Add Llama4 support (#5092)

Co-authored-by: Cheng Wan <cwan39@gatech.edu>
Co-authored-by: fzyzcjy <ch271828n@outlook.com>
Co-authored-by: ispobock <ispobaoke@163.com>

python/sglang/srt/layers/radix_attention.py

@@ -35,6 +35,7 @@ class RadixAttention(nn.Module):
         sliding_window_size: int = -1,
         is_cross_attention: bool = False,
         prefix: str = "",
+        use_irope: bool = False,
     ):
         super().__init__()
         self.tp_q_head_num = num_heads
@@ -50,6 +51,7 @@ class RadixAttention(nn.Module):
         self.is_cross_attention = is_cross_attention
         self.k_scale = None
         self.v_scale = None
+        self.use_irope = use_irope
 
     def forward(
         self,

python/sglang/srt/layers/rotary_embedding.py

@@ -733,6 +733,69 @@ class Llama3RotaryEmbedding(RotaryEmbedding):
         return new_freqs
 
 
+class Llama4VisionRotaryEmbedding(RotaryEmbedding):
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position_embeddings: int,
+        base: int,
+        is_neox_style: bool,
+        dtype: torch.dtype,
+    ):
+        super().__init__(
+            head_size, rotary_dim, max_position_embeddings, base, is_neox_style, dtype
+        )
+
+    def _compute_inv_freq(self, base: Union[int, float]) -> torch.Tensor:
+        inv_freqs = super()._compute_inv_freq(base)
+        inv_freqs = inv_freqs[: (self.rotary_dim // 2)]
+        return inv_freqs
+
+    def _compute_cos_sin_cache(self) -> torch.Tensor:
+        inv_freq = self._compute_inv_freq(self.base)
+
+        # self.max_position_embeddings here is number of image patches
+        # i.e. (image_size // patch_size) ** 2
+        num_patches = self.max_position_embeddings
+        img_idx = torch.arange(num_patches, dtype=torch.int32).reshape(num_patches, 1)
+        img_idx = torch.cat([img_idx, img_idx[:1]], dim=0)
+        img_idx[-1, -1] = -2  # set to ID_CLS_TOKEN
+        num_patches_single_dim = int(math.sqrt(num_patches))
+        frequencies_x = img_idx % num_patches_single_dim
+        frequencies_y = img_idx // num_patches_single_dim
+        freqs_x = (
+            (frequencies_x + 1)[..., None] * inv_freq[None, None, :]
+        ).repeat_interleave(2, dim=-1)
+        freqs_y = (
+            (frequencies_y + 1)[..., None] * inv_freq[None, None, :]
+        ).repeat_interleave(2, dim=-1)
+        freqs = torch.cat([freqs_x, freqs_y], dim=-1).float().contiguous()[..., ::2]
+        freqs = freqs.masked_fill(img_idx.reshape(-1, 1, 1) < 0, 0)
+        cache = torch.view_as_complex(
+            torch.stack([torch.cos(freqs), torch.sin(freqs)], dim=-1)
+        )
+        return cache
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        self.cos_sin_cache: torch.Tensor = self.cos_sin_cache.to(query.device)
+        query_ = torch.view_as_complex(query.float().reshape(*query.shape[:-1], -1, 2))
+        key_ = torch.view_as_complex(key.float().reshape(*key.shape[:-1], -1, 2))
+        broadcast_shape = [
+            d if i == 1 or i == (query_.ndim - 1) else 1
+            for i, d in enumerate(query_.shape)
+        ]
+        freqs_ci = self.cos_sin_cache.view(*broadcast_shape)
+        query_out = torch.view_as_real(query_ * freqs_ci).flatten(3)
+        key_out = torch.view_as_real(key_ * freqs_ci).flatten(3)
+        return query_out.type_as(query), key_out.type_as(key)
+
+
 class MRotaryEmbedding(RotaryEmbedding):
     """Rotary Embedding with Multimodal Sections."""
 

