init

transformers/tests/models/data2vec/test_modeling_data2vec_audio.py

@@ -0,0 +1,716 @@
+# Copyright 2022 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.
+"""Testing suite for the PyTorch Data2VecAudio model."""
+
+import math
+import unittest
+
+import numpy as np
+from datasets import load_dataset
+
+from tests.test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
+from transformers import Data2VecAudioConfig, is_torch_available
+from transformers.testing_utils import require_torch, require_torchcodec, slow, torch_device
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, _config_zero_init
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        Data2VecAudioForAudioFrameClassification,
+        Data2VecAudioForCTC,
+        Data2VecAudioForSequenceClassification,
+        Data2VecAudioForXVector,
+        Data2VecAudioModel,
+        Wav2Vec2Processor,
+    )
+    from transformers.models.data2vec.modeling_data2vec_audio import _compute_mask_indices
+
+
+class Data2VecAudioModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        seq_length=1024,  # speech is longer
+        is_training=False,
+        hidden_size=16,
+        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,
+        intermediate_size=20,
+        layer_norm_eps=1e-5,
+        hidden_act="gelu",
+        initializer_range=0.02,
+        mask_time_prob=0.5,
+        mask_time_length=2,
+        vocab_size=32,
+        num_adapter_layers=1,
+        adapter_stride=2,
+        tdnn_dim=(32, 32),
+        tdnn_kernel=(5, 3),
+        tdnn_dilation=(1, 2),
+        xvector_output_dim=32,
+        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_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.num_adapter_layers = num_adapter_layers
+        self.adapter_stride = adapter_stride
+        self.mask_time_prob = mask_time_prob
+        self.mask_time_length = mask_time_length
+        self.scope = scope
+        self.tdnn_dim = tdnn_dim
+        self.tdnn_kernel = tdnn_kernel
+        self.tdnn_dilation = tdnn_dilation
+        self.xvector_output_dim = xvector_output_dim
+
+        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
+
+        self.adapter_output_seq_length = (self.output_seq_length - 1) // adapter_stride + 1
+
+    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 Data2VecAudioConfig(
+            hidden_size=self.hidden_size,
+            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,
+            mask_time_prob=self.mask_time_prob,
+            mask_time_length=self.mask_time_length,
+            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,
+            num_adapter_layers=self.num_adapter_layers,
+            adapter_stride=self.adapter_stride,
+            tdnn_dim=self.tdnn_dim,
+            tdnn_kernel=self.tdnn_kernel,
+            tdnn_dilation=self.tdnn_dilation,
+            xvector_output_dim=self.xvector_output_dim,
+        )
+
+    def create_and_check_model(self, config, input_values, attention_mask):
+        model = Data2VecAudioModel(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_model_with_adapter(self, config, input_values, attention_mask):
+        config.add_adapter = True
+        model = Data2VecAudioModel(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.adapter_output_seq_length, self.hidden_size)
+        )
+
+    def create_and_check_model_with_adapter_proj_dim(self, config, input_values, attention_mask):
+        config.add_adapter = True
+        config.output_hidden_size = 8
+        model = Data2VecAudioModel(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.adapter_output_seq_length, config.output_hidden_size),
+        )
+
+    def check_ctc_loss(self, config, input_values, *args):
+        model = Data2VecAudioForCTC(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 = Data2VecAudioForSequenceClassification(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 = Data2VecAudioForCTC(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 = Data2VecAudioForSequenceClassification(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_xvector_training(self, config, input_values, *args):
+        config.ctc_zero_infinity = True
+        model = Data2VecAudioForXVector(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 = Data2VecAudioForCTC(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 self.parent.assertRaises(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 Data2VecAudioModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (
+        (
+            Data2VecAudioForCTC,
+            Data2VecAudioModel,
