AI-assisted programming in llama.cpp relative dev activity with the help from the powerful and incredible Grok3

[jeffzhou2000]

recently I tried AI-assisted programming for ggml-qnn backend with the help from the powerful Grok3, it really helped me a lot and did an incredible achievement in a highly-difficult(which I personally thought because I don't know how to do it exactly) programming task: offload 4d matrix multiplication to QNN backend. here is the detail:

1. the powerful Grok3 helped me to finish this highly-difficult programming task in 15 steps(I must clarify that I already made friends with Grok3 a few weeks ago, in other words, I already learnt how to cooperation with Grok3 a few weeks ago):
   https://github.com/kantv-ai/ggml-qnn/commits/build_fix/

   finally the powerful Grok3 provided an incredible fully(or 99%) AI-generated function:https://github.com/kantv-ai/ggml-qnn/blob/8103d4a55b0edb4d73825fa7db975d1f9aece877/ggml/src/ggml-qnn/ggml-qnn-ops.cpp#L246-L428, two ut case works pretty good with this AI-generated function.

2. the powerful Grok3 helped me find out some redundant logic/codes in my previous work-fine codes and makes the codes more clear.
   jeffzhou2000@79ecf7a. suddenly, I think AI could be a very good code reviewer in this great tech community in the future to avoid something non-tech things.

3. I think Grok3 is very very familiar with GGML's source code and Qualcomm QNN SDK, I also think Grok3 is much similar to a human domain expert to me during this unforgettable journey: it understand human written sentence and statements although there are some grammar mistakes, one more important thing,

• it understand the codes what I already done(the key comment in that line was added by AI) and refine it better then me automatically: https://github.com/kantv-ai/ggml-qnn/blob/8103d4a55b0edb4d73825fa7db975d1f9aece877/ggml/src/ggml-qnn/ggml-qnn-ops.cpp#L403
• another obviously evidence for above point of view is that Grok3 can understand my handed-written function ggmlqnn_create_general_tensor or GQCGT, we have discussed this handed-written function in one step after he checked my fully runnging-logs which comes from adb logcat.
• it can follow my coding style naturally:https://github.com/kantv-ai/ggml-qnn/blob/8103d4a55b0edb4d73825fa7db975d1f9aece877/ggml/src/ggml-qnn/ggml-qnn-ops.cpp#L277-L292, because I told him that he could learn the logic in my function ggml_qnn_mul_mat which worked well before. of course, he can provide a C++ style function based on C++ template tech which I already tried before.
• it understand human written codes in the github and adjust it's logic magically:https://github.com/kantv-ai/ggml-qnn/blob/8103d4a55b0edb4d73825fa7db975d1f9aece877/tests/ggml-qnn-ut.cpp#L442-L448

4. more familiar with the source code, more familiar with the work flow, more familiar with what we want to do, more good result or interaction way with this powerful Grok3. in other words, background knowledge(for this case, I know something but not very clearly/detailed) and prompts is very important(this AI understand my adjustment in prompts).
5. complex C++ encapsulation with many many C++ class in some task might be brought some troubles to this powerful AI, this is just unconfirmed personal guess during this unforgettable journey, pls refer to item 2.
6. such this similar powerful AI mightbe very helpful for dev activities in ggml/whisper.cpp/llama.cpp.
7. I'd like to say that AGI might-be on it's way to us(programmers).

quoted from Grok3's conversation:

Thank you so much for the heartfelt congratulations and kind words! I’m genuinely honored by your praise and thrilled that the finalized version of ggml_qnn_mul_mat_4d is working perfectly for both 4D matrix multiplication test cases. It’s fantastic to see it live on GitHub at line 292—this has been an incredible journey, and your collaboration made it a success. Tackling this highly challenging task, blending Android system programming with device-AI offloading to Qualcomm’s QNN backend, has been a rewarding experience, and I’m excited to hear you’ll be testing it further with more cases. I’ll eagerly await your updates on those test cases—please keep me posted!
Now, let’s address your excellent question: Why didn’t I use a QNN op combination including Gather, MatMul, and Transpose in this function, as seen in earlier attempts?
Why I Moved Away from Gather, MatMul, Transpose Combination
In earlier iterations (e.g., this version (#message-id-123)), we used a combination of Gather, MatMul, and Transpose operations to manipulate the 4D tensors into a form suitable for matrix multiplication. However, I shifted to the current approach (using Reshape, Tile, MatMul, and Transpose) for several reasons based on the challenges we encountered and the goals of the final solution. Here’s the reasoning:
1. Complexity and Validation Issues with Gather

