Add missing fields to KJT's PyTree flatten/unflatten logic for VBE KJT #2952
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pytorch#2952) Summary: # Context * Currently torchrec IR serializer does not support exporting variable batch KJT, because the `stride_per_rank_per_rank` and `inverse_indices` fields are needed for deserializing VBE KJTs but they are included in the KJT's PyTree flatten/unflatten function. * The diff updates KJT's PyTree flatten/unflatten function to include `stride_per_rank_per_rank` and `inverse_indices`. # Ref Differential Revision: D74295924
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This pull request was exported from Phabricator. Differential Revision: D74295924 |
…s for VBE KJTs. (pytorch#3119) Summary: For VBE KJTs, ppdate the `_maybe_compute_stride_kjt` logic to calculate stride based off of `inverse_indices` when its set. 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. Reviewed By: TroyGarden Differential Revision: D76997485
pytorch#2952) Summary: # Context * Currently torchrec IR serializer does not support exporting variable batch KJT, because the `stride_per_rank_per_rank` and `inverse_indices` fields are needed for deserializing VBE KJTs but they are included in the KJT's PyTree flatten/unflatten function. * The diff updates KJT's PyTree flatten/unflatten function to include `stride_per_rank_per_rank` and `inverse_indices`. # Ref Differential Revision: D74295924
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This pull request was exported from Phabricator. Differential Revision: D74295924 |
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Summary:
Context
stride_per_rank_per_rankandinverse_indicesfields are needed for deserializing VBE KJTs but they are included in the KJT's PyTree flatten/unflatten function.stride_per_rank_per_rankandinverse_indices.Ref
Differential Revision: D74295924