[DTensor] Replaced neg dim normalization with assert in helper #114141
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/114141
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This was referenced Nov 20, 2023
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ghstack-source-id: 960cfc2099b449157a1d7950e6ee9c18fcfadc94 Pull Request resolved: #114141
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…lper" This is a replacement for #113922. I think we can still leave the check for negative shard dimension in `compute_local_shape_and_global_offset` and replace the normalization logic with an assert. This should provide us a stack trace to see which user-facing API did not normalize the dim as expected. [ghstack-poisoned]
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**Overview** Generally, I think we can try to freeze as many of these classes used in DTensor sharding propagation as possible so that we can cache hashes. This PR targets hashing `DTensorSpec`, which turns out to be relatively expensive. **Details** It looks like `tensor_meta` is only updated in `_wrap_output_spec_tensor_meta`, which only runs if the propagation was not cached: https://github.com/pytorch/pytorch/blob/ae94c7e491e22f58d3df66571c1a568e51d70acd/torch/distributed/_tensor/sharding_prop.py#L137 https://github.com/pytorch/pytorch/blob/ae94c7e491e22f58d3df66571c1a568e51d70acd/torch/distributed/_tensor/sharding_prop.py#L153 In that case, I think we can cache the hash for the `DTensorSpec` and only update it when one of the hashed attributes changes, which we only really expect to happen for `tensor_meta`. To ensure correctness, we need that all hashed attributes are immutable. - `DeviceMesh` caches its hash: https://github.com/pytorch/pytorch/blob/a9134fa99a8986adf478a12db2ea5729d24554db/torch/distributed/_device_mesh.py#L181 - This PR makes each `Placement` a frozen `dataclass`, making them immutable (relying on the fact that they do not have references to any mutable objects). - `TensorMeta` is a `NamedTuple` of `torch.Size`, `Tuple[int, ...]`, and `torch.dtype`, so it is immutable: https://github.com/pytorch/pytorch/blob/9916d8a9eaaf2c05c131f2a2dbe9eabeeaa9dffc/torch/distributed/_tensor/placement_types.py#L369-L375 **Example** For some simple small GPT model: Before: 0.125 ms <img width="509" alt="Screenshot 2023-11-16 at 10 08 05 PM" src="https://github.com/pytorch/pytorch/assets/31054793/10e59401-f635-431f-80b5-1b48df3a706e"> After: 0.048 ms <img width="294" alt="Screenshot 2023-11-16 at 10 08 47 PM" src="https://github.com/pytorch/pytorch/assets/31054793/09a3b0b9-f68c-4afc-bca1-c29a4b01c2fb"> The overall Adam CPU step time decreases from 7.647 ms to 6.451 ms. Pull Request resolved: #113915 Approved by: https://github.com/wanchaol ghstack dependencies: #113919, #113924, #114134, #113925, #113930, #114141
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This is a nit change to save one `isinstance` call for when `dim` is not `None` but the placement is not `Shard`. Pull Request resolved: #114140 Approved by: https://github.com/Skylion007, https://github.com/wanchaol ghstack dependencies: #113919, #113924, #114134, #113925, #113930, #114141, #113915
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This is a forward fix for #113781. We lazily compute the hash so that we do not try to compute the hash on `SymInt`s (for the stride) during Dynamo tracing. Tested via: ``` python test/distributed/_tensor/test_dtensor_compile.py -k test_2d_fsdp_tp_ac_compile ``` Pull Request resolved: #114322 Approved by: https://github.com/wanchaol ghstack dependencies: #113919, #113924, #114134, #113925, #113930, #114141, #113915, #114140
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Stack from ghstack (oldest at bottom):
isinstancecall inis_shard#114140DTensorSpec#113915distribute_tensor#113930grad_placementswas tuple #113925from_local#114134redistribute#113924_Partial,Replicatefrozen dataclasses #113919This is a replacement for #113922. I think we can still leave the check for negative shard dimension in
compute_local_shape_and_global_offsetand replace the normalization logic with an assert. This should provide us a stack trace to see which user-facing API did not normalize the dim as expected.