Add pynccl all-gatherv and reducescatterv (#20154)

Signed-off-by: Trevor Morris <tmorris@nvidia.com>
Signed-off-by: mgoin <mgoin64@gmail.com>
Co-authored-by: mgoin <mgoin64@gmail.com>

tests/distributed/test_pynccl.py

@@ -4,6 +4,7 @@
 import multiprocessing
 import os
 
+import numpy as np
 import pytest
 import torch
 import torch.distributed
@@ -177,6 +178,38 @@ def test_pynccl_all_gather():
     distributed_run(all_gather_worker_fn, 2)
 
 
+@worker_fn_wrapper
+def all_gatherv_worker_fn():
+    pynccl_comm = PyNcclCommunicator(get_world_group().cpu_group,
+                                     device=get_world_group().device)
+
+    rank = pynccl_comm.rank
+    world_size = pynccl_comm.world_size
+    device = f'cuda:{pynccl_comm.rank}'
+
+    assert world_size <= 8
+    sizes = [81, 20, 57, 52, 81, 5, 49, 49][:world_size]
+    num_elems = sizes[rank]
+    tensor = torch.arange(num_elems, dtype=torch.float32,
+                          device=device) + rank * 100
+    result = torch.zeros(sum(sizes), dtype=torch.float32, device=device)
+
+    expected = torch.cat([
+        torch.arange(sizes[r], dtype=torch.float32) + r * 100
+        for r in range(world_size)
+    ]).to(device)
+
+    pynccl_comm.all_gatherv(result, tensor, sizes=sizes)
+    torch.cuda.synchronize()
+    torch.testing.assert_close(result, expected, rtol=1e-5, atol=1e-8)
+
+
+@pytest.mark.skipif(torch.cuda.device_count() < 2,
+                    reason="Need at least 2 GPUs to run the test.")
+def test_pynccl_all_gatherv():
+    distributed_run(all_gatherv_worker_fn, 2)
+
+
 @worker_fn_wrapper
 def reduce_scatter_worker_fn():
     pynccl_comm = PyNcclCommunicator(get_world_group().cpu_group,
@@ -214,6 +247,43 @@ def test_pynccl_reduce_scatter():
     distributed_run(reduce_scatter_worker_fn, 2)
 
 
+@worker_fn_wrapper
+def reduce_scatterv_worker_fn():
+    pynccl_comm = PyNcclCommunicator(get_world_group().cpu_group,
+                                     device=get_world_group().device)
+
+    rank = pynccl_comm.rank
+    world_size = pynccl_comm.world_size
+    device = f'cuda:{pynccl_comm.rank}'
+
+    assert world_size <= 8
+    sizes = [81, 20, 57, 52, 81, 5, 49, 49][:world_size]
+    num_elems = sum(sizes)
+    tensor = torch.arange(num_elems, dtype=torch.float32,
+                          device=device) + rank * 100
+    result = torch.zeros(sizes[rank], dtype=torch.float32, device=device)
+
+    # Calculate expected result for this rank's chunk
+    all_tensors = [
+        torch.arange(num_elems, dtype=torch.float32) + r * 100
+        for r in range(world_size)
+    ]
+    sizes_cumsum = np.cumsum(sizes)
+    start = 0 if rank == 0 else sizes_cumsum[rank - 1]
+    end = sizes_cumsum[rank]
+    expected = sum(tensor[start:end] for tensor in all_tensors).to(device)
+
+    pynccl_comm.reduce_scatterv(result, tensor, sizes=sizes)
+    torch.cuda.synchronize()
+    torch.testing.assert_close(result, expected, rtol=1e-5, atol=1e-8)
+
+
+@pytest.mark.skipif(torch.cuda.device_count() < 2,
+                    reason="Need at least 2 GPUs to run the test.")
+def test_pynccl_reduce_scatterv():
+    distributed_run(reduce_scatterv_worker_fn, 2)
+
+
 @pytest.mark.skipif(torch.cuda.device_count() < 2,
                     reason="Need at least 2 GPUs to run the test.")
 def test_pynccl_with_cudagraph():

vllm/distributed/device_communicators/base_device_communicator.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import threading
-from typing import Optional
+from typing import Optional, Union
 from weakref import WeakValueDictionary
 
 import torch
@@ -138,6 +138,14 @@ class DeviceCommunicatorBase:
                                               input_size[dim + 1:])
         return output_tensor
 
