[gpt-oss] Add gpt-oss bf16 support

vllm/distributed/device_communicators/cpu_communicator.py

@@ -0,0 +1,145 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import os
+from typing import Optional
+
+import torch
+from torch.distributed import ProcessGroup
+
+from vllm.platforms import current_platform
+from vllm.platforms.interface import CpuArchEnum
+
+from .base_device_communicator import DeviceCommunicatorBase
+
+
+class CpuCommunicator(DeviceCommunicatorBase):
+
+    def __init__(self,
+                 cpu_group: ProcessGroup,
+                 device: Optional[torch.device] = None,
+                 device_group: Optional[ProcessGroup] = None,
+                 unique_name: str = ""):
+        super().__init__(cpu_group, device, device_group, unique_name)
+        self.dist_module = torch.distributed
+
+        if (current_platform.get_cpu_architecture()
+                == CpuArchEnum.X86) and hasattr(
+                    torch.ops._C,
+                    "init_shm_manager") and unique_name.startswith("tp"):
+            self.dist_module = _CPUSHMDistributed(self)
+
+    def all_reduce(self, input_):
+        self.dist_module.all_reduce(input_, group=self.device_group)
+        return input_
+
+    def gather(self,
+               input_: torch.Tensor,
+               dst: int = 0,
+               dim: int = -1) -> Optional[torch.Tensor]:
+        """
+        NOTE: We assume that the input tensor is on the same device across
+        all the ranks.
+        NOTE: `dst` is the local rank of the destination rank.
+        """
+        world_size = self.world_size
+        assert -input_.dim() <= dim < input_.dim(), (
+            f"Invalid dim ({dim}) for input tensor with shape {input_.size()}")
+        if dim < 0:
+            # Convert negative dim to positive.
+            dim += input_.dim()
+
+        # Allocate output tensor.
+        if self.rank_in_group == dst:
+            gather_list = [torch.empty_like(input_) for _ in range(world_size)]
+        else:
+            gather_list = None
+
+        # Gather.
+        self.dist_module.gather(input_,
+                                gather_list,
+                                dst=self.ranks[dst],
+                                group=self.device_group)
+
+        if self.rank_in_group == dst:
+            output_tensor = torch.cat(gather_list, dim=dim)
+        else:
+            output_tensor = None
+        return output_tensor
+
+    def all_gather(self, input_: torch.Tensor, dim: int = -1) -> torch.Tensor:
+        if dim < 0:
+            # Convert negative dim to positive.
+            dim += input_.dim()
+        input_size = input_.size()
+        # NOTE: we have to use concat-style all-gather here,
+        # stack-style all-gather has compatibility issues with
+        # torch.compile . see https://github.com/pytorch/pytorch/issues/138795
+        output_size = (input_size[0] * self.world_size, ) + input_size[1:]
+        # Allocate output tensor.
+        output_tensor = torch.empty(output_size,
+                                    dtype=input_.dtype,
+                                    device=input_.device)
+        # All-gather.
+        self.dist_module.all_gather_into_tensor(output_tensor,
+                                                input_,
+                                                group=self.device_group)
+
+        # Reshape
+        output_tensor = output_tensor.reshape((self.world_size, ) + input_size)
+        output_tensor = output_tensor.movedim(0, dim)
+        output_tensor = output_tensor.reshape(input_size[:dim] +
+                                              (self.world_size *
+                                               input_size[dim], ) +
+                                              input_size[dim + 1:])
+        return output_tensor
+
+
+class _CPUSHMDistributed:
+
+    def __init__(self, communicator: CpuCommunicator):
+        instance_identifier = os.environ["VLLM_DIST_IDENT"]
+        unique_name = communicator.unique_name
+        instance_identifier = f"{instance_identifier}-{unique_name}"
+        self.communicator = communicator
+
+        group_ranks = [str(rank) for rank in self.communicator.ranks]
+        shm_group_identifier = f"[{'-'.join(group_ranks)}]"
+        self.group_name = f"{instance_identifier}-{shm_group_identifier}-cpushm"
+
+        self.handle = self._init_cpu_shm()
+
+    def _init_cpu_shm(self) -> int:
+        handle = torch.ops._C.init_shm_manager(
+            self.group_name,
+            self.communicator.world_size,
+            self.communicator.rank,
+        )
+        torch.distributed.barrier(self.communicator.device_group)
+        torch.ops._C.join_shm_manager(
+            handle,
+            self.group_name,
+        )
+        torch.distributed.barrier(self.communicator.device_group)
+
+        return handle
+
+    def all_reduce(self,
+                   input: torch.Tensor,
+                   group: Optional[ProcessGroup] = None) -> None:
+        torch.ops._C.shm_allreduce(self.handle, input)
+
+    def gather(self,
+               input: torch.Tensor,
+               gather_list: Optional[list[torch.Tensor]],
+               dst: int = -1,
+               group: Optional[ProcessGroup] = None) -> None:
+        # Note: different from the torch gather, here we use local dst rank.
+        torch.ops._C.shm_gather(self.handle, input, gather_list,
+                                torch.distributed.get_group_rank(group, dst))
+
+    def all_gather_into_tensor(self,
+                               output: torch.Tensor,
+                               input: torch.Tensor,
+                               group: Optional[ProcessGroup] = None) -> None:
+        torch.ops._C.shm_all_gather(self.handle, input, output)