# Copyright 2020 The HuggingFace 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 json import os import tempfile import unittest import pytest from packaging import version from transformers import AutoTokenizer, ModernBertConfig, PreTrainedModel, is_torch_available from transformers.models.auto import get_values from transformers.testing_utils import ( CaptureLogger, require_flash_attn, require_torch, require_torch_accelerator, 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 ( MODEL_FOR_PRETRAINING_MAPPING, ModernBertForMaskedLM, ModernBertForMultipleChoice, ModernBertForQuestionAnswering, ModernBertForSequenceClassification, ModernBertForTokenClassification, ModernBertModel, logging, ) class ModernBertModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, pad_token_id=0, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_activation="gelu", mlp_dropout=0.0, attention_dropout=0.0, embedding_dropout=0.0, classifier_dropout=0.0, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, 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_labels = use_labels self.vocab_size = vocab_size self.pad_token_id = pad_token_id 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_activation = hidden_activation self.mlp_dropout = mlp_dropout self.attention_dropout = attention_dropout self.embedding_dropout = embedding_dropout self.classifier_dropout = classifier_dropout 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.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]) 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, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): """ Returns a tiny configuration by default. """ config = ModernBertConfig( vocab_size=self.vocab_size, pad_token_id=self.pad_token_id, 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_activation=self.hidden_activation, mlp_dropout=self.mlp_dropout, attention_dropout=self.attention_dropout, embedding_dropout=self.embedding_dropout, classifier_dropout=self.classifier_dropout, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, initializer_range=self.initializer_range, ) if test := os.environ.get("PYTEST_CURRENT_TEST", None): test_name = test.split(":")[-1].split(" ")[0] # If we're testing `test_retain_grad_hidden_states_attentions`, we normally get an error # that compilation doesn't work. Users can then set compile=False when loading the model, # much like here. We're testing whether it works once they've done that. # If we're testing `test_inputs_embeds_matches_input_ids`, then we'd like to test with `reference_compile` # set to False, otherwise the input_ids with compiled input embeddings will not match the inputs_embeds # with atol=1e-8 and rtol=1e-5 if test_name in ("test_retain_grad_hidden_states_attentions", "test_inputs_embeds_matches_input_ids"): config.reference_compile = False # Some tests require attentions to be outputted, in that case we'll set the attention implementation to eager # as the others don't support outputted attentions if test_name in ( "test_attention_outputs", "test_hidden_states_output", "test_retain_grad_hidden_states_attentions", ): config._attn_implementation = "eager" return config def create_and_check_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels): model = ModernBertModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask) result = model(input_ids) 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, input_mask, sequence_labels, token_labels, choice_labels ): model = ModernBertForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, 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, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = ModernBertForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, 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, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = ModernBertForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_for_multiple_choice( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = ModernBertForMultipleChoice(config=config) model.to(torch_device) model.eval() multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() result = model( multiple_choice_inputs_ids, attention_mask=multiple_choice_input_mask, labels=choice_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_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 ModernBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( ModernBertModel, ModernBertForMaskedLM, ModernBertForSequenceClassification, ModernBertForTokenClassification, ModernBertForQuestionAnswering, ModernBertForMultipleChoice, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": ModernBertModel, "fill-mask": ModernBertForMaskedLM, "text-classification": ModernBertForSequenceClassification, "token-classification": ModernBertForTokenClassification, "zero-shot": ModernBertForSequenceClassification, "question-answering": ModernBertForQuestionAnswering, } if