[dtensor] refactor sharding prop to handle cross mesh computation#147869
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[dtensor] refactor sharding prop to handle cross mesh computation#147869
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as titled, this PR moves the same mesh check from the sharding propagation level to each individual operator level. This is to allow more flexibility for each individual operator to check the operator can be run on the same mesh or not. For example, before this PR if user have two DTensor params that lives on different DeviceMesh, and want to run `for_each` operator on them individually, it would error out with cross mesh error. But for foreach computation there could be DTensors that live on different meshes, as long as the the mesh are the same in a "zipped way". This should also fix #134212
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/147869
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I didn't check if a certain op needs validate or not. Let me know if you need me to.
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…iple meshes (#157682) We are seeing more and more use cases where parameters in a model (under the same optimizer group) are put on different meshes. E.g. - when FSDP and TP are both applied, some parameters are sharded only on the FSDP mesh but not TP mesh (see #153268). - in [dp2ep Expert Parallel](pytorch/torchtitan#1324), the routed experts are sharded on the (global FSDP \ EP) mesh for smaller FSDP and on the EP mesh for EP, whereas other params are sharded on the global FSDP mesh for FSDP. This PR is, in some sense, a continuation of #147869 to tackle the problem when fused optimizers are used. In such cases, the [`fused_adam`](https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/native_functions.yaml#L15786) / `fused_adamw` has a scalar tensor arg `state_steps` which gets automatically cast to DTensor on the default [`compute_mesh`](https://github.com/pytorch/pytorch/blob/main/torch/distributed/tensor/_dispatch.py#L350) (one of the multiple meshes), even though the it could correspond to different meshes. To avoid hitting the cross-mesh propagation exception in `common_pointwise_strategy` and followup redistribute problems, we manually set the target mesh and placements to be the same as input mesh and placements, so that no redistribute will be triggered. This also helps bypass the situation where [`generate_redistribute_costs`](https://github.com/pytorch/pytorch/pull/157682/files#diff-eea32a36dd2d4e58307bc5229402e48048b2ecaef64a7c085495fba1ee10ac89R597) returns infinite cost due to cross mesh redistribute. Moreover, this PR has minimal scope (restricted to the `fused_ops`) and doesn't need to modify other files such as `_sharding_prop.py`. Pull Request resolved: #157682 Approved by: https://github.com/wanchaol
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as titled, this PR moves the same mesh check from the sharding propagation level to each individual operator level.
This is to allow more flexibility for each individual operator to check the operator can be run on the same mesh or not. For example, before this PR if user have two DTensor params that lives on different DeviceMesh, and want to run
for_eachoperator on them individually, it would error out with cross mesh error. But for foreach computation there could be DTensors that live on different meshes, as long as the the mesh are the same in a "zipped way".This should also fix #134212
Fixes #ISSUE_NUMBER
cc @H-Huang @awgu @kwen2501 @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o