[prototype] Expert Parallel #714
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The expert-choice MoE implementation is mostly from torchtune: pytorch/torchtune#1902 The PR requires (ad hoc) changes to pytorch: pytorch/pytorch#141937 Issue tracking: - [ ] [dp2ep] how to apply FSDP only to the non-MoE modules? - [ ] [dp2ep] `shard_dim_alltoall` not robust (especially during backward with more than 1D) - [ ] [tp2ep] backward efficiency may not optimized (e.g. right now `aten.scatter.src` only supports [replicate](https://github.com/pytorch/pytorch/blob/main/torch/distributed/tensor/_ops/_tensor_ops.py#L368) sharding prop) - [ ] [tp2ep] using DTensor (e.g. in "tp2ep"), the backward `aten.scatter.src` requires `_allow_implicit_replication` (maybe because in backward some tensor is not generated as DTensor) - [ ] some other issues tracked in pytorch/pytorch#141937 - [ ] `torch.compile` fails on `torch.topk` Haven't worked on - softmax scoring instead of sigmoid (can be done similarly, would incur extra communications) - part of DP (e.g. CP) to EP Not considering - shared expert overlapping - token-choice MoE [ghstack-poisoned]
lessw2020
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Dec 4, 2024
| if self.use_sigmoid: | ||
| scores = torch.sigmoid(scores.to(torch.float32)).to(x.dtype) | ||
| else: | ||
| scores = F.softmax(scores.to(torch.float32), dim=0).to(x.dtype) |
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one comment here - it's not clear imo if the optimal order is softmax and then topk, or topk and then softmax.
It does not change the routing, but it changes the weights.
In my nanogpt MoE it has the option to toggle the ordering.
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somehow I lost access to this ghstack, moving to #725 instead |
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Stack from ghstack (oldest at bottom):
The expert-choice MoE implementation is mostly from torchtune: pytorch/torchtune#1902
The PR requires (ad hoc) changes to pytorch: pytorch/pytorch#141937
Issue tracking:
fully_shard)shard_dim_alltoallnot robust (especially during backward with more than 1D)aten.scatter.srconly supports replicate sharding prop)aten.scatter.srcrequires_allow_implicit_replication(maybe because in backward some tensor is not generated as DTensor)torch.compilefails ontorch.topkHaven't worked on
Not considering