is_torch_available() else {} ) model_split_percents = [0.5, 0.8, 0.9] # special case for ForPreTraining model def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if inputs_dict.get("output_attentions", False): inputs_dict["output_attentions"] = True if return_labels: if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING): inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs_dict["next_sentence_label"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) return inputs_dict def setUp(self): self.model_tester = ModernBertModelTester(self) self.config_tester = ConfigTester(self, config_class=ModernBertConfig, hidden_size=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_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) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs) def test_for_warning_if_padding_and_no_attention_mask(self): ( config, input_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = self.model_tester.prepare_config_and_inputs() # Set pad tokens in the input_ids input_ids[0, 0] = config.pad_token_id # Check for warnings if the attention_mask is missing. logger = logging.get_logger("transformers.modeling_utils") # clear cache so we can test the warning is emitted (from `warning_once`). logger.warning_once.cache_clear() with CaptureLogger(logger) as cl: model = ModernBertModel(config=config) model.to(torch_device) model.eval() model(input_ids, attention_mask=None) self.assertIn("We strongly recommend passing in an `attention_mask`", cl.out) @unittest.skip("ModernBert doesn't use separate classes for SDPA, but a function instead.") def test_sdpa_can_dispatch_non_composite_models(self): pass @slow def test_model_from_pretrained(self): model_name = "google-bert/bert-base-uncased" model = ModernBertModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_flash_attn @require_torch_accelerator @pytest.mark.flash_attn_test @slow def test_flash_attn_2_inference_equivalence_right_padding(self): self.skipTest(reason="ModernBert flash attention does not support right padding") @require_flash_attn @require_torch_accelerator @pytest.mark.flash_attn_test @slow def test_flash_attn_2_conversion(self): self.skipTest(reason="ModernBert doesn't use the ModernBertFlashAttention2 class method.") @pytest.mark.torch_compile_test def test_saved_config_excludes_reference_compile(self): config = ModernBertConfig(reference_compile=True) with tempfile.TemporaryDirectory() as tmpdirname: config.save_pretrained(tmpdirname) with open(os.path.join(tmpdirname, "config.json")) as f: config_dict = json.load(f) self.assertNotIn("reference_compile", config_dict) @require_flash_attn @require_torch_accelerator @pytest.mark.flash_attn_test def test_flash_attention_dispatches_by_default(self): "ModernBert should dispatch to FA2 by default, not SDPA" config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config=config) self.assertTrue(model.config._attn_implementation == "flash_attention_2") # This is overloaded because the model handles padding / unpadding on its own, thus ModernBertForMultipleChoice has # a different hidden states shape when using FA2. def flash_attn_inference_equivalence( self, attn_implementation: str, padding_side: str, atol: float = 4e-2, rtol: float = 4e-2 ): r""" Tests the equivalence between the eager and flash attention implementations. This test is only for inference and runs with `dtype=torch.bfloat16`. """ if not self.has_attentions: self.skipTest(reason="Model architecture does not support attentions") # This flag is used to know if the test was skipped for all `self.all_model_classes` or not _has_run_at_least_one_model = False for model_class in self.all_model_classes: # Custom kernel which needs the mask interface to be properly usable on these models if not model_class._supports_attention_backend and not attn_implementation.startswith("flash_attention"): continue config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # flash attention variants does not always support arbitrary headim config = self._prepare_config_headdim(config, 16) # forcing the prefill size to go over sliding window size to check for SWA correctness if getattr(config, "sliding_window", None): config.sliding_window = 2 model = model_class(config) if not all( submodel._supports_flash_attn for submodel in model.modules() if isinstance(submodel, PreTrainedModel) ): continue # If we end up here, at least one model class