Use CuPy for CUDA graphs

requirements.txt

@@ -12,3 +12,4 @@ pydantic >= 2.0  # Required for OpenAI server.
 aioprometheus[starlette]
 pynvml == 11.5.0
 triton >= 2.1.0
+cupy-cuda12x == 12.3.0  # Required for CUDA graphs. CUDA 11.8 users should install cupy-cuda11x instead.

vllm/engine/llm_engine.py

@@ -283,7 +283,7 @@ def _init_workers_ray(self, placement_group: "PlacementGroup",
             is_driver_worker=True,
         )
 
-        self._run_workers("init_model")
+        self._run_workers("init_model", cupy_port=get_open_port())
         self._run_workers(
             "load_model",
             max_concurrent_workers=self.parallel_config.

vllm/model_executor/parallel_utils/communication_op.py

@@ -1,14 +1,15 @@
 from collections import namedtuple
 from typing import Any, Dict, List, Optional, Union
 
-from torch.distributed import ProcessGroup
-
 import torch
+from torch.distributed import ProcessGroup
 
+from vllm.model_executor.parallel_utils import cupy_utils
 from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
     get_tensor_model_parallel_group,
+    is_cupy_nccl_enabled_for_all_reduce,
 )
 from vllm.model_executor.parallel_utils.custom_all_reduce import custom_all_reduce
 
@@ -31,8 +32,12 @@ def tensor_model_parallel_all_reduce(input_: torch.Tensor) -> torch.Tensor:
     out = custom_all_reduce(input_)
     if out is not None:
         return out
-    torch.distributed.all_reduce(input_,
-                                 group=get_tensor_model_parallel_group())
+    if is_cupy_nccl_enabled_for_all_reduce():
+        # TODO: support multiple parallel groups.
+        cupy_utils.all_reduce(input_)
+    else:
+        torch.distributed.all_reduce(input_,
+                                     group=get_tensor_model_parallel_group())
     return input_
 
 

