molmo.py

import math
import re
from array import array
from dataclasses import dataclass
from functools import lru_cache, partial
from typing import Iterable, List, Mapping, Optional, Set, Tuple, TypedDict

import torch
from einops import rearrange
from PIL import Image
from torch import nn
from torch.nn import functional as F
from transformers import PretrainedConfig

from vllm.attention import Attention, AttentionMetadata
from vllm.attention.layer import MultiHeadAttention
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
                              get_tensor_model_parallel_world_size,
                              split_tensor_along_last_dim,
                              tensor_model_parallel_all_gather)
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData,
                         InputContext, token_inputs)
from vllm.model_executor import SamplingMetadata
from vllm.model_executor.layers.activation import QuickGELU, SiluAndMul
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                               MergedColumnParallelLinear,
                                               QKVParallelLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
    ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
from vllm.multimodal.inputs import NestedTensors, PlaceholderRange
from vllm.multimodal.utils import cached_get_tokenizer
from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,
                           SequenceData)
from vllm.transformers_utils.processor import get_processor

from .interfaces import SupportsMultiModal, SupportsPP
from .utils import (AutoWeightsLoader, WeightsMapper, is_pp_missing_parameter,
                    make_empty_intermediate_tensors_factory, make_layers,
                    maybe_prefix, merge_multimodal_embeddings)

# TODO: hard-coded for now. Consider making it configurable.
VIT_LAYERS = [-2, -9]
NUM_PREFIX_TOKENS = 1
ADDITIONAL_VOCAB_SIZE = 128
DEFAULT_IMAGE_PATCH_TOKEN_ID = 152066
DEFAULT_IM_START_TOKEN_ID = 152067
DEFAULT_IM_END_TOKEN_ID = 152064
DEFAULT_IM_COL_TOKEN_ID = 152065


class MolmoImageInputs(TypedDict):
    images: torch.Tensor
    """Shape:
    `(batch_size, num_crops, num_patch, patch_dim)`
    """

    image_input_idx: torch.Tensor
    """Shape:
    `(batch_size, num_crops, num_patch)`
    """

    seq_len: torch.Tensor
    """Shape:
    `(batch_size, )`
    """

    image_masks: Optional[torch.Tensor]
    """Shape:
    `(batch_size, num_crops, num_patch)`
    """

    image_start_end: Tuple[int, int]
    """Starting and ending index of placeholder 
    tokens
    """


@dataclass
class VisionBackboneConfig:
    image_default_input_size: Tuple[int, int] = (336, 336)
    image_patch_size: int = 14
    image_pos_patch_size: int = 14
    image_emb_dim: int = 1024
    image_num_heads: int = 16
    image_num_key_value_heads: int = 16
    image_num_layers: int = 23
    image_mlp_dim: int = 4096
    image_mlp_activations: str = "quick_gelu"
    image_num_pos: int = 577
    image_norm_eps: float = 1e-5

    def __post_init__(self):
        self.image_default_input_size = tuple(
            self.image_default_input_size)  # type: ignore[assignment]

    @property
    def image_num_patch(self):
        h, w = self.image_default_input_size
        return h // self.image_patch_size, w // self.image_patch_size


class ViTMLP(nn.Module):
    """MLP used in Vision Transformer."""

    def __init__(
        self,
        config: VisionBackboneConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.w1 = ColumnParallelLinear(
            config.image_emb_dim,
            config.image_mlp_dim,
            bias=True,
            quant_config=quant_config,
        )
        # Activation function.
        assert config.image_mlp_activations == "quick_gelu"
        self.act = QuickGELU()
        self.w2 = RowParallelLinear(
            config.image_mlp_dim,
            config.image_emb_dim,
            bias=True,
            quant_config=quant_config,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x, _ = self.w1(x)
        x = self.act(x)
        x, _ = self.w2(x)
        return x


class MultiHeadDotProductAttention(nn.Module):
    """Multi-head attention used in Vision Transformer."""

    def __init__(
        self,
        config: VisionBackboneConfig,
        use_bias: bool = True,
        nlayers: int = 1,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()

        self.hidden_size = config.image_emb_dim
        self.total_num_heads = config.image_num_heads
        tp_size = get_tensor_model_parallel_world_size()

        assert self.hidden_size % self.total_num_heads == 0
        assert self.total_num_heads % tp_size == 0

        self.num_heads = self.total_num_heads // tp_size
        self.head_dim = self.hidden_size // self.total_num_heads

        self.total_num_kv_heads = config.image_num_key_value_heads
        if self.total_num_kv_heads >= tp_size:
            assert self.total_num_kv_heads % tp_size == 0
        else:
            assert tp_size % self.total_num_kv_heads == 0

