# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # Copyright 2021, 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. import copy import tempfile import unittest import paddle from parameterized import parameterized_class from paddlenlp.transformers import ( AutoTokenizer, MBartConfig, MBartForConditionalGeneration, MBartForQuestionAnswering, MBartForSequenceClassification, MBartModel, ) from paddlenlp.transformers.mbart.modeling import MBartDecoder from tests.testing_utils import PaddleNLPModelTest, slow from ..test_generation_utils import GenerationTesterMixin from ..test_modeling_common import ModelTesterMixin, ids_tensor def prepare_mbart_inputs_dict( config, input_ids, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, ): if attention_mask is None: attention_mask = (input_ids == config.pad_token_id).astype("float32").unsqueeze([1, 2]) * -1e4 if decoder_attention_mask is None: decoder_attention_mask = (decoder_input_ids == config.pad_token_id).astype("float32").unsqueeze([1, 2]) * -1e4 return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": attention_mask, } class MBartModelTester: 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, decoder_start_token_id=2, activation_function="relu", activation_dropout=0.0, init_std=0.02, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training 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 self.decoder_start_token_id = decoder_start_token_id self.activation_function = activation_function self.activation_dropout = activation_dropout self.init_std = init_std # forcing a certain token to be generated, sets all other tokens to -inf # if however the token to be generated is already at -inf then it can lead token # `nan` values and thus break generation self.forced_bos_token_id = None self.forced_eos_token_id = None def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size, dtype="int64") input_ids = paddle.clip(ids_tensor([self.batch_size, self.seq_length], self.vocab_size, dtype="int64"), 3) input_ids[:, -1] = self.eos_token_id # Eos Token decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size, dtype="int64") config = self.get_config() inputs_dict = prepare_mbart_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def get_config(self): return MBartConfig( 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, forced_bos_token_id=self.forced_bos_token_id, decoder_start_token_id=self.decoder_start_token_id, activation_function=self.activation_function, activation_dropout=self.activation_dropout, init_std=self.init_std, ) 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 = MBartModel(config).get_decoder() model.eval() input_ids = inputs_dict["input_ids"] attention_mask = inputs_dict["attention_mask"] cache = model.decoder.gen_cache(paddle.randn(shape=[input_ids.shape[0], input_ids.shape[1], config.d_model])) # first forward pass outputs = model( input_ids, decoder_attention_mask=attention_mask, cache=cache, return_dict=self.parent.return_dict ) output, past_key_values = outputs[:2] # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size, dtype="int64") next_attn_mask = (1 - ids_tensor((self.batch_size, 3), 2, dtype="int64").unsqueeze([1, 2])).astype( "float32" ) * -1e4 # append to next input_ids and next_input_ids = paddle.concat([input_ids, next_tokens], axis=-1) next_attention_mask = paddle.concat([attention_mask, next_attn_mask], axis=-1) output_from_no_past = model( next_input_ids, decoder_attention_mask=next_attention_mask, cache=None, return_dict=self.parent.return_dict ) if self.parent.return_dict: output_from_no_past = output_from_no_past[0] output_from_past = model(next_tokens, decoder_attention_mask=next_attention_mask, cache=past_key_values)[0] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1], dtype="int64").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(paddle.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) @parameterized_class( ("return_dict",), [ [False], [True], ], ) class MBartModelTest(ModelTesterMixin, GenerationTesterMixin, PaddleNLPModelTest): base_model_class = MBartModel all_model_classes = ( MBartModel, MBartForConditionalGeneration, MBartForSequenceClassification, MBartForQuestionAnswering, ) all_generative_model_classes = {MBartForConditionalGeneration: (MBartModel, "mbart")} is_encoder_decoder = True test_missing_keys = False return_dict = False def setUp(self): self.model_tester = MBartModelTester(self) 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 = self._make_model_instance(config, model_class) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model_class.from_pretrained(tmpdirname) # assign a model but never use 