[content] train_dynamics.py (python)
from genie import Genie, restore_genie_components
from utils.dataloader import get_dataloader
from utils.lr_utils import get_lr_schedule
from utils.parameter_utils import count_parameters_by_component


@dataclass
class Args:
    # Experiment
    num_steps: int = 200_000
    seed: int = 0
    seq_len: int = 16
    image_channels: int = 3
    image_height: int = 90
    image_width: int = 160
    data_dir: str = ""
    save_ckpt: bool = False
    restore_ckpt: bool = False
    # Optimization
    batch_size: int = 36
    init_lr: float = 0.0
    max_lr: float = 3e-5
    decay_end: float = 0.0
    wsd_decay_steps: int = (
        10000  # NOTE: wsd_decay_steps will only be used when using a wsd-schedule
    )
    warmup_steps: int = 5000
    lr_schedule: str = "wsd"  # supported options: wsd, cos
    # Tokenizer
    tokenizer_dim: int = 512
    tokenizer_ffn_dim: int = 2048
    latent_patch_dim: int = 32
    num_patch_latents: int = 1024
    patch_size: int = 4
    tokenizer_num_blocks: int = 4
    tokenizer_num_heads: int = 8
    tokenizer_checkpoint: str = ""
    # LAM
    lam_dim: int = 512
    lam_ffn_dim: int = 2048
    latent_action_dim: int = 32
    num_latent_actions: int = 6
    lam_patch_size: int = 16
    lam_num_blocks: int = 4
    lam_num_heads: int = 8
    lam_checkpoint: str = ""
    # Dynamics
    dyna_dim: int = 512
    dyna_ffn_dim: int = 2048
    dyna_num_blocks: int = 6
    dyna_num_heads: int = 8
    dropout: float = 0.0
    mask_limit: float = 0.5
    param_dtype = jnp.float32
    dtype = jnp.bfloat16
    use_flash_attention: bool = True
    # Logging
    log: bool = False
    entity: str = ""
    project: str = ""
    name: str = "train_dynamics"
    tags: list[str] = field(default_factory=lambda: ["dynamics"])
    log_interval: int = 5
    log_image_interval: int = 250
    ckpt_dir: str = ""
    log_checkpoint_interval: int = 25000
    log_checkpoint_keep_period: int = 20000
    log_gradients: bool = False
    wandb_id: str = ""


args = tyro.cli(Args)


def dynamics_loss_fn(
    model: Genie, inputs: dict
) -> tuple[jax.Array, tuple[jax.Array, dict]]:
    """Compute masked dynamics loss"""
    inputs["videos"] = inputs["videos"].astype(args.dtype) / 255.0
    model.train()
    outputs = model(inputs, training=True)
    mask = outputs["mask"]
    outputs["token_logits"] = outputs["token_logits"].astype(jnp.float32)
    ce_loss = optax.softmax_cross_entropy_with_integer_labels(
        outputs["token_logits"], outputs["video_tokens"]
    )
    ce_loss = (mask * ce_loss).sum() / mask.sum()
    acc = outputs["token_logits"].argmax(-1) == outputs["video_tokens"]
    acc = (mask * acc).sum() / mask.sum()
    select_probs = jax.nn.softmax(outputs["token_logits"])
    gt = inputs["videos"].clip(0, 1).reshape(-1, *inputs["videos"].shape[2:])
    recon = outputs["recon"].clip(0, 1).reshape(-1, *outputs["recon"].shape[2:])
    psnr = jnp.asarray(pix.psnr(gt, recon)).mean()
    ssim = jnp.asarray(pix.ssim(gt, recon)).mean()
    _, index_counts_lam = jnp.unique_counts(
        jnp.ravel(outputs["lam_indices"]), size=args.num_latent_actions, fill_value=0
    )
    _, index_counts_tokenizer = jnp.unique_counts(
        jnp.ravel(outputs["video_tokens"]), size=args.num_patch_latents, fill_value=0
    )
    codebook_usage_lam = (index_counts_lam != 0).mean()
    codebook_usage_tokenizer = (index_counts_tokenizer != 0).mean()
    metrics = dict(
        cross_entropy_loss=ce_loss,
        masked_token_accuracy=acc,
        select_logit=outputs["token_logits"].max(-1).mean(),
        select_p=select_probs.max(-1).mean(),
        entropy=jax.scipy.special.entr(select_probs).sum(-1).mean(),
        psnr=psnr,
        ssim=ssim,
        codebook_usage_lam=codebook_usage_lam,
        codebook_usage_tokenizer=codebook_usage_tokenizer,
    )
    return ce_loss, (outputs["recon"], metrics)


