[Model]: Add support for Aria model (#10514)

Signed-off-by: xffxff <1247714429@qq.com>
Co-authored-by: Isotr0py <2037008807@qq.com>

docs/source/models/supported_models.rst

@@ -476,6 +476,12 @@ Text Generation
     - Example HF Models
     - :ref:`LoRA <lora>`
     - :ref:`PP <distributed_serving>`
+  * - :code:`AriaForConditionalGeneration`
+    - Aria
+    - T + I
+    - :code:`rhymes-ai/Aria`
+    - 
+    - ✅︎
   * - :code:`Blip2ForConditionalGeneration`
     - BLIP-2
     - T + I\ :sup:`E`

examples/offline_inference_vision_language.py

@@ -402,6 +402,23 @@ def run_idefics3(question: str, modality: str):
     return llm, prompt, stop_token_ids
 
 
+# Aria
+def run_aria(question: str, modality: str):
+    assert modality == "image"
+    model_name = "rhymes-ai/Aria"
+
+    llm = LLM(model=model_name,
+              tokenizer_mode="slow",
+              trust_remote_code=True,
+              dtype="bfloat16")
+
+    prompt = (f"<|im_start|>user\n<fim_prefix><|img|><fim_suffix>\n{question}"
+              "<|im_end|>\n<|im_start|>assistant\n")
+
+    stop_token_ids = [93532, 93653, 944, 93421, 1019, 93653, 93519]
+    return llm, prompt, stop_token_ids
+
+
 model_example_map = {
     "llava": run_llava,
     "llava-next": run_llava_next,
@@ -423,6 +440,7 @@ model_example_map = {
     "molmo": run_molmo,
     "glm4v": run_glm4v,
     "idefics3": run_idefics3,
+    "aria": run_aria,
 }
 
 

examples/offline_inference_vision_language_multi_image.py

@@ -321,6 +321,25 @@ def load_idefics3(question, image_urls: List[str]) -> ModelRequestData:
     )
 
 
+def load_aria(question, image_urls: List[str]) -> ModelRequestData:
+    model_name = "rhymes-ai/Aria"
+    llm = LLM(model=model_name,
+              tokenizer_mode="slow",
+              trust_remote_code=True,
+              dtype="bfloat16",
+              limit_mm_per_prompt={"image": len(image_urls)})
+    placeholders = "<fim_prefix><|img|><fim_suffix>\n" * len(image_urls)
+    prompt = (f"<|im_start|>user\n{placeholders}{question}<|im_end|>\n"
+              "<|im_start|>assistant\n")
+    stop_token_ids = [93532, 93653, 944, 93421, 1019, 93653, 93519]
+    return ModelRequestData(
+        llm=llm,
+        prompt=prompt,
+        stop_token_ids=stop_token_ids,
+        image_data=[fetch_image(url) for url in image_urls],
+        chat_template=None)
+
+
 model_example_map = {
     "phi3_v": load_phi3v,
     "h2ovl_chat": load_h2onvl,
@@ -330,6 +349,7 @@ model_example_map = {
     "qwen_vl_chat": load_qwenvl_chat,
     "mllama": load_mllama,
     "idefics3": load_idefics3,
+    "aria": load_aria,
 }
 
 

tests/models/registry.py

@@ -43,6 +43,8 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
                                          trust_remote_code=True),
     "ArcticForCausalLM": _HfExamplesInfo("Snowflake/snowflake-arctic-instruct",
                                          trust_remote_code=True),
+    "AriaForConditionalGeneration": _HfExamplesInfo("rhymes-ai/Aria",
+                                                    trust_remote_code=True),
     "BaiChuanForCausalLM": _HfExamplesInfo("baichuan-inc/Baichuan-7B",
                                          trust_remote_code=True),
     "BaichuanForCausalLM": _HfExamplesInfo("baichuan-inc/Baichuan2-7B-chat",

vllm/entrypoints/chat_utils.py

@@ -412,6 +412,8 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
                 return ""
             if model_type == "idefics3":
                 return "<image>"
+            if model_type == "aria":
+                return "<|fim_prefix|><|img|><|fim_suffix|>"
 
             raise TypeError(f"Unknown {modality} model type: {model_type}")
         elif modality == "audio":

