# Copyright 2022 Google LongT5 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 tempfile import unittest from functools import cached_property from transformers import LongT5Config, is_torch_available from transformers.models.auto import get_values from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch import torch.nn.functional as F from transformers import ( MODEL_FOR_QUESTION_ANSWERING_MAPPING, AutoTokenizer, LongT5EncoderModel, LongT5ForConditionalGeneration, LongT5Model, ) class LongT5ModelTester: def __init__( self, parent, vocab_size=99, batch_size=13, encoder_seq_length=7, decoder_seq_length=9, local_radius=5, encoder_attention_type="local", global_block_size=3, # 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, large_model_config_path="google/long-t5-local-large", ): self.parent = parent self.batch_size = batch_size self.encoder_seq_length = encoder_seq_length self.decoder_seq_length = decoder_seq_length self.local_radius = local_radius self.block_len = local_radius + 1 self.encoder_attention_type = encoder_attention_type self.global_block_size = global_block_size # 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 self.large_model_config_path = large_model_config_path def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size) 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 LongT5Config( vocab_size=166, # longt5 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, local_radius=self.local_radius, encoder_attention_type=self.encoder_attention_type, global_block_size=self.global_block_size, ) def get_config(self): return LongT5Config( 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, local_radius=self.local_radius, encoder_attention_type=self.encoder_attention_type, global_block_size=self.global_block_size, ) def check_prepare_lm_labels_via_shift_left( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = LongT5Model(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 = LongT5Model(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) def create_and_check_with_lm_head( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = LongT5ForConditionalGeneration(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_decoder_model_past( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = LongT5Model(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 = LongT5Model(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 = LongT5Model(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 = LongT5ForConditionalGeneration(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_encoder_decoder_shared_weights( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): for model_class in [LongT5Model, LongT5ForConditionalGeneration]: 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, ) 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() 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 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 LongT5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (LongT5Model, LongT5ForConditionalGeneration) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": LongT5Model, "summarization": LongT5ForConditionalGeneration, "text2text-generation": LongT5ForConditionalGeneration, "translation": LongT5ForConditionalGeneration, } if is_torch_available() else {} ) test_resize_embeddings = True is_encoder_decoder = True def setUp(self): self.model_tester = LongT5ModelTester(self) self.config_tester = ConfigTester(self, config_class=LongT5Config, d_model=37) 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_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_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, ) # overwrite because T5 doesn't accept position ids as input and expects `decoder_input_ids` def test_custom_4d_attention_mask(self): for model_class in self.all_generative_model_classes: config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config).to(device=torch_device, dtype=torch.float32) ( input_ids, _, input_ids_shared_prefix, mask_shared_prefix, _, ) = self._get_custom_4d_mask_test_data() logits = model.forward( decoder_input_ids=input_ids, input_ids=input_dict["input_ids"][:3], ).logits # logits.shape == torch.Size([3, 4, ...]) logits_shared_prefix = model( input_ids=input_dict["input_ids"][:1], decoder_input_ids=input_ids_shared_prefix, decoder_attention_mask=mask_shared_prefix, )[0] # logits_shared_prefix.shape == torch.Size([1, 6, ...]) out_last_tokens = logits[:, -1, :] # last tokens in each batch line out_shared_prefix_last_tokens = logits_shared_prefix[0, -3:, :] # last three tokens # comparing softmax-normalized logits: normalized_0 = F.softmax(out_last_tokens) normalized_1 = F.softmax(out_shared_prefix_last_tokens) torch.testing.assert_close(normalized_0, normalized_1, rtol=1e-3, atol=1e-4) 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) @slow def test_model_from_pretrained(self): model_name = "google/long-t5-local-base" model = LongT5Model.from_pretrained(model_name) self.assertIsNotNone(model) def test_attention_outputs(self): if not self.has_attentions: self.skipTest(reason="has_attentions is set to False") else: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) chunk_length = getattr(self.model_tester, "chunk_length", None) block_len = getattr(self.model_tester, "block_len", None) if chunk_length is not None and hasattr(self.model_tester, "num_hashes"): encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, block_len, 3 * block_len], ) out_len = len(outputs) if self.is_encoder_decoder: correct_outlen = 5 # loss is at first position if "labels" in inputs_dict: correct_outlen += 1 # loss is added to beginning # Question Answering model returns start_logits and end_logits if model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING): correct_outlen += 1 # start_logits and end_logits instead of only 1 output if "past_key_values" in outputs: correct_outlen += 1 # past_key_values have been returned self.assertEqual(out_len, correct_outlen) # decoder attentions decoder_attentions = outputs.decoder_attentions self.assertIsInstance(decoder_attentions, (list, tuple)) self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length], ) # cross attentions cross_attentions = outputs.cross_attentions self.assertIsInstance(cross_attentions, (list, tuple)) self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(cross_attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, decoder_seq_length, encoder_key_length, ], ) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types elif self.is_encoder_decoder: added_hidden_states = 2 else: added_hidden_states = 1 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, block_len, 3 * block_len], ) def _check_encoder_attention_for_generate(self, attentions, batch_size, config, prompt_length): block_len = getattr(self.model_tester, "block_len", None) encoder_expected_shape = (batch_size, 2, config.num_attention_heads, block_len, 3 * block_len) self.assertIsInstance(attentions, tuple) self.assertListEqual( [layer_attentions.shape for layer_attentions in attentions], [encoder_expected_shape] * len(attentions), ) @unittest.skip( reason="This architecture has tied weights by default and there is no way to remove it, check: https://github.com/huggingface/transformers/pull/31771#issuecomment-2210915245" ) def test_load_save_without_tied_weights(self): pass @unittest.skip(reason="LongT5 has no separate base model without a head.") def test_model_base_model_prefix(self): pass @require_torch class LongT5TGlobalModelTest(LongT5ModelTest): def setUp(self): self.model_tester = LongT5ModelTester( self, encoder_attention_type="transient-global", large_model_config_path="google/long-t5-tglobal-large" ) self.config_tester = ConfigTester(self, config_class=LongT5Config, d_model=37) def test_attention_outputs(self): if not self.has_attentions: self.skipTest(reason="has_attentions is set to False") else: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) chunk_length = getattr(self.model_tester, "chunk_length", None) block_len = getattr(self.model_tester, "block_len", None) global_block_size = getattr(self.model_tester, "global_block_size", None) global_seq_len = encoder_seq_length // global_block_size if chunk_length is not None and hasattr(self.model_tester, "num_hashes"): encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, block_len, 3 * block_len + global_seq_len], ) out_len = len(outputs) if self.is_encoder_decoder: correct_outlen = 5 # loss is at first position if "labels" in inputs_dict: correct_outlen += 1 # loss is added to beginning # Question Answering model returns start_logits and end_logits if model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING): correct_outlen += 1 # start_logits and end_logits instead of only 1 output if "past_key_values" in outputs: correct_outlen += 1 # past_key_values have been returned self.assertEqual(out_len, correct_outlen) # decoder attentions decoder_attentions = outputs.decoder_attentions self.assertIsInstance(decoder_attentions, (list, tuple)) self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length], ) # cross attentions cross_attentions = outputs.cross_attentions self.assertIsInstance(cross_attentions, (list, tuple)) self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(cross_attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, decoder_seq_length, encoder_key_length, ], ) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types elif self.is_encoder_decoder: added_hidden_states = 2 else: added_hidden_states = 1 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, block_len, 3 * block_len + global_seq_len], ) def _check_encoder_attention_for_generate(self, attentions, batch_size, config, prompt_length): block_len = getattr(self.model_tester, "block_len", None) global_block_size = getattr(self.model_tester, "global_block_size", None) global_seq_length = prompt_length // global_block_size encoder_expected_shape = ( batch_size, 2, config.num_attention_heads, block_len, 3 * block_len + global_seq_length, ) self.assertIsInstance(attentions, tuple) self.assertListEqual( [layer_attentions.shape for layer_attentions in attentions], [encoder_expected_shape] * len(attentions), ) @unittest.skip(reason="LongT5 has no separate base model without a head.") def test_model_base_model_prefix(self): pass class LongT5EncoderOnlyModelTester: def __init__( self, parent, vocab_size=99, batch_size=13, encoder_seq_length=7, local_radius=5, encoder_attention_type="local", global_block_size=3, # 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, large_model_config_path="google/long-t5-local-large", ): self.parent = parent self.batch_size = batch_size self.encoder_seq_length = encoder_seq_length self.local_radius = local_radius self.block_len = local_radius + 1 self.encoder_attention_type = encoder_attention_type self.global_block_size = global_block_size # 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 self.large_model_config_path = large_model_config_path 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 = LongT5Config( 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, local_radius=self.local_radius, encoder_attention_type=self.encoder_attention_type, global_block_size=self.global_block_size, ) return ( config, input_ids, attention_mask, ) def create_and_check_model( self, config, input_ids, attention_mask, ): model = LongT5EncoderModel(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 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 LongT5EncoderOnlyModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (LongT5EncoderModel,) if is_torch_available() else () test_resize_embeddings = False def setUp(self): self.model_tester = LongT5EncoderOnlyModelTester(self) self.config_tester = ConfigTester(self, config_class=LongT5Config, 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) def test_attention_outputs(self): if not self.has_attentions: self.skipTest(reason="has_attentions is set to False") else: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True block_len = getattr(self.model_tester, "block_len", 4) for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, block_len, 3 * block_len], ) out_len = len(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types elif self.is_encoder_decoder: added_hidden_states = 2 else: added_hidden_states = 1 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, block_len, 3 * block_len], ) @unittest.skip( reason="This architecture has tied weights by default and there is no way to remove it, check: https://github.com/huggingface/transformers/pull/31771#issuecomment-2210915245" ) def test_load_save_without_tied_weights(self): pass class LongT5EncoderOnlyTGlobalModelTest(LongT5EncoderOnlyModelTest): def setUp(self): self.model_tester = LongT5EncoderOnlyModelTester( self, encoder_attention_type="transient-global", large_model_config_path="google/long-t5-tglobal-large" ) self.config_tester = ConfigTester(self, config_class=LongT5Config, d_model=37) def test_attention_outputs(self): if not self.has_attentions: self.skipTest(reason="has_attentions is set to False") else: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True block_len = getattr(self.model_tester, "block_len", None) seq_len = getattr(self.model_tester, "seq_length", None) global_block_size = getattr(self.model_tester, "global_block_size", 4) global_seq_len = seq_len // global_block_size for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, block_len, 3 * block_len + global_seq_len], ) out_len = len(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types elif self.is_encoder_decoder: added_hidden_states = 2 else: added_hidden_states = 1 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, block_len, 3 * block_len + global_seq_len], ) def use_task_specific_params(model, task): model.config.update(model.config.task_specific_params[task]) @require_torch @require_sentencepiece @require_tokenizers class LongT5ModelIntegrationTests(unittest.TestCase): @cached_property def model(self): return LongT5ForConditionalGeneration.from_pretrained("Stancld/longt5-tglobal-large-16384-pubmed-3k_steps").to( torch_device ) @cached_property def tokenizer(self): return AutoTokenizer.from_pretrained("Stancld/longt5-tglobal-large-16384-pubmed-3k_steps") def expected_summary(self): return [ "background : coronary artery disease ( cad ) is the emerging cause of morbidity and mortality in" " developing world . it provides an excellent resolution for visualization of the coronaryarteries for" " catheter - based or operating interventions . although the association of this technique with major" " complications such as mortality is highly uncommon , it is frequently associated with various cardiac" " and