ggml : change ggml_graph_compute() API to not require context (#1999)

* ggml_graph_compute: deprecate using ggml_context, try resolve issue #287

* rewrite: no longer consider backward compitability; plan and make_plan

* minor: rename ctx as plan; const

* remove ggml_graph_compute from tests/test-grad0.c, but current change breaks backward

* add static ggml_graph_compute_sugar()

* minor: update comments

* reusable buffers

* ggml : more consistent naming + metal fixes

* ggml : fix docs

* tests : disable grad / opt + minor naming changes

* ggml : add ggml_graph_compute_with_ctx()

- backwards compatible API
- deduplicates a lot of copy-paste

* ci : enable test-grad0

* examples : factor out plan allocation into a helper function

* llama : factor out plan stuff into a helper function

* ci : fix env

* llama : fix duplicate symbols + refactor example benchmark

* ggml : remove obsolete assert + refactor n_tasks section

* ggml : fix indentation in switch

* llama : avoid unnecessary bool

* ggml : remove comments from source file and match order in header

---------

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

ggml-metal.m

@@ -25,6 +25,8 @@ struct ggml_metal_buffer {
 };
 
 struct ggml_metal_context {
+    int n_cb;
+
     float * logits;
 
     id<MTLDevice>       device;
@@ -86,11 +88,12 @@ static NSString * const msl_library_source = @"see metal.metal";
 @implementation GGMLMetalClass
 @end
 
-struct ggml_metal_context * ggml_metal_init(void) {
+struct ggml_metal_context * ggml_metal_init(int n_cb) {
     fprintf(stderr, "%s: allocating\n", __func__);
 
     struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context));
 
+    ctx->n_cb   = n_cb;
     ctx->device = MTLCreateSystemDefaultDevice();
     ctx->queue  = [ctx->device newCommandQueue];
     ctx->n_buffers = 0;
@@ -208,6 +211,10 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
     free(ctx);
 }
 
+void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb) {
+    ctx->n_cb = n_cb;
+}
+
 // finds the Metal buffer that contains the tensor data on the GPU device
 // the assumption is that there is 1-to-1 mapping between the host and device memory buffers, so we can find the
 // Metal buffer based on the host memory pointer
@@ -354,7 +361,7 @@ void ggml_metal_graph_compute(
     // create multiple command buffers and enqueue them
     // then, we encode the graph into the command buffers in parallel
 
-    const int n_cb = gf->n_threads;
+    const int n_cb = ctx->n_cb;
 
     NSMutableArray * command_buffers = [NSMutableArray arrayWithCapacity:n_cb];