+            Data2VecAudioForSequenceClassification,
+            Data2VecAudioForAudioFrameClassification,
+            Data2VecAudioForXVector,
+        )
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "audio-classification": Data2VecAudioForSequenceClassification,
+            "automatic-speech-recognition": Data2VecAudioForCTC,
+            "feature-extraction": Data2VecAudioModel,
+        }
+        if is_torch_available()
+        else {}
+    )
+    test_pruning = False
+    test_headmasking = False
+
+    def setUp(self):
+        self.model_tester = Data2VecAudioModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=Data2VecAudioConfig, 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_with_adapter(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model_with_adapter(*config_and_inputs)
+
+    def test_model_with_adapter_proj_dim(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model_with_adapter_proj_dim(*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_xvector_train(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.check_xvector_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)
+
+    @unittest.skip(reason="Data2VecAudio has no inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="`input_ids` is renamed to `input_values`")
+    def test_forward_signature(self):
+        pass
+
+    @unittest.skip(reason="Data2VecAudio has no tokens embeddings")
+    def test_resize_tokens_embeddings(self):
+        pass
+
+    @unittest.skip(reason="Data2VecAudio has no inputs_embeds")
+    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
+
+        # force eager attention to support output attentions
+        config._attn_implementation = "eager"
+
+        # 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",
+                    "masked_spec_embed",
+                    "codevectors",
+                    "quantizer.weight_proj.weight",
+                    "project_hid.weight",
+                    "project_hid.bias",
+                    "project_q.weight",
+                    "project_q.bias",
+                    "feature_projection.projection.weight",
+                    "feature_projection.projection.bias",
+                    "objective.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, "codevectors") and module.codevectors is not None:
+            module.codevectors.data.fill_(3)
+        if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
+            module.masked_spec_embed.data.fill_(3)
+
+    def test_mask_feature_prob_ctc(self):
+        model = Data2VecAudioForCTC.from_pretrained(
+            "hf-internal-testing/tiny-random-data2vec-seq-class", mask_feature_prob=0.2, mask_feature_length=2
+        )
+        model.to(torch_device).train()
+        processor = Wav2Vec2Processor.from_pretrained(
+            "hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True
+        )
+
+        batch_duration_in_seconds = [1, 3, 2, 6]
+        input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
+
+        batch = processor(
+            input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
+        )
+
+        logits = model(
+            input_values=batch["input_values"].to(torch_device),
+            attention_mask=batch["attention_mask"].to(torch_device),
+        ).logits
+
+        self.assertEqual(logits.shape, (4, 1498, 32))
+
+    def test_mask_time_prob_ctc(self):
+        model = Data2VecAudioForCTC.from_pretrained(
+            "facebook/data2vec-audio-base-960h", mask_time_prob=0.2, mask_time_length=2
+        )
+        model.to(torch_device).train()
+        processor = Wav2Vec2Processor.from_pretrained(
+            "hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True
+        )
+
+        batch_duration_in_seconds = [1, 3, 2, 6]
+        input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
+
+        batch = processor(
+            input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
+        )
+
+        logits = model(
+            input_values=batch["input_values"].to(torch_device),
+            attention_mask=batch["attention_mask"].to(torch_device),
+        ).logits
+
+        self.assertEqual(logits.shape, (4, 299, 32))
+
+    @unittest.skip(reason="Feed forward chunking is not implemented")
+    def test_feed_forward_chunking(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        model = Data2VecAudioModel.from_pretrained("facebook/data2vec-audio-base")
+        self.assertIsNotNone(model)
+
+
+@require_torch
+class Data2VecAudioUtilsTest(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_low_prob(self):