python/sglang/srt/managers/multimodal_processors/mllama4.py

+from typing import List, Mapping, Optional, Tuple, Union
+
+import torch
+from PIL import Image
+from transformers import Llama4Processor
+from transformers.image_utils import SizeDict
+from transformers.models.llama4.image_processing_llama4 import (
+    find_supported_resolutions,
+    get_best_fit,
+)
+
+from sglang.srt.managers.multimodal_processors.base_processor import (
+    BaseMultimodalProcessor,
+    MultimodalSpecialTokens,
+)
+from sglang.srt.managers.schedule_batch import Modality, MultimodalDataItem
+from sglang.srt.models.mllama4 import Llama4ForConditionalGeneration
+from sglang.srt.utils import load_image
+
+
+class Mllama4ImageProcessor(BaseMultimodalProcessor):
+    models = [Llama4ForConditionalGeneration]
+
+    def __init__(self, hf_config, server_args, _processor):
+        super().__init__(hf_config, server_args, _processor)
+        self.vision_config = hf_config.vision_config
+        self.text_config = hf_config.text_config
+        self.multimodal_tokens = MultimodalSpecialTokens(
+            image_token=_processor.image_token
+        )
+
+    async def process_mm_data_async(
+        self,
+        image_data: List[Union[str, bytes]],
+        input_text,
+        max_req_input_len=None,
+        *args,
+        **kwargs,
+    ):
+        if not image_data:
+            return None
+
+        if isinstance(input_text, list):
+            assert len(input_text) and isinstance(input_text[0], int)
+            input_text = self._processor.tokenizer.decode(input_text)
+
+        # Process images and text using the base processor's load_mm_data method
+        processed_data = self.load_mm_data(
+            prompt=input_text,
+            multimodal_tokens=self.multimodal_tokens,
+            max_req_input_len=max_req_input_len or 4096,
+            image_data=image_data,
+            return_text=True,
+        )
+
+        # Process the images using the processor
+        processor = Llama4Processor.from_pretrained(
+            self.server_args.model_path, **kwargs
+        )
+
+        # Process the prompt and images
+        image_inputs = processor(
+            text=processed_data.input_text,
+            images=processed_data.images,
+            return_tensors="pt",
+        )
+
+        # Handle image resolutions and aspect ratios
+        if "pixel_values" in image_inputs:
+            image_processor = processor.image_processor
+            tokenizer = self._processor.tokenizer
+
+            # Calculate tile size and find supported resolutions
+            tile_size = self.vision_config.image_size
+            max_num_tiles = getattr(self.vision_config, "max_patches", 1)
+
+            possible_resolutions = find_supported_resolutions(
+                max_num_chunks=max_num_tiles,
+                patch_size=SizeDict(height=tile_size, width=tile_size),
+            )
+
+            # Find best fit for each image
+            best_fit_sizes = [
+                get_best_fit(
+                    (image.size[1], image.size[0]),  # (height, width)
+                    torch.tensor(possible_resolutions),
+                    resize_to_max_canvas=image_processor.resize_to_max_canvas,
+                )
+                for image in processed_data.images
+            ]
+
+            # Calculate aspect ratios and patches per image
+            aspect_ratios = [
+                (image_size[0] // tile_size, image_size[1] // tile_size)
+                for image_size in best_fit_sizes
+            ]
+
+            patches_per_image = [
+                1 if r_h * r_w == 1 else 1 + r_h * r_w for (r_h, r_w) in aspect_ratios
+            ]
+
+            # Add to image_inputs
+            image_inputs["aspect_ratios"] = aspect_ratios
+            image_inputs["patches_per_image"] = torch.tensor(patches_per_image)
+
+            # Process embed_is_patch
+            vocab = tokenizer.get_vocab()
+            patch_id = vocab.get(processor.img_patch_token, -1)
+            image_end_id = vocab.get(processor.end_of_img_token, -1)
+
+            if patch_id != -1 and image_end_id != -1:
+                input_ids = image_inputs["input_ids"].view(-1)
+
+                # Remove BOS token if present
+                if input_ids.size(0) > 0 and input_ids[0] == tokenizer.bos_token_id:
+                    input_ids = input_ids[1:]
+
+                # Find image end indices and split input_ids
+                image_end_indices = (input_ids == image_end_id).nonzero().view(-1)
+
+                if image_end_indices.size(0) > 0:
+                    # Split at image boundaries
+                    split_indices = (image_end_indices + 1)[:-1]
+                    split_input_ids = torch.tensor_split(input_ids, split_indices)
+                    split_input_ids = [x for x in split_input_ids if x.numel() > 0]
+
+                    # Create embed_is_patch for each image
+                    embed_is_patch = []
+                    for per_image_input_ids in split_input_ids:
+                        embed_is_patch.append(per_image_input_ids == patch_id)
+
+                    image_inputs["embed_is_patch"] = embed_is_patch
+
+        # Convert to the format expected by SGLang
+        image_inputs["input_ids"] = image_inputs["input_ids"].tolist()[0]
+
+        # Add metadata for image processing
+        image_inputs["mm_items"] = [
+            MultimodalDataItem(
+                pixel_values=image_inputs["pixel_values"],
+                modality=Modality.IMAGE,
+                # Add additional metadata needed for Llama4 vision processing
+                embed_is_patch=image_inputs.get("embed_is_patch", None),
+                aspect_ratios=image_inputs.get("aspect_ratios", None),
+                patches_per_image=image_inputs.get("patches_per_image", None),
+            )
+        ]
+
+        return image_inputs
+
+    def get_patch_per_chunk(self):
+        """Calculate patches per chunk based on vision config"""
+        image_size = self.vision_config.image_size
+        patch_size = self.vision_config.patch_size
+
+        assert (
+            image_size % patch_size == 0
+        ), f"chunk size {image_size} should be multiple of patch_size {patch_size}"
+
+        ds_ratio = int(round(1.0 / (self.vision_config.pixel_shuffle_ratio**2)))
+        return (image_size // patch_size) ** 2 // ds_ratio

