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47 lines
2.3 KiB
Markdown
47 lines
2.3 KiB
Markdown
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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rendered properly in your Markdown viewer.
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# Expert parallelism
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[Expert parallelism](https://huggingface.co/spaces/nanotron/ultrascale-playbook?section=expert_parallelism) is a parallelism strategy for [mixture-of-experts (MoE) models](https://huggingface.co/blog/moe). Each expert's feedforward layer lives on a different hardware accelerator. A router dispatches tokens to the appropriate experts and gathers the results. This approach scales models to far larger parameter counts without increasing computation cost because each token activates only a few experts.
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## DistributedConfig
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Enable expert parallelism with the [`DistributedConfig`] class and the `enable_expert_parallel` argument.
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```py
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from transformers.distributed.configuration_utils import DistributedConfig
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distributed_config = DistributedConfig(enable_expert_parallel=True)
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model = AutoModelForCausalLM.from_pretrained(
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"openai/gpt-oss-120b",
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distributed_config=distributed_config,
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)
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```
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> [!TIP]
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> Expert parallelism automatically enables [tensor parallelism](./perf_infer_gpu_multi) for attention layers.
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This argument switches to the `ep_plan` (expert parallel plan) defined in each MoE model's config file. The [`GroupedGemmParallel`] class splits expert weights so each device loads only its local experts. The `ep_router` routes tokens to experts and an all-reduce operation combines their outputs.
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Launch your inference script with [torchrun](https://pytorch.org/docs/stable/elastic/run.html) and specify how many devices to use. The number of devices must evenly divide the total number of experts.
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```zsh
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torchrun --nproc-per-node 8 your_script.py
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```
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