[Feat][Bugfix] Enable additional dimension for Flashinfer MLA and fix routing dtype (#36931)

Signed-off-by: Dimitrios Bariamis <12195802+dbari@users.noreply.github.com>
Co-authored-by: Dimitrios Bariamis <12195802+dbari@users.noreply.github.com>

vllm/model_executor/models/deepseek_v2.py

@@ -47,7 +47,11 @@ from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import Attention
 from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
-from vllm.model_executor.layers.fused_moe import GateLinear, SharedFusedMoE
+from vllm.model_executor.layers.fused_moe import (
+    GateLinear,
+    RoutingMethodType,
+    SharedFusedMoE,
+)
 from vllm.model_executor.layers.layernorm import LayerNorm, RMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
@@ -333,8 +337,12 @@ class DeepseekV2MoE(nn.Module):
         # NOTE(rob): this is a hack until we finish off the PR for
         # merging TRTLLM kernels into the MK framework. Then we can
         # query the MonolithicMK for the expected router logits.
+        # NOTE(dbari): Use BF16 if routing is not Deepseek, e.g. Mistral Large 3
         self.gate.set_out_dtype(
-            torch.float32 if self.experts.quant_method.is_monolithic else torch.bfloat16
+            torch.float32
+            if self.experts.quant_method.is_monolithic
+            and self.experts.routing_method_type == RoutingMethodType.DeepSeekV3
+            else torch.bfloat16
         )
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
@@ -1197,6 +1205,11 @@ class DeepseekV2Model(nn.Module):
             if inputs_embeds is not None:
                 hidden_states = inputs_embeds
             else:
+                if input_ids is None:
+                    raise ValueError(
+                        "Either input_ids or inputs_embeds must be provided "
+                        "to DeepseekV2Model.forward"
+                    )
                 hidden_states = self.embed_input_ids(input_ids)
             residual = None
         else: