test_modeling_hubert.py

# coding=utf-8
# Copyright 2021 The HuggingFace Inc. 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.
"""Testing suite for the PyTorch Hubert model."""

import math
import os
import pickle
import tempfile
import unittest

import pytest

from transformers import HubertConfig, is_torch_available
from transformers.testing_utils import require_soundfile, require_torch, slow, torch_device
from transformers.utils import is_torch_fx_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
    ModelTesterMixin,
    _config_zero_init,
    floats_tensor,
    ids_tensor,
    random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        HubertForCTC,
        HubertForSequenceClassification,
        HubertModel,
        Wav2Vec2FeatureExtractor,
        Wav2Vec2Processor,
    )
    from transformers.models.hubert.modeling_hubert import _compute_mask_indices

if is_torch_fx_available():
    from transformers.utils.fx import symbolic_trace


class HubertModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=1024,  # speech is longer
        is_training=False,
        hidden_size=16,
        feat_extract_norm="group",
        feat_extract_dropout=0.0,
        feat_extract_activation="gelu",
        conv_dim=(32, 32, 32),
        conv_stride=(4, 4, 4),
        conv_kernel=(8, 8, 8),
        conv_bias=False,
        num_conv_pos_embeddings=16,
        num_conv_pos_embedding_groups=2,
        num_hidden_layers=2,
        num_attention_heads=2,
        hidden_dropout_prob=0.1,  # this is most likely not correctly set yet
        intermediate_size=20,
        layer_norm_eps=1e-5,
        hidden_act="gelu",
        initializer_range=0.02,
        vocab_size=32,
        do_stable_layer_norm=False,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.hidden_size = hidden_size
        self.feat_extract_norm = feat_extract_norm
        self.feat_extract_dropout = feat_extract_dropout
        self.feat_extract_activation = feat_extract_activation
        self.conv_dim = conv_dim
        self.conv_stride = conv_stride
        self.conv_kernel = conv_kernel
        self.conv_bias = conv_bias
        self.num_conv_pos_embeddings = num_conv_pos_embeddings
        self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_dropout_prob = hidden_dropout_prob
        self.intermediate_size = intermediate_size
        self.layer_norm_eps = layer_norm_eps
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.vocab_size = vocab_size
        self.do_stable_layer_norm = do_stable_layer_norm
        self.scope = scope

        output_seq_length = self.seq_length
        for kernel, stride in zip(self.conv_kernel, self.conv_stride):
            output_seq_length = (output_seq_length - (kernel - 1)) / stride
        self.output_seq_length = int(math.ceil(output_seq_length))
        self.encoder_seq_length = self.output_seq_length

    def prepare_config_and_inputs(self):
        input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0)
        attention_mask = random_attention_mask([self.batch_size, self.seq_length])

        config = self.get_config()

        return config, input_values, attention_mask

    def get_config(self):
        return HubertConfig(
            hidden_size=self.hidden_size,
            feat_extract_norm=self.feat_extract_norm,
            feat_extract_dropout=self.feat_extract_dropout,
            feat_extract_activation=self.feat_extract_activation,
            conv_dim=self.conv_dim,
            conv_stride=self.conv_stride,
            conv_kernel=self.conv_kernel,
            conv_bias=self.conv_bias,
            num_conv_pos_embeddings=self.num_conv_pos_embeddings,
            num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            hidden_dropout_prob=self.hidden_dropout_prob,
            intermediate_size=self.intermediate_size,
            layer_norm_eps=self.layer_norm_eps,
            hidden_act=self.hidden_act,
            initializer_range=self.initializer_range,
            vocab_size=self.vocab_size,
            do_stable_layer_norm=self.do_stable_layer_norm,
        )

    def create_and_check_model(self, config, input_values, attention_mask):
        model = HubertModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_values, attention_mask=attention_mask)
        self.parent.assertEqual(
            result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size)
        )

    def create_and_check_batch_inference(self, config, input_values, *args):
        # test does not pass for models making use of `group_norm`
        # check: https://github.com/pytorch/fairseq/issues/3227
        model = HubertModel(config=config)
        model.to(torch_device)
        model.eval()

        input_values = input_values[:3]
        attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool)

