File size: 2,552 Bytes
a7c2243 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from pathlib import Path
import torch
from omegaconf import OmegaConf
from nemo.core.config import hydra_runner
from nemo.utils.model_utils import import_class_by_path
@dataclass
class HfExportConfig:
# Name of the model class to be imported, e.g. nemo.collections.speechlm2.models.DuplexS2SModel
class_path: str
# Path to PyTorch Lightning checkpoint file (normal ckpt) or directory (distributed ckpt)
ckpt_path: str
# Path to the experiment's config, used to instantiate the model class.
ckpt_config: str
# Path where we should save the HuggingFace Hub compatible checkpoint
output_dir: str
# Dtype used for stored parameters
dtype: str = "bfloat16"
def load_checkpoint(model: torch.nn.Module, checkpoint_path: str):
if Path(checkpoint_path).is_dir():
from torch.distributed.checkpoint import load
state_dict = {"state_dict": model.state_dict()}
load(state_dict, checkpoint_id=checkpoint_path)
model.load_state_dict(state_dict["state_dict"])
else:
ckpt_data = torch.load(checkpoint_path, map_location="cpu")
model.load_state_dict(ckpt_data["state_dict"])
@hydra_runner(config_name="HfExportConfig", schema=HfExportConfig)
def main(cfg: HfExportConfig):
"""
Read PyTorch Lightning checkpoint and export the model to HuggingFace Hub format.
The resulting model can be then initialized via ModelClass.from_pretrained(path).
Also supports distributed checkpoints for models trained with FSDP2/TP.
"""
model_cfg = OmegaConf.to_container(OmegaConf.load(cfg.ckpt_config).model, resolve=True)
model_cfg["torch_dtype"] = cfg.dtype
cls = import_class_by_path(cfg.class_path)
model = cls(model_cfg)
load_checkpoint(model, cfg.ckpt_path)
model = model.to(getattr(torch, cfg.dtype))
model.save_pretrained(cfg.output_dir)
if __name__ == "__main__":
main()
|