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transformers/docs/source/zh/perf_infer_gpu_multi.md

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+
+# 多GPU推理
+
+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。
+
+要启用张量并行，只需在调用 [`~AutoModelForCausalLM.from_pretrained`] 时传递参数 `tp_plan="auto"`：
+
+```python
+import os
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
+
+# 初始化分布式环境
+rank = int(os.environ["RANK"])
+device = torch.device(f"cuda:{rank}")
+torch.cuda.set_device(device)
+torch.distributed.init_process_group("nccl", device_id=device)
+
+# 获取支持张量并行的模型
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    tp_plan="auto",
+)
+
+# 准备输入tokens
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+prompt = "Can I help"
+inputs = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
+
+# 分布式运行
+outputs = model(inputs)
+```
+
+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：
+
+```
+torchrun --nproc-per-node 4 demo.py
+```
+
+目前，PyTorch 张量并行支持以下模型：
+* [Llama](https://huggingface.co/docs/transformers/model_doc/llama#transformers.LlamaModel)
+
+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。
+
+### 预期性能提升
+
+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。
+
+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：
+
+<div style="text-align: center">
+<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">
+</div>