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[doc] Fix a bug about booster api in the documentation. #3898

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Merged
ver217 merged 2 commits into from
Jun 6, 2023
Merged

[doc] Fix a bug about booster api in the documentation. #3898

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9727a0b
fix docs about booster api usage
Jun 5, 2023
7ef29b4
Merge branch 'hpcaitech:main' into fix_booster_doc
Fridge003 Jun 5, 2023
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4 changes: 2 additions & 2 deletions colossalai/booster/booster.py
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Expand Up @@ -25,11 +25,11 @@ class Booster:
Examples:
```python
colossalai.launch(...)
plugin = GeminiPlugin(stage=3, ...)
plugin = GeminiPlugin(...)
booster = Booster(precision='fp16', plugin=plugin)

model = GPT2()
optimizer = Adam(model.parameters())
optimizer = HybridAdam(model.parameters())
dataloader = Dataloader(Dataset)
lr_scheduler = LinearWarmupScheduler()
criterion = GPTLMLoss()
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4 changes: 2 additions & 2 deletions docs/source/en/features/zero_with_chunk.md
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Expand Up @@ -195,7 +195,7 @@ def get_data(batch_size, seq_len, vocab_size):
Finally, we define a model which uses Gemini + ZeRO DDP and define our training loop, As we pre-train GPT in this example, we just use a simple language model loss:

```python
from torch.optim import Adam
from colossalai.nn.optimizer import HybridAdam

from colossalai.booster import Booster
from colossalai.zero import ColoInitContext
Expand All @@ -211,7 +211,7 @@ def main():

# build criterion
criterion = GPTLMLoss()
optimizer = Adam(model.parameters(), lr=0.001)
optimizer = HybridAdam(model.parameters(), lr=0.001)

torch.manual_seed(123)
default_pg = ProcessGroup(tp_degree=args.tp_degree)
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4 changes: 2 additions & 2 deletions docs/source/zh-Hans/features/zero_with_chunk.md
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Expand Up @@ -197,7 +197,7 @@ def get_data(batch_size, seq_len, vocab_size):
最后，使用booster注入 Gemini + ZeRO DDP 特性, 并定义训练循环。由于我们在这个例子中对GPT进行预训练，因此只使用了一个简单的语言模型损失函数：

```python
from torch.optim import Adam
from colossalai.nn.optimizer import HybridAdam

from colossalai.booster import Booster
from colossalai.zero import ColoInitContext
Expand All @@ -213,7 +213,7 @@ def main():

# build criterion
criterion = GPTLMLoss()
optimizer = Adam(model.parameters(), lr=0.001)
optimizer = HybridAdam(model.parameters(), lr=0.001)

torch.manual_seed(123)
default_pg = ProcessGroup(tp_degree=args.tp_degree)
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