modeling_otter.py

from typing import Optional, List

import torch
import torch.nn as nn
from transformers.modeling_utils import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
from einops import rearrange, repeat
from accelerate.hooks import add_hook_to_module, AlignDevicesHook

from .configuration_otter import OtterConfig

import sys

# The package importlib_metadata is in a different place, depending on the python version.
if sys.version_info < (3, 8):
    import importlib_metadata
else:
    import importlib.metadata as importlib_metadata

XFORMERS_AVAIL = False
try:
    import xformers.ops as xops
    from xformers_model import CLIPVisionModel, LlamaForCausalLM
    from transformers import LlamaTokenizer

    _xformers_version = importlib_metadata.version("xformers")
    print(f"Successfully imported xformers version {_xformers_version}")
except ImportError:
    from transformers import CLIPVisionModel, LlamaForCausalLM, LlamaTokenizer

    XFORMERS_AVAIL = False
    print(
        "No xformers found. You are recommended to install xformers via `pip install xformers` or `conda install -c xformers xformers`"
    )

# from transformers import CLIPVisionModel, LlamaForCausalLM, LlamaTokenizer

__KNOWN_DECODER_LAYERS_ATTR_NAMES = {
    "opt": "model.decoder.layers",
    "gptneo": "transformer.h",
    "gptj": "transformer.h",
    "gpt-j": "transformer.h",
    "pythia": "gpt_neox.layers",
    "llama": "model.layers",
}


def _infer_decoder_layers_attr_name(model: nn.Module):
    for k in __KNOWN_DECODER_LAYERS_ATTR_NAMES:
        if k.lower() in model.__class__.__name__.lower():
            return __KNOWN_DECODER_LAYERS_ATTR_NAMES[k]

    raise ValueError(
        f"We require the attribute name for the nn.ModuleList in the decoder storing the transformer block layers. Please supply this string manually."
    )


def extend_instance(obj, mixin):
    """Apply mixins to a class instance after creation"""
    base_cls = obj.__class__
    base_cls_name = obj.__class__.__name__
    obj.__class__ = type(
        base_cls_name, (mixin, base_cls), {}
    )  # mixin needs to go first for our forward() logic to work


def getattr_recursive(obj, att):
    """
    Return nested attribute of obj
    Example: getattr_recursive(obj, 'a.b.c') is equivalent to obj.a.b.c
    """
    if att == "":
        return obj
    i = att.find(".")
    if i < 0:
        return getattr(obj, att)
    else:
        return getattr_recursive(getattr(obj, att[:i]), att[i + 1 :])


def setattr_recursive(obj, att, val):
    """
    Set nested attribute of obj
    Example: setattr_recursive(obj, 'a.b.c', val) is equivalent to obj.a.b.c = val
    """
    if "." in att:
        obj = getattr_recursive(obj, ".".join(att.split(".")[:-1]))
    setattr(obj, att.split(".")[-1], val)


def exists(val):
    return val is not None


class OtterPerceiverBlock(nn.Module):
    def __init__(self, *, dim: int, dim_head: int = 64, heads: int = 8, mult: int = 4):
        super().__init__()
        self.scale = dim_head**-0.5
        self.heads = heads
        inner_dim = dim_head * heads
        ff_dim = dim * mult
        self.norm_media = nn.LayerNorm(dim)
        self.norm_latents = nn.LayerNorm(dim)

        self.to_q = nn.Linear(dim, inner_dim, bias=False)
        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
        self.to_out = nn.Linear(inner_dim, dim, bias=False)
        self.feed_forward = nn.ModuleList(
            [
                nn.LayerNorm(dim),
                nn.Linear(dim, ff_dim, bias=False),
                nn.GELU(),
                nn.Linear(ff_dim, dim, bias=False),
            ]
        )