vllm/model_executor/parallel_utils/cupy_utils.py

@@ -0,0 +1,130 @@
+"""CuPy utilities for all-reduce.
+
+We use CuPy all-reduce instead of torch.distributed.all_reduce when capturing
+CUDA graphs, because torch.distributed.all_reduce causes errors when capturing
+CUDA graphs.
+
+NOTE: We use CuPy 12.3 since CuPy 13.0 does not support Python 3.8.
+TODO: Remove this file when torch.distributed.all_reduce is fixed.
+"""
+import contextlib
+
+import torch
+from torch.distributed import ReduceOp
+
+try:
+    import cupy
+    from cupy.cuda import nccl
+    from cupyx.distributed import NCCLBackend
+except ImportError as e:
+    cupy = e
+    nccl = None
+
+    class NCCLBackend:
+        ...
+
+
+_OP_MAPPING = {
+    ReduceOp.SUM: "sum",
+    ReduceOp.PRODUCT: "prod",
+    ReduceOp.MIN: "min",
+    ReduceOp.MAX: "max",
+}
+
+
+class NCCLBackendWithBFloat16(NCCLBackend):
+    # This is enough to add bfloat16 support for most operations,
+    # but broadcast will fail (will require changes in compiled
+    # cupy code).
+    def _get_nccl_dtype_and_count(self, array, count=None):
+        nccl_dtype, count = super()._get_nccl_dtype_and_count(array, count)
+        torch_dtype = getattr(array, "_torch_dtype", None)
+        if torch_dtype is torch.bfloat16:
+            nccl_dtype = nccl.NCCL_BFLOAT16
+        return nccl_dtype, count
+
+    def barrier(self) -> None:
+        raise RuntimeError(
+            "Currently, CuPy NCCL barrier is not supported since the TCP "
+            "store is immediately stopped after the initialization.")
+
+
+_NCCL_BACKEND = None
+_WORLD_SIZE = 0
+
+
+def is_initialized() -> bool:
+    """Returns whether the NCCL backend is initialized."""
+    return _NCCL_BACKEND is not None
+
+
+@contextlib.contextmanager
+def set_cupy_stream(stream: torch.cuda.Stream):
+    """Set the cuda stream for communication"""
+    cupy_stream = cupy.cuda.ExternalStream(stream.cuda_stream,
+                                           stream.device_index)
+    with cupy_stream:
+        yield
+
+
+def init_process_group(world_size: int, rank: int, host: str,
+                       port: int) -> None:
+    """Initializes the CuPy NCCL backend.
+
+    # TODO: handle NCCL timeouts.
+    """
+    assert not is_initialized()
+
+    if isinstance(cupy, Exception):
+        raise ImportError(
+            "NCCLBackend is not available. Please install cupy.") from cupy
+
+    # TODO(woosuk): Create TP and PP process groups for CuPy.
+    global _NCCL_BACKEND
+    global _WORLD_SIZE
+    assert world_size > 0, f"{world_size=} should be a positive integer"
+    assert 0 <= rank < world_size, (
+        f"{rank=} should be a integer between [0, {world_size})")
+
+    cupy.cuda.runtime.setDevice(torch.cuda.current_device())
+    _NCCL_BACKEND = NCCLBackendWithBFloat16(world_size, rank, host, port)
+    _WORLD_SIZE = world_size
+
+    # Stop the TCP store to prevent the deadlock issues at termination time.
+    # FIXME(woosuk): This is hacky. Find a more robust solution.
+    if rank == 0 and hasattr(_NCCL_BACKEND, "_store"):
+        _NCCL_BACKEND._store.stop()
+
+
+def all_reduce(input_: torch.Tensor, op=ReduceOp.SUM) -> None:
+    """All-reduces the input tensor across the process group."""
+    assert input_.is_cuda, f"{input_} should be a cuda tensor"
+    # Hack to support bfloat16
+    torch_dtype = input_.dtype
+    if torch_dtype is torch.bfloat16:
+        # We need to view as float16, otherwise
+        # cupy will fail. This will not change
+        # the underlying data.
+        input_ = input_.view(torch.float16)
+    cupy_input = cupy.asarray(input_)
+    cupy_input._torch_dtype = torch_dtype  # pylint: disable=protected-access
+    _NCCL_BACKEND.all_reduce(in_array=cupy_input,
+                             out_array=cupy_input,
+                             op=_OP_MAPPING[op])
+
+
+def destroy_process_group() -> None:
+    """Destroys the NCCL backend."""
+    global _NCCL_BACKEND
+    global _WORLD_SIZE
+    _NCCL_BACKEND = None
+    _WORLD_SIZE = 0
+
+
+def get_world_size() -> int:
+    """Returns the world size."""
+    return _WORLD_SIZE
+
+
+def get_nccl_backend():
+    return _NCCL_BACKEND

vllm/model_executor/parallel_utils/parallel_state.py

@@ -3,9 +3,12 @@
 # https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/parallel_state.py
 # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
 """Tensor and pipeline parallel groups."""
+import contextlib
 
 import torch
 
+from vllm.model_executor.parallel_utils import cupy_utils
+
 # Tensor model parallel group that the current rank belongs to.
 _TENSOR_MODEL_PARALLEL_GROUP = None
 # Pipeline model parallel group that the current rank belongs to.
@@ -206,3 +209,37 @@ def destroy_model_parallel():
     _PIPELINE_MODEL_PARALLEL_GROUP = None
     global _PIPELINE_GLOBAL_RANKS
     _PIPELINE_GLOBAL_RANKS = None
+
+    # Destroy the cupy states if any.
+    cupy_utils.destroy_process_group()
+
+
+# Whether to use cupy for nccl all reduce.
+# We use cupy for all reduce when using CUDA graph, because torch.distributed
+# is not well supported by CUDA graph.
+_ENABLE_CUPY_FOR_ALL_REDUCE = False
+
+
+@contextlib.contextmanager
+def with_cupy_nccl_for_all_reduce():
+    """use CuPy nccl instead of torch.distributed for all reduce"""
+    tp_size = get_tensor_model_parallel_world_size()
+    if tp_size == 1:
+        # No-op.
+        # NOTE(woosuk): We don't initialize CuPy when tp_size is 1.
+        yield
+    else:
+        global _ENABLE_CUPY_FOR_ALL_REDUCE
+        old = _ENABLE_CUPY_FOR_ALL_REDUCE
+        _ENABLE_CUPY_FOR_ALL_REDUCE = True
+
+        stream = torch.cuda.current_stream()
+        with cupy_utils.set_cupy_stream(stream):
+            yield
+        _ENABLE_CUPY_FOR_ALL_REDUCE = old
+
+
+def is_cupy_nccl_enabled_for_all_reduce():
+    """check if CuPy nccl is enabled for all reduce"""
+    global _ENABLE_CUPY_FOR_ALL_REDUCE
+    return _ENABLE_CUPY_FOR_ALL_REDUCE

