Create a new tensor arg for stride_per_key_per_rank to facilitate torch.export #2950
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…s for VBE KJTs. (pytorch#2949) Summary: Update the `_maybe_compute_stride_kjt` logic to calculate stride based off of `inverse_indices` for VBE KJTs. Currently, stride of VBE KJT with `stride_per_key_per_rank` is calculated as the max "stride per key". This is different from the batch size of the EBC output KeyedTensor which is based off of inverse_indices. This causes issues in IR module serialization: debug doc. Differential Revision: D74273083
…ch.export (pytorch#2950) Summary: # Context * Currently torchrec IR serializer can't handle variable batch KJT use case. * To support VBE KJT, the `stride_per_key_per_rank` field needs to be flattened as a variable in the pytree flatten spec for a VBE KJT to be unflattened correctly by`torch.export`. * Currently `stride_per_key_per_rank` is a List. To flatten the `stride_per_key_per_rank` info as a variable we have to add a new tensor field for it. # Ref Differential Revision: D74207283
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This pull request was exported from Phabricator. Differential Revision: D74207283 |
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…ch.export (pytorch#2950) Summary: # Context * Currently torchrec IR serializer can't handle variable batch KJT use case. * To support VBE KJT, the `stride_per_key_per_rank` field needs to be flattened as a variable in the pytree flatten spec for a VBE KJT to be unflattened correctly by`torch.export`. * Currently `stride_per_key_per_rank` is a List. To flatten the `stride_per_key_per_rank` info as a variable we have to add a new tensor field for it. # Ref Differential Revision: D74207283
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Summary:
Context
stride_per_key_per_rankfield needs to be flattened as a variable in the pytree flatten spec for a VBE KJT to be unflattened correctly by`torch.export.stride_per_key_per_rankis a List. To flatten thestride_per_key_per_rankinfo as a variable we have to add a new tensor field for it.Ref
Differential Revision: D74207283