# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
All the functions to build the relevant models and modules
from the Hydra config.
"""
import typing as tp
import audiocraft
import omegaconf
import torch
from .encodec import CompressionModel, EncodecModel, InterleaveStereoCompressionModel
from .lm import LMModel
from .seanet import SEANetEncoder, SEANetDecoder
from .codebooks_patterns import (
CodebooksPatternProvider,
DelayedPatternProvider,
MusicLMPattern,
ParallelPatternProvider,
UnrolledPatternProvider,
CoarseFirstPattern,
)
from .conditioners import (
BaseConditioner,
ChromaStemConditioner,
CLAPEmbeddingConditioner,
ConditionFuser,
ConditioningProvider,
LUTConditioner,
T5Conditioner,
)
from .unet import DiffusionUnet
import audiocraft.quantization as qt
from .utils.utils import dict_from_config
from .diffusion_schedule import MultiBandProcessor, SampleProcessor
def get_quantizer(quantizer: str, cfg: omegaconf.DictConfig, dimension: int) -> qt.BaseQuantizer:
klass = {
'no_quant': qt.DummyQuantizer,
'rvq': qt.ResidualVectorQuantizer
}[quantizer]
kwargs = dict_from_config(getattr(cfg, quantizer))
if quantizer != 'no_quant':
kwargs['dimension'] = dimension
return klass(**kwargs)
def get_encodec_autoencoder(encoder_name: str, cfg: omegaconf.DictConfig):
if encoder_name == 'seanet':
kwargs = dict_from_config(getattr(cfg, 'seanet'))
encoder_override_kwargs = kwargs.pop('encoder')
decoder_override_kwargs = kwargs.pop('decoder')
encoder_kwargs = {**kwargs, **encoder_override_kwargs}
decoder_kwargs = {**kwargs, **decoder_override_kwargs}
encoder = SEANetEncoder(**encoder_kwargs)
decoder = SEANetDecoder(**decoder_kwargs)
return encoder, decoder
else:
raise KeyError(f"Unexpected compression model {cfg.compression_model}")
def get_compression_model(cfg: omegaconf.DictConfig) -> CompressionModel:
"""Instantiate a compression model."""
if cfg.compression_model == 'encodec':
kwargs = dict_from_config(getattr(cfg, 'encodec'))
encoder_name = kwargs.pop('autoencoder')
quantizer_name = kwargs.pop('quantizer')
encoder, decoder = get_encodec_autoencoder(encoder_name, cfg)
quantizer = get_quantizer(quantizer_name, cfg, encoder.dimension)
frame_rate = kwargs['sample_rate'] // encoder.hop_length
renormalize = kwargs.pop('renormalize', False)
# deprecated params
kwargs.pop('renorm', None)
return EncodecModel(encoder, decoder, quantizer,
frame_rate=frame_rate, renormalize=renormalize, **kwargs).to(cfg.device)
else:
raise KeyError(f"Unexpected compression model {cfg.compression_model}")
def get_lm_model(cfg: omegaconf.DictConfig) -> LMModel:
"""Instantiate a transformer LM."""
if cfg.lm_model in ['transformer_lm', 'transformer_lm_magnet']:
kwargs = dict_from_config(getattr(cfg, 'transformer_lm'))
n_q = kwargs['n_q']
q_modeling = kwargs.pop('q_modeling', None)
codebooks_pattern_cfg = getattr(cfg, 'codebooks_pattern')
attribute_dropout = dict_from_config(getattr(cfg, 'attribute_dropout'))
cls_free_guidance = dict_from_config(getattr(cfg, 'classifier_free_guidance'))
cfg_prob, cfg_coef = cls_free_guidance['training_dropout'], cls_free_guidance['inference_coef']
fuser = get_condition_fuser(cfg)
condition_provider = get_conditioner_provider(kwargs["dim"], cfg).to(cfg.device)
if len(fuser.fuse2cond['cross']) > 0: # enforce cross-att programmatically
kwargs['cross_attention'] = True
if codebooks_pattern_cfg.modeling is None:
assert q_modeling is not None, \
"LM model should either have a codebook pattern defined or transformer_lm.q_modeling"
codebooks_pattern_cfg = omegaconf.OmegaConf.create(
{'modeling': q_modeling, 'delay': {'delays': list(range(n_q))}}
)
pattern_provider = get_codebooks_pattern_provider(n_q, codebooks_pattern_cfg)
# lm_class = MagnetLMModel if cfg.lm_model == 'transformer_lm_magnet' else LMModel
lm_class = LMModel # hard coded D
print(f'{lm_class=}\n\n\n\n=====================')
return lm_class(
pattern_provider=pattern_provider,
condition_provider=condition_provider,
fuser=fuser,
cfg_dropout=cfg_prob,
cfg_coef=cfg_coef,
attribute_dropout=attribute_dropout,
dtype=getattr(torch, cfg.dtype),
device=cfg.device,
**kwargs
).to(cfg.device)
else:
raise KeyError(f"Unexpected LM model {cfg.lm_model}")
def get_conditioner_provider(output_dim: int, cfg: omegaconf.DictConfig) -> ConditioningProvider:
"""Instantiate a conditioning model."""
device = cfg.device
duration = cfg.dataset.segment_duration
cfg = getattr(cfg, 'conditioners')
dict_cfg = {} if cfg is None else dict_from_config(cfg)
conditioners: tp.Dict[str, BaseConditioner] = {}
condition_provider_args = dict_cfg.pop('args', {})
condition_provider_args.pop('merge_text_conditions_p', None)
condition_provider_args.pop('drop_desc_p', None)
for cond, cond_cfg in dict_cfg.items():
model_type = cond_cfg['model']
model_args = cond_cfg[model_type]
if model_type == 't5':
conditioners[str(cond)] = T5Conditioner(output_dim=output_dim, device=device, **model_args)
elif model_type == 'lut':
conditioners[str(cond)] = LUTConditioner(output_dim=output_dim, **model_args)
elif model_type == 'chroma_stem':
conditioners[str(cond)] = ChromaStemConditioner(
output_dim=output_dim,
duration=duration,
device=device,
**model_args
)
elif model_type == 'clap':
conditioners[str(cond)] = CLAPEmbeddingConditioner(
output_dim=output_dim,
device=device,
**model_args
)
else:
raise ValueError(f"Unrecognized conditioning model: {model_type}")
conditioner = ConditioningProvider(conditioners, device=device, **condition_provider_args)
return conditioner
def get_condition_fuser(cfg: omegaconf.DictConfig) -> ConditionFuser:
"""Instantiate a condition fuser object."""
fuser_cfg = getattr(cfg, 'fuser')
fuser_methods = ['sum', 'cross', 'prepend', 'input_interpolate']
fuse2cond = {k: fuser_cfg[k] for k in fuser_methods}
kwargs = {k: v for k, v in fuser_cfg.items() if k not in fuser_methods}
fuser = ConditionFuser(fuse2cond=fuse2cond, **kwargs)
return fuser
def get_codebooks_pattern_provider(n_q: int, cfg: omegaconf.DictConfig) -> CodebooksPatternProvider:
"""Instantiate a codebooks pattern provider object."""
pattern_providers = {
'parallel': ParallelPatternProvider,
'delay': DelayedPatternProvider,
'unroll': UnrolledPatternProvider,
'coarse_first': CoarseFirstPattern,
'musiclm': MusicLMPattern,
}
name = cfg.modeling
kwargs = dict_from_config(cfg.get(name)) if hasattr(cfg, name) else {}
klass = pattern_providers[name]
return klass(n_q, **kwargs)
def get_debug_compression_model(device='cpu', sample_rate: int = 32000):
"""Instantiate a debug compression model to be used for unit tests."""
assert sample_rate in [16000, 32000], "unsupported sample rate for debug compression model"
model_ratios = {
16000: [10, 8, 8], # 25 Hz at 16kHz
32000: [10, 8, 16] # 25 Hz at 32kHz
}
ratios: tp.List[int] = model_ratios[sample_rate]
frame_rate = 25
seanet_kwargs: dict = {
'n_filters': 4,
'n_residual_layers': 1,
'dimension': 32,
'ratios': ratios,
}
encoder = SEANetEncoder(**seanet_kwargs)
decoder = SEANetDecoder(**seanet_kwargs)
quantizer = qt.ResidualVectorQuantizer(dimension=32, bins=400, n_q=4)
init_x = torch.randn(8, 32, 128)
quantizer(init_x, 1) # initialize kmeans etc.
compression_model = EncodecModel(
encoder, decoder, quantizer,
frame_rate=frame_rate, sample_rate=sample_rate, channels=1).to(device)
return compression_model.eval()
def get_diffusion_model(cfg: omegaconf.DictConfig):
# TODO Find a way to infer the channels from dset
channels = cfg.channels
num_steps = cfg.schedule.num_steps
return DiffusionUnet(
chin=channels, num_steps=num_steps, **cfg.diffusion_unet)
def get_processor(cfg, sample_rate: int = 24000):
sample_processor = SampleProcessor()
if cfg.use:
kw = dict(cfg)
kw.pop('use')
kw.pop('name')
if cfg.name == "multi_band_processor":
sample_processor = MultiBandProcessor(sample_rate=sample_rate, **kw)
return sample_processor
def get_debug_lm_model(device='cpu'):
"""Instantiate a debug LM to be used for unit tests."""
pattern = DelayedPatternProvider(n_q=4)
dim = 16
providers = {
'description': LUTConditioner(n_bins=128, dim=dim, output_dim=dim, tokenizer="whitespace"),
}
condition_provider = ConditioningProvider(providers)
fuser = ConditionFuser(
{'cross': ['description'], 'prepend': [],
'sum': [], 'input_interpolate': []})
lm = LMModel(
pattern, condition_provider, fuser,
n_q=4, card=400, dim=dim, num_heads=4, custom=True, num_layers=2,
cross_attention=True, causal=True)
return lm.to(device).eval()
def get_wrapped_compression_model(
compression_model: CompressionModel,
cfg: omegaconf.DictConfig) -> CompressionModel:
if hasattr(cfg, 'interleave_stereo_codebooks'):
if cfg.interleave_stereo_codebooks.use:
kwargs = dict_from_config(cfg.interleave_stereo_codebooks)
kwargs.pop('use')
compression_model = InterleaveStereoCompressionModel(compression_model, **kwargs)
if hasattr(cfg, 'compression_model_n_q'):
if cfg.compression_model_n_q is not None:
compression_model.set_num_codebooks(cfg.compression_model_n_q)
return compression_model