Enable ModelOpt Llama4 fp8 checkpoint deployment in SGLang (#7129)

python/sglang/srt/layers/moe/fused_moe_triton/layer.py

@@ -649,6 +649,27 @@ class FusedMoE(torch.nn.Module):
         loaded_weight: torch.tensor,
         tp_rank: int,
     ):
+        """Load w2 weights for down projection.
+
+        Args:
+            expert_data: The expert data tensor to load into
+            shard_dim: The dimension to shard along
+            shard_id: The shard ID (must be "w2")
+            loaded_weight: The weight tensor to load from
+            tp_rank: The tensor parallel rank
+        """
+        if not isinstance(expert_data, torch.Tensor) or not isinstance(
+            loaded_weight, torch.Tensor
+        ):
+            raise ValueError("expert_data and loaded_weight must be torch.Tensor")
+
+        if expert_data.dim() != 2 or loaded_weight.dim() != 2:
+            raise ValueError(
+                f"Expected 2D tensors, got expert_data shape {expert_data.shape} and loaded_weight shape {loaded_weight.shape}"
+            )
+
+        if shard_id != "w2":
+            raise ValueError(f"shard_id must be 'w2', got {shard_id}")
 
         # Index the loaded weight for tp sharding.
         # down_proj: "RowParallel" so tp sharding on input_dim
@@ -669,6 +690,10 @@ class FusedMoE(torch.nn.Module):
             if not self.use_presharded_weights:
                 if self.use_triton_kernels:
                     loaded_weight = loaded_weight.transpose(-2, -1)
+                if shard_size * tp_rank + shard_size > loaded_weight.shape[shard_dim]:
+                    raise ValueError(
+                        f"Shard size {shard_size} at rank {tp_rank} exceeds loaded_weight dimension {loaded_weight.shape[shard_dim]}"
+                    )
                 loaded_weight = loaded_weight.narrow(
                     shard_dim, shard_size * tp_rank, shard_size
                 )
@@ -795,8 +820,21 @@ class FusedMoE(torch.nn.Module):
                 tp_rank=tp_rank,
             )
             return
+
         if "ModelOpt" in self.quant_method.__class__.__name__:
-            if "weight_scale_2" in weight_name or "input_scale" in weight_name:
+            # Determine per-tensor weight scale patterns based on variant
+            is_fp4_variant = (
+                "ModelOptNvFp4FusedMoEMethod" in self.quant_method.__class__.__name__
+            )
+
+            # FP4 uses "weight_scale_2" for per-tensor, FP8 uses "weight_scale" for per-tensor
+            per_tensor_conditions = (
+                "weight_scale_2" in weight_name
+                if is_fp4_variant
+                else "weight_scale" in weight_name
+            ) or "input_scale" in weight_name
+
+            if per_tensor_conditions:
                 self._load_per_tensor_weight_scale(
                     shard_id=shard_id,
                     param=param,

python/sglang/srt/layers/quantization/modelopt_quant.py

@@ -26,6 +26,7 @@ from sglang.srt.layers.quantization.kv_cache import BaseKVCacheMethod
 from sglang.srt.layers.quantization.utils import (
     convert_to_channelwise,
     is_layer_skipped,
+    per_tensor_dequantize,
     requantize_with_max_scale,
 )
 from sglang.srt.layers.radix_attention import RadixAttention
@@ -110,7 +111,12 @@ class ModelOptFp8Config(QuantizationConfig):
         self, layer: torch.nn.Module, prefix: str
     ) -> Optional["QuantizeMethodBase"]:
         if self.exclude_modules and any(
-            module in prefix for module in self.exclude_modules
+            module in prefix
+            or (
+                prefix.startswith("language_model.")
+                and module in prefix.removeprefix("language_model.")
+            )
+            for module in self.exclude_modules
         ):
             return None
 
@@ -119,6 +125,12 @@ class ModelOptFp8Config(QuantizationConfig):
         if self.kv_cache_quant_method and isinstance(layer, RadixAttention):
             return ModelOptFp8KVCacheMethod(self)
 
+        # Add MoE support
+        from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
+
+        if isinstance(layer, FusedMoE):
+            return ModelOptFp8MoEMethod(self)
+
         return None
 
     def get_scaled_act_names(self) -> List[str]:
@@ -234,6 +246,237 @@ class ModelOptFp8KVCacheMethod(BaseKVCacheMethod):
         super().__init__(quant_config)
 