python/sglang/srt/model_executor/model_runner.py

@@ -128,6 +128,7 @@ class ModelRunner:
             self.model_config.attention_arch == AttentionArch.MLA
             and not server_args.disable_mla
         )
+        self.attention_chunk_size = model_config.attention_chunk_size
 
         # Model-specific adjustment
         self.model_specific_adjustment()

python/sglang/srt/models/llama.py

@@ -63,6 +63,7 @@ class LlamaMLP(nn.Module):
         hidden_act: str,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
+        reduce_results: bool = True,
     ) -> None:
         super().__init__()
         self.gate_up_proj = MergedColumnParallelLinear(
@@ -78,6 +79,7 @@ class LlamaMLP(nn.Module):
             bias=False,
             quant_config=quant_config,
             prefix=add_prefix("down_proj", prefix),
+            reduce_results=reduce_results,
         )
         if hidden_act != "silu":
             raise ValueError(
@@ -281,7 +283,7 @@ class LlamaModel(nn.Module):
         self.layers = make_layers(
             config.num_hidden_layers,
             lambda idx, prefix: LlamaDecoderLayer(
-                config=config, quant_config=quant_config, layer_id=idx, prefix=prefix
+                config=config, layer_id=idx, quant_config=quant_config, prefix=prefix
             ),
             prefix="model.layers",
         )
@@ -375,9 +377,7 @@ class LlamaForCausalLM(nn.Module):
         super().__init__()
         self.config = config
         self.quant_config = quant_config
-        self.model = LlamaModel(
-            config, quant_config=quant_config, prefix=add_prefix("model", prefix)
-        )
+        self.model = self._init_model(config, quant_config, add_prefix("model", prefix))
         # Llama 3.2 1B Instruct set tie_word_embeddings to True
         # Llama 3.1 8B Instruct set tie_word_embeddings to False
         if self.config.tie_word_embeddings:
@@ -402,6 +402,14 @@ class LlamaForCausalLM(nn.Module):
 
         self.capture_aux_hidden_states = False
 
+    def _init_model(
+        self,
+        config: LlamaConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        return LlamaModel(config, quant_config=quant_config, prefix=prefix)
+
     @torch.no_grad()
     def forward(
         self,

