# coding=utf-8 # Copyright 2024 HuggingFace Inc. # # 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. import gc import unittest import torch from datasets import load_dataset from parameterized import parameterized from diffusers import AutoencoderOobleck from diffusers.utils.testing_utils import ( backend_empty_cache, enable_full_determinism, floats_tensor, slow, torch_all_close, torch_device, ) from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin enable_full_determinism() class AutoencoderOobleckTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase): model_class = AutoencoderOobleck main_input_name = "sample" base_precision = 1e-2 def get_autoencoder_oobleck_config(self, block_out_channels=None): init_dict = { "encoder_hidden_size": 12, "decoder_channels": 12, "decoder_input_channels": 6, "audio_channels": 2, "downsampling_ratios": [2, 4], "channel_multiples": [1, 2], } return init_dict @property def dummy_input(self): batch_size = 4 num_channels = 2 seq_len = 24 waveform = floats_tensor((batch_size, num_channels, seq_len)).to(torch_device) return {"sample": waveform, "sample_posterior": False} @property def input_shape(self): return (2, 24) @property def output_shape(self): return (2, 24) def prepare_init_args_and_inputs_for_common(self): init_dict = self.get_autoencoder_oobleck_config() inputs_dict = self.dummy_input return init_dict, inputs_dict def test_enable_disable_slicing(self): init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() torch.manual_seed(0) model = self.model_class(**init_dict).to(torch_device) inputs_dict.update({"return_dict": False}) torch.manual_seed(0) output_without_slicing = model(**inputs_dict, generator=torch.manual_seed(0))[0] torch.manual_seed(0) model.enable_slicing() output_with_slicing = model(**inputs_dict, generator=torch.manual_seed(0))[0] self.assertLess( (output_without_slicing.detach().cpu().numpy() - output_with_slicing.detach().cpu().numpy()).max(), 0.5, "VAE slicing should not affect the inference results", ) torch.manual_seed(0) model.disable_slicing() output_without_slicing_2 = model(**inputs_dict, generator=torch.manual_seed(0))[0] self.assertEqual( output_without_slicing.detach().cpu().numpy().all(), output_without_slicing_2.detach().cpu().numpy().all(), "Without slicing outputs should match with the outputs when slicing is manually disabled.", ) @unittest.skip("Test unsupported.") def test_forward_with_norm_groups(self): pass @unittest.skip("No attention module used in this model") def test_set_attn_processor_for_determinism(self): return @unittest.skip( "The convolution layers of AutoencoderOobleck are wrapped with torch.nn.utils.weight_norm. This causes the hook's pre_forward to not " "cast the module weights to compute_dtype (as required by forward pass). As a result, forward pass errors out. To fix:\n" "1. Make sure `nn::Module::to` works with `torch.nn.utils.weight_norm` wrapped convolution layer.\n" "2. Unskip this test." ) def test_layerwise_casting_inference(self): pass @unittest.skip( "The convolution layers of AutoencoderOobleck are wrapped with torch.nn.utils.weight_norm. This causes the hook's pre_forward to not " "cast the module weights to compute_dtype (as required by forward pass). As a result, forward pass errors out. To fix:\n" "1. Make sure `nn::Module::to` works with `torch.nn.utils.weight_norm` wrapped convolution layer.\n" "2. Unskip this test." ) def test_layerwise_casting_memory(self): pass @slow class AutoencoderOobleckIntegrationTests(unittest.TestCase): def tearDown(self): # clean up the VRAM after each test super().tearDown() gc.collect() backend_empty_cache(torch_device) def _load_datasamples(self, num_samples): ds = load_dataset( "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True ) # automatic decoding with librispeech speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"] return torch.nn.utils.rnn.pad_sequence( [torch.from_numpy(x["array"]) for x in speech_samples], batch_first=True ) def get_audio(self, audio_sample_size=2097152, fp16=False): dtype = torch.float16 if fp16 else torch.float32 audio = self._load_datasamples(2).to(torch_device).to(dtype) # pad / crop to audio_sample_size audio = torch.nn.functional.pad(audio[:, :audio_sample_size], pad=(0, audio_sample_size - audio.shape[-1])) # todo channel audio = audio.unsqueeze(1).repeat(1, 2, 1).to(torch_device) return audio def get_oobleck_vae_model(self, model_id="stabilityai/stable-audio-open-1.0", fp16=False): torch_dtype = torch.float16 if fp16 else torch.float32 model = AutoencoderOobleck.from_pretrained( model_id, subfolder="vae", torch_dtype=torch_dtype, ) model.to(torch_device) return model def get_generator(self, seed=0): generator_device = "cpu" if not torch_device.startswith("cuda") else "cuda" if torch_device != "mps": return torch.Generator(device=generator_device).manual_seed(seed) return torch.manual_seed(seed) @parameterized.expand( [ # fmt: off [33, [1.193e-4, 6.56e-05, 1.314e-4, 3.80e-05, -4.01e-06], 0.001192], [44, [2.77e-05, -2.65e-05, 1.18e-05, -6.94e-05, -9.57e-05], 0.001196], # fmt: on ] ) def test_stable_diffusion(self, seed, expected_slice, expected_mean_absolute_diff): model = self.get_oobleck_vae_model() audio = self.get_audio() generator = self.get_generator(seed) with torch.no_grad(): sample = model(audio, generator=generator, sample_posterior=True).sample assert sample.shape == audio.shape assert ((sample - audio).abs().mean() - expected_mean_absolute_diff).abs() <= 1e-6 output_slice = sample[-1, 1, 5:10].cpu() expected_output_slice = torch.tensor(expected_slice) assert torch_all_close(output_slice, expected_output_slice, atol=1e-5) def test_stable_diffusion_mode(self): model = self.get_oobleck_vae_model() audio = self.get_audio() with torch.no_grad(): sample = model(audio, sample_posterior=False).sample assert sample.shape == audio.shape @parameterized.expand( [ # fmt: off [33, [1.193e-4, 6.56e-05, 1.314e-4, 3.80e-05, -4.01e-06], 0.001192], [44, [2.77e-05, -2.65e-05, 1.18e-05, -6.94e-05, -9.57e-05], 0.001196], # fmt: on ] ) def test_stable_diffusion_encode_decode(self, seed, expected_slice, expected_mean_absolute_diff): model = self.get_oobleck_vae_model() audio = self.get_audio() generator = self.get_generator(seed) with torch.no_grad(): x = audio posterior = model.encode(x).latent_dist z = posterior.sample(generator=generator) sample = model.decode(z).sample # (batch_size, latent_dim, sequence_length) assert posterior.mean.shape == (audio.shape[0], model.config.decoder_input_channels, 1024) assert sample.shape == audio.shape assert ((sample - audio).abs().mean() - expected_mean_absolute_diff).abs() <= 1e-6 output_slice = sample[-1, 1, 5:10].cpu() expected_output_slice = torch.tensor(expected_slice) assert torch_all_close(output_slice, expected_output_slice, atol=1e-5)