Tests: Musicgen tests + `make fix-copies` (#29734)
* make fix-copies * some tests fixed * tests fixed
This commit is contained in:
Joao Gante
GitHub
parent
66ce9593fd
commit
1a5c500f12
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@ -1294,7 +1294,7 @@ class MusicgenMelodyForCausalLM(MusicgenMelodyPreTrainedModel):
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)
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# 11. run greedy search
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outputs = self.greedy_search(
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outputs = self._greedy_search(
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input_ids,
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logits_processor=logits_processor,
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stopping_criteria=stopping_criteria,
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@ -1319,7 +1319,7 @@ class MusicgenMelodyForCausalLM(MusicgenMelodyPreTrainedModel):
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)
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# 12. run sample
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outputs = self.sample(
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outputs = self._sample(
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input_ids,
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logits_processor=logits_processor,
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logits_warper=logits_warper,
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@ -257,105 +257,6 @@ class MusicgenDecoderTest(ModelTesterMixin, GenerationTesterMixin, PipelineTeste
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warper_kwargs = {}
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return process_kwargs, warper_kwargs
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# override since we don't expect the outputs of `.generate` and `.greedy_search` to be the same, since we perform
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# additional post-processing in the former
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def test_greedy_generate_dict_outputs(self):
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for model_class in self.greedy_sample_model_classes:
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# disable cache
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config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
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config.use_cache = False
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model = model_class(config).to(torch_device).eval()
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output_generate = self._greedy_generate(
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model=model,
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input_ids=input_ids.to(torch_device),
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attention_mask=attention_mask.to(torch_device),
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max_length=max_length,
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output_scores=True,
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output_hidden_states=True,
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output_attentions=True,
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return_dict_in_generate=True,
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)
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self.assertIsInstance(output_generate, GenerateDecoderOnlyOutput)
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self.assertNotIn(config.pad_token_id, output_generate)
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# override since we don't expect the outputs of `.generate` and `.greedy_search` to be the same, since we perform
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# additional post-processing in the former
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def test_greedy_generate_dict_outputs_use_cache(self):
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for model_class in self.greedy_sample_model_classes:
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# enable cache
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config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
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config.use_cache = True
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config.is_decoder = True
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model = model_class(config).to(torch_device).eval()
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output_generate = self._greedy_generate(
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model=model,
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input_ids=input_ids.to(torch_device),
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attention_mask=attention_mask.to(torch_device),
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max_length=max_length,
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output_scores=True,
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output_hidden_states=True,
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output_attentions=True,
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return_dict_in_generate=True,
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)
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self.assertIsInstance(output_generate, GenerateDecoderOnlyOutput)
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# override since we don't expect the outputs of `.generate` and `.sample` to be the same, since we perform
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# additional post-processing in the former
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def test_sample_generate(self):
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for model_class in self.greedy_sample_model_classes:
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config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
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model = model_class(config).to(torch_device).eval()
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process_kwargs, logits_warper_kwargs = self._get_logits_processor_and_warper_kwargs(
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input_ids.shape[-1],
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max_length=max_length,
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)
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# check `generate()` and `sample()` are equal
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output_generate = self._sample_generate(
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model=model,
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input_ids=input_ids.to(torch_device),
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attention_mask=attention_mask.to(torch_device),
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max_length=max_length,
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num_return_sequences=3,
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logits_warper_kwargs=logits_warper_kwargs,
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process_kwargs=process_kwargs,
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)
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self.assertIsInstance(output_generate, torch.Tensor)
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# override since we don't expect the outputs of `.generate` and `.sample` to be the same, since we perform
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# additional post-processing in the former
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def test_sample_generate_dict_output(self):
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for model_class in self.greedy_sample_model_classes:
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# disable cache
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config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
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config.use_cache = False
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model = model_class(config).to(torch_device).eval()
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process_kwargs, logits_warper_kwargs = self._get_logits_processor_and_warper_kwargs(
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input_ids.shape[-1],
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max_length=max_length,
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)
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output_generate = self._sample_generate(
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model=model,
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input_ids=input_ids.to(torch_device),
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attention_mask=attention_mask.to(torch_device),
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max_length=max_length,
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num_return_sequences=1,
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logits_warper_kwargs=logits_warper_kwargs,
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process_kwargs=process_kwargs,