+        # with these settings num_masked_spans=0.5, which means probabilistic rounding
+        # ensures that in 5 out of 10 method calls, num_masked_spans=0, and in
+        # the other 5 out of 10, cases num_masked_spans=1
+        n_trials = 100
+        batch_size = 4
+        sequence_length = 100
+        mask_prob = 0.05
+        mask_length = 10
+
+        count_dimensions_masked = 0
+        count_dimensions_not_masked = 0
+
+        for _ in range(n_trials):
+            mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
+            mask = torch.from_numpy(mask).to(torch_device)
+
+            num_masks = torch.sum(mask).item()
+
+            if num_masks > 0:
+                count_dimensions_masked += 1
+            else:
+                count_dimensions_not_masked += 1
+
+        # as we test for at least 10 masked dimension and at least
+        # 10 non-masked dimension, this test could fail with probability:
+        # P(100 coin flips, at most 9 heads) = 1.66e-18
+        self.assertGreater(count_dimensions_masked, int(n_trials * 0.1))
+        self.assertGreater(count_dimensions_not_masked, int(n_trials * 0.1))
+
+    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)
+
+    def test_compute_mask_indices_attn_mask_overlap(self):
+        batch_size = 4
+        sequence_length = 80
+        mask_prob = 0.5
+        mask_length = 4
+
+        attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
+        attention_mask[:2, sequence_length // 2 :] = 0
+
+        mask = _compute_mask_indices(
+            (batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask
+        )
+        mask = torch.from_numpy(mask).to(torch_device)
+
+        for batch_sum in mask.sum(axis=-1):
+            self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)
+
+        self.assertTrue(mask[:2, sequence_length // 2 :].sum() == 0)
+
+    def test_compute_mask_indices_short_audio(self):
+        batch_size = 4
+        sequence_length = 100
+        mask_prob = 0.05
+        mask_length = 10
+
+        attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
+        # force one example to be heavily padded
+        attention_mask[0, 5:] = 0
+
+        mask = _compute_mask_indices(
+            (batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask, min_masks=2
+        )
+
+        # make sure that non-padded examples cannot be padded
+        self.assertFalse(mask[0][attention_mask[0].to(torch.bool).cpu()].any())
+
+
+@require_torch
+@require_torchcodec
+@slow
+class Data2VecAudioModelIntegrationTest(unittest.TestCase):
+    def _load_datasamples(self, num_samples):
+        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):
+        ds = load_dataset("anton-l/superb_dummy", task, split="test")
+
+        return ds[:num_samples]
+
+    def test_inference_ctc_normal(self):
+        model = Data2VecAudioForCTC.from_pretrained("facebook/data2vec-audio-base-960h")
+        model.to(torch_device)
+        processor = Wav2Vec2Processor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2", do_lower_case=True)
+        input_speech = self._load_datasamples(1)
+
+        input_values = processor(input_speech, return_tensors="pt").input_values.to(torch_device)
+
+        with torch.no_grad():
+            logits = model(input_values).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"]
+        self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
+
+    def test_inference_ctc_batched(self):
+        model = Data2VecAudioForCTC.from_pretrained("facebook/data2vec-audio-base-960h").to(torch_device)
+        processor = Wav2Vec2Processor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2", do_lower_case=True)
+
+        input_speech = self._load_datasamples(4)
+
+        inputs = processor(input_speech, return_tensors="pt", padding=True)
+
+        input_values = inputs.input_values.to(torch_device)
+
+        with torch.no_grad():
+            logits = model(input_values).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",
+            "the cut on his chest still dripping blood the ache of his overstrained eyes even the soaring arena around"
+            " him with thousands of spectators were trivialities not worth thinking about",
+            "his instant of panic was followed by a small sharp blow high on his chest",
+        ]
+        self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)

transformers/tests/models/data2vec/test_modeling_data2vec_text.py

@@ -0,0 +1,663 @@
+# Copyright 2022 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.
+"""Testing suite for the PyTorch Data2VecAudio model."""
