tester.create_and_check_decoder_model_attention_mask_past( config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ) def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_generate_with_past_key_values(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_generate_with_past_key_values(*config_and_inputs) def test_encoder_decoder_shared_weights(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_encoder_decoder_shared_weights(*config_and_inputs) @unittest.skipIf(torch_device == "cpu", "Cant do half precision") def test_model_fp16_forward(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_fp16_forward(*config_and_inputs) def test_v1_1_resize_embeddings(self): config = self.model_tester.prepare_config_and_inputs()[0] self.model_tester.check_resize_embeddings_t5_v1_1(config) @slow def test_model_from_pretrained(self): for model_name in T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = T5Model.from_pretrained(model_name) self.assertIsNotNone(model) @unittest.skip("Test has a segmentation fault on torch 1.8.0") def test_export_to_onnx(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() model = T5Model(config_and_inputs[0]).to(torch_device) with tempfile.TemporaryDirectory() as tmpdirname: torch.onnx.export( model, (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]), f"{tmpdirname}/t5_test.onnx", export_params=True, opset_version=9, input_names=["input_ids", "decoder_input_ids"], ) def test_generate_with_head_masking(self): attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"] config_and_inputs = self.model_tester.prepare_config_and_inputs() config = config_and_inputs[0] max_length = config_and_inputs[1].shape[-1] + 3 model = T5ForConditionalGeneration(config).eval() model.to(torch_device) head_masking = { "head_mask": torch.zeros(config.num_layers, config.num_heads, device=torch_device), "decoder_head_mask": torch.zeros(config.num_decoder_layers, config.num_heads, device=torch_device), "cross_attn_head_mask": torch.zeros(config.num_decoder_layers, config.num_heads, device=torch_device), } for attn_name, (name, mask) in zip(attention_names, head_masking.items()): head_masks = {name: mask} # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified if name == "head_mask": head_masks["decoder_head_mask"] = torch.ones( config.num_decoder_layers, config.num_heads, device=torch_device ) out = model.generate( config_and_inputs[1], num_beams=1, max_length=max_length, output_attentions=True, return_dict_in_generate=True, **head_masks, ) # We check the state of decoder_attentions and cross_attentions just from the last step attn_weights = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1] self.assertEqual(sum([w.sum().item() for w in attn_weights]), 0.0) @unittest.skip("Does not work on the tiny model as we keep hitting edge cases.") def test_disk_offload(self): pass class T5EncoderOnlyModelTester: 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 get_large_model_config(self): return T5Config.from_pretrained("t5-base") 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 = T5EncoderModel(config=config) model.to(torch_device) model.eval() 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.size(), (self.batch_size, self.encoder_seq_length, self.hidden_size)) def create_and_check_model_fp16_forward( self, config, input_ids, attention_mask, ): model = T5EncoderModel(config=config).to(torch_device).half().eval() output = model(input_ids, attention_mask=attention_mask)["last_hidden_state"] self.parent.assertFalse(torch.isnan(output).any().item()) 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 T5EncoderOnlyModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (T5EncoderModel,) if is_torch_available() else () test_pruning = False test_resize_embeddings = False test_model_parallel = True all_parallelizable_model_classes = (T5EncoderModel,) if is_torch_available() else () def setUp(self): self.model_tester = T5EncoderOnlyModelTester(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) @unittest.skipIf(torch_device == "cpu", "Cant do half precision") def test_model_fp16_forward(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_fp16_forward(*config_and_inputs) def use_task_specific_params(model, task): model.config.update(model.config.task_specific_params[task]) @require_torch @require_accelerate @require_tokenizers @slow class T5ModelFp16Tests(unittest.TestCase): def test_fp16_fp32_conversion(self): r""" A test to check whether the argument `keep_in_fp32_modules` correctly does its job """ orig_import = __import__ accelerate_mock = unittest.mock.Mock() # mock import of accelerate def import_accelerate_mock(name, *args, **kwargs): if name == "accelerate": if accelerate_available: return accelerate_mock else: raise ImportError return orig_import(name, *args, **kwargs) # Load without using `accelerate` with unittest.mock.patch("builtins.