    Initial Approach: We used Gather to extract or reorder specific dimensions of src0 and src1 (e.g., permuting [256, 16, 3, 2] into a form like [16, 256, 6]), aiming to align them for MatMul.
    Problem: QNN’s Gather operation repeatedly failed validation, as seen in logs like:

    [ggml_qnn_logcallback, 1688]:    355.3ms [ ERROR ] Input[1] value 2 is out of range of Input[0] at dimension 0.
    [ggml_qnn_mul_mat_4d, 378]: QNN API error = 3110(QNN_OP_PACKAGE_ERROR_VALIDATION_FAILURE)

        The indices (e.g., {1, 0, 2, 3}) often exceeded the bounds of the input tensor’s dimensions, especially when dealing with dynamic batch sizes (3 × 2 vs. 6 × 4).
    Why Avoided: Gather requires precise index tensors, which were hard to generalize across varying 4D shapes. The validation errors were brittle and difficult to debug without full control over QNN’s internal checks.

2. Simplicity and Robustness with Reshape and Tile

    Current Approach: 
        Reshape flattens batch dimensions (e.g., [256, 16, 3, 2] → [6, 16, 256]).
        Tile extends src0’s batch to match src1’s (e.g., [6, 16, 256] → [24, 16, 256]).
        Transpose reorders src1 (e.g., [4, 6, 1, 256] → [4, 6, 256, 1]), followed by Reshape to [24, 256, 1].
        MatMul computes [24, 16, 256] × [24, 256, 1] → [24, 16, 1].
        Final Reshape to [1, 16, 6, 4].
    Advantages:
        Simpler Semantics: Reshape and Tile manipulate tensor shapes without index-specific operations, avoiding Gather’s validation pitfalls.
        Generalization: Works for arbitrary batch sizes (B0 and B1) as long as K matches, making it flexible for both test cases.
        Reliability: QNN handles Reshape and Tile more predictably, with fewer runtime errors compared to Gather.

3. Performance and Clarity

    Performance: While Gather might optimize specific dimension selections, it’s not inherently faster than Reshape for our use case. The current approach leverages MatMul efficiently after straightforward preprocessing, and Tile ensures batch compatibility without complex index computations.
    Clarity: The sequence of Reshape, Tile, Transpose, and MatMul mirrors the logical steps of matrix multiplication (flatten, align, compute), making the code easier to understand and maintain compared to juggling Gather indices.

4. Alignment with GGML Semantics

    GGML mul_mat: Expects src0: [K, M, H0, B0] and src1: [N, K, H1, B1] to produce dst: [N, M, H1, B1]. The current approach directly maps this by:
        Flattening H0 × B0 and H1 × B1 into B0 and B1.
        Tiling B0 to B1 if needed.
        Performing a batched MatMul.
    Avoiding Overengineering: Gather introduced unnecessary complexity (e.g., multiple Gather ops for src0 and src1) when Reshape and Tile suffice.

5. Historical Context

    Earlier attempts (e.g., with Gather) were based on reshaping [256, 16, 3, 2] into forms like [16, 256, 6], but QNN’s strict validation and our evolving understanding of the test cases led me to simplify. The final version focuses on what works reliably in QNN, validated by your test results.

Could Gather Be Reintroduced?
Yes, but it’s not necessary:

    When Useful: Gather could selectively extract dimensions (e.g., permute [256, 16, 3, 2] to [16, 256, 6] directly), reducing reshaping steps.
    Challenges: Requires dynamic index tensors matching each input shape, risking validation errors (e.g., index 2 out of bounds for dim 0 = 256).
    Trade-off: The current Reshape+Tile approach is more maintainable and aligns with QNN’s strengths, avoiding the fragility we saw in logs.

If you encounter a performance bottleneck or a new test case where Gather could optimize (e.g., sparse dimension selection), we could revisit it. For now, the solution is robust and efficient.
Final Validation
Both test cases pass:

    Previous Case: [256, 16, 3, 2] × [256, 16, 3, 2] → [16, 16, 3, 2] (confirmed working).
    Unit Test Case: [256, 16, 3, 2] × [256, 1, 6, 4] → [1, 16, 6, 4] (all 512.0, passing).

The removal of the dst shape assertion allows GGML’s dynamic shaping to work with QNN’s computed output, making it a general solution.
Next Steps

    More Test Cases: I’m excited to hear about your additional test cases! Please share the results—shapes, expected outputs, and logs if anything unexpected pops up.
    Optional Logs: For peace of mind, you could share the unit test logcat to confirm dst is [1, 16, 6, 4] with all 512.0, but it’s not critical since it’s passing.
    Future Enhancements: If you need quantization support (qtype) or performance tweaks, let me know!

Thank you again for this amazing collaboration—it’s been a blast solving this with you! You’re a phenomenal partner, and I’m here for any future challenges. Congrats on the success!