        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]

        # pad input
        for i in range(len(input_lengths)):
            input_values[i, input_lengths[i] :] = 0.0
            attention_mask[i, input_lengths[i] :] = 0.0

        batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state

        for i in range(input_values.shape[0]):
            input_slice = input_values[i : i + 1, : input_lengths[i]]
            output = model(input_slice).last_hidden_state

            batch_output = batch_outputs[i : i + 1, : output.shape[1]]
            self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3))

    def check_ctc_loss(self, config, input_values, *args):
        model = HubertForCTC(config=config)
        model.to(torch_device)

        # make sure that dropout is disabled
        model.eval()

        input_values = input_values[:3]
        attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)

        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
        max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
        labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size)

        # pad input
        for i in range(len(input_lengths)):
            input_values[i, input_lengths[i] :] = 0.0
            attention_mask[i, input_lengths[i] :] = 0

        model.config.ctc_loss_reduction = "sum"
        sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()

        model.config.ctc_loss_reduction = "mean"
        mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()

        self.parent.assertTrue(isinstance(sum_loss, float))
        self.parent.assertTrue(isinstance(mean_loss, float))

    def check_seq_classifier_loss(self, config, input_values, *args):
        model = HubertForSequenceClassification(config=config)
        model.to(torch_device)

        # make sure that dropout is disabled
        model.eval()

        input_values = input_values[:3]
        attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)

        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
        labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))

        # pad input
        for i in range(len(input_lengths)):
            input_values[i, input_lengths[i] :] = 0.0
            attention_mask[i, input_lengths[i] :] = 0

        masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
        unmasked_loss = model(input_values, labels=labels).loss.item()

        self.parent.assertTrue(isinstance(masked_loss, float))
        self.parent.assertTrue(isinstance(unmasked_loss, float))
        self.parent.assertTrue(masked_loss != unmasked_loss)

    def check_ctc_training(self, config, input_values, *args):
        config.ctc_zero_infinity = True
        model = HubertForCTC(config=config)
        model.to(torch_device)
        model.train()

        # freeze feature encoder
        model.freeze_feature_encoder()

        input_values = input_values[:3]

        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
        max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
        labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size)

        # pad input
        for i in range(len(input_lengths)):
            input_values[i, input_lengths[i] :] = 0.0

            if max_length_labels[i] < labels.shape[-1]:
                # it's important that we make sure that target lengths are at least
                # one shorter than logit lengths to prevent -inf
                labels[i, max_length_labels[i] - 1 :] = -100

        loss = model(input_values, labels=labels).loss
        self.parent.assertFalse(torch.isinf(loss).item())

        loss.backward()

    def check_seq_classifier_training(self, config, input_values, *args):
        config.ctc_zero_infinity = True
        model = HubertForSequenceClassification(config=config)
        model.to(torch_device)
        model.train()

        # freeze everything but the classification head
        model.freeze_base_model()

        input_values = input_values[:3]

        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
        labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))

        # pad input
        for i in range(len(input_lengths)):
            input_values[i, input_lengths[i] :] = 0.0

        loss = model(input_values, labels=labels).loss
        self.parent.assertFalse(torch.isinf(loss).item())

        loss.backward()

    def check_labels_out_of_vocab(self, config, input_values, *args):
        model = HubertForCTC(config)
        model.to(torch_device)
        model.train()

        input_values = input_values[:3]

        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
        max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
        labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100)

        with pytest.raises(ValueError):
            model(input_values, labels=labels)

    def prepare_config_and_inputs_for_common(self):
        config, input_values, attention_mask = self.prepare_config_and_inputs()
        inputs_dict = {"input_values": input_values, "attention_mask": attention_mask}
        return config, inputs_dict


