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[Tests] reduce the model size in the audioldm2 fast test #7846

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Merged
sayakpaul merged 5 commits into from
Jul 23, 2024
Merged

[Tests] reduce the model size in the audioldm2 fast test #7846

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2c93db4
chore: initial model size reduction
ariG23498 May 2, 2024
9fde9a4
chore: fixing expected values for failing tests
ariG23498 May 2, 2024
fa0c9de
requested edits
ariG23498 May 3, 2024
af41994
Merge branch 'main' into aritra/audioldm2-test-fast
sayakpaul May 6, 2024
4a94e0d
Merge branch 'main' into aritra/audioldm2-test-fast
sayakpaul Jul 23, 2024
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65 changes: 42 additions & 23 deletions tests/pipelines/audioldm2/test_audioldm2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -73,14 +73,15 @@ class AudioLDM2PipelineFastTests(PipelineTesterMixin, unittest.TestCase):
def get_dummy_components(self):
torch.manual_seed(0)
unet = AudioLDM2UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
block_out_channels=(8, 16),
layers_per_block=1,
norm_num_groups=8,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=([None, 16, 32], [None, 16, 32]),
cross_attention_dim=(8, 16),
)
scheduler = DDIMScheduler(
beta_start=0.00085,
Expand All @@ -91,9 +92,10 @@ def get_dummy_components(self):
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
block_out_channels=[8, 16],
in_channels=1,
out_channels=1,
norm_num_groups=8,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
Expand All @@ -102,32 +104,34 @@ def get_dummy_components(self):
text_branch_config = ClapTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=16,
hidden_size=8,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=2,
num_hidden_layers=2,
num_attention_heads=1,
num_hidden_layers=1,
pad_token_id=1,
vocab_size=1000,
projection_dim=16,
projection_dim=8,
)
audio_branch_config = ClapAudioConfig(
spec_size=64,
spec_size=8,
window_size=4,
num_mel_bins=64,
num_mel_bins=8,
intermediate_size=37,
layer_norm_eps=1e-05,
depths=[2, 2],
num_attention_heads=[2, 2],
num_hidden_layers=2,
depths=[1, 1],
num_attention_heads=[1, 1],
num_hidden_layers=1,
hidden_size=192,
projection_dim=16,
projection_dim=8,
patch_size=2,
patch_stride=2,
patch_embed_input_channels=4,
)
text_encoder_config = ClapConfig.from_text_audio_configs(
text_config=text_branch_config, audio_config=audio_branch_config, projection_dim=16
text_config=text_branch_config,
audio_config=audio_branch_config,
projection_dim=16,
)
text_encoder = ClapModel(text_encoder_config)
tokenizer = RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta", model_max_length=77)
Expand All @@ -141,17 +145,17 @@ def get_dummy_components(self):
d_model=32,
d_ff=37,
d_kv=8,
num_heads=2,
num_layers=2,
num_heads=1,
num_layers=1,
)
text_encoder_2 = T5EncoderModel(text_encoder_2_config)
tokenizer_2 = T5Tokenizer.from_pretrained("hf-internal-testing/tiny-random-T5Model", model_max_length=77)

torch.manual_seed(0)
language_model_config = GPT2Config(
n_embd=16,
n_head=2,
n_layer=2,
n_head=1,
n_layer=1,
vocab_size=1000,
n_ctx=99,
n_positions=99,
Expand All @@ -160,7 +164,11 @@ def get_dummy_components(self):
language_model.config.max_new_tokens = 8

torch.manual_seed(0)
projection_model = AudioLDM2ProjectionModel(text_encoder_dim=16, text_encoder_1_dim=32, langauge_model_dim=16)
projection_model = AudioLDM2ProjectionModel(
text_encoder_dim=16,
text_encoder_1_dim=32,
langauge_model_dim=16,
)

vocoder_config = SpeechT5HifiGanConfig(
model_in_dim=8,
Expand Down Expand Up @@ -220,7 +228,18 @@ def test_audioldm2_ddim(self):

audio_slice = audio[:10]
expected_slice = np.array(
[0.0025, 0.0018, 0.0018, -0.0023, -0.0026, -0.0020, -0.0026, -0.0021, -0.0027, -0.0020]
[
2.602e-03,
1.729e-03,
1.863e-03,
-2.219e-03,
-2.656e-03,
-2.017e-03,
-2.648e-03,
-2.115e-03,
-2.502e-03,
-2.081e-03,
]
)

assert np.abs(audio_slice - expected_slice).max() < 1e-4
Expand Down Expand Up @@ -361,7 +380,7 @@ def test_audioldm2_negative_prompt(self):

audio_slice = audio[:10]
expected_slice = np.array(
[0.0025, 0.0018, 0.0018, -0.0023, -0.0026, -0.0020, -0.0026, -0.0021, -0.0027, -0.0020]
[0.0026, 0.0017, 0.0018, -0.0022, -0.0026, -0.002, -0.0026, -0.0021, -0.0025, -0.0021]
)

assert np.abs(audio_slice - expected_slice).max() < 1e-4
Expand All @@ -388,7 +407,7 @@ def test_audioldm2_num_waveforms_per_prompt(self):
assert audios.shape == (batch_size, 256)

# test num_waveforms_per_prompt for single prompt
num_waveforms_per_prompt = 2
num_waveforms_per_prompt = 1
audios = audioldm_pipe(prompt, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt).audios

assert audios.shape == (num_waveforms_per_prompt, 256)
Expand Down