| import argparse |
| import os |
| from train.train import train |
|
|
| from accelerate.logging import get_logger |
|
|
|
|
| def parse_args(input_args=None): |
| parser = argparse.ArgumentParser(description="Main script for training RDT.") |
| parser.add_argument( |
| "--config_path", |
| type=str, |
| default="configs/base.yaml", |
| help="Path to the configuration file. Default is `configs/base.yaml`.", |
| ) |
| parser.add_argument( |
| "--deepspeed", |
| type=str, |
| default=None, |
| help="Enable DeepSpeed and pass the path to its config file or an already initialized DeepSpeed config dictionary", |
| ) |
| parser.add_argument( |
| "--pretrained_text_encoder_name_or_path", |
| type=str, |
| default=None, |
| help="Pretrained text encoder name or path if not the same as model_name", |
| ) |
| parser.add_argument( |
| "--pretrained_vision_encoder_name_or_path", |
| type=str, |
| default=None, |
| help="Pretrained vision encoder name or path if not the same as model_name", |
| ) |
| |
| parser.add_argument( |
| "--output_dir", |
| type=str, |
| default="checkpoints", |
| help="The output directory where the model predictions and checkpoints will be written.", |
| ) |
| parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") |
|
|
| parser.add_argument( |
| "--load_from_hdf5", |
| action="store_true", |
| default=False, |
| help=( |
| "Whether to load the dataset directly from HDF5 files. " |
| "If False, the dataset will be loaded using producer-consumer pattern, " |
| "where the producer reads TFRecords and saves them to buffer, and the consumer reads from buffer." |
| ) |
| ) |
| parser.add_argument( |
| "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader." |
| ) |
| parser.add_argument( |
| "--sample_batch_size", type=int, default=8, help="Batch size (per device) for the sampling dataloader." |
| ) |
| parser.add_argument( |
| "--num_sample_batches", type=int, default=2, help="Number of batches to sample from the dataset." |
| ) |
| parser.add_argument("--num_train_epochs", type=int, default=1) |
| parser.add_argument( |
| "--max_train_steps", |
| type=int, |
| default=None, |
| help="Total number of training steps to perform. If provided, overrides num_train_epochs.", |
| ) |
| parser.add_argument( |
| "--checkpointing_period", |
| type=int, |
| default=500, |
| help=( |
| "Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. " |
| "In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference." |
| "Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components." |
| "See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step" |
| "instructions." |
| ), |
| ) |
| parser.add_argument( |
| "--checkpoints_total_limit", |
| type=int, |
| default=None, |
| help=( |
| "Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`." |
| " See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state" |
| " for more details" |
| ), |
| ) |
| parser.add_argument( |
| "--resume_from_checkpoint", |
| type=str, |
| default=None, |
| help=( |
| "Whether training should be resumed from a previous checkpoint. Use a path saved by" |
| ' `--checkpointing_period`, or `"latest"` to automatically select the last available checkpoint.' |
| ), |
| ) |
| parser.add_argument( |
| "--pretrained_model_name_or_path", |
| type=str, |
| default=None, |
| help=( |
| "Path or name of a pretrained checkpoint to load the model from.\n" |
| " This can be either:\n" |
| " - a string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co, e.g., `robotics-diffusion-transformer/rdt-1b`,\n" |
| " - a path to a *directory* containing model weights saved using [`~RDTRunner.save_pretrained`] method, e.g., `./my_model_directory/`.\n" |
| " - a path to model checkpoint (*.pt), .e.g, `my_model_directory/checkpoint-10000/pytorch_model/mp_rank_00_model_states.pt`" |
| " - `None` if you are randomly initializing model using configuration at `config_path`." |
| ) |
| ) |
| parser.add_argument( |
| "--gradient_accumulation_steps", |
| type=int, |
| default=1, |
| help="Number of updates steps to accumulate before performing a backward/update pass.", |
| ) |
| parser.add_argument( |
| "--gradient_checkpointing", |
| action="store_true", |
| help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", |
| ) |
| parser.add_argument( |
| "--learning_rate", |
| type=float, |
| default=5e-6, |
| help="Initial learning rate (after the potential warmup period) to use.", |
| ) |
| parser.add_argument( |
| "--cond_mask_prob", |
| type=float, |
| default=0.1, |
| help=( |
| "The probability to randomly mask the conditions (except states) during training. " |
| "If set to 0, the conditions are not masked." |
| ), |
| ) |
| parser.add_argument( |
| "--cam_ext_mask_prob", |
| type=float, |
| default=-1.0, |
| help=( |