+    def all_gatherv(
+        self,
+        input_: Union[torch.Tensor, list[torch.Tensor]],
+        dim: int = 0,
+        sizes: Optional[list[int]] = None
+    ) -> Union[torch.Tensor, list[torch.Tensor]]:
+        raise NotImplementedError
+
     def reduce_scatter(self,
                        input_: torch.Tensor,
                        dim: int = -1) -> torch.Tensor:
@@ -172,6 +180,12 @@ class DeviceCommunicatorBase:
         # Reshape before returning
         return output_tensor.movedim(0, dim).contiguous()
 
+    def reduce_scatterv(self,
+                        input_: torch.Tensor,
+                        dim: int = -1,
+                        sizes: Optional[list[int]] = None) -> torch.Tensor:
+        raise NotImplementedError
+
     def gather(self,
                input_: torch.Tensor,
                dst: int = 0,

vllm/distributed/device_communicators/cuda_communicator.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from typing import Optional
+from typing import Optional, Union
 
 import torch
 from torch.distributed import ProcessGroup
@@ -142,6 +142,42 @@ class CudaCommunicator(DeviceCommunicatorBase):
         # Reshape before returning
         return output.movedim(0, dim).contiguous()
 
+    def reduce_scatterv(self,
+                        input_: torch.Tensor,
+                        dim: int = -1,
+                        sizes: Optional[list[int]] = None):
+        world_size = self.world_size
+        pynccl_comm = self.pynccl_comm
+        assert pynccl_comm is not None
+        if dim < 0:
+            # Convert negative dim to positive.
+            dim += input_.dim()
+
+        # Note: This will produce an incorrect answer if we don't make
+        # the input_tensor contiguous. Possible bug in reduce_scatter_tensor?
+        input_tensor = input_.movedim(0, dim).contiguous()
+
+        if sizes is not None:
+            assert len(sizes) == world_size
+            assert input_tensor.shape[0] == sum(sizes)
+            chunk_size = sizes[self.rank_in_group]
+        else:
+            assert input_tensor.shape[0] % world_size == 0
+            chunk_size = input_tensor.shape[0] // world_size
+        output_shape = (chunk_size, ) + input_tensor.shape[1:]
+
+        output = torch.empty(output_shape,
+                             dtype=input_tensor.dtype,
+                             device=input_tensor.device)
+
+        if sizes is not None:
+            pynccl_comm.reduce_scatterv(output, input_, sizes=sizes)
+        else:
+            pynccl_comm.reduce_scatter(output, input_)
+
+        # Reshape before returning
+        return output.movedim(0, dim).contiguous()
+
     def send(self, tensor: torch.Tensor, dst: Optional[int] = None) -> None:
         """Sends a tensor to the destination rank in a non-blocking way"""
         """NOTE: `dst` is the local rank of the destination rank."""
@@ -180,6 +216,51 @@ class CudaCommunicator(DeviceCommunicatorBase):
             self.all2all_manager.destroy()
             self.all2all_manager = None
 
+    def all_gatherv(self,
+                    input_: Union[torch.Tensor, list[torch.Tensor]],
+                    dim: int = 0,
+                    sizes: Optional[list[int]] = None):
+        if dim != 0:
+            raise NotImplementedError("only dim 0 all-gatherv is supported")
+        world_size = self.world_size
+        pynccl_comm = self.pynccl_comm
+        assert pynccl_comm is not None and not pynccl_comm.disabled
+
+        # 'sizes' is not needed if all inputs in the same group have the same
+        # shape
+        if sizes is not None and all(s == sizes[0] for s in sizes):
+            sizes = None
+
+        def _all_gather_single(input_: torch.Tensor,
+                               sizes: Optional[list[int]] = None):
+            input_size = input_.size()
+            if sizes is not None:
+                assert len(sizes) == world_size
+                assert input_.shape[dim] == sizes[self.rank_in_group]
+                output_size = (sum(sizes), ) + input_size[1:]
+            else:
+                output_size = (input_size[0] * world_size, ) + input_size[1:]
+            # Allocate output tensor.
+            output_tensor = torch.empty(output_size,
+                                        dtype=input_.dtype,
+                                        device=input_.device)
+            if sizes is not None:
+                pynccl_comm.all_gatherv(output_tensor, input_, sizes=sizes)
+            else:
+                pynccl_comm.all_gather(output_tensor, input_)
+            return output_tensor
+
+        if isinstance(input_, torch.Tensor):
+            return _all_gather_single(input_, sizes)
+
+        output_list = []
+        pynccl_comm.group_start()
+        for inp in input_:
+            output_list.append(_all_gather_single(inp, sizes=sizes))
+        pynccl_comm.group_end()
+
+        return output_list
+
     def dispatch(
             self, hidden_states: torch.Tensor,
             router_logits: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:

vllm/distributed/device_communicators/pynccl.py

@@ -152,6 +152,40 @@ class PyNcclCommunicator:
             ncclDataTypeEnum.from_torch(input_tensor.dtype), self.comm,
             cudaStream_t(stream.cuda_stream))
 
+    def all_gatherv(
+        self,
+        output_tensor: torch.Tensor,
+        input_tensor: torch.Tensor,
+        sizes: list[int],
+        stream=None,
+    ):
+        if self.disabled:
+            return
+        # nccl communicator created on a specific device
+        # will only work on tensors on the same device
+        # otherwise it will cause "illegal memory access"
+        assert input_tensor.device == self.device, (
+            f"this nccl communicator is created to work on {self.device}, "
+            f"but the input tensor is on {input_tensor.device}")
+        if stream is None:
+            stream = current_stream()
+        assert output_tensor.shape[0] == sum(sizes)
+        split_offset = 0
+        self.nccl.ncclGroupStart()
+        for root, split_size in enumerate(sizes):
+            dst_slice = output_tensor[split_offset:split_offset + split_size]
+            self.nccl.ncclBroadcast(
+                buffer_type(input_tensor.data_ptr()),
+                buffer_type(dst_slice.data_ptr()),
+                dst_slice.numel(),
+                ncclDataTypeEnum.from_torch(input_tensor.dtype),
+                root,
+                self.comm,
+                cudaStream_t(stream.cuda_stream),
+            )
+            split_offset += split_size
+        self.nccl.ncclGroupEnd()
+
     def reduce_scatter(self,
                        output_tensor: torch.Tensor,
                        input_tensor: torch.Tensor,
@@ -174,6 +208,38 @@ class PyNcclCommunicator:
             ncclRedOpTypeEnum.from_torch(op), self.comm,
             cudaStream_t(stream.cuda_stream))
 
+    def reduce_scatterv(
+        self,
+        output_tensor: torch.Tensor,
+        input_tensor: torch.Tensor,
+        sizes: list[int],
+        op: ReduceOp = ReduceOp.SUM,
+        stream=None,
+    ):
+        if self.disabled:
+            return
+        # nccl communicator created on a specific device
+        # will only work on tensors on the same device
+        # otherwise it will cause "illegal memory access"
+        assert input_tensor.device == self.device, (
+            f"this nccl communicator is created to work on {self.device}, "
+            f"but the input tensor is on {input_tensor.device}")
+        if stream is None:
+            stream = current_stream()
+
+        split_offset = 0
+        self.nccl.ncclGroupStart()
+        for root, split_size in enumerate(sizes):
+            chunk = input_tensor[split_offset:split_offset + split_size, ...]
+            self.nccl.ncclReduce(
+                buffer_type(chunk.data_ptr()),
+                buffer_type(output_tensor.data_ptr()), chunk.numel(),
+                ncclDataTypeEnum.from_torch(input_tensor.dtype),
+                ncclRedOpTypeEnum.from_torch(op), root, self.comm,
+                cudaStream_t(stream.cuda_stream))
+            split_offset += split_size
+        self.nccl.ncclGroupEnd()
+
     def send(self, tensor: torch.Tensor, dst: int, stream=None):
         if self.disabled:
             return
@@ -216,3 +282,9 @@ class PyNcclCommunicator:
         self.nccl.ncclBroadcast(sendbuff, recvbuff, tensor.numel(),
                                 ncclDataTypeEnum.from_torch(tensor.dtype), src,
                                 self.comm, cudaStream_t(stream.cuda_stream))
+
+    def group_start(self):
+        self.nccl.ncclGroupStart()
+
+    def group_end(self):
+        self.nccl.ncclGroupEnd()

vllm/distributed/device_communicators/pynccl_wrapper.py

@@ -154,6 +154,17 @@ class NCCLLibrary:
             ncclRedOp_t, ncclComm_t, cudaStream_t
         ]),
 
