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from dataclasses import dataclass\nimport os\nimport time\n\nimport einops\nfrom flax.training import orbax_utils\nfrom flax.training.train_state import TrainState\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax\nfrom orbax.checkpoint import PyTreeCheckpointer\nimport numpy as np\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\n\nfrom genie import Genie, restore_genie_components\nfrom models.tokenizer import TokenizerVQVAE\nfrom models.lam import LatentActionModel\nfrom utils.dataloader import get_dataloader\n\nts = int(time.time())\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_000\n seed: int = 0\n seq_len: int = 16\n image_channels: int = 3\n image_height: int = 90\n image_width: int = 160\n data_dir: str = "data_tfrecords/coinrun"\n # Optimization\n batch_size: int = 36\n min_lr: float = 3e-6\n max_lr: float = 3e-5\n warmup_steps: int = 5000\n # Tokenizer\n tokenizer_dim: int = 512\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 8\n tokenizer_num_heads: int = 8\n tokenizer_checkpoint: str = ""\n # LAM\n lam_dim: int = 512\n latent_action_dim: int = 32\n num_latent_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 8\n lam_num_heads: int = 8\n lam_checkpoint: str = ""\n # Dynamics\n dyna_dim: int = 512\n dyna_num_blocks: int = 12\n dyna_num_heads: int = 8\n dropout: float = 0.0\n mask_limit: float = 0.5\n # Logging\n log: bool = False\n entity: str = ""\n project: str = ""\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = ""\n log_checkpoint_interval: int = 25000\n log_gradients: bool = False\n\n\nargs = tyro.cli(Args)\n\n\ndef dynamics_loss_fn(params, state, inputs):\n """Compute masked dynamics loss"""\n outputs = state.apply_fn(\n params,\n inputs,\n training=True,\n rngs={"params": inputs["rng"], "dropout": inputs["dropout_rng"]},\n )\n mask = outputs["mask"]\n ce_loss = optax.softmax_cross_entropy_with_integer_labels(\n outputs["token_logits"], outputs["video_tokens"]\n )\n ce_loss = (mask * ce_loss).sum() / mask.sum()\n acc = outputs["token_logits"].argmax(-1) == outputs["video_tokens"]\n acc = (mask * acc).sum() / mask.sum()\n select_probs = jax.nn.softmax(outputs["token_logits"])\n metrics = dict(\n cross_entropy_loss=ce_loss,\n masked_token_accuracy=acc,\n select_logit=outputs["token_logits"].max(-1).mean(),\n select_p=select_probs.max(-1).mean(),\n entropy=jax.scipy.special.entr(select_probs).sum(-1).mean(),\n )\n return ce_loss, (outputs["recon"], metrics)\n\n\n@jax.jit\ndef train_step(state, inputs):\n """Update state and compute metrics"""\n grad_fn = jax.value_and_grad(dynamics_loss_fn, has_aux=True, allow_int=True)\n (loss, (recon, metrics)), grads = grad_fn(state.params, state, inputs)\n state = state.apply_gradients(grads=grads)\n if args.log_gradients:\n metrics["gradients_std/"] = jax.tree.map(\n lambda x: x.std(), grads["params"]["dynamics"]\n )\n return state, loss, recon, metrics\n\n\nif __name__ == "__main__":\n jax.distributed.initialize()\n num_devices = jax.device_count()\n if num_devices == 0:\n raise ValueError("No JAX devices found.")\n print(f"Running on {num_devices} devices.")\n\n if args.batch_size % num_devices != 0:\n raise ValueError(\n f"Global batch size {args.batch_size} must be divisible by "\n f"number of devices {num_devices}."\n )\n\n per_device_batch_size_for_init = args.batch_size // num_devices\n\n rng = jax.random.PRNGKey(args.seed)\n if args.log and jax.process_index() == 0:\n wandb.init(entity=args.entity, project=args.project, group="debug", config=args)\n\n # --- Initialize model ---\n genie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n latent_action_dim=args.latent_action_dim,\n num_latent_actions=args.num_latent_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n # Dynamics\n dyna_dim=args.dyna_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n dropout=args.dropout,\n mask_limit=args.mask_limit,\n )\n rng, _rng = jax.random.split(rng)\n image_shape = (args.image_height, args.image_width, args.image_channels)\n dummy_inputs = dict(\n videos=jnp.zeros(\n (per_device_batch_size_for_init, args.seq_len, *image_shape),\n dtype=jnp.float32,\n ),\n action=jnp.zeros(\n (per_device_batch_size_for_init, args.seq_len), dtype=jnp.float32\n ),\n mask_rng=_rng,\n )\n rng, _rng = jax.random.split(rng)\n init_params = genie.init(_rng, dummy_inputs)\n\n # --- Initialize optimizer ---\n lr_schedule = optax.warmup_cosine_decay_schedule(\n args.min_lr, args.max_lr, args.warmup_steps, args.num_steps\n )\n tx = optax.adamw(learning_rate=lr_schedule, b1=0.9, b2=0.9, weight_decay=1e-4)\n train_state = TrainState.create(apply_fn=genie.apply, params=init_params, tx=tx)\n\n device_mesh_arr = create_device_mesh((num_devices,))\n mesh = Mesh(devices=device_mesh_arr, axis_names=("data",))\n\n replicated_sharding = NamedSharding(mesh, PartitionSpec())\n train_state = jax.device_put(train_state, replicated_sharding)\n\n # --- Restore checkpoint ---\n train_state = restore_genie_components(\n train_state, replicated_sharding, dummy_inputs, rng, args\n )\n\n # --- TRAIN LOOP ---\n tfrecord_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith(".tfrecord")\n ]\n dataloader = get_dataloader(\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n tfrecord_files,\n args.seq_len,\n args.batch_size,\n *image_shape,\n )\n step = 0\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n rng, _rng, _rng_dropout, _rng_mask = jax.random.split(rng, 4)\n\n videos_sharding = NamedSharding(\n mesh, PartitionSpec("data", None, None, None, None)\n )\n videos = jax.make_array_from_process_local_data(videos_sharding, videos)\n\n inputs = dict(\n videos=videos,\n rng=_rng,\n dropout_rng=_rng_dropout,\n mask_rng=_rng_mask,\n )\n start_time = time.time()\n train_state, loss, recon, metrics = train_step(train_state, inputs)\n elapsed_time = (time.time() - start_time) * 1000\n print(f"Step {step}, loss: {loss}, step time: {elapsed_time}ms")\n step += 1\n\n # --- Logging ---\n if args.log:\n if step % args.log_interval == 0 and jax.process_index() == 0:\n wandb.log(\n {\n "loss": loss,\n "step": step,\n "step_time_ms": elapsed_time,\n **metrics,\n }\n )\n if step % args.log_image_interval == 0:\n gt_seq = inputs["videos"][0]\n recon_seq = recon[0].clip(0, 1)\n comparison_seq = jnp.concatenate((gt_seq, recon_seq), axis=1)\n comparison_seq = einops.rearrange(\n comparison_seq * 255, "t h w c -> h (t w) c"\n )\n if jax.process_index() == 0:\n log_images = dict(\n image=wandb.Image(np.asarray(gt_seq[args.seq_len - 1])),\n recon=wandb.Image(np.asarray(recon_seq[args.seq_len - 1])),\n true_vs_recon=wandb.Image(\n np.asarray(comparison_seq.astype(np.uint8))\n ),\n )\n wandb.log(log_images)\n if step % args.log_checkpoint_interval == 0:\n ckpt = {"model": train_state}\n orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n save_args = orbax_utils.save_args_from_target(ckpt)\n orbax_checkpointer.save(\n os.path.join(os.getcwd(), args.ckpt_dir, f"genie_{ts}_{step}"),\n ckpt,\n save_args=save_args,\n )\n if step >= args.num_steps:\n break\n
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