# Copyright 2024 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 Wav2Vec2-BERT model.""" import tempfile import unittest from datasets import load_dataset from transformers import Wav2Vec2BertConfig, is_torch_available from transformers.testing_utils import ( is_flaky, require_torch, require_torch_accelerator, require_torch_fp16, slow, torch_device, ) from ...test_configuration_common import ConfigTester from ...test_modeling_common import ( ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask, ) from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( AutoFeatureExtractor, Wav2Vec2BertForAudioFrameClassification, Wav2Vec2BertForCTC, Wav2Vec2BertForSequenceClassification, Wav2Vec2BertForXVector, Wav2Vec2BertModel, ) from transformers.models.wav2vec2.modeling_wav2vec2 import _sample_negative_indices from transformers.models.wav2vec2_bert.modeling_wav2vec2_bert import ( _compute_mask_indices, ) # Copied from tests.models.wav2vec2_conformer.test_modeling_wav2vec2_conformer.Wav2Vec2ConformerModelTester with Conformer->Bert, input_values->input_features class Wav2Vec2BertModelTester: # Ignore copy def __init__( self, parent, batch_size=13, seq_length=200, # speech is longer is_training=False, hidden_size=16, feature_projection_input_dim=16, 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, do_stable_layer_norm=False, num_adapter_layers=2, adapter_stride=2, tdnn_dim=(32, 32), tdnn_kernel=(5, 3), tdnn_dilation=(1, 2), xvector_output_dim=32, position_embeddings_type="relative", 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.feature_projection_input_dim = feature_projection_input_dim 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.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 self.position_embeddings_type = position_embeddings_type self.output_seq_length = self.seq_length self.encoder_seq_length = self.output_seq_length self.adapter_output_seq_length = self.output_seq_length for _ in range(num_adapter_layers): self.adapter_output_seq_length = (self.adapter_output_seq_length - 1) // adapter_stride + 1 # Ignore copy def prepare_config_and_inputs(self, position_embeddings_type="relative"): input_shape = [self.batch_size, self.seq_length, self.feature_projection_input_dim] input_features = floats_tensor(input_shape, self.vocab_size) attention_mask = random_attention_mask([self.batch_size, self.seq_length]) config = self.get_config(position_embeddings_type=position_embeddings_type) return config, input_features, attention_mask # Ignore copy def get_config(self, position_embeddings_type="relative"): return Wav2Vec2BertConfig( hidden_size=self.hidden_size, feature_projection_input_dim=self.feature_projection_input_dim, 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, do_stable_layer_norm=self.do_stable_layer_norm, 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, position_embeddings_type=position_embeddings_type, ) def create_and_check_model(self, config, input_features, attention_mask): model = Wav2Vec2BertModel(config=config) model.to(torch_device) model.eval() result = model(input_features, 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_features, attention_mask): config.add_adapter = True model = Wav2Vec2BertModel(config=config) model.to(torch_device) model.eval() result = model(input_features, 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_for_ctc(self, config, input_features, attention_mask): config.add_adapter = True config.output_hidden_size = 2 * config.hidden_size model = Wav2Vec2BertForCTC(config=config) model.to(torch_device) model.eval() result = model(input_features, attention_mask=attention_mask) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.adapter_output_seq_length, self.vocab_size) ) # Ignore copy def create_and_check_model_with_intermediate_ffn_before_adapter(self, config, input_features, attention_mask): config.add_adapter = True config.use_intermediate_ffn_before_adapter = True model = Wav2Vec2BertModel(config=config) model.to(torch_device) model.eval() result = model(input_features, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.adapter_output_seq_length, config.output_hidden_size), ) # also try with different adapter proj dim config.output_hidden_size = 8 model = Wav2Vec2BertModel(config=config) model.to(torch_device) model.eval() result = model(input_features, 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 create_and_check_model_with_adapter_proj_dim(self, config, input_features, attention_mask): config.add_adapter = True config.output_hidden_size = 8 model = Wav2Vec2BertModel(config=config) model.to(torch_device) model.eval() result = model(input_features, 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 create_and_check_model_float16(self, config, input_features, attention_mask): model = Wav2Vec2BertModel(config=config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model = Wav2Vec2BertModel.from_pretrained(tmpdirname, dtype=torch.float16) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_features.type(dtype=torch.float16), attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size) ) def check_ctc_loss(self, config, input_features, *args): model = Wav2Vec2BertForCTC(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_features = input_features[:3] # Ignore copy attention_mask = torch.ones(input_features.shape[:2], device=torch_device, dtype=torch.long) input_lengths = [input_features.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_features.shape[0], min(max_length_labels) - 1), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_features[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 model.config.ctc_loss_reduction = "sum" sum_loss = model(input_features, attention_mask=attention_mask, labels=labels).loss.item() model.config.ctc_loss_reduction = "mean" mean_loss = model(input_features, 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_features, *args): model = Wav2Vec2BertForSequenceClassification(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_features = input_features[:3] # Ignore copy attention_mask = torch.ones(input_features.shape[:2], device=torch_device, dtype=torch.long) input_lengths = [input_features.shape[1] // i for i in [4, 2, 1]] labels = ids_tensor((input_features.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_features[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 masked_loss = model(input_features, attention_mask=attention_mask, labels=labels).loss.item() unmasked_loss = model(input_features, 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_features, *args): config.ctc_zero_infinity = True model = Wav2Vec2BertForCTC(config=config) model.to(torch_device) model.train() # Ignore copy input_features = input_features[:3] input_lengths = [input_features.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_features.shape[0], max(max_length_labels) - 2), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_features[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_features, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_seq_classifier_training(self, config, input_features, *args): config.ctc_zero_infinity = True model = Wav2Vec2BertForSequenceClassification(config=config) model.to(torch_device) model.train() # freeze everything but the classification head model.freeze_base_model() input_features = input_features[:3] # Ignore copy input_lengths = [input_features.shape[1] // i for i in [4, 2, 1]] labels = ids_tensor((input_features.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_features[i, input_lengths[i] :] = 0.0 loss = model(input_features, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_xvector_training(self, config, input_features, *args): config.ctc_zero_infinity = True model = Wav2Vec2BertForXVector(config=config) model.to(torch_device) model.train() # freeze everything but the classification head model.freeze_base_model() input_features = input_features[:3] input_lengths = [input_features.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_features.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_features[i, input_lengths[i] :] = 0.0 loss = model(input_features, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_labels_out_of_vocab(self, config, input_features, *args): model = Wav2Vec2BertForCTC(config) model.to(torch_device) model.train() input_features = input_features[:3] input_lengths = [input_features.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_features.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100) with self.parent.assertRaises(ValueError): model(input_features, labels=labels) def prepare_config_and_inputs_for_common(self): config, input_features, attention_mask = self.prepare_config_and_inputs() inputs_dict = {"input_features": input_features, "attention_mask": attention_mask} return config, inputs_dict @require_torch class Wav2Vec2BertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): # Ignore copy all_model_classes = ( ( Wav2Vec2BertForCTC, Wav2Vec2BertModel, Wav2Vec2BertForSequenceClassification, Wav2Vec2BertForAudioFrameClassification, Wav2Vec2BertForXVector, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "audio-classification": Wav2Vec2BertForSequenceClassification, "automatic-speech-recognition": Wav2Vec2BertForCTC, "feature-extraction": Wav2Vec2BertModel, } if