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HuggingFace Accelerate 教程：4行代码实现PyTorch分布式训练与混合精度优化

huggingface-accelerate by orchestra-research/ai-research-skills

66 周安装量

5,500 GitHub Stars

GitHub

安装命令

npx skills add https://github.com/orchestra-research/ai-research-skills --skill huggingface-accelerate

AI/机器学习 PyTorch 分布式系统

🇨🇳中文介绍

HuggingFace Accelerate - 统一分布式训练

快速开始

Accelerate 将分布式训练简化到只需 4 行代码。

安装：

pip install accelerate

转换 PyTorch 脚本（4 行）：

import torch
+ from accelerate import Accelerator

+ accelerator = Accelerator()

  model = torch.nn.Transformer()
  optimizer = torch.optim.Adam(model.parameters())
  dataloader = torch.utils.data.DataLoader(dataset)

+ model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)

  for batch in dataloader:
      optimizer.zero_grad()
      loss = model(batch)
-     loss.backward()
+     accelerator.backward(loss)
      optimizer.step()

运行（单一命令）：

accelerate launch train.py

常见工作流

工作流 1：从单 GPU 到多 GPU

原始脚本：

# train.py
import torch

model = torch.nn.Linear(10, 2).to('cuda')
optimizer = torch.optim.Adam(model.parameters())
dataloader = torch.utils.data.DataLoader(dataset, batch_size=32)

for epoch in range(10):
    for batch in dataloader:
        batch = batch.to('cuda')
        optimizer.zero_grad()
        loss = model(batch).mean()
        loss.backward()
        optimizer.step()

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工作流 2：混合精度训练

启用 FP16/BF16：

from accelerate import Accelerator

# FP16（带梯度缩放）
accelerator = Accelerator(mixed_precision='fp16')

# BF16（无缩放，更稳定）
accelerator = Accelerator(mixed_precision='bf16')

# FP8（H100+）
accelerator = Accelerator(mixed_precision='fp8')

model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)

# 其他一切都是自动的！
for batch in dataloader:
    with accelerator.autocast():  # 可选，自动完成
        loss = model(batch)
    accelerator.backward(loss)

工作流 3：DeepSpeed ZeRO 集成

启用 DeepSpeed ZeRO-2：

from accelerate import Accelerator

accelerator = Accelerator(
    mixed_precision='bf16',
    deepspeed_plugin={
        "zero_stage": 2,  # ZeRO-2
        "offload_optimizer": False,
        "gradient_accumulation_steps": 4
    }
)

# 代码与之前相同！
model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)

或通过配置：

accelerate config
# 选择：DeepSpeed → ZeRO-2

deepspeed_config.json：

{
    "fp16": {"enabled": false},
    "bf16": {"enabled": true},
    "zero_optimization": {
        "stage": 2,
        "offload_optimizer": {"device": "cpu"},
        "allgather_bucket_size": 5e8,
        "reduce_bucket_size": 5e8
    }
}

accelerate launch --config_file deepspeed_config.json train.py

工作流 4：FSDP（全分片数据并行）

from accelerate import Accelerator, FullyShardedDataParallelPlugin

fsdp_plugin = FullyShardedDataParallelPlugin(
    sharding_strategy="FULL_SHARD",  # 相当于 ZeRO-3
    auto_wrap_policy="TRANSFORMER_AUTO_WRAP",
    cpu_offload=False
)

accelerator = Accelerator(
    mixed_precision='bf16',
    fsdp_plugin=fsdp_plugin
)

model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)

或通过配置：

accelerate config
# 选择：FSDP → 全分片 → 无 CPU 卸载

工作流 5：梯度累积

from accelerate import Accelerator

accelerator = Accelerator(gradient_accumulation_steps=4)

model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)

for batch in dataloader:
    with accelerator.accumulate(model):  # 处理累积
        optimizer.zero_grad()
        loss = model(batch)
        accelerator.backward(loss)
        optimizer.step()

有效批次大小：batch_size * num_gpus * gradient_accumulation_steps

何时使用与替代方案对比

在以下情况使用 Accelerate：

希望最简单的分布式训练
需要适用于任何硬件的单一脚本
使用 HuggingFace 生态系统
需要灵活性（DDP/DeepSpeed/FSDP/Megatron）
需要快速原型设计

4 行代码：最小的代码改动
统一 API：DDP、DeepSpeed、FSDP、Megatron 使用相同代码
自动化：设备放置、混合精度、分片
交互式配置：无需手动设置启动器
单一启动：随处可用

在以下情况使用替代方案：

PyTorch Lightning：需要回调函数、高级抽象
Ray Train：多节点编排、超参数调优
DeepSpeed：直接 API 控制、高级功能
原始 DDP：最大控制、最小抽象

