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."""