fix the performance issue of tbo pd separation

vllm/attention/layer.py

@@ -7,6 +7,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
+from vllm.two_batch_overlap.v1.two_batch_overlap_v1 import tbo_maybe_save_kv_layer_to_connector
 import vllm.envs as envs
 from vllm.attention import AttentionType
 from vllm.attention.backends.abstract import AttentionBackend
@@ -480,7 +481,10 @@ def unified_attention(
     output = self.impl.forward(self, query, key, value, kv_cache,
                                attn_metadata)
 
-    maybe_save_kv_layer_to_connector(layer_name, kv_cache)
+    if envs.VLLM_ENABLE_TBO:
+        tbo_maybe_save_kv_layer_to_connector(layer_name, kv_cache)
+    else:
+        maybe_save_kv_layer_to_connector(layer_name, kv_cache)
     return output
 
 
@@ -528,7 +532,10 @@ def unified_attention_with_output(
                       output_scale=output_scale,
                       output_block_scale=output_block_scale)
 
-    maybe_save_kv_layer_to_connector(layer_name, kv_cache)
+    if envs.VLLM_ENABLE_TBO:
+        tbo_maybe_save_kv_layer_to_connector(layer_name, kv_cache)
+    else:
+        maybe_save_kv_layer_to_connector(layer_name, kv_cache)
 
 
 def unified_attention_with_output_fake(

vllm/distributed/kv_transfer/kv_connector/v1/p2p/p2p_nccl_connector.py

@@ -7,6 +7,7 @@ from typing import TYPE_CHECKING, Any, Optional
 import regex as re
 import torch
 
+from vllm import envs
 from vllm.config import VllmConfig
 from vllm.distributed.kv_transfer.kv_connector.v1.base import (
     KVConnectorBase_V1, KVConnectorMetadata, KVConnectorRole)
@@ -262,6 +263,8 @@ class P2pNcclConnector(KVConnectorBase_V1):
                 torch.Tensor: A tensor containing the extracted KV slices.
                 Returns None if the layout is unsupported.
             """
+            if envs.VLLM_ENABLE_TBO:
+                slot_mapping = slot_mapping.pin_memory().to(device=layer.device, non_blocking=True) 
             if (isinstance(attn_metadata, MLACommonMetadata)
                     or layer.shape[1] == 2):  # MLA or FlashInfer
                 return layer[block_ids, ...]

vllm/two_batch_overlap/v1/two_batch_overlap_v1.py

@@ -162,6 +162,14 @@ def init_two_batch_overlap():
         tbo_obj_v1 = TwoBatchOverlap()
     tbo_obj_v1.init_tbo_thread()
 
+def tbo_maybe_save_kv_layer_to_connector(layer_name, kv_cache):
+    from vllm.attention.layer import maybe_save_kv_layer_to_connector
+    if envs.VLLM_ENABLE_TBO and tbo_obj_v1 != None and tbo_obj_v1.tbo_running:
+        tid = threading.get_ident()
+        if tid == tbo_obj_v1.left_tid:
+            return
+    maybe_save_kv_layer_to_connector(layer_name, kv_cache)
+
 def tbo_all_reduce_v1(obj):
     if envs.VLLM_ENABLE_TBO and tbo_obj_v1 != None and tbo_obj_v1.tbo_running:
         tid = threading.get_ident()