问题：设备放置错误

不要手动移动到设备：

# 错误
batch = batch.to('cuda')

# 正确
# Accelerate 在 prepare() 后自动处理

问题：梯度累积不工作

使用上下文管理器：

# 正确
with accelerator.accumulate(model):
    optimizer.zero_grad()
    accelerator.backward(loss)
    optimizer.step()

问题：分布式环境下的检查点保存

使用 accelerator 方法：

# 仅在主进程上保存
if accelerator.is_main_process:
    accelerator.save_state('checkpoint/')

# 在所有进程上加载
accelerator.load_state('checkpoint/')

问题：使用 FSDP 时结果不同

确保相同的随机种子：

from accelerate.utils import set_seed
set_seed(42)

Megatron 集成：有关张量并行、流水线并行和序列并行设置的详细信息，请参阅 references/megatron-integration.md。

自定义插件：有关创建自定义分布式插件和高级配置的详细信息，请参阅 references/custom-plugins.md。

性能调优：有关性能分析、内存优化和最佳实践的详细信息，请参阅 references/performance.md。

CPU：可用（慢）
单 GPU：可用
多 GPU：DDP（默认）、DeepSpeed 或 FSDP
多节点：DDP、DeepSpeed、FSDP、Megatron
TPU：支持
Apple MPS：支持

启动器要求：

DDP：torch.distributed.run（内置）
DeepSpeed：deepspeed（pip install deepspeed）
FSDP：PyTorch 1.12+（内置）
Megatron：自定义设置

文档：https://huggingface.co/docs/accelerate
GitHub：https://github.com/huggingface/accelerate
版本：1.11.0+
教程："加速你的脚本"
示例：https://github.com/huggingface/accelerate/tree/main/examples
使用者：HuggingFace Transformers、TRL、PEFT 及所有 HF 库

🇺🇸English

HuggingFace Accelerate - Unified Distributed Training

Quick start

Accelerate simplifies distributed training to 4 lines of code.

Installation :

pip install accelerate

Convert PyTorch script (4 lines):

import torch
+ from accelerate import Accelerator

+ accelerator = Accelerator()

  model = torch.nn.Transformer()
  optimizer = torch.optim.Adam(model.parameters())
  dataloader = torch.utils.data.DataLoader(dataset)

+ model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)

  for batch in dataloader:
      optimizer.zero_grad()
      loss = model(batch)
-     loss.backward()
+     accelerator.backward(loss)
      optimizer.step()

Run (single command):

accelerate launch train.py

Common workflows

Workflow 1: From single GPU to multi-GPU

Original script :

# train.py
import torch

model = torch.nn.Linear(10, 2).to('cuda')
optimizer = torch.optim.Adam(model.parameters())
dataloader = torch.utils.data.DataLoader(dataset, batch_size=32)

for epoch in range(10):
    for batch in dataloader:
        batch = batch.to('cuda')
        optimizer.zero_grad()
        loss = model(batch).mean()
        loss.backward()
        optimizer.step()

With Accelerate (4 lines added):

# train.py
import torch
from accelerate import Accelerator  # +1

accelerator = Accelerator()  # +2

model = torch.nn.Linear(10, 2)
optimizer = torch.optim.Adam(model.parameters())
dataloader = torch.utils.data.DataLoader(dataset, batch_size=32)

model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)  # +3

for epoch in range(10):
    for batch in dataloader:
        # No .to('cuda') needed - automatic!
        optimizer.zero_grad()
        loss = model(batch).mean()
        accelerator.backward(loss)  # +4
        optimizer.step()

Configure (interactive):

accelerate config

Questions :

Which machine? (single/multi GPU/TPU/CPU)
How many machines? (1)
Mixed precision? (no/fp16/bf16/fp8)
DeepSpeed? (no/yes)

Launch (works on any setup):

# Single GPU
accelerate launch train.py

# Multi-GPU (8 GPUs)
accelerate launch --multi_gpu --num_processes 8 train.py

# Multi-node
accelerate launch --multi_gpu --num_processes 16 \
  --num_machines 2 --machine_rank 0 \
  --main_process_ip $MASTER_ADDR \
  train.py

Workflow 2: Mixed precision training

Enable FP16/BF16 :

from accelerate import Accelerator

# FP16 (with gradient scaling)
accelerator = Accelerator(mixed_precision='fp16')

# BF16 (no scaling, more stable)
accelerator = Accelerator(mixed_precision='bf16')

# FP8 (H100+)
accelerator = Accelerator(mixed_precision='fp8')

model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)