@nnx.jit
def train_step(
    model: Genie, optimizer: nnx.Optimizer, inputs: dict
) -> tuple[jax.Array, jax.Array, dict]:
    """Update state and compute metrics"""

    def loss_fn(model: Genie) -> tuple[jax.Array, tuple[jax.Array, dict]]:
        return dynamics_loss_fn(model, inputs)

    (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(model)
    optimizer.update(grads)
    if args.log_gradients:
        metrics["gradients_std/"] = jax.tree.map(
            lambda x: x.std(), grads["params"]["dynamics"]
        )
    return loss, recon, metrics


if __name__ == "__main__":
    jax.distributed.initialize()
    num_devices = jax.device_count()
    if num_devices == 0:
        raise ValueError("No JAX devices found.")
    print(f"Running on {num_devices} devices.")

    if args.batch_size % num_devices != 0:
        raise ValueError(
            f"Global batch size {args.batch_size} must be divisible by "
            f"number of devices {num_devices}."
        )

    per_device_batch_size_for_init = args.batch_size // num_devices

    rng = jax.random.key(args.seed)

    # --- Initialize model ---
    rng, _rng = jax.random.split(rng)
    rngs = nnx.Rngs(_rng)
    genie = Genie(
        # Tokenizer
        in_dim=args.image_channels,
        tokenizer_dim=args.tokenizer_dim,
        tokenizer_ffn_dim=args.tokenizer_ffn_dim,
        latent_patch_dim=args.latent_patch_dim,
        num_patch_latents=args.num_patch_latents,
        patch_size=args.patch_size,
        tokenizer_num_blocks=args.tokenizer_num_blocks,
        tokenizer_num_heads=args.tokenizer_num_heads,
        # LAM
        lam_dim=args.lam_dim,
        lam_ffn_dim=args.lam_ffn_dim,
        latent_action_dim=args.latent_action_dim,
        num_latent_actions=args.num_latent_actions,
        lam_patch_size=args.lam_patch_size,
        lam_num_blocks=args.lam_num_blocks,
        lam_num_heads=args.lam_num_heads,
        lam_co_train=not args.lam_checkpoint,
        # Dynamics
        dyna_dim=args.dyna_dim,
        dyna_ffn_dim=args.dyna_ffn_dim,
        dyna_num_blocks=args.dyna_num_blocks,
        dyna_num_heads=args.dyna_num_heads,
        dropout=args.dropout,
        mask_limit=args.mask_limit,
        param_dtype=args.param_dtype,
        dtype=args.dtype,
        use_flash_attention=args.use_flash_attention,
        rngs=rngs,
    )

    _, params, _ = nnx.split(genie, nnx.Param, ...)
    param_counts = count_parameters_by_component(params)

    if args.log and jax.process_index() == 0:
        wandb_init_kwargs = {
            "entity": args.entity,
            "project": args.project,
            "name": args.name,
            "tags": args.tags,
            "group": "debug",
            "config": args,
        }

        if args.wandb_id:
            wandb_init_kwargs.update(
                {
                    "id": args.wandb_id,
                    "resume": "allow",
                }
            )
        wandb.init(**wandb_init_kwargs)

        wandb.config.update({"model_param_count": param_counts})

    print("Parameter counts:")
    print(param_counts)

    # --- Initialize optimizer ---
    lr_schedule = get_lr_schedule(
        args.lr_schedule,
        args.init_lr,
        args.max_lr,
        args.decay_end,
        args.num_steps,
        args.warmup_steps,
        args.wsd_decay_steps,
    )
    tx = optax.adamw(
        learning_rate=lr_schedule,
        b1=0.9,
        b2=0.9,
        weight_decay=1e-4,
        mu_dtype=args.dtype,
    )
    optimizer = nnx.Optimizer(genie, tx)