    def forward(self, x: torch.Tensor, latents: torch.Tensor) -> torch.Tensor:
        """
        Args:
            x (torch.Tensor): image features
                shape (b, T, n1, D)
            latent (torch.Tensor): latent features
                shape (b, T, n2, D)
        """
        x = self.norm_media(x)
        residual_latents = latents
        latents = self.norm_latents(latents)

        h = self.heads

        q = self.to_q(latents)
        kv_input = torch.cat((x, latents), dim=-2)
        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
        q = rearrange(q, "b t n (h d) -> b h t n d", h=h)
        k = rearrange(k, "b t n (h d) -> b h t n d", h=h)
        v = rearrange(v, "b t n (h d) -> b h t n d", h=h)
        q = q * self.scale

        # attention
        sim = torch.einsum("... i d, ... j d  -> ... i j", q, k)
        sim = sim - sim.amax(dim=-1, keepdim=True).detach()
        attn = sim.softmax(dim=-1)

        out = torch.einsum("... i j, ... j d -> ... i d", attn, v)
        out = rearrange(out, "b h t n d -> b t n (h d)", h=h)
        out = self.to_out(out) + residual_latents
        residual_out = out
        for layer in self.feed_forward:
            out = layer(out)
        return out + residual_out


class OtterPerceiverResampler(nn.Module):
    def __init__(
        self,
        *,
        dim: int,
        depth: int = 6,
        dim_head: int = 64,
        heads: int = 8,
        num_latents: int = 64,
        max_num_media: Optional[int] = None,
        max_num_frames: Optional[int] = None,
        ff_mult: int = 4,
    ):
        super().__init__()
        self.latents = nn.Parameter(torch.randn(num_latents, dim))
        self.frame_embs = (
            nn.Parameter(torch.randn(max_num_frames, dim))
            if exists(max_num_frames)
            else None
        )
        self.media_time_embs = (
            nn.Parameter(torch.randn(max_num_media, 1, dim))
            if exists(max_num_media)
            else None
        )

        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(
                OtterPerceiverBlock(
                    dim=dim, dim_head=dim_head, heads=heads, mult=ff_mult
                )
            )

        self.norm = nn.LayerNorm(dim)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Args:
            x (torch.Tensor): image features
                shape (b, T, F, v, D)
        Returns:
            shape (b, T, n, D) where n is self.num_latents
        """
        b, T, F, v = x.shape[:4]

        # frame and media time embeddings
        if exists(self.frame_embs):
            frame_embs = repeat(self.frame_embs[:F], "F d -> b T F v d", b=b, T=T, v=v)
            x = x + frame_embs
        x = rearrange(
            x, "b T F v d -> b T (F v) d"
        )  # flatten the frame and spatial dimensions
        if exists(self.media_time_embs):
            x = x + self.media_time_embs[:T]

        # blocks
        latents = repeat(self.latents, "n d -> b T n d", b=b, T=T)
        for block in self.layers:
            latents = block(x, latents)
        return self.norm(latents)


class OtterMaskedCrossAttention(nn.Module):
    def __init__(
        self,
        *,
        dim: int,
        dim_visual: int,
        dim_head: int = 64,
        heads: int = 8,
        only_attend_immediate_media: bool = True,
        only_attend_previous: bool = True,
    ):
        super().__init__()
        self.scale = dim_head**-0.5
        self.heads = heads
        inner_dim = dim_head * heads

        self.norm = nn.LayerNorm(dim)

        self.to_q = nn.Linear(dim, inner_dim, bias=False)
        self.to_kv = nn.Linear(dim_visual, inner_dim * 2, bias=False)
        self.to_out = nn.Linear(inner_dim, dim, bias=False)

