# 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 Musicgen model. """ import copy import inspect import math import unittest import numpy as np from transformers import ( EncodecConfig, MusicgenConfig, MusicgenDecoderConfig, MusicgenProcessor, PretrainedConfig, T5Config, ) from transformers.testing_utils import is_torch_available, require_torch, slow, torch_device from transformers.utils import cached_property from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( MusicgenForCausalLM, MusicgenForConditionalGeneration, MusicgenModel, set_seed, ) from transformers.generation import ( GreedySearchDecoderOnlyOutput, GreedySearchEncoderDecoderOutput, InfNanRemoveLogitsProcessor, LogitsProcessorList, SampleDecoderOnlyOutput, SampleEncoderDecoderOutput, ) def _config_zero_init(config): configs_no_init = copy.deepcopy(config) for key in configs_no_init.__dict__.keys(): if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key: setattr(configs_no_init, key, 1e-10) if isinstance(getattr(configs_no_init, key, None), PretrainedConfig): no_init_subconfig = _config_zero_init(getattr(configs_no_init, key)) setattr(configs_no_init, key, no_init_subconfig) return configs_no_init def prepare_musicgen_decoder_inputs_dict( config, input_ids, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, cross_attn_head_mask=None, ): if attention_mask is None: attention_mask = input_ids.reshape(-1, config.num_codebooks, input_ids.shape[-1])[:, 0, :] attention_mask = attention_mask.ne(config.pad_token_id) if head_mask is None: head_mask = torch.ones(config.num_hidden_layers, config.num_attention_heads, device=torch_device) if encoder_attention_mask is None and encoder_hidden_states is not None: encoder_attention_mask = torch.ones(encoder_hidden_states.shape[:2], device=torch_device) if cross_attn_head_mask is None: cross_attn_head_mask = torch.ones(config.num_hidden_layers, config.num_attention_heads, device=torch_device) return { "input_ids": input_ids, "attention_mask": attention_mask, "encoder_hidden_states": encoder_hidden_states, "encoder_attention_mask": encoder_attention_mask, "head_mask": head_mask, "cross_attn_head_mask": cross_attn_head_mask, } class MusicgenDecoderTester: def __init__( self, parent, batch_size=2, seq_length=7, is_training=False, use_labels=False, vocab_size=99, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=100, pad_token_id=99, bos_token_id=99, num_codebooks=4, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.num_codebooks = num_codebooks def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size * self.num_codebooks, self.seq_length], self.vocab_size) encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]) config = self.get_config() inputs_dict = prepare_musicgen_decoder_inputs_dict( config, input_ids, encoder_hidden_states=encoder_hidden_states ) return config, inputs_dict def get_config(self): config = MusicgenDecoderConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, d_ff=self.intermediate_size, pad_token_id=self.pad_token_id, decoder_start_token_id=self.bos_token_id, bos_token_id=self.bos_token_id, num_codebooks=self.num_codebooks, tie_word_embeddings=False, ) return config def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict @require_torch class MusicgenDecoderTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (MusicgenModel, MusicgenForCausalLM) if is_torch_available() else () greedy_sample_model_classes = ( (MusicgenForCausalLM,) if is_torch_available() else () ) # we don't want to run all the generation tests, only a specific subset pipeline_model_mapping = {} test_pruning = False test_resize_embeddings = False def setUp(self): self.model_tester = MusicgenDecoderTester(self) self.config_tester = ConfigTester(self, config_class=MusicgenDecoderConfig, hidden_size=16) def test_config(self): self.config_tester.run_common_tests() # override since we have to compute the input embeddings over codebooks 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"] embed_tokens = model.get_input_embeddings() input_ids = input_ids.reshape(-1, config.num_codebooks, input_ids.shape[-1]) inputs["inputs_embeds"] = sum( [embed_tokens[codebook](input_ids[:, codebook]) for codebook in range(config.num_codebooks)] ) with torch.no_grad(): model(**inputs)[0] # override since we have embeddings / LM heads over multiple codebooks def test_model_common_attributes(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) first_embed = model.get_input_embeddings()[0] self.assertIsInstance(first_embed, torch.nn.Embedding) lm_heads = model.get_output_embeddings() self.assertTrue(lm_heads is None or isinstance(lm_heads[0], torch.nn.Linear)) # skip as this model doesn't support all arguments tested def test_model_outputs_equivalence(self): pass # skip as this model has multiple inputs embeds and lm heads that should not be tied def test_tie_model_weights(self): pass # skip as this model has multiple inputs embeds and lm heads that should not be tied def test_tied_weights_keys(self): pass def _get_input_ids_and_config(self, batch_size=2): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_ids = inputs_dict["input_ids"] # take max batch_size sequence_length = input_ids.shape[-1] input_ids = input_ids[: batch_size * config.num_codebooks, :] # generate max 3 tokens max_length = input_ids.shape[-1] + 3 attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long) return config, input_ids, attention_mask, max_length @staticmethod def _get_logits_processor_and_kwargs( input_length, eos_token_id, forced_bos_token_id=None, forced_eos_token_id=None, max_length=None, diversity_penalty=None, ): process_kwargs = { "min_length": input_length + 1 if max_length is None else max_length - 1, } logits_processor = LogitsProcessorList() return process_kwargs, logits_processor # override since we don't expect the outputs of `.generate` and `.greedy_search` to be the same, since we perform # additional post-processing in the former def test_greedy_generate_dict_outputs(self): for model_class in self.greedy_sample_model_classes: # disable cache config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() config.use_cache = False model = model_class(config).to(torch_device).eval() output_greedy, output_generate = self._greedy_generate( model=model, input_ids=input_ids.to(torch_device), attention_mask=attention_mask.to(torch_device), max_length=max_length, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) self.assertIsInstance(output_greedy, GreedySearchDecoderOnlyOutput) self.assertIsInstance(output_generate, GreedySearchDecoderOnlyOutput) self.assertNotIn(config.pad_token_id, output_generate) # override since we don't expect the outputs of `.generate` and `.greedy_search` to be the same, since we perform # additional post-processing in the former def test_greedy_generate_dict_outputs_use_cache(self): for model_class in self.greedy_sample_model_classes: # enable cache config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() config.use_cache = True config.is_decoder = True model = model_class(config).to(torch_device).eval() output_greedy, output_generate = self._greedy_generate( model=model, input_ids=input_ids.to(torch_device), attention_mask=attention_mask.to(torch_device), max_length=max_length, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) self.assertIsInstance(output_greedy, GreedySearchDecoderOnlyOutput) self.assertIsInstance(output_generate, GreedySearchDecoderOnlyOutput) # override since we don't expect the outputs of `.generate` and `.sample` to be the same, since we perform # additional post-processing in the former def test_sample_generate(self): for model_class in self.greedy_sample_model_classes: config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() model = model_class(config).to(torch_device).eval() process_kwargs, logits_processor = self._get_logits_processor_and_kwargs( input_ids.shape[-1], model.config.eos_token_id, forced_bos_token_id=model.config.forced_bos_token_id, forced_eos_token_id=model.config.forced_eos_token_id, max_length=max_length, ) logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=2) # check `generate()` and `sample()` are equal output_sample, output_generate = self._sample_generate( model=model, input_ids=input_ids.to(torch_device), attention_mask=attention_mask.to(torch_device), max_length=max_length, num_return_sequences=3, logits_processor=logits_processor, logits_warper=logits_warper, logits_warper_kwargs=logits_warper_kwargs, process_kwargs=process_kwargs, ) self.assertIsInstance(output_sample, torch.Tensor) self.assertIsInstance(output_generate, torch.Tensor) # override since we don't expect the outputs of `.generate` and `.sample` to be the same, since we perform # additional post-processing in the former def test_sample_generate_dict_output(self): for model_class in self.greedy_sample_model_classes: # disable cache config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() config.use_cache = False model = model_class(config).to(torch_device).eval() process_kwargs, logits_processor = self._get_logits_processor_and_kwargs( input_ids.shape[-1], model.config.eos_token_id, forced_bos_token_id=model.config.forced_bos_token_id, forced_eos_token_id=model.config.forced_eos_token_id, max_length=max_length, ) logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1) output_sample, output_generate = self._sample_generate( model=model, input_ids=input_ids.to(torch_device), attention_mask=attention_mask.to(torch_device), max_length=max_length, num_return_sequences=1, logits_processor=logits_processor, logits_warper=logits_warper, logits_warper_kwargs=logits_warper_kwargs, process_kwargs=process_kwargs, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) self.assertIsInstance(output_sample, SampleDecoderOnlyOutput) self.assertIsInstance(output_generate, SampleDecoderOnlyOutput) def prepare_musicgen_inputs_dict( config, input_ids, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, ): if decoder_attention_mask is None: decoder_attention_mask = decoder_input_ids.reshape( -1, config.decoder.num_codebooks, decoder_input_ids.shape[-1] )[:, 0, :] decoder_attention_mask = decoder_attention_mask.ne(config.decoder.pad_token_id) if head_mask is None: head_mask = torch.ones( config.text_encoder.num_hidden_layers, config.text_encoder.num_attention_heads, device=torch_device ) if decoder_head_mask is None: decoder_head_mask = torch.ones( config.decoder.num_hidden_layers, config.decoder.num_attention_heads, device=torch_device ) if cross_attn_head_mask is None: cross_attn_head_mask = torch.ones( config.decoder.num_hidden_layers, config.decoder.num_attention_heads, device=torch_device ) return { "input_ids": input_ids, "attention_mask": attention_mask, "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } class MusicgenTester: def __init__( self, parent, batch_size=2, seq_length=7, is_training=False, use_labels=False, vocab_size=99, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=100, pad_token_id=99, bos_token_id=99, num_codebooks=4, num_filters=4, codebook_size=128, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.num_codebooks = num_codebooks self.num_filters = num_filters self.codebook_size = codebook_size def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) decoder_input_ids = ids_tensor([self.batch_size * self.num_codebooks, self.seq_length], self.vocab_size) config = self.get_config() inputs_dict = prepare_musicgen_inputs_dict(config, input_ids, decoder_input_ids=decoder_input_ids) return config, inputs_dict def get_config(self): text_encoder_config = T5Config( vocab_size=self.vocab_size, d_model=self.hidden_size, d_ff=self.intermediate_size, num_layers=self.num_hidden_layers, num_heads=self.num_attention_heads, ) audio_encoder_config = EncodecConfig( hidden_size=self.vocab_size, compress=1, num_filters=self.num_filters, codebook_size=self.codebook_size, codebook_dim=self.vocab_size, ) decoder_config = MusicgenDecoderConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, ffn_dim=self.intermediate_size, pad_token_id=self.pad_token_id, decoder_start_token_id=self.bos_token_id, bos_token_id=self.bos_token_id, num_codebooks=self.num_codebooks, tie_word_embeddings=False, ) config = MusicgenConfig.from_sub_models_config(text_encoder_config, audio_encoder_config, decoder_config) return config def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict @require_torch class MusicgenTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (MusicgenForConditionalGeneration,) if is_torch_available() else () greedy_sample_model_classes = (MusicgenForConditionalGeneration,) if is_torch_available() else () pipeline_model_mapping = {"text-to-audio": MusicgenForConditionalGeneration} if is_torch_available() else {} test_pruning = False # training is not supported yet for MusicGen test_headmasking = False test_resize_embeddings = False # not to test torchscript as the model tester doesn't prepare `input_values` and `padding_mask` # (and `torchscript` hates `None` values). test_torchscript = False def setUp(self): self.model_tester = MusicgenTester(self) def _check_output_with_attentions(self, outputs, config, input_ids, decoder_input_ids): text_encoder_config = config.text_encoder decoder_config = config.decoder encoder_attentions = outputs["encoder_attentions"] self.assertEqual(len(encoder_attentions), text_encoder_config.num_hidden_layers) self.assertEqual( encoder_attentions[0].shape[-3:], (text_encoder_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]), ) decoder_attentions = outputs["decoder_attentions"] num_decoder_layers = decoder_config.num_hidden_layers