+
+import inspect
+import tempfile
+import unittest
+
+from tests.test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
+from transformers import Data2VecTextConfig, is_torch_available
+from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
+
+from ...generation.test_utils import GenerationTesterMixin
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        Data2VecTextForCausalLM,
+        Data2VecTextForMaskedLM,
+        Data2VecTextForMultipleChoice,
+        Data2VecTextForQuestionAnswering,
+        Data2VecTextForSequenceClassification,
+        Data2VecTextForTokenClassification,
+        Data2VecTextModel,
+        DataCollatorWithFlattening,
+    )
+    from transformers.models.data2vec.modeling_data2vec_text import Data2VecTextEmbeddings
+
+
+class Data2VecTextModelTester:
+    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 Data2VecTextConfig(
+            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,
+        )
+
+    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 = Data2VecTextModel(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 = Data2VecTextModel(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 = Data2VecTextForCausalLM(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_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 = Data2VecTextForCausalLM(config=config).to(torch_device).eval()
+
+        # make sure that ids don't start with pad token
+        mask = input_ids.ne(config.pad_token_id).long()
+        input_ids = input_ids * mask
+
+        # 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)
+
+        # make sure that ids don't start with pad token
+        mask = next_tokens.ne(config.pad_token_id).long()
+        next_tokens = next_tokens * mask
+        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_masked_lm(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = Data2VecTextForMaskedLM(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 = Data2VecTextForTokenClassification(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 = Data2VecTextForMultipleChoice(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 = Data2VecTextForQuestionAnswering(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 Data2VecTextModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (
+        (
+            Data2VecTextForCausalLM,
+            Data2VecTextForMaskedLM,
+            Data2VecTextModel,
+            Data2VecTextForSequenceClassification,
+            Data2VecTextForTokenClassification,
+            Data2VecTextForMultipleChoice,
+            Data2VecTextForQuestionAnswering,
+        )
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": Data2VecTextModel,
+            "fill-mask": Data2VecTextForMaskedLM,
+            "question-answering": Data2VecTextForQuestionAnswering,
+            "text-classification": Data2VecTextForSequenceClassification,
+            "text-generation": Data2VecTextForCausalLM,
+            "token-classification": Data2VecTextForTokenClassification,
+            "zero-shot": Data2VecTextForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
+    model_split_percents = [0.5, 0.9]
+
+    # 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 = Data2VecTextModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=Data2VecTextConfig, 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", "relative_key", "relative_key_query"]:
+            config_and_inputs[0].position_embedding_type = type
+            config_and_inputs[0]._attn_implementation = "eager"
+            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_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_decoder_model_past_with_large_inputs_relative_pos_emb(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
+        config_and_inputs[0].position_embedding_type = "relative_key"
+        config_and_inputs[0]._attn_implementation = "eager"
+        self.model_tester.create_and_check_decoder_model_past_large_inputs(*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):
+        model_name = "facebook/data2vec-text-base"
+        model = Data2VecTextModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+    def test_create_position_ids_respects_padding_index(self):
+        """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 Data2VecTextForTextEmbeddings.padding_idx + 1
+        """
+        config = self.model_tester.prepare_config_and_inputs()[0]
+        model = Data2VecTextEmbeddings(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 = Data2VecTextEmbeddings.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):
+        """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 Data2VecTextForTextEmbeddings.padding_idx + 1
+        """
+        config = self.model_tester.prepare_config_and_inputs()[0]
+        embeddings = Data2VecTextEmbeddings(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, embeddings.padding_idx)
+        self.assertEqual(position_ids.shape, expected_positions.shape)
+        self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
+
+    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/head_mask/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 Data2VecTextModelIntegrationTest(TestCasePlus):
+    @slow
+    def test_inference_masked_lm(self):
+        model = Data2VecTextForMaskedLM.from_pretrained("facebook/data2vec-text-base")
+
+        input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
+        with torch.no_grad():
+            output = model(input_ids)[0]
+        expected_shape = torch.Size((1, 11, 50265))
+        self.assertEqual(output.shape, expected_shape)
+        # compare the actual values for a slice.
+        expected_slice = torch.tensor([[[0.2328, 0.0000, 1.1710], [2.2525, 0.0000, 1.9937], [2.1280, 0.0000, 1.8691]]])
+
+        torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
+
+    @slow
+    def test_inference_no_head(self):
+        model = Data2VecTextModel.from_pretrained("facebook/data2vec-text-base")
+
+        input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
+        with torch.no_grad():
+            output = model(input_ids)[0]
+        # compare the actual values for a slice.
+        expected_slice = torch.tensor(
+            [[[0.1998, -0.0379, 0.0024], [-0.0971, -0.2214, -0.1798], [-0.0789, -0.2400, -0.1898]]]
+        )
+
+        torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)

transformers/tests/models/data2vec/test_modeling_data2vec_vision.py

@@ -0,0 +1,385 @@
+# Copyright 2022 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 Data2VecVision model."""
