LLM JNI: auto-detect eval_mode from .pte methods (kv-only vs hybrid)

Replace the hardcoded eval_mode=0 in the QNN_LLAMA branch with a runtime
check on the loaded module's method names: if the .pte exposes a
prefill_forward graph, switch to EvalMode::kHybrid (1) — the runner can
then batch the entire prompt through prefill_forward in one parallel pass
instead of running 52 ms/token sequentially through kv_forward. Falls
back to kKVCached (0) when only kv_forward exists, matching the current
.pte behaviour exactly so this is a safe in-place upgrade ahead of the
hybrid re-export.

Sanity-tested with the kv-only Qwen3-4B .pte already on the tablet:
  Prompt 'Bonjour, ça va ?' → "Bonjour ! Ça va, merci de me demander ça.
  Tu as une question ?", TTFT 2728 ms, total 4158 ms — no change vs the
  hardcoded eval_mode=0 build.

Once the hybrid Qwen3-4B export finishes (~50 min compile, both
prefill_forward + kv_forward graphs), the same JNI binary will pick up
the new .pte and TTFT should drop to <1 s.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

executorch-patches/llm_in_process_jni.patch

@@ -14,10 +14,10 @@ index e93731e..4951e1d 100644
      ${EXECUTORCH_SOURCE_DIR}/third-party/pybind11
      ${CMAKE_CURRENT_BINARY_DIR}/pybind11
 diff --git a/extension/android/jni/jni_layer_llama.cpp b/extension/android/jni/jni_layer_llama.cpp
-index 45f2414..7c4e1aa 100644
+index 45f2414..ae3d79f 100644
 --- a/extension/android/jni/jni_layer_llama.cpp
 +++ b/extension/android/jni/jni_layer_llama.cpp
-@@ -171,14 +171,35 @@ class ExecuTorchLlmJni : public facebook::jni::HybridClass<ExecuTorchLlmJni> {
+@@ -171,14 +171,44 @@ class ExecuTorchLlmJni : public facebook::jni::HybridClass<ExecuTorchLlmJni> {
            model_path->toStdString().c_str(),
            data_files_vector,
            executorch::extension::Module::LoadMode::MmapUseMlockIgnoreErrors);
@@ -40,6 +40,15 @@ index 45f2414..7c4e1aa 100644
 +        kv_bitwidth = static_cast<example::KvBitWidth>(
 +            module->get("get_kv_io_bit_width").get().toScalar().to<int64_t>());
 +      }
++      // Auto-detect eval_mode: kv-only (0) if the .pte only carries
++      // kv_forward, hybrid (1) if it also has prefill_forward (which lets the
++      // runner batch the prompt prefill — TTFT drops from ~52 ms/token to
++      // sub-ms after the one-shot prefill graph). Same JNI binary works with
++      // both export modes, no code change needed when the .pte is upgraded.
++      int eval_mode = 0;
++      if (module->method_names()->count("prefill_forward") > 0) {
++        eval_mode = 1; // EvalMode::kHybrid
++      }
 +      auto make_runner = [&](auto sample) -> std::unique_ptr<llm::IRunner> {
 +        using T = decltype(sample);
 +        return std::make_unique<example::Runner<T>>(
@@ -50,7 +59,7 @@ index 45f2414..7c4e1aa 100644
 +            /* performance_output_path */ "",
 +            /* dump_logits_path */ "",
 +            /* temperature */ 0.0f, // greedy
-+            /* eval_mode */ 0, // EvalMode::kKVCached
++            eval_mode,
 +            /* shared_buffer */ true);
 +      };
 +      if (kv_bitwidth == example::KvBitWidth::kWidth16) {