python/sglang/srt/models/llama4.py

+# Copyright 2023-2024 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+# Adapted from
+# https://github.com/vllm-project/vllm/blob/v0.8.3/vllm/model_executor/models/llama4.py
+"""Inference-only LLaMA model compatible with HuggingFace weights."""
+
+import logging
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+from transformers import Llama4TextConfig
+
+from sglang.srt.distributed import (
+    get_tensor_model_parallel_world_size,
+    tensor_model_parallel_all_reduce,
+)
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.linear import (
+    QKVParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
+from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.models.llama import LlamaForCausalLM, LlamaMLP
+from sglang.srt.utils import add_prefix, get_compiler_backend, make_layers
+
+logger = logging.getLogger(__name__)
+
+
+class Llama4MoE(nn.Module):
+
+    @torch.compile(dynamic=True, backend=get_compiler_backend())
+    @staticmethod
+    def custom_routing_function(
+        hidden_states: torch.Tensor,
+        gating_output: torch.Tensor,
+        topk: int,
+        renormalize: bool,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        router_scores_aK, router_indices_aK = torch.topk(gating_output, topk, dim=-1)
+        router_scores_aK = torch.sigmoid(router_scores_aK.float()).to(
+            hidden_states.dtype
+        )
+        return (
+            router_scores_aK.view(-1).reshape(router_scores_aK.shape),
+            router_indices_aK.to(torch.int32),
+        )
+
+    def __init__(
+        self,
+        config: Llama4TextConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.top_k = config.num_experts_per_tok
+
+        intermediate_size_moe = config.intermediate_size
+        self.router = ReplicatedLinear(
+            config.hidden_size,
+            config.num_local_experts,
+            bias=False,
+            quant_config=None,
+            prefix=add_prefix("router", prefix),
+        )
+
+        self.experts = FusedMoE(
+            num_experts=config.num_local_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            custom_routing_function=Llama4MoE.custom_routing_function,
+            intermediate_size=intermediate_size_moe,
+            reduce_results=False,
+            renormalize=False,
+            quant_config=quant_config,
+            apply_router_weight_on_input=True,
+            prefix=add_prefix("experts", prefix),
+        )
+
+        self.shared_expert = LlamaMLP(
+            hidden_size=config.hidden_size,
+            intermediate_size=intermediate_size_moe,
+            hidden_act="silu",
+            quant_config=quant_config,
+            prefix=add_prefix("shared_expert", prefix),
+            reduce_results=False,  # We need to do scatter before reduce
+        )
+
+    def forward(self, hidden_states):
+        # router_scores: [num_tokens, num_experts]
+        router_logits, _ = self.router(hidden_states)
+        shared_out = self.shared_expert(hidden_states)
+        routed_out = self.experts(
+            hidden_states=hidden_states,
+            router_logits=router_logits,
+        )
+        out_aD = routed_out + shared_out
+
+        if self.tp_size > 1:
+            out_aD = tensor_model_parallel_all_reduce(out_aD)
+
+        return out_aD
+
+
+class Llama4Attention(nn.Module):
+
+    def __init__(
+        self,
+        config: Llama4TextConfig,
+        layer_id: int,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[Dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        bias_o_proj: bool = False,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.layer_id = layer_id
+        self.hidden_size = hidden_size
+        self.use_rope = int((layer_id + 1) % 4 != 0)
+        self.use_qk_norm = config.use_qk_norm and self.use_rope
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = config.head_dim
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.attn_temperature_tuning = config.attn_temperature_tuning
+        self.floor_scale = config.floor_scale
+        self.attn_scale = config.attn_scale
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+        self.n_rep = self.num_heads // self.num_kv_heads
+        self.qk_norm = (
+            RMSNorm(
+                hidden_size=self.head_dim,
+                eps=config.rms_norm_eps,
+            )
+            if self.use_qk_norm
+            else None
+        )
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=hidden_size,
+            head_size=self.head_dim,
+            total_num_heads=self.total_num_heads,
+            total_num_kv_heads=self.total_num_kv_heads,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=add_prefix("qkv_proj", prefix),
+        )
+
+        self.o_proj = RowParallelLinear(
+            input_size=self.total_num_heads * self.head_dim,
+            output_size=hidden_size,
+            bias=bias_o_proj,
+            quant_config=quant_config,
+            prefix=add_prefix("o_proj", prefix),
+        )
+        is_neox_style = True
+        is_gguf = quant_config and quant_config.get_name() == "gguf"
+        if is_gguf and config.model_type in ["llama", "llama4"]:
+            is_neox_style = False
+
+        self.rotary_emb = (
+            get_rope(
+                self.head_dim,
+                rotary_dim=self.head_dim,
+                max_position=max_position_embeddings,
+                base=int(rope_theta),
