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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import threading
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from weakref import WeakValueDictionary
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import torch
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import torch.distributed as dist
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from torch.distributed import ProcessGroup
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class Cache:
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def __init__(self):
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self._cache: WeakValueDictionary = WeakValueDictionary()
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self._lock = threading.RLock() # Reentrant lock for thread safety
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def get_or_create(self, kwargs, func):
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# Create a hashable key from the kwargs
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key = tuple(sorted((k, v) for k, v in kwargs.items()))
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with self._lock:
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instance = self._cache.get(key)
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if instance is None:
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instance = func(**kwargs)
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self._cache[key] = instance
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return instance
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class All2AllManagerBase:
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rank: int
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world_size: int
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def __init__(self, cpu_group):
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self.cpu_group = cpu_group
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# compute some common properties
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from vllm.distributed.parallel_state import (
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get_dp_group,
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get_tp_group,
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in_the_same_node_as,
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)
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# all2all lives in ep group, which is merged from dp and tp group
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self.dp_group = get_dp_group()
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self.tp_group = get_tp_group()
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# no self.ep_group since self.ep_group is still in construction
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# when we create this object
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self.dp_rank = self.dp_group.rank_in_group
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self.dp_world_size = self.dp_group.world_size
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self.rank = dist.get_rank(cpu_group)
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self.world_size = dist.get_world_size(cpu_group)
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# all2all communication often has separate implementations for
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# intra-node and inter-node communication
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self.internode = not all(in_the_same_node_as(cpu_group, source_rank=0))
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def get_handle(self, kwargs):
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# get a handle for the all2all communication,
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# based on the kwargs.
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# different layers can have different configs,
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# e.g. one layer has hidden size 1024, another has 2048.
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# usually the underlying implementation caches the handle
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# and reuse it for the same config.
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raise NotImplementedError
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def dispatch(
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self,
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hidden_states: torch.Tensor,
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router_logits: torch.Tensor,
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is_sequence_parallel: bool = False,
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):
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raise NotImplementedError
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def set_num_sms(self, num_sms: int):
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pass
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def max_sms_used(self) -> int | None:
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return None # None means it could use the whole GPU
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def combine(self, hidden_states: torch.Tensor, is_sequence_parallel: bool = False):
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raise NotImplementedError
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def destroy(self):
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pass
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class DeviceCommunicatorBase:
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"""
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Base class for device-specific communicator.
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It can use the `cpu_group` to initialize the communicator.
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If the device has PyTorch integration (PyTorch can recognize its
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communication backend), the `device_group` will also be given.
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"""
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def __init__(
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self,
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cpu_group: ProcessGroup,
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device: torch.device | None = None,
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device_group: ProcessGroup | None = None,
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unique_name: str = "",
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):
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self.device = device or torch.device("cpu")
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self.cpu_group = cpu_group
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self.device_group = device_group
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self.unique_name = unique_name
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self.rank = dist.get_rank(cpu_group)
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self.world_size = dist.get_world_size(cpu_group)
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self.ranks = dist.get_process_group_ranks(cpu_group)
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self.global_rank = dist.get_rank()
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self.global_world_size = dist.get_world_size()
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self.rank_in_group = dist.get_group_rank(self.cpu_group, self.global_rank)
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use_ep = False
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all2all_backend = None
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from vllm.config import get_current_vllm_config
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config = get_current_vllm_config()
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if config is not None:
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# as long as we use data parallel (coupled data parallel
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# where all data parallel ranks execute forward together),
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# we initialize the all2all manager used in expert parallel.
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use_ep = config.parallel_config.data_parallel_size > 1
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all2all_backend = config.parallel_config.all2all_backend
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self.is_ep_communicator = "ep" in unique_name
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self.use_all2all = self.is_ep_communicator and use_ep
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self.all2all_backend = all2all_backend
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self.all2all_manager: All2AllManagerBase | None = None
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def all_reduce(self, input_: torch.Tensor) -> torch.Tensor:
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dist.all_reduce(input_, group=self.device_group)
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return input_
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def all_gather(self, input_: torch.Tensor, dim: int = -1) -> torch.Tensor:
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if dim < 0:
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# Convert negative dim to positive.
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dim += input_.dim()
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input_size = input_.size()
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# NOTE: we have to use concat-style all-gather here,
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# stack-style all-gather has compatibility issues with
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# torch.compile . see https://github.com/pytorch/pytorch/issues/138795
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output_size = (input_size[0] * self.world_size,) + input_size[1:]
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# Allocate output tensor.
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output_tensor = torch.empty(
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output_size, dtype=input_.dtype, device=input_.device
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)
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# All-gather.