        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)

        self.wq = ColumnParallelLinear(
            nlayers * self.hidden_size,
            self.total_num_heads * self.head_dim,
            bias=use_bias,
            quant_config=quant_config,
        )
        self.wk = ColumnParallelLinear(
            nlayers * self.hidden_size,
            self.total_num_kv_heads * self.head_dim,
            bias=use_bias,
            quant_config=quant_config,
        )
        self.wv = ColumnParallelLinear(
            nlayers * self.hidden_size,
            self.total_num_kv_heads * self.head_dim,
            bias=use_bias,
            quant_config=quant_config,
        )
        self.wo = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            self.hidden_size,
            bias=use_bias,
            quant_config=quant_config,
        )

        self.scale = self.head_dim**-0.5
        self.attn = MultiHeadAttention(self.num_heads,
                                       self.head_dim,
                                       self.scale,
                                       num_kv_heads=self.num_kv_heads)

    def forward(self,
                inputs_q: torch.Tensor,
                inputs_kv: Optional[torch.Tensor] = None) -> torch.Tensor:

        if inputs_kv is not None:
            inputs_k = inputs_kv
            inputs_v = inputs_kv
        else:
            inputs_k = inputs_q
            inputs_v = inputs_q

        xq, _ = self.wq(inputs_q)
        xk, _ = self.wk(inputs_k)
        xv, _ = self.wv(inputs_v)

        output = self.attn(xq, xk, xv)
        output, _ = self.wo(output)

        return output


class ResidualAttentionBlock(nn.Module):
    """Residual attention block used in Vision Transformer."""

    def __init__(
        self,
        config: VisionBackboneConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.attention = MultiHeadDotProductAttention(
            config, quant_config=quant_config)
        self.feed_forward = ViTMLP(config, quant_config)
        self.attention_norm = nn.LayerNorm(
            config.image_emb_dim,
            eps=config.image_norm_eps,
        )
        self.ffn_norm = nn.LayerNorm(
            config.image_emb_dim,
            eps=config.image_norm_eps,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = x + self.attention(self.attention_norm(x))
        x = x + self.feed_forward(self.ffn_norm(x))
        return x


class BlockCollection(nn.Module):
    """Collection of residual attention blocks used in Vision Transformer."""

    def __init__(
        self,
        config: VisionBackboneConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.resblocks = nn.ModuleList([
            ResidualAttentionBlock(config, quant_config)
            for _ in range(config.image_num_layers)
        ])

    def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
        hidden_states = []
        for r in self.resblocks:
            x = r(x)
            hidden_states.append(x)
        return hidden_states


def _expand_token(token: torch.Tensor, batch_size: int) -> torch.Tensor:
    return token.view(1, 1, -1).expand(batch_size, -1, -1)


class VisionTransformer(nn.Module):
    """Vision Transformer used in Vision Backbone."""

    def __init__(
        self,
        config: VisionBackboneConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        scale = config.image_emb_dim**-0.5
        self.patch_num = config.image_num_patch
        self.class_embedding = nn.Parameter(
            torch.randn(config.image_emb_dim) * scale)
        self.num_prefix_tokens: int = NUM_PREFIX_TOKENS
        self.positional_embedding = nn.Parameter(
            torch.randn(config.image_num_pos, config.image_emb_dim) * scale)
        image_patch_size = config.image_patch_size
        self.patch_embedding = nn.Linear(
            image_patch_size * image_patch_size * 3,
            config.image_emb_dim,
            bias=False,
        )
        self.pre_ln = nn.LayerNorm(config.image_emb_dim,
                                   eps=config.image_norm_eps)
        self.transformer = BlockCollection(config, quant_config)

    def add_pos_emb(self, x: torch.Tensor, patch_num: int) -> torch.Tensor:
        cls_emb = self.positional_embedding[0:1]
        pos_emb = self.positional_embedding[1:]

        pos_emb = pos_emb.reshape(
            (int(math.sqrt(pos_emb.shape[0])),
             int(math.sqrt(pos_emb.shape[0])), pos_emb.shape[1]))

        (patch_num_0, patch_num_1) = patch_num

        if pos_emb.shape[0] != patch_num_0 or pos_emb.shape[1] != patch_num_1:
            # from https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
            pos_emb = pos_emb.unsqueeze(0).permute(0, 3, 1, 2)
            pos_emb = F.interpolate(
                pos_emb,
                size=(patch_num_0, patch_num_1),
                mode="bicubic",
                align_corners=False,
                antialias=True,
            )
            pos_emb = pos_emb.permute(0, 2, 3, 1).squeeze(0)

        pos_emb = pos_emb.reshape(-1, pos_emb.shape[-1])
        x = x + torch.cat([cls_emb[None, :, :], pos_emb[None, :, :]],
                          dim=1).to(x.dtype)
        return x

    def forward(self,
                x: torch.Tensor,
                patch_num: int = None) -> List[torch.Tensor]:
        """
        : param x: (batch_size, num_patch, n_pixels)
        """
        if patch_num is None:
            patch_num = self.patch_num
        B, N, D = x.shape

        x = self.patch_embedding(x)

        # class embeddings and positional embeddings
        x = torch.cat(
            [_expand_token(self.class_embedding, x.shape[0]).to(x.dtype), x],
            dim=1)
        x = self.add_pos_emb(x, patch_num)

        x = self.pre_ln(x)

        hidden_states = self.transformer(x)
        return hidden_states


class MolmoAttention(nn.Module):
    """Molmo's LLM attention."""