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_inputs_embeds_for_mbart(self): # NOTE: rewrite test inputs embeds for mbart model since scaler not equal to 1.0 # get config for model and inputs_dict for model forward config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() scaler = config.d_model**0.5 # test all model classes for model_class in self.all_model_classes: model = self._make_model_instance(config, model_class) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) with paddle.no_grad(): ids_output = model(**inputs) 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) * scaler else: inputs["inputs_embeds"] = wte(encoder_input_ids) * scaler inputs["decoder_inputs_embeds"] = wte(decoder_input_ids) * scaler with paddle.no_grad(): embeds_output = model(**inputs) if isinstance(ids_output, tuple): ids_output = ids_output[0] if isinstance(embeds_output, tuple): embeds_output = embeds_output[0] self.assertTrue(paddle.allclose(ids_output, embeds_output, rtol=1e-4, atol=1e-4)) 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 paddle.allclose(a, b, atol=atol): return True raise except Exception: pct_different = (paddle.greater_than((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 paddle.to_tensor(tok_lst, dtype="int64") class AbstractSeq2SeqIntegrationTest(PaddleNLPModelTest): maxDiff = 1000 # longer string compare tracebacks checkpoint_name = None @classmethod def setUpClass(cls): cls.tokenizer = AutoTokenizer.from_pretrained(cls.checkpoint_name) return cls def model(self): """Only load the model if needed.""" model = MBartForConditionalGeneration.from_pretrained(self.checkpoint_name) model.eval() return model @parameterized_class( ("return_dict",), [ [False], [True], ], ) class MBartEnroIntegrationTest(AbstractSeq2SeqIntegrationTest): checkpoint_name = "mbart-large-en-ro" src_text = [ " UN Chief Says There Is No Military Solution in Syria", """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""", ] tgt_text = [ "Şeful ONU declară că nu există o soluţie militară în Siria", 'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar acordat de Rusia Siriei este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria a milioane de oameni.', ] expected_src_tokens = [8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, 250004] return_dict = False @slow @unittest.skip("Skip for miss model weight.") def test_enro_generate_one(self): batch = self.tokenizer( ["UN Chief Says There Is No Military Solution in Syria"], return_tensors="pd", return_token_type_ids=False ) model = self.model() translated_tokens = model.generate(**batch, max_length=128)[0] decoded = self.tokenizer.batch_decode(translated_tokens, skip_special_tokens=True) self.assertEqual(self.tgt_text[0], decoded[0]) @slow @unittest.skip("Skip for miss model weight.") def test_enro_generate_batch(self): batch = self.tokenizer( self.src_text, return_tensors="pd", padding=True, truncation=True, return_token_type_ids=False ) model = self.model() translated_tokens = model.generate(**batch, max_length=128, decode_strategy="greedy_search")[0] decoded = self.tokenizer.batch_decode(translated_tokens, skip_special_tokens=True) for i in range(len(self.tgt_text)): assert str(self.tgt_text[i]) == str(decoded[i]), f"{i}" def test_mbart_fast_forward(self): config = MBartConfig( 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, ) lm_model = MBartForConditionalGeneration(config) context = paddle.to_tensor([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]], dtype="int64") summary = paddle.to_tensor([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]], dtype="int64") loss, logits = lm_model( input_ids=context, decoder_input_ids=summary, labels=summary, return_dict=self.return_dict )[:2] expected_shape = [*summary.shape, config.vocab_size] self.assertIsInstance(loss.item(), float) self.assertEqual(logits.shape, expected_shape) class MBartCC25IntegrationTest(AbstractSeq2SeqIntegrationTest): checkpoint_name = "mbart-large-cc25" src_text = [ " UN Chief Says There Is No Military Solution in Syria", " I ate lunch twice yesterday", ] tgt_text = ["Şeful ONU declară că nu există o soluţie militară în Siria", "to be padded"] @slow @unittest.skip("Skip for miss model weight.") def test_fill_mask(self): inputs = self.tokenizer(["One of the best I ever read!"], return_tensors="pd") model = self.model() outputs = model.generate(inputs["input_ids"], decoder_start_token_id=self.tokenizer.lang_code_to_id["en_XX"])[ 0 ] prediction = self.tokenizer.batch_decode(outputs, clean_up_tokenization_spaces=True, skip_special_tokens=True)[ 0 ] self.assertEqual(prediction, "of the best books I ever read!") class MBartStandaloneDecoderModelTester: 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=4, encoder_attention_heads=4, decoder_attention_heads=4, max_position_embeddings=30, 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.