[DTensor] Support matmul in inference_mode #142197
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/142197
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit ac1c07d with merge base 61dc5e9 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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XilunWu
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| dx = distribute_tensor(x, device_mesh, [Replicate()]) | ||
| dA = distribute_tensor(A, device_mesh, [Shard(0)]) |
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i remember that people complain the var name dX dA indicates gradients. Names such as x_dist A_distare preferred. cc @awgu
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wz337
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Fixes #142190 . The solution is to add a `decompose_handler` for `aten.matmul`, similar to how we handle `aten.linear`. With the decomposition, `aten.matmul` becomes `aten.mm` which has sharding strategy registered with DTensor. Pull Request resolved: #142197 Approved by: https://github.com/XilunWu, https://github.com/wz337
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Stack from ghstack (oldest at bottom):
Fixes #142190 .
The solution is to add a
decompose_handlerforaten.matmul, similar to how we handleaten.linear.With the decomposition,
aten.matmulbecomesaten.mmwhich has sharding strategy registered with DTensor.cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o