[DeepseekR1-FP4] Add Support for nvidia/DeepSeekR1-FP4 model (#6853)

Signed-off-by: Pavani Majety <pmajety@nvidia.com>

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

@@ -556,7 +556,8 @@ class FusedMoE(torch.nn.Module):
             loaded_weight = loaded_weight.to(param.data.device)
 
             if (
-                param.data[expert_id] != 1
+                "compressed" in self.quant_method.__class__.__name__.lower()
+                and param.data[expert_id] != 1
                 and (param.data[expert_id] - loaded_weight).abs() > 1e-5
             ):
                 raise ValueError(
@@ -580,6 +581,23 @@ 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:
+                self._load_per_tensor_weight_scale(
+                    shard_id=shard_id,
+                    param=param,
+                    loaded_weight=loaded_weight,
+                    expert_id=expert_id,
+                )
+            elif "weight" in weight_name:
+                self._load_model_weight_or_group_weight_scale(
+                    shard_id=shard_id,
+                    shard_dim=shard_dim,
+                    loaded_weight=loaded_weight,
+                    expert_data=expert_data,
+                    tp_rank=tp_rank,
+                )
+            return
 
         # Case weight scales and zero_points
         if "scale" in weight_name or "zero" in weight_name:

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

 # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/modelopt.py
 
 import logging
-from typing import Any, Dict, List, Optional
+from typing import Any, Callable, Dict, List, Optional
 
 import torch
 from torch.nn.parameter import Parameter
 
-from sglang.srt.layers.linear import LinearBase, LinearMethodBase
+from sglang.srt.layers.linear import (
+    LinearBase,
+    LinearMethodBase,
+    UnquantizedLinearMethod,
+)
+from sglang.srt.layers.moe.cutlass_moe_params import CutlassMoEParams, CutlassMoEType
 from sglang.srt.layers.parameter import ModelWeightParameter, PerTensorScaleParameter
 from sglang.srt.layers.quantization.base_config import (
     QuantizationConfig,
@@ -15,10 +20,12 @@ from sglang.srt.layers.quantization.base_config import (
 from sglang.srt.layers.quantization.fp8_utils import (
     apply_fp8_linear,
     cutlass_fp8_supported,
+    is_sm100_supported,
 )
 from sglang.srt.layers.quantization.kv_cache import BaseKVCacheMethod
 from sglang.srt.layers.quantization.utils import (
     convert_to_channelwise,
+    is_layer_skipped,
     requantize_with_max_scale,
 )
 from sglang.srt.layers.radix_attention import RadixAttention
@@ -270,9 +277,16 @@ class ModelOptFp4Config(QuantizationConfig):
             )
         is_checkpoint_nvfp4_serialized = "NVFP4" in quant_method
         kv_cache_quant_algo = quant_config["kv_cache_quant_algo"]
+        if not kv_cache_quant_algo:
+            kv_cache_quant_algo = "auto"
         group_size = quant_config["group_size"]
         exclude_modules = quant_config["exclude_modules"]
         if not (group_size and kv_cache_quant_algo and exclude_modules):
+            logger.warning(
+                f"group_size: {group_size},"
+                f"kv_cache_quant_algo: {kv_cache_quant_algo},"
+                f"exclude_modules: {exclude_modules}"
+            )
             raise ValueError(
                 "NVFP4 quantization requires group size and "
                 "kv_cache_quant_algo specified in "
@@ -285,19 +299,30 @@ class ModelOptFp4Config(QuantizationConfig):
             exclude_modules,
         )
 
+    def is_layer_excluded(self, prefix: str, exclude_modules: list):
+        import regex as re
+
+        for pattern in exclude_modules:
+            regex_str = pattern.replace(".", r"\.").replace("*", r".*")
+            if re.fullmatch(regex_str, prefix):
+                return True
+        return False
+
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
     ) -> Optional["QuantizeMethodBase"]:
-        if self.exclude_modules and any(
-            module in prefix for module in self.exclude_modules
-        ):
-            return None
+        from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
 
         if isinstance(layer, LinearBase):
+            if is_layer_skipped(prefix, self.exclude_modules) or self.is_layer_excluded(
+                prefix, self.exclude_modules
+            ):
+                return UnquantizedLinearMethod()
             return ModelOptFp4LinearMethod(self)
         if self.kv_cache_quant_algo and isinstance(layer, RadixAttention):
             return ModelOptFp8KVCacheMethod(self)
-
+        elif isinstance(layer, FusedMoE):
+            return ModelOptNvFp4FusedMoEMethod(self)
         return None
 
