model: adapt mllama4 to VisionAttention (#8512)

Co-authored-by: root <mickjagger19@icloud.com>

python/sglang/srt/hf_transformers_utils.py

@@ -14,7 +14,6 @@
 """Utilities for Huggingface Transformers."""
 
 import contextlib
-import logging
 import os
 import warnings
 from pathlib import Path
@@ -45,7 +44,7 @@ from sglang.srt.configs import (
 )
 from sglang.srt.configs.internvl import InternVLChatConfig
 from sglang.srt.connector import create_remote_connector
-from sglang.srt.utils import is_remote_url, lru_cache_frozenset
+from sglang.srt.utils import is_remote_url, logger, lru_cache_frozenset
 
 _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
     ChatGLMConfig.model_type: ChatGLMConfig,
@@ -317,15 +316,31 @@ def get_processor(
 
     if config.model_type not in {"llava", "clip"}:
         kwargs["use_fast"] = use_fast
+    try:
+        processor = AutoProcessor.from_pretrained(
+            tokenizer_name,
+            *args,
+            trust_remote_code=trust_remote_code,
+            revision=revision,
+            **kwargs,
+        )
 
-    processor = AutoProcessor.from_pretrained(
-        tokenizer_name,
-        *args,
-        trust_remote_code=trust_remote_code,
-        revision=revision,
-        **kwargs,
-    )
-
+    except ValueError as e:
+        error_message = str(e)
+        if "does not have a slow version" in error_message:
+            logger.info(
+                f"Processor {tokenizer_name} does not have a slow version. Automatically use fast version"
+            )
+            kwargs["use_fast"] = True
+            processor = AutoProcessor.from_pretrained(
+                tokenizer_name,
+                *args,
+                trust_remote_code=trust_remote_code,
+                revision=revision,
+                **kwargs,
+            )
+        else:
+            raise e
     tokenizer = get_tokenizer_from_processor(processor)
 
     attach_additional_stop_token_ids(tokenizer)

python/sglang/srt/layers/attention/vision.py

@@ -4,7 +4,7 @@ import dataclasses
 import functools
 import math
 from functools import lru_cache, partial
-from typing import Any, Optional, Tuple, Union
+from typing import Any, Callable, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -308,6 +308,7 @@ class VisionFlash3Attention(nn.Module):
         cu_seqlens = cu_seqlens.to(dtype=torch.int32).to(q.device)
         seq_lens = cu_seqlens[1:] - cu_seqlens[:-1]
         max_seqlen = seq_lens.max().item()
+
         output = flash_attn_varlen_func(
             q,
             k,
@@ -358,6 +359,9 @@ class VisionAttention(nn.Module):
         qkv_bias: bool = True,
         qk_normalization: bool = False,
         layer_norm_eps: float = 1e-06,
+        customized_position_embedding_applier: Callable[
+            [torch.Tensor, torch.Tensor, Any, Any], Tuple[torch.Tensor, torch.Tensor]
+        ] = None,
         **kwargs,
     ):
         super().__init__()
@@ -392,6 +396,7 @@ class VisionAttention(nn.Module):
                 self.dummy_dim, eps=layer_norm_eps, var_hidden_size=embed_dim
             )
 
+        # priority: server_args > passed qkv_backend > sdpa
         if global_server_args_dict["mm_attention_backend"] is None:
             if qkv_backend is None:
                 qkv_backend = "sdpa"
@@ -401,6 +406,9 @@ class VisionAttention(nn.Module):
 
         print_info_once(f"Using {qkv_backend} as multimodal attention backend.")
 
