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 ibert only supports absolute embedding ensure that the default position ids only assign a sequential this is a regression test for https github comhuggingfacetransformersissues1761 the position ids should be masked with the embedding object s padding index therefore the first available nonpadding position index is ibertembeddings paddingidx 1 ensure that the default position ids only assign a sequential this is a regression test for https github comhuggingfacetransformersissues1761 the position ids should be masked with the embedding object s padding index therefore the first available nonpadding position index is ibertembeddings paddingidx 1 override override override scaling factor should follow the symmetric quantization rule quantization error should not exceed the scaling factor first pass after the first pass xmin and xmax should be initialized with x min and x max scaling factor should follow the symmetric quantization rule quantization error should not exceed the scaling factor output should be integer second pass from the second pass xmin and xmax should be updated with moving average scaling factor should follow the symmetric quantization rule quantization error should not exceed the scaling factor output should be integer third pass with eval in eval mode minmax and scaling factor must be fixed test if identity and identityscalingfactor are given should add the input values scaling factor should follow the symmetric quantization rule output of the normal linear layer and the quantized linear layer should be similar output of the quantized linear layer should be integer output of the normal gelu and the quantized gelu should be similar output of the quantized gelu layer should be integer output of the normal softmax and the quantized softmax should be similar output of the quantized gelu layer should be integer output of the quantize softmax should not exceed the outputbit some random matrix output of the normal ln and the quantized ln should be similar output of the quantized gelu layer should be integer helper function that quantizes the given model recursively convert all the quantmode attributes as true ibert should be equivalent to roberta if not quantized test coped from testmodelingroberta py ibert should be similar to roberta if quantized ibert should be equivalent to roberta if not quantized test coped from testmodelingroberta py ibert should be similar to roberta if quantized 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 i bert only supports absolute embedding ensure that the default position ids only assign a sequential this is a regression test for https github com huggingface transformers issues 1761 the position ids should be masked with the embedding object s padding index therefore the first available non padding position index is ibertembeddings padding_idx 1 ensure that the default position ids only assign a sequential this is a regression test for https github com huggingface transformers issues 1761 the position ids should be masked with the embedding object s padding index therefore the first available non padding position index is ibertembeddings padding_idx 1 override override i bert does not support chunking override scaling factor should follow the symmetric quantization rule quantization error should not exceed the scaling factor first pass after the first pass x_min and x_max should be initialized with x min and x max scaling factor should follow the symmetric quantization rule quantization error should not exceed the scaling factor output should be integer second pass from the second pass x_min and x_max should be updated with moving average scaling factor should follow the symmetric quantization rule quantization error should not exceed the scaling factor output should be integer third pass with eval in eval mode min max and scaling factor must be fixed test if identity and identity_scaling_factor are given should add the input values scaling factor should follow the symmetric quantization rule output of the normal linear layer and the quantized linear layer should be similar output of the quantized linear layer should be integer output of the normal gelu and the quantized gelu should be similar output of the quantized gelu layer should be integer output of the normal softmax and the quantized softmax should be similar output of the quantized gelu layer should be integer output of the quantize softmax should not exceed the output_bit some random matrix output of the normal ln and the quantized ln should be similar output of the quantized gelu layer should be integer helper function that quantizes the given model recursively convert all the quant_mode attributes as true i bert should be equivalent to roberta if not quantized test coped from test_modeling_roberta py i bert should be similar to roberta if quantized i bert should be equivalent to roberta if not quantized test coped from test_modeling_roberta py i bert should be similar to roberta if quantized
import copy
import unittest

from transformers import IBertConfig, 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 torch import nn

    from transformers import (
        IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        IBertForMaskedLM,
        IBertForMultipleChoice,
        IBertForQuestionAnswering,
        IBertForSequenceClassification,
        IBertForTokenClassification,
        IBertModel,
    )
    from transformers.models.ibert.modeling_ibert import (
        IBertEmbeddings,
        IntGELU,
        IntLayerNorm,
        IntSoftmax,
        QuantAct,
        QuantEmbedding,
        QuantLinear,
        create_position_ids_from_input_ids,
    )


class IBertModelTester:
    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):
        return IBertConfig(
            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,
            initializer_range=self.initializer_range,
            quant_mode=True,
        )

    def get_pipeline_config(self):
        config = self.get_config()
        config.vocab_size = 300
        return config

    def create_and_check_model(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = IBertModel(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_for_masked_lm(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = IBertForMaskedLM(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_token_classification(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        config.num_labels = self.num_labels
        model = IBertForTokenClassification(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 = IBertForMultipleChoice(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 create_and_check_for_question_answering(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = IBertForQuestionAnswering(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 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 IBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    test_pruning = False
    test_torchscript = False
    test_head_masking = False
    test_resize_embeddings = False

