move allocation workaround out of ggml-alloc.c

ggml/include/ggml-backend.h

@@ -261,6 +261,9 @@ extern "C" {
 
     GGML_API enum ggml_backend_meta_split_state ggml_backend_meta_get_split_state(const struct ggml_tensor * tensor, bool assume_sync);
 
+    // temporary workaround to statically allocate tensors from a context in a deduplicated way:
+    GGML_API struct ggml_backend_buffer * ggml_backend_meta_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft);
+
     //
     // Backend registry
     //

ggml/src/ggml-alloc.c

@@ -1,6 +1,5 @@
 #include "ggml-alloc.h"
 #include "ggml-backend-impl.h"
-#include "ggml-backend.h"
 #include "ggml.h"
 #include "ggml-impl.h"
 #include <assert.h>
@@ -1241,9 +1240,6 @@ size_t ggml_backend_alloc_ctx_tensors_from_buft_size(struct ggml_context * ctx,
 }
 
 ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) {
-    if (ggml_backend_buft_is_meta(buft)) {
-        return ggml_backend_meta_alloc_ctx_tensors_from_buft(ctx, buft);
-    }
     size_t nbytes_total = 0;
     return ggml_backend_alloc_ctx_tensors_from_buft_impl(ctx, buft, &nbytes_total, /*no_alloc =*/ false);
 }

ggml/src/ggml-backend-impl.h

@@ -254,9 +254,6 @@ extern "C" {
 #    define GGML_BACKEND_DL_SCORE_IMPL(score_fn)
 #endif
 
-    // temporary workaround to statically allocate tensors from a context in a deduplicated way:
-    GGML_API struct ggml_backend_buffer * ggml_backend_meta_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft);
-
 #ifdef  __cplusplus
 }
 #endif

src/llama-kv-cache.cpp

@@ -187,7 +187,11 @@ llama_kv_cache::llama_kv_cache(
                 t->buffer = buf; // set dummy buffer for KV cache so that the backend scheduler won't try to allocate it
             }
         } else {
-            buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx.get(), buft); // real buffer
+            if (ggml_backend_buft_is_meta(buft)) {
+                buf = ggml_backend_meta_alloc_ctx_tensors_from_buft(ctx.get(), buft);
+            } else {
+                buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx.get(), buft); // real buffer
+            }
         }
         if (!buf) {
             throw std::runtime_error("failed to allocate buffer for kv cache");

src/llama-memory-recurrent.cpp

@@ -1,5 +1,6 @@
 #include "llama-memory-recurrent.h"
 
+#include "ggml-backend.h"
 #include "llama-impl.h"
 #include "llama-io.h"
 #include "llama-batch.h"
@@ -101,7 +102,8 @@ llama_memory_recurrent::llama_memory_recurrent(
 
     // allocate tensors and initialize the buffers to avoid NaNs in the padding
     for (auto & [buft, ctx] : ctx_map) {
-        ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx.get(), buft);
+        ggml_backend_buffer_t buf = ggml_backend_buft_is_meta(buft) ?
+            ggml_backend_meta_alloc_ctx_tensors_from_buft(ctx.get(), buft) : ggml_backend_alloc_ctx_tensors_from_buft(ctx.get(), buft);
         if (!buf) {
             throw std::runtime_error("failed to allocate buffer for rs cache");
         }

src/llama-model.cpp

@@ -7504,7 +7504,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                     t->buffer = buf; // set dummy buffer for weights so that the backend scheduler won't try to allocate them
                 }
             } else {
-                buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); // real buffer
+                if (ggml_backend_buft_is_meta(buft)) {
+                    buf = ggml_backend_meta_alloc_ctx_tensors_from_buft(ctx, buft); // real buffer
+                } else {
+                    buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); // real buffer
+                }
             }
             if (buf == nullptr) {
                 throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft)));