 
+class ModelOptFp8MoEMethod:
+    """MoE method for ModelOpt FP8.
+    Supports loading FP8 checkpoints with static weight scale and activation scale.
+
+    Args:
+        quant_config: The ModelOpt quantization config.
+    """
+
+    def __new__(cls, *args, **kwargs):
+        """
+        Dynamic class composition pattern.
+
+        This allows us to effectively "inject" FusedMoEMethodBase as a parent class
+        at runtime while avoiding circular import issues.
+        """
+        from sglang.srt.layers.moe.fused_moe_triton import FusedMoEMethodBase
+
+        if not hasattr(cls, "_initialized"):
+            original_init = cls.__init__
+            new_cls = type(
+                cls.__name__,
+                (FusedMoEMethodBase,),
+                {
+                    "__init__": original_init,
+                    **{k: v for k, v in cls.__dict__.items() if k != "__dict__"},
+                },
+            )
+            obj = super(new_cls, new_cls).__new__(new_cls)
+            obj.__init__(*args, **kwargs)
+            return obj
+        return super().__new__(cls)
+
+    def __init__(self, quant_config: ModelOptFp8Config):
+        self.quant_config = quant_config
+        self.cutlass_fp8_supported = cutlass_fp8_supported()
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        num_experts: int,
+        hidden_size: int,
+        intermediate_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
+
+        # Use FP8 dtype if checkpoint is serialized, otherwise use the default dtype
+        weight_dtype = (
+            torch.float8_e4m3fn
+            if self.quant_config.is_checkpoint_fp8_serialized
+            else params_dtype
+        )
+        weight_loader = extra_weight_attrs.get("weight_loader")
+
+        w13_weight = ModelWeightParameter(
+            data=torch.empty(
+                num_experts, 2 * intermediate_size, hidden_size, dtype=weight_dtype
+            ),
+            input_dim=2,
+            output_dim=1,
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("w13_weight", w13_weight)
+
+        w2_weight = ModelWeightParameter(
+            data=torch.empty(
+                num_experts, hidden_size, intermediate_size, dtype=weight_dtype
+            ),
+            input_dim=2,
+            output_dim=1,
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("w2_weight", w2_weight)
+
+        if self.quant_config.is_checkpoint_fp8_serialized:
+            # WEIGHT SCALES - Per-tensor scaling for ModelOpts
+            # Allocate 2 scales for w1 and w3 respectively.
+            # They will be combined to a single scale after weight loading.
+            w13_weight_scale = PerTensorScaleParameter(
+                data=torch.full(
+                    (num_experts, 2),
+                    torch.finfo(torch.float32).min,
+                    dtype=torch.float32,
+                ),
+                weight_loader=weight_loader,
+            )
+            w2_weight_scale = PerTensorScaleParameter(
+                data=torch.full(
+                    (num_experts,), torch.finfo(torch.float32).min, dtype=torch.float32
+                ),
+                weight_loader=weight_loader,
+            )
+            layer.register_parameter("w13_weight_scale", w13_weight_scale)
+            layer.register_parameter("w2_weight_scale", w2_weight_scale)
+
+            # Set weight loader attributes for scales
+            extra_weight_attrs.update(
+                {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value}
+            )
+
+            # INPUT SCALES - Per-tensor scaling for ModelOpt
+            w13_input_scale = PerTensorScaleParameter(
+                data=torch.full((num_experts,), 1.0, dtype=torch.float32),
+                weight_loader=weight_loader,
+            )
+            w2_input_scale = PerTensorScaleParameter(
+                data=torch.full((num_experts,), 1.0, dtype=torch.float32),
+                weight_loader=weight_loader,
+            )
+            layer.register_parameter("w13_input_scale", w13_input_scale)
+            layer.register_parameter("w2_input_scale", w2_input_scale)
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        """Process FP8 MoE weights after loading from serialized checkpoint.
+
+        Only supports pre-quantized checkpoints with FP8 weights and scales.
+        """
+
+        layer.w13_weight = Parameter(layer.w13_weight.data, requires_grad=False)
+        layer.w2_weight = Parameter(layer.w2_weight.data, requires_grad=False)
+
+        # Handle scale parameters
