[Refactor] Remove dead code in kv connector and model runner (#38383)

Signed-off-by: yewentao256 <zhyanwentao@126.com>

tests/v1/kv_connector/unit/test_kv_connector_lifecycle.py

@@ -6,6 +6,7 @@ from vllm.distributed.kv_transfer.kv_connector.v1.example_connector import (  #
 )
 from vllm.distributed.kv_transfer.kv_transfer_state import (
     ensure_kv_transfer_initialized,
+    ensure_kv_transfer_shutdown,
     get_kv_transfer_group,
 )
 from vllm.v1.core.sched.output import CachedRequestData, SchedulerOutput
@@ -57,4 +58,4 @@ def test_kv_connector_mixin_clears_metadata():
         assert connector.call_record.get("clear_connector_metadata", 0) == 1
     finally:
         # Ensure we clean up the global connector between tests
-        KVConnectorModelRunnerMixin.ensure_kv_transfer_shutdown()
+        ensure_kv_transfer_shutdown()

vllm/v1/worker/cpu_model_runner.py

@@ -102,10 +102,6 @@ class CPUModelRunner(GPUModelRunner):
         # so stale KV cache data never affects computation.
         pass
 
-    def get_dp_padding(self, num_tokens: int) -> tuple[int, torch.Tensor | None]:
-        # Note: For CPU backend, dp padding is not required for now.
-        return 0, None
-
 
 @contextmanager
 def _torch_cuda_wrapper():

vllm/v1/worker/ec_connector_model_runner_mixin.py

@@ -34,14 +34,6 @@ class ECConnectorModelRunnerMixin:
         connector = get_ec_transfer()
         connector.save_caches(encoder_cache=encoder_cache, mm_hash=mm_hash)
 
-    @staticmethod
-    def get_finished_ec_transfers(
-        scheduler_output: "SchedulerOutput",
-    ) -> tuple[set[str] | None, set[str] | None]:
-        if has_ec_transfer():
-            return get_ec_transfer().get_finished(scheduler_output.finished_req_ids)
-        return None, None
-
     @staticmethod
     def maybe_get_ec_connector_output(
         scheduler_output: "SchedulerOutput",

vllm/v1/worker/gpu/kv_connector.py

@@ -97,11 +97,6 @@ class ActiveKVConnector(KVConnector):
             self.kv_connector.clear_connector_metadata()
         return output
 
-    def clear_metadata(self) -> None:
-        """Clear the connector metadata. Call this after draft model runs."""
-        if not self._disabled:
-            self.kv_connector.clear_connector_metadata()
-
     def no_forward(self, scheduler_output: "SchedulerOutput") -> ModelRunnerOutput:
         if self._disabled:
             return EMPTY_MODEL_RUNNER_OUTPUT

vllm/v1/worker/gpu_model_runner.py

@@ -451,7 +451,6 @@ class GPUModelRunner(
         # Model-related.
         self.num_query_heads = model_config.get_num_attention_heads(parallel_config)
         self.inputs_embeds_size = model_config.get_inputs_embeds_size()
-        self.attention_chunk_size = model_config.attention_chunk_size
         # Only relevant for models using ALiBi (e.g, MPT)
         self.use_alibi = model_config.uses_alibi
 
@@ -594,7 +593,6 @@ class GPUModelRunner(
         # NOTE(rob): num_prompt_logprobs only includes reqs
         # that are currently in the prefill phase.
         self.num_prompt_logprobs: dict[str, int] = {}
-        self.comm_stream = torch.cuda.Stream()
 
         # Input Batch
         # NOTE(Chen): Ideally, we should initialize the input batch inside

vllm/v1/worker/kv_connector_model_runner_mixin.py

@@ -13,11 +13,7 @@ import torch
 
 from vllm.config import VllmConfig
 from vllm.config.cache import CacheDType
-from vllm.distributed.kv_transfer import (
-    ensure_kv_transfer_shutdown,
-    get_kv_transfer_group,
-    has_kv_transfer_group,
-)
+from vllm.distributed.kv_transfer import get_kv_transfer_group, has_kv_transfer_group
 from vllm.distributed.kv_transfer.kv_connector.base import KVConnectorBase
 from vllm.forward_context import get_forward_context, set_forward_context
 from vllm.logger import init_logger
@@ -38,12 +34,6 @@ logger = init_logger(__name__)
 
 # Defined as a kv connector functionality mixin for ModelRunner (GPU, TPU)
 class KVConnectorModelRunnerMixin:
-    @staticmethod
-    def ensure_kv_transfer_shutdown() -> None:
-        # has_kv_transfer_group can be None during interpreter shutdown.
-        if has_kv_transfer_group and has_kv_transfer_group():  # type: ignore[truthy-function]
-            ensure_kv_transfer_shutdown()
-
     @staticmethod
     def kv_connector_no_forward(
         scheduler_output: "SchedulerOutput", vllm_config: VllmConfig

vllm/v1/worker/xpu_model_runner.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from contextlib import contextmanager
-from typing import TYPE_CHECKING
 
 import torch
 
 from vllm.config import VllmConfig
-from vllm.logger import init_logger
 from vllm.utils.torch_utils import supports_xpu_graph
 from vllm.v1.worker.gpu.model_runner import (
     GPUModelRunner as GPUModelRunnerV2,
 )
 from vllm.v1.worker.gpu_model_runner import GPUModelRunner
 
-if TYPE_CHECKING:
-    pass
-
-logger = init_logger(__name__)
-
 
 class XPUModelRunner(GPUModelRunner):
     """A model runner for XPU devices."""
@@ -47,19 +40,16 @@ class XPUModelRunnerV2(GPUModelRunnerV2):
 
 @contextmanager
 def _torch_cuda_wrapper():
-    try:
-        # replace cuda APIs with xpu APIs, this should work by default
-        torch.cuda.Stream = torch.xpu.Stream
-        torch.cuda.default_stream = torch.xpu.current_stream
-        torch.cuda.current_stream = torch.xpu.current_stream
-        torch.cuda.stream = torch.xpu.stream
-        torch.cuda.mem_get_info = torch.xpu.mem_get_info
-        torch.cuda.Event = torch.Event
-        torch.cuda.set_stream = torch.xpu.set_stream
-        if supports_xpu_graph():
-            torch.cuda.graph = torch.xpu.graph
-            torch.cuda.CUDAGraph = torch.xpu.XPUGraph
-            torch.cuda.graph_pool_handle = torch.xpu.graph_pool_handle
-        yield
-    finally:
-        pass
+    # replace cuda APIs with xpu APIs, this should work by default
+    torch.cuda.Stream = torch.xpu.Stream
+    torch.cuda.default_stream = torch.xpu.current_stream
+    torch.cuda.current_stream = torch.xpu.current_stream
+    torch.cuda.stream = torch.xpu.stream
+    torch.cuda.mem_get_info = torch.xpu.mem_get_info
+    torch.cuda.Event = torch.Event
+    torch.cuda.set_stream = torch.xpu.set_stream
+    if supports_xpu_graph():
+        torch.cuda.graph = torch.xpu.graph
+        torch.cuda.CUDAGraph = torch.xpu.XPUGraph
+        torch.cuda.graph_pool_handle = torch.xpu.graph_pool_handle
+    yield