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| # coding=utf-8 | |
| # Copyright 2021 The HuggingFace Inc. team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ Testing suite for the PyTorch Hubert model. """ | |
| import math | |
| import os | |
| import pickle | |
| import tempfile | |
| import unittest | |
| import pytest | |
| from transformers import HubertConfig, is_torch_available | |
| from transformers.testing_utils import require_soundfile, require_torch, slow, torch_device | |
| from transformers.utils import is_torch_fx_available | |
| from ...test_configuration_common import ConfigTester | |
| from ...test_modeling_common import ( | |
| ModelTesterMixin, | |
| _config_zero_init, | |
| floats_tensor, | |
| ids_tensor, | |
| random_attention_mask, | |
| ) | |
| from ...test_pipeline_mixin import PipelineTesterMixin | |
| if is_torch_available(): | |
| import torch | |
| from transformers import ( | |
| HubertForCTC, | |
| HubertForSequenceClassification, | |
| HubertModel, | |
| Wav2Vec2FeatureExtractor, | |
| Wav2Vec2Processor, | |
| ) | |
| from transformers.models.hubert.modeling_hubert import _compute_mask_indices | |
| if is_torch_fx_available(): | |
| from transformers.utils.fx import symbolic_trace | |
| class HubertModelTester: | |
| def __init__( | |
| self, | |
| parent, | |
| batch_size=13, | |
| seq_length=1024, # speech is longer | |
| is_training=False, | |
| hidden_size=16, | |
| feat_extract_norm="group", | |
| feat_extract_dropout=0.0, | |
| feat_extract_activation="gelu", | |
| conv_dim=(32, 32, 32), | |
| conv_stride=(4, 4, 4), | |
| conv_kernel=(8, 8, 8), | |
| conv_bias=False, | |
| num_conv_pos_embeddings=16, | |
| num_conv_pos_embedding_groups=2, | |
| num_hidden_layers=2, | |
| num_attention_heads=2, | |
| hidden_dropout_prob=0.1, # this is most likely not correctly set yet | |
| intermediate_size=20, | |
| layer_norm_eps=1e-5, | |
| hidden_act="gelu", | |
| initializer_range=0.02, | |
| vocab_size=32, | |
| do_stable_layer_norm=False, | |
| scope=None, | |
| ): | |
| self.parent = parent | |
| self.batch_size = batch_size | |
| self.seq_length = seq_length | |
| self.is_training = is_training | |
| self.hidden_size = hidden_size | |
| self.feat_extract_norm = feat_extract_norm | |
| self.feat_extract_dropout = feat_extract_dropout | |
| self.feat_extract_activation = feat_extract_activation | |
| self.conv_dim = conv_dim | |
| self.conv_stride = conv_stride | |
| self.conv_kernel = conv_kernel | |
| self.conv_bias = conv_bias | |
| self.num_conv_pos_embeddings = num_conv_pos_embeddings | |
| self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.hidden_dropout_prob = hidden_dropout_prob | |
| self.intermediate_size = intermediate_size | |
| self.layer_norm_eps = layer_norm_eps | |
| self.hidden_act = hidden_act | |
| self.initializer_range = initializer_range | |
| self.vocab_size = vocab_size | |
| self.do_stable_layer_norm = do_stable_layer_norm | |
| self.scope = scope | |
| output_seq_length = self.seq_length | |
| for kernel, stride in zip(self.conv_kernel, self.conv_stride): | |
| output_seq_length = (output_seq_length - (kernel - 1)) / stride | |
| self.output_seq_length = int(math.ceil(output_seq_length)) | |
| self.encoder_seq_length = self.output_seq_length | |
| def prepare_config_and_inputs(self): | |
| input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0) | |
| attention_mask = random_attention_mask([self.batch_size, self.seq_length]) | |
| config = self.get_config() | |
| return config, input_values, attention_mask | |
| def get_config(self): | |
| return HubertConfig( | |
| hidden_size=self.hidden_size, | |
| feat_extract_norm=self.feat_extract_norm, | |
| feat_extract_dropout=self.feat_extract_dropout, | |
| feat_extract_activation=self.feat_extract_activation, | |
| conv_dim=self.conv_dim, | |
| conv_stride=self.conv_stride, | |
| conv_kernel=self.conv_kernel, | |
| conv_bias=self.conv_bias, | |
| num_conv_pos_embeddings=self.num_conv_pos_embeddings, | |
| num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups, | |
| num_hidden_layers=self.num_hidden_layers, | |