    # FIXME: switch to create_hybrid_device_mesh for runs spanning multiple nodes
    device_mesh_arr = create_device_mesh((num_devices,))
    mesh = Mesh(devices=device_mesh_arr, axis_names=("data",))

    replicated_sharding = NamedSharding(mesh, PartitionSpec())
    videos_sharding = NamedSharding(mesh, PartitionSpec("data", None, None, None, None))

    model_state = nnx.state(optimizer.model)
    model_sharded_state = jax.lax.with_sharding_constraint(
        model_state, replicated_sharding
    )
    nnx.update(optimizer.model, model_sharded_state)
    optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)
    optimizer_sharded_state = jax.lax.with_sharding_constraint(
        optimizer_state, replicated_sharding
    )
    nnx.update(optimizer, optimizer_sharded_state)

    # --- Initialize checkpoint manager ---
    step = 0
    handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()
    handler_registry.add(
        "model_state", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler
    )
    handler_registry.add(
        "model_state", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler
    )
    handler_registry.add(
        "dataloader_state",
        grain.checkpoint.CheckpointSave,
        cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),
    )
    handler_registry.add(
        "dataloader_state",
        grain.checkpoint.CheckpointRestore,
        cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),
    )

    checkpoint_options = ocp.CheckpointManagerOptions(
        save_interval_steps=args.log_checkpoint_interval,
        max_to_keep=3,
        keep_period=args.log_checkpoint_keep_period,
        step_format_fixed_length=6,
        cleanup_tmp_directories=True,
    )

    checkpoint_manager = ocp.CheckpointManager(
        args.ckpt_dir,
        options=checkpoint_options,
        handler_registry=handler_registry,
    )

    # --- Create DataLoaderIterator from dataloader ---
    image_shape = (args.image_height, args.image_width, args.image_channels)
    array_record_files = [
        os.path.join(args.data_dir, x)
        for x in os.listdir(args.data_dir)
        if x.endswith(".array_record")
    ]
    grain_dataloader = get_dataloader(
        array_record_files,
        args.seq_len,
        # NOTE: We deliberately pass the global batch size
        # The dataloader shards the dataset across all processes
        args.batch_size,
        *image_shape,
        num_workers=8,
        prefetch_buffer_size=1,
        seed=args.seed,
    )
    initial_state = grain_dataloader._create_initial_state()
    grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)

    # --- Restore checkpoint ---
    if args.restore_ckpt:
        abstract_optimizer = nnx.eval_shape(lambda: optimizer)
        abstract_optimizer_state = nnx.state(abstract_optimizer)
        restored = checkpoint_manager.restore(
            checkpoint_manager.latest_step(),
            args=ocp.args.Composite(
                model_state=ocp.args.PyTreeRestore(abstract_optimizer_state),  # type: ignore
                dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator),  # type: ignore
            ),
        )
        restored_optimizer_state = restored["model_state"]
        nnx.update(optimizer, restored_optimizer_state)
        grain_iterator = restored["dataloader_state"]
        step = checkpoint_manager.latest_step() or 0
        print(f"Restored dataloader and model state from step {step}")
    else:
        # Restore from pre-trained tokenizer (and LAM)
        optimizer = restore_genie_components(optimizer, replicated_sharding, rng, args)
        # NOTE: We have to remove the (unused) tokenizer vq dropout due flax.nnx lazily initializing modules.
        # Specifically, the first dynamics model checkpoint will contain the vq dropout module,
        # but the first full restore will fail due to nnx not initializing the module when
        # dropout is set to 0.0.
        del optimizer.model.tokenizer.vq.drop

    # --- TRAIN LOOP ---
    dataloader = (
        jax.make_array_from_process_local_data(videos_sharding, elem)
        for elem in grain_iterator
    )
    print(f"Starting training from step {step}...")
    while step < args.num_steps:
        for videos in dataloader:
            # --- Train step ---
            rng, _rng_mask = jax.random.split(rng, 2)
            inputs = dict(videos=videos, mask_rng=_rng_mask)
            loss, recon, metrics = train_step(genie, optimizer, inputs)