+        # ncclResult_t  ncclReduce(
+        #   const void* sendbuff, void* recvbuff, size_t count,
+        #   ncclDataType_t datatype, ncclRedOp_t op, int root,
+        #   ncclComm_t comm,  cudaStream_t stream);
+        # note that cudaStream_t is a pointer type, so the last argument
+        # is a pointer
+        Function("ncclReduce", ncclResult_t, [
+            buffer_type, buffer_type, ctypes.c_size_t, ncclDataType_t,
+            ncclRedOp_t, ctypes.c_int, ncclComm_t, cudaStream_t
+        ]),
+
         # ncclResult_t  ncclAllGather(
         #   const void* sendbuff, void* recvbuff, size_t count,
         #   ncclDataType_t datatype, ncclComm_t comm,
@@ -207,6 +218,10 @@ class NCCLLibrary:
         # it is better not to call it at all.
         # ncclResult_t  ncclCommDestroy(ncclComm_t comm);
         Function("ncclCommDestroy", ncclResult_t, [ncclComm_t]),
+        # ncclResult_t ncclGroupStart();
+        Function("ncclGroupStart", ncclResult_t, []),
+        # ncclResult_t ncclGroupEnd();
+        Function("ncclGroupEnd", ncclResult_t, []),
     ]
 
     # class attribute to store the mapping from the path to the library
@@ -300,6 +315,18 @@ class NCCLLibrary:
                                                      datatype, op, comm,
                                                      stream))
 
+    def ncclReduce(self, sendbuff: buffer_type, recvbuff: buffer_type,
+                   count: int, datatype: int, op: int, root: int,
+                   comm: ncclComm_t, stream: cudaStream_t) -> None:
+        # `datatype` actually should be `ncclDataType_t`
+        # and `op` should be `ncclRedOp_t`
+        # both are aliases of `ctypes.c_int`
+        # when we pass int to a function, it will be converted to `ctypes.c_int`
+        # by ctypes automatically
+        self.NCCL_CHECK(self._funcs["ncclReduce"](sendbuff, recvbuff, count,
+                                                  datatype, op, root, comm,
+                                                  stream))
+
     def ncclReduceScatter(self, sendbuff: buffer_type, recvbuff: buffer_type,
                           count: int, datatype: int, op: int, comm: ncclComm_t,
                           stream: cudaStream_t) -> None:
@@ -342,6 +369,12 @@ class NCCLLibrary:
     def ncclCommDestroy(self, comm: ncclComm_t) -> None:
         self.NCCL_CHECK(self._funcs["ncclCommDestroy"](comm))
 
+    def ncclGroupStart(self) -> None:
+        self.NCCL_CHECK(self._funcs["ncclGroupStart"]())
+
+    def ncclGroupEnd(self) -> None:
+        self.NCCL_CHECK(self._funcs["ncclGroupEnd"]())
+
 
 __all__ = [
     "NCCLLibrary", "ncclDataTypeEnum", "ncclRedOpTypeEnum", "ncclUniqueId",

vllm/distributed/parallel_state.py

@@ -383,6 +383,12 @@ class GroupCoordinator:
                               dim: int) -> torch.Tensor:
         return self.device_communicator.all_gather(input_, dim)
 
+    def all_gatherv(self,
+                    input_: Union[torch.Tensor, list[torch.Tensor]],
+                    dim: int = 0,
+                    sizes: Optional[list[int]] = None):
+        return self.device_communicator.all_gatherv(input_, dim, sizes)
+
     def reduce_scatter(self,
                        input_: torch.Tensor,
                        dim: int = -1) -> torch.Tensor:
@@ -401,6 +407,12 @@ class GroupCoordinator:
         else:
             return self._reduce_scatter_out_place(input_, dim)
 
+    def reduce_scatterv(self,
+                        input_: torch.Tensor,
+                        dim: int = -1,
+                        sizes: Optional[list[int]] = None) -> torch.Tensor:
+        return self.device_communicator.reduce_scatterv(input_, dim, sizes)
+
     def _reduce_scatter_out_place(self, input_: torch.Tensor,
                                   dim: int) -> torch.Tensor:
         return self.device_communicator.reduce_scatter(input_, dim)