| num_attention_heads=self.num_attention_heads, | |
| hidden_dropout_prob=self.hidden_dropout_prob, | |
| intermediate_size=self.intermediate_size, | |
| layer_norm_eps=self.layer_norm_eps, | |
| hidden_act=self.hidden_act, | |
| initializer_range=self.initializer_range, | |
| vocab_size=self.vocab_size, | |
| do_stable_layer_norm=self.do_stable_layer_norm, | |
| ) | |
| def create_and_check_model(self, config, input_values, attention_mask): | |
| model = HubertModel(config=config) | |
| model.to(torch_device) | |
| model.eval() | |
| result = model(input_values, attention_mask=attention_mask) | |
| self.parent.assertEqual( | |
| result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size) | |
| ) | |
| def create_and_check_batch_inference(self, config, input_values, *args): | |
| # test does not pass for models making use of `group_norm` | |
| # check: https://github.com/pytorch/fairseq/issues/3227 | |
| model = HubertModel(config=config) | |
| model.to(torch_device) | |
| model.eval() | |
| input_values = input_values[:3] | |
| attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool) | |
| input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] | |
| # pad input | |
| for i in range(len(input_lengths)): | |
| input_values[i, input_lengths[i] :] = 0.0 | |
| attention_mask[i, input_lengths[i] :] = 0.0 | |
| batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state | |
| for i in range(input_values.shape[0]): | |
| input_slice = input_values[i : i + 1, : input_lengths[i]] | |
| output = model(input_slice).last_hidden_state | |
| batch_output = batch_outputs[i : i + 1, : output.shape[1]] | |
| self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3)) | |
| def check_ctc_loss(self, config, input_values, *args): | |
| model = HubertForCTC(config=config) | |
| model.to(torch_device) | |
| # make sure that dropout is disabled | |
| model.eval() | |
| input_values = input_values[:3] | |
| attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) | |
| input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] | |
| max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) | |
| labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size) | |
| # pad input | |
| for i in range(len(input_lengths)): | |
| input_values[i, input_lengths[i] :] = 0.0 | |
| attention_mask[i, input_lengths[i] :] = 0 | |
| model.config.ctc_loss_reduction = "sum" | |
| sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() | |
| model.config.ctc_loss_reduction = "mean" | |
| mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() | |
| self.parent.assertTrue(isinstance(sum_loss, float)) | |
| self.parent.assertTrue(isinstance(mean_loss, float)) | |
| def check_seq_classifier_loss(self, config, input_values, *args): | |
| model = HubertForSequenceClassification(config=config) | |
| model.to(torch_device) | |
| # make sure that dropout is disabled | |
| model.eval() | |
| input_values = input_values[:3] | |
| attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) | |
| input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] | |
| labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) | |
| # pad input | |
| for i in range(len(input_lengths)): | |
| input_values[i, input_lengths[i] :] = 0.0 | |
| attention_mask[i, input_lengths[i] :] = 0 | |
| masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() | |
| unmasked_loss = model(input_values, labels=labels).loss.item() | |
| self.parent.assertTrue(isinstance(masked_loss, float)) | |
| self.parent.assertTrue(isinstance(unmasked_loss, float)) | |
| self.parent.assertTrue(masked_loss != unmasked_loss) | |
| def check_ctc_training(self, config, input_values, *args): | |
| config.ctc_zero_infinity = True | |
| model = HubertForCTC(config=config) | |
| model.to(torch_device) | |
| model.train() | |
| # freeze feature encoder | |
| model.freeze_feature_encoder() | |
| input_values = input_values[:3] | |
| input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] | |
| max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) | |
| labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size) | |
| # pad input | |
| for i in range(len(input_lengths)): | |