+        self.customized_position_embedding_applier = (
+            customized_position_embedding_applier
+        )
         self.qkv_backend = QKV_BACKEND_IMPL[qkv_backend](
             head_dim=self.head_size,
             num_heads=self.num_attention_heads_per_partition,
@@ -473,13 +481,13 @@ class VisionAttention(nn.Module):
         if x.dim() == 2:
             x = x.unsqueeze(0)
         assert x.dim() == 3, x.shape
-        bsz, s, _ = x.shape
+        x_shape = x.shape
+        bsz, s, _ = x_shape
         head = self.num_attention_heads_per_partition
         kv_head = self.num_attention_kv_heads_per_partition
         if self.use_qkv_parallel:
             # [b, s, embed_dim] --> [b, s, embed_dim]
             qkv, _ = self.qkv_proj(x)
-
             q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
 
             # [b, s, embed_dim] --> [b * s, head, head_size]
@@ -508,16 +516,25 @@ class VisionAttention(nn.Module):
             ]
 
         if position_embeddings is not None:
-            cos, sin = position_embeddings
             original_shape = q.shape
-            # [total_tokens, head, head_size]
-            q = q.view(-1, head, self.head_size)
-            k = k.view(-1, head, self.head_size)
 
-            q, k = apply_rotary_pos_emb(q, k, cos, sin)
+            if self.customized_position_embedding_applier is not None:
+                q, k = self.customized_position_embedding_applier(
+                    q, k, position_embeddings, x_shape
+                )
+                q = q.view(original_shape)
+                k = k.view(original_shape)
+            else:
+                cos, sin = position_embeddings
+
+                # [total_tokens, head, head_size]
+                q = q.view(-1, head, self.head_size)
+                k = k.view(-1, head, self.head_size)
+
+                q, k = apply_rotary_pos_emb(q, k, cos, sin)
 
-            q = q.view(original_shape)
-            k = k.view(original_shape)
+                q = q.view(original_shape)
+                k = k.view(original_shape)
 
         if q.dim() == 4:
             # [b, s, head, head_size] --> [b * s, head, head_size]

python/sglang/srt/managers/tokenizer_manager.py

@@ -70,7 +70,6 @@ from sglang.srt.managers.io_struct import (
     BatchMultimodalOut,
     BatchStrOut,
     BatchTokenIDOut,
-    BlockReqType,
     CloseSessionReqInput,
     ConfigureLoggingReq,
     EmbeddingReqInput,
@@ -202,13 +201,29 @@ class TokenizerManager:
 
         if self.model_config.is_multimodal:
             import_processors()
-            _processor = get_processor(
-                server_args.tokenizer_path,
-                tokenizer_mode=server_args.tokenizer_mode,
-                trust_remote_code=server_args.trust_remote_code,
-                revision=server_args.revision,
-                use_fast=not server_args.disable_fast_image_processor,
-            )
+            try:
+                _processor = get_processor(
+                    server_args.tokenizer_path,
+                    tokenizer_mode=server_args.tokenizer_mode,
+                    trust_remote_code=server_args.trust_remote_code,
+                    revision=server_args.revision,
+                    use_fast=not server_args.disable_fast_image_processor,
+                )
+            except ValueError as e:
+                error_message = str(e)
+                if "does not have a slow version" in error_message:
+                    logger.info(
+                        f"Processor {server_args.tokenizer_path} does not have a slow version. Automatically use fast version"
+                    )
+                    _processor = get_processor(
+                        server_args.tokenizer_path,
+                        tokenizer_mode=server_args.tokenizer_mode,
+                        trust_remote_code=server_args.trust_remote_code,
+                        revision=server_args.revision,
+                        use_fast=True,
+                    )
+                else:
+                    raise e
             transport_mode = _determine_tensor_transport_mode(self.server_args)
 
