Remove all-to-all operation from TSP/CP in Attention Segmentation Mask

[dinodeep]

Description

When performing TSP/CP with packed sequences, the segmentation mask produced for attention from ids could introduce a large all-to-all operation (transporting bytes on the order of O(S^2)). This PR avoids that by introducing an all-gather on the small non-broadcasted to reduce communication.

More specifically, this line

mask = decoder_segment_ids[:, :, None] == decoder_key_segment_ids[:, None, :]

would produce an all-to-all on the broadcasted decoder_key_segment_ids[:, None, :] when running with TSP/CP since decoder_segment_ids is [B, S] following the input data sharding. The sequence dimension is sharded under TSP/CP constraints; however, this introduces a large all-to-all collective when constructing the mask.

To fix this, we add a sharding constraint on the right hand side of the equation. This all-gathers decoder_key_segment_ids on the sequence dimension for the right-hand side prior to broadcasting for a much smaller collective with the same sharded output. This reduces communication latency and can improve performance when training with large sequence lengths.

Tests

I observed all-to-all operations (300-400us) in the HLO for a Llama3-70b training run executing with tensor_sequence_parallelism. Then, after running the same configuration but with the new changes, I did not observe an all-to-all instruction in the HLO, and instead, the operation was replaced with a small all-gather (30us latency).

Checklist

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[codecov]

Codecov Report

❌ Patch coverage is 50.00000% with 2 lines in your changes missing coverage. Please review.

Files with missing lines	Patch %	Lines
src/maxtext/layers/attention_op.py	33.33%	1 Missing and 1 partial ⚠️

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