        # whether for text to only attend to immediate preceding image, or all previous images
        self.only_attend_immediate_media = only_attend_immediate_media
        self.only_attend_previous = only_attend_previous

    def forward(
        self,
        x: torch.Tensor,
        media: torch.Tensor,
        media_locations: Optional[torch.BoolTensor] = None,
        attend_previous: bool = True,
    ) -> torch.Tensor:
        """
        Args:
            x (torch.Tensor): text features
                shape (B, T_txt, D_txt)
            media (torch.Tensor): image features
                shape (B, T_img, n, D_img) where n is the dim of the latents
            media_locations: boolean mask identifying the media tokens in x
                shape (B, T_txt)
            attend_previous: bool
                If false, ignores immediately preceding image and starts attending when following image
        """
        _, T_img, n = media.shape[:3]
        h = self.heads

        x = self.norm(x)

        q = self.to_q(x)
        media = rearrange(media, "b t n d -> b (t n) d")

        k, v = self.to_kv(media).chunk(2, dim=-1)
        if not XFORMERS_AVAIL:
            q = rearrange(q, "b n (h d) -> b h n d", h=h)
            k = rearrange(k, "b n (h d) -> b h n d", h=h)
            v = rearrange(v, "b n (h d) -> b h n d", h=h)
            q = q * self.scale

            sim = torch.einsum("... i d, ... j d -> ... i j", q, k)

            if exists(media_locations):
                # at each boolean of True, increment the time counter (relative to media time)
                text_time = media_locations.cumsum(dim=-1)
                media_time = torch.arange(T_img, device=x.device) + 1

                if not attend_previous:
                    text_time[~media_locations] += 1
                    # make sure max is still the number of images in the sequence
                    text_time[
                        text_time
                        > repeat(
                            torch.count_nonzero(media_locations, dim=1),
                            "b -> b i",
                            i=text_time.shape[1],
                        )
                    ] = 0

                # text time must equal media time if only attending to most immediate image
                # otherwise, as long as text time is greater than media time (if attending to all previous images / media)
                mask_op = torch.eq if self.only_attend_immediate_media else torch.ge

                text_to_media_mask = mask_op(
                    rearrange(text_time, "b i -> b 1 i 1"),
                    repeat(media_time, "j -> 1 1 1 (j n)", n=n),
                )
                sim = sim.masked_fill(~text_to_media_mask, -torch.finfo(sim.dtype).max)

            sim = sim - sim.amax(dim=-1, keepdim=True).detach()
            attn = sim.softmax(dim=-1)

            if exists(media_locations) and self.only_attend_immediate_media:
                # any text without a preceding media needs to have attention zeroed out
                text_without_media_mask = text_time == 0
                text_without_media_mask = rearrange(
                    text_without_media_mask, "b i -> b 1 i 1"
                )
                attn = attn.masked_fill(text_without_media_mask, 0.0)

            out = torch.einsum("... i j, ... j d -> ... i d", attn, v)
            out = rearrange(out, "b h n d -> b n (h d)")
        else:
            q = rearrange(q, "b n (h d) -> b n h d", h=h)
            k = rearrange(k, "b n (h d) -> b n h d", h=h)
            v = rearrange(v, "b n (h d) -> b n h d", h=h)
            attn_mask = None
            out = xops.memory_efficient_attention(
                q, k, v, attn_bias=attn_mask, scale=self.scale
            )
        return self.to_out(out)


class OtterGatedCrossAttentionBlock(nn.Module):
    def __init__(
        self,
        *,
        dim: int,
        dim_visual: int,
        dim_head: int = 64,
        heads: int = 8,
        ff_mult: int = 4,
        only_attend_immediate_media: bool = True,
        only_attend_previous: bool = True,
    ):
        super().__init__()
        self.attn = OtterMaskedCrossAttention(
            dim=dim,
            dim_visual=dim_visual,
            dim_head=dim_head,
            heads=heads,
            only_attend_immediate_media=only_attend_immediate_media,
            only_attend_previous=only_attend_previous,
        )
        self.attn_gate = nn.Parameter(torch.tensor([0.0]))
        self.feed_forward = nn.ModuleList(
            [
                nn.LayerNorm(dim),
                nn.Linear(dim, dim * ff_mult, bias=False),
                nn.GELU(),
                nn.Linear(dim * ff_mult, dim, bias=False),
            ]
        )
        self.ff_gate = nn.Parameter(torch.tensor([0.0]))

