llama : ggml-backend integration (#4766)

* llama : ggml-backend integration

* ggml-backend : add names to buffers

* fix unmap after loading

* batched-bench : add tensor_split param

* llama : check for null tensor_split

* ggml-backend : increase GGML_MAX_BACKENDS

* improve graph splitting, partial fix for --no-kv-offload

* cuda : add ggml-backend split buffer support

* cuda : do not create buffer types for devices that don't exist (fixes usage without CUDA devices available)

* ggml : fix null backend dereference (#4807)

* ggml : fix null backend dereference

* ggml : also check ggml_backend_is_cpu

* test-backend-ops : check buffer allocation failures

* llama : add cparam (split_mode) and command line argument (--split-mode, -sm) to configure the split mode (none, layer or row)

* ggml : fix mul_mat_id work size

* llama : rewrite session kv load/set without graphs

* minor

* llama : only initialize used backends, free backends on context free

* llama : abort ctx if cuda backend init fails

* llama : rewrite lora with ggml-backend and compute on CPU

ggml-ci

* llama : only map to a backend buffer the region of the file mapping containing the tensors used in the buffer

* opencl : add ggml-backend buffer type

* cuda : only use batched_cublas with batched mat muls (fixes fp16 tg perf)

* llama : on Metal, by default offload the full model

ggml-ci

* metal : page align the data ptr (#4854)

* Apply suggestions from code review

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

* cuda : fix split buffer free

* address review comments

* llama-bench : add split-mode parameter

* fix whitespace

* opencl : fix double initialization

* server : add --split-mode parameter

* use async copy and compute to improve multi-gpu performance

ggml-ci

* use async memcpys to copy the graph outputs to the CPU

* fix opencl

* use a host buffer for the cpu compute buffer for faster copies to the gpu

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

1 files changed, 26 insertions, 4 deletions

diff --git a/ggml.c b/ggml.c
index f5caeba0..6dbd7626 100644
--- a/ggml.c
+++ b/ggml.c
@@ -2354,6 +2354,10 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
 }
 
 void ggml_free(struct ggml_context * ctx) {
+    if (ctx == NULL) {
+        return;
+    }
+
     // make this function thread safe
     ggml_critical_section_start();
 
@@ -4362,6 +4366,23 @@ struct ggml_tensor * ggml_cpy(
     return ggml_cpy_impl(ctx, a, b);
 }
 
+struct ggml_tensor * ggml_cast(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        enum   ggml_type      type) {
+    bool is_node = false;
+
+    struct ggml_tensor * result = ggml_new_tensor(ctx, type, GGML_MAX_DIMS, a->ne);
+    ggml_format_name(result, "%s (copy)", a->name);
+
+    result->op   = GGML_OP_CPY;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = result;
+
+    return result;
+}
+
 // ggml_cont
 
 static struct ggml_tensor * ggml_cont_impl(
@@ -14871,7 +14892,7 @@ size_t ggml_hash_find_or_insert(struct ggml_hash_set hash_set, struct ggml_tenso
     return i;
 }
 
-static struct ggml_hash_set ggml_hash_set_new(size_t size) {
+struct ggml_hash_set ggml_hash_set_new(size_t size) {
     size = ggml_hash_size(size);
     struct ggml_hash_set result;
     result.size = size;
@@ -16620,7 +16641,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
     return GGML_EXIT_SUCCESS;
 }
 
-struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
+struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threads) {
     if (n_threads <= 0) {
         n_threads = GGML_DEFAULT_N_THREADS;
     }
@@ -16682,14 +16703,15 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
                 } break;
             case GGML_OP_MUL_MAT_ID:
                 {
+                    cur = 0;
                     const struct ggml_tensor * src0 = node->src[2];
                     const struct ggml_tensor * src1 = node->src[1];
                     const enum ggml_type vec_dot_type = type_traits[src0->type].vec_dot_type;
                     if (src1->type != vec_dot_type) {
-                        cur = ggml_row_size(vec_dot_type, ggml_nelements(src1));
+                        cur += ggml_row_size(vec_dot_type, ggml_nelements(src1));
                     }
                     const int n_as = ggml_get_op_params_i32(node, 1);
-                    cur = GGML_PAD(cur, sizeof(int64_t));        // align
+                    cur += GGML_PAD(cur, sizeof(int64_t));       // align
                     cur += n_as * sizeof(int64_t);               // matrix_row_counts
                     cur += n_as * src1->ne[1] * sizeof(int64_t); // matrix_rows
                 } break;