# 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 PLBART model.""" import copy import tempfile import unittest from functools import cached_property from transformers import PLBartConfig, 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 ( AutoTokenizer, PLBartForCausalLM, PLBartForConditionalGeneration, PLBartForSequenceClassification, PLBartModel, ) from transformers.models.plbart.modeling_plbart import PLBartDecoder, PLBartEncoder def prepare_plbart_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, } class PLBartModelTester: 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=100, 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) input_ids = input_ids.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 = self.get_config() inputs_dict = prepare_plbart_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def get_config(self): return PLBartConfig( 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, ) 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 = PLBartModel(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_with_past_key_values = model( next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values ) output_from_past = output_with_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 check_encoder_decoder_model_standalone(self, config, inputs_dict): model = PLBartModel(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 = PLBartEncoder.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 = PLBartDecoder.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 PLBartModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( (PLBartModel, PLBartForConditionalGeneration, PLBartForSequenceClassification) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": PLBartModel, "summarization": PLBartForConditionalGeneration, "text-classification": PLBartForSequenceClassification, "text-generation": PLBartForCausalLM, "text2text-generation": PLBartForConditionalGeneration, "translation": PLBartForConditionalGeneration, "zero-shot": PLBartForSequenceClassification, } if is_torch_available() else {} ) is_encoder_decoder = True test_missing_keys = False # TODO: Fix the failed tests def is_pipeline_test_to_skip( self, pipeline_test_case_name, config_class, model_architecture, tokenizer_name, image_processor_name, feature_extractor_name, processor_name, ): if pipeline_test_case_name == "TranslationPipelineTests": # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`. # `PLBartConfig` was never used in pipeline tests: cannot create a simple tokenizer. return True return False def setUp(self): self.model_tester = PLBartModelTester(self) self.config_tester = ConfigTester(self, config_class=PLBartConfig) 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) # PLBartForSequenceClassification 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 (PLBartModel, PLBartForConditionalGeneration): 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 = PLBartForConditionalGeneration(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) @unittest.skip(reason="Failing since #26752") def test_sample_generate(self): pass @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 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) @require_torch @require_sentencepiece @require_tokenizers class AbstractSeq2SeqIntegrationTest(unittest.TestCase): maxDiff = 1000 # longer string compare tracebacks checkpoint_name = None @classmethod def setUpClass(cls): cls.tokenizer = AutoTokenizer.from_pretrained(cls.checkpoint_name, use_fast=False) return cls @cached_property def model(self): """Only load the model if needed.""" model = PLBartForConditionalGeneration.from_pretrained(self.checkpoint_name).to(torch_device) if "cuda" in torch_device: model = model.half() return model @require_torch @require_sentencepiece @require_tokenizers class PLBartJavaCsIntegrationTest(AbstractSeq2SeqIntegrationTest): checkpoint_name = "uclanlp/plbart-java-cs" src_text = [ "public int maximum(int a, int b, int c){return Math.max(a, Math.max(b, c));}", "public int product(int a, int b, int c){return a*b*c;}", ] tgt_text = [ "public int maximum(int a, int b, int c){return Math.Max(", "public int Product(int a, int b, int c){return a * b *", ] @slow def test_java_cs_generate_one(self): batch = self.tokenizer( ["public int maximum(int a, int b, int c){return Math.max(a, Math.max(b, c));}"], return_tensors="pt" ) batch = batch.to(torch_device) translated_tokens = self.model.generate(**batch) decoded = self.tokenizer.decode(translated_tokens, skip_special_tokens=True) self.assertEqual(self.tgt_text[0], decoded[0]) # self.assertEqual(self.tgt_text[1], decoded[1]) @slow def test_java_cs_generate_batch(self): batch = self.tokenizer(self.src_text, return_tensors="pt", padding=True, truncation=True) batch = batch.to(torch_device) translated_tokens = self.model.generate(**batch) decoded = self.tokenizer.decode(translated_tokens, skip_special_tokens=True) assert self.tgt_text == decoded def test_plbart_java_cs_config(self): plbart_models = ["uclanlp/plbart-java-cs"] expected = {"scale_embedding": True} for name in plbart_models: config = PLBartConfig.from_pretrained(name) for k, v in expected.items(): try: self.assertEqual(v, getattr(config, k)) except AssertionError as e: e.args += (name, k) raise def test_plbart_fast_forward(self): config = PLBartConfig( vocab_size=99, d_model=24, encoder_layers=2, decoder_layers=2, encoder_attention_heads=2, decoder_attention_heads=2, encoder_ffn_dim=32, decoder_ffn_dim=32, max_position_embeddings=48, add_final_layer_norm=True, ) lm_model = PLBartForConditionalGeneration(config).to(torch_device) context = torch.tensor( [[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]], device=torch_device, dtype=torch.long ) summary = torch.tensor([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]], device=torch_device, dtype=torch.long) result = lm_model(input_ids=context, decoder_input_ids=summary, labels=summary) expected_shape = (*summary.shape, config.vocab_size) self.assertEqual(result.logits.shape, expected_shape) @require_torch @require_sentencepiece @require_tokenizers @slow class PLBartBaseIntegrationTest(AbstractSeq2SeqIntegrationTest): checkpoint_name = "uclanlp/plbart-base" src_text = ["Is 0 the first Fibonacci number ?", "Find the sum of all prime numbers ."] tgt_text = ["0 the first Fibonacci number?", "the sum of all prime numbers.......... the the"] def test_base_generate(self): inputs = self.tokenizer([self.src_text[0]], return_tensors="pt").to(torch_device) src_lan = self.tokenizer._convert_lang_code_special_format("en_XX") translated_tokens = self.model.generate( input_ids=inputs["input_ids"].to(torch_device), decoder_start_token_id=self.tokenizer.lang_code_to_id[src_lan], ) decoded = self.tokenizer.decode(translated_tokens, skip_special_tokens=True) self.assertEqual(self.tgt_text[0], decoded[0]) def test_fill_mask(self): inputs = self.tokenizer(["Is 0 the Fibonacci ?"], return_tensors="pt").to(torch_device) src_lan = self.tokenizer._convert_lang_code_special_format("en_XX") outputs = self.model.generate( inputs["input_ids"], decoder_start_token_id=self.tokenizer.lang_code_to_id[src_lan], num_beams=1 ) prediction: str = self.tokenizer.decode(outputs, clean_up_tokenization_spaces=True, skip_special_tokens=True)[ 0 ] self.assertEqual(prediction, "0 0 the 0 the 0 the 0 the 0 the 0 the 0 the 0 the") class PLBartStandaloneDecoderModelTester: 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 = PLBartConfig( vocab_size=self.vocab_size, d_model=self.d_model, decoder_layers=self.decoder_layers, num_hidden_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 = PLBartDecoder(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 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, attention_mask, lm_labels, ): model = PLBartDecoder(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, attention_mask=attn_mask)["last_hidden_state"] output_from_past = model( next_tokens, attention_mask=attn_mask, 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 self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) 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 PLBartStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (PLBartDecoder, PLBartForCausalLM) if is_torch_available() else () is_encoder_decoder = False def setUp(self): self.model_tester = PLBartStandaloneDecoderModelTester(self, is_training=False) self.config_tester = ConfigTester(self, config_class=PLBartConfig) 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