[Cuda2CPU][P/D] Add cuda2cpu support in NixlConnector

tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh

@@ -1,6 +1,31 @@
 #!/bin/bash
 set -xe
 
+# Parse command line arguments
+KV_BUFFER_DEVICE="cuda"  # Default to cuda
+while [[ $# -gt 0 ]]; do
+  case $1 in
+    --kv_buffer_device)
+      KV_BUFFER_DEVICE="$2"
+      shift 2
+      ;;
+    *)
+      echo "Unknown option $1"
+      echo "Usage: $0 [--kv_buffer_device <cuda|cpu>]"
+      exit 1
+      ;;
+  esac
+done
+
+echo "Running accuracy tests with kv_buffer_device=$KV_BUFFER_DEVICE"
+
+# Build the kv-transfer-config once
+if [[ "$KV_BUFFER_DEVICE" == "cuda" ]]; then
+  KV_CONFIG='{"kv_connector":"NixlConnector","kv_role":"kv_both"}'
+else
+  KV_CONFIG="{\"kv_connector\":\"NixlConnector\",\"kv_role\":\"kv_both\",\"kv_buffer_device\":\"$KV_BUFFER_DEVICE\"}"
+fi
+
 # Models to run
 MODELS=(
     "Qwen/Qwen3-0.6B"
@@ -79,7 +104,7 @@ run_tests_for_model() {
 
     # Calculate port number (base port + instance number)
     PORT=$((8100 + i))
-    # Calculate side channel port. Avoid clash with with TP workers. 
+    # Calculate side channel port. Avoid clash with with TP workers.
     SIDE_CHANNEL_PORT=$((5559 + i))
 
     echo "Starting prefill instance $i on GPU $GPU_ID, port $PORT"
@@ -93,7 +118,7 @@ run_tests_for_model() {
     --enforce-eager \
     --gpu-memory-utilization 0.2 \
     --tensor-parallel-size $PREFILLER_TP_SIZE \
-    --kv-transfer-config '{\"kv_connector\":\"NixlConnector\",\"kv_role\":\"kv_both\"}'"
+    --kv-transfer-config '$KV_CONFIG'"
 
     if [ -n "$model_args" ]; then
     FULL_CMD="$BASE_CMD $model_args"
@@ -128,7 +153,7 @@ run_tests_for_model() {
     --enforce-eager \
     --gpu-memory-utilization 0.2 \
     --tensor-parallel-size $DECODER_TP_SIZE \
-    --kv-transfer-config '{\"kv_connector\":\"NixlConnector\",\"kv_role\":\"kv_both\"}'"
+    --kv-transfer-config '$KV_CONFIG'"
 
     if [ -n "$model_args" ]; then
     FULL_CMD="$BASE_CMD $model_args"

tests/v1/kv_connector/nixl_integration/run_edge_case_test.sh

@@ -1,6 +1,33 @@
 #!/bin/bash
 set -xe
 
+# Parse command line arguments
+KV_BUFFER_DEVICE="cuda"  # Default to cuda
+PREFILL_GPU_ID=4         # Default GPU IDs
+DECODE_GPU_ID=5
+while [[ $# -gt 0 ]]; do
+  case $1 in
+    --kv_buffer_device)
+      KV_BUFFER_DEVICE="$2"
+      shift 2
+      ;;
+    *)
+      echo "Unknown option $1"
+      echo "Usage: $0 [--kv_buffer_device <cuda|cpu>]"
+      exit 1
+      ;;
+  esac
+done
+
+echo "Running edge case tests with kv_buffer_device=$KV_BUFFER_DEVICE (GPUs: $PREFILL_GPU_ID, $DECODE_GPU_ID)"
+
+# Build the kv-transfer-config once
+if [[ "$KV_BUFFER_DEVICE" == "cuda" ]]; then
+  KV_CONFIG='{"kv_connector":"NixlConnector","kv_role":"kv_both"}'
+else
+  KV_CONFIG="{\"kv_connector\":\"NixlConnector\",\"kv_role\":\"kv_both\",\"kv_buffer_device\":\"$KV_BUFFER_DEVICE\"}"
+fi
+
 # Models to run
 MODELS=(
     "Qwen/Qwen3-0.6B"
@@ -50,15 +77,15 @@ run_tests_for_model() {
 
   # Get model-specific arguments
   local model_args=$(get_model_args "$model_name")
-  
+
   # Start prefill instance
   PREFILL_PORT=8001
 