| "The probability to randomly mask the external camera image during training. " |
| "If set to < 0, the external camera image is masked with the probability of `cond_mask_prob`." |
| ), |
| ) |
| parser.add_argument( |
| "--state_noise_snr", |
| type=float, |
| default=None, |
| help=( |
| "The signal-to-noise ratio (SNR, unit: dB) for adding noise to the states. " |
| "Default is None, which means no noise is added." |
| ), |
| ) |
| parser.add_argument( |
| "--image_aug", |
| action="store_true", |
| default=False, |
| help="Whether or not to apply image augmentation (ColorJitter, blur, noise, etc) to the input images.", |
| ) |
| parser.add_argument( |
| "--precomp_lang_embed", |
| action="store_true", |
| default=False, |
| help="Whether or not to use precomputed language embeddings.", |
| ) |
| parser.add_argument( |
| "--scale_lr", |
| action="store_true", |
| default=False, |
| help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", |
| ) |
| parser.add_argument( |
| "--lr_scheduler", |
| type=str, |
| default="constant", |
| help=( |
| 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' |
| ' "constant", "constant_with_warmup"]' |
| ), |
| ) |
| parser.add_argument( |
| "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler." |
| ) |
| parser.add_argument( |
| "--lr_num_cycles", |
| type=int, |
| default=1, |
| help="Number of hard resets of the lr in cosine_with_restarts scheduler.", |
| ) |
| parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.") |
| parser.add_argument( |
| "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes." |
| ) |
| parser.add_argument( |
| "--dataloader_num_workers", |
| type=int, |
| default=0, |
| help=( |
| "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process." |
| ), |
| ) |
| parser.add_argument("--alpha", type=float, default=0.9, help="The moving average coefficient for each dataset's loss.") |
| parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.") |
| parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.") |
| parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.") |
| parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer") |
| parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") |
| parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") |
| parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.") |
| parser.add_argument( |
| "--hub_model_id", |
| type=str, |
| default=None, |
| help="The name of the repository to keep in sync with the local `output_dir`.", |
| ) |
| parser.add_argument( |
| "--logging_dir", |
| type=str, |
| default="logs", |
| help=( |
| "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" |
| " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." |
| ), |
| ) |
| parser.add_argument( |
| "--allow_tf32", |
| action="store_true", |
| help=( |
| "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" |
| " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" |
| ), |
| ) |
| parser.add_argument( |
| "--report_to", |
| type=str, |
| default="tensorboard", |
| help=( |
| 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' |
| ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.' |
| ), |
| ) |
| parser.add_argument( |
| "--sample_period", |
| type=int, |
| default=-1, |
| help=( |
| "Run sampling every X steps. During the sampling phase, the model will sample a trajectory" |
| " and report the error between the sampled trajectory and groud-truth trajectory" |
| " in the training batch." |
| ), |
| ) |
| parser.add_argument( |
| "--mixed_precision", |
| type=str, |
| default=None, |
| choices=["no", "fp16", "bf16"], |
| help=( |
| "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" |
| " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" |
| " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." |
| ), |
| ) |
|
|
| parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") |
| parser.add_argument( |
| "--set_grads_to_none", |
| action="store_true", |
| help=( |
| "Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain" |
| " behaviors, so disable this argument if it causes any problems. More info:" |
| " https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html" |
| ), |
| ) |
|
|
| parser.add_argument('--dataset_type', |
| type=str, |
| default="pretrain", |
| required=False, |
| help="Whether to load the pretrain dataset or finetune dataset." |
| ) |
|
|
| if input_args is not None: |
| args = parser.parse_args(input_args) |
| else: |
| args = parser.parse_args() |
|
|
| env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) |
| if env_local_rank != -1 and env_local_rank != args.local_rank: |
| args.local_rank = env_local_rank |
|
|
| return args |
|
|
|
|
| if __name__ == "__main__": |
| logger = get_logger(__name__) |
| args = parse_args() |
| train(args, logger) |
|
|