    all_model_classes = (
        (
            IBertForMaskedLM,
            IBertModel,
            IBertForSequenceClassification,
            IBertForTokenClassification,
            IBertForMultipleChoice,
            IBertForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    pipeline_model_mapping = (
        {
            "feature-extraction": IBertModel,
            "fill-mask": IBertForMaskedLM,
            "question-answering": IBertForQuestionAnswering,
            "text-classification": IBertForSequenceClassification,
            "token-classification": IBertForTokenClassification,
            "zero-shot": IBertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )

    def setUp(self):
        self.model_tester = IBertModelTester(self)
        self.config_tester = ConfigTester(self, config_class=IBertConfig, 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_various_embeddings(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        
        for type in ["absolute"]:
            config_and_inputs[0].position_embedding_type = type
            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_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_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)

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

    def test_create_position_ids_respects_padding_index(self):
        
        config = self.model_tester.prepare_config_and_inputs()[0]
        model = IBertEmbeddings(config=config)

        input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]])
        expected_positions = torch.as_tensor(
            [[0 + model.padding_idx + 1, 1 + model.padding_idx + 1, 2 + model.padding_idx + 1, model.padding_idx]]
        )

        position_ids = create_position_ids_from_input_ids(input_ids, model.padding_idx)
        self.assertEqual(position_ids.shape, expected_positions.shape)
        self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))

    def test_create_position_ids_from_inputs_embeds(self):
        
        config = self.model_tester.prepare_config_and_inputs()[0]
        embeddings = IBertEmbeddings(config=config)

        inputs_embeds = torch.empty(2, 4, 30)
        expected_single_positions = [
            0 + embeddings.padding_idx + 1,
            1 + embeddings.padding_idx + 1,
            2 + embeddings.padding_idx + 1,
            3 + embeddings.padding_idx + 1,
        ]
        expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions])
        position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds)
        self.assertEqual(position_ids.shape, expected_positions.shape)
        self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))

    
    def test_model_common_attributes(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            self.assertIsInstance(model.get_input_embeddings(), QuantEmbedding)
            model.set_input_embeddings(nn.Embedding(10, 10))
            x = model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(x, nn.Linear))

    
    def test_feed_forward_chunking(self):
        pass  

    
    def test_inputs_embeds(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))

            if not self.is_encoder_decoder:
                input_ids = inputs["input_ids"]
                del inputs["input_ids"]
            else:
                encoder_input_ids = inputs["input_ids"]
                decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
                del inputs["input_ids"]
                inputs.pop("decoder_input_ids", None)

            wte = model.get_input_embeddings()
            if not self.is_encoder_decoder:
                embed, embed_scaling_factor = wte(input_ids)
                inputs["inputs_embeds"] = embed
            else:
                inputs["inputs_embeds"] = wte(encoder_input_ids)
                inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)

            with torch.no_grad():
                model(**inputs)[0]


@require_torch
class IBertModelIntegrationTest(unittest.TestCase):
    def test_quant_embedding(self):
        weight_bit = 8
        embedding = QuantEmbedding(2, 4, quant_mode=True, weight_bit=weight_bit)
        embedding_weight = torch.tensor([[-1.0, -2.0, -3.0, -4.0], [5.0, 6.0, 7.0, 8.0]])
        embedding.weight = nn.Parameter(embedding_weight)

        expected_scaling_factor = embedding_weight.abs().max() / (2 ** (weight_bit - 1) - 1)
        x, x_scaling_factor = embedding(torch.tensor(0))
        y, y_scaling_factor = embedding(torch.tensor(1))

        
        self.assertTrue(torch.allclose(x_scaling_factor, expected_scaling_factor, atol=1e-4))
        self.assertTrue(torch.allclose(x_scaling_factor, expected_scaling_factor, atol=1e-4))
        self.assertTrue(torch.allclose(y_scaling_factor, expected_scaling_factor, atol=1e-4))

        
        self.assertTrue(torch.allclose(x, embedding_weight[0], atol=expected_scaling_factor))
        self.assertTrue(torch.allclose(y, embedding_weight[1], atol=expected_scaling_factor))

    def test_quant_act(self):
        def _test_range():
            act = QuantAct(activation_bit, act_range_momentum, quant_mode=True)

            
            x = torch.tensor([[-1.0, -2.0, -3.0, -4.0], [5.0, 6.0, 7.0, 8.0]])
            x_scaling_factor = torch.tensor(1.0)
            y, y_scaling_factor = act(x, x_scaling_factor)
            y_int = y / y_scaling_factor