vllm/test_utils.py

@@ -15,8 +15,11 @@ def init_test_distributed_environment(
                                      tensor_parallel_size,
                                      worker_use_ray=True)
     distributed_init_method = f"tcp://localhost:{distributed_init_port}"
-    init_distributed_environment(parallel_config, rank,
-                                 distributed_init_method)
+    init_distributed_environment(
+        parallel_config,
+        rank,
+        cupy_port=None,
+        distributed_init_method=distributed_init_method)
 
 
 def multi_process_tensor_parallel(

vllm/worker/model_runner.py

@@ -5,11 +5,15 @@
 import torch
 import torch.nn as nn
 
-from vllm.config import DeviceConfig, ModelConfig, LoRAConfig, ParallelConfig, SchedulerConfig
+from vllm.config import (DeviceConfig, ModelConfig, LoRAConfig, ParallelConfig,
+                         SchedulerConfig)
 from vllm.logger import init_logger
 from vllm.model_executor import get_model, InputMetadata, SamplingMetadata
 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
 from vllm.sampling_params import SamplingParams, SamplingType
 from vllm.sequence import SamplerOutput, SequenceData, SequenceGroupMetadata
@@ -644,6 +648,10 @@ def list_loras(self) -> Set[int]:
 
     @torch.inference_mode()
     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()
+
         assert not self.model_config.enforce_eager
         logger.info("Capturing the model for CUDA graphs. This may lead to "
                     "unexpected consequences if the model is not static. To "
@@ -674,6 +682,12 @@ def capture_model(self, kv_caches: List[KVCache]) -> None:
 
         # 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():
             for batch_size in reversed(batch_size_capture_list):
                 # Create dummy input_metadata.
@@ -713,6 +727,14 @@ def capture_model(self, kv_caches: List[KVCache]) -> None:
         # This usually takes < 10 seconds.
         logger.info(f"Graph capturing finished in {elapsed_time:.0f} secs.")
 
+    def __del__(self) -> None:
+        # Delete the CUDA graphs before deleting the CuPy NCCL communicator.
+        # NOTE(woosuk): This is necessary because otherwise deadlocks can
+        # happen.
+        # FIXME(woosuk): This is a bit hacky. Find a more robust solution.
+        self.graph_runners.clear()
+        self.cupy_nccl_backend = None
+
 
 class CUDAGraphRunner:
 
@@ -734,25 +756,29 @@ 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).
-        self.model(
-            input_ids,
-            positions,
-            kv_caches,
-            input_metadata,
-        )
-        torch.cuda.synchronize()
-
-        # Capture the graph.
-        self.graph = torch.cuda.CUDAGraph()
-        with torch.cuda.graph(self.graph, pool=memory_pool):
-            hidden_states = self.model(
+        with with_cupy_nccl_for_all_reduce():
+            self.model(
                 input_ids,
                 positions,
                 kv_caches,
                 input_metadata,
             )
         torch.cuda.synchronize()
 
+        # Capture the graph.
+        # NOTE(woosuk): Python 3.8 does not support multi-line with statements.
+        # 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():
+                hidden_states = self.model(
+                    input_ids,
+                    positions,
+                    kv_caches,
+                    input_metadata,
+                )
+        torch.cuda.synchronize()
+
         # Save the input and output buffers.
         self.input_buffers = {
             "input_ids": input_ids,