    def __init__(
        self,
        config: PretrainedConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
        self.tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = config.num_attention_heads

        assert self.hidden_size % self.total_num_heads == 0
        assert self.total_num_heads % self.tp_size == 0

        self.num_heads = self.total_num_heads // self.tp_size
        self.total_num_kv_heads = config.num_key_value_heads \
            or self.total_num_heads
        if self.total_num_kv_heads >= self.tp_size:
            assert self.total_num_kv_heads % self.tp_size == 0
        else:
            assert self.tp_size % self.total_num_kv_heads == 0

        self.num_kv_heads = max(1, self.total_num_kv_heads // self.tp_size)
        self.head_dim = self.hidden_size // self.total_num_heads
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.max_position_embeddings = config.max_position_embeddings
        self.rope_theta = config.rope_theta

        # Attention input projection. Projects x -> (q, k, v)
        self.qkv_proj = QKVParallelLinear(
            self.hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=config.qkv_bias,
            quant_config=quant_config,
        )

        self.tp_rank: Optional[int] = None
        self.k_norm: Optional[nn.Module] = None
        self.q_norm: Optional[nn.Module] = None
        if config.attention_layer_norm:
            self.tp_rank = get_tensor_model_parallel_rank()
            self.k_norm = RMSNorm(self.total_num_kv_heads * self.head_dim,
                                  eps=config.layer_norm_eps)
            self.q_norm = RMSNorm(config.hidden_size,
                                  eps=config.layer_norm_eps)

        # Rotary embeddings.
        self.rotary_emb = get_rope(
            self.head_dim,
            rotary_dim=self.head_dim,
            max_position=self.max_position_embeddings,
            base=self.rope_theta,
        )
        self.scaling = self.head_dim**-0.5
        self.attn = Attention(self.num_heads,
                              self.head_dim,
                              self.scaling,
                              num_kv_heads=self.num_kv_heads,
                              cache_config=cache_config,
                              quant_config=quant_config,
                              prefix=f"{prefix}.attn")

        # Attention output projection.
        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            self.hidden_size,
            bias=False,
            quant_config=quant_config,
        )

    def _apply_qk_norm(self, q: torch.Tensor,
                       k: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        if self.tp_size > 1:
            q = tensor_model_parallel_all_gather(q.contiguous())
            k = tensor_model_parallel_all_gather(k.contiguous())
        q = self.q_norm.forward_native(q)
        k = self.k_norm.forward_native(k)
        if self.tp_size > 1:
            splitter = partial(split_tensor_along_last_dim,
                               num_partitions=self.tp_size)
            q = splitter(q)[self.tp_rank]
            k = splitter(k)[self.tp_rank]
        return q, k

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
        if self.q_norm is not None and self.k_norm is not None:
            q, k = self._apply_qk_norm(q, k)
        q, k = self.rotary_emb(positions, q, k)
        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
        output, _ = self.o_proj(attn_output)
        return output


class MolmoMLP(nn.Module):
    """Molmo's LLM mlp."""

    def __init__(
        self,
        config: PretrainedConfig,
        input_dim: Optional[int] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size // 2

        # Feed-forward input projection.
        self.gate_up_proj = MergedColumnParallelLinear(
            input_dim or self.hidden_size,
            [self.intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
        )

        # Activation function.
        self.act_fn = SiluAndMul()

        # Feed-forward output projection.
        self.down_proj = RowParallelLinear(
            self.intermediate_size,
            self.hidden_size,
            bias=False,
            quant_config=quant_config,
        )

    def forward(
        self,
        x: torch.Tensor,
    ) -> torch.Tensor:
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


class MolmoDecoderLayer(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        # Attention block.
        self.self_attn = MolmoAttention(config,
                                        cache_config,
                                        quant_config,
                                        prefix=f"{prefix}.self_attn")

        # MLP block.
        self.mlp = MolmoMLP(config, quant_config=quant_config)

        # LayerNorm
        assert config.layer_norm_type == "rms"
        self.input_layernorm = RMSNorm(config.hidden_size,
                                       eps=config.layer_norm_eps)
        self.post_attention_layernorm = RMSNorm(config.hidden_size,
                                                eps=config.layer_norm_eps)

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
        residual: Optional[torch.Tensor],
    ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
        # Self Attention
        if residual is None:
            residual = hidden_states
            hidden_states = self.input_layernorm(hidden_states)
        else:
            hidden_states, residual = self.input_layernorm(
                hidden_states, residual)
        hidden_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
            kv_cache=kv_cache,
            attn_metadata=attn_metadata,
        )

        hidden_states, residual = self.post_attention_layernorm(
            hidden_states, residual)
        hidden_states = self.mlp(hidden_states)
        return hidden_states, residual


class MolmoDecoderNormAfterLayer(MolmoDecoderLayer):