    def forward(
        self,
        x: torch.Tensor,
        media: torch.Tensor,
        media_locations: Optional[torch.BoolTensor] = None,
        attend_previous: bool = True,
    ) -> torch.Tensor:
        x = (
            self.attn(
                x,
                media,
                media_locations=media_locations,
                attend_previous=attend_previous,
            )
            * self.attn_gate.tanh()
            + x
        )
        residual_x = x
        for ff in self.feed_forward:
            x = ff(x)
        x = x * self.ff_gate.tanh() + residual_x

        return x


class OtterLayer(nn.Module):
    def __init__(self, gated_cross_attn_layer: nn.Module, decoder_layer: nn.Module):
        super().__init__()
        self.gated_cross_attn_layer = gated_cross_attn_layer
        self.decoder_layer = decoder_layer
        self.vis_x = None
        self.media_locations = None

    def is_conditioned(self) -> bool:
        """Check whether the layer is conditioned."""
        return self.vis_x is not None

    # Used this great idea from this implementation of Otter (https://github.com/dhansmair/otter-mini/)
    def condition_vis_x(self, vis_x) -> None:
        self.vis_x = vis_x

    def condition_media_locations(self, media_locations) -> None:
        self.media_locations = media_locations

    def condition_attend_previous(self, attend_previous) -> None:
        self.attend_previous = attend_previous

    def forward(
        self,
        lang_x: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        **decoder_layer_kwargs,
    ):
        if self.gated_cross_attn_layer is None:
            return self.decoder_layer(
                lang_x, attention_mask=attention_mask, **decoder_layer_kwargs
            )

        if self.vis_x is None:
            raise ValueError("vis_x must be conditioned before forward pass")

        if self.media_locations is None:
            raise ValueError("media_locations must be conditioned before forward pass")

        lang_x = self.gated_cross_attn_layer(
            lang_x,
            self.vis_x,
            media_locations=self.media_locations,
            attend_previous=self.attend_previous,
        )
        lang_x = self.decoder_layer(
            lang_x, attention_mask=attention_mask, **decoder_layer_kwargs
        )
        return lang_x


class OtterLMMixin(nn.Module):
    """
    Mixin to add cross-attention layers to a language model.
    """

    def set_decoder_layers_attr_name(self, decoder_layers_attr_name):
        self.decoder_layers_attr_name = decoder_layers_attr_name

    def _get_decoder_layers(self):
        return getattr_recursive(self, self.decoder_layers_attr_name)

    def _set_decoder_layers(self, value):
        setattr_recursive(self, self.decoder_layers_attr_name, value)

    def init_otter(
        self,
        media_token_id: int,
        vis_hidden_size: int,
        cross_attn_every_n_layers: int,
        use_media_placement_augmentation: bool,
        only_attend_previous: bool,
    ):
        """
        Initialize Otter by adding a new gated cross attn to the decoder. Store the media token id for computing the media locations.
        """

        gated_cross_attn_layers = nn.ModuleList(
            [
                OtterGatedCrossAttentionBlock(
                    dim=self.config.hidden_size,
                    dim_visual=vis_hidden_size,
                    only_attend_previous=only_attend_previous,
                )
                if (layer_idx + 1) % cross_attn_every_n_layers == 0
                else None
                for layer_idx, _ in enumerate(self._get_decoder_layers())
            ]
        )
        self._set_decoder_layers(
            nn.ModuleList(
                [
                    OtterLayer(gated_cross_attn_layer, decoder_layer)
                    for gated_cross_attn_layer, decoder_layer in zip(
                        gated_cross_attn_layers, self._get_decoder_layers()
                    )
                ]
            )
        )
        self.media_token_id = media_token_id
        self.use_media_placement_augmentation = use_media_placement_augmentation
        self.only_attend_previous = only_attend_previous
        self.initialized_otter = True

