[DeviceMesh] Initialized mesh tensor with CPU context #124767
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This was referenced Apr 23, 2024
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/124767
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wz337
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Apr 23, 2024
| mesh = torch.arange(math.prod(mesh_shape), dtype=torch.int).view(mesh_shape) | ||
| # Always initialize the mesh's tensor on CPU, regardless of what the | ||
| # external device type has been set to be (e.g. meta) | ||
| with torch.device("cpu"): |
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Oh I think with this, we can remove the .detach().to("cpu") here:
https://github.com/pytorch/pytorch/blob/main/torch/distributed/device_mesh.py#L215
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I was worrying that someone out there might init a DeviceMesh object directly instead of through init_device_mesh, so leaving that there might be safer.
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Just remember we had a check right above it, but ye I think we can keep it to make sure the dtype is consistent.
https://github.com/pytorch/pytorch/blob/main/torch/distributed/device_mesh.py#L212-L213
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This PR adds a `DeviceMesh.from_group()` static method to convert an existing process group to a device mesh. Motivation: We need `DeviceMesh.from_group()` to allow FSDP2 to interoperate with distributed libraries that do not use `DeviceMesh` for all parallelisms. Pull Request resolved: #124787 Approved by: https://github.com/wanchaol ghstack dependencies: #124651, #124741, #124767, #124768, #124780
This was referenced Apr 25, 2024
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This PR adds a `DeviceMesh.from_group()` static method to convert an existing process group to a device mesh. Motivation: We need `DeviceMesh.from_group()` to allow FSDP2 to interoperate with distributed libraries that do not use `DeviceMesh` for all parallelisms. Pull Request resolved: pytorch#124787 Approved by: https://github.com/wanchaol ghstack dependencies: pytorch#124651, pytorch#124741, pytorch#124767, pytorch#124768, pytorch#124780
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This PR renames the `FSDP` class to `FSDPModule`. This is a BC breaking change. The rationale is that `FSDPModule` is more descriptive since `fully_shard` is a module-level API (applied to a `module` arg), so the `FSDP` class will always correspond to a module. Also, users commonly import `FullyShardedDataParallel` as `FSDP`, so this can help avoid some name conflict in some cases. Pull Request resolved: #124955 Approved by: https://github.com/wanchaol, https://github.com/wconstab ghstack dependencies: #124651, #124741, #124767, #124768, #124780, #124787
This was referenced Apr 29, 2024
petrex
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This PR makes sure to construct the `DeviceMesh`'s `mesh` tensor on CPU device in `init_device_mesh()`. This means that we can call `init_device_mesh()` under meta-device context and still construct the correct `mesh` tensor. Pull Request resolved: pytorch#124767 Approved by: https://github.com/wz337 ghstack dependencies: pytorch#124651, pytorch#124741
pytorch-bot bot
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This PR adds a `DeviceMesh.from_group()` static method to convert an existing process group to a device mesh. Motivation: We need `DeviceMesh.from_group()` to allow FSDP2 to interoperate with distributed libraries that do not use `DeviceMesh` for all parallelisms. Pull Request resolved: #124787 Approved by: https://github.com/wanchaol ghstack dependencies: #124651, #124741, #124767, #124768, #124780
pytorch-bot bot
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This PR renames the `FSDP` class to `FSDPModule`. This is a BC breaking change. The rationale is that `FSDPModule` is more descriptive since `fully_shard` is a module-level API (applied to a `module` arg), so the `FSDP` class will always correspond to a module. Also, users commonly import `FullyShardedDataParallel` as `FSDP`, so this can help avoid some name conflict in some cases. Pull Request resolved: #124955 Approved by: https://github.com/wanchaol, https://github.com/wconstab ghstack dependencies: #124651, #124741, #124767, #124768, #124780, #124787
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Stack from ghstack (oldest at bottom):
DeviceMesh.from_group()#124787.to(cpu)#124768This PR makes sure to construct the
DeviceMesh'smeshtensor on CPU device ininit_device_mesh(). This means that we can callinit_device_mesh()under meta-device context and still construct the correctmeshtensor.cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @penguinwu @fegin @XilunWu @wanchaol @fduwjj @wz337 @tianyu-l @wconstab @yf225 @chauhang @d4l3k