Checkpoints#

There are three main ways to load pretrained checkpoints in NeMo:

  • Using the restore_from() method to load a local checkpoint file (.nemo), or

  • Converting a partially trained .ckpt (intermediate) checkpoint to .nemo format.

  • Converting HuggingFace public checkpoints to .nemo format.

Refer to the following sections for instructions and examples for each.

Note that these instructions are for loading fully trained checkpoints for evaluation or fine-tuning.

Loading .nemo Checkpoints#

NeMo automatically saves checkpoints of a model that is trained in a .nemo format. Alternatively, to manually save the model at any point, issue model.save_to(<checkpoint_path>.nemo).

If there is a local .nemo checkpoint that you’d like to load, use the restore_from() method:

import nemo.collections.multimodal as nemo_multimodal
model = nemo_multimodal.models.<MODEL_BASE_CLASS>.restore_from(restore_path="<path/to/checkpoint/file.nemo>")

Where the model base class is the MM model class of the original checkpoint.

Converting Intermediate Checkpoints#

To evaluate a partially trained checkpoint, you may need to convert it to .nemo format. script to convert the checkpoint <ADD convert_ckpt_to_nemo.py PATH>.

python -m torch.distributed.launch --nproc_per_node=<tensor_model_parallel_size> * <pipeline_model_parallel_size> \
  convert_ckpt_to_nemo.py \
  --checkpoint_folder <path_to_PTL_checkpoints_folder> \
  --checkpoint_name <checkpoint_name> \
  --nemo_file_path <path_to_output_nemo_file> \
  --tensor_model_parallel_size <tensor_model_parallel_size> \
  --pipeline_model_parallel_size <pipeline_model_parallel_size>

Converting HuggingFace Checkpoints#

To fully utilize the optimized training pipeline and framework/TRT inference pipeline of NeMo, we provide scripts to convert popular checkpoints on HuggingFace into NeMo format. Once converted, you can perform fine-tuning or inference on such checkpoints.

Stable Diffusion & ControlNet#

We provide script to convert the Huggingface checkpoint <ADD convert_hf_ckpt_to_nemo.py PATH> to .nemo format, which can then be used within our inference pipeline.

python convert_hf_ckpt_to_nemo.py \
  --ckpt_path <path_to_HF_checkpoints> \
  --hparams_file <path_to_hparams_file> \
  --nemo_file_path <path_to_output_nemo_file> \
  --model_type <model_to_be_converted> \
  --nemo_clip_path <clip_ckpt_in_nemo_format>

  • hparams_file: Config file to be combined with model weights to generate .nemo checkpoint. It can be generated from a dummy run and can be found at, for example, nemo_experiments/stable-diffusion-train/version_0/hparams.yaml.

  • model_type: We support converting stable_diffusion and controlnet checkpoint in this script.

  • nemo_clip_path: It’s required only when the cond_stage_config in hparams_file refer to a NeMo CLIP model. It will be ignored when cond_stage_config refer to Hugginface CLIP. See Model Configuration for more details.

Imagen#

We will provide conversion script if Imagen research team releases their checkpoint in the future. Conversion script for DeepFloyd IF models will be provided in the next release.

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