[content] train_lam.py (python)
from utils.parameter_utils import count_parameters_by_component

ts = int(time.time())


@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 = "data_tfrecords/coinrun"
    checkpoint: str = ""
    # Optimization
    batch_size: int = 36
    vq_beta: float = 0.25
    min_lr: float = 3e-6
    max_lr: float = 3e-5
    warmup_steps: int = 5000
    vq_reset_thresh: int = 50
    # LAM
    model_dim: int = 512
    latent_dim: int = 32
    num_latents: int = 6
    patch_size: int = 16
    num_blocks: int = 8
    num_heads: int = 8
    dropout: float = 0.0
    codebook_dropout: float = 0.0
    # Logging
    log: bool = False
    entity: str = ""
    project: str = ""
    name: str = "train_lam"
    tags: list[str] = field(default_factory=lambda: ["lam"])
    log_interval: int = 5
    log_image_interval: int = 250
    ckpt_dir: str = ""
    log_checkpoint_interval: int = 10000


args = tyro.cli(Args)


def lam_loss_fn(params, state, inputs):
    # --- Compute loss ---
    outputs = state.apply_fn(
        params, inputs, training=True, rngs={"dropout": inputs["rng"]}
    )
    gt_future_frames = inputs["videos"][:, 1:]
    mse = jnp.square(gt_future_frames - outputs["recon"]).mean()
    q_loss = jnp.square(jax.lax.stop_gradient(outputs["emb"]) - outputs["z"]).mean()
    commitment_loss = jnp.square(
        outputs["emb"] - jax.lax.stop_gradient(outputs["z"])
    ).mean()
    loss = mse + q_loss + args.vq_beta * commitment_loss

    # --- Compute validation metrics ---
    gt = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])
    recon = outputs["recon"].clip(0, 1).reshape(-1, *outputs["recon"].shape[2:])
    psnr = pix.psnr(gt, recon).mean()
    ssim = pix.ssim(gt, recon).mean()
    count_fn = jax.vmap(lambda i: (outputs["indices"] == i).sum())
    index_counts = count_fn(jnp.arange(args.num_latents))
    metrics = dict(
        loss=loss,
        mse=mse,
        q_loss=q_loss,
        commitment_loss=commitment_loss,
        psnr=psnr,
        ssim=ssim,
        codebook_usage=(index_counts != 0).mean(),
    )
    return loss, (outputs["recon"], index_counts, metrics)


@jax.jit
def train_step(state, inputs, action_last_active):
    # --- Update model ---
    rng, inputs["rng"] = jax.random.split(inputs["rng"])
    grad_fn = jax.value_and_grad(lam_loss_fn, has_aux=True, allow_int=True)
    (loss, (recon, idx_counts, metrics)), grads = grad_fn(state.params, state, inputs)
    state = state.apply_gradients(grads=grads)

    # --- Reset inactive latent actions ---
    codebook = state.params["params"]["vq"]["codebook"]
    num_codes = len(codebook)
    active_codes = idx_counts != 0.0
    action_last_active = jnp.where(active_codes, 0, action_last_active + 1)
    p_code = active_codes / active_codes.sum()
    reset_idxs = jax.random.choice(rng, num_codes, shape=(num_codes,), p=p_code)
    do_reset = action_last_active >= args.vq_reset_thresh
    new_codebook = jnp.where(
        jnp.expand_dims(do_reset, -1), codebook[reset_idxs], codebook
    )
    state.params["params"]["vq"]["codebook"] = new_codebook
    action_last_active = jnp.where(do_reset, 0, action_last_active)
    return state, loss, recon, action_last_active, 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.PRNGKey(args.seed)
<<<<<<< Updated upstream
=======
    if args.log and jax.process_index() == 0:
        wandb.init(
            entity=args.entity,
            project=args.project,
            name=args.name,
            tags=args.tags,
            group="debug",
            config=args
        )
>>>>>>> Stashed changes

    # --- Initialize model ---
    lam = LatentActionModel(
        in_dim=args.image_channels,
        model_dim=args.model_dim,
        latent_dim=args.latent_dim,
        num_latents=args.num_latents,
        patch_size=args.patch_size,
        num_blocks=args.num_blocks,
        num_heads=args.num_heads,
        dropout=args.dropout,
        codebook_dropout=args.codebook_dropout,
    )
    # Track when each action was last sampled
    action_last_active = jnp.zeros(args.num_latents)
    image_shape = (args.image_height, args.image_width, args.image_channels)
    rng, _rng = jax.random.split(rng)
    inputs = dict(
        videos=jnp.zeros(
            (per_device_batch_size_for_init, args.seq_len, *image_shape),
            dtype=jnp.float32,
        ),
        rng=_rng,
    )
    rng, _rng = jax.random.split(rng)
    init_params = lam.init(_rng, inputs)

    param_counts = count_parameters_by_component(init_params)

    if args.log and jax.process_index() == 0:
        wandb.init(
            entity=args.entity,
            project=args.project,
            name=args.name,
            tags=args.tags,
            group="debug",
            config=args,
        )
        wandb.config.update({"model_param_count": param_counts})

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

    # --- Initialize optimizer ---
    lr_schedule = optax.warmup_cosine_decay_schedule(
        args.min_lr, args.max_lr, args.warmup_steps, args.num_steps
    )
    tx = optax.adamw(learning_rate=lr_schedule, b1=0.9, b2=0.9, weight_decay=1e-4)
    train_state = TrainState.create(apply_fn=lam.apply, params=init_params, tx=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)
    )
    train_state = jax.device_put(train_state, replicated_sharding)
    action_last_active = jax.device_put(action_last_active, replicated_sharding)

    # --- Load checkpoint ---
    step = 0
    if args.checkpoint:
        restore_target = {"model": train_state}
        restore_args = orbax_utils.restore_args_from_target(restore_target)
        train_state.params["params"].update(
            PyTreeCheckpointer()
            .restore(args.checkpoint, item=restore_target, restore_args=restore_args)[
                "model"
            ]
            .params["params"]
        )
        # Assume checkpoint is of the form tokenizer_<timestamp>_<step>
        step += int(args.checkpoint.split("_")[-1])

    # --- TRAIN LOOP ---
    tfrecord_files = [
        os.path.join(args.data_dir, x)
        for x in os.listdir(args.data_dir)
        if x.endswith(".tfrecord")
    ]
    dataloader = get_dataloader(
        # NOTE: We deliberately pass the global batch size
        # The dataloader shards the dataset across all processes
        tfrecord_files,
        args.seq_len,
        args.batch_size,
        *image_shape,
    )
    dataloader = (jax.make_array_from_process_local_data(videos_sharding, elem) for elem in dataloader) # type: ignore
    print(f"Starting training from step {step}...")
    while step < args.num_steps:
        # for videos in dataloader:
        # npy_path = "overfit_dir/single_sample_corner.npy"
        npy_path = "overfit_dir/single_batch_12_elems.npy"
        videos = np.load(npy_path)
        print("batch shape: ", videos.shape)
        while(True):
            # --- Train step ---
            rng, _rng = jax.random.split(rng)