From 886f690e76cdf647bb38851abca7b59add27dd95 Mon Sep 17 00:00:00 2001
From: HMJ0628 <2383422508@qq.com>
Date: Tue, 17 Dec 2024 01:22:35 +0800
Subject: [PATCH] Translating "translate perf_infer_gpu_multi.md" to Chinese
 (#35271)

add "translate perf_infer_gpu_multi"
---
 docs/source/zh/_toctree.yml            |  2 +
 docs/source/zh/perf_infer_gpu_multi.md | 68 ++++++++++++++++++++++++++
 2 files changed, 70 insertions(+)
 create mode 100644 docs/source/zh/perf_infer_gpu_multi.md

diff --git a/docs/source/zh/_toctree.yml b/docs/source/zh/_toctree.yml
index 572f4b8572..2cce86b659 100644
--- a/docs/source/zh/_toctree.yml
+++ b/docs/source/zh/_toctree.yml
@@ -69,6 +69,8 @@
       title: 完全分片数据并行
     - local: perf_train_special
       title: 在 Apple silicon 芯片上进行 PyTorch 训练
+    - local: perf_infer_gpu_multi
+      title: 多GPU推理
     - local: perf_train_cpu
       title: 在CPU上进行高效训练
     - local: perf_hardware
diff --git a/docs/source/zh/perf_infer_gpu_multi.md b/docs/source/zh/perf_infer_gpu_multi.md
new file mode 100644
index 0000000000..ee523bc604
--- /dev/null
+++ b/docs/source/zh/perf_infer_gpu_multi.md
@@ -0,0 +1,68 @@
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# 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.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="huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/Meta-Llama-3-8B-Instruct, seqlen = 512, python, w_ compile.png">
+</div>