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| # 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. | |
| """ | |
| Utility functions to load from the checkpoints. | |
| Each checkpoint is a torch.saved dict with the following keys: | |
| - 'xp.cfg': the hydra config as dumped during training. This should be used | |
| to rebuild the object using the audiocraft.models.builders functions, | |
| - 'model_best_state': a readily loadable best state for the model, including | |
| the conditioner. The model obtained from `xp.cfg` should be compatible | |
| with this state dict. In the case of a LM, the encodec model would not be | |
| bundled along but instead provided separately. | |
| Those functions also support loading from a remote location with the Torch Hub API. | |
| They also support overriding some parameters, in particular the device and dtype | |
| of the returned model. | |
| """ | |
| from pathlib import Path | |
| from huggingface_hub import hf_hub_download | |
| import typing as tp | |
| import os | |
| from omegaconf import OmegaConf, DictConfig | |
| import torch | |
| from . import builders | |
| from .encodec import CompressionModel | |
| def get_audiocraft_cache_dir() -> tp.Optional[str]: | |
| return os.environ.get('AUDIOCRAFT_CACHE_DIR', None) | |
| def _get_state_dict( | |
| file_or_url_or_id: tp.Union[Path, str], | |
| filename: tp.Optional[str] = None, | |
| device='cpu', | |
| cache_dir: tp.Optional[str] = None, | |
| ): | |
| if cache_dir is None: | |
| cache_dir = get_audiocraft_cache_dir() | |
| # Return the state dict either from a file or url | |
| file_or_url_or_id = str(file_or_url_or_id) | |
| assert isinstance(file_or_url_or_id, str) | |
| if os.path.isfile(file_or_url_or_id): | |
| return torch.load(file_or_url_or_id, map_location=device) | |
| if os.path.isdir(file_or_url_or_id): | |
| file = f"{file_or_url_or_id}/{filename}" | |
| return torch.load(file, map_location=device) | |
| elif file_or_url_or_id.startswith('https://'): | |
| return torch.hub.load_state_dict_from_url(file_or_url_or_id, map_location=device, check_hash=True) | |
| else: | |
| assert filename is not None, "filename needs to be defined if using HF checkpoints" | |
| file = hf_hub_download(repo_id=file_or_url_or_id, filename=filename, cache_dir=cache_dir) | |
| return torch.load(file, map_location=device) | |
| def load_compression_model_ckpt(file_or_url_or_id: tp.Union[Path, str], cache_dir: tp.Optional[str] = None): | |
| return _get_state_dict(file_or_url_or_id, filename="compression_state_dict.bin", cache_dir=cache_dir) | |
| def load_compression_model(file_or_url_or_id: tp.Union[Path, str], device='cpu', cache_dir: tp.Optional[str] = None): | |
| pkg = load_compression_model_ckpt(file_or_url_or_id, cache_dir=cache_dir) | |
| if 'pretrained' in pkg: | |
| return CompressionModel.get_pretrained(pkg['pretrained'], device=device) | |
| cfg = OmegaConf.create(pkg['xp.cfg']) | |
| cfg.device = str(device) | |
| model = builders.get_compression_model(cfg) | |
| model.load_state_dict(pkg['best_state']) | |
| model.eval() | |
| return model | |
| def load_lm_model_ckpt(file_or_url_or_id: tp.Union[Path, str], cache_dir: tp.Optional[str] = None): | |
| return _get_state_dict(file_or_url_or_id, filename="state_dict.bin", cache_dir=cache_dir) | |
| def _delete_param(cfg: DictConfig, full_name: str): | |
| parts = full_name.split('.') | |
| for part in parts[:-1]: | |
| if part in cfg: | |
| cfg = cfg[part] | |
| else: | |
| return | |
| OmegaConf.set_struct(cfg, False) | |
| if parts[-1] in cfg: | |
| del cfg[parts[-1]] | |
| OmegaConf.set_struct(cfg, True) | |
| def load_lm_model(file_or_url_or_id: tp.Union[Path, str], device='cpu', cache_dir: tp.Optional[str] = None): | |
| pkg = load_lm_model_ckpt(file_or_url_or_id, cache_dir=cache_dir) | |
| cfg = OmegaConf.create(pkg['xp.cfg']) | |
| cfg.device = str(device) | |
| if cfg.device == 'cpu': | |
| cfg.dtype = 'float32' | |
| else: | |
| cfg.dtype = 'float16' | |
| _delete_param(cfg, 'conditioners.self_wav.chroma_stem.cache_path') | |
| _delete_param(cfg, 'conditioners.args.merge_text_conditions_p') | |
| _delete_param(cfg, 'conditioners.args.drop_desc_p') | |
| model = builders.get_lm_model(cfg) | |
| model.load_state_dict(pkg['best_state']) | |
| model.eval() | |
| model.cfg = cfg | |
| return model | |
| def load_mbd_ckpt(file_or_url_or_id: tp.Union[Path, str], cache_dir: tp.Optional[str] = None): | |
| return _get_state_dict(file_or_url_or_id, filename="all_in_one.pt", cache_dir=cache_dir) | |
| def load_diffusion_models(file_or_url_or_id: tp.Union[Path, str], device='cpu', cache_dir: tp.Optional[str] = None): | |
| pkg = load_mbd_ckpt(file_or_url_or_id, cache_dir=cache_dir) | |
| models = [] | |
| processors = [] | |
| cfgs = [] | |
| sample_rate = pkg['sample_rate'] | |
| for i in range(pkg['n_bands']): | |
| cfg = pkg[i]['cfg'] | |
| model = builders.get_diffusion_model(cfg) | |
| model_dict = pkg[i]['model_state'] | |
| model.load_state_dict(model_dict) | |
| model.to(device) | |
| processor = builders.get_processor(cfg=cfg.processor, sample_rate=sample_rate) | |
| processor_dict = pkg[i]['processor_state'] | |
| processor.load_state_dict(processor_dict) | |
| processor.to(device) | |
| models.append(model) | |
| processors.append(processor) | |
| cfgs.append(cfg) | |
| return models, processors, cfgs | |