+        if hasattr(layer, "w13_weight_scale") and layer.w13_weight_scale is not None:
+            # Fp8 moe kernel needs single weight scale for w13 per expert.
+            # We take the max of the w1 and w3 scales then dequant and requant each expert.
+            if layer.w13_weight_scale.dim() == 2:  # Shape: (num_experts, 2)
+                from sglang.srt.layers.quantization.fp8_kernel import scaled_fp8_quant
+
+                # Get the maximum scale across w1 and w3 for each expert
+                max_w13_scales = layer.w13_weight_scale.max(dim=1).values
+
+                # Requantize each expert's weights using the combined scale
+                # w13_weight has shape (num_experts, 2 * intermediate_size, hidden_size)
+                # where the first intermediate_size rows are w1, the next are w3
+                intermediate_size = layer.w13_weight.shape[1] // 2
+                for expert_id in range(layer.w13_weight.shape[0]):
+                    start = 0
+                    for shard_id in range(2):  # w1 and w3
+                        # Dequantize using the original scale for this shard
+                        dq_weight = per_tensor_dequantize(
+                            layer.w13_weight[expert_id][
+                                start : start + intermediate_size, :
+                            ],
+                            layer.w13_weight_scale[expert_id][shard_id],
+                        )
+                        # Requantize using the combined max scale
+                        (
+                            layer.w13_weight[expert_id][
+                                start : start + intermediate_size, :
+                            ],
+                            _,
+                        ) = scaled_fp8_quant(dq_weight, max_w13_scales[expert_id])
+
+                        start += intermediate_size
+
+                # Update the scale parameter to be per-expert instead of per-shard
+                layer.w13_weight_scale = Parameter(max_w13_scales, requires_grad=False)
+            else:
+                layer.w13_weight_scale = Parameter(
+                    layer.w13_weight_scale.data, requires_grad=False
+                )
+
+        if hasattr(layer, "w2_weight_scale") and layer.w2_weight_scale is not None:
+            layer.w2_weight_scale = Parameter(
+                layer.w2_weight_scale.data, requires_grad=False
+            )
+        if hasattr(layer, "w13_input_scale") and layer.w13_input_scale is not None:
+            layer.w13_input_scale = Parameter(
+                layer.w13_input_scale.max(), requires_grad=False
+            )
+        if hasattr(layer, "w2_input_scale") and layer.w2_input_scale is not None:
+            layer.w2_input_scale = Parameter(
+                layer.w2_input_scale.max(), requires_grad=False
+            )
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool,
+        use_grouped_topk: bool,
+        topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
+        num_fused_shared_experts: Optional[int] = None,
+        custom_routing_function: Optional[Callable] = None,
+        correction_bias: Optional[torch.Tensor] = None,
+        activation: str = "silu",
+        apply_router_weight_on_input: bool = False,
+        inplace: bool = True,
+        no_combine: bool = False,
+        routed_scaling_factor: Optional[float] = None,
+    ) -> torch.Tensor:
+        from sglang.srt.layers.moe.fused_moe_triton.fused_moe import fused_experts
+        from sglang.srt.layers.moe.topk import select_experts
+
+        # Expert selection
+        topk_weights, topk_ids = select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            num_fused_shared_experts=num_fused_shared_experts,
+            custom_routing_function=custom_routing_function,
+            correction_bias=correction_bias,
+            routed_scaling_factor=routed_scaling_factor,
+        )
+
+        return fused_experts(
+            x,
+            layer.w13_weight,
+            layer.w2_weight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            inplace=inplace,
+            activation=activation,
+            use_fp8_w8a8=True,
+            per_channel_quant=False,  # ModelOpt uses per-tensor quantization
+            w1_scale=layer.w13_weight_scale,
+            w2_scale=layer.w2_weight_scale,
+            a1_scale=layer.w13_input_scale,
+            a2_scale=layer.w2_input_scale,
+            no_combine=no_combine,
+        )
+
+
 class ModelOptFp4Config(QuantizationConfig):
     """Config class for FP4."""
 