    def forward(self, *input, **kwargs):
        """Condition the Otter layers on the media locations before forward()"""
        if not self.initialized_otter:
            raise ValueError(
                "Otter layers are not initialized. Please call `init_otter` first."
            )

        input_ids = kwargs["input_ids"] if "input_ids" in kwargs else input[0]
        media_locations = input_ids == self.media_token_id
        # IMPORTANT: Force `attend_previous` to True when we place training data as <image>caption<|endofchunk|>
        # attend_previous = (
        #     (random.random() < 0.5) if self.use_media_placement_augmentation else False
        # )
        attend_previous = self.only_attend_previous

        for layer in self.get_decoder().layers:
            layer.condition_media_locations(media_locations)
            layer.condition_attend_previous(attend_previous)

        return super().forward(
            *input, **kwargs
        )  # Call the other parent's forward method

    def is_conditioned(self) -> bool:
        """Check whether all decoder layers are already conditioned."""
        return all(l.is_conditioned() for l in self._get_decoder_layers())

    def clear_conditioned_layers(self) -> None:
        for layer in self._get_decoder_layers():
            layer.condition_vis_x(None)
            layer.condition_media_locations(None)
            layer.condition_attend_previous(None)


class OtterPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = OtterConfig
    base_model_prefix = "otter"
    supports_gradient_checkpointing = True
    _no_split_modules = ["OtterPerceiverBlock", "CLIPEncoderLayer", "OtterLayer"]

    def _init_weights(self, module):
        """Otter requires no specific initialization"""
        return super()._init_weights(module)


class OtterModel(OtterPreTrainedModel):
    config_class = OtterConfig

    def __init__(
        self,
        config: OtterConfig,
    ):
        super().__init__(config)
        text_tokenizer = LlamaTokenizer.from_pretrained(
            config.text_config._name_or_path
        )
        lang_encoder = LlamaForCausalLM(config=config.text_config)
        vision_encoder = CLIPVisionModel(config=config.vision_config)

        text_tokenizer.add_special_tokens(
            {"additional_special_tokens": ["<|endofchunk|>", "<image>", "<answer>"]}
        )
        if text_tokenizer.pad_token is None:
            text_tokenizer.add_special_tokens({"pad_token": "<PAD>"})
        self.text_tokenizer = text_tokenizer
        self.eoc_token_id = text_tokenizer.encode("<|endofchunk|>")[-1]
        self.media_token_id = text_tokenizer.encode("<image>")[-1]

        extend_instance(lang_encoder, OtterLMMixin)
        decoder_layers_attr_name = _infer_decoder_layers_attr_name(lang_encoder)
        lang_encoder.set_decoder_layers_attr_name(decoder_layers_attr_name)
        lang_encoder.resize_token_embeddings(len(text_tokenizer))
        self.lang_encoder = lang_encoder

        self.cross_attn_every_n_layers = config.cross_attn_every_n_layers
        self.use_media_placement_augmentation = config.use_media_placement_augmentation
        self.only_attend_previous = config.only_attend_previous

        vision_encoder.output_tokens = True
        self.vision_encoder = vision_encoder

        self.vis_dim = 1024
        self.perceiver = OtterPerceiverResampler(dim=self.vis_dim)

        self.lang_encoder.init_otter(
            media_token_id=self.media_token_id,
            vis_hidden_size=self.vis_dim,
            cross_attn_every_n_layers=self.cross_attn_every_n_layers,
            use_media_placement_augmentation=self.use_media_placement_augmentation,
            only_attend_previous=self.only_attend_previous,
        )
        self.post_init()

    def get_input_embeddings(self) -> nn.Module:
        return self.lang_encoder.get_input_embeddings()

    def set_input_embeddings(self, new_embeddings):
        self.lang_encoder.set_input_embeddings(new_embeddings)