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
        residual: Optional[torch.Tensor],
    ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
        # Self Attention
        residual = hidden_states
        hidden_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
            kv_cache=kv_cache,
            attn_metadata=attn_metadata,
        )

        hidden_states = self.input_layernorm(hidden_states)
        hidden_states = hidden_states + residual
        residual = hidden_states

        hidden_states = self.mlp(hidden_states)
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = hidden_states + residual
        residual = None
        return hidden_states, residual


class MolmoVisionBackbone(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        vision_config: VisionBackboneConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        self.vit_layers = VIT_LAYERS
        self.image_num_patch = vision_config.image_num_patch
        self.llm_patches_per_crop = (
            (self.image_num_patch[0] + 1) // 2,
            (self.image_num_patch[1] + 1) // 2,
        )
        self.image_vit = VisionTransformer(vision_config,
                                           quant_config=quant_config)
        self.num_prefix_tokens = self.image_vit.num_prefix_tokens
        assert self.num_prefix_tokens in {
            0, 1
        }, "Only 0 or 1 prefix tokens are supported"
        self.image_pooling_2d = MultiHeadDotProductAttention(
            vision_config,
            nlayers=len(self.vit_layers),
            quant_config=quant_config)
        self.image_projector = MolmoMLP(
            config,
            input_dim=vision_config.image_emb_dim,
            quant_config=quant_config,
        )

        image_dim = vision_config.image_emb_dim * len(self.vit_layers)
        self.pad_embed = nn.Parameter(torch.zeros((2, image_dim)))

    @property
    def dtype(self) -> torch.dtype:
        return self.image_vit.patch_embedding.weight.dtype

    @property
    def device(self) -> torch.device:
        return self.image_vit.patch_embedding.weight.device

    def encode_image(self, images: torch.Tensor) -> torch.Tensor:
        """
        : param images: (batch_size, num_crops, num_patch, n_pixels)
        """
        B, T, N, D = images.shape

        mask = ~torch.all(
            images.view(B * T, N, D) == -1, dim=(1, 2), keepdim=True)

        images = images.view(B * T, N, D)
        image_features = self.image_vit(images)

        if self.vit_layers is not None:
            features = []
            for layer in self.vit_layers:
                features.append(image_features[layer])
            image_features = torch.cat(features, dim=-1)
        else:
            image_features = image_features[-1]

        if self.num_prefix_tokens > 0:
            image_features = image_features[:, 1:]

        image_features = image_features * mask
        image_features = image_features.view(B, T, N, -1)

        return image_features

    def forward(
        self, images: torch.Tensor, image_masks: torch.Tensor
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:

        # image_features: (batch_size, num_crops(=num_image), num_patch, nximage_emb_dim) # noqa: E501
        batch_size, num_image = images.shape[:2]
        images = images.to(device=self.device, dtype=self.dtype)
        image_features = self.encode_image(images)

        og_dtype = image_features.dtype
        assert image_masks is not None
        pad_embed = self.pad_embed[:, None, None, None, :]
        all_pad = image_masks == 0
        partial_pad = torch.logical_and(
            image_masks < 1,
            torch.logical_not(all_pad)).to(dtype=torch.float32)
        all_pad = all_pad.to(dtype=torch.float32)
        image_features = image_features + pad_embed[0] * torch.unsqueeze(
            all_pad, -1)
        image_features = image_features + pad_embed[1] * torch.unsqueeze(
            partial_pad, -1)

        image_features = image_features.to(og_dtype)

        image_features = image_features.reshape(
            (batch_size, num_image) + self.image_num_patch + (-1, ), )

        if self.image_num_patch[0] % 2 == 1:
            # Pad so we can still pool 2x2 patches
            image_features = F.pad(
                image_features,
                (0, 0, 0, 1, 0, 1, 0, 0, 0, 0),
            )

        # image pooling
        image_features = rearrange(
            image_features,
            'b n (h dh) (w dw) c -> (b n h w) (dh dw) c',
            dh=2,
            dw=2,
        )

        query = image_features.mean(-2, keepdim=True)
        image_features = self.image_pooling_2d(query, image_features)

        h, w = self.llm_patches_per_crop
        image_features = image_features.view(batch_size, num_image, h * w, -1)

        image_features = self.image_projector(image_features)

        # image_features: (batch_size, num_image, num_patch, d_model)
        return image_features

    def load_weights(self, weights: Iterable[Tuple[str,
                                                   torch.Tensor]]) -> Set[str]:
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]
        params_dict = dict(self.named_parameters())
        loaded_params: Set[str] = set()

        for name, loaded_weight in weights:
            for (param_name, weight_name, shard_id) in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                if is_pp_missing_parameter(name, self):
                    continue
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                if name.endswith(".bias") and name not in params_dict:
                    continue
                if is_pp_missing_parameter(name, self):
                    continue
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params


@support_torch_compile
class MolmoModel(nn.Module):

