tests/models/bert/test_modeling_bert.py

# 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 inspect
import tempfile
import unittest

import pytest
from packaging import version

from transformers import AutoTokenizer, BertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_PRETRAINING_MAPPING,
        BertForMaskedLM,
        BertForMultipleChoice,
        BertForNextSentencePrediction,
        BertForPreTraining,
        BertForQuestionAnswering,
        BertForSequenceClassification,
        BertForTokenClassification,
        BertLMHeadModel,
        BertModel,
        DataCollatorWithFlattening,
    )


class BertModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=7,
        is_training=True,
        use_input_mask=True,
        use_token_type_ids=True,
        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,
        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.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])

        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):
        """
        Returns a tiny configuration by default.
        """
        return BertConfig(
            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,
            hidden_dropout_prob=self.hidden_dropout_prob,
            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
            max_position_embeddings=self.max_position_embeddings,
            type_vocab_size=self.type_vocab_size,
            is_decoder=False,
            initializer_range=self.initializer_range,
        )

    def prepare_config_and_inputs_for_decoder(self):
        (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
        ) = self.prepare_config_and_inputs()

        config.is_decoder = True
        encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
        encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )

    def create_and_check_model(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = BertModel(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, token_type_ids=token_type_ids)
        result = model(input_ids)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))

    def create_and_check_model_as_decoder(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    ):
        config.add_cross_attention = True
        model = BertModel(config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
        )
        result = model(
            input_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            encoder_hidden_states=encoder_hidden_states,
        )
        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))

    def create_and_check_for_causal_lm(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    ):
        model = BertLMHeadModel(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_masked_lm(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = BertForMaskedLM(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_model_for_causal_lm_as_decoder(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    ):
        config.add_cross_attention = True
        model = BertLMHeadModel(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,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
        )
        result = model(
            input_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            labels=token_labels,
            encoder_hidden_states=encoder_hidden_states,
        )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))

    def create_and_check_decoder_model_past_large_inputs(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    ):
        config.is_decoder = True
        config.add_cross_attention = True
        model = BertLMHeadModel(config=config).to(torch_device).eval()

        # first forward pass
        outputs = model(
            input_ids,
            attention_mask=input_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            use_cache=True,
        )
        past_key_values = outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
        next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)

        # append to next input_ids and
        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
        next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)

        output_from_no_past = model(
            next_input_ids,
            attention_mask=next_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            output_hidden_states=True,
        )["hidden_states"][0]
        output_from_past = model(
            next_tokens,
            attention_mask=next_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
            output_hidden_states=True,
        )["hidden_states"][0]

        # 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 create_and_check_for_next_sequence_prediction(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = BertForNextSentencePrediction(config=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, 2))

    def create_and_check_for_pretraining(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = BertForPreTraining(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,
            next_sentence_label=sequence_labels,
        )
        self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
        self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2))

    def create_and_check_for_question_answering(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = BertForQuestionAnswering(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            start_positions=sequence_labels,
            end_positions=sequence_labels,
        )
        self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
        self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))

    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 = BertForSequenceClassification(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 = BertForTokenClassification(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 create_and_check_for_multiple_choice(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        config.num_choices = self.num_choices
        model = BertForMultipleChoice(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_token_type_ids = token_type_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,
            token_type_ids=multiple_choice_token_type_ids,
            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,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
        ) = config_and_inputs
        inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
        return config, inputs_dict


@require_torch
class BertModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            BertModel,
            BertLMHeadModel,
            BertForMaskedLM,
            BertForMultipleChoice,
            BertForNextSentencePrediction,
            BertForPreTraining,
            BertForQuestionAnswering,
            BertForSequenceClassification,
            BertForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    pipeline_model_mapping = (
        {
            "feature-extraction": BertModel,
            "fill-mask": BertForMaskedLM,
            "question-answering": BertForQuestionAnswering,
            "text-classification": BertForSequenceClassification,
            "text-generation": BertLMHeadModel,
            "token-classification": BertForTokenClassification,
            "zero-shot": BertForSequenceClassification,
        }
        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 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

    # Overwriting to add `is_decoder` flag
    def prepare_config_and_inputs_for_generate(self, batch_size=2):
        config, inputs = super().prepare_config_and_inputs_for_generate(batch_size)
        config.is_decoder = True
        return config, inputs

    def setUp(self):
        self.model_tester = BertModelTester(self)
        self.config_tester = ConfigTester(self, config_class=BertConfig, 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_model_as_decoder(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)

    def test_model_as_decoder_with_default_input_mask(self):
        (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        ) = self.model_tester.prepare_config_and_inputs_for_decoder()

        input_mask = None

        self.model_tester.create_and_check_model_as_decoder(
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )

    def test_for_causal_lm(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_for_causal_lm(*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_causal_lm_decoder(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_for_causal_lm_as_decoder(*config_and_inputs)

    def test_decoder_model_past_with_large_inputs(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*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_next_sequence_prediction(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_next_sequence_prediction(*config_and_inputs)

    def test_for_pretraining(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*config_and_inputs)

    def test_for_question_answering(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*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)

    @slow
    def test_model_from_pretrained(self):
        model_name = "google-bert/bert-base-uncased"
        model = BertModel.from_pretrained(model_name)
        self.assertIsNotNone(model)

    def attention_mask_padding_matches_padding_free_with_position_ids(
        self, attn_implementation: str, fa_kwargs: bool = False
    ):
        """
        Overwritten to account for the embeddings that rely on position ids.
        """
        if not self.has_attentions:
            self.skipTest(reason="Model architecture does not support attentions")

        max_new_tokens = 30
        support_flag = {
            "sdpa": "_supports_sdpa",
            "flash_attention_2": "_supports_flash_attn",
            "flash_attention_3": "_supports_flash_attn",
        }

        for model_class in self.all_generative_model_classes:
            if attn_implementation != "eager" and not getattr(model_class, support_flag[attn_implementation]):
                self.skipTest(f"{model_class.__name__} does not support {attn_implementation}")

            # can't infer if new attn mask API is supported by assume that only model with attention backend support it
            if not model_class._supports_attention_backend:
                self.skipTest(f"{model_class.__name__} does not support new attention mask API")

            if model_class._is_stateful:  # non-transformer models most probably have no packing support
                self.skipTest(f"{model_class.__name__} doesn't support packing!")

