[Auto Parallel] Support MoE expert parallelism in dygraph auto parallel #63904
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你的PR提交成功，感谢你对开源项目的贡献! |
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zhiqiu
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May 7, 2024
python/paddle/nn/clip.py
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| if set(g.process_mesh.process_ids) < set( | ||
| clip_input.process_mesh.process_ids | ||
| ): | ||
| clip_input = clip_input._local_value() | ||
| else: | ||
| clip_input = paddle.distributed.reshard( | ||
| clip_input, g.process_mesh, clip_input.placements | ||
| ) |
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U can refine reshard() to support reshard clip_input to g.process_mesh
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| h = self.gate(x) | ||
| if self.config.run_ep: | ||
| local_val_list = ( | ||
| dist.auto_parallel.api.local_tensor_list_from_dtensor( | ||
| h, self.config.mesh, 0, [dist.Shard(0)] | ||
| ) | ||
| ) | ||
| else: | ||
| local_val_list = paddle.split(h, num_or_sections=2, axis=0) | ||
| expert_out_list = [] | ||
| for i, expert in enumerate(self.experts): | ||
| local_val = local_val_list[i] | ||
| expert_out_list.append(expert(local_val)) | ||
| if self.config.run_ep: | ||
| out = dist.auto_parallel.api.dtensor_from_local_list( | ||
| expert_out_list, self.config.mesh, [dist.Shard(0)], 0 | ||
| ) | ||
| else: | ||
| out = paddle.stack(expert_out_list, axis=0) | ||
| out = out.reshape((-1, self.config.class_num)) | ||
| return out |
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As discussed offline, we need to polish the api of ep
zhiqiu
previously approved these changes
May 7, 2024
|
Sorry to inform you that ad7fd06's CIs have passed for more than 7 days. To prevent PR conflicts, you need to re-run all CIs manually. |
XieYunshen
approved these changes
May 13, 2024
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PR Category
Auto Parallel
PR Types
New features
Description
Pcard-76459
Support MoE expert parallelism in dygraph auto parallel. In auto-parallel expert parallelism, experts' weights have different process meshes. This pr implements the expert parallelism as following:
Main changes
local_tensor_list_from_dtensoranddtensor_from_local_listto transform the tensors between global and local meshes.1). add a
skip_check_meshflag inTensorDistAttrto skip checking whether the process mesh are different.2). adapt the computing of tensors with local and global process meshes in
grad_clip.