[Bugfix] fix pipeline parallelism bug introduced by async-scheduling refactor work (#4973)

### What this PR does / why we need it?
Currently, when using pipeline parallel and pd disaggregate,
model_runner will return None on non-last-pp-rank stages in
`sample_tokens`, which will cause assert error in vllm
KVOutputAggregator on [this
line](https://github.com/vllm-project/vllm/blob/main/vllm/distributed/kv_transfer/kv_connector/utils.py#L84).

In fact, all pp workers should return a model_runner_output which
contains kv_connector_output to do aggregate in Enginecore scheduler
process to ensure all kv transfer is finished for kv cache releasing
later.

To fix this issue, this PR follows gpu_model_runner in vllm, passing
kv_connector_output in `sample_tokens` to make sure all ranks will
return a ModelRunnerOutput, in non-last-pp-rank workers, it will return
EMPTY_MODEL_RUNNER_OUTPUT with kv_connector_output.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?

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

---------

Signed-off-by: lidenghui <lidenghui1110@gmail.com>

vllm_ascend/worker/model_runner_v1.py

@@ -21,7 +21,7 @@ import math
 import time
 from collections import defaultdict
 from contextlib import contextmanager, nullcontext
-from copy import deepcopy
+from copy import copy, deepcopy
 from dataclasses import dataclass
 from multiprocessing import Manager
 from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Union
@@ -189,7 +189,6 @@ class ExecuteModelState(NamedTuple):
     hidden_states: torch.Tensor
     sample_hidden_states: torch.Tensor
     aux_hidden_states: list[torch.Tensor] | None
-    kv_connector_output: KVConnectorOutput | None
     attn_metadata: dict[str, Any]
     positions: torch.Tensor
 
@@ -1450,6 +1449,7 @@ class NPUModelRunner(GPUModelRunner):
                 # For mid-pipeline stages, return the hidden states.
                 if not broadcast_pp_output:
                     hidden_states.kv_connector_output = kv_connector_output
+                    self.kv_connector_output = kv_connector_output
                     if need_dump:
                         assert self.debugger is not None
                         self.debugger.stop()
@@ -1496,19 +1496,32 @@ class NPUModelRunner(GPUModelRunner):
                 hidden_states,
                 sample_hidden_states,
                 aux_hidden_states,
-                kv_connector_output,
                 attn_metadata,
                 positions,
             )
+            self.kv_connector_output = kv_connector_output
         return None
 
     @torch.inference_mode
     def sample_tokens(
         self, grammar_output: "GrammarOutput | None"
     ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors:
+        kv_connector_output = self.kv_connector_output
+        self.kv_connector_output = None
+
         if self.execute_model_state is None:
             # Nothing to do (PP non-final rank case), output isn't used.
-            return None  # noqa
+            if not kv_connector_output:
+                return None  # noqa
+            # In case of PP with kv transfer, we need to pass through the
+            # kv_connector_output
+            if kv_connector_output.is_empty():
+                return EMPTY_MODEL_RUNNER_OUTPUT
+
+            output = copy(EMPTY_MODEL_RUNNER_OUTPUT)
+            output.kv_connector_output = kv_connector_output
+            return output
+
         need_dump = self.dump_enable and self.debugger is not None
         # Unpack ephemeral state.
         (
@@ -1517,8 +1530,7 @@ class NPUModelRunner(GPUModelRunner):
             spec_decode_metadata,
             hidden_states,
             sample_hidden_states,
-            aux_hidden_states,  # noqa
+            aux_hidden_states,
-            kv_connector_output,
             attn_metadata,
             positions,
         ) = self.execute_model_state