[content] genie.py (python)
from models.dynamics import DynamicsMaskGIT
from models.lam import LatentActionModel
from models.tokenizer import TokenizerVQVAE


class Genie(nnx.Module):
    """Genie model"""

    def __init__(
        self,
        in_dim: int,
        tokenizer_dim: int,
        tokenizer_ffn_dim: int,
        latent_patch_dim: int,
        num_patch_latents: int,
        patch_size: int,
        tokenizer_num_blocks: int,
        tokenizer_num_heads: int,
        lam_dim: int,
        lam_ffn_dim: int,
        latent_action_dim: int,
        num_latent_actions: int,
        lam_patch_size: int,
        lam_num_blocks: int,
        lam_num_heads: int,
        lam_co_train: bool,
        dyna_dim: int,
        dyna_ffn_dim: int,
        dyna_num_blocks: int,
        dyna_num_heads: int,
        param_dtype: jnp.dtype,
        dtype: jnp.dtype,
        use_flash_attention: bool,
        rngs: nnx.Rngs,
        dropout: float = 0.0,
        mask_limit: float = 0.0,
    ):
        # --- Tokenizer ---
        self.in_dim = in_dim
        self.tokenizer_dim = tokenizer_dim
        self.tokenizer_ffn_dim = tokenizer_ffn_dim
        self.latent_patch_dim = latent_patch_dim
        self.num_patch_latents = num_patch_latents
        self.patch_size = patch_size
        self.tokenizer_num_blocks = tokenizer_num_blocks
        self.tokenizer_num_heads = tokenizer_num_heads
        # --- LAM ---
        self.lam_dim = lam_dim
        self.lam_ffn_dim = lam_ffn_dim
        self.latent_action_dim = latent_action_dim
        self.num_latent_actions = num_latent_actions
        self.lam_patch_size = lam_patch_size
        self.lam_num_blocks = lam_num_blocks
        self.lam_num_heads = lam_num_heads
        self.lam_co_train = lam_co_train
        # --- Dynamics ---
        self.dyna_dim = dyna_dim
        self.dyna_ffn_dim = dyna_ffn_dim
        self.dyna_num_blocks = dyna_num_blocks
        self.dyna_num_heads = dyna_num_heads
        self.param_dtype = param_dtype
        self.dtype = dtype
        self.use_flash_attention = use_flash_attention
        self.dropout = dropout
        self.mask_limit = mask_limit

        self.tokenizer = TokenizerVQVAE(
            in_dim=self.in_dim,
            model_dim=self.tokenizer_dim,
            ffn_dim=self.tokenizer_ffn_dim,
            latent_dim=self.latent_patch_dim,
            num_latents=self.num_patch_latents,
            patch_size=self.patch_size,
            num_blocks=self.tokenizer_num_blocks,
            num_heads=self.tokenizer_num_heads,
            dropout=0.0,
            codebook_dropout=0.0,
            param_dtype=self.param_dtype,
            dtype=self.dtype,
            use_flash_attention=self.use_flash_attention,
            rngs=rngs,
        )
        self.lam = LatentActionModel(
            in_dim=self.in_dim,
            model_dim=self.lam_dim,
            ffn_dim=self.lam_ffn_dim,
            latent_dim=self.latent_patch_dim,
            num_latents=self.num_latent_actions,
            patch_size=self.lam_patch_size,
            num_blocks=self.lam_num_blocks,
            num_heads=self.lam_num_heads,
            dropout=0.0,
            codebook_dropout=0.0,
            param_dtype=self.param_dtype,
            dtype=self.dtype,
            use_flash_attention=self.use_flash_attention,
            rngs=rngs,
        )
        self.dynamics = DynamicsMaskGIT(
            model_dim=self.dyna_dim,
            ffn_dim=self.dyna_ffn_dim,
            num_latents=self.num_patch_latents,
            latent_action_dim=self.latent_action_dim,
            num_blocks=self.dyna_num_blocks,
            num_heads=self.dyna_num_heads,
            dropout=self.dropout,
            mask_limit=self.mask_limit,
            param_dtype=self.param_dtype,
            dtype=self.dtype,
            use_flash_attention=self.use_flash_attention,
            rngs=rngs,
        )

    def __call__(
        self, batch: Dict[str, jax.Array], training: bool = True
    ) -> Dict[str, jax.Array]:
        videos_BTHWC = batch["videos"]
        tokenizer_outputs = self.tokenizer.vq_encode(videos_BTHWC, training=False)
        token_indices_BTN = tokenizer_outputs["indices"]
        lam_outputs = self.lam.vq_encode(videos_BTHWC, training=False)
        z_q_BTm11L = lam_outputs["z_q"]
        action_indices_E = lam_outputs["indices"]
        latent_actions_BTm11L = jax.lax.cond(
            self.lam_co_train,
            lambda: z_q_BTm11L,
            lambda: jax.lax.stop_gradient(z_q_BTm11L),
        )
        outputs = dict(
            video_tokens=jax.lax.stop_gradient(token_indices_BTN),
            latent_actions=latent_actions_BTm11L,
        )
        outputs["mask_rng"] = batch["mask_rng"]
        dyna_logits_BTNV, dyna_mask = self.dynamics(outputs, training)
        outputs["token_logits"] = dyna_logits_BTNV
        if dyna_mask is not None:
            outputs["mask"] = dyna_mask
        mle_indices_BTN = jnp.argmax(outputs["token_logits"], axis=-1)
        H, W = batch["videos"].shape[2:4]
        outputs["recon"] = self.tokenizer.decode(mle_indices_BTN, (H, W))
        outputs["lam_indices"] = action_indices_E
        return outputs