noncardiac complications.materials and methods : in aortic stenosis , we aimed to report the" " diagnostic performance of 128-slice computed tomography coronary angiogram in 50 patients undergoing for" " major noncoron ary cardiac surgery referred" ] @slow def test_summarization(self): model = self.model tok = self.tokenizer ARTICLE = """coronary artery disease ( cad ) is the emerging cause of morbidity and mortality in developing world . \n it provides an excellent resolution for visualization of the coronary arteries for catheter - based or operating interventions . \n although the association of this technique with major complications such as mortality is highly uncommon , it is frequently associated with various cardiac and noncardiac complications . computed tomography ( ct ) coronary angiography is a promising technique for the evaluation of cad noninvasively . \n it assesses disease within the coronary artery and provides qualitative and quantitative information about nonobstructive atherosclerotic plaque burden within the vessel wall . \n thus , ct angiography - based disease evaluation may provide clinically more significant information than conventional angiography . the introduction of multi - slice computed tomography ( msct ) technology such as 64-slice , 12 8-slice , 256-slice , and now 320-slice msct has produced a high diagnostic accuracy of ct coronary angiography . \n it has consistently showed to have a very high negative predictive value ( well above 90% ) in ruling out patients with s ignificant cad defined as coronary luminal stenosis of > 50% . \n the american college of cardiology / american heart association recommends that coronary angiography should be performed before valve surgery in men aged > 40 years , women aged > 35 years with coronary risk factors and in postmenopausal women . \n the prevalence of cad in patients undergoing valve replacement is 2040% in developed countries . in the previous studies , \n the incidence of angiographically p roven cad in acquired valvular diseases has been shown to vary widely from 9% to 41% . in aortic stenosis , \n we aimed to report the diagnostic performance of 128-slice ct coronary angiography in 50 patients undergoing for major noncoron ary cardiac surgery referred for diagnostic invasive coronary angiography to assess the extent and severity of coronary stenosis . \n during january 2013 to december 2014 , we enrolled fifty major noncoronary cardiac surgery patients sche duled for invasive coronary angiography who fulfilled the following inclusion criteria of age 40 years , having low or intermediate probability of cad , left ventricular ejection fraction ( lvef ) > 35% , and patient giving informed conse nt for undergoing msct and conventional coronary angiography . \n those having any contraindication for contrast injection , lvef < 35% , high pretest probability of cad , and hemodynamic instability were excluded from the study . \n pati ents with heart rates of > 70 bpm received ( unless they had known overt heart failure or electrocardiogram ( ecg ) atrioventricular conduction abnormalities ) a single oral dose of 100 mg metoprolol 45 min before the scan . \n patients w ith heart rates of > 80 bpm received an additional oral dose of metoprolol if not contraindicated . \n all patients were scanned with a 128-slice ct scanner ( siemens , somatom definition as ) equipped with a new feature in msct technolog y , so - called z - axis flying - focus technology . \n the central 32 detector rows acquire 0.6-mm slices , and the flying - focus spot switches back and forth between 2 z positions between each reading . \n two slices per detector row a re acquired , which results in a higher oversampling rate in the z - axis , thereby reducing artifacts related to the spiral acquisition and improving spatial resolution down to 0.4 mm . \n a bolus of 6580 ml contrast material ( omnipaque ) was injected through an arm vein at a flow rate of 5 ml / s . \n a bolus tracking technique was used to synchronize the arrival of contrast in the coronary arteries with the initiation of the scan . to monitor the arrival of contrast m aterial , \n axial scans were obtained at the level of the ascending aorta with a delay of 10 s after the start of the contrast injection . \n the scan was automatically started when a threshold of 150 hounsfield units was reached in a re gion of interest positioned in the ascending aorta . \n images were reconstructed with ecg gating to obtain optimal , motion - free image quality . \n all scans were performed within 2 weeks of the msct coronary diagnostic angiogram . a s ingle observer unaware of the multi - slice ct results identified coronary lesion as a single vessel , double vessel , or triple vessel disease . \n all lesion , regardless of size , were included for comparison with ct coronary angiograp hy . \n lesions were