was not skipped _has_run_at_least_one_model = True with tempfile.TemporaryDirectory() as tmpdirname: # Save the model so we can reload with correct attention model.save_pretrained(tmpdirname) # Create first inputs without attention mask main_input = inputs_dict[model.main_input_name] # Only keep first batch sequence if isinstance(main_input, torch.Tensor): main_input = main_input[:1] # Fix the dtype if torch.is_floating_point(main_input): main_input = main_input.to(torch.bfloat16) first_inputs = {model.main_input_name: main_input, "output_hidden_states": True} # Some models have main input name which is different from input_ids, but require input_ids... e.g. BarkFine if model.main_input_name != "input_ids" and "input_ids" in inputs_dict: first_inputs["input_ids"] = inputs_dict["input_ids"][:1] # If we have some pixel values, use them as well if model.main_input_name != "pixel_values" and "pixel_values" in inputs_dict: # NOTE: this fixes qwen2_5_vl/omni because test break w/ pixel values if "image_grid_thw" in inputs_dict: continue first_inputs["pixel_values"] = inputs_dict["pixel_values"][:1].to(torch.bfloat16) if model.config.is_encoder_decoder: decoder_input_ids = inputs_dict.get("decoder_input_ids", first_inputs.get("input_ids")) if decoder_input_ids is not None: first_inputs["decoder_input_ids"] = decoder_input_ids[:1] # Create attention mask with padding dummy_attention_mask = inputs_dict.get("attention_mask", None) if dummy_attention_mask is not None: dummy_attention_mask = dummy_attention_mask[:1] if padding_side == "left": dummy_attention_mask[:, 1:] = 1 dummy_attention_mask[:, 0] = 0 else: dummy_attention_mask[:, :-1] = 1 dummy_attention_mask[:, -1] = 0 # Create second inputs with attention mask and padding second_inputs = copy.deepcopy(first_inputs) if dummy_attention_mask is not None: second_inputs["attention_mask"] = dummy_attention_mask if model.config.is_encoder_decoder: second_inputs["decoder_attention_mask"] = dummy_attention_mask # Use prepare for class to account for special attributes (e.g. in QnA models) first_inputs = self._prepare_for_class(first_inputs, model_class) first_inputs = { k: v.to(torch_device) if isinstance(v, torch.Tensor) else v for k, v in first_inputs.items() } second_inputs = self._prepare_for_class(second_inputs, model_class) second_inputs = { k: v.to(torch_device) if isinstance(v, torch.Tensor) else v for k, v in second_inputs.items() } model = model_class.from_pretrained( tmpdirname, dtype=torch.bfloat16, attn_implementation="eager", device_map=torch_device ) # First run without attention mask outputs = model(**first_inputs) retrieve_logits = model_class == ModernBertForMultipleChoice logits_1_eager = outputs.logits if retrieve_logits else outputs.hidden_states[-1] # Second run with attention mask and padding outputs = model(**second_inputs) logits_2_eager = outputs.logits if retrieve_logits else outputs.hidden_states[-1] # Switch to FA del model model = model_class.from_pretrained( tmpdirname, dtype=torch.bfloat16, attn_implementation=attn_implementation, device_map=torch_device ) outputs = model(**first_inputs) logits_1_fa = outputs.logits if retrieve_logits else outputs.hidden_states[-1] # Second run with attention mask and padding outputs = model(**second_inputs) logits_2_fa = outputs.logits if retrieve_logits else outputs.hidden_states[-1] # Check the results torch.testing.assert_close(logits_1_eager, logits_1_fa, atol=atol, rtol=rtol) if padding_side == "left": torch.testing.assert_close(logits_2_eager[1:], logits_2_fa[1:], atol=atol, rtol=rtol) # Check it can run in training mode model.train() _ = model(**second_inputs) else: torch.testing.assert_close(logits_2_eager[:-1], logits_2_fa[:-1], atol=atol, rtol=rtol) # In this case, the test should appear as skipped, not successful if not _has_run_at_least_one_model: self.skipTest( f"Model architecture does not support {attn_implementation}, or setting its attention dynamically" ) @require_torch class ModernBertModelIntegrationTest(unittest.TestCase): @slow def test_inference_masked_lm(self): if version.parse(torch.__version__) < version.parse("2.4.0"): self.skipTest(reason="This test requires torch >= 2.4 to run.") model = ModernBertForMaskedLM.from_pretrained( "answerdotai/ModernBERT-base", reference_compile=False, attn_implementation="sdpa" ) tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base") inputs = tokenizer("Hello World!", return_tensors="pt") with torch.no_grad(): output = model(**inputs)[0] expected_shape = torch.Size((1, 5, 50368)) self.assertEqual(output.shape, expected_shape) # compare the actual values for a slice. expected_slice = torch.tensor( [[[3.8387, -0.2017, 12.2839], [3.6300, 0.6869, 14.7123], [-5.1137, -3.8122, 11.9874]]] ) torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) @slow def test_inference_no_head(self): if version.parse(torch.