[KVPOOl]Support pp (#4761)

### What this PR does / why we need it?
Support pp for kv pool

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: baxingpiaochong <771405853@qq.com>

vllm_ascend/distributed/kvpool/config_data.py

@@ -21,6 +21,8 @@ class KeyMetadata:
     pcp_rank: int
     """ Initialize the current decode context model parallel rank """
     dcp_rank: int
+    """ Initialize the current pipeline parallel rank """
+    pp_rank: int
 
 
 @dataclass(order=True)
@@ -34,6 +36,7 @@ class PoolKey:
             self.key_metadata.head_or_tp_rank,
             self.key_metadata.pcp_rank,
             self.key_metadata.dcp_rank,
+            self.key_metadata.pp_rank,
             self.chunk_hash,
         ))
 
@@ -41,8 +44,8 @@ class PoolKey:
         return (
             f"{self.key_metadata.model_name}"
             f"@pcp{self.key_metadata.pcp_rank}@dcp{self.key_metadata.dcp_rank}"
-            f"@head_or_tp_rank:{self.key_metadata.head_or_tp_rank}@{self.chunk_hash}"
-        )
+            f"@head_or_tp_rank:{self.key_metadata.head_or_tp_rank}"
+            f"@pp_rank:{self.key_metadata.pp_rank}@{self.chunk_hash}")
 
     def split_layers(self, num_layers: int) -> List["LayerPoolKey"]:
         """Split the key into multiple keys for each layer"""

vllm_ascend/distributed/kvpool/pool_worker.py

@@ -48,6 +48,8 @@ class KVPoolWorker:
         self.use_layerwise = use_layerwize
         self.tp_rank = get_tensor_model_parallel_rank()
         self.tp_size = get_tensor_model_parallel_world_size()
+        self.pp_size = parallel_config.pipeline_parallel_size
+        self.pp_rank = (parallel_config.rank // self.tp_size) % self.pp_size
 
         self.pcp_size = get_pcp_group().world_size
         self.pcp_rank = get_pcp_group(
@@ -87,6 +89,7 @@ class KVPoolWorker:
             self.head_or_tp_rank,
             self.pcp_rank,
             self.dcp_rank,
+            self.pp_rank,
         )
 
         self.token_database = ChunkedTokenDatabase(self.metadata,
@@ -555,6 +558,12 @@ class KVPoolWorker:
                         "@head_or_tp_rank:0", f"@head_or_tp_rank:{i}", 1)
                     multi_tp_keys.append(new_str)
 
+            for i in range(1, self.pp_size):
+                for item in keys:
+                    new_str = item.replace(  # type: ignore[attr-defined]
+                        "@pp_rank:0", f"@pp_rank:{i}", 1)
+                    multi_tp_keys.append(new_str)
+
             res = self.m_store.exists(
                 multi_tp_keys)  # type: ignore[assignment]
             num_block = len(keys)

vllm_ascend/worker/model_runner_v1.py

@@ -2450,6 +2450,7 @@ class NPUModelRunner(LoRAModelRunnerMixin, ECConnectorModelRunnerMixin):
                     attn_metadata, self.with_prefill, maybe_padded_num_tokens,
                     input_ids, positions, intermediate_tensors, inputs_embeds)
 
+            self.maybe_wait_for_kv_save()
             finished_sending, finished_recving = self.get_finished_kv_transfer(
                 scheduler_output)
 
@@ -2711,7 +2712,7 @@ class NPUModelRunner(LoRAModelRunnerMixin, ECConnectorModelRunnerMixin):
                     # ngram and other speculative decoding methods use the sampled
                     # tokens on the CPU, so they are run after bookkeeping.
                     propose_draft_token_ids(valid_sampled_token_ids)
-            self.maybe_wait_for_kv_save()
+
             if has_kv_transfer_group():
                 get_kv_transfer_group().clear_connector_metadata()