Important

NeMo 2.0 is an experimental feature and currently released in the dev container only: nvcr.io/nvidia/nemo:dev. Please refer to NeMo 2.0 overview for information on getting started.

Common Configuration Files

This section provides a detailed overview of the NeMo configuration file setup specific to models within the NeMo Multimodal Language Model collection. For foundational knowledge about setting up and executing experiments common to all NeMo models, such as the Experiment Manager and PyTorch Lightning trainer parameters, refer to the core documentation.

Within the configuration files of the NeMo Multimodal Language Model, details concerning dataset(s), augmentation, optimization parameters, and model architectural specifications are central. This page explores each of these aspects.

Discover exemplary configuration files for all NeMo Multimodal Language Model scripts in the config directory of the examples.

Dataset Configuration

The configuration file delineates parameters for training, validation, and testing datasets under the train, validation, and test sections, respectively. Depending on the specific task, the configuration might include parameters related to dataset augmentations, image resolution filtering, among others.

All initialization parameters supported by the Dataset class utilized in the experiment can be defined in the config file. For a comprehensive list of Datasets and their associated parameters, consult the Datasets section of the API.

A representative training configuration appears as:

data:
    num_workers: 16 # Number of workers for the dataloader
    train:
      dataset_path: # Paths to wdinfo files specifying training datasets
        - dataset1.pkl
        - dataset2.pkl
    validation: # Paths to validation dataset files (pkl or tar)
      dataset_path:
        - dataset3.pkl
    webdataset:
      use_webdataset: True     # Enable the use of webdataset
      infinite_sampler: False  # Set to False for correct training resumption and accurate epoch counting
      local_root_path: ???     # Specify the directory where the dataset is located
      verbose: False           # Enable verbose debugging information if set to True

As outlined in Datasets, the utilization of the webdataset format is imperative for the vision-language contrastive training pipeline. Due to the extensive data requirements, it’s beneficial to follow the Webdataset Multinode Training Guideline for recommended practices.

data:
  train:
    augmentations:
      resize_smallest_side: 256 # Adjust the image's smallest side to the specified dimension
      center_crop_h_w: 256, 256 # Perform center cropping
      horizontal_flip: False # Apply horizontal flip
    filterings:
      resolution:
        method: larger
        value: 256

This configuration uses the same data class as the text2image setup. Enabling the train.filterings and train.augmentations sections offers flexibility to filter specific images (and their text pairs) for particular requirements without the need to recreate the entire webdataset. The provided example demonstrates filtering to retain only images with resolutions exceeding 256x256 for training. To concatenate multiple webdatasets, simply list all corresponding wdinfo files under train.dataset_path.

Trainer Configuration

This section outlines arguments for the Pytorch Lightning Trainer Object.

trainer:
  devices: 1 # number of GPUs (0 for CPU), or list of the GPUs to use e.g. [0, 1]
  num_nodes: 1
  max_epochs: -1
  max_steps: 2500000 # precedence over max_epochs
  logger: False  # Provided by exp_manager
  precision: bf16 # Should be set to 16 for O1 and O2 to enable the AMP.
  accelerator: gpu
  log_every_n_steps: 5  # Interval of logging.
  resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc.
  num_sanity_val_steps: 10 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it
  enable_checkpointing: False # Provided by exp_manager
  accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models
  gradient_clip_val: 1.0
  benchmark: False
  enable_model_summary: True

For a detailed list of arguments, refer to the Pytorch Lightning Trainer API section.

Experiment Manager Configurations

The NeMo Experiment Manager provides a streamlined approach to manage various tasks such as logging, saving, and resuming.

exp_manager:
  exp_dir: null  # exp_dir for your experiment, if None, defaults to "./nemo_experiments"
  name: ${name}
  create_wandb_logger: True
  wandb_logger_kwargs: # Whether you want exp_manger to create a Wandb logger
    name: training-session
    project: text2img
    group: nemo
    resume: True
  create_tensorboard_logger: True  # Whether you want exp_manger to create a tb logger
  create_checkpoint_callback: True  # Whether you want exp_manager to create a modelcheckpoint callback
  checkpoint_callback_params:
    monitor: reduced_train_loss
    save_top_k: 5
    every_n_epochs: 0 # Save checkpoint frequency.
    every_n_train_steps: 1000 # Mutually exclusive with every_n_epochs. It is recommended to set this if training on large-scale dataset.
    filename: '${name}--{reduced_train_loss:.2f}-{step}-{consumed_samples}'
  resume_if_exists: True
  resume_ignore_no_checkpoint: True
  resume_from_checkpoint: ${model.resume_from_checkpoint}
  ema:
    enable: True
    decay: 0.9999
    validate_original_weights: False
    every_n_steps: 1
    cpu_offload: False

Optimizer Configurations

optim:
  name: fused_adam
  lr: 0.0001
  eps: 1e-8
  betas: [ 0.9, 0.999 ]
  weight_decay: 0.01
  sched:
    name: WarmupPolicy
    warmup_steps: 10000
    warmup_ratio: null

The default optimizer used is fused_adam. For details on all supported optimizers, refer to the NeMo user guide. The learning rate scheduler can be specified in the optim.sched section.

Model Configurations

Each configuration file should detail the model architecture used for the experiment.

The parameters commonly shared across most multimodal language models include:

Parameter	Datatype	Description
micro_batch_size	int	Micro batch size that fits on each GPU
global_batch_size	int	Global batch size considering gradient accumulation and data parallelism
tensor_model_parallel_size	int	Intra-layer model parallelism
pipeline_model_parallel_size	int	Inter-layer model parallelism
seed	int	Seed used in training

CLIP

For model-specific configurations, refer to clip.