venv/lib/python3.10/site-packages/deepspeed/moe/experts.py

# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0

# DeepSpeed Team

import copy
from typing import List, Optional

import torch
from torch import nn


class Experts(nn.Module):

    def __init__(self, expert: nn.Module, num_local_experts: int = 1, expert_group_name: Optional[str] = None) -> None:
        super(Experts, self).__init__()

        self.deepspeed_experts = nn.ModuleList([copy.deepcopy(expert) for _ in range(num_local_experts)])
        self.num_local_experts = num_local_experts

        # TODO: revisit allreduce for moe.gate...
        for expert in self.deepspeed_experts:
            # TODO: Create param groups to handle expert + data case (e.g. param.group = moe_group)
            for param in expert.parameters():
                param.allreduce = False
                param.group_name = expert_group_name

    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
        chunks = inputs.chunk(self.num_local_experts, dim=1)
        expert_outputs: List[torch.Tensor] = []

        for chunk, expert in zip(chunks, self.deepspeed_experts):
            out = expert(chunk)
            if isinstance(out, tuple):
                out = out[0]  # Ignore the bias term for now
            expert_outputs += [out]

        return torch.cat(expert_outputs, dim=1)