Update num_tokens_across_dp to use nccl instead of gloo

vllm/envs.py

@@ -92,6 +92,7 @@
     VLLM_ALLOW_RUNTIME_LORA_UPDATING: bool = False
     VLLM_SKIP_P2P_CHECK: bool = False
     VLLM_DISABLED_KERNELS: list[str] = []
+    VLLM_DISABLE_NCCL_DP_PADDING: bool = False
     VLLM_USE_V1: bool = True
     VLLM_ROCM_USE_AITER: bool = False
     VLLM_ROCM_USE_AITER_PAGED_ATTN: bool = False
@@ -739,6 +740,10 @@ def get_vllm_port() -> Optional[int]:
     lambda: [] if "VLLM_DISABLED_KERNELS" not in os.environ else os.environ[
         "VLLM_DISABLED_KERNELS"].split(","),
 
+    "VLLM_DISABLE_NCCL_DP_PADDING":
+    lambda: (os.getenv("VLLM_DISABLE_NCCL_DP_PADDING", "False").lower() in
+             ("true", "1")),
+
     # If set, use the V1 code path.
     "VLLM_USE_V1":
     lambda: bool(int(os.getenv("VLLM_USE_V1", "1"))),

vllm/forward_context.py

@@ -13,6 +13,7 @@
 import vllm.envs as envs
 from vllm.config import CUDAGraphMode, ParallelConfig, VllmConfig
 from vllm.logger import init_logger
+from vllm.platforms import current_platform
 
 if TYPE_CHECKING:
     from vllm.attention.backends.abstract import AttentionMetadata
@@ -75,14 +76,26 @@ def num_tokens_across_dp(num_tokens: int, dp_size: int,
         Gather the num_tokens across all DP ranks and return results in a
         CPU tensor of size dp_size.
         """
+        from vllm.distributed.parallel_state import get_dp_group
+        device = current_platform.device_type
+        group = get_dp_group().device_group
+
+        # Transfering this tensor from GPU to CPU will introduce a GPU sync
+        # point that could adversely affect performance of vllm with asynch
+        # scheduling. This environment variable exists to quickly disable
+        # this optimization if we run into this case.
+        if envs.VLLM_DISABLE_NCCL_DP_PADDING:
+            logger.info_once(
+                "Using CPU all reduce to syncronize DP padding between ranks.")
+            device = "cpu"
+            group = get_dp_group().cpu_group
         num_tokens_across_dp = [0] * dp_size
         num_tokens_across_dp[dp_rank] = num_tokens
         num_tokens_tensor = torch.tensor(num_tokens_across_dp,
-                                         device="cpu",
+                                         device=device,
                                          dtype=torch.int32)
-        from vllm.distributed.parallel_state import get_dp_group
-        dist.all_reduce(num_tokens_tensor, group=get_dp_group().cpu_group)
-        return num_tokens_tensor
+        dist.all_reduce(num_tokens_tensor, group=group)
+        return num_tokens_tensor.cpu()
 
     @staticmethod
     def make(