Input batch sharding strategy BATCH #884
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apoorvtintin
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ruomingp
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Dec 14, 2024
| @@ -607,6 +610,7 @@ def host_to_global_device_array( | |||
| host_arrays: Nested[Union[np.ndarray, Tensor]], | |||
| *, | |||
| partition: DataPartitionType = DataPartitionType.FULL, | |||
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I think @markblee plans to remove the DataPartitionType enum and rely on https://jax.readthedocs.io/en/latest/_autosummary/jax.make_array_from_process_local_data.html to support flexible partition specs.
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Thanks that sounds promising.
Hello @markblee, let me know if this PR is needed till you make your changes, or if you have your design in mind I can reshape the PR to make it compatible with your design.
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Axlearn currently supports DataPartitionType strategies FULL and REPLICATED for input batches. This PR adds support for a third sharding strategy DataPartitionType.BATCH.
This sharding strategy shards the input batch on sharding axis inferred as "batch_axis". This ensures batches are replicated on the "model" dimension and thus avoids unnecessary collectives to reshard input batches when Tensor parallelism is used.