             # We want to parallelize the image pre-processing so we create an executor for it

python/sglang/srt/models/llama4.py

@@ -241,13 +241,22 @@ class Llama4Attention(nn.Module):
             if self.use_qk_norm
             else None
         )
+
+        qkv_quant_config = quant_config
+        o_quant_config = quant_config
+        if quant_config and hasattr(quant_config, "ignore") and quant_config.ignore:
+            if add_prefix("q_proj", prefix) in quant_config.ignore:
+                qkv_quant_config = None
+            if add_prefix("o_proj", prefix) in quant_config.ignore:
+                o_quant_config = 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,
+            quant_config=qkv_quant_config,
             prefix=add_prefix("qkv_proj", prefix),
             tp_rank=attn_tp_rank,
             tp_size=attn_tp_size,
@@ -257,7 +266,7 @@ class Llama4Attention(nn.Module):
             input_size=self.total_num_heads * self.head_dim,
             output_size=hidden_size,
             bias=bias_o_proj,
-            quant_config=quant_config,
+            quant_config=o_quant_config,
             prefix=add_prefix("o_proj", prefix),
             tp_rank=attn_tp_rank,
             tp_size=attn_tp_size,

python/sglang/srt/models/mllama4.py

 import json as json_lib
 import logging
+import math
 import os
 from collections.abc import Iterable
 from typing import List, Optional, Set, Tuple
 
 import torch
 from torch import nn
-from transformers import Llama4Config
+from transformers import Llama4Config, Llama4VisionConfig
 from transformers.models.llama4.modeling_llama4 import (
     Llama4MultiModalProjector,
-    Llama4VisionModel,
+    vision_apply_rotary_emb,
 )
 
+from sglang.srt.layers.attention.vision import VisionAttention
+from sglang.srt.layers.linear import (
+    ColumnParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
 from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
 from sglang.srt.layers.quantization import QuantizationConfig
@@ -26,10 +33,10 @@ from sglang.srt.managers.schedule_batch import (
     global_server_args_dict,
 )
 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, is_cpu
+from sglang.srt.utils import is_cpu
 
 _is_cpu = is_cpu()
+
 from sglang.srt.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
@@ -39,6 +46,376 @@ from sglang.srt.utils import add_prefix
 logger = logging.getLogger(__name__)
 