| input_values[i, input_lengths[i] :] = 0.0 | |
| if max_length_labels[i] < labels.shape[-1]: | |
| # it's important that we make sure that target lengths are at least | |
| # one shorter than logit lengths to prevent -inf | |
| labels[i, max_length_labels[i] - 1 :] = -100 | |
| loss = model(input_values, labels=labels).loss | |
| self.parent.assertFalse(torch.isinf(loss).item()) | |
| loss.backward() | |
| def check_seq_classifier_training(self, config, input_values, *args): | |
| config.ctc_zero_infinity = True | |
| model = HubertForSequenceClassification(config=config) | |
| model.to(torch_device) | |
| model.train() | |
| # freeze everything but the classification head | |
| model.freeze_base_model() | |
| input_values = input_values[:3] | |
| input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] | |
| labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) | |
| # pad input | |
| for i in range(len(input_lengths)): | |
| input_values[i, input_lengths[i] :] = 0.0 | |
| loss = model(input_values, labels=labels).loss | |
| self.parent.assertFalse(torch.isinf(loss).item()) | |
| loss.backward() | |
| def check_labels_out_of_vocab(self, config, input_values, *args): | |
| model = HubertForCTC(config) | |
| model.to(torch_device) | |
| model.train() | |
| input_values = input_values[:3] | |
| input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] | |
| max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) | |
| labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100) | |
| with pytest.raises(ValueError): | |
| model(input_values, labels=labels) | |
| def prepare_config_and_inputs_for_common(self): | |
| config, input_values, attention_mask = self.prepare_config_and_inputs() | |
| inputs_dict = {"input_values": input_values, "attention_mask": attention_mask} | |
| return config, inputs_dict | |
| class HubertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): | |
| all_model_classes = (HubertForCTC, HubertForSequenceClassification, HubertModel) if is_torch_available() else () | |
| pipeline_model_mapping = ( | |
| { | |
| "audio-classification": HubertForSequenceClassification, | |
| "automatic-speech-recognition": HubertForCTC, | |
| "feature-extraction": HubertModel, | |
| } | |
| if is_torch_available() | |
| else {} | |
| ) | |
| fx_compatible = True | |
| test_pruning = False | |
| test_headmasking = False | |
| def setUp(self): | |
| self.model_tester = HubertModelTester(self) | |
| self.config_tester = ConfigTester(self, config_class=HubertConfig, hidden_size=37) | |
| def test_config(self): | |
| self.config_tester.run_common_tests() | |
| def test_model(self): | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.create_and_check_model(*config_and_inputs) | |
| def test_ctc_loss_inference(self): | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.check_ctc_loss(*config_and_inputs) | |
| def test_seq_classifier_loss_inference(self): | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.check_seq_classifier_loss(*config_and_inputs) | |
| def test_ctc_train(self): | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.check_ctc_training(*config_and_inputs) | |
| def test_seq_classifier_train(self): | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.check_seq_classifier_training(*config_and_inputs) | |
| def test_labels_out_of_vocab(self): | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.check_labels_out_of_vocab(*config_and_inputs) | |
| # Hubert has no inputs_embeds | |
| def test_inputs_embeds(self): | |
| pass | |
| # `input_ids` is renamed to `input_values` | |
| def test_forward_signature(self): | |
| pass | |
| # Hubert cannot resize token embeddings | |
| # since it has no tokens embeddings | |
| def test_resize_tokens_embeddings(self): | |
| pass | |
| # Hubert has no inputs_embeds | |
| # and thus the `get_input_embeddings` fn | |
| # is not implemented | |
| def test_model_common_attributes(self): | |
| pass | |
| def test_retain_grad_hidden_states_attentions(self): | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| config.output_hidden_states = True | |
| config.output_attentions = True | |
