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# 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
@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)
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