 
+class Llama4VisionMLP(nn.Module):
+
+    def __init__(
+        self,
+        input_size: int,
+        intermediate_size: int,
+        output_size: int,
+        bias: bool,
+        output_activation: bool,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
+        super().__init__()
+        cls_fc1 = ReplicatedLinear if use_data_parallel else ColumnParallelLinear
+        self.fc1 = cls_fc1(
+            input_size=input_size,
+            output_size=intermediate_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc1",
+        )
+        cls_fc2 = ReplicatedLinear if use_data_parallel else RowParallelLinear
+        self.fc2 = cls_fc2(
+            input_size=intermediate_size,
+            output_size=output_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc2",
+        )
+        self.activation_fn = nn.GELU()
+        self.output_activation = output_activation
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states, _ = self.fc2(hidden_states)
+        if self.output_activation:
+            return self.activation_fn(hidden_states)
+        return hidden_states
+
+
+def pixel_shuffle(input_tensor, shuffle_ratio):
+    # input_tensor: [batch_size, num_patches, channels]
+    batch_size, num_patches, channels = input_tensor.shape
+    patch_size = int(math.sqrt(num_patches))
+
+    input_tensor = input_tensor.view(batch_size, patch_size, patch_size, -1)
+    batch_size, height, width, channels = input_tensor.size()
+
+    reshaped_tensor = input_tensor.view(
+        batch_size, height, int(width * shuffle_ratio), int(channels / shuffle_ratio)
+    )
+    reshaped_tensor = reshaped_tensor.permute(0, 2, 1, 3).contiguous()
+
+    reshaped_tensor = reshaped_tensor.view(
+        batch_size,
+        int(height * shuffle_ratio),
+        int(width * shuffle_ratio),
+        int(channels / (shuffle_ratio**2)),
+    )
+    reshaped_tensor = reshaped_tensor.permute(0, 2, 1, 3).contiguous()
+
+    output_tensor = reshaped_tensor.view(batch_size, -1, reshaped_tensor.shape[-1])
+    return output_tensor
+
+
+class Llama4VisionPixelShuffleMLP(nn.Module):
+
+    def __init__(
+        self,
+        config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
+        super().__init__()
+        self.pixel_shuffle_ratio = config.pixel_shuffle_ratio
+        self.mlp = Llama4VisionMLP(
+            input_size=config.intermediate_size,
+            intermediate_size=config.projector_input_dim,
+            output_size=config.projector_output_dim,
+            bias=config.multi_modal_projector_bias,
+            output_activation=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+            use_data_parallel=use_data_parallel,
+        )
+
+    def forward(self, encoded_patches: torch.Tensor) -> torch.Tensor:
+        encoded_patches = pixel_shuffle(encoded_patches, self.pixel_shuffle_ratio)
+        return self.mlp(encoded_patches)
+
+
+def apply_position_embedding(q, k, freqs_ci, shape):
+    # [batch_size_times_num_tiles, num_channels]
+    input_shape = shape[:2]
+    # [batch_size_times_num_tiles, num_channels, num_heads, head_dim]
+    hidden_shape = (*input_shape, *q.shape[-2:])
+    q = q.view(hidden_shape)
+    k = k.view(hidden_shape)
+    q, k = vision_apply_rotary_emb(q, k, freqs_ci)
+    return q, k
+
+
+class Llama4VisionEncoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: Llama4VisionConfig,
+        quant_config: Optional[QuantizationConfig],
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.num_attention_heads = config.num_attention_heads
+        self.intermediate_size = config.intermediate_size
+
+        self.self_attn = VisionAttention(
+            self.hidden_size,
+            self.num_attention_heads,
+            self.hidden_size,
+            use_qkv_parallel=True,
+            # vision_model is explicitly ignored in Maverick-17B-128E-Instruct-FP8
+            quant_config=None,
+            dropout=0.0,
+            qkv_backend="sdpa",
+            softmax_in_single_precision=False,
+            flatten_batch=False,
+            prefix=add_prefix("self_attn", prefix),
+            qkv_bias=True,
+            customized_position_embedding_applier=apply_position_embedding,
+        )
+        self.mlp = Llama4VisionMLP(
+            input_size=config.hidden_size,
+            intermediate_size=config.intermediate_size,
+            output_size=config.hidden_size,
+            bias=True,
+            output_activation=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+            use_data_parallel=use_data_parallel,
+        )
+
+        self.input_layernorm = nn.LayerNorm(config.hidden_size)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size)
+
+    def forward(
+        self,
+        hidden_state: torch.Tensor,
+        freqs_ci: torch.Tensor,
+    ):
+        # Self Attention
+        residual = hidden_state
+        hidden_state = self.input_layernorm(hidden_state)
+        hidden_state = self.self_attn(hidden_state, position_embeddings=freqs_ci)
+        hidden_state = residual + hidden_state
+
+        # Feed forward
+        residual = hidden_state
+        hidden_state = self.post_attention_layernorm(hidden_state)
+        hidden_state = self.mlp(hidden_state)
+        hidden_state = residual + hidden_state
+
+        outputs = hidden_state
+        return outputs
+
+
+class Llama4VisionEncoder(nn.Module):
+
+    def __init__(
+        self,
+        config: Llama4VisionConfig,
+        quant_config: Optional[QuantizationConfig],
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
+        super().__init__()
+        self.config = config
+        self.layers = nn.ModuleList(
+            [
+                Llama4VisionEncoderLayer(
+                    config,
+                    quant_config=quant_config,
+                    prefix=f"{prefix}.layers.{layer_idx}",
+                    use_data_parallel=use_data_parallel,
+                )
+                for layer_idx in range(config.num_hidden_layers)
+            ]
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        freqs_ci: torch.Tensor,  # TODO: move this to an attribute instead of keeping it around
+    ) -> torch.Tensor:
+        r"""
+        Args:
+            hidden_states (`torch.FloatTensor` of shape
+                    `(batch_size, sequence_length, hidden_size)`):