__import__", side_effect=import_accelerate_mock): accelerate_available = False model = T5ForConditionalGeneration.from_pretrained("t5-small", torch_dtype=torch.float16) self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.float32) self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wi.weight.dtype == torch.float16) # Load without in bf16 model = T5ForConditionalGeneration.from_pretrained("t5-small", torch_dtype=torch.bfloat16) self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.bfloat16) self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wi.weight.dtype == torch.bfloat16) # Load using `accelerate` in bf16 model = T5ForConditionalGeneration.from_pretrained("t5-small", torch_dtype=torch.bfloat16, device_map="auto") self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.bfloat16) self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wi.weight.dtype == torch.bfloat16) # Load using `accelerate` in bf16 model = T5ForConditionalGeneration.from_pretrained( "t5-small", torch_dtype=torch.bfloat16, low_cpu_mem_usage=True ) self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.bfloat16) self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wi.weight.dtype == torch.bfloat16) # Load without using `accelerate` model = T5ForConditionalGeneration.from_pretrained( "t5-small", torch_dtype=torch.float16, low_cpu_mem_usage=True ) self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.float32) self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wi.weight.dtype == torch.float16) # Load using `accelerate` model = T5ForConditionalGeneration.from_pretrained("t5-small", torch_dtype=torch.float16, device_map="auto") self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.float32) self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wi.weight.dtype == torch.float16) @require_torch @require_sentencepiece @require_tokenizers class T5ModelIntegrationTests(unittest.TestCase): @cached_property def model(self): return T5ForConditionalGeneration.from_pretrained("t5-base").to(torch_device) @cached_property def tokenizer(self): return T5Tokenizer.from_pretrained("t5-base") @slow def test_torch_quant(self): r""" Test that a simple `torch.quantization.quantize_dynamic` call works on a T5 model. """ model_name = "google/flan-t5-small" tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) model = torch.quantization.quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8) input_text = "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids _ = model.generate(input_ids) @slow def test_small_generation(self): model = T5ForConditionalGeneration.from_pretrained("t5-small").to(torch_device) model.config.max_length = 8 model.config.num_beams = 1 model.config.do_sample = False tokenizer = T5Tokenizer.from_pretrained("t5-small") input_ids = tokenizer("summarize: Hello there", return_tensors="pt").input_ids.to(torch_device) sequences = model.generate(input_ids) output_str = tokenizer.batch_decode(sequences, skip_special_tokens=True)[0] self.assertTrue(output_str == "Hello there!") @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 = T5ForConditionalGeneration.from_pretrained("t5-small").to(torch_device) tokenizer = T5Tokenizer.from_pretrained("t5-small") input_ids = tokenizer("Hello there", return_tensors="pt").input_ids labels = tokenizer("Hi I am", return_tensors="pt").input_ids loss = model(input_ids.to(torch_device), labels=labels.to(torch_device)).loss mtf_score = -(labels.shape[-1] * loss.item()) EXPECTED_SCORE = -19.0845 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 = T5ForConditionalGeneration.from_pretrained("google/t5-v1_1-small").to(torch_device) tokenizer = T5Tokenizer.from_pretrained("google/t5-v1_1-small") input_ids = tokenizer("Hello there", return_tensors="pt").input_ids labels = tokenizer("Hi I am", return_tensors="pt").input_ids loss = model(input_ids.to(torch_device), labels=labels.to(torch_device)).loss mtf_score = -(labels.shape[-1] * loss.item()) EXPECTED_SCORE = -59.0293 