codingutf8 2020 the huggingface team 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 orglicenseslicense2 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 bertformultiplechoice behaves incorrectly in jit environments because distilbertformultiplechoice requires inputs with different shapes we need to override this test because distilbertformultiplechoice requires inputs with different shapes we need to override this test coding utf 8 2020 the huggingface team 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 bertformultiplechoice behaves incorrectly in jit environments because distilbertformultiplechoice requires inputs with different shapes we need to override this test because distilbertformultiplechoice requires inputs with different shapes we need to override this test
import os
import tempfile
import unittest

import pytest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import 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 (
        DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DistilBertForMaskedLM,
        DistilBertForMultipleChoice,
        DistilBertForQuestionAnswering,
        DistilBertForSequenceClassification,
        DistilBertForTokenClassification,
        DistilBertModel,
    )


class DistilBertModelTester(object):
    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,
        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])

        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):
        return DistilBertConfig(
            vocab_size=self.vocab_size,
            dim=self.hidden_size,
            n_layers=self.num_hidden_layers,
            n_heads=self.num_attention_heads,
            hidden_dim=self.intermediate_size,
            hidden_act=self.hidden_act,
            dropout=self.hidden_dropout_prob,
            attention_dropout=self.attention_probs_dropout_prob,
            max_position_embeddings=self.max_position_embeddings,
            initializer_range=self.initializer_range,
        )

    def create_and_check_distilbert_model(
        self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = DistilBertModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, 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_distilbert_for_masked_lm(
        self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = DistilBertForMaskedLM(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_distilbert_for_question_answering(
        self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = DistilBertForQuestionAnswering(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids, attention_mask=input_mask, 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_distilbert_for_sequence_classification(
        self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        config.num_labels = self.num_labels
        model = DistilBertForSequenceClassification(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_distilbert_for_token_classification(
        self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        config.num_labels = self.num_labels
        model = DistilBertForTokenClassification(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_distilbert_for_multiple_choice(
        self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        config.num_choices = self.num_choices
        model = DistilBertForMultipleChoice(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 DistilBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            DistilBertModel,
            DistilBertForMaskedLM,
            DistilBertForMultipleChoice,
            DistilBertForQuestionAnswering,
            DistilBertForSequenceClassification,
            DistilBertForTokenClassification,
        )
        if is_torch_available()
        else None
    )
    pipeline_model_mapping = (
        {
            "feature-extraction": DistilBertModel,
            "fill-mask": DistilBertForMaskedLM,
            "question-answering": DistilBertForQuestionAnswering,
            "text-classification": DistilBertForSequenceClassification,
            "token-classification": DistilBertForTokenClassification,
            "zero-shot": DistilBertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    fx_compatible = True
    test_pruning = True
    test_resize_embeddings = True
    test_resize_position_embeddings = True

    def setUp(self):
        self.model_tester = DistilBertModelTester(self)
        self.config_tester = ConfigTester(self, config_class=DistilBertConfig, dim=37)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_distilbert_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_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_distilbert_for_masked_lm(*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_distilbert_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_distilbert_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_distilbert_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_distilbert_for_multiple_choice(*config_and_inputs)

    @slow
    def test_model_from_pretrained(self):
        for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = DistilBertModel.from_pretrained(model_name)
            self.assertIsNotNone(model)

    @slow
    @require_torch_accelerator
    def test_torchscript_device_change(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
            
            if model_class == DistilBertForMultipleChoice:
                return

            config.torchscript = True
            model = model_class(config=config)

            inputs_dict = self._prepare_for_class(inputs_dict, model_class)
            traced_model = torch.jit.trace(
                model, (inputs_dict["input_ids"].to("cpu"), inputs_dict["attention_mask"].to("cpu"))
            )

            with tempfile.TemporaryDirectory() as tmp:
                torch.jit.save(traced_model, os.path.join(tmp, "traced_model.pt"))
                loaded = torch.jit.load(os.path.join(tmp, "traced_model.pt"), map_location=torch_device)
                loaded(inputs_dict["input_ids"].to(torch_device), inputs_dict["attention_mask"].to(torch_device))