+                rope_scaling=rope_scaling if rope_scaling != "default" else None,
+                is_neox_style=is_neox_style,
+            )
+            if self.use_rope
+            else None
+        )
+
+        self.attn = RadixAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            layer_id=layer_id,
+            prefix=add_prefix("attn", prefix),
+            use_irope=self.use_rope,
+        )
+
+    def _get_attn_scale(self, positions: torch.Tensor) -> torch.Tensor:
+        floor = torch.floor((positions + 1.0) / self.floor_scale)
+        attn_scale = torch.log(floor + 1.0) * self.attn_scale + 1.0
+
+        return attn_scale.unsqueeze(-1)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> 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.rotary_emb is not None:
+            q, k = self.rotary_emb(positions, q, k)
+
+        if self.qk_norm is not None:
+            # TODO: support float
+            q = q.reshape(-1, self.head_dim).contiguous().bfloat16()
+            k = k.reshape(-1, self.head_dim).contiguous().bfloat16()
+            q = self.qk_norm(q).to(q.dtype)
+            k = self.qk_norm(k).to(k.dtype)
+            q = q.reshape(-1, self.q_size)
+            k = k.reshape(-1, self.kv_size)
+
+        # We are applying temperature tuning (https://arxiv.org/abs/2501.19399) to NoPE layers, where
+        # the inference-time temperature tuning function is customized to not affect short context
+        # while working at very long context
+        # https://arxiv.org/abs/2501.19399
+        if self.attn_temperature_tuning and not self.use_rope:
+            attn_scale = self._get_attn_scale(positions)
+            q = (q * attn_scale).to(q.dtype)
+
+        attn_output = self.attn(q, k, v, forward_batch)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class Llama4DecoderLayer(nn.Module):
+    def __init__(
+        self,
+        config: Llama4TextConfig,
+        layer_id: int = 0,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.layer_id = layer_id
+        self.hidden_size = config.hidden_size
+        rope_theta = config.rope_theta
+        rope_scaling = config.rope_scaling
+        max_position_embeddings = config.max_position_embeddings
+
+        self.self_attn = Llama4Attention(
+            config=config,
+            layer_id=layer_id,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            quant_config=quant_config,
+            bias=False,
+            bias_o_proj=False,
+            prefix=add_prefix("self_attn", prefix),
+        )
+        is_moe_layer = (layer_id + 1) % config.interleave_moe_layer_step == 0
+        if is_moe_layer:
+            self.feed_forward = Llama4MoE(
+                config=config,
+                quant_config=quant_config,
+                prefix=add_prefix("feed_forward", prefix),
+            )
+        else:
+            self.feed_forward = LlamaMLP(
+                hidden_size=self.hidden_size,
+                intermediate_size=config.intermediate_size_mlp,
+                hidden_act="silu",
+                quant_config=quant_config,
+                prefix=add_prefix("feed_forward", prefix),
+            )
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+        residual: Optional[torch.Tensor],
+    ) -> 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,
+            forward_batch=forward_batch,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
+        hidden_states = self.feed_forward(hidden_states)
+        return hidden_states, residual
+
+
+class Llama4Model(nn.Module):
+    def __init__(
+        self,
+        config: Llama4TextConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            quant_config=quant_config,
+            prefix=add_prefix("embed_tokens", prefix),
+        )
+        self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda idx, prefix: Llama4DecoderLayer(
+                config=config, layer_id=idx, quant_config=quant_config, prefix=prefix
+            ),
+            prefix="model.layers",
+        )
+
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.layers_to_capture = []
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, List[torch.Tensor]]]:
+        if input_embeds is None:
+            hidden_states = self.embed_tokens(input_ids)
+        else:
+            hidden_states = input_embeds
+        residual = None
+        aux_hidden_states = []
+        for i in range(len(self.layers)):
+            if i in self.layers_to_capture:
+                aux_hidden_states.append(hidden_states + residual)
+            layer = self.layers[i]
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                forward_batch,
+                residual,
+            )
+        hidden_states, _ = self.norm(hidden_states, residual)
+
+        if len(aux_hidden_states) == 0:
+            return hidden_states
+
+        return hidden_states, aux_hidden_states
+
+
+class Llama4ForCausalLM(LlamaForCausalLM):
+
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+        "gate_up_proj": ["gate_proj", "up_proj"],
+    }
+
+    def __init__(
+        self,
+        config: Llama4TextConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__(config, quant_config, prefix)
+
+    def _init_model(
+        self,
+        config: Llama4TextConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        return Llama4Model(config, quant_config=quant_config, prefix=prefix)
+
+
+EntryClass = [Llama4ForCausalLM]

