# coding=utf-8 # Copyright 2022 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 numpy as np import torch from diffusers import DDPMScheduler, MidiProcessor, SpectrogramDiffusionPipeline from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, T5FilmDecoder from diffusers.utils import require_torch_gpu, skip_mps, slow, torch_device from diffusers.utils.testing_utils import require_note_seq, require_onnxruntime from ...pipeline_params import TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS, TOKENS_TO_AUDIO_GENERATION_PARAMS from ...test_pipelines_common import PipelineTesterMixin torch.backends.cuda.matmul.allow_tf32 = False MIDI_FILE = "./tests/fixtures/elise_format0.mid" class SpectrogramDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = SpectrogramDiffusionPipeline required_optional_params = PipelineTesterMixin.required_optional_params - { "callback", "latents", "callback_steps", "output_type", "num_images_per_prompt", } test_attention_slicing = False test_cpu_offload = False batch_params = TOKENS_TO_AUDIO_GENERATION_PARAMS params = TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS def get_dummy_components(self): torch.manual_seed(0) notes_encoder = SpectrogramNotesEncoder( max_length=2048, vocab_size=1536, d_model=768, dropout_rate=0.1, num_layers=1, num_heads=1, d_kv=4, d_ff=2048, feed_forward_proj="gated-gelu", ) continuous_encoder = SpectrogramContEncoder( input_dims=128, targets_context_length=256, d_model=768, dropout_rate=0.1, num_layers=1, num_heads=1, d_kv=4, d_ff=2048, feed_forward_proj="gated-gelu", ) decoder = T5FilmDecoder( input_dims=128, targets_length=256, max_decoder_noise_time=20000.0, d_model=768, num_layers=1, num_heads=1, d_kv=4, d_ff=2048, dropout_rate=0.1, ) scheduler = DDPMScheduler() components = { "notes_encoder": notes_encoder.eval(), "continuous_encoder": continuous_encoder.eval(), "decoder": decoder.eval(), "scheduler": scheduler, "melgan": None, } return components def get_dummy_inputs(self, device, seed=0): if str(device).startswith("mps"): generator = torch.manual_seed(seed) else: generator = torch.Generator(device=device).manual_seed(seed) inputs = { "input_tokens": [ [1134, 90, 1135, 1133, 1080, 112, 1132, 1080, 1133, 1079, 133, 1132, 1079, 1133, 1] + [0] * 2033 ], "generator": generator, "num_inference_steps": 4, "output_type": "mel", } return inputs def test_spectrogram_diffusion(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() pipe = SpectrogramDiffusionPipeline(**components) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) output = pipe(**inputs) mel = output.audios mel_slice = mel[0, -3:, -3:] assert mel_slice.shape == (3, 3) expected_slice = np.array( [-11.512925, -4.788215, -0.46172905, -2.051715, -10.539147, -10.970963, -9.091634, 4.0, 4.0] ) assert np.abs(mel_slice.flatten() - expected_slice).max() < 1e-2 @skip_mps def test_save_load_local(self): return super().test_save_load_local() @skip_mps def test_dict_tuple_outputs_equivalent(self): return super().test_dict_tuple_outputs_equivalent() @skip_mps def test_save_load_optional_components(self): return super().test_save_load_optional_components() @skip_mps def test_attention_slicing_forward_pass(self): return super().test_attention_slicing_forward_pass() def test_inference_batch_single_identical(self): pass def test_inference_batch_consistent(self): pass @skip_mps def test_progress_bar(self): return super().test_progress_bar() @slow @require_torch_gpu @require_onnxruntime @require_note_seq class PipelineIntegrationTests(unittest.TestCase): def tearDown(self): # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def test_callback(self): # TODO - test that pipeline can decode tokens in a callback # so that music can be played live device = torch_device pipe = SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion") melgan = pipe.melgan pipe.melgan = None pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) def callback(step, mel_output): # decode mel to audio audio = melgan(input_features=mel_output.astype(np.float32))[0] assert len(audio[0]) == 81920 * (step + 1) # simulate that audio is played return audio processor = MidiProcessor() input_tokens = processor(MIDI_FILE) input_tokens = input_tokens[:3] generator = torch.manual_seed(0) pipe(input_tokens, num_inference_steps=5, generator=generator, callback=callback, output_type="mel") def test_spectrogram_fast(self): device = torch_device pipe = SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion") pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) processor = MidiProcessor() input_tokens = processor(MIDI_FILE) # just run two denoising loops input_tokens = input_tokens[:2] generator = torch.manual_seed(0) output = pipe(input_tokens, num_inference_steps=2, generator=generator) audio = output.audios[0] assert abs(np.abs(audio).sum() - 3612.841) < 1e-1 def test_spectrogram(self): device = torch_device pipe = SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion") pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) processor = MidiProcessor() input_tokens = processor(MIDI_FILE) # just run 4 denoising loops input_tokens = input_tokens[:4] generator = torch.manual_seed(0) output = pipe(input_tokens, num_inference_steps=100, generator=generator) audio = output.audios[0] assert abs(np.abs(audio).sum() - 9389.1111) < 5e-2