python/sglang/srt/models/mllama4.py

+import json as json_lib
+import logging
+import os
 from collections.abc import Iterable
 from typing import List, Optional, Set, Tuple
 
@@ -19,6 +22,13 @@ from sglang.srt.model_loader.weight_utils import default_weight_loader
 from sglang.srt.utils import add_prefix, is_cpu
 
 _is_cpu = is_cpu()
+from sglang.srt.model_loader.weight_utils import (
+    default_weight_loader,
+    maybe_remap_kv_scale_name,
+)
+from sglang.srt.utils import add_prefix
+
+logger = logging.getLogger(__name__)
 
 
 class Llama4ForConditionalGeneration(nn.Module):
@@ -37,19 +47,85 @@ class Llama4ForConditionalGeneration(nn.Module):
         self.config = config
         self.quant_config = quant_config
 
-        self.vision_model = Llama4VisionModel(config.vision_config)
-        self.multi_modal_projector = Llama4MultiModalProjector(config)
+        # Check if this is a text-only model (modelopt fp8 llama4 has no vision components)
+        self.has_vision = self._has_vision_weights(config)
+        if not self.has_vision:
+            logger.warning(
+                "No vision weights found in checkpoint. Model will run in text-only mode. "
+                "Multimodal capabilities (image processing) will be unavailable."
+            )
+
+        if self.has_vision:
+            self.vision_model = Llama4VisionModel(config.vision_config)
+            self.multi_modal_projector = Llama4MultiModalProjector(config)
+        else:
+            self.vision_model = None
+            self.multi_modal_projector = None
 
         # Initialize the language model
         from sglang.srt.models.llama4 import Llama4ForCausalLM
 
         self.language_model = Llama4ForCausalLM(
-            config.text_config,
+            config.text_config if hasattr(config, "text_config") else config,
             quant_config=quant_config,
             prefix=add_prefix("language_model", prefix),
         )
 
-        self.logits_processor = LogitsProcessor(config.text_config)
+        self.logits_processor = LogitsProcessor(
+            config.text_config if hasattr(config, "text_config") else config
+        )
+
+    def _has_vision_weights(self, config) -> bool:
+        """Check if the model has vision components by examining the checkpoint."""
+        model_path = getattr(config, "_name_or_path", None)
+        if not model_path:
+            return False
+
+        # Check if this is a local path first
+        if os.path.isdir(model_path):
+            index_file = os.path.join(model_path, "model.safetensors.index.json")
+            if os.path.exists(index_file):
+                return self._check_vision_weights_in_index(index_file)
+
+        # For HuggingFace models, we need to check the actual checkpoint
+        # The config might say it's multimodal, but the checkpoint might be text-only
+        try:
+            # Try to access the HuggingFace cache directory
+            from huggingface_hub import try_to_load_from_cache
+
+            # Check if index file exists in cache
+            index_file_path = try_to_load_from_cache(
+                repo_id=model_path,
+                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)
+
+        except Exception:
+            # If we can't access the cache, fall back to config-based detection
+            pass
+
+        # Fallback， assume text-only
+        return False
+
+    def _check_vision_weights_in_index(self, index_file: str) -> bool:
+        """Check if the model.safetensors.index.json contains vision weights."""
+        try:
+            with open(index_file, "r") as f:
+                index_data = json_lib.load(f)
+
+            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
+                for pattern in vision_patterns
+            )
+        except (OSError, json_lib.JSONDecodeError, KeyError):
+            return False
 
     def pad_input_ids(self, input_ids: List[int], mm_inputs: MultimodalInputs):
         pattern = MultiModalityDataPaddingPatternMultimodalTokens()
@@ -59,6 +135,10 @@ class Llama4ForConditionalGeneration(nn.Module):
         self,
         items: List[MultimodalDataItem],
     ) -> torch.Tensor:
+        # 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.pixel_values for item in items])
             .to(next(self.vision_model.parameters()).device)
@@ -79,11 +159,14 @@ class Llama4ForConditionalGeneration(nn.Module):
         **kwargs: object,
     ) -> torch.Tensor:
 
+        # For text-only models, pass None for image_data_embedding_func
+        image_embedding_func = self.get_image_feature if self.has_vision else None
+
         hs = general_mm_embed_routine(
             input_ids=input_ids,
             forward_batch=forward_batch,
             language_model=self.language_model,
-            image_data_embedding_func=self.get_image_feature,
+            image_data_embedding_func=image_embedding_func,
             positions=positions,
         )
 
@@ -124,7 +207,6 @@ class Llama4ForConditionalGeneration(nn.Module):
         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"),
@@ -137,11 +219,12 @@ class Llama4ForConditionalGeneration(nn.Module):
         ]
 
         params_dict = dict(self.named_parameters())
+        num_experts = (
+            self.config.text_config.num_local_experts
+            if hasattr(self.config, "text_config")
+            else self.config.num_local_experts
+        )
 
