258 lines
9.2 KiB
Python
258 lines
9.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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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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from vllm.logger import init_logger
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from .base_device_communicator import DeviceCommunicatorBase
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logger = init_logger(__name__)
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class XpuCommunicator(DeviceCommunicatorBase):
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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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super().__init__(cpu_group, device, device_group, unique_name)
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if self.use_all2all:
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if self.all2all_backend == "naive":
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from .all2all import NaiveAll2AllManager
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self.all2all_manager = NaiveAll2AllManager(self.cpu_group)
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logger.info("Using naive all2all manager.")
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elif self.all2all_backend == "allgather_reducescatter":
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from .all2all import AgRsAll2AllManager
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self.all2all_manager = AgRsAll2AllManager(self.cpu_group)
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logger.info("Using AgRs manager on XPU device.")
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else: # type: ignore[has-type]
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logger.warning(
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"`%s` all2all manager is not supported on XPU. "
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"Falling back to AgRs manager for XPU, "
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"which is the Default backend",
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self.all2all_backend, # type: ignore[has-type]
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)
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from .all2all import AgRsAll2AllManager
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self.all2all_manager = AgRsAll2AllManager(self.cpu_group)
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logger.info("Using AgRs manager on XPU device.")
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def all_reduce(self, input_) -> 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 reduce_scatter(self, input_: torch.Tensor, dim: int = -1):
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world_size = self.world_size
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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 = torch.empty(
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output_shape, dtype=input_tensor.dtype, device=input_tensor.device
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)
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dist.reduce_scatter_tensor(output, input_tensor)
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# Reshape before returning
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return output.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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):
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world_size = self.world_size
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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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if sizes is not None:
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assert len(sizes) == world_size
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assert input_tensor.shape[0] == sum(sizes)
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chunk_size = sizes[self.rank_in_group]
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else:
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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 = torch.empty(
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output_shape, dtype=input_tensor.dtype, device=input_tensor.device
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)
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if sizes is not None and sizes.count(sizes[0]) != len(sizes):
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# if inputs shape in different ranks is not the same using reduce_scatter
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input_splits = list(input_tensor.split(sizes, dim=0))
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dist.reduce_scatter(output, input_splits)
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else:
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dist.reduce_scatter_tensor(output, input_tensor)
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# Reshape before returning
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return output.movedim(0, dim).contiguous()
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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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):
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if dim != 0:
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raise NotImplementedError("only dim 0 all-gatherv is supported")
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world_size = self.world_size
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# 'sizes' is not needed if all inputs in the same group have the same
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# shape
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if sizes is not None and all(s == sizes[0] for s in sizes):
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sizes = None
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def _all_gather_single(input_: torch.Tensor, sizes: list[int] | None = None):
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input_size = input_.size()
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if sizes is not None:
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assert len(sizes) == world_size
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assert input_.shape[dim] == sizes[self.rank_in_group], (
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f"{input_.shape[dim]} != {sizes[self.rank_in_group]}"
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)
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output_size = (sum(sizes),) + input_size[1:]
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else:
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output_size = (input_size[0] * 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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if sizes is not None:
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all_gather_list = []
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for size in sizes:
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all_gather_list.append(
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torch.empty(
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(size,) + input_.shape[1:],
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dtype=input_.dtype,
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device=input_.device,
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)
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)
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dist.all_gather(all_gather_list, input_)
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output_tensor = torch.cat(all_gather_list, dim=0)
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else:
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dist.all_gather([output_tensor], input_)
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return output_tensor
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if isinstance(input_, torch.Tensor):
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return _all_gather_single(input_, sizes)
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output_list = []
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for inp in input_:
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output_list.append(_all_gather_single(inp, sizes=sizes))
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return output_list
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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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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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# For xpu path, gather doesn't work properly together with ray
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# cluster so we use all_gather instead for now.
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input_size = input_.size()
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# Allocate output tensor.
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output_tensor = torch.empty(
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(self.world_size,) + input_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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if self.rank_in_group == dst:
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# Reshape
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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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else:
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output_tensor = None
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return output_tensor
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def broadcast(self, input_: torch.Tensor, src: int = 0) -> None:
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dist.broadcast(input_, src=src, group=self.device_group)
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def dispatch_router_logits(
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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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extra_tensors: list[torch.Tensor] | None = None,
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) -> (
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tuple[torch.Tensor, torch.Tensor]
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| tuple[torch.Tensor, torch.Tensor, list[torch.Tensor]]
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):
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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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assert self.all2all_manager is not None
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return self.all2all_manager.dispatch_router_logits(
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hidden_states,
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router_logits,
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is_sequence_parallel,
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extra_tensors,
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)
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def dispatch(
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self,
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hidden_states: torch.Tensor,
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topk_weights: torch.Tensor,
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topk_ids: torch.Tensor,
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is_sequence_parallel: bool = False,
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extra_tensors: list[torch.Tensor] | None = None,
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) -> (
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tuple[torch.Tensor, torch.Tensor, torch.Tensor]
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| tuple[torch.Tensor, torch.Tensor, torch.Tensor, list[torch.Tensor]]
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):
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"""
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Dispatch the hidden states and topk weights/ids 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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assert self.all2all_manager is not None
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return self.all2all_manager.dispatch(
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hidden_states,
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topk_weights,
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topk_ids,
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is_sequence_parallel,
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extra_tensors=extra_tensors,
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)
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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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assert self.all2all_manager is not None
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return self.all2all_manager.combine(
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hidden_states,
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is_sequence_parallel,
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)
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