[content] train_tokenizer.py (python)
    data_dir: str = "data_tfrecords/coinrun"
    checkpoint: str = ""
    # Optimization
    vq_beta: float = 0.25
    batch_size: int = 48
    min_lr: float = 0.0
    max_lr: float = 3e-4
    warmup_steps: int = 10000
    # Tokenizer
    model_dim: int = 512
    latent_dim: int = 32
    num_latents: int = 1024
    patch_size: int = 4
    num_blocks: int = 8
    num_heads: int = 8
    dropout: float = 0.0
    codebook_dropout: float = 0.01
    # Logging
    log_dir: str = "logs/" 
    loggers: list[str] = field(default_factory=lambda: ["console"])
    entity: str = ""
    project: str = ""
    name: str = "train_tokenizer"
    tags: list[str] = field(default_factory=lambda: ["tokenizer"])
    log_interval: int = 5
    log_image_interval: int = 250
    ckpt_dir: str = ""
    log_checkpoint_interval: int = 10000
    log_gradients: bool = False


args = tyro.cli(Args)


def tokenizer_loss_fn(params, state, inputs):
    # --- Compute loss ---
    outputs = state.apply_fn(
        params,
        inputs,
        training=True,
        rngs={"params": inputs["rng"], "dropout": inputs["dropout_rng"]},
    )
    mse = jnp.square(inputs["videos"] - 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 = inputs["videos"].clip(0, 1).reshape(-1, *inputs["videos"].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()
    _, index_counts = jnp.unique_counts(
        jnp.ravel(outputs["indices"]), size=args.num_latents, fill_value=0
    )
    codebook_usage = (index_counts != 0).mean()
    metrics = dict(
        loss=loss,
        mse=mse,
        q_loss=q_loss,
        commitment_loss=commitment_loss,
        psnr=psnr,
        ssim=ssim,
        codebook_usage=codebook_usage,
    )
    return loss, (outputs["recon"], metrics)


@jax.jit
def train_step(state, inputs):
    grad_fn = jax.value_and_grad(tokenizer_loss_fn, has_aux=True, allow_int=True)
    (loss, (recon, metrics)), grads = grad_fn(state.params, state, inputs)
    state = state.apply_gradients(grads=grads)
    if args.log_gradients:
        metrics["encoder_gradients_std/"] = jax.tree.map(
            lambda x: x.std(), grads["params"]["encoder"]
        )
        metrics["vq_gradients_std/"] = jax.tree.map(
            lambda x: x.std(), grads["params"]["vq"]
        )
        metrics["decoder_gradients_std/"] = jax.tree.map(
            lambda x: x.std(), grads["params"]["decoder"]
        )
    return state, 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.PRNGKey(args.seed)

    # --- Initialize model ---
    tokenizer = TokenizerVQVAE(
        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,
    )
    rng, _rng = jax.random.split(rng)
    image_shape = (args.image_height, args.image_width, args.image_channels)
    inputs = dict(
        videos=jnp.zeros(
            (per_device_batch_size_for_init, args.seq_len, *image_shape),
            dtype=jnp.float32,
        ),
    )
    init_params = tokenizer.init(_rng, inputs)

    param_counts = count_parameters_by_component(init_params)

    if jax.process_index() == 0:
        cfg = vars(args).copy()
        cfg["model_param_count"] = param_counts
        logger = CompositeLogger(args.loggers, cfg)

    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=tokenizer.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)

    # --- 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,
        seed=args.seed,
    )
    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:
            # --- Train step ---
            rng, _rng, _rng_dropout = jax.random.split(rng, 3)

            inputs = dict(videos=videos, rng=_rng, dropout_rng=_rng_dropout)
            train_state, loss, recon, metrics = train_step(train_state, inputs)
            step += 1

            # --- Logging ---
            if step % args.log_interval == 0 and jax.process_index() == 0:
                logger.log_metrics(
                    {
                        "loss": loss,
                        **metrics,
                    },
                    step
                )
            if step % args.log_image_interval == 0:
                gt_seq = inputs["videos"][0]
                recon_seq = recon[0].clip(0, 1)
                comparison_seq = jnp.concatenate((gt_seq, recon_seq), axis=1)
                comparison_seq = einops.rearrange(
                    comparison_seq * 255, "t h w c -> h (t w) c"
                )
                # NOTE: Process-dependent control flow deliberately happens
                # after indexing operation since it must not contain code
                # sections that lead to cross-accelerator communication.
                if jax.process_index() == 0:
                    log_images = dict(
                        image=np.asarray(gt_seq[0]).astype(np.uint8),
                        recon=np.asarray(recon_seq[0]).astype(np.uint8),