classified as having nonsignificant disease ( luminal irregularities or < 50% stenosis ) or as having significant stenosis . \n stenosis was evaluated in two orthogonal views and classified as significant if the mean lumen diameter reduction was 50% using a validated quantitative coronary angiography ( qca ) . \n all scans were analyzed independently by a radiologist and a cardiologist who were unaware of the results of conventional coronary angiograp hy . \n total calcium scores of all patients were calculated with dedicated software and expressed as agatston scores . \n the agatston score is a commonly used scoring method that calculates the total amount of calcium on the basis of th e number , areas , and peak hounsfield units of the detected calcified lesions . \n all available coronary segments were visually scored for the presence of > 50% considered as significant stenosis . \n maximum intensity projections were used to identify coronary lesions and ( curved ) multiplanar reconstructions to classify lesions as significant or nonsignificant . \n data were analyzed using statistical system spss version 20 software ( chicago , il , usa ) . \n the di agnostic performance of ct coronary angiography for the detection of significant lesions in coronary arteries with qca as the standard of reference is presented as sensitivity , specificity , positive and negative predictive values , and positive and negative likelihood ratios with the corresponding exact 95% of confidence interval ( cis ) . \n comparison between ct and conventional coronary angiography was performed on the two level vessel by vessel ( no or any disease p er vessel ) , and patient by patient ( no or any disease per patient ) . \n all scans were performed within 2 weeks of the msct coronary diagnostic angiogram . a single observer unaware of the multi - slice ct results identified coronary lesion as a single vessel , double vessel , or triple vessel disease . \n all lesion , regardless of size , were included for comparison with ct coronary angiography . \n lesions were classified as having nonsignificant disease ( luminal irregularities or < 50% stenosis ) or as having significant stenosis . \n stenosis was evaluated in two orthogonal views and classified as significant if the mean lumen diameter reduction was 50% using a validated quantitative coronary an giography ( qca ) . \n all scans were analyzed independently by a radiologist and a cardiologist who were unaware of the results of conventional coronary angiography . \n total calcium scores of all patients were calculated with dedicated software and expressed as agatston scores . \n the agatston score is a commonly used scoring method that calculates the total amount of calcium on the basis of the number , areas , and peak hounsfield units of the detected calcified lesi ons . \n all available coronary segments were visually scored for the presence of > 50% considered as significant stenosis . \n maximum intensity projections were used to identify coronary lesions and ( curved ) multiplanar reconstruction s to classify lesions as significant or nonsignificant . \n data were analyzed using statistical system spss version 20 software ( chicago , il , usa ) . \n the diagnostic performance of ct coronary angiography for the detection of signif icant lesions in coronary arteries with qca as the standard of reference is presented as sensitivity , specificity , positive and negative predictive values , and positive and negative likelihood ratios with the corresponding exact 95% of confidence interval ( cis ) . \n comparison between ct and conventional coronary angiography was performed on the two level vessel by vessel ( no or any disease per vessel ) , and patient by patient ( no or any disease per patient ) . \n in this study , 29 ( 58% ) subjects were female , and 21 ( 42% ) were male showing an average age of 50.36 8.39 years . \n of fifty patients 24 ( 48% ) , 13 ( 26% ) , eight ( 16% ) , and five ( 10% ) underwent mitral valve replacement , double valve replacement ( dvr ) , aortic valve replacement , and other surgeries , respectively . \n high distribution of cad risk factors such as hypertension ( 24% ) , smoking ( 22% ) , and dyslipidemia ( 18% ) was observed in the stu dy group . \n the mean creatinine level was 0.766 0.17 and average dye used in conventional angiography was 48.5 26.6 whereas for ct angiography it was 72.8 6.32 . \n average radiation dose in conventional coronary angiography and msct coronary angiography was 5.2 msv and 9.2 msv , respectively . \n the majority of the patients had sinus rhythm ( 68% ) , whereas atrial fibrillation was found in 32% of the subjects . \n patients included in the study had low to intermed iate probability of cad . in this study , three patients had complications after conventional angiography . \n complications were of local site