            
            expected_x_min, expected_x_max = x.min(), x.max()
            self.assertTrue(torch.allclose(act.x_min, expected_x_min, atol=1e-4))
            self.assertTrue(torch.allclose(act.x_max, expected_x_max, atol=1e-4))

            
            expected_range = torch.max(expected_x_min.abs(), expected_x_max.abs())
            expected_scaling_factor = expected_range / (2 ** (activation_bit - 1) - 1)
            self.assertTrue(torch.allclose(y_scaling_factor, expected_scaling_factor, atol=1e-4))

            
            self.assertTrue(torch.allclose(x, y, atol=expected_scaling_factor))

            
            self.assertTrue(torch.allclose(y_int, y_int.round(), atol=1e-4))

            
            x = torch.tensor([[-1.0, -2.0, -3.0, -4.0], [5.0, 6.0, 7.0, 8.0]]) * 2
            x_scaling_factor = torch.tensor(1.0)
            y, y_scaling_factor = act(x, x_scaling_factor)
            y_int = y / y_scaling_factor

            
            expected_x_min = expected_x_min * act_range_momentum + x.min() * (1 - act_range_momentum)
            expected_x_max = expected_x_max * act_range_momentum + x.max() * (1 - act_range_momentum)
            self.assertTrue(torch.allclose(act.x_min, expected_x_min, atol=1e-4))
            self.assertTrue(torch.allclose(act.x_max, expected_x_max, atol=1e-4))

            
            expected_range = torch.max(expected_x_min.abs(), expected_x_max.abs())
            expected_scaling_factor = expected_range / (2 ** (activation_bit - 1) - 1)
            self.assertTrue(torch.allclose(y_scaling_factor, expected_scaling_factor, atol=1e-4))

            
            x = x.clamp(min=-expected_range, max=expected_range)
            self.assertTrue(torch.allclose(x, y, atol=expected_scaling_factor))

            
            self.assertTrue(torch.allclose(y_int, y_int.round(), atol=1e-4))

            
            act.eval()
            x = torch.tensor([[-1.0, -2.0, -3.0, -4.0], [5.0, 6.0, 7.0, 8.0]]) * 3

            
            self.assertTrue(torch.allclose(act.x_min, expected_x_min, atol=1e-4))
            self.assertTrue(torch.allclose(act.x_max, expected_x_max, atol=1e-4))
            self.assertTrue(torch.allclose(y_scaling_factor, expected_scaling_factor, atol=1e-4))

        def _test_identity():
            
            
            act = QuantAct(activation_bit, act_range_momentum, quant_mode=True)
            x = torch.tensor([[-1.0, -2.0, -3.0, -4.0], [5.0, 6.0, 7.0, 8.0]])
            y = torch.tensor([[6.0, -7.0, 1.0, -2.0], [3.0, -4.0, -8.0, 5.0]])
            x_scaling_factor = torch.tensor(1.0)
            y_scaling_factor = torch.tensor(0.5)
            z, z_scaling_factor = act(x, x_scaling_factor, y, y_scaling_factor)
            z_int = z / z_scaling_factor
            self.assertTrue(torch.allclose(x + y, z, atol=0.1))
            self.assertTrue(torch.allclose(z_int, z_int.round(), atol=1e-4))

        activation_bit = 8
        act_range_momentum = 0.95
        _test_range()
        _test_identity()

    def test_quant_linear(self):
        def _test(per_channel):
            linear_q = QuantLinear(2, 4, quant_mode=True, per_channel=per_channel, weight_bit=weight_bit)
            linear_dq = QuantLinear(2, 4, quant_mode=False, per_channel=per_channel, weight_bit=weight_bit)
            linear_weight = torch.tensor([[-1.0, 2.0, 3.0, -4.0], [5.0, -6.0, -7.0, 8.0]]).T
            linear_q.weight = nn.Parameter(linear_weight)
            linear_dq.weight = nn.Parameter(linear_weight)

            q, q_scaling_factor = linear_q(x, x_scaling_factor)
            q_int = q / q_scaling_factor
            dq, dq_scaling_factor = linear_dq(x, x_scaling_factor)

            if per_channel:
                q_max = linear_weight.abs().max(dim=1).values
            else:
                q_max = linear_weight.abs().max()
            expected_scaling_factor = q_max / (2 ** (weight_bit - 1) - 1)

            
            self.assertTrue(torch.allclose(linear_q.fc_scaling_factor, expected_scaling_factor, atol=1e-4))

            
            self.assertTrue(torch.allclose(q, dq, atol=0.5))