vllm/model_executor/models/aria.py

@@ -0,0 +1,695 @@
+import math
+from typing import Iterable, List, Optional, Set, Tuple, TypedDict, Union
+
+import torch
+import torch.nn as nn
+from torch.nn.init import trunc_normal_
+from transformers import LlamaConfig
+
+from vllm.attention import AttentionMetadata
+from vllm.config import CacheConfig, QuantizationConfig, VllmConfig
+from vllm.distributed import get_tensor_model_parallel_rank
+from vllm.inputs import INPUT_REGISTRY, token_inputs
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    get_compressed_tensors_cache_scale)
+from vllm.model_executor.layers.sampler import (Sampler, SamplerOutput,
+                                                SamplingMetadata)
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.models.idefics2_vision_model import (
+    Idefics2VisionTransformer)
+from vllm.model_executor.models.interfaces import SupportsMultiModal
+from vllm.model_executor.models.llama import (LlamaDecoderLayer, LlamaMLP,
+                                              LlamaModel)
+from vllm.model_executor.models.utils import (AutoWeightsLoader, WeightsMapper,
+                                              is_pp_missing_parameter,
+                                              make_layers, maybe_prefix,
+                                              merge_multimodal_embeddings)
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.base import MultiModalInputs
+from vllm.multimodal.image import cached_get_image_processor
+from vllm.multimodal.inputs import NestedTensors
+from vllm.multimodal.utils import (cached_get_tokenizer,
+                                   repeat_and_pad_placeholder_tokens)
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.configs.aria import (AriaMoELMConfig,
+                                                  AriaVisionConfig)
+
+from .utils import flatten_bn
+
+
+class AriaImagePixelInputs(TypedDict):
+    pixel_values: torch.Tensor
+    pixel_mask: Optional[torch.Tensor]
+    """
+    Shape: 
+        pixel_values: `(batch_size * num_images, num_channels, height, width)`
+        pixel_mask: `(batch_size * num_images, height, width)`
+    """
+
+
+class AriaVisionTransformer(Idefics2VisionTransformer):
+    """
+    AriaVisionTransformer is a modified version of Idefics2VisionTransformer
+    that replaces the post-layernorm with an identity layer.
+    """
+
+    def __init__(
+        self,
+        config: AriaVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__(config, quant_config, prefix)
+        self.post_layernorm = nn.Identity()
+
+
+class AriaVisionModel(nn.Module):
+    config_class = AriaVisionConfig
+
+    def __init__(
+        self,
+        config: AriaVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.vision_model = AriaVisionTransformer(
+            config,
+            quant_config,
+            prefix=f"{prefix}.vision_model",
+        )
+
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        pixel_mask: Optional[torch.BoolTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.BoolTensor]]:
+        patch_attention_mask = self._create_patch_attention_mask(pixel_mask)
+
+        vit_oup = self.vision_model(
+            pixel_values=pixel_values,
+            patch_attention_mask=patch_attention_mask,
+        )
+
+        image_atts = self._create_image_attention_mask(patch_attention_mask)
+
+        return vit_oup, image_atts
+
+    def _create_patch_attention_mask(self, pixel_mask):
+        if pixel_mask is None:
+            return None
+
+        patches_subgrid = pixel_mask.unfold(
+            dimension=1,
+            size=self.vision_model.config.patch_size,
+            step=self.vision_model.config.patch_size,
+        ).unfold(
+            dimension=2,
+            size=self.vision_model.config.patch_size,
+            step=self.vision_model.config.patch_size,
+        )
+        return (patches_subgrid.sum(dim=(-1, -2)) > 0).bool()
+
+    def _create_image_attention_mask(self, patch_attention_mask):
+        if patch_attention_mask is None:
+            return None
+
+        flattened_mask = patch_attention_mask.flatten(1)
+        return torch.logical_not(flattened_mask)
+
+
+class FFN(nn.Module):
+
+    def __init__(self, embed_dim, ff_dim, output_dim):
+        super().__init__()
+        self.linear_in = ColumnParallelLinear(embed_dim, ff_dim, bias=False)
+        self.linear_out = RowParallelLinear(ff_dim, output_dim, bias=False)
+        self.act = get_act_fn("gelu_new")
+
+    def forward(self, hidden_states):
+        hidden_states, _ = self.linear_in(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.linear_out(hidden_states)
+        return hidden_states
+
+
+class CrossAttention(nn.Module):
+
+    def __init__(self, kv_dim, embed_dim, num_heads, drop_out_rate=0):
+        super().__init__()
+        self.num_heads = num_heads
+        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=False)
+        self.k_proj = nn.Linear(kv_dim, embed_dim, bias=False)
+        self.v_proj = nn.Linear(kv_dim, embed_dim, bias=False)
+
+        self.multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
+        self.linear = nn.Linear(embed_dim, embed_dim)
+        self.dropout = nn.Dropout(drop_out_rate)
+
+        self.layer_norm = nn.LayerNorm(embed_dim)
+        self.ln_kv = nn.LayerNorm(kv_dim)
+
+    def forward(self, x, hidden_states, attn_mask=None, add_residual=False):
+        normed_hidden_states = self.layer_norm(hidden_states)
+        query = self.q_proj(normed_hidden_states).permute(1, 0, 2)
+
+        x = self.ln_kv(x)
+        key = self.k_proj(x).permute(1, 0, 2)
+        value = self.v_proj(x).permute(1, 0, 2)
+
+        attn_output, _ = self.multihead_attn(query,
+                                             key,
+                                             value,
+                                             attn_mask=attn_mask)
+
+        attn_output = attn_output.permute(1, 0, 2)
+
+        if add_residual:
+            attn_output = hidden_states + self.dropout(
+                self.linear(attn_output))
+        else:
+            attn_output = self.dropout(self.linear(attn_output))
+
+        return attn_output
+
+
+class AriaProjector(nn.Module):
+    """
+    A projection module with one cross attention layer and one FFN layer, which
+    projects ViT's outputs into MoE's inputs.
+
+    Args:
+        patch_to_query_dict (dict): Maps patch numbers to their corresponding
+        query numbers,
+            e.g., {1225: 128, 4900: 256}. This allows for different query sizes
+            based on image resolution.
+        embed_dim (int): Embedding dimension. 
+        num_heads (int): Number of attention heads. 
+        kv_dim (int): Dimension of key and value. 
+        ff_dim (int): Hidden dimension of the feed-forward network. 
+        output_dim (int): Output dimension. 
+        norm_layer (nn.Module): Normalization layer. Default is nn.LayerNorm.
+
+    Outputs:
+        A tensor with the shape of (batch_size, query_number, output_dim)
+    """
+
+    def __init__(
+        self,
+        patch_to_query_dict,
+        embed_dim,
+        num_heads,
+        kv_dim,
+        ff_dim,
+        output_dim,
+        norm_layer=nn.LayerNorm,
+    ):
+        super().__init__()
+        self.patch_to_query_dict = patch_to_query_dict
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+
+        self.query = nn.Parameter(
+            torch.zeros(max(patch_to_query_dict.values()), self.embed_dim))
+
+        trunc_normal_(self.query, std=0.02)
+
+        self.cross_attn = CrossAttention(kv_dim, embed_dim, num_heads)
+
+        self.ln_ffn = norm_layer(embed_dim)
+        self.ffn = FFN(embed_dim, ff_dim, output_dim)
+
+    def forward(self, x, attn_mask=None):
+        bs = x.shape[0]
+        queries = self.query.unsqueeze(0).repeat(bs, 1, 1)
+
+        query_num = self.patch_to_query_dict.get(x.shape[1], None)
+        assert (query_num is not None
+                ), f"Query number for {x.shape[1]} patches is not provided"
+
+        queries = queries[:, :query_num, :]
+
+        if attn_mask is not None:
+            attn_mask = attn_mask.repeat_interleave(self.num_heads, 0)
+            attn_mask = attn_mask.unsqueeze(1).expand(-1, queries.size(1), -1)
+
+        attention_out = self.cross_attn(x, queries, attn_mask=attn_mask)
+
+        out = self.ffn(self.ln_ffn(attention_out))
+
+        return out
+
+
+class AriaFusedMoE(FusedMoE):
+
+    def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor,
+                      shard_id: str) -> Set[str]:
+        # Override the weight_loader to handle the expert weights in the Aria
+        # model, which are already packed with experts, and merge the gate and
+        # up weights for each expert.
+        # Note: Loading expert weights with quantization is not supported
+        tp_rank = get_tensor_model_parallel_rank()
+        if shard_id == 'w13':
+            # the shape of loaded_weight is
+            # (num_experts, hidden_size, 2 * moe_intermediate_size)
+            if self.tp_size > 1:
+                up, gate = loaded_weight.chunk(2, dim=-1)
+                up_current_rank = up.chunk(self.tp_size, dim=-1)[tp_rank]
+                gate_current_rank = gate.chunk(self.tp_size, dim=-1)[tp_rank]
+                up_and_gate = torch.cat([up_current_rank, gate_current_rank],
+                                        dim=-1).transpose(1, 2)
+                param.data.copy_(up_and_gate)
+            else:
+                param.data.copy_(loaded_weight.transpose(1, 2))
+        elif shard_id == 'w2':
+            # the shape of loaded_weight is
+            # (num_experts, moe_intermediate_size, hidden_size)
+            if self.tp_size > 1:
+                down_current_rank = loaded_weight.chunk(self.tp_size,
+                                                        dim=1)[tp_rank]
+                param.data.copy_(down_current_rank.transpose(1, 2))
+            else:
+                param.data.copy_(loaded_weight.transpose(1, 2))
+
+
+class MoELayer(nn.Module):
+    """
+    Mixture of Experts (MoE) Layer for the AriaMoE model.
+
+    This layer implements the MoE mechanism, which routes input tokens to
+    different experts based on a routing algorithm, processes them through the
+    experts, and then combines the outputs.
+    """
+
+    def __init__(
+        self,
+        config: AriaMoELMConfig,
+        quant_config: Optional[QuantizationConfig],
+    ) -> None:
+        super().__init__()
+        self.config = config
+
+        self.router_weight = nn.Parameter(
+            torch.empty(
+                (self.config.moe_num_experts, self.config.hidden_size)))
+
+        self.experts = AriaFusedMoE(
+            num_experts=config.moe_num_experts,
+            top_k=config.moe_topk,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.moe_intermediate_size,
+            quant_config=quant_config,
