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# 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)
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