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.max_position_embeddings = max_position_embeddings self.use_cache = use_cache 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, dtype="int64") attention_mask = None if self.use_attention_mask: attention_mask = ids_tensor([self.batch_size, 1, 1, self.decoder_seq_length], vocab_size=2, dtype="int64") lm_labels = None if self.parent.use_labels: lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size, dtype="int64") config = MBartConfig( embed_tokens=None, vocab_size=self.vocab_size, d_model=self.d_model, decoder_layers=self.decoder_layers, decoder_ffn_dim=self.decoder_ffn_dim, decoder_attention_heads=self.decoder_attention_heads, max_position_embeddings=self.max_position_embeddings, ) return ( config, input_ids, attention_mask, lm_labels, ) def create_and_check_decoder_model_past( self, config, input_ids, attention_mask, lm_labels, ): # self.use_cache = True model = MBartDecoder(config) model.eval() encoder_output = paddle.randn(shape=input_ids.shape + [self.d_model]) origin_cache = model.decoder.gen_cache(encoder_output) # first forward pass outputs = model(input_ids, cache=origin_cache, return_dict=self.parent.return_dict) # outputs_use_cache_conf = model(input_ids, return_dict=self.parent.return_dict) outputs_no_past = model(input_ids, cache=None, return_dict=self.parent.return_dict) # self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) # didn't support using cache by config yet if not self.parent.return_dict: self.parent.assertTrue(len(outputs) == len((outputs_no_past,)) + 1) else: self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) past_key_values = outputs[1] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size, dtype="int64") # append to next input_ids and next_input_ids = paddle.concat([input_ids, next_tokens], axis=-1) output_from_no_past = model(next_input_ids, return_dict=self.parent.return_dict) if self.parent.return_dict: output_from_no_past = output_from_no_past[0] output_from_past = model(next_tokens, cache=past_key_values, return_dict=self.parent.return_dict)[0] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1], dtype="int64").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 paddle.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 = MBartDecoder(config) model.eval() # create attention mask attn_mask = paddle.ones(input_ids.shape, dtype="int64") half_seq_length = input_ids.shape[-1] // 2 attn_mask[:, half_seq_length:] = 0 attn_mask = attn_mask.unsqueeze([1, 2]) encoder_output = paddle.randn(shape=input_ids.shape + [self.d_model]) origin_cache = model.decoder.gen_cache(encoder_output) # first forward pass past_key_values = model( input_ids, # attention_mask=attn_mask, decoder_attention_mask=attn_mask, cache=origin_cache, return_dict=self.parent.return_dict, )[1] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size, dtype="int64") # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length, dtype="int64").item() + 1 random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size, dtype="int64").squeeze(-1) input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens # append to next input_ids and attn_mask next_input_ids = paddle.concat([input_ids, next_tokens], axis=-1) attn_mask = paddle.concat( [attn_mask, paddle.ones((attn_mask.shape[0], 1, 1, 1), dtype="int64")], axis=-1, ) # get two different outputs output_from_no_past = model( next_input_ids, decoder_attention_mask=attn_mask, return_dict=self.parent.return_dict ) if self.parent.return_dict: output_from_no_past = output_from_no_past[0] output_from_past = model( next_tokens, decoder_attention_mask=attn_mask, cache=past_key_values, return_dict=self.parent.return_dict )[0] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1], dtype="int64").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 paddle.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 @parameterized_class( ("return_dict", "use_labels"), [ [False, False], [False, True], [True, False], [True, True], ], ) class MBartStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, PaddleNLPModelTest): base_model_class = MBartModel all_model_classes = () use_test_model_name_list = False all_generative_model_classes = {} is_encoder_decoder = False use_labels = False def setUp(self): self.model_tester = MBartStandaloneDecoderModelTester(self, is_training=False) 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) def test_retain_grad_hidden_states_attentions(self): # decoder cannot keep gradients return def test_model_name_list(self): pass