     def get_scaled_act_names(self) -> List[str]:
@@ -461,3 +486,305 @@ class ModelOptFp4LinearMethod(LinearMethodBase):
         if bias is not None:
             out = out + bias
         return out.view(*output_shape)
+
+
+class ModelOptNvFp4FusedMoEMethod:
+    """
+       MoE Method for FP4 Quantization with Blockscales and PerTensorScales
+    Args:
+        quant_config: NVFP4 Quant Config
+    """
+
+    def __new__(cls, *args, **kwargs):
+        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: ModelOptFp4Config):
+        self.quant_config = quant_config
+        if not is_sm100_supported():
+            raise ValueError(
+                "Current platform does not support NVFP4"
+                " quantization. Please use Blackwell and"
+                " above."
+            )
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        num_experts: int,
+        hidden_size: int,
+        intermediate_size_per_partition: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        if not self.quant_config.is_checkpoint_nvfp4_serialized:
+            raise ValueError(
+                "NVFP4 quantization was selected, "
+                " dynamic quantization is not supported."
+            )
+
+        layer.num_experts = num_experts
+        layer.params_dtype = params_dtype
+        layer.quant_config = self.quant_config
+        weight_dtype = torch.uint8
+        weight_scale_dtype = torch.float8_e4m3fn
+        weight_loader = extra_weight_attrs.get("weight_loader")
+        # GEMM 1
+        w13_weight = ModelWeightParameter(
+            data=torch.empty(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                # 2 fp4 items are packed in the input dimension
+                hidden_size // 2,
+                dtype=weight_dtype,
+            ),
+            input_dim=1,
+            output_dim=2,
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("w13_weight", w13_weight)
+
+        # GEMM 2
+        w2_weight = ModelWeightParameter(
+            data=torch.empty(
+                num_experts,
+                hidden_size,
+                # 2 fp4 items are packed in the input dimension
+                intermediate_size_per_partition // 2,
+                dtype=weight_dtype,
+            ),
+            input_dim=1,
+            output_dim=2,
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("w2_weight", w2_weight)
+
+        w13_weight_scale = ModelWeightParameter(
+            data=torch.empty(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                # 2 fp4 items are packed in the input dimension
+                hidden_size // self.quant_config.group_size,
+                dtype=weight_scale_dtype,
+            ),
+            input_dim=1,
+            output_dim=2,
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("w13_weight_scale", w13_weight_scale)
+
+        w2_weight_scale = ModelWeightParameter(
+            data=torch.empty(
+                num_experts,
+                hidden_size,
+                # 2 fp4 items are packed in the input dimension
+                intermediate_size_per_partition // self.quant_config.group_size,
+                dtype=weight_scale_dtype,
+            ),
+            input_dim=1,
+            output_dim=2,
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("w2_weight_scale", w2_weight_scale)
+
+        from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
+
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.BLOCK.value}
+        )
+
+        w13_weight_scale_2 = PerTensorScaleParameter(
+            data=torch.empty(num_experts, 2, dtype=torch.float32),
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("w13_weight_scale_2", w13_weight_scale_2)
+
+        w2_weight_scale_2 = PerTensorScaleParameter(
+            data=torch.empty(num_experts, dtype=torch.float32),
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("w2_weight_scale_2", w2_weight_scale_2)
+
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value}
+        )
+
+        w13_input_scale = PerTensorScaleParameter(
+            data=torch.empty(num_experts, 2, dtype=torch.float32),
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("w13_input_scale", w13_input_scale)
+
+        w2_input_scale = PerTensorScaleParameter(
+            data=torch.empty(num_experts, dtype=torch.float32),
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("w2_input_scale", w2_input_scale)
+
+    def swizzle_blockscale(self, scale: torch.tensor):
+        assert scale.dtype == torch.float8_e4m3fn
+        # Pad and blockwise interleave weight_scale
+        scale_ndim = scale.ndim
+        if scale.ndim == 2:
+            scale = scale.unsqueeze(0)