self.assertEqual(len(decoder_attentions), num_decoder_layers) self.assertEqual( decoder_attentions[0].shape[-3:], (decoder_config.num_attention_heads, decoder_input_ids.shape[-1], decoder_input_ids.shape[-1]), ) cross_attentions = outputs["cross_attentions"] self.assertEqual(len(cross_attentions), num_decoder_layers) cross_attention_input_seq_len = decoder_input_ids.shape[-1] self.assertEqual( cross_attentions[0].shape[-3:], (decoder_config.num_attention_heads, cross_attention_input_seq_len, input_ids.shape[-1]), ) def check_musicgen_model_output_attentions( self, model_class, config, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, **kwargs, ): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, output_attentions=True, **kwargs, ) self._check_output_with_attentions(outputs, config, input_ids, decoder_input_ids) def check_musicgen_model_output_attentions_from_config( self, model_class, config, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, **kwargs, ): # Similar to `check_musicgen_model_output_attentions`, but with `output_attentions` triggered from the # config file. Contrarily to most models, changing the model's config won't work -- the defaults are loaded # from the inner models' configurations. config.output_attentions = True # model config -> won't work model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, **kwargs, ) self.assertTrue( all(key not in outputs for key in ["encoder_attentions", "decoder_attentions", "cross_attentions"]) ) config.text_encoder.output_attentions = True # inner model config -> will work config.audio_encoder.output_attentions = True config.decoder.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, **kwargs, ) self._check_output_with_attentions(outputs, config, input_ids, decoder_input_ids) # override since changing `output_attentions` from the top-level model config won't work def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: self.check_musicgen_model_output_attentions(model_class, config, **inputs_dict) self.check_musicgen_model_output_attentions_from_config(model_class, config, **inputs_dict) # override since we have a specific forward signature for musicgen 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_ids", "attention_mask", "input_values", "padding_mask", "decoder_input_ids", "decoder_attention_mask", ] expected_arg_names.extend( ["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"] if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names else ["encoder_outputs"] ) self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) # override since changing `gradient_checkpointing` from the top-level model config won't work def test_gradient_checkpointing_backward_compatibility(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if not model_class.supports_gradient_checkpointing: continue config.text_encoder.gradient_checkpointing = True config.audio_encoder.gradient_checkpointing = True config.decoder.gradient_checkpointing = True model = model_class(config) self.assertTrue(model.is_gradient_checkpointing) # skip as this model has multiple inputs embeds and lm heads that should not be tied def test_tie_model_weights(self): pass # skip as this model has multiple inputs embeds and lm heads that should not be tied def test_tied_model_weights_key_ignore(self): pass # skip as this model has multiple inputs embeds and lm heads that should not be tied def test_tied_weights_keys(self): pass # override since changing `output_hidden_states` / `output_attentions` from the top-level model config won't work def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.text_encoder.output_hidden_states = True config.audio_encoder.output_hidden_states = True config.decoder.output_hidden_states = True config.text_encoder.output_attentions = True config.decoder.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) inputs = self._prepare_for_class(inputs_dict, model_class) outputs = model(**inputs) output = outputs[0] encoder_hidden_states = outputs.encoder_hidden_states[0] encoder_hidden_states.retain_grad() decoder_hidden_states = outputs.decoder_hidden_states[0] decoder_hidden_states.retain_grad() if self.has_attentions: encoder_attentions = outputs.encoder_attentions[0] encoder_attentions.retain_grad() decoder_attentions = outputs.decoder_attentions[0] decoder_attentions.retain_grad() cross_attentions = outputs.cross_attentions[0] cross_attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(encoder_hidden_states.grad) self.assertIsNotNone(decoder_hidden_states.grad) if self.has_attentions: self.assertIsNotNone(encoder_attentions.grad) self.assertIsNotNone(decoder_attentions.grad) self.assertIsNotNone(cross_attentions.grad) # override since changing `output_hidden_states` from the top-level model config won't work 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.encoder_hidden_states expected_num_layers = self.model_tester.num_hidden_layers + 1 self.assertEqual(len(hidden_states), expected_num_layers) seq_length = self.model_tester.seq_length self.assertListEqual( list(hidden_states[0].shape[-2:]), [seq_length, self.model_tester.hidden_size], ) hidden_states = outputs.decoder_hidden_states self.assertIsInstance(hidden_states, (list, tuple)) self.assertEqual(len(hidden_states), expected_num_layers) self.assertListEqual( list(hidden_states[0].shape[-2:]), [seq_length, self.model_tester.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.text_encoder.output_hidden_states = True config.audio_encoder.output_hidden_states = True config.decoder.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) # override since the conv layers and lstm's in encodec are exceptions 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"] ignore_init = ["lstm"] 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", ) elif not any(x in name for x in ignore_init): 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", ) # override since we have embeddings / LM heads over multiple codebooks def test_model_common_attributes(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) self.assertIsInstance(model.get_input_embeddings(), torch.nn.Embedding) lm_heads = model.get_output_embeddings() self.assertTrue(lm_heads is None or isinstance(lm_heads[0], torch.nn.Linear)) def _get_input_ids_and_config(self, batch_size=2): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_ids = inputs_dict["input_ids"] # take max batch_size sequence_length = input_ids.shape[-1] input_ids = input_ids[:batch_size, :] attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long) # generate max 3 tokens decoder_input_ids = inputs_dict["decoder_input_ids"] max_length = decoder_input_ids.shape[-1] + 3 decoder_input_ids = decoder_input_ids[: batch_size * config.decoder.num_codebooks, :] return config, input_ids, attention_mask, decoder_input_ids, max_length # override since the `input_ids` cannot be used as the `decoder_input_ids` for musicgen (input / outputs are # different modalities -> different shapes) def _greedy_generate( self, model, input_ids, attention_mask, decoder_input_ids, max_length, output_scores=False, output_attentions=False, output_hidden_states=False, return_dict_in_generate=False, ): logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs( input_ids.shape[-1], eos_token_id=model.config.eos_token_id, forced_bos_token_id=model.config.forced_bos_token_id, forced_eos_token_id=model.config.forced_eos_token_id, max_length=max_length, ) model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {} output_generate = model.generate( input_ids, do_sample=False, num_beams=1, max_length=max_length, output_attentions=output_attentions, output_hidden_states=output_hidden_states, output_scores=output_scores, return_dict_in_generate=return_dict_in_generate, remove_invalid_values=True, **logits_process_kwargs, **model_kwargs, ) encoder_outputs, input_ids, attention_mask = self._get_encoder_outputs( model, input_ids, attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) with torch.no_grad(): model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {} output_greedy = model.greedy_search( decoder_input_ids, max_length=max_length, logits_processor=logits_processor, output_attentions=output_attentions, output_hidden_states=output_hidden_states, output_scores=output_scores, return_dict_in_generate=return_dict_in_generate, encoder_outputs=encoder_outputs, **model_kwargs, ) return output_greedy, output_generate # override since the `input_ids` cannot be used as the `decoder_input_ids` for musicgen (input / outputs are # different modalities -> different shapes) def _sample_generate( self, model, input_ids, attention_mask, decoder_input_ids, max_length, num_return_sequences, logits_processor, logits_warper, logits_warper_kwargs, process_kwargs, output_scores=False, output_attentions=False, output_hidden_states=False, return_dict_in_generate=False, ): torch.manual_seed(0) model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {} output_generate = model.generate( input_ids, do_sample=True, num_beams=1, max_length=max_length, num_return_sequences=num_return_sequences, output_scores=output_scores, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict_in_generate=return_dict_in_generate, remove_invalid_values=True, **logits_warper_kwargs, **process_kwargs, **model_kwargs, ) torch.manual_seed(0) encoder_outputs, input_ids, attention_mask = self._get_encoder_outputs( model, input_ids, attention_mask, num_interleave=num_return_sequences, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) # prevent flaky generation test failures logits_processor.append(InfNanRemoveLogitsProcessor()) with torch.no_grad(): model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {} output_sample = model.sample( decoder_input_ids.repeat_interleave(num_return_sequences, dim=0), max_length=max_length, logits_processor=logits_processor, logits_warper=logits_warper, output_scores=output_scores, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict_in_generate=return_dict_in_generate, encoder_outputs=encoder_outputs, **model_kwargs, ) return output_sample, output_generate @staticmethod def _get_logits_processor_and_kwargs( input_length, eos_token_id, forced_bos_token_id=None, forced_eos_token_id=None, max_length=None, diversity_penalty=None, ): process_kwargs = { "min_length": input_length + 1 if max_length is None else max_length - 1, } logits_processor = LogitsProcessorList() return process_kwargs, logits_processor def test_greedy_generate_dict_outputs(self): for model_class in self.greedy_sample_model_classes: # disable cache config, input_ids, attention_mask, decoder_input_ids, max_length = self._get_input_ids_and_config() config.use_cache = False model = model_class(config).to(torch_device).eval() output_greedy, output_generate = self._greedy_generate( model=model, input_ids=input_ids.to(torch_device), attention_mask=attention_mask.to(torch_device), decoder_input_ids=decoder_input_ids, max_length=max_length, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) self.assertIsInstance(output_greedy, GreedySearchEncoderDecoderOutput) self.assertIsInstance(output_generate, GreedySearchEncoderDecoderOutput) self.assertNotIn(config.pad_token_id, output_generate) def test_greedy_generate_dict_outputs_use_cache(self): for model_class in self.greedy_sample_model_classes: # enable cache config, input_ids, attention_mask, decoder_input_ids, max_length = self._get_input_ids_and_config() config.use_cache = True config.is_decoder = True model = model_class(config).to(torch_device).eval() output_greedy, output_generate = self._greedy_generate( model=model, input_ids=input_ids.to(torch_device), attention_mask=attention_mask.to(torch_device), decoder_input_ids=decoder_input_ids, max_length=max_length, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) self.assertIsInstance(output_greedy, GreedySearchEncoderDecoderOutput) self.assertIsInstance(output_generate, GreedySearchEncoderDecoderOutput) def test_sample_generate(self): for model_class in self.greedy_sample_model_classes: config, input_ids, attention_mask, decoder_input_ids, max_length = self._get_input_ids_and_config() model = model_class(config).to(torch_device).eval() process_kwargs, logits_processor = self._get_logits_processor_and_kwargs( input_ids.shape[-1], model.config.eos_token_id, forced_bos_token_id=model.config.forced_bos_token_id, forced_eos_token_id=model.config.forced_eos_token_id, max_length=max_length, ) logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=2) # check `generate()` and `sample()` are equal output_sample, output_generate = self._sample_generate( model=model, input_ids=input_ids.to(torch_device), attention_mask=attention_mask.to(torch_device), decoder_input_ids=decoder_input_ids, max_length=max_length, num_return_sequences=1, logits_processor=logits_processor, logits_warper=logits_warper, logits_warper_kwargs=logits_warper_kwargs, process_kwargs=process_kwargs, ) self.assertIsInstance(output_sample, torch.Tensor) self.assertIsInstance(output_generate, torch.Tensor) def test_sample_generate_dict_output(self): for model_class in self.greedy_sample_model_classes: # disable cache config, input_ids, attention_mask, decoder_input_ids, max_length = self._get_input_ids_and_config() config.use_cache = False model = model_class(config).to(torch_device).eval() process_kwargs, logits_processor = self._get_logits_processor_and_kwargs( input_ids.shape[-1], model.config.eos_token_id, forced_bos_token_id=model.config.forced_bos_token_id, forced_eos_token_id=model.config.forced_eos_token_id, max_length=max_length, ) logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1) output_sample, output_generate = self._sample_generate( model=model, input_ids=input_ids.to(torch_device), attention_mask=attention_mask.to(torch_device), decoder_input_ids=decoder_input_ids, max_length=max_length, num_return_sequences=3, logits_processor=logits_processor, logits_warper=logits_warper, logits_warper_kwargs=logits_warper_kwargs, process_kwargs=process_kwargs, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) self.assertIsInstance(output_sample, SampleEncoderDecoderOutput) self.assertIsInstance(output_generate, SampleEncoderDecoderOutput) def test_generate_without_input_ids(self): config, _, _, _, max_length = self._get_input_ids_and_config() # if no bos token id => cannot generate from None if config.bos_token_id is None: return for model_class in self.greedy_sample_model_classes: model = model_class(config).to(torch_device) model.eval() output_ids_generate = model.generate(do_sample=False, max_length=max_length, remove_invalid_values=True) self.assertIsNotNone(output_ids_generate) def test_generate_fp16(self): config, input_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.greedy_sample_model_classes: model = model_class(config).eval().to(torch_device) if torch_device == "cuda": model.half() # greedy model.generate(input_dict["input_ids"], attention_mask=input_dict["attention_mask"], max_new_tokens=10) # sampling model.generate( input_dict["input_ids"], attention_mask=input_dict["attention_mask"], do_sample=True, max_new_tokens=10 ) def get_bip_bip(bip_duration=0.125, duration=0.5, sample_rate=32000): """Produces a series of 'bip bip' sounds at a given frequency.""" timesteps = np.arange(int(duration * sample_rate)) / sample_rate wav = np.cos(2 * math.pi * 440 * timesteps) time_period = (timesteps % (2 * bip_duration)) / (2 * bip_duration) envelope = time_period >= 0.5 return wav * envelope def place_dict_on_device(dict_to_place, device): for key in dict_to_place: if dict_to_place[key] is not None and isinstance(dict_to_place[key], torch.Tensor): dict_to_place[key] = dict_to_place[key].to(device) return dict_to_place @require_torch class MusicgenIntegrationTests(unittest.TestCase): @cached_property def model(self): return MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small").to(torch_device) @cached_property def processor(self): return MusicgenProcessor.from_pretrained("facebook/musicgen-small") @slow def test_logits_text_prompt(self): model = self.model processor = self.processor inputs = processor(text=["80s music", "Club techno"], padding=True, return_tensors="pt") # prepare the encoder inputs input_ids = inputs.input_ids.to(torch_device) attention_mask = inputs.attention_mask.to(torch_device) # prepare the decoder inputs pad_token_id = model.generation_config.pad_token_id decoder_input_ids = ( torch.ones((input_ids.shape[0] * model.decoder.num_codebooks, 1), dtype=torch.long).to(torch_device) * pad_token_id ) with torch.no_grad(): logits = model( input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, ).logits # fmt: off EXPECTED_LOGITS = torch.tensor( [ -0.9708, -3.0149, -4.6415, -1.4754, -0.2786, -2.3523, -2.6049, -6.7467, -1.0206, -3.2984, -3.3968, -1.5108, -1.5786, -3.1493, -1.1503, -0.0545, ] ) # fmt: on self.assertTrue(logits.shape == (*decoder_input_ids.shape, model.decoder.config.vocab_size)) self.assertTrue(torch.allclose(logits[0, 0, :16].cpu(), EXPECTED_LOGITS, atol=1e-4)) @slow def test_logits_text_audio_prompt(self): model = self.model processor = self.processor audio = [get_bip_bip(duration=0.5), get_bip_bip(duration=1.0)] text = ["80s music", "Club techno"] inputs = processor(audio=audio, text=text, padding=True, return_tensors="pt") # prepare the text encoder inputs input_ids = inputs.input_ids.to(torch_device) attention_mask = inputs.attention_mask.to(torch_device) # prepare the audio encoder inputs input_values = inputs.input_values.to(torch_device) padding_mask = inputs.padding_mask.to(torch_device) with torch.no_grad(): logits = model( input_ids, attention_mask=attention_mask, input_values=input_values, padding_mask=padding_mask, ).logits # fmt: off EXPECTED_LOGITS = torch.tensor( [ 0.1841, -2.9324, -0.7898, 0.1857, 0.4971, -2.8685, -1.6525, -1.6541, 2.7757, -2.5942, -3.0959, -1.0120, -1.0147, -0.4605, -0.8885, 0.6820, ] ) # fmt: on self.assertTrue(logits.shape == (8, 50, 2048)) self.assertTrue(torch.allclose(logits[0, -1, :16].cpu(), EXPECTED_LOGITS, atol=1e-4)) @slow def test_generate_unconditional_greedy(self): model = self.model # only generate 1 sample with greedy - since it's deterministic all elements of the batch will be the same