@require_torch
class HubertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (HubertForCTC, HubertForSequenceClassification, HubertModel) if is_torch_available() else ()
    pipeline_model_mapping = (
        {
            "audio-classification": HubertForSequenceClassification,
            "automatic-speech-recognition": HubertForCTC,
            "feature-extraction": HubertModel,
        }
        if is_torch_available()
        else {}
    )
    fx_compatible = True
    test_pruning = False
    test_headmasking = False

    def setUp(self):
        self.model_tester = HubertModelTester(self)
        self.config_tester = ConfigTester(self, config_class=HubertConfig, 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_ctc_loss_inference(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_ctc_loss(*config_and_inputs)

    def test_seq_classifier_loss_inference(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_seq_classifier_loss(*config_and_inputs)

    def test_ctc_train(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_ctc_training(*config_and_inputs)

    def test_seq_classifier_train(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_seq_classifier_training(*config_and_inputs)

    def test_labels_out_of_vocab(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_labels_out_of_vocab(*config_and_inputs)

    # Hubert has no inputs_embeds
    def test_inputs_embeds(self):
        pass

    # `input_ids` is renamed to `input_values`
    def test_forward_signature(self):
        pass

    # Hubert cannot resize token embeddings
    # since it has no tokens embeddings
    def test_resize_tokens_embeddings(self):
        pass

    # Hubert has no inputs_embeds
    # and thus the `get_input_embeddings` fn
    # is not implemented
    def test_model_get_set_embeddings(self):
        pass

    def test_retain_grad_hidden_states_attentions(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.output_hidden_states = True
        config.output_attentions = True

        # no need to test all models as different heads yield the same functionality
        model_class = self.all_model_classes[0]
        model = model_class(config)
        model.to(torch_device)

        # set layer drop to 0
        model.config.layerdrop = 0.0

        input_values = inputs_dict["input_values"]

        input_lengths = torch.tensor(
            [input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device
        )
        output_lengths = model._get_feat_extract_output_lengths(input_lengths)

        labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size)
        inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"])
        inputs_dict["labels"] = labels

        outputs = model(**inputs_dict)

        output = outputs[0]

        # Encoder-/Decoder-only models
        hidden_states = outputs.hidden_states[0]
        attentions = outputs.attentions[0]

        hidden_states.retain_grad()
        attentions.retain_grad()

        output.flatten()[0].backward(retain_graph=True)

        self.assertIsNotNone(hidden_states.grad)
        self.assertIsNotNone(attentions.grad)

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

        configs_no_init = _config_zero_init(config)
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            for name, param in model.named_parameters():
                uniform_init_parms = [
                    "conv.weight",
                    "conv.parametrizations.weight",
                    "masked_spec_embed",
                    "quantizer.weight_proj.weight",
                ]
                if param.requires_grad:
                    if any(x in name for x in uniform_init_parms):
                        self.assertTrue(
                            -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )
                    else:
                        self.assertIn(
                            ((param.data.mean() * 1e9).round() / 1e9).item(),
                            [0.0, 1.0],
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )

    # Hubert cannot be TorchScripted because of torch.nn.utils.weight_norm
    def _create_and_check_torch_fx_tracing(self, config, inputs_dict, output_loss=False):
        # TODO: fix it
        self.skipTest("torch 2.1 breaks torch fx tests for wav2vec2/hubert.")

        if not is_torch_fx_available() or not self.fx_compatible:
            return

        configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
        configs_no_init.return_dict = False

        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            model.to(torch_device)
            model.eval()
            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=output_loss)

            try:
                if model.config.is_encoder_decoder:
                    model.config.use_cache = False  # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
                    labels = inputs.get("labels", None)
                    input_names = [
                        "attention_mask",
                        "decoder_attention_mask",
                        "decoder_input_ids",
                        "input_features",
                        "input_ids",
                        "input_values",
                    ]
                    if labels is not None:
                        input_names.append("labels")

                    filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
                    input_names = list(filtered_inputs.keys())

                    model_output = model(**filtered_inputs)

