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55 lines
2.0 KiB
55 lines
2.0 KiB
# Low Level ZeRO
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>Low Level ZeRO == ZeRO-DP stage 1 and 2, we would denote it as ZeRO.
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## Design:
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### Notion
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`p32` denotes the param copy in the optimizer
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`p` denotes the model param
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`g` denotes the gradient
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### INIT
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In low level zero(1, 2), `p32` is split. Different from the previous implement, we split each `p32` evenly by world_size. Thus, rank0 got a param list as `[p00, p10]`, rank1 got a param list as `[p-01, p-11]`, etc.
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<img width="840" alt="image" src="https://github.com/hpcaitech/ColossalAI/assets/74758262/f7758d7d-c5e5-44a4-a121-3aba8b05c904">
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For the detailed implementation, we first pad `p` for it can be split by world_size if needed. Then, we would view it to the shape `[world_size, -1]`, and each rank got its own part `p32` by cloning.
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### BWD
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To leverage the communication, a gradient would be added to a bucket first. When the bucket is full, each `g` in it would be reshaped as `[world_size, -1]`. And the `[local_rank]` parts would be united.
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The data structure looks like this:
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```
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{
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0: [g-00, g-10],
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1: [g-01, g-11],
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2: [g-02, g-12]
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}
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```
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After that, the gradients would be flattened by rank, and the data structure looks like this:
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```
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# g-0 means flatten([g-00, g-10])
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{
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0: [g-0],
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1: [g-1],
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2: [g-2]
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}
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```
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For zero1, we iterate the dictionary and do `all_reduce`. For zero2, we can just do `reduce-scatter`.
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### Optim
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For each rank gets its own `p32` and the counterpart `g`, it is quite easy to do `optim.step()`.
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However, we have to consider a situation of layer drop, for instance:
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```
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class MlpModel(nn.Module):
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def __init__(self):
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super(MlpModel, self).__init__()
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self.linear1 = nn.Linear(128, 256)
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self.drop_linear = nn.Linear(256, 256)
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self.linear2 = nn.Linear(256, 512)
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def forward(self, x):
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x = self.linear1(x)
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x = self.linear2(x)
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return x
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```
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And the solution is to build a mapping of `p32`, `p`, and `g`. Before `optim.step()`, we collect `p` which `requires_grad=True` and `p.grad != None` as a real working param. And select the counterpart `p32` and `g`.
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