# Copyright (c) 2026 LightSeek Foundation # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. """Abstract base class for communication backends.""" from abc import ABC, abstractmethod import torch from tokenspeed.runtime.distributed.mapping import Group class CommBackend(ABC): """Interface that all communication backends must implement. All group parameters are tuples of global ranks, e.g. (0, 1, 2, 3). Process groups are looked up from pg_manager, not created here. """ # ---- Collective ops ---- @abstractmethod def all_reduce( self, tensor: torch.Tensor, group: Group, op=None ) -> torch.Tensor: ... @abstractmethod def all_gather( self, tensor: torch.Tensor, group: Group, dim: int = 0 ) -> torch.Tensor: ... @abstractmethod def all_gather_into_tensor( self, output: torch.Tensor, input: torch.Tensor, group: Group ) -> None: ... @abstractmethod def reduce_scatter(self, tensor: torch.Tensor, group: Group) -> torch.Tensor: ... @abstractmethod def all_to_all_single( self, output: torch.Tensor, input: torch.Tensor, group: Group ) -> None: """Even-split all_to_all. output and input must have same numel divisible by len(group). """ ... # ---- Token-aware ops (uneven token distribution) ---- @abstractmethod def token_all_gather( self, tensor: torch.Tensor, group: Group, scattered_num_tokens: list[int], ) -> torch.Tensor: ... @abstractmethod def token_reduce_scatter( self, tensor: torch.Tensor, group: Group, scattered_num_tokens: list[int], ) -> torch.Tensor: ...