server: optionally evict the KV cache too (Phase 2 of VRAM sharing)

Extend on-demand device residency to the KV cache so that when a model's KV
plus another model would not fit in VRAM, the KV can also be evicted to a host
shadow (D2H on release, H2D on restore) instead of only the weights.

- llama_memory_i: add release_device_buffers()/restore_device_buffers()
  (default no-op). Implemented in llama_kv_cache (D2H shadow of the live
  ctxs_bufs, freed and reallocated like the weights); llama_memory_hybrid and
  llama_kv_cache_iswa delegate to their child caches.
- llama_context::release_device(evict_kv): also evict the memory's device
  buffers when requested; restore_device() rebuilds them. Public API
  llama_context_release_device gains an evict_kv flag.
- server: LLAMA_SLEEP_EVICT_KV=1 enables it. Off by default (weights-only),
  since the KV shadow adds a D2H/H2D copy of the live cache each cycle.

Validated on RX 580 (Vulkan), 4B @ 32k ctx: weights-only cold VRAM 1750 MB
(KV stays); weights+KV cold VRAM 726 MB (KV freed, ~1 GB reclaimed). KV
survives the round-trip: prompt cache reused after the cycle (prompt_n 4 vs
42), correct output.

Assisted-by: Claude

src/llama-kv-cache.cpp

@@ -1959,6 +1959,80 @@ size_t llama_kv_cache::total_size() const {
     return size;
 }
 
+void llama_kv_cache::release_device_buffers() {
+    // NOTE: the caller must have synchronized the backend so nothing references these buffers.
+    // The KV cache is read-write, so its host shadow is (re)captured fresh on every release.
+    if (dev_released) {
+        return;
+    }
+    dev_shadows.assign(ctxs_bufs.size(), device_buffer_shadow{});
+    size_t freed = 0;
+    for (size_t i = 0; i < ctxs_bufs.size(); ++i) {
+        ggml_context * ctx = ctxs_bufs[i].first.get();
+        ggml_backend_buffer_t buf = ctxs_bufs[i].second.get();
+        if (buf == nullptr || ggml_backend_buffer_is_host(buf) || ggml_backend_buffer_get_size(buf) == 0) {
+            continue; // only real device (VRAM) buffers are evictable
+        }
+        auto & sh = dev_shadows[i];
+        sh.releasable = true;
+        sh.buft = ggml_backend_buffer_get_type(buf);
+
+        // capture live contents compactly in stable iteration order (skip views, which alias a base)
+        size_t total = 0;
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
+            if (t->view_src == nullptr) { total += ggml_nbytes(t); }
+        }
+        sh.data.resize(total);
+        size_t off = 0;
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
+            if (t->view_src != nullptr) { continue; }
+            const size_t n = ggml_nbytes(t);
+            ggml_backend_tensor_get(t, sh.data.data() + off, 0, n);
+            off += n;
+        }
+
+        freed += ggml_backend_buffer_get_size(buf);
+        ctxs_bufs[i].second.reset(); // free the device buffer
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
+            t->buffer = nullptr;
+            t->data   = nullptr;
+        }
+    }
+    dev_released = true;
+    if (freed > 0) {
+        LLAMA_LOG_INFO("%s: released %.2f MiB of KV cache from device\n", __func__, freed / 1024.0 / 1024.0);
+    }
+}
+
+bool llama_kv_cache::restore_device_buffers() {
+    if (!dev_released) {
+        return true;
+    }
+    for (size_t i = 0; i < ctxs_bufs.size(); ++i) {
+        auto & sh = dev_shadows[i];
+        if (!sh.releasable) {
+            continue;
+        }
+        ggml_context * ctx = ctxs_bufs[i].first.get();
+        ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, sh.buft);
+        if (buf == nullptr) {
+            LLAMA_LOG_ERROR("%s: failed to reallocate KV cache device buffer (out of VRAM?)\n", __func__);
+            return false;
+        }
+        size_t off = 0;
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
+            if (t->view_src != nullptr) { continue; }
+            const size_t n = ggml_nbytes(t);
+            ggml_backend_tensor_set(t, sh.data.data() + off, 0, n);
+            off += n;
+        }
+        ctxs_bufs[i].second.reset(buf);
+    }
+    dev_released = false;
+    dev_shadows.clear(); // recaptured on next release
+    return true;
+}
+
 size_t llama_kv_cache::size_k_bytes() const {
     size_t size_k_bytes = 0;