[Misc] Remove duplicated DeepSeek V2/V3 model definition

vllm/config.py

@@ -754,7 +754,6 @@ def get_hidden_size(self) -> int:
 
     @property
     def is_deepseek_mla(self) -> bool:
-        # TODO add deepseek_v3
         return (hasattr(self.hf_text_config, "model_type")) \
                 and (self.hf_text_config.model_type in \
                     ('deepseek_v2', 'deepseek_v3'))\

vllm/model_executor/models/deepseek_v2.py

@@ -21,7 +21,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""Inference-only DeepseekV2 model."""
+"""Inference-only DeepseekV2/DeepseekV3 model."""
 from typing import Any, Dict, Iterable, List, Optional, Set, Tuple, Union
 
 import torch
@@ -115,23 +115,32 @@ def __init__(
             raise ValueError(f"Unsupported activation: {config.hidden_act}. "
                              "Only silu is supported for now.")
 
-        self.experts = FusedMoE(num_experts=config.n_routed_experts,
-                                top_k=config.num_experts_per_tok,
-                                hidden_size=config.hidden_size,
-                                intermediate_size=config.moe_intermediate_size,
-                                reduce_results=False,
-                                renormalize=config.norm_topk_prob,
-                                quant_config=quant_config,
-                                use_grouped_topk=True,
-                                num_expert_group=config.n_group,
-                                topk_group=config.topk_group,
-                                prefix=f"{prefix}.experts")
-
         self.gate = ReplicatedLinear(config.hidden_size,
                                      config.n_routed_experts,
                                      bias=False,
                                      quant_config=None,
                                      prefix=f"{prefix}.gate")
+        if config.topk_method == "noaux_tc":
+            self.gate.e_score_correction_bias = nn.Parameter(
+                torch.empty(config.n_routed_experts))
+        else:
+            self.gate.e_score_correction_bias = None
+
+        self.experts = FusedMoE(
+            num_experts=config.n_routed_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.moe_intermediate_size,
+            reduce_results=False,
+            renormalize=config.norm_topk_prob,
+            quant_config=quant_config,
+            use_grouped_topk=True,
+            num_expert_group=config.n_group,
+            topk_group=config.topk_group,
+            prefix=f"{prefix}.experts",
+            scoring_func=config.scoring_func,
+            e_score_correction_bias=self.gate.e_score_correction_bias)
+
         if config.n_shared_experts is not None:
             intermediate_size = (config.moe_intermediate_size *
                                  config.n_shared_experts)
@@ -732,6 +741,15 @@ def load_weights(self, weights: Iterable[Tuple[str,
         for name, loaded_weight in weights:
             if "rotary_emb.inv_freq" in name:
                 continue
+
+            # TODO(simon): support nextn predict layers
+            if hasattr(self.config, "num_nextn_predict_layers"
+                       ) and self.config.num_nextn_predict_layers > 0:
+                assert self.config.num_nextn_predict_layers == 1
+                layer_idx = self.config.num_hidden_layers
+                if name.startswith(f"model.layers.{layer_idx}"):
+                    continue
+
             for (param_name, weight_name, shard_id) in stacked_params_mapping:
                 # Skip non-stacked layers and experts (experts handled below).
                 if weight_name not in name:
@@ -793,3 +811,7 @@ def load_weights(self, weights: Iterable[Tuple[str,
                     weight_loader(param, loaded_weight)
             loaded_params.add(name)
         return loaded_params
+
+
+class DeepseekV3ForCausalLM(DeepseekV2ForCausalLM):
+    pass