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Update 05_ddp.md #525

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Update 05_ddp.md
XuHu0529 Mar 1, 2023
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添加batch_size是单机单卡除以2的说明
brandonliu2 Apr 14, 2023
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7 changes: 6 additions & 1 deletion cn/docs/parallelism/05_ddp.md
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Expand Up @@ -35,8 +35,10 @@
download=True,
)

sampler = flow.utils.data.distributed.DistributedSampler(training_data)
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train_dataloader = flow.utils.data.DataLoader(
training_data, BATCH_SIZE, shuffle=True
training_data, BATCH_SIZE, shuffle=(sampler is None), sampler=sampler
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)

model = flowvision.models.mobilenet_v2().to(DEVICE)
Expand All @@ -48,6 +50,7 @@

for t in range(EPOCH_NUM):
print(f"Epoch {t+1}\n-------------------------------")
train_dataloader.sampler.set_epoch(t)
size = len(train_dataloader.dataset)
for batch, (x, y) in enumerate(train_dataloader):
x = x.to_global(placement=PLACEMENT, sbp=S0)
Expand Down Expand Up @@ -88,6 +91,8 @@
y = y.to_global(placement=PLACEMENT, sbp=S0)
```

- 需要注意的是，在进行分布式并行训练时，代码中规定的`BATCH_SIZE`为每一台机器的本地值而非`GLOBAL_BATCH_SIZE`,故上述代码单机双卡`BATCH_SIZE=64`的训练效果与单机单卡`BATCH_SIZE=128`一致。
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Suggested change
- 需要注意的是，在进行分布式并行训练时，代码中规定的`BATCH_SIZE`为每一台机器的本地值而非`GLOBAL_BATCH_SIZE`,故上述代码单机双卡`BATCH_SIZE=64`的训练效果与单机单卡`BATCH_SIZE=128`一致。
- 需要注意的是，在进行分布式并行训练时，代码中规定的 `BATCH_SIZE` 为每一台机器的本地值而非`GLOBAL_BATCH_SIZE`,故上述代码单机双卡 `BATCH_SIZE=64` 的训练效果与单机单卡 `BATCH_SIZE=128` 一致。

中英文之间、中文和数字之间要有空格。

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其实我觉得这句不用加这里，因为它如果懂 global tensor，应该自己懂这个道理。
如果真要解释，是不是把 global tensor 那篇文章多做解释，解释下各种 sbp 下，to global 后的 global tensor 的形状。

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其实我觉得这句不用加这里，因为它如果懂 global tensor，应该自己懂这个道理。
如果真要解释，是不是把 global tensor 那篇文章多做解释，解释下各种 sbp 下，to global 后的 global tensor 的形状。

好的，global tensor的文档中已经有相应的tensor形状变化的解释以及例子。因为客户在微信聊天记录里问了一下这个batch_size=64是local还是global，我想着这里再解释一遍。


这样，按照 [常见的分布式并行策略](./01_introduction.md) 中的介绍，我们就通过对数据进行 `split(0)` 切分，对模型进行广播，进行了分布式数据并行训练。

## 使用 DistributedDataParallel 做数据并行训练
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