Don't use cupy NCCL for AMD backends

vllm/model_executor/parallel_utils/custom_all_reduce.py

@@ -67,6 +67,10 @@ def get_handle() -> Optional["CustomAllreduce"]:
     return _CA_HANDLE
 
 
+def is_initialized() -> bool:
+    return _CA_HANDLE is not None
+
+
 @contextmanager
 def capture():
     try:

vllm/worker/model_runner.py

@@ -1,3 +1,4 @@
+import contextlib
 import time
 from typing import Dict, List, Optional, Tuple, Set, Union
 
@@ -9,9 +10,9 @@
                          SchedulerConfig)
 from vllm.logger import init_logger
 from vllm.model_executor import get_model, InputMetadata, SamplingMetadata
+from vllm.model_executor.parallel_utils import cupy_utils
 from vllm.model_executor.parallel_utils.communication_op import (
     broadcast_tensor_dict)
-from vllm.model_executor.parallel_utils.cupy_utils import get_nccl_backend
 from vllm.model_executor.parallel_utils.parallel_state import (
     with_cupy_nccl_for_all_reduce)
 from vllm.model_executor.parallel_utils import custom_all_reduce
@@ -650,7 +651,7 @@ def list_loras(self) -> Set[int]:
     def capture_model(self, kv_caches: List[KVCache]) -> None:
         # NOTE(woosuk): This is a hack to ensure that the NCCL backend is never
         # deleted before the CUDA graphs.
-        self.cupy_nccl_backend = get_nccl_backend()
+        self.cupy_nccl_backend = cupy_utils.get_nccl_backend()
 
         assert not self.model_config.enforce_eager
         logger.info("Capturing the model for CUDA graphs. This may lead to "
@@ -680,15 +681,15 @@ def capture_model(self, kv_caches: List[KVCache]) -> None:
             bs for bs in _BATCH_SIZES_TO_CAPTURE if bs <= graph_batch_size
         ]
 
-        # NOTE: Capturing the largest batch size first may help reduce the
-        # memory usage of CUDA graph.
         # NOTE(woosuk): There are 3 backends for all-reduce: custom all-reduce
         # kernel, CuPy NCCL, and PyTorch NCCL. When using CUDA graph, we use
         # either custom all-reduce kernel or CuPy NCCL. When not using CUDA
         # graph, we use either custom all-reduce kernel or PyTorch NCCL.
         # We always prioritize using custom all-reduce kernel but fall back
         # to PyTorch or CuPy NCCL if it is disabled or not supported.
         with custom_all_reduce.capture():
+            # NOTE: Capturing the largest batch size first may help reduce the
+            # memory usage of CUDA graph.
             for batch_size in reversed(batch_size_capture_list):
                 # Create dummy input_metadata.
                 input_metadata = InputMetadata(
@@ -756,7 +757,7 @@ def capture(
         # Run the model once without capturing the graph.
         # This is to make sure that the captured graph does not include the
         # kernel launches for initial benchmarking (e.g., Triton autotune).
-        with with_cupy_nccl_for_all_reduce():
+        with _maybe_cupy_nccl():
             self.model(
                 input_ids,
                 positions,
@@ -770,7 +771,7 @@ def capture(
         # https://stackoverflow.com/questions/31039022/python-multi-line-with-statement
         self.graph = torch.cuda.CUDAGraph()
         with torch.cuda.graph(self.graph, pool=memory_pool):  # noqa: SIM117
-            with with_cupy_nccl_for_all_reduce():
+            with _maybe_cupy_nccl():
                 hidden_states = self.model(
                     input_ids,
                     positions,
@@ -821,6 +822,15 @@ def __call__(self, *args, **kwargs):
         return self.forward(*args, **kwargs)
 
 
+@contextlib.contextmanager
+def _maybe_cupy_nccl():
+    if cupy_utils.is_initialized() and not custom_all_reduce.is_initialized():
+        with with_cupy_nccl_for_all_reduce():
+            yield
+    else:
+        yield
+
+
 def _pad_to_max(x: List[int], max_len: int, pad: int) -> List[int]:
     assert len(x) <= max_len
     return x + [pad] * (max_len - len(x))

vllm/worker/worker.py

@@ -19,6 +19,7 @@
 from vllm.worker.cache_engine import CacheEngine
 from vllm.worker.model_runner import ModelRunner
 from vllm.lora.request import LoRARequest
+from vllm.utils import is_hip
 
 
 class Worker:
@@ -264,7 +265,8 @@ def init_distributed_environment(
                 "cupy.distributed is already initialized but the cupy world "
                 "size does not match parallel_config.world_size "
                 f"({cupy_world_size} vs. {parallel_config.world_size}).")
-    elif parallel_config.world_size > 1 and cupy_port is not None:
+    elif (parallel_config.world_size > 1 and cupy_port is not None
+          and not is_hip()):
         # NOTE(woosuk): We don't initialize CuPy process group when world size
         # is 1.
         # TODO(woosuk): Support multi-node connection.