+                Optionally, instead of passing `input_ids` you can choose to
+                directly pass an embedded representation. This is useful if you
+                want more control over how to convert `input_ids` indices into
+                associated vectors than the model's internal embedding
+                lookup matrix.
+        """
+
+        for encoder_layer in self.layers:
+            layer_outputs = encoder_layer(hidden_states, freqs_ci=freqs_ci)
+            hidden_states = layer_outputs
+
+        return hidden_states
+
+
+class Llama4UnfoldConvolution(nn.Module):
+
+    def __init__(
+        self,
+        config: Llama4VisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
+        super().__init__()
+        kernel_size = config.patch_size
+        if isinstance(kernel_size, int):
+            kernel_size = (kernel_size, kernel_size)
+        self.unfold = torch.nn.Unfold(kernel_size=kernel_size, stride=config.patch_size)
+        params = {
+            "input_size": config.num_channels * kernel_size[0] * kernel_size[1],
+            "output_size": config.hidden_size,
+            "bias": False,
+            "quant_config": quant_config,
+            "prefix": f"{prefix}.linear",
+        }
+        if use_data_parallel:
+            cls = ReplicatedLinear
+        else:
+            cls = ColumnParallelLinear
+            params["gather_output"] = True
+        self.linear = cls(**params)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.unfold(hidden_states)
+        hidden_states = hidden_states.permute(0, 2, 1)
+        hidden_states, _ = self.linear(hidden_states)
+        return hidden_states
+
+
+class Llama4VisionRotaryEmbedding(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        idx = config.image_size // config.patch_size
+        img_idx = torch.arange(idx**2, dtype=torch.int32).reshape(idx**2, 1)
+        img_idx = torch.cat([img_idx, img_idx[:1]], dim=0)
+        img_idx[-1, -1] = -2  # ID_CLS_TOKEN
+        frequencies_x = img_idx % idx  # get the coordinates of the 2d matrix along x
+        frequencies_y = img_idx // idx  # get the coordinates of the 2d matrix along y
+        freq_dim = config.hidden_size // config.num_attention_heads // 2
+        rope_freq = 1.0 / (
+            config.rope_theta
+            ** (torch.arange(0, freq_dim, 2)[: (freq_dim // 2)].float() / freq_dim)
+        )
+        freqs_x = (
+            (frequencies_x + 1)[..., None] * rope_freq[None, None, :]
+        ).repeat_interleave(2, dim=-1)
+        freqs_y = (
+            (frequencies_y + 1)[..., None] * rope_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)
+        freq_cis = torch.view_as_complex(
+            torch.stack([torch.cos(freqs), torch.sin(freqs)], dim=-1)
+        )
+        self.freqs_ci = freq_cis  # idx**2, idx**2, idx * 2
+
+    def forward(self, hidden_states):
+        return self.freqs_ci.to(hidden_states.device)
+
+
+class Llama4VisionModel(nn.Module):
+
+    def __init__(
+        self,
+        config: Llama4VisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+        self.hidden_size = config.hidden_size
+        self.num_channels = config.num_channels
+
+        self.num_patches = (self.image_size // self.patch_size) ** 2 + 1
+        self.scale = config.hidden_size**-0.5
+
+        self.patch_embedding = Llama4UnfoldConvolution(
+            config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.patch_embedding",
+        )
+
+        self.class_embedding = nn.Parameter(self.scale * torch.randn(self.hidden_size))
+        self.positional_embedding_vlm = nn.Parameter(
+            self.scale * torch.randn(self.num_patches, self.hidden_size)
+        )
+
+        self.rotary_embedding = Llama4VisionRotaryEmbedding(config)
+
+        # layer norms
+        self.layernorm_pre = nn.LayerNorm(self.hidden_size, eps=1e-5)
+        self.layernorm_post = nn.LayerNorm(self.hidden_size, eps=1e-5)
+
+        # encoders
+        self.model = Llama4VisionEncoder(
+            config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.model",
+        )
+        self.vision_adapter = Llama4VisionPixelShuffleMLP(
+            config,
+            quant_config,
+            prefix=f"{prefix}.vision_adapter",
+        )
+
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+    ) -> torch.Tensor:
+        # Patch embedding
+        hidden_state = self.patch_embedding(pixel_values)
+        num_tiles, num_patches, hidden_dim = hidden_state.shape
+
+        # Add cls token
+        class_embedding = self.class_embedding.expand(
+            hidden_state.shape[0], 1, hidden_state.shape[-1]
+        )
+        hidden_state = torch.cat([hidden_state, class_embedding], dim=1)
+        num_patches += 1
+
+        # Position embeddings
+        hidden_state = hidden_state.reshape(
+            num_tiles,
+            1,
+            num_patches,
+            hidden_dim,
+        )
+        positional_embedding = self.positional_embedding_vlm.to(
+            dtype=hidden_state.dtype, device=hidden_state.device
+        )
+        hidden_state = hidden_state + positional_embedding
+        hidden_state = self.layernorm_pre(hidden_state)
+        hidden_state = hidden_state.view(num_tiles, -1, hidden_dim)
+        freqs_ci = self.rotary_embedding(pixel_values)
+        # Apply encoder
+        hidden_state = self.model(hidden_state, freqs_ci=freqs_ci)
+        hidden_state = self.layernorm_post(hidden_state)
+
+        # Remove CLS token output
+        hidden_state = hidden_state[:, :-1, :]
+
+        # now, we use Llama4VisionPixelShuffle + mlp to project embeddings
+        hidden_state = self.vision_adapter(hidden_state)
+
+        return hidden_state
+
+
 class Llama4ForConditionalGeneration(nn.Module):
     packed_modules_mapping = {
         "qkv_proj": ["q_proj", "k_proj", "v_proj"],
@@ -60,7 +437,8 @@ class Llama4ForConditionalGeneration(nn.Module):
         if not self.has_vision_weights:
             logger.warning(
                 "No vision weights found in checkpoint. Model will run in text-only mode. "
-                "Multimodal capabilities (image processing) will be unavailable."
+                "Multimodal capabilities (vision understanding) will be unavailable. "
+                "Please not that this warning might be inaccurate if the weights haven't been fully downloaded"
             )
 
