mirror of https://github.com/hpcaitech/ColossalAI
140 lines
5.5 KiB
Markdown
140 lines
5.5 KiB
Markdown
# 分布式优化器
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Author: Wenxuan Tan, Junwen Duan, Renjie Mao
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**相关论文**
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- [Adafactor: Adaptive Learning Rates with Sublinear Memory Cost](https://arxiv.org/abs/1804.04235)
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- [CAME: Confidence-guided Adaptive Memory Efficient Optimization](https://arxiv.org/abs/2307.02047)
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- [GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection](https://arxiv.org/abs/2403.03507)
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- [Large Batch Optimization for Deep Learning: Training BERT in 76 minutes](https://arxiv.org/pdf/1904.00962)
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## 介绍
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除了广泛采用的Adam和SGD外，许多现代优化器需要逐层统计信息以有效更新参数，因此无法直接应用于模型层在多个设备上分片的并行设置。我们以提供了优化的分布式实现，，并且通过plugin与Tensor Parallel、DDP和ZeRO无缝集成。
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## 优化器
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Adafactor 是一种首次采用非负矩阵分解（NMF）的 Adam 变体，用于减少内存占用。CAME 通过引入一个置信度矩阵来改进 NMF 的效果。GaLore 通过将梯度投影到低秩空间，并使用 8 位块状量化进一步减少内存占用。Lamb 允许使用巨大的批量大小而不失准确性，通过按其 Lipschitz 常数的倒数界定的逐层自适应更新实现
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## 使用
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现在我们展示如何使用分布式 Adafactor 与 booster API 结合 Tensor Parallel 和 ZeRO 2。即使您不使用distributed optimizer，plugin 也会自动将optimizer转换为分布式版本以方便使用。
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### step 1. 导包
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```python
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from transformers import LlamaModel, LlamaConfig
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from colossalai.nn.optimizer.distributed_adafactor import DistributedAdaFactor
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from colossalai.booster import Booster
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from colossalai.booster.plugin import HybridParallelPlugin
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import colossalai
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import torch
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```
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### step 2. 初始化分布式
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我们需要先初始化分布式环境. 为了展示, 我们使用 `colossal run --nproc_per_node 4`. 更多初始化方式请参考 [Launch Colossal-AI](../basics/launch_colossalai.md)
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```python
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colossalai.launch_from_torch()
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```
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### step 3. 初始化模型和优化器
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```python
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configuration = LlamaConfig()
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model = LlamaModel(configuration).cuda()
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criterion = lambda x: x.mean()
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dist_optim = DistributedAdaFactor(model.parameters())
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```
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### step 4.初始化booster和plugin
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```python
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plugin = HybridParallelPlugin(tp_size=2, zero_stage=2, pp_size=1, enable_all_optimization=True)
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booster = Booster(plugin=plugin)
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# You should also pass in your own dataset.
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model, dist_optim, criterion, dataloader, _ = booster.boost(model, dist_optim, criterion)
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```
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### step 5.训练
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```python
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steps = 10
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for step in range(steps):
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input_ids = torch.ones(1, 100, device="cuda", dtype=torch.int)
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attention_mask = input_ids.clone()
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outputs = model(input_ids.cuda(), attention_mask.cuda())
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loss = criterion(outputs.last_hidden_state)
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booster.backward(loss, dist_optim)
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dist_optim.step()
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dist_optim.zero_grad()
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```
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### GaLore的特殊初期
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对于 GaLore，我们需要为每个参数组指定投影rank，以及量化和分页优化器参数。有关量化的详细信息，请参考 bitandbytes.
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```python
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from colossalai.nn.optimizer.galore import get_galore_param_groups
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from colossalai.nn.optimizer import DistGaloreAwamW
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optim = DistGaloreAwamW(
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get_galore_param_groups(model, decay=1e-2, rank=8),
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lr=lr,
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betas=(beta1, beta2),
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eps=eps,
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nbits=8,
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percentile_clipping=100,
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block_wise=True,
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min_8bit_size=4096,
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)
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```
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## 兼容性
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<table>
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<tr>
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<th nowrap="nowrap">Optimizer/Plugin</th>
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<th nowrap="nowrap" align="center">Hybrid Parallel Plugin</th>
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<th nowrap="nowrap" align="center">Low Level Zero Plugin</th>
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<th nowrap="nowrap" align="center">Torch DDP Plugin</th>
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<th nowrap="nowrap" align="center">Gemini Plugin</th>
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<th nowrap="nowrap" align="center">Moe Hybrid Plugin</th>
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</tr>
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<tr>
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<td nowrap="nowrap" align="center" title="Lamb">Lamb</td>
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<td nowrap="nowrap" align="center">✔️</td>
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<td nowrap="nowrap" align="center">✔️</td>
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<td nowrap="nowrap" align="center">✔️</td>
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<td nowrap="nowrap" align="center">❌</td>
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<td nowrap="nowrap" align="center">❌</td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="center" title="GaLore">GaLore</td>
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<td nowrap="nowrap" align="center">✔️</td>
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<td nowrap="nowrap" align="center">✔️</td>
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<td nowrap="nowrap" align="center">✔️</td>
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<td nowrap="nowrap" align="center">❌</td>
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<td nowrap="nowrap" align="center">❌</td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="center" title="Adafactor">Adafactor</td>
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<td nowrap="nowrap" align="center">✔️</td>
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<td nowrap="nowrap" align="center">✔️</td>
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<td nowrap="nowrap" align="center">✔️</td>
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<td nowrap="nowrap" align="center">❌</td>
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<td nowrap="nowrap" align="center">❌</td>
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</tr>
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<tr>
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<td nowrap="nowrap" align="center" title="CAME">CAME</td>
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<td nowrap="nowrap" align="center">✔️</td>
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<td nowrap="nowrap" align="center">✔️</td>
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<td nowrap="nowrap" align="center">✔️</td>
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<td nowrap="nowrap" align="center">❌</td>
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<td nowrap="nowrap" align="center">❌</td>
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</tr>
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<tr>
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<td colspan="39"></td>
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</tr>
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</table>
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<!-- doc-test-command: colossalai run --nproc_per_node 4 distributed_optimizers.py -->
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## API 参考
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{{ autodoc:colossalai.nn.optimizer.distributed_adafactor.DistributedAdaFactor }}
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{{ autodoc:colossalai.nn.optimizer.distributed_lamb.DistributedLamb }}
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{{ autodoc:colossalai.nn.optimizer.distributed_galore.DistGaloreAwamW }}
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{{ autodoc:colossalai.nn.optimizer.distributed_came.DistributedCAME }}
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