# coding=utf-8 # Copyright 2023 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 CLAP model. """ import inspect import os import tempfile import unittest import numpy as np from datasets import load_dataset from transformers import ClapAudioConfig, ClapConfig, ClapProcessor, ClapTextConfig from transformers.testing_utils import require_torch, slow, torch_device from transformers.utils import is_torch_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 torch import nn from transformers import ( ClapAudioModel, ClapAudioModelWithProjection, ClapModel, ClapTextModel, ClapTextModelWithProjection, ) from transformers.models.clap.modeling_clap import CLAP_PRETRAINED_MODEL_ARCHIVE_LIST class ClapAudioModelTester: def __init__( self, parent, batch_size=12, image_size=60, num_mel_bins=16, window_size=4, spec_size=64, patch_size=2, patch_stride=2, seq_length=16, freq_ratio=2, num_channels=3, is_training=True, hidden_size=256, patch_embeds_hidden_size=32, projection_dim=32, num_hidden_layers=4, num_heads=[2, 2, 2, 2], intermediate_size=37, dropout=0.1, attention_dropout=0.1, initializer_range=0.02, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_mel_bins = num_mel_bins self.window_size = window_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.hidden_size = hidden_size self.projection_dim = projection_dim self.num_hidden_layers = num_hidden_layers self.num_heads = num_heads self.num_attention_heads = num_heads[0] self.seq_length = seq_length self.spec_size = spec_size self.freq_ratio = freq_ratio self.patch_stride = patch_stride self.patch_embeds_hidden_size = patch_embeds_hidden_size self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.initializer_range = initializer_range self.scope = scope def prepare_config_and_inputs(self): input_features = floats_tensor([self.batch_size, 1, self.hidden_size, self.num_mel_bins]) config = self.get_config() return config, input_features def get_config(self): return ClapAudioConfig( image_size=self.image_size, patch_size=self.patch_size, num_mel_bins=self.num_mel_bins, window_size=self.window_size, num_channels=self.num_channels, hidden_size=self.hidden_size, patch_stride=self.patch_stride, projection_dim=self.projection_dim, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_heads, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, initializer_range=self.initializer_range, spec_size=self.spec_size, freq_ratio=self.freq_ratio, patch_embeds_hidden_size=self.patch_embeds_hidden_size, ) def create_and_check_model(self, config, input_features): model = ClapAudioModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_features) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_model_with_projection(self, config, input_features): model = ClapAudioModelWithProjection(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_features) self.parent.assertEqual(result.audio_embeds.shape, (self.batch_size, self.projection_dim)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_features = config_and_inputs inputs_dict = {"input_features": input_features} return config, inputs_dict @require_torch class ClapAudioModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as CLAP does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (ClapAudioModel, ClapAudioModelWithProjection) if is_torch_available() else () fx_compatible = False test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = ClapAudioModelTester(self) self.config_tester = ConfigTester(self, config_class=ClapAudioConfig, has_text_modality=False, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="ClapAudioModel does not use inputs_embeds") def test_inputs_embeds(self): pass def test_model_common_attributes(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_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) self.assertListEqual( list(hidden_states[0].shape[-2:]), [self.model_tester.patch_embeds_hidden_size, self.model_tester.patch_embeds_hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) @unittest.skip(reason="ClapAudioModel does not output any loss term in the forward pass") def test_retain_grad_hidden_states_attentions(self): pass def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["input_features"] self.assertListEqual(arg_names[:1], expected_arg_names) 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_projection(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_projection(*config_and_inputs) @unittest.skip(reason="ClapAudioModel does not output any loss term in the forward pass") def test_training(self): pass @unittest.skip(reason="ClapAudioModel does not output any loss term in the forward pass") def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="ClapAudioModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="ClapAudioModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): for model_name in CLAP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = ClapAudioModel.from_pretrained(model_name) self.assertIsNotNone(model) @slow def test_model_with_projection_from_pretrained(self): for model_name in