         self.has_vision = (
@@ -68,7 +446,12 @@ class Llama4ForConditionalGeneration(nn.Module):
         )
 
         if self.has_vision:
-            self.vision_model = Llama4VisionModel(config.vision_config)
+            self.vision_model = Llama4VisionModel(
+                config.vision_config,
+                quant_config=quant_config,
+                prefix=add_prefix("vision_model", prefix),
+            )
+
             self.multi_modal_projector = Llama4MultiModalProjector(config)
         else:
             self.vision_model = None
@@ -112,7 +495,6 @@ class Llama4ForConditionalGeneration(nn.Module):
                 filename="model.safetensors.index.json",
                 cache_dir=None,
             )
-
             if index_file_path and os.path.exists(index_file_path):
                 return self._check_vision_weights_in_index(index_file_path)
 
@@ -120,7 +502,7 @@ class Llama4ForConditionalGeneration(nn.Module):
             # If we can't access the cache, fall back to config-based detection
             pass
 
-        # Fallback， assume text-only
+        # Fallback, assume text-only
         return False
 
     def _check_vision_weights_in_index(self, index_file: str) -> bool:
@@ -131,7 +513,6 @@ class Llama4ForConditionalGeneration(nn.Module):
 
             vision_patterns = ["vision_model", "vision_tower", "multi_modal_projector"]
             weight_names = index_data.get("weight_map", {}).keys()
-
             return any(
                 pattern in weight_name
                 for weight_name in weight_names
@@ -150,17 +531,17 @@ class Llama4ForConditionalGeneration(nn.Module):
         # For text-only models, return None or raise an error
         if not self.has_vision or self.vision_model is None:
             raise ValueError("Vision model not available for text-only checkpoint")
-
         pixel_values = (
             torch.concat([item.feature for item in items])
             .to(next(self.vision_model.parameters()).device)
             .type(next(self.vision_model.parameters()).dtype)
         )
+        image_features = self.vision_model(pixel_values)
 