unconditional_inputs = model.get_unconditional_inputs(num_samples=1) unconditional_inputs = place_dict_on_device(unconditional_inputs, device=torch_device) output_values = model.generate(**unconditional_inputs, do_sample=False, max_new_tokens=5) # fmt: off EXPECTED_VALUES = torch.tensor( [ 0.0056, 0.0064, 0.0063, 0.0054, 0.0042, 0.0033, 0.0024, 0.0015, 0.0015, 0.0010, 0.0004, -0.0012, -0.0036, -0.0055, -0.0067, -0.0071, ] ) # fmt: on self.assertTrue(output_values.shape == (1, 1, 3200)) self.assertTrue(torch.allclose(output_values[0, 0, :16].cpu(), EXPECTED_VALUES, atol=1e-4)) @slow def test_generate_unconditional_sampling(self): model = self.model # for stochastic sampling we can generate multiple outputs unconditional_inputs = model.get_unconditional_inputs(num_samples=2) unconditional_inputs = place_dict_on_device(unconditional_inputs, device=torch_device) set_seed(0) output_values = model.generate(**unconditional_inputs, do_sample=True, max_new_tokens=10) # fmt: off EXPECTED_VALUES = torch.tensor( [ -0.0099, -0.0140, 0.0079, 0.0080, -0.0046, 0.0065, -0.0068, -0.0185, 0.0105, 0.0059, 0.0329, 0.0249, -0.0204, -0.0341, -0.0465, 0.0053, ] ) # fmt: on self.assertTrue(output_values.shape == (2, 1, 4480)) self.assertTrue(torch.allclose(output_values[0, 0, :16].cpu(), EXPECTED_VALUES, atol=1e-4)) @slow def test_generate_text_prompt_greedy(self): model = self.model processor = self.processor inputs = processor(text=["80s music", "Club techno"], padding=True, return_tensors="pt") # prepare the encoder inputs input_ids = inputs.input_ids.to(torch_device) attention_mask = inputs.attention_mask.to(torch_device) output_values = model.generate( input_ids, attention_mask=attention_mask, do_sample=False, guidance_scale=None, max_new_tokens=10 ) # fmt: off EXPECTED_VALUES = torch.tensor( [ -1.1998e-04, -2.2302e-04, 4.6296e-04, 1.0524e-03, 2.4827e-04, -4.0288e-05, -1.2468e-04, 4.9846e-05, 7.1485e-04, 4.4197e-04, ] ) # fmt: on self.assertTrue(output_values.shape == (2, 1, 4480)) self.assertTrue(torch.allclose(output_values[0, 0, :10].cpu(), EXPECTED_VALUES, atol=1e-4)) @slow def test_generate_text_prompt_greedy_with_classifier_free_guidance(self): model = self.model processor = self.processor inputs = processor(text=["80s music", "Club techno"], padding=True, return_tensors="pt") # prepare the encoder inputs input_ids = inputs.input_ids.to(torch_device) attention_mask = inputs.attention_mask.to(torch_device) output_values = model.generate( input_ids, attention_mask=attention_mask, do_sample=False, guidance_scale=3, max_new_tokens=10 ) # fmt: off EXPECTED_VALUES = torch.tensor( [ 0.0283, 0.0246, 0.0650, 0.0640, 0.0599, 0.0711, 0.0420, 0.0112, 0.0511, 0.0746, 0.1363, 0.1213, 0.0185, -0.0578, -0.0908, 0.0443, ] ) # fmt: on self.assertTrue(output_values.shape == (2, 1, 4480)) self.assertTrue(torch.allclose(output_values[0, 0, :16].cpu(), EXPECTED_VALUES, atol=1e-4)) @slow def test_generate_text_prompt_sampling(self): model = self.model processor = self.processor inputs = processor(text=["80s music", "Club techno"], padding=True, return_tensors="pt") # prepare the encoder inputs input_ids = inputs.input_ids.to(torch_device) attention_mask = inputs.attention_mask.to(torch_device) set_seed(0) output_values = model.generate( input_ids, attention_mask=attention_mask, do_sample=True, guidance_scale=None, max_new_tokens=10 ) # fmt: off EXPECTED_VALUES = torch.tensor( [ -0.0111, -0.0154, 0.0047, 0.0058, -0.0068, 0.0012, -0.0109, -0.0229, 0.0010, -0.0038, 0.0167, 0.0042, -0.0421, -0.0610, -0.0764, -0.0326, ] ) # fmt: on self.assertTrue(output_values.shape == (2, 1, 4480)) self.assertTrue(torch.allclose(output_values[0, 0, :16].cpu(), EXPECTED_VALUES, atol=1e-4)) @slow def test_generate_text_audio_prompt(self): model = self.model processor = self.processor audio = [get_bip_bip(duration=0.5), get_bip_bip(duration=1.0)] text = ["80s music", "Club techno"] inputs = processor(audio=audio, text=text, padding=True, return_tensors="pt") inputs = place_dict_on_device(inputs, device=torch_device) output_values = model.generate(**inputs, do_sample=False, guidance_scale=None, max_new_tokens=10) # fmt: off EXPECTED_VALUES = torch.tensor( [ -0.0036, -0.0130, -0.0261, -0.0384, -0.0557, -0.0718, -0.0680, -0.0632, -0.0529, -0.0403, -0.0289, -0.0198, -0.0136, -0.0101, -0.0095, -0.0040, ] ) # fmt: on self.assertTrue( output_values.shape == (2, 1, 36480) ) # input values take shape 32000 and we generate from there self.assertTrue(torch.allclose(output_values[0, 0, -16:].cpu(), EXPECTED_VALUES, atol=1e-4))