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tests/models/bark/test_modeling_bark.py
1 119 строк
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Cyril Vallez
Remove deprecation cycle for inputs embeds (#45885)
11 май 2026, 10:28
Не верифицирован
11 май 2026, 10:28
2d6815e
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# 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 Bark model.""" import copy import inspect import tempfile import unittest from functools import cached_property from transformers import ( BarkCausalModel, BarkCoarseConfig, BarkConfig, BarkFineConfig, BarkSemanticConfig, is_torch_available, ) from transformers.models.bark.generation_configuration_bark import ( BarkCoarseGenerationConfig, BarkFineGenerationConfig, BarkSemanticGenerationConfig, ) from transformers.testing_utils import ( backend_torch_accelerator_module, require_torch, require_torch_accelerator, require_torch_fp16, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ..encodec.test_modeling_encodec import EncodecModelTester if is_torch_available(): import torch from transformers import ( BarkCoarseModel, BarkFineModel, BarkModel, BarkProcessor, BarkSemanticModel, ) class BarkSemanticModelTester: def __init__( self, parent, batch_size=3, # need batch_size != num_hidden_layers seq_length=4, is_training=False, # for now training is not supported use_input_mask=True, use_labels=True, vocab_size=33, output_vocab_size=33, hidden_size=16, num_hidden_layers=2, num_attention_heads=2, intermediate_size=15, dropout=0.1, window_size=256, initializer_range=0.02, n_codes_total=8, # for BarkFineModel n_codes_given=1, # for BarkFineModel ): 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.output_vocab_size = output_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.dropout = dropout self.window_size = window_size self.initializer_range = initializer_range self.bos_token_id = output_vocab_size - 1 self.eos_token_id = output_vocab_size - 1 self.pad_token_id = output_vocab_size - 1 self.n_codes_total = n_codes_total self.n_codes_given = n_codes_given self.is_encoder_decoder = False 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]) config = self.get_config() inputs_dict = { "input_ids": input_ids, "attention_mask": input_mask, } return config, inputs_dict def get_config(self): return BarkSemanticConfig( vocab_size=self.vocab_size, output_vocab_size=self.output_vocab_size, hidden_size=self.hidden_size, num_layers=self.num_hidden_layers, num_heads=self.num_attention_heads, use_cache=True, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, window_size=self.window_size, ) def get_pipeline_config(self): config = self.get_config() config.vocab_size = 300 config.output_vocab_size = 300 return config def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = BarkSemanticModel(config=config).to(torch_device).eval() input_ids = inputs_dict["input_ids"] attention_mask = inputs_dict["attention_mask"] # first forward pass outputs = model(input_ids, attention_mask=attention_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["logits"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "logits" ] # 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)) # test no attention_mask works outputs = model(input_ids, use_cache=True) _, past_key_values = outputs.to_tuple() output_from_no_past = model(next_input_ids)["logits"] output_from_past = model(next_tokens, past_key_values=past_key_values)["logits"] 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() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) class BarkCoarseModelTester: def __init__( self, parent, batch_size=3, # need batch_size != num_hidden_layers seq_length=4, is_training=False, # for now training is not supported use_input_mask=True, use_labels=True, vocab_size=33, output_vocab_size=33, hidden_size=16, num_hidden_layers=2, num_attention_heads=2, intermediate_size=15, dropout=0.1, window_size=256, initializer_range=0.02, n_codes_total=8, # for BarkFineModel n_codes_given=1, # for BarkFineModel ): 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.output_vocab_size = output_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.dropout = dropout self.window_size = window_size self.initializer_range = initializer_range self.bos_token_id = output_vocab_size - 1 self.eos_token_id = output_vocab_size - 1 self.pad_token_id = output_vocab_size - 1 self.n_codes_total = n_codes_total self.n_codes_given = n_codes_given self.is_encoder_decoder = False 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]) config = self.get_config() inputs_dict = { "input_ids": input_ids, "attention_mask": input_mask, } return config, inputs_dict def get_config(self): return BarkCoarseConfig( vocab_size=self.vocab_size, output_vocab_size=self.output_vocab_size, hidden_size=self.hidden_size, num_layers=self.num_hidden_layers, num_heads=self.num_attention_heads, use_cache=True, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, window_size=self.window_size, ) def get_pipeline_config(self): config = self.get_config() config.vocab_size = 300 config.output_vocab_size = 300 return config def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = BarkCoarseModel(config=config).to(torch_device).eval() input_ids = inputs_dict["input_ids"] attention_mask = inputs_dict["attention_mask"] # first forward pass outputs = model(input_ids, attention_mask=attention_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["logits"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "logits" ] # 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)) # test no attention_mask works outputs = model(input_ids, use_cache=True) _, past_key_values = outputs.to_tuple() output_from_no_past = model(next_input_ids)["logits"] output_from_past = model(next_tokens, past_key_values=past_key_values)["logits"] 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() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) class BarkFineModelTester: def __init__( self, parent, batch_size=3, # need batch_size != num_hidden_layers seq_length=4, is_training=False, # for now training is not supported use_input_mask=True, use_labels=True, vocab_size=33, output_vocab_size=33, hidden_size=16, num_hidden_layers=2, num_attention_heads=2, intermediate_size=15, dropout=0.1, window_size=256, initializer_range=0.02, n_codes_total=8, # for BarkFineModel n_codes_given=1, # for BarkFineModel ): 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.output_vocab_size = output_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.dropout = dropout self.window_size = window_size self.initializer_range = initializer_range self.bos_token_id = output_vocab_size - 1 self.eos_token_id = output_vocab_size - 1 self.pad_token_id = output_vocab_size - 1 self.n_codes_total = n_codes_total self.n_codes_given = n_codes_given self.is_encoder_decoder = False def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length, self.n_codes_total], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) config = self.get_config() # randint between self.n_codes_given - 1 and self.n_codes_total - 1 codebook_idx = ids_tensor((1,), self.n_codes_total - self.n_codes_given).item() + self.n_codes_given inputs_dict = { "codebook_idx": codebook_idx, "input_ids": input_ids, "attention_mask": input_mask, } return config, inputs_dict def get_config(self): return BarkFineConfig( vocab_size=self.vocab_size, output_vocab_size=self.output_vocab_size, hidden_size=self.hidden_size, num_layers=self.num_hidden_layers, num_heads=self.num_attention_heads, use_cache=True, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, window_size=self.window_size, ) def get_pipeline_config(self): config = self.get_config() config.vocab_size = 300 config.output_vocab_size = 300 return config def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = BarkFineModel(config=config).to(torch_device).eval() input_ids = inputs_dict["input_ids"] attention_mask = inputs_dict["attention_mask"] # first forward pass outputs = model(input_ids, attention_mask=attention_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["logits"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "logits" ] # 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)) # test no attention_mask works outputs = model(input_ids, use_cache=True) _, past_key_values = outputs.to_tuple() output_from_no_past = model(next_input_ids)["logits"] output_from_past = model(next_tokens, past_key_values=past_key_values)["logits"] 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() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) class BarkModelTester: def __init__( self, parent, semantic_kwargs=None, coarse_acoustics_kwargs=None, fine_acoustics_kwargs=None, codec_kwargs=None, is_training=False, # for now training is not supported ): if semantic_kwargs is None: semantic_kwargs = {} if coarse_acoustics_kwargs is None: coarse_acoustics_kwargs = {} if fine_acoustics_kwargs is None: fine_acoustics_kwargs = {} if codec_kwargs is None: codec_kwargs = {} self.parent = parent self.semantic_model_tester = BarkSemanticModelTester(parent, **semantic_kwargs) self.coarse_acoustics_model_tester = BarkCoarseModelTester(parent, **coarse_acoustics_kwargs) self.fine_acoustics_model_tester = BarkFineModelTester(parent, **fine_acoustics_kwargs) self.codec_model_tester = EncodecModelTester(parent, **codec_kwargs) self.is_training = is_training def get_config(self): return BarkConfig( semantic_config=self.semantic_model_tester.get_config(), coarse_acoustics_config=self.coarse_acoustics_model_tester.get_config(), fine_acoustics_config=self.fine_acoustics_model_tester.get_config(), codec_config=self.codec_model_tester.get_config(), ) def get_pipeline_config(self): config = self.get_config() # follow the `get_pipeline_config` of the sub component models config.semantic_config.vocab_size = 300 config.coarse_acoustics_config.vocab_size = 300 config.fine_acoustics_config.vocab_size = 300 config.semantic_config.output_vocab_size = 300 config.coarse_acoustics_config.output_vocab_size = 300 config.fine_acoustics_config.output_vocab_size = 300 return config @require_torch class BarkSemanticModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (BarkSemanticModel,) if is_torch_available() else () # `BarkSemanticModel` inherits from `BarkCausalModel`, but requires an advanced generation config. # `BarkCausalModel` does not, so we run generation tests there. all_generative_model_classes = (BarkCausalModel,) if is_torch_available() else () is_encoder_decoder = False test_missing_keys = False test_resize_embeddings = True def setUp(self): self.model_tester = BarkSemanticModelTester(self) self.config_tester = ConfigTester(self, config_class=BarkSemanticConfig, n_embd=37) def test_config(self): self.config_tester.run_common_tests() def test_save_load_strict(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(info["missing_keys"], set()) def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) @require_torch_fp16 def test_generate_fp16(self): config, input_dict = self.model_tester.prepare_config_and_inputs() input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) model = self.all_generative_model_classes[0](config).eval().to(torch_device) model.half() model.generate(input_ids, attention_mask=attention_mask) model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3) @unittest.skip("Bark has no base model due to special archiecture") def test_model_base_model_prefix(self): pass @require_torch class BarkCoarseModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (BarkCoarseModel,) if is_torch_available() else () # `BarkCoarseModel` inherits from `BarkCausalModel`, but requires an advanced generation config. # `BarkCausalModel` does not, so we run generation tests there. all_generative_model_classes = (BarkCausalModel,) if is_torch_available() else () is_encoder_decoder = False test_missing_keys = False test_resize_embeddings = True def setUp(self): self.model_tester = BarkCoarseModelTester(self) self.config_tester = ConfigTester(self, config_class=BarkCoarseConfig, n_embd=37) def test_config(self): self.config_tester.run_common_tests() def test_save_load_strict(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(info["missing_keys"], set()) def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) @require_torch_fp16 def test_generate_fp16(self): config, input_dict = self.model_tester.prepare_config_and_inputs() input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) model = self.all_generative_model_classes[0](config).eval().to(torch_device) model.half() model.generate(input_ids, attention_mask=attention_mask) model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3) @unittest.skip("Bark has no base model due to special archiecture") def test_model_base_model_prefix(self): pass @require_torch class BarkFineModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (BarkFineModel,) if is_torch_available() else () is_encoder_decoder = False test_missing_keys = False test_resize_embeddings = True def setUp(self): self.model_tester = BarkFineModelTester(self) self.config_tester = ConfigTester(self, config_class=BarkFineConfig, n_embd=37) def test_config(self): self.config_tester.run_common_tests() def test_save_load_strict(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(info["missing_keys"], set()) @unittest.skip(reason="FineModel relies on codebook idx and does not return same logits") def test_inputs_embeds_matches_input_ids(self): pass def test_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) input_ids = inputs["input_ids"] del inputs["input_ids"] wte = model.get_input_embeddings()[inputs_dict["codebook_idx"]] inputs["inputs_embeds"] = wte(input_ids[:, :, inputs_dict["codebook_idx"]]) with torch.no_grad(): model(**inputs)[0] @require_torch_fp16 def test_generate_fp16(self): config, input_dict = self.model_tester.prepare_config_and_inputs() input_ids = input_dict["input_ids"] # take first codebook channel model = self.all_model_classes[0](config).eval().to(torch_device) model.half() # toy generation_configs semantic_generation_config = BarkSemanticGenerationConfig(semantic_vocab_size=0) coarse_generation_config = BarkCoarseGenerationConfig(n_coarse_codebooks=config.n_codes_given) fine_generation_config = BarkFineGenerationConfig( max_fine_history_length=config.block_size // 2, max_fine_input_length=config.block_size, n_fine_codebooks=config.n_codes_total, ) codebook_size = config.vocab_size - 1 model.generate( input_ids, history_prompt=None, temperature=None, semantic_generation_config=semantic_generation_config, coarse_generation_config=coarse_generation_config, fine_generation_config=fine_generation_config, codebook_size=codebook_size, ) model.generate( input_ids, history_prompt=None, temperature=0.7, semantic_generation_config=semantic_generation_config, coarse_generation_config=coarse_generation_config, fine_generation_config=fine_generation_config, codebook_size=codebook_size, ) 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 = ["codebook_idx", "input_ids"] self.assertListEqual(arg_names[:2], expected_arg_names) def test_model_get_set_embeddings(self): # one embedding layer per codebook config, inputs_dict = 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()[0], (torch.nn.Embedding)) model.set_input_embeddings( torch.nn.ModuleList([torch.nn.Embedding(10, 10) for _ in range(config.n_codes_total)]) ) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x[0], torch.nn.Linear)) def test_resize_tokens_embeddings(self): # resizing tokens_embeddings of a ModuleList original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if not self.test_resize_embeddings: self.skipTest(reason="test_resize_embeddings is False") for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) if self.model_tester.is_training is False: model.eval() model_vocab_size = config.vocab_size # Retrieve the embeddings and clone theme model_embed_list = model.resize_token_embeddings(model_vocab_size) cloned_embeddings_list = [model_embed.weight.clone() for model_embed in model_embed_list] # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_embed_list = model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.vocab_size, model_vocab_size + 10) # Check that it actually resizes the embeddings matrix for each codebook for model_embed, cloned_embeddings in zip(model_embed_list, cloned_embeddings_list): self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model_embed_list = model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.vocab_size, model_vocab_size - 15) for model_embed, cloned_embeddings in zip(model_embed_list, cloned_embeddings_list): self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) # Input ids should be clamped to the maximum size of the vocabulary inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that adding and removing tokens has not modified the first part of the embedding matrix. # only check for the first embedding matrix models_equal = True for p1, p2 in zip(cloned_embeddings_list[0], model_embed_list[0].weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_resize_embeddings_untied(self): # resizing tokens_embeddings of a ModuleList original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if not self.test_resize_embeddings: self.skipTest(reason="test_resize_embeddings is False") original_config.tie_word_embeddings = False for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config).to(torch_device) model.eval() # if no output embeddings -> leave test if model.get_output_embeddings() is None: continue # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_vocab_size = config.vocab_size model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.vocab_size, model_vocab_size + 10) output_embeds_list = model.get_output_embeddings() for output_embeds in output_embeds_list: self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix output_embeds_list = model.get_output_embeddings() for output_embeds in output_embeds_list: self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) # Input ids should be clamped to the maximum size of the vocabulary inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) @require_torch @slow class BarkModelIntegrationTests(unittest.TestCase): @cached_property def model(self): return BarkModel.from_pretrained("suno/bark", revision="refs/pr/25", trust_remote_code=True).to(torch_device) @cached_property def processor(self): return BarkProcessor.from_pretrained("suno/bark") @cached_property def inputs(self): input_ids = self.processor("In the light of the moon, a little egg lay on a leaf", voice_preset="en_speaker_6") for k, v in input_ids.items(): input_ids[k] = v.to(torch_device) return input_ids @cached_property def semantic_generation_config(self): semantic_generation_config = BarkSemanticGenerationConfig(**self.model.generation_config.semantic_config) return semantic_generation_config @cached_property def coarse_generation_config(self): coarse_generation_config = BarkCoarseGenerationConfig(**self.model.generation_config.coarse_acoustics_config) return coarse_generation_config @cached_property def fine_generation_config(self): fine_generation_config = BarkFineGenerationConfig(**self.model.generation_config.fine_acoustics_config) return fine_generation_config def test_model_can_generate(self): # Bark has custom generate without inheriting GenerationMixin. This test could prevent regression. self.assertTrue(self.model.can_generate()) def test_generate_semantic(self): input_ids = self.inputs # check first ids expected_output_ids = [7363, 321, 41, 1461, 6915, 952, 326, 41, 41, 927,] # fmt: skip # greedy decoding with torch.no_grad(): output_ids = self.model.semantic.generate( **input_ids, do_sample=False, temperature=1.0, semantic_generation_config=self.semantic_generation_config, ) self.assertListEqual(output_ids[0, : len(expected_output_ids)].tolist(), expected_output_ids) def test_generate_semantic_early_stop(self): input_ids = self.inputs min_eos_p = 0.01 # check first ids expected_output_ids = [7363, 321, 41, 1461, 6915, 952, 326, 41, 41, 927,] # fmt: skip # Should be able to read min_eos_p from kwargs with torch.no_grad(): torch.manual_seed(0) output_ids_without_min_eos_p = self.model.semantic.generate( **input_ids, do_sample=False, temperature=0.9, semantic_generation_config=self.semantic_generation_config, ) torch.manual_seed(0) output_ids_kwargs = self.model.semantic.generate( **input_ids, do_sample=False, temperature=0.9, semantic_generation_config=self.semantic_generation_config, min_eos_p=min_eos_p, ) self.assertListEqual(output_ids_without_min_eos_p[0, : len(expected_output_ids)].tolist(), expected_output_ids) self.assertLess(len(output_ids_kwargs[0, :].tolist()), len(output_ids_without_min_eos_p[0, :].tolist())) # Should be able to read min_eos_p from the semantic generation config self.semantic_generation_config.min_eos_p = min_eos_p with torch.no_grad(): torch.manual_seed(0) output_ids = self.model.semantic.generate( **input_ids, do_sample=False, temperature=0.9, semantic_generation_config=self.semantic_generation_config, ) self.assertEqual(output_ids.shape, output_ids_kwargs.shape) self.assertLess(len(output_ids[0, :].tolist()), len(output_ids_without_min_eos_p[0, :].tolist())) self.assertListEqual(output_ids[0, : len(expected_output_ids)].tolist(), expected_output_ids) def test_generate_coarse(self): input_ids = self.inputs history_prompt = input_ids["history_prompt"] # check first ids expected_output_ids = [11018, 11391, 10651, 11418, 10857, 11620, 10642, 11366, 10312, 11528, 10531, 11516, 10474, 11051, 10524, 11051, ] # fmt: skip with torch.no_grad(): output_ids = self.model.semantic.generate( **input_ids, do_sample=False, temperature=1.0, semantic_generation_config=self.semantic_generation_config, ) output_ids = self.model.coarse_acoustics.generate( output_ids, history_prompt=history_prompt, do_sample=False, temperature=1.0, semantic_generation_config=self.semantic_generation_config, coarse_generation_config=self.coarse_generation_config, codebook_size=self.model.generation_config.codebook_size, ) self.assertListEqual(output_ids[0, : len(expected_output_ids)].tolist(), expected_output_ids) def test_generate_fine(self): input_ids = self.inputs history_prompt = input_ids["history_prompt"] # fmt: off expected_output_ids = [ [1018, 651, 857, 642, 312, 531, 474, 524, 524, 776,], [367, 394, 596, 342, 504, 492, 27, 27, 822, 822,], [961, 955, 221, 955, 955, 686, 939, 939, 479, 176,], [638, 365, 218, 944, 853, 363, 639, 22, 884, 456,], [302, 912, 524, 38, 174, 209, 879, 23, 910, 227,], [440, 673, 861, 666, 372, 558, 49, 172, 232, 342,], [244, 358, 123, 356, 586, 520, 499, 877, 542, 637,], [806, 685, 905, 848, 803, 810, 921, 208, 625, 203,], ] # fmt: on with torch.no_grad(): output_ids = self.model.semantic.generate( **input_ids, do_sample=False, temperature=1.0, semantic_generation_config=self.semantic_generation_config, ) output_ids = self.model.coarse_acoustics.generate( output_ids, history_prompt=history_prompt, do_sample=False, temperature=1.0, semantic_generation_config=self.semantic_generation_config, coarse_generation_config=self.coarse_generation_config, codebook_size=self.model.generation_config.codebook_size, ) # greedy decoding output_ids = self.model.fine_acoustics.generate( output_ids, history_prompt=history_prompt, temperature=None, semantic_generation_config=self.semantic_generation_config, coarse_generation_config=self.coarse_generation_config, fine_generation_config=self.fine_generation_config, codebook_size=self.model.generation_config.codebook_size, ) self.assertListEqual(output_ids[0, :, : len(expected_output_ids[0])].tolist(), expected_output_ids) def test_generate_end_to_end(self): input_ids = self.inputs with torch.no_grad(): self.model.generate(**input_ids) self.model.generate(**{key: val for (key, val) in input_ids.items() if key != "history_prompt"}) def test_generate_end_to_end_with_args(self): input_ids = self.inputs with torch.no_grad(): self.model.generate(**input_ids, do_sample=True, temperature=0.6, penalty_alpha=0.6) self.model.generate(**input_ids, do_sample=True, temperature=0.6, num_beams=4) def test_generate_batching(self): args = {"do_sample": False, "temperature": None} s1 = "I love HuggingFace" s2 = "In the light of the moon, a little egg lay on a leaf" voice_preset = "en_speaker_6" input_ids = self.processor([s1, s2], voice_preset=voice_preset).to(torch_device) # generate in batch outputs, audio_lengths = self.model.generate(**input_ids, **args, return_output_lengths=True) # generate one-by-one s1 = self.processor(s1, voice_preset=voice_preset).to(torch_device) s2 = self.processor(s2, voice_preset=voice_preset).to(torch_device) output1 = self.model.generate(**s1, **args) output2 = self.model.generate(**s2, **args) # up until the coarse acoustic model (included), results are the same # the fine acoustic model introduces small differences # first verify if same length (should be the same because it's decided in the coarse model) self.assertEqual(tuple(audio_lengths), (output1.shape[1], output2.shape[1])) # then assert almost equal torch.testing.assert_close(outputs[0, : audio_lengths[0]], output1.squeeze(), rtol=2e-3, atol=2e-3) torch.testing.assert_close(outputs[1, : audio_lengths[1]], output2.squeeze(), rtol=2e-3, atol=2e-3) # now test single input with return_output_lengths = True outputs, _ = self.model.generate(**s1, **args, return_output_lengths=True) self.assertTrue((outputs == output1).all().item()) def test_generate_end_to_end_with_sub_models_args(self): input_ids = self.inputs with torch.no_grad(): torch.manual_seed(0) self.model.generate( **input_ids, do_sample=False, temperature=1.0, coarse_do_sample=True, coarse_temperature=0.7 ) output_ids_without_min_eos_p = self.model.generate( **input_ids, do_sample=True, temperature=0.9, coarse_do_sample=True, coarse_temperature=0.7, fine_temperature=0.3, ) output_ids_with_min_eos_p = self.model.generate( **input_ids, do_sample=True, temperature=0.9, coarse_temperature=0.7, fine_temperature=0.3, min_eos_p=0.1, ) self.assertLess( len(output_ids_with_min_eos_p[0, :].tolist()), len(output_ids_without_min_eos_p[0, :].tolist()) ) @require_torch_accelerator def test_generate_end_to_end_with_offload(self): input_ids = self.inputs with torch.no_grad(): # standard generation output_with_no_offload = self.model.generate(**input_ids, do_sample=False, temperature=1.0) torch_accelerator_module = backend_torch_accelerator_module(torch_device) torch_accelerator_module.empty_cache() memory_before_offload = torch_accelerator_module.memory_allocated() model_memory_footprint = self.model.get_memory_footprint() # activate cpu offload self.model.enable_cpu_offload() memory_after_offload = torch_accelerator_module.memory_allocated() # checks if the model have been offloaded # CUDA memory usage after offload should be near 0, leaving room to small differences room_for_difference = 1.1 self.assertGreater( (memory_before_offload - model_memory_footprint) * room_for_difference, memory_after_offload ) # checks if device is the correct one self.assertEqual(self.model.device.type, torch_device) # checks if hooks exist self.assertTrue(hasattr(self.model.semantic, "_hf_hook")) # output with cpu offload output_with_offload = self.model.generate(**input_ids, do_sample=False, temperature=1.0) # checks if same output self.assertListAlmostEqual(output_with_no_offload.squeeze().tolist(), output_with_offload.squeeze().tolist()) def assertListAlmostEqual(self, list1, list2, tol=1e-6): self.assertEqual(len(list1), len(list2)) for a, b in zip(list1, list2): self.assertAlmostEqual(a, b, delta=tol)