# Everything else is automatic!
for batch in dataloader:
    with accelerator.autocast():  # Optional, done automatically
        loss = model(batch)
    accelerator.backward(loss)

Workflow 3: DeepSpeed ZeRO integration

Enable DeepSpeed ZeRO-2 :

from accelerate import Accelerator

accelerator = Accelerator(
    mixed_precision='bf16',
    deepspeed_plugin={
        "zero_stage": 2,  # ZeRO-2
        "offload_optimizer": False,
        "gradient_accumulation_steps": 4
    }
)

# Same code as before!
model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)

Or via config :

accelerate config
# Select: DeepSpeed → ZeRO-2

deepspeed_config.json :

{
    "fp16": {"enabled": false},
    "bf16": {"enabled": true},
    "zero_optimization": {
        "stage": 2,
        "offload_optimizer": {"device": "cpu"},
        "allgather_bucket_size": 5e8,
        "reduce_bucket_size": 5e8
    }
}

Launch :

accelerate launch --config_file deepspeed_config.json train.py

Workflow 4: FSDP (Fully Sharded Data Parallel)

Enable FSDP :

from accelerate import Accelerator, FullyShardedDataParallelPlugin

fsdp_plugin = FullyShardedDataParallelPlugin(
    sharding_strategy="FULL_SHARD",  # ZeRO-3 equivalent
    auto_wrap_policy="TRANSFORMER_AUTO_WRAP",
    cpu_offload=False
)

accelerator = Accelerator(
    mixed_precision='bf16',
    fsdp_plugin=fsdp_plugin
)

model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)

Or via config :

accelerate config
# Select: FSDP → Full Shard → No CPU Offload

Workflow 5: Gradient accumulation

Accumulate gradients :

from accelerate import Accelerator

accelerator = Accelerator(gradient_accumulation_steps=4)

model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)

for batch in dataloader:
    with accelerator.accumulate(model):  # Handles accumulation
        optimizer.zero_grad()
        loss = model(batch)
        accelerator.backward(loss)
        optimizer.step()

Effective batch size : batch_size * num_gpus * gradient_accumulation_steps

When to use vs alternatives

Use Accelerate when :

Want simplest distributed training
Need single script for any hardware
Use HuggingFace ecosystem
Want flexibility (DDP/DeepSpeed/FSDP/Megatron)
Need quick prototyping

Key advantages :

4 lines : Minimal code changes
Unified API : Same code for DDP, DeepSpeed, FSDP, Megatron
Automatic : Device placement, mixed precision, sharding
Interactive config : No manual launcher setup
Single launch : Works everywhere

Use alternatives instead :

PyTorch Lightning : Need callbacks, high-level abstractions
Ray Train : Multi-node orchestration, hyperparameter tuning
DeepSpeed : Direct API control, advanced features
Raw DDP : Maximum control, minimal abstraction

Common issues

Issue: Wrong device placement

Don't manually move to device:

# WRONG
batch = batch.to('cuda')

# CORRECT
# Accelerate handles it automatically after prepare()

Issue: Gradient accumulation not working

Use context manager:

# CORRECT
with accelerator.accumulate(model):
    optimizer.zero_grad()
    accelerator.backward(loss)
    optimizer.step()

Issue: Checkpointing in distributed

Use accelerator methods:

# Save only on main process
if accelerator.is_main_process:
    accelerator.save_state('checkpoint/')

# Load on all processes
accelerator.load_state('checkpoint/')

Issue: Different results with FSDP

Ensure same random seed:

from accelerate.utils import set_seed
set_seed(42)

Advanced topics

Megatron integration : See references/megatron-integration.md for tensor parallelism, pipeline parallelism, and sequence parallelism setup.

Custom plugins : See references/custom-plugins.md for creating custom distributed plugins and advanced configuration.

Performance tuning : See references/performance.md for profiling, memory optimization, and best practices.

Hardware requirements

CPU : Works (slow)
Single GPU : Works
Multi-GPU : DDP (default), DeepSpeed, or FSDP
Multi-node : DDP, DeepSpeed, FSDP, Megatron
TPU : Supported
Apple MPS : Supported

Launcher requirements :

DDP : torch.distributed.run (built-in)
DeepSpeed : deepspeed (pip install deepspeed)
FSDP : PyTorch 1.12+ (built-in)
Megatron : Custom setup

Resources

Docs: https://huggingface.co/docs/accelerate
GitHub: https://github.com/huggingface/accelerate
Version: 1.11.0+
Tutorial: "Accelerate your scripts"
Examples: https://github.com/huggingface/accelerate/tree/main/examples
Used by: HuggingFace Transformers, TRL, PEFT, all HF libraries

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超能力技能使用指南：AI助手技能调用优先级与工作流程详解

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