Fix MTP with Deepseek R1 Fp4 (#7376)

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

@@ -330,6 +330,12 @@ class FusedMoE(torch.nn.Module):
         self.tp_rank = get_tensor_model_parallel_rank()
         self.num_experts = num_experts
         self.expert_map = None
+
+        if enable_flashinfer_moe and quant_config is None:
+            logger.warning("Disable flashinfer MoE when quantization config is None.")
+            enable_flashinfer_moe = False
+            enable_ep_moe = False
+
         self.enable_flashinfer_moe = enable_flashinfer_moe
         if enable_ep_moe:
             assert (

python/sglang/srt/models/deepseek_nextn.py

@@ -44,6 +44,12 @@ class DeepseekModelNextN(nn.Module):
         prefix: str = "",
     ) -> None:
         super().__init__()
+        if quant_config is not None and quant_config.get_name() == "modelopt_fp4":
+            logger.warning(
+                "Overriding DeepseekV3ForCausalLMNextN quant config for modelopt_fp4 Deepseek model."
+            )
+            quant_config = None
+
         self.vocab_size = config.vocab_size
 
         self.embed_tokens = VocabParallelEmbedding(

python/sglang/srt/models/deepseek_v2.py

@@ -2201,7 +2201,7 @@ class DeepseekV2ForCausalLM(nn.Module):
                             q_a_proj_weight = cached_a_proj[q_a_proj_name]
                             kv_a_proj_weight = cached_a_proj[kv_a_proj_name]
                             cat_dim = 0
-                            if (
+                            if self.quant_config is not None and (
                                 self.quant_config.get_name() == "awq"
                                 or self.quant_config.get_name() == "moe_wna16"
                             ):
@@ -2232,6 +2232,13 @@ class DeepseekV2ForCausalLM(nn.Module):
                             for scale in ["k_scale", "v_scale"]:
                                 if scale in name:
                                     name = name.replace(f"{scale[0]}_proj", "attn_mqa")
+                                    break
+                        if name not in params_dict:
+                            # modelopt ckpt contains not needed weights for MTP module:
+                            # model.decoder.self_attn.attn_mqa.v_scale and
+                            # model.decoder.self_attn.attn_mqa.k_scale
+                            logger.warning(f"{name} not found in params_dict.")
+                            continue
                         param = params_dict[name]
                         weight_loader = getattr(
                             param, "weight_loader", default_weight_loader