+
+import unittest
+from functools import cached_property
+
+import pytest
+
+from transformers import Data2VecVisionConfig
+from transformers.testing_utils import (
+    require_torch,
+    require_torch_multi_gpu,
+    require_vision,
+    slow,
+    torch_device,
+)
+from transformers.utils import (
+    is_torch_available,
+    is_vision_available,
+)
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+    from torch import nn
+
+    from transformers import (
+        Data2VecVisionForImageClassification,
+        Data2VecVisionForSemanticSegmentation,
+        Data2VecVisionModel,
+    )
+    from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import BeitImageProcessor
+
+
+class Data2VecVisionModelTester:
+    def __init__(
+        self,
+        parent,
+        vocab_size=100,
+        batch_size=13,
+        image_size=30,
+        patch_size=2,
+        num_channels=3,
+        is_training=True,
+        use_labels=True,
+        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,
+        type_sequence_label_size=10,
+        initializer_range=0.02,
+        num_labels=3,
+        scope=None,
+        out_indices=[0, 1, 2, 3],
+        attn_implementation="eager",
+        mask_ratio=0.5,
+    ):
+        self.parent = parent
+        self.vocab_size = 100
+        self.batch_size = batch_size
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.is_training = is_training
+        self.use_labels = use_labels
+        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.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.scope = scope
+        self.out_indices = out_indices
+        self.num_labels = num_labels
+
+        # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
+        num_patches = (image_size // patch_size) ** 2
+        self.seq_length = num_patches + 1
+        self.mask_length = self.seq_length - 1
+        self.num_masks = int(mask_ratio * self.seq_length)
+        self.attn_implementation = attn_implementation
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+
+        labels = None
+        pixel_labels = None
+        if self.use_labels:
+            labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+            pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)
+
+        config = self.get_config()
+
+        return config, pixel_values, labels, pixel_labels
+
+    def get_config(self):
+        return Data2VecVisionConfig(
+            vocab_size=self.vocab_size,
+            image_size=self.image_size,
+            patch_size=self.patch_size,
+            num_channels=self.num_channels,
+            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,
+            is_decoder=False,
+            initializer_range=self.initializer_range,
+            out_indices=self.out_indices,
+            attn_implementation=self.attn_implementation,
+        )
+
+    def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
+        model = Data2VecVisionModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values)
+        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
+        num_patches = (self.image_size // self.patch_size) ** 2
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
+
+    def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels):
+        config.num_labels = self.type_sequence_label_size
+        model = Data2VecVisionForImageClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values, labels=labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
+
+    def create_and_check_for_image_segmentation(self, config, pixel_values, labels, pixel_labels):
+        config.num_labels = self.num_labels
+        model = Data2VecVisionForSemanticSegmentation(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values)
+        self.parent.assertEqual(
+            result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)
+        )
+        result = model(pixel_values, labels=pixel_labels)
+        self.parent.assertEqual(
+            result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)
+        )
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values, labels, pixel_labels = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_torch
+class Data2VecVisionModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as Data2VecVision does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (
+        (Data2VecVisionModel, Data2VecVisionForImageClassification, Data2VecVisionForSemanticSegmentation)
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "image-feature-extraction": Data2VecVisionModel,
+            "image-classification": Data2VecVisionForImageClassification,
+            "image-segmentation": Data2VecVisionForSemanticSegmentation,
+        }
+        if is_torch_available()
+        else {}
+    )
+
+    test_pruning = False
+    test_resize_embeddings = False
+    test_head_masking = False
+
+    def setUp(self):
+        self.model_tester = Data2VecVisionModelTester(self)
+        self.config_tester = ConfigTester(
+            self, config_class=Data2VecVisionConfig, has_text_modality=False, hidden_size=37
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(
+        reason="Will fix only if requested by the community: it fails with `torch._dynamo.exc.InternalTorchDynamoError: IndexError: list index out of range`. Without compile, the test pass."