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 = T5ForConditionalGeneration.from_pretrained("google/byt5-small").to(torch_device) tokenizer = ByT5Tokenizer.from_pretrained("google/byt5-small") input_ids = tokenizer("Hello there", return_tensors="pt").input_ids labels = tokenizer("Hi I am", return_tensors="pt").input_ids loss = model(input_ids.to(torch_device), labels=labels.to(torch_device)).loss mtf_score = -(labels.shape[-1] * loss.item()) EXPECTED_SCORE = -60.7397 self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4) @slow def test_summarization(self): model = self.model tok = self.tokenizer 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 .", ] use_task_specific_params(model, "summarization") dct = tok( [model.config.prefix + x for x in [FRANCE_ARTICLE, SHORTER_ARTICLE, IRAN_ARTICLE, ARTICLE_SUBWAY]], padding="max_length", truncation=True, return_tensors="pt", ).to(torch_device) self.assertEqual(512, dct["input_ids"].shape[1]) hypotheses_batch = model.generate( **dct, 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.batch_decode(hypotheses_batch, skip_special_tokens=True, clean_up_tokenization_spaces=False) self.assertListEqual( expected_summaries, decoded, ) @slow def test_translation_en_to_de(self): model = self.model tok = self.tokenizer use_task_specific_params(model, "translation_en_to_de") en_text = '"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 + en_text, return_tensors="pt") input_ids = input_ids.to(torch_device) output = model.generate(input_ids) 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 # t5-base tok = self.tokenizer use_task_specific_params(model, "translation_en_to_fr") 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. " ) input_ids = tok.encode(model.config.prefix + en_text, return_tensors="pt") input_ids = input_ids.to(torch_device) 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) 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." ) self.assertEqual(translation, new_truncated_translation) @slow def test_translation_en_to_ro(self): model = self.model tok = self.tokenizer use_task_specific_params(model, "translation_en_to_ro") en_text = "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." inputs = tok(model.config.prefix + en_text, return_tensors="pt").to(torch_device) output = model.generate(**inputs) translation = tok.decode(output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False) self.assertEqual(translation, expected_translation) @slow def test_contrastive_search_t5(self): article = ( " 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." ) article = "summarize: " + article.strip() t5_tokenizer = AutoTokenizer.from_pretrained("flax-community/t5-base-cnn-dm") t5_model = T5ForConditionalGeneration.from_pretrained("flax-community/t5-base-cnn-dm").to(torch_device) input_ids = t5_tokenizer( article, add_special_tokens=False, truncation=True, max_length=512, return_tensors="pt" ).input_ids.to(torch_device) outputs = t5_model.generate(input_ids, penalty_alpha=0.5, top_k=5, max_length=64) generated_text = t5_tokenizer.batch_decode(outputs, skip_special_tokens=True) self.assertListEqual( generated_text, [ "Liana Barrientos has been married 10 times, nine of them in the Bronx. Her husbands filed for " "permanent residence after the marriages, prosecutors say." ], ) @require_torch class TestAsymmetricT5(unittest.TestCase): def build_model_and_check_forward_pass(self, **kwargs): tester = T5ModelTester(self, **kwargs) config, *inputs = tester.prepare_config_and_inputs() ( input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ) = inputs model = T5ForConditionalGeneration(config=config).to(torch_device).eval() outputs = model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, labels=lm_labels, ) # outputs = model(*inputs) assert len(outputs) == 4 assert outputs["logits"].size() == (tester.batch_size, tester.decoder_seq_length, tester.vocab_size) assert outputs["loss"].size() == () return model def test_small_decoder(self): # num_hidden_layers is passed to T5Config as num_layers model = self.build_model_and_check_forward_pass(decoder_layers=1, num_hidden_layers=2) assert len(model.encoder.block) == 2 assert len(model.decoder.block) == 1 def test_defaulting_to_symmetry(self): # num_hidden_layers is passed to T5Config as num_layers model = self.build_model_and_check_forward_pass(num_hidden_layers=2) assert len(model.decoder.block) == len(model.encoder.block) == 2