# Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Testing suite for the PyTorch PEGASUS-X model.""" import copy import math import tempfile import unittest from functools import cached_property from transformers import is_torch_available from transformers.testing_utils import ( require_sentencepiece, require_tokenizers, require_torch, require_torch_fp16, 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 from transformers import PegasusTokenizer, PegasusXConfig, PegasusXForConditionalGeneration, PegasusXModel from transformers.models.pegasus_x.modeling_pegasus_x import PegasusXDecoder, PegasusXEncoder def prepare_pegasus_x_inputs_dict( config, input_ids, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, ): if attention_mask is None: attention_mask = input_ids.ne(config.pad_token_id) if decoder_attention_mask is None: decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": attention_mask, } @require_torch class PegasusXModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_labels=False, vocab_size=99, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=50, eos_token_id=2, pad_token_id=1, bos_token_id=0, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training 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.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp( 3, ) input_ids[:, -1] = self.eos_token_id # Eos Token decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) config = PegasusXConfig( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, stagger_local_blocks=False, ) inputs_dict = prepare_pegasus_x_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = PegasusXModel(config=config).get_decoder().to(torch_device).eval() input_ids = inputs_dict["input_ids"] attention_mask = inputs_dict["attention_mask"] # 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_attn_mask = ids_tensor((self.batch_size, 3), 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_attn_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-2)) def check_encoder_decoder_model_standalone(self, config, inputs_dict): model = PegasusXModel(config=config).to(torch_device).eval() outputs = model(**inputs_dict) encoder_last_hidden_state = outputs.encoder_last_hidden_state last_hidden_state = outputs.last_hidden_state with tempfile.TemporaryDirectory() as tmpdirname: encoder = model.get_encoder() encoder.save_pretrained(tmpdirname) encoder = PegasusXEncoder.from_pretrained(tmpdirname).to(torch_device) encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"])[ 0 ] self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3) with tempfile.TemporaryDirectory() as tmpdirname: decoder = model.get_decoder() decoder.save_pretrained(tmpdirname) decoder = PegasusXDecoder.from_pretrained(tmpdirname).to(torch_device) last_hidden_state_2 = decoder( input_ids=inputs_dict["decoder_input_ids"], attention_mask=inputs_dict["decoder_attention_mask"], encoder_hidden_states=encoder_last_hidden_state, encoder_attention_mask=inputs_dict["attention_mask"], )[0] self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3) @require_torch class PegasusXModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (PegasusXModel, PegasusXForConditionalGeneration) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": PegasusXModel, "summarization": PegasusXForConditionalGeneration, "text2text-generation": PegasusXForConditionalGeneration, "translation": PegasusXForConditionalGeneration, } if is_torch_available() else {} ) is_encoder_decoder = True test_missing_keys = False def setUp(self): self.model_tester = PegasusXModelTester(self) self.config_tester = ConfigTester(self, config_class=PegasusXConfig) def test_config(self): self.config_tester.run_common_tests() def test_save_load_strict(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(info["missing_keys"], set()) 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_encoder_decoder_model_standalone(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs) def test_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in (PegasusXModel, PegasusXForConditionalGeneration): 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] @require_torch_fp16 def test_generate_fp16(self): config, input_dict = self.model_tester.prepare_config_and_inputs() input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) model = PegasusXForConditionalGeneration(config).eval().to(torch_device) model.half() model.generate(input_ids, attention_mask=attention_mask) model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3) def test_attention_outputs(self): 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) 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._from_config(config, attn_implementation="eager") config = model.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]["local"].shape[-4:]), [ self.model_tester.num_attention_heads, math.ceil(encoder_seq_length / model.config.block_size), model.config.block_size, model.config.block_size + model.config.num_global_tokens, ], ) 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 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]["local"].shape[-4:]), [ self.model_tester.num_attention_heads, math.ceil(encoder_seq_length / model.config.block_size), model.config.block_size, model.config.block_size + model.config.num_global_tokens, ], ) def _check_encoder_attention_for_generate(self, attentions, batch_size, config, prompt_length): encoder_expected_shape = ( batch_size, config.num_attention_heads, math.ceil(prompt_length / config.block_size), config.block_size, config.block_size + config.num_global_tokens, ) self.assertIsInstance(attentions, tuple) self.assertListEqual( [layer_attentions["local"].shape for layer_attentions in