Support attention data parallelism

[khatwanimohit]

Description

This work was done in collaboration with @richjames0 and @NicoGrande

This PR introduces attention data parallelism (attn_dp) to optimize memory efficiency when the number of KV heads is less than tensor parallelism. The attention DP degree is auto-calculated based on the ratio of tensor parallelism to KV heads, ensuring optimal sharding without manual configuration.
New logical axes (attn_activation_length, attn_activation_embed) and corresponding sharding rules have been added to support attention-specific tensor partitioning separate from the rest of the model

Notice 1: Once all tests pass, the "pull ready" label will automatically be assigned.
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Tests

Ran Llama3.1-8B by reducing num_kv_heads to 2 and ici_tensor_parallelism=8. This will auto-calculate attn_dp and set it to 4.
vllm mesh: mesh=Mesh('data': 1, 'attn_dp': 4, 'expert': 1, 'model': 2, axis_types=(Auto, Auto, Auto, Auto))

Attention shardings:

llama2.py:158] bfloat16[96,1,4096]............................................................. (None, None, ('model', 'attn_dp')).
attentions.py:1014] bfloat16[96,1,4096]............................................................. (None, 'attn_dp', 'model').
attentions.py:1078] bfloat16[96,1,32,128]........................................................... (None, 'attn_dp', 'model', None).
attentions.py:1079] bfloat16[96,1,8,128]............................................................ (None, 'attn_dp', 'model', None).
linears.py:527] bfloat16[96,1,14336]............................................................ (None, None, ('model', 'attn_dp')).

Weight shardings:

maxtext_utils.py:1197] decoder/decoder_norm/scale/value................................................ bfloat16[4096] (('model', 'attn_dp'),)
maxtext_utils.py:1197] decoder/layers_0/mlp/wi_0/kernel/value.......................................... bfloat16[4096,14336] (None, ('model', 'attn_dp'))
maxtext_utils.py:1197] decoder/layers_0/mlp/wi_1/kernel/value.......................................... bfloat16[4096,14336] (None, ('model', 'attn_dp'))
maxtext_utils.py:1197] decoder/layers_0/mlp/wo/kernel/value............................................ bfloat16[14336,4096] (('model', 'attn_dp'), None)
maxtext_utils.py:1197] decoder/layers_0/post_self_attention_layer_norm/scale/value..................... bfloat16[4096] (('model', 'attn_dp'),)
maxtext_utils.py:1197] decoder/layers_0/pre_self_attention_layer_norm/scale/value...................... bfloat16[4096] (('model', 'attn_dp'),)
maxtext_utils.py:1197] decoder/layers_0/self_attention/key/kernel/value................................ bfloat16[4096,8,128] (None, 'model', None)
maxtext_utils.py:1197] decoder/layers_0/self_attention/out/kernel/value................................ bfloat16[32,128,4096] ('model', None, None)
maxtext_utils.py:1197] decoder/layers_0/self_attention/query/kernel/value.............................. bfloat16[4096,32,128] (None, 'model', None)
maxtext_utils.py:1197] decoder/layers_0/self_attention/value/kernel/value.............................. bfloat16[4096,8,128] (None, 'model', None)
decoder/logits_dense/kernel/value............................................... bfloat16[4096,128256] (None, ('model', 'attn_dp'))
token_embedder/embedding/value.................................................. bfloat16[128256,4096] (('model', 'attn_dp'), None)

Running Qwen3-30b-moe with tp=8, it has 4 kv heads so model=4 and attn_dp=2
Command:

NEW_MODEL_DESIGN=1 python3 -m MaxText.vllm_decode     --model_name qwen3-30b-a3b  --hf_model_name Qwen/Qwen3-30B-A3B     --hf_config_path src/MaxText/integration/vllm/maxtext_vllm_adapter  --ici_tensor_parallelism 8  --gpu_memory_utilization 0.5  --prompt "Suggest some famous landmarks in London" --debug_sharding true --enable_dp_attention true --load_parameters_path gs://parambole-qwen3-moe-verification/unscanned/qwen3-30b-a3b-thinking-2507/14_08_2025/0/items 2>&1 | tee attn_output

Output: https://paste.googleplex.com/5980793354715136

Checklist

Before submitting this PR, please make sure (put X in square brackets):

• I have performed a self-review of my code. For an optional AI review, add the gemini-review label.
• I have necessary comments in my code, particularly in hard-to-understand areas.
• I have run end-to-end tests tests and provided workload links above if applicable.
• I have made or will make corresponding changes to the doc if needed, including adding new documentation pages to the relevant Table of Contents (toctree directive) as explained in our documentation.

[codecov]

Codecov Report

❌ Patch coverage is 30.43478% with 16 lines in your changes missing coverage. Please review.

Files with missing lines	Patch %	Lines
src/MaxText/layers/moe.py	0.00%	5 Missing and 1 partial ⚠️
src/MaxText/vllm_decode.py	0.00%	5 Missing ⚠️
src/MaxText/model_creation_utils.py	25.00%	3 Missing ⚠️
src/MaxText/train_compile.py	0.00%	1 Missing ⚠️
src/MaxText/train_utils.py	0.00%	1 Missing ⚠️

📢 Thoughts on this report? Let us know!

[NicoGrande]

LGTM! Awesome work!

[NuojCheng]

It is surprising to see

attentions.py:1014] bfloat16[96,1,4096]............................................................. (None, 'attn_dp', 'model').

with 'attn_dp' = 4. It should produce error instead. Is it a typo?

[khatwanimohit]

It is surprising to see

attentions.py:1014] bfloat16[96,1,4096]............................................................. (None, 'attn_dp', 'model').

with 'attn_dp' = 4. It should produce error instead. Is it a typo?

I just re-ran Llama3.1-8B with 2 kv heads and here are the updated shardings. You are right, this should have errored out, not sure what happened earlier. I remember in that run earlier we were missing a env variable (NEW_MODE_DESIGN=1)in vLLM which was responsible for using new shardings which includes attn_dp

[NuojCheng]

Thank you Mohit! LGTM