    def get_output_embeddings(self) -> nn.Module:
        return self.lang_encoder.get_output_embeddings()

    def set_output_embeddings(self, new_embeddings):
        self.lang_encoder.set_output_embeddings(new_embeddings)

    def get_image_encoder(self) -> nn.Module:
        return self.vision_encoder

    def get_lang_encoder(self) -> nn.Module:
        return self.lang_encoder

    def tie_weights(self):
        return super().tie_weights()

    def init_weights(self):
        # Freeze all parameters in vision encoder
        for param in self.vision_encoder.parameters():
            param.requires_grad = False
        # Freeze all parameters in lang encoders except gated_cross_attn_layers
        for name, param in self.lang_encoder.named_parameters():
            if "gated_cross_attn_layer" not in name:
                param.requires_grad = False
        # Unfreeze LM input embeddings
        self.lang_encoder.get_input_embeddings().requires_grad_(True)

    def forward(
        self,
        vision_x: torch.Tensor,
        lang_x: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        use_cached_vision_x: bool = False,
        clear_conditioned_layers: bool = True,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        use_cache: bool = False,
        **kwargs,
    ) -> CausalLMOutputWithPast:
        """
        Forward pass of Otter.

        Args:
            vision_x (torch.Tensor): Vision input
                shape (B, T_img, F, C, H, W) with F=1
            lang_x (torch.Tensor): Language input ids
                shape (B, T_txt)
            attention_mask (torch.Tensor, optional): Attention mask. Defaults to None.
            labels (torch.Tensor, optional): Labels. Defaults to None.
            clear_conditioned_layers: if True, clear the conditioned layers
                once the foward pass is completed. Set this to false if the
                same set of images will be reused in another subsequent
                forward pass.
            past_key_values: pre-computed values to pass to language model.
                See past_key_values documentation in Hugging Face
                CausalLM models.
            use_cache: whether to use cached key values. See use_cache
                documentation in Hugging Face CausalLM models.
        """
        assert (
            vision_x is not None
        ) or use_cached_vision_x, (
            "Must provide either vision_x or use_cached_vision_x to True."
        )

        if use_cached_vision_x:
            # Case: use cached; vision_x should be cached and other
            # vision-related inputs should not be provided.
            assert (
                vision_x is None
            ), "Expect vision_x to be None when use_cached_vision_x is True."
            assert self.lang_encoder.is_conditioned()

        else:
            # Case: do not use caching (i.e. this is a standard forward pass);
            self._encode_vision_x(vision_x=vision_x)

        output = self.lang_encoder(
            input_ids=lang_x,
            attention_mask=attention_mask,
            labels=labels,
            past_key_values=past_key_values,
            use_cache=use_cache,
            **kwargs,
        )

        if clear_conditioned_layers:
            self.lang_encoder.clear_conditioned_layers()

        return output

    def _encode_vision_x(self, vision_x: torch.Tensor):
        """
        Compute media tokens from vision input by passing it through vision encoder and conditioning language model.
        Args:
            vision_x (torch.Tensor): Vision input
                shape (B, T_img, F, C, H, W)
                Images in the same chunk are collated along T_img, and frames are collated along F
                Currently only F=1 is supported (single-frame videos)

        rearrange code based on https://github.com/dhansmair/flamingo-mini
        """

        assert vision_x.ndim == 6, "vision_x should be of shape (b, T_img, F, C, H, W)"
        b, T, F = vision_x.shape[:3]

        vision_x = rearrange(vision_x, "b T F c h w -> (b T F) c h w")
        with torch.no_grad():
            vision_x = self.vision_encoder(vision_x)[0][:, 1:, :]
        vision_x = rearrange(vision_x, "(b T F) v d -> b T F v d", b=b, T=T, F=F)

        vision_x = self.perceiver(vision_x)  # reshapes to (b, T, n, d)