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dist.all_gather_into_tensor(output_tensor, input_, group=self.device_group)
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# Reshape
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output_tensor = output_tensor.reshape((self.world_size,) + input_size)
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output_tensor = output_tensor.movedim(0, dim)
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output_tensor = output_tensor.reshape(
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input_size[:dim]
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+ (self.world_size * input_size[dim],)
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+ input_size[dim + 1 :]
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)
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return output_tensor
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def all_gatherv(
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self,
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input_: torch.Tensor | list[torch.Tensor],
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dim: int = 0,
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sizes: list[int] | None = None,
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) -> torch.Tensor | list[torch.Tensor]:
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raise NotImplementedError
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def reduce_scatter(self, input_: torch.Tensor, dim: int = -1) -> torch.Tensor:
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world_size = self.world_size
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# Bypass the function if we are using only 1 GPU.
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if world_size == 1:
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return input_
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assert -input_.dim() <= dim < input_.dim(), (
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f"Invalid dim ({dim}) for input tensor with shape {input_.size()}"
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)
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if dim < 0:
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# Convert negative dim to positive.
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dim += input_.dim()
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# Note: This will produce an incorrect answer if we don't make
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# the input_tensor contiguous. Possible bug in reduce_scatter_tensor?
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input_tensor = input_.movedim(0, dim).contiguous()
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assert input_tensor.shape[0] % world_size == 0
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chunk_size = input_tensor.shape[0] // world_size
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output_shape = (chunk_size,) + input_tensor.shape[1:]
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output_tensor = torch.empty(
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output_shape, dtype=input_tensor.dtype, device=input_tensor.device
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)
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# Perform reduce-scatter operation
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torch.distributed.reduce_scatter_tensor(
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output_tensor, input_tensor, group=self.device_group
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)
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# Reshape before returning
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return output_tensor.movedim(0, dim).contiguous()
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def reduce_scatterv(
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self, input_: torch.Tensor, dim: int = -1, sizes: list[int] | None = None
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) -> torch.Tensor:
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raise NotImplementedError
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def gather(
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self, input_: torch.Tensor, dst: int = 0, dim: int = -1
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) -> torch.Tensor | None:
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"""
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NOTE: We assume that the input tensor is on the same device across
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all the ranks.
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NOTE: `dst` is the local rank of the destination rank.
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"""
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world_size = self.world_size
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assert -input_.dim() <= dim < input_.dim(), (
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f"Invalid dim ({dim}) for input tensor with shape {input_.size()}"
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)
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if dim < 0:
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# Convert negative dim to positive.
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dim += input_.dim()
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# Allocate output tensor.
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if self.rank_in_group == dst:
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gather_list = [torch.empty_like(input_) for _ in range(world_size)]
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else:
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gather_list = None
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# Gather.
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torch.distributed.gather(
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input_, gather_list, dst=self.ranks[dst], group=self.device_group
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)
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if self.rank_in_group == dst:
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output_tensor = torch.cat(gather_list, dim=dim)
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else:
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output_tensor = None
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return output_tensor
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def send(self, tensor: torch.Tensor, dst: int | None = None) -> None:
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"""Sends a tensor to the destination rank in a blocking way"""
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"""NOTE: `dst` is the local rank of the destination rank."""
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if dst is None:
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dst = (self.rank_in_group + 1) % self.world_size
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torch.distributed.send(tensor, self.ranks[dst], self.device_group)
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def recv(
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self, size: torch.Size, dtype: torch.dtype, src: int | None = None
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) -> torch.Tensor:
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"""Receives a tensor from the source rank."""
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"""NOTE: `src` is the local rank of the source rank."""
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if src is None:
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src = (self.rank_in_group - 1) % self.world_size
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tensor = torch.empty(size, dtype=dtype, device=self.device)
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torch.distributed.recv(tensor, self.ranks[src], self.device_group)
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return tensor
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def destroy(self):
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pass
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def prepare_communication_buffer_for_model(self, model: torch.nn.Module) -> None:
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"""
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Prepare the communication buffer for the model.
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"""
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if not self.is_ep_communicator:
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return
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moe_modules = [
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module
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for module in model.modules()
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# TODO(bnell): Should use isinstance but can't. Maybe search for
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# presence of quant_method.maybe_init_modular_kernel?
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if (
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module.__class__.__name__ == "FusedMoE"
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or module.__class__.__name__ == "SharedFusedMoE"
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)
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]
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for module in moe_modules:
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module.maybe_init_modular_kernel()
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def dispatch(
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self,
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hidden_states: torch.Tensor,
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router_logits: torch.Tensor,
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is_sequence_parallel: bool = False,
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) -> tuple[torch.Tensor, torch.Tensor]:
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"""
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Dispatch the hidden states and router logits to the appropriate device.
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This is a no-op in the base class.
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"""
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return hidden_states, router_logits
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def combine(
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self, hidden_states: torch.Tensor, is_sequence_parallel: bool = False
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) -> torch.Tensor:
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"""
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Combine the hidden states and router logits from the appropriate device.
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This is a no-op in the base class.
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"""
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return hidden_states
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