[tab] genie.py (python)
from typing import Dict, Any

import optax
import jax
import jax.numpy as jnp
import flax.linen as nn
from jax import NamedSharding
from flax.training.train_state import TrainState
from flax.training import orbax_utils
from orbax.checkpoint import PyTreeCheckpointer

from models.dynamics import DynamicsMaskGIT
from models.lam import LatentActionModel
from models.tokenizer import TokenizerVQVAE


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

    # --- Tokenizer ---
    in_dim: int
    tokenizer_dim: int
    latent_patch_dim: int
    num_patch_latents: int
    patch_size: int
    tokenizer_num_blocks: int
    tokenizer_num_heads: int
    # --- LAM ---
    lam_dim: int
    latent_action_dim: int
    num_latent_actions: int
    lam_patch_size: int
    lam_num_blocks: int
    lam_num_heads: int
    # --- Dynamics ---
    dyna_dim: int
    dyna_num_blocks: int
    dyna_num_heads: int
    dropout: float = 0.0
    mask_limit: float = 0.0

    def setup(self):
        self.tokenizer = TokenizerVQVAE(
            in_dim=self.in_dim,
            model_dim=self.tokenizer_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,
        )
        self.lam = LatentActionModel(
            in_dim=self.in_dim,
            model_dim=self.lam_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,
        )
        self.dynamics = DynamicsMaskGIT(
            model_dim=self.dyna_dim,
            num_latents=self.num_patch_latents,
            num_blocks=self.dyna_num_blocks,
            num_heads=self.dyna_num_heads,
            dropout=self.dropout,
            mask_limit=self.mask_limit,
        )

    def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:
        tokenizer_outputs = self.tokenizer.vq_encode(batch["videos"], training=False)
        lam_outputs = self.lam.vq_encode(batch["videos"], training=False)
        outputs = dict(
            video_tokens=jax.lax.stop_gradient(tokenizer_outputs["indices"]),
            latent_actions=jax.lax.stop_gradient(lam_outputs["z_q"]),
        )
        outputs["mask_rng"] = batch["mask_rng"]
        dyna_outputs = self.dynamics(outputs, training)
        outputs.update(dyna_outputs)
        mle_indices = jnp.argmax(outputs["token_logits"], axis=-1)
        outputs["recon"] = self.tokenizer.decode(
            mle_indices, batch["videos"].shape[2:4]
        )
        return outputs

    @nn.compact
    def sample_mihir(
        self,
        batch: Dict[str, Any],
        seq_len: int,
        steps: int,
        temperature: float,
        sample_argmax: bool,
    ) -> Any:
        # B == batch_size
        # T == num_frames (input)
        # N == num_patches
        # S == seq_len
        # A == action_space
        # D == latent_dim
        # --- Encode videos and actions ---
        tokenizer_out = self.tokenizer.vq_encode(batch["videos"], training=False)
        token_idxs = tokenizer_out["indices"] # (B, T, N)
        B, T, N = token_idxs.shape
        S = seq_len
        print("token_idxs shape:", token_idxs.shape)
        pad_shape = (B, S - T, N)
        pad = jnp.zeros(pad_shape, dtype=token_idxs.dtype)
        token_idxs = jnp.concatenate([token_idxs, pad], axis=1) # shape (B, S, N)
        action_tokens = self.lam.vq.get_codes(batch["latent_actions"])

        # begin potential forloop (from T to S)
        initial_T = T
        for T in range(initial_T, S):
            print(f"sampling frame {T}")
            # Create a mask that is 1 (True) where we just padded
            # token_idxs shape: (B, S, N), T = original length, S = seq_len
            # mask is True for padded positions (i.e., t >= T)
            mask = (jnp.arange(S)[None, :, None] >= T)  # shape (1, S, 1)
            mask = jnp.broadcast_to(mask, (B, S, N))    # shape (B, S, N)
            init_mask = mask.astype(bool)
            token_idxs *= ~init_mask
            #print("token_idxs[0,:,0]:", token_idxs[0,:,0])
            #print("init_mask[0,:,0]:", init_mask[0,:,0])

            assert init_mask.shape == (B, S, N), "Wrong mask shape"

            # --- Initialize MaskGIT ---
            init_carry = (
                batch["rng"],
                init_mask,
                token_idxs,
                action_tokens,
            )
            MaskGITLoop = nn.scan(
                MaskGITStepMihir,
                variable_broadcast="params",
                split_rngs={"params": False},
                in_axes=0,
                out_axes=0,
                length=steps,
            )

