[v0.11.0][Fix] Prevent memory leak in MLA decode graph (#3743)

[yiz-liu]

What this PR does / why we need it?

The cache for MLA decode graph parameters was holding strong references to tensors, preventing them from being garbage collected and leading to increased memory usage.

This change wraps the cached tensors in weak references, allowing them to be deallocated when no longer in use and reducing overall memory pressure.

Does this PR introduce any user-facing change?

None.

How was this patch tested?

None.

[github-actions]

👋 Hi! Thank you for contributing to the vLLM Ascend project. The following points will speed up your PR merge:‌‌

• A PR should do only one thing, smaller PRs enable faster reviews.
• Every PR should include unit tests and end-to-end tests ‌to ensure it works and is not broken by other future PRs.
• Write the commit message by fulfilling the PR description to help reviewer and future developers understand.

If CI fails, you can run linting and testing checks locally according Contributing and Testing.

[gemini-code-assist]

Code Review

This pull request effectively addresses a memory leak in the MLA decode graph by introducing weak references for cached tensors. The changes are consistently applied across attention_v1.py and mla_v1.py, ensuring that temporary tensors and workspace buffers are wrapped in weak_ref_tensors before being cached. This allows for proper garbage collection and reduces memory pressure. The related graph update logic in acl_graph.py has been correctly adjusted to accommodate these changes. The correction to remove the weak reference from block_tables is also appropriate, as it is a per-request input that needs to remain accessible. Overall, the changes are well-implemented and solve the stated problem.