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()

        config = vllm_config.model_config.hf_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config

        self.config = config

        self.embedding_size = config.embedding_size or config.vocab_size
        self.embedding_size += ADDITIONAL_VOCAB_SIZE
        self.embed_tokens = VocabParallelEmbedding(
            self.embedding_size,
            config.hidden_size,
            quant_config=quant_config,
        )

        decoder_layer = MolmoDecoderNormAfterLayer if config.norm_after \
            else MolmoDecoderLayer
        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
            lambda prefix: decoder_layer(
                config, cache_config, quant_config, prefix=prefix),
            prefix=f"{prefix}.layers",
        )

        assert config.layer_norm_type == "rms"
        self.norm = RMSNorm(config.hidden_size, config.layer_norm_eps)

        self.make_empty_intermediate_tensors = (
            make_empty_intermediate_tensors_factory(
                ["hidden_states", "residual"], config.hidden_size))

    def get_input_embeddings(
        self,
        input_ids: torch.Tensor,
    ) -> torch.Tensor:
        return self.embed_tokens(input_ids)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        if get_pp_group().is_first_rank:
            if inputs_embeds is not None:
                hidden_states = inputs_embeds
            else:
                hidden_states = self.embed_tokens(input_ids)
            residual = None
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]

        # Apply blocks one-by-one.
        for i in range(self.start_layer, self.end_layer):
            layer = self.layers[i]
            hidden_states, residual = layer(
                positions,
                hidden_states,
                kv_caches[i - self.start_layer],
                attn_metadata,
                residual,
            )
        if not get_pp_group().is_last_rank:
            return IntermediateTensors({
                "hidden_states": hidden_states,
                "residual": residual
            })
        if residual is not None:
            hidden_states, _ = self.norm(hidden_states, residual)
        else:
            hidden_states = self.norm(hidden_states)
        return hidden_states

    def load_weights(self, weights: Iterable[Tuple[str,
                                                   torch.Tensor]]) -> Set[str]:
        params_dict = dict(self.named_parameters())
        loaded_params: Set[str] = set()

        for name, loaded_weight in weights:
            if "gate_up_proj" in name:
                up_proj, gate_proj = loaded_weight.chunk(2, dim=0)
                loaded_weight = torch.cat([gate_proj, up_proj], dim=0)

            if name.endswith(".bias") and name not in params_dict:
                continue
            if is_pp_missing_parameter(name, self):
                continue

            param = params_dict[name]
            weight_loader = getattr(param, "weight_loader",
                                    default_weight_loader)
            weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params


cached_get_processor = lru_cache(get_processor)


def get_num_patches(num_tiles: int, crop_patches: int, left_margin: int,
                    right_margin: int, pooling_size: int) -> int:
    crop_window_patches = crop_patches - (left_margin + right_margin)
    if num_tiles > 1:
        left_crop_window_patches = (crop_window_patches + left_margin +
                                    pooling_size -
                                    1) // pooling_size * pooling_size
        middle_crop_window_patches = (crop_window_patches + pooling_size -
                                      1) // pooling_size * pooling_size
        right_crop_window_patches = (crop_window_patches + right_margin +
                                     pooling_size -
                                     1) // pooling_size * pooling_size
        return left_crop_window_patches + (
            num_tiles -
            2) * middle_crop_window_patches + right_crop_window_patches
    else:
        single_crop_window_patches = (crop_patches + pooling_size -
                                      1) // pooling_size * pooling_size
        return single_crop_window_patches


def get_tokens(tiling_h: int, tiling_w: int, crop_patches: int,
               left_margin: int, right_margin: int, pooling_size: int) -> int:
    h = get_num_patches(tiling_h, crop_patches, left_margin, right_margin,
                        pooling_size)
    w = get_num_patches(tiling_w, crop_patches, left_margin, right_margin,
                        pooling_size)
    per_row = w // pooling_size + 1
    joint = per_row * (h // pooling_size) + 2
    image_token_length = (crop_patches + pooling_size - 1) // pooling_size
    resize = (image_token_length + 1) * image_token_length + 2
    return resize + joint


def get_max_tokens(max_crops: int, crop_patches: int, left_margin: int,
                   right_margin: int, pooling_size: int) -> int:
    tilings = []
    for i in range(1, max_crops + 1):
        for j in range(1, max_crops + 1):
            if i * j <= max_crops:
                tilings.append((i, j))
    tokens = [
        get_tokens(tilings[i][0], tilings[i][1], crop_patches, left_margin,
                   right_margin, pooling_size) for i in range(len(tilings))
    ]
    return max(tokens)


def get_max_molmo_image_tokens(ctx: InputContext) -> int:
    processor = cached_get_processor(
        ctx.model_config.model,
        trust_remote_code=ctx.model_config.trust_remote_code,
        revision=ctx.model_config.code_revision)
    image_processor = processor.image_processor
    max_llm_image_tokens = get_max_tokens(
        image_processor.max_crops,
        image_processor.base_image_input_size[0] //
        image_processor.image_patch_size,
        image_processor.overlap_margins[0],
        image_processor.overlap_margins[1],
        2,
    )
    return max_llm_image_tokens