            # --- Run MaskGIT loop ---
            loop_fn = MaskGITLoop(
                dynamics=self.dynamics,
                tokenizer=self.tokenizer,
                temperature=temperature,
                sample_argmax=sample_argmax,
                steps=steps,
            )
            final_carry, _ = loop_fn(init_carry, jnp.arange(steps))
            token_idxs = final_carry[2]

        new_frame_pixels = self.tokenizer.decode(
            token_idxs,
            video_hw=batch["videos"].shape[2:4],
        )
        return new_frame_pixels

    @nn.compact
    def sample(
        self,
        batch: Dict[str, Any],
        steps: int = 25,
        temperature: int = 1,
        sample_argmax: bool = False,
    ) -> Any:
        # B == batch_size
        # T == num_frames (input)
        # N == num_patches
        # S == seq_len
        # A == action_space
        # D == latent_dim
        # --- Encode videos and actions ---
        tokenizer_out = self.tokenizer.vq_encode(batch["videos"], training=False)
        token_idxs = tokenizer_out["indices"]# (B, T, N)
        new_frame_idxs = jnp.zeros_like(token_idxs)[:, 0]  # (B, N) 
        action_tokens = self.lam.vq.get_codes(batch["latent_actions"]) # (B, S, A, D)

        # --- Initialize MaskGIT ---
        init_mask = jnp.ones_like(token_idxs, dtype=bool)[:, 0] # (B, N)
        init_carry = (
            batch["rng"],
            new_frame_idxs,
            init_mask,
            token_idxs,
            action_tokens,
        )
        MaskGITLoop = nn.scan(
            MaskGITStep,
            variable_broadcast="params",
            split_rngs={"params": False},
            in_axes=0,
            out_axes=0,
            length=steps,
        )

        # --- Run MaskGIT loop ---
        loop_fn = MaskGITLoop(
            dynamics=self.dynamics,
            tokenizer=self.tokenizer,
            temperature=temperature,
            sample_argmax=sample_argmax,
            steps=steps,
        )
        final_carry, _ = loop_fn(init_carry, jnp.arange(steps))
        new_frame_idxs = final_carry[1]
        new_frame_pixels = self.tokenizer.decode(
            jnp.expand_dims(new_frame_idxs, 1),
            video_hw=batch["videos"].shape[2:4],
        )
        return new_frame_pixels

    def vq_encode(self, batch, training) -> Dict[str, Any]:
        # --- Preprocess videos ---
        lam_output = self.lam.vq_encode(batch["videos"], training=training)
        return lam_output["indices"]


class MaskGITStepMihir(nn.Module):
    dynamics: nn.Module
    tokenizer: nn.Module
    temperature: float
    sample_argmax: bool
    steps: int

    @nn.compact
    def __call__(self, carry, x):
        rng, mask, token_idxs, action_tokens = carry
        step = x
        B, S, N = token_idxs.shape[:3]

        # --- Construct + encode video ---
        vid_embed = self.dynamics.patch_embed(token_idxs)
        # Mask vid_embed: set to mask_token where mask==1, else keep vid_embed
        # mask: (B, S, N), vid_embed: (B, S, N, D), mask_token: (D,)
        mask_token = self.dynamics.mask_token  # (1,1, 1, D,)
        # Expand mask to (B, S, N, 1) for broadcasting
        mask_expanded = mask[..., None]
        vid_embed = jnp.where(mask_expanded, mask_token, vid_embed)

        # --- Predict transition ---
        act_embed = self.dynamics.action_up(action_tokens)
        vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))
        unmasked_ratio = jnp.cos(jnp.pi * (step + 1) / (self.steps * 2))
        step_temp = self.temperature * (1.0 - unmasked_ratio)
        final_logits_old = self.dynamics.dynamics(vid_embed)[:, -1] / step_temp
        final_logits= self.dynamics.dynamics(vid_embed) / step_temp
        # jax.debug.print("final_logits shape: {}", final_logits.shape)
        # jax.debug.print("final_logits_tmp shape: {}", final_logits_tmp.shape)