+            reduce_results=True,
+        )
+        self.shared_experts = LlamaMLP(
+            config.hidden_size,
+            config.moe_intermediate_size * config.moe_num_shared_experts,
+            "silu",
+            quant_config=quant_config,
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass of the MoE Layer.
+
+        Args:
+            hidden_states (torch.Tensor): Input tensor of shape (batch_size,
+            sequence_length, hidden_size).
+
+        Returns:
+            torch.Tensor: Output tensor after passing through the MoE layer.
+        """
+
+        router_output = torch.nn.functional.linear(hidden_states,
+                                                   self.router_weight)
+
+        shared_expert_output = self.shared_experts(hidden_states)
+        sparse_expert_output = self.experts(hidden_states, router_output)
+
+        return sparse_expert_output + shared_expert_output
+
+
+class MoEDecoderLayer(LlamaDecoderLayer):
+    """
+    Custom Decoder Layer for the AriaMoE model which modifies the standard
+    `LlamaDecoderLayer` by replacing the traditional MLP with a Mixture of
+    Experts (MoE) Layer.
+    """
+
+    def __init__(
+        self,
+        config: LlamaConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__(config, cache_config, quant_config, prefix)
+        self.mlp = MoELayer(config, quant_config=quant_config)
+
+
+class AriaMoELMModel(LlamaModel):
+    """
+    Custom LlamaModel for the AriaMoE model which modifies the standard
+    LlamaModel by replacing the `LlamaDecoderLayer` with `MoEDecoderLayer`.
+    """
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        # FIXME: this is a hack to disable the compilation of the model
+        self.do_not_compile = True
+
+        self.layers = None
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: MoEDecoderLayer(
+                config=config,
+                cache_config=cache_config,
+                quant_config=quant_config,
+                prefix=prefix,
+            ),
+            prefix=f"{prefix}.layers",
+        )
+
+    # Adapted from LlamaModel.load_weights with the modification of adding
+    # the expert weights mapping to `stacked_params_mapping`
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
+            ("experts.w13_weight", "experts.fc1.weight", 'w13'),
+            ("experts.w2_weight", "experts.fc2.weight", 'w2'),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: Set[str] = set()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if ("rotary_emb.cos_cached" in name
+                    or "rotary_emb.sin_cached" in name):
+                # Models trained using ColossalAI may include these tensors in
+                # the checkpoint. Skip them.
+                continue
+            if scale_name := get_compressed_tensors_cache_scale(name):
+                # Loading kv cache scales for compressed-tensors quantization
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                loaded_weight = loaded_weight[0]
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+            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:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Remapping the name of FP8 kv-scale.
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    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
+
+
+def build_mm_projector(config):
+    return AriaProjector(
+        patch_to_query_dict=config.projector_patch_to_query_dict,
+        embed_dim=config.vision_config.hidden_size,
+        num_heads=config.vision_config.num_attention_heads,
+        kv_dim=config.vision_config.hidden_size,
+        ff_dim=config.text_config.hidden_size,
+        output_dim=config.text_config.hidden_size,
+    )
+
+
+def get_max_multimodal_tokens(ctx):
+    return max(ctx.model_config.hf_config.image_size2tokens.values())
+
+
+def input_mapper_for_aria(ctx, data):
+    return MultiModalInputs(data)
+
+
+def input_processor(ctx, llm_inputs):
+    multi_modal_data = llm_inputs.get("multi_modal_data")
+    # if it is pure text input, use it as is
+    if multi_modal_data is None or "image" not in multi_modal_data:
+        return llm_inputs
+
+    model_config = ctx.model_config
+
+    tokenizer = cached_get_tokenizer(model_config.tokenizer)
+    image_processor = cached_get_image_processor(
+        model_config.model, trust_remote_code=model_config.trust_remote_code)
+    hf_config = model_config.hf_config
+
+    # prepare image tokens, the max_image_size is used to determine the number
+    # of patch_size for every image
+    max_image_size = multi_modal_data.pop("max_image_size", 980)
+    _split_image = multi_modal_data.pop("split_image", False)
+
+    assert isinstance(max_image_size,
+                      (int, float)), "max_image_size should be float or int"
+    images = (multi_modal_data["image"] if isinstance(
+        multi_modal_data["image"], list) else [multi_modal_data["image"]])
+
+    image_inputs = image_processor.preprocess(images,
+                                              max_image_size=max_image_size,
+                                              split_image=_split_image,
+                                              return_tensors="pt").data
+    image_inputs['pixel_values'] = image_inputs['pixel_values'].to(
+        ctx.model_config.dtype)
+    num_crops = image_inputs.pop("num_crops")
+
+    prompt_token_ids = llm_inputs["prompt_token_ids"]
+    if num_crops.sum().item() > 0:
+        _, prompt_token_ids, _ = repeat_and_pad_placeholder_tokens(
+            tokenizer,
+            None,
+            prompt_token_ids,
+            placeholder_token_id=hf_config.image_token_index,
+            repeat_count=num_crops,
+        )
+
+    repeat_count = [hf_config.image_size2tokens[max_image_size]
+                    ] * sum(num_crops).item()
+    new_prompt, new_token_ids, _ = repeat_and_pad_placeholder_tokens(
+        tokenizer,
+        None,
+        prompt_token_ids,
+        placeholder_token_id=hf_config.image_token_index,
+        repeat_count=repeat_count,
+    )
+
+    return token_inputs(
+        prompt_token_ids=new_token_ids,
+        prompt=new_prompt,
+        multi_modal_data={"image": image_inputs},
+    )
+
+
+@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_multimodal_tokens)
+@MULTIMODAL_REGISTRY.register_image_input_mapper(input_mapper_for_aria)
+@INPUT_REGISTRY.register_input_processor(input_processor)
+class AriaForConditionalGeneration(nn.Module, SupportsMultiModal):
+    """
+    Aria model for conditional generation tasks.
+
+    This model combines a vision tower, a multi-modal projector, and a language
+    model to perform tasks that involve both image and text inputs.
+    """
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+
+        # prepare the image_size to tokens mapping for the image preprocess, see
+        # input_processor
+        config.image_size2tokens = {
+            int(math.sqrt(k) * config.vision_config.patch_size): v
+            for k, v in config.projector_patch_to_query_dict.items()
+        }
+        self.config = config
+        self.vision_tower = AriaVisionModel(config.vision_config)
+        self.multi_modal_projector = build_mm_projector(config)
+        self.vocab_size = config.text_config.vocab_size
+        self.language_model = AriaMoELMModel(
+            vllm_config=vllm_config.with_hf_config(config.text_config),
+            prefix=maybe_prefix(prefix, "language_model.model"),
+        )
+        self.pad_token_id = (self.config.pad_token_id
+                             if self.config.pad_token_id is not None else -1)
+        self.unpadded_vocab_size = config.text_config.vocab_size
+        self.lm_head = ParallelLMHead(
+            self.unpadded_vocab_size,
+            config.text_config.hidden_size,
+            org_num_embeddings=self.language_model.org_vocab_size,
+            quant_config=quant_config,
+        )
+        logit_scale = getattr(config, "logit_scale", 1.0)
+        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                self.vocab_size, logit_scale)
+        self.sampler = Sampler()
+
+    def _validate_image_sizes(
+            self, images: List[torch.Tensor]) -> List[torch.Tensor]:
+        if not all(img.shape == images[0].shape for img in images):
+            raise ValueError("All images must be the same size")
+        return images
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[AriaImagePixelInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        pixel_mask = kwargs.pop("pixel_mask", None)
+
+        if pixel_values is None:
+            return None
+
+        if not isinstance(pixel_values, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of pixel values. "
+                             f"Got type: {type(pixel_values)}")
+
+        pixel_values = self._validate_image_sizes(pixel_values)
+        pixel_values = flatten_bn(pixel_values, concat=True)
+        if pixel_mask is not None:
+            pixel_mask = flatten_bn(pixel_mask, concat=True)
+
+        return AriaImagePixelInputs(
+            pixel_values=pixel_values,
+            pixel_mask=pixel_mask,
+        )
+
+    def _process_image_input(
+        self, image_input: AriaImagePixelInputs
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        assert self.vision_tower is not None
+
+        pixel_values = image_input['pixel_values']
+        pixel_mask = image_input['pixel_mask']
+
+        image_feature, image_attn_mask = self.vision_tower(
+            pixel_values, pixel_mask=pixel_mask)
+        return self.multi_modal_projector(image_feature, image_attn_mask)
+
+    def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return None
+        multimodal_embeddings = self._process_image_input(image_input)
+        return multimodal_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[NestedTensors] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, multimodal_embeddings,
+                self.config.image_token_index)
+        return inputs_embeds
+
+    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,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if inputs_embeds is None:
+            multimodal_embeddings = self.get_multimodal_embeddings(**kwargs)
+            # always pass the input via `inputs_embeds`
+            # to make sure the computation graph is consistent
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      multimodal_embeddings)
+            input_ids = None
+
+        hidden_states = self.language_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_prefix={
+                "language_model.model": "language_model",
+                "language_model.lm_head": "lm_head",
+            },
+            orig_to_new_suffix={
+                "router.weight": "router_weight",
+            },
+        )
+
+        loader = AutoWeightsLoader(self)
+        loader.load_weights(weights, mapper=hf_to_vllm_mapper)