# NOTE: preprocessing for the image data has been included in the
# 'input_processor_for_molmo' function
def image_input_mapper_for_molmo(
    ctx: InputContext,
    data: object,
):
    if isinstance(data, list):
        data = data[0]
    return MultiModalKwargs(data)


def dummy_data_for_molmo(ctx: InputContext, seq_len: int,
                         mm_counts: Mapping[str, int]):
    processor = cached_get_processor(
        ctx.model_config.model,
        trust_remote_code=ctx.model_config.trust_remote_code,
        revision=ctx.model_config.code_revision)
    image_processor = processor.image_processor

    base_image_input_d = image_processor.image_patch_size
    left_margin, right_margin = image_processor.overlap_margins
    max_crops = image_processor.max_crops

    # Assume: prompt_token_ids always starts with bos_token_id followed image tokens # noqa: E501
    max_llm_image_tokens = get_max_molmo_image_tokens(ctx)
    if seq_len - max_llm_image_tokens - 1 < 0:
        raise RuntimeError(
            f"Molmo cannot process {max_crops} crops in a prompt, "
            "please increase max_model_len or reduce number of crops")

    # The vertical image has the maximum number of image tokens due to column tokens. # noqa: E501
    tiling = (max_crops, 1)
    total_margin_pixels = base_image_input_d * (right_margin + left_margin)
    crop_patches = image_processor.base_image_input_size[
        0] // base_image_input_d
    crop_window_patches = crop_patches - (right_margin + left_margin)
    crop_window_size = crop_window_patches * base_image_input_d

    h = crop_window_size * tiling[0] + total_margin_pixels
    w = crop_window_size * tiling[1] + total_margin_pixels

    dummy_image = Image.new("RGB", (w, h), color="red")

    out = processor.process("dummy prompt", dummy_image)

    token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE,
                      out["input_ids"][:1 + max_llm_image_tokens])
    token_ids += array(VLLM_TOKEN_ID_ARRAY_TYPE,
                       [0]) * (seq_len - max_llm_image_tokens - 1)
    dummy_seqdata = SequenceData(token_ids)
    dummy_imgdata = {
        "images": out["images"],
        "image_input_idx": out["image_input_idx"],
    }
    if "image_masks" in out:
        dummy_imgdata["image_masks"] = out["image_masks"]
    dummy_imgdata["seq_len"] = torch.tensor(seq_len, dtype=torch.long)
    size = 0
    offset = -1
    for i in range(len(token_ids)):
        if token_ids[i] in (DEFAULT_IMAGE_PATCH_TOKEN_ID,
                            DEFAULT_IM_START_TOKEN_ID, DEFAULT_IM_END_TOKEN_ID,
                            DEFAULT_IM_COL_TOKEN_ID):
            if offset < 0:
                offset = i
            size += 1
    dummy_imgdata["image_start_end"] = (offset, offset + size)
    return DummyData(seq_data=dummy_seqdata,
                     multi_modal_data={"image": dummy_imgdata},
                     multi_modal_placeholders={
                         "image":
                         [PlaceholderRange(offset=offset, length=size)]
                     })


def pad_images(
    max_total_crops: int,
    images: torch.Tensor,
    image_input_idx: torch.Tensor,
    image_masks: Optional[torch.Tensor] = None,
):
    n = max_total_crops - images.shape[0]
    images = F.pad(images, (0, 0, 0, 0, 0, n), value=-1)
    image_input_idx = F.pad(image_input_idx, (0, 0, 0, n), value=-1)
    if image_masks is not None:
        image_masks = F.pad(image_masks, (0, 0, 0, n), value=-1)
    return images, image_input_idx, image_masks


def input_processor_for_molmo(ctx: InputContext, inputs: DecoderOnlyInputs):
    prompt = inputs.get("prompt")
    multi_modal_data = inputs.get("multi_modal_data")
    image = None if multi_modal_data is None else multi_modal_data.get("image")

    model_config = ctx.model_config
    processor = cached_get_processor(
        ctx.model_config.model,
        trust_remote_code=model_config.trust_remote_code,
        revision=ctx.model_config.code_revision)
    tokenizer = cached_get_tokenizer(
        model_config.tokenizer,
        trust_remote_code=model_config.trust_remote_code)

    # NOTE: message formatting for raw text prompt is only applied for
    # offline inference; for online inference, the prompt is always in
    # instruction format and tokenized.
    if prompt is not None and re.match(r"^User:[\s\S]*?(Assistant:)*$",
                                       prompt):
        out = processor.process(prompt, image, message_format="none")
    elif prompt is not None:
        out = processor.process(prompt, image)
    else:
        out = processor.process(None, image, tokens=inputs["prompt_token_ids"])

