Fixed paged|FA2 kernel loading logic and UT.

[YangKai0616]

What does this PR do?

1. [General] When running the test case tests/generation/test_continuous_batching.py::ContinuousBatchingTest::test_continuous_batching_parity_qwen_flash, it reports error as follows:

                flash_attn_func = getattr(kernel, "flash_attn_func", None)
                flash_attn_varlen_func = getattr(kernel, "flash_attn_varlen_func", None)
                if flash_attn_varlen_func is None:
>                   raise ValueError(
                        f"Could not find the currently requested flash attention implementation at `{implementation}`."
                        "Make sure that you request a valid kernel from the hub, e.g. `kernels-community/flash-attn2`."
                    )
E                   ValueError: Could not find the currently requested flash attention implementation at `flash_attention_2`.Make sure that you request a valid kernel from the hub, e.g. `kernels-community/flash-attn2`.

src/transformers/modeling_flash_attention_utils.py:115: ValueError

The root cause is a kernel loading failure of paged|flash_attention_2. This PR fixes the issue.

2. [XPU] The above test case reports error E AssertionError: Test request_id = 'req_1' failed, no expected output was provided..... after successfully running on both XPU and CUDA. However, on the XPU side, the test case can stably pass by using require_deterministic_for_xpu.

For enabling the related tests on XPU, please refer to PR #42536.

[YangKai0616]

@vasqu , please help review, thanks!

[vasqu]

I would like to extend this a bit to allow general CB support for kernels without 2x loading the kernel.

The idea is to modify the fallback to include the paged| prefix directly + load the kernel properly via lazy_import_paged_flash_attention. This should allow us to use the prefix version directly as well, e.g. attn_implementation="paged|kernels-community/flash-attn2"

[vasqu]

Let's simplify a bit, introducing another variable will be even more confusing on second thought. We can avoid that

[YangKai0616]

Strange, the CI failing example tests/models/mvp/test_modeling_mvp.py::MvpHeadTests::test_generate_beam_search can pass on both CUDA (A100) and XPU for me (with torch 2.9).

[vasqu]

Yup, not sure what's going on. It's an annoying flaky test. Added a new commit to simplify something, let's see if CI passes this time otherwise I'll try to get a core maintainer to merge if necessary.

[vasqu]

Forgot to approve but lgtm now