mirror of https://github.com/InternLM/InternLM
54 lines
1.8 KiB
Python
54 lines
1.8 KiB
Python
"""
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The file has been adapted from the following files:
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https://github.com/microsoft/DeepSpeed/blob/master/deepspeed/moe/experts.py
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Git commit hash: f3943cf9109226ed3ecf2d5dbb639a11cd925555
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We retain the following license from the original files:
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"""
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# Copyright (c) Microsoft Corporation.
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# SPDX-License-Identifier: Apache-2.0
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# DeepSpeed Team
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from typing import Union, cast
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import torch
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from torch.nn import Module, ModuleList
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class Experts(torch.nn.Module):
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"""
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Local Experts.
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"""
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def __init__(self, experts: Union[Module, ModuleList], num_local_experts=1, expert_group_name=None):
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super().__init__()
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# TODO: We can not deepcopy FeedForward since it contains a process_group in submodules
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# self.experts = torch.nn.ModuleList([copy.deepcopy(expert) for i in range(num_local_experts)])
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if isinstance(experts, ModuleList):
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self.experts = cast(ModuleList, experts)
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else:
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self.experts = ModuleList([experts])
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self.num_local_experts = num_local_experts
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# TODO: revisit allreduce for moe.gate...
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for expert in self.experts:
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# TODO: Create param groups to handle expert + data case (e.g. param.group = moe_group)
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for _, param in expert.named_parameters():
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param.belong_expert = True
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param.group_name = expert_group_name
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def forward(self, inputs):
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chunks = inputs.chunk(self.num_local_experts, dim=1)
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expert_outputs = []
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for chunk, expert in zip(chunks, self.experts):
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out = expert(chunk)
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if isinstance(out, tuple):
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out = out[0] # Ignore the bias term for now
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expert_outputs += [out]
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expert_output = torch.cat(expert_outputs, dim=1)
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return expert_output
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