    image_processor = processor.image_processor
    max_total_crops = 1 + image_processor.max_crops
    if image is not None:
        images, image_input_idx, image_masks = pad_images(
            max_total_crops,
            out["images"],
            out["image_input_idx"],
            out.get("image_masks"),
        )
    else:
        base_image_input_size = image_processor.base_image_input_size
        image_patch_size = image_processor.image_patch_size
        image_num_patch = (
            base_image_input_size[0] // image_patch_size,
            base_image_input_size[1] // image_patch_size,
        )
        n_pixels = image_patch_size * image_patch_size * 3
        n_patches = image_num_patch[0] * image_num_patch[1]

        image_length_w = image_processor.image_token_length_w
        image_length_h = image_processor.image_token_length_h
        tokens_per_image = image_length_w * image_length_h
        images = torch.full(
            (max_total_crops, n_patches, n_pixels),
            -1,
            dtype=torch.float32,
        )
        image_input_idx = torch.full(
            (max_total_crops, tokens_per_image),
            -1,
            dtype=torch.int32,
        )
        if image_processor.image_padding_mask:
            image_masks = torch.full(
                (max_total_crops, n_patches),
                -1,
                dtype=torch.float32,
            )

    image_data = dict(
        images=images,
        image_input_idx=image_input_idx,
    )
    if image_masks is not None:
        image_data["image_masks"] = image_masks

    new_prompt_token_ids = out["input_ids"].tolist()
    image_data["seq_len"] = torch.tensor(len(new_prompt_token_ids),
                                         dtype=torch.long)

    multi_modal_data = dict(image=image_data)
    size = 0
    offset = -1
    for i in range(len(new_prompt_token_ids)):
        if new_prompt_token_ids[i] in (DEFAULT_IMAGE_PATCH_TOKEN_ID,
                                       DEFAULT_IM_START_TOKEN_ID,
                                       DEFAULT_IM_END_TOKEN_ID,
                                       DEFAULT_IM_COL_TOKEN_ID):
            if offset < 0:
                offset = i
            size += 1
    image_data["image_start_end"] = (offset, offset + size)

    prompt = inputs.get("prompt")
    if prompt is None:
        prompt = tokenizer.decode(new_prompt_token_ids)

    return token_inputs(
        prompt_token_ids=new_prompt_token_ids,
        prompt=prompt,
        multi_modal_data=multi_modal_data,
        multi_modal_placeholders={
            "image": [PlaceholderRange(offset=offset, length=size)]
        },
    )


@MULTIMODAL_REGISTRY.register_image_input_mapper(image_input_mapper_for_molmo)
@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_molmo_image_tokens)
@INPUT_REGISTRY.register_dummy_data(dummy_data_for_molmo)
@INPUT_REGISTRY.register_input_processor(input_processor_for_molmo)
class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        multimodal_config = vllm_config.model_config.multimodal_config
        self.config = config
        self.multimodal_config = multimodal_config

        vision_config = VisionBackboneConfig()
        self.vision_backbone = MolmoVisionBackbone(config, vision_config,
                                                   quant_config)
        self.model = MolmoModel(vllm_config=vllm_config,
                                prefix=maybe_prefix(prefix, "model"))

        if self.config.weight_tying:
            self.lm_head = self.model.transformer.wte
        else:
            self.lm_head = ParallelLMHead(
                config.embedding_size or config.vocab_size,
                config.hidden_size,
                quant_config=quant_config,
            )

        self.logits_processor = LogitsProcessor(config.embedding_size
                                                or config.vocab_size)
        self.sampler = get_sampler()

        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors)

    def _parse_and_validate_image_input(
        self,
        **kwargs: object,
    ) -> Optional[MolmoImageInputs]:
        images = kwargs.pop("images", None)
        image_masks = kwargs.pop("image_masks", None)
        image_start_end = kwargs.pop("image_start_end", None)
        if images is None:
            return None

        image_input_idx = kwargs.pop("image_input_idx", None)
        seq_len = kwargs.pop("seq_len", None)
        if image_input_idx is None:
            raise ValueError("image_input_idx is required for Molmo model.")
        if seq_len is None:
            raise ValueError("seq_len is required for Molmo model.")
        if not isinstance(seq_len, torch.Tensor):
            seq_len = torch.tensor(seq_len)

        return MolmoImageInputs(
            images=images,
            image_input_idx=image_input_idx,
            seq_len=seq_len,
            image_masks=image_masks,
            image_start_end=image_start_end,
        )

    def _process_image_input(
        self,
        image_input: MolmoImageInputs,
    ) -> torch.Tensor:

        image_features = self.vision_backbone(
            images=image_input["images"],
            image_masks=image_input["image_masks"],
        )

        return image_features

    def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]:
        image_input = self._parse_and_validate_image_input(**kwargs)
        if image_input is None:
            return None
        image_features = self._process_image_input(image_input)
        image_input_idx = image_input["image_input_idx"]
        seq_len = image_input["seq_len"]
        batch_size, num_image, num_patch = image_features.shape[:3]
        assert image_input_idx.shape == (batch_size, num_image, num_patch)