__version__) < version.parse("2.4.0"): self.skipTest(reason="This test requires torch >= 2.4 to run.") model = ModernBertModel.from_pretrained( "answerdotai/ModernBERT-base", reference_compile=False, attn_implementation="sdpa" ) tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base") inputs = tokenizer("Hello World!", return_tensors="pt") with torch.no_grad(): output = model(**inputs)[0] expected_shape = torch.Size((1, 5, 768)) self.assertEqual(output.shape, expected_shape) # compare the actual values for a slice. expected_slice = torch.tensor( [[[0.3151, -0.6417, -0.7027], [-0.7834, -1.5810, 0.4576], [1.0614, -0.7268, -0.0871]]] ) torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) @slow def test_inference_token_classification(self): if version.parse(torch.__version__) < version.parse("2.4.0"): self.skipTest(reason="This test requires torch >= 2.4 to run.") model = ModernBertForTokenClassification.from_pretrained( "hf-internal-testing/tiny-random-ModernBertForTokenClassification", reference_compile=False, attn_implementation="sdpa", ) tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-ModernBertForTokenClassification") inputs = tokenizer("Hello World!", return_tensors="pt") with torch.no_grad(): output = model(**inputs)[0] expected_shape = torch.Size((1, 5, 2)) self.assertEqual(output.shape, expected_shape) expected = torch.tensor( [[[2.0159, 4.6569], [-0.9430, 3.1595], [-3.8770, 3.2653], [1.5752, 4.5167], [-1.6939, 1.2524]]] ) torch.testing.assert_close(output, expected, rtol=1e-4, atol=1e-4) @slow def test_inference_sequence_classification(self): if version.parse(torch.__version__) < version.parse("2.4.0"): self.skipTest(reason="This test requires torch >= 2.4 to run.") model = ModernBertForSequenceClassification.from_pretrained( "hf-internal-testing/tiny-random-ModernBertForSequenceClassification", reference_compile=False, attn_implementation="sdpa", ) tokenizer = AutoTokenizer.from_pretrained( "hf-internal-testing/tiny-random-ModernBertForSequenceClassification" ) 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([[1.6466, 4.5662]]) torch.testing.assert_close(output, expected, rtol=1e-4, atol=1e-4) @pytest.mark.torch_export_test @slow def test_export(self): if version.parse(torch.__version__) < version.parse("2.4.0"): self.skipTest(reason="This test requires torch >= 2.4 to run.") bert_model = "answerdotai/ModernBERT-base" device = "cpu" attn_implementation = "sdpa" max_length = 512 tokenizer = AutoTokenizer.from_pretrained(bert_model) inputs = tokenizer( "the man worked as a [MASK].", return_tensors="pt", padding="max_length", max_length=max_length, ) model = ModernBertForMaskedLM.from_pretrained( bert_model, device_map=device, attn_implementation=attn_implementation, ) logits = model(**inputs).logits eg_predicted_mask = tokenizer.decode(logits[0, 6].topk(5).indices) self.assertEqual(eg_predicted_mask.split(), ["lawyer", "mechanic", "teacher", "doctor", "waiter"]) exported_program = torch.export.export( model, args=(inputs["input_ids"],), kwargs={"attention_mask": inputs["attention_mask"]}, strict=True, ) result = exported_program.module().forward(inputs["input_ids"], inputs["attention_mask"]) ep_predicted_mask = tokenizer.decode(result.logits[0, 6].topk(5).indices) self.assertEqual(eg_predicted_mask, ep_predicted_mask) @slow def test_inference_multiple_choice(self): if version.parse(torch.__version__) < version.parse("2.4.0"): self.skipTest(reason="This test requires torch >= 2.4 to run.") tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base") model = ( ModernBertForMultipleChoice.from_pretrained( "netique/ModernBertForMultipleChoice", reference_compile=False, attn_implementation="sdpa", ) .eval() .to(torch_device) ) prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced." choices = [ "It is eaten with a fork and a knife.", "It is eaten while held in the hand.", "It also walks on the sidewalks.", "It is a common drink.", ] labels = torch.tensor([0], device=torch_device) encoding = tokenizer([prompt for _ in choices], choices, return_tensors="pt", padding=True) outputs = model(**{k: v.unsqueeze(0).to(torch_device) for k, v in encoding.items()}, labels=labels) expected_logits = torch.tensor([[0.1973, 0.2041, 0.1835, 0.1896]]) logits = outputs.logits.to("cpu") self.assertTrue( torch.allclose(logits, expected_logits, atol=1e-4, rtol=1e-4), f"Logits: {logits.tolist()}\nExpected: {expected_logits.tolist()}", )