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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
import torch
from . import builders
HF_MODEL_CHECKPOINTS_MAP = {
"small": "facebook/musicgen-small",
"medium": "facebook/musicgen-medium",
"large": "facebook/musicgen-large",
"melody": "facebook/musicgen-melody",
}
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,
):
# 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)
elif file_or_url_or_id in HF_MODEL_CHECKPOINTS_MAP:
assert filename is not None, "filename needs to be defined if using HF checkpoints"
repo_id = HF_MODEL_CHECKPOINTS_MAP[file_or_url_or_id]
file = hf_hub_download(repo_id=repo_id, filename=filename, cache_dir=cache_dir)
return torch.load(file, map_location=device)
else:
raise ValueError(f"{file_or_url_or_id} is not a valid name, path or link that can be loaded.")
def load_compression_model(file_or_url_or_id: tp.Union[Path, str], device='cpu', cache_dir: tp.Optional[str] = None):
pkg = _get_state_dict(file_or_url_or_id, filename="compression_state_dict.bin", cache_dir=cache_dir)
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(file_or_url_or_id: tp.Union[Path, str], device='cpu', cache_dir: tp.Optional[str] = None):
pkg = _get_state_dict(file_or_url_or_id, filename="state_dict.bin", 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'
model = builders.get_lm_model(cfg)
model.load_state_dict(pkg['best_state'])
model.eval()
model.cfg = cfg
return model
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