mirror of
https://github.com/huggingface/diffusers.git
synced 2025-12-17 09:54:41 +08:00
250 lines
8.7 KiB
Python
250 lines
8.7 KiB
Python
# coding=utf-8
|
|
# Copyright 2025 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 ...testing_utils import (
|
|
backend_empty_cache,
|
|
enable_full_determinism,
|
|
floats_tensor,
|
|
slow,
|
|
torch_all_close,
|
|
torch_device,
|
|
)
|
|
from ..test_modeling_common import ModelTesterMixin
|
|
from .testing_utils import AutoencoderTesterMixin
|
|
|
|
|
|
enable_full_determinism()
|
|
|
|
|
|
class AutoencoderOobleckTests(ModelTesterMixin, AutoencoderTesterMixin, 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("No attention module used in this model")
|
|
def test_set_attn_processor_for_determinism(self):
|
|
return
|
|
|
|
@unittest.skip(
|
|
"Test not supported because of 'weight_norm_fwd_first_dim_kernel' not implemented for 'Float8_e4m3fn'"
|
|
)
|
|
def test_layerwise_casting_training(self):
|
|
return super().test_layerwise_casting_training()
|
|
|
|
@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(torch_device) else torch_device
|
|
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)
|