70 lines
2.7 KiB
Markdown
70 lines
2.7 KiB
Markdown
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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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http://www.apache.org/licenses/LICENSE-2.0
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-->
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# 多GPU推理
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某些模型现已支持内置的**张量并行**（Tensor Parallelism, TP），并通过 PyTorch 实现。张量并行技术将模型切分到多个 GPU 上，从而支持更大的模型尺寸，并对诸如矩阵乘法等计算任务进行并行化。
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要启用张量并行，只需在调用 [`~AutoModelForCausalLM.from_pretrained`] 时传递参数 `tp_plan="auto"`：
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```python
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import os
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
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# 初始化分布式环境
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rank = int(os.environ["RANK"])
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device = torch.device(f"cuda:{rank}")
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torch.cuda.set_device(device)
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torch.distributed.init_process_group("nccl", device_id=device)
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# 获取支持张量并行的模型
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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tp_plan="auto",
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)
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# 准备输入tokens
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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prompt = "Can I help"
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inputs = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
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# 分布式运行
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outputs = model(inputs)
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```
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您可以使用 `torchrun` 命令启动上述脚本，多进程模式会自动将每个进程映射到一张 GPU：
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```
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torchrun --nproc-per-node 4 demo.py
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```
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目前，PyTorch 张量并行支持以下模型：
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* [Llama](https://huggingface.co/docs/transformers/model_doc/llama#transformers.LlamaModel)
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如果您希望对其他模型添加张量并行支持，可以通过提交 GitHub Issue 或 Pull Request 来提出请求。
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### 预期性能提升
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对于推理场景（尤其是处理大批量或长序列的输入），张量并行可以显著提升计算速度。
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以下是 [Llama](https://huggingface.co/docs/transformers/model_doc/llama#transformers.LlamaModel) 模型在序列长度为 512 且不同批量大小情况下的单次前向推理的预期加速效果：
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<div style="text-align: center">
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/Meta-Llama-3-8B-Instruct%2C%20seqlen%20%3D%20512%2C%20python%2C%20w_%20compile.png">
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</div>
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