vLLM/vllm/distributed/device_communicators/xpu_communicator.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project


import torch
import torch.distributed as dist
from torch.distributed import ProcessGroup

import vllm.envs as envs
from vllm.logger import init_logger

from .base_device_communicator import DeviceCommunicatorBase

logger = init_logger(__name__)


class XpuCommunicator(DeviceCommunicatorBase):
    def __init__(
        self,
        cpu_group: ProcessGroup,
        device: torch.device | None = None,
        device_group: ProcessGroup | None = None,
        unique_name: str = "",
        use_all2all: bool = False,
    ):
        super().__init__(
            cpu_group, device, device_group, unique_name, use_all2all=use_all2all
        )
        self.ca_comm: None = None
        if self.use_all2all:
            if self.all2all_backend in ("naive", "allgather_reducescatter"):
                from .all2all import AgRsAll2AllManager

                self.all2all_manager = AgRsAll2AllManager(self.cpu_group)
                logger.info("Using AgRs manager on XPU device.")

            else:  # type: ignore[has-type]
                logger.warning(
                    "`%s` all2all manager is not supported on XPU. "
                    "Falling back to AgRs manager for XPU, "
                    "which is the Default backend",
                    self.all2all_backend,  # type: ignore[has-type]
                )
                from .all2all import AgRsAll2AllManager

                self.all2all_manager = AgRsAll2AllManager(self.cpu_group)
                logger.info("Using AgRs manager on XPU device.")

    def _fixed_rank_sum(self, input_: torch.Tensor) -> torch.Tensor:
        flat_input = input_.reshape(-1)
        gathered = torch.empty(
            (self.world_size, flat_input.numel()),
            dtype=input_.dtype,
            device=input_.device,
        )
        dist.all_gather_single(gathered.view(-1), flat_input, group=self.device_group)
        output = gathered[0].clone()
        for rank in range(1, self.world_size):
            output.add_(gathered[rank])
        return output.view(input_.shape)

    def all_reduce(self, input_: torch.Tensor) -> torch.Tensor:
        if envs.VLLM_BATCH_INVARIANT:
            # XCCL changes its reduction order with message size. Gather first
            # and accumulate in rank order, independent of the token batch.
            return self._fixed_rank_sum(input_)

        output = input_.clone()
        dist.all_reduce(output, group=self.device_group)
        return output

    def reduce_scatter(self, input_: torch.Tensor, dim: int = -1):
        world_size = self.world_size

        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()

        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 envs.VLLM_BATCH_INVARIANT:
            reduced = self._fixed_rank_sum(input_tensor)
            start = self.rank_in_group * chunk_size
            output.copy_(reduced.narrow(0, start, chunk_size))
        else:
            dist.reduce_scatter_tensor(output, input_tensor, group=self.device_group)

        # Reshape before returning
        return output.movedim(0, dim).contiguous()

    def reduce_scatterv(
        self, input_: torch.Tensor, dim: int = -1, sizes: list[int] | None = None
    ):
        world_size = self.world_size

        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 envs.VLLM_BATCH_INVARIANT:
            reduced = self._fixed_rank_sum(input_tensor)
            start = (
                self.rank_in_group * chunk_size
                if sizes is None
                else sum(sizes[: self.rank_in_group])
            )
            output.copy_(reduced.narrow(0, start, chunk_size))
        elif sizes is None or sizes.count(sizes[0]) == len(sizes):
            dist.reduce_scatter_tensor(output, input_tensor, group=self.device_group)
        elif torch.xpu.device_count() < self.world_size:
            # uneven reduce_scatter is not supported when the
            # process cannot see every device in the group (
            # e.g. external-LBDP).
            max_size = max(sizes)
            padded = input_tensor.new_zeros(
                (world_size * max_size,) + input_tensor.shape[1:]
            )
            for rank, split in enumerate(input_tensor.split(sizes, dim=0)):
                padded[rank * max_size : rank * max_size + sizes[rank]] = split
            padded_output = input_tensor.new_empty((max_size,) + input_tensor.shape[1:])
            dist.reduce_scatter_tensor(padded_output, padded, group=self.device_group)
            output = padded_output[:chunk_size]
        else:
            input_splits = list(input_tensor.split(sizes, dim=0))
            dist.reduce_scatter(output, input_splits, group=self.device_group)
        # Reshape before returning
        return output.movedim(0, dim).contiguous()

