Repository: ggml-org/llama.cpp
Pull request: memory: respect unified KV cache in hybrid memory for eval tasks

## Overview

The hybrid memory paths (`llama-memory-hybrid.cpp` and `llama-memory-hybrid-iswa.cpp`) always used sequential equal split, ignoring the unified KV cache flag. This caused hellaswag, winogrande, and multiple-choice evaluations to fail on hybrid models (models with both attention and recurrent/SSM layers, such as Qwen3.5-35B-A3B) with:

```
  split_equal: sequential split is not supported when there are
  coupled sequences in the input batch (you may need to use the
  -kvu flag)
```

PR #19954 fixed this for `llama-kv-cache-iswa.cpp` by automatically enabling unified KV mode and setting n_parallel >= 4 for multi-choice eval tasks. However, the hybrid memory paths were not updated.

This commit mirrors the iswa fix: use non-sequential split when KV cache is unified (n_stream == 1), which is automatically set by llama-perplexity for hellaswag/winogrande/multiple-choice since #19954.

Tested on Qwen3.5-35B-A3B (hybrid attention+SSM MoE model):
- HellaSwag: 83.0% (400 tasks)
- Winogrande: 74.5% (400 tasks)
- MMLU: 41.2%
- ARC-Challenge: 56.2%
- TruthfulQA: 37.7% All previously failed with llama_decode() error.

## Additional information

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# Requirements

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- I have read and agree with the [contributing guidelines](https://github.com/ggml-org/llama.cpp/blob/master/CONTRIBUTING.md)
- AI usage disclosure: Claude was used during a benchmarking session for my quants and bumped into this as I could not have run evals - I've checked the related PRs, and guided it to do the changes needed

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Changed files:
filename: src/llama-memory-hybrid-iswa.cpp
patch: @@ -73,9 +73,9 @@ llama_memory_context_ptr llama_memory_hybrid_iswa::init_batch(llama_batch_allocr
                 // if all tokens are output, split by sequence
                 ubatch = balloc.split_seq(n_ubatch);
             } else {
-                // TODO: non-sequential equal split can be done if using unified KV cache
-                //       for simplicity, we always use sequential equal split for now
-                ubatch = balloc.split_equal(n_ubatch, true);
+                // Use non-sequential split when KV cache is unified (needed for hellaswag/winogrande/multiple-choice)
+                const bool unified = (mem_attn->get_base()->get_n_stream() == 1);
+                ubatch = balloc.split_equal(n_ubatch, !unified);
             }
 
             if (ubatch.n_tokens == 0) {

filename: src/llama-memory-hybrid.cpp
patch: @@ -73,9 +73,9 @@ llama_memory_context_ptr llama_memory_hybrid::init_batch(llama_batch_allocr & ba
                 // if all tokens are output, split by sequence
                 ubatch = balloc.split_seq(n_ubatch);
             } else {
-                // TODO: non-sequential equal split can be done if using unified KV cache
-                //       for simplicity, we always use sequential equal split for now
-                ubatch = balloc.split_equal(n_ubatch, true);
+                // Use non-sequential split when KV cache is unified (needed for hellaswag/winogrande/multiple-choice)
+                const bool unified = (mem_attn->get_n_stream() == 1);
+                ubatch = balloc.split_equal(n_ubatch, !unified);
             }
 
             if (ubatch.n_tokens == 0) {