            
            self.assertTrue(torch.allclose(q_int, q_int.round(), atol=1e-4))

        weight_bit = 8
        x = torch.tensor([[2.0, -5.0], [-3.0, 4.0]])
        x_scaling_factor = torch.tensor([1.0])
        _test(True)
        _test(False)

    def test_int_gelu(self):
        gelu_q = IntGELU(quant_mode=True)
        gelu_dq = nn.GELU()

        x_int = torch.arange(-10000, 10001, 1)
        x_scaling_factor = torch.tensor(0.001)
        x = x_int * x_scaling_factor

        q, q_scaling_factor = gelu_q(x, x_scaling_factor)
        q_int = q / q_scaling_factor
        dq = gelu_dq(x)

        
        self.assertTrue(torch.allclose(q, dq, atol=0.5))

        
        self.assertTrue(torch.allclose(q_int, q_int.round(), atol=1e-4))

    def test_force_dequant_gelu(self):
        x_int = torch.arange(-10000, 10001, 1)
        x_scaling_factor = torch.tensor(0.001)
        x = x_int * x_scaling_factor

        gelu_dq = IntGELU(quant_mode=False)
        gelu_fdqs_dict = {
            True: [
                IntGELU(quant_mode=True, force_dequant="nonlinear"),
                IntGELU(quant_mode=True, force_dequant="gelu"),
            ],
            False: [
                IntGELU(quant_mode=True, force_dequant="none"),
                IntGELU(quant_mode=True, force_dequant="softmax"),
                IntGELU(quant_mode=True, force_dequant="layernorm"),
            ],
        }

        dq, dq_scaling_factor = gelu_dq(x, x_scaling_factor)
        for label, gelu_fdqs in gelu_fdqs_dict.items():
            for gelu_fdq in gelu_fdqs:
                q, q_scaling_factor = gelu_fdq(x, x_scaling_factor)
                if label:
                    self.assertTrue(torch.allclose(q, dq, atol=1e-4))
                else:
                    self.assertFalse(torch.allclose(q, dq, atol=1e-4))

    def test_int_softmax(self):
        output_bit = 8
        softmax_q = IntSoftmax(output_bit, quant_mode=True)
        softmax_dq = nn.Softmax()

        def _test(array):
            x_int = torch.tensor(array)
            x_scaling_factor = torch.tensor(0.1)
            x = x_int * x_scaling_factor

            q, q_scaling_factor = softmax_q(x, x_scaling_factor)
            q_int = q / q_scaling_factor
            dq = softmax_dq(x)

            
            self.assertTrue(torch.allclose(q, dq, atol=0.5))

            
            self.assertTrue(torch.allclose(q_int, q_int.round(), atol=1e-4))

            
            self.assertTrue(q.abs().max() < 2**output_bit)

        array = [[i + j for j in range(10)] for i in range(-10, 10)]
        _test(array)
        array = [[i + j for j in range(50)] for i in range(-10, 10)]
        _test(array)
        array = [[i + 100 * j for j in range(2)] for i in range(-10, 10)]
        _test(array)

    def test_force_dequant_softmax(self):
        output_bit = 8
        array = [[i + j for j in range(10)] for i in range(-10, 10)]
        x_int = torch.tensor(array)
        x_scaling_factor = torch.tensor(0.1)
        x = x_int * x_scaling_factor

        softmax_dq = IntSoftmax(output_bit, quant_mode=False)
        softmax_fdqs_dict = {
            True: [
                IntSoftmax(output_bit, quant_mode=True, force_dequant="nonlinear"),
                IntSoftmax(output_bit, quant_mode=True, force_dequant="softmax"),
            ],
            False: [
                IntSoftmax(output_bit, quant_mode=True, force_dequant="none"),
                IntSoftmax(output_bit, quant_mode=True, force_dequant="gelu"),
                IntSoftmax(output_bit, quant_mode=True, force_dequant="layernorm"),
            ],
        }

        dq, dq_scaling_factor = softmax_dq(x, x_scaling_factor)
        for label, softmax_fdqs in softmax_fdqs_dict.items():
            for softmax_fdq in softmax_fdqs:
                q, q_scaling_factor = softmax_fdq(x, x_scaling_factor)
                if label:
                    self.assertTrue(torch.allclose(q, dq, atol=1e-4))
                else:
                    self.assertFalse(torch.allclose(q, dq, atol=1e-4))

    def test_int_layernorm(self):
        output_bit = 8

        
        array = [[[i * j * j + j for j in range(5, 15)]] for i in range(-10, 10)]
        x_int = torch.tensor(array)
        x_scaling_factor = torch.tensor(0.1)
        x = x_int * x_scaling_factor

