mlapo add qdown output

[h1074112368]

What this PR does / why we need it?

This PR adds mlapo operation support qdown of output.

Does this PR introduce any user-facing change?

mlapo operation add enable_inner_out of input

How was this patch tested?

CI passed with new added/existing test.

• vLLM version: v0.12.0
• vLLM main: vllm-project/vllm@ad32e3e

[gemini-code-assist]

Code Review

This pull request introduces a new 'qdown' output, controlled by the enable_inner_out flag. The changes correctly propagate this new parameter from the PyTorch operator definition down to the kernel implementation, adding new logic paths and updating function signatures accordingly. However, my review identified a critical bug related to duplicated arguments being passed to the kernel implementation, which could lead to incorrect computations. Additionally, there is a significant code duplication issue in the new kernel logic, which impacts maintainability. Addressing these points will improve the correctness and quality of the code.

[gemini-code-assist]

The mla_preprocess_impl function is called with sin_ptr and cos_ptr passed for both (sin1, cos1) and (sin2, cos2) arguments. The kernel implementation seems to expect two distinct pairs of sin/cos tensors. Passing the same pointers for both pairs is likely a bug and could lead to incorrect calculations.

The mla_preprocess function signature should probably be updated to accept a second pair of sin/cos tensors, or if this is intentional, it should be clearly documented why the same tensors are passed twice.

[gemini-code-assist]

There is significant code duplication across the new case statements for _INNER keys. The logic inside case KEY_BF16_CACHEMODE_0_QUANTMODE_0_INNER, case KEY_BF16_CACHEMODE_1_QUANTMODE_0_INNER, and case KEY_BF16_CACHEMODE_3_QUANTMODE_0_INNER is identical except for the CacheMode template parameter (0, 1, 3) for MLAPO_BF16_INNER::MLAOperation.

This duplication makes the code harder to read and maintain. Any future changes to this logic will need to be applied in three places, increasing the risk of errors.

To reduce duplication, you could use a helper function templated on the CacheMode to encapsulate the common logic. For example:

template <int CacheMode>
__aicore__ void ProcessInnerOp(MlaTilingData& mlaTilingData, GM_ADDR tiling, /* other params */) {
    MLAPO_BF16_INNER::MLAOperation<__bf16, CacheMode, DataFormat::NZ, DataFormat::NZ, DataFormat::ND,
                             QuantMode::PER_TENSOR_ASYMM_QUANT>
        op(mlaTilingData, tiling);
    op.Init(hiddenState, quantScale1, quantOffset1, wdqkv, bias1, gamma2, beta2,
                      quantScale2, quantOffset2, gamma3, sin1, cos1, sin2, cos2, keycache, slotMapping, wuq,
                      bias2, wuk, descale1, descale2, ctkvScale, qnopeScale, q, keycacheOut, q2, keycacheOut2,
                      s1, s2, s3, s4, s5, innerOut);
    if ASCEND_IS_AIC {
        op.ProcessCube();
    }
    if ASCEND_IS_AIV {
        op.ProcessVector();
    }
}

// Then in the switch statement:
case KEY_BF16_CACHEMODE_0_QUANTMODE_0_INNER:
    ProcessInnerOp<0>(mlaTilingData, tiling, ...);
    break;
case KEY_BF16_CACHEMODE_1_QUANTMODE_0_INNER:
    ProcessInnerOp<1>(mlaTilingData, tiling, ...);
    break;
case KEY_BF16_CACHEMODE_3_QUANTMODE_0_INNER:
    ProcessInnerOp<3>(mlaTilingData, tiling, ...);
    break;

Since adding a new function might be a larger change, you could also use a macro to achieve a similar result within the current function body.

[github-actions]

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

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[MengqingCao]

https://github.com/vllm-project/vllm-ascend/blob/main/tests/e2e/nightly/ops/test_mla_preprocess.py

[github-actions]

This pull request has conflicts, please resolve those before we can evaluate the pull request.

[github-actions]

This pull request has conflicts, please resolve those before we can evaluate the pull request.