| # no need to test all models as different heads yield the same functionality | |
| model_class = self.all_model_classes[0] | |
| model = model_class(config) | |
| model.to(torch_device) | |
| # set layer drop to 0 | |
| model.config.layerdrop = 0.0 | |
| input_values = inputs_dict["input_values"] | |
| input_lengths = torch.tensor( | |
| [input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device | |
| ) | |
| output_lengths = model._get_feat_extract_output_lengths(input_lengths) | |
| labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size) | |
| inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"]) | |
| inputs_dict["labels"] = labels | |
| outputs = model(**inputs_dict) | |
| output = outputs[0] | |
| # Encoder-/Decoder-only models | |
| hidden_states = outputs.hidden_states[0] | |
| attentions = outputs.attentions[0] | |
| hidden_states.retain_grad() | |
| attentions.retain_grad() | |
| output.flatten()[0].backward(retain_graph=True) | |
| self.assertIsNotNone(hidden_states.grad) | |
| self.assertIsNotNone(attentions.grad) | |
| def test_initialization(self): | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| configs_no_init = _config_zero_init(config) | |
| for model_class in self.all_model_classes: | |
| model = model_class(config=configs_no_init) | |
| for name, param in model.named_parameters(): | |
| uniform_init_parms = [ | |
| "conv.weight", | |
| "masked_spec_embed", | |
| "quantizer.weight_proj.weight", | |
| ] | |
| if param.requires_grad: | |
| if any(x in name for x in uniform_init_parms): | |
| self.assertTrue( | |
| -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0, | |
| msg=f"Parameter {name} of model {model_class} seems not properly initialized", | |
| ) | |
| else: | |
| self.assertIn( | |
| ((param.data.mean() * 1e9).round() / 1e9).item(), | |
| [0.0, 1.0], | |
| msg=f"Parameter {name} of model {model_class} seems not properly initialized", | |
| ) | |
| # Hubert cannot be TorchScripted because of torch.nn.utils.weight_norm | |
| def _create_and_check_torch_fx_tracing(self, config, inputs_dict, output_loss=False): | |
| if not is_torch_fx_available() or not self.fx_compatible: | |
| return | |
| configs_no_init = _config_zero_init(config) # To be sure we have no Nan | |
| configs_no_init.return_dict = False | |
| for model_class in self.all_model_classes: | |
| model = model_class(config=configs_no_init) | |
| model.to(torch_device) | |
| model.eval() | |
| inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=output_loss) | |
| try: | |
| if model.config.is_encoder_decoder: | |
| model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward | |
| labels = inputs.get("labels", None) | |
| input_names = [ | |
| "attention_mask", | |
| "decoder_attention_mask", | |
| "decoder_input_ids", | |
| "input_features", | |
| "input_ids", | |
| "input_values", | |
| ] | |
| if labels is not None: | |
| input_names.append("labels") | |
| filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names} | |
| input_names = list(filtered_inputs.keys()) | |
| model_output = model(**filtered_inputs) | |
| traced_model = symbolic_trace(model, input_names) | |
| traced_output = traced_model(**filtered_inputs) | |
| else: | |
| input_names = [ | |
| "attention_mask", | |
| "bbox", | |
| "input_features", | |
| "input_ids", | |
| "input_values", | |
| "pixel_values", | |
| "token_type_ids", | |
| "visual_feats", | |
| "visual_pos", | |
| ] | |
| labels = inputs.get("labels", None) | |
| start_positions = inputs.get("start_positions", None) | |
| end_positions = inputs.get("end_positions", None) | |
| if labels is not None: | |
| input_names.append("labels") | |
| if start_positions is not None: | |
| input_names.append("start_positions") | |
| if end_positions is not None: | |
| input_names.append("end_positions") | |
| filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names} | |
| input_names = list(filtered_inputs.keys()) | |
| model_output = model(**filtered_inputs) | |
| traced_model = symbolic_trace(model, input_names) | |
| traced_output = traced_model(**filtered_inputs) | |
| except Exception as e: | |
