optimze attn

[momo609]

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[gemini-code-assist]

Summary of Changes

Hello @momo609, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a specialized tensor parallelism group dedicated to attention layers, aiming to optimize the distribution and processing of attention weights. By creating distinct linear layer implementations that leverage this new group, the change allows for more granular control over how attention mechanisms are sharded across devices, potentially leading to performance improvements or reduced memory footprint for large language models.

Highlights

• Dedicated Attention Tensor Parallelism Group: Introduced a new _LM_HEAD_TP group and associated utility functions (get_lm_tp_group, get_lm_tensor_model_parallel_world_size, get_lm_tensor_model_parallel_rank) in vllm/distributed/parallel_state.py. This group is specifically initialized for 'lm_head_tp' and uses a message queue broadcaster, allowing for distinct tensor parallelism configurations for attention layers.
• Specialized Attention Linear Layers: Added new linear layer classes, AttnColumnParallelLinear and AttnRowParallelLinear, and refactored AttnQKVParallelLinear in vllm/model_executor/layers/linear.py. These classes are designed to leverage the new _LM_HEAD_TP group for their tensor parallelism calculations, including determining shard sizes, ranks, and weight loading, enabling optimized sharding strategies for attention weights.
• Qwen2 Model Integration: Updated the Qwen2Attention class in vllm/model_executor/models/qwen2.py to utilize the newly introduced AttnQKVParallelLinear and AttnRowParallelLinear classes for its QKV and output projections. This demonstrates the application of the attention-specific tensor parallelism for improved efficiency or memory usage.

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[gemini-code-assist]

Code Review

The pull request introduces a new tensor parallel group for the LM head, aiming to optimize attention mechanisms. The changes involve modifications to the parallel state and linear layers to accommodate this new group. The review focuses on providing suggestions for improved documentation and clarity within the code.

[gemini-code-assist]

Consider adding a more descriptive error message that includes the specific group that failed to initialize.

Suggested change

	assert _LM_HEAD_TP is not None, ("lm tensor model parallel group is not initialized")
	assert _LM_HEAD_TP is not None, ("LM head tensor model parallel group is not initialized.")