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tests/models/modernbert/test_modeling_modernbert.py

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+# 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 os
+import unittest
+
+import pytest
+
+from transformers import AutoTokenizer, ModernBertConfig, is_torch_available
+from transformers.models.auto import get_values
+from transformers.testing_utils import (
+    Expectations,
+    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,
+    )
+
+
+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]
+
+            # 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,
+        }
+        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=32)
+
+    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)
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "google-bert/bert-base-uncased"
+        model = ModernBertModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+
+@require_torch
+class ModernBertModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference_masked_lm(self):
+        model = ModernBertForMaskedLM.from_pretrained("answerdotai/ModernBERT-base", 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):
+        model = ModernBertModel.from_pretrained("answerdotai/ModernBERT-base", 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):
+        model = ModernBertForTokenClassification.from_pretrained(
+            "hf-internal-testing/tiny-random-ModernBertForTokenClassification",
+            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):
+        model = ModernBertForSequenceClassification.from_pretrained(
+            "hf-internal-testing/tiny-random-ModernBertForSequenceClassification",
+            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)
+
+    @slow
+    def test_inference_multiple_choice(self):
+        tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base")
+        model = (
+            ModernBertForMultipleChoice.from_pretrained(
+                "netique/ModernBertForMultipleChoice",
+                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()}",
+        )
+
+    @require_flash_attn
+    @require_torch_accelerator
+    @pytest.mark.flash_attn_test
+    @slow
+    def test_inference_masked_lm_flash_attention_2(self):
+        model = ModernBertForMaskedLM.from_pretrained(
+            "answerdotai/ModernBERT-base", dtype=torch.float16, attn_implementation="flash_attention_2"
+        ).to(torch_device)
+        tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base")
+
+        inputs = tokenizer("Hello World!", return_tensors="pt").to(torch_device)
+        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 = Expectations(
+            {
+                ("cuda", None): torch.tensor(
+                    [[[3.8203, -0.2125, 12.2812], [3.6055, 0.6797, 14.6875], [-5.1094, -3.8105, 11.9922]]],
+                    dtype=torch.float16,
+                ),
+                ("rocm", None): torch.tensor(
+                    [[[3.8262, -0.2073, 12.2812], [3.6348, 0.6841, 14.6953], [-5.1172, -3.8125, 11.9922]]],
+                    dtype=torch.float16,
+                ),
+                ("xpu", None): torch.tensor(
+                    [[[3.8555, -0.1993, 12.2969], [3.6387, 0.6943, 14.7109], [-5.1172, -3.8086, 11.9844]]],
+                    dtype=torch.float16,
+                ),
+            }
+        ).get_expectation()
+        torch.testing.assert_close(output[:, :3, :3].cpu(), expected_slice, rtol=1e-4, atol=1e-4)