    def all_gatherv(
        self,
        input_: torch.Tensor | list[torch.Tensor],
        dim: int = 0,
        sizes: list[int] | None = None,
    ):
        if dim != 0:
            raise NotImplementedError("only dim 0 all-gatherv is supported")
        world_size = self.world_size

        # '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: list[int] | None = None):
            input_size = input_.size()
            if sizes is not None:
                assert len(sizes) == world_size
                assert input_.shape[dim] == sizes[self.rank_in_group], (
                    f"{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:
                all_gather_list = []
                for size in sizes:
                    all_gather_list.append(
                        torch.empty(
                            (size,) + input_.shape[1:],
                            dtype=input_.dtype,
                            device=input_.device,
                        )
                    )
                dist.all_gather(all_gather_list, input_, group=self.device_group)
                output_tensor = torch.cat(all_gather_list, dim=0)
            else:
                dist.all_gather([output_tensor], input_, group=self.device_group)
            return output_tensor

        if isinstance(input_, torch.Tensor):
            return _all_gather_single(input_, sizes)

        output_list = []
        for inp in input_:
            output_list.append(_all_gather_single(inp, sizes=sizes))
        return output_list

    def gather(
        self, input_: torch.Tensor, dst: int = 0, dim: int = -1
    ) -> torch.Tensor | None:
        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()
        # For xpu path, gather doesn't work properly together with ray
        # cluster so we use all_gather instead for now.
        input_size = input_.size()
        # Allocate output tensor.
        output_tensor = torch.empty(
            (self.world_size,) + input_size, dtype=input_.dtype, device=input_.device
        )
        # All-gather.
        dist.all_gather_single(output_tensor, input_, group=self.device_group)
        if self.rank_in_group == dst:
            # Reshape
            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 :]
            )
        else:
            output_tensor = None
        return output_tensor

    def broadcast(self, input_: torch.Tensor, src: int = 0) -> None:
        dist.broadcast(input_, src=src, group=self.device_group)

    def dispatch_router_logits(
        self,
        hidden_states: torch.Tensor,
        router_logits: torch.Tensor,
        is_sequence_parallel: bool = False,
        extra_tensors: list[torch.Tensor] | None = None,
    ) -> (
        tuple[torch.Tensor, torch.Tensor]
        | tuple[torch.Tensor, torch.Tensor, list[torch.Tensor]]
    ):
        """Dispatch the hidden states and router logits to the appropriate device.
        This is a no-op in the base class.
        """
        assert self.all2all_manager is not None
        return self.all2all_manager.dispatch_router_logits(
            hidden_states,
            router_logits,
            is_sequence_parallel,
            extra_tensors,
        )

    def dispatch(
        self,
        hidden_states: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        is_sequence_parallel: bool = False,
        extra_tensors: list[torch.Tensor] | None = None,
    ) -> (
        tuple[torch.Tensor, torch.Tensor, torch.Tensor]
        | tuple[torch.Tensor, torch.Tensor, torch.Tensor, list[torch.Tensor]]
    ):
        """Dispatch the hidden states and topk weights/ids to the appropriate device.
        This is a no-op in the base class.
        """
        assert self.all2all_manager is not None
        return self.all2all_manager.dispatch(
            hidden_states,
            topk_weights,
            topk_ids,
            is_sequence_parallel,
            extra_tensors=extra_tensors,
        )

    def combine(
        self, hidden_states: torch.Tensor, is_sequence_parallel: bool = False
    ) -> torch.Tensor:
        """Combine the hidden states and router logits from the appropriate device.
        This is a no-op in the base class.
        """
        assert self.all2all_manager is not None
        return self.all2all_manager.combine(
            hidden_states,
            is_sequence_parallel,
        )