-        image_outputs = self.vision_model(pixel_values, output_hidden_states=False)
-        image_features = image_outputs.last_hidden_state
         vision_flat = image_features.view(-1, image_features.size(-1))
+
         projected_vision_flat = self.multi_modal_projector(vision_flat)
+
         return projected_vision_flat
 
     def forward(
@@ -246,31 +627,47 @@ class Llama4ForConditionalGeneration(nn.Module):
             num_experts=num_experts,
         )
 
+        loaded_params = set()
+
         for name, loaded_weight in weights:
             if self._should_skip_weight(name):
                 continue
 
             name = self._transform_weight_name(name)
 
-            if "vision" not in name:
+            if "vision" in name:
+                name = name.replace(".self_attn.o_proj", ".self_attn.proj")
+            else:
                 name, loaded_weight = self.permute_qk_weight_for_rotary(
                     name, loaded_weight
                 )
 
             if self._handle_scale_remapping(name, params_dict):
+                loaded_params.add(name)
                 continue
 
             if self._handle_stacked_params(
-                name, loaded_weight, stacked_params_mapping, params_dict
+                name, loaded_weight, stacked_params_mapping, params_dict, loaded_params
             ):
                 continue
 
             if self._handle_expert_weights(
-                name, loaded_weight, expert_params_mapping, params_dict, num_experts
+                name,
+                loaded_weight,
+                expert_params_mapping,
+                params_dict,
+                num_experts,
+                loaded_params,
             ):
                 continue
 
+            loaded_params.add(name)
             self._handle_default_weight(name, loaded_weight, params_dict)
+        unloaded_params = params_dict.keys() - loaded_params
+        if unloaded_params:
+            logger.warning(
+                f"Some weights are not initialized from checkpoints {unloaded_params}"
+            )
 
     def _should_skip_weight(self, name: str) -> bool:
         """Check if we should skip loading this weight."""
@@ -301,11 +698,13 @@ class Llama4ForConditionalGeneration(nn.Module):
         loaded_weight: torch.Tensor,
         stacked_params_mapping: list,
         params_dict: dict,
+        loaded_params: set,
     ) -> bool:
         """Handle stacked parameter loading. Returns True if handled."""
         for param_name, weight_name, shard_id in stacked_params_mapping:
-            if weight_name in name and "vision" not in name:
+            if weight_name in name:
                 transformed_name = name.replace(weight_name, param_name)
+                loaded_params.add(transformed_name)
                 param = params_dict[transformed_name]
                 param.weight_loader(param, loaded_weight, shard_id)
                 return True
@@ -318,6 +717,7 @@ class Llama4ForConditionalGeneration(nn.Module):
         expert_params_mapping: list,
         params_dict: dict,
         num_experts: int,
+        loaded_params: set,
     ) -> bool:
         """Handle expert weight loading for MoE (Mixture of Experts) layers.
 
@@ -336,16 +736,16 @@ class Llama4ForConditionalGeneration(nn.Module):
 
         if "experts.gate_up_proj" not in name and "experts.down_proj" not in name:
             return self._handle_other_expert_params(
-                name, loaded_weight, expert_params_mapping, params_dict
+                name, loaded_weight, expert_params_mapping, params_dict, loaded_params
             )
 
         if "scale" in name:
             return self._handle_expert_scale_params(
-                name, loaded_weight, params_dict, num_experts
+                name, loaded_weight, params_dict, num_experts, loaded_params
             )
         else:
             return self._handle_expert_weight_params(
-                name, loaded_weight, params_dict, num_experts
+                name, loaded_weight, params_dict, num_experts, loaded_params
             )
 
     def _handle_other_expert_params(
@@ -354,6 +754,7 @@ class Llama4ForConditionalGeneration(nn.Module):
         loaded_weight: torch.Tensor,
         expert_params_mapping: list,
         params_dict: dict,
+        loaded_params: set,
     ) -> bool:
         """Handle expert parameters that are not gate_up_proj or down_proj weights.
 