CLAP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = ClapAudioModelWithProjection.from_pretrained(model_name) self.assertIsNotNone(model) self.assertTrue(hasattr(model, "audio_projection")) class ClapTextModelTester: def __init__( self, parent, batch_size=12, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=32, projection_dim=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, max_position_embeddings=512, initializer_range=0.02, scope=None, projection_hidden_act="relu", ): 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_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.projection_dim = projection_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.scope = scope self.projection_hidden_act = projection_hidden_act 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]) if input_mask is not None: batch_size, seq_length = input_mask.shape rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,)) for batch_idx, start_index in enumerate(rnd_start_indices): input_mask[batch_idx, :start_index] = 1 input_mask[batch_idx, start_index:] = 0 config = self.get_config() return config, input_ids, input_mask def get_config(self): return ClapTextConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, projection_dim=self.projection_dim, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, projection_hidden_act=self.projection_hidden_act, ) def create_and_check_model(self, config, input_ids, input_mask): model = ClapTextModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_ids, attention_mask=input_mask) 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_with_projection(self, config, input_ids, input_mask): model = ClapTextModelWithProjection(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_ids, attention_mask=input_mask) 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.text_embeds.shape, (self.batch_size, self.projection_dim)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, input_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class ClapTextModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (ClapTextModel, ClapTextModelWithProjection) if is_torch_available() else () fx_compatible = False test_pruning = False test_head_masking = False def setUp(self): self.model_tester = ClapTextModelTester(self) self.config_tester = ConfigTester(self, config_class=ClapTextConfig, 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_projection(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_with_projection(*config_and_inputs) @unittest.skip(reason="ClapTextModel does not output any loss term in the forward pass") def test_training(self): pass @unittest.skip(reason="ClapTextModel does not output any loss term in the forward pass") def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="ClapTextModel does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="ClapTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="ClapTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): for model_name in CLAP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = ClapTextModel.from_pretrained(model_name) self.assertIsNotNone(model) @slow def test_model_with_projection_from_pretrained(self): for model_name in CLAP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = ClapTextModelWithProjection.from_pretrained(model_name) self.assertIsNotNone(model) self.assertTrue(hasattr(model, "text_projection")) class ClapModelTester: def __init__(self, parent, text_kwargs=None, audio_kwargs=None, is_training=True): if text_kwargs is None: text_kwargs = {} if audio_kwargs is None: audio_kwargs = {} self.parent = parent self.text_model_tester = ClapTextModelTester(parent, **text_kwargs) self.audio_model_tester = ClapAudioModelTester(parent, **audio_kwargs) self.is_training = is_training def prepare_config_and_inputs(self): _, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() _, input_features = self.audio_model_tester.prepare_config_and_inputs() config = self.get_config() return config, input_ids, attention_mask, input_features def get_config(self): return ClapConfig.from_text_audio_configs( self.text_model_tester.get_config(), self.audio_model_tester.get_config(), projection_dim=64 ) def create_and_check_model(self, config, input_ids, attention_mask, input_features): model = ClapModel(config).to(torch_device).eval() with torch.no_grad(): result = model(input_ids, input_features, attention_mask) self.parent.assertEqual( result.logits_per_audio.shape, (self.audio_model_tester.batch_size, self.text_model_tester.batch_size) ) self.parent.assertEqual( result.logits_per_text.shape, (self.text_model_tester.batch_size, self.audio_model_tester.batch_size) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask, input_features = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, "input_features": input_features, "return_loss": True, } return config, inputs_dict @require_torch class ClapModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (ClapModel,) if is_torch_available() else () pipeline_model_mapping = {"feature-extraction": ClapModel} if is_torch_available() else {} fx_compatible = False test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False def setUp(self): self.model_tester = ClapModelTester(self) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @unittest.skip(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="ClapModel does not have input/output embeddings") def test_model_common_attributes(self): pass # override as the `logit_scale` parameter initilization is different for CLAP 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(): if param.requires_grad: # check if `logit_scale` is initilized as per the original implementation if name == "logit_scale": self.assertAlmostEqual( param.data.item(), np.log(1 / 0.07), delta=1e-3, 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", ) def _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True 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() try: input_ids = inputs_dict["input_ids"] input_features = inputs_dict["input_features"] # CLAP needs input_features traced_model = torch.jit.trace(model, (input_ids, input_features)) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") model.to(torch_device) model.eval() loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) models_equal = True for layer_name, p1 in model_state_dict.items(): p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_load_audio_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save ClapConfig and check if we can load ClapAudioConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) audio_config = ClapAudioConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.audio_config.to_dict(), audio_config.to_dict()) # Save ClapConfig and check if we can load ClapTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = ClapTextConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict()) @slow def test_model_from_pretrained(self): for model_name in CLAP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = ClapModel.from_pretrained(model_name) self.assertIsNotNone(model) @slow @require_torch class ClapModelIntegrationTest(unittest.TestCase): paddings = ["repeatpad", "repeat", "pad"] def test_integration_unfused(self): EXPECTED_MEANS_UNFUSED = { "repeatpad": 0.0024, "pad": 0.0020, "repeat": 0.0023, } librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") audio_sample = librispeech_dummy[-1] model_id = "laion/clap-htsat-unfused" model = ClapModel.from_pretrained(model_id).to(torch_device) processor = ClapProcessor.from_pretrained(model_id) for padding in self.paddings: inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt", padding=padding).to( torch_device ) audio_embed = model.get_audio_features(**inputs) expected_mean = EXPECTED_MEANS_UNFUSED[padding] self.assertTrue( torch.allclose(audio_embed.cpu().mean(), torch.tensor([expected_mean]), atol=1e-3, rtol=1e-3) ) def test_integration_fused(self): EXPECTED_MEANS_FUSED = { "repeatpad": 0.00069, "repeat": 0.00196, "pad": -0.000379, } librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") audio_sample = librispeech_dummy[-1] model_id = "laion/clap-htsat-fused" model = ClapModel.from_pretrained(model_id).to(torch_device) processor = ClapProcessor.from_pretrained(model_id) for padding in self.paddings: inputs = processor( audios=audio_sample["audio"]["array"], return_tensors="pt", padding=padding, truncation="fusion" ).to(torch_device) audio_embed = model.get_audio_features(**inputs) expected_mean = EXPECTED_MEANS_FUSED[padding] self.assertTrue( torch.allclose(audio_embed.cpu().mean(), torch.tensor([expected_mean]), atol=1e-3, rtol=1e-3) ) def test_batched_fused(self): EXPECTED_MEANS_FUSED = { "repeatpad": 0.0010, "repeat": 0.0020, "pad": 0.0006, } librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") audio_samples = [sample["array"] for sample in librispeech_dummy[0:4]["audio"]] model_id = "laion/clap-htsat-fused" model = ClapModel.from_pretrained(model_id).to(torch_device) processor = ClapProcessor.from_pretrained(model_id) for padding in self.paddings: inputs = processor(audios=audio_samples, return_tensors="pt", padding=padding, truncation="fusion").to( torch_device ) audio_embed = model.get_audio_features(**inputs) expected_mean = EXPECTED_MEANS_FUSED[padding] self.assertTrue( torch.allclose(audio_embed.cpu().mean(), torch.tensor([expected_mean]), atol=1e-3, rtol=1e-3) ) def test_batched_unfused(self): EXPECTED_MEANS_FUSED = { "repeatpad": 0.0016, "repeat": 0.0019, "pad": 0.0019, } librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") audio_samples = [sample["array"] for sample in librispeech_dummy[0:4]["audio"]] model_id = "laion/clap-htsat-unfused" model = ClapModel.from_pretrained(model_id).to(torch_device) processor = ClapProcessor.from_pretrained(model_id) for padding in self.paddings: inputs = processor(audios=audio_samples, return_tensors="pt", padding=padding).to(torch_device) audio_embed = model.get_audio_features(**inputs) expected_mean = EXPECTED_MEANS_FUSED[padding] self.assertTrue( torch.allclose(audio_embed.cpu().mean(), torch.tensor([expected_mean]), atol=1e-3, rtol=1e-3) )