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output_scores=True,
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output_hidden_states=True,
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output_attentions=True,
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return_dict_in_generate=True,
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)
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self.assertIsInstance(output_generate, GenerateDecoderOnlyOutput)
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def test_greedy_generate_stereo_outputs(self):
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for model_class in self.greedy_sample_model_classes:
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config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
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@ -55,8 +55,6 @@ if is_torch_available():
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)
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from transformers.generation import (
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GenerateDecoderOnlyOutput,
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InfNanRemoveLogitsProcessor,
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LogitsProcessorList,
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)
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if is_torchaudio_available():
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@ -248,142 +246,24 @@ class MusicgenMelodyDecoderTest(ModelTesterMixin, GenerationTesterMixin, unittes
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return config, input_ids, attention_mask, max_length
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@staticmethod
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def _get_logits_processor_and_kwargs(
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def _get_logits_processor_and_warper_kwargs(
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input_length,
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eos_token_id,
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forced_bos_token_id=None,
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forced_eos_token_id=None,
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max_length=None,
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diversity_penalty=None,
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):
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process_kwargs = {
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"min_length": input_length + 1 if max_length is None else max_length - 1,
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}
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logits_processor = LogitsProcessorList()
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return process_kwargs, logits_processor
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# override since we don't expect the outputs of `.generate` and `.greedy_search` to be the same, since we perform
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# additional post-processing in the former
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def test_greedy_generate_dict_outputs(self):
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for model_class in self.greedy_sample_model_classes:
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# disable cache
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config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
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config.use_cache = False
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model = model_class(config).to(torch_device).eval()
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output_greedy, output_generate = self._greedy_generate(
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model=model,
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input_ids=input_ids.to(torch_device),
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attention_mask=attention_mask.to(torch_device),
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max_length=max_length,
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output_scores=True,
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output_hidden_states=True,
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output_attentions=True,
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return_dict_in_generate=True,
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)
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self.assertIsInstance(output_greedy, GenerateDecoderOnlyOutput)
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self.assertIsInstance(output_generate, GenerateDecoderOnlyOutput)
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self.assertNotIn(config.pad_token_id, output_generate)
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# override since we don't expect the outputs of `.generate` and `.greedy_search` to be the same, since we perform
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# additional post-processing in the former
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def test_greedy_generate_dict_outputs_use_cache(self):
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for model_class in self.greedy_sample_model_classes:
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# enable cache
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config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
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config.use_cache = True
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config.is_decoder = True
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model = model_class(config).to(torch_device).eval()
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output_greedy, output_generate = self._greedy_generate(
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model=model,
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input_ids=input_ids.to(torch_device),
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attention_mask=attention_mask.to(torch_device),
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max_length=max_length,
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output_scores=True,
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output_hidden_states=True,
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output_attentions=True,
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return_dict_in_generate=True,
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)
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self.assertIsInstance(output_greedy, GenerateDecoderOnlyOutput)
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self.assertIsInstance(output_generate, GenerateDecoderOnlyOutput)
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# override since we don't expect the outputs of `.generate` and `.sample` to be the same, since we perform
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# additional post-processing in the former
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def test_sample_generate(self):
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for model_class in self.greedy_sample_model_classes:
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config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
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model = model_class(config).to(torch_device).eval()
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process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
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input_ids.shape[-1],
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model.config.eos_token_id,
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forced_bos_token_id=model.config.forced_bos_token_id,
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forced_eos_token_id=model.config.forced_eos_token_id,
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max_length=max_length,
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)
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logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=2)
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# check `generate()` and `sample()` are equal
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output_sample, output_generate = self._sample_generate(
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model=model,
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input_ids=input_ids.to(torch_device),
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attention_mask=attention_mask.to(torch_device),
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max_length=max_length,
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num_return_sequences=3,
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logits_processor=logits_processor,
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logits_warper=logits_warper,
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logits_warper_kwargs=logits_warper_kwargs,
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process_kwargs=process_kwargs,
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)