hematoma , acute kidney injury managed conservatively , and acute heart failure . \n a patient who developed hematoma was obese female patients with body mass index > 30 kg / m . \n the patient suffered from pseudoaneurysm , had hospitalized for 9 days , which leads to increased morbidity and cost of hospital stay . \n the diagnos tic accuracy of ct coronary angiography was evaluated regarding true positive , true negative values and is presented in table 1 . the overall sensitivity and \n specificity of ct angiography technique was 100% ( 95% ci : 39.76%100% ) and 91.30% ( 95% ci : 79.21%97.58% ) , respectively [ table 2 ] . \n the positive predictive value ( 50% ; 95% ci : 15.70%84.30% ) and negative predictive value ( 100% ; 95% ci : 91.59%100% ) of ct angiography were also fairly high in these patients . \n recent reports from multiple studies demonstrated that recent - generation msct scanners showed promise for noninvasive detection of coronary stenosis however , until now no studies were found regarding the clinical efficacy or prognostic value of 128-slice ct coronary angiography versus conventional invasive coronary angiography in the diagnosis of patients planned for major noncoronary surgeries such as dvr , bentall , atrial septal defect closure , etc . in our study , we reported 8% cad prevalence in patients planned for major noncoronary cardiac surgery . \n we performed conventional and msct coronary angiography in all patients and the results showed that ct coronary angiography with i nvasive coronary angiography as the reference standard had a considerably high sensitivity ( 100% ) and specificity ( 95.65% ) . \n the health economic model using invasive coronary angiography as the reference standard showed that at a p retest probability of cad of 70% or lower , ct coronary angiography resulted in lower cost per patient with a true positive diagnosis . at a pretest probability of cad of 70% or higher , invasive coronary angiography was associated with a lower cost per patient with a true positive diagnosis . in our study population , \n two patients developed local site complications in the form of hematoma and pseudoaneurysm after conventional angiography . \n hence , msct coronary ang iography will be more favorable in female obese patients with intermediate likelihood of cad . \n hence , msct coronary angiography will be cost - effective in patients of valvular heart diseases . \n however , ct angiography suffers from a drawback that average amount of dye used in msct coronary angiography were 72.8 6.32 ml which is higher than average amount of dye required for conventional angiography ( 48.6 26.6 ml ) . \n hence , the use of ct coronary angiography could not be used in patients with known renal dysfunction , where reduction of contrast dye load is highly advocated . \n our results show that 128-slice ct coronary angiography is a reliable technique to detect coronary stenosis in pat ients planned for noncoronary cardiac surgery . \n although there has been important technological progress in the development of ct coronary angiography , its clinical application remains limited . \n a study wth large numbers of patient s is required for the recommendation of only ct coronary angiography for the coronary evaluation in major non - cardiac surgeries . \n mehta institute of cardiology and research center ( affiliated to bj medical college , ahmedabad , guja rat , india ) . \n u.n . mehta institute of cardiology and research center ( affiliated to bj medical college , ahmedabad , gujarat , india ) . \n """ dct = tok( [ARTICLE], max_length=1024, padding="max_length", truncation=True, return_tensors="pt", ).to(torch_device) 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( self.expected_summary(), decoded, ) @slow def test_inference_hidden_states(self): model = self.model input_ids = torch.tensor( [[100, 19, 3, 9, 7142, 1200, 145, 8, 1252, 14145, 2034, 812, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.long, device=torch_device, ) decoder_input_ids = torch.tensor( [[100, 19, 3, 9, 7142, 1200, 145, 8, 1252, 14145, 2034, 812, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.long, device=torch_device, ) attention_mask = torch.tensor( [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.long, device=torch_device, ) output = model( input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, output_hidden_states=True ) # check if encoder_outputs match expected_output_slice = torch.tensor([0.0629, -0.1294, -0.0089, 0.0772, 0.0663], device=torch_device) torch.testing.assert_close( output.encoder_hidden_states[-1][0, 0, :5], expected_output_slice, rtol=1e-4, atol=1e-4 ) # check if logits match expected_output_slice = torch.tensor([5.5231, 6.1058, 3.1766, 8.2391, -5.9453], device=torch_device) torch.testing.assert_close(output.logits[0, 0, :5], expected_output_slice, rtol=1e-4, atol=1e-4)