            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            if config.is_encoder_decoder:
                self.skipTest("Model is an encoder-decoder")

            if 0 not in inputs_dict.get("attention_mask", []) or "attention_mask" not in inputs_dict:
                self.skipTest("Model dummy inputs should contain padding in their attention mask")

            if "input_ids" not in inputs_dict or inputs_dict["input_ids"].ndim != 2:
                self.skipTest("Model dummy inputs should contain text input ids")

            # make sure that all models have enough positions for generation
            dummy_input_ids = inputs_dict["input_ids"]
            if hasattr(config, "max_position_embeddings"):
                config.max_position_embeddings = max_new_tokens + dummy_input_ids.shape[1] + 1

            model = model_class(config)
            if "position_ids" not in inspect.signature(model.forward).parameters:
                self.skipTest("Model does not support position_ids")

            if (not fa_kwargs) and "position_ids" not in inspect.signature(model.forward).parameters:
                continue  # this model doesn't accept position ids as input

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname)

                # Drop all keys except for the minimal set. Hard to manipulate with multimodals etc
                inputs_dict = {k: v for k, v in inputs_dict.items() if k in ["input_ids", "attention_mask"]}

                # Ensure left padding, to adapt for some models
                if 0 in inputs_dict["attention_mask"][:, -1]:
                    inputs_dict["attention_mask"] = inputs_dict["attention_mask"].flip(1)
                dummy_attention_mask = inputs_dict["attention_mask"]
                dummy_input_ids[~dummy_attention_mask.bool()] = config.get_text_config().pad_token_id

                # Main difference to other models, we need to prepare position ids according to the attention mask
                # as we use it to extract embeddings that rely on the correct position - naively increasing sequences do
                # not suffice anymore atp. The solution here calculates an increasing sequences for all 1s and puts 0s else.
                inputs_dict["position_ids"] = ((inputs_dict["attention_mask"] == 1).long().cumsum(dim=1) - 1) * (
                    inputs_dict["attention_mask"] == 1
                ).long()

                model = (
                    model_class.from_pretrained(
                        tmpdirname,
                        dtype=torch.bfloat16,
                        attn_implementation=attn_implementation,
                    )
                    .to(torch_device)
                    .eval()
                )

                if fa_kwargs:
                    # flatten
                    features = [
                        {"input_ids": i[a.bool()].tolist()} for i, a in zip(dummy_input_ids, dummy_attention_mask)
                    ]

                    # add position_ids + fa_kwargs
                    data_collator = DataCollatorWithFlattening(return_tensors="pt", return_flash_attn_kwargs=True)
                    batch = data_collator(features)
                    padfree_inputs_dict = {
                        k: t.to(torch_device) if torch.is_tensor(t) else t for k, t in batch.items()
                    }
                else:
                    # create packed position_ids
                    position_ids = (
                        torch.cat([torch.arange(length) for length in dummy_attention_mask.sum(1).tolist()])
                        .long()
                        .unsqueeze(0)
                        .to(torch_device)
                    )
                    padfree_inputs_dict = {
                        "input_ids": dummy_input_ids[dummy_attention_mask.bool()].unsqueeze(0),
                        "position_ids": position_ids,
                    }

                # We need to do simple forward without cache in order to trigger packed SDPA/flex/eager attention path
                res_padded = model(**inputs_dict, use_cache=False)
                res_padfree = model(**padfree_inputs_dict, use_cache=False)

                logits_padded = res_padded.logits[dummy_attention_mask.bool()]
                logits_padfree = res_padfree.logits[0]

                # acceptable numerical instability
                tol = torch.finfo(torch.bfloat16).eps
                torch.testing.assert_close(logits_padded, logits_padfree, rtol=tol, atol=tol)


@require_torch
class BertModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference_no_head_absolute_embedding(self):
        model = BertModel.from_pretrained("google-bert/bert-base-uncased")
        input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
        attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
        with torch.no_grad():
            output = model(input_ids, attention_mask=attention_mask)[0]
        expected_shape = torch.Size((1, 11, 768))
        self.assertEqual(output.shape, expected_shape)
        expected_slice = torch.tensor([[[0.4249, 0.1008, 0.7531], [0.3771, 0.1188, 0.7467], [0.4152, 0.1098, 0.7108]]])

        torch.testing.assert_close(output[:, 1:4, 1:4], expected_slice, rtol=1e-4, atol=1e-4)

    @slow
    @pytest.mark.torch_export_test
    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 = "google-bert/bert-base-uncased"
        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 = BertForMaskedLM.from_pretrained(
            bert_model,
            device_map=device,
            attn_implementation=attn_implementation,
            use_cache=True,
        )

        logits = model(**inputs).logits
        eg_predicted_mask = tokenizer.decode(logits[0, 6].topk(5).indices)
        self.assertEqual(eg_predicted_mask.split(), ["carpenter", "waiter", "barber", "mechanic", "salesman"])

        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)