# 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 warnings import omegaconf import torch from src.audiocraft import modules # upstream imported this from a pip-installed `audiocraft` package; we use # audiocraft locally under src/audiocraft, which defines the same classes from src.audiocraft.models.encodec import CompressionModel, EncodecModel # noqa from .lm_cc import LMModel from src.audiocraft.modules.codebooks_patterns import ( CodebooksPatternProvider, DelayedPatternProvider, ParallelPatternProvider, UnrolledPatternProvider, # VALLEPattern, MusicLMPattern, ) from src.audiocraft.modules.conditioners import ( BaseConditioner, ConditioningProvider, LUTConditioner, T5Conditioner, ConditionFuser, ChromaStemConditioner, ) from src.audiocraft import quantization as qt from src.audiocraft.utils.utils import dict_from_config 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 = modules.SEANetEncoder(**encoder_kwargs) decoder = modules.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', None) renorm = kwargs.pop('renorm') if renormalize is None: renormalize = renorm is not None warnings.warn("You are using a deprecated EnCodec model. Please migrate to new renormalization.") 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 == 'transformer_lm': 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 programatically 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) return LMModel( 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") cfg = omegaconf.OmegaConf.create({}) if cfg is None else cfg conditioners: tp.Dict[str, BaseConditioner] = {} with omegaconf.open_dict(cfg): condition_provider_args = 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 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": model_args.pop('cache_path', None) conditioners[str(cond)] = ChromaStemConditioner( output_dim=output_dim, duration=duration, 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, # 'valle': VALLEPattern, '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'): """Instantiate a debug compression model to be used for unit tests. """ seanet_kwargs = { 'n_filters': 4, 'n_residual_layers': 1, 'dimension': 32, 'ratios': [10, 8, 16] # 25 Hz at 32kHz } encoder = modules.SEANetEncoder(**seanet_kwargs) decoder = modules.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=25, sample_rate=32000, channels=1).to(device) return compression_model.eval() 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()