vllm/model_executor/models/registry.py

@@ -133,6 +133,7 @@ _CROSS_ENCODER_MODELS = {
 
 _MULTIMODAL_MODELS = {
     # [Decoder-only]
+    "AriaForConditionalGeneration": ("aria", "AriaForConditionalGeneration"),
     "Blip2ForConditionalGeneration": ("blip2", "Blip2ForConditionalGeneration"),
     "ChameleonForConditionalGeneration": ("chameleon", "ChameleonForConditionalGeneration"),  # noqa: E501
     "ChatGLMModel": ("chatglm", "ChatGLMForCausalLM"),

vllm/transformers_utils/configs/aria.py

@@ -0,0 +1,47 @@
+from transformers.models.idefics2.configuration_idefics2 import (
+    Idefics2VisionConfig)
+from transformers.models.llama.configuration_llama import LlamaConfig
+
+
+class AriaVisionConfig(Idefics2VisionConfig):
+    model_type = "aria_vision_model"
+
+
+class AriaMoELMConfig(LlamaConfig):
+    """
+    Configuration class for AriaMoE language model.
+
+    This class extends the LlamaConfig to include additional parameters specific
+    to the Mixture of Experts (MoE) architecture.
+    """
+
+    model_type = "aria_moe_lm"
+
+    def __init__(
+        self,
+        moe_intermediate_size: int = 4096,
+        moe_num_experts: int = 8,
+        moe_topk: int = 2,
+        moe_num_shared_experts: int = 2,
+        **kwargs,
+    ):
+        """
+        Initialize the AriaMoELMConfig.
+
+        Args:
+            moe_intermediate_size (int): The intermediate size for MoE layers.
+                Default is 4096.
+            moe_num_experts (int): The number of experts in the MoE layer.
+                Default is 8.
+            moe_topk (int): The number of top experts to route to for each 
+                token. Default is 2.
+            moe_num_shared_experts (int): The number of shared experts. Default
+                is 2. 
+            **kwargs: Additional keyword arguments to be passed to the parent
+                LlamaConfig.
+        """
+        super().__init__(**kwargs)
+        self.moe_intermediate_size = moe_intermediate_size
+        self.moe_num_experts = moe_num_experts
+        self.moe_topk = moe_topk
+        self.moe_num_shared_experts = moe_num_shared_experts