[Core] Cleanup TPU model runner for MM (#23894)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

vllm/v1/worker/tpu_model_runner.py

@@ -808,31 +808,6 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         return per_layer_attn_metadata, logits_indices, padded_num_reqs,\
             num_reqs, end_index
 
-    def _scatter_placeholders(
-        self,
-        embeds: torch.Tensor,
-        is_embed: Optional[torch.Tensor],
-    ) -> torch.Tensor:
-        if is_embed is None:
-            return embeds
-
-        placeholders = embeds.new_full(
-            (is_embed.shape[0], embeds.shape[-1]),
-            fill_value=torch.nan,
-        )
-        placeholders[is_embed] = embeds
-        return placeholders
-
-    def _gather_placeholders(
-        self,
-        placeholders: torch.Tensor,
-        is_embed: Optional[torch.Tensor],
-    ) -> torch.Tensor:
-        if is_embed is None:
-            return placeholders
-
-        return placeholders[is_embed]
-
     def _execute_mm_encoder(self, scheduler_output: "SchedulerOutput"):
         scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
         if not scheduled_encoder_inputs:
@@ -892,12 +867,7 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         # NOTE (NickLucche) here we diverge from logic in other runners, as we
         # assume to only have whole mm items to process. Hence we avoid the
         # intrinsic dynamism that `scatter_mm_placeholders` introduces.
-        for (mm_hash, pos_info), output in zip(
-                mm_hashes_pos,
-                encoder_outputs,
-        ):
-            if req_id not in self.encoder_cache:
-                self.encoder_cache[req_id] = {}
+        for (mm_hash, pos_info), output in zip(mm_hashes_pos, encoder_outputs):
             assert pos_info.is_embed is None, "Expected all positions to be"\
                 " contiguous and embeddings."
             self.encoder_cache[mm_hash] = output