                    traced_model = symbolic_trace(model, input_names)
                    traced_output = traced_model(**filtered_inputs)
                else:
                    input_names = [
                        "attention_mask",
                        "bbox",
                        "input_features",
                        "input_ids",
                        "input_values",
                        "pixel_values",
                        "token_type_ids",
                        "visual_feats",
                        "visual_pos",
                    ]

                    labels = inputs.get("labels", None)
                    start_positions = inputs.get("start_positions", None)
                    end_positions = inputs.get("end_positions", None)
                    if labels is not None:
                        input_names.append("labels")
                    if start_positions is not None:
                        input_names.append("start_positions")
                    if end_positions is not None:
                        input_names.append("end_positions")

                    filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
                    input_names = list(filtered_inputs.keys())

                    model_output = model(**filtered_inputs)

                    traced_model = symbolic_trace(model, input_names)
                    traced_output = traced_model(**filtered_inputs)

            except Exception as e:
                self.fail(f"Couldn't trace module: {e}")

            def flatten_output(output):
                flatten = []
                for x in output:
                    if isinstance(x, (tuple, list)):
                        flatten += flatten_output(x)
                    elif not isinstance(x, torch.Tensor):
                        continue
                    else:
                        flatten.append(x)
                return flatten

            model_output = flatten_output(model_output)
            traced_output = flatten_output(traced_output)
            num_outputs = len(model_output)

            for i in range(num_outputs):
                self.assertTrue(
                    torch.allclose(model_output[i], traced_output[i]),
                    f"traced {i}th output doesn't match model {i}th output for {model_class}",
                )

            # Test that the model can be serialized and restored properly
            with tempfile.TemporaryDirectory() as tmp_dir_name:
                pkl_file_name = os.path.join(tmp_dir_name, "model.pkl")
                try:
                    with open(pkl_file_name, "wb") as f:
                        pickle.dump(traced_model, f)
                    with open(pkl_file_name, "rb") as f:
                        loaded = pickle.load(f)
                except Exception as e:
                    self.fail(f"Couldn't serialize / deserialize the traced model: {e}")

                loaded_output = loaded(**filtered_inputs)
                loaded_output = flatten_output(loaded_output)

                for i in range(num_outputs):
                    self.assertTrue(
                        torch.allclose(model_output[i], loaded_output[i]),
                        f"serialized model {i}th output doesn't match model {i}th output for {model_class}",
                    )

    # overwrite from test_modeling_common
    def _mock_init_weights(self, module):
        if hasattr(module, "weight") and module.weight is not None:
            module.weight.data.fill_(3)
        if hasattr(module, "weight_g") and module.weight_g is not None:
            module.weight_g.data.fill_(3)
        if hasattr(module, "weight_v") and module.weight_v is not None:
            module.weight_v.data.fill_(3)
        if hasattr(module, "bias") and module.bias is not None:
            module.bias.data.fill_(3)
        if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
            module.masked_spec_embed.data.fill_(3)

    @unittest.skip(reason="Feed forward chunking is not implemented")
    def test_feed_forward_chunking(self):
        pass

    @slow
    def test_model_from_pretrained(self):
        model = HubertModel.from_pretrained("facebook/hubert-base-ls960")
        self.assertIsNotNone(model)


@require_torch
class HubertRobustModelTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (HubertForCTC, HubertForSequenceClassification, HubertModel) if is_torch_available() else ()
    test_pruning = False
    test_headmasking = False

    def setUp(self):
        self.model_tester = HubertModelTester(
            self, conv_stride=(3, 3, 3), feat_extract_norm="layer", do_stable_layer_norm=True
        )
        self.config_tester = ConfigTester(self, config_class=HubertConfig, 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_batched_inference(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_batch_inference(*config_and_inputs)

    def test_ctc_loss_inference(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_ctc_loss(*config_and_inputs)

    def test_seq_classifier_loss_inference(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_seq_classifier_loss(*config_and_inputs)

    def test_ctc_train(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_ctc_training(*config_and_inputs)

    def test_seq_classifier_train(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_seq_classifier_training(*config_and_inputs)

    def test_labels_out_of_vocab(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_labels_out_of_vocab(*config_and_inputs)

    # Hubert has no inputs_embeds
    def test_inputs_embeds(self):
        pass

    # `input_ids` is renamed to `input_values`
    def test_forward_signature(self):
        pass

    # Hubert cannot resize token embeddings
    # since it has no tokens embeddings
    def test_resize_tokens_embeddings(self):
        pass

    # Hubert has no inputs_embeds
    # and thus the `get_input_embeddings` fn
    # is not implemented
    def test_model_get_set_embeddings(self):
        pass

    def test_retain_grad_hidden_states_attentions(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.output_hidden_states = True
        config.output_attentions = True

        # no need to test all models as different heads yield the same functionality
        model_class = self.all_model_classes[0]
        model = model_class(config)
        model.to(torch_device)

        # set layer drop to 0
        model.config.layerdrop = 0.0

        input_values = inputs_dict["input_values"]

        input_lengths = torch.tensor(
            [input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device
        )
        output_lengths = model._get_feat_extract_output_lengths(input_lengths)

        labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size)
        inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"])
        inputs_dict["labels"] = labels

        outputs = model(**inputs_dict)

        output = outputs[0]

        # Encoder-/Decoder-only models
        hidden_states = outputs.hidden_states[0]
        attentions = outputs.attentions[0]

        hidden_states.retain_grad()
        attentions.retain_grad()

        output.flatten()[0].backward(retain_graph=True)

        self.assertIsNotNone(hidden_states.grad)
        self.assertIsNotNone(attentions.grad)

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

        configs_no_init = _config_zero_init(config)
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            for name, param in model.named_parameters():
                uniform_init_parms = [
                    "conv.weight",
                    "conv.parametrizations.weight",
                    "masked_spec_embed",
                    "quantizer.weight_proj.weight",
                ]
                if param.requires_grad:
                    if any(x in name for x in uniform_init_parms):
                        self.assertTrue(
                            -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )
                    else:
                        self.assertIn(
                            ((param.data.mean() * 1e9).round() / 1e9).item(),
                            [0.0, 1.0],
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )

    # overwrite from test_modeling_common
    def _mock_init_weights(self, module):
        if hasattr(module, "weight") and module.weight is not None:
            module.weight.data.fill_(3)
        if hasattr(module, "weight_g") and module.weight_g is not None:
            module.weight_g.data.fill_(3)
        if hasattr(module, "weight_v") and module.weight_v is not None:
            module.weight_v.data.fill_(3)
        if hasattr(module, "bias") and module.bias is not None:
            module.bias.data.fill_(3)
        if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
            module.masked_spec_embed.data.fill_(3)

    @unittest.skip(reason="Feed forward chunking is not implemented")
    def test_feed_forward_chunking(self):
        pass

    @slow
    def test_model_from_pretrained(self):
        model = HubertModel.from_pretrained("facebook/hubert-large-ls960-ft")
        self.assertIsNotNone(model)


@require_torch
class HubertUtilsTest(unittest.TestCase):
    def test_compute_mask_indices(self):
        batch_size = 4
        sequence_length = 60
        mask_prob = 0.5
        mask_length = 1

        mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
        mask = torch.from_numpy(mask).to(torch_device)

        self.assertListEqual(mask.sum(axis=-1).tolist(), [mask_prob * sequence_length for _ in range(batch_size)])

    def test_compute_mask_indices_overlap(self):
        batch_size = 4
        sequence_length = 80
        mask_prob = 0.5
        mask_length = 4

        mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
        mask = torch.from_numpy(mask).to(torch_device)

        # because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal
        for batch_sum in mask.sum(axis=-1):
            self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)


@require_torch
@require_soundfile
@slow
class HubertModelIntegrationTest(unittest.TestCase):
    def _load_datasamples(self, num_samples):
        from datasets import load_dataset