+        assert scale.ndim == 3
+        B, M, K = scale.shape
+        round_up_multiple = lambda x, m: (x + m - 1) // m * m
+        M_padded = round_up_multiple(M, 128)
+        K_padded = round_up_multiple(K, 4)
+        padded_scale = torch.zeros((B, M_padded, K_padded), dtype=scale.dtype)
+        padded_scale[:B, :M, :K] = scale
+        batches, rows, cols = padded_scale.shape
+        assert rows % 128 == 0
+        assert cols % 4 == 0
+        padded_scale = padded_scale.reshape(batches, rows // 128, 4, 32, cols // 4, 4)
+        swizzled_scale = padded_scale.permute((0, 1, 4, 3, 2, 5))
+        swizzled_scale = swizzled_scale.contiguous().cuda()
+        return (
+            swizzled_scale.reshape(M, K)
+            if scale_ndim == 2
+            else swizzled_scale.reshape(B, M, K)
+        )
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+
+        # GEMM 1
+        if not torch.allclose(
+            layer.w13_weight_scale_2[:, 0], layer.w13_weight_scale_2[:, 1]
+        ):
+            logger.warning_once(
+                "w1_weight_scale_2 must match w3_weight_scale_2. "
+                "Accuracy may be affected."
+            )
+
+        w13_weight_scale_2 = layer.w13_weight_scale_2[:, 0]
+        layer.w13_weight_scale_2 = Parameter(w13_weight_scale_2, requires_grad=False)
+
+        w13_input_scale = layer.w13_input_scale.max(dim=1).values.to(torch.float32)
+        layer.g1_alphas = Parameter(
+            (w13_input_scale * w13_weight_scale_2).to(torch.float32),
+            requires_grad=False,
+        )
+
+        assert (
+            layer.w13_weight_scale.shape[2] % 16 == 0
+        ), "Expected weight_scale.dim(1) to be divisible by 16"
+        assert (
+            layer.w13_weight_scale.dtype == torch.float8_e4m3fn
+        ), "Weight Blockscale must be represented as FP8-E4M3"
+        w13_blockscale_swizzled = self.swizzle_blockscale(layer.w13_weight_scale)
+
+        layer.w13_blockscale_swizzled = Parameter(
+            w13_blockscale_swizzled, requires_grad=False
+        )
+
+        # This is for quantization, so we need to invert it.
+        layer.w13_input_scale_quant = Parameter(
+            (1 / w13_input_scale).to(torch.float32), requires_grad=False
+        )
+
+        layer.w13_weight = Parameter(layer.w13_weight.data, requires_grad=False)
+
+        # GEMM 2
+        layer.g2_alphas = Parameter(
+            (layer.w2_input_scale * layer.w2_weight_scale_2).to(torch.float32),
+            requires_grad=False,
+        )
+
+        # This is for quantization, so we need to invert it.
+        layer.w2_input_scale_quant = Parameter(
+            (1 / layer.w2_input_scale).to(torch.float32), requires_grad=False
+        )
+
+        assert (
+            layer.w2_weight_scale.shape[2] % 16 == 0
+        ), "Expected weight_scale.dim(1) to be divisible by 16"
+        assert (
+            layer.w2_weight_scale.dtype == torch.float8_e4m3fn
+        ), "Weight Blockscale must be represented as FP8-E4M3"
+        w2_blockscale_swizzled = self.swizzle_blockscale(layer.w2_weight_scale)
+
+        layer.w2_blockscale_swizzled = Parameter(
+            w2_blockscale_swizzled, requires_grad=False
+        )
+        layer.w2_weight = Parameter(layer.w2_weight.data, requires_grad=False)
+
+        device = layer.w13_weight.device
+        layer.cutlass_moe_params = CutlassMoEParams(
+            CutlassMoEType.BlockscaledFP4,
+            device,
+            num_experts=layer.num_experts,
+            intermediate_size_per_partition=layer.w2_weight.shape[2] * 2,  # n
+            hidden_size=layer.w13_weight.shape[2] * 2,
+        )  # k
+
+    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:
+
+        assert activation == "silu", "Only SiLU activation is supported."
+
+        from sglang.srt.layers.moe.fused_moe_triton.fused_moe import fused_experts
+        from sglang.srt.layers.moe.topk import select_experts
+
+        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,
+        )
+
+        from sglang.srt.layers.moe.cutlass_moe import cutlass_moe_fp4
+
+        return cutlass_moe_fp4(
+            a=x,
+            a1_gscale=layer.w13_input_scale_quant,
+            w1_fp4=layer.w13_weight,
+            w1_blockscale=layer.w13_blockscale_swizzled,
+            w1_alphas=layer.g1_alphas,
+            a2_gscale=layer.w2_input_scale_quant,
+            w2_fp4=layer.w2_weight,
+            w2_blockscale=layer.w2_blockscale_swizzled,
+            w2_alphas=layer.g2_alphas,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            params=layer.cutlass_moe_params,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+        ).to(x.dtype)