python/sglang/srt/models/mllama4.py

+# TODO: add Aapted from vllm/mllama4.py
+from collections.abc import Iterable
+from typing import Optional, Set, Tuple
+
+import torch
+from torch import nn
+from transformers import Llama4Config
+
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.quantization import QuantizationConfig
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.utils import add_prefix
+
+
+class Llama4ForConditionalGeneration(nn.Module):
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+    }
+
+    def __init__(
+        self,
+        config: Llama4Config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+
+        # Initialize the language model
+        from sglang.srt.models.llama4 import Llama4ForCausalLM
+
+        self.language_model = Llama4ForCausalLM(
+            config.text_config,
+            quant_config=quant_config,
+            prefix=add_prefix("language_model", prefix),
+        )
+
+        self.logits_processor = LogitsProcessor(config.text_config)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        **kwargs: object,
+    ) -> torch.Tensor:
+
+        return self.language_model(input_ids, positions, forward_batch)
+
+    def permute_qk_weight_for_rotary(
+        self,
+        name: str,
+        loaded_weight: torch.Tensor,
+    ) -> Tuple[str, torch.Tensor]:
+
+        def permute(w: torch.Tensor, n_heads: int):
+            attn_in = self.language_model.config.head_dim * n_heads
+            attn_out = self.language_model.config.hidden_size
+
+            return (
+                w.view(n_heads, attn_in // n_heads // 2, 2, attn_out)
+                .transpose(1, 2)
+                .reshape(attn_in, attn_out)
+            )
+
+        modules = name.split(".")
+
+        # rotary embeds should be sliced
+        if ("wk" in modules or "k_proj" in modules) and modules[-1] == "weight":
+            loaded_weight = permute(
+                loaded_weight, self.language_model.config.num_key_value_heads
+            )
+        elif ("wq" in modules or "q_proj" in modules) and modules[-1] == "weight":
+            loaded_weight = permute(
+                loaded_weight, self.language_model.config.num_attention_heads
+            )
+
+        return name, loaded_weight
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]) -> Set[str]:
+
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".self_attn.qkv_proj", ".self_attn.q_proj", "q"),
+            (".self_attn.qkv_proj", ".self_attn.k_proj", "k"),
+            (".self_attn.qkv_proj", ".self_attn.v_proj", "v"),
+            (".shared_expert.gate_up_proj", ".shared_expert.gate_proj", 0),
+            (".shared_expert.gate_up_proj", ".shared_expert.up_proj", 1),
+            (".feed_forward.gate_up_proj", ".feed_forward.gate_proj", 0),
+            (".feed_forward.gate_up_proj", ".feed_forward.up_proj", 1),
+        ]
+
+        params_dict = dict(self.named_parameters())
+
+        num_experts = self.config.text_config.num_local_experts
+
+        for name, loaded_weight in weights:
+
+            if name.startswith("vision_model") or name.startswith(
+                "multi_modal_projector"
+            ):
+                continue
+
+            name, loaded_weight = self.permute_qk_weight_for_rotary(name, loaded_weight)
+
+            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)
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                if ".experts" in name:
+                    if ".gate_up_proj" in name:
+                        name_list = [
+                            name.replace(".experts.gate_up_proj", ".experts.w13_weight")
+                        ] * 2
+                        loaded_weight_list = loaded_weight.chunk(2, dim=-1)
+                        shard_id_list = ["w1", "w3"]
+                    else:
+                        name_list = [
+                            name.replace(".experts.down_proj", ".experts.w2_weight")
+                        ]
+                        shard_id_list = ["w2"]
+                        loaded_weight_list = [loaded_weight]
+                    for name, loaded_weight, shard_id in zip(
+                        name_list, loaded_weight_list, shard_id_list
+                    ):
+                        param = params_dict[name]
+                        weight_loader = param.weight_loader
+                        for expert_id in range(num_experts):
+                            weight_loader(
+                                param,
+                                loaded_weight[expert_id].T,
+                                name,
+                                shard_id=shard_id,
+                                expert_id=expert_id,
+                            )
+                else:
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+                    param = params_dict[name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+
+
+EntryClass = Llama4ForConditionalGeneration