# Copyright 2025 Nicolas Boizard, Duarte M. Alves, Hippolyte Gisserot-Boukhlef and the EuroBert 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 EuroBERT model.""" import unittest from transformers import AutoTokenizer, EuroBertConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( EuroBertForMaskedLM, EuroBertForSequenceClassification, EuroBertForTokenClassification, EuroBertModel, ) class EuroBertModelTester: if is_torch_available(): base_model_class = EuroBertModel def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, pad_token_id=0, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids 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.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.pad_token_id = pad_token_id self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]).to(torch_device) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): return EuroBertConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = EuroBertModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = EuroBertForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_for_sequence_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = EuroBertForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = EuroBertForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class EuroBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( EuroBertModel, EuroBertForMaskedLM, EuroBertForSequenceClassification, EuroBertForTokenClassification, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": EuroBertModel, "fill-mask": EuroBertForMaskedLM, "text-classification": EuroBertForSequenceClassification, "token-classification": EuroBertForTokenClassification, "zero-shot": EuroBertForSequenceClassification, } if is_torch_available() else {} ) model_tester_class = EuroBertModelTester # Need to use `0.8` instead of `0.9` for `test_cpu_offload` # This is because we are hitting edge cases with the causal_mask buffer model_split_percents = [0.5, 0.7, 0.8] # used in `test_torch_compile_for_training` _torch_compile_train_cls = EuroBertForMaskedLM if is_torch_available() else None def setUp(self): self.model_tester = EuroBertModelTester(self) self.config_tester = ConfigTester(self, config_class=EuroBertConfig, hidden_size=32, num_attention_heads=2) 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_model_various_embeddings(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: config_and_inputs[0].position_embedding_type = type self.model_tester.create_and_check_model(*config_and_inputs) def test_eurobert_sequence_classification_model(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = EuroBertForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) def test_eurobert_sequence_classification_model_for_single_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "single_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = EuroBertForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) def test_eurobert_sequence_classification_model_for_multi_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "multi_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor( [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size ).to(torch.float) model = EuroBertForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) @unittest.skip(reason="EuroBert buffers include complex numbers, which breaks this test") def test_save_load_fast_init_from_base(self): pass def test_model_loading_old_rope_configs(self): def _reinitialize_config(base_config, new_kwargs): # Reinitialize the config with the new kwargs, forcing the config to go through its __init__ validation # steps. base_config_dict = base_config.to_dict() new_config = EuroBertConfig.from_dict(config_dict={**base_config_dict, **new_kwargs}) return new_config # from untouched config -> ✅ base_config, model_inputs = self.model_tester.prepare_config_and_inputs_for_common() original_model = EuroBertForMaskedLM(base_config).to(torch_device) original_model(**model_inputs) # from a config with the expected rope configuration -> ✅ config = _reinitialize_config(base_config, {"rope_scaling": {"rope_type": "linear", "factor": 10.0}}) original_model = EuroBertForMaskedLM(config).to(torch_device) original_model(**model_inputs) # from a config with the old rope configuration ('type' instead of 'rope_type') -> ✅ we gracefully handle BC config = _reinitialize_config(base_config, {"rope_scaling": {"type": "linear", "factor": 10.0}}) original_model = EuroBertForMaskedLM(config).to(torch_device) original_model(**model_inputs) # from a config with both 'type' and 'rope_type' -> ✅ they can coexist (and both are present in the config) config = _reinitialize_config( base_config, {"rope_scaling": {"type": "linear", "rope_type": "linear", "factor": 10.0}} ) self.assertTrue(config.rope_scaling["type"] == "linear") self.assertTrue(config.rope_scaling["rope_type"] == "linear") original_model = EuroBertForMaskedLM(config).to(torch_device) original_model(**model_inputs) # from a config with parameters in a bad range ('factor' should be >= 1.0) -> ⚠️ throws a warning with self.assertLogs("transformers.modeling_rope_utils", level="WARNING") as logs: config = _reinitialize_config(base_config, {"rope_scaling": {"rope_type": "linear", "factor": -999.0}}) original_model = EuroBertForMaskedLM(config).to(torch_device) original_model(**model_inputs) self.assertEqual(len(logs.output), 1) self.assertIn("factor field", logs.output[0]) # from a config with unknown parameters ('foo' isn't a rope option) -> ⚠️ throws a warning with self.assertLogs("transformers.modeling_rope_utils", level="WARNING") as logs: config = _reinitialize_config( base_config, {"rope_scaling": {"rope_type": "linear", "factor": 10.0, "foo": "bar"}} ) original_model = EuroBertForMaskedLM(config).to(torch_device) original_model(**model_inputs) self.assertEqual(len(logs.output), 1) self.assertIn("Unrecognized keys", logs.output[0]) # from a config with specific rope type but missing one of its mandatory parameters -> ❌ throws exception with self.assertRaises(KeyError): config = _reinitialize_config(base_config, {"rope_scaling": {"rope_type": "linear"}}) # missing "factor" @require_torch class EuroBertIntegrationTest(unittest.TestCase): @slow def test_inference_masked_lm(self): model = EuroBertForMaskedLM.from_pretrained("EuroBERT/EuroBERT-210m", attn_implementation="sdpa") tokenizer = AutoTokenizer.from_pretrained("EuroBERT/EuroBERT-210m") inputs = tokenizer("Hello World!", return_tensors="pt") with torch.no_grad(): output = model(**inputs)[0] expected_shape = torch.Size((1, 4, 128256)) self.assertEqual(output.shape, expected_shape) # compare the actual values for a slice. expected_slice = torch.tensor([[[2.2926, 2.4539, 1.8910], [5.9669, 3.8567, 0.0723], [2.4965, 2.7193, 1.9904]]]) torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) @slow def test_inference_no_head(self): model = EuroBertModel.from_pretrained("EuroBERT/EuroBERT-210m", attn_implementation="sdpa") tokenizer = AutoTokenizer.from_pretrained("EuroBERT/EuroBERT-210m") inputs = tokenizer("Hello World!", return_tensors="pt") with torch.no_grad(): output = model(**inputs)[0] expected_shape = torch.Size((1, 4, 768)) self.assertEqual(output.shape, expected_shape) # compare the actual values for a slice. expected_slice = torch.tensor( [[[1.2437, 1.8956, 50.9435], [-4.5560, -0.1686, -1.2776], [1.6557, 1.9383, 50.1393]]] ) torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) @slow def test_inference_token_classification(self): model = EuroBertForTokenClassification.from_pretrained( "hf-internal-testing/tiny-random-EuroBertForTokenClassification", attn_implementation="sdpa", ) tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-EuroBertForTokenClassification") inputs = tokenizer("Hello World!", return_tensors="pt") with torch.no_grad(): output = model(**inputs)[0] expected_shape = torch.Size((1, 4, 2)) self.assertEqual(output.shape, expected_shape) expected = torch.tensor([[[-1.0817, -5.3000], [5.6100, -5.2878], [3.4393, -8.8765], [-0.0329, -3.8588]]]) torch.testing.assert_close(output, expected, rtol=1e-4, atol=1e-4) @slow def test_inference_sequence_classification(self): model = EuroBertForSequenceClassification.from_pretrained( "hf-internal-testing/tiny-random-EuroBertForSequenceClassification", attn_implementation="sdpa", ) tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-EuroBertForSequenceClassification") inputs = tokenizer("Hello World!", return_tensors="pt") with torch.no_grad(): output = model(**inputs)[0] expected_shape = torch.Size((1, 2)) self.assertEqual(output.shape, expected_shape) expected = torch.tensor([[-1.8948, 6.2092]]) torch.testing.assert_close(output, expected, rtol=1e-4, atol=1e-4)