+    )
+    @pytest.mark.torch_compile_test
+    def test_sdpa_can_compile_dynamic(self):
+        pass
+
+    @unittest.skip(reason="Data2VecVision does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @require_torch_multi_gpu
+    @unittest.skip(
+        reason="Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"
+    )
+    def test_multi_gpu_data_parallel_forward(self):
+        pass
+
+    def test_model_get_set_embeddings(self):
+        config, _ = 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(), (nn.Module))
+            x = model.get_output_embeddings()
+            self.assertTrue(x is None or isinstance(x, nn.Linear))
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_for_image_segmentation(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_image_segmentation(*config_and_inputs)
+
+    def test_training(self):
+        if not self.model_tester.is_training:
+            self.skipTest(reason="model_tester.is_training is set to False")
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+
+        for model_class in self.all_model_classes:
+            if model_class.__name__ in MODEL_MAPPING_NAMES.values():
+                continue
+
+            model = model_class(config)
+            model.to(torch_device)
+            model.train()
+            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+            loss = model(**inputs).loss
+            loss.backward()
+
+    def test_training_gradient_checkpointing(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        if not self.model_tester.is_training:
+            self.skipTest(reason="model_tester.is_training is set to False")
+
+        config.use_cache = False
+        config.return_dict = True
+
+        for model_class in self.all_model_classes:
+            if model_class.__name__ in MODEL_MAPPING_NAMES.values() or not model_class.supports_gradient_checkpointing:
+                continue
+            # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
+            # this can then be incorporated into _prepare_for_class in test_modeling_common.py
+            elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
+                batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape
+                inputs_dict["labels"] = torch.zeros(
+                    [self.model_tester.batch_size, height, width], device=torch_device
+                ).long()
+            model = model_class(config)
+            model.gradient_checkpointing_enable()
+            model.to(torch_device)
+            model.train()
+            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+            loss = model(**inputs).loss
+            loss.backward()
+
+    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():
+                # we skip lambda parameters as these require special initial values
+                # determined by config.layer_scale_init_value
+                if "lambda" in name:
+                    continue
+                if param.requires_grad:
+                    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",
+                    )
+
+    def test_for_image_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "facebook/data2vec-vision-base-ft1k"
+        model = Data2VecVisionModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+    return image
+
+
+@require_torch
+@require_vision
+class Data2VecVisionModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_image_processor(self):
+        return (
+            BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k") if is_vision_available() else None
+        )
+
+    @slow
+    def test_inference_image_classification_head_imagenet_1k(self):
+        model = Data2VecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k").to(
+            torch_device
+        )
+
+        image_processor = self.default_image_processor
+        image = prepare_img()
+        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs)
+        logits = outputs.logits
+
+        # verify the logits
+        expected_shape = torch.Size((1, 1000))
+        self.assertEqual(logits.shape, expected_shape)
+
+        expected_slice = torch.tensor([0.3277, -0.1395, 0.0911]).to(torch_device)
+
+        torch.testing.assert_close(logits[0, :3], expected_slice, rtol=1e-4, atol=1e-4)
+
+        expected_top2 = [model.config.label2id[i] for i in ["remote control, remote", "tabby, tabby cat"]]
+        self.assertEqual(logits[0].topk(2).indices.tolist(), expected_top2)
+
+    @slow
+    def test_inference_interpolate_pos_encoding(self):
+        model_name = "facebook/data2vec-vision-base-ft1k"
+        model = Data2VecVisionModel.from_pretrained(model_name, **{"use_absolute_position_embeddings": True}).to(
+            torch_device
+        )
+
+        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+        processor = BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k")
+        inputs = processor(images=image, return_tensors="pt", size={"height": 480, "width": 480})
+        pixel_values = inputs.pixel_values.to(torch_device)
+
+        # with interpolate_pos_encoding being True the model should process the higher resolution image
+        # successfully and produce the expected output.
+        with torch.no_grad():
+            outputs = model(pixel_values, interpolate_pos_encoding=True)
+
+        # num_cls_tokens + (height / patch_size) * (width / patch_size)
+        # 1 + (480 / 16) * (480 / 16) = 901
+        expected_shape = torch.Size((1, 901, 768))
+        self.assertEqual(outputs.last_hidden_state.shape, expected_shape)