        # --- Sample new tokens for final frame ---
        if self.sample_argmax:
            sampled_token_idxs_old = jnp.argmax(final_logits_old, axis=-1)
            sampled_token_idxs = jnp.argmax(final_logits, axis=-1)
            # jax.debug.print("sampled_token_idxs shape: {}", sampled_token_idxs.shape)
            # jax.debug.print("sampled_token_idxs_tmp shape: {}", sampled_token_idxs_tmp.shape)
        else:
            rng, _rng = jax.random.split(rng)
            sampled_token_idxs = jnp.where(
                step == self.steps - 1,
                jnp.argmax(final_logits, axis=-1),
                jax.random.categorical(_rng, final_logits),
            )
        gather_fn = jax.vmap(jax.vmap(lambda x, y: x[y]))
        gather_fn_tmp = jax.vmap(jax.vmap(jax.vmap(lambda x, y: x[y])))
        final_token_probs_old = gather_fn(jax.nn.softmax(final_logits_old), sampled_token_idxs_old)
        final_token_probs = gather_fn_tmp(jax.nn.softmax(final_logits), sampled_token_idxs)
        # jax.debug.print("final_token_probs shape: {}", final_token_probs.shape)
        # jax.debug.print("final_token_probs_tmp shape: {}", final_token_probs_tmp.shape)
        final_token_probs += ~mask
        # Update masked tokens only
        token_idxs = jnp.where(mask, sampled_token_idxs, token_idxs)

        # --- Update mask ---
        num_unmasked_tokens = jnp.round(N * (1.0 - unmasked_ratio)).astype(int)
        idx_mask = jnp.arange(final_token_probs.shape[-1]) > num_unmasked_tokens
        sorted_idxs = jnp.argsort(final_token_probs, axis=-1, descending=True)
        mask_update_fn = jax.vmap(lambda msk, ids: msk.at[ids].set(idx_mask))
        new_mask = mask_update_fn(mask, sorted_idxs)

        new_carry = (rng, new_mask, token_idxs, action_tokens)
        return new_carry, None


class MaskGITStep(nn.Module):
    dynamics: nn.Module
    tokenizer: nn.Module
    temperature: float
    sample_argmax: bool
    steps: int

    @nn.compact
    def __call__(self, carry, x):
        rng, final_token_idxs, mask, token_idxs, action_tokens = carry
        step = x
        B, T, N = token_idxs.shape[:3]

        # --- Construct + encode video ---
        vid_token_idxs = jnp.concatenate(
            (token_idxs, jnp.expand_dims(final_token_idxs, 1)), axis=1
        ) # (B, T+1, N)
        vid_embed = self.dynamics.patch_embed(vid_token_idxs) # (B, T+1, N, D)
        curr_masked_frame = jnp.where(
            jnp.expand_dims(mask, -1), # (B, N, 1)
            self.dynamics.mask_token[0], # (B, 1, D)
            vid_embed[:, -1], # (B, N, D)
        ) # (B, N, D)
        vid_embed = vid_embed.at[:, -1].set(curr_masked_frame)

        # --- Predict transition ---
        act_embed = self.dynamics.action_up(action_tokens)
        vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))
        unmasked_ratio = jnp.cos(jnp.pi * (step + 1) / (self.steps * 2))
        step_temp = self.temperature * (1.0 - unmasked_ratio)
        final_logits = self.dynamics.dynamics(vid_embed)[:, -1] / step_temp

        # --- Sample new tokens for final frame ---
        if self.sample_argmax:
            sampled_token_idxs = jnp.argmax(final_logits, axis=-1)
        else:
            rng, _rng = jax.random.split(rng)
            sampled_token_idxs = jnp.where(
                step == self.steps - 1,
                jnp.argmax(final_logits, axis=-1),
                jax.random.categorical(_rng, final_logits),
            )
        gather_fn = jax.vmap(jax.vmap(lambda x, y: x[y]))
        final_token_probs = gather_fn(jax.nn.softmax(final_logits), sampled_token_idxs)
        final_token_probs += ~mask
        # Update masked tokens only
        new_token_idxs = jnp.where(mask, sampled_token_idxs, final_token_idxs)