        ln_q = IntLayerNorm(x.shape[1:], 1e-5, quant_mode=True, output_bit=output_bit)
        ln_dq = nn.LayerNorm(x.shape[1:], 1e-5)

        ln_q.weight = nn.Parameter(torch.ones(x.shape[1:]))
        ln_q.bias = nn.Parameter(torch.ones(x.shape[1:]))
        ln_dq.weight = nn.Parameter(torch.ones(x.shape[1:]))
        ln_dq.bias = nn.Parameter(torch.ones(x.shape[1:]))

        q, q_scaling_factor = ln_q(x, x_scaling_factor)
        q_int = q / q_scaling_factor
        dq = ln_dq(x)

        
        self.assertTrue(torch.allclose(q, dq, atol=0.5))

        
        self.assertTrue(torch.allclose(q_int, q_int.round(), atol=1e-4))

    def test_force_dequant_layernorm(self):
        output_bit = 8
        array = [[[i * j * j + j for j in range(5, 15)]] for i in range(-10, 10)]
        x_int = torch.tensor(array)
        x_scaling_factor = torch.tensor(0.1)
        x = x_int * x_scaling_factor

        ln_dq = IntLayerNorm(x.shape[1:], 1e-5, quant_mode=False, output_bit=output_bit)
        ln_fdqs_dict = {
            True: [
                IntLayerNorm(x.shape[1:], 1e-5, quant_mode=True, output_bit=output_bit, force_dequant="nonlinear"),
                IntLayerNorm(x.shape[1:], 1e-5, quant_mode=True, output_bit=output_bit, force_dequant="layernorm"),
            ],
            False: [
                IntLayerNorm(x.shape[1:], 1e-5, quant_mode=True, output_bit=output_bit, force_dequant="none"),
                IntLayerNorm(x.shape[1:], 1e-5, quant_mode=True, output_bit=output_bit, force_dequant="gelu"),
                IntLayerNorm(x.shape[1:], 1e-5, quant_mode=True, output_bit=output_bit, force_dequant="softmax"),
            ],
        }

        ln_dq.weight = nn.Parameter(torch.ones(x.shape[1:]))
        ln_dq.bias = nn.Parameter(torch.ones(x.shape[1:]))
        dq, dq_scaling_factor = ln_dq(x, x_scaling_factor)
        for label, ln_fdqs in ln_fdqs_dict.items():
            for ln_fdq in ln_fdqs:
                ln_fdq.weight = nn.Parameter(torch.ones(x.shape[1:]))
                ln_fdq.bias = nn.Parameter(torch.ones(x.shape[1:]))
                q, q_scaling_factor = ln_fdq(x, x_scaling_factor)
                if label:
                    self.assertTrue(torch.allclose(q, dq, atol=1e-4))
                else:
                    self.assertFalse(torch.allclose(q, dq, atol=1e-4))

    def quantize(self, model):
        
        
        if hasattr(model, "quant_mode"):
            model.quant_mode = True
        elif type(model) == nn.Sequential:
            for n, m in model.named_children():
                self.quantize(m)
        elif type(model) == nn.ModuleList:
            for n in model:
                self.quantize(n)
        else:
            for attr in dir(model):
                mod = getattr(model, attr)
                if isinstance(mod, nn.Module) and mod != model:
                    self.quantize(mod)

    @slow
    def test_inference_masked_lm(self):
        
        
        model = IBertForMaskedLM.from_pretrained("kssteven/ibert-roberta-base")
        input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
        output = model(input_ids)[0]
        expected_shape = torch.Size((1, 11, 50265))
        self.assertEqual(output.shape, expected_shape)
        expected_slice = torch.tensor(
            [[[33.8802, -4.3103, 22.7761], [4.6539, -2.8098, 13.6253], [1.8228, -3.6898, 8.8600]]]
        )
        self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))

        
        self.quantize(model)
        output = model(input_ids)[0]
        self.assertEqual(output.shape, expected_shape)
        self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=0.1))

    @slow
    def test_inference_classification_head(self):
        
        
        model = IBertForSequenceClassification.from_pretrained("kssteven/ibert-roberta-large-mnli")
        input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
        output = model(input_ids)[0]
        expected_shape = torch.Size((1, 3))
        self.assertEqual(output.shape, expected_shape)
        expected_tensor = torch.tensor([[-0.9469, 0.3913, 0.5118]])
        self.assertTrue(torch.allclose(output, expected_tensor, atol=1e-4))

        
        self.quantize(model)
        output = model(input_ids)[0]
        self.assertEqual(output.shape, expected_shape)
        self.assertTrue(torch.allclose(output, expected_tensor, atol=0.1))