        for layer in self.lang_encoder._get_decoder_layers():
            layer.condition_vis_x(vision_x)


class OtterForConditionalGeneration(OtterPreTrainedModel):
    config_class = OtterConfig

    def __init__(
        self,
        config: OtterConfig,
    ):
        super().__init__(config)
        text_tokenizer = LlamaTokenizer.from_pretrained(
            config.text_config._name_or_path
        )
        lang_encoder = LlamaForCausalLM(config=config.text_config)
        vision_encoder = CLIPVisionModel(config=config.vision_config)

        text_tokenizer.add_special_tokens(
            {"additional_special_tokens": ["<|endofchunk|>", "<image>", "<answer>"]}
        )
        if text_tokenizer.pad_token is None:
            text_tokenizer.add_special_tokens({"pad_token": "<PAD>"})
        self.text_tokenizer = text_tokenizer
        self.eoc_token_id = text_tokenizer.encode("<|endofchunk|>")[-1]
        self.media_token_id = text_tokenizer.encode("<image>")[-1]

        extend_instance(lang_encoder, OtterLMMixin)
        decoder_layers_attr_name = _infer_decoder_layers_attr_name(lang_encoder)
        lang_encoder.set_decoder_layers_attr_name(decoder_layers_attr_name)
        lang_encoder.resize_token_embeddings(len(text_tokenizer))
        self.lang_encoder = lang_encoder

        self.cross_attn_every_n_layers = config.cross_attn_every_n_layers
        self.use_media_placement_augmentation = config.use_media_placement_augmentation

        vision_encoder.output_tokens = True
        self.vision_encoder = vision_encoder

        self.vis_dim = 1024
        self.perceiver = OtterPerceiverResampler(dim=self.vis_dim)

        self.only_attend_previous = config.only_attend_previous

        self.lang_encoder.init_otter(
            media_token_id=self.media_token_id,
            vis_hidden_size=self.vis_dim,
            cross_attn_every_n_layers=self.cross_attn_every_n_layers,
            use_media_placement_augmentation=self.use_media_placement_augmentation,
            only_attend_previous=self.only_attend_previous,
        )
        self.post_init()

    def get_input_embeddings(self) -> nn.Module:
        return self.lang_encoder.get_input_embeddings()

    def set_input_embeddings(self, new_embeddings):
        self.lang_encoder.set_input_embeddings(new_embeddings)

    def get_output_embeddings(self) -> nn.Module:
        return self.lang_encoder.get_output_embeddings()

    def set_output_embeddings(self, new_embeddings):
        self.lang_encoder.set_output_embeddings(new_embeddings)

    def get_image_encoder(self) -> nn.Module:
        return self.vision_encoder

    def get_lang_encoder(self) -> nn.Module:
        return self.lang_encoder

    def init_weights(self):
        # Freeze all parameters in vision encoder
        for param in self.vision_encoder.parameters():
            param.requires_grad = False
        # Freeze all parameters in lang encoders except gated_cross_attn_layers
        for name, param in self.lang_encoder.named_parameters():
            if "gated_cross_attn_layer" not in name:
                param.requires_grad = False
        # Unfreeze LM input and output embeddings
        self.lang_encoder.get_input_embeddings().requires_grad_(True)
        self.lang_encoder.get_output_embeddings().requires_grad_(True)

    def forward(
        self,
        vision_x: torch.Tensor,
        lang_x: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        use_cached_vision_x: bool = False,
        clear_conditioned_layers: bool = True,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        use_cache: bool = False,
        **kwargs,
    ) -> CausalLMOutputWithPast:
        """
        Forward pass of Otter.

        Args:
            vision_x (torch.Tensor): Vision input
                shape (B, T_img, F, C, H, W) with F=1
            lang_x (torch.Tensor): Language input ids
                shape (B, T_txt)
            attention_mask (torch.Tensor, optional): Attention mask. Defaults to None.
            labels (torch.Tensor, optional): Labels. Defaults to None.
            clear_conditioned_layers: if True, clear the conditioned layers
                once the foward pass is completed. Set this to false if the
                same set of images will be reused in another subsequent
                forward pass.
            past_key_values: pre-computed values to pass to language model.
                See past_key_values documentation in Hugging Face
                CausalLM models.
            use_cache: whether to use cached key values. See use_cache
                documentation in Hugging Face CausalLM models.
        """
        assert (
            vision_x is not None
        ) or use_cached_vision_x, (
            "Must provide either vision_x or use_cached_vision_x to True."
        )

        if use_cached_vision_x:
            # Case: use cached; vision_x should be cached and other
            # vision-related inputs should not be provided.
            assert (
                vision_x is None
            ), "Expect vision_x to be None when use_cached_vision_x is True."
            assert self.lang_encoder.is_conditioned()

        else:
            # Case: do not use caching (i.e. this is a standard forward pass);
            self._encode_vision_x(vision_x=vision_x)

        output = self.lang_encoder(
            input_ids=lang_x,
            attention_mask=attention_mask,
            labels=labels,
            past_key_values=past_key_values,
            use_cache=use_cache,
            **kwargs,
        )

        if clear_conditioned_layers:
            self.lang_encoder.clear_conditioned_layers()

        return output

    def _encode_vision_x(self, vision_x: torch.Tensor):
        """
        Compute media tokens from vision input by passing it through vision encoder and conditioning language model.
        Args:
            vision_x (torch.Tensor): Vision input
                shape (B, T_img, F, C, H, W)
                Images in the same chunk are collated along T_img, and frames are collated along F
                Currently only F=1 is supported (single-frame videos)

        rearrange code based on https://github.com/dhansmair/flamingo-mini
        """

        assert vision_x.ndim == 6, "vision_x should be of shape (b, T_img, F, C, H, W)"
        b, T, F = vision_x.shape[:3]

        vision_x = rearrange(vision_x, "b T F c h w -> (b T F) c h w")
        with torch.no_grad():
            vision_x = self.vision_encoder(vision_x)[0][:, 1:, :]
        vision_x = rearrange(vision_x, "(b T F) v d -> b T F v d", b=b, T=T, F=F)

        vision_x = self.perceiver(vision_x)  # reshapes to (b, T, n, d)

        for layer in self.lang_encoder._get_decoder_layers():
            layer.condition_vis_x(vision_x)

    @torch.no_grad()
    def generate(
        self,
        vision_x: torch.Tensor,
        lang_x: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        **generate_kwargs,
    ):
        """
        Generate text conditioned on vision and language inputs.

        Args:
            vision_x (torch.Tensor): Vision input
                shape (B, T_img, F, C, H, W)
                images in the same chunk are collated along T_img, and frames are collated along F
                currently only F=1 is supported (single-frame videos)
            lang_x (torch.Tensor): Language input
                shape (B, T_txt)
            max_length (int, optional): Maximum length of the output. Defaults to None.
            attention_mask (torch.Tensor, optional): Attention mask. Defaults to None.
        Returns:
            torch.Tensor: lang_x with generated tokens appended to it
        """
        if hasattr(self, "_hf_hook"):
            # add a hook to make sure that the output of lang_encoder is mapped to the same device as the lang_x
            hook = AlignDevicesHook(
                execution_device=lang_x.device,
                io_same_device=True,
                place_submodules=False,
            )
            add_hook_to_module(self.lang_encoder, hook)
        num_beams = generate_kwargs.get("num_beams", 1)
        if num_beams > 1:
            vision_x = vision_x.repeat_interleave(num_beams, dim=0)
        self._encode_vision_x(vision_x=vision_x)
        output = self.lang_encoder.generate(
            lang_x,
            attention_mask=attention_mask,
            eos_token_id=self.eoc_token_id,
            **generate_kwargs,
        )

        self.lang_encoder.clear_conditioned_layers()
        return output