is_torch_available() else {} ) def setUp(self): self.model_tester = Wav2Vec2BertModelTester(self) self.config_tester = ConfigTester(self, config_class=Wav2Vec2BertConfig, 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) @is_flaky(description="Get lager difference with A10 and even with the new `5e-4` still flaky") def test_batching_equivalence(self, atol=5e-4, rtol=5e-4): super().test_batching_equivalence(atol=atol, rtol=rtol) def test_model_with_relative(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="relative") self.model_tester.create_and_check_model(*config_and_inputs) # Ignore copy def test_model_with_relative_key(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="relative_key") self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_rotary(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="rotary") self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_no_rel_pos(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type=None) 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_for_ctc(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_adapter_for_ctc(*config_and_inputs) # Ignore copy def test_model_with_intermediate_ffn_before_adapter(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_intermediate_ffn_before_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) @require_torch_accelerator @require_torch_fp16 def test_model_float16_with_relative(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="relative") self.model_tester.create_and_check_model_float16(*config_and_inputs) # Ignore copy @require_torch_accelerator @require_torch_fp16 def test_model_float16_with_relative_key(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="relative_key") self.model_tester.create_and_check_model_float16(*config_and_inputs) @require_torch_accelerator @require_torch_fp16 def test_model_float16_with_rotary(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="rotary") self.model_tester.create_and_check_model_float16(*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) # Ignore copy @unittest.skip(reason="Wav2Vec2Bert has no inputs_embeds") def test_inputs_embeds(self): pass # Ignore copy @unittest.skip(reason="`input_ids` is renamed to `input_features`") def test_forward_signature(self): pass # Ignore copy @unittest.skip(reason="Wav2Vec2Bert has no tokens embeddings") def test_resize_tokens_embeddings(self): pass # Ignore copy @unittest.skip(reason="Wav2Vec2Bert 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 # 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_features = inputs_dict["input_features"] input_lengths = torch.tensor( [input_features.shape[1] for _ in range(input_features.shape[0])], dtype=torch.long, device=torch_device ) output_lengths = model._get_feat_extract_output_lengths(input_lengths) labels = ids_tensor((input_features.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) # overwrite from test_modeling_common def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.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.fill_(3) if hasattr(module, "pos_bias_u") and module.pos_bias_u is not None: module.pos_bias_u.data.fill_(3) if hasattr(module, "pos_bias_v") and module.pos_bias_v is not None: module.pos_bias_v.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) # Ignore copy @unittest.skip(reason="Kept to make #Copied from working") def test_mask_feature_prob_ctc(self): pass # Ignore copy @unittest.skip(reason="Kept to make #Copied from working") def test_mask_time_prob_ctc(self): pass @unittest.skip(reason="Feed forward chunking is not implemented") def test_feed_forward_chunking(self): pass @slow def test_model_from_pretrained(self): # Ignore copy model = Wav2Vec2BertModel.from_pretrained("facebook/w2v-bert-2.0") self.assertIsNotNone(model) @require_torch # Copied from tests.models.wav2vec2_conformer.test_modeling_wav2vec2_conformer.Wav2Vec2ConformerUtilsTest with Conformer->Bert, input_values->input_features class Wav2Vec2BertUtilsTest(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()) # Ignore copy @unittest.skip(reason="Kept to make #Copied from working. Test a class used for pretraining, not yet supported.") def test_compute_perplexity(self): pass def test_sample_negatives(self): batch_size = 2 sequence_length = 10 hidden_size = 4 num_negatives = 3 features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view( sequence_length, hidden_size ) # each value in vector consists of same value features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous() # sample negative indices sampled_negative_indices = _sample_negative_indices((batch_size, sequence_length), num_negatives, None) sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device) negatives = features.view(-1, hidden_size)[sampled_negative_indices.long().view(-1)] negatives = negatives.view(batch_size, sequence_length, -1, hidden_size).permute(2, 0, 1, 3) self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size)) # make sure no negatively sampled vector is actually a positive one for negative in negatives: self.assertTrue(((negative - features) == 0).sum() == 0.0) # make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim self.assertTrue(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1)) def test_sample_negatives_with_mask(self): batch_size = 2 sequence_length = 10 hidden_size = 4 num_negatives = 3 # second half of last input tensor is padded mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device) mask[-1, sequence_length // 2 :] = 0 features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view( sequence_length, hidden_size ) # each value in vector consists of same value features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous() # replace masked feature vectors with -100 to test that those are not sampled features = torch.where(mask[:, :, None].expand(features.shape).bool(), features, -100) # sample negative indices sampled_negative_indices = _sample_negative_indices( (batch_size, sequence_length), num_negatives, mask.cpu().numpy() ) sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device) negatives = features.view(-1, hidden_size)[sampled_negative_indices.long().view(-1)] negatives = negatives.view(batch_size, sequence_length, -1, hidden_size).permute(2, 0, 1, 3) self.assertTrue((negatives >= 0).all().item()) self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size)) # make sure no negatively sampled vector is actually a positive one for negative in negatives: self.assertTrue(((negative - features) == 0).sum() == 0.0) # make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim self.assertTrue(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1)) @require_torch @slow class Wav2Vec2BertModelIntegrationTest(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)]) speech_samples = speech_samples[:num_samples]["audio"] return [x["array"] for x in speech_samples] def test_inference_w2v2_bert(self): model = Wav2Vec2BertModel.from_pretrained("facebook/w2v-bert-2.0") model.to(torch_device) feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/w2v-bert-2.0") input_speech = self._load_datasamples(2) inputs = feature_extractor(input_speech, return_tensors="pt", padding=True).to(torch_device) model.eval() with torch.no_grad(): outputs = model(**inputs, output_attentions=True) # fmt: off expected_slice_0 = torch.tensor( [[-0.0098, -0.0570, -0.1286, 0.0439, -0.1037, -0.0235], [-0.0767, 0.0574, -0.3224, 0.0482, 0.0440, -0.0193], [ 0.0220, -0.0878, -0.2027, -0.0028, -0.0666, 0.0721], [ 0.0307, -0.1099, 0.0273, -0.0416, -0.0715, 0.0094], [ 0.0758, -0.0291, 0.1084, 0.0004, -0.0751, -0.0116], [ 0.0349, -0.0343, -0.0098, 0.0415, -0.0617, 0.0241], [-0.0193, -0.0171, 0.1965, 0.0797, -0.0308, 0.2033], [-0.0323, -0.0315, 0.0948, 0.0944, -0.0254, 0.1241], [-0.0493, 0.0010, -0.1762, 0.0034, -0.0787, 0.0832], [ 0.0043, -0.1228, -0.0739, 0.0266, -0.0337, -0.0068]] ).to(torch_device) # fmt: on # fmt: off expected_slice_1 = torch.tensor( [[-0.0348, -0.0521, -0.3036, 0.0285, -0.0715, -0.0453], [-0.0102, 0.0114, -0.3266, 0.0027, -0.0558, 0.0038], [ 0.0454, 0.0148, -0.2418, -0.0392, -0.0455, 0.0478], [-0.0013, 0.0825, -0.1730, -0.0091, -0.0426, 0.0360], [-0.0227, 0.0687, -0.1168, 0.0569, -0.0160, 0.0759], [-0.0318, 0.0562, -0.0508, 0.0605, 0.0150, 0.0953], [-0.0415, 0.0438, 0.0233, 0.0336, 0.0262, 0.0860], [-0.0163, 0.0048, 0.0807, 0.0119, 0.0712, 0.0158], [ 0.0244, -0.0145, 0.0262, -0.0237, 0.0283, -0.0125], [-0.0587, -0.0516, -0.0368, -0.0196, 0.0307, -0.1434]] ).to(torch_device) # fmt: on self.assertTrue((outputs.last_hidden_state[0, 25:35, 4:10] - expected_slice_0).abs().max() <= 1e-4) self.assertTrue((outputs.last_hidden_state[1, 25:35, 4:10] - expected_slice_1).abs().max() <= 1e-4) self.assertAlmostEqual(outputs.last_hidden_state[1].mean().item(), 3.3123e-05) self.assertAlmostEqual(outputs.last_hidden_state[1].std().item(), 0.1545, delta=2e-5) self.assertListEqual(list(outputs.last_hidden_state.shape), [2, 326, 1024])