llama : initial ggml-backend integration (#4520)

* llama : initial ggml-backend integration

* add ggml-metal

* cuda backend can be used though ggml-backend with LLAMA_GGML_BACKEND_CUDA_TEST
access all tensor data with ggml_backend_tensor_get/set

* add ggml_backend_buffer_clear
zero-init KV cache buffer

* add ggml_backend_buffer_is_hos, used to avoid copies if possible when accesing tensor data

* disable gpu backends with ngl 0

* more accurate mlock

* unmap offloaded part of the model

* use posix_fadvise64(.., POSIX_FADV_SEQUENTIAL) to improve performance with mmap

* update quantize and lora

* update session copy/set to use ggml-backend

ggml-ci

* use posix_fadvise instead of posix_fadvise64

* ggml_backend_alloc_ctx_tensors_from_buft : remove old print

* llama_mmap::align_offset : use pointers instead of references for out parameters

* restore progress_callback behavior

* move final progress_callback call to load_all_data

* cuda : fix fprintf format string (minor)

* do not offload scales

* llama_mmap : avoid unmapping the same fragments again in the destructor

* remove unnecessary unmap

* metal : add default log function that prints to stderr, cleanup code

ggml-ci

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

ggml.c

@@ -2383,20 +2383,8 @@ size_t ggml_get_mem_size(const struct ggml_context * ctx) {
 size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) {
     size_t max_size = 0;
 
-    struct ggml_object * obj = ctx->objects_begin;
-
-    while (obj != NULL) {
-        if (obj->type == GGML_OBJECT_TENSOR) {
-            struct ggml_tensor * tensor = (struct ggml_tensor *) ((char *) ctx->mem_buffer + obj->offs);
-
-            const size_t size = ggml_nbytes(tensor);
-
-            if (max_size < size) {
-                max_size = size;
-            }
-        }
-
-        obj = obj->next;
+    for (struct ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor != NULL; tensor = ggml_get_next_tensor(ctx, tensor)) {
+        max_size = MAX(max_size, ggml_nbytes(tensor));
     }
 
     return max_size;
@@ -3093,7 +3081,7 @@ struct ggml_tensor * ggml_view_tensor(
     return result;
 }
 
-struct ggml_tensor * ggml_get_first_tensor(struct ggml_context * ctx) {
+struct ggml_tensor * ggml_get_first_tensor(const struct ggml_context * ctx) {
     struct ggml_object * obj = ctx->objects_begin;
 
     char * const mem_buffer = ctx->mem_buffer;
@@ -3109,7 +3097,7 @@ struct ggml_tensor * ggml_get_first_tensor(struct ggml_context * ctx) {
     return NULL;
 }
 
-struct ggml_tensor * ggml_get_next_tensor(struct ggml_context * ctx, struct ggml_tensor * tensor) {
+struct ggml_tensor * ggml_get_next_tensor(const struct ggml_context * ctx, struct ggml_tensor * tensor) {
     struct ggml_object * obj = (struct ggml_object *) ((char *)tensor - GGML_OBJECT_SIZE);
     obj = obj->next;
 
@@ -19213,6 +19201,10 @@ char * gguf_get_tensor_name(const struct gguf_context * ctx, int i) {
     return ctx->infos[i].name.data;
 }
 
+enum ggml_type gguf_get_tensor_type(const struct gguf_context * ctx, int i) {
+    return ctx->infos[i].type;
+}
+
 // returns the index
 static int gguf_get_or_add_key(struct gguf_context * ctx, const char * key) {
     const int idx = gguf_find_key(ctx, key);