-        num_experts = self.config.text_config.num_local_experts
-
-        # Params for weights, fp8 weight scales, fp8 activation scales
-        # (param_name, weight_name, expert_id, shard_id)
         expert_params_mapping = FusedMoE.make_expert_params_mapping(
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
@@ -150,81 +233,279 @@ class Llama4ForConditionalGeneration(nn.Module):
         )
 
         for name, loaded_weight in weights:
-            if not "vision" in name:
+            if self._should_skip_weight(name):
+                continue
+
+            name = self._transform_weight_name(name)
+
+            if "vision" not in name:
                 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
-
-                if "vision" 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
+            if self._handle_scale_remapping(name, params_dict):
+                continue
+
+            if self._handle_stacked_params(
+                name, loaded_weight, stacked_params_mapping, params_dict
+            ):
+                continue
+
+            if self._handle_expert_weights(
+                name, loaded_weight, expert_params_mapping, params_dict, num_experts
+            ):
+                continue
+
+            self._handle_default_weight(name, loaded_weight, params_dict)
+
+    def _should_skip_weight(self, name: str) -> bool:
+        """Check if we should skip loading this weight."""
+        return "vision" in name and not self.has_vision
+
+    def _transform_weight_name(self, name: str) -> str:
+        """Transform weight name by adding language_model prefix if needed."""
+        if (
+            not name.startswith("language_model.")
+            and "vision" not in name
+            and "multi_modal_projector" not in name
+        ):
+            return f"language_model.{name}"
+        return name
+
+    def _handle_scale_remapping(self, name: str, params_dict: dict) -> bool:
+        """Handle scale parameter remapping. Returns True if handled."""
+        if "scale" in name and "expert" not in name:
+            remapped_name = maybe_remap_kv_scale_name(name, params_dict)
+            return remapped_name is None
+        return False
+
+    def _handle_stacked_params(
+        self,
+        name: str,
+        loaded_weight: torch.Tensor,
+        stacked_params_mapping: list,
+        params_dict: dict,
+    ) -> 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:
+                transformed_name = name.replace(weight_name, param_name)
+                param = params_dict[transformed_name]
+                param.weight_loader(param, loaded_weight, shard_id)
+                return True
+        return False
+
+    def _handle_expert_weights(
+        self,
+        name: str,
+        loaded_weight: torch.Tensor,
+        expert_params_mapping: list,
+        params_dict: dict,
+        num_experts: int,
+    ) -> bool:
+        """Handle expert weight loading for MoE (Mixture of Experts) layers.
+
+        Args:
+            name: Parameter name from the checkpoint
+            loaded_weight: The weight tensor to be loaded
+            expert_params_mapping: Mapping of parameter names to expert configurations
+            params_dict: Dictionary of model parameters
+            num_experts: Total number of experts in the MoE layer
+
+        Returns:
+            bool: True if the parameter was handled (is an expert parameter), False otherwise
+        """
+        if ".experts" not in name:
+            return False
+
+        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
+            )
+
+        if "scale" in name:
+            return self._handle_expert_scale_params(
+                name, loaded_weight, params_dict, num_experts
+            )
+        else:
+            return self._handle_expert_weight_params(
+                name, loaded_weight, params_dict, num_experts
+            )
+
+    def _handle_other_expert_params(
+        self,
+        name: str,
+        loaded_weight: torch.Tensor,
+        expert_params_mapping: list,
+        params_dict: dict,
+    ) -> bool:
+        """Handle expert parameters that are not gate_up_proj or down_proj weights.
+
+        Args:
+            name: Parameter name from the checkpoint
+            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
+
+        Returns:
+            bool: True if parameter was found and handled, False otherwise
+        """
+        for param_name, weight_name, expert_id, shard_id in expert_params_mapping:
+            if weight_name in name:
+                transformed_name = name.replace(weight_name, param_name)
+                param = params_dict[transformed_name]
+                param.weight_loader(
+                    param, loaded_weight, name, shard_id=shard_id, expert_id=expert_id
+                )
+                return True
+        return False
+
+    def _transform_expert_name(
+        self, name: str, is_weight: bool = False
+    ) -> Tuple[str, str, List[str]]:
+        """Transform expert parameter name and get shard information.
+
+        Args:
+            name: The original parameter name
+            is_weight: Whether this is a weight parameter (adds _weight suffix)
+
+        Returns:
+            Tuple of (transformed_name, shard_id, shard_id_list)
+        """
+        suffix = "_weight" if is_weight else ""
+
+        if ".gate_up_proj" in name:
+            transformed_name = name.replace(
+                ".experts.gate_up_proj", f".experts.w13{suffix}"
+            )
+            shard_id = "w13"
+            shard_id_list = ["w1", "w3"]
