[TPU] kv cache update kernel supports dynamic grid

[yaochengji]

Purpose

In the kv_cache_update TPU kernel, we pad the input tensor slices to prevent recompilation, but this resulted in numerous trivial DMA copies. We now use the dynamic grid features of a Pallas kernel to eliminate most of them. It can provide more than 1% e2e throughput gain usually.

Test Plan

pytest -s -v tests/v1/tpu/test_kv_cache_update_kernel.py

Test Result

Passed

[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 aims to improve the end-to-end throughput of the kv_cache_update TPU kernel by leveraging dynamic grid features of Pallas. This change eliminates the need for padding input tensor slices, which previously led to inefficient trivial DMA copies, thereby optimizing performance.

Highlights

• Performance Optimization: The primary goal is to achieve over 1% e2e throughput gain by switching the kv_cache_update TPU kernel from using padded input tensor slices to a dynamic grid approach. This change reduces unnecessary DMA copies.
• Dynamic Grid Implementation: The Pallas kernel for KV cache updates now dynamically determines its grid size based on the actual number of slices (num_kv_update_slices) rather than a padded size, utilizing a new cdiv utility for ceiling division.
• API Changes: The kv_cache_update_op and its underlying Pallas kernel (kv_cache_update) now accept an additional num_kv_update_slices parameter to facilitate the dynamic grid sizing.
• Metadata Updates: The PallasMetadata struct has been extended to include num_kv_update_slices, ensuring this dynamic information is propagated through the system.
• Test Suite Adaptation: The existing test for kv_cache_update_kernel has been updated to correctly pass the new num_kv_update_slices parameter, ensuring the changes are validated.

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[yaochengji]

@bythew3i , could you take a look?

[github-actions]

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[gemini-code-assist]

Code Review

The pull request introduces dynamic grid support for the kv_cache_update TPU kernel, aiming to eliminate unnecessary DMA copies and improve e2e throughput. The changes involve modifications to the test script and the kernel implementation, as well as updates to the worker to pass the number of KV update slices. The code appears well-structured and addresses the stated purpose. I've added a few comments to improve readability and highlight a potential platform-specific dependency.

[gemini-code-assist]

It would be helpful to include a space after the concatenation operator (+) for improved readability.

    "Tensor num_kv_update_slices, int page_size, int num_slices_per_block)" \
    "-> Tensor", )

[gemini-code-assist]

Consider adding a brief comment to clarify what slot_mapping_metadata represents. This will improve code readability.

Suggested change

	# calcute the slot mapping
	slot_mapping_metadata = self._get_slot_mapping_metadata(
	num_reqs, num_scheduled_tokens_per_req)
	num_kv_update_slices = slot_mapping_metadata.shape[0]
	# calculate the slot mapping
	slot_mapping_metadata = self._get_slot_mapping_metadata(
	num_reqs, num_scheduled_tokens_per_req)

[vanbasten23]

Thanks Chengji!

Assuming this pr is the same as https://github.com/vllm-project/tpu_commons/pull/87, then it LGTM

[mgoin]

Nice, LGTM!