attentions], [encoder_expected_shape] * len(attentions), ) def _check_encoder_hidden_states_for_generate(self, hidden_states, batch_size, config, prompt_length): encoder_expected_shape = (batch_size, self.round_up(prompt_length, config.block_size), config.hidden_size) self.assertIsInstance(hidden_states, tuple) # Only the last layer will have the hidden states truncated back to token level self.assertListEqual( [layer_hidden_states.shape for layer_hidden_states in hidden_states[:-1]], [encoder_expected_shape] * (len(hidden_states) - 1), ) # Only the last layer will have the hidden states truncated back to token level self.assertEqual( hidden_states[-1][0].shape, (batch_size, prompt_length, config.hidden_size), ) def test_hidden_states_output(self): def _check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) if hasattr(self.model_tester, "encoder_seq_length"): seq_length = self.model_tester.encoder_seq_length if hasattr(self.model_tester, "chunk_length") and self.model_tester.chunk_length > 1: seq_length = seq_length * self.model_tester.chunk_length else: seq_length = self.model_tester.seq_length self.assertListEqual( list(hidden_states[0].shape[-2:]), [self.round_up(seq_length, config.block_size), self.model_tester.hidden_size], ) if config.is_encoder_decoder: hidden_states = outputs.decoder_hidden_states self.assertIsInstance(hidden_states, (list, tuple)) self.assertEqual(len(hidden_states), expected_num_layers) seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) self.assertListEqual( list(hidden_states[0].shape[-2:]), [decoder_seq_length, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True _check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True _check_hidden_states_output(inputs_dict, config, model_class) def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = self.has_attentions # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) inputs = self._prepare_for_class(inputs_dict, model_class) outputs = model(**inputs) output = outputs[0] if config.is_encoder_decoder: # Seq2Seq models encoder_hidden_states = outputs.encoder_hidden_states[0] encoder_hidden_states.retain_grad() decoder_hidden_states = outputs.decoder_hidden_states[0] decoder_hidden_states.retain_grad() if self.has_attentions: encoder_attentions = outputs.encoder_attentions[0] encoder_attentions["local"].retain_grad() encoder_attentions["global"].retain_grad() decoder_attentions = outputs.decoder_attentions[0] decoder_attentions.retain_grad() cross_attentions = outputs.cross_attentions[0] cross_attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(encoder_hidden_states.grad) self.assertIsNotNone(decoder_hidden_states.grad) if self.has_attentions: self.assertIsNotNone(encoder_attentions["local"].grad) self.assertIsNotNone(encoder_attentions["global"].grad) self.assertIsNotNone(decoder_attentions.grad) self.assertIsNotNone(cross_attentions.grad) else: # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] hidden_states.retain_grad() if self.has_attentions: attentions = outputs.attentions[0] attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) if self.has_attentions: self.assertIsNotNone(attentions.grad) @classmethod def round_up(cls, n, k): return math.ceil(n / k) * k def assert_tensors_close(a, b, atol=1e-12, prefix=""): """If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error.""" if a is None and b is None: return True try: if torch.allclose(a, b, atol=atol): return True raise Exception except Exception: pct_different = (torch.gt((a - b).abs(), atol)).float().mean().item() if a.numel() > 100: msg = f"tensor values are {pct_different:.1%} percent different." else: msg = f"{a} != {b}" if prefix: msg = prefix + ": " + msg raise AssertionError(msg) def _long_tensor(tok_lst): return torch.tensor(tok_lst, dtype=torch.long, device=torch_device) TOLERANCE = 1e-4 @require_torch @require_sentencepiece @require_tokenizers @slow class PegasusXModelIntegrationTests(unittest.TestCase): @cached_property def default_tokenizer(self): return PegasusTokenizer.from_pretrained("google/pegasus-x-base") def test_inference_no_head(self): model = PegasusXModel.from_pretrained("google/pegasus-x-base").to(torch_device) input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) decoder_input_ids = _long_tensor([[2, 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588]]) inputs_dict = prepare_pegasus_x_inputs_dict(model.config, input_ids, decoder_input_ids) with torch.no_grad(): output = model(**inputs_dict)[0] expected_shape = torch.Size((1, 11, 768)) self.assertEqual(output.shape, expected_shape) # change to expected output here expected_slice = torch.tensor( [[[0.0702, -0.1552, 0.1192], [0.0836, -0.1848, 0.1304], [0.0673, -0.1686, 0.1045]]], device=torch_device ) torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=TOLERANCE, atol=TOLERANCE) def test_inference_head(self): model = PegasusXForConditionalGeneration.from_pretrained("google/pegasus-x-base").to(torch_device) # change to intended input input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) decoder_input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) inputs_dict = prepare_pegasus_x_inputs_dict(model.config, input_ids, decoder_input_ids) with torch.no_grad(): output = model(**inputs_dict)[0] expected_shape = torch.Size((1, 11, model.config.vocab_size)) self.assertEqual(output.shape, expected_shape) # change to expected output here expected_slice = torch.tensor( [[[0.0, 9.5705185, 1.5897303], [0.0, 9.833374, 1.5828674], [0.0, 10.429961, 1.5643371]]], device=torch_device, ) torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=TOLERANCE, atol=TOLERANCE) def test_seq_to_seq_generation(self): hf = PegasusXForConditionalGeneration.from_pretrained("google/pegasus-x-base-arxiv").to(torch_device) tok = PegasusTokenizer.from_pretrained("google/pegasus-x-base") batch_input = [ "While large pretrained Transformer models have proven highly capable at tackling natural language tasks," " handling long sequence inputs continues to be a significant challenge. One such task is long input" " summarization, where inputs are longer than the maximum input context of most pretrained models. Through" " an extensive set of experiments, we investigate what model architectural changes and pretraining" " paradigms can most efficiently adapt a pretrained Transformer for long input summarization. We find that" " a staggered, block-local Transformer with global encoder tokens strikes a good balance of performance" " and efficiency, and that an additional pretraining phase on long sequences meaningfully improves" " downstream summarization performance. Based on our findings, we introduce PEGASUS-X, an extension of the" " PEGASUS model with additional long input pretraining to handle inputs of up to 16K tokens. PEGASUS-X" " achieves strong performance on long input summarization tasks comparable with much larger models while" " adding few additional parameters and not requiring model parallelism to train." ] # The below article tests that we don't add any hypotheses outside of the top n_beams dct = tok.batch_encode_plus( batch_input, max_length=512, padding="max_length", truncation="only_first", return_tensors="pt", ) hypotheses_batch = hf.generate( input_ids=dct["input_ids"].to(torch_device), attention_mask=dct["attention_mask"].to(torch_device), num_beams=2, max_length=32, ) EXPECTED = [ "we investigate the performance of a new pretrained model for long input summarization. the model is a" " superposition of two well -" ] generated = tok.batch_decode( hypotheses_batch.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True ) assert generated == EXPECTED class PegasusXStandaloneDecoderModelTester: def __init__( self, parent, vocab_size=99, batch_size=13, d_model=16, decoder_seq_length=7, is_training=True, is_decoder=True, use_attention_mask=True, use_cache=False, use_labels=True, decoder_start_token_id=2, decoder_ffn_dim=32, decoder_layers=2, encoder_attention_heads=4, decoder_attention_heads=4, max_position_embeddings=50, is_encoder_decoder=False, pad_token_id=0, bos_token_id=1, eos_token_id=2, scope=None, ): self.parent = parent self.batch_size = batch_size 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.d_model = d_model self.hidden_size = d_model self.num_hidden_layers = decoder_layers self.decoder_layers = decoder_layers self.decoder_ffn_dim = decoder_ffn_dim self.encoder_attention_heads = encoder_attention_heads self.decoder_attention_heads = decoder_attention_heads self.num_attention_heads = decoder_attention_heads self.eos_token_id = eos_token_id self.bos_token_id = bos_token_id self.pad_token_id = pad_token_id self.decoder_start_token_id = decoder_start_token_id self.use_cache = use_cache self.max_position_embeddings = max_position_embeddings self.is_encoder_decoder = is_encoder_decoder self.scope = None self.decoder_key_length = decoder_seq_length self.base_model_out_len = 2 self.decoder_attention_idx = 1 def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) attention_mask = None if self.use_attention_mask: 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 = PegasusXConfig( vocab_size=self.vocab_size, d_model=self.d_model, decoder_layers=self.decoder_layers, decoder_ffn_dim=self.decoder_ffn_dim, encoder_attention_heads=self.encoder_attention_heads, decoder_attention_heads=self.decoder_attention_heads, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, use_cache=self.use_cache, pad_token_id=self.pad_token_id, decoder_start_token_id=self.decoder_start_token_id, max_position_embeddings=self.max_position_embeddings, is_encoder_decoder=self.is_encoder_decoder, ) return ( config, input_ids, attention_mask, lm_labels, ) def create_and_check_decoder_model_past( self, config, input_ids, attention_mask, lm_labels, ): config.use_cache = True model = PegasusXDecoder(config=config).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) past_key_values = outputs["past_key_values"] # 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[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice assert 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, attention_mask, lm_labels, ): model = PegasusXDecoder(config=config).to(torch_device).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 past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"] # 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)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past_key_values, use_cache=True)["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[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, attention_mask, lm_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict @require_torch class PegasusXStandaloneDecoderModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (PegasusXDecoder,) if is_torch_available() else () is_encoder_decoder = False def setUp( self, ): self.model_tester = PegasusXStandaloneDecoderModelTester(self, is_training=False) self.config_tester = ConfigTester(self, config_class=PegasusXConfig) def test_config(self): self.config_tester.run_common_tests() 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_attn_mask_past(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) @unittest.skip(reason="Decoder cannot keep gradients") def test_retain_grad_hidden_states_attentions(self): return @unittest.skip(reason="Decoder cannot keep gradients") def test_flex_attention_with_grads(): return