+        else:  # down_proj
+            transformed_name = name.replace(
+                ".experts.down_proj", f".experts.w2{suffix}"
+            )
+            shard_id = "w2"
+            shard_id_list = ["w2"]
+
+        return transformed_name, shard_id, shard_id_list
+
+    def _handle_expert_scale_params(
+        self,
+        name: str,
+        loaded_weight: torch.Tensor,
+        params_dict: dict,
+        num_experts: int,
+    ) -> bool:
+        """Handle quantization scale parameters for expert weights.
+
+        Args:
+            name: Parameter name containing scale information
+            loaded_weight: Scale tensor to be loaded
+            params_dict: Dictionary of model parameters
+            num_experts: Total number of experts for broadcast operations
+
+        Returns:
+            bool: True (always handles scale parameters)
+        """
+        import re
+
+        # Check if this matches the expert parameter pattern: experts.{expert_id}.{param_name}
+        expert_match = re.search(r"experts\.(\d+)\.", name)
+
+        # Transform name
+        transformed_name, _, _ = self._transform_expert_name(name)
+
+        if transformed_name not in params_dict:
+            return True
+
+        param = params_dict[transformed_name]
+
+        # Handle scale parameters
+        if expert_match:
+            # If we have a specific expert ID, only load for that expert
+            expert_id = int(expert_match.group(1))
+            # For scale parameters, we can directly set the value
+            param.data[expert_id] = loaded_weight
+        else:
+            # No expert ID found - this is a single scale for all experts
+            # Load the same scale for all experts
+            for expert_id in range(num_experts):
+                param.data[expert_id] = loaded_weight
+
+        return True
+
+    def _handle_expert_weight_params(
+        self,
+        name: str,
+        loaded_weight: torch.Tensor,
+        params_dict: dict,
+        num_experts: int,
+    ) -> bool:
+        """Handle actual weight tensors for expert layers (gate_up_proj and down_proj).
+
+        Args:
+            name: Parameter name (should contain gate_up_proj or down_proj)
+            loaded_weight: Weight tensor(s) to be loaded
+            params_dict: Dictionary of model parameters
+            num_experts: Total number of experts for tensor distribution
+
+        Returns:
+            bool: True (always handles weight parameters)
+        """
+        # Transform name and get shard info
+        transformed_name, _, shard_id_list = self._transform_expert_name(
+            name, is_weight=True
+        )
+
+        if ".gate_up_proj" in name:
+            loaded_weight_list = loaded_weight.chunk(2, dim=-1)
+        else:  # down_proj
+            loaded_weight_list = [loaded_weight]
+
+        for param_name, weight_chunk, shard_id in zip(
+            [transformed_name] * len(shard_id_list), loaded_weight_list, shard_id_list
+        ):
+            if param_name not in params_dict:
+                continue
+
+            param = params_dict[param_name]
+            weight_loader = param.weight_loader
+
+            # Handle the case where loaded_weight might be a single tensor for all experts
+            if weight_chunk.dim() == 2:
+                # Single tensor case - load for all experts
+                for expert_id in range(num_experts):
+                    weight_loader(
+                        param,
+                        weight_chunk.T,
+                        param_name,
+                        shard_id=shard_id,
+                        expert_id=expert_id,
+                    )
             else:
-                if ".experts" in name:
-                    # NOTE: llama4 fp8 has different weight format for experts
-                    if (
-                        "experts.gate_up_proj" not in name
-                        and "experts.down_proj" not in name
-                    ):
-                        for mapping in expert_params_mapping:
-                            param_name, weight_name, expert_id, shard_id = 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,
-                                name,
-                                shard_id=shard_id,
-                                expert_id=expert_id,
-                            )
-                            break
-                    else:
-                        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
+                # Multiple experts case - load each expert's weights
+                for expert_id in range(num_experts):
+                    weight_loader(
+                        param,
+                        weight_chunk[expert_id].T,
+                        param_name,
+                        shard_id=shard_id,
+                        expert_id=expert_id,
                     )
-                    weight_loader(param, loaded_weight)
+
+        return True
+
+    def _handle_default_weight(
+        self, name: str, loaded_weight: torch.Tensor, params_dict: dict
+    ):
+        """Handle default weight loading."""
+        # Skip loading extra bias for GPTQ models
+        if name.endswith(".bias") and name not in params_dict:
+            return
+
+        param = params_dict[name]
+        weight_loader = getattr(param, "weight_loader", default_weight_loader)
+        weight_loader(param, loaded_weight)
 
     def set_eagle3_layers_to_capture(self, layer_ids: Optional[List[int]] = None):
         if hasattr(self.language_model, "set_eagle3_layers_to_capture"):