@@ -362,6 +763,7 @@ class Llama4ForConditionalGeneration(nn.Module):
             loaded_weight: The weight tensor to be loaded
             expert_params_mapping: List of tuples mapping checkpoint names to model parameters
             params_dict: Dictionary of model parameters
+            loaded_params: Set of loaded parameter names
 
         Returns:
             bool: True if parameter was found and handled, False otherwise
@@ -373,6 +775,7 @@ class Llama4ForConditionalGeneration(nn.Module):
                 param.weight_loader(
                     param, loaded_weight, name, shard_id=shard_id, expert_id=expert_id
                 )
+                loaded_params.add(transformed_name)
                 return True
         return False
 
@@ -411,6 +814,7 @@ class Llama4ForConditionalGeneration(nn.Module):
         loaded_weight: torch.Tensor,
         params_dict: dict,
         num_experts: int,
+        loaded_params: set,
     ) -> bool:
         """Handle quantization scale parameters for expert weights.
 
@@ -419,6 +823,7 @@ class Llama4ForConditionalGeneration(nn.Module):
             loaded_weight: Scale tensor to be loaded
             params_dict: Dictionary of model parameters
             num_experts: Total number of experts for broadcast operations
+            loaded_params: Set of loaded parameter names
 
         Returns:
             bool: True (always handles scale parameters)
@@ -447,6 +852,7 @@ class Llama4ForConditionalGeneration(nn.Module):
             # Load the same scale for all experts
             for expert_id in range(num_experts):
                 param.data[expert_id] = loaded_weight
+        loaded_params.add(transformed_name)
 
         return True
 
@@ -456,6 +862,7 @@ class Llama4ForConditionalGeneration(nn.Module):
         loaded_weight: torch.Tensor,
         params_dict: dict,
         num_experts: int,
+        loaded_params: set,
     ) -> bool:
         """Handle actual weight tensors for expert layers (gate_up_proj and down_proj).
 
@@ -464,6 +871,7 @@ class Llama4ForConditionalGeneration(nn.Module):
             loaded_weight: Weight tensor(s) to be loaded
             params_dict: Dictionary of model parameters
             num_experts: Total number of experts for tensor distribution
+            loaded_params: Set of loaded parameter names
 
         Returns:
             bool: True (always handles weight parameters)
@@ -486,6 +894,7 @@ class Llama4ForConditionalGeneration(nn.Module):
 
             param = params_dict[param_name]
             weight_loader = param.weight_loader
+            loaded_params.add(param_name)
 
             # Handle the case where loaded_weight might be a single tensor for all experts
             if weight_chunk.dim() == 2:

python/sglang/srt/multimodal/processors/base_processor.py

@@ -12,7 +12,6 @@ import torch
 from PIL import Image
 from transformers import BaseImageProcessorFast
 
-from sglang.srt.managers.mm_utils import TransportProxyTensor
 from sglang.srt.managers.schedule_batch import Modality, MultimodalDataItem
 from sglang.srt.utils import load_audio, load_image, load_video, logger
 
@@ -218,8 +217,10 @@ class BaseMultimodalProcessor(ABC):
                 kwargs["audio"] = audios
 
         processor = self._processor
-        if hasattr(processor, "image_processor") and isinstance(
-            processor.image_processor, BaseImageProcessorFast
+        if (
+            hasattr(processor, "image_processor")
+            and isinstance(processor.image_processor, BaseImageProcessorFast)
+            and not self.server_args.disable_fast_image_processor
         ):
             kwargs["device"] = "cuda"
         result = processor.__call__(