python/sglang/srt/models/deepseek_v2.py

@@ -1746,7 +1746,7 @@ class DeepseekV2ForCausalLM(nn.Module):
                 global_server_args_dict["disable_shared_experts_fusion"] = False
                 log_info_on_rank0(
                     logger,
-                    "Deepseek V3/R1 with fp8 can use shared experts fusion optimization when SM version >=90. Shared experts fusion optimization is enabled.",
+                    "Deepseek V3/R1 with fp8/fp4 can use shared experts fusion optimization when SM version >=90. Shared experts fusion optimization is enabled.",
                 )
 
     def get_input_embeddings(self) -> nn.Embedding:
@@ -1926,6 +1926,7 @@ class DeepseekV2ForCausalLM(nn.Module):
                 self_attn.use_deep_gemm_bmm = True
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]], is_nextn=False):
+
         if is_nextn:
             if hasattr(self.config, "num_nextn_predict_layers"):
                 num_nextn_layers = self.config.num_nextn_predict_layers
@@ -1982,6 +1983,21 @@ class DeepseekV2ForCausalLM(nn.Module):
                         "up_proj.qzeros",
                         "up_proj.scales",
                     ]
+                elif self.quant_config.get_name() == "modelopt_fp4":
+                    suffix_list = [
+                        "down_proj.weight",
+                        "down_proj.weight_scale",
+                        "down_proj.weight_scale_2",
+                        "down_proj.input_scale",
+                        "gate_proj.weight",
+                        "gate_proj.weight_scale",
+                        "gate_proj.weight_scale_2",
+                        "gate_proj.input_scale",
+                        "up_proj.weight",
+                        "up_proj.weight_scale",
+                        "up_proj.weight_scale_2",
+                        "up_proj.input_scale",
+                    ]
                 else:
                     raise ValueError(
                         f"Unsupported shared expert fusion for quantization: {self.quant_config.get_name()}."
@@ -2125,7 +2141,6 @@ class DeepseekV2ForCausalLM(nn.Module):
                     # Skip loading extra bias for GPTQ models.
                     if name.endswith(".bias") and name not in params_dict:
                         continue
-
                     if fuse_qkv_a_proj and (
                         "q_a_proj" in name or "kv_a_proj_with_mqa" in name
                     ):
@@ -2151,9 +2166,12 @@ class DeepseekV2ForCausalLM(nn.Module):
                             fused_weight = torch.cat(
                                 [q_a_proj_weight, kv_a_proj_weight], dim=0
                             )
-
-                            param_name = name.replace(
-                                "q_a_proj", "fused_qkv_a_proj_with_mqa"
+                            param_name = (
+                                name.replace("q_a_proj", "fused_qkv_a_proj_with_mqa")
+                                if "q_a_proj" in name
+                                else name.replace(
+                                    "kv_a_proj_with_mqa", "fused_qkv_a_proj_with_mqa"
+                                )
                             )
                             param = params_dict[param_name]
 
@@ -2164,6 +2182,16 @@ class DeepseekV2ForCausalLM(nn.Module):
                             cached_a_proj.pop(q_a_proj_name)
                             cached_a_proj.pop(kv_a_proj_name)
                     else:
+                        if (
+                            "k_scale" in name or "v_scale" in name
+                        ) and name not in params_dict:
+                            # modelopt attn kv scale is named differently
+                            if any(scale in name for scale in ["k_scale", "v_scale"]):
+                                name = name.replace("_proj", "attn_mqa")
+                            else:
+                                logger.warning(
+                                    f"Unknown scale found in checkpoint: {name}"
+                                )
                         param = params_dict[name]
                         weight_loader = getattr(
                             param, "weight_loader", default_weight_loader