| self.fail(f"Couldn't trace module: {e}") | |
| def flatten_output(output): | |
| flatten = [] | |
| for x in output: | |
| if isinstance(x, (tuple, list)): | |
| flatten += flatten_output(x) | |
| elif not isinstance(x, torch.Tensor): | |
| continue | |
| else: | |
| flatten.append(x) | |
| return flatten | |
| model_output = flatten_output(model_output) | |
| traced_output = flatten_output(traced_output) | |
| num_outputs = len(model_output) | |
| for i in range(num_outputs): | |
| self.assertTrue( | |
| torch.allclose(model_output[i], traced_output[i]), | |
| f"traced {i}th output doesn't match model {i}th output for {model_class}", | |
| ) | |
| # Test that the model can be serialized and restored properly | |
| with tempfile.TemporaryDirectory() as tmp_dir_name: | |
| pkl_file_name = os.path.join(tmp_dir_name, "model.pkl") | |
| try: | |
| with open(pkl_file_name, "wb") as f: | |
| pickle.dump(traced_model, f) | |
| with open(pkl_file_name, "rb") as f: | |
| loaded = pickle.load(f) | |
| except Exception as e: | |
| self.fail(f"Couldn't serialize / deserialize the traced model: {e}") | |
| loaded_output = loaded(**filtered_inputs) | |
| loaded_output = flatten_output(loaded_output) | |
| for i in range(num_outputs): | |
| self.assertTrue( | |
| torch.allclose(model_output[i], loaded_output[i]), | |
| f"serialized model {i}th output doesn't match model {i}th output for {model_class}", | |
| ) | |
| # overwrite from test_modeling_common | |
| def _mock_init_weights(self, module): | |
| if hasattr(module, "weight") and module.weight is not None: | |
| module.weight.data.fill_(3) | |
| if hasattr(module, "weight_g") and module.weight_g is not None: | |
| module.weight_g.data.fill_(3) | |
| if hasattr(module, "weight_v") and module.weight_v is not None: | |
| module.weight_v.data.fill_(3) | |
| if hasattr(module, "bias") and module.bias is not None: | |
| module.bias.data.fill_(3) | |
| if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None: | |
| module.masked_spec_embed.data.fill_(3) | |
| def test_feed_forward_chunking(self): | |
| pass | |
| def test_model_from_pretrained(self): | |
| model = HubertModel.from_pretrained("facebook/hubert-base-ls960") | |
| self.assertIsNotNone(model) | |
| class HubertRobustModelTest(ModelTesterMixin, unittest.TestCase): | |
| all_model_classes = (HubertForCTC, HubertForSequenceClassification, HubertModel) if is_torch_available() else () | |
| test_pruning = False | |
| test_headmasking = False | |
| def setUp(self): | |
| self.model_tester = HubertModelTester( | |
| self, conv_stride=(3, 3, 3), feat_extract_norm="layer", do_stable_layer_norm=True | |
| ) | |
| self.config_tester = ConfigTester(self, config_class=HubertConfig, hidden_size=37) | |
| def test_config(self): | |
| self.config_tester.run_common_tests() | |
| def test_model(self): | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.create_and_check_model(*config_and_inputs) | |
| def test_batched_inference(self): | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.create_and_check_batch_inference(*config_and_inputs) | |
| def test_ctc_loss_inference(self): | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.check_ctc_loss(*config_and_inputs) | |
| def test_seq_classifier_loss_inference(self): | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.check_seq_classifier_loss(*config_and_inputs) | |
| def test_ctc_train(self): | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.check_ctc_training(*config_and_inputs) | |
| def test_seq_classifier_train(self): | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.check_seq_classifier_training(*config_and_inputs) | |
| def test_labels_out_of_vocab(self): | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.check_labels_out_of_vocab(*config_and_inputs) | |
| # Hubert has no inputs_embeds | |
| def test_inputs_embeds(self): | |
| pass | |
| # `input_ids` is renamed to `input_values` | |
| def test_forward_signature(self): | |
| pass | |
| # Hubert cannot resize token embeddings | |
| # since it has no tokens embeddings | |
| def test_resize_tokens_embeddings(self): | |
| pass | |
| # Hubert has no inputs_embeds | |