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self.assertIsInstance(output_sample, torch.Tensor)
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self.assertIsInstance(output_generate, torch.Tensor)
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# override since we don't expect the outputs of `.generate` and `.sample` to be the same, since we perform
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# additional post-processing in the former
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def test_sample_generate_dict_output(self):
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for model_class in self.greedy_sample_model_classes:
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# disable cache
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config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
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config.use_cache = False
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model = model_class(config).to(torch_device).eval()
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process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
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input_ids.shape[-1],
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model.config.eos_token_id,
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forced_bos_token_id=model.config.forced_bos_token_id,
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forced_eos_token_id=model.config.forced_eos_token_id,
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max_length=max_length,
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)
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logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1)
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output_sample, output_generate = self._sample_generate(
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model=model,
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input_ids=input_ids.to(torch_device),
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attention_mask=attention_mask.to(torch_device),
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max_length=max_length,
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num_return_sequences=1,
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logits_processor=logits_processor,
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logits_warper=logits_warper,
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logits_warper_kwargs=logits_warper_kwargs,
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process_kwargs=process_kwargs,
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output_scores=True,
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output_hidden_states=True,
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output_attentions=True,
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return_dict_in_generate=True,
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)
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self.assertIsInstance(output_sample, GenerateDecoderOnlyOutput)
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self.assertIsInstance(output_generate, GenerateDecoderOnlyOutput)
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warper_kwargs = {}
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return process_kwargs, warper_kwargs
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def test_greedy_generate_stereo_outputs(self):
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for model_class in self.greedy_sample_model_classes:
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config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
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config.audio_channels = 2
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model = model_class(config).to(torch_device).eval()
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output_greedy, output_generate = self._greedy_generate(
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output_generate = self._greedy_generate(
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model=model,
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input_ids=input_ids.to(torch_device),
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attention_mask=attention_mask.to(torch_device),
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@ -394,9 +274,7 @@ class MusicgenMelodyDecoderTest(ModelTesterMixin, GenerationTesterMixin, unittes
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return_dict_in_generate=True,
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)
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self.assertIsInstance(output_greedy, GenerateDecoderOnlyOutput)
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self.assertIsInstance(output_generate, GenerateDecoderOnlyOutput)
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self.assertNotIn(config.pad_token_id, output_generate)
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@ -817,10 +695,8 @@ class MusicgenMelodyTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
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attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long)
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# generate max 3 tokens
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decoder_input_ids = inputs_dict["decoder_input_ids"]
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max_length = decoder_input_ids.shape[-1] + 3
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decoder_input_ids = decoder_input_ids[: batch_size * config.decoder.num_codebooks, :]
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return config, input_ids, attention_mask, decoder_input_ids, max_length
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max_length = 3
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return config, input_ids, attention_mask, max_length
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# override since the `input_ids` cannot be used as the `decoder_input_ids` for musicgen_melody (input / outputs are
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# different modalities -> different shapes)
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@ -829,18 +705,14 @@ class MusicgenMelodyTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
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model,
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input_ids,
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attention_mask,
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decoder_input_ids,
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max_length,
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output_scores=False,
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output_attentions=False,
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output_hidden_states=False,
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return_dict_in_generate=False,
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):
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logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
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logits_process_kwargs, _ = self._get_logits_processor_and_warper_kwargs(
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input_ids.shape[-1],
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eos_token_id=model.config.eos_token_id,
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forced_bos_token_id=model.config.forced_bos_token_id,
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forced_eos_token_id=model.config.forced_eos_token_id,
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max_length=max_length,
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)
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@ -859,34 +731,17 @@ class MusicgenMelodyTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
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**model_kwargs,
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)
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with torch.no_grad():
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model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
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output_greedy = model.greedy_search(
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decoder_input_ids,
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max_length=max_length,
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logits_processor=logits_processor,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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output_scores=output_scores,
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return_dict_in_generate=return_dict_in_generate,
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# Ignore copy
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**model_kwargs,
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)
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return output_greedy, output_generate
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return output_generate