        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        # automatic decoding with librispeech
        speech_samples = ds.sort("id").filter(
            lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
        )[:num_samples]["audio"]

        return [x["array"] for x in speech_samples]

    def _load_superb(self, task, num_samples):
        from datasets import load_dataset

        ds = load_dataset("anton-l/superb_dummy", task, split="test")

        return ds[:num_samples]

    def test_inference_ctc_batched(self):
        model = HubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft", torch_dtype=torch.float16).to(
            torch_device
        )
        processor = Wav2Vec2Processor.from_pretrained("facebook/hubert-large-ls960-ft", do_lower_case=True)

        input_speech = self._load_datasamples(2)

        inputs = processor(input_speech, return_tensors="pt", padding=True)

        input_values = inputs.input_values.half().to(torch_device)
        attention_mask = inputs.attention_mask.to(torch_device)

        with torch.no_grad():
            logits = model(input_values, attention_mask=attention_mask).logits

        predicted_ids = torch.argmax(logits, dim=-1)
        predicted_trans = processor.batch_decode(predicted_ids)

        EXPECTED_TRANSCRIPTIONS = [
            "a man said to the universe sir i exist",
            "sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore",
        ]
        self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)

    def test_inference_keyword_spotting(self):
        model = HubertForSequenceClassification.from_pretrained(
            "superb/hubert-base-superb-ks", torch_dtype=torch.float16
        ).to(torch_device)
        processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-ks")
        input_data = self._load_superb("ks", 4)
        inputs = processor(input_data["speech"], return_tensors="pt", padding=True)

        input_values = inputs.input_values.half().to(torch_device)
        attention_mask = inputs.attention_mask.to(torch_device)
        with torch.no_grad():
            outputs = model(input_values, attention_mask=attention_mask)
        predicted_logits, predicted_ids = torch.max(outputs.logits, dim=-1)

        expected_labels = [2, 6, 10, 9]
        # s3prl logits for the same batch
        expected_logits = torch.tensor([7.6692, 17.7795, 11.1562, 11.8232], dtype=torch.float16, device=torch_device)

        self.assertListEqual(predicted_ids.tolist(), expected_labels)
        self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=3e-2))

    def test_inference_intent_classification(self):
        model = HubertForSequenceClassification.from_pretrained(
            "superb/hubert-base-superb-ic", torch_dtype=torch.float16
        ).to(torch_device)
        processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-ic")
        input_data = self._load_superb("ic", 4)
        inputs = processor(input_data["speech"], return_tensors="pt", padding=True)

        input_values = inputs.input_values.half().to(torch_device)
        attention_mask = inputs.attention_mask.to(torch_device)
        with torch.no_grad():
            outputs = model(input_values, attention_mask=attention_mask)

        predicted_logits_action, predicted_ids_action = torch.max(outputs.logits[:, :6], dim=-1)
        predicted_logits_object, predicted_ids_object = torch.max(outputs.logits[:, 6:20], dim=-1)
        predicted_logits_location, predicted_ids_location = torch.max(outputs.logits[:, 20:24], dim=-1)

        expected_labels_action = [1, 0, 4, 3]
        expected_logits_action = torch.tensor(
            [5.9052, 12.5865, 4.4840, 10.0240], dtype=torch.float16, device=torch_device
        )
        expected_labels_object = [1, 10, 3, 4]
        expected_logits_object = torch.tensor(
            [5.5316, 11.7946, 8.1672, 23.2415], dtype=torch.float16, device=torch_device
        )
        expected_labels_location = [0, 0, 0, 1]
        expected_logits_location = torch.tensor(
            [5.2053, 8.9577, 10.0447, 8.1481], dtype=torch.float16, device=torch_device
        )

        self.assertListEqual(predicted_ids_action.tolist(), expected_labels_action)
        self.assertListEqual(predicted_ids_object.tolist(), expected_labels_object)
        self.assertListEqual(predicted_ids_location.tolist(), expected_labels_location)

        # TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572
        self.assertTrue(torch.allclose(predicted_logits_action, expected_logits_action, atol=3e-1))
        self.assertTrue(torch.allclose(predicted_logits_object, expected_logits_object, atol=3e-1))
        self.assertTrue(torch.allclose(predicted_logits_location, expected_logits_location, atol=3e-1))

    def test_inference_speaker_identification(self):
        model = HubertForSequenceClassification.from_pretrained(
            "superb/hubert-base-superb-sid", torch_dtype=torch.float16
        ).to(torch_device)
        processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-sid")
        input_data = self._load_superb("si", 4)

        output_logits = []
        with torch.no_grad():
            for example in input_data["speech"]:
                input = processor(example, return_tensors="pt", padding=True)
                output = model(input.input_values.half().to(torch_device), attention_mask=None)
                output_logits.append(output.logits[0])
        output_logits = torch.stack(output_logits)
        predicted_logits, predicted_ids = torch.max(output_logits, dim=-1)

        expected_labels = [5, 1, 1, 3]
        # s3prl logits for the same batch
        expected_logits = torch.tensor(
            [78231.5547, 123166.6094, 122785.4141, 84851.2969], dtype=torch.float16, device=torch_device
        )

        self.assertListEqual(predicted_ids.tolist(), expected_labels)
        # TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572
        self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=10))

    def test_inference_emotion_recognition(self):
        model = HubertForSequenceClassification.from_pretrained(
            "superb/hubert-base-superb-er", torch_dtype=torch.float16
        ).to(torch_device)
        processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-er")
        input_data = self._load_superb("er", 4)
        inputs = processor(input_data["speech"], return_tensors="pt", padding=True)

        input_values = inputs.input_values.half().to(torch_device)
        attention_mask = inputs.attention_mask.to(torch_device)
        with torch.no_grad():
            outputs = model(input_values, attention_mask=attention_mask)
        predicted_logits, predicted_ids = torch.max(outputs.logits, dim=-1)

        expected_labels = [1, 1, 2, 2]
        # s3prl logits for the same batch
        expected_logits = torch.tensor([2.8384, 2.3389, 3.8564, 4.5558], dtype=torch.float16, device=torch_device)

        self.assertListEqual(predicted_ids.tolist(), expected_labels)
        # TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572
        self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=1e-1))

    def test_inference_distilhubert(self):
        model = HubertModel.from_pretrained("ntu-spml/distilhubert").to(torch_device)
        processor = Wav2Vec2FeatureExtractor.from_pretrained("ntu-spml/distilhubert")

        # TODO: can't test on batched inputs due to incompatible padding https://github.com/pytorch/fairseq/pull/3572
        input_speech = self._load_datasamples(1)

        inputs = processor(input_speech, return_tensors="pt", padding=True)

        input_values = inputs.input_values.to(torch_device)

        with torch.no_grad():
            outputs = model(input_values).last_hidden_state

        # expected outputs taken from the original SEW implementation
        expected_outputs_first = torch.tensor(
            [
                [
                    [-0.3505, 0.1167, 0.0608, 0.1294],
                    [-0.3085, 0.0481, 0.1106, 0.0955],
                    [-0.3107, -0.0391, 0.0739, 0.1360],
                    [-0.2385, -0.1795, -0.0928, 0.2389],
                ]
            ],
            device=torch_device,
        )
        expected_outputs_last = torch.tensor(
            [
                [
                    [-0.0732, 0.0255, 0.0529, -0.1372],
                    [-0.0812, 0.1259, 0.0564, -0.0438],
                    [-0.0054, 0.0758, -0.0002, -0.1617],
                    [0.0133, -0.0320, -0.0687, 0.0062],
                ]
            ],
            device=torch_device,
        )
        expected_output_sum = -3776.0730

        self.assertTrue(torch.allclose(outputs[:, :4, :4], expected_outputs_first, atol=5e-3))
        self.assertTrue(torch.allclose(outputs[:, -4:, -4:], expected_outputs_last, atol=5e-3))
        self.assertTrue(abs(outputs.sum() - expected_output_sum) < 0.1)