| # and thus the `get_input_embeddings` fn | |
| # is not implemented | |
| def test_model_common_attributes(self): | |
| pass | |
| def test_retain_grad_hidden_states_attentions(self): | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| config.output_hidden_states = True | |
| config.output_attentions = True | |
| # no need to test all models as different heads yield the same functionality | |
| model_class = self.all_model_classes[0] | |
| model = model_class(config) | |
| model.to(torch_device) | |
| # set layer drop to 0 | |
| model.config.layerdrop = 0.0 | |
| input_values = inputs_dict["input_values"] | |
| input_lengths = torch.tensor( | |
| [input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device | |
| ) | |
| output_lengths = model._get_feat_extract_output_lengths(input_lengths) | |
| labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size) | |
| inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"]) | |
| inputs_dict["labels"] = labels | |
| outputs = model(**inputs_dict) | |
| output = outputs[0] | |
| # Encoder-/Decoder-only models | |
| hidden_states = outputs.hidden_states[0] | |
| attentions = outputs.attentions[0] | |
| hidden_states.retain_grad() | |
| attentions.retain_grad() | |
| output.flatten()[0].backward(retain_graph=True) | |
| self.assertIsNotNone(hidden_states.grad) | |
| self.assertIsNotNone(attentions.grad) | |
| def test_initialization(self): | |
| config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
| configs_no_init = _config_zero_init(config) | |
| for model_class in self.all_model_classes: | |
| model = model_class(config=configs_no_init) | |
| for name, param in model.named_parameters(): | |
| uniform_init_parms = [ | |
| "conv.weight", | |
| "masked_spec_embed", | |
| "quantizer.weight_proj.weight", | |
| ] | |
| if param.requires_grad: | |
| if any(x in name for x in uniform_init_parms): | |
| self.assertTrue( | |
| -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0, | |
| msg=f"Parameter {name} of model {model_class} seems not properly initialized", | |
| ) | |
| else: | |
| self.assertIn( | |
| ((param.data.mean() * 1e9).round() / 1e9).item(), | |
| [0.0, 1.0], | |
| msg=f"Parameter {name} of model {model_class} seems not properly initialized", | |
| ) | |
| # overwrite from test_modeling_common | |
| def _mock_init_weights(self, module): | |
| if hasattr(module, "weight") and module.weight is not None: | |
| module.weight.data.fill_(3) | |
| if hasattr(module, "weight_g") and module.weight_g is not None: | |
| module.weight_g.data.fill_(3) | |
| if hasattr(module, "weight_v") and module.weight_v is not None: | |
| module.weight_v.data.fill_(3) | |
| if hasattr(module, "bias") and module.bias is not None: | |
| module.bias.data.fill_(3) | |
| if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None: | |
| module.masked_spec_embed.data.fill_(3) | |
| def test_feed_forward_chunking(self): | |
| pass | |
| def test_model_from_pretrained(self): | |
| model = HubertModel.from_pretrained("facebook/hubert-large-ls960-ft") | |
| self.assertIsNotNone(model) | |
| class HubertUtilsTest(unittest.TestCase): | |
| def test_compute_mask_indices(self): | |
| batch_size = 4 | |
| sequence_length = 60 | |
| mask_prob = 0.5 | |
| mask_length = 1 | |
| mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) | |
| mask = torch.from_numpy(mask).to(torch_device) | |
| self.assertListEqual(mask.sum(axis=-1).tolist(), [mask_prob * sequence_length for _ in range(batch_size)]) | |
| def test_compute_mask_indices_overlap(self): | |
| batch_size = 4 | |
| sequence_length = 80 | |
| mask_prob = 0.5 | |
| mask_length = 4 | |
| mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) | |
| mask = torch.from_numpy(mask).to(torch_device) | |
| # because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal | |
| for batch_sum in mask.sum(axis=-1): | |
| self.assertTrue(int(batch_sum) <= mask_prob * sequence_length) | |
| class HubertModelIntegrationTest(unittest.TestCase): | |
| def _load_datasamples(self, num_samples): | |
| from datasets import load_dataset | |
| ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") | |