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# override since the `input_ids` cannot be used as the `decoder_input_ids` for musicgen_melody (input / outputs are
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# different modalities -> different shapes)
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# Ignore copy
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def _sample_generate(
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self,
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model,
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input_ids,
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attention_mask,
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decoder_input_ids,
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max_length,
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num_return_sequences,
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logits_processor,
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logits_warper,
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logits_warper_kwargs,
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process_kwargs,
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output_scores=False,
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@ -912,53 +767,31 @@ class MusicgenMelodyTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
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**model_kwargs,
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)
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torch.manual_seed(0)
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# prevent flaky generation test failures
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logits_processor.append(InfNanRemoveLogitsProcessor())
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with torch.no_grad():
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model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
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output_sample = model.sample(
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decoder_input_ids.repeat_interleave(num_return_sequences, dim=0),
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max_length=max_length,
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logits_processor=logits_processor,
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logits_warper=logits_warper,
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output_scores=output_scores,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict_in_generate=return_dict_in_generate,
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**model_kwargs,
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)
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return output_sample, output_generate
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return output_generate
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@staticmethod
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def _get_logits_processor_and_kwargs(
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def _get_logits_processor_and_warper_kwargs(
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input_length,
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eos_token_id,
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forced_bos_token_id=None,
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forced_eos_token_id=None,
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max_length=None,
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diversity_penalty=None,
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):
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process_kwargs = {
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"min_length": input_length + 1 if max_length is None else max_length - 1,
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}
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logits_processor = LogitsProcessorList()
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return process_kwargs, logits_processor
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warper_kwargs = {}
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return process_kwargs, warper_kwargs
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def test_greedy_generate_dict_outputs(self):
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for model_class in self.greedy_sample_model_classes:
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# disable cache
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config, input_ids, attention_mask, decoder_input_ids, max_length = self._get_input_ids_and_config()
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config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
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config.use_cache = False
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model = model_class(config).to(torch_device).eval()
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output_greedy, output_generate = self._greedy_generate(
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output_generate = self._greedy_generate(
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model=model,
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input_ids=input_ids.to(torch_device),
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attention_mask=attention_mask.to(torch_device),
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decoder_input_ids=decoder_input_ids,
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max_length=max_length,
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output_scores=True,
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output_hidden_states=True,
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@ -966,7 +799,6 @@ class MusicgenMelodyTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
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return_dict_in_generate=True,
|
||||
)
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self.assertIsInstance(output_greedy, GenerateDecoderOnlyOutput)
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self.assertIsInstance(output_generate, GenerateDecoderOnlyOutput)
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||||
|
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self.assertNotIn(config.pad_token_id, output_generate)
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|
|
@ -974,16 +806,15 @@ class MusicgenMelodyTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
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|||
def test_greedy_generate_dict_outputs_use_cache(self):
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for model_class in self.greedy_sample_model_classes:
|
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# enable cache
|
||||
config, input_ids, attention_mask, decoder_input_ids, max_length = self._get_input_ids_and_config()
|
||||
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(
|
||||
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,
|
||||
|
|
@ -991,64 +822,48 @@ class MusicgenMelodyTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
|
|||
return_dict_in_generate=True,
|
||||
)
|
||||
|
||||
self.assertIsInstance(output_greedy, GenerateDecoderOnlyOutput)
|
||||
self.assertIsInstance(output_generate, GenerateDecoderOnlyOutput)
|
||||
|
||||
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()
|
||||
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(
|
||||
process_kwargs, logits_warper_kwargs = self._get_logits_processor_and_warper_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(
|
||||
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, 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(
|
||||
process_kwargs, logits_warper_kwargs = self._get_logits_processor_and_warper_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(
|
||||
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,
|
||||
|
|
@ -1057,11 +872,10 @@ class MusicgenMelodyTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
|
|||
return_dict_in_generate=True,
|
||||
)
|
||||
|
||||
self.assertIsInstance(output_sample, GenerateDecoderOnlyOutput)
|
||||
self.assertIsInstance(output_generate, GenerateDecoderOnlyOutput)
|
||||
|
||||
def test_generate_without_input_ids(self):
|
||||
config, _, _, _, max_length = self._get_input_ids_and_config()
|
||||
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:
|
||||
|
|
@ -1090,15 +904,14 @@ class MusicgenMelodyTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
|
|||
|
||||
def test_greedy_generate_stereo_outputs(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()
|
||||
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
|
||||
config.audio_channels = 2
|
||||
|
||||
model = model_class(config).to(torch_device).eval()
|
||||
output_greedy, output_generate = self._greedy_generate(
|
||||
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,
|
||||
|
|
@ -1106,7 +919,6 @@ class MusicgenMelodyTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
|
|||
return_dict_in_generate=True,
|
||||
)
|
||||
|
||||
self.assertIsInstance(output_greedy, GenerateDecoderOnlyOutput)
|
||||
self.assertIsInstance(output_generate, GenerateDecoderOnlyOutput)
|
||||
|
||||
self.assertNotIn(config.pad_token_id, output_generate)
|
||||
|
|
|
|||
Loading…
Reference in New Issue