    
    @require_flash_attn
    @require_torch_accelerator
    @pytest.mark.flash_attn_test
    @slow
    def test_flash_attn_2_inference(self):
        import torch

        for model_class in self.all_model_classes:
            dummy_input = torch.LongTensor(
                [
                    [1, 2, 3, 4],
                    [1, 2, 8, 9],
                    [1, 2, 11, 12],
                    [1, 2, 13, 14],
                ]
            ).to(torch_device)
            dummy_attention_mask = torch.LongTensor(
                [
                    [0, 1, 1, 1],
                    [0, 1, 1, 1],
                    [0, 1, 1, 1],
                    [0, 1, 1, 1],
                ]
            ).to(torch_device)

            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            model = model_class(config)

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname)
                model_fa = model_class.from_pretrained(
                    tmpdirname, torch_dtype=torch.bfloat16, use_flash_attention_2=True
                )
                model_fa.to(torch_device)

                model = model_class.from_pretrained(
                    tmpdirname, torch_dtype=torch.bfloat16, use_flash_attention_2=False
                )
                model.to(torch_device)

                logits = model(dummy_input, output_hidden_states=True).hidden_states[-1]
                logits_fa = model_fa(dummy_input, output_hidden_states=True).hidden_states[-1]

                self.assertTrue(torch.allclose(logits_fa, logits, atol=4e-2, rtol=4e-2))

                output_fa = model_fa(dummy_input, attention_mask=dummy_attention_mask, output_hidden_states=True)
                logits_fa = output_fa.hidden_states[-1]

                output = model(dummy_input, attention_mask=dummy_attention_mask, output_hidden_states=True)
                logits = output.hidden_states[-1]

                self.assertTrue(torch.allclose(logits_fa[1:], logits[1:], atol=4e-2, rtol=4e-2))

    
    @require_flash_attn
    @require_torch_accelerator
    @pytest.mark.flash_attn_test
    @slow
    def test_flash_attn_2_inference_padding_right(self):
        import torch

        for model_class in self.all_model_classes:
            dummy_input = torch.LongTensor(
                [
                    [1, 2, 3, 4],
                    [1, 2, 8, 9],
                    [1, 2, 11, 12],
                    [1, 2, 13, 14],
                ]
            ).to(torch_device)
            dummy_attention_mask = torch.LongTensor(
                [
                    [0, 1, 1, 1],
                    [0, 1, 1, 1],
                    [0, 1, 1, 1],
                    [0, 1, 1, 1],
                ]
            ).to(torch_device)

            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            model = model_class(config)

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname)
                model_fa = model_class.from_pretrained(
                    tmpdirname, torch_dtype=torch.bfloat16, use_flash_attention_2=True
                )
                model_fa.to(torch_device)

                model = model_class.from_pretrained(
                    tmpdirname, torch_dtype=torch.bfloat16, use_flash_attention_2=False
                )
                model.to(torch_device)

                logits = model(dummy_input, output_hidden_states=True).hidden_states[-1]
                logits_fa = model_fa(dummy_input, output_hidden_states=True).hidden_states[-1]

                self.assertTrue(torch.allclose(logits_fa, logits, atol=4e-2, rtol=4e-2))

                output_fa = model_fa(dummy_input, attention_mask=dummy_attention_mask, output_hidden_states=True)
                logits_fa = output_fa.hidden_states[-1]

                output = model(dummy_input, attention_mask=dummy_attention_mask, output_hidden_states=True)
                logits = output.hidden_states[-1]

                self.assertTrue(torch.allclose(logits_fa[:-1], logits[:-1], atol=4e-2, rtol=4e-2))


@require_torch
class DistilBertModelIntergrationTest(unittest.TestCase):
    @slow
    def test_inference_no_head_absolute_embedding(self):
        model = DistilBertModel.from_pretrained("distilbert-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(
            [[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]
        )

        self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))