| # automatic decoding with librispeech | |
| speech_samples = ds.sort("id").filter( | |
| lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)] | |
| )[:num_samples]["audio"] | |
| return [x["array"] for x in speech_samples] | |
| def _load_superb(self, task, num_samples): | |
| from datasets import load_dataset | |
| ds = load_dataset("anton-l/superb_dummy", task, split="test") | |
| return ds[:num_samples] | |
| def test_inference_ctc_batched(self): | |
| model = HubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft", torch_dtype=torch.float16).to( | |
| torch_device | |
| ) | |
| processor = Wav2Vec2Processor.from_pretrained("facebook/hubert-large-ls960-ft", do_lower_case=True) | |
| input_speech = self._load_datasamples(2) | |
| inputs = processor(input_speech, return_tensors="pt", padding=True) | |
| input_values = inputs.input_values.half().to(torch_device) | |
| attention_mask = inputs.attention_mask.to(torch_device) | |
| with torch.no_grad(): | |
| logits = model(input_values, attention_mask=attention_mask).logits | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| predicted_trans = processor.batch_decode(predicted_ids) | |
| EXPECTED_TRANSCRIPTIONS = [ | |
| "a man said to the universe sir i exist", | |
| "sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore", | |
| ] | |
| self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS) | |
| def test_inference_keyword_spotting(self): | |
| model = HubertForSequenceClassification.from_pretrained( | |
| "superb/hubert-base-superb-ks", torch_dtype=torch.float16 | |
| ).to(torch_device) | |
| processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-ks") | |
| input_data = self._load_superb("ks", 4) | |
| inputs = processor(input_data["speech"], return_tensors="pt", padding=True) | |
| input_values = inputs.input_values.half().to(torch_device) | |
| attention_mask = inputs.attention_mask.to(torch_device) | |
| with torch.no_grad(): | |
| outputs = model(input_values, attention_mask=attention_mask) | |
| predicted_logits, predicted_ids = torch.max(outputs.logits, dim=-1) | |
| expected_labels = [2, 6, 10, 9] | |
| # s3prl logits for the same batch | |
| expected_logits = torch.tensor([7.6692, 17.7795, 11.1562, 11.8232], dtype=torch.float16, device=torch_device) | |
| self.assertListEqual(predicted_ids.tolist(), expected_labels) | |
| self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=3e-2)) | |
| def test_inference_intent_classification(self): | |
| model = HubertForSequenceClassification.from_pretrained( | |
| "superb/hubert-base-superb-ic", torch_dtype=torch.float16 | |
| ).to(torch_device) | |
| processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-ic") | |
| input_data = self._load_superb("ic", 4) | |
| inputs = processor(input_data["speech"], return_tensors="pt", padding=True) | |
| input_values = inputs.input_values.half().to(torch_device) | |
| attention_mask = inputs.attention_mask.to(torch_device) | |
| with torch.no_grad(): | |
| outputs = model(input_values, attention_mask=attention_mask) | |
| predicted_logits_action, predicted_ids_action = torch.max(outputs.logits[:, :6], dim=-1) | |
| predicted_logits_object, predicted_ids_object = torch.max(outputs.logits[:, 6:20], dim=-1) | |
| predicted_logits_location, predicted_ids_location = torch.max(outputs.logits[:, 20:24], dim=-1) | |
| expected_labels_action = [1, 0, 4, 3] | |
| expected_logits_action = torch.tensor( | |
| [5.9052, 12.5865, 4.4840, 10.0240], dtype=torch.float16, device=torch_device | |
| ) | |
| expected_labels_object = [1, 10, 3, 4] | |
| expected_logits_object = torch.tensor( | |
| [5.5316, 11.7946, 8.1672, 23.2415], dtype=torch.float16, device=torch_device | |
| ) | |
| expected_labels_location = [0, 0, 0, 1] | |
| expected_logits_location = torch.tensor( | |
| [5.2053, 8.9577, 10.0447, 8.1481], dtype=torch.float16, device=torch_device | |
| ) | |
| self.assertListEqual(predicted_ids_action.tolist(), expected_labels_action) | |
| self.assertListEqual(predicted_ids_object.tolist(), expected_labels_object) | |
| self.assertListEqual(predicted_ids_location.tolist(), expected_labels_location) | |
| # TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572 | |
| self.assertTrue(torch.allclose(predicted_logits_action, expected_logits_action, atol=3e-1)) | |
| self.assertTrue(torch.allclose(predicted_logits_object, expected_logits_object, atol=3e-1)) | |
| self.assertTrue(torch.allclose(predicted_logits_location, expected_logits_location, atol=3e-1)) | |
| def test_inference_speaker_identification(self): | |
| model = HubertForSequenceClassification.from_pretrained( | |
| "superb/hubert-base-superb-sid", torch_dtype=torch.float16 | |
| ).to(torch_device) | |
| processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-sid") | |
| input_data = self._load_superb("si", 4) | |
| output_logits = [] | |
| with torch.no_grad(): | |
| for example in input_data["speech"]: | |
| input = processor(example, return_tensors="pt", padding=True) | |
| output = model(input.input_values.half().to(torch_device), attention_mask=None) | |
| output_logits.append(output.logits[0]) | |
| output_logits = torch.stack(output_logits) | |
| predicted_logits, predicted_ids = torch.max(output_logits, dim=-1) | |
| expected_labels = [5, 1, 1, 3] | |
| # s3prl logits for the same batch | |
| expected_logits = torch.tensor( | |
| [78231.5547, 123166.6094, 122785.4141, 84851.2969], dtype=torch.float16, device=torch_device | |
| ) | |
| self.assertListEqual(predicted_ids.tolist(), expected_labels) | |
| # TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572 | |
| self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=10)) | |
| def test_inference_emotion_recognition(self): | |
| model = HubertForSequenceClassification.from_pretrained( | |
| "superb/hubert-base-superb-er", torch_dtype=torch.float16 | |
| ).to(torch_device) | |
| processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-er") | |
| input_data = self._load_superb("er", 4) | |
| inputs = processor(input_data["speech"], return_tensors="pt", padding=True) | |
| input_values = inputs.input_values.half().to(torch_device) | |
| attention_mask = inputs.attention_mask.to(torch_device) | |
| with torch.no_grad(): | |
| outputs = model(input_values, attention_mask=attention_mask) | |
| predicted_logits, predicted_ids = torch.max(outputs.logits, dim=-1) | |
| expected_labels = [1, 1, 2, 2] | |
| # s3prl logits for the same batch | |
| expected_logits = torch.tensor([2.8384, 2.3389, 3.8564, 4.5558], dtype=torch.float16, device=torch_device) | |
| self.assertListEqual(predicted_ids.tolist(), expected_labels) | |
| # TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572 | |
| self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=1e-1)) | |
| def test_inference_distilhubert(self): | |
| model = HubertModel.from_pretrained("ntu-spml/distilhubert").to(torch_device) | |
| processor = Wav2Vec2FeatureExtractor.from_pretrained("ntu-spml/distilhubert") | |
| # TODO: can't test on batched inputs due to incompatible padding https://github.com/pytorch/fairseq/pull/3572 | |
| input_speech = self._load_datasamples(1) | |
| inputs = processor(input_speech, return_tensors="pt", padding=True) | |
| input_values = inputs.input_values.to(torch_device) | |
| with torch.no_grad(): | |
| outputs = model(input_values).last_hidden_state | |
| # expected outputs taken from the original SEW implementation | |
| expected_outputs_first = torch.tensor( | |
| [ | |
| [ | |
| [-0.3505, 0.1167, 0.0608, 0.1294], | |
| [-0.3085, 0.0481, 0.1106, 0.0955], | |
| [-0.3107, -0.0391, 0.0739, 0.1360], | |
| [-0.2385, -0.1795, -0.0928, 0.2389], | |
| ] | |
| ], | |
| device=torch_device, | |
| ) | |
| expected_outputs_last = torch.tensor( | |
| [ | |
| [ | |
| [-0.0732, 0.0255, 0.0529, -0.1372], | |
| [-0.0812, 0.1259, 0.0564, -0.0438], | |
| [-0.0054, 0.0758, -0.0002, -0.1617], | |
| [0.0133, -0.0320, -0.0687, 0.0062], | |
| ] | |
| ], | |
| device=torch_device, | |
| ) | |
| expected_output_sum = -3776.0730 | |
| self.assertTrue(torch.allclose(outputs[:, :4, :4], expected_outputs_first, atol=5e-3)) | |
| self.assertTrue(torch.allclose(outputs[:, -4:, -4:], expected_outputs_last, atol=5e-3)) | |
| self.assertTrue(abs(outputs.sum() - expected_output_sum) < 0.1) | |