[TPU] support attention head dim smaller than 128

[yaochengji]

Purpose

To support models whose head dim is smaller than 128 on TPU.
Note: for head_dim which is a multiply of 128, we will support it in a separate PR.

Test Plan

pytest -s -v tests/v1/tpu/test_basic.py::test_phi3

Test Result

Passed.

[github-actions]

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[gemini-code-assist]

Summary of Changes

Hello @yaochengji, 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 enables VLLM to run models with attention head dimensions less than 128 on TPU devices. This is achieved by strategically padding tensors to meet the hardware's requirement for dimensions to be multiples of 128 during attention computation and KV cache operations, and then unpadding the final output. A dedicated test case for a relevant model has been included.

Highlights

• TPU Support Expansion: I've added support for attention head dimensions smaller than 128 when running on TPU, addressing a previous limitation.
• Padding Implementation: To accommodate TPU hardware requirements, I've implemented padding for query, key, and value tensors, as well as the KV cache, to ensure the head dimension is a multiple of 128 during computation. The padding is removed from the final output.
• New Test Case: A new test (test_phi3) has been added specifically for models like Phi-3 (which has a head size of 96) to verify the new functionality on TPU.

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[gemini-code-assist]

Code Review

This pull request adds support for attention head dimensions smaller than 128 on TPUs. The changes are well-targeted, and a new test case validates the functionality. The review includes suggestions to enhance maintainability and clarify a potentially redundant check.

[vanbasten23]

Is it more of a Pallas kernel requirement?

[yaochengji]

I believe the fundamental issue lies with the TPU hardware. We need to implement padding, either within the model or the kernel. In this case, we've opted to pad at the model level.

[vanbasten23]

Looks good. Thanks Chengji.

[mgoin]

Great idea!