-  BASE_CMD="CUDA_VISIBLE_DEVICES=0 VLLM_NIXL_SIDE_CHANNEL_PORT=5559 vllm serve $model_name \
+  BASE_CMD="CUDA_VISIBLE_DEVICES=$PREFILL_GPU_ID VLLM_NIXL_SIDE_CHANNEL_PORT=5559 vllm serve $model_name \
   --port $PREFILL_PORT \
   --enforce-eager \
   --gpu-memory-utilization 0.2 \
-  --kv-transfer-config '{\"kv_connector\":\"NixlConnector\",\"kv_role\":\"kv_both\"}'"
+  --kv-transfer-config '$KV_CONFIG'"
 
   if [ -n "$model_args" ]; then
   FULL_CMD="$BASE_CMD $model_args"
@@ -72,11 +99,11 @@ run_tests_for_model() {
   DECODE_PORT=8002
 
   # Build the command with or without model-specific args
-  BASE_CMD="CUDA_VISIBLE_DEVICES=1 VLLM_NIXL_SIDE_CHANNEL_PORT=6000 vllm serve $model_name \
+  BASE_CMD="CUDA_VISIBLE_DEVICES=$DECODE_GPU_ID VLLM_NIXL_SIDE_CHANNEL_PORT=6000 vllm serve $model_name \
   --port $DECODE_PORT \
   --enforce-eager \
   --gpu-memory-utilization 0.2 \
-  --kv-transfer-config '{\"kv_connector\":\"NixlConnector\",\"kv_role\":\"kv_both\"}'"
+  --kv-transfer-config '$KV_CONFIG'"
 
   if [ -n "$model_args" ]; then
   FULL_CMD="$BASE_CMD $model_args"

vllm/config/kv_transfer.py

@@ -28,8 +28,8 @@ class KVTransferConfig:
     """The engine id for KV transfers."""
 
     kv_buffer_device: Optional[str] = "cuda"
-    """The device used by kv connector to buffer the KV cache.
-    Currently only support 'cuda'."""
+    """The device used by kv connector to buffer the KV cache. Choices are 
+    'cuda' and 'cpu'."""
 
     kv_buffer_size: float = 1e9
     """The buffer size for TorchDistributedConnector. Measured in number of

vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py

@@ -67,7 +67,10 @@
 # Supported platforms and types of kv transfer buffer.
 # {device: tuple of supported kv buffer types}
 _NIXL_SUPPORTED_DEVICE = {
-    "cuda": ("cuda", ),
+    "cuda": (
+        "cuda",
+        "cpu",
+    ),
     "tpu": ("cpu", ),
     "xpu": ("cpu", ),
 }
@@ -687,6 +690,9 @@ def initialize_host_xfer_buffer(
 
     def set_host_xfer_buffer_ops(self, copy_operation: CopyBlocksOp):
         """Assign copy (d2h, h2d) operations when host buffer is used."""
+        # Set a no-op if the host buffer is not cpu.
+        if self.kv_buffer_device != "cpu":
+            return
         assert self.use_host_buffer
         self.copy_blocks = copy_operation
 

vllm/platforms/cuda.py

@@ -494,6 +494,30 @@ def check_if_supports_dtype(cls, torch_dtype: torch.dtype):
                     "You can use float16 instead by explicitly setting the "
                     "`dtype` flag in CLI, for example: --dtype=half.")
 
+    @classmethod
+    def insert_blocks_to_device(
+        cls,
+        src_cache: torch.Tensor,
+        dst_cache: torch.Tensor,
+        src_block_indices: torch.Tensor,
+        dst_block_indices: torch.Tensor,
+    ) -> None:
+        """Copy blocks from src_cache to dst_cache on GPU."""
+        _src_cache = src_cache[:, src_block_indices]
+        dst_cache[:, dst_block_indices] = _src_cache.to(dst_cache.device)
+
+    @classmethod
+    def swap_out_blocks_to_host(
+        cls,
+        src_cache: torch.Tensor,
+        dst_cache: torch.Tensor,
+        src_block_indices: torch.Tensor,
+        dst_block_indices: torch.Tensor,
+    ) -> None:
+        """Copy blocks from GPU to host (CPU)."""
+        _src_cache = src_cache[:, src_block_indices]
+        dst_cache[:, dst_block_indices] = _src_cache.cpu()
+
     @classmethod
     def support_hybrid_kv_cache(cls) -> bool:
         return True

vllm/v1/worker/gpu_model_runner.py

@@ -3973,10 +3973,9 @@ def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
             self.drafter.validate_same_kv_cache_group(kv_cache_config)
 
         if has_kv_transfer_group():
-            get_kv_transfer_group().register_kv_caches(kv_caches)
-            if self.device.type == 'xpu':
-                get_kv_transfer_group().set_host_xfer_buffer_ops(
-                    copy_kv_blocks)
+            kv_transfer_group = get_kv_transfer_group()
+            kv_transfer_group.register_kv_caches(kv_caches)
+            kv_transfer_group.set_host_xfer_buffer_ops(copy_kv_blocks)
 
         if self.dcp_world_size > 1:
             layer_names = self.attn_groups[0][0].layer_names