audiocraft/models/builders.py

# 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 audiocraft
import omegaconf
import torch

from .encodec import CompressionModel, EncodecModel, FlattenedCompressionModel  # noqa
from .lm import LMModel
from ..modules.codebooks_patterns import (
    CodebooksPatternProvider,
    DelayedPatternProvider,
    ParallelPatternProvider,
    UnrolledPatternProvider,
    VALLEPattern,
    MusicLMPattern,
)
from ..modules.conditioners import (
    BaseConditioner,
    ConditioningProvider,
    LUTConditioner,
    T5Conditioner,
    ConditionFuser,
    ChromaStemConditioner,
)
from .. import quantization as qt
from ..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 = audiocraft.modules.SEANetEncoder(**encoder_kwargs)
        decoder = audiocraft.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', {})
    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 = audiocraft.modules.SEANetEncoder(**seanet_kwargs)
    decoder = audiocraft.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()