        # insert the image feature into the embedding.
        image_features = image_features.view(batch_size, num_image * num_patch,
                                             -1)
        image_input_idx = image_input_idx.view(batch_size,
                                               num_image * num_patch)

        valid = image_input_idx >= 0
        image_features = image_features * valid[:, :, None].to(
            image_features.dtype)
        image_features = image_features.view(
            batch_size * num_image * num_patch, -1).contiguous()

        image_input_idx = image_input_idx * valid.to(image_input_idx.dtype)
        offset = torch.cat([seq_len.new_zeros(1),
                            seq_len.cumsum(dim=0)[:-1]],
                           dim=0)[:, None]
        image_input_idx = image_input_idx + offset.to(image_input_idx.dtype)
        image_input_idx = image_input_idx.flatten()[:, None]
        mat = image_input_idx == torch.arange(
            seq_len.sum().item(), device=image_features.device)[None, :]
        mat = mat.to(image_features.dtype)

        # Note: In this original implementation from AI2, the final
        # vision_embeddings will be always be the same length
        # of input embeddings.
        vision_embeddings = torch.einsum('nd,nm->md', image_features, mat)

        # Split by the sizes of the input sequences. For each full embedding,
        # extract the actual vision embeddings to be merged.
        vision_embeddings = list(vision_embeddings.split(seq_len.tolist()))
        for i in range(len(vision_embeddings)):
            start, end = image_input['image_start_end'][i]
            vision_embeddings[i] = vision_embeddings[i][start:end]

        return vision_embeddings

    def get_input_embeddings(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: Optional[NestedTensors] = None,
    ) -> torch.Tensor:
        inputs_embeds = self.model.get_input_embeddings(input_ids)
        if multimodal_embeddings is not None:
            inputs_embeds = merge_multimodal_embeddings(
                input_ids, inputs_embeds, multimodal_embeddings, [
                    DEFAULT_IMAGE_PATCH_TOKEN_ID, DEFAULT_IM_START_TOKEN_ID,
                    DEFAULT_IM_END_TOKEN_ID, DEFAULT_IM_COL_TOKEN_ID
                ])
        return inputs_embeds

    def forward(
        self,
        input_ids: torch.LongTensor,
        positions: torch.LongTensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        **kwargs: object,
    ) -> SamplerOutput:

        if intermediate_tensors is not None:
            inputs_embeds = None

        # NOTE: In v1, inputs_embeds is always generated at model runner, this
        # condition is for v0 compatibility.
        elif inputs_embeds is None:
            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
            inputs_embeds = self.get_input_embeddings(input_ids,
                                                      vision_embeddings)
            input_ids = None

        hidden_states = self.model(input_ids,
                                   positions,
                                   kv_caches,
                                   attn_metadata,
                                   intermediate_tensors,
                                   inputs_embeds=inputs_embeds)

        return hidden_states

    def compute_logits(self, hidden_states: torch.Tensor,
                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
        logits = self.logits_processor(self.lm_head, hidden_states,
                                       sampling_metadata)
        return logits

    def sample(
        self,
        logits: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
        next_tokens = self.sampler(logits, sampling_metadata)
        return next_tokens

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        hf_to_vllm_mapper = WeightsMapper(
            orig_to_new_substr={
                # vision backbone mapping
                "image_projector.w1.": "image_projector.gate_proj.",
                "image_projector.w3.": "image_projector.up_proj.",
                "image_projector.w2.": "image_projector.down_proj.",
                # language backbone mapping
                "att_proj": "self_attn.qkv_proj",
                "attn_out": "self_attn.o_proj",
                "q_norm": "self_attn.q_norm",
                "k_norm": "self_attn.k_norm",
                "ff_proj": "mlp.gate_up_proj",
                "ff_out": "mlp.down_proj",
                "attn_norm": "input_layernorm",
                "ff_norm": "post_attention_layernorm",
            },
            orig_to_new_prefix={
                # vision backbone mapping
                "model.vision_backbone.": "vision_backbone.",
                # language backbone mapping
                "model.transformer.blocks.": "model.layers.",
                "model.transformer.ln_f.": "model.norm.",
                # lm_head is renamed to model.transformer.mlp.down_proj firstly,
                # we need to run a second renaming for it
                "model.transformer.mlp.down_proj.": "lm_head.",
            },
        )
        loader = AutoWeightsLoader(self)
        weights = _get_weights_with_merged_embedding(weights)
        return loader.load_weights(weights, mapper=hf_to_vllm_mapper)


def _get_weights_with_merged_embedding(
    weights: Iterable[Tuple[str, torch.Tensor]]
) -> Iterable[Tuple[str, torch.Tensor]]:
    embedding_weights = {}
    for name, weight in weights:
        if "wte.embedding" in name:
            embedding_weights["embedding"] = weight
        elif "wte.new_embedding" in name:
            embedding_weights["new_embedding"] = weight
        else:
            yield (name, weight)
    # this is compatible with most of quantization,
    # because they won't quantize embed_tokens
    embedding_weights = torch.cat(
        [embedding_weights["embedding"], embedding_weights["new_embedding"]],
        dim=0,
    )
    yield ("model.embed_tokens.weight", embedding_weights)