        # --- Update mask ---
        num_unmasked_tokens = jnp.round(N * (1.0 - unmasked_ratio)).astype(int)
        idx_mask = jnp.arange(final_token_probs.shape[-1]) > num_unmasked_tokens
        sorted_idxs = jnp.argsort(final_token_probs, axis=-1, descending=True)
        mask_update_fn = jax.vmap(lambda msk, ids: msk.at[ids].set(idx_mask))
        new_mask = mask_update_fn(mask, sorted_idxs)

        new_carry = (rng, new_token_idxs, new_mask, token_idxs, action_tokens)
        return new_carry, None


def restore_genie_components(
    train_state: TrainState,
    sharding: NamedSharding,
    inputs: Dict[str, jax.Array],
    rng: jax.Array,
    args,
):
    """Restore pre-trained Genie components"""
    rng, _rng = jax.random.split(rng)

    dummy_tokenizer = TokenizerVQVAE(
        in_dim=args.image_channels,
        model_dim=args.tokenizer_dim,
        latent_dim=args.latent_patch_dim,
        num_latents=args.num_patch_latents,
        patch_size=args.patch_size,
        num_blocks=args.tokenizer_num_blocks,
        num_heads=args.tokenizer_num_heads,
        dropout=args.dropout,
        codebook_dropout=args.dropout,
    )
    dummy_lam = LatentActionModel(
        in_dim=args.image_channels,
        model_dim=args.lam_dim,
        latent_dim=args.latent_patch_dim,
        num_latents=args.num_latent_actions,
        patch_size=args.lam_patch_size,
        num_blocks=args.lam_num_blocks,
        num_heads=args.lam_num_heads,
        dropout=args.dropout,
        codebook_dropout=args.dropout,
    )
    tokenizer_init_params = dummy_tokenizer.init(_rng, inputs)
    lam_init_params = dummy_lam.init(_rng, inputs)

    # dummy values since we only use tx to initialize the dummy train states
    dummy_tx = optax.adamw(
        learning_rate=optax.constant_schedule(args.max_lr),
        b1=0.9,
        b2=0.9,
        weight_decay=1e-4,
    )

    dummy_tokenizer_train_state = TrainState.create(
        apply_fn=dummy_tokenizer.apply, params=tokenizer_init_params, tx=dummy_tx
    )
    dummy_lam_train_state = TrainState.create(
        apply_fn=dummy_lam.apply, params=lam_init_params, tx=dummy_tx
    )

    def create_abstract_sharded_pytree(pytree_template, sharding_spec):
        """Replaces arrays in a pytree with ShapeDtypeStructs having the given sharding."""

        def map_fn(leaf_template):
            if hasattr(leaf_template, "shape") and hasattr(leaf_template, "dtype"):
                return jax.ShapeDtypeStruct(
                    leaf_template.shape, leaf_template.dtype, sharding=sharding_spec
                )
            return leaf_template

        return jax.tree_util.tree_map(map_fn, pytree_template)

    abstract_sharded_tokenizer_state = create_abstract_sharded_pytree(
        dummy_tokenizer_train_state, sharding
    )
    abstract_sharded_lam_state = create_abstract_sharded_pytree(
        dummy_lam_train_state, sharding
    )

    tokenizer_restore_target = {"model": abstract_sharded_tokenizer_state}
    lam_restore_target = {"model": abstract_sharded_lam_state}

    tokenizer_restore_args = orbax_utils.restore_args_from_target(
        tokenizer_restore_target
    )
    lam_restore_args = orbax_utils.restore_args_from_target(lam_restore_target)

    restored_tokenizer_params = (
        PyTreeCheckpointer()
        .restore(
            args.tokenizer_checkpoint,
            item=tokenizer_restore_target,
            restore_args=tokenizer_restore_args,
        )["model"]
        .params["params"]
    )
    restored_lam_params = (
        PyTreeCheckpointer()
        .restore(
            args.lam_checkpoint, item=lam_restore_target, restore_args=lam_restore_args
        )["model"]
        .params["params"]
    )
    # Genie does not initialize all LAM modules, thus we omit those extra modules during restoration
    # (f.srambical) FIXME: Currently, this is a small HBM memory crunch since the LAM's decoder is loaded into HBM and immediately dicarded.
    # A workaround would be to restore to host memory first, and only move the weights to HBM after pruning the decoder
    restored_lam_params = {
        k: v
        for k, v in restored_lam_params.items()
        if k in train_state.params["params"]["lam"]
    }

    train_state.params["params"